diff --git a/.bazelrc b/.bazelrc index a99570d29..725581524 100644 --- a/.bazelrc +++ b/.bazelrc @@ -1,10 +1,10 @@ # ------ ALL SETTINGS ------ -run --incompatible_disallow_dict_plus=false --incompatible_disable_deprecated_attr_params=false --incompatible_depset_is_not_iterable=false --incompatible_no_support_tools_in_action_inputs=false -build --incompatible_disallow_dict_plus=false --incompatible_disable_deprecated_attr_params=false --incompatible_depset_is_not_iterable=false --incompatible_no_support_tools_in_action_inputs=false -test --incompatible_disallow_dict_plus=false --incompatible_disable_deprecated_attr_params=false --incompatible_depset_is_not_iterable=false --incompatible_no_support_tools_in_action_inputs=false -coverage --incompatible_disallow_dict_plus=false --incompatible_disable_deprecated_attr_params=false --incompatible_depset_is_not_iterable=false --incompatible_no_support_tools_in_action_inputs=false +run --incompatible_depset_is_not_iterable=false --jobs=2 +build --incompatible_depset_is_not_iterable=false --jobs=2 +test --incompatible_depset_is_not_iterable=false --jobs=2 +coverage --incompatible_depset_is_not_iterable=false --jobs=2 # ------ OPTIMIZATION ------ @@ -31,6 +31,7 @@ coverage:cc_coverage --instrument_test_targets --experimental_cc_coverage --comb run:valgrind --run_under="valgrind --leak-check=full" test:valgrind --run_under="valgrind --leak-check=full" +run:asan --linkopt -fsanitize=address test:asan --linkopt -fsanitize=address # === GTESTS === diff --git a/.gitignore b/.gitignore index 8391c7ab7..efb86f91d 100644 --- a/.gitignore +++ b/.gitignore @@ -77,5 +77,7 @@ html/ !.gitmodules !.astylerc !.bazelrc +!models/*.pbx +!models/**/*.pbx !rocnnet/pretrained/*.pbx !rocnnet/notebooks/*.pbx diff --git a/BUILD.bazel b/BUILD.bazel index d267e47a3..342c5b449 100644 --- a/BUILD.bazel +++ b/BUILD.bazel @@ -18,3 +18,20 @@ filegroup( "BUILD.bazel", ], ) + +filegroup( + name = "test_models", + srcs = glob([ + "models/test/*.pbx", + "models/test/*.txt", + "models/test/*.json", + ]) +) + +filegroup( + name = "models", + srcs = glob([ + "models/*.pbx", + "models/*.json", + ]) +) diff --git a/Makefile b/Makefile index 4e6f0df6d..84e7e9ecb 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ COVERAGE_INFO_FILE := bazel-out/_coverage/_coverage_report.dat COVERAGE_IGNORE := 'external/*' '**/test/*' 'testutil/*' '**/genfiles/*' 'dbg/*' -CCOVER := bazel coverage --config asan --action_env="ASAN_OPTIONS=detect_leaks=0" --config gtest --config cc_coverage +CCOVER := bazel coverage --config asan --action_env="ASAN_OPTIONS=detect_leaks=0" --config gtest --config cc_coverage --define EAD_CFG=MIN ADE_TEST := //ade:test @@ -26,8 +26,8 @@ print_vars: rocnnet_py_build: bazel build --config $(CC)_eigen_optimal //rocnnet:rocnnet_py -rocnnet_py_export: rocnnet_py_build - cp -f bazel-bin/rocnnet/*.so rocnnet/notebooks/rocnnet +rocnnet_py_export: bazel-bin/rocnnet/rocnnet.so bazel-bin/ead/tenncor.so bazel-bin/ead/ead.so + cp -f bazel-bin/rocnnet/rocnnet.so rocnnet/notebooks/rocnnet cp -f bazel-bin/ead/*.so rocnnet/notebooks/ead diff --git a/ade/functor.hpp b/ade/functor.hpp index 1bf4b5559..2e1fb9d7e 100644 --- a/ade/functor.hpp +++ b/ade/functor.hpp @@ -22,7 +22,7 @@ struct Functor final : public iFunctor { if (0 == args.size()) { - logs::fatalf("cannot perform %s with no arguments", + logs::fatalf("cannot perform `%s` with no arguments", opcode.name_.c_str()); } @@ -32,7 +32,7 @@ struct Functor final : public iFunctor Shape ishape = args[i].shape(); if (false == ishape.compatible_after(shape, 0)) { - logs::fatalf("cannot perform %s with incompatible shapes %s " + logs::fatalf("cannot perform `%s` with incompatible shapes %s " "and %s", opcode.name_.c_str(), shape.to_string().c_str(), ishape.to_string().c_str()); } diff --git a/ade/grad_def.hpp b/ade/grad_def.hpp index 6ba773d5b..70f258926 100644 --- a/ade/grad_def.hpp +++ b/ade/grad_def.hpp @@ -76,7 +76,7 @@ struct iGradientBuilder std::list parents; std::transform(pathmap.begin(), pathmap.end(), std::back_inserter(parents), - [](std::pair> parent) + [](std::pair> parent) { return static_cast(parent.first); }); diff --git a/ade/ileaf.hpp b/ade/ileaf.hpp index 34ace0618..02f47e481 100644 --- a/ade/ileaf.hpp +++ b/ade/ileaf.hpp @@ -25,6 +25,9 @@ struct iLeaf : public iTensor, public iData { visiter.visit(this); } + + /// Return true if leaf is immutable, otherwise false + virtual bool is_const (void) const = 0; }; /// Leaf smart pointer diff --git a/ade/shape.hpp b/ade/shape.hpp index 9040e957e..5817b2b18 100644 --- a/ade/shape.hpp +++ b/ade/shape.hpp @@ -22,16 +22,18 @@ namespace ade /// Type used for shape rank using RankT = uint8_t; -// /// Type used for shape dimension -// #if !defined(SDIM_BYTES) || SDIM_BYTES <= 1 -// using DimT = uint8_t; -// #elif SDIM_BYTES <= 2 +#define SDIM_BYTES 2 + +/// Type used for shape dimension +#if !defined(SDIM_BYTES) || SDIM_BYTES <= 1 +using DimT = uint8_t; +#elif SDIM_BYTES <= 2 using DimT = uint16_t; -// #elif SDIM_BYTES <= 4 -// using DimT = uint32_t; -// #else -// using DimT = uint64_t; -// #endif +#elif SDIM_BYTES <= 4 +using DimT = uint32_t; +#else +using DimT = uint64_t; +#endif /// Type used for coordinate dimensions using CDimT = double; diff --git a/ade/src/coord.cpp b/ade/src/coord.cpp index 197243a95..3f03eee5f 100644 --- a/ade/src/coord.cpp +++ b/ade/src/coord.cpp @@ -79,9 +79,10 @@ CoordptrT reduce (RankT rank, std::vector red) logs::fatalf("cannot reduce shape rank %d beyond rank_cap with n_red %d", rank, n_red); } - if (0 == n_red) + if (0 == n_red || std::all_of(red.begin(), red.end(), + [](DimT d) { return 1 == d; })) { - logs::warn("reducing with empty vector ... will do nothing"); + logs::warn("reducing scalar ... will do nothing"); return identity; } diff --git a/ade/test/common.hpp b/ade/test/common.hpp index 592e1a15e..5a8a90842 100644 --- a/ade/test/common.hpp +++ b/ade/test/common.hpp @@ -46,6 +46,11 @@ struct MockTensor : public ade::iLeaf return 0; } + bool is_const (void) const override + { + return true; + } + ade::Shape shape_; }; diff --git a/ade/test/test_coord.cpp b/ade/test/test_coord.cpp index 462bfb335..6a7dd3610 100644 --- a/ade/test/test_coord.cpp +++ b/ade/test/test_coord.cpp @@ -200,7 +200,7 @@ TEST(COORD, Reduce) rank + 1, red.size()); EXPECT_FATAL(ade::reduce(rank + 1, red), fatalmsg.c_str()); - EXPECT_WARN(ade::reduce(0, {}), "reducing with empty vector ... will do nothing"); + EXPECT_WARN(ade::reduce(0, {}), "reducing scalar ... will do nothing"); } diff --git a/ade/test/test_functor.cpp b/ade/test/test_functor.cpp index c00cba860..bfb484c42 100644 --- a/ade/test/test_functor.cpp +++ b/ade/test/test_functor.cpp @@ -31,10 +31,10 @@ TEST(FUNCTOR, Shapes) EXPECT_ARREQ(shape, gotshape); EXPECT_FATAL(ade::Functor::get(ade::Opcode{"MOCK", 0}, {}), - "cannot perform MOCK with no arguments"); + "cannot perform `MOCK` with no arguments"); std::string fatalmsg = fmts::sprintf( - "cannot perform MOCK with incompatible shapes %s and %s", + "cannot perform `MOCK` with incompatible shapes %s and %s", shape.to_string().c_str(), badshape.to_string().c_str()); EXPECT_FATAL(ade::Functor::get(ade::Opcode{"MOCK", 0}, { ade::identity_map(leaf), diff --git a/ade/test/test_grad.cpp b/ade/test/test_grad.cpp index 9843c1606..48662327f 100644 --- a/ade/test/test_grad.cpp +++ b/ade/test/test_grad.cpp @@ -42,7 +42,7 @@ struct MockGradientBuilder final : public ade::iGradientBuilder return ade::TensptrT(ade::Functor::get(ade::Opcode{"FUNC4", 3}, {op->get_children()[arg_idx]})); } - return ade::TensptrT(new LabelledMockTensor("other", op->shape()));; + return ade::TensptrT(new LabelledMockTensor("other", op->shape())); } ade::TensptrT chain_rule (ade::FuncptrT op, const ade::TensptrT& local_der, @@ -135,20 +135,20 @@ TEST(GRAD, BuilderStandardV) "(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf2[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " `--(1[94\\78\\70\\82\\62\\29\\38\\1])", + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:leaf2[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " `--(constant:1[94\\78\\70\\82\\62\\29\\38\\1])", gl); EXPECT_GRAPHEQ( "(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf2[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(leaf2[94\\78\\70\\82\\62\\29\\38\\1])\n" - " `--(1[94\\78\\70\\82\\62\\29\\38\\1])", + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:leaf2[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:leaf2[94\\78\\70\\82\\62\\29\\38\\1])\n" + " `--(constant:1[94\\78\\70\\82\\62\\29\\38\\1])", gl2); } @@ -180,32 +180,32 @@ TEST(GRAD, BuilderDiamond) " `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC4[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(other[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(1[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:other[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:1[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(other[94\\78\\70\\82\\62\\29\\38\\1])\n" - " `--(1[94\\78\\70\\82\\62\\29\\38\\1])", + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:other[94\\78\\70\\82\\62\\29\\38\\1])\n" + " `--(constant:1[94\\78\\70\\82\\62\\29\\38\\1])", gl); } @@ -237,45 +237,45 @@ TEST(GRAD, TadPole) "(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(other[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:other[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC4[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(other[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(1[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:other[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:1[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC4[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | `--(FUNC4[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC2[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC3[94\\78\\70\\82\\62\\29\\38\\1])\n" " | | `--(FUNC[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | | `--(leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" - " | `--(other[94\\78\\70\\82\\62\\29\\38\\1])\n" - " `--(1[94\\78\\70\\82\\62\\29\\38\\1])", + " | | `--(constant:leaf[94\\78\\70\\82\\62\\29\\38\\1])\n" + " | `--(constant:other[94\\78\\70\\82\\62\\29\\38\\1])\n" + " `--(constant:1[94\\78\\70\\82\\62\\29\\38\\1])", gl); } diff --git a/ade/test/test_traveler.cpp b/ade/test/test_traveler.cpp index c25fa7024..b0faf6912 100644 --- a/ade/test/test_traveler.cpp +++ b/ade/test/test_traveler.cpp @@ -59,10 +59,10 @@ TEST(TRAVELER, PathFinder) { ASSERT_HAS(finder.parents_, g.get()); - EXPECT_HAS(finder.parents_[g.get()], 1); + EXPECT_ARRHAS(finder.parents_[g.get()], 1); ASSERT_HAS(finder.parents_, f.get()); - EXPECT_HAS(finder.parents_[f.get()], 0); + EXPECT_ARRHAS(finder.parents_[f.get()], 0); } finder.parents_.clear(); @@ -72,7 +72,7 @@ TEST(TRAVELER, PathFinder) ASSERT_HASNOT(finder.parents_, g.get()); ASSERT_HAS(finder.parents_, f.get()); - EXPECT_HAS(finder.parents_[f.get()], 0); + EXPECT_ARRHAS(finder.parents_[f.get()], 0); } ade::PathFinder finder2(c.get()); @@ -80,7 +80,7 @@ TEST(TRAVELER, PathFinder) { ASSERT_HAS(finder2.parents_, g.get()); - EXPECT_HAS(finder2.parents_[g.get()], 0); + EXPECT_ARRHAS(finder2.parents_[g.get()], 0); } finder2.parents_.clear(); diff --git a/ade/traveler.hpp b/ade/traveler.hpp index 36a77f46d..183cece2c 100644 --- a/ade/traveler.hpp +++ b/ade/traveler.hpp @@ -109,6 +109,8 @@ struct GraphStat final : public iTraveler std::unordered_map> graphsize_; }; +using ParentMapT = std::unordered_map>; + /// Traveler that paints paths to a target tensor /// All nodes in the path are added as keys to the parents_ map with the values /// being a boolean vector denoting nodes leading to target @@ -116,9 +118,6 @@ struct GraphStat final : public iTraveler /// x is true if the ith child leads to target struct PathFinder final : public iTraveler { - /// Type for mapping function nodes in path to boolean vector - using ParentMapT = std::unordered_map>; - PathFinder (const iTensor* target) : target_(target) {} /// Implementation of iTraveler @@ -150,7 +149,7 @@ struct PathFinder final : public iTraveler } if (false == path.empty()) { - parents_[func] = path; + parents_[func] = std::vector(path.begin(), path.end()); } } } @@ -165,8 +164,6 @@ struct PathFinder final : public iTraveler /// Traveler that for each child tracks the relationship to all parents struct ParentFinder final : public iTraveler { - using ParentMapT = std::unordered_map>; - /// Implementation of iTraveler void visit (iLeaf* leaf) override { diff --git a/cfg/BUILD.bazel b/cfg/BUILD.bazel index 5ca6ee25f..efc97340a 100644 --- a/cfg/BUILD.bazel +++ b/cfg/BUILD.bazel @@ -9,6 +9,11 @@ filegroup( srcs = ["ead.yml"], ) +filegroup( + name = "ead_min", + srcs = ["ead_min.yml"], +) + filegroup( name = "optimizations", srcs = ["optimizations.rules"], diff --git a/cfg/ead.yml b/cfg/ead.yml index b0feaca29..c7c410511 100644 --- a/cfg/ead.yml +++ b/cfg/ead.yml @@ -462,27 +462,8 @@ api: type: ead::NodeptrT val: | // - // (todo) report offset out of rank_cap - ade::CoordT slicings; - std::fill(slicings.begin(), slicings.end(), ade::rank_cap); - slicings[0] = offset; - slicings[1] = extent; - slicings[2] = dimension; return ead::make_functor(ade::Opcode{"SLICE",::age::SLICE}, { - ead::FuncArg(arg, - std::make_shared( - [=](ade::MatrixT fwd) - { - for (ade::RankT i = 0; i < ade::rank_cap; ++i) - { - fwd[i][i] = 1; - } - fwd[ade::rank_cap][dimension] = - extent - arg->shape().at(dimension); - }), - std::make_shared(slicings, false) - ) - }); + ead::slice_map(arg, offset, extent, dimension)}); - template: typename T name: pad args: @@ -496,27 +477,8 @@ api: type: ead::NodeptrT val: | // - // (todo) report if dimension out of rank_cap - ade::CoordT paddings; - std::fill(paddings.begin(), paddings.end(), ade::rank_cap); - paddings[0] = padding.first; - paddings[1] = padding.second; - paddings[2] = dimension; return ead::make_functor(ade::Opcode{"PAD",::age::PAD}, { - ead::FuncArg(arg, - std::make_shared( - [=](ade::MatrixT fwd) - { - for (ade::RankT i = 0; i < ade::rank_cap; ++i) - { - fwd[i][i] = 1; - } - fwd[ade::rank_cap][dimension] = - padding.first + padding.second; - }), - std::make_shared(paddings, false) - ) - }); + ead::pad_map(arg, padding, dimension)}); - template: typename T name: matmul args: diff --git a/cfg/ead_min.yml b/cfg/ead_min.yml new file mode 100644 index 000000000..5c74fd707 --- /dev/null +++ b/cfg/ead_min.yml @@ -0,0 +1,952 @@ +--- +dtype: + DOUBLE: double + FLOAT: float +opcode: + operator_path: ead/operator.hpp + params: ade::Shape shape, ead::EigenptrT& out, std::vector>& in + opcalls: + ABS: out = ead::abs(shape,in[0]); + NEG: out = ead::neg(shape,in[0]); + SIN: out = ead::sin(shape,in[0]); + COS: out = ead::cos(shape,in[0]); + TAN: out = ead::tan(shape,in[0]); + EXP: out = ead::exp(shape,in[0]); + LOG: out = ead::log(shape,in[0]); + SQRT: out = ead::sqrt(shape,in[0]); + ROUND: out = ead::round(shape,in[0]); + SIGMOID: out = ead::sigmoid(shape,in[0]); + SIGMOID_GRAD: out = ead::sigmoid_grad(shape,in[0]); + TANH: out = ead::tanh(shape,in[0]); + SQUARE: out = ead::square(shape,in[0]); + CUBE: out = ead::cube(shape,in[0]); + POW: out = ead::pow(shape,in[0],in[1]); + ADD: out = ead::add(shape,in[0],in[1]); + SUB: out = ead::sub(shape,in[0],in[1]); + MUL: out = ead::mul(shape,in[0],in[1]); + DIV: out = ead::div(shape,in[0],in[1]); + MIN: out = ead::min(shape,in[0],in[1]); + MAX: out = ead::max(shape,in[0],in[1]); + EQ: out = ead::eq(shape,in[0],in[1]); + NEQ: out = ead::neq(shape,in[0],in[1]); + LT: out = ead::lt(shape,in[0],in[1]); + GT: out = ead::gt(shape,in[0],in[1]); + RAND_UNIF: out = ead::rand_uniform(shape,in[0],in[1]); + REDUCE_SUM: out = ead::reduce_sum(shape,in[0]); + REDUCE_PROD: out = ead::reduce_prod(shape,in[0]); + REDUCE_MIN: out = ead::reduce_min(shape,in[0]); + REDUCE_MAX: out = ead::reduce_max(shape,in[0]); + PERMUTE: out = ead::permute(shape,in[0]); + EXTEND: out = ead::extend(shape,in[0]); + MATMUL: out = ead::matmul(shape,in[0],in[1]); + CONV: out = ead::convolution(shape,in[0],in[1]); + SLICE: out = ead::slice(shape,in[0]); + PAD: out = ead::pad(shape,in[0]); + CONV_IMG_GRAD: out = ead::convolution_image_grad(shape,in[0],in[1]); + CONV_KRN_GRAD: out = ead::convolution_kernel_grad(shape,in[0],in[1]); + SELECT: out = ead::select(shape, in[0], in[1], in[2]); +api: + pybind_type: float + includes: + - '"ead/constant.hpp"' + - '"ead/variable.hpp"' + - '"ead/functor.hpp"' + - '"tag/group.hpp"' + - '"tag/prop.hpp"' + namespaces: + tenncor: + - template: typename T + name: abs + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"ABS",::age::ABS},{ead::identity_map(arg)}); + - template: typename T + name: neg + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"NEG",::age::NEG},{ead::identity_map(arg)}); + - template: typename T + name: sin + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"SIN",::age::SIN},{ead::identity_map(arg)}); + - template: typename T + name: cos + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"COS",::age::COS},{ead::identity_map(arg)}); + - template: typename T + name: tan + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"TAN",::age::TAN},{ead::identity_map(arg)}); + - template: typename T + name: exp + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"EXP",::age::EXP},{ead::identity_map(arg)}); + - template: typename T + name: log + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"LOG",::age::LOG},{ead::identity_map(arg)}); + - template: typename T + name: sqrt + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"SQRT",::age::SQRT},{ead::identity_map(arg)}); + - template: typename T + name: round + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"ROUND",::age::ROUND},{ead::identity_map(arg)}); + - template: typename T + name: sigmoid + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"SIGMOID",::age::SIGMOID},{ead::identity_map(arg)}); + - template: typename T + name: sigmoid_grad + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"SIGMOID_GRAD",::age::SIGMOID_GRAD},{ead::identity_map(arg)}); + - template: typename T + name: tanh + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"TANH",::age::TANH},{ead::identity_map(arg)}); + - template: typename T + name: square + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"SQUARE",::age::SQUARE},{ead::identity_map(arg)}); + - template: typename T + name: cube + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"CUBE",::age::CUBE},{ead::identity_map(arg)}); + - template: typename T + name: pow + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"POW",::age::POW},{ead::identity_map(arg1),ead::identity_map(arg2)}); + - template: typename T + name: add + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: | + // + auto out = ead::make_functor(ade::Opcode{"ADD",::age::ADD}, { + ead::identity_map(arg1), + ead::identity_map(arg2), + }); + tag::get_property_reg().property_tag(out->get_tensor(), tag::commutative_tag); + tag::get_group_reg().group_tag(out->get_tensor(), "sum"); + return out; + - template: typename T + name: sub + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"SUB",::age::SUB},{ead::identity_map(arg1),ead::identity_map(arg2)}); + - template: typename T + name: mul + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: | + // + auto out = ead::make_functor(ade::Opcode{"MUL",::age::MUL}, { + ead::identity_map(arg1), + ead::identity_map(arg2), + }); + tag::get_property_reg().property_tag(out->get_tensor(), tag::commutative_tag); + tag::get_group_reg().group_tag(out->get_tensor(), "prod"); + return out; + - template: typename T + name: div + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"DIV",::age::DIV},{ead::identity_map(arg1),ead::identity_map(arg2)}); + - template: typename T + name: eq + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: | + // + auto out = ead::make_functor(ade::Opcode{"EQ",::age::EQ}, { + ead::identity_map(arg1), + ead::identity_map(arg2), + }); + tag::get_property_reg().property_tag(out->get_tensor(), tag::commutative_tag); + return out; + - template: typename T + name: neq + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: | + // + auto out = ead::make_functor(ade::Opcode{"NEQ",::age::NEQ}, { + ead::identity_map(arg1), + ead::identity_map(arg2), + }); + tag::get_property_reg().property_tag(out->get_tensor(), tag::commutative_tag); + return out; + - template: typename T + name: lt + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"LT",::age::LT},{ead::identity_map(arg1),ead::identity_map(arg2)}); + - template: typename T + name: gt + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"GT",::age::GT},{ead::identity_map(arg1),ead::identity_map(arg2)}); + - template: typename T + name: min + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: | + // + auto out = ead::make_functor(ade::Opcode{"MIN",::age::MIN}, { + ead::identity_map(arg1), + ead::identity_map(arg2), + }); + tag::get_property_reg().property_tag(out->get_tensor(), tag::commutative_tag); + return out; + - template: typename T + name: max + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: | + // + auto out = ead::make_functor(ade::Opcode{"MAX",::age::MAX}, { + ead::identity_map(arg1), + ead::identity_map(arg2), + }); + tag::get_property_reg().property_tag(out->get_tensor(), tag::commutative_tag); + return out; + - template: typename T + name: if_then_else + args: + - dtype: ead::NodeptrT + name: condition + - dtype: ead::NodeptrT + name: then + - dtype: ead::NodeptrT + name: otherwise + out: + type: ead::NodeptrT + val: | + // + return ead::make_functor(ade::Opcode{"SELECT",::age::SELECT},{ + ead::identity_map(condition), + ead::identity_map(then), + ead::identity_map(otherwise) + }); + - template: typename T + name: permute + args: + - dtype: ead::NodeptrT + name: arg + - dtype: std::vector + name: order + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"PERMUTE",::age::PERMUTE},{ead::permute_map(arg,order)}); + - template: typename T + name: extend + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: offset + - dtype: std::vector + name: xlist + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"EXTEND",::age::EXTEND},{ead::extend_map(arg,offset,xlist)}); + - template: typename T + name: reduce_sum + args: + - dtype: ead::NodeptrT + name: tens + - dtype: ade::RankT + name: offset + default: '0' + - dtype: ade::RankT + name: ndims + default: ade::rank_cap + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"REDUCE_SUM",::age::REDUCE_SUM},{ead::reduce_map(tens,offset,ndims)}); + description: sum values ignoring coordinate indices between start and end + - template: typename T + name: reduce_prod + args: + - dtype: ead::NodeptrT + name: tens + - dtype: ade::RankT + name: offset + default: '0' + - dtype: ade::RankT + name: ndims + default: ade::rank_cap + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"REDUCE_PROD",::age::REDUCE_PROD},{ead::reduce_map(tens,offset,ndims)}); + description: multiply values ignoring coordinate indices between start and end + - template: typename T + name: reduce_min + args: + - dtype: ead::NodeptrT + name: tens + - dtype: ade::RankT + name: offset + default: '0' + - dtype: ade::RankT + name: ndims + default: ade::rank_cap + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"REDUCE_MIN",::age::REDUCE_MIN},{ead::reduce_map(tens,offset,ndims)}); + description: min values ignoring coordinate indices between start and end + - template: typename T + name: reduce_max + args: + - dtype: ead::NodeptrT + name: tens + - dtype: ade::RankT + name: offset + default: '0' + - dtype: ade::RankT + name: ndims + default: ade::rank_cap + out: + type: ead::NodeptrT + val: return ead::make_functor(ade::Opcode{"REDUCE_MAX",::age::REDUCE_MAX},{ead::reduce_map(tens,offset,ndims)}); + description: max values ignoring coordinate indices between start and end + - template: typename T + name: n_elems + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ead::make_constant_scalar(arg->get_tensor()->shape().n_elems(), ade::Shape()); + - template: typename T + name: n_dims + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: rank + out: + type: ead::NodeptrT + val: return ead::make_constant_scalar(arg->get_tensor()->shape().at(rank), ade::Shape()); + - template: typename T + name: slice + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: offset + - dtype: ade::RankT + name: extent + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: | + // + return ead::make_functor(ade::Opcode{"SLICE",::age::SLICE}, { + ead::slice_map(arg, offset, extent, dimension)}); + - template: typename T + name: pad + args: + - dtype: ead::NodeptrT + name: arg + - dtype: std::pair + name: padding + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: | + // + return ead::make_functor(ade::Opcode{"PAD",::age::PAD}, { + ead::pad_map(arg, padding, dimension)}); + - template: typename T + name: matmul + args: + - dtype: ead::NodeptrT + name: a + - dtype: ead::NodeptrT + name: b + out: + type: ead::NodeptrT + val: | + // + ade::Shape ashape = a->get_tensor()->shape(); + ade::Shape bshape = b->get_tensor()->shape(); + ade::DimT ncommon = ashape.at(0); + ade::DimT nrow = ashape.at(1); + ade::DimT ncol = bshape.at(0); + if (ncommon != bshape.at(1)) + { + logs::fatalf("invalid matmul shapes %s and %s", + ashape.to_string().c_str(), bshape.to_string().c_str()); + } + + ade::CoordptrT left_shaper(new ade::CoordMap( + [=](ade::MatrixT fwd) + { + for (ade::RankT i = 3; i < ade::mat_dim; ++i) + { + fwd[i][i] = 1; + } + fwd[2][0] = ncol; + fwd[1][1] = 1; + fwd[0][2] = 1.0 / ncommon; + } + )); + + ade::CoordptrT right_shaper(new ade::CoordMap( + [=](ade::MatrixT fwd) + { + for (ade::RankT i = 3; i < ade::mat_dim; ++i) + { + fwd[i][i] = 1; + } + fwd[0][0] = 1; + fwd[2][1] = nrow; + fwd[1][2] = 1.0 / ncommon; + } + )); + return ead::make_functor(ade::Opcode{"MATMUL",::age::MATMUL}, { + ead::FuncArg(a, left_shaper, nullptr), + ead::FuncArg(b, right_shaper, nullptr) + }); + - template: typename T + name: convolution + args: + - dtype: ead::NodeptrT + name: input + - dtype: ead::NodeptrT + name: kernel + - dtype: std::vector + name: dims + out: + type: ead::NodeptrT + val: | + // + ade::Shape inshape = input->get_tensor()->shape(); + ade::Shape kernelshape = kernel->get_tensor()->shape(); + ade::CoordptrT input_shaper(new ade::CoordMap( + [kernelshape](ade::MatrixT fwd) + { + for (ade::RankT i = 0; i < ade::rank_cap; ++i) + { + fwd[i][i] = 1; + } + for (ade::RankT i = 0; i < ade::rank_cap; ++i) + { + fwd[ade::rank_cap][i] = -kernelshape.at(i) + 1; + } + } + )); + + ade::CoordptrT kernel_shaper(new ade::CoordMap( + [inshape](ade::MatrixT fwd) + { + for (ade::RankT i = 0; i < ade::rank_cap; ++i) + { + fwd[i][i] = -1; + } + for (ade::RankT i = 0; i < ade::rank_cap; ++i) + { + fwd[ade::rank_cap][i] = inshape.at(i) + 1; + } + } + )); + + ade::CoordT kernel_dims; + auto it = kernel_dims.begin(); + std::fill(it, kernel_dims.end(), ade::rank_cap); + std::copy(dims.begin(), dims.end(), it); + return ead::make_functor(ade::Opcode{"CONV",::age::CONV}, { + ead::FuncArg(input, input_shaper, nullptr), + ead::FuncArg(kernel, kernel_shaper, + std::make_shared(kernel_dims, true)), + }); + - template: typename T + name: reduce_sum_1d + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: | + // + auto red = ::tenncor::reduce_sum(arg, dimension, 1); + + std::vector indices(ade::rank_cap); + auto bt = indices.begin(); + auto it = bt + dimension; + std::iota(bt, it, 0); + std::iota(it, indices.end(), dimension + 1); + indices[ade::rank_cap - 1] = dimension; + return ::tenncor::permute(red, indices); + - template: typename T + name: reduce_prod_1d + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: | + // + auto red = ::tenncor::reduce_prod(arg, dimension, 1); + + std::vector indices(ade::rank_cap); + auto bt = indices.begin(); + auto it = bt + dimension; + std::iota(bt, it, 0); + std::iota(it, indices.end(), dimension + 1); + indices[ade::rank_cap - 1] = dimension; + return ::tenncor::permute(red, indices); + - template: typename T + name: reduce_min_1d + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: | + // + auto red = ::tenncor::reduce_min(arg, dimension, 1); + + std::vector indices(ade::rank_cap); + auto bt = indices.begin(); + auto it = bt + dimension; + std::iota(bt, it, 0); + std::iota(it, indices.end(), dimension + 1); + indices[ade::rank_cap - 1] = dimension; + return ::tenncor::permute(red, indices); + - template: typename T + name: reduce_max_1d + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: | + // + auto red = ::tenncor::reduce_max(arg, dimension, 1); + + std::vector indices(ade::rank_cap); + auto bt = indices.begin(); + auto it = bt + dimension; + std::iota(bt, it, 0); + std::iota(it, indices.end(), dimension + 1); + indices[ade::rank_cap - 1] = dimension; + return ::tenncor::permute(red, indices); + - template: typename T + name: transpose + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ::tenncor::permute(arg, {1, 0}); + - template: typename T + name: reduce_mean + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: return ::tenncor::div(::tenncor::reduce_sum(arg), ::tenncor::n_elems(arg)); + - template: typename T + name: reduce_mean_1d + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: | + // + auto red = ::tenncor::reduce_sum_1d(arg, dimension); + auto dim = ead::make_constant_scalar(arg->shape().at(dimension), red->shape()); + return ::tenncor::div(red, dim); + - template: typename T + name: reduce_l2norm + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: offset + default: '0' + - dtype: ade::RankT + name: ndims + default: ade::rank_cap + out: + type: ead::NodeptrT + val: return ::tenncor::sqrt(::tenncor::reduce_sum(::tenncor::square(arg), offset, ndims)); + - template: typename T + name: reduce_l2norm_1d + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: dimension + out: + type: ead::NodeptrT + val: return ::tenncor::sqrt(::tenncor::reduce_sum_1d(::tenncor::square(arg), dimension)); + - template: typename T + name: clip_by_range + args: + - dtype: ead::NodeptrT + name: arg + - dtype: T + name: minval + - dtype: T + name: maxval + out: + type: ead::NodeptrT + val: | + // + if (minval > maxval) + { + logs::fatal("min value is below max"); + } + ade::Shape shape = arg->shape(); + auto lo = ead::make_constant_scalar(minval, shape); + auto hi = ead::make_constant_scalar(maxval, shape); + auto out = ::tenncor::max(::tenncor::min(arg, hi), lo); + tag::recursive_group_tag(out->get_tensor(), "clip_by_range", { + arg->get_tensor().get(), + lo->get_tensor().get(), + hi->get_tensor().get(), + }); + return out; + - template: typename T + name: clip_by_l2norm + args: + - dtype: ead::NodeptrT + name: arg + - dtype: T + name: upper + out: + type: ead::NodeptrT + val: | + // + if (upper == 0) + { + logs::fatal("cannot clip_by_norm with a upper limit of 0"); + } + ade::Shape shape = arg->shape(); + auto limit = ead::make_constant_scalar(upper, shape); + auto norm = ::tenncor::extend(::tenncor::reduce_l2norm(arg), 0, + std::vector(shape.begin(), shape.end())); + auto out = ::tenncor::if_then_else(::tenncor::lt(norm, limit), + arg, ::tenncor::div(::tenncor::mul(arg, limit), norm)); + tag::recursive_group_tag(out->get_tensor(), "clip_by_l2norm", { + arg->get_tensor().get(), + limit->get_tensor().get(), + }); + return out; + description: 'clip by l2norm ((todo) allow l2norm to be configurable)' + - template: typename T + name: sum + args: + - dtype: ead::NodesT + name: args + out: + type: ead::NodeptrT + val: | + // + if (args.empty()) + { + logs::fatal("cannot sum without arguments"); + } + ead::NodeptrT out = args[0]; + for (size_t i = 1, n = args.size(); i < n; ++i) + { + out = ::tenncor::add(out, args[i]); + } + return out; + - template: typename T + name: prod + args: + - dtype: ead::NodesT + name: args + out: + type: ead::NodeptrT + val: | + // + if (args.empty()) + { + logs::fatal("cannot sum without arguments"); + } + ead::NodeptrT out = args[0]; + for (size_t i = 1, n = args.size(); i < n; ++i) + { + out = ::tenncor::mul(out, args[i]); + } + return out; + - template: typename T + name: softmax + args: + - dtype: ead::NodeptrT + name: arg + - dtype: ade::RankT + name: offset + default: '0' + - dtype: ade::RankT + name: ndims + default: ade::rank_cap + out: + type: ead::NodeptrT + val: | + // + auto exarg = exp(arg); + ade::Shape shape = exarg->shape(); + auto it = shape.begin() + offset; + std::vector xlist(it, it + ndims); + auto out = ::tenncor::div(exarg, + ::tenncor::extend(::tenncor::reduce_sum(exarg, offset, offset+ndims), + offset, xlist)); + tag::recursive_group_tag(out->get_tensor(), "softmax", { + arg->get_tensor().get()}); + return out; + - template: typename T + name: sign + args: + - dtype: ead::NodeptrT + name: x + out: + type: ead::NodeptrT + val: return ::tenncor::pow(x,ead::make_constant_scalar(0,x->shape())); + tenncor::random: + - template: typename T + name: rand_unif + args: + - dtype: ead::NodeptrT + name: arg1 + - dtype: ead::NodeptrT + name: arg2 + out: + type: ead::NodeptrT + val: | + // + return ead::make_functor( + ade::Opcode{"RAND_UNIF",::age::RAND_UNIF},{ + ead::identity_map(arg1), + ead::identity_map(arg2) + }); + - template: typename T + name: rand_binom_one + args: + - dtype: ead::NodeptrT + name: arg + out: + type: ead::NodeptrT + val: | + // + const ade::Shape& shape = arg->get_tensor()->shape(); + auto trial = ::tenncor::random::rand_unif( + ead::convert_to_node(ead::make_variable_scalar((T) 0, shape)), + ead::convert_to_node(ead::make_variable_scalar((T) 1, shape))); + return ::tenncor::lt(trial, arg); + tenncor::nn: + - template: typename T + name: relu + args: + - dtype: ead::NodeptrT + name: x + out: + type: ead::NodeptrT + val: return ::tenncor::max(x,ead::make_constant_scalar(0,x->shape())); + - template: typename T + name: conv2d + args: + - dtype: ead::NodeptrT + name: image + - dtype: ead::NodeptrT + name: kernel + out: + type: ead::NodeptrT + val: | + // + // image must be in form [in, width, height, batch] + // kernel must be in form [out, in, width, height] + // see https://www.tensorflow.org/api_docs/python/tf/nn/conv2d + ade::DimT nfilters = kernel->shape().at(0); + ead::NodesT convolveds; + convolveds.reserve(nfilters); + for (ade::DimT i = 0; i < nfilters; ++i) + { + auto filter = ::tenncor::permute( + ::tenncor::slice(kernel, i, 1, 0), + {1, 2, 3, 0}); + auto conved = ::tenncor::convolution(image, filter, + {0, 1, 2}); + auto padded = ::tenncor::pad(conved, + {i, nfilters - i - 1}, 0); + convolveds.push_back(padded); + } + auto out = ::tenncor::sum(convolveds); + tag::recursive_group_tag(out->get_tensor(), "conv2d", { + image->get_tensor().get(), + kernel->get_tensor().get() + }); + return out; + - template: typename T + name: fully_connect + args: + - dtype: ead::NodesT + name: inputs + - dtype: ead::NodesT + name: weights + - dtype: ead::NodeptrT + name: bias + out: + type: ead::NodeptrT + val: | + // + if (weights.empty()) + { + logs::fatal("cannot create a fully connected layer without weights"); + } + size_t ninputs = inputs.size(); + if (ninputs != weights.size()) + { + logs::fatalf( + "number of inputs (%d) must equal the number of weights (%d)", + ninputs, weights.size()); + } + std::unordered_set ignores = { + inputs[0]->get_tensor().get(), + weights[0]->get_tensor().get() + }; + auto out = ::tenncor::matmul(inputs[0], weights[0]); + for (size_t i = 1; i < ninputs; ++i) + { + ignores.emplace(inputs[i]->get_tensor().get()); + ignores.emplace(weights[i]->get_tensor().get()); + out = ::tenncor::add(out, ::tenncor::matmul(inputs[i], weights[i])); + } + if (nullptr != bias) + { + const ade::Shape& shape = out->shape(); + out = ::tenncor::add(out, ::tenncor::extend(bias, 1, {shape.at(1)})); + ignores.emplace(bias->get_tensor().get()); + } + tag::recursive_group_tag(out->get_tensor(), "fully_connect", ignores); + return out; diff --git a/dbg/grpc/session.hpp b/dbg/grpc/session.hpp index 97a8fca05..abe13a65d 100644 --- a/dbg/grpc/session.hpp +++ b/dbg/grpc/session.hpp @@ -58,8 +58,8 @@ struct InteractiveSession final : public ead::iSession InteractiveSession (std::shared_ptr channel, ClientConfig client_cfg = ClientConfig(), tag::TagRegistry& registry = tag::get_reg()) : - client_(channel, client_cfg), - registry_(registry) + registry_(registry), + client_(channel, client_cfg) { logs::infof("created session: %s", sess_id_.c_str()); } diff --git a/dbg/python/stream.cpp b/dbg/python/stream.cpp index b49297902..d0c47bb91 100644 --- a/dbg/python/stream.cpp +++ b/dbg/python/stream.cpp @@ -92,7 +92,7 @@ PYBIND11_MODULE(stream_dbg, m) } else { - logs::warnf("failed to print graph to file '%s'", + logs::warnf("failed to print graph to file `%s`", filename.c_str()); } }, @@ -112,7 +112,7 @@ PYBIND11_MODULE(stream_dbg, m) } else { - logs::warnf("failed to write csv to file '%s'", filename.c_str()); + logs::warnf("failed to write csv to file `%s`", filename.c_str()); } }, "Stream csv of graph edges to file", @@ -134,7 +134,7 @@ PYBIND11_MODULE(stream_dbg, m) } else { - logs::warnf("failed to write csv to file '%s'", filename.c_str()); + logs::warnf("failed to write csv to file `%s`", filename.c_str()); } }, "Return csv of graph edges of multiple roots to file", diff --git a/dbg/stream/ade.hpp b/dbg/stream/ade.hpp index 7ecad7ce6..4177d766e 100644 --- a/dbg/stream/ade.hpp +++ b/dbg/stream/ade.hpp @@ -8,6 +8,7 @@ #include +#include "ade/ileaf.hpp" #include "ade/functor.hpp" #include "dbg/stream/tree.hpp" @@ -45,6 +46,10 @@ struct PrettyEquation final { out << it->second << "="; } + if (auto var = dynamic_cast(root)) + { + out << (var->is_const() ? "constant:" : "variable:"); + } out << root->to_string(); if (showshape_) { @@ -54,7 +59,7 @@ struct PrettyEquation final }) {} /// Stream equation of ptr to out - void print (std::ostream& out, ade::TensptrT& ptr) + void print (std::ostream& out, const ade::TensptrT& ptr) { drawer_.print(out, ptr.get()); } diff --git a/docs/_r_e_a_d_m_e_8md.html b/docs/_r_e_a_d_m_e_8md.html deleted file mode 100644 index 9bc684ede..000000000 --- a/docs/_r_e_a_d_m_e_8md.html +++ /dev/null @@ -1,76 +0,0 @@ - - - - - - - -Tenncor: README.md File Reference - - - - - - - - - -
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9 #include "ade/functor.hpp"
10 #include "ade/traveler.hpp"
11 #include "ade/iopfunc.hpp"
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struct  ade::iCoordMap
 Interface for transforming coordinates and reversing the coordinate. More...
 
struct  ade::CoordMap
 
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using ade::CoordptrT = std::shared_ptr< iCoordMap >
 Type of iCoordMap smartpointer. More...
 
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bool ade::is_identity (iCoordMap *coorder)
 Checks if the coord mapper is an identity mapper. More...
 
CoordptrT ade::reduce (RankT rank, std::vector< DimT > red)
 
CoordptrT ade::extend (RankT rank, std::vector< DimT > ext)
 
CoordptrT ade::permute (std::vector< RankT > order)
 
CoordptrT ade::flip (RankT dim)
 
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CoordptrT ade::identity
 Identity matrix instance. More...
 
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1 
9 #include <functional>
10 
11 #include "ade/matops.hpp"
12 
13 #ifndef ADE_COORD_HPP
14 #define ADE_COORD_HPP
15 
16 namespace ade
17 {
18 
20 struct iCoordMap
21 {
22  virtual ~iCoordMap (void) = default;
23 
25  virtual iCoordMap* connect (const iCoordMap& rhs) const = 0;
26 
28  virtual void forward (CoordT::iterator out,
29  CoordT::const_iterator in) const = 0;
30 
33  virtual iCoordMap* reverse (void) const = 0;
34 
36  virtual std::string to_string (void) const = 0;
37 
40  virtual void access (std::function<void(const MatrixT&)> cb) const = 0;
41 
43  virtual bool is_bijective (void) const = 0;
44 };
45 
48 struct CoordMap final : public iCoordMap
49 {
50  CoordMap (std::function<void(MatrixT)> init)
51  {
52  std::memset(fwd_, 0, mat_size);
53  fwd_[rank_cap][rank_cap] = 1;
54  init(fwd_);
55  }
56 
58  iCoordMap* connect (const iCoordMap& rhs) const override
59  {
60  return new CoordMap([&](MatrixT out)
61  {
62  rhs.access([&](const MatrixT& in)
63  {
64  matmul(out, fwd_, in);
65  });
66  });
67  }
68 
70  void forward (CoordT::iterator out,
71  CoordT::const_iterator in) const override;
72 
74  iCoordMap* reverse (void) const override
75  {
76  return new CoordMap([this](MatrixT m)
77  {
78  inverse(m, this->fwd_);
79  });
80  }
81 
83  std::string to_string (void) const override
84  {
85  return ade::to_string(fwd_);
86  }
87 
89  void access (std::function<void(const MatrixT&)> cb) const override
90  {
91  cb(fwd_);
92  }
93 
95  bool is_bijective (void) const override
96  {
97  return (int) determinant(fwd_) != 0;
98  }
99 
100 private:
102  MatrixT fwd_;
103 };
104 
106 using CoordptrT = std::shared_ptr<iCoordMap>;
107 
109 extern CoordptrT identity;
110 
112 bool is_identity (iCoordMap* coorder);
113 
118 CoordptrT reduce (RankT rank, std::vector<DimT> red);
119 
124 CoordptrT extend (RankT rank, std::vector<DimT> ext);
125 
134 CoordptrT permute (std::vector<RankT> order);
135 
139 CoordptrT flip (RankT dim);
140 
141 }
142 
143 #endif // ADE_COORD_HPP
ade::iCoordMap::forward
virtual void forward(CoordT::iterator out, CoordT::const_iterator in) const =0
Forward transform coordinates.
-
ade::flip
CoordptrT flip(RankT dim)
-
ade::extend
CoordptrT extend(RankT rank, std::vector< DimT > ext)
-
ade::reduce
CoordptrT reduce(RankT rank, std::vector< DimT > red)
-
ade::iCoordMap::is_bijective
virtual bool is_bijective(void) const =0
Return true if this instance maps coordinates/shapes bijectively.
-
ade::iCoordMap::~iCoordMap
virtual ~iCoordMap(void)=default
-
ade::iCoordMap::to_string
virtual std::string to_string(void) const =0
Return string representation of coordinate transformer.
-
ade::CoordMap::is_bijective
bool is_bijective(void) const override
Implementation of iCoordMap.
Definition: coord.hpp:95
-
ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
-
ade::CoordMap::access
void access(std::function< void(const MatrixT &)> cb) const override
Implementation of iCoordMap.
Definition: coord.hpp:89
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ade::CoordMap::CoordMap
CoordMap(std::function< void(MatrixT)> init)
Definition: coord.hpp:50
- -
ade::iCoordMap::access
virtual void access(std::function< void(const MatrixT &)> cb) const =0
-
ade::matmul
void matmul(MatrixT out, const MatrixT &lhs, const MatrixT &rhs)
Apply matrix multiplication for lhs and rhs to out matrix.
-
ade::CoordMap::connect
iCoordMap * connect(const iCoordMap &rhs) const override
Implementation of iCoordMap.
Definition: coord.hpp:58
-
ade::mat_size
const size_t mat_size
Number of bytes in a homogeneous matrix.
Definition: matops.hpp:25
-
ade::identity
CoordptrT identity
Identity matrix instance.
-
ade::iCoordMap
Interface for transforming coordinates and reversing the coordinate.
Definition: coord.hpp:20
-
ade::MatrixT
double[mat_dim][mat_dim] MatrixT
Coordinate transformation matrix (using homogeneous)
Definition: matops.hpp:28
-
ade
Definition: coord.hpp:16
-
ade::CoordMap
Definition: coord.hpp:48
-
ade::CoordMap::to_string
std::string to_string(void) const override
Implementation of iCoordMap.
Definition: coord.hpp:83
-
ade::CoordMap::forward
void forward(CoordT::iterator out, CoordT::const_iterator in) const override
Implementation of iCoordMap.
-
ade::RankT
uint8_t RankT
Type used for shape rank.
Definition: shape.hpp:23
-
ade::determinant
double determinant(const MatrixT &mat)
Return the determinant of matrix.
-
ade::CoordMap::reverse
iCoordMap * reverse(void) const override
Implementation of iCoordMap.
Definition: coord.hpp:74
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ade::iCoordMap::reverse
virtual iCoordMap * reverse(void) const =0
-
ade::permute
CoordptrT permute(std::vector< RankT > order)
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ade::CoordMap::fwd_
MatrixT fwd_
Forward transformation matrix.
Definition: coord.hpp:102
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ade::rank_cap
const RankT rank_cap
Number of dimsensions in a shape/coordinate.
Definition: shape.hpp:45
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ade::to_string
std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
-
ade::iCoordMap::connect
virtual iCoordMap * connect(const iCoordMap &rhs) const =0
Return matmul(this, rhs)
-
ade::inverse
void inverse(MatrixT out, const MatrixT &in)
Inverse in matrix and dump to out matrix.
-
ade::is_identity
bool is_identity(iCoordMap *coorder)
Checks if the coord mapper is an identity mapper.
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struct  ade::FuncArg
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using ade::ArgsT = std::vector< FuncArg >
 Type of functor arguments. More...
 
using ade::TensT = std::vector< TensptrT >
 Vector representation of ade tensor pointers. More...
 
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Shape ade::apply_shaper (const CoordptrT &shaper, Shape inshape)
 
FuncArg ade::identity_map (TensptrT tensor)
 Return FuncArg that identity maps input tensor. More...
 
FuncArg ade::reduce_1d_map (TensptrT tensor, RankT rank)
 
FuncArg ade::reduce_map (TensptrT tensor, RankT rank, std::vector< DimT > red)
 
FuncArg ade::extend_map (TensptrT tensor, RankT rank, std::vector< DimT > ext)
 
FuncArg ade::permute_map (TensptrT tensor, std::vector< RankT > order)
 
FuncArg ade::flip_map (TensptrT tensor, RankT dim)
 Return FuncArg that flips input tensor along dimension. More...
 
ArgsT ade::to_args (TensT tens)
 Return ArgsT with each tensor in TensT attached to identity mapper. More...
 
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1 
9 #include "ade/itensor.hpp"
10 #include "ade/coord.hpp"
11 
12 #ifndef ADE_FUNCARG_HPP
13 #define ADE_FUNCARG_HPP
14 
15 namespace ade
16 {
17 
18 Shape apply_shaper (const CoordptrT& shaper, Shape inshape);
19 
21 struct FuncArg final
22 {
24  FuncArg (TensptrT tensor, CoordptrT shaper) :
25  tensor_(tensor), shaper_(shaper)
26  {
27  if (tensor_ == nullptr)
28  {
29  logs::fatal("cannot map a null tensor");
30  }
31  map_io_ = tensor_->shape().n_elems() > shape().n_elems();
32  if (shaper == identity || map_io_)
33  {
34  coorder_ = shaper;
35  }
36  else
37  {
38  coorder_ = CoordptrT(shaper->reverse());
39  }
40  }
41 
43  FuncArg (TensptrT tensor, CoordptrT shaper,
44  bool map_io, CoordptrT coorder) :
45  tensor_(tensor), shaper_(shaper),
46  map_io_(map_io), coorder_(coorder)
47  {
48  if (tensor_ == nullptr)
49  {
50  logs::fatal("cannot map a null tensor");
51  }
52  }
53 
55  Shape shape (void) const
56  {
57  return apply_shaper(shaper_, tensor_->shape());
58  }
59 
61  TensptrT get_tensor (void) const
62  {
63  return tensor_;
64  }
65 
67  CoordptrT get_shaper (void) const
68  {
69  return shaper_;
70  }
71 
74  bool map_io (void) const
75  {
76  return map_io_;
77  }
78 
80  CoordptrT get_coorder (void) const
81  {
82  return coorder_;
83  }
84 
85 private:
87  TensptrT tensor_;
88 
90  CoordptrT shaper_;
91 
94  bool map_io_;
95 
97  CoordptrT coorder_;
98 };
99 
101 using ArgsT = std::vector<FuncArg>;
102 
104 using TensT = std::vector<TensptrT>;
105 
107 FuncArg identity_map (TensptrT tensor);
108 
112 FuncArg reduce_1d_map (TensptrT tensor, RankT rank);
113 
118 FuncArg reduce_map (TensptrT tensor,
119  RankT rank, std::vector<DimT> red);
120 
125 FuncArg extend_map (TensptrT tensor,
126  RankT rank, std::vector<DimT> ext);
127 
131 FuncArg permute_map (TensptrT tensor, std::vector<RankT> order);
132 
134 FuncArg flip_map (TensptrT tensor, RankT dim);
135 
137 ArgsT to_args (TensT tens);
138 
139 }
140 
141 #endif // ADE_FUNCARG_HPP
ade::FuncArg::FuncArg
FuncArg(TensptrT tensor, CoordptrT shaper)
Construct FuncArg auto deducing coorder_ and map_io_ flag.
Definition: funcarg.hpp:24
-
ade::extend_map
FuncArg extend_map(TensptrT tensor, RankT rank, std::vector< DimT > ext)
-
ade::identity_map
FuncArg identity_map(TensptrT tensor)
Return FuncArg that identity maps input tensor.
-
ade::FuncArg::coorder_
CoordptrT coorder_
Coordinate mapper.
Definition: funcarg.hpp:97
-
ade::permute_map
FuncArg permute_map(TensptrT tensor, std::vector< RankT > order)
-
ade::FuncArg::map_io_
bool map_io_
Definition: funcarg.hpp:94
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ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
-
ade::Shape::n_elems
NElemT n_elems(void) const
Return the total number of elements represented by the shape.
Definition: shape.hpp:116
-
ade::to_args
ArgsT to_args(TensT tens)
Return ArgsT with each tensor in TensT attached to identity mapper.
-
ade::FuncArg::map_io
bool map_io(void) const
Definition: funcarg.hpp:74
-
ade::identity
CoordptrT identity
Identity matrix instance.
-
ade::FuncArg::get_coorder
CoordptrT get_coorder(void) const
Return coord map for coordinates.
Definition: funcarg.hpp:80
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ade
Definition: coord.hpp:16
-
ade::flip_map
FuncArg flip_map(TensptrT tensor, RankT dim)
Return FuncArg that flips input tensor along dimension.
-
ade::apply_shaper
Shape apply_shaper(const CoordptrT &shaper, Shape inshape)
-
ade::reduce_map
FuncArg reduce_map(TensptrT tensor, RankT rank, std::vector< DimT > red)
-
ade::RankT
uint8_t RankT
Type used for shape rank.
Definition: shape.hpp:23
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
-
ade::Shape
Definition: shape.hpp:60
-
ade::TensT
std::vector< TensptrT > TensT
Vector representation of ade tensor pointers.
Definition: funcarg.hpp:104
-
ade::FuncArg::tensor_
TensptrT tensor_
Tensor reference.
Definition: funcarg.hpp:87
-
ade::FuncArg::get_shaper
CoordptrT get_shaper(void) const
Return shaper coord map.
Definition: funcarg.hpp:67
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ade::FuncArg::FuncArg
FuncArg(TensptrT tensor, CoordptrT shaper, bool map_io, CoordptrT coorder)
Construct FuncArg with specific coorder_ and map_io_ flag.
Definition: funcarg.hpp:43
-
ade::FuncArg::shaper_
CoordptrT shaper_
Shape mapper.
Definition: funcarg.hpp:90
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ade::FuncArg::shape
Shape shape(void) const
Return shape of tensor filtered through coordinate mapper.
Definition: funcarg.hpp:55
-
ade::FuncArg::get_tensor
TensptrT get_tensor(void) const
Return tensor being mapped.
Definition: funcarg.hpp:61
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ade::reduce_1d_map
FuncArg reduce_1d_map(TensptrT tensor, RankT rank)
-
ade::ArgsT
std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
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1 
9 #include "ade/ifunctor.hpp"
10 
11 #ifndef ADE_FUNCTOR_HPP
12 #define ADE_FUNCTOR_HPP
13 
14 namespace ade
15 {
16 
18 struct Functor final : public iFunctor
19 {
21  static Functor* get (Opcode opcode, ArgsT args)
22  {
23  if (0 == args.size())
24  {
25  logs::fatalf("cannot perform %s with no arguments",
26  opcode.name_.c_str());
27  }
28 
29  Shape shape = args[0].shape();
30  for (size_t i = 1, n = args.size(); i < n; ++i)
31  {
32  Shape ishape = args[i].shape();
33  if (false == ishape.compatible_after(shape, 0))
34  {
35  logs::fatalf("cannot perform %s with incompatible shapes %s "
36  "and %s", opcode.name_.c_str(), shape.to_string().c_str(),
37  ishape.to_string().c_str());
38  }
39  }
40  return new Functor(opcode, shape, args);
41  }
42 
43  static Functor* get (const Functor& other)
44  {
45  return new Functor(other);
46  }
47 
48  static Functor* get (Functor&& other)
49  {
50  return new Functor(std::move(other));
51  }
52 
53  Functor& operator = (const Functor& other) = delete;
54 
55  Functor& operator = (Functor&& other) = delete;
56 
58  const Shape& shape (void) const override
59  {
60  return shape_;
61  }
62 
64  std::string to_string (void) const override
65  {
66  return opcode_.name_;
67  }
68 
70  Opcode get_opcode (void) const override
71  {
72  return opcode_;
73  }
74 
76  const ArgsT& get_children (void) const override
77  {
78  return args_;
79  }
80 
82  void update_child (FuncArg arg, size_t index) override
83  {
84  logs::warn("ade::Functor does not allow editing of children");
85  }
86 
87 private:
88  Functor (Opcode opcode, Shape shape, ArgsT args) :
89  opcode_(opcode), shape_(shape), args_(args) {}
90 
91  Functor (const Functor& other) = default;
92 
93  Functor (Functor&& other) = default;
94 
96  Opcode opcode_;
97 
99  Shape shape_;
100 
102  ArgsT args_;
103 };
104 
105 }
106 
107 #endif // ADE_FUNCTOR_HPP
ade::Functor
Functor of the graph mapping to operators specified by opcode argument.
Definition: functor.hpp:18
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ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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csv_to_png.args
args
Definition: csv_to_png.py:105
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ade::Opcode
Encoding of operation.
Definition: ifunctor.hpp:18
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ade::Opcode::name_
std::string name_
String representation of operation.
Definition: ifunctor.hpp:21
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ade::Functor::operator=
Functor & operator=(const Functor &other)=delete
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ade::Shape::to_string
std::string to_string(void) const
Return string representation of shape.
Definition: shape.hpp:146
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ade::Functor::get_opcode
Opcode get_opcode(void) const override
Implementation of iFunctor.
Definition: functor.hpp:70
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ade::Functor::opcode_
Opcode opcode_
Operation encoding.
Definition: functor.hpp:96
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ade::Functor::get_children
const ArgsT & get_children(void) const override
Implementation of iFunctor.
Definition: functor.hpp:76
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ade
Definition: coord.hpp:16
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ade::Functor::args_
ArgsT args_
Tensor arguments (and children)
Definition: functor.hpp:102
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ade::Functor::update_child
void update_child(FuncArg arg, size_t index) override
Implementation of iFunctor.
Definition: functor.hpp:82
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
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ade::Functor::to_string
std::string to_string(void) const override
Implementation of iTensor.
Definition: functor.hpp:64
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ade::index
NElemT index(Shape shape, CoordT coord)
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ade::Shape::compatible_after
bool compatible_after(const Shape &other, RankT idx) const
Definition: shape.hpp:134
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ade::Shape
Definition: shape.hpp:60
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ade::Functor::shape_
Shape shape_
Shape info built at construction time according to arguments.
Definition: functor.hpp:99
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Functor(Opcode opcode, Shape shape, ArgsT args)
Definition: functor.hpp:88
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ade::Functor::shape
const Shape & shape(void) const override
Implementation of iTensor.
Definition: functor.hpp:58
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std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
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ileaf.hpp
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1 
9 #include "ade/itensor.hpp"
10 #include "ade/idata.hpp"
11 
12 #ifndef ADE_ILEAF_HPP
13 #define ADE_ILEAF_HPP
14 
15 namespace ade
16 {
17 
19 struct iLeaf : public iTensor, public iData
20 {
21  virtual ~iLeaf (void) = default;
22 
24  void accept (iTraveler& visiter) override
25  {
26  visiter.visit(this);
27  }
28 };
29 
31 using LeafptrT = std::shared_ptr<iLeaf>;
32 
33 }
34 
35 #endif // ADE_ILEAF_HPP
ade::LeafptrT
std::shared_ptr< iLeaf > LeafptrT
Leaf smart pointer.
Definition: ileaf.hpp:31
- -
ade::iLeaf
Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
-
ade::iData
Interface for unveiling data.
Definition: idata.hpp:19
-
ade
Definition: coord.hpp:16
-
ade::iTraveler
Interface to travel through graph, treating iLeaf and iFunctor differently.
Definition: itensor.hpp:22
-
ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
-
ade::iTraveler::visit
virtual void visit(iLeaf *leaf)=0
Visit leaf node.
- -
ade::iLeaf::accept
void accept(iTraveler &visiter) override
Implementation of iTensor.
Definition: ileaf.hpp:24
-
ade::iLeaf::~iLeaf
virtual ~iLeaf(void)=default
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coord.cpp File Reference
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#include "ade/coord.hpp"
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-} diff --git a/docs/ade_8hpp__incl.md5 b/docs/ade_8hpp__incl.md5 deleted file mode 100644 index 5016c0996..000000000 --- a/docs/ade_8hpp__incl.md5 +++ /dev/null @@ -1 +0,0 @@ -6ac843c1dfab75beb7ea4980331af09c \ No newline at end of file diff --git a/docs/ade_8hpp_source.html b/docs/ade_8hpp_source.html deleted file mode 100644 index f0e9737fb..000000000 --- a/docs/ade_8hpp_source.html +++ /dev/null @@ -1,83 +0,0 @@ - - - - - - - -Tenncor: ade/ade.hpp Source File - - - - - - - - - -
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1 
9 #include "ade/functor.hpp"
10 #include "ade/traveler.hpp"
11 #include "ade/iopfunc.hpp"
- - -
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ade_csv.hpp File Reference
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#include <unordered_map>
-#include <unordered_set>
-#include <utility>
-#include "ade/ileaf.hpp"
-#include "ade/ifunctor.hpp"
-#include "estd/estd.hpp"
-#include "dbg/stream/ade.hpp"
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- - - - - - - - -

-Classes

struct  CSVEquation
 
struct  CSVEquation::Edge
 
struct  CSVEquation::Node
 
- - - -

-Typedefs

using GetTypeF = std::function< NODE_TYPE(ade::iFunctor *)>
 
- - - -

-Enumerations

enum  NODE_TYPE { VARIABLE = 0, -FUNCTOR, -CACHED_FUNC - }
 
- - - -

-Functions

static void multiline_replace (std::string &multiline)
 
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-Variables

const char label_delim = ':'
 
-

Typedef Documentation

- -

◆ GetTypeF

- -
-
- - - - -
using GetTypeF = std::function<NODE_TYPE(ade::iFunctor*)>
-
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Enumeration Type Documentation

- -

◆ NODE_TYPE

- -
-
- - - - -
enum NODE_TYPE
-
- - - - -
Enumerator
VARIABLE 
FUNCTOR 
CACHED_FUNC 
- -
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Function Documentation

- -

◆ multiline_replace()

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static void multiline_replace (std::string & multiline)
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Variable Documentation

- -

◆ label_delim

- -
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- - diff --git a/docs/ade__csv_8hpp__dep__incl.map b/docs/ade__csv_8hpp__dep__incl.map deleted file mode 100644 index f92a25dd8..000000000 --- a/docs/ade__csv_8hpp__dep__incl.map +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/docs/ade__csv_8hpp__dep__incl.md5 b/docs/ade__csv_8hpp__dep__incl.md5 deleted file mode 100644 index 01ea5a61f..000000000 --- a/docs/ade__csv_8hpp__dep__incl.md5 +++ /dev/null @@ -1 +0,0 @@ -6f810a1b6b8af56dddb0795365f1d08f \ No newline at end of file diff --git a/docs/ade__csv_8hpp__dep__incl.png b/docs/ade__csv_8hpp__dep__incl.png deleted file mode 100644 index 8325124bb..000000000 Binary files a/docs/ade__csv_8hpp__dep__incl.png and /dev/null differ diff --git a/docs/ade__csv_8hpp__incl.map b/docs/ade__csv_8hpp__incl.map deleted file mode 100644 index 25b36bd88..000000000 --- a/docs/ade__csv_8hpp__incl.map +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - - - - - - - - diff --git a/docs/ade__csv_8hpp__incl.md5 b/docs/ade__csv_8hpp__incl.md5 deleted file mode 100644 index be253d461..000000000 --- a/docs/ade__csv_8hpp__incl.md5 +++ /dev/null @@ -1 +0,0 @@ -f0af18fa87bc038a023ae52880e97d07 \ No newline at end of file diff --git a/docs/ade__csv_8hpp__incl.png b/docs/ade__csv_8hpp__incl.png deleted file mode 100644 index c69aad253..000000000 Binary files a/docs/ade__csv_8hpp__incl.png and /dev/null differ diff --git a/docs/ade__csv_8hpp_source.html b/docs/ade__csv_8hpp_source.html deleted file mode 100644 index 6f0159a2c..000000000 --- a/docs/ade__csv_8hpp_source.html +++ /dev/null @@ -1,125 +0,0 @@ - - - - - - - -Tenncor: dbg/stream/ade_csv.hpp Source File - - - - - - - - - -
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1 #include <unordered_map>
2 #include <unordered_set>
3 #include <utility>
4 
5 #include "ade/ileaf.hpp"
6 #include "ade/ifunctor.hpp"
7 
8 #include "estd/estd.hpp"
9 
10 #include "dbg/stream/ade.hpp"
11 
12 #ifndef DBG_ADE_CSV_HPP
13 #define DBG_ADE_CSV_HPP
14 
15 const char label_delim = ':';
16 
17 static void multiline_replace (std::string& multiline)
18 {
19  size_t i = 0;
20  char nline = '\n';
21  while ((i = multiline.find(nline, i)) != std::string::npos)
22  {
23  multiline.replace(i, 1, "\\");
24  }
25 }
26 
27 enum NODE_TYPE
28 {
29  VARIABLE = 0,
30  FUNCTOR,
31  CACHED_FUNC,
32 };
33 
34 using GetTypeF = std::function<NODE_TYPE(ade::iFunctor*)>;
35 
36 struct CSVEquation final : public ade::iTraveler
37 {
38  CSVEquation (GetTypeF get_ftype =
39  [](ade::iFunctor* func) { return FUNCTOR; }) :
40  get_ftype_(get_ftype) {}
41 
42  void visit (ade::iLeaf* leaf) override
43  {
44  if (estd::has(nodes_, leaf))
45  {
46  return;
47  }
48  std::string label;
49  auto it = labels_.find(leaf);
50  if (labels_.end() != it)
51  {
52  label = it->second + "=";
53  }
54  label += leaf->to_string();
55  if (showshape_)
56  {
57  label += leaf->shape().to_string();
58  }
59  nodes_.emplace(leaf, Node{
60  label,
61  VARIABLE,
62  nodes_.size(),
63  });
64  }
65 
66  void visit (ade::iFunctor* func) override
67  {
68  if (estd::has(nodes_, func))
69  {
70  return;
71  }
72  std::string funcstr;
73  auto it = labels_.find(func);
74  if (labels_.end() != it)
75  {
76  funcstr = it->second + "=";
77  }
78  funcstr += func->to_string();
79  if (showshape_)
80  {
81  funcstr += func->shape().to_string();
82  }
83  nodes_.emplace(func, Node{
84  funcstr,
85  get_ftype_(func),
86  nodes_.size(),
87  });
88  auto& children = func->get_children();
89  for (size_t i = 0, n = children.size(); i < n; ++i)
90  {
91  const ade::FuncArg& child = children[i];
92  auto coorder = child.get_coorder().get();
93  auto tens = child.get_tensor().get();
94  if (ade::is_identity(coorder))
95  {
96  coorder = nullptr;
97  }
98  else
99  {
100  std::string coordstr = coorder->to_string();
101  multiline_replace(coordstr);
102  coorders_.emplace(coorder, coordstr);
103  }
104  edges_.push_back(Edge{
105  func,
106  tens,
107  coorder,
108  fmts::to_string(i),
109  });
110  tens->accept(*this);
111  }
112  }
113 
114  void to_stream (std::ostream& out)
115  {
116  size_t nnodes = nodes_.size();
117  for (size_t i = 0, nedges = edges_.size(); i < nedges; ++i)
118  {
119  const Edge& edge = edges_[i];
120  auto& parent_node = nodes_[edge.func_];
121  auto& child_node = nodes_[edge.child_];
122  std::string color = child_node.ntype_ == CACHED_FUNC ?
123  "red" : "white";
124  if (nullptr == edge.coorder_)
125  {
126  out << parent_node.id_ << label_delim
127  << parent_node.label_ << ','
128  << child_node.id_ << label_delim
129  << child_node.label_ << ','
130  << edge.edge_label_ << ','
131  << color << '\n';
132  }
133  else
134  {
135  out << parent_node.id_ << label_delim
136  << parent_node.label_ << ','
137  << nnodes + i << label_delim
138  << coorders_[edge.coorder_] << ','
139  << edge.edge_label_ << ','
140  << color << '\n';
141 
142  out << nnodes + i << label_delim
143  << coorders_[edge.coorder_] << ','
144  << child_node.id_ << label_delim
145  << child_node.label_ << ','
146  << edge.edge_label_ << ','
147  << color << '\n';
148  }
149  }
150  }
151 
152  bool showshape_ = false;
153 
154  struct Edge
155  {
156  ade::iFunctor* func_;
157 
158  ade::iTensor* child_;
159 
160  ade::iCoordMap* coorder_;
161 
162  std::string edge_label_;
163  };
164 
165  struct Node
166  {
167  std::string label_;
168 
169  NODE_TYPE ntype_;
170 
171  size_t id_;
172  };
173 
175  LabelsMapT labels_;
176 
177  std::vector<Edge> edges_;
178 
179  std::unordered_map<ade::iTensor*,Node> nodes_;
180 
181  std::unordered_map<ade::iCoordMap*,std::string> coorders_;
182 
183  GetTypeF get_ftype_;
184 };
185 
186 #endif // DBG_ADE_CSV_HPP
CSVEquation::edges_
std::vector< Edge > edges_
Definition: ade_csv.hpp:177
-
CSVEquation::visit
void visit(ade::iFunctor *func) override
Visit functor node.
Definition: ade_csv.hpp:66
-
NODE_TYPE
NODE_TYPE
Definition: ade_csv.hpp:27
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CSVEquation::Node::id_
size_t id_
Definition: ade_csv.hpp:171
-
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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CSVEquation::Edge::func_
ade::iFunctor * func_
Definition: ade_csv.hpp:156
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CSVEquation::CSVEquation
CSVEquation(GetTypeF get_ftype=[](ade::iFunctor *func) { return FUNCTOR;})
Definition: ade_csv.hpp:38
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CSVEquation::nodes_
std::unordered_map< ade::iTensor *, Node > nodes_
Definition: ade_csv.hpp:179
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CSVEquation::showshape_
bool showshape_
Definition: ade_csv.hpp:152
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CSVEquation::labels_
LabelsMapT labels_
For every label associated with a tensor, show LABEL=value in the tree.
Definition: ade_csv.hpp:175
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CSVEquation::Node::ntype_
NODE_TYPE ntype_
Definition: ade_csv.hpp:169
-
ade::iTensor::shape
virtual const Shape & shape(void) const =0
Return the shape held by this tensor.
-
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std::string to_string(void) const
Return string representation of shape.
Definition: shape.hpp:146
-
ade::iLeaf
Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
-
CSVEquation::Edge::coorder_
ade::iCoordMap * coorder_
Definition: ade_csv.hpp:160
-
LabelsMapT
std::unordered_map< ade::iTensor *, std::string > LabelsMapT
Definition: ade.hpp:18
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static void multiline_replace(std::string &multiline)
Definition: ade_csv.hpp:17
-
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Definition: ade_csv.hpp:30
-
ade::iTensor::to_string
virtual std::string to_string(void) const =0
Return the string representation of the tensor.
-
ade::FuncArg::get_coorder
CoordptrT get_coorder(void) const
Return coord map for coordinates.
Definition: funcarg.hpp:80
-
ade::iCoordMap
Interface for transforming coordinates and reversing the coordinate.
Definition: coord.hpp:20
-
CSVEquation::get_ftype_
GetTypeF get_ftype_
Definition: ade_csv.hpp:183
-
CSVEquation::to_stream
void to_stream(std::ostream &out)
Definition: ade_csv.hpp:114
-
ade::iTraveler
Interface to travel through graph, treating iLeaf and iFunctor differently.
Definition: itensor.hpp:22
-
ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
-
ade::iFunctor::get_children
virtual const ArgsT & get_children(void) const =0
Return children nodes as a vector of raw pointers.
-
CSVEquation::Edge::edge_label_
std::string edge_label_
Definition: ade_csv.hpp:162
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
-
CSVEquation
Definition: ade_csv.hpp:36
-
GetTypeF
std::function< NODE_TYPE(ade::iFunctor *)> GetTypeF
Definition: ade_csv.hpp:34
- -
CSVEquation::Node::label_
std::string label_
Definition: ade_csv.hpp:167
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const char label_delim
Definition: ade_csv.hpp:15
-
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Definition: ade_csv.hpp:31
-
CSVEquation::Node
Definition: ade_csv.hpp:165
-
ade::FuncArg::get_tensor
TensptrT get_tensor(void) const
Return tensor being mapped.
Definition: funcarg.hpp:61
-
ade::to_string
std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
-
CSVEquation::Edge
Definition: ade_csv.hpp:154
-
VARIABLE
Definition: ade_csv.hpp:29
- -
CSVEquation::visit
void visit(ade::iLeaf *leaf) override
Visit leaf node.
Definition: ade_csv.hpp:42
-
CSVEquation::Edge::child_
ade::iTensor * child_
Definition: ade_csv.hpp:158
-
CSVEquation::coorders_
std::unordered_map< ade::iCoordMap *, std::string > coorders_
Definition: ade_csv.hpp:181
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ade::is_identity
bool is_identity(iCoordMap *coorder)
Checks if the coord mapper is an identity mapper.
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struct  opt::Symbol
 
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struct  opt::CandArg
 
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 opt
 
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using opt::CtxValT = std::set< ade::TensptrT >
 
using opt::ContexT = std::map< std::string, CtxValT >
 
using opt::CtxsT = std::unordered_set< ContexT, boost::hash< ContexT > >
 
using opt::CandsT = std::unordered_map< Symbol, CtxsT, SymbolHash >
 
using opt::CandArgsT = std::vector< CandArg >
 
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enum  opt::CAND_TYPE { opt::SCALAR = 0, -opt::CONST, -opt::INTERM, -opt::CONVRT - }
 
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bool opt::operator== (const Symbol &lhs, const Symbol &rhs)
 
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1 #include <string>
2 #include <unordered_map>
3 
4 #include <boost/functional/hash.hpp>
5 
6 #include "ade/itensor.hpp"
7 
8 #ifndef OPT_CAND_HPP
9 #define OPT_CAND_HPP
10 
11 namespace opt
12 {
13 
14 using CtxValT = std::set<ade::TensptrT>;
15 
16 using ContexT = std::map<std::string,CtxValT>;
17 
18 using CtxsT = std::unordered_set<ContexT,boost::hash<ContexT>>;
19 
20 enum CAND_TYPE
21 {
22  SCALAR = 0,
23  CONST,
24  INTERM,
25  CONVRT,
26 };
27 
28 struct Symbol final
29 {
30  CAND_TYPE type_;
31 
32  // SCALAR -> scalar label
33  // INTERM -> intermediate id
34  // CONVRT -> conversion ref
35  std::string reference_;
36 };
37 
38 struct SymbolHash final
39 {
40  size_t operator() (const Symbol& sym) const
41  {
42  size_t seed = 0;
43  boost::hash_combine(seed, sym.type_);
44  boost::hash_combine(seed, sym.reference_);
45  return seed;
46  }
47 };
48 
49 inline bool operator == (const Symbol& lhs, const Symbol& rhs)
50 {
51  return lhs.type_ == rhs.type_ && lhs.reference_ == rhs.reference_;
52 }
53 
54 using CandsT = std::unordered_map<Symbol,CtxsT,SymbolHash>;
55 
56 struct CandArg
57 {
58  ade::TensptrT tensor_;
59 
60  CandsT candidates_;
61 
62  ade::CoordptrT shaper_;
63 
64  ade::CoordptrT coorder_;
65 };
66 
67 using CandArgsT = std::vector<CandArg>;
68 
69 }
70 
71 #endif // OPT_CAND_HPP
opt::CtxValT
std::set< ade::TensptrT > CtxValT
Definition: candidate.hpp:14
-
opt::CandArg::candidates_
CandsT candidates_
Definition: candidate.hpp:60
-
opt::CONST
Definition: candidate.hpp:23
-
opt::Symbol::reference_
std::string reference_
Definition: candidate.hpp:35
- -
ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
-
opt
Definition: candidate.hpp:11
-
opt::CtxsT
std::unordered_set< ContexT, boost::hash< ContexT > > CtxsT
Definition: candidate.hpp:18
-
opt::SymbolHash::operator()
size_t operator()(const Symbol &sym) const
Definition: candidate.hpp:40
-
opt::CandArgsT
std::vector< CandArg > CandArgsT
Definition: candidate.hpp:67
-
opt::SymbolHash
Definition: candidate.hpp:38
-
opt::CAND_TYPE
CAND_TYPE
Definition: candidate.hpp:20
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opt::ContexT
std::map< std::string, CtxValT > ContexT
Definition: candidate.hpp:16
-
opt::INTERM
Definition: candidate.hpp:24
-
opt::CandArg::coorder_
ade::CoordptrT coorder_
Definition: candidate.hpp:64
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
opt::CandsT
std::unordered_map< Symbol, CtxsT, SymbolHash > CandsT
Definition: candidate.hpp:54
-
opt::CandArg
Definition: candidate.hpp:56
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opt::Symbol::type_
CAND_TYPE type_
Definition: candidate.hpp:30
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opt::CandArg::shaper_
ade::CoordptrT shaper_
Definition: candidate.hpp:62
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opt::Symbol
Definition: candidate.hpp:28
-
opt::CONVRT
Definition: candidate.hpp:25
-
opt::SCALAR
Definition: candidate.hpp:22
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opt::operator==
bool operator==(const Symbol &lhs, const Symbol &rhs)
Definition: candidate.hpp:49
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ade::TensptrT tensor_
Definition: candidate.hpp:58
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1 #include <chrono>
2 
3 #include <grpc/grpc.h>
4 #include <grpcpp/channel.h>
5 #include <grpcpp/client_context.h>
6 
7 #include "jobs/managed_job.hpp"
8 #include "jobs/sequence.hpp"
9 
10 #include "dbg/grpc/tenncor.grpc.pb.h"
11 
12 #ifndef DBG_GRPC_CLIENT_HPP
13 #define DBG_GRPC_CLIENT_HPP
14 
15 namespace dbg
16 {
17 
18 static const size_t max_attempts = 10;
19 
20 static const size_t data_sync_interval = 50;
21 
22 struct ClientConfig
23 {
24  ClientConfig (void) = default;
25 
26  ClientConfig (std::chrono::duration<int64_t,std::milli> request_duration,
27  std::chrono::duration<int64_t,std::milli> stream_duration) :
28  request_duration_(request_duration), stream_duration_(stream_duration) {}
29 
30  std::chrono::duration<int64_t,std::milli> request_duration_ =
31  std::chrono::milliseconds(250);
32 
33  std::chrono::duration<int64_t,std::milli> stream_duration_ =
34  std::chrono::milliseconds(10000);
35 };
36 
37 struct GraphEmitterClient final
38 {
39  GraphEmitterClient (std::shared_ptr<grpc::ChannelInterface> channel,
40  ClientConfig cfg) :
41  stub_(tenncor::GraphEmitter::NewStub(channel)),
42  cfg_(cfg),
43  connected_(true)
44  {
45  jobs::ManagedJob healthjob(
46  [this](std::future<void> stop_it)
47  {
48  tenncor::Empty empty;
49  do
50  {
51  grpc::ClientContext context;
52  tenncor::CreateGraphResponse response;
53  // set context deadline
54  std::chrono::time_point<std::chrono::system_clock> deadline =
55  std::chrono::system_clock::now() +
56  std::chrono::milliseconds(1000);
57  context.set_deadline(deadline);
58  grpc::Status status =
59  stub_->HealthCheck(&context, empty, &empty);
60  this->connected_ = status.ok();
61 
62  std::this_thread::sleep_for(
63  std::chrono::milliseconds(1000));
64  }
65  while (stop_it.wait_for(std::chrono::milliseconds(1)) ==
66  std::future_status::timeout);
67  });
68  health_checker_ = std::move(healthjob);
69  }
70 
72  void create_graph (tenncor::CreateGraphRequest& request)
73  {
74  // retries sending creation request unless stop_it times out
75  sequential_jobs_.attach_job(
76  [this](std::future<void> dependency, std::future<void> stop_it,
77  tenncor::CreateGraphRequest request)
78  {
79  if (dependency.valid())
80  {
81  dependency.get(); // wait for dependency completion
82  }
83  std::string sid = fmts::to_string(
84  std::this_thread::get_id());
85  for (size_t attempt = 0;
86  stop_it.wait_for(std::chrono::milliseconds(1)) ==
87  std::future_status::timeout && attempt < max_attempts;
88  ++attempt)
89  {
90  grpc::ClientContext context;
91  tenncor::CreateGraphResponse response;
92  // set context deadline
93  std::chrono::time_point<std::chrono::system_clock> deadline =
94  std::chrono::system_clock::now() + cfg_.request_duration_;
95  context.set_deadline(deadline);
96 
97  grpc::Status status = this->stub_->CreateGraph(
98  &context, request, &response);
99  if (status.ok())
100  {
101  auto res_status = response.status();
102  if (tenncor::Status::OK != res_status)
103  {
104  logs::errorf("%s: %s",
105  tenncor::Status_Name(res_status).c_str(),
106  response.message().c_str());
107  }
108  else
109  {
110  logs::infof("%s: CreateGraphRequest success: %s",
111  sid.c_str(), response.message().c_str());
112  return;
113  }
114  }
115  else
116  {
117  logs::errorf(
118  "%s: CreateGraphRequest attempt %d failure: %s",
119  sid.c_str(), attempt,
120  status.error_message().c_str());
121  }
122  std::this_thread::sleep_for(
123  std::chrono::milliseconds(attempt * 1000));
124  }
125  logs::warnf("%s: CreateGraphRequest terminating", sid.c_str());
126  }, std::move(request));
127  }
128 
130  void update_graph (tenncor::UpdateGraphRequest& request)
131  {
132  // retries sending creation request unless stop_it times out
133  sequential_jobs_.attach_job(
134  [this](std::future<void> dependency, std::future<void> stop_it,
135  tenncor::UpdateGraphRequest request)
136  {
137  if (dependency.valid())
138  {
139  dependency.get(); // wait for dependency completion
140  }
141  std::string sid = fmts::to_string(
142  std::this_thread::get_id());
143  for (size_t attempt = 0;
144  stop_it.wait_for(std::chrono::milliseconds(1)) ==
145  std::future_status::timeout && attempt < max_attempts;
146  ++attempt)
147  {
148  grpc::ClientContext context;
149  tenncor::UpdateGraphResponse response;
150  // set context deadline
151  std::chrono::time_point<std::chrono::system_clock> deadline =
152  std::chrono::system_clock::now() + cfg_.request_duration_;
153  context.set_deadline(deadline);
154 
155  grpc::Status status = this->stub_->UpdateGraph(
156  &context, request, &response);
157  if (status.ok())
158  {
159  auto res_status = response.status();
160  if (tenncor::Status::OK != res_status)
161  {
162  logs::errorf("%s: %s",
163  tenncor::Status_Name(res_status).c_str(),
164  response.message().c_str());
165  }
166  else
167  {
168  logs::infof("%s: UpdateGraphRequest success: %s",
169  sid.c_str(), response.message().c_str());
170  return;
171  }
172  }
173  else
174  {
175  logs::errorf(
176  "%s: UpdateGraphRequest attempt %d failure: %s",
177  sid.c_str(), attempt,
178  status.error_message().c_str());
179  }
180  std::this_thread::sleep_for(
181  std::chrono::milliseconds(attempt * 1000));
182  }
183  logs::warnf("%s: UpdateGraphRequest terminating", sid.c_str());
184  }, std::move(request));
185  }
186 
188  void update_node_data (
189  std::vector<tenncor::UpdateNodeDataRequest>& requests,
190  size_t update_it)
191  {
192  sequential_jobs_.attach_job(
193  [this](std::future<void> dependency,
194  std::future<void> stop_it,
195  std::vector<tenncor::UpdateNodeDataRequest> requests, size_t update_it)
196  {
197  if (dependency.valid())
198  {
199  dependency.get(); // wait for dependency completion
200  }
201  std::string sid = fmts::to_string(
202  std::this_thread::get_id());
203  tenncor::UpdateNodeDataResponse response;
204  grpc::ClientContext context;
205  // set context deadline
206  std::chrono::time_point<std::chrono::system_clock> deadline =
207  std::chrono::system_clock::now() +
208  std::chrono::milliseconds(cfg_.stream_duration_);
209  context.set_deadline(deadline);
210  std::unique_ptr<grpc::ClientWriterInterface<
211  tenncor::UpdateNodeDataRequest>> writer(
212  stub_->UpdateNodeData(&context, &response));
213 
214  for (auto& request : requests)
215  {
216  if (stop_it.wait_for(std::chrono::milliseconds(1)) !=
217  std::future_status::timeout)
218  {
219  break;
220  }
221  if (false == writer->Write(request))
222  {
223  logs::errorf("failed to write update %d", update_it);
224  break;
225  }
226  }
227  writer->WritesDone();
228 
229  grpc::Status status = writer->Finish();
230  if (status.ok())
231  {
232  auto res_status = response.status();
233  if (tenncor::Status::OK != res_status)
234  {
235  logs::errorf("%s: %s",
236  tenncor::Status_Name(res_status).c_str(),
237  response.message().c_str());
238  }
239  else
240  {
241  return;
242  }
243  }
244  else
245  {
246  logs::errorf(
247  "UpdateNodeData failure: %s",
248  status.error_message().c_str());
249  }
250  logs::warnf("%s: UpdateNodeData terminating", sid.c_str());
251  }, std::move(requests), std::move(update_it));
252  }
253 
254  bool is_connected (void)
255  {
256  return connected_;
257  }
258 
259  void join (void)
260  {
261  sequential_jobs_.join();
262  }
263 
264  void clear (void)
265  {
266  sequential_jobs_.stop();
267  }
268 
269 private:
270  std::unique_ptr<tenncor::GraphEmitter::Stub> stub_;
271 
272  ClientConfig cfg_;
273 
274  // every request from emitter has dependency on the previous request
275  jobs::Sequence sequential_jobs_;
276 
277  // connection state
278  std::atomic<bool> connected_;
279  jobs::ManagedJob health_checker_;
280 };
281 
282 }
283 
284 #endif // DBG_GRPC_CLIENT_HPP
dbg::GraphEmitterClient::GraphEmitterClient
GraphEmitterClient(std::shared_ptr< grpc::ChannelInterface > channel, ClientConfig cfg)
Definition: client.hpp:39
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std::atomic< bool > connected_
Definition: client.hpp:278
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Definition: client.hpp:15
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Add job that pass CreateGraphRequest.
Definition: client.hpp:72
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Definition: client.hpp:30
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Definition: client.hpp:22
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Definition: client.hpp:279
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Add job that pass UpdateGraphRequest.
Definition: client.hpp:130
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Definition: client.hpp:188
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Definition: client.hpp:272
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Definition: client.hpp:270
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Definition: client.hpp:20
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Definition: client.hpp:264
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Definition: client.hpp:254
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Definition: client.hpp:33
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Definition: client.hpp:259
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struct  ead::ConstantNode< T >
 
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template<typename T >
NodeptrT< T > ead::make_constant_scalar (T scalar, ade::Shape shape)
 
template<typename T >
NodeptrT< T > ead::make_constant (T *data, ade::Shape shape)
 
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static const size_t ead::label_limit = 5
 
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1 #include "tag/prop.hpp"
2 
3 #include "ead/ileaf.hpp"
4 #include "ead/inode.hpp"
5 
6 #ifndef EAD_CONSTANT_HPP
7 #define EAD_CONSTANT_HPP
8 
9 namespace ead
10 {
11 
12 static const size_t label_limit = 5;
13 
14 template <typename T>
15 struct Constant final : public iLeaf<T>
16 {
17  static Constant<T>* get (T* data, ade::Shape shape);
18 
19  static Constant<T>* get_scalar (T scalar, ade::Shape shape)
20  {
21  size_t n = shape.n_elems();
22  T buffer[n];
23  std::fill(buffer, buffer + n, scalar);
24  return Constant<T>::get(buffer, shape);
25  }
26 
27  Constant (const Constant<T>& other) = delete;
28 
29  Constant (Constant<T>&& other) = delete;
30 
31  Constant<T>& operator = (const Constant<T>& other) = delete;
32 
33  Constant<T>& operator = (Constant<T>&& other) = delete;
34 
36  std::string to_string (void) const override
37  {
38  const T* data = this->data_.data();
39  if (is_scalar())
40  {
41  if (0 == data[0]) // prevent -0
42  {
43  return "0";
44  }
45  return fmts::to_string(data[0]);
46  }
47  size_t nelems = this->shape_.n_elems();
48  auto out = fmts::to_string(data,
49  data + std::min(label_limit, nelems));
50  if (nelems > label_limit)
51  {
52  out += "...";
53  }
54  return out;
55  }
56 
57  bool is_const (void) const override
58  {
59  return true;
60  }
61 
62  bool is_scalar (void) const
63  {
64  const T* data = this->data_.data();
65  size_t nelems = this->shape_.n_elems();
66  return std::all_of(data + 1, data + nelems,
67  [&](const T& e) { return e == data[0]; });
68  }
69 
70 private:
71  Constant (T* data, ade::Shape shape) :
72  iLeaf<T>(data, shape) {}
73 };
74 
75 template <typename T>
76 struct ConstantNode final : public iNode<T>
77 {
78  ConstantNode (std::shared_ptr<Constant<T>> cst) : cst_(cst) {}
79 
80  T* data (void) override
81  {
82  return (T*) cst_->data();
83  }
84 
85  void update (void) override {}
86 
87  ade::TensptrT get_tensor (void) override
88  {
89  return cst_;
90  }
91 
92 private:
93  std::shared_ptr<Constant<T>> cst_;
94 };
95 
96 template <typename T>
97 Constant<T>* Constant<T>::get (T* data, ade::Shape shape)
98 {
99  static bool registered = register_builder<Constant<T>,T>(
100  [](ade::TensptrT tens)
101  {
102  return std::make_shared<ConstantNode<T>>(
103  std::static_pointer_cast<Constant<T>>(tens));
104  });
105  assert(registered);
106 
107  return new Constant(data, shape);
108 }
109 
110 template <typename T>
111 NodeptrT<T> make_constant_scalar (T scalar, ade::Shape shape)
112 {
113  auto out = std::make_shared<ConstantNode<T>>(
114  std::shared_ptr<Constant<T>>(Constant<T>::get_scalar(scalar, shape))
115  );
116  tag::get_property_reg().property_tag(out->get_tensor(), tag::immutable_tag);
117  return out;
118 }
119 
120 template <typename T>
121 NodeptrT<T> make_constant (T* data, ade::Shape shape)
122 {
123  auto out = std::make_shared<ConstantNode<T>>(
124  std::shared_ptr<Constant<T>>(Constant<T>::get(data, shape))
125  );
126  tag::get_property_reg().property_tag(out->get_tensor(), tag::immutable_tag);
127  return out;
128 }
129 
130 }
131 
132 #endif // EAD_CONSTANT_HPP
ead::Constant::to_string
std::string to_string(void) const override
Implementation of iTensor.
Definition: constant.hpp:36
-
ead::make_constant_scalar
NodeptrT< T > make_constant_scalar(T scalar, ade::Shape shape)
Definition: constant.hpp:111
-
ead::iLeaf::shape
const ade::Shape & shape(void) const override
Implementation of iTensor.
Definition: ileaf.hpp:25
-
ead::ConstantNode::cst_
std::shared_ptr< Constant< T > > cst_
Definition: constant.hpp:93
-
ead::NodeptrT
std::shared_ptr< iNode< T > > NodeptrT
Definition: inode.hpp:31
-
ead::Constant::Constant
Constant(T *data, ade::Shape shape)
Definition: constant.hpp:71
-
ead::ConstantNode::get_tensor
ade::TensptrT get_tensor(void) override
Definition: constant.hpp:87
-
ead::ConstantNode::ConstantNode
ConstantNode(std::shared_ptr< Constant< T >> cst)
Definition: constant.hpp:78
-
ead
Definition: constant.hpp:9
-
ade::Shape::n_elems
NElemT n_elems(void) const
Return the total number of elements represented by the shape.
Definition: shape.hpp:116
-
ead::Constant::is_const
bool is_const(void) const override
Definition: constant.hpp:57
-
ead::iLeaf::data_
TensorT< T > data_
Data Source.
Definition: ileaf.hpp:69
-
ead::min
EigenptrT< T > min(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
Definition: operator.hpp:893
-
ead::Constant::operator=
Constant< T > & operator=(const Constant< T > &other)=delete
-
ead::iNode
Definition: inode.hpp:14
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ead::make_constant
NodeptrT< T > make_constant(T *data, ade::Shape shape)
Definition: constant.hpp:121
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ead::Constant::get_scalar
static Constant< T > * get_scalar(T scalar, ade::Shape shape)
Definition: constant.hpp:19
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ead::label_limit
static const size_t label_limit
Definition: constant.hpp:12
-
ead::Constant::is_scalar
bool is_scalar(void) const
Definition: constant.hpp:62
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ade::Shape
Definition: shape.hpp:60
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ead::iLeaf::shape_
ade::Shape shape_
Shape utility to avoid excessive conversion between data_.dimensions()
Definition: ileaf.hpp:72
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ead::Constant::get
static Constant< T > * get(T *data, ade::Shape shape)
Definition: constant.hpp:97
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tag::get_property_reg
PropertyRegistry & get_property_reg(void)
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tag::immutable_tag
const std::string immutable_tag
Definition: prop.hpp:14
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ead::iLeaf
Definition: ileaf.hpp:20
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ead::ConstantNode::update
void update(void) override
Definition: constant.hpp:85
-
ead::ConstantNode
Definition: constant.hpp:76
-
ead::ConstantNode::data
T * data(void) override
Definition: constant.hpp:80
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ade::to_string
std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
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ead::iLeaf::data
void * data(void) override
Implementation of iData.
Definition: ileaf.hpp:31
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ead::Constant::Constant
Constant(const Constant< T > &other)=delete
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tag::PropertyRegistry::property_tag
void property_tag(ade::TensrefT tens, std::string property)
Definition: prop.hpp:52
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Definition: constant.hpp:15
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def csv_to_png._str_clean (str)
 
def csv_to_png._apply_styles (graph, styles)
 
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def csv_to_png.print_graph (callgraph, outname)
 
def csv_to_png.csv_to_png (lines, outpath)
 
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dictionary csv_to_png._styles
 
 csv_to_png.parser = argparse.ArgumentParser()
 
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using pbm::TensT = std::vector< ade::TensptrT >
 Tensptr vector type. More...
 
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 String list type used for paths. More...
 
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1 
9 #include <list>
10 
11 #include "ade/ade.hpp"
12 
13 #include "pbm/graph.pb.h"
14 
15 #ifndef PBM_COMMON_HPP
16 #define PBM_COMMON_HPP
17 
18 namespace pbm
19 {
20 
22 using TensT = std::vector<ade::TensptrT>;
23 
25 using StringsT = std::list<fmts::string>;
26 
27 struct iSaver
28 {
29  virtual std::string save_leaf (bool& is_const, ade::iLeaf* leaf) = 0;
30 
31  virtual std::vector<double> save_shaper (const ade::CoordptrT& mapper) = 0;
32 
33  virtual std::vector<double> save_coorder (const ade::CoordptrT& mapper) = 0;
34 };
35 
36 struct iLoader
37 {
38  virtual ade::TensptrT generate_leaf (const char* data, ade::Shape shape,
39  std::string typelabel, std::string label, bool is_const) = 0;
40 
41  virtual ade::TensptrT generate_func (std::string opname, ade::ArgsT args) = 0;
42 
43  virtual ade::CoordptrT generate_shaper (std::vector<double> coord) = 0;
44 
45  virtual ade::CoordptrT generate_coorder (
46  std::string opname, std::vector<double> coord) = 0;
47 };
48 
49 }
50 
51 #endif // PBM_COMMON_HPP
csv_to_png.args
args
Definition: csv_to_png.py:105
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pbm::iSaver::save_shaper
virtual std::vector< double > save_shaper(const ade::CoordptrT &mapper)=0
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ade::iLeaf
Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
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ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
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virtual ade::TensptrT generate_func(std::string opname, ade::ArgsT args)=0
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pbm::StringsT
std::list< fmts::string > StringsT
String list type used for paths.
Definition: data.hpp:25
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ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
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virtual std::vector< double > save_coorder(const ade::CoordptrT &mapper)=0
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ade::Shape
Definition: shape.hpp:60
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pbm::iLoader::generate_coorder
virtual ade::CoordptrT generate_coorder(std::string opname, std::vector< double > coord)=0
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Definition: data.hpp:36
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std::vector< ade::TensptrT > TensT
Tensptr vector type.
Definition: data.hpp:22
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pbm::iSaver
Definition: data.hpp:27
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virtual std::string save_leaf(bool &is_const, ade::iLeaf *leaf)=0
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virtual ade::TensptrT generate_leaf(const char *data, ade::Shape shape, std::string typelabel, std::string label, bool is_const)=0
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virtual ade::CoordptrT generate_shaper(std::vector< double > coord)=0
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pbm
Definition: data.hpp:18
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std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
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1 #include <grpc/grpc.h>
2 #include <grpcpp/create_channel.h>
3 #include <grpcpp/security/credentials.h>
4 
5 #include <boost/uuid/uuid.hpp>
6 #include <boost/uuid/uuid_generators.hpp>
7 #include <boost/uuid/uuid_io.hpp>
8 
9 #include "jobs/scope_guard.hpp"
10 
11 #include "ead/session.hpp"
12 
13 #include "tag/tag.hpp"
14 
15 #include "dbg/grpc/client.hpp"
16 
17 #ifndef DBG_SESSION_HPP
18 #define DBG_SESSION_HPP
19 
20 namespace dbg
21 {
22 
23 static const std::string tag_str_key = "name";
24 
25 static const std::string tag_node_type = "node_type";
26 
27 static const std::string edge_label_fmt = "parent-child-%d";
28 
29 struct EdgeInfo
30 {
31  size_t parent_;
32  size_t child_;
33  std::string label_;
34 };
35 
36 struct EdgeInfoHash final
37 {
38  size_t operator() (const EdgeInfo& edge) const
39  {
40  std::stringstream ss;
41  ss << edge.parent_ << ","
42  << edge.child_ << ","
43  << edge.label_;
44  return std::hash<std::string>()(ss.str());
45  }
46 };
47 
48 inline bool operator == (const EdgeInfo& lhs, const EdgeInfo& rhs)
49 {
50  EdgeInfoHash hasher;
51  return hasher(lhs) == hasher(rhs);
52 }
53 
54 struct InteractiveSession final : public ead::iSession
55 {
56  static boost::uuids::random_generator uuid_gen_;
57 
58  InteractiveSession (std::shared_ptr<grpc::ChannelInterface> channel,
59  ClientConfig client_cfg = ClientConfig(),
60  tag::TagRegistry& registry = tag::get_reg()) :
61  client_(channel, client_cfg),
62  registry_(registry)
63  {
64  logs::infof("created session: %s", sess_id_.c_str());
65  }
66 
67  InteractiveSession (std::string host,
68  ClientConfig client_cfg = ClientConfig()) :
69  InteractiveSession(grpc::CreateChannel(host,
70  grpc::InsecureChannelCredentials()), client_cfg) {}
71 
72  void track (ade::TensT roots) override
73  {
74  sess_.track(roots);
75 
76  // setup request
77  tenncor::CreateGraphRequest request;
78  auto payload = request.mutable_payload();
79  payload->set_graph_id(sess_id_);
80  for (auto& statpair : sess_.stat_.graphsize_)
81  {
82  auto tens = statpair.first;
83  auto& range = statpair.second;
84  size_t id = node_ids_.size() + 1;
85  if (false == estd::has(node_ids_, tens))
86  {
87  node_ids_.emplace(tens, id);
88  // add to request
89  auto node = payload->add_nodes();
90  node->set_id(id);
91  auto tags = node->mutable_tags();
92  {
93  tenncor::Strings tag_str;
94  tag_str.add_strings(tens->to_string());
95  tags->insert({tag_str_key, tag_str});
96  }
97  {
98  tenncor::Strings type_str;
99  if (0 == range.upper_)
100  {
101  type_str.add_strings("leaf");
102  }
103  else
104  {
105  type_str.add_strings("functor");
106  }
107  tags->insert({tag_node_type, type_str});
108  }
109  {
110  auto inner_tags = registry_.get_tags(tens);
111  std::map<std::string,tenncor::Strings> outer_tags;
112  for (auto& itags : inner_tags)
113  {
114  google::protobuf::RepeatedPtrField<std::string>
115  field(itags.second.begin(), itags.second.end());
116  tenncor::Strings otags;
117  otags.mutable_strings()->Swap(&field);
118  outer_tags.emplace(itags.first, otags);
119  }
120  tags->insert(outer_tags.begin(), outer_tags.end());
121  }
122  auto s = tens->shape();
123  google::protobuf::RepeatedField<uint32_t> shape(
124  s.begin(), s.end());
125  node->mutable_shape()->Swap(&shape);
126  auto location = node->mutable_location();
127  location->set_maxheight(range.upper_);
128  location->set_minheight(range.lower_);
129  }
130  }
131  for (auto& statpair : sess_.stat_.graphsize_)
132  {
133  auto tens = statpair.first;
134  auto& range = statpair.second;
135  if (range.upper_ > 0)
136  {
137  auto f = static_cast<ade::iFunctor*>(tens);
138  auto& children = f->get_children();
139  for (size_t i = 0, n = children.size(); i < n; ++i)
140  {
141  auto& child = children[i];
142  auto child_tens = child.get_tensor().get();
143  auto shaper = child.get_shaper();
144  auto coorder = child.get_coorder();
145  std::string label = fmts::sprintf(edge_label_fmt, i);
146  EdgeInfo edgeinfo{
147  node_ids_[f],
148  node_ids_[child_tens],
149  label,
150  };
151  if (false == estd::has(edges_, edgeinfo))
152  {
153  edges_.emplace(edgeinfo);
154  // add to request
155  auto edge = payload->add_edges();
156  edge->set_parent(node_ids_[f]);
157  edge->set_child(node_ids_[child_tens]);
158  edge->set_label(label);
159  if (false == ade::is_identity(shaper.get()))
160  {
161  edge->set_shaper(shaper->to_string());
162  }
163  if (false == ade::is_identity(coorder.get()))
164  {
165  edge->set_coorder(coorder->to_string());
166  }
167  }
168  }
169  }
170  }
171 
172  client_.create_graph(request);
173  }
174 
175  void update (ead::TensSetT updated = {},
176  ead::TensSetT ignores = {}) override
177  {
178  jobs::ScopeGuard defer([this]() { ++this->update_it_; });
179 
180  // ignore any node data updates when
181  // not connected or out of sync interval
182  if (false == client_.is_connected() || 0 < update_it_ % data_sync_interval)
183  {
184  sess_.update(updated, ignores);
185  return;
186  }
187 
188  // basic copy over from session::update
189  std::unordered_map<ade::iOperableFunc*,ead::SizeT> fulfilments;
190  for (ade::iTensor* unodes : updated)
191  {
192  auto& node_parents = sess_.parents_[unodes];
193  for (auto& node_parent : node_parents)
194  {
195  ++fulfilments[node_parent].d;
196  }
197  }
198 
199  std::vector<tenncor::UpdateNodeDataRequest> requests;
200  requests.reserve(sess_.requirements_.size());
201 
202  for (auto& statpair : sess_.stat_.graphsize_)
203  {
204  if (0 == statpair.second.upper_)
205  {
206  auto leaf = static_cast<ade::iLeaf*>(statpair.first);
207  age::_GENERATED_DTYPE dtype =
208  (age::_GENERATED_DTYPE) leaf->type_code();
209  std::vector<float> data;
210  size_t nelems = leaf->shape().n_elems();
211  age::type_convert(data, leaf->data(), dtype, nelems);
212 
213  tenncor::UpdateNodeDataRequest request;
214  auto payload = request.mutable_payload();
215  payload->set_graph_id(sess_id_);
216  payload->set_node_id(node_ids_[leaf]);
217  google::protobuf::RepeatedField<float> field(
218  data.begin(), data.end());
219  payload->mutable_data()->Swap(&field);
220  requests.push_back(request);
221  }
222  }
223 
224  // ignored nodes and its dependers will never fulfill requirement
225  for (auto& op : sess_.requirements_)
226  {
227  // fulfilled and not ignored
228  if (fulfilments[op.first].d >= op.second &&
229  false == estd::has(ignores, op.first))
230  {
231  op.first->update();
232  age::_GENERATED_DTYPE dtype =
233  (age::_GENERATED_DTYPE) op.first->type_code();
234  std::vector<float> data;
235  size_t nelems = op.first->shape().n_elems();
236  age::type_convert(data, op.first->data(), dtype, nelems);
237  auto& op_parents = sess_.parents_[op.first];
238  for (auto& op_parent : op_parents)
239  {
240  ++fulfilments[op_parent].d;
241  }
242 
243  // create requests (bulk of the overhead)
244  tenncor::UpdateNodeDataRequest request;
245  auto payload = request.mutable_payload();
246  payload->set_graph_id(sess_id_);
247  payload->set_node_id(node_ids_[op.first]);
248  google::protobuf::RepeatedField<float> field(
249  data.begin(), data.end());
250  payload->mutable_data()->Swap(&field);
251  requests.push_back(request);
252  }
253  }
254 
255  client_.update_node_data(requests, update_it_);
256  }
257 
258  void update_target (ead::TensSetT targeted,
259  ead::TensSetT updated = {}) override
260  {
261  jobs::ScopeGuard defer([this]() { ++this->update_it_; });
262 
263  // ignore any node data updates when
264  // not connected or out of sync interval
265  if (false == client_.is_connected() || 0 < update_it_ % data_sync_interval)
266  {
267  sess_.update_target(targeted, updated);
268  return;
269  }
270 
271  // basic copy over from session::update
272  ade::OnceTraveler traveler;
273  for (auto& tens : targeted)
274  {
275  tens->accept(traveler);
276  }
277 
278  std::unordered_map<ade::iOperableFunc*,ead::SizeT> fulfilments;
279  for (ade::iTensor* unodes : updated)
280  {
281  auto& node_parents = sess_.parents_[unodes];
282  for (auto& node_parent : node_parents)
283  {
284  ++fulfilments[node_parent].d;
285  }
286  }
287 
288  std::vector<tenncor::UpdateNodeDataRequest> requests;
289  requests.reserve(sess_.requirements_.size());
290 
291  for (auto& statpair : sess_.stat_.graphsize_)
292  {
293  if (0 == statpair.second.upper_)
294  {
295  auto leaf = static_cast<ade::iLeaf*>(statpair.first);
296  age::_GENERATED_DTYPE dtype =
297  (age::_GENERATED_DTYPE) leaf->type_code();
298  std::vector<float> data;
299  size_t nelems = leaf->shape().n_elems();
300  age::type_convert(data, leaf->data(), dtype, nelems);
301 
302  tenncor::UpdateNodeDataRequest request;
303  auto payload = request.mutable_payload();
304  payload->set_graph_id(sess_id_);
305  payload->set_node_id(node_ids_[leaf]);
306  google::protobuf::RepeatedField<float> field(
307  data.begin(), data.end());
308  payload->mutable_data()->Swap(&field);
309  requests.push_back(request);
310  }
311  }
312 
313  // ignored nodes and its dependers will never fulfill requirement
314  for (auto& op : sess_.requirements_)
315  {
316  // fulfilled and not ignored
317  if (estd::has(traveler.visited_, op.first) &&
318  fulfilments[op.first].d >= op.second)
319  {
320  op.first->update();
321  age::_GENERATED_DTYPE dtype =
322  (age::_GENERATED_DTYPE) op.first->type_code();
323  std::vector<float> data;
324  size_t nelems = op.first->shape().n_elems();
325  age::type_convert(data, op.first->data(), dtype, nelems);
326  auto& op_parents = sess_.parents_[op.first];
327  for (auto& op_parent : op_parents)
328  {
329  ++fulfilments[op_parent].d;
330  }
331 
332  // create requests (bulk of the overhead)
333  tenncor::UpdateNodeDataRequest request;
334  auto payload = request.mutable_payload();
335  payload->set_graph_id(sess_id_);
336  payload->set_node_id(node_ids_[op.first]);
337  google::protobuf::RepeatedField<float> field(
338  data.begin(), data.end());
339  payload->mutable_data()->Swap(&field);
340  requests.push_back(request);
341  }
342  }
343 
344  client_.update_node_data(requests, update_it_);
345  }
346 
347  void optimize (const opt::OptCtx& rules)
348  {
349  sess_.optimize(rules);
350 
351  // update graph
352  node_ids_.clear();
353  edges_.clear();
354 
355  // setup request
356  tenncor::UpdateGraphRequest request;
357  auto payload = request.mutable_payload();
358  payload->set_graph_id(sess_id_);
359  for (auto& statpair : sess_.stat_.graphsize_)
360  {
361  auto tens = statpair.first;
362  auto& range = statpair.second;
363  size_t id = node_ids_.size() + 1;
364  if (false == estd::has(node_ids_, tens))
365  {
366  node_ids_.emplace(tens, id);
367  // add to request
368  auto node = payload->add_nodes();
369  node->set_id(id);
370  auto tags = node->mutable_tags();
371  {
372  tenncor::Strings tag_str;
373  tag_str.add_strings(tens->to_string());
374  tags->insert({tag_str_key, tag_str});
375  }
376  {
377  tenncor::Strings type_str;
378  if (0 == range.upper_)
379  {
380  type_str.add_strings("leaf");
381  }
382  else
383  {
384  type_str.add_strings("functor");
385  }
386  tags->insert({tag_node_type, type_str});
387  }
388  {
389  auto inner_tags = registry_.get_tags(tens);
390  std::map<std::string,tenncor::Strings> outer_tags;
391  for (auto& itags : inner_tags)
392  {
393  google::protobuf::RepeatedPtrField<std::string>
394  field(itags.second.begin(), itags.second.end());
395  tenncor::Strings otags;
396  otags.mutable_strings()->Swap(&field);
397  outer_tags.emplace(itags.first, otags);
398  }
399  tags->insert(outer_tags.begin(), outer_tags.end());
400  }
401  auto s = tens->shape();
402  google::protobuf::RepeatedField<uint32_t> shape(
403  s.begin(), s.end());
404  node->mutable_shape()->Swap(&shape);
405  auto location = node->mutable_location();
406  location->set_maxheight(range.upper_);
407  location->set_minheight(range.lower_);
408  }
409  }
410  for (auto& statpair : sess_.stat_.graphsize_)
411  {
412  auto tens = statpair.first;
413  auto& range = statpair.second;
414  if (range.upper_ > 0)
415  {
416  auto f = static_cast<ade::iFunctor*>(tens);
417  auto& children = f->get_children();
418  for (size_t i = 0, n = children.size(); i < n; ++i)
419  {
420  auto& child = children[i];
421  auto child_tens = child.get_tensor().get();
422  auto shaper = child.get_shaper();
423  auto coorder = child.get_coorder();
424  std::string label = fmts::sprintf(edge_label_fmt, i);
425  EdgeInfo edgeinfo{
426  node_ids_[f],
427  node_ids_[child_tens],
428  label,
429  };
430  if (false == estd::has(edges_, edgeinfo))
431  {
432  edges_.emplace(edgeinfo);
433  // add to request
434  auto edge = payload->add_edges();
435  edge->set_parent(node_ids_[f]);
436  edge->set_child(node_ids_[child_tens]);
437  edge->set_label(label);
438  if (false == ade::is_identity(shaper.get()))
439  {
440  edge->set_shaper(shaper->to_string());
441  }
442  if (false == ade::is_identity(coorder.get()))
443  {
444  edge->set_coorder(coorder->to_string());
445  }
446  }
447  }
448  }
449  }
450 
451  client_.update_graph(request);
452  }
453 
454  // join indefinitely
455  void join (void)
456  {
457  client_.join();
458  }
459 
460  // join until specified deadline, then terminate all jobs in the client
461  void join_then_stop (
462  const std::chrono::time_point<std::chrono::system_clock>& deadline)
463  {
464  std::condition_variable client_done;
465  std::thread timed_killer(
466  [&]()
467  {
468  std::mutex mtx;
469  std::unique_lock<std::mutex> lck(mtx);
470  client_done.wait_until(lck, deadline);
471  this->client_.clear();
472  });
473  client_.join();
474  client_done.notify_one();
475  timed_killer.join();
476  }
477 
478  void stop (void)
479  {
480  client_.clear();
481  }
482 
483  std::string get_session_id (void) const
484  {
485  return sess_id_;
486  }
487 
488  std::unique_ptr<tenncor::GraphEmitter::Stub> stub_;
489 
490  ead::Session sess_;
491 
492  tag::TagRegistry& registry_;
493 
494 private:
495  std::string sess_id_ = boost::uuids::to_string(
496  InteractiveSession::uuid_gen_());
497 
498  std::unordered_map<ade::iTensor*,size_t> node_ids_;
499 
500  std::unordered_set<EdgeInfo,EdgeInfoHash> edges_;
501 
502  size_t update_it_ = 0;
503 
504  GraphEmitterClient client_;
505 };
506 
507 boost::uuids::random_generator InteractiveSession::uuid_gen_;
508 
509 }
510 
511 #endif // DBG_SESSION_HPP
ead::Session::parents_
std::unordered_map< ade::iTensor *, std::unordered_set< ade::iOperableFunc * > > parents_
Definition: session.hpp:176
-
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
-
ead::Session
Definition: session.hpp:42
-
dbg::InteractiveSession::InteractiveSession
InteractiveSession(std::shared_ptr< grpc::ChannelInterface > channel, ClientConfig client_cfg=ClientConfig(), tag::TagRegistry &registry=tag::get_reg())
Definition: session.hpp:58
-
dbg::InteractiveSession::stub_
std::unique_ptr< tenncor::GraphEmitter::Stub > stub_
Definition: session.hpp:488
-
dbg::InteractiveSession::edges_
std::unordered_set< EdgeInfo, EdgeInfoHash > edges_
Definition: session.hpp:500
-
ead::Session::optimize
void optimize(const opt::OptCtx &rules)
Definition: session.hpp:162
-
ead::Session::update
void update(TensSetT updated={}, TensSetT ignores={}) override
Definition: session.hpp:101
-
ade::OnceTraveler
Extremely generic traveler that visits every node in the graph once.
Definition: traveler.hpp:22
-
dbg::InteractiveSession::uuid_gen_
static boost::uuids::random_generator uuid_gen_
Definition: session.hpp:56
-
dbg::InteractiveSession
Definition: session.hpp:54
-
dbg::EdgeInfo::label_
std::string label_
Definition: session.hpp:33
-
tag::TagRegistry::get_tags
TagRepsT get_tags(const ade::iTensor *tens)
Definition: tag.hpp:152
-
tag::TagRegistry
Definition: tag.hpp:135
-
dbg::edge_label_fmt
static const std::string edge_label_fmt
Definition: session.hpp:27
-
ade::OnceTraveler::visited_
std::unordered_set< iTensor * > visited_
Definition: traveler.hpp:57
-
dbg
Definition: client.hpp:15
-
dbg::GraphEmitterClient::create_graph
void create_graph(tenncor::CreateGraphRequest &request)
Add job that pass CreateGraphRequest.
Definition: client.hpp:72
-
ead::TensSetT
std::unordered_set< ade::iTensor * > TensSetT
Definition: session.hpp:17
-
dbg::EdgeInfo::parent_
size_t parent_
Definition: session.hpp:31
-
ead::iSession
Definition: session.hpp:19
-
dbg::InteractiveSession::join
void join(void)
Definition: session.hpp:455
-
ade::iLeaf
Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
-
dbg::ClientConfig
Definition: client.hpp:22
- -
dbg::InteractiveSession::sess_id_
std::string sess_id_
Definition: session.hpp:495
-
dbg::InteractiveSession::InteractiveSession
InteractiveSession(std::string host, ClientConfig client_cfg=ClientConfig())
Definition: session.hpp:67
-
opt::OptCtx
Definition: optimize.hpp:12
-
dbg::InteractiveSession::node_ids_
std::unordered_map< ade::iTensor *, size_t > node_ids_
Definition: session.hpp:498
-
dbg::InteractiveSession::client_
GraphEmitterClient client_
Definition: session.hpp:504
-
dbg::EdgeInfoHash::operator()
size_t operator()(const EdgeInfo &edge) const
Definition: session.hpp:38
-
ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
-
dbg::GraphEmitterClient::update_graph
void update_graph(tenncor::UpdateGraphRequest &request)
Add job that pass UpdateGraphRequest.
Definition: client.hpp:130
-
dbg::InteractiveSession::join_then_stop
void join_then_stop(const std::chrono::time_point< std::chrono::system_clock > &deadline)
Definition: session.hpp:461
-
ead::Session::track
void track(ade::TensT roots) override
Definition: session.hpp:44
-
dbg::InteractiveSession::stop
void stop(void)
Definition: session.hpp:478
-
ade::iFunctor::get_children
virtual const ArgsT & get_children(void) const =0
Return children nodes as a vector of raw pointers.
-
ead::Session::requirements_
std::vector< std::pair< ade::iOperableFunc *, size_t > > requirements_
Definition: session.hpp:178
-
dbg::operator==
bool operator==(const EdgeInfo &lhs, const EdgeInfo &rhs)
Definition: session.hpp:48
-
dbg::GraphEmitterClient::update_node_data
void update_node_data(std::vector< tenncor::UpdateNodeDataRequest > &requests, size_t update_it)
Add job that streams UpdateNodeDataRequest.
Definition: client.hpp:188
-
dbg::data_sync_interval
static const size_t data_sync_interval
Definition: client.hpp:20
-
dbg::InteractiveSession::track
void track(ade::TensT roots) override
Definition: session.hpp:72
-
tag::get_reg
TagRegistry & get_reg(void)
-
dbg::tag_node_type
static const std::string tag_node_type
Definition: session.hpp:25
-
dbg::InteractiveSession::update
void update(ead::TensSetT updated={}, ead::TensSetT ignores={}) override
Definition: session.hpp:175
-
dbg::GraphEmitterClient::clear
void clear(void)
Definition: client.hpp:264
-
dbg::EdgeInfoHash
Definition: session.hpp:36
-
dbg::InteractiveSession::update_it_
size_t update_it_
Definition: session.hpp:502
-
ade::TensT
std::vector< TensptrT > TensT
Vector representation of ade tensor pointers.
Definition: funcarg.hpp:104
- -
dbg::GraphEmitterClient
Definition: client.hpp:37
-
dbg::GraphEmitterClient::is_connected
bool is_connected(void)
Definition: client.hpp:254
-
dbg::InteractiveSession::get_session_id
std::string get_session_id(void) const
Definition: session.hpp:483
-
ade::GraphStat::graphsize_
std::unordered_map< iTensor *, estd::NumRange< size_t > > graphsize_
Definition: traveler.hpp:109
-
ade::iData::type_code
virtual size_t type_code(void) const =0
Return data type encoding.
-
dbg::InteractiveSession::registry_
tag::TagRegistry & registry_
Definition: session.hpp:492
-
dbg::tag_str_key
static const std::string tag_str_key
Definition: session.hpp:23
-
dbg::GraphEmitterClient::join
void join(void)
Definition: client.hpp:259
-
dbg::InteractiveSession::sess_
ead::Session sess_
Definition: session.hpp:490
-
ead::Session::stat_
ade::GraphStat stat_
Definition: session.hpp:173
-
ead::Session::update_target
void update_target(TensSetT target, TensSetT updated={}) override
Definition: session.hpp:129
-
dbg::InteractiveSession::update_target
void update_target(ead::TensSetT targeted, ead::TensSetT updated={}) override
Definition: session.hpp:258
-
ade::to_string
std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
-
dbg::EdgeInfo::child_
size_t child_
Definition: session.hpp:32
-
dbg::EdgeInfo
Definition: session.hpp:29
-
dbg::InteractiveSession::optimize
void optimize(const opt::OptCtx &rules)
Definition: session.hpp:347
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ade::is_identity
bool is_identity(iCoordMap *coorder)
Checks if the coord mapper is an identity mapper.
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-Classes

struct  PrettyEquation
 Use PrettyTree to render ade::TensptrT graph as an ascii art. More...
 
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using LabelsMapT = std::unordered_map< ade::iTensor *, std::string >
 
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ade.hpp dbg

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Purpose: Draw an equation graph as an ascii tree

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1 
9 #include <unordered_map>
10 
11 #include "ade/functor.hpp"
12 
13 #include "dbg/stream/tree.hpp"
14 
15 #ifndef DBG_ADE_HPP
16 #define DBG_ADE_HPP
17 
18 using LabelsMapT = std::unordered_map<ade::iTensor*,std::string>;
19 
21 struct PrettyEquation final
22 {
23  PrettyEquation (void) : drawer_(
24  [](ade::iTensor*& root) -> std::vector<ade::iTensor*>
25  {
26  if (ade::iFunctor* f = dynamic_cast<ade::iFunctor*>(root))
27  {
28  auto& children = f->get_children();
29  std::vector<ade::iTensor*> tens(children.size());
30  std::transform(children.begin(), children.end(), tens.begin(),
31  [](const ade::FuncArg& child)
32  {
33  return child.get_tensor().get();
34  });
35  return tens;
36  }
37  return {};
38  },
39  [this](std::ostream& out, ade::iTensor*& root)
40  {
41  if (root)
42  {
43  auto it = this->labels_.find(root);
44  if (this->labels_.end() != it)
45  {
46  out << it->second << "=";
47  }
48  out << root->to_string();
49  if (showshape_)
50  {
51  out << root->shape().to_string();
52  }
53  }
54  }) {}
55 
57  void print (std::ostream& out, ade::TensptrT& ptr)
58  {
59  drawer_.print(out, ptr.get());
60  }
61 
62  void print (std::ostream& out, ade::iTensor* ptr)
63  {
64  drawer_.print(out, ptr);
65  }
66 
68  LabelsMapT labels_;
69 
70  bool showshape_ = false;
71 
72 private:
74  PrettyTree<ade::iTensor*> drawer_;
75 };
76 
77 #endif // DBG_ADE_HPP
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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PrettyEquation::PrettyEquation
PrettyEquation(void)
Definition: ade.hpp:23
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PrettyEquation::labels_
LabelsMapT labels_
For every label associated with a tensor, show LABEL=value in the tree.
Definition: ade.hpp:68
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LabelsMapT
std::unordered_map< ade::iTensor *, std::string > LabelsMapT
Definition: ade.hpp:18
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PrettyTree::print
void print(std::ostream &out, T root)
Definition: tree.hpp:33
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PrettyEquation::showshape_
bool showshape_
Definition: ade.hpp:70
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ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
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ade
Definition: coord.hpp:16
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PrettyEquation::drawer_
PrettyTree< ade::iTensor * > drawer_
Actual ascii renderer.
Definition: ade.hpp:74
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ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
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PrettyTree
Definition: tree.hpp:22
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PrettyEquation::print
void print(std::ostream &out, ade::iTensor *ptr)
Definition: ade.hpp:62
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PrettyEquation::print
void print(std::ostream &out, ade::TensptrT &ptr)
Stream equation of ptr to out.
Definition: ade.hpp:57
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
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PrettyEquation
Use PrettyTree to render ade::TensptrT graph as an ascii art.
Definition: ade.hpp:21
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def dbg_wrapper.graph_to_csvimg (root, outpath, showshape=False)
 
def dbg_wrapper.multigraph_to_csvimg (roots, outpath, showshape=False)
 
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struct  Branch
 
struct  Subgraph
 
struct  Arg
 
struct  Conversion
 
struct  Group
 
struct  Property
 
struct  Statement
 
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-Enumerations

enum  SUBGRAPH_TYPE { SCALAR = 0, -ANY, -BRANCH - }
 
enum  STMT_TYPE { SYMBOL_DEF = 0, -PROPERTY_DEF, -CONVERSION - }
 
enum  PTR_TYPE { STATEMENT = 0, -ARGUMENT - }
 
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void subgraph_recursive_free (void *ptr)
 
void arg_recursive_free (void *ptr)
 
void conversion_recursive_free (void *ptr)
 
void statement_recursive_free (void *ptr)
 
void statements_free (struct PtrList *stmts)
 
int parse_str (struct PtrList **stmts, const char *str)
 
int parse_file (struct PtrList **stmts, FILE *file)
 
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1 #include <stdio.h>
2 
3 #include "opt/parse/list.h"
4 
5 #ifndef PARSE_DEF_H
6 #define PARSE_DEF_H
7 
8 struct Branch
9 {
10  int is_group_;
11  struct PtrList* args_;
12  char label_[32];
13  char variadic_[32];
14 };
15 
16 enum SUBGRAPH_TYPE
17 {
18  SCALAR = 0,
19  ANY,
20  BRANCH,
21 };
22 
23 struct Subgraph
24 {
25  enum SUBGRAPH_TYPE type_;
26  union
27  {
28  double scalar_;
29  char* any_;
30  struct Branch* branch_;
31  } val_;
32 };
33 
34 void subgraph_recursive_free (void* ptr);
35 
36 struct Arg
37 {
38  struct Subgraph* subgraph_;
39  struct NumList* shaper_;
40  struct NumList* coorder_;
41 };
42 
43 void arg_recursive_free (void* ptr);
44 
45 struct Conversion
46 {
47  struct Subgraph* source_;
48  struct Subgraph* dest_;
49 };
50 
51 void conversion_recursive_free (void* ptr);
52 
53 struct Group
54 {
55  char ref_[32];
56  char tag_[32];
57 };
58 
59 struct Property
60 {
61  char label_[32];
62  char property_[32];
63  int is_group_;
64 };
65 
66 enum STMT_TYPE
67 {
68  SYMBOL_DEF = 0,
69  PROPERTY_DEF,
70  CONVERSION,
71 };
72 
73 struct Statement
74 {
75  enum STMT_TYPE type_;
76  void* val_;
77 };
78 
79 enum PTR_TYPE
80 {
81  STATEMENT = 0,
82  ARGUMENT,
83 };
84 
85 void statement_recursive_free (void* ptr);
86 
87 #ifdef __cplusplus
88 extern "C" {
89 #endif
90 
91 // wrapper around Statement recursive free
92 extern void statements_free (struct PtrList* stmts);
93 
94 extern int parse_str (struct PtrList** stmts, const char* str);
95 
96 extern int parse_file (struct PtrList** stmts, FILE* file);
97 
98 #ifdef __cplusplus
99 }
100 #endif
101 
102 #endif // PARSE_DEF_H
STMT_TYPE
STMT_TYPE
Definition: def.h:66
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Definition: list.h:35
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Definition: def.h:82
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Property::label_
char label_[32]
Definition: def.h:61
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struct Branch * branch_
Definition: def.h:30
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char tag_[32]
Definition: def.h:56
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Definition: def.h:68
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struct Subgraph * dest_
Definition: def.h:48
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int is_group_
Definition: def.h:63
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CONVERSION
Definition: def.h:70
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Definition: def.h:36
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parse_str
int parse_str(struct PtrList **stmts, const char *str)
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enum SUBGRAPH_TYPE type_
Definition: def.h:25
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int is_group_
Definition: def.h:10
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Definition: def.h:45
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Statement::val_
void * val_
Definition: def.h:76
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enum STMT_TYPE type_
Definition: def.h:75
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Definition: list.h:13
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Definition: def.h:20
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Definition: def.h:39
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Definition: def.h:11
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Definition: def.h:8
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Definition: def.h:40
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ANY
Definition: def.h:19
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Definition: def.h:23
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char ref_[32]
Definition: def.h:55
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char variadic_[32]
Definition: def.h:13
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statements_free
void statements_free(struct PtrList *stmts)
-
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Definition: def.h:81
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Definition: def.h:16
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Definition: def.h:79
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Definition: def.h:53
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Definition: def.h:47
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union Subgraph::@0 val_
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char property_[32]
Definition: def.h:62
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char label_[32]
Definition: def.h:12
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Definition: def.h:69
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Definition: def.h:38
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Definition: def.h:28
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Definition: def.h:59
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Definition: def.h:73
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Definition: def.h:18
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- padding-left: 0; -} - -div.classindex span.ai { - display: inline-block; -} - -div.groupHeader { - margin-left: 16px; - margin-top: 12px; - font-weight: bold; -} - -div.groupText { - margin-left: 16px; - font-style: italic; -} - -body { - background-color: white; - color: black; - margin: 0; -} - -div.contents { - margin-top: 10px; - margin-left: 12px; - margin-right: 8px; -} - -td.indexkey { - background-color: #EBEFF6; - font-weight: bold; - border: 1px solid #C4CFE5; - margin: 2px 0px 2px 0; - padding: 2px 10px; - white-space: nowrap; - vertical-align: top; -} - -td.indexvalue { - background-color: #EBEFF6; - border: 1px solid #C4CFE5; - padding: 2px 10px; - margin: 2px 0px; -} - -tr.memlist { - background-color: #EEF1F7; -} - -p.formulaDsp { - text-align: center; -} - -img.formulaDsp { - -} - -img.formulaInl { - vertical-align: middle; -} - -div.center { - text-align: center; - margin-top: 0px; - margin-bottom: 0px; - padding: 0px; -} - -div.center img { - border: 0px; -} - -address.footer { - text-align: right; - padding-right: 12px; -} - -img.footer { - border: 0px; - vertical-align: middle; -} - -/* @group Code Colorization */ - -span.keyword { - color: #008000 -} - -span.keywordtype { - color: #604020 -} - -span.keywordflow { - color: #e08000 -} - -span.comment { - color: #800000 -} - -span.preprocessor { - color: #806020 -} - -span.stringliteral { - color: #002080 -} - -span.charliteral { - color: #008080 -} - -span.vhdldigit { - color: #ff00ff -} - -span.vhdlchar { - color: #000000 -} - -span.vhdlkeyword { - color: #700070 -} - -span.vhdllogic { - color: #ff0000 -} - -blockquote { - background-color: #F7F8FB; - border-left: 2px solid #9CAFD4; - margin: 0 24px 0 4px; - padding: 0 12px 0 16px; -} - -/* @end */ - -/* -.search { - color: #003399; - font-weight: bold; -} - -form.search { - margin-bottom: 0px; - margin-top: 0px; -} - -input.search { - font-size: 75%; - color: #000080; - font-weight: normal; - background-color: #e8eef2; -} -*/ - -td.tiny { - font-size: 75%; -} - -.dirtab { - padding: 4px; - border-collapse: collapse; - border: 1px solid #A3B4D7; -} - -th.dirtab { - background: #EBEFF6; - font-weight: bold; -} - -hr { - height: 0px; - border: none; - border-top: 1px solid #4A6AAA; -} - -hr.footer { - height: 1px; -} - -/* @group Member Descriptions */ - -table.memberdecls { - border-spacing: 0px; - padding: 0px; -} - -.memberdecls td, .fieldtable tr { - -webkit-transition-property: background-color, box-shadow; - -webkit-transition-duration: 0.5s; - -moz-transition-property: background-color, box-shadow; - -moz-transition-duration: 0.5s; - -ms-transition-property: background-color, box-shadow; - -ms-transition-duration: 0.5s; - -o-transition-property: background-color, box-shadow; - -o-transition-duration: 0.5s; - transition-property: background-color, box-shadow; - transition-duration: 0.5s; -} - -.memberdecls td.glow, .fieldtable tr.glow { - background-color: cyan; - box-shadow: 0 0 15px cyan; -} - -.mdescLeft, .mdescRight, -.memItemLeft, .memItemRight, -.memTemplItemLeft, .memTemplItemRight, .memTemplParams { - background-color: #F9FAFC; - border: none; - margin: 4px; - padding: 1px 0 0 8px; -} - -.mdescLeft, .mdescRight { - padding: 0px 8px 4px 8px; - color: #555; -} - -.memSeparator { - border-bottom: 1px solid #DEE4F0; - line-height: 1px; - margin: 0px; - padding: 0px; -} - -.memItemLeft, .memTemplItemLeft { - white-space: nowrap; -} - -.memItemRight { - width: 100%; -} - -.memTemplParams { - color: #4665A2; - white-space: nowrap; - font-size: 80%; -} - -/* @end */ - -/* @group Member Details */ - -/* Styles for detailed member documentation */ - -.memtitle { - padding: 8px; - border-top: 1px solid #A8B8D9; - border-left: 1px solid #A8B8D9; - border-right: 1px solid #A8B8D9; - border-top-right-radius: 4px; - border-top-left-radius: 4px; - margin-bottom: -1px; - background-image: url('nav_f.png'); - background-repeat: repeat-x; - background-color: #E2E8F2; - line-height: 1.25; - font-weight: 300; - float:left; -} - -.permalink -{ - font-size: 65%; - display: inline-block; - vertical-align: middle; -} - -.memtemplate { - font-size: 80%; - color: #4665A2; - font-weight: normal; - margin-left: 9px; -} - -.memnav { - background-color: #EBEFF6; - border: 1px solid #A3B4D7; - text-align: center; - margin: 2px; - margin-right: 15px; - padding: 2px; -} - -.mempage { - width: 100%; -} - -.memitem { - padding: 0; - margin-bottom: 10px; - margin-right: 5px; - -webkit-transition: box-shadow 0.5s linear; - -moz-transition: box-shadow 0.5s linear; - -ms-transition: box-shadow 0.5s linear; - -o-transition: box-shadow 0.5s linear; - transition: box-shadow 0.5s linear; - display: table !important; - width: 100%; -} - -.memitem.glow { - box-shadow: 0 0 15px cyan; -} - -.memname { - font-weight: 400; - margin-left: 6px; -} - -.memname td { - vertical-align: bottom; -} - -.memproto, dl.reflist dt { - border-top: 1px solid #A8B8D9; - border-left: 1px solid #A8B8D9; - border-right: 1px solid #A8B8D9; - padding: 6px 0px 6px 0px; - color: #253555; - font-weight: bold; - text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); - background-color: #DFE5F1; - /* opera specific markup */ - box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); - border-top-right-radius: 4px; - /* firefox specific markup */ - -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; - -moz-border-radius-topright: 4px; - /* webkit specific markup */ - -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); - -webkit-border-top-right-radius: 4px; - -} - -.overload { - font-family: "courier new",courier,monospace; - font-size: 65%; -} - -.memdoc, dl.reflist dd { - border-bottom: 1px solid #A8B8D9; - border-left: 1px solid #A8B8D9; - border-right: 1px solid #A8B8D9; - padding: 6px 10px 2px 10px; - background-color: #FBFCFD; - border-top-width: 0; - background-image:url('nav_g.png'); - background-repeat:repeat-x; - background-color: #FFFFFF; - /* opera specific markup */ - border-bottom-left-radius: 4px; - border-bottom-right-radius: 4px; - box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); - /* firefox specific markup */ - -moz-border-radius-bottomleft: 4px; - -moz-border-radius-bottomright: 4px; - -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; - /* webkit specific markup */ - -webkit-border-bottom-left-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); -} - -dl.reflist dt { - padding: 5px; -} - -dl.reflist dd { - margin: 0px 0px 10px 0px; - padding: 5px; -} - -.paramkey { - text-align: right; -} - -.paramtype { - white-space: nowrap; -} - -.paramname { - color: #602020; - white-space: nowrap; -} -.paramname em { - font-style: normal; -} -.paramname code { - line-height: 14px; -} - -.params, .retval, .exception, .tparams { - margin-left: 0px; - padding-left: 0px; -} - -.params .paramname, .retval .paramname { - font-weight: bold; - vertical-align: top; -} - -.params .paramtype { - font-style: italic; - vertical-align: top; -} - -.params .paramdir { - font-family: "courier new",courier,monospace; - vertical-align: top; -} - -table.mlabels { - border-spacing: 0px; -} - -td.mlabels-left { - width: 100%; - padding: 0px; -} - -td.mlabels-right { - vertical-align: bottom; - padding: 0px; - white-space: nowrap; -} - -span.mlabels { - margin-left: 8px; -} - -span.mlabel { - background-color: #728DC1; - border-top:1px solid #5373B4; - border-left:1px solid #5373B4; - border-right:1px solid #C4CFE5; - border-bottom:1px solid #C4CFE5; - text-shadow: none; - color: white; - margin-right: 4px; - padding: 2px 3px; - border-radius: 3px; - font-size: 7pt; - white-space: nowrap; - vertical-align: middle; -} - - - -/* @end */ - -/* these are for tree view inside a (index) page */ - -div.directory { - margin: 10px 0px; - border-top: 1px solid #9CAFD4; - border-bottom: 1px solid #9CAFD4; - width: 100%; -} - -.directory table { - border-collapse:collapse; -} - -.directory td { - margin: 0px; - padding: 0px; - vertical-align: top; -} - -.directory td.entry { - white-space: nowrap; - padding-right: 6px; - padding-top: 3px; -} - -.directory td.entry a { - outline:none; -} - -.directory td.entry a img { - border: none; -} - -.directory td.desc { - width: 100%; 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- height: 22px; - display: inline-block; -} - -.iconfopen { - width: 24px; - height: 18px; - margin-bottom: 4px; - background-image:url('folderopen.png'); - background-position: 0px -4px; - background-repeat: repeat-y; - vertical-align:top; - display: inline-block; -} - -.iconfclosed { - width: 24px; - height: 18px; - margin-bottom: 4px; - background-image:url('folderclosed.png'); - background-position: 0px -4px; - background-repeat: repeat-y; - vertical-align:top; - display: inline-block; -} - -.icondoc { - width: 24px; - height: 18px; - margin-bottom: 4px; - background-image:url('doc.png'); - background-position: 0px -4px; - background-repeat: repeat-y; - vertical-align:top; - display: inline-block; -} - -table.directory { - font: 400 14px Roboto,sans-serif; -} - -/* @end */ - -div.dynheader { - margin-top: 8px; - -webkit-touch-callout: none; - -webkit-user-select: none; - -khtml-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; -} - -address { - font-style: normal; - color: #2A3D61; -} - -table.doxtable caption { - caption-side: top; -} - -table.doxtable { - border-collapse:collapse; - margin-top: 4px; - margin-bottom: 4px; -} - -table.doxtable td, table.doxtable th { - border: 1px solid #2D4068; - padding: 3px 7px 2px; -} - -table.doxtable th { - background-color: #374F7F; - color: #FFFFFF; - font-size: 110%; - padding-bottom: 4px; - padding-top: 5px; -} - -table.fieldtable { - /*width: 100%;*/ - margin-bottom: 10px; - border: 1px solid #A8B8D9; - border-spacing: 0px; - -moz-border-radius: 4px; - -webkit-border-radius: 4px; - border-radius: 4px; - -moz-box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 2px; - -webkit-box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); - box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); -} - -.fieldtable td, .fieldtable th { - padding: 3px 7px 2px; -} - -.fieldtable td.fieldtype, .fieldtable td.fieldname { - white-space: nowrap; - border-right: 1px solid #A8B8D9; - border-bottom: 1px solid #A8B8D9; - vertical-align: top; -} - -.fieldtable td.fieldname { - padding-top: 3px; 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def dtypes._handle_get_types (dtypes)
 
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string dtypes._header_template
 
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string dtypes._plugin_id = "DTYPE"
 
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struct  ead::CoordMap
 
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using ead::CoordptrT = std::shared_ptr< CoordMap >
 Type of iCoordMap smartpointer. More...
 
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CoordptrT ead::reduce (std::vector< ade::RankT > red_dims)
 
CoordptrT ead::extend (ade::RankT rank, std::vector< ade::DimT > ext)
 
CoordptrT ead::permute (std::vector< ade::RankT > dims)
 
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1 #include "ade/coord.hpp"
2 
3 #ifndef EAD_COORD_HPP
4 #define EAD_COORD_HPP
5 
6 namespace ead
7 {
8 
9 struct CoordMap final : public ade::iCoordMap
10 {
11  CoordMap (ade::CoordT indices, bool bijective) :
12  indices_(indices), bijective_(bijective) {}
13 
14  ade::iCoordMap* connect (const ade::iCoordMap& rhs) const override
15  {
16  return nullptr;
17  }
18 
19  void forward (ade::CoordT::iterator out,
20  ade::CoordT::const_iterator in) const override
21  {
22  std::copy(indices_.begin(), indices_.end(), out);
23  }
24 
25  iCoordMap* reverse (void) const override
26  {
27  return nullptr;
28  }
29 
30  std::string to_string (void) const override
31  {
32  return fmts::to_string(indices_.begin(), indices_.end());
33  }
34 
35  void access (std::function<void(const ade::MatrixT&)> cb) const override {}
36 
37  bool is_bijective (void) const override
38  {
39  return bijective_;
40  }
41 
42 private:
43  ade::CoordT indices_;
44 
45  bool bijective_;
46 };
47 
49 using CoordptrT = std::shared_ptr<CoordMap>;
50 
51 CoordptrT reduce (std::vector<ade::RankT> red_dims);
52 
53 CoordptrT extend (ade::RankT rank, std::vector<ade::DimT> ext);
54 
55 CoordptrT permute (std::vector<ade::RankT> dims);
56 
57 }
58 
59 #endif // EAD_COORD_HPP
ead::CoordMap::indices_
ade::CoordT indices_
Definition: coord.hpp:43
-
ead::reduce
CoordptrT reduce(std::vector< ade::RankT > red_dims)
-
ead::CoordMap::forward
void forward(ade::CoordT::iterator out, ade::CoordT::const_iterator in) const override
Definition: coord.hpp:19
-
ead::extend
CoordptrT extend(ade::RankT rank, std::vector< ade::DimT > ext)
-
ead::CoordMap
Definition: coord.hpp:9
- -
ead
Definition: constant.hpp:9
-
ead::CoordMap::bijective_
bool bijective_
Definition: coord.hpp:45
-
ead::CoordMap::to_string
std::string to_string(void) const override
Return string representation of coordinate transformer.
Definition: coord.hpp:30
-
ead::CoordMap::is_bijective
bool is_bijective(void) const override
Return true if this instance maps coordinates/shapes bijectively.
Definition: coord.hpp:37
-
ade::iCoordMap
Interface for transforming coordinates and reversing the coordinate.
Definition: coord.hpp:20
-
ade::MatrixT
double[mat_dim][mat_dim] MatrixT
Coordinate transformation matrix (using homogeneous)
Definition: matops.hpp:28
-
ead::CoordMap::CoordMap
CoordMap(ade::CoordT indices, bool bijective)
Definition: coord.hpp:11
-
ade::RankT
uint8_t RankT
Type used for shape rank.
Definition: shape.hpp:23
-
ead::CoordMap::connect
ade::iCoordMap * connect(const ade::iCoordMap &rhs) const override
Return matmul(this, rhs)
Definition: coord.hpp:14
-
ade::CoordT
std::array< CDimT, rank_cap > CoordT
Definition: shape.hpp:54
-
ead::CoordMap::access
void access(std::function< void(const ade::MatrixT &)> cb) const override
Definition: coord.hpp:35
-
ead::permute
CoordptrT permute(std::vector< ade::RankT > dims)
-
ade::to_string
std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
-
ead::CoordptrT
std::shared_ptr< CoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:49
-
ead::CoordMap::reverse
iCoordMap * reverse(void) const override
Definition: coord.hpp:25
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template<typename T >
FuncArg< T > ead::identity_map (NodeptrT< T > node)
 
template<typename T >
FuncArg< T > ead::reduce_map (NodeptrT< T > node, ade::RankT offset, ade::RankT ndims)
 
template<typename T >
FuncArg< T > ead::extend_map (NodeptrT< T > node, ade::RankT rank, std::vector< ade::DimT > ext)
 
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1 #include "ade/funcarg.hpp"
2 
3 #include "ead/coord.hpp"
4 #include "ead/inode.hpp"
5 
6 #ifndef EAD_FUNCARG_HPP
7 #define EAD_FUNCARG_HPP
8 
9 namespace ead
10 {
11 
12 template <typename T>
13 struct FuncArg final
14 {
16  FuncArg (NodeptrT<T> node, ade::CoordptrT shaper, CoordptrT coorder) :
17  node_(node), shaper_(shaper), coorder_(coorder)
18  {
19  if (node_ == nullptr)
20  {
21  logs::fatal("cannot map a null node");
22  }
23  }
24 
26  ade::Shape shape (void) const
27  {
28  return ade::apply_shaper(shaper_, node_->get_tensor()->shape());
29  }
30 
32  ade::TensptrT get_tensor (void) const
33  {
34  return node_->get_tensor();
35  }
36 
37  NodeptrT<T> get_node (void) const
38  {
39  return node_;
40  }
41 
43  ade::CoordptrT get_shaper (void) const
44  {
45  return shaper_;
46  }
47 
50  bool map_io (void) const
51  {
52  // map in to out only if bijective
53  return nullptr == coorder_ || coorder_->is_bijective();
54  }
55 
57  CoordptrT get_coorder (void) const
58  {
59  return coorder_;
60  }
61 
62 private:
64  NodeptrT<T> node_;
65 
67  ade::CoordptrT shaper_;
68 
70  CoordptrT coorder_;
71 };
72 
73 template <typename T>
74 using ArgsT = std::vector<FuncArg<T>>;
75 
76 template <typename T>
77 FuncArg<T> identity_map (NodeptrT<T> node)
78 {
79  return FuncArg<T>(node, ade::identity, nullptr);
80 }
81 
82 template <typename T>
83 FuncArg<T> reduce_map (NodeptrT<T> node, ade::RankT offset, ade::RankT ndims)
84 {
85  if (offset >= ade::rank_cap)
86  {
87  logs::fatalf("cannot dimensions [%d,...] greater or equal to %d",
88  offset, ade::rank_cap);
89  }
90 
91  ade::RankT n = std::min<ade::RankT>(offset + ndims, ade::rank_cap);
92  ade::Shape shape = node->get_tensor()->shape();
93  std::vector<ade::RankT> dims; // dims are allowed to be non-contiguous
94  std::vector<ade::DimT> slist;
95  dims.reserve(n);
96  slist.reserve(n);
97 
98  for (ade::RankT i = offset; i < n; ++i)
99  {
100  if (shape.at(i) > 1)
101  {
102  dims.push_back(i);
103  }
104  slist.push_back(shape.at(i));
105  }
106 
107  return FuncArg<T>(node, ade::reduce(offset, slist), reduce(dims));
108 }
109 
110 template <typename T>
111 FuncArg<T> extend_map (NodeptrT<T> node,
112  ade::RankT rank, std::vector<ade::DimT> ext)
113 {
114  return FuncArg<T>(node, ade::extend(rank, ext), extend(rank, ext));
115 }
116 
117 template <typename T>
118 FuncArg<T> permute_map (NodeptrT<T> node, std::vector<ade::RankT> order)
119 {
120  return FuncArg<T>(node, ade::permute(order), permute(order));
121 }
122 
123 }
124 
125 #endif // EAD_FUNCARG_HPP
ead::FuncArg::shape
ade::Shape shape(void) const
Return shape of tensor filtered through coordinate mapper.
Definition: funcarg.hpp:26
-
ead::reduce
CoordptrT reduce(std::vector< ade::RankT > red_dims)
-
ead::extend
CoordptrT extend(ade::RankT rank, std::vector< ade::DimT > ext)
-
ead::FuncArg::get_node
NodeptrT< T > get_node(void) const
Definition: funcarg.hpp:37
-
ade::extend
CoordptrT extend(RankT rank, std::vector< DimT > ext)
-
ade::reduce
CoordptrT reduce(RankT rank, std::vector< DimT > red)
-
ead::extend_map
FuncArg< T > extend_map(NodeptrT< T > node, ade::RankT rank, std::vector< ade::DimT > ext)
Definition: funcarg.hpp:111
-
ead::NodeptrT
std::shared_ptr< iNode< T > > NodeptrT
Definition: inode.hpp:31
-
ead::reduce_map
FuncArg< T > reduce_map(NodeptrT< T > node, ade::RankT offset, ade::RankT ndims)
Definition: funcarg.hpp:83
-
ead::FuncArg::node_
NodeptrT< T > node_
Tensor reference.
Definition: funcarg.hpp:64
-
ead::ArgsT
std::vector< FuncArg< T > > ArgsT
Definition: funcarg.hpp:74
-
ead::FuncArg::map_io
bool map_io(void) const
Definition: funcarg.hpp:50
-
ead::FuncArg::get_tensor
ade::TensptrT get_tensor(void) const
Return tensor being mapped.
Definition: funcarg.hpp:32
- - -
ead
Definition: constant.hpp:9
-
ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
-
ead::FuncArg
Definition: funcarg.hpp:13
-
ade::identity
CoordptrT identity
Identity matrix instance.
-
ead::permute_map
FuncArg< T > permute_map(NodeptrT< T > node, std::vector< ade::RankT > order)
Definition: funcarg.hpp:118
-
ead::FuncArg::coorder_
CoordptrT coorder_
Coordinate mapper.
Definition: funcarg.hpp:70
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ead::FuncArg::get_shaper
ade::CoordptrT get_shaper(void) const
Return shaper coord map.
Definition: funcarg.hpp:43
- -
ade::apply_shaper
Shape apply_shaper(const CoordptrT &shaper, Shape inshape)
-
ade::RankT
uint8_t RankT
Type used for shape rank.
Definition: shape.hpp:23
-
ead::FuncArg::shaper_
ade::CoordptrT shaper_
Shape mapper.
Definition: funcarg.hpp:67
-
ead::identity_map
FuncArg< T > identity_map(NodeptrT< T > node)
Definition: funcarg.hpp:77
-
ade::Shape
Definition: shape.hpp:60
-
ead::FuncArg::FuncArg
FuncArg(NodeptrT< T > node, ade::CoordptrT shaper, CoordptrT coorder)
Construct FuncArg with specific coorder_ and map_io_ flag.
Definition: funcarg.hpp:16
-
ade::permute
CoordptrT permute(std::vector< RankT > order)
-
ead::permute
CoordptrT permute(std::vector< ade::RankT > dims)
-
ade::rank_cap
const RankT rank_cap
Number of dimsensions in a shape/coordinate.
Definition: shape.hpp:45
-
ead::FuncArg::get_coorder
CoordptrT get_coorder(void) const
Return coord map for coordinates.
Definition: funcarg.hpp:57
-
ead::CoordptrT
std::shared_ptr< CoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:49
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struct  ead::Functor< T >
 
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template<typename T >
NodeptrT< T > ead::make_functor (ade::Opcode opcode, ArgsT< T > args)
 
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1 #include "ade/iopfunc.hpp"
2 
3 #include "ead/generated/opcode.hpp"
4 
5 #include "ead/funcarg.hpp"
6 #include "ead/constant.hpp"
7 #include "ead/operator.hpp"
8 
9 #ifndef EAD_FUNCTOR_HPP
10 #define EAD_FUNCTOR_HPP
11 
12 namespace ead
13 {
14 
15 template <typename T>
16 struct Functor final : public ade::iOperableFunc
17 {
18  static Functor<T>* get (ade::Opcode opcode, ArgsT<T> args);
19 
20  static Functor<T>* get (Functor<T>&& other)
21  {
22  return new Functor<T>(std::move(other));
23  }
24 
25  Functor (const Functor<T>& other) = delete;
26 
27  Functor<T>& operator = (const Functor<T>& other) = delete;
28 
29  Functor<T>& operator = (Functor<T>&& other) = delete;
30 
32  const ade::Shape& shape (void) const override
33  {
34  return shape_;
35  }
36 
38  std::string to_string (void) const override
39  {
40  return opcode_.name_;
41  }
42 
44  ade::Opcode get_opcode (void) const override
45  {
46  return opcode_;
47  }
48 
50  const ade::ArgsT& get_children (void) const override
51  {
52  return args_;
53  }
54 
56  void update_child (ade::FuncArg arg, size_t index) override
57  {
58  ade::Shape arg_shape = arg.shape();
59  if (false == arg_shape.compatible_after(shape_, 0))
60  {
61  logs::fatalf("cannot update child %d to argument with "
62  "incompatible shape %s (requires shape %s)",
63  index, arg_shape.to_string().c_str(),
64  shape_.to_string().c_str());
65  }
66  args_[index] = arg;
67  uninitialize();
68  // warning: does not notify parents of data destruction
69  }
70 
72  void update (void) override
73  {
74  if (is_uninit())
75  {
76  initialize();
77  }
78  return out_->assign();
79  }
80 
82  void* data (void) override
83  {
84  if (is_uninit())
85  {
86  initialize();
87  }
88  return out_->get_ptr();
89  }
90 
92  const void* data (void) const override
93  {
94  if (is_uninit())
95  {
96  logs::fatal("cannot get data of uninitialized functor");
97  }
98  return out_->get_ptr();
99  }
100 
102  size_t type_code (void) const override
103  {
104  return age::get_type<T>();
105  }
106 
108  std::string type_label (void) const override
109  {
110  return age::name_type(age::get_type<T>());
111  }
112 
114  size_t nbytes (void) const override
115  {
116  return sizeof(T) * shape_.n_elems();
117  }
118 
119  bool is_uninit (void) const
120  {
121  return nullptr == out_;
122  }
123 
124  void uninitialize (void)
125  {
126  out_ = nullptr;
127  }
128 
129  void initialize (void)
130  {
131  std::vector<OpArg<T>> datamaps;
132  for (const ade::FuncArg& arg : args_)
133  {
134  auto tens = arg.get_tensor();
135  auto coorder = static_cast<CoordMap*>(arg.get_coorder().get());
136  datamaps.push_back(OpArg<T>{
137  NodeConverters<T>::to_node(tens)->data(),
138  tens->shape(),
139  coorder
140  });
141  }
142  age::typed_exec<T>((age::_GENERATED_OPCODE) opcode_.code_,
143  shape_, out_, datamaps);
144  }
145 
146 private:
147  Functor (ade::Opcode opcode, ade::Shape shape, ade::ArgsT args) :
148  opcode_(opcode), shape_(shape), args_(args)
149  {
150  // initialize();
151  }
152 
153  Functor (Functor<T>&& other) = default;
154 
155  EigenptrT<T> out_ = nullptr;
156 
158  ade::Opcode opcode_;
159 
161  ade::Shape shape_;
162 
164  ade::ArgsT args_;
165 };
166 
167 template <typename T>
168 struct FunctorNode final : public iNode<T>
169 {
170  FunctorNode (std::shared_ptr<Functor<T>> f) : func_(f) {}
171 
172  T* data (void) override
173  {
174  return (T*) func_->data();
175  }
176 
177  void update (void) override
178  {
179  func_->update();
180  }
181 
182  ade::TensptrT get_tensor (void) override
183  {
184  return func_;
185  }
186 
187 private:
188  std::shared_ptr<Functor<T>> func_;
189 };
190 
191 template <typename T>
192 Functor<T>* Functor<T>::get (ade::Opcode opcode, ArgsT<T> args)
193 {
194  static bool registered = register_builder<Functor<T>,T>(
195  [](ade::TensptrT tens)
196  {
197  return std::make_shared<FunctorNode<T>>(
198  std::static_pointer_cast<Functor<T>>(tens));
199  });
200  assert(registered);
201 
202  size_t nargs = args.size();
203  if (0 == nargs)
204  {
205  logs::fatalf("cannot perform %s with no arguments",
206  opcode.name_.c_str());
207  }
208 
209  ade::Shape shape = args[0].shape();
210  for (size_t i = 1, n = nargs; i < n; ++i)
211  {
212  ade::Shape ishape = args[i].shape();
213  if (false == ishape.compatible_after(shape, 0))
214  {
215  logs::fatalf("cannot perform %s with incompatible shapes %s "
216  "and %s", opcode.name_.c_str(), shape.to_string().c_str(),
217  ishape.to_string().c_str());
218  }
219  }
220 
221  ade::ArgsT input_args;
222  input_args.reserve(nargs);
223  std::transform(args.begin(), args.end(),
224  std::back_inserter(input_args),
225  [](FuncArg<T>& arg)
226  {
227  return ade::FuncArg(
228  arg.get_tensor(),
229  arg.get_shaper(),
230  arg.map_io(),
231  arg.get_coorder());
232  });
233  return new Functor<T>(opcode, shape, input_args);
234 }
235 
236 template <typename T>
237 NodeptrT<T> make_functor (ade::Opcode opcode, ArgsT<T> args)
238 {
239  return std::make_shared<FunctorNode<T>>(
240  std::shared_ptr<Functor<T>>(Functor<T>::get(opcode, args))
241  );
242 }
243 
244 }
245 
246 #endif // EAD_FUNCTOR_HPP
ead::Functor::data
const void * data(void) const override
Implementation of iData.
Definition: functor.hpp:92
-
ead::Functor::operator=
Functor< T > & operator=(const Functor< T > &other)=delete
-
ead::Functor::type_code
size_t type_code(void) const override
Implementation of iData.
Definition: functor.hpp:102
-
csv_to_png.args
args
Definition: csv_to_png.py:105
-
ead::Functor::get_children
const ade::ArgsT & get_children(void) const override
Implementation of iFunctor.
Definition: functor.hpp:50
-
ead::Functor::update
void update(void) override
Implementation of iOperableFunc.
Definition: functor.hpp:72
-
ade::Opcode
Encoding of operation.
Definition: ifunctor.hpp:18
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ead::Functor::args_
ade::ArgsT args_
Tensor arguments (and children)
Definition: functor.hpp:164
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ead::CoordMap
Definition: coord.hpp:9
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ead::FunctorNode::func_
std::shared_ptr< Functor< T > > func_
Definition: functor.hpp:188
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ade::Opcode::name_
std::string name_
String representation of operation.
Definition: ifunctor.hpp:21
-
ead::NodeptrT
std::shared_ptr< iNode< T > > NodeptrT
Definition: inode.hpp:31
-
ead::ArgsT
std::vector< FuncArg< T > > ArgsT
Definition: funcarg.hpp:74
-
ead::Functor::shape
const ade::Shape & shape(void) const override
Implementation of iTensor.
Definition: functor.hpp:32
-
csv_to_png.nargs
nargs
Definition: csv_to_png.py:101
-
ead::Functor::update_child
void update_child(ade::FuncArg arg, size_t index) override
Implementation of iFunctor.
Definition: functor.hpp:56
- -
ead::Functor::get_opcode
ade::Opcode get_opcode(void) const override
Implementation of iFunctor.
Definition: functor.hpp:44
-
ade::Shape::to_string
std::string to_string(void) const
Return string representation of shape.
Definition: shape.hpp:146
-
ead
Definition: constant.hpp:9
- -
ead::FunctorNode::FunctorNode
FunctorNode(std::shared_ptr< Functor< T >> f)
Definition: functor.hpp:170
-
ade::Shape::n_elems
NElemT n_elems(void) const
Return the total number of elements represented by the shape.
Definition: shape.hpp:116
-
ead::Functor::Functor
Functor(const Functor< T > &other)=delete
-
ead::FuncArg
Definition: funcarg.hpp:13
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ead::NodeConverters::to_node
static NodeptrT< T > to_node(ade::TensptrT tens)
Definition: inode.hpp:44
-
ead::Functor::uninitialize
void uninitialize(void)
Definition: functor.hpp:124
-
ead::Functor::data
void * data(void) override
Implementation of iData.
Definition: functor.hpp:82
-
ead::iNode
Definition: inode.hpp:14
-
ead::EigenptrT
std::shared_ptr< iEigen< T > > EigenptrT
Definition: eigen.hpp:76
-
ead::FunctorNode::data
T * data(void) override
Definition: functor.hpp:172
-
ead::OpArg
Definition: operator.hpp:27
-
ead::FunctorNode::update
void update(void) override
Definition: functor.hpp:177
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ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ead::Functor::initialize
void initialize(void)
Definition: functor.hpp:129
-
ead::Functor::get
static Functor< T > * get(ade::Opcode opcode, ArgsT< T > args)
Definition: functor.hpp:192
-
ead::Functor::is_uninit
bool is_uninit(void) const
Definition: functor.hpp:119
-
ead::Functor::Functor
Functor(ade::Opcode opcode, ade::Shape shape, ade::ArgsT args)
Definition: functor.hpp:147
-
ade::iOperableFunc
A functor node with direct access to evaluated data.
Definition: iopfunc.hpp:20
-
ead::Functor::opcode_
ade::Opcode opcode_
Operation encoding.
Definition: functor.hpp:158
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
-
ade::index
NElemT index(Shape shape, CoordT coord)
- -
ade::Shape::compatible_after
bool compatible_after(const Shape &other, RankT idx) const
Definition: shape.hpp:134
-
ade::Shape
Definition: shape.hpp:60
-
ade::Opcode::code_
size_t code_
Numerical encoding of operation.
Definition: ifunctor.hpp:24
-
ead::Functor::nbytes
size_t nbytes(void) const override
Implementation of iData.
Definition: functor.hpp:114
-
ead::Functor
Definition: functor.hpp:16
-
ead::Functor::out_
EigenptrT< T > out_
Definition: functor.hpp:155
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ead::make_functor
NodeptrT< T > make_functor(ade::Opcode opcode, ArgsT< T > args)
Definition: functor.hpp:237
-
ade::FuncArg::shape
Shape shape(void) const
Return shape of tensor filtered through coordinate mapper.
Definition: funcarg.hpp:55
-
ead::FunctorNode::get_tensor
ade::TensptrT get_tensor(void) override
Definition: functor.hpp:182
-
ead::Functor::type_label
std::string type_label(void) const override
Implementation of iData.
Definition: functor.hpp:108
-
ead::Functor::to_string
std::string to_string(void) const override
Implementation of iTensor.
Definition: functor.hpp:38
-
ead::Functor::shape_
ade::Shape shape_
Shape info built at construction time according to arguments.
Definition: functor.hpp:161
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ead::FunctorNode
Definition: functor.hpp:168
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ade::ArgsT
std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
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1 
9 #include "ade/ileaf.hpp"
10 
11 #include "ead/eigen.hpp"
12 
13 #ifndef EAD_ILEAF_HPP
14 #define EAD_ILEAF_HPP
15 
16 namespace ead
17 {
18 
19 template <typename T>
20 struct iLeaf : public ade::iLeaf
21 {
22  virtual ~iLeaf (void) = default;
23 
25  const ade::Shape& shape (void) const override
26  {
27  return shape_;
28  }
29 
31  void* data (void) override
32  {
33  return data_.data();
34  }
35 
37  const void* data (void) const override
38  {
39  return data_.data();
40  }
41 
43  size_t type_code (void) const override
44  {
45  return age::get_type<T>();
46  }
47 
49  std::string type_label (void) const override
50  {
51  return age::name_type(age::get_type<T>());
52  }
53 
55  size_t nbytes (void) const override
56  {
57  return sizeof(T) * shape_.n_elems();
58  }
59 
60  // todo: deprecate (with is_mutable)
61  virtual bool is_const (void) const = 0;
62 
63 protected:
64  iLeaf (T* data, ade::Shape shape) :
65  data_(make_tensmap(data, shape)),
66  shape_(shape) {}
67 
69  TensorT<T> data_;
70 
72  ade::Shape shape_;
73 };
74 
75 }
76 
77 #endif // EAD_ILEAF_HPP
ead::iLeaf::type_code
size_t type_code(void) const override
Implementation of iData.
Definition: ileaf.hpp:43
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ead::iLeaf::is_const
virtual bool is_const(void) const =0
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ead::iLeaf::shape
const ade::Shape & shape(void) const override
Implementation of iTensor.
Definition: ileaf.hpp:25
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ead::iLeaf::iLeaf
iLeaf(T *data, ade::Shape shape)
Definition: ileaf.hpp:64
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ead::iLeaf::~iLeaf
virtual ~iLeaf(void)=default
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ead::make_tensmap
TensMapT< T > make_tensmap(T *data, const ade::Shape &shape)
Definition: eigen.hpp:164
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ead
Definition: constant.hpp:9
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ade::iLeaf
Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
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ade::Shape::n_elems
NElemT n_elems(void) const
Return the total number of elements represented by the shape.
Definition: shape.hpp:116
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ead::iLeaf::data_
TensorT< T > data_
Data Source.
Definition: ileaf.hpp:69
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ead::TensorT
Eigen::Tensor< T, 8 > TensorT
Definition: eigen.hpp:22
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ead::iLeaf::nbytes
size_t nbytes(void) const override
Implementation of iData.
Definition: ileaf.hpp:55
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ade::Shape
Definition: shape.hpp:60
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ead::iLeaf::shape_
ade::Shape shape_
Shape utility to avoid excessive conversion between data_.dimensions()
Definition: ileaf.hpp:72
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ead::iLeaf
Definition: ileaf.hpp:20
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ead::iLeaf::type_label
std::string type_label(void) const override
Implementation of iData.
Definition: ileaf.hpp:49
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ead::iLeaf::data
void * data(void) override
Implementation of iData.
Definition: ileaf.hpp:31
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ead::iLeaf::data
const void * data(void) const override
Implementation of iData.
Definition: ileaf.hpp:37
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struct  ead::ScalarConvr< T >
 
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1 #include "opt/parse.hpp"
2 
3 #include "ead/ead.hpp"
4 
5 #ifndef EAD_PARSE_HPP
6 #define EAD_PARSE_HPP
7 
8 namespace ead
9 {
10 
11 static std::vector<double> vectorize (::NumList* list)
12 {
13  std::vector<double> arr;
14  for (auto it = list->head_; nullptr != it; it = it->next_)
15  {
16  arr.push_back(it->val_);
17  }
18  return arr;
19 }
20 
21 static CoordptrT coorderize (::NumList* list)
22 {
23  CoordptrT out = nullptr;
24  if (nullptr == list)
25  {
26  return out;
27  }
28  std::vector<double> clist = vectorize(list);
29  if (clist.size() > 0)
30  {
31  ade::CoordT carr;
32  std::copy(clist.begin(), clist.end(), carr.begin());
33  out = std::make_shared<CoordMap>(carr, false); // todo: figure out bijectivity
34  }
35  return out;
36 }
37 
38 template <typename T>
39 struct ScalarConvr final : public opt::iConverter
40 {
41  ScalarConvr (double scalar) : scalar_(scalar) {}
42 
43  ade::TensptrT build (const opt::ContexT& ctx,
44  ade::Shape outshape) const override
45  {
46  return ead::make_constant_scalar((T) scalar_,
47  outshape)->get_tensor();
48  }
49 
50  std::string to_string (void) const override
51  {
52  return fmts::to_string(scalar_);
53  }
54 
55  double scalar_;
56 };
57 
58 template <typename T>
59 struct AnyConvr final : public opt::iConverter
60 {
61  AnyConvr (std::string any_id) : any_id_(any_id) {}
62 
63  ade::TensptrT build (const opt::ContexT& ctx,
64  ade::Shape outshape) const override
65  {
66  const opt::CtxValT& val = estd::must_getf(ctx, any_id_,
67  "cannot find any id %s in conversion", any_id_.c_str());
68  if (val.size() != 1)
69  {
70  logs::fatal("context value is not any");
71  }
72  return *(val.begin());
73  }
74 
75  std::string to_string (void) const override
76  {
77  return any_id_;
78  }
79 
80  std::string any_id_;
81 };
82 
83 struct BuilderArg final
84 {
85  BuilderArg (opt::ConvptrT arg,
86  ade::CoordptrT shaper, CoordptrT coorder) :
87  arg_(arg), shaper_(shaper), coorder_(coorder)
88  {
89  if (nullptr == arg)
90  {
91  logs::fatal("created a rule argument with null argument");
92  }
93  }
94 
95  opt::ConvptrT arg_;
96 
97  ade::CoordptrT shaper_;
98 
99  CoordptrT coorder_;
100 };
101 
102 using BuilderArgsT = std::vector<BuilderArg>;
103 
104 template <typename T>
105 struct FuncConvr final : public opt::iConverter
106 {
107  FuncConvr (std::string op, BuilderArgsT args) :
108  opcode_({op, age::get_op(op)}), args_(args) {}
109 
110  ade::TensptrT build (const opt::ContexT& ctx,
111  ade::Shape outshape) const override
112  {
113  ArgsT<T> args;
114  for (auto& arg : args_)
115  {
116  ade::Shape childshape = outshape;
117  if (ade::is_identity(arg.shaper_.get()))
118  {
119  childshape = ade::apply_shaper(arg.shaper_, childshape);
120  }
121  auto tens = arg.arg_->build(ctx, childshape);
122  args.push_back(FuncArg<T>(
123  NodeConverters<T>::to_node(tens),
124  arg.shaper_, arg.coorder_));
125  }
126  return make_functor(opcode_, args)->get_tensor();
127  }
128 
129  std::string to_string (void) const override
130  {
131  std::vector<std::string> args;
132  args.reserve(args_.size());
133  std::transform(args_.begin(), args_.end(),
134  std::back_inserter(args),
135  [](const BuilderArg& arg)
136  {
137  return arg.arg_->to_string();
138  });
139  return opcode_.name_ + fmts::sprintf("(%s)", fmts::join(",",
140  args.begin(), args.end()).c_str());
141  }
142 
143  ade::Opcode opcode_;
144 
145  BuilderArgsT args_;
146 };
147 
148 template <typename T>
149 struct GroupConvr final : public opt::iConverter
150 {
151  GroupConvr (std::string group, BuilderArgsT args, std::string variadic) :
152  group_(group), args_(args), variadic_(variadic)
153  {
154  assert(group_ == "sum" || group_ == "prod"); // todo: generalize this for ordered-groups
155  }
156 
157  ade::TensptrT build (const opt::ContexT& ctx,
158  ade::Shape outshape) const override
159  {
160  ade::TensT args;
161  for (auto& arg : args_)
162  {
163  ade::Shape childshape = outshape;
164  if (ade::is_identity(arg.shaper_.get()))
165  {
166  childshape = ade::apply_shaper(arg.shaper_, childshape);
167  }
168  args.push_back(arg.arg_->build(ctx, childshape));
169  }
170 
171  if (variadic_.size() > 0)
172  {
173  opt::CtxValT varargs = estd::try_get(
174  ctx, variadic_, opt::CtxValT());
175  args.insert(args.end(), varargs.begin(), varargs.end());
176  }
177  ead::NodesT<T> outs;
178  outs.reserve(args.size());
179  std::transform(args.begin(), args.end(),
180  std::back_inserter(outs),
181  [](ade::TensptrT tens)
182  {
183  return NodeConverters<T>::to_node(tens);
184  });
185  if (group_ == "sum")
186  {
187  return tenncor::sum(outs)->get_tensor();
188  }
189  return tenncor::prod(outs)->get_tensor();
190  }
191 
192  std::string to_string (void) const override
193  {
194  std::vector<std::string> args;
195  args.reserve(args_.size());
196  std::transform(args_.begin(), args_.end(),
197  std::back_inserter(args),
198  [](const BuilderArg& arg)
199  {
200  return arg.arg_->to_string();
201  });
202  if (variadic_.size() > 0)
203  {
204  args.push_back(".." + variadic_);
205  }
206  return fmts::sprintf("group:%s(%s)", group_.c_str(),
207  fmts::join(",", args.begin(), args.end()).c_str());
208  }
209 
210  std::string group_;
211 
212  BuilderArgsT args_;
213 
214  std::string variadic_;
215 };
216 
217 template <typename T>
218 struct ConverterBuilder final : public opt::iConverterBuilder
219 {
220  opt::CstConvertF build_cconv (void) const override
221  {
222  return [](ade::iTensor* tens)
223  {
224  ade::TensptrT out = nullptr;
225  if (auto f = dynamic_cast<ade::iOperableFunc*>(tens))
226  {
227  f->update();
228  T* data = (T*) f->data();
229  out = ead::make_constant(data, tens->shape())->get_tensor();
230  }
231  return out;
232  };
233  }
234 
235  opt::ConvptrT build (const ::Subgraph* sg,
236  const opt::RulesContext& ctx) const override
237  {
238  if (NULL == sg)
239  {
240  logs::fatal("cannot make builder with null subgraph");
241  }
242  opt::ConvptrT out;
243  switch (sg->type_)
244  {
245  case SCALAR:
246  out = std::make_shared<ScalarConvr<T>>(sg->val_.scalar_);
247  break;
248  case ANY:
249  {
250  std::string symbol(sg->val_.any_);
251  if (false == estd::has(ctx.symbols_, symbol))
252  {
253  logs::fatalf("undeclared symbol '%s'", symbol.c_str());
254  }
255  out = std::make_shared<AnyConvr<T>>(symbol);
256  }
257  break;
258  case BRANCH:
259  {
260  ::Branch* branch = sg->val_.branch_;
261  if (nullptr == branch)
262  {
263  logs::fatal("subgraph ended at nullptr branch");
264  }
265  BuilderArgsT args;
266  for (auto it = branch->args_->head_; nullptr != it; it = it->next_)
267  {
268  ::Arg* arg = (::Arg*) it->val_;
269  opt::ConvptrT warg = build(arg->subgraph_, ctx);
270  ade::CoordptrT shaper = this->shaperize(arg->shaper_);
271  CoordptrT coorder = ead::coorderize(arg->coorder_);
272  args.push_back(BuilderArg(warg, shaper, coorder));
273  }
274  std::string label(branch->label_);
275  if (branch->is_group_)
276  {
277  std::string variadic(branch->variadic_);
278  if (variadic.size() > 0 && false == estd::has(ctx.symbols_, variadic))
279  {
280  logs::warnf("unknown variadic %s", variadic.c_str());
281  variadic = "";
282  }
283  out = std::make_shared<GroupConvr<T>>(
284  label, args, variadic);
285  }
286  else
287  {
288  out = std::make_shared<FuncConvr<T>>(label, args);
289  }
290  }
291  break;
292  default:
293  logs::fatalf("unknown subgraph node type %d", sg->type_);
294  }
295  return out;
296  }
297 
298  ade::CoordptrT shaperize (::NumList* list) const override
299  {
300  ade::CoordptrT out = nullptr;
301  if (nullptr == list)
302  {
303  return out;
304  }
305  std::vector<double> slist = vectorize(list);
306  if (slist.size() > 0)
307  {
308  out = std::make_shared<ade::CoordMap>(
309  [&slist](ade::MatrixT m)
310  {
311  for (size_t i = 0; i < ade::mat_dim; ++i)
312  {
313  for (size_t j = 0; j < ade::mat_dim; ++j)
314  {
315  size_t index = i * ade::mat_dim + j;
316  if (index < slist.size())
317  {
318  m[i][j] = slist[index];
319  }
320  }
321  }
322  });
323  }
324  return out;
325  }
326 
327  ade::CoordptrT coorderize (::NumList* list) const override
328  {
329  return ead::coorderize(list);
330  }
331 };
332 
333 template <typename T>
334 opt::OptCtx parse (std::string content)
335 {
336  static ConverterBuilder<T> builder;
337  return opt::parse(content, builder);
338 }
339 
340 template <typename T>
341 opt::OptCtx parse_file (std::string filename)
342 {
343  static ConverterBuilder<T> builder;
344  return opt::parse_file(filename, builder);
345 }
346 
347 }
348 
349 #endif // EAD_PARSE_HPP
opt::CtxValT
std::set< ade::TensptrT > CtxValT
Definition: candidate.hpp:14
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opt::RulesContext::symbols_
std::unordered_set< std::string > symbols_
Definition: parse.hpp:12
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ead::NodeConverters
Definition: inode.hpp:40
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ead::FuncConvr::FuncConvr
FuncConvr(std::string op, BuilderArgsT args)
Definition: parse.hpp:107
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csv_to_png.args
args
Definition: csv_to_png.py:105
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ead::AnyConvr::AnyConvr
AnyConvr(std::string any_id)
Definition: parse.hpp:61
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ead::coorderize
static CoordptrT coorderize(::NumList *list)
Definition: parse.hpp:21
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ade::Opcode
Encoding of operation.
Definition: ifunctor.hpp:18
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ead::make_constant_scalar
NodeptrT< T > make_constant_scalar(T scalar, ade::Shape shape)
Definition: constant.hpp:111
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ead::ConverterBuilder::build
opt::ConvptrT build(const ::Subgraph *sg, const opt::RulesContext &ctx) const override
Definition: parse.hpp:235
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ade::Opcode::name_
std::string name_
String representation of operation.
Definition: ifunctor.hpp:21
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ead::ArgsT
std::vector< FuncArg< T > > ArgsT
Definition: funcarg.hpp:74
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Arg
Definition: def.h:36
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ead::BuilderArg::coorder_
CoordptrT coorder_
Definition: parse.hpp:99
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NumList::head_
struct NumNode * head_
Definition: list.h:15
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Branch::is_group_
int is_group_
Definition: def.h:10
-
ead::GroupConvr::group_
std::string group_
Definition: parse.hpp:210
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ead::ConverterBuilder::shaperize
ade::CoordptrT shaperize(::NumList *list) const override
Definition: parse.hpp:298
-
ead::AnyConvr::to_string
std::string to_string(void) const override
Definition: parse.hpp:75
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ead::FuncConvr::build
ade::TensptrT build(const opt::ContexT &ctx, ade::Shape outshape) const override
Definition: parse.hpp:110
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ead
Definition: constant.hpp:9
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NumNode::next_
struct NumNode * next_
Definition: list.h:9
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ead::parse
opt::OptCtx parse(std::string content)
Definition: parse.hpp:334
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ead::ConverterBuilder::build_cconv
opt::CstConvertF build_cconv(void) const override
Definition: parse.hpp:220
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ead::GroupConvr::GroupConvr
GroupConvr(std::string group, BuilderArgsT args, std::string variadic)
Definition: parse.hpp:151
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NumList
Definition: list.h:13
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ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
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BRANCH
Definition: def.h:20
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opt::iConverterBuilder
Definition: parse.hpp:19
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ead::GroupConvr::variadic_
std::string variadic_
Definition: parse.hpp:214
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opt::CstConvertF
std::function< ade::TensptrT(ade::iTensor *)> CstConvertF
Definition: optimize.hpp:10
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Arg::shaper_
struct NumList * shaper_
Definition: def.h:39
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Branch::args_
struct PtrList * args_
Definition: def.h:11
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ead::FuncArg
Definition: funcarg.hpp:13
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ead::NodeConverters::to_node
static NodeptrT< T > to_node(ade::TensptrT tens)
Definition: inode.hpp:44
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opt::OptCtx
Definition: optimize.hpp:12
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ead::FuncConvr::args_
BuilderArgsT args_
Definition: parse.hpp:145
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ead::BuilderArgsT
std::vector< BuilderArg > BuilderArgsT
Definition: parse.hpp:102
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Branch
Definition: def.h:8
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opt::ContexT
std::map< std::string, CtxValT > ContexT
Definition: candidate.hpp:16
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ead::AnyConvr
Definition: parse.hpp:59
-
ead::ScalarConvr::build
ade::TensptrT build(const opt::ContexT &ctx, ade::Shape outshape) const override
Definition: parse.hpp:43
-
ead::GroupConvr::build
ade::TensptrT build(const opt::ContexT &ctx, ade::Shape outshape) const override
Definition: parse.hpp:157
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Arg::coorder_
struct NumList * coorder_
Definition: def.h:40
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ead::AnyConvr::build
ade::TensptrT build(const opt::ContexT &ctx, ade::Shape outshape) const override
Definition: parse.hpp:63
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ade::MatrixT
double[mat_dim][mat_dim] MatrixT
Coordinate transformation matrix (using homogeneous)
Definition: matops.hpp:28
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ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
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ANY
Definition: def.h:19
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ead::make_constant
NodeptrT< T > make_constant(T *data, ade::Shape shape)
Definition: constant.hpp:121
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ead::FuncConvr::opcode_
ade::Opcode opcode_
Definition: parse.hpp:143
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opt::ConvptrT
std::shared_ptr< iConverter > ConvptrT
Definition: iconverter.hpp:23
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Branch::variadic_
char variadic_[32]
Definition: def.h:13
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opt::parse
OptCtx parse(std::string content, const iConverterBuilder &builder)
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ead::ScalarConvr
Definition: parse.hpp:39
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ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
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ead::ConverterBuilder::coorderize
ade::CoordptrT coorderize(::NumList *list) const override
Definition: parse.hpp:327
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opt::RulesContext
Definition: parse.hpp:9
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ade::apply_shaper
Shape apply_shaper(const CoordptrT &shaper, Shape inshape)
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ead::BuilderArg::BuilderArg
BuilderArg(opt::ConvptrT arg, ade::CoordptrT shaper, CoordptrT coorder)
Definition: parse.hpp:85
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ade::mat_dim
const RankT mat_dim
Number of rows and columns for the homogeneous matrix.
Definition: matops.hpp:22
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ead::BuilderArg
Definition: parse.hpp:83
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ead::GroupConvr::args_
BuilderArgsT args_
Definition: parse.hpp:212
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ead::BuilderArg::arg_
opt::ConvptrT arg_
Definition: parse.hpp:95
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ade::index
NElemT index(Shape shape, CoordT coord)
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ead::BuilderArg::shaper_
ade::CoordptrT shaper_
Definition: parse.hpp:97
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ade::Shape
Definition: shape.hpp:60
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ead::AnyConvr::any_id_
std::string any_id_
Definition: parse.hpp:80
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ade::CoordT
std::array< CDimT, rank_cap > CoordT
Definition: shape.hpp:54
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opt::parse_file
OptCtx parse_file(std::string filename, const iConverterBuilder &builder)
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ade::TensT
std::vector< TensptrT > TensT
Vector representation of ade tensor pointers.
Definition: funcarg.hpp:104
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ead::ScalarConvr::scalar_
double scalar_
Definition: parse.hpp:55
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ead::ScalarConvr::to_string
std::string to_string(void) const override
Definition: parse.hpp:50
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ead::ScalarConvr::ScalarConvr
ScalarConvr(double scalar)
Definition: parse.hpp:41
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opt::iConverter
Definition: iconverter.hpp:13
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Definition: parse.hpp:149
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Definition: list.h:37
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NodeptrT< T > make_functor(ade::Opcode opcode, ArgsT< T > args)
Definition: functor.hpp:237
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Definition: def.h:12
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static std::vector< double > vectorize(::NumList *list)
Definition: parse.hpp:11
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struct Subgraph * subgraph_
Definition: def.h:38
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Definition: parse.hpp:105
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std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
-
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std::shared_ptr< CoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:49
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Definition: parse.hpp:218
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opt::OptCtx parse_file(std::string filename)
Definition: parse.hpp:341
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Definition: list.h:31
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Definition: def.h:18
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Definition: inode.hpp:34
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bool is_identity(iCoordMap *coorder)
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1 #include <list>
2 #include <unordered_set>
3 
4 #include "ade/traveler.hpp"
5 
6 #include "opt/optimize.hpp"
7 
8 #include "ead/constant.hpp"
9 #include "ead/functor.hpp"
10 
11 #ifndef EAD_SESSION_HPP
12 #define EAD_SESSION_HPP
13 
14 namespace ead
15 {
16 
17 using TensSetT = std::unordered_set<ade::iTensor*>;
18 
19 struct iSession
20 {
21  virtual ~iSession (void) = default;
22 
23  virtual void track (ade::TensT roots) = 0;
24 
28  virtual void update (TensSetT updated = {}, TensSetT ignores = {}) = 0;
29 
30  virtual void update_target (TensSetT target, TensSetT updated = {}) = 0;
31 };
32 
33 struct SizeT
34 {
35  size_t d = 0;
36 
37  operator size_t() const { return d; }
38 };
39 
40 // for each leaf node, iteratively update the parents
41 // don't update parent node if it is part of ignored set
42 struct Session final : public iSession
43 {
44  void track (ade::TensT roots) override
45  {
46  tracked_.insert(roots.begin(), roots.end());
47  ade::ParentFinder pfinder;
48  for (ade::TensptrT& root : roots)
49  {
50  root->accept(pfinder);
51  root->accept(stat_);
52  }
53  auto& statmap = stat_.graphsize_;
54 
55  std::list<ade::iOperableFunc*> all_ops;
56  for (auto& statpair : statmap)
57  {
58  if (0 < statpair.second.upper_)
59  {
60  // ensure we only track operable functors
61  auto op = dynamic_cast<ade::iOperableFunc*>(statpair.first);
62  if (nullptr == op)
63  {
64  logs::fatalf("cannot track non-operable functor %s",
65  statpair.first->to_string().c_str());
66  }
67  all_ops.push_back(op);
68  }
69  }
70  all_ops.sort(
71  [&statmap](ade::iOperableFunc* a, ade::iOperableFunc* b)
72  {
73  return statmap[a].upper_ < statmap[b].upper_;
74  });
75  requirements_.clear();
76  for (ade::iOperableFunc* op : all_ops)
77  {
78  auto& args = op->get_children();
79  TensSetT unique_children;
80  for (const ade::FuncArg& arg : args)
81  {
82  auto tens = arg.get_tensor().get();
83  if (0 < statmap[tens].upper_) // ignore leaves
84  {
85  unique_children.emplace(tens);
86  }
87  }
88  requirements_.push_back({op, unique_children.size()});
89  }
90 
91  for (auto& assocs : pfinder.parents_)
92  {
93  for (auto& parent_pair : assocs.second)
94  {
95  parents_[assocs.first].emplace(
96  static_cast<ade::iOperableFunc*>(parent_pair.first));
97  }
98  }
99  }
100 
101  void update (TensSetT updated = {}, TensSetT ignores = {}) override
102  {
103  std::unordered_map<ade::iOperableFunc*,SizeT> fulfilments;
104  for (ade::iTensor* unodes : updated)
105  {
106  auto& node_parents = parents_[unodes];
107  for (auto& node_parent : node_parents)
108  {
109  ++fulfilments[node_parent].d;
110  }
111  }
112  // ignored nodes and its dependers will never fulfill requirement
113  for (auto& op : requirements_)
114  {
115  // fulfilled and not ignored
116  if (fulfilments[op.first].d >= op.second &&
117  false == estd::has(ignores, op.first))
118  {
119  op.first->update();
120  auto& op_parents = parents_[op.first];
121  for (auto& op_parent : op_parents)
122  {
123  ++fulfilments[op_parent].d;
124  }
125  }
126  }
127  }
128 
129  void update_target (TensSetT target, TensSetT updated = {}) override
130  {
131  ade::OnceTraveler targetted;
132  for (auto& tens : target)
133  {
134  tens->accept(targetted);
135  }
136  std::unordered_map<ade::iOperableFunc*,SizeT> fulfilments;
137  for (ade::iTensor* unodes : updated)
138  {
139  auto& node_parents = parents_[unodes];
140  for (auto& node_parent : node_parents)
141  {
142  ++fulfilments[node_parent].d;
143  }
144  }
145  // ignored nodes and its dependers will never fulfill requirement
146  for (auto& op : requirements_)
147  {
148  // is relevant to target, is fulfilled and not ignored
149  if (estd::has(targetted.visited_, op.first) &&
150  fulfilments[op.first].d >= op.second)
151  {
152  op.first->update();
153  auto& op_parents = parents_[op.first];
154  for (auto& op_parent : op_parents)
155  {
156  ++fulfilments[op_parent].d;
157  }
158  }
159  }
160  }
161 
162  void optimize (const opt::OptCtx& rules)
163  {
164  ade::TensT tracked(tracked_.begin(), tracked_.end());
165  opt::optimize(tracked, rules);
166  stat_.graphsize_.clear();
167  parents_.clear();
168  track(tracked);
169  }
170 
171  std::unordered_set<ade::TensptrT> tracked_;
172 
173  ade::GraphStat stat_;
174 
175  std::unordered_map<ade::iTensor*,
176  std::unordered_set<ade::iOperableFunc*>> parents_;
177 
178  std::vector<std::pair<ade::iOperableFunc*,size_t>> requirements_; // todo: test minimal requirements
179 };
180 
181 }
182 
183 #endif // EAD_SESSION_HPP
ead::Session::parents_
std::unordered_map< ade::iTensor *, std::unordered_set< ade::iOperableFunc * > > parents_
Definition: session.hpp:176
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Definition: session.hpp:42
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csv_to_png.args
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Definition: csv_to_png.py:105
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void optimize(const opt::OptCtx &rules)
Definition: session.hpp:162
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void update(TensSetT updated={}, TensSetT ignores={}) override
Definition: session.hpp:101
-
ade::OnceTraveler
Extremely generic traveler that visits every node in the graph once.
Definition: traveler.hpp:22
- -
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virtual void update_target(TensSetT target, TensSetT updated={})=0
-
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std::unordered_set< iTensor * > visited_
Definition: traveler.hpp:57
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std::unordered_set< ade::iTensor * > TensSetT
Definition: session.hpp:17
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Definition: session.hpp:19
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Definition: constant.hpp:9
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Traveler that maps each tensor to its subtree&#39;s maximum depth.
Definition: traveler.hpp:61
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Definition: optimize.hpp:12
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ead::iSession::update
virtual void update(TensSetT updated={}, TensSetT ignores={})=0
-
ade::ParentFinder::parents_
std::unordered_map< iTensor *, ParentMapT > parents_
Tracks child to parents relationship.
Definition: traveler.hpp:194
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ead::Session::tracked_
std::unordered_set< ade::TensptrT > tracked_
Definition: session.hpp:171
-
ead::iSession::track
virtual void track(ade::TensT roots)=0
-
ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
-
ade::iOperableFunc
A functor node with direct access to evaluated data.
Definition: iopfunc.hpp:20
-
ead::Session::track
void track(ade::TensT roots) override
Definition: session.hpp:44
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std::vector< std::pair< ade::iOperableFunc *, size_t > > requirements_
Definition: session.hpp:178
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ead::SizeT
Definition: session.hpp:33
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ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
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ade::TensT
std::vector< TensptrT > TensT
Vector representation of ade tensor pointers.
Definition: funcarg.hpp:104
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Traveler that for each child tracks the relationship to all parents.
Definition: traveler.hpp:166
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std::unordered_map< iTensor *, estd::NumRange< size_t > > graphsize_
Definition: traveler.hpp:109
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ade::TensT optimize(ade::TensT roots, const OptCtx &opts)
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ade::GraphStat stat_
Definition: session.hpp:173
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void update_target(TensSetT target, TensSetT updated={}) override
Definition: session.hpp:129
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size_t d
Definition: session.hpp:35
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eigen.hpp File Reference
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#include "Eigen/Core"
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struct  ead::iEigen< T >
 
struct  ead::EigenTensOp< T, EigenSource, EigenArgs >
 
struct  ead::EigenMatOp< T, EigenSource, EigenArgs >
 
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using ead::DimensionsT = std::array< Eigen::Index, 8 >
 
template<typename T >
using ead::MatrixT = Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor >
 
template<typename T >
using ead::TensorT = Eigen::Tensor< T, 8 >
 
template<typename T >
using ead::TensMapT = Eigen::TensorMap< TensorT< T > >
 
template<typename T >
using ead::MatMapT = Eigen::Map< MatrixT< T > >
 
template<typename T >
using ead::EigenptrT = std::shared_ptr< iEigen< T > >
 
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template<typename T >
MatMapT< T > ead::tens_to_matmap (TensorT< T > &tens)
 
template<typename T >
MatMapT< T > ead::mat_to_matmap (MatrixT< T > &mat)
 
template<typename T >
MatMapT< T > ead::tensmap_to_matmap (TensMapT< T > &tens)
 
template<typename T >
TensMapT< T > ead::mat_to_tensmap (MatrixT< T > &mat)
 
template<typename T >
TensMapT< T > ead::tens_to_tensmap (TensorT< T > &tens)
 
template<typename T , typename EigenSource , typename EigenArgs >
EigenptrT< T > ead::make_eigentensor (DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
 
template<typename T , typename EigenSource , typename EigenArgs >
EigenptrT< T > ead::make_eigenmatrix (DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
 
template<typename T >
TensorT< T > ead::make_tensor (const ade::Shape &shape)
 
template<typename T >
MatMapT< T > ead::make_matmap (T *data, const ade::Shape &shape)
 
template<typename T >
TensMapT< T > ead::make_tensmap (T *data, const ade::Shape &shape)
 
template<typename T >
ade::Shape ead::get_shape (const TensorT< T > &tens)
 
template<typename T >
ade::Shape ead::get_shape (const TensMapT< T > &tens)
 
DimensionsT ead::shape_convert (ade::Shape shape)
 
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eigen.hpp
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1 #include "Eigen/Core"
2 #include "unsupported/Eigen/CXX11/Tensor"
3 
4 #include "ade/shape.hpp"
5 
6 #include "ead/generated/dtype.hpp"
7 
8 #ifndef EAD_EIGEN_HPP
9 #define EAD_EIGEN_HPP
10 
11 namespace ead
12 {
13 
14 // eigen shape
15 using DimensionsT = std::array<Eigen::Index,8>;
16 
17 // 4 base eigen types
18 template <typename T>
19 using MatrixT = Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic,Eigen::RowMajor>;
20 
21 template <typename T>
22 using TensorT = Eigen::Tensor<T,8>;
23 
24 template <typename T>
25 using TensMapT = Eigen::TensorMap<TensorT<T>>;
26 
27 template <typename T>
28 using MatMapT = Eigen::Map<MatrixT<T>>;
29 
30 // conversions between 4 base eigen types
31 template <typename T>
32 inline MatMapT<T> tens_to_matmap (TensorT<T>& tens)
33 {
34  return MatMapT<T>(tens.data(),
35  tens.dimension(1), tens.dimension(0));
36 }
37 
38 template <typename T>
39 inline MatMapT<T> mat_to_matmap (MatrixT<T>& mat)
40 {
41  return MatMapT<T>(mat.data(), mat.rows(), mat.cols());
42 }
43 
44 template <typename T>
45 inline MatMapT<T> tensmap_to_matmap (TensMapT<T>& tens)
46 {
47  return MatMapT<T>(tens.data(),
48  tens.dimension(1), tens.dimension(0));
49 }
50 
51 template <typename T>
52 inline TensMapT<T> mat_to_tensmap (MatrixT<T>& mat)
53 {
54  return TensMapT<T>(mat.data(),
55  mat.cols(), mat.rows(), 1,1,1,1,1,1);
56 }
57 
58 template <typename T>
59 inline TensMapT<T> tens_to_tensmap (TensorT<T>& tens)
60 {
61  return TensMapT<T>(tens.data(), tens.dimensions());
62 }
63 
64 // generic eigen bridge wrapper
65 template <typename T>
66 struct iEigen
67 {
68  virtual ~iEigen (void) = default;
69 
70  virtual void assign (void) = 0;
71 
72  virtual T* get_ptr (void) = 0;
73 };
74 
75 template <typename T>
76 using EigenptrT = std::shared_ptr<iEigen<T>>;
77 
78 template <typename T, typename EigenSource, typename EigenArgs>
79 struct EigenTensOp final : public iEigen<T>
80 {
81  EigenTensOp (DimensionsT dims,
82  std::function<EigenSource(EigenArgs&)> make_base, EigenArgs args) :
83  args_(args), tensorbase_(make_base(args_)), data_(dims) {}
84 
85  void assign (void) override
86  {
87  data_ = tensorbase_.reshape(data_.dimensions());
88  }
89 
90  T* get_ptr (void) override
91  {
92  return data_.data();
93  }
94 
95  EigenArgs args_;
96 
97  EigenSource tensorbase_;
98 
99  TensorT<T> data_;
100 };
101 
102 template <typename T, typename EigenSource, typename EigenArgs>
103 struct EigenMatOp final : public iEigen<T>
104 {
105  EigenMatOp (DimensionsT dims,
106  std::function<EigenSource(EigenArgs&)> make_base, EigenArgs args) :
107  args_(args), matrixbase_(make_base(args_)),
108  data_(dims.at(1), dims.at(0)) {}
109 
110  void assign (void) override
111  {
112  data_ = matrixbase_;
113  }
114 
115  T* get_ptr (void) override
116  {
117  return data_.data();
118  }
119 
120  EigenArgs args_;
121 
122  EigenSource matrixbase_;
123 
124  MatrixT<T> data_;
125 };
126 
127 template <typename T, typename EigenSource, typename EigenArgs>
128 inline EigenptrT<T> make_eigentensor (DimensionsT dims,
129  std::function<EigenSource(EigenArgs&)> make_base, EigenArgs args)
130 {
131  return std::make_shared<EigenTensOp<T,EigenSource,EigenArgs>>(
132  dims, make_base, args);
133 }
134 
135 template <typename T, typename EigenSource, typename EigenArgs>
136 inline EigenptrT<T> make_eigenmatrix (DimensionsT dims,
137  std::function<EigenSource(EigenArgs&)> make_base, EigenArgs args)
138 {
139  return std::make_shared<EigenMatOp<T,EigenSource,EigenArgs>>(
140  dims, make_base, args);
141 }
142 
143 template <typename T>
144 inline TensorT<T> make_tensor (const ade::Shape& shape)
145 {
146  std::array<Eigen::Index,ade::rank_cap> slist;
147  std::copy(shape.begin(), shape.end(), slist.begin());
148  TensorT<T> out(slist);
149  out.setZero();
150  return out;
151 }
152 
153 template <typename T>
154 inline MatMapT<T> make_matmap (T* data, const ade::Shape& shape)
155 {
156  if (nullptr == data)
157  {
158  logs::fatal("cannot get matmap from nullptr");;
159  }
160  return MatMapT<T>(data, shape.at(1), shape.at(0));
161 }
162 
163 template <typename T>
164 inline TensMapT<T> make_tensmap (T* data, const ade::Shape& shape)
165 {
166  std::array<Eigen::Index,ade::rank_cap> slist;
167  std::copy(shape.begin(), shape.end(), slist.begin());
168  if (nullptr == data)
169  {
170  logs::fatal("cannot get tensmap from nullptr");;
171  }
172  return TensMapT<T>(data, slist);
173 }
174 
175 template <typename T>
176 ade::Shape get_shape (const TensorT<T>& tens)
177 {
178  auto slist = tens.dimensions();
179  return ade::Shape(std::vector<ade::DimT>(slist.begin(), slist.end()));
180 }
181 
182 template <typename T>
183 ade::Shape get_shape (const TensMapT<T>& tens)
184 {
185  auto slist = tens.dimensions();
186  return ade::Shape(std::vector<ade::DimT>(slist.begin(), slist.end()));
187 }
188 
189 DimensionsT shape_convert (ade::Shape shape);
190 
191 }
192 
193 #endif // EAD_EIGEN_HPP
ead::tens_to_tensmap
TensMapT< T > tens_to_tensmap(TensorT< T > &tens)
Definition: eigen.hpp:59
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ead::EigenMatOp::EigenMatOp
EigenMatOp(DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
Definition: eigen.hpp:105
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ead::get_shape
ade::Shape get_shape(const TensorT< T > &tens)
Definition: eigen.hpp:176
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csv_to_png.args
args
Definition: csv_to_png.py:105
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ead::EigenTensOp::get_ptr
T * get_ptr(void) override
Definition: eigen.hpp:90
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ade::Shape::at
DimT at(RankT idx) const
Return DimT element at idx for any index in range [0:rank_cap)
Definition: shape.hpp:106
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ead::make_tensmap
TensMapT< T > make_tensmap(T *data, const ade::Shape &shape)
Definition: eigen.hpp:164
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void assign(void) override
Definition: eigen.hpp:85
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EigenptrT< T > make_eigentensor(DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
Definition: eigen.hpp:128
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Definition: constant.hpp:9
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ead::iEigen::~iEigen
virtual ~iEigen(void)=default
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ead::DimensionsT
std::array< Eigen::Index, 8 > DimensionsT
Definition: eigen.hpp:15
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iterator end(void)
Return end iterator of internal array.
Definition: shape.hpp:160
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EigenTensOp(DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
Definition: eigen.hpp:81
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TensMapT< T > mat_to_tensmap(MatrixT< T > &mat)
Definition: eigen.hpp:52
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MatMapT< T > mat_to_matmap(MatrixT< T > &mat)
Definition: eigen.hpp:39
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Definition: eigen.hpp:19
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iterator begin(void)
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Definition: shape.hpp:154
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Definition: eigen.hpp:154
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Definition: eigen.hpp:76
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Definition: eigen.hpp:66
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Definition: eigen.hpp:103
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DimensionsT shape_convert(ade::Shape shape)
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Definition: eigen.hpp:124
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T * get_ptr(void) override
Definition: eigen.hpp:115
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ade::Shape
Definition: shape.hpp:60
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EigenSource tensorbase_
Definition: eigen.hpp:97
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ead::tens_to_matmap
MatMapT< T > tens_to_matmap(TensorT< T > &tens)
Definition: eigen.hpp:32
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Definition: eigen.hpp:95
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MatMapT< T > tensmap_to_matmap(TensMapT< T > &tens)
Definition: eigen.hpp:45
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TensorT< T > make_tensor(const ade::Shape &shape)
Definition: eigen.hpp:144
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EigenArgs args_
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ead::EigenMatOp::matrixbase_
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Definition: eigen.hpp:79
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TensorT< T > data_
Definition: eigen.hpp:99
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ead::iEigen::get_ptr
virtual T * get_ptr(void)=0
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Eigen::Map< MatrixT< T > > MatMapT
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1 
9 #include <list>
10 
11 #include "ade/traveler.hpp"
12 
13 #ifndef ADE_GRAD_DEF_HPP
14 #define ADE_GRAD_DEF_HPP
15 
16 namespace ade
17 {
18 
28 struct iGradientBuilder
29 {
30  virtual ~iGradientBuilder (void) = default;
31 
34  virtual TensptrT local_derivative (FuncptrT op, size_t arg_idx) const = 0;
35 
41  virtual TensptrT chain_rule (FuncptrT op, const TensptrT& local_der,
42  TensptrT supcomp_grad, size_t arg_idx) const = 0;
43 
45  virtual TensptrT get_const_one (Shape shape) const = 0;
46 
48  virtual TensptrT get_const_zero (Shape shape) const = 0;
49 
51  virtual TensptrT add (TensptrT& lhs, TensptrT& rhs) const = 0;
52 
54  TensptrT derive (TensptrT root, TensptrT target) const
55  {
56  if (root == target)
57  {
58  return get_const_one(target->shape());
59  }
60 
61  PathFinder finder(target.get());
62  root->accept(finder);
63 
64  auto& pathmap = finder.parents_;
65  // no path to wrt
66  if (pathmap.empty())
67  {
68  return get_const_zero(target->shape());
69  }
70  // else there exists a path to wrt
71  // using pathfinder, breadth first traverse from this to wrt
72  GraphStat stat;
73  root->accept(stat);
74  auto owners = track_owners({root});
75 
76  std::list<iFunctor*> parents;
77  std::transform(pathmap.begin(), pathmap.end(),
78  std::back_inserter(parents),
79  [](std::pair<iTensor*,std::unordered_set<size_t>> parent)
80  {
81  return static_cast<iFunctor*>(parent.first);
82  });
83  parents.sort(
84  [&](iFunctor* a, iFunctor* b)
85  {
86  size_t aheight = stat.graphsize_[a].upper_;
87  size_t bheight = stat.graphsize_[b].upper_;
88  if (aheight == bheight) // make traversal more deterministic
89  {
90  return a->to_string() > b->to_string();
91  }
92  return aheight > bheight;
93  });
94 
95  // map functor to its respective super composite derivative
96  // let L = root, F = key functor, value of F in grads is dL/dF
97  std::unordered_map<const iTensor*,TensT> grads = {
98  {root.get(), {get_const_one(root->shape())}}
99  };
100  for (iFunctor* parent : parents)
101  {
102  TensT& gargs = grads[parent];
103  TensptrT bwd = gargs[0];
104  for (size_t i = 1, n = gargs.size(); i < n; ++i)
105  {
106  bwd = add(bwd, gargs[i]);
107  }
108 
109  auto& grad_indices = pathmap[parent];
110  ArgsT children = parent->get_children();
111  size_t nchildren = children.size();
112  // assert: all nnary-children use identity mapping,
113  // so no children-arg is direct mapping
114  TensT args(nchildren);
115  std::transform(children.begin(), children.end(), args.begin(),
116  [](FuncArg& arg)
117  {
118  return arg.get_tensor();
119  });
120  // for each painted child, calculate dThis/dChild
121  // go through grads in order
122  std::list<size_t> ordered(grad_indices.begin(), grad_indices.end());
123  ordered.sort();
124  for (size_t i : ordered)
125  {
126  auto parent_ptr = std::static_pointer_cast<iFunctor>(
127  owners[parent].lock());
128  auto local = local_derivative(parent_ptr, i);
129  auto grad_step = chain_rule(parent_ptr, local, bwd, i);
130  grads[args[i].get()].push_back(grad_step);
131  }
132  }
133  TensT& outargs = grads[target.get()];
134  TensptrT out = outargs[0];
135  for (size_t i = 1, n = outargs.size(); i < n; ++i)
136  {
137  out = add(out, outargs[i]);
138  }
139  return out;
140  }
141 };
142 
143 }
144 
145 #endif // ADE_GRAD_DEF_HPP
ade::FuncptrT
std::shared_ptr< iFunctor > FuncptrT
Functor smart pointer.
Definition: ifunctor.hpp:49
-
ade::iGradientBuilder
Definition: grad_def.hpp:28
-
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
-
csv_to_png.args
args
Definition: csv_to_png.py:105
-
ade::iGradientBuilder::chain_rule
virtual TensptrT chain_rule(FuncptrT op, const TensptrT &local_der, TensptrT supcomp_grad, size_t arg_idx) const =0
- -
ade::iGradientBuilder::~iGradientBuilder
virtual ~iGradientBuilder(void)=default
-
ade::iGradientBuilder::get_const_one
virtual TensptrT get_const_one(Shape shape) const =0
Return tensor representing 1 constant.
-
ade::GraphStat
Traveler that maps each tensor to its subtree&#39;s maximum depth.
Definition: traveler.hpp:61
-
ade::PathFinder::parents_
ParentMapT parents_
Map of parent nodes in path.
Definition: traveler.hpp:162
-
ade::iTensor::to_string
virtual std::string to_string(void) const =0
Return the string representation of the tensor.
-
ade::iGradientBuilder::local_derivative
virtual TensptrT local_derivative(FuncptrT op, size_t arg_idx) const =0
-
ade::PathFinder
Definition: traveler.hpp:117
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ade
Definition: coord.hpp:16
-
ade::iGradientBuilder::get_const_zero
virtual TensptrT get_const_zero(Shape shape) const =0
Return tensor representing 0 constant.
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
-
ade::Shape
Definition: shape.hpp:60
-
ade::TensT
std::vector< TensptrT > TensT
Vector representation of ade tensor pointers.
Definition: funcarg.hpp:104
-
ade::GraphStat::graphsize_
std::unordered_map< iTensor *, estd::NumRange< size_t > > graphsize_
Definition: traveler.hpp:109
-
ade::iGradientBuilder::add
virtual TensptrT add(TensptrT &lhs, TensptrT &rhs) const =0
Return functor representing lhs + rhs.
-
ade::iGradientBuilder::derive
TensptrT derive(TensptrT root, TensptrT target) const
Return derivative of root with respect to target.
Definition: grad_def.hpp:54
-
ade::ArgsT
std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
-
ade::track_owners
OwnerMapT track_owners(TensT roots)
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template<typename T >
NodeptrT< T > ead::reduce_grad (const ade::FuncArg &child, NodeptrT< T > bwd, size_t idx)
 
template<typename T >
NodeptrT< T > ead::permute_grad (ade::iFunctor *fwd, NodeptrT< T > bwd, size_t idx)
 
template<typename T >
NodeptrT< T > ead::extend_grad (ade::iFunctor *fwd, NodeptrT< T > bwd, size_t idx)
 
template<typename T >
NodeptrT< T > ead::derive (NodeptrT< T > root, NodeptrT< T > target)
 Derive root with respect to target and optimized. More...
 
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9 #include <list>
10 
11 #include "ade/grad_def.hpp"
12 
13 #include "ead/generated/api.hpp"
14 
15 #include "ead/constant.hpp"
16 
17 #ifndef EAD_GRADER_HPP
18 #define EAD_GRADER_HPP
19 
20 namespace ead
21 {
22 
23 template <typename T>
24 NodeptrT<T> reduce_grad (const ade::FuncArg& child,
25  NodeptrT<T> bwd, size_t idx)
26 {
27  const ade::Shape& shape = child.get_tensor()->shape();
28  ade::CoordptrT revshaper(child.get_shaper()->reverse());
29  CoordptrT revcoord;
30  {
31  auto coorder = child.get_coorder();
32  assert(nullptr != coorder);
33  ade::CoordT dims;
34  coorder->forward(dims.begin(), dims.begin());
35  ade::CoordT bcast;
36  std::fill(bcast.begin(), bcast.end(), 1);
37  for (ade::RankT d : dims)
38  {
39  if (d < ade::rank_cap)
40  {
41  bcast[d] = shape.at(d);
42  }
43  }
44  revcoord = std::make_shared<CoordMap>(bcast, false);
45  }
46  return make_functor<T>(ade::Opcode{"EXTEND",age::EXTEND}, {
47  FuncArg<T>(bwd, revshaper, revcoord)
48  });
49 }
50 
51 template <typename T>
52 NodeptrT<T> permute_grad (ade::iFunctor* fwd,
53  NodeptrT<T> bwd, size_t idx)
54 {
55  const auto& child = fwd->get_children()[0];
56  ade::CoordptrT revshaper(child.get_shaper()->reverse());
57  CoordptrT revcoord;
58  {
59  auto coorder = child.get_coorder();
60  assert(nullptr != coorder);
61  ade::CoordT dims;
62  coorder->forward(dims.begin(), dims.begin());
63 
64  ade::CoordT order;
65  for (ade::RankT i = 0; i < ade::rank_cap; ++i)
66  {
67  order[dims[i]] = i;
68  }
69  revcoord = std::make_shared<CoordMap>(order, true);
70  }
71  return make_functor<T>(ade::Opcode{"PERMUTE",age::PERMUTE},{
72  FuncArg<T>(bwd, revshaper, revcoord)
73  });
74 }
75 
76 template <typename T>
77 NodeptrT<T> extend_grad (ade::iFunctor* fwd,
78  NodeptrT<T> bwd, size_t idx)
79 {
80  const auto& child = fwd->get_children()[0];
81  ade::CoordptrT revshaper(child.get_shaper()->reverse());
82  CoordptrT revcoord;
83  {
84  auto coorder = child.get_coorder();
85  assert(nullptr != coorder);
86  ade::CoordT dims;
87  coorder->forward(dims.begin(), dims.begin());
88  std::vector<ade::RankT> red_dims;
89  for (ade::RankT i = 0; i < ade::rank_cap; ++i)
90  {
91  if (dims[i] > 1)
92  {
93  red_dims.push_back(i);
94  }
95  }
96  revcoord = reduce(red_dims);
97  }
98  return make_functor<T>(ade::Opcode{"REDUCE_SUM",age::REDUCE_SUM},{
99  FuncArg<T>(bwd, revshaper, revcoord)
100  });
101 }
102 
103 template <typename T>
104 struct GradientBuilder final : public ade::iGradientBuilder
105 {
107  ade::TensptrT local_derivative (ade::FuncptrT op,
108  size_t arg_idx) const override
109  {
110  const ade::ArgsT& args = op->get_children();
111  ade::TensptrT out;
112  ade::Opcode opcode = op->get_opcode();
113  switch ((age::_GENERATED_OPCODE) opcode.code_)
114  {
115  case age::ABS:
116  out = tenncor::div(NodeConverters<T>::to_node(args[0].get_tensor()),
117  NodeConverters<T>::to_node(op))->get_tensor();
118  break;
119  case age::NEG:
120  out = make_constant_scalar<T>(
121  -1, args[0].get_tensor()->shape())->get_tensor();
122  break;
123  case age::SIN:
124  out = tenncor::cos(NodeConverters<T>::to_node(args[0].get_tensor()))->get_tensor();
125  break;
126  case age::COS:
127  out = tenncor::neg(tenncor::sin(
128  NodeConverters<T>::to_node(args[0].get_tensor())))->get_tensor();
129  break;
130  case age::TAN:
131  out = tenncor::div(make_constant_scalar<T>(1,
132  args[0].get_tensor()->shape()),
133  tenncor::pow(
134  tenncor::cos(NodeConverters<T>::to_node(args[0].get_tensor())),
135  make_constant_scalar<T>(2, args[0].get_tensor()->shape())
136  )
137  )->get_tensor();
138  break;
139  case age::EXP:
140  out = op;
141  break;
142  case age::LOG:
143  out = tenncor::div(
144  make_constant_scalar<T>(1,args[0].get_tensor()->shape()),
145  NodeConverters<T>::to_node(args[0].get_tensor())
146  )->get_tensor();
147  break;
148  case age::SQRT:
149  out = tenncor::div(
150  make_constant_scalar<T>(1,args[0].get_tensor()->shape()),
151  tenncor::mul(
152  make_constant_scalar<T>(2,
153  args[0].get_tensor()->shape()),
154  NodeConverters<T>::to_node(op)
155  )
156  )->get_tensor();
157  break;
158  case age::SQUARE:
159  out = tenncor::mul(
160  make_constant_scalar<T>(2, args[0].get_tensor()->shape()),
161  NodeConverters<T>::to_node(args[0].get_tensor())
162  )->get_tensor();
163  break;
164  case age::CUBE:
165  out = tenncor::mul(
166  make_constant_scalar<T>(3, args[0].get_tensor()->shape()),
167  tenncor::square(NodeConverters<T>::to_node(args[0].get_tensor()))
168  )->get_tensor();
169  break;
170  case age::SIGMOID:
171  out = tenncor::sigmoid_grad(
172  NodeConverters<T>::to_node(args[0].get_tensor()))->get_tensor();
173  break;
174  case age::SIGMOID_GRAD:
175  out = tenncor::mul(
176  NodeConverters<T>::to_node(op),
177  tenncor::sub(
178  make_constant_scalar<T>(1,
179  args[0].get_tensor()->shape()),
180  tenncor::mul(
181  make_constant_scalar<T>(2,
182  args[0].get_tensor()->shape()),
183  tenncor::sigmoid(NodeConverters<T>::to_node(args[0].get_tensor()))
184  )
185  )
186  )->get_tensor();
187  break;
188  case age::TANH:
189  out = tenncor::sub(
190  make_constant_scalar<T>(1,args[0].get_tensor()->shape()),
191  tenncor::square(NodeConverters<T>::to_node(op))
192  )->get_tensor();
193  break;
194  case age::ROUND:
195  case age::REDUCE_SUM:
196  case age::EXTEND:
197  case age::PERMUTE:
198  case age::ADD:
199  case age::SLICE:
200  case age::PAD:
201  out = get_const_one(args[0].get_tensor()->shape());
202  break;
203  case age::MUL:
204  case age::CONV:
205  out = args[(size_t)(arg_idx==0)].get_tensor();
206  break;
207  case age::MAX:
208  case age::MIN:
209  out = tenncor::eq(NodeConverters<T>::to_node(op),
210  NodeConverters<T>::to_node(args[arg_idx].get_tensor()))->get_tensor();;
211  break;
212  case age::POW:
213  out = (arg_idx==0 ?
214  tenncor::mul(
215  NodeConverters<T>::to_node(args[1].get_tensor()),
216  tenncor::pow(
217  NodeConverters<T>::to_node(args[0].get_tensor()),
218  tenncor::sub(
219  NodeConverters<T>::to_node(args[1].get_tensor()),
220  make_constant_scalar<T>(1,
221  args[0].get_tensor()->shape())
222  )
223  )
224  ) :
225  tenncor::mul(tenncor::log(NodeConverters<T>::to_node(args[0].get_tensor())),
226  NodeConverters<T>::to_node(op))
227  )->get_tensor();;
228  break;
229  case age::SUB:
230  out = make_constant_scalar<T>(arg_idx == 0 ?
231  1 : -1, args[0].get_tensor()->shape())->get_tensor();
232  break;
233  case age::DIV:
234  {
235  auto denom = NodeConverters<T>::to_node(args[1].get_tensor());
236  out = (arg_idx==0 ?
237  tenncor::div(
238  make_constant_scalar<T>(1,
239  args[0].get_tensor()->shape()),
240  denom
241  ) :
242  tenncor::div(
243  tenncor::div(
244  tenncor::neg(NodeConverters<T>::to_node(args[0].get_tensor())),
245  denom),
246  denom
247  ))->get_tensor();
248  }
249  break;
250  case age::EQ:
251  case age::NEQ:
252  case age::GT:
253  case age::LT:
254  case age::RAND_UNIF:
255  case age::SELECT:
256  out = get_const_zero(args[0].get_tensor()->shape());
257  break;
258  case age::REDUCE_PROD: // todo: prevent divide by zero
259  out = tenncor::div(
260  reduce_grad(args[0], NodeConverters<T>::to_node(op), arg_idx),
261  NodeConverters<T>::to_node(args[0].get_tensor())
262  )->get_tensor();
263  break;
264  case age::REDUCE_MAX:
265  case age::REDUCE_MIN:
266  out = tenncor::eq(
267  reduce_grad(args[0], NodeConverters<T>::to_node(op), arg_idx),
268  NodeConverters<T>::to_node(args[0].get_tensor())
269  )->get_tensor();
270  break;
271  case age::MATMUL:
272  {
273  NodeptrT<T> lhs = NodeConverters<T>::to_node(args[0].get_tensor());
274  NodeptrT<T> rhs = NodeConverters<T>::to_node(args[1].get_tensor());
275  out = (0 == arg_idx ?
276  // ext_rhs
277  tenncor::permute(tenncor::extend(rhs, 2, {
278  lhs->shape().at(1)}), {0,2,1}) :
279  // ext_lhs
280  tenncor::permute(tenncor::extend(lhs, 2, {
281  rhs->shape().at(0)}), {2,1,0})
282  )->get_tensor();
283  }
284  break;
285  case age::CONV_IMG_GRAD:
286  logs::fatal("cannot derive CONV_IMG_GRAD");
287  break;
288  case age::CONV_KRN_GRAD:
289  logs::fatal("cannot derive CONV_KRN_GRAD");
290  break;
291  default:
292  logs::fatalf("Unknown op %s", opcode.name_.c_str());
293  }
294  return out;
295  }
296 
298  ade::TensptrT chain_rule (ade::FuncptrT op, const ade::TensptrT& local_der,
299  ade::TensptrT supcomp_grad, size_t arg_idx) const override
300  {
301  NodeptrT<T> out;
302  ade::Opcode opcode = op->get_opcode();
303  switch (opcode.code_)
304  {
305  case age::ABS:
306  case age::NEG:
307  case age::SIN:
308  case age::COS:
309  case age::TAN:
310  case age::EXP:
311  case age::LOG:
312  case age::SQRT:
313  case age::SQUARE:
314  case age::CUBE:
315  case age::ROUND:
316  case age::SIGMOID:
317  case age::SIGMOID_GRAD:
318  case age::TANH:
319  case age::ADD:
320  case age::MUL:
321  case age::MAX:
322  case age::MIN:
323  case age::POW:
324  case age::SUB:
325  case age::DIV:
326  case age::EQ:
327  case age::NEQ:
328  case age::GT:
329  case age::LT:
330  case age::RAND_UNIF:
331  out = tenncor::mul(NodeConverters<T>::to_node(local_der),
332  NodeConverters<T>::to_node(supcomp_grad));
333  break;
334  case age::REDUCE_MAX:
335  case age::REDUCE_MIN:
336  case age::REDUCE_PROD:
337  case age::REDUCE_SUM:
338  out = tenncor::mul(NodeConverters<T>::to_node(local_der), reduce_grad(
339  op->get_children()[0], NodeConverters<T>::to_node(supcomp_grad), arg_idx));
340  break;
341  case age::EXTEND:
342  out = tenncor::mul(NodeConverters<T>::to_node(local_der), extend_grad(
343  op.get(), NodeConverters<T>::to_node(supcomp_grad), arg_idx));
344  break;
345  case age::PERMUTE:
346  out = tenncor::mul(NodeConverters<T>::to_node(local_der), permute_grad(
347  op.get(), NodeConverters<T>::to_node(supcomp_grad), arg_idx));
348  break;
349  case age::MATMUL:
350  out = tenncor::reduce_sum(
351  tenncor::permute(
352  tenncor::mul(NodeConverters<T>::to_node(local_der),
353  tenncor::extend(NodeConverters<T>::to_node(supcomp_grad), 2, {
354  op->get_children()[0].
355  get_tensor()->shape().at(0)
356  })),
357  0 == arg_idx ?
358  std::vector<ade::RankT>{2, 1, 0} :
359  std::vector<ade::RankT>{0, 2, 1}), 2, 1);
360  break;
361  case age::CONV:
362  {
363  ade::Opcode opcode;
364  auto args = op->get_children();
365  ade::CoordptrT fwd_shaper =
366  args[(size_t)(0 == arg_idx)].get_shaper();
367  ade::CoordptrT rev_shaper(
368  args[arg_idx].get_shaper()->reverse());
369  if (arg_idx == 0)
370  {
371  opcode = ade::Opcode{"CONV_IMG_GRAD",
372  age::CONV_IMG_GRAD};
373  }
374  else
375  {
376  opcode = ade::Opcode{"CONV_KRN_GRAD",
377  age::CONV_KRN_GRAD};
378  }
379  ade::CoordptrT full_shaper(
380  fwd_shaper->connect(*rev_shaper));
381  out = make_functor<T>(opcode, {
382  FuncArg<T>(NodeConverters<T>::to_node(local_der), full_shaper, nullptr),
383  FuncArg<T>(NodeConverters<T>::to_node(supcomp_grad), rev_shaper, nullptr),
384  });
385  }
386  break;
387  case age::SLICE:
388  {
389  ade::CoordT slicings;
390  auto& child = op->get_children()[0];
391  child.get_coorder()->forward(
392  slicings.begin(), slicings.begin());
393  ade::DimT dimension = slicings[2];
394  ade::DimT dim = child.get_tensor()->shape().at(dimension);
395  ade::DimT left_pad = slicings[0];
396  ade::DimT right_pad = dim - (left_pad + slicings[1]);
397  out = tenncor::mul(NodeConverters<T>::to_node(local_der),
398  tenncor::pad(NodeConverters<T>::to_node(supcomp_grad),
399  std::pair<ade::DimT,ade::DimT>{
400  left_pad, right_pad}, dimension));
401  }
402  break;
403  case age::PAD:
404  {
405  ade::CoordT paddings;
406  auto& child = op->get_children()[0];
407  child.get_coorder()->forward(
408  paddings.begin(), paddings.begin());
409  ade::DimT dimension = paddings[2];
410  ade::DimT dim = op->shape().at(dimension);
411  ade::DimT offset = paddings[0];
412  ade::DimT extent = dim - paddings[1] - offset;
413  out = tenncor::mul(NodeConverters<T>::to_node(local_der),
414  tenncor::slice(NodeConverters<T>::to_node(supcomp_grad),
415  offset, extent, dimension));
416  }
417  case age::SELECT:
418  {
419  if (0 == arg_idx)
420  {
421  out = NodeConverters<T>::to_node(local_der);
422  break;
423  }
424  auto condition = NodeConverters<T>::to_node(
425  op->get_children()[0].get_tensor());
426  auto then = NodeConverters<T>::to_node(supcomp_grad);
427  auto otherwise = make_constant_scalar<T>(0, op->shape());
428  if (1 < arg_idx)
429  {
430  std::swap(then, otherwise);
431  }
432  out = tenncor::if_then_else(condition, then, otherwise);
433  }
434  break;
435  case age::CONV_IMG_GRAD:
436  logs::fatal("cannot derive CONV_IMG_GRAD");
437  break;
438  case age::CONV_KRN_GRAD:
439  logs::fatal("cannot derive CONV_KRN_GRAD");
440  break;
441  default:
442  logs::fatalf("Unknown op %s", opcode.name_.c_str());
443  }
444  return out->get_tensor();
445  }
446 
448  ade::TensptrT get_const_one (ade::Shape shape) const override
449  {
450  return make_constant_scalar<T>(1, shape)->get_tensor();
451  }
452 
454  ade::TensptrT get_const_zero (ade::Shape shape) const override
455  {
456  return make_constant_scalar<T>(0, shape)->get_tensor();
457  }
458 
460  ade::TensptrT add (ade::TensptrT& lhs, ade::TensptrT& rhs) const override
461  {
462  return ade::TensptrT(Functor<T>::get(ade::Opcode{"ADD", age::ADD}, {
463  identity_map(NodeConverters<T>::to_node(lhs)),
464  identity_map(NodeConverters<T>::to_node(rhs))
465  }));
466  }
467 };
468 
470 template <typename T>
471 NodeptrT<T> derive (NodeptrT<T> root, NodeptrT<T> target)
472 {
473  GradientBuilder<T> builder;
474  ade::TensptrT derivative = builder.derive(
475  root->get_tensor(), target->get_tensor());
476  return NodeConverters<T>::to_node(derivative);
477 }
478 
479 }
480 
481 #endif // EAD_GRADER_HPP
ead::neg
EigenptrT< T > neg(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:209
-
ead::sub
EigenptrT< T > sub(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
Definition: operator.hpp:633
-
ade::FuncptrT
std::shared_ptr< iFunctor > FuncptrT
Functor smart pointer.
Definition: ifunctor.hpp:49
-
ead::log
EigenptrT< T > log(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:356
-
ead::NodeConverters
Definition: inode.hpp:40
-
ead::mul
EigenptrT< T > mul(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
Definition: operator.hpp:662
-
ade::iGradientBuilder
Definition: grad_def.hpp:28
-
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
-
ead::square
EigenptrT< T > square(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:513
-
csv_to_png.args
args
Definition: csv_to_png.py:105
-
ead::reduce
CoordptrT reduce(std::vector< ade::RankT > red_dims)
-
ead::eq
EigenptrT< T > eq(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
Definition: operator.hpp:726
-
ade::Opcode
Encoding of operation.
Definition: ifunctor.hpp:18
-
ade::extend
CoordptrT extend(RankT rank, std::vector< DimT > ext)
-
ade::Opcode::name_
std::string name_
String representation of operation.
Definition: ifunctor.hpp:21
-
ead::NodeptrT
std::shared_ptr< iNode< T > > NodeptrT
Definition: inode.hpp:31
-
ead::GradientBuilder::get_const_one
ade::TensptrT get_const_one(ade::Shape shape) const override
Implementation of iGradientBuilder.
Definition: grader.hpp:448
-
ead::sin
EigenptrT< T > sin(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:234
-
ade::Shape::at
DimT at(RankT idx) const
Return DimT element at idx for any index in range [0:rank_cap)
Definition: shape.hpp:106
-
ead
Definition: constant.hpp:9
- -
ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
-
ead::div
EigenptrT< T > div(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
Definition: operator.hpp:694
-
ade::DimT
uint16_t DimT
Definition: shape.hpp:29
-
ead::reduce_sum
EigenptrT< T > reduce_sum(ade::Shape &outshape, const OpArg< T > &in) template< typename T > EigenptrT< T > reduce_prod(ade
Definition: operator.hpp:87
-
ead::GradientBuilder::add
ade::TensptrT add(ade::TensptrT &lhs, ade::TensptrT &rhs) const override
Implementation of iGradientBuilder.
Definition: grader.hpp:460
-
ead::extend_grad
NodeptrT< T > extend_grad(ade::iFunctor *fwd, NodeptrT< T > bwd, size_t idx)
Definition: grader.hpp:77
-
ead::FuncArg
Definition: funcarg.hpp:13
-
ead::NodeConverters::to_node
static NodeptrT< T > to_node(ade::TensptrT tens)
Definition: inode.hpp:44
-
ead::slice
EigenptrT< T > slice(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:133
-
grad_def.hpp
-
ade::FuncArg::get_coorder
CoordptrT get_coorder(void) const
Return coord map for coordinates.
Definition: funcarg.hpp:80
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ead::GradientBuilder::local_derivative
ade::TensptrT local_derivative(ade::FuncptrT op, size_t arg_idx) const override
Implementation of iGradientBuilder.
Definition: grader.hpp:107
-
ead::derive
NodeptrT< T > derive(NodeptrT< T > root, NodeptrT< T > target)
Derive root with respect to target and optimized.
Definition: grader.hpp:471
-
ade::iFunctor::get_children
virtual const ArgsT & get_children(void) const =0
Return children nodes as a vector of raw pointers.
-
ead::pad
EigenptrT< T > pad(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:158
-
ade::RankT
uint8_t RankT
Type used for shape rank.
Definition: shape.hpp:23
-
ead::identity_map
FuncArg< T > identity_map(NodeptrT< T > node)
Definition: funcarg.hpp:77
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
-
ade::Shape
Definition: shape.hpp:60
-
ade::Opcode::code_
size_t code_
Numerical encoding of operation.
Definition: ifunctor.hpp:24
-
ade::CoordT
std::array< CDimT, rank_cap > CoordT
Definition: shape.hpp:54
-
ead::cos
EigenptrT< T > cos(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:268
-
ade::permute
CoordptrT permute(std::vector< RankT > order)
-
ead::Functor
Definition: functor.hpp:16
-
ade::FuncArg::get_shaper
CoordptrT get_shaper(void) const
Return shaper coord map.
Definition: funcarg.hpp:67
-
ead::permute_grad
NodeptrT< T > permute_grad(ade::iFunctor *fwd, NodeptrT< T > bwd, size_t idx)
Definition: grader.hpp:52
-
ade::FuncArg::get_tensor
TensptrT get_tensor(void) const
Return tensor being mapped.
Definition: funcarg.hpp:61
-
ead::sigmoid
EigenptrT< T > sigmoid(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:429
-
ead::GradientBuilder
Definition: grader.hpp:104
-
ade::rank_cap
const RankT rank_cap
Number of dimsensions in a shape/coordinate.
Definition: shape.hpp:45
-
ead::pow
EigenptrT< T > pow(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
Definition: operator.hpp:562
-
ead::CoordptrT
std::shared_ptr< CoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:49
-
ead::GradientBuilder::get_const_zero
ade::TensptrT get_const_zero(ade::Shape shape) const override
Implementation of iGradientBuilder.
Definition: grader.hpp:454
-
ead::reduce_grad
NodeptrT< T > reduce_grad(const ade::FuncArg &child, NodeptrT< T > bwd, size_t idx)
Definition: grader.hpp:24
-
ade::iGradientBuilder::derive
TensptrT derive(TensptrT root, TensptrT target) const
Return derivative of root with respect to target.
Definition: grad_def.hpp:54
-
ead::GradientBuilder::chain_rule
ade::TensptrT chain_rule(ade::FuncptrT op, const ade::TensptrT &local_der, ade::TensptrT supcomp_grad, size_t arg_idx) const override
Implementation of iGradientBuilder.
Definition: grader.hpp:298
-
ead::sigmoid_grad
EigenptrT< T > sigmoid_grad(ade::Shape &outshape, const OpArg< T > &in)
Definition: operator.hpp:452
-
ade::ArgsT
std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
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#include <boost/uuid/uuid.hpp>
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struct  tag::GroupTag
 
struct  tag::GroupRegistry
 
struct  tag::Subgraph
 
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 tag
 
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using tag::TensSetT = std::unordered_set< TensKey, TensKeyHash >
 
using tag::AGroupsT = std::map< std::string, std::unordered_set< std::string > >
 
using tag::AdjMapT = std::unordered_map< ade::iTensor *, AGroupsT >
 
using tag::SgraphptrT = std::shared_ptr< Subgraph >
 
using tag::SubgraphsT = std::unordered_set< SgraphptrT >
 
using tag::SubgraphAssocsT = std::unordered_map< ade::iTensor *, SubgraphsT >
 
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GroupRegistry & tag::get_group_reg (void)
 
void tag::recursive_group_tag (ade::TensrefT tens, std::string group, std::unordered_set< ade::iTensor *> stops, GroupRegistry &registry=get_group_reg())
 
void tag::adjacencies (AdjMapT &out, ade::TensT roots, GroupRegistry &registry=get_group_reg())
 
void tag::beautify_groups (SubgraphAssocsT &out, const AdjMapT &adjs)
 
void tag::filter_head (SubgraphAssocsT &out, const SubgraphAssocsT &assocs)
 
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const std::string tag::groups_key = "groups"
 
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group.hpp
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1 #include <boost/uuid/uuid.hpp>
2 #include <boost/uuid/uuid_generators.hpp>
3 #include <boost/uuid/uuid_io.hpp>
4 
5 #include "tag/tag.hpp"
6 
7 #ifndef TAG_GROUP_HPP
8 #define TAG_GROUP_HPP
9 
10 namespace tag
11 {
12 
13 using TensSetT = std::unordered_set<TensKey,TensKeyHash>;
14 
15 const std::string groups_key = "groups";
16 
23 struct GroupTag final : public iTag
24 {
25  GroupTag (std::string init_label) : labels_({init_label}) {}
26 
27  size_t tag_id (void) const override
28  {
29  return tag_id_;
30  }
31 
32  void absorb (TagptrT&& other) override
33  {
34  std::set<std::string>& olabels =
35  static_cast<GroupTag*>(other.get())->labels_;
36  labels_.insert(olabels.begin(), olabels.end());
37  }
38 
39  TagRepsT get_tags (void) const override
40  {
41  TagRepsT out;
42  out.emplace(groups_key,
43  std::vector<std::string>(labels_.begin(), labels_.end()));
44  return out;
45  }
46 
47 private:
48  std::set<std::string> labels_;
49 
50  static size_t tag_id_;
51 };
52 
53 struct GroupRegistry final
54 {
55  GroupRegistry (TagRegistry& registry = get_reg()) : tag_reg_(registry) {}
56 
57  void group_tag (ade::TensrefT tens, std::string tag)
58  {
59  tag_reg_.add_tag(tens, TagptrT(new GroupTag(tag)));
60 
61  auto& gtens = groups_[tag];
62  auto it = gtens.find(TensKey(tens.lock().get()));
63  // clear out previous entry that is expired
64  if (gtens.end() != it && it->expired())
65  {
66  gtens.erase(tens.lock().get());
67  }
68  gtens.emplace(tens);
69  }
70 
71  std::unordered_map<std::string,TensSetT> groups_;
72 
73  TagRegistry& tag_reg_;
74 };
75 
76 GroupRegistry& get_group_reg (void);
77 
78 void recursive_group_tag (ade::TensrefT tens, std::string group,
79  std::unordered_set<ade::iTensor*> stops,
80  GroupRegistry& registry = get_group_reg());
81 
82 using AGroupsT = std::map<std::string,std::unordered_set<std::string>>;
83 
84 using AdjMapT = std::unordered_map<ade::iTensor*,AGroupsT>;
85 
86 void adjacencies (AdjMapT& out, ade::TensT roots,
87  GroupRegistry& registry = get_group_reg());
88 
89 struct Subgraph final : public ade::iTraveler
90 {
91  Subgraph (std::string group) : group_(group) {}
92 
94  void visit (ade::iLeaf* leaf) override
95  {
96  if (false == estd::has(content_, leaf))
97  {
98  content_.emplace(leaf);
99  children_.erase(leaf);
100  }
101  }
102 
104  void visit (ade::iFunctor* func) override
105  {
106  if (false == estd::has(content_, func))
107  {
108  content_.emplace(func);
109  children_.erase(func);
110 
111  auto& children = func->get_children();
112  for (auto& child : children)
113  {
114  auto tens = child.get_tensor();
115  if (false == estd::has(content_, tens.get()))
116  {
117  children_.emplace(tens.get(), tens);
118  }
119  }
120  }
121  }
122 
123  std::string group_;
124 
125  std::unordered_set<ade::iTensor*> content_;
126 
127  // todo: order subgraphs children somehow
128  std::unordered_map<ade::iTensor*,ade::TensptrT> children_;
129 };
130 
131 using SgraphptrT = std::shared_ptr<Subgraph>;
132 
133 using SubgraphsT = std::unordered_set<SgraphptrT>;
134 
135 using SubgraphAssocsT = std::unordered_map<ade::iTensor*,SubgraphsT>;
136 
137 void beautify_groups (SubgraphAssocsT& out, const AdjMapT& adjs);
138 
139 // look for associations where the tensor key is the
140 // max height tensor of the mapped subgraph
141 // dump filtered associations in out
142 void filter_head (SubgraphAssocsT& out, const SubgraphAssocsT& assocs);
143 
144 }
145 
146 #endif // TAG_GROUP_HPP
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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tag::beautify_groups
void beautify_groups(SubgraphAssocsT &out, const AdjMapT &adjs)
-
tag::GroupTag::absorb
void absorb(TagptrT &&other) override
Definition: group.hpp:32
-
tag::GroupRegistry::group_tag
void group_tag(ade::TensrefT tens, std::string tag)
Definition: group.hpp:57
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tag::TagRegistry::add_tag
void add_tag(ade::TensrefT tens, TagptrT tag)
Definition: tag.hpp:137
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tag::recursive_group_tag
void recursive_group_tag(ade::TensrefT tens, std::string group, std::unordered_set< ade::iTensor *> stops, GroupRegistry &registry=get_group_reg())
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tag::groups_key
const std::string groups_key
Definition: group.hpp:15
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tag::iTag
Definition: tag.hpp:16
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tag::Subgraph::Subgraph
Subgraph(std::string group)
Definition: group.hpp:91
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tag::TagRegistry
Definition: tag.hpp:135
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tag::GroupTag::GroupTag
GroupTag(std::string init_label)
Definition: group.hpp:25
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tag::AdjMapT
std::unordered_map< ade::iTensor *, AGroupsT > AdjMapT
Definition: group.hpp:84
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tag::GroupRegistry::GroupRegistry
GroupRegistry(TagRegistry &registry=get_reg())
Definition: group.hpp:55
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tag
Definition: group.hpp:10
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ade::iLeaf
Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
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tag::TagptrT
std::unique_ptr< iTag > TagptrT
Definition: tag.hpp:27
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tag::TagRepsT
std::map< std::string, std::vector< std::string > > TagRepsT
Definition: tag.hpp:12
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tag::Subgraph
Definition: group.hpp:89
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tag::Subgraph::group_
std::string group_
Definition: group.hpp:123
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tag::Subgraph::visit
void visit(ade::iLeaf *leaf) override
Implementation of iTraveler.
Definition: group.hpp:94
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tag::Subgraph::visit
void visit(ade::iFunctor *func) override
Implementation of iTraveler.
Definition: group.hpp:104
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tag::GroupTag::tag_id
size_t tag_id(void) const override
Definition: group.hpp:27
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tag::SubgraphsT
std::unordered_set< SgraphptrT > SubgraphsT
Definition: group.hpp:133
-
ade::iTraveler
Interface to travel through graph, treating iLeaf and iFunctor differently.
Definition: itensor.hpp:22
-
ade::iFunctor::get_children
virtual const ArgsT & get_children(void) const =0
Return children nodes as a vector of raw pointers.
-
tag::GroupRegistry::tag_reg_
TagRegistry & tag_reg_
Definition: group.hpp:73
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tag::TensKey
Definition: tag.hpp:95
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tag::get_reg
TagRegistry & get_reg(void)
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tag::AGroupsT
std::map< std::string, std::unordered_set< std::string > > AGroupsT
Definition: group.hpp:82
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tag::Subgraph::children_
std::unordered_map< ade::iTensor *, ade::TensptrT > children_
Definition: group.hpp:128
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tag::GroupRegistry
Definition: group.hpp:53
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ade::TensrefT
std::weak_ptr< iTensor > TensrefT
Tensor weak pointers.
Definition: itensor.hpp:52
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ade::TensT
std::vector< TensptrT > TensT
Vector representation of ade tensor pointers.
Definition: funcarg.hpp:104
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tag::filter_head
void filter_head(SubgraphAssocsT &out, const SubgraphAssocsT &assocs)
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tag::get_group_reg
GroupRegistry & get_group_reg(void)
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void adjacencies(AdjMapT &out, ade::TensT roots, GroupRegistry &registry=get_group_reg())
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tag::TensSetT
std::unordered_set< TensKey, TensKeyHash > TensSetT
Definition: group.hpp:13
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std::unordered_set< ade::iTensor * > content_
Definition: group.hpp:125
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tag::SgraphptrT
std::shared_ptr< Subgraph > SgraphptrT
Definition: group.hpp:131
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tag::SubgraphAssocsT
std::unordered_map< ade::iTensor *, SubgraphsT > SubgraphAssocsT
Definition: group.hpp:135
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tag::GroupTag::get_tags
TagRepsT get_tags(void) const override
Definition: group.hpp:39
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tag::GroupTag
Definition: group.hpp:23
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tag::GroupRegistry::groups_
std::unordered_map< std::string, TensSetT > groups_
Definition: group.hpp:71
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tag::GroupTag::tag_id_
static size_t tag_id_
Definition: group.hpp:50
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Definition: group.hpp:48
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2 #include "opt/parse/def.h"
3 }
4 
5 #include "opt/candidate.hpp"
6 
7 #ifndef OPT_ICONVERTER_HPP
8 #define OPT_ICONVERTER_HPP
9 
10 namespace opt
11 {
12 
13 struct iConverter
14 {
15  virtual ~iConverter (void) = default;
16 
17  virtual ade::TensptrT build (
18  const ContexT& ctx, ade::Shape outshape) const = 0;
19 
20  virtual std::string to_string (void) const = 0;
21 };
22 
23 using ConvptrT = std::shared_ptr<iConverter>;
24 
25 }
26 
27 #endif // OPT_ICONVERTER_HPP
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idata.hpp
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1 
9 #include <cstdlib>
10 #include <string>
11 
12 #ifndef ADE_IDATA_HPP
13 #define ADE_IDATA_HPP
14 
15 namespace ade
16 {
17 
19 struct iData
20 {
21  virtual ~iData (void) = default;
22 
24  virtual void* data (void) = 0;
25 
27  virtual const void* data (void) const = 0;
28 
30  virtual size_t type_code (void) const = 0;
31 
33  virtual std::string type_label (void) const = 0;
34 
36  virtual size_t nbytes (void) const = 0;
37 };
38 
39 }
40 
41 #endif // ADE_IDATA_HPP
ade::iData::~iData
virtual ~iData(void)=default
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ade::iData::nbytes
virtual size_t nbytes(void) const =0
Return number of bytes in the data.
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ade::iData
Interface for unveiling data.
Definition: idata.hpp:19
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ade
Definition: coord.hpp:16
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ade::iData::type_label
virtual std::string type_label(void) const =0
Return data type label (for better readability)
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ade::iData::data
virtual void * data(void)=0
Return pointer to internal data.
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ade::iData::type_code
virtual size_t type_code(void) const =0
Return data type encoding.
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ifunctor.hpp File Reference
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#include "ade/funcarg.hpp"
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struct  ade::Opcode
 Encoding of operation. More...
 
struct  ade::iFunctor
 Interface of iOperation-defined operation node. More...
 
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 ade
 
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using ade::FuncptrT = std::shared_ptr< iFunctor >
 Functor smart pointer. More...
 
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9 #include "ade/funcarg.hpp"
10 
11 #ifndef ADE_IFUNCTOR_HPP
12 #define ADE_IFUNCTOR_HPP
13 
14 namespace ade
15 {
16 
18 struct Opcode final
19 {
21  std::string name_;
22 
24  size_t code_;
25 };
26 
28 struct iFunctor : public iTensor
29 {
30  virtual ~iFunctor (void) = default;
31 
33  void accept (iTraveler& visiter) override
34  {
35  visiter.visit(this);
36  }
37 
39  virtual Opcode get_opcode (void) const = 0;
40 
42  virtual const ArgsT& get_children (void) const = 0;
43 
45  virtual void update_child (FuncArg arg, size_t index) = 0;
46 };
47 
49 using FuncptrT = std::shared_ptr<iFunctor>;
50 
51 }
52 
53 #endif // ADE_IFUNCTOR_HPP
ade::FuncptrT
std::shared_ptr< iFunctor > FuncptrT
Functor smart pointer.
Definition: ifunctor.hpp:49
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ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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virtual ~iFunctor(void)=default
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virtual void update_child(FuncArg arg, size_t index)=0
Update child at specified index.
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ade::Opcode
Encoding of operation.
Definition: ifunctor.hpp:18
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ade::Opcode::name_
std::string name_
String representation of operation.
Definition: ifunctor.hpp:21
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ade::iFunctor::accept
void accept(iTraveler &visiter) override
Implementation of iTensor.
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ade::iFunctor::get_opcode
virtual Opcode get_opcode(void) const =0
Return operation encoding.
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ade
Definition: coord.hpp:16
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ade::iTraveler
Interface to travel through graph, treating iLeaf and iFunctor differently.
Definition: itensor.hpp:22
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ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
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ade::iFunctor::get_children
virtual const ArgsT & get_children(void) const =0
Return children nodes as a vector of raw pointers.
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ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
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ade::index
NElemT index(Shape shape, CoordT coord)
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ade::Opcode::code_
size_t code_
Numerical encoding of operation.
Definition: ifunctor.hpp:24
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ade::iTraveler::visit
virtual void visit(iLeaf *leaf)=0
Visit leaf node.
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ade::ArgsT
std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
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Tenncor libraries help developers build and evaluate tensor equations and its derivatives. A tensor is an N-dimensional container that organizes its content by some shape. An M by N matrix for instance, is a 2-dimensional tensor with a shape of [N, M] (according to Tenncor's x-y-z-... coordinate notation).

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High-level diagram available: https://drive.google.com/file/d/1PrsFa7Duj4Whlu_m0lmFr5JGikGnU3gC/view?usp=sharing

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This module supplies syntax tree for equation and generates derivative. Constraints to the equation is limited to each tensor's shape.

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This module is contains debug libraries for ADE Graphs.

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This module tags ADE tensors with labels.

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Building

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Tenncor uses bazel 0.28+.

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Download bazel: https://docs.bazel.build/versions/master/install.html

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inode.hpp File Reference
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#include "estd/estd.hpp"
-#include "ade/itensor.hpp"
-#include "ead/eigen.hpp"
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struct  ead::iNode< T >
 
struct  ead::NodeConverters< T >
 
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template<typename T >
using ead::NodeptrT = std::shared_ptr< iNode< T > >
 
template<typename T >
using ead::NodesT = std::vector< NodeptrT< T > >
 
template<typename T >
using ead::NodeBuilderF = std::function< NodeptrT< T >(ade::TensptrT)>
 
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template<typename TensType , typename T >
bool ead::register_builder (NodeBuilderF< T > builder)
 
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1 #include "estd/estd.hpp"
2 
3 #include "ade/itensor.hpp"
4 
5 #include "ead/eigen.hpp"
6 
7 #ifndef EAD_INODE_HPP
8 #define EAD_INODE_HPP
9 
10 namespace ead
11 {
12 
13 template <typename T>
14 struct iNode
15 {
16  virtual ~iNode (void) = default;
17 
18  ade::Shape shape (void)
19  {
20  return get_tensor()->shape();
21  }
22 
23  virtual T* data (void) = 0;
24 
25  virtual void update (void) = 0;
26 
27  virtual ade::TensptrT get_tensor (void) = 0;
28 };
29 
30 template <typename T>
31 using NodeptrT = std::shared_ptr<iNode<T>>;
32 
33 template <typename T>
34 using NodesT = std::vector<NodeptrT<T>>;
35 
36 template <typename T>
37 using NodeBuilderF = std::function<NodeptrT<T>(ade::TensptrT)>;
38 
39 template <typename T>
40 struct NodeConverters final
41 {
42  static std::unordered_map<size_t,NodeBuilderF<T>> builders_;
43 
44  static NodeptrT<T> to_node (ade::TensptrT tens)
45  {
46  const std::type_info& tp = typeid(*tens);
47  return estd::must_getf(builders_, tp.hash_code(),
48  "unknown tensor type %s with %s dtype",
49  tp.name(), age::name_type(age::get_type<T>()).c_str())(tens);
50  }
51 
52  NodeConverters (void) = delete;
53 };
54 
55 template <typename T>
56 std::unordered_map<size_t,NodeBuilderF<T>> NodeConverters<T>::builders_;
57 
58 template <typename TensType, typename T>
59 bool register_builder (NodeBuilderF<T> builder)
60 {
61  const std::type_info& tp = typeid(TensType);
62  return NodeConverters<T>::builders_.
63  emplace(tp.hash_code(), builder).second;
64 }
65 
66 }
67 
68 #endif // EAD_INODE_HPP
ead::NodeConverters
Definition: inode.hpp:40
-
ead::NodeptrT
std::shared_ptr< iNode< T > > NodeptrT
Definition: inode.hpp:31
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ead::register_builder
bool register_builder(NodeBuilderF< T > builder)
Definition: inode.hpp:59
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ead
Definition: constant.hpp:9
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ead::NodeConverters::NodeConverters
NodeConverters(void)=delete
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ead::iNode::~iNode
virtual ~iNode(void)=default
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ead::iNode::get_tensor
virtual ade::TensptrT get_tensor(void)=0
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ead::NodeConverters::to_node
static NodeptrT< T > to_node(ade::TensptrT tens)
Definition: inode.hpp:44
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ead::iNode
Definition: inode.hpp:14
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ead::iNode::shape
ade::Shape shape(void)
Definition: inode.hpp:18
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ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
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virtual void update(void)=0
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ade::Shape
Definition: shape.hpp:60
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ead::NodeBuilderF
std::function< NodeptrT< T >(ade::TensptrT)> NodeBuilderF
Definition: inode.hpp:37
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virtual T * data(void)=0
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static std::unordered_map< size_t, NodeBuilderF< T > > builders_
Definition: inode.hpp:42
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std::vector< NodeptrT< T > > NodesT
Definition: inode.hpp:34
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1 
10 #include "ade/ifunctor.hpp"
11 #include "ade/idata.hpp"
12 
13 #ifndef ADE_OPFUNC_HPP
14 #define ADE_OPFUNC_HPP
15 
16 namespace ade
17 {
18 
20 struct iOperableFunc : public iFunctor, public iData
21 {
22  virtual ~iOperableFunc (void) = default;
23 
25  virtual void update (void) = 0;
26 };
27 
28 }
29 
30 #endif // ADE_OPFUNC_HPP
ade::iOperableFunc::~iOperableFunc
virtual ~iOperableFunc(void)=default
-
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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virtual void update(void)=0
Update local data-cache using this functor&#39;s operation.
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Interface for unveiling data.
Definition: idata.hpp:19
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A functor node with direct access to evaluated data.
Definition: iopfunc.hpp:20
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 Interface of traversible and differentiable nodes with shape information. More...
 
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 Tensor smart pointer. More...
 
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1 
9 #include "ade/shape.hpp"
10 
11 #ifndef ADE_INTERFACE_HPP
12 #define ADE_INTERFACE_HPP
13 
14 namespace ade
15 {
16 
17 struct iLeaf;
18 
19 struct iFunctor;
20 
22 struct iTraveler
23 {
24  virtual ~iTraveler (void) = default;
25 
27  virtual void visit (iLeaf* leaf) = 0;
28 
30  virtual void visit (iFunctor* func) = 0;
31 };
32 
34 struct iTensor
35 {
36  virtual ~iTensor (void) = default;
37 
39  virtual void accept (iTraveler& visiter) = 0;
40 
42  virtual const Shape& shape (void) const = 0;
43 
45  virtual std::string to_string (void) const = 0;
46 };
47 
49 using TensptrT = std::shared_ptr<iTensor>;
50 
52 using TensrefT = std::weak_ptr<iTensor>;
53 
54 }
55 
56 #endif // ADE_INTERFACE_HPP
ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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virtual const Shape & shape(void) const =0
Return the shape held by this tensor.
-
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Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
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virtual ~iTraveler(void)=default
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ade::iTensor::to_string
virtual std::string to_string(void) const =0
Return the string representation of the tensor.
-
ade::iTensor::accept
virtual void accept(iTraveler &visiter)=0
Obtain concrete information on either leaf or functor implementations.
- -
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ade
Definition: coord.hpp:16
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ade::iTraveler
Interface to travel through graph, treating iLeaf and iFunctor differently.
Definition: itensor.hpp:22
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ade::iTensor
Interface of traversible and differentiable nodes with shape information.
Definition: itensor.hpp:34
-
ade::Shape
Definition: shape.hpp:60
-
ade::TensrefT
std::weak_ptr< iTensor > TensrefT
Tensor weak pointers.
Definition: itensor.hpp:52
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virtual void visit(iLeaf *leaf)=0
Visit leaf node.
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struct  opt::VoterArg
 
struct  opt::SegVArgs
 
struct  opt::OrdrHasher
 
struct  opt::CommHasher
 
struct  opt::iVoter
 
struct  opt::VoterPool
 
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using opt::VoterArgsT = std::vector< VoterArg >
 
using opt::VotptrT = std::shared_ptr< iVoter >
 
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void opt::sort_vargs (VoterArgsT &args)
 
bool opt::operator== (const VoterArgsT &lhs, const VoterArgsT &rhs)
 
bool opt::operator== (const SegVArgs &lhs, const SegVArgs &rhs)
 
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1 extern "C" {
2 #include "opt/parse/def.h"
3 }
4 
5 #include "opt/stats.hpp"
6 #include "opt/candidate.hpp"
7 
8 #ifndef OPT_IVOTER_HPP
9 #define OPT_IVOTER_HPP
10 
11 namespace opt
12 {
13 
14 struct VoterArg final
15 {
16  VoterArg (std::string label,
17  ade::CoordptrT shaper,
18  ade::CoordptrT coorder,
19  SUBGRAPH_TYPE type) :
20  label_(label),
21  shaper_(shaper),
22  coorder_(coorder),
23  type_(type) {}
24 
25  // return true if arg matches this
26  // only add to ctxs if matches
27  bool match (CtxsT& ctxs, const CandArg& arg) const
28  {
29  // match arg.shaper_ and arg.coorder_
30  if (false == is_equal(arg.shaper_, shaper_) ||
31  false == is_equal(arg.coorder_, coorder_))
32  {
33  return false;
34  }
35  switch (type_)
36  {
37  case ::SUBGRAPH_TYPE::SCALAR:
38  // look for scalar candidate in arg.candidates
39  return estd::has(arg.candidates_, Symbol{
40  CAND_TYPE::SCALAR,
41  label_,
42  });
43  case ::SUBGRAPH_TYPE::BRANCH:
44  {
45  // look for intermediate candidate in arg.candidates
46  auto it = arg.candidates_.find(Symbol{
47  CAND_TYPE::INTERM,
48  label_,
49  });
50  if (arg.candidates_.end() == it)
51  {
52  return false;
53  }
54  auto& cand_ctxs = it->second;
55  CtxsT matching_ctxs;
56  if (ctxs.empty())
57  {
58  matching_ctxs = cand_ctxs;
59  }
60  for (const ContexT& ctx : ctxs)
61  {
62  for (ContexT cand_tx : cand_ctxs)
63  {
64  // if the cand_tx map and ctx have a
65  // non-conflicting intersection, mark as matched
66  if (std::all_of(ctx.begin(), ctx.end(),
67  [&](const std::pair<std::string,CtxValT>& ctxpair)
68  {
69  auto ait = cand_tx.find(ctxpair.first);
70  if (cand_tx.end() == ait)
71  {
72  return true;
73  }
74  auto& ctens = ctxpair.second;
75  auto& cand_ctens = ait->second;
76  return std::equal(ctens.begin(), ctens.end(),
77  cand_ctens.begin());
78  }))
79  {
80  // merge
81  cand_tx.insert(ctx.begin(), ctx.end());
82  matching_ctxs.emplace(cand_tx);
83  }
84  }
85  }
86  bool has_match = matching_ctxs.size() > 0;
87  if (has_match)
88  {
89  ctxs = matching_ctxs;
90  }
91  return has_match;
92  }
93  case ::SUBGRAPH_TYPE::ANY:
94  {
95  CtxsT matching_ctxs;
96  if (ctxs.empty())
97  {
98  matching_ctxs.emplace(ContexT{{label_, {arg.tensor_}}});
99  }
100  for (ContexT ctx : ctxs)
101  {
102  auto it = ctx.find(label_);
103  if (ctx.end() == it || estd::has(it->second, arg.tensor_))
104  {
105  // matching ANY
106  ctx.emplace(label_, CtxValT{arg.tensor_});
107  matching_ctxs.emplace(ctx);
108  }
109  }
110  bool has_match = matching_ctxs.size() > 0;
111  if (has_match)
112  {
113  ctxs = matching_ctxs;
114  }
115  return has_match;
116  }
117  }
118  return true;
119  }
120 
121  std::string label_;
122 
123  ade::CoordptrT shaper_;
124 
125  ade::CoordptrT coorder_;
126 
127  SUBGRAPH_TYPE type_;
128 };
129 
130 using VoterArgsT = std::vector<VoterArg>;
131 
132 struct SegVArgs
133 {
134  size_t size (void) const
135  {
136  return scalars_.size() + branches_.size() + anys_.size();
137  }
138 
139  VoterArgsT scalars_;
140 
141  VoterArgsT branches_;
142 
143  VoterArgsT anys_;
144 };
145 
146 void sort_vargs (VoterArgsT& args);
147 
148 struct OrdrHasher final
149 {
150  size_t operator() (const VoterArgsT& args) const
151  {
152  size_t seed = 0;
153  hash_combine(seed, args);
154  return seed;
155  }
156 
157  void hash_combine (size_t& seed, const VoterArgsT& args) const
158  {
159  for (const VoterArg& arg : args)
160  {
161  std::tuple<std::string,std::string,std::string,size_t>
162  hash_target = {
163  arg.label_,
164  to_string(arg.shaper_),
165  to_string(arg.coorder_),
166  arg.type_,
167  };
168  boost::hash_combine(seed, hash_target);
169  }
170  }
171 };
172 
173 inline bool operator == (const VoterArgsT& lhs, const VoterArgsT& rhs)
174 {
175  return lhs.size() == rhs.size() &&
176  std::equal(lhs.begin(), lhs.end(), rhs.begin(),
177  [](const VoterArg& l, const VoterArg& r)
178  {
179  return l.label_ == r.label_ &&
180  is_equal(l.shaper_, r.shaper_) &&
181  is_equal(l.coorder_, r.coorder_) &&
182  l.type_ == r.type_;
183  });
184 }
185 
186 struct CommHasher final
187 {
188  size_t operator() (const SegVArgs& args) const
189  {
190  size_t seed = 0;
191  hasher_.hash_combine(seed, args.scalars_);
192  hasher_.hash_combine(seed, args.branches_);
193  hasher_.hash_combine(seed, args.anys_);
194  return seed;
195  }
196 
197  OrdrHasher hasher_;
198 };
199 
200 inline bool operator == (const SegVArgs& lhs, const SegVArgs& rhs)
201 {
202  return lhs.scalars_ == rhs.scalars_ &&
203  lhs.branches_ == rhs.branches_ &&
204  lhs.anys_ == rhs.anys_;
205 }
206 
207 // select candidates
208 struct iVoter
209 {
210  virtual ~iVoter (void) = default;
211 
212  virtual void emplace (VoterArgsT args, Symbol cand) = 0;
213 
214  virtual CandsT inspect (const CandArgsT& args) const = 0;
215 };
216 
217 using VotptrT = std::shared_ptr<iVoter>;
218 
219 struct VoterPool
220 {
221  std::unordered_set<std::string> immutables_;
222 
223  std::unordered_map<std::string,VotptrT> branches_;
224 };
225 
226 }
227 
228 #endif // OPT_IVOTER_HPP
opt::CtxValT
std::set< ade::TensptrT > CtxValT
Definition: candidate.hpp:14
-
opt::CandArg::candidates_
CandsT candidates_
Definition: candidate.hpp:60
-
csv_to_png.args
args
Definition: csv_to_png.py:105
-
opt::iVoter::~iVoter
virtual ~iVoter(void)=default
-
opt::CommHasher::operator()
size_t operator()(const SegVArgs &args) const
Definition: ivoter.hpp:188
- -
opt::iVoter::inspect
virtual CandsT inspect(const CandArgsT &args) const =0
- -
opt::SegVArgs::branches_
VoterArgsT branches_
Definition: ivoter.hpp:141
- -
opt::VoterPool::branches_
std::unordered_map< std::string, VotptrT > branches_
Definition: ivoter.hpp:223
-
opt::VoterArg::shaper_
ade::CoordptrT shaper_
Definition: ivoter.hpp:123
-
opt::is_equal
bool is_equal(ade::CoordptrT a, ade::CoordptrT b)
-
ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
-
opt
Definition: candidate.hpp:11
-
opt::SegVArgs::anys_
VoterArgsT anys_
Definition: ivoter.hpp:143
-
BRANCH
Definition: def.h:20
-
opt::CtxsT
std::unordered_set< ContexT, boost::hash< ContexT > > CtxsT
Definition: candidate.hpp:18
-
opt::VotptrT
std::shared_ptr< iVoter > VotptrT
Definition: ivoter.hpp:217
-
opt::CandArgsT
std::vector< CandArg > CandArgsT
Definition: candidate.hpp:67
-
opt::VoterArg::label_
std::string label_
Definition: ivoter.hpp:121
-
opt::SegVArgs
Definition: ivoter.hpp:132
-
opt::OrdrHasher::hash_combine
void hash_combine(size_t &seed, const VoterArgsT &args) const
Definition: ivoter.hpp:157
-
opt::ContexT
std::map< std::string, CtxValT > ContexT
Definition: candidate.hpp:16
-
opt::VoterArg::type_
SUBGRAPH_TYPE type_
Definition: ivoter.hpp:127
-
opt::OrdrHasher
Definition: ivoter.hpp:148
-
opt::INTERM
Definition: candidate.hpp:24
-
opt::CandArg::coorder_
ade::CoordptrT coorder_
Definition: candidate.hpp:64
-
opt::VoterArg::VoterArg
VoterArg(std::string label, ade::CoordptrT shaper, ade::CoordptrT coorder, SUBGRAPH_TYPE type)
Definition: ivoter.hpp:16
-
ANY
Definition: def.h:19
-
opt::CandsT
std::unordered_map< Symbol, CtxsT, SymbolHash > CandsT
Definition: candidate.hpp:54
-
opt::CandArg
Definition: candidate.hpp:56
-
opt::SegVArgs::scalars_
VoterArgsT scalars_
Definition: ivoter.hpp:139
-
opt::sort_vargs
void sort_vargs(VoterArgsT &args)
-
opt::OrdrHasher::operator()
size_t operator()(const VoterArgsT &args) const
Definition: ivoter.hpp:150
-
opt::iVoter
Definition: ivoter.hpp:208
-
SUBGRAPH_TYPE
SUBGRAPH_TYPE
Definition: def.h:16
-
opt::CommHasher::hasher_
OrdrHasher hasher_
Definition: ivoter.hpp:197
-
opt::VoterArg::coorder_
ade::CoordptrT coorder_
Definition: ivoter.hpp:125
-
opt::to_string
std::string to_string(ade::CoordptrT c)
-
opt::VoterPool::immutables_
std::unordered_set< std::string > immutables_
Definition: ivoter.hpp:221
-
opt::CommHasher
Definition: ivoter.hpp:186
-
opt::CandArg::shaper_
ade::CoordptrT shaper_
Definition: candidate.hpp:62
-
opt::Symbol
Definition: candidate.hpp:28
-
opt::SegVArgs::size
size_t size(void) const
Definition: ivoter.hpp:134
-
opt::VoterArgsT
std::vector< VoterArg > VoterArgsT
Definition: ivoter.hpp:130
-
opt::iVoter::emplace
virtual void emplace(VoterArgsT args, Symbol cand)=0
-
SCALAR
Definition: def.h:18
-
opt::VoterPool
Definition: ivoter.hpp:219
-
opt::operator==
bool operator==(const Symbol &lhs, const Symbol &rhs)
Definition: candidate.hpp:49
-
opt::CandArg::tensor_
ade::TensptrT tensor_
Definition: candidate.hpp:58
-
opt::VoterArg
Definition: ivoter.hpp:14
-
opt::VoterArg::match
bool match(CtxsT &ctxs, const CandArg &arg) const
Definition: ivoter.hpp:27
-
- -
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-
-
list.c File Reference
-
-
-
#include <string.h>
-#include "opt/parse/list.h"
-
-Include dependency graph for list.c:
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struct  NumNode
 
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struct NumList * new_numlist (void)
 
void numlist_clear (struct NumList *list)
 
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struct PtrList * new_ptrlist (size_t type)
 
void ptrlist_clear (struct PtrList *list, void(*val_mgr)(void *))
 
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1 #include <assert.h>
2 #include <stdlib.h>
3 
4 #ifndef PARSE_LIST_H
5 #define PARSE_LIST_H
6 
7 struct NumNode
8 {
9  struct NumNode* next_;
10  double val_;
11 };
12 
13 struct NumList
14 {
15  struct NumNode* head_;
16  struct NumNode* tail_;
17 };
18 
19 struct NumList* new_numlist (void);
20 
21 // frees all node in list and reset list head and tail to null
22 void numlist_clear (struct NumList* list);
23 
24 void numlist_free (struct NumList* list);
25 
26 void numlist_pushback (struct NumList* list, double val);
27 
28 
29 struct PtrNode
30 {
31  struct PtrNode* next_;
32  void* val_;
33 };
34 
35 struct PtrList
36 {
37  struct PtrNode* head_;
38  struct PtrNode* tail_;
39  size_t type_;
40 };
41 
42 struct PtrList* new_ptrlist (size_t type);
43 
44 // frees all node in list and reset list head and tail to null
45 // every val pointer in list passes as a parameter of callback val_mgr
46 // for memory management
47 // null val_mgr are ignored (todo: maybe warn?)
48 void ptrlist_clear (struct PtrList* list, void (*val_mgr)(void*));
49 
50 // val_mgr is used to clear list before freeing
51 void ptrlist_free (struct PtrList* list, void (*val_mgr)(void*));
52 
53 void ptrlist_pushback (struct PtrList* list, void* val);
54 
55 #endif // PARSE_LIST_H
new_numlist
struct NumList * new_numlist(void)
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Definition: list.h:35
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void * val_
Definition: list.h:32
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struct NumNode * head_
Definition: list.h:15
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struct NumNode * next_
Definition: list.h:9
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Definition: list.h:13
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void numlist_clear(struct NumList *list)
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struct PtrNode * tail_
Definition: list.h:38
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size_t type_
Definition: list.h:39
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Definition: list.h:7
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Definition: list.h:29
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struct PtrNode * head_
Definition: list.h:37
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Definition: list.h:31
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Definition: list.h:16
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void numlist_pushback(struct NumList *list, double val)
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struct  pbm::PathedTens
 Tree node for labeling Tensptrs. More...
 
struct  pbm::GraphInfo
 Contains all information necessary to recreate labelled ADE graph. More...
 
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template<typename LOAD , typename std::enable_if< std::is_base_of< iLoader, LOAD >::value >::type * = nullptr>
void pbm::load_graph (GraphInfo &out, const cortenn::Graph &in)
 Return graph info through out available from in graph. More...
 
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1 
9 #include "pbm/data.hpp"
10 
11 #ifndef PBM_LOAD_HPP
12 #define PBM_LOAD_HPP
13 
14 namespace pbm
15 {
16 
18 struct PathedTens final
19 {
20  ~PathedTens (void)
21  {
22  for (auto& child : children_)
23  {
24  delete child.second;
25  }
26  }
27 
30  void join (PathedTens* other)
31  {
32  {
33  std::vector<std::string> labels;
34  for (auto opair : other->tens_)
35  {
36  if (estd::has(tens_, opair.first))
37  {
38  labels.push_back(opair.first);
39  }
40  }
41  if (tens_.size() > 0)
42  {
43  logs::warnf("duplicate base labels %s",
44  fmts::to_string(labels.begin(), labels.end()).c_str());
45  }
46  }
47 
48  for (auto cpair : other->children_)
49  {
50  std::string label = cpair.first;
51  auto it = children_.find(label);
52  if (children_.end() == it)
53  {
54  children_.emplace(label, cpair.second);
55  }
56  else
57  {
58  it->second->join(cpair.second);
59  }
60  }
61  }
62 
64  ade::TensptrT get_labelled (StringsT path) const
65  {
66  return get_labelled(path.begin(), path.end());
67  }
68 
70  void set_labelled (StringsT path, ade::TensptrT tens)
71  {
72  set_labelled(path.begin(), path.end(), tens);
73  }
74 
77  ade::TensptrT get_labelled (
78  StringsT::iterator path_begin,
79  StringsT::iterator path_end) const
80  {
81  if (path_begin == path_end)
82  {
83  return nullptr;
84  }
85  auto path_it = path_begin++;
86  if (path_begin == path_end)
87  {
88  auto it = tens_.find(*path_it);
89  if (tens_.end() != it)
90  {
91  return it->second;
92  }
93  }
94  else
95  {
96  auto it = children_.find(*path_it);
97  if (nullptr != it->second)
98  {
99  return it->second->get_labelled(path_begin, path_end);
100  }
101  }
102  return nullptr;
103  }
104 
106  void set_labelled (StringsT::iterator path_begin,
107  StringsT::iterator path_end, ade::TensptrT tens)
108  {
109  if (path_begin == path_end)
110  {
111  return;
112  }
113  std::string label = *(path_begin++);
114  if (path_begin == path_end)
115  {
116  tens_.emplace(label, tens);
117  return;
118  }
119  PathedTens* child;
120  auto it = children_.find(label);
121  if (it == children_.end())
122  {
123  child = new PathedTens();
124  children_.emplace(label, child);
125  }
126  else
127  {
128  assert(nullptr != it->second);
129  child = it->second;
130  }
131  child->set_labelled(path_begin, path_end, tens);
132  }
133 
135  std::unordered_map<std::string,PathedTens*> children_;
136 
138  std::unordered_map<std::string,ade::TensptrT> tens_;
139 };
140 
142 struct GraphInfo final
143 {
145  std::unordered_set<ade::TensptrT> roots_;
146 
148  PathedTens tens_;
149 };
150 
152 template <typename LOAD, typename std::enable_if<
153  std::is_base_of<iLoader,LOAD>::value>::type* = nullptr>
154 void load_graph (GraphInfo& out, const cortenn::Graph& in)
155 {
156  LOAD loader;
157  auto nodes = in.nodes();
158  TensT invec;
159  for (const cortenn::Node& node : nodes)
160  {
161  auto pb_labels = node.labels();
162  if (node.has_source())
163  {
164  std::string src_label;
165  if (pb_labels.size() > 0)
166  {
167  src_label = *(pb_labels.rbegin());
168  }
169  const cortenn::Source& source = node.source();
170  std::string sstr = source.shape();
171  ade::Shape shape(std::vector<ade::DimT>(sstr.begin(), sstr.end()));
172  std::string data = source.data();
173  ade::TensptrT leaf = loader.generate_leaf(data.c_str(),
174  shape, source.typelabel(), src_label, source.is_const());
175  invec.push_back(leaf);
176  if (false == pb_labels.empty())
177  {
178  StringsT labels(pb_labels.begin(), pb_labels.end());
179  out.tens_.set_labelled(labels.begin(), labels.end(), leaf);
180  }
181  out.roots_.emplace(leaf);
182  }
183  else
184  {
185  cortenn::Functor func = node.functor();
186  auto nodeargs = func.args();
187  std::string opname = func.opname();
188  ade::ArgsT args;
189  for (auto nodearg : nodeargs)
190  {
191  ade::TensptrT arg = invec[nodearg.idx()];
192  auto shaper_pb = nodearg.shaper();
193  auto coorder_pb = nodearg.coord();
194  std::vector<double> shaper_vec(shaper_pb.begin(), shaper_pb.end());
195  std::vector<double> coord_vec(coorder_pb.begin(), coorder_pb.end());
196  ade::CoordptrT shaper = loader.generate_shaper(shaper_vec);
197  ade::CoordptrT coord = loader.generate_coorder(opname, coord_vec);
198  args.push_back(
199  ade::FuncArg(arg, shaper, nodearg.fwd(), coord));
200  out.roots_.erase(invec[nodearg.idx()]);
201  }
202  ade::TensptrT f = loader.generate_func(opname, args);
203  invec.push_back(f);
204  if (false == pb_labels.empty())
205  {
206  StringsT labels(pb_labels.begin(), pb_labels.end());
207  out.tens_.set_labelled(labels.begin(), labels.end(), f);
208  }
209  out.roots_.emplace(f);
210  }
211  }
212 }
213 
214 }
215 
216 #endif // PBM_GRAPH_HPP
pbm::PathedTens
Tree node for labeling Tensptrs.
Definition: load.hpp:18
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csv_to_png.args
args
Definition: csv_to_png.py:105
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pbm::PathedTens::get_labelled
ade::TensptrT get_labelled(StringsT::iterator path_begin, StringsT::iterator path_end) const
Definition: load.hpp:77
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pbm::load_graph
void load_graph(GraphInfo &out, const cortenn::Graph &in)
Return graph info through out available from in graph.
Definition: load.hpp:154
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pbm::PathedTens::get_labelled
ade::TensptrT get_labelled(StringsT path) const
Return tensor associated with input path if found otherwise nullptr.
Definition: load.hpp:64
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~PathedTens(void)
Definition: load.hpp:20
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ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
-
pbm::GraphInfo
Contains all information necessary to recreate labelled ADE graph.
Definition: load.hpp:142
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pbm::PathedTens::set_labelled
void set_labelled(StringsT path, ade::TensptrT tens)
Set input path to reference tensor.
Definition: load.hpp:70
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pbm::StringsT
std::list< fmts::string > StringsT
String list type used for paths.
Definition: data.hpp:25
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pbm::GraphInfo::tens_
PathedTens tens_
Labelled tensors.
Definition: load.hpp:148
-
ade::TensptrT
std::shared_ptr< iTensor > TensptrT
Tensor smart pointer.
Definition: itensor.hpp:49
-
ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
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pbm::PathedTens::children_
std::unordered_map< std::string, PathedTens * > children_
Map of labels to branching nodes.
Definition: load.hpp:135
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ade::Shape
Definition: shape.hpp:60
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pbm::PathedTens::join
void join(PathedTens *other)
Definition: load.hpp:30
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pbm::TensT
std::vector< ade::TensptrT > TensT
Tensptr vector type.
Definition: data.hpp:22
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ade::to_string
std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
-
pbm::GraphInfo::roots_
std::unordered_set< ade::TensptrT > roots_
Set of all roots (Tensptrs without any parent)
Definition: load.hpp:145
-
pbm
Definition: data.hpp:18
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pbm::PathedTens::tens_
std::unordered_map< std::string, ade::TensptrT > tens_
Map of labels to tensor leaves.
Definition: load.hpp:138
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ade::ArgsT
std::vector< FuncArg > ArgsT
Type of functor arguments.
Definition: funcarg.hpp:101
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pbm::PathedTens::set_labelled
void set_labelled(StringsT::iterator path_begin, StringsT::iterator path_end, ade::TensptrT tens)
Set path between iterators begin and end to reference tensor.
Definition: load.hpp:106
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1 #include "ade/traveler.hpp"
2 
3 #include "tag/group.hpp"
4 
5 #include "opt/ivoter.hpp"
6 
7 #ifndef OPT_MATCHER_HPP
8 #define OPT_MATCHER_HPP
9 
10 namespace opt
11 {
12 
13 const std::string group_prefix = "group:";
14 
15 // approach: we have matcher, voters, and candidates
16 // - matchers map:
17 // - functors to voters (by functor's opname)
18 // - leaves to ScalarCand (if it is immutable and scalar)
19 // - anything that is not matched to a voter or candidate has no
20 // candidate and marked as ANY
21 // - voters are created by the parser, and only accept functor arguments
22 // voters generate a list of candidates based on the arguments,
23 // the list of candidates can be empty and marked as ANY
24 // - candidates can be a scalar, an intermediate candidate,
25 // or a convertible candidate. when built:
26 // - scalar candidates take a constant scalar node
27 // - convertible candidates take on it's converted subgraph
28 // - intermediate candidates do nothing
29 // Using the matcher, the optimizer makes a best attempt at
30 // mapping tensor to zero to many candidates.
31 // The optimizer is responsible for selecting the best candidates
32 struct Matcher final : public ade::iTraveler
33 {
34  Matcher (void) = default;
35 
36  Matcher (const VoterPool& voters) : voters_(voters) {}
37 
39  void visit (ade::iLeaf* leaf) override
40  {
41  if (false == estd::has(candidates_, leaf))
42  {
43  if (tag::get_property_reg().has_property(leaf, tag::immutable_tag))
44  {
45  std::string const_str = leaf->to_string();
46  CandsT cands = {
47  {Symbol{CAND_TYPE::CONST, const_str}, CtxsT{}}
48  };
49  if (is_scalar(leaf))
50  {
51  // match against scalar maps
52  if (estd::has(voters_.immutables_, const_str))
53  {
54  cands.emplace(
55  Symbol{CAND_TYPE::SCALAR, const_str}, CtxsT{});
56  }
57  }
58  candidates_.emplace(leaf, cands);
59  }
60  else
61  {
62  candidates_.emplace(leaf, CandsT{});
63  }
64  }
65  }
66 
68  void visit (ade::iFunctor* func) override
69  {
70  if (false == estd::has(candidates_, func))
71  {
72  auto& children = func->get_children();
73  for (auto& child : children)
74  {
75  child.get_tensor()->accept(*this);
76  }
77 
78  if (std::all_of(children.begin(), children.end(),
79  [this](const ade::FuncArg& child) -> bool
80  {
81  auto ctens = child.get_tensor().get();
82  return estd::has(this->candidates_[ctens],
83  Symbol{CAND_TYPE::CONST, ctens->to_string()});
84  }))
85  {
86  // all children are constants
87  // therefore mark this as constant
88  std::string const_str = func->to_string();
89  candidates_.emplace(func, CandsT{
90  {Symbol{CAND_TYPE::CONST, const_str}, CtxsT{}},
91  });
92 
93  // mark as scalar if func's children are scalar
94  // in order to propagate scalar info to parents
95  if (scalarize_)
96  {
97  if (std::all_of(children.begin(), children.end(),
98  [this](const ade::FuncArg& child) -> bool
99  {
100  auto ctens = child.get_tensor().get();
101  std::string scalar_str = scalarize_(ctens);
102  return estd::has(this->candidates_[ctens],
103  Symbol{CAND_TYPE::SCALAR, scalar_str});
104  }))
105  {
106  std::string scalar_str = scalarize_(func);
107  candidates_[func].emplace(
108  Symbol{CAND_TYPE::SCALAR, scalar_str}, CtxsT{});
109  }
110  }
111  return;
112  }
113 
114  CandsT out_cands;
115  // functor
116  std::string opname = func->get_opcode().name_;
117  auto it = voters_.branches_.find(opname);
118  if (voters_.branches_.end() != it)
119  {
120  CandArgsT args;
121  args.reserve(children.size());
122  for (auto& child : children)
123  {
124  auto ctens = child.get_tensor();
125  args.push_back(CandArg{
126  ctens,
127  candidates_[ctens.get()],
128  child.get_shaper(),
129  child.get_coorder(),
130  });
131  }
132  out_cands = it->second->inspect(args);
133  }
134 
135  // do the same for functors that are the "head" of groups
136  tag::SubgraphsT sgs;
137  if (estd::get(sgs, group_head_, func))
138  {
139  // look for candidates in each of the potential subgraphs
140  for (tag::SgraphptrT sg : sgs)
141  {
142  auto bit = voters_.branches_.find(
143  group_prefix + sg->group_);
144  if (voters_.branches_.end() != bit)
145  {
146  // todo: store sg->children_ as ade::ArgsT
147  CandArgsT args;
148  args.reserve(children.size());
149  for (auto& sgcpair : sg->children_)
150  {
151  auto ctens = sgcpair.second;
152  args.push_back(CandArg{
153  ctens,
154  candidates_[sgcpair.first],
155  ade::identity,
156  ade::CoordptrT(),
157  });
158  }
159  CandsT group_cands = bit->second->inspect(args);
160  out_cands.insert(
161  group_cands.begin(), group_cands.end());
162  }
163  }
164  }
165 
166  candidates_.emplace(func, out_cands);
167  }
168  }
169 
170  // created by parser
171  VoterPool voters_;
172 
173  // generated as visited
174  std::unordered_map<ade::iTensor*,CandsT> candidates_;
175 
176  // heads for functors
177  tag::SubgraphAssocsT group_head_;
178 
179  // functor for returning constant representation of tensor
180  std::function<std::string(ade::iTensor*)> scalarize_;
181 };
182 
183 }
184 
185 #endif // OPT_MATCHER_HPP
opt::Matcher::scalarize_
std::function< std::string(ade::iTensor *)> scalarize_
Definition: matcher.hpp:180
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opt::Matcher::voters_
VoterPool voters_
Definition: matcher.hpp:171
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ade::iFunctor
Interface of iOperation-defined operation node.
Definition: ifunctor.hpp:28
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csv_to_png.args
args
Definition: csv_to_png.py:105
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opt::is_scalar
bool is_scalar(ade::iLeaf *leaf)
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opt::CONST
Definition: candidate.hpp:23
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Matcher(void)=default
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std::string name_
String representation of operation.
Definition: ifunctor.hpp:21
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opt::Matcher::candidates_
std::unordered_map< ade::iTensor *, CandsT > candidates_
Definition: matcher.hpp:174
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opt::VoterPool::branches_
std::unordered_map< std::string, VotptrT > branches_
Definition: ivoter.hpp:223
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ade::iLeaf
Leaf of the graph commonly representing the variable in an equation.
Definition: ileaf.hpp:19
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ade::CoordptrT
std::shared_ptr< iCoordMap > CoordptrT
Type of iCoordMap smartpointer.
Definition: coord.hpp:106
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opt
Definition: candidate.hpp:11
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opt::CtxsT
std::unordered_set< ContexT, boost::hash< ContexT > > CtxsT
Definition: candidate.hpp:18
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std::vector< CandArg > CandArgsT
Definition: candidate.hpp:67
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ade::iTensor::to_string
virtual std::string to_string(void) const =0
Return the string representation of the tensor.
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virtual Opcode get_opcode(void) const =0
Return operation encoding.
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ade::identity
CoordptrT identity
Identity matrix instance.
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opt::CandsT
std::unordered_map< Symbol, CtxsT, SymbolHash > CandsT
Definition: candidate.hpp:54
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opt::CandArg
Definition: candidate.hpp:56
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tag::SubgraphsT
std::unordered_set< SgraphptrT > SubgraphsT
Definition: group.hpp:133
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opt::Matcher::Matcher
Matcher(const VoterPool &voters)
Definition: matcher.hpp:36
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ade::iTraveler
Interface to travel through graph, treating iLeaf and iFunctor differently.
Definition: itensor.hpp:22
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opt::Matcher::visit
void visit(ade::iLeaf *leaf) override
Implementation of iTraveler.
Definition: matcher.hpp:39
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ade::iFunctor::get_children
virtual const ArgsT & get_children(void) const =0
Return children nodes as a vector of raw pointers.
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void visit(ade::iFunctor *func) override
Implementation of iTraveler.
Definition: matcher.hpp:68
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ade::FuncArg
Coordinate mapper and tensor pair.
Definition: funcarg.hpp:21
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opt::VoterPool::immutables_
std::unordered_set< std::string > immutables_
Definition: ivoter.hpp:221
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tag::SubgraphAssocsT group_head_
Definition: matcher.hpp:177
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Definition: candidate.hpp:28
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PropertyRegistry & get_property_reg(void)
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const std::string immutable_tag
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std::shared_ptr< Subgraph > SgraphptrT
Definition: group.hpp:131
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Definition: matcher.hpp:32
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std::unordered_map< ade::iTensor *, SubgraphsT > SubgraphAssocsT
Definition: group.hpp:135
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SCALAR
Definition: def.h:18
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const std::string group_prefix
Definition: matcher.hpp:13
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Definition: ivoter.hpp:219
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 ade
 
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using ade::MatrixT = double[mat_dim][mat_dim]
 Coordinate transformation matrix (using homogeneous) More...
 
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std::string ade::to_string (const MatrixT &mat)
 Return the string representation of input matrix. More...
 
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 Return the determinant of matrix. More...
 
void ade::inverse (MatrixT out, const MatrixT &in)
 Inverse in matrix and dump to out matrix. More...
 
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const RankT ade::mat_dim = rank_cap + 1
 Number of rows and columns for the homogeneous matrix. More...
 
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 Number of bytes in a homogeneous matrix. More...
 
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1 
10 #include <cassert>
11 #include <cstring>
12 
13 #include "ade/shape.hpp"
14 
15 #ifndef ADE_MATOPS_HPP
16 #define ADE_MATOPS_HPP
17 
18 namespace ade
19 {
20 
22 const RankT mat_dim = rank_cap + 1;
23 
25 const size_t mat_size = sizeof(double) * mat_dim * mat_dim;
26 
28 using MatrixT = double[mat_dim][mat_dim];
29 
31 std::string to_string (const MatrixT& mat);
32 
34 double determinant (const MatrixT& mat);
35 
37 void inverse (MatrixT out, const MatrixT& in);
38 
40 void matmul (MatrixT out, const MatrixT& lhs, const MatrixT& rhs);
41 
42 }
43 
44 #endif
ade::matmul
void matmul(MatrixT out, const MatrixT &lhs, const MatrixT &rhs)
Apply matrix multiplication for lhs and rhs to out matrix.
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const size_t mat_size
Number of bytes in a homogeneous matrix.
Definition: matops.hpp:25
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double[mat_dim][mat_dim] MatrixT
Coordinate transformation matrix (using homogeneous)
Definition: matops.hpp:28
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Definition: coord.hpp:16
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const RankT mat_dim
Number of rows and columns for the homogeneous matrix.
Definition: matops.hpp:22
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uint8_t RankT
Type used for shape rank.
Definition: shape.hpp:23
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ade::determinant
double determinant(const MatrixT &mat)
Return the determinant of matrix.
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ade::rank_cap
const RankT rank_cap
Number of dimsensions in a shape/coordinate.
Definition: shape.hpp:45
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ade::to_string
std::string to_string(const MatrixT &mat)
Return the string representation of input matrix.
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void inverse(MatrixT out, const MatrixT &in)
Inverse in matrix and dump to out matrix.
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Provides straight forward ADE iLeaf implementation using Variable to store Eigen Tensors.

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Single line comment are double slashes (//).

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Symbol declaration has the following syntax:

symbol A // this declares A

Property Mapping

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A conversion identifies an ADE subgraph and defines a new subgraph to convert to given specied symbols and scalars.

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Conversion statement has the following syntax:

<Subgraph to identify> => <Subgraph to convert>

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Edge info is a JSON object that contain keys coorder or shaper. Edge info requires the edge from the parent to the particular argument to match the included attributes.

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    #include <grpc/grpc.h>
    -#include <grpcpp/server.h>
    -#include <grpcpp/server_builder.h>
    -#include <grpcpp/server_context.h>
    -#include <grpcpp/security/server_credentials.h>
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    struct  GraphEmitterImpl
     
    struct  GraphEmitterImpl::Node
     
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    int main (int argc, char **argv)
     
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    ◆ main()

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    struct  CoordMap
     
    struct  FuncArg
     Coordinate mapper and tensor pair. More...
     
    struct  Functor
     Functor of the graph mapping to operators specified by opcode argument. More...
     
    struct  GraphStat
     Traveler that maps each tensor to its subtree's maximum depth. More...
     
    struct  HeightMatrix
     
    struct  iCoordMap
     Interface for transforming coordinates and reversing the coordinate. More...
     
    struct  iData
     Interface for unveiling data. More...
     
    struct  iFunctor
     Interface of iOperation-defined operation node. More...
     
    struct  iGradientBuilder
     
    struct  iLeaf
     Leaf of the graph commonly representing the variable in an equation. More...
     
    struct  iOperableFunc
     A functor node with direct access to evaluated data. More...
     
    struct  iTensor
     Interface of traversible and differentiable nodes with shape information. More...
     
    struct  iTraveler
     Interface to travel through graph, treating iLeaf and iFunctor differently. More...
     
    struct  OnceTraveler
     Extremely generic traveler that visits every node in the graph once. More...
     
    struct  Opcode
     Encoding of operation. More...
     
    struct  ParentFinder
     Traveler that for each child tracks the relationship to all parents. More...
     
    struct  PathFinder
     
    struct  Shape
     
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    using CoordptrT = std::shared_ptr< iCoordMap >
     Type of iCoordMap smartpointer. More...
     
    using ArgsT = std::vector< FuncArg >
     Type of functor arguments. More...
     
    using TensT = std::vector< TensptrT >
     Vector representation of ade tensor pointers. More...
     
    using FuncptrT = std::shared_ptr< iFunctor >
     Functor smart pointer. More...
     
    using LeafptrT = std::shared_ptr< iLeaf >
     Leaf smart pointer. More...
     
    using TensptrT = std::shared_ptr< iTensor >
     Tensor smart pointer. More...
     
    using TensrefT = std::weak_ptr< iTensor >
     Tensor weak pointers. More...
     
    using MatrixT = double[mat_dim][mat_dim]
     Coordinate transformation matrix (using homogeneous) More...
     
    using RankT = uint8_t
     Type used for shape rank. More...
     
    using DimT = uint16_t
     
    using CDimT = double
     Type used for coordinate dimensions. More...
     
    using NElemT = uint64_t
     
    using ShapeT = std::array< DimT, rank_cap >
     Array type used to hold dimension info in Shape. More...
     
    using CoordT = std::array< CDimT, rank_cap >
     
    using OwnerMapT = std::unordered_map< iTensor *, TensrefT >
     Map between tensor and its corresponding smart pointer. More...
     
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    bool is_identity (iCoordMap *coorder)
     Checks if the coord mapper is an identity mapper. More...
     
    CoordptrT reduce (RankT rank, std::vector< DimT > red)
     
    CoordptrT extend (RankT rank, std::vector< DimT > ext)
     
    CoordptrT permute (std::vector< RankT > order)
     
    CoordptrT flip (RankT dim)
     
    Shape apply_shaper (const CoordptrT &shaper, Shape inshape)
     
    FuncArg identity_map (TensptrT tensor)
     Return FuncArg that identity maps input tensor. More...
     
    FuncArg reduce_1d_map (TensptrT tensor, RankT rank)
     
    FuncArg reduce_map (TensptrT tensor, RankT rank, std::vector< DimT > red)
     
    FuncArg extend_map (TensptrT tensor, RankT rank, std::vector< DimT > ext)
     
    FuncArg permute_map (TensptrT tensor, std::vector< RankT > order)
     
    FuncArg flip_map (TensptrT tensor, RankT dim)
     Return FuncArg that flips input tensor along dimension. More...
     
    ArgsT to_args (TensT tens)
     Return ArgsT with each tensor in TensT attached to identity mapper. More...
     
    std::string to_string (const MatrixT &mat)
     Return the string representation of input matrix. More...
     
    double determinant (const MatrixT &mat)
     Return the determinant of matrix. More...
     
    void inverse (MatrixT out, const MatrixT &in)
     Inverse in matrix and dump to out matrix. More...
     
    void matmul (MatrixT out, const MatrixT &lhs, const MatrixT &rhs)
     Apply matrix multiplication for lhs and rhs to out matrix. More...
     
    NElemT index (Shape shape, CoordT coord)
     
    CoordT coordinate (Shape shape, NElemT idx)
     
    OwnerMapT track_owners (TensT roots)
     
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    -Variables

    CoordptrT identity
     Identity matrix instance. More...
     
    const RankT mat_dim = rank_cap + 1
     Number of rows and columns for the homogeneous matrix. More...
     
    const size_t mat_size = sizeof(double) * mat_dim * mat_dim
     Number of bytes in a homogeneous matrix. More...
     
    const RankT rank_cap = 8
     Number of dimsensions in a shape/coordinate. More...
     
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    Detailed Description

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    coord.hpp ade

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    Purpose: Define shape/coordinate transformation functions

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    funcarg.hpp ade

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    Purpose: Define functor argument wrapper to carryover shape and coordinate mappers

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    functor.hpp ade

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    Purpose: Define functor nodes of an equation graph

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    grad_def.hpp ade

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    Purpose: Define gradient builder interface for building derivatives

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    ileaf.hpp ade

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    Purpose: Define common interface for node unveiling data information

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    ileaf.hpp ade

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    Purpose: Define leafs for tensor equation graph

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    opfunc.hpp ade

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    Purpose: Define functor nodes directly hold/manipulate data This differs from Functor which should not directly manipulate data

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    itensor.hpp ade

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    Purpose: Define interfaces and building blocks for an equation graph

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    matops.hpp ade

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    Purpose: Define matrix operations for coordinate transformation This functions are here to avoid external dependencies in ADE

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    shape.hpp ade

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    Purpose: Define shapes models and coordinate to flattened index mapping

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    traveler.hpp ade

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    Purpose: Define common traveler implementations

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    Typedef Documentation

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    ◆ ArgsT

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    Array type used to hold dimension info when transforming coordinates Coordinates are allowed to be negative, negative dimensions are counted backward from the corresponding shape dimension For example, given shape=[5], coord=[-1] is the same as coord=[4]

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    ◆ FuncptrT

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    using ade::FuncptrT = typedef std::shared_ptr<iFunctor>
    -
    - -

    Functor smart pointer.

    - -
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    ◆ LeafptrT

    - -
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    using ade::LeafptrT = typedef std::shared_ptr<iLeaf>
    -
    - -

    Leaf smart pointer.

    - -
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    ◆ MatrixT

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    using ade::MatrixT = typedef double[mat_dim][mat_dim]
    -
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    Coordinate transformation matrix (using homogeneous)

    - -
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    ◆ NElemT

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    using ade::NElemT = typedef uint64_t
    -
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    Type used for flattened index DimT having 8 bits and shape comprising of 8 DimT values means a maximum flattened index of (2 ^ 8) ^ 8 = 2 ^ 64

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    ◆ OwnerMapT

    - -
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    using ade::OwnerMapT = typedef std::unordered_map<iTensor*,TensrefT>
    -
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    Map between tensor and its corresponding smart pointer.

    - -
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    ◆ RankT

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    using ade::RankT = typedef uint8_t
    -
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    Type used for shape rank.

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    ◆ ShapeT

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    using ade::ShapeT = typedef std::array<DimT,rank_cap>
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    Array type used to hold dimension info in Shape.

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    ◆ TensptrT

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    Tensor smart pointer.

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    ◆ TensrefT

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    Tensor weak pointers.

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    ◆ TensT

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    using ade::TensT = typedef std::vector<TensptrT>
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    Vector representation of ade tensor pointers.

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    Function Documentation

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    ◆ apply_shaper()

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    Shape ade::apply_shaper (const CoordptrT & shaper,
    Shape inshape 
    )
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    ◆ coordinate()

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    CoordT ade::coordinate (Shape shape,
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    )
    -
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    Return the coordinate of a flat index according to shape Coordinate dimensions are 0-based For example [0, 0, ..., 0] <-> 0

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    ◆ determinant()

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    Return the determinant of matrix.

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    ◆ extend()

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    CoordptrT ade::extend (RankT rank,
    std::vector< DimT > ext 
    )
    -
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    Return coordinate mapper multiplying dimensions after rank by values in ext vector For example, given coordinate [6, 6, 2, 2], rank=2, and ext=[3, 3], mapper forward transforms to coordinate [6, 6, 6, 6]

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    FuncArg ade::extend_map (TensptrT tensor,
    RankT rank,
    std::vector< DimT > ext 
    )
    -
    -

    Return FuncArg that extends input tensor by rank and extension vector E.g.: tensor w/ shape [2, 1, 1], rank = 1, red = [3, 4] gets mapped to [2, 3, 4]

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    ◆ flip()

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    CoordptrT ade::flip (RankT dim)
    -
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    Return coordinate mapper flipping coordinate value at specified dimension Flipped dimension with original value x is represented as -x-1 (see CoordT definition)

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    ◆ flip_map()

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    FuncArg ade::flip_map (TensptrT tensor,
    RankT dim 
    )
    -
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    Return FuncArg that flips input tensor along dimension.

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    ◆ identity_map()

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    FuncArg ade::identity_map (TensptrT tensor)
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    Return FuncArg that identity maps input tensor.

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    ◆ index()

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    CoordT coord 
    )
    -
    -

    Return the flat index mapped by coord according to shape For example, 2-D tensor has indices in place of value as follows: [[0, 1, ..., n-1], [n, n+1, ..., 2*n-1]] The index follows the equation: index = coord[0]+coord[1]*shape[0]+... Invalid coordinate where the coordinate value is beyond the dimension for any index will report error

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    ◆ inverse()

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    void ade::inverse (MatrixT out,
    const MatrixT & in 
    )
    -
    - -

    Inverse in matrix and dump to out matrix.

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    ◆ is_identity()

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    bool ade::is_identity (iCoordMap * coorder)
    -
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    Checks if the coord mapper is an identity mapper.

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    ◆ matmul()

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    void ade::matmul (MatrixT out,
    const MatrixT & lhs,
    const MatrixT & rhs 
    )
    -
    - -

    Apply matrix multiplication for lhs and rhs to out matrix.

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    ◆ permute()

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    CoordptrT ade::permute (std::vector< RankT > order)
    -
    -

    Return coordinate mapper permuting coordinate according to input order Order is a vector of indices of the dimensions to appear in order Indices not referenced by order but less than rank_cap will be appended by numerical order For example, given coordinate [1, 2, 3, 4], order=[1, 3], mapper forward transforms to coordinate [2, 4, 1, 3] Returned coordinate mapper will be a CoordMap instance, so inversibility requires order indices be unique, otherwise throw fatal error

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    ◆ permute_map()

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    FuncArg ade::permute_map (TensptrT tensor,
    std::vector< RankT > order 
    )
    -
    -

    Return FuncArg that permutes input tensor by order E.g.: tensor w/ shape [2, 3, 4], order = [1, 2, 0] gets mapped to [3, 4, 2]

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    ◆ reduce()

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    CoordptrT ade::reduce (RankT rank,
    std::vector< DimT > red 
    )
    -
    -

    Return coordinate mapper dividing dimensions after rank by values in red vector For example, given coordinate [2, 2, 6, 6], rank=2, and red=[3, 3], mapper forward transforms to coordinate [2, 2, 2, 2]

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    ◆ reduce_1d_map()

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    FuncArg ade::reduce_1d_map (TensptrT tensor,
    RankT rank 
    )
    -
    -

    Return FuncArg that reduces input tensor at rank then snip the dimension at rank E.g.: tensor w/ shape [2, 3, 4], rank = 1 gets mapped to [2, 4]

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    ◆ reduce_map()

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    FuncArg ade::reduce_map (TensptrT tensor,
    RankT rank,
    std::vector< DimT > red 
    )
    -
    -

    Return FuncArg that reduces input tensor by units in reduction vector after specified rank E.g.: tensor w/ shape [2, 3, 4], rank = 0, red = [2, 3] gets mapped to [1, 1, 4]

    - -
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    ◆ to_args()

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    ArgsT ade::to_args (TensT tens)
    -
    - -

    Return ArgsT with each tensor in TensT attached to identity mapper.

    - -
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    ◆ to_string()

    - -
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    std::string ade::to_string (const MatrixT & mat)
    -
    - -

    Return the string representation of input matrix.

    - -
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    - -

    ◆ track_owners()

    - -
    -
    - - - - - - - - -
    OwnerMapT ade::track_owners (TensT roots)
    -
    -

    Travelers will lose smart pointer references, This utility function will grab reference maps of root's subtree

    - -
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    Variable Documentation

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    ◆ identity

    - -
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    CoordptrT ade::identity
    -
    - -

    Identity matrix instance.

    - -
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    ◆ mat_dim

    - -
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    const RankT ade::mat_dim = rank_cap + 1
    -
    - -

    Number of rows and columns for the homogeneous matrix.

    - -
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    ◆ mat_size

    - -
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    const size_t ade::mat_size = sizeof(double) * mat_dim * mat_dim
    -
    - -

    Number of bytes in a homogeneous matrix.

    - -
    -
    - -

    ◆ rank_cap

    - -
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    Number of dimsensions in a shape/coordinate.

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    string prog_description = 'Generate c++ glue layer mapping ADE and some data-processing library.'
     
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    Function Documentation

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    class  APIsPlugin
     
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    def _parse_args (arg, accept_def=True)
     
    def _nullcheck (args)
     
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    def _template_defn_func (api)
     
    def _defn_func (api)
     
    def _handle_api_header (apis)
     
    def _handle_api_source (apis)
     
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    string _ns_template
     
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    string _source_template
     
    string _decl_tmp
     
    string _template_defn_tmp
     
    string _defn_tmp
     
    string _plugin_id = "API"
     
    string api_header = 'api.hpp'
     
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    Function Documentation

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     accept_def = True 
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    4 '''
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    2 {outtype} {funcname} ({args})
    3 {{
    4  if ({null_check})
    5  {{
    6  logs::fatal("cannot {funcname} with a null argument");
    7  }}
    8  {block}
    9 }}
    10 '''
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    1 = '''
    2 #ifndef _GENERATED_API_HPP
    3 #define _GENERATED_API_HPP
    4 
    5 //>>> hdr_namespaces
    6 {hdr_namespaces}
    7 
    8 //>>> template_namespaces
    9 {template_namespaces}
    10 
    11 #endif // _GENERATED_API_HPP
    12 '''
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    1 = '''
    2 //>>> namespace
    3 namespace {namespace}
    4 {{
    5 
    6 //>>> funcs
    7 {funcs}
    8 
    9 }}
    10 '''
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    1 = '''
    2 #ifdef _GENERATED_API_HPP
    3 
    4 //>>> src_namespaces
    5 {src_namespaces}
    6 
    7 #endif
    8 '''
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    2 template <{template_args}>
    3 {outtype} {funcname} ({args})
    4 {{
    5  if ({null_check})
    6  {{
    7  logs::fatal("cannot {funcname} with a null argument");
    8  }}
    9  {block}
    10 }}
    11 '''
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    -Generated by   -doxygen - 1.8.14 -
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    def _str_clean (str)
     
    def _apply_styles (graph, styles)
     
    def read_graph (lines)
     
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    dictionary _styles
     
     parser = argparse.ArgumentParser()
     
     nargs
     
     default
     
     help
     
     args = parser.parse_args()
     
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    Detailed Description

    -
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    Function Documentation

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    1 = {
    2  'graph': {
    3  'label': 'Operation Graph',
    4  'fontsize': '16',
    5  'fontcolor': 'white',
    6  'bgcolor': '#333333',
    7  'rankdir': 'BT',
    8  },
    9  'nodes': {
    10  'fontname': 'Helvetica',
    11  'shape': 'hexagon',
    12  'fontcolor': 'white',
    13  'color': 'white',
    14  'style': 'filled',
    15  'fillcolor': '#006699',
    16  },
    17  'edges': {
    18  'style': 'dashed',
    19  # 'color': 'white',
    20  'arrowhead': 'open',
    21  'fontname': 'Courier',
    22  'fontsize': '12',
    23  'fontcolor': 'white',
    24  }
    25 }
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    static const size_t max_attempts = 10
     
    static const size_t data_sync_interval = 50
     
    static const std::string tag_str_key = "name"
     
    static const std::string tag_node_type = "node_type"
     
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    def graph_to_csvimg (root, outpath, showshape=False)
     
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    class  DTypesPlugin
     
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    def _handle_enumeration (dtypes)
     
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    def _handle_type2names (dtypes)
     
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    string _header_template
     
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    2  out = std::vector<OUTTYPE>(({dtype}*) input, ({dtype}*) input + nelems);
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    5  return {code};
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     internal
     
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    struct  AnyConvr
     
    struct  BuilderArg
     
    struct  Constant
     
    struct  ConstantNode
     
    struct  ConverterBuilder
     
    struct  CoordMap
     
    struct  EADLoader
     Unmarshal cortenn::Source as Variable containing context of source. More...
     
    struct  EADSaver
     
    struct  EigenMatOp
     
    struct  EigenTensOp
     
    struct  FuncArg
     
    struct  FuncConvr
     
    struct  Functor
     
    struct  FunctorNode
     
    struct  GradientBuilder
     
    struct  GroupConvr
     
    struct  iEigen
     
    struct  iLeaf
     
    struct  iNode
     
    struct  iSession
     
    struct  NodeConverters
     
    struct  OpArg
     
    struct  ScalarConvr
     
    struct  Session
     
    struct  SizeT
     
    struct  Variable
     Leaf node containing data. More...
     
    struct  VariableNode
     
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    using CoordptrT = std::shared_ptr< CoordMap >
     Type of iCoordMap smartpointer. More...
     
    using DimensionsT = std::array< Eigen::Index, 8 >
     
    template<typename T >
    using MatrixT = Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor >
     
    template<typename T >
    using TensorT = Eigen::Tensor< T, 8 >
     
    template<typename T >
    using TensMapT = Eigen::TensorMap< TensorT< T > >
     
    template<typename T >
    using MatMapT = Eigen::Map< MatrixT< T > >
     
    template<typename T >
    using EigenptrT = std::shared_ptr< iEigen< T > >
     
    template<typename T >
    using ArgsT = std::vector< FuncArg< T > >
     
    template<typename T >
    using NodeptrT = std::shared_ptr< iNode< T > >
     
    template<typename T >
    using NodesT = std::vector< NodeptrT< T > >
     
    template<typename T >
    using NodeBuilderF = std::function< NodeptrT< T >(ade::TensptrT)>
     
    template<typename OP , size_t N, typename T >
    using ReduceOutT = Eigen::TensorReductionOp< OP, const std::array< ade::RankT, N >, const TensMapT< T > >
     
    using BuilderArgsT = std::vector< BuilderArg >
     
    using EngineT = std::default_random_engine
     RNG engine used. More...
     
    template<typename T >
    using GenF = std::function< T()>
     
    using TensSetT = std::unordered_set< ade::iTensor * >
     
    template<typename T >
    using VarptrT = std::shared_ptr< VariableNode< T > >
     
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    -Functions

    template<typename T >
    NodeptrT< T > make_constant_scalar (T scalar, ade::Shape shape)
     
    template<typename T >
    NodeptrT< T > make_constant (T *data, ade::Shape shape)
     
    CoordptrT reduce (std::vector< ade::RankT > red_dims)
     
    CoordptrT extend (ade::RankT rank, std::vector< ade::DimT > ext)
     
    CoordptrT permute (std::vector< ade::RankT > dims)
     
    template<typename T >
    MatMapT< T > tens_to_matmap (TensorT< T > &tens)
     
    template<typename T >
    MatMapT< T > mat_to_matmap (MatrixT< T > &mat)
     
    template<typename T >
    MatMapT< T > tensmap_to_matmap (TensMapT< T > &tens)
     
    template<typename T >
    TensMapT< T > mat_to_tensmap (MatrixT< T > &mat)
     
    template<typename T >
    TensMapT< T > tens_to_tensmap (TensorT< T > &tens)
     
    template<typename T , typename EigenSource , typename EigenArgs >
    EigenptrT< T > make_eigentensor (DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
     
    template<typename T , typename EigenSource , typename EigenArgs >
    EigenptrT< T > make_eigenmatrix (DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
     
    template<typename T >
    TensorT< T > make_tensor (const ade::Shape &shape)
     
    template<typename T >
    MatMapT< T > make_matmap (T *data, const ade::Shape &shape)
     
    template<typename T >
    TensMapT< T > make_tensmap (T *data, const ade::Shape &shape)
     
    template<typename T >
    ade::Shape get_shape (const TensorT< T > &tens)
     
    template<typename T >
    ade::Shape get_shape (const TensMapT< T > &tens)
     
    DimensionsT shape_convert (ade::Shape shape)
     
    template<typename T >
    FuncArg< T > identity_map (NodeptrT< T > node)
     
    template<typename T >
    FuncArg< T > reduce_map (NodeptrT< T > node, ade::RankT offset, ade::RankT ndims)
     
    template<typename T >
    FuncArg< T > extend_map (NodeptrT< T > node, ade::RankT rank, std::vector< ade::DimT > ext)
     
    template<typename T >
    FuncArg< T > permute_map (NodeptrT< T > node, std::vector< ade::RankT > order)
     
    template<typename T >
    NodeptrT< T > make_functor (ade::Opcode opcode, ArgsT< T > args)
     
    template<typename T >
    NodeptrT< T > reduce_grad (const ade::FuncArg &child, NodeptrT< T > bwd, size_t idx)
     
    template<typename T >
    NodeptrT< T > permute_grad (ade::iFunctor *fwd, NodeptrT< T > bwd, size_t idx)
     
    template<typename T >
    NodeptrT< T > extend_grad (ade::iFunctor *fwd, NodeptrT< T > bwd, size_t idx)
     
    template<typename T >
    NodeptrT< T > derive (NodeptrT< T > root, NodeptrT< T > target)
     Derive root with respect to target and optimized. More...
     
    template<typename TensType , typename T >
    bool register_builder (NodeBuilderF< T > builder)
     
    static bool is_2d (ade::Shape shape)
     
    template<typename T >
    EigenptrT< T > reduce_sum (ade::Shape &outshape, const OpArg< T > &in) template< typename T > EigenptrT< T > reduce_prod(ade
     
    template<typename T >
    EigenptrT< T > permute (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > slice (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > pad (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > abs (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > neg (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > sin (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > cos (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > tan (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > exp (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > log (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > sqrt (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > round (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > sigmoid (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > sigmoid_grad (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > tanh (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > square (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > cube (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > pow (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > add (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > sub (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > mul (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > div (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > eq (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > neq (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > lt (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > gt (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > min (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > max (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > rand_uniform (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > select (ade::Shape &outshape, const OpArg< T > &condition, const OpArg< T > &then, const OpArg< T > &otherwise)
     
    template<typename T >
    EigenptrT< T > matmul (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > convolution (ade::Shape &outshape, const OpArg< T > &input, const OpArg< T > &kernel)
     
    template<typename T >
    EigenptrT< T > convolution_image_grad (ade::Shape &imageshape, const OpArg< T > &kernel, const OpArg< T > &super_composite)
     
    template<typename T >
    EigenptrT< T > convolution_kernel_grad (ade::Shape &kernelshape, const OpArg< T > &image, const OpArg< T > &super_composite)
     
    static std::vector< double > vectorize (::NumList *list)
     
    static CoordptrT coorderize (::NumList *list)
     
    template<typename T >
    opt::OptCtx parse (std::string content)
     
    template<typename T >
    opt::OptCtx parse_file (std::string filename)
     
    EngineT & get_engine (void)
     Return global random generator. More...
     
    template<typename T , typename std::enable_if< std::is_integral< T >::value >::type * = nullptr>
    T unif (const T &a, const T &b)
     
    template<typename T , typename std::enable_if< std::is_integral< T >::value >::type * = nullptr>
    GenF< T > unif_gen (const T &a, const T &b)
     
    template<typename T , typename std::enable_if<!std::is_integral< T >::value >::type * = nullptr>
    GenF< T > unif_gen (T a, T b)
     
    template<typename T , typename std::enable_if<!std::is_integral< T >::value >::type * = nullptr>
    GenF< T > norm_gen (T mean, T stdev)
     
    static bool is_big_endian (void)
     
    template<typename T >
    NodeptrT< T > convert_to_node (VarptrT< T > var)
     
    template<typename T >
    VarptrT< T > make_variable_scalar (T scalar, ade::Shape shape, std::string label="")
     
    template<typename T >
    VarptrT< T > make_variable (ade::Shape shape, std::string label="")
     
    template<typename T >
    VarptrT< T > make_variable (T *data, ade::Shape shape, std::string label="")
     
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    -Variables

    static const size_t label_limit = 5
     
    static std::unordered_set< size_t > non_bijectives
     
    -

    Detailed Description

    -

    grader.hpp ead

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    Purpose: Implement ead gradient definition for supported operations

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    ileaf.hpp ead

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    Purpose: Define interfaces and building blocks for an equation graph

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    Purpose: Define functions manipulating tensor data values No function in this file makes any attempt to check for nullptrs

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    Purpose: Define functions for marshal and unmarshal data sources

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    ◆ EigenptrT

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    using ead::EigenptrT = typedef std::shared_ptr<iEigen<T> >
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    ◆ EngineT

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    using ead::EngineT = typedef std::default_random_engine
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    RNG engine used.

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    ◆ GenF

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    using ead::GenF = typedef std::function<T()>
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    ◆ MatMapT

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    using ead::MatMapT = typedef Eigen::Map<MatrixT<T> >
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    ◆ MatrixT

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    using ead::MatrixT = typedef Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic,Eigen::RowMajor>
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    ◆ NodeBuilderF

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    using ead::NodeBuilderF = typedef std::function<NodeptrT<T>(ade::TensptrT)>
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    ◆ NodeptrT

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    using ead::NodeptrT = typedef std::shared_ptr<iNode<T> >
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    ◆ NodesT

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    using ead::NodesT = typedef std::vector<NodeptrT<T> >
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    ◆ ReduceOutT

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    using ead::ReduceOutT = typedef Eigen::TensorReductionOp<OP, const std::array<ade::RankT,N>,const TensMapT<T> >
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    ◆ TensMapT

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    using ead::TensMapT = typedef Eigen::TensorMap<TensorT<T> >
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    ◆ TensorT

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    using ead::TensorT = typedef Eigen::Tensor<T,8>
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    ◆ TensSetT

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    using ead::TensSetT = typedef std::unordered_set<ade::iTensor*>
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    ◆ VarptrT

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    using ead::VarptrT = typedef std::shared_ptr<VariableNode<T> >
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    Function Documentation

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    ◆ abs()

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    EigenptrT<T> ead::abs (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    Given reference to output array, and input vector ref, make output elements take absolute value of inputs

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    ◆ add()

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    EigenptrT<T> ead::add (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    ◆ convert_to_node()

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    NodeptrT<T> ead::convert_to_node (VarptrT< T > var)
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    ◆ convolution()

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    EigenptrT<T> ead::convolution (ade::Shape & outshape,
    const OpArg< T > & input,
    const OpArg< T > & kernel 
    )
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    ◆ convolution_image_grad()

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    EigenptrT<T> ead::convolution_image_grad (ade::Shape & imageshape,
    const OpArg< T > & kernel,
    const OpArg< T > & super_composite 
    )
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    ◆ convolution_kernel_grad()

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    EigenptrT<T> ead::convolution_kernel_grad (ade::Shape & kernelshape,
    const OpArg< T > & image,
    const OpArg< T > & super_composite 
    )
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    ◆ coorderize()

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    static CoordptrT ead::coorderize (::NumList * list)
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    ◆ cos()

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    EigenptrT<T> ead::cos (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    Given reference to output array, and input vector ref, make output elements take cosine of inputs

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    ◆ cube()

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    EigenptrT<T> ead::cube (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    ◆ derive()

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    NodeptrT<T> ead::derive (NodeptrT< T > root,
    NodeptrT< T > target 
    )
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    Derive root with respect to target and optimized.

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    ◆ div()

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    EigenptrT<T> ead::div (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments a, and b, for every pair of mapped elements sharing the same index divide Only accept 2 arguments

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    ◆ eq()

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    EigenptrT<T> ead::eq (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments a, and b, for every pair of mapped elements sharing the same index apply == operator Only accept 2 arguments

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    ◆ exp()

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    EigenptrT<T> ead::exp (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    Given reference to output array, and input vector ref, make output elements take exponent of inputs

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    ◆ extend()

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    CoordptrT ead::extend (ade::RankT rank,
    std::vector< ade::DimT > ext 
    )
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    ◆ extend_grad()

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    NodeptrT<T> ead::extend_grad (ade::iFunctor * fwd,
    NodeptrT< T > bwd,
    size_t idx 
    )
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    ◆ extend_map()

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    FuncArg<T> ead::extend_map (NodeptrT< T > node,
    ade::RankT rank,
    std::vector< ade::DimT > ext 
    )
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    ◆ get_engine()

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    EngineT& ead::get_engine (void )
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    Return global random generator.

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    ◆ get_shape() [1/2]

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    ade::Shape ead::get_shape (const TensorT< T > & tens)
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    ◆ get_shape() [2/2]

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    ade::Shape ead::get_shape (const TensMapT< T > & tens)
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    ◆ gt()

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    EigenptrT<T> ead::gt (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments a, and b, for every pair of mapped elements sharing the same index apply > operator Only accept 2 arguments

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    ◆ identity_map()

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    FuncArg<T> ead::identity_map (NodeptrT< T > node)
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    ◆ is_2d()

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    static bool ead::is_2d (ade::Shape shape)
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    ◆ is_big_endian()

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    static bool ead::is_big_endian (void )
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    ◆ log()

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    EigenptrT<T> ead::log (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    Given reference to output array, and input vector ref, make output elements take natural log of inputs

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    ◆ lt()

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    EigenptrT<T> ead::lt (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments a, and b, for every pair of mapped elements sharing the same index apply < operator Only accept 2 arguments

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    ◆ make_constant()

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    NodeptrT<T> ead::make_constant (T * data,
    ade::Shape shape 
    )
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    ◆ make_constant_scalar()

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    NodeptrT<T> ead::make_constant_scalar (T scalar,
    ade::Shape shape 
    )
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    ◆ make_eigenmatrix()

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    EigenptrT<T> ead::make_eigenmatrix (DimensionsT dims,
    std::function< EigenSource(EigenArgs &)> make_base,
    EigenArgs args 
    )
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    ◆ make_eigentensor()

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    EigenptrT<T> ead::make_eigentensor (DimensionsT dims,
    std::function< EigenSource(EigenArgs &)> make_base,
    EigenArgs args 
    )
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    ◆ make_functor()

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    NodeptrT<T> ead::make_functor (ade::Opcode opcode,
    ArgsT< T > args 
    )
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    ◆ make_matmap()

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    MatMapT<T> ead::make_matmap (T * data,
    const ade::Shape & shape 
    )
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    ◆ make_tensmap()

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    TensMapT<T> ead::make_tensmap (T * data,
    const ade::Shape & shape 
    )
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    ◆ make_tensor()

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    TensorT<T> ead::make_tensor (const ade::Shape & shape)
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    ◆ make_variable() [1/2]

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    VarptrT<T> ead::make_variable (ade::Shape shape,
    std::string label = "" 
    )
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    ◆ make_variable() [2/2]

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    VarptrT<T> ead::make_variable (T * data,
    ade::Shape shape,
    std::string label = "" 
    )
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    ◆ make_variable_scalar()

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    VarptrT<T> ead::make_variable_scalar (T scalar,
    ade::Shape shape,
    std::string label = "" 
    )
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    ◆ mat_to_matmap()

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    MatMapT<T> ead::mat_to_matmap (MatrixT< T > & mat)
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    ◆ mat_to_tensmap()

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    TensMapT<T> ead::mat_to_tensmap (MatrixT< T > & mat)
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    ◆ matmul()

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    EigenptrT<T> ead::matmul (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    ◆ max()

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    EigenptrT<T> ead::max (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments, for every mapped index i in range [0:max_nelems], take the maximum all elements for all arguments

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    ◆ min()

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    EigenptrT<T> ead::min (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments, for every mapped index i in range [0:max_nelems], take the minimum all elements for all arguments

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    ◆ mul()

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    EigenptrT<T> ead::mul (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    ◆ neg()

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    EigenptrT<T> ead::neg (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    Given reference to output array, and input vector ref, make output elements take negatives of inputs

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    ◆ neq()

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    EigenptrT<T> ead::neq (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments a, and b, for every pair of mapped elements sharing the same index apply != operator Only accept 2 arguments

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    ◆ norm_gen()

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    GenF<T> ead::norm_gen (T mean,
    T stdev 
    )
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    ◆ pad()

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    EigenptrT<T> ead::pad (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    ◆ parse()

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    opt::OptCtx ead::parse (std::string content)
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    ◆ parse_file()

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    opt::OptCtx ead::parse_file (std::string filename)
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    ◆ permute() [1/2]

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    CoordptrT ead::permute (std::vector< ade::RankT > dims)
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    ◆ permute() [2/2]

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    EigenptrT<T> ead::permute (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    ◆ permute_grad()

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    NodeptrT<T> ead::permute_grad (ade::iFunctor * fwd,
    NodeptrT< T > bwd,
    size_t idx 
    )
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    ◆ permute_map()

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    FuncArg<T> ead::permute_map (NodeptrT< T > node,
    std::vector< ade::RankT > order 
    )
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    ◆ pow()

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    EigenptrT<T> ead::pow (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments a, and b, for every pair of mapped elements sharing the same index apply std::pow operator Only accept 2 arguments

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    ◆ rand_uniform()

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    EigenptrT<T> ead::rand_uniform (ade::Shape & outshape,
    const OpArg< T > & a,
    const OpArg< T > & b 
    )
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    Given arguments a, and b, for every pair of mapped elements sharing the same index apply std::uniform_distributon function Only accept 2 arguments

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    ◆ reduce()

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    CoordptrT ead::reduce (std::vector< ade::RankT > red_dims)
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    ◆ reduce_grad()

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    NodeptrT<T> ead::reduce_grad (const ade::FuncArg & child,
    NodeptrT< T > bwd,
    size_t idx 
    )
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    ◆ reduce_map()

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    FuncArg<T> ead::reduce_map (NodeptrT< T > node,
    ade::RankT offset,
    ade::RankT ndims 
    )
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    ◆ reduce_sum()

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    EigenptrT<T> ead::reduce_sum (ade::Shape & outshape,
    const OpArg< T > & in 
    )
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    Variable Documentation

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    -     		-static
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    -Initial value:
    =
    {
    age::REDUCE_SUM,
    age::REDUCE_PROD,
    age::REDUCE_MIN,
    age::REDUCE_MAX,
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    template<size_t N>
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    def _handle_ops (params, opcalls)
     
    def _handle_code2names (params, opcalls)
     
    def _handle_name2codes (params, opcalls)
     
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    struct  CandArg
     
    struct  CommHasher
     
    struct  CommVoter
     
    struct  iConverter
     
    struct  iConverterBuilder
     
    struct  iVoter
     
    struct  Matcher
     
    struct  OptCtx
     
    struct  OrdrHasher
     
    struct  OrdrVoter
     
    struct  RulesContext
     
    struct  SegVArgs
     
    struct  Symbol
     
    struct  SymbolHash
     
    struct  VariadicVoter
     
    struct  VoterArg
     
    struct  VoterPool
     
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    -Typedefs

    using CtxValT = std::set< ade::TensptrT >
     
    using ContexT = std::map< std::string, CtxValT >
     
    using CtxsT = std::unordered_set< ContexT, boost::hash< ContexT > >
     
    using CandsT = std::unordered_map< Symbol, CtxsT, SymbolHash >
     
    using CandArgsT = std::vector< CandArg >
     
    using ConvptrT = std::shared_ptr< iConverter >
     
    using VoterArgsT = std::vector< VoterArg >
     
    using VotptrT = std::shared_ptr< iVoter >
     
    using CstConvertF = std::function< ade::TensptrT(ade::iTensor *)>
     
    using ImmutablesT = std::vector< ade::LeafptrT >
     
    using HFunctorsT = std::vector< std::vector< ade::FuncptrT > >
     
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    enum  CAND_TYPE { SCALAR = 0, -CONST, -INTERM, -CONVRT - }
     
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    bool operator== (const Symbol &lhs, const Symbol &rhs)
     
    void sort_vargs (VoterArgsT &args)
     
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    bool operator== (const SegVArgs &lhs, const SegVArgs &rhs)
     
    ade::TensT optimize (ade::TensT roots, const OptCtx &opts)
     
    OptCtx parse (std::string content, const iConverterBuilder &builder)
     
    OptCtx parse_file (std::string filename, const iConverterBuilder &builder)
     
    void replace_parents (const ade::ParentFinder &pfinder, ade::iTensor *source, ade::TensptrT target)
     
    template<typename T >
    std::vector< T > remove_duplicates (ade::TensT &roots, std::vector< T > tens, const ade::ParentFinder &pfinder, tag::TagRegistry &registry=tag::get_reg())
     
    void populate_graph (ImmutablesT &immutables, HFunctorsT &functors, const ade::TensT &roots)
     
    void remove_all_duplicates (ade::TensT &roots, ImmutablesT &immutables, HFunctorsT &functors)
     
    bool is_scalar (ade::iLeaf *leaf)
     
    std::string to_string (ade::CoordptrT c)
     
    bool lt (ade::CoordptrT a, ade::CoordptrT b)
     
    bool is_equal (ade::CoordptrT a, ade::CoordptrT b)
     
    bool lt (std::unordered_set< ade::iTensor *> priorities, ade::iLeaf *a, ade::iLeaf *b)
     
    bool is_equal (ade::iLeaf *a, ade::iLeaf *b)
     
    bool lt (std::unordered_set< ade::iTensor *> priorities, ade::iFunctor *a, ade::iFunctor *b)
     
    bool is_equal (ade::iFunctor *a, ade::iFunctor *b)
     
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    const std::string group_prefix = "group:"
     
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    Typedef Documentation

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    ◆ CandArgsT

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    ◆ CandsT

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    ◆ ContexT

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    using opt::CstConvertF = typedef std::function<ade::TensptrT(ade::iTensor*)>
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    ◆ CtxsT

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    using opt::HFunctorsT = typedef std::vector<std::vector<ade::FuncptrT> >
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    ◆ ImmutablesT

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    ◆ VoterArgsT

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    using opt::VoterArgsT = typedef std::vector<VoterArg>
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    ◆ VotptrT

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    using opt::VotptrT = typedef std::shared_ptr<iVoter>
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    ◆ CAND_TYPE

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    enum opt::CAND_TYPE
    -
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    Enumerator
    SCALAR 
    CONST 
    INTERM 
    CONVRT 
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    ◆ is_equal() [1/3]

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    )
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    )
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    )
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    )
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    ade::iLeaf * a,
    ade::iLeaf * b 
    )
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    )
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    )
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    ◆ optimize()

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    ade::TensT opt::optimize (ade::TensT roots,
    const OptCtx & opts 
    )
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    const iConverterBuilder & builder 
    )
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    const iConverterBuilder & builder 
    )
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    HFunctorsT & functors,
    const ade::TensT & roots 
    )
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    )
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    std::vector< T > tens,
    const ade::ParentFinder & pfinder,
    tag::TagRegistry & registry = tag::get_reg() 
    )
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    ade::iTensor * source,
    ade::TensptrT target 
    )
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    struct  GraphInfo
     Contains all information necessary to recreate labelled ADE graph. More...
     
    struct  GraphSaver
     Graph serialization traveler. More...
     
    struct  iLoader
     
    struct  iSaver
     
    struct  PathedTens
     Tree node for labeling Tensptrs. More...
     
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    using TensT = std::vector< ade::TensptrT >
     Tensptr vector type. More...
     
    using StringsT = std::list< fmts::string >
     String list type used for paths. More...
     
    using PathedMapT = std::unordered_map< ade::TensptrT, StringsT >
     Map Tensptrs to a string path type. More...
     
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    template<typename LOAD , typename std::enable_if< std::is_base_of< iLoader, LOAD >::value >::type * = nullptr>
    void load_graph (GraphInfo &out, const cortenn::Graph &in)
     Return graph info through out available from in graph. More...
     
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    Purpose: Define functions for marshaling equation graph

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    Purpose: Define functions for marshal and unmarshal equation graph

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    Purpose: Define functions for marshaling equation graph

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    )
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    class  PyAPIsPlugin
     
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    def _sub_pybind (stmt, source)
     
    def _strip_template_prefix (template)
     
    def _wrap_func (idx, api, namespace)
     
    def _handle_pybind (pybind_type)
     
    def _handle_pybind_type (pybind_type)
     
    def _handle_unique_wrap (pybind_type, apis, namespace)
     
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    string _pybindt = 'PybindT'
     
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     apis,
     module_name,
     first_module 
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     apis,
     namespace 
    )
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     source 
    )
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    ◆ _wrap_func()

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    def pyapis._wrap_func ( idx,
     api,
     namespace 
    )
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    Variable Documentation

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    ◆ _func_fmt

    - -
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    string pyapis._func_fmt
    -     		-private
    -
    -Initial value:
    1 = '''
    2 {outtype} {funcname}_{idx} ({param_decl})
    3 {{
    4  return {namespace}::{funcname}({args});
    5 }}
    6 '''
    -
    -
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    ◆ _header_template

    - -
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    - - - - -
    string pyapis._header_template
    -     		-private
    -
    -Initial value:
    1 = '''
    2 // type to replace template arguments in pybind
    3 using {pybind} = {pybind_type};
    4 //>>> ^ pybind, pybind_type
    5 '''
    -
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    ◆ _plugin_id

    - -
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    string pyapis._plugin_id = 'PYBINDER'
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    ◆ _pybindt

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    string pyapis._pybindt = 'PybindT'
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    ◆ _source_template

    - -
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    string pyapis._source_template
    -     		-private
    -
    -Initial value:
    1 = '''
    2 namespace py = pybind11;
    3 
    4 namespace pyage
    5 {{
    6 
    7 //>>> unique_wrap
    8 {unique_wrap}
    9 
    10 }}
    11 
    12 //>>> modname
    13 PYBIND11_MODULE({modname}, m_{modname})
    14 {{
    15  m_{modname}.doc() = "pybind for {modname} api";
    16 
    17  //>>> modname
    18  py::class_<ade::iTensor,ade::TensptrT> tensor(m_{modname}, "Tensor");
    19 
    20  //>>> defs
    21  {defs}
    22 }}
    23 '''
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    ade::Shape p2cshape (std::vector< py::ssize_t > &pyshape)
     
    std::vector< ade::DimT > c2pshape (const ade::Shape &cshape)
     
    template<typename T >
    py::array typedata_to_array (ead::iNode< PybindT > *tnode, py::dtype dtype)
     
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     Nade
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     ▼Nead
     Ninternal
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     Nopt
     Npbm
     Npyapis
     Npyead
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     Ntemplate
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    struct  GroupRegistry
     
    struct  GroupTag
     
    struct  iTag
     
    struct  PropertyRegistry
     
    struct  PropTag
     PropTag (properties tag) define node properties. More...
     
    struct  Subgraph
     
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    struct  TagRegistry
     
    struct  TensKey
     
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    using TensSetT = std::unordered_set< TensKey, TensKeyHash >
     
    using AGroupsT = std::map< std::string, std::unordered_set< std::string > >
     
    using AdjMapT = std::unordered_map< ade::iTensor *, AGroupsT >
     
    using SgraphptrT = std::shared_ptr< Subgraph >
     
    using SubgraphsT = std::unordered_set< SgraphptrT >
     
    using SubgraphAssocsT = std::unordered_map< ade::iTensor *, SubgraphsT >
     
    using TagRepsT = std::map< std::string, std::vector< std::string > >
     
    using TagptrT = std::unique_ptr< iTag >
     
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    GroupRegistry & get_group_reg (void)
     
    void recursive_group_tag (ade::TensrefT tens, std::string group, std::unordered_set< ade::iTensor *> stops, GroupRegistry &registry=get_group_reg())
     
    void adjacencies (AdjMapT &out, ade::TensT roots, GroupRegistry &registry=get_group_reg())
     
    void beautify_groups (SubgraphAssocsT &out, const AdjMapT &adjs)
     
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    bool operator== (const TensKey &lhs, const TensKey &rhs)
     
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    const std::string groups_key = "groups"
     
    const std::string props_key = "properties"
     
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    )
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    std::unordered_set< ade::iTensor *> stops,
    GroupRegistry & registry = get_group_reg() 
    )
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    class  opcodes.OpcodesPlugin
     
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    def opcodes._handle_opcodes (params, opcalls)
     
    def opcodes._handle_params (params, opcalls)
     
    def opcodes._handle_ops (params, opcalls)
     
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    string opcodes._header_template
     
    string opcodes._source_template
     
    string opcodes._plugin_id = "OPCODE"
     
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    #include "ead/eigen.hpp"
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    struct  ead::OpArg< T >
     
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     ead
     
     ead::internal
     
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    #define _EAD_INTERNAL_V2A_CASE(N, PROCESS, RED)
     
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    template<typename OP , size_t N, typename T >
    using ead::ReduceOutT = Eigen::TensorReductionOp< OP, const std::array< ade::RankT, N >, const TensMapT< T > >
     
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    static bool ead::is_2d (ade::Shape shape)
     
    template<size_t N>
    std::array< ade::RankT, N > ead::internal::dim_copy (std::vector< ade::RankT > d)
     
    template<typename T >
    EigenptrT< T > ead::reduce_sum (ade::Shape &outshape, const OpArg< T > &in) template< typename T > EigenptrT< T > reduce_prod(ade
     
    template<typename T >
    EigenptrT< T > ead::permute (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::slice (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::pad (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::abs (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::neg (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::sin (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::cos (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::tan (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::exp (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::log (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::sqrt (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::round (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::sigmoid (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::sigmoid_grad (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::tanh (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::square (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::cube (ade::Shape &outshape, const OpArg< T > &in)
     
    template<typename T >
    EigenptrT< T > ead::pow (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::add (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::sub (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::mul (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::div (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::eq (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::neq (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::lt (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::gt (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::min (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::max (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::rand_uniform (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::select (ade::Shape &outshape, const OpArg< T > &condition, const OpArg< T > &then, const OpArg< T > &otherwise)
     
    template<typename T >
    EigenptrT< T > ead::matmul (ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
     
    template<typename T >
    EigenptrT< T > ead::convolution (ade::Shape &outshape, const OpArg< T > &input, const OpArg< T > &kernel)
     
    template<typename T >
    EigenptrT< T > ead::convolution_image_grad (ade::Shape &imageshape, const OpArg< T > &kernel, const OpArg< T > &super_composite)
     
    template<typename T >
    EigenptrT< T > ead::convolution_kernel_grad (ade::Shape &kernelshape, const OpArg< T > &image, const OpArg< T > &super_composite)
     
    -

    Macro Definition Documentation

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    ◆ _EAD_INTERNAL_V2A

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    #define _EAD_INTERNAL_V2A( PROCESS,
     RED 
    )
    -
    -Value:
    {\
    assert(nullptr != in.coorder_);\
    ade::CoordT coord;\
    in.coorder_->forward(coord.begin(), coord.begin());\
    std::vector<ade::RankT> vdims;\
    std::copy_if(coord.begin(), coord.end(), std::back_inserter(vdims),\
    [](ade::RankT d) { return d < ade::rank_cap; });\
    switch (vdims.size()) {\
    _EAD_INTERNAL_V2A_CASE(0, PROCESS, RED)\
    _EAD_INTERNAL_V2A_CASE(1, PROCESS, RED)\
    _EAD_INTERNAL_V2A_CASE(2, PROCESS, RED)\
    _EAD_INTERNAL_V2A_CASE(3, PROCESS, RED)\
    _EAD_INTERNAL_V2A_CASE(4, PROCESS, RED)\
    _EAD_INTERNAL_V2A_CASE(5, PROCESS, RED)\
    _EAD_INTERNAL_V2A_CASE(6, PROCESS, RED)\
    _EAD_INTERNAL_V2A_CASE(7, PROCESS, RED)\
    default: break;\
    } return make_eigentensor<T,ReduceOutT<RED,8,T>,TensMapT<T>>(\
    shape_convert(outshape), [vdims](TensMapT<T>& in) {\
    return in.PROCESS(ead::internal::dim_copy<8>(vdims));\
    }, make_tensmap(in.data_, in.shape_));\
    }
    ead::make_tensmap
    TensMapT< T > make_tensmap(T *data, const ade::Shape &shape)
    Definition: eigen.hpp:164
    -
    ade::RankT
    uint8_t RankT
    Type used for shape rank.
    Definition: shape.hpp:23
    -
    ade::CoordT
    std::array< CDimT, rank_cap > CoordT
    Definition: shape.hpp:54
    -
    ade::rank_cap
    const RankT rank_cap
    Number of dimsensions in a shape/coordinate.
    Definition: shape.hpp:45
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     PROCESS,
     RED 
    )
    -
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    case N: return make_eigentensor<T,ReduceOutT<RED,N,T>,TensMapT<T>>(\
    shape_convert(outshape), [vdims](TensMapT<T>& in) {\
    return in.PROCESS(ead::internal::dim_copy<N>(vdims)); },\
    make_tensmap(in.data_, in.shape_));
    -
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    operator.hpp
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    1 
    10 #include "ead/eigen.hpp"
    11 #include "ead/coord.hpp"
    12 #include "ead/random.hpp"
    13 
    14 #ifndef EAD_OPERATOR_HPP
    15 #define EAD_OPERATOR_HPP
    16 
    17 namespace ead
    18 {
    19 
    20 static inline bool is_2d (ade::Shape shape)
    21 {
    22  return std::all_of(shape.begin() + 2, shape.end(),
    23  [](ade::DimT dim) { return 1 == dim; });
    24 }
    25 
    26 template <typename T>
    27 struct OpArg final
    28 {
    29  OpArg (T* data, ade::Shape shape, CoordMap* coorder) :
    30  data_(data), shape_(shape), coorder_(coorder) {}
    31 
    32  T* data_;
    33 
    34  ade::Shape shape_;
    35 
    36  CoordMap* coorder_ = nullptr;
    37 };
    38 
    39 template <typename OP, size_t N, typename T>
    40 using ReduceOutT = Eigen::TensorReductionOp<OP,
    41  const std::array<ade::RankT,N>,const TensMapT<T>>;
    42 
    43 namespace internal
    44 {
    45 
    46 template <size_t N>
    47 inline std::array<ade::RankT,N> dim_copy (std::vector<ade::RankT> d)
    48 {
    49  std::array<ade::RankT,N> out;
    50  auto it = d.begin();
    51  std::copy(it, it + N, out.begin());
    52  return out;
    53 }
    54 
    55 #define _EAD_INTERNAL_V2A_CASE(N, PROCESS, RED)\
    56 case N: return make_eigentensor<T,ReduceOutT<RED,N,T>,TensMapT<T>>(\
    57 shape_convert(outshape), [vdims](TensMapT<T>& in) {\
    58 return in.PROCESS(ead::internal::dim_copy<N>(vdims)); },\
    59 make_tensmap(in.data_, in.shape_));
    60 
    61 #define _EAD_INTERNAL_V2A(PROCESS, RED) {\
    62  assert(nullptr != in.coorder_);\
    63  ade::CoordT coord;\
    64  in.coorder_->forward(coord.begin(), coord.begin());\
    65  std::vector<ade::RankT> vdims;\
    66  std::copy_if(coord.begin(), coord.end(), std::back_inserter(vdims),\
    67  [](ade::RankT d) { return d < ade::rank_cap; });\
    68  switch (vdims.size()) {\
    69  _EAD_INTERNAL_V2A_CASE(0, PROCESS, RED)\
    70  _EAD_INTERNAL_V2A_CASE(1, PROCESS, RED)\
    71  _EAD_INTERNAL_V2A_CASE(2, PROCESS, RED)\
    72  _EAD_INTERNAL_V2A_CASE(3, PROCESS, RED)\
    73  _EAD_INTERNAL_V2A_CASE(4, PROCESS, RED)\
    74  _EAD_INTERNAL_V2A_CASE(5, PROCESS, RED)\
    75  _EAD_INTERNAL_V2A_CASE(6, PROCESS, RED)\
    76  _EAD_INTERNAL_V2A_CASE(7, PROCESS, RED)\
    77  default: break;\
    78  } return make_eigentensor<T,ReduceOutT<RED,8,T>,TensMapT<T>>(\
    79  shape_convert(outshape), [vdims](TensMapT<T>& in) {\
    80  return in.PROCESS(ead::internal::dim_copy<8>(vdims));\
    81  }, make_tensmap(in.data_, in.shape_));\
    82 }
    83 
    84 }
    85 
    86 template <typename T>
    87 EigenptrT<T> reduce_sum (ade::Shape& outshape, const OpArg<T>& in)
    88 _EAD_INTERNAL_V2A(sum, Eigen::internal::SumReducer<T>)
    89 
    90 template <typename T>
    91 EigenptrT<T> reduce_prod (ade::Shape& outshape, const OpArg<T>& in)
    92 _EAD_INTERNAL_V2A(prod, Eigen::internal::ProdReducer<T>)
    93 
    94 template <typename T>
    95 EigenptrT<T> reduce_min (ade::Shape& outshape, const OpArg<T>& in)
    96 _EAD_INTERNAL_V2A(minimum, Eigen::internal::MinReducer<T>)
    97 
    98 template <typename T>
    99 EigenptrT<T> reduce_max (ade::Shape& outshape, const OpArg<T>& in)
    100 _EAD_INTERNAL_V2A(maximum, Eigen::internal::MaxReducer<T>)
    101 
    102 template <typename T>
    103 EigenptrT<T> extend (ade::Shape& outshape, const OpArg<T>& in)
    104 {
    105  assert(nullptr != in.coorder_);
    106  ade::CoordT coord;
    107  in.coorder_->forward(coord.begin(), coord.begin());
    108  return make_eigentensor<T,Eigen::TensorBroadcastingOp<
    109  const ade::CoordT,const TensMapT<T>>,TensMapT<T>>(
    110  shape_convert(outshape),
    111  [coord](TensMapT<T>& in)
    112  {
    113  return in.broadcast(coord);
    114  }, make_tensmap(in.data_, in.shape_));
    115 }
    116 
    117 template <typename T>
    118 EigenptrT<T> permute (ade::Shape& outshape, const OpArg<T>& in)
    119 {
    120  assert(nullptr != in.coorder_);
    121  ade::CoordT reorder;
    122  in.coorder_->forward(reorder.begin(), reorder.begin());
    123  return make_eigentensor<T,Eigen::TensorShufflingOp<
    124  const ade::CoordT,TensMapT<T>>,TensMapT<T>>(
    125  shape_convert(outshape),
    126  [reorder](TensMapT<T>& in)
    127  {
    128  return in.shuffle(reorder);
    129  }, make_tensmap(in.data_, in.shape_));
    130 }
    131 
    132 template <typename T>
    133 EigenptrT<T> slice (ade::Shape& outshape, const OpArg<T>& in)
    134 {
    135  assert(nullptr != in.coorder_);
    136  ade::CoordT slicing;
    137  in.coorder_->forward(slicing.begin(), slicing.begin());
    138  ade::ShapeT offset;
    139  ade::ShapeT extent;
    140  std::fill(offset.begin(), offset.end(), 0);
    141  std::copy(in.shape_.begin(), in.shape_.end(), extent.begin());
    142  ade::RankT dimension = slicing[2];
    143  offset[dimension] = slicing[0];
    144  extent[dimension] = slicing[1];
    145  return make_eigentensor<T,Eigen::TensorSlicingOp<
    146  const ade::ShapeT, const ade::ShapeT,
    147  ead::TensMapT<T>
    148  >,
    149  TensMapT<T>>(
    150  shape_convert(outshape),
    151  [&offset, &extent](TensMapT<T>& in)
    152  {
    153  return in.slice(offset, extent);
    154  }, make_tensmap(in.data_, in.shape_));
    155 }
    156 
    157 template <typename T>
    158 EigenptrT<T> pad (ade::Shape& outshape, const OpArg<T>& in)
    159 {
    160  assert(nullptr != in.coorder_);
    161  ade::CoordT padding;
    162  in.coorder_->forward(padding.begin(), padding.begin());
    163  std::array<std::pair<ade::DimT,ade::DimT>,ade::rank_cap> paddings;
    164  for (ade::RankT i = 0; i < ade::rank_cap; ++i)
    165  {
    166  paddings[i] = std::make_pair(0, 0);
    167  }
    168  paddings[padding[2]] = std::make_pair(padding[0], padding[1]);
    169  return make_eigentensor<T,Eigen::TensorPaddingOp<
    170  const std::array<std::pair<ade::DimT,ade::DimT>,ade::rank_cap>,
    171  const ead::TensMapT<T>
    172  >,
    173  TensMapT<T>>(
    174  shape_convert(outshape),
    175  [&paddings](TensMapT<T>& in)
    176  {
    177  return in.pad(paddings);
    178  }, make_tensmap(in.data_, in.shape_));
    179 }
    180 
    183 template <typename T>
    184 EigenptrT<T> abs (ade::Shape& outshape, const OpArg<T>& in)
    185 {
    186  if (is_2d(outshape))
    187  {
    188  // use matrix when possible
    189  return make_eigenmatrix<T,Eigen::CwiseUnaryOp<
    190  Eigen::internal::scalar_abs_op<T>,const MatMapT<T>>,
    191  MatMapT<T>>(shape_convert(outshape),
    192  [](MatMapT<T>& in)
    193  {
    194  return in.cwiseAbs();
    195  }, make_matmap(in.data_, in.shape_));
    196  }
    197  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    198  Eigen::internal::scalar_abs_op<T>,const TensMapT<T>>,
    199  TensMapT<T>>(shape_convert(outshape),
    200  [](TensMapT<T>& in)
    201  {
    202  return in.abs();
    203  }, make_tensmap(in.data_, in.shape_));
    204 }
    205 
    208 template <typename T>
    209 EigenptrT<T> neg (ade::Shape& outshape, const OpArg<T>& in)
    210 {
    211  if (is_2d(outshape))
    212  {
    213  // use matrix when possible
    214  return make_eigenmatrix<T,Eigen::CwiseUnaryOp<
    215  Eigen::internal::scalar_opposite_op<T>,const MatMapT<T>>,
    216  MatMapT<T>>(shape_convert(outshape),
    217  [](MatMapT<T>& in)
    218  {
    219  return -in;
    220  }, make_matmap(in.data_, in.shape_));
    221  }
    222  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    223  Eigen::internal::scalar_opposite_op<T>,const TensMapT<T>>,
    224  TensMapT<T>>(shape_convert(outshape),
    225  [](TensMapT<T>& in)
    226  {
    227  return -in;
    228  }, make_tensmap(in.data_, in.shape_));
    229 }
    230 
    233 template <typename T>
    234 EigenptrT<T> sin (ade::Shape& outshape, const OpArg<T>& in)
    235 {
    236 #ifdef __cpp_if_constexpr
    237  if constexpr(!std::is_integral<T>::value)
    238  {
    239  if (is_2d(outshape))
    240  {
    241  // use matrix when possible
    242  return make_eigenmatrix<T,
    243  typename Eigen::ArrayWrapper<MatMapT<T>>::SinReturnType,
    244  MatMapT<T>>(shape_convert(outshape),
    245  [](MatMapT<T>& in)
    246  {
    247  return in.array().sin();
    248  }, make_matmap(in.data_, in.shape_));
    249  }
    250  }
    251 #endif
    252  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    253  std::function<T(const T&)>,const TensMapT<T>>,
    254  TensMapT<T>>(shape_convert(outshape),
    255  [](TensMapT<T>& in)
    256  {
    257  return in.unaryExpr(std::function<T(const T&)>(
    258  [](const T& a) -> T
    259  {
    260  return std::sin(a);
    261  }));
    262  }, make_tensmap(in.data_, in.shape_));
    263 }
    264 
    267 template <typename T>
    268 EigenptrT<T> cos (ade::Shape& outshape, const OpArg<T>& in)
    269 {
    270 #ifdef __cpp_if_constexpr
    271  if constexpr(!std::is_integral<T>::value)
    272  {
    273  if (is_2d(outshape))
    274  {
    275  // use matrix when possible
    276  return make_eigenmatrix<T,
    277  typename Eigen::ArrayWrapper<MatMapT<T>>::CosReturnType,
    278  MatMapT<T>>(shape_convert(outshape),
    279  [](MatMapT<T>& in)
    280  {
    281  return in.array().cos();
    282  }, make_matmap(in.data_, in.shape_));
    283  }
    284  }
    285 #endif
    286  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    287  std::function<T(const T&)>,const TensMapT<T>>,
    288  TensMapT<T>>(shape_convert(outshape),
    289  [](TensMapT<T>& in)
    290  {
    291  return in.unaryExpr(std::function<T(const T&)>(
    292  [](const T& a) -> T
    293  {
    294  return std::cos(a);
    295  }));
    296  }, make_tensmap(in.data_, in.shape_));
    297 }
    298 
    301 template <typename T>
    302 EigenptrT<T> tan (ade::Shape& outshape, const OpArg<T>& in)
    303 {
    304  if (is_2d(outshape))
    305  {
    306  // use matrix when possible
    307  return make_eigenmatrix<T,
    308  typename Eigen::ArrayWrapper<MatMapT<T>>::TanReturnType,
    309  MatMapT<T>>(shape_convert(outshape),
    310  [](MatMapT<T>& in)
    311  {
    312  return in.array().tan();
    313  }, make_matmap(in.data_, in.shape_));
    314  }
    315  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    316  std::function<T(const T&)>,const TensMapT<T>>,
    317  TensMapT<T>>(shape_convert(outshape),
    318  [](TensMapT<T>& in)
    319  {
    320  return in.unaryExpr(std::function<T(const T&)>(
    321  [](const T& a) -> T
    322  {
    323  return std::tan(a);
    324  }));
    325  }, make_tensmap(in.data_, in.shape_));
    326 }
    327 
    330 template <typename T>
    331 EigenptrT<T> exp (ade::Shape& outshape, const OpArg<T>& in)
    332 {
    333  if (is_2d(outshape))
    334  {
    335  // use matrix when possible
    336  return make_eigenmatrix<T,
    337  typename Eigen::ArrayWrapper<MatMapT<T>>::ExpReturnType,
    338  MatMapT<T>>(shape_convert(outshape),
    339  [](MatMapT<T>& in)
    340  {
    341  return in.array().exp();
    342  }, make_matmap(in.data_, in.shape_));
    343  }
    344  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    345  Eigen::internal::scalar_exp_op<T>,const TensMapT<T>>,
    346  TensMapT<T>>(shape_convert(outshape),
    347  [](TensMapT<T>& in)
    348  {
    349  return in.exp();
    350  }, make_tensmap(in.data_, in.shape_));
    351 }
    352 
    355 template <typename T>
    356 EigenptrT<T> log (ade::Shape& outshape, const OpArg<T>& in)
    357 {
    358  if (is_2d(outshape))
    359  {
    360  // use matrix when possible
    361  return make_eigenmatrix<T,
    362  typename Eigen::ArrayWrapper<MatMapT<T>>::LogReturnType,
    363  MatMapT<T>>(shape_convert(outshape),
    364  [](MatMapT<T>& in)
    365  {
    366  return in.array().log();
    367  }, make_matmap(in.data_, in.shape_));
    368  }
    369  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    370  Eigen::internal::scalar_log_op<T>,const TensMapT<T>>,
    371  TensMapT<T>>(shape_convert(outshape),
    372  [](TensMapT<T>& in)
    373  {
    374  return in.log();
    375  }, make_tensmap(in.data_, in.shape_));
    376 }
    377 
    380 template <typename T>
    381 EigenptrT<T> sqrt (ade::Shape& outshape, const OpArg<T>& in)
    382 {
    383  if (is_2d(outshape))
    384  {
    385  // use matrix when possible
    386  return make_eigenmatrix<T,Eigen::CwiseUnaryOp<
    387  Eigen::internal::scalar_sqrt_op<T>,const MatMapT<T>>,
    388  MatMapT<T>>(shape_convert(outshape),
    389  [](MatMapT<T>& in)
    390  {
    391  return in.cwiseSqrt();
    392  }, make_matmap(in.data_, in.shape_));
    393  }
    394  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    395  Eigen::internal::scalar_sqrt_op<T>,const TensMapT<T>>,
    396  TensMapT<T>>(shape_convert(outshape),
    397  [](TensMapT<T>& in)
    398  {
    399  return in.sqrt();
    400  }, make_tensmap(in.data_, in.shape_));
    401 }
    402 
    405 template <typename T>
    406 EigenptrT<T> round (ade::Shape& outshape, const OpArg<T>& in)
    407 {
    408  if (is_2d(outshape))
    409  {
    410  // use matrix when possible
    411  return make_eigenmatrix<T,
    412  typename Eigen::ArrayWrapper<MatMapT<T>>::RoundReturnType,
    413  MatMapT<T>>(shape_convert(outshape),
    414  [](MatMapT<T>& in)
    415  {
    416  return in.array().round();
    417  }, make_matmap(in.data_, in.shape_));
    418  }
    419  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    420  Eigen::internal::scalar_round_op<T>,const TensMapT<T>>,
    421  TensMapT<T>>(shape_convert(outshape),
    422  [](TensMapT<T>& in)
    423  {
    424  return in.round();
    425  }, make_tensmap(in.data_, in.shape_));
    426 }
    427 
    428 template <typename T>
    429 EigenptrT<T> sigmoid (ade::Shape& outshape, const OpArg<T>& in)
    430 {
    431  if (is_2d(outshape))
    432  {
    433  // use matrix when possible
    434  return make_eigenmatrix<T,Eigen::CwiseUnaryOp<
    435  Eigen::internal::scalar_sigmoid_op<T>,const MatMapT<T>>,
    436  MatMapT<T>>(shape_convert(outshape),
    437  [](MatMapT<T>& in)
    438  {
    439  return in.unaryExpr(Eigen::internal::scalar_sigmoid_op<T>());
    440  }, make_matmap(in.data_, in.shape_));
    441  }
    442  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    443  Eigen::internal::scalar_sigmoid_op<T>,const TensMapT<T>>,
    444  TensMapT<T>>(shape_convert(outshape),
    445  [](TensMapT<T>& in)
    446  {
    447  return in.sigmoid();
    448  }, make_tensmap(in.data_, in.shape_));
    449 }
    450 
    451 template <typename T>
    452 EigenptrT<T> sigmoid_grad (ade::Shape& outshape, const OpArg<T>& in)
    453 {
    454  if (is_2d(outshape))
    455  {
    456  // use matrix when possible
    457  return make_eigenmatrix<T,
    458  Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<T>,
    459  const Eigen::CwiseUnaryOp<Eigen::internal::scalar_sigmoid_op<T>,
    460  const MatMapT<T>>,
    461  const Eigen::CwiseUnaryOp<Eigen::internal::bind1st_op<
    462  Eigen::internal::scalar_difference_op<T>>,
    463  const Eigen::CwiseUnaryOp<Eigen::internal::scalar_sigmoid_op<T>,
    464  const MatMapT<T>>>>,
    465  MatMapT<T>>(shape_convert(outshape),
    466  [](MatMapT<T>& in)
    467  {
    468  auto out = in.unaryExpr(Eigen::internal::scalar_sigmoid_op<T>());
    469  return out.cwiseProduct(out.unaryExpr(
    470  Eigen::internal::bind1st_op<Eigen::internal::scalar_difference_op<T>>(1)));
    471  }, make_matmap(in.data_, in.shape_));
    472  }
    473  return make_eigentensor<T,
    474  Eigen::TensorCwiseBinaryOp<Eigen::internal::scalar_product_op<T>,
    475  const Eigen::TensorCwiseUnaryOp<Eigen::internal::scalar_sigmoid_op<T>,
    476  const TensMapT<T>>,
    477  const Eigen::TensorCwiseUnaryOp<Eigen::internal::bind1st_op<
    478  Eigen::internal::scalar_difference_op<T>>,
    479  const Eigen::TensorCwiseUnaryOp<Eigen::internal::scalar_sigmoid_op<T>,
    480  const TensMapT<T>>>>,
    481  TensMapT<T>>(shape_convert(outshape),
    482  [](TensMapT<T>& in)
    483  {
    484  auto out = in.sigmoid();
    485  return out * (1 - out);
    486  }, make_tensmap(in.data_, in.shape_));
    487 }
    488 
    489 template <typename T>
    490 EigenptrT<T> tanh (ade::Shape& outshape, const OpArg<T>& in)
    491 {
    492  if (is_2d(outshape))
    493  {
    494  // use matrix when possible
    495  return make_eigenmatrix<T,Eigen::CwiseUnaryOp<
    496  Eigen::internal::scalar_tanh_op<T>,const MatMapT<T>>,
    497  MatMapT<T>>(shape_convert(outshape),
    498  [](MatMapT<T>& in)
    499  {
    500  return in.unaryExpr(Eigen::internal::scalar_tanh_op<T>());
    501  }, make_matmap(in.data_, in.shape_));
    502  }
    503  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    504  Eigen::internal::scalar_tanh_op<T>,const TensMapT<T>>,
    505  TensMapT<T>>(shape_convert(outshape),
    506  [](TensMapT<T>& in)
    507  {
    508  return in.tanh();
    509  }, make_tensmap(in.data_, in.shape_));
    510 }
    511 
    512 template <typename T>
    513 EigenptrT<T> square (ade::Shape& outshape, const OpArg<T>& in)
    514 {
    515  if (is_2d(outshape))
    516  {
    517  // use matrix when possible
    518  return make_eigenmatrix<T,Eigen::CwiseUnaryOp<
    519  Eigen::internal::scalar_square_op<T>,const MatMapT<T>>,
    520  MatMapT<T>>(shape_convert(outshape),
    521  [](MatMapT<T>& in)
    522  {
    523  return in.unaryExpr(Eigen::internal::scalar_square_op<T>());
    524  }, make_matmap(in.data_, in.shape_));
    525  }
    526  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    527  Eigen::internal::scalar_square_op<T>,const TensMapT<T>>,
    528  TensMapT<T>>(shape_convert(outshape),
    529  [](TensMapT<T>& in)
    530  {
    531  return in.square();
    532  }, make_tensmap(in.data_, in.shape_));
    533 }
    534 
    535 template <typename T>
    536 EigenptrT<T> cube (ade::Shape& outshape, const OpArg<T>& in)
    537 {
    538  if (is_2d(outshape))
    539  {
    540  // use matrix when possible
    541  return make_eigenmatrix<T,Eigen::CwiseUnaryOp<
    542  Eigen::internal::scalar_cube_op<T>,const MatMapT<T>>,
    543  MatMapT<T>>(shape_convert(outshape),
    544  [](MatMapT<T>& in)
    545  {
    546  return in.unaryExpr(Eigen::internal::scalar_cube_op<T>());
    547  }, make_matmap(in.data_, in.shape_));
    548  }
    549  return make_eigentensor<T,Eigen::TensorCwiseUnaryOp<
    550  Eigen::internal::scalar_cube_op<T>,const TensMapT<T>>,
    551  TensMapT<T>>(shape_convert(outshape),
    552  [](TensMapT<T>& in)
    553  {
    554  return in.cube();
    555  }, make_tensmap(in.data_, in.shape_));
    556 }
    557 
    561 template <typename T>
    562 EigenptrT<T> pow (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    563 {
    564  if (is_2d(outshape))
    565  {
    566  // use matrix when possible
    567  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    568  std::function<T(const T&,const T&)>,
    569  const MatMapT<T>,const MatMapT<T>>,
    570  std::vector<MatMapT<T>>>(shape_convert(outshape),
    571  [](std::vector<MatMapT<T>>& args)
    572  {
    573  return args[0].binaryExpr(args[1],
    574  std::function<T(const T&,const T&)>(
    575  [](const T& a, const T& b) -> T
    576  {
    577  return std::pow(a, b);
    578  }));
    579  }, {
    580  make_matmap(a.data_, a.shape_),
    581  make_matmap(b.data_, b.shape_)});
    582  }
    583  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    584  std::function<T(const T&,const T&)>,
    585  const TensMapT<T>,const TensMapT<T>>,
    586  std::vector<TensMapT<T>>>(shape_convert(outshape),
    587  [](std::vector<TensMapT<T>>& args)
    588  {
    589  return args[0].binaryExpr(args[1],
    590  std::function<T(const T&,const T&)>(
    591  [](const T& a, const T& b) -> T
    592  {
    593  return std::pow(a, b);
    594  }));
    595  }, {
    596  make_tensmap(a.data_, a.shape_),
    597  make_tensmap(b.data_, b.shape_)});
    598 }
    599 
    600 template <typename T>
    601 EigenptrT<T> add (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    602 {
    603  if (is_2d(outshape))
    604  {
    605  // use matrix when possible
    606  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    607  Eigen::internal::scalar_sum_op<T>,
    608  const MatMapT<T>,const MatMapT<T>>,
    609  std::vector<MatMapT<T>>>(shape_convert(outshape),
    610  [](std::vector<MatMapT<T>>& args)
    611  {
    612  return args[0] + args[1];
    613  }, {
    614  make_matmap(a.data_, a.shape_),
    615  make_matmap(b.data_, b.shape_)});
    616  }
    617  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    618  Eigen::internal::scalar_sum_op<T>,
    619  const TensMapT<T>,const TensMapT<T>>,
    620  std::vector<TensMapT<T>>>(shape_convert(outshape),
    621  [](std::vector<TensMapT<T>>& args)
    622  {
    623  return args[0] + args[1];
    624  }, {
    625  make_tensmap(a.data_, a.shape_),
    626  make_tensmap(b.data_, b.shape_)});
    627 }
    628 
    632 template <typename T>
    633 EigenptrT<T> sub (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    634 {
    635  if (is_2d(outshape))
    636  {
    637  // use matrix when possible
    638  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    639  Eigen::internal::scalar_difference_op<T>,
    640  const MatMapT<T>,const MatMapT<T>>,
    641  std::vector<MatMapT<T>>>(shape_convert(outshape),
    642  [](std::vector<MatMapT<T>>& args)
    643  {
    644  return args[0] - args[1];
    645  }, {
    646  make_matmap(a.data_, a.shape_),
    647  make_matmap(b.data_, b.shape_)});
    648  }
    649  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    650  Eigen::internal::scalar_difference_op<T>,
    651  const TensMapT<T>,const TensMapT<T>>,
    652  std::vector<TensMapT<T>>>(shape_convert(outshape),
    653  [](std::vector<TensMapT<T>>& args)
    654  {
    655  return args[0] - args[1];
    656  }, {
    657  make_tensmap(a.data_, a.shape_),
    658  make_tensmap(b.data_, b.shape_)});
    659 }
    660 
    661 template <typename T>
    662 EigenptrT<T> mul (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    663 {
    664  if (is_2d(outshape))
    665  {
    666  // use matrix when possible
    667  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    668  Eigen::internal::scalar_product_op<T>,
    669  const MatMapT<T>,const MatMapT<T>>,
    670  std::vector<MatMapT<T>>>(shape_convert(outshape),
    671  [](std::vector<MatMapT<T>>& args)
    672  {
    673  return args[0].cwiseProduct(args[1]);
    674  }, {
    675  make_matmap(a.data_, a.shape_),
    676  make_matmap(b.data_, b.shape_)});
    677  }
    678  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    679  Eigen::internal::scalar_product_op<T>,
    680  const TensMapT<T>,const TensMapT<T>>,
    681  std::vector<TensMapT<T>>>(shape_convert(outshape),
    682  [](std::vector<TensMapT<T>>& args)
    683  {
    684  return args[0] * args[1];
    685  }, {
    686  make_tensmap(a.data_, a.shape_),
    687  make_tensmap(b.data_, b.shape_)});
    688 }
    689 
    693 template <typename T>
    694 EigenptrT<T> div (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    695 {
    696  if (is_2d(outshape))
    697  {
    698  // use matrix when possible
    699  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    700  Eigen::internal::scalar_quotient_op<T>,
    701  const MatMapT<T>,const MatMapT<T>>,
    702  std::vector<MatMapT<T>>>(shape_convert(outshape),
    703  [](std::vector<MatMapT<T>>& args)
    704  {
    705  return args[0].cwiseQuotient(args[1]);
    706  }, {
    707  make_matmap(a.data_, a.shape_),
    708  make_matmap(b.data_, b.shape_)});
    709  }
    710  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    711  Eigen::internal::scalar_quotient_op<T>,
    712  const TensMapT<T>,const TensMapT<T>>,
    713  std::vector<TensMapT<T>>>(shape_convert(outshape),
    714  [](std::vector<TensMapT<T>>& args)
    715  {
    716  return args[0] / args[1];
    717  }, {
    718  make_tensmap(a.data_, a.shape_),
    719  make_tensmap(b.data_, b.shape_)});
    720 }
    721 
    725 template <typename T>
    726 EigenptrT<T> eq (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    727 {
    728  if (is_2d(outshape))
    729  {
    730  // use matrix when possible
    731  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    732  std::function<T(const T&,const T&)>,
    733  const MatMapT<T>,const MatMapT<T>>,
    734  std::vector<MatMapT<T>>>(shape_convert(outshape),
    735  [](std::vector<MatMapT<T>>& args)
    736  {
    737  return args[0].binaryExpr(args[1],
    738  std::function<T(const T&,const T&)>(
    739  [](const T& a, const T& b) -> T
    740  {
    741  return a == b;
    742  }));
    743  }, {
    744  make_matmap(a.data_, a.shape_),
    745  make_matmap(b.data_, b.shape_)});
    746  }
    747  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    748  std::function<T(const T&,const T&)>,
    749  const TensMapT<T>,const TensMapT<T>>,
    750  std::vector<TensMapT<T>>>(shape_convert(outshape),
    751  [](std::vector<TensMapT<T>>& args)
    752  {
    753  return args[0].binaryExpr(args[1],
    754  std::function<T(const T&,const T&)>(
    755  [](const T& a, const T& b) -> T
    756  {
    757  return a == b;
    758  }));
    759  }, {
    760  make_tensmap(a.data_, a.shape_),
    761  make_tensmap(b.data_, b.shape_)});
    762 }
    763 
    767 template <typename T>
    768 EigenptrT<T> neq (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    769 {
    770  if (is_2d(outshape))
    771  {
    772  // use matrix when possible
    773  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    774  std::function<T(const T&,const T&)>,
    775  const MatMapT<T>,const MatMapT<T>>,
    776  std::vector<MatMapT<T>>>(shape_convert(outshape),
    777  [](std::vector<MatMapT<T>>& args)
    778  {
    779  return args[0].binaryExpr(args[1],
    780  std::function<T(const T&,const T&)>(
    781  [](const T& a, const T& b) -> T
    782  {
    783  return a != b;
    784  }));
    785  }, {
    786  make_matmap(a.data_, a.shape_),
    787  make_matmap(b.data_, b.shape_)});
    788  }
    789  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    790  std::function<T(const T&,const T&)>,
    791  const TensMapT<T>,const TensMapT<T>>,
    792  std::vector<TensMapT<T>>>(shape_convert(outshape),
    793  [](std::vector<TensMapT<T>>& args)
    794  {
    795  return args[0].binaryExpr(args[1],
    796  std::function<T(const T&,const T&)>(
    797  [](const T& a, const T& b) -> T
    798  {
    799  return a != b;
    800  }));
    801  }, {
    802  make_tensmap(a.data_, a.shape_),
    803  make_tensmap(b.data_, b.shape_)});
    804 }
    805 
    809 template <typename T>
    810 EigenptrT<T> lt (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    811 {
    812  if (is_2d(outshape))
    813  {
    814  // use matrix when possible
    815  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    816  std::function<T(const T&,const T&)>,
    817  const MatMapT<T>,const MatMapT<T>>,
    818  std::vector<MatMapT<T>>>(shape_convert(outshape),
    819  [](std::vector<MatMapT<T>>& args)
    820  {
    821  return args[0].binaryExpr(args[1],
    822  std::function<T(const T&,const T&)>(
    823  [](const T& a, const T& b) -> T
    824  {
    825  return a < b;
    826  }));
    827  }, {
    828  make_matmap(a.data_, a.shape_),
    829  make_matmap(b.data_, b.shape_)});
    830  }
    831  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    832  std::function<T(const T&,const T&)>,
    833  const TensMapT<T>,const TensMapT<T>>,
    834  std::vector<TensMapT<T>>>(shape_convert(outshape),
    835  [](std::vector<TensMapT<T>>& args)
    836  {
    837  return args[0].binaryExpr(args[1],
    838  std::function<T(const T&,const T&)>(
    839  [](const T& a, const T& b) -> T
    840  {
    841  return a < b;
    842  }));
    843  }, {
    844  make_tensmap(a.data_, a.shape_),
    845  make_tensmap(b.data_, b.shape_)});
    846 }
    847 
    851 template <typename T>
    852 EigenptrT<T> gt (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    853 {
    854  if (is_2d(outshape))
    855  {
    856  // use matrix when possible
    857  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    858  std::function<T(const T&,const T&)>,
    859  const MatMapT<T>,const MatMapT<T>>,
    860  std::vector<MatMapT<T>>>(shape_convert(outshape),
    861  [](std::vector<MatMapT<T>>& args)
    862  {
    863  return args[0].binaryExpr(args[1],
    864  std::function<T(const T&,const T&)>(
    865  [](const T& a, const T& b) -> T
    866  {
    867  return a > b;
    868  }));
    869  }, {
    870  make_matmap(a.data_, a.shape_),
    871  make_matmap(b.data_, b.shape_)});
    872  }
    873  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    874  std::function<T(const T&,const T&)>,
    875  const TensMapT<T>,const TensMapT<T>>,
    876  std::vector<TensMapT<T>>>(shape_convert(outshape),
    877  [](std::vector<TensMapT<T>>& args)
    878  {
    879  return args[0].binaryExpr(args[1],
    880  std::function<T(const T&,const T&)>(
    881  [](const T& a, const T& b) -> T
    882  {
    883  return a > b;
    884  }));
    885  }, {
    886  make_tensmap(a.data_, a.shape_),
    887  make_tensmap(b.data_, b.shape_)});
    888 }
    889 
    892 template <typename T>
    893 EigenptrT<T> min (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    894 {
    895  if (is_2d(outshape))
    896  {
    897  // use matrix when possible
    898  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    899  Eigen::internal::scalar_min_op<T,T>,
    900  const MatMapT<T>,const MatMapT<T>>,
    901  std::vector<MatMapT<T>>>(shape_convert(outshape),
    902  [](std::vector<MatMapT<T>>& args)
    903  {
    904  return args[0].cwiseMin(args[1]);
    905  }, {
    906  make_matmap(a.data_, a.shape_),
    907  make_matmap(b.data_, b.shape_)});
    908  }
    909  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    910  Eigen::internal::scalar_min_op<T>,
    911  const TensMapT<T>,const TensMapT<T>>,
    912  std::vector<TensMapT<T>>>(shape_convert(outshape),
    913  [](std::vector<TensMapT<T>>& args)
    914  {
    915  return args[0].cwiseMin(args[1]);
    916  }, {
    917  make_tensmap(a.data_, a.shape_),
    918  make_tensmap(b.data_, b.shape_)});
    919 }
    920 
    923 template <typename T>
    924 EigenptrT<T> max (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    925 {
    926  if (is_2d(outshape))
    927  {
    928  // use matrix when possible
    929  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    930  Eigen::internal::scalar_max_op<T,T>,
    931  const MatMapT<T>,const MatMapT<T>>,
    932  std::vector<MatMapT<T>>>(shape_convert(outshape),
    933  [](std::vector<MatMapT<T>>& args)
    934  {
    935  return args[0].cwiseMax(args[1]);
    936  }, {
    937  make_matmap(a.data_, a.shape_),
    938  make_matmap(b.data_, b.shape_)});
    939  }
    940  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    941  Eigen::internal::scalar_max_op<T>,
    942  const TensMapT<T>,const TensMapT<T>>,
    943  std::vector<TensMapT<T>>>(shape_convert(outshape),
    944  [](std::vector<TensMapT<T>>& args)
    945  {
    946  return args[0].cwiseMax(args[1]);
    947  }, {
    948  make_tensmap(a.data_, a.shape_),
    949  make_tensmap(b.data_, b.shape_)});
    950 }
    951 
    955 template <typename T>
    956 EigenptrT<T> rand_uniform (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    957 {
    958  if (is_2d(outshape))
    959  {
    960  // use matrix when possible
    961  return make_eigenmatrix<T,Eigen::CwiseBinaryOp<
    962  std::function<T(const T&,const T&)>,
    963  const MatMapT<T>,const MatMapT<T>>,
    964  std::vector<MatMapT<T>>>(shape_convert(outshape),
    965  [](std::vector<MatMapT<T>>& args)
    966  {
    967  return args[0].binaryExpr(args[1],
    968  std::function<T(const T&,const T&)>(unif<T>));
    969  }, {
    970  make_matmap(a.data_, a.shape_),
    971  make_matmap(b.data_, b.shape_)});
    972  }
    973  return make_eigentensor<T,Eigen::TensorCwiseBinaryOp<
    974  std::function<T(const T&,const T&)>,
    975  const TensMapT<T>,const TensMapT<T>>,
    976  std::vector<TensMapT<T>>>(shape_convert(outshape),
    977  [](std::vector<TensMapT<T>>& args)
    978  {
    979  return args[0].binaryExpr(args[1],
    980  std::function<T(const T&,const T&)>(unif<T>));
    981  }, {
    982  make_tensmap(a.data_, a.shape_),
    983  make_tensmap(b.data_, b.shape_)});
    984 }
    985 
    986 template <typename T>
    987 EigenptrT<T> select (ade::Shape& outshape,
    988  const OpArg<T>& condition,
    989  const OpArg<T>& then, const OpArg<T>& otherwise)
    990 {
    991  if (is_2d(outshape))
    992  {
    993  // use matrix when possible
    994  return make_eigenmatrix<T,
    995  Eigen::Select<MatMapT<T>,MatMapT<T>,MatMapT<T>>,
    996  std::vector<MatMapT<T>>>(shape_convert(outshape),
    997  [](std::vector<MatMapT<T>>& args) -> Eigen::Select<MatMapT<T>,MatMapT<T>,MatMapT<T>>
    998  {
    999  return args[0].select(args[1], args[2]);
    1000  }, {
    1001  make_matmap(condition.data_, condition.shape_),
    1002  make_matmap(then.data_, then.shape_),
    1003  make_matmap(otherwise.data_, otherwise.shape_)});
    1004  }
    1005  return make_eigentensor<T,
    1006  Eigen::TensorSelectOp<const TensMapT<T>,
    1007  const TensMapT<T>,const TensMapT<T>>,
    1008  std::vector<TensMapT<T>>>(shape_convert(outshape),
    1009  [](std::vector<TensMapT<T>>& args) -> Eigen::TensorSelectOp<const TensMapT<T>,const TensMapT<T>,const TensMapT<T>>
    1010  {
    1011  return args[0].select(args[1], args[2]);
    1012  }, {
    1013  make_tensmap(condition.data_, condition.shape_),
    1014  make_tensmap(then.data_, then.shape_),
    1015  make_tensmap(otherwise.data_, otherwise.shape_)});
    1016 }
    1017 
    1018 template <typename T>
    1019 EigenptrT<T> matmul (ade::Shape& outshape, const OpArg<T>& a, const OpArg<T>& b)
    1020 {
    1021  assert(is_2d(outshape));
    1022  return make_eigenmatrix<T,Eigen::Product<MatMapT<T>,MatMapT<T>>,
    1023  std::vector<MatMapT<T>>>(shape_convert(outshape),
    1024  [](std::vector<MatMapT<T>>& args)
    1025  {
    1026  return args[0] * args[1];
    1027  }, {
    1028  make_matmap(a.data_, a.shape_),
    1029  make_matmap(b.data_, b.shape_)});
    1030 }
    1031 
    1032 template <typename T>
    1033 EigenptrT<T> convolution (ade::Shape& outshape, const OpArg<T>& input, const OpArg<T>& kernel)
    1034 {
    1035  assert(nullptr != kernel.coorder_);
    1036  ade::CoordT kernel_dims;
    1037  kernel.coorder_->forward(kernel_dims.begin(), kernel_dims.begin());
    1038  ade::ShapeT dims;
    1039  std::copy(kernel_dims.begin(), kernel_dims.end(), dims.begin());
    1040 
    1041  return make_eigentensor<T,Eigen::TensorConvolutionOp<
    1042  const ade::ShapeT,
    1043  const TensMapT<T>,const TensMapT<T>>,
    1044  std::vector<TensMapT<T>>>(shape_convert(outshape),
    1045  [&](std::vector<TensMapT<T>>& args)
    1046  {
    1047  return args[0].convolve(args[1], dims);
    1048  }, {
    1049  make_tensmap(input.data_, input.shape_),
    1050  make_tensmap(kernel.data_, kernel.shape_)});
    1051 }
    1052 
    1053 template <typename T>
    1054 EigenptrT<T> convolution_image_grad (ade::Shape& imageshape,
    1055  const OpArg<T>& kernel, const OpArg<T>& super_composite)
    1056 {
    1057  return make_eigentensor<T,
    1058  Eigen::TensorReductionOp<Eigen::internal::SumReducer<T>,
    1059  const ade::ShapeT,
    1060  const Eigen::TensorCwiseBinaryOp<
    1061  Eigen::internal::scalar_product_op<T,T>,
    1062  const Eigen::TensorBroadcastingOp<
    1063  const std::array<ade::DimT,ade::rank_cap+1>,
    1064  const Eigen::TensorReshapingOp<
    1065  const std::array<ade::DimT,ade::rank_cap+1>,
    1066  Eigen::TensorReverseOp<
    1067  const std::array<bool,ade::rank_cap>,
    1068  ead::TensMapT<T>
    1069  >
    1070  >
    1071  >,
    1072  const Eigen::TensorPatchOp<
    1073  const ade::ShapeT,
    1074  const Eigen::TensorPaddingOp<
    1075  const std::array<std::pair<int,int>,ade::rank_cap>,
    1076  const ead::TensMapT<T>
    1077  >
    1078  >
    1079  >
    1080  >,
    1081  std::vector<TensMapT<T>>>(shape_convert(imageshape),
    1082  [&](std::vector<TensMapT<T>>& args)
    1083  {
    1084  auto& outshape = super_composite.shape_;
    1085 
    1086  ade::ShapeT patch_dims;
    1087  std::copy(outshape.begin(), outshape.end(), patch_dims.begin());
    1088  Eigen::array<std::pair<int,int>,ade::rank_cap> paddings;
    1089  for (ade::RankT i = 0; i < ade::rank_cap; ++i)
    1090  {
    1091  int paddsize = outshape.at(i) - 1;
    1092  paddings[i] = std::make_pair(paddsize, paddsize);
    1093  }
    1094  auto patched = args[0].pad(paddings)
    1095  .extract_patches(patch_dims);
    1096 
    1097  std::array<bool,ade::rank_cap> revflags;
    1098  std::fill(revflags.begin(), revflags.end(), true);
    1099  std::array<ade::DimT,ade::rank_cap+1> pshape;
    1100  std::copy(outshape.begin(), outshape.end(), pshape.begin());
    1101  pshape[ade::rank_cap] = 1;
    1102  std::array<ade::DimT,ade::rank_cap+1> expansion;
    1103  std::fill(expansion.begin(), expansion.end(), 1);
    1104  expansion[ade::rank_cap] = imageshape.n_elems();
    1105  auto partial = args[1]
    1106  .reverse(revflags)
    1107  .reshape(pshape)
    1108  .broadcast(expansion) * patched;
    1109 
    1110  ade::ShapeT shapespace;
    1111  std::iota(shapespace.begin(), shapespace.end(), 0);
    1112  return partial.sum(shapespace);
    1113  }, {
    1114  make_tensmap(kernel.data_, kernel.shape_),
    1115  make_tensmap(super_composite.data_, super_composite.shape_)});
    1116 }
    1117 
    1118 template <typename T>
    1119 EigenptrT<T> convolution_kernel_grad (ade::Shape& kernelshape,
    1120  const OpArg<T>& image, const OpArg<T>& super_composite)
    1121 {
    1122  return make_eigentensor<T,
    1123  Eigen::TensorReductionOp<Eigen::internal::SumReducer<T>,
    1124  const ade::ShapeT,
    1125  const Eigen::TensorCwiseBinaryOp<
    1126  Eigen::internal::scalar_product_op<T,T>,
    1127  const Eigen::TensorBroadcastingOp<
    1128  const std::array<ade::DimT,ade::rank_cap+1>,
    1129  const Eigen::TensorReshapingOp<
    1130  const std::array<ade::DimT,ade::rank_cap+1>,
    1131  ead::TensMapT<T>
    1132  >
    1133  >,
    1134  const Eigen::TensorPatchOp<
    1135  const ade::ShapeT,
    1136  const ead::TensMapT<T>
    1137  >
    1138  >
    1139  >,
    1140  std::vector<TensMapT<T>>>(shape_convert(kernelshape),
    1141  [&](std::vector<TensMapT<T>>& args)
    1142  {
    1143  auto& outshape = super_composite.shape_;
    1144 
    1145  ade::ShapeT patch_dims;
    1146  std::copy(outshape.begin(), outshape.end(), patch_dims.begin());
    1147  auto patched = args[0].extract_patches(patch_dims);
    1148 
    1149  std::array<ade::DimT,ade::rank_cap+1> pshape;
    1150  std::copy(outshape.begin(), outshape.end(), pshape.begin());
    1151  pshape[ade::rank_cap] = 1;
    1152  std::array<ade::DimT,ade::rank_cap+1> expansion;
    1153  std::fill(expansion.begin(), expansion.end(), 1);
    1154  expansion[ade::rank_cap] = kernelshape.n_elems();
    1155  auto partial = args[1]
    1156  .reshape(pshape)
    1157  .broadcast(expansion) * patched;
    1158 
    1159  ade::ShapeT shapespace;
    1160  std::iota(shapespace.begin(), shapespace.end(), 0);
    1161  return partial.sum(shapespace);
    1162  }, {
    1163  make_tensmap(image.data_, image.shape_),
    1164  make_tensmap(super_composite.data_, super_composite.shape_)});
    1165 }
    1166 
    1167 }
    1168 
    1169 #endif // EAD_OPERATOR_HPP
    ead::neg
    EigenptrT< T > neg(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:209
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    ead::sub
    EigenptrT< T > sub(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:633
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    ead::rand_uniform
    EigenptrT< T > rand_uniform(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:956
    -
    ead::log
    EigenptrT< T > log(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:356
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    ead::mul
    EigenptrT< T > mul(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:662
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    ead::square
    EigenptrT< T > square(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:513
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    csv_to_png.args
    args
    Definition: csv_to_png.py:105
    -
    ead::CoordMap::forward
    void forward(ade::CoordT::iterator out, ade::CoordT::const_iterator in) const override
    Definition: coord.hpp:19
    -
    ead::extend
    CoordptrT extend(ade::RankT rank, std::vector< ade::DimT > ext)
    -
    ead::eq
    EigenptrT< T > eq(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:726
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    ead::CoordMap
    Definition: coord.hpp:9
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    ade::ShapeT
    std::array< DimT, rank_cap > ShapeT
    Array type used to hold dimension info in Shape.
    Definition: shape.hpp:48
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    ead::cube
    EigenptrT< T > cube(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:536
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    ead::sin
    EigenptrT< T > sin(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:234
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    ead::lt
    EigenptrT< T > lt(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:810
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    ead::make_tensmap
    TensMapT< T > make_tensmap(T *data, const ade::Shape &shape)
    Definition: eigen.hpp:164
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    ead::make_eigentensor
    EigenptrT< T > make_eigentensor(DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
    Definition: eigen.hpp:128
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    ead
    Definition: constant.hpp:9
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    ade::Shape::end
    iterator end(void)
    Return end iterator of internal array.
    Definition: shape.hpp:160
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    ead::internal::dim_copy
    std::array< ade::RankT, N > dim_copy(std::vector< ade::RankT > d)
    Definition: operator.hpp:47
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    ade::Shape::n_elems
    NElemT n_elems(void) const
    Return the total number of elements represented by the shape.
    Definition: shape.hpp:116
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    ead::tan
    EigenptrT< T > tan(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:302
    -
    ead::OpArg::data_
    T * data_
    Definition: operator.hpp:32
    -
    ead::div
    EigenptrT< T > div(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:694
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    ead::convolution
    EigenptrT< T > convolution(ade::Shape &outshape, const OpArg< T > &input, const OpArg< T > &kernel)
    Definition: operator.hpp:1033
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    ade::DimT
    uint16_t DimT
    Definition: shape.hpp:29
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    ead::reduce_sum
    EigenptrT< T > reduce_sum(ade::Shape &outshape, const OpArg< T > &in) template< typename T > EigenptrT< T > reduce_prod(ade
    Definition: operator.hpp:87
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    ead::TensMapT
    Eigen::TensorMap< TensorT< T > > TensMapT
    Definition: eigen.hpp:25
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    ead::min
    EigenptrT< T > min(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:893
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    ead::convolution_kernel_grad
    EigenptrT< T > convolution_kernel_grad(ade::Shape &kernelshape, const OpArg< T > &image, const OpArg< T > &super_composite)
    Definition: operator.hpp:1119
    -
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    EigenptrT< T > slice(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:133
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    ead::convolution_image_grad
    EigenptrT< T > convolution_image_grad(ade::Shape &imageshape, const OpArg< T > &kernel, const OpArg< T > &super_composite)
    Definition: operator.hpp:1054
    -
    ade::Shape::begin
    iterator begin(void)
    Return begin iterator of internal array.
    Definition: shape.hpp:154
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    OpArg(T *data, ade::Shape shape, CoordMap *coorder)
    Definition: operator.hpp:29
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    ead::is_2d
    static bool is_2d(ade::Shape shape)
    Definition: operator.hpp:20
    -
    ead::make_matmap
    MatMapT< T > make_matmap(T *data, const ade::Shape &shape)
    Definition: eigen.hpp:154
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    ead::OpArg::coorder_
    CoordMap * coorder_
    Definition: operator.hpp:36
    -
    ead::EigenptrT
    std::shared_ptr< iEigen< T > > EigenptrT
    Definition: eigen.hpp:76
    -
    ead::OpArg
    Definition: operator.hpp:27
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    ead::gt
    EigenptrT< T > gt(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:852
    -
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    EigenptrT< T > matmul(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:1019
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    ead::OpArg::shape_
    ade::Shape shape_
    Definition: operator.hpp:34
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    ead::shape_convert
    DimensionsT shape_convert(ade::Shape shape)
    -
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    EigenptrT< T > pad(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:158
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    ead::round
    EigenptrT< T > round(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:406
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    ead::ReduceOutT
    Eigen::TensorReductionOp< OP, const std::array< ade::RankT, N >, const TensMapT< T > > ReduceOutT
    Definition: operator.hpp:41
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    ade::RankT
    uint8_t RankT
    Type used for shape rank.
    Definition: shape.hpp:23
    -
    ead::select
    EigenptrT< T > select(ade::Shape &outshape, const OpArg< T > &condition, const OpArg< T > &then, const OpArg< T > &otherwise)
    Definition: operator.hpp:987
    -
    ade::Shape
    Definition: shape.hpp:60
    -
    ead::neq
    EigenptrT< T > neq(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:768
    -
    ead::max
    EigenptrT< T > max(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:924
    -
    ade::CoordT
    std::array< CDimT, rank_cap > CoordT
    Definition: shape.hpp:54
    - -
    ead::cos
    EigenptrT< T > cos(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:268
    -
    ead::tanh
    EigenptrT< T > tanh(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:490
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    ead::abs
    EigenptrT< T > abs(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:184
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    ead::exp
    EigenptrT< T > exp(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:331
    -
    ead::permute
    CoordptrT permute(std::vector< ade::RankT > dims)
    -
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    #define _EAD_INTERNAL_V2A(PROCESS, RED)
    Definition: operator.hpp:61
    -
    ead::sigmoid
    EigenptrT< T > sigmoid(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:429
    -
    ade::rank_cap
    const RankT rank_cap
    Number of dimsensions in a shape/coordinate.
    Definition: shape.hpp:45
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    ead::pow
    EigenptrT< T > pow(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:562
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    ead::MatMapT
    Eigen::Map< MatrixT< T > > MatMapT
    Definition: eigen.hpp:28
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    ead::sqrt
    EigenptrT< T > sqrt(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:381
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    ead::add
    EigenptrT< T > add(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:601
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    ead::sigmoid_grad
    EigenptrT< T > sigmoid_grad(ade::Shape &outshape, const OpArg< T > &in)
    Definition: operator.hpp:452
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    ead::make_eigenmatrix
    EigenptrT< T > make_eigenmatrix(DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
    Definition: eigen.hpp:136
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    1 #include "opt/optimize.hpp"
    2 
    3 #ifndef OPT_PARSE_HPP
    4 #define OPT_PARSE_HPP
    5 
    6 namespace opt
    7 {
    8 
    9 struct RulesContext final
    10 {
    11  // maps declared symbol to any id
    12  std::unordered_set<std::string> symbols_;
    13 
    14  // maps functor/group label to properties set
    15  std::unordered_map<std::string,
    16  std::unordered_set<std::string>> properties_;
    17 };
    18 
    19 struct iConverterBuilder
    20 {
    21  virtual ~iConverterBuilder (void) = default;
    22 
    23  // return constant converter
    24  virtual CstConvertF build_cconv (void) const = 0;
    25 
    26  virtual ConvptrT build (const ::Subgraph* sg, const RulesContext& ctx) const = 0;
    27 
    28  // extended interface to create shaper
    29  virtual ade::CoordptrT shaperize (::NumList* list) const = 0;
    30 
    31  // extended interface to create coorder
    32  virtual ade::CoordptrT coorderize (::NumList* list) const = 0;
    33 };
    34 
    35 OptCtx parse (std::string content, const iConverterBuilder& builder);
    36 
    37 OptCtx parse_file (std::string filename, const iConverterBuilder& builder);
    38 
    39 }
    40 
    41 #endif // OPT_PARSE_HPP
    opt::RulesContext::symbols_
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    Definition: parse.hpp:12
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    Definition: optimize.hpp:10
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    Definition: iconverter.hpp:23
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    Definition: parse.hpp:16
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    1 #include "opt/matcher.hpp"
    2 #include "opt/iconverter.hpp"
    3 
    4 #ifndef OPT_OPTIMIZE_HPP
    5 #define OPT_OPTIMIZE_HPP
    6 
    7 namespace opt
    8 {
    9 
    10 using CstConvertF = std::function<ade::TensptrT(ade::iTensor*)>;
    11 
    12 struct OptCtx
    13 {
    14  VoterPool voters_;
    15 
    16  CstConvertF const_conv_;
    17 
    18  std::unordered_map<std::string,ConvptrT> converts_;
    19 };
    20 
    21 ade::TensT optimize (ade::TensT roots, const OptCtx& opts);
    22 
    23 }
    24 
    25 #endif // OPT_OPTIMIZE_HPP
    opt::OptCtx::const_conv_
    CstConvertF const_conv_
    Definition: optimize.hpp:16
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    Definition: candidate.hpp:11
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    std::function< ade::TensptrT(ade::iTensor *)> CstConvertF
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    Definition: optimize.hpp:18
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    ade::TensT
    std::vector< TensptrT > TensT
    Vector representation of ade tensor pointers.
    Definition: funcarg.hpp:104
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    ade::TensT optimize(ade::TensT roots, const OptCtx &opts)
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    1 #include "tag/tag.hpp"
    2 
    3 #ifndef TAG_PROP_HPP
    4 #define TAG_PROP_HPP
    5 
    6 namespace tag
    7 {
    8 
    9 const std::string props_key = "properties";
    10 
    11 // some property tags
    12 const std::string commutative_tag = "commutative";
    13 
    14 const std::string immutable_tag = "immutable";
    15 
    17 struct PropTag final : public iTag
    18 {
    19  PropTag (std::string init_label) : labels_({init_label}) {}
    20 
    21  size_t tag_id (void) const override
    22  {
    23  return tag_id_;
    24  }
    25 
    26  void absorb (TagptrT&& other) override
    27  {
    28  std::unordered_set<std::string>& olabels =
    29  static_cast<PropTag*>(other.get())->labels_;
    30  labels_.insert(olabels.begin(), olabels.end());
    31  }
    32 
    33  TagRepsT get_tags (void) const override
    34  {
    35  TagRepsT out;
    36  out.emplace(props_key, std::vector<std::string>(
    37  labels_.begin(), labels_.end()));
    38  return out;
    39  }
    40 
    41 private:
    42  std::unordered_set<std::string> labels_;
    43 
    44  static size_t tag_id_;
    45 };
    46 
    47 struct PropertyRegistry final
    48 {
    49  PropertyRegistry (TagRegistry& registry = get_reg()) :
    50  tag_reg_(registry) {}
    51 
    52  void property_tag (ade::TensrefT tens, std::string property)
    53  {
    54  tag_reg_.add_tag(tens, TagptrT(new PropTag(property)));
    55  }
    56 
    57  bool has_property (const ade::iTensor* tens, std::string property)
    58  {
    59  auto reps = tag_reg_.get_tags(tens);
    60  auto it = reps.find(props_key);
    61  if (reps.end() == it)
    62  {
    63  return false;
    64  }
    65  return estd::arr_has(it->second, property);
    66  }
    67 
    68  TagRegistry& tag_reg_;
    69 };
    70 
    71 PropertyRegistry& get_property_reg (void);
    72 
    73 }
    74 
    75 #endif // TAG_PROP_HPP
    tag::TagRegistry::add_tag
    void add_tag(ade::TensrefT tens, TagptrT tag)
    Definition: tag.hpp:137
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    PropTag(std::string init_label)
    Definition: prop.hpp:19
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    Definition: tag.hpp:16
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    TagRepsT get_tags(const ade::iTensor *tens)
    Definition: tag.hpp:152
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    Definition: tag.hpp:135
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    Definition: prop.hpp:21
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    const std::string props_key
    Definition: prop.hpp:9
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    PropTag (properties tag) define node properties.
    Definition: prop.hpp:17
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    Definition: group.hpp:10
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    Definition: tag.hpp:27
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    Definition: tag.hpp:12
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    Definition: prop.hpp:44
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    const std::string commutative_tag
    Definition: prop.hpp:12
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    void absorb(TagptrT &&other) override
    Definition: prop.hpp:26
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    TagRegistry & tag_reg_
    Definition: prop.hpp:68
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    PropertyRegistry(TagRegistry &registry=get_reg())
    Definition: prop.hpp:49
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    Interface of traversible and differentiable nodes with shape information.
    Definition: itensor.hpp:34
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    Definition: prop.hpp:47
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    Definition: prop.hpp:42
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    TagRegistry & get_reg(void)
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    std::weak_ptr< iTensor > TensrefT
    Tensor weak pointers.
    Definition: itensor.hpp:52
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    PropertyRegistry & get_property_reg(void)
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    const std::string immutable_tag
    Definition: prop.hpp:14
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    TagRepsT get_tags(void) const override
    Definition: prop.hpp:33
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    bool has_property(const ade::iTensor *tens, std::string property)
    Definition: prop.hpp:57
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    Definition: prop.hpp:52
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    1 #include <random>
    2 #include <type_traits>
    3 #include <functional>
    4 
    5 #ifndef EAD_RANDOM_HPP
    6 #define EAD_RANDOM_HPP
    7 
    8 namespace ead
    9 {
    10 
    12 using EngineT = std::default_random_engine;
    13 
    14 template <typename T>
    15 using GenF = std::function<T()>;
    16 
    18 EngineT& get_engine (void);
    19 
    20 template <typename T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
    21 T unif (const T& a, const T& b)
    22 {
    23  std::uniform_int_distribution<T> dist(a, b);
    24  return dist(get_engine());
    25 }
    26 
    27 template <typename T, typename std::enable_if<!std::is_integral<T>::value>::type* = nullptr>
    28 T unif (const T& a, const T& b)
    29 {
    30  std::uniform_real_distribution<T> dist(a, b);
    31  return dist(get_engine());
    32 }
    33 
    34 template <typename T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
    35 GenF<T> unif_gen (const T& a, const T& b)
    36 {
    37  std::uniform_int_distribution<T> dist(a, b);
    38  return std::bind(dist, get_engine());;
    39 }
    40 
    41 template <typename T, typename std::enable_if<!std::is_integral<T>::value>::type* = nullptr>
    42 GenF<T> unif_gen (T a, T b)
    43 {
    44  std::uniform_real_distribution<T> dist(a, b);
    45  return std::bind(dist, get_engine());
    46 }
    47 
    48 template <typename T, typename std::enable_if<!std::is_integral<T>::value>::type* = nullptr>
    49 GenF<T> norm_gen (T mean, T stdev)
    50 {
    51  std::normal_distribution<T> dist(mean, stdev);
    52  return std::bind(dist, get_engine());
    53 }
    54 
    55 }
    56 
    57 #endif // EAD_RANDOM_HPP
    ead::norm_gen
    GenF< T > norm_gen(T mean, T stdev)
    Definition: random.hpp:49
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    ead::GenF
    std::function< T()> GenF
    Definition: random.hpp:15
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    Definition: constant.hpp:9
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    T unif(const T &a, const T &b)
    Definition: random.hpp:21
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    GenF< T > unif_gen(const T &a, const T &b)
    Definition: random.hpp:35
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    ead::get_engine
    EngineT & get_engine(void)
    Return global random generator.
    -
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    std::default_random_engine EngineT
    RNG engine used.
    Definition: random.hpp:12
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    using opt::ImmutablesT = std::vector< ade::LeafptrT >
     
    using opt::HFunctorsT = std::vector< std::vector< ade::FuncptrT > >
     
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    void opt::replace_parents (const ade::ParentFinder &pfinder, ade::iTensor *source, ade::TensptrT target)
     
    template<typename T >
    std::vector< T > opt::remove_duplicates (ade::TensT &roots, std::vector< T > tens, const ade::ParentFinder &pfinder, tag::TagRegistry &registry=tag::get_reg())
     
    void opt::populate_graph (ImmutablesT &immutables, HFunctorsT &functors, const ade::TensT &roots)
     
    void opt::remove_all_duplicates (ade::TensT &roots, ImmutablesT &immutables, HFunctorsT &functors)
     
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    1 #include "opt/stats.hpp"
    2 
    3 #ifndef OPT_RMDUPS_HPP
    4 #define OPT_RMDUPS_HPP
    5 
    6 namespace opt
    7 {
    8 
    9 void replace_parents (const ade::ParentFinder& pfinder,
    10  ade::iTensor* source, ade::TensptrT target);
    11 
    12 template <typename T>
    13 std::vector<T> remove_duplicates (ade::TensT& roots, std::vector<T> tens,
    14  const ade::ParentFinder& pfinder,
    15  tag::TagRegistry& registry = tag::get_reg())
    16 {
    17  if (tens.empty())
    18  {
    19  return {};
    20  }
    21 
    22  std::unordered_set<ade::iTensor*> priorities;
    23  std::unordered_map<ade::iTensor*,std::vector<size_t>> rindices;
    24  for (size_t i = 0, n = roots.size(); i < n; ++i)
    25  {
    26  ade::TensptrT& root = roots[i];
    27  priorities.emplace(root.get());
    28  rindices[root.get()].push_back(i);
    29  }
    30 
    31  std::sort(tens.begin(), tens.end(),
    32  [&priorities](T& a, T& b) { return lt(priorities, a.get(), b.get()); });
    33  T last = tens[0];
    34  std::vector<T> uniques = {last};
    35  size_t n = tens.size();
    36  uniques.reserve(n - 1);
    37  for (size_t i = 1; i < n; ++i)
    38  {
    39  T& cur = tens[i];
    40  if (is_equal(last.get(), cur.get()))
    41  {
    42  logs::debugf("replacing %s", cur->to_string().c_str());
    43  // remove equivalent node
    44  replace_parents(pfinder, cur.get(), last);
    45 
    46  auto it = rindices.find(cur.get());
    47  if (rindices.end() != it)
    48  {
    49  for (size_t ridx : it->second)
    50  {
    51  roots[ridx] = last;
    52  }
    53  }
    54 
    55  // todo: mark parents as uninitialized, reinitialize entire graph, or uninitialize everything to begin with
    56 
    57  // inherit tags
    58  registry.move_tags(last, cur.get());
    59  }
    60  else
    61  {
    62  uniques.push_back(cur);
    63  last = cur;
    64  }
    65  }
    66  return uniques;
    67 }
    68 
    69 using ImmutablesT = std::vector<ade::LeafptrT>;
    70 
    71 using HFunctorsT = std::vector<std::vector<ade::FuncptrT>>;
    72 
    73 // identify immutable leaves and organize functors by maxheight
    74 void populate_graph (ImmutablesT& immutables, HFunctorsT& functors,
    75  const ade::TensT& roots);
    76 
    77 // delete and update equivalent immutable leaves and functors
    78 void remove_all_duplicates (ade::TensT& roots,
    79  ImmutablesT& immutables, HFunctorsT& functors);
    80 
    81 }
    82 
    83 #endif // OPT_RMDUPS_HPP
    opt::lt
    bool lt(ade::CoordptrT a, ade::CoordptrT b)
    - -
    opt::remove_duplicates
    std::vector< T > remove_duplicates(ade::TensT &roots, std::vector< T > tens, const ade::ParentFinder &pfinder, tag::TagRegistry &registry=tag::get_reg())
    Definition: rmdups.hpp:13
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    tag::TagRegistry
    Definition: tag.hpp:135
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    opt::HFunctorsT
    std::vector< std::vector< ade::FuncptrT > > HFunctorsT
    Definition: rmdups.hpp:71
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    opt::is_equal
    bool is_equal(ade::CoordptrT a, ade::CoordptrT b)
    -
    opt
    Definition: candidate.hpp:11
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    opt::replace_parents
    void replace_parents(const ade::ParentFinder &pfinder, ade::iTensor *source, ade::TensptrT target)
    -
    ade::TensptrT
    std::shared_ptr< iTensor > TensptrT
    Tensor smart pointer.
    Definition: itensor.hpp:49
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    ade::iTensor
    Interface of traversible and differentiable nodes with shape information.
    Definition: itensor.hpp:34
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    opt::populate_graph
    void populate_graph(ImmutablesT &immutables, HFunctorsT &functors, const ade::TensT &roots)
    -
    opt::remove_all_duplicates
    void remove_all_duplicates(ade::TensT &roots, ImmutablesT &immutables, HFunctorsT &functors)
    -
    tag::get_reg
    TagRegistry & get_reg(void)
    -
    opt::ImmutablesT
    std::vector< ade::LeafptrT > ImmutablesT
    Definition: rmdups.hpp:69
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    ade::TensT
    std::vector< TensptrT > TensT
    Vector representation of ade tensor pointers.
    Definition: funcarg.hpp:104
    -
    ade::ParentFinder
    Traveler that for each child tracks the relationship to all parents.
    Definition: traveler.hpp:166
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    1 
    9 #include <list>
    10 #include <unordered_set>
    11 
    12 #include "ade/traveler.hpp"
    13 #include "ade/functor.hpp"
    14 
    15 #include "pbm/data.hpp"
    16 
    17 #ifndef PBM_SAVE_HPP
    18 #define PBM_SAVE_HPP
    19 
    20 namespace pbm
    21 {
    22 
    24 using PathedMapT = std::unordered_map<ade::TensptrT,StringsT>;
    25 
    27 template <typename SAVER,
    28  typename std::enable_if<
    29  std::is_base_of<iSaver,SAVER>::value>::type* = nullptr>
    30 struct GraphSaver final : public ade::iTraveler
    31 {
    33  void visit (ade::iLeaf* leaf) override
    34  {
    35  if (false == estd::has(visited_, leaf))
    36  {
    37  leaf->accept(stat);
    38  leaves_.push_back(leaf);
    39  visited_.emplace(leaf);
    40  }
    41  }
    42 
    44  void visit (ade::iFunctor* func) override
    45  {
    46  if (false == estd::has(visited_, func))
    47  {
    48  func->accept(stat);
    49  funcs_.push_back(func);
    50  visited_.emplace(func);
    51 
    52  ade::ArgsT children = func->get_children();
    53  for (auto& child : children)
    54  {
    55  child.get_tensor()->accept(*this);
    56  }
    57  }
    58  }
    59 
    61  void save (cortenn::Graph& out, PathedMapT labels = PathedMapT())
    62  {
    63  std::unordered_map<ade::iTensor*,StringsT> raw_labels;
    64  for (auto lpair : labels)
    65  {
    66  raw_labels[lpair.first.get()] = lpair.second;
    67  }
    68 
    69  // sort functions from the root with the smallest subtree to the largest
    70  // this ensures every children of a node appears before the parent,
    71  // as is the order of node creations
    72  funcs_.sort(
    73  [&](ade::iTensor* a, ade::iTensor* b)
    74  {
    75  return stat.graphsize_[a].upper_ < stat.graphsize_[b].upper_;
    76  });
    77 
    78  std::vector<ade::iFunctor*> funcs(funcs_.begin(), funcs_.end());
    79  std::vector<ade::iLeaf*> leaves(leaves_.begin(), leaves_.end());
    80 
    81  // all nodes in leaf appear before funcs
    82  std::unordered_map<ade::iTensor*,size_t> ordermap;
    83  size_t nleaves = leaves.size();
    84  for (size_t i = 0; i < nleaves; ++i)
    85  {
    86  ade::iLeaf* tens = leaves[i];
    87  ordermap[tens] = i;
    88 
    89  cortenn::Node* pb_node = out.add_nodes();
    90  auto it = raw_labels.find(tens);
    91  if (raw_labels.end() != it)
    92  {
    93  google::protobuf::RepeatedPtrField<std::string> vec(
    94  it->second.begin(), it->second.end());
    95  pb_node->mutable_labels()->Swap(&vec);
    96  }
    97  save_data(*pb_node->mutable_source(), tens);
    98  }
    99  for (size_t i = 0, n = funcs.size(); i < n; ++i)
    100  {
    101  ade::iFunctor* f = funcs[i];
    102  ordermap[f] = nleaves + i;
    103 
    104  cortenn::Node* pb_node = out.add_nodes();
    105  auto it = raw_labels.find(f);
    106  if (raw_labels.end() != it)
    107  {
    108  google::protobuf::RepeatedPtrField<std::string> vec(
    109  it->second.begin(), it->second.end());
    110  pb_node->mutable_labels()->Swap(&vec);
    111  }
    112  cortenn::Functor* func = pb_node->mutable_functor();
    113  ade::Opcode opcode = f->get_opcode();
    114  func->set_opname(opcode.name_);
    115  const ade::ArgsT& children = f->get_children();
    116  for (auto& child : children)
    117  {
    118  cortenn::NodeArg* arg = func->add_args();
    119  ade::iTensor* tens = child.get_tensor().get();
    120  arg->set_idx(ordermap[tens]);
    121  std::vector<double> shaper =
    122  saver_.save_shaper(child.get_shaper());
    123  std::vector<double> coorder =
    124  saver_.save_coorder(child.get_coorder());
    125  google::protobuf::RepeatedField<double> shaper_vec(
    126  shaper.begin(), shaper.end());
    127  google::protobuf::RepeatedField<double> coorder_vec(
    128  coorder.begin(), coorder.end());
    129  arg->mutable_shaper()->Swap(&shaper_vec);
    130  arg->mutable_coord()->Swap(&coorder_vec);
    131  arg->set_fwd(child.map_io());
    132  }
    133  }
    134  }
    135 
    137  std::list<ade::iLeaf*> leaves_;
    138 
    140  std::list<ade::iFunctor*> funcs_;
    141 
    143  std::unordered_set<ade::iTensor*> visited_;
    144 
    146  ade::GraphStat stat;
    147 
    148 private:
    149  void save_data (cortenn::Source& out, ade::iLeaf* in)
    150  {
    151  const ade::Shape& shape = in->shape();
    152  bool is_const = false;
    153  out.set_shape(std::string(shape.begin(), shape.end()));
    154  out.set_data(saver_.save_leaf(is_const, in));
    155  out.set_typelabel(in->type_label());
    156  out.set_is_const(is_const);
    157  }
    158 
    159  SAVER saver_;
    160 };
    161 
    162 }
    163 
    164 #endif // PBM_SAVE_HPP
    pbm::GraphSaver::saver_
    SAVER saver_
    Definition: save.hpp:159
    -
    ade::iFunctor
    Interface of iOperation-defined operation node.
    Definition: ifunctor.hpp:28
    -
    pbm::GraphSaver::funcs_
    std::list< ade::iFunctor * > funcs_
    List of functions visited (by depth-first)
    Definition: save.hpp:140
    - -
    ade::Opcode
    Encoding of operation.
    Definition: ifunctor.hpp:18
    -
    pbm::GraphSaver::save_data
    void save_data(cortenn::Source &out, ade::iLeaf *in)
    Definition: save.hpp:149
    -
    ade::Opcode::name_
    std::string name_
    String representation of operation.
    Definition: ifunctor.hpp:21
    -
    pbm::GraphSaver::visit
    void visit(ade::iFunctor *func) override
    Implementation of iTraveler.
    Definition: save.hpp:44
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    ade::iTensor::shape
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    Return the shape held by this tensor.
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    pbm::GraphSaver::save
    void save(cortenn::Graph &out, PathedMapT labels=PathedMapT())
    Marshal all equation graphs in roots vector to protobuf object.
    Definition: save.hpp:61
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    ade::iFunctor::accept
    void accept(iTraveler &visiter) override
    Implementation of iTensor.
    Definition: ifunctor.hpp:33
    -
    ade::iLeaf
    Leaf of the graph commonly representing the variable in an equation.
    Definition: ileaf.hpp:19
    -
    ade::Shape::end
    iterator end(void)
    Return end iterator of internal array.
    Definition: shape.hpp:160
    -
    ade::GraphStat
    Traveler that maps each tensor to its subtree&#39;s maximum depth.
    Definition: traveler.hpp:61
    -
    ade::Shape::begin
    iterator begin(void)
    Return begin iterator of internal array.
    Definition: shape.hpp:154
    -
    ade::iFunctor::get_opcode
    virtual Opcode get_opcode(void) const =0
    Return operation encoding.
    - -
    pbm::GraphSaver::leaves_
    std::list< ade::iLeaf * > leaves_
    List of leaves visited (left to right)
    Definition: save.hpp:137
    -
    ade::iTraveler
    Interface to travel through graph, treating iLeaf and iFunctor differently.
    Definition: itensor.hpp:22
    -
    ade::iTensor
    Interface of traversible and differentiable nodes with shape information.
    Definition: itensor.hpp:34
    -
    ade::iFunctor::get_children
    virtual const ArgsT & get_children(void) const =0
    Return children nodes as a vector of raw pointers.
    -
    pbm::GraphSaver::visit
    void visit(ade::iLeaf *leaf) override
    Implementation of iTraveler.
    Definition: save.hpp:33
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    ade::Shape
    Definition: shape.hpp:60
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    pbm::GraphSaver::stat
    ade::GraphStat stat
    Internal traveler.
    Definition: save.hpp:146
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    pbm::PathedMapT
    std::unordered_map< ade::TensptrT, StringsT > PathedMapT
    Map Tensptrs to a string path type.
    Definition: save.hpp:24
    -
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    virtual std::string type_label(void) const =0
    Return data type label (for better readability)
    - -
    ade::GraphStat::graphsize_
    std::unordered_map< iTensor *, estd::NumRange< size_t > > graphsize_
    Definition: traveler.hpp:109
    -
    pbm::GraphSaver
    Graph serialization traveler.
    Definition: save.hpp:30
    -
    pbm::GraphSaver::visited_
    std::unordered_set< ade::iTensor * > visited_
    Visited nodes.
    Definition: save.hpp:143
    -
    ade::iLeaf::accept
    void accept(iTraveler &visiter) override
    Implementation of iTensor.
    Definition: ileaf.hpp:24
    -
    pbm
    Definition: data.hpp:18
    -
    ade::ArgsT
    std::vector< FuncArg > ArgsT
    Type of functor arguments.
    Definition: funcarg.hpp:101
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    - - diff --git a/docs/search/namespaces_5.js b/docs/search/namespaces_5.js deleted file mode 100644 index 348cc51a6..000000000 --- a/docs/search/namespaces_5.js +++ /dev/null @@ -1,6 +0,0 @@ -var searchData= -[ - ['pbm',['pbm',['../namespacepbm.html',1,'']]], - ['pyapis',['pyapis',['../namespacepyapis.html',1,'']]], - ['pyead',['pyead',['../namespacepyead.html',1,'']]] -]; diff --git a/docs/search/namespaces_6.html b/docs/search/namespaces_6.html deleted file mode 100644 index 25c75178c..000000000 --- a/docs/search/namespaces_6.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/namespaces_6.js b/docs/search/namespaces_6.js deleted file mode 100644 index 365d30d17..000000000 --- a/docs/search/namespaces_6.js +++ /dev/null @@ -1,5 +0,0 @@ -var searchData= -[ - ['tag',['tag',['../namespacetag.html',1,'']]], - ['template',['template',['../namespacetemplate.html',1,'']]] -]; diff --git a/docs/search/nomatches.html b/docs/search/nomatches.html deleted file mode 100644 index b1ded27e9..000000000 --- a/docs/search/nomatches.html +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - - -
    -
    No Matches
    -
    - - diff --git a/docs/search/pages_0.html b/docs/search/pages_0.html deleted file mode 100644 index d7528582d..000000000 --- a/docs/search/pages_0.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/pages_0.js b/docs/search/pages_0.js deleted file mode 100644 index bbbbf3197..000000000 --- a/docs/search/pages_0.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['ade_20_28automatic_20differentiation_20engine_29',['ADE (Automatic Differentiation Engine)',['../md_ade__r_e_a_d_m_e__a_d_e.html',1,'']]] -]; diff --git a/docs/search/pages_1.html b/docs/search/pages_1.html deleted file mode 100644 index 924fb482e..000000000 --- a/docs/search/pages_1.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/pages_1.js b/docs/search/pages_1.js deleted file mode 100644 index cf3dc74cc..000000000 --- a/docs/search/pages_1.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['debugger_20_28dbg_29',['Debugger (DBG)',['../md_dbg__r_e_a_d_m_e__d_b_g.html',1,'']]] -]; diff --git a/docs/search/pages_2.html b/docs/search/pages_2.html deleted file mode 100644 index ffc6d9295..000000000 --- a/docs/search/pages_2.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/pages_2.js b/docs/search/pages_2.js deleted file mode 100644 index 00a1dd361..000000000 --- a/docs/search/pages_2.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['ead_20_28eigen_2dade_29',['EAD (Eigen-ADE)',['../md_ead__r_e_a_d_m_e__e_a_d.html',1,'']]] -]; diff --git a/docs/search/pages_3.html b/docs/search/pages_3.html deleted file mode 100644 index f3746246f..000000000 --- a/docs/search/pages_3.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/pages_3.js b/docs/search/pages_3.js deleted file mode 100644 index 2e5b65096..000000000 --- a/docs/search/pages_3.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['optimization_20rules_20explained_20in_20natural_20language',['Optimization Rules Explained in Natural Language',['../md_opt_rules.html',1,'']]] -]; diff --git a/docs/search/pages_4.html b/docs/search/pages_4.html deleted file mode 100644 index 593494e9e..000000000 --- a/docs/search/pages_4.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/pages_4.js b/docs/search/pages_4.js deleted file mode 100644 index 6dfe0c49e..000000000 --- a/docs/search/pages_4.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['pbm_20_28protobuf_20marshaller_29',['PBM (Protobuf Marshaller)',['../md_pbm__r_e_a_d_m_e__p_b_m.html',1,'']]] -]; diff --git a/docs/search/pages_5.html b/docs/search/pages_5.html deleted file mode 100644 index 7033bd6df..000000000 --- a/docs/search/pages_5.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/pages_5.js b/docs/search/pages_5.js deleted file mode 100644 index 54dc59b9b..000000000 --- a/docs/search/pages_5.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['tenncor',['Tenncor',['../index.html',1,'']]] -]; diff --git a/docs/search/search.css b/docs/search/search.css deleted file mode 100644 index 3cf9df94a..000000000 --- a/docs/search/search.css +++ /dev/null @@ -1,271 +0,0 @@ -/*---------------- Search Box */ - -#FSearchBox { - float: left; -} - -#MSearchBox { - white-space : nowrap; - float: none; - margin-top: 8px; - right: 0px; - width: 170px; - height: 24px; - z-index: 102; -} - -#MSearchBox .left -{ - display:block; - position:absolute; - left:10px; - width:20px; - height:19px; - background:url('search_l.png') no-repeat; - background-position:right; -} - -#MSearchSelect { - display:block; - position:absolute; - width:20px; - height:19px; -} - -.left #MSearchSelect { - left:4px; -} - -.right #MSearchSelect { - right:5px; -} - -#MSearchField { - display:block; - position:absolute; - height:19px; - background:url('search_m.png') repeat-x; - border:none; - width:115px; - margin-left:20px; - padding-left:4px; - color: #909090; - outline: none; - font: 9pt Arial, Verdana, sans-serif; - -webkit-border-radius: 0px; -} - -#FSearchBox #MSearchField { - margin-left:15px; -} - -#MSearchBox .right { - display:block; - position:absolute; - right:10px; - top:8px; - width:20px; - height:19px; - background:url('search_r.png') no-repeat; - background-position:left; -} - -#MSearchClose { - display: none; - position: absolute; - top: 4px; - background : none; - border: none; - margin: 0px 4px 0px 0px; - padding: 0px 0px; - outline: none; -} - -.left #MSearchClose { - left: 6px; -} - -.right #MSearchClose { - right: 2px; -} - -.MSearchBoxActive #MSearchField { - color: #000000; -} - -/*---------------- Search filter selection */ - -#MSearchSelectWindow { - display: none; - position: absolute; - left: 0; top: 0; - border: 1px solid #90A5CE; - background-color: #F9FAFC; - z-index: 10001; - padding-top: 4px; - padding-bottom: 4px; - -moz-border-radius: 4px; - -webkit-border-top-left-radius: 4px; - -webkit-border-top-right-radius: 4px; - -webkit-border-bottom-left-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); -} - -.SelectItem { - font: 8pt Arial, Verdana, sans-serif; - padding-left: 2px; - padding-right: 12px; - border: 0px; -} - -span.SelectionMark { - margin-right: 4px; - font-family: monospace; - outline-style: none; - text-decoration: none; -} - -a.SelectItem { - display: block; - outline-style: none; - color: #000000; - text-decoration: none; - padding-left: 6px; - padding-right: 12px; -} - -a.SelectItem:focus, -a.SelectItem:active { - color: #000000; - outline-style: none; - text-decoration: none; -} - -a.SelectItem:hover { - color: #FFFFFF; - background-color: #3D578C; - outline-style: none; - text-decoration: none; - cursor: pointer; - display: block; -} - -/*---------------- Search results window */ - -iframe#MSearchResults { - width: 60ex; - height: 15em; -} - -#MSearchResultsWindow { - display: none; - position: absolute; - left: 0; top: 0; - border: 1px solid #000; - background-color: #EEF1F7; - z-index:10000; -} - -/* ----------------------------------- */ - - -#SRIndex { - clear:both; - padding-bottom: 15px; -} - -.SREntry { - font-size: 10pt; - padding-left: 1ex; -} - -.SRPage .SREntry { - font-size: 8pt; - padding: 1px 5px; -} - -body.SRPage { - margin: 5px 2px; -} - -.SRChildren { - padding-left: 3ex; padding-bottom: .5em -} - -.SRPage .SRChildren { - display: none; -} - -.SRSymbol { - font-weight: bold; - color: #425E97; - font-family: Arial, Verdana, sans-serif; - text-decoration: none; - outline: none; -} - -a.SRScope { - display: block; - color: #425E97; - font-family: Arial, Verdana, sans-serif; - text-decoration: none; - outline: none; -} - -a.SRSymbol:focus, a.SRSymbol:active, -a.SRScope:focus, a.SRScope:active { - text-decoration: underline; -} - -span.SRScope { - padding-left: 4px; -} - -.SRPage .SRStatus { - padding: 2px 5px; - font-size: 8pt; - font-style: italic; -} - -.SRResult { - display: none; -} - -DIV.searchresults { - margin-left: 10px; - margin-right: 10px; -} - -/*---------------- External search page results */ - -.searchresult { - background-color: #F0F3F8; -} - -.pages b { - color: white; - padding: 5px 5px 3px 5px; - background-image: url("../tab_a.png"); - background-repeat: repeat-x; - text-shadow: 0 1px 1px #000000; -} - -.pages { - line-height: 17px; - margin-left: 4px; - text-decoration: none; -} - -.hl { - font-weight: bold; -} - -#searchresults { - margin-bottom: 20px; -} - -.searchpages { - margin-top: 10px; -} - diff --git a/docs/search/search.js b/docs/search/search.js deleted file mode 100644 index a554ab9cb..000000000 --- a/docs/search/search.js +++ /dev/null @@ -1,814 +0,0 @@ -/* - @licstart The following is the entire license notice for the - JavaScript code in this file. - - Copyright (C) 1997-2017 by Dimitri van Heesch - - This program is free software; you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation; either version 2 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License along - with this program; if not, write to the Free Software Foundation, Inc., - 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. - - @licend The above is the entire license notice - for the JavaScript code in this file - */ -function convertToId(search) -{ - var result = ''; - for (i=0;i do a search - { - this.Search(); - } - } - - this.OnSearchSelectKey = function(evt) - { - var e = (evt) ? evt : window.event; // for IE - if (e.keyCode==40 && this.searchIndex0) // Up - { - this.searchIndex--; - this.OnSelectItem(this.searchIndex); - } - else if (e.keyCode==13 || e.keyCode==27) - { - this.OnSelectItem(this.searchIndex); - this.CloseSelectionWindow(); - this.DOMSearchField().focus(); - } - return false; - } - - // --------- Actions - - // Closes the results window. - this.CloseResultsWindow = function() - { - this.DOMPopupSearchResultsWindow().style.display = 'none'; - this.DOMSearchClose().style.display = 'none'; - this.Activate(false); - } - - this.CloseSelectionWindow = function() - { - this.DOMSearchSelectWindow().style.display = 'none'; - } - - // Performs a search. - this.Search = function() - { - this.keyTimeout = 0; - - // strip leading whitespace - var searchValue = this.DOMSearchField().value.replace(/^ +/, ""); - - var code = searchValue.toLowerCase().charCodeAt(0); - var idxChar = searchValue.substr(0, 1).toLowerCase(); - if ( 0xD800 <= code && code <= 0xDBFF && searchValue > 1) // surrogate pair - { - idxChar = searchValue.substr(0, 2); - } - - var resultsPage; - var resultsPageWithSearch; - var hasResultsPage; - - var idx = indexSectionsWithContent[this.searchIndex].indexOf(idxChar); - if (idx!=-1) - { - var hexCode=idx.toString(16); - resultsPage = this.resultsPath + '/' + indexSectionNames[this.searchIndex] + '_' + hexCode + '.html'; - resultsPageWithSearch = resultsPage+'?'+escape(searchValue); - hasResultsPage = true; - } - else // nothing available for this search term - { - resultsPage = this.resultsPath + '/nomatches.html'; - resultsPageWithSearch = resultsPage; - hasResultsPage = false; - } - - window.frames.MSearchResults.location = resultsPageWithSearch; - var domPopupSearchResultsWindow = this.DOMPopupSearchResultsWindow(); - - if (domPopupSearchResultsWindow.style.display!='block') - { - var domSearchBox = this.DOMSearchBox(); - this.DOMSearchClose().style.display = 'inline'; - if (this.insideFrame) - { - var domPopupSearchResults = this.DOMPopupSearchResults(); - domPopupSearchResultsWindow.style.position = 'relative'; - domPopupSearchResultsWindow.style.display = 'block'; - var width = document.body.clientWidth - 8; // the -8 is for IE :-( - domPopupSearchResultsWindow.style.width = width + 'px'; - domPopupSearchResults.style.width = width + 'px'; - } - else - { - var domPopupSearchResults = this.DOMPopupSearchResults(); - var left = getXPos(domSearchBox) + 150; // domSearchBox.offsetWidth; - var top = getYPos(domSearchBox) + 20; // domSearchBox.offsetHeight + 1; - domPopupSearchResultsWindow.style.display = 'block'; - left -= domPopupSearchResults.offsetWidth; - domPopupSearchResultsWindow.style.top = top + 'px'; - domPopupSearchResultsWindow.style.left = left + 'px'; - } - } - - this.lastSearchValue = searchValue; - this.lastResultsPage = resultsPage; - } - - // -------- Activation Functions - - // Activates or deactivates the search panel, resetting things to - // their default values if necessary. - this.Activate = function(isActive) - { - if (isActive || // open it - this.DOMPopupSearchResultsWindow().style.display == 'block' - ) - { - this.DOMSearchBox().className = 'MSearchBoxActive'; - - var searchField = this.DOMSearchField(); - - if (searchField.value == this.searchLabel) // clear "Search" term upon entry - { - searchField.value = ''; - this.searchActive = true; - } - } - else if (!isActive) // directly remove the panel - { - this.DOMSearchBox().className = 'MSearchBoxInactive'; - this.DOMSearchField().value = this.searchLabel; - this.searchActive = false; - this.lastSearchValue = '' - this.lastResultsPage = ''; - } - } -} - -// ----------------------------------------------------------------------- - -// The class that handles everything on the search results page. -function SearchResults(name) -{ - // The number of matches from the last run of . - this.lastMatchCount = 0; - this.lastKey = 0; - this.repeatOn = false; - - // Toggles the visibility of the passed element ID. - this.FindChildElement = function(id) - { - var parentElement = document.getElementById(id); - var element = parentElement.firstChild; - - while (element && element!=parentElement) - { - if (element.nodeName == 'DIV' && element.className == 'SRChildren') - { - return element; - } - - if (element.nodeName == 'DIV' && element.hasChildNodes()) - { - element = element.firstChild; - } - else if (element.nextSibling) - { - element = element.nextSibling; - } - else - { - do - { - element = element.parentNode; - } - while (element && element!=parentElement && !element.nextSibling); - - if (element && element!=parentElement) - { - element = element.nextSibling; - } - } - } - } - - this.Toggle = function(id) - { - var element = this.FindChildElement(id); - if (element) - { - if (element.style.display == 'block') - { - element.style.display = 'none'; - } - else - { - element.style.display = 'block'; - } - } - } - - // Searches for the passed string. If there is no parameter, - // it takes it from the URL query. - // - // Always returns true, since other documents may try to call it - // and that may or may not be possible. - this.Search = function(search) - { - if (!search) // get search word from URL - { - search = window.location.search; - search = search.substring(1); // Remove the leading '?' - search = unescape(search); - } - - search = search.replace(/^ +/, ""); // strip leading spaces - search = search.replace(/ +$/, ""); // strip trailing spaces - search = search.toLowerCase(); - search = convertToId(search); - - var resultRows = document.getElementsByTagName("div"); - var matches = 0; - - var i = 0; - while (i < resultRows.length) - { - var row = resultRows.item(i); - if (row.className == "SRResult") - { - var rowMatchName = row.id.toLowerCase(); - rowMatchName = rowMatchName.replace(/^sr\d*_/, ''); // strip 'sr123_' - - if (search.length<=rowMatchName.length && - rowMatchName.substr(0, search.length)==search) - { - row.style.display = 'block'; - matches++; - } - else - { - row.style.display = 'none'; - } - } - i++; - } - document.getElementById("Searching").style.display='none'; - if (matches == 0) // no results - { - document.getElementById("NoMatches").style.display='block'; - } - else // at least one result - { - document.getElementById("NoMatches").style.display='none'; - } - this.lastMatchCount = matches; - return true; - } - - // return the first item with index index or higher that is visible - this.NavNext = function(index) - { - var focusItem; - while (1) - { - var focusName = 'Item'+index; - focusItem = document.getElementById(focusName); - if (focusItem && focusItem.parentNode.parentNode.style.display=='block') - { - break; - } - else if (!focusItem) // last element - { - break; - } - focusItem=null; - index++; - } - return focusItem; - } - - this.NavPrev = function(index) - { - var focusItem; - while (1) - { - var focusName = 'Item'+index; - focusItem = document.getElementById(focusName); - if (focusItem && focusItem.parentNode.parentNode.style.display=='block') - { - break; - } - else if (!focusItem) // last element - { - break; - } - focusItem=null; - index--; - } - return focusItem; - } - - this.ProcessKeys = function(e) - { - if (e.type == "keydown") - { - this.repeatOn = false; - this.lastKey = e.keyCode; - } - else if (e.type == "keypress") - { - if (!this.repeatOn) - { - if (this.lastKey) this.repeatOn = true; - return false; // ignore first keypress after keydown - } - } - else if (e.type == "keyup") - { - this.lastKey = 0; - this.repeatOn = false; - } - return this.lastKey!=0; - } - - this.Nav = function(evt,itemIndex) - { - var e = (evt) ? evt : window.event; // for IE - if (e.keyCode==13) return true; - if (!this.ProcessKeys(e)) return false; - - if (this.lastKey==38) // Up - { - var newIndex = itemIndex-1; - var focusItem = this.NavPrev(newIndex); - if (focusItem) - { - var child = this.FindChildElement(focusItem.parentNode.parentNode.id); - if (child && child.style.display == 'block') // children visible - { - var n=0; - var tmpElem; - while (1) // search for last child - { - tmpElem = document.getElementById('Item'+newIndex+'_c'+n); - if (tmpElem) - { - focusItem = tmpElem; - } - else // found it! - { - break; - } - n++; - } - } - } - if (focusItem) - { - focusItem.focus(); - } - else // return focus to search field - { - parent.document.getElementById("MSearchField").focus(); - } - } - else if (this.lastKey==40) // Down - { - var newIndex = itemIndex+1; - var focusItem; - var item = document.getElementById('Item'+itemIndex); - var elem = this.FindChildElement(item.parentNode.parentNode.id); - if (elem && elem.style.display == 'block') // children visible - { - focusItem = document.getElementById('Item'+itemIndex+'_c0'); - } - if (!focusItem) focusItem = this.NavNext(newIndex); - if (focusItem) focusItem.focus(); - } - else if (this.lastKey==39) // Right - { - var item = document.getElementById('Item'+itemIndex); - var elem = this.FindChildElement(item.parentNode.parentNode.id); - if (elem) elem.style.display = 'block'; - } - else if (this.lastKey==37) // Left - { - var item = document.getElementById('Item'+itemIndex); - var elem = this.FindChildElement(item.parentNode.parentNode.id); - if (elem) elem.style.display = 'none'; - } - else if (this.lastKey==27) // Escape - { - parent.searchBox.CloseResultsWindow(); - parent.document.getElementById("MSearchField").focus(); - } - else if (this.lastKey==13) // Enter - { - return true; - } - return false; - } - - this.NavChild = function(evt,itemIndex,childIndex) - { - var e = (evt) ? evt : window.event; // for IE - if (e.keyCode==13) return true; - if (!this.ProcessKeys(e)) return false; - - if (this.lastKey==38) // Up - { - if (childIndex>0) - { - var newIndex = childIndex-1; - document.getElementById('Item'+itemIndex+'_c'+newIndex).focus(); - } - else // already at first child, jump to parent - { - document.getElementById('Item'+itemIndex).focus(); - } - } - else if (this.lastKey==40) // Down - { - var newIndex = childIndex+1; - var elem = document.getElementById('Item'+itemIndex+'_c'+newIndex); - if (!elem) // last child, jump to parent next parent - { - elem = this.NavNext(itemIndex+1); - } - if (elem) - { - elem.focus(); - } - } - else if (this.lastKey==27) // Escape - { - parent.searchBox.CloseResultsWindow(); - parent.document.getElementById("MSearchField").focus(); - } - else if (this.lastKey==13) // Enter - { - return true; - } - return false; - } -} - -function setKeyActions(elem,action) -{ - elem.setAttribute('onkeydown',action); - elem.setAttribute('onkeypress',action); - elem.setAttribute('onkeyup',action); -} - -function setClassAttr(elem,attr) -{ - elem.setAttribute('class',attr); - elem.setAttribute('className',attr); -} - -function createResults() -{ - var results = document.getElementById("SRResults"); - for (var e=0; e - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/typedefs_0.js b/docs/search/typedefs_0.js deleted file mode 100644 index 880e367a9..000000000 --- a/docs/search/typedefs_0.js +++ /dev/null @@ -1,6 +0,0 @@ -var searchData= -[ - ['adjmapt',['AdjMapT',['../namespacetag.html#a9cba46943ca1c23cb7f1c104a02ef2b5',1,'tag']]], - ['agroupst',['AGroupsT',['../namespacetag.html#adc952604313a2e54d27b022e4334e420',1,'tag']]], - ['argst',['ArgsT',['../namespaceade.html#a9db559f9967702ac0d7ce882e73e8f15',1,'ade::ArgsT()'],['../namespaceead.html#ab8fc1bf6d7132e0d7993b4d951780216',1,'ead::ArgsT()']]] -]; diff --git a/docs/search/typedefs_1.html b/docs/search/typedefs_1.html deleted file mode 100644 index 7af807db4..000000000 --- a/docs/search/typedefs_1.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
    No Matches
    - -
    - - diff --git a/docs/search/typedefs_1.js b/docs/search/typedefs_1.js deleted file mode 100644 index 5a8efee3d..000000000 --- a/docs/search/typedefs_1.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['builderargst',['BuilderArgsT',['../namespaceead.html#ad4e749f733e8f8c103a941723c8f1d97',1,'ead']]] -]; diff --git a/docs/search/typedefs_10.html b/docs/search/typedefs_10.html deleted file mode 100644 index d8e5942ad..000000000 --- a/docs/search/typedefs_10.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - -
    -
    Loading...
    -
    - -
    Searching...
    -
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    ade::TensptrT
    std::shared_ptr< iTensor > TensptrT
    Tensor smart pointer.
    Definition: itensor.hpp:49
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    1 
    9 #include "pbm/data.hpp"
    10 
    11 #include "ead/generated/opcode.hpp"
    12 #include "ead/generated/dtype.hpp"
    13 
    14 #include "ead/coord.hpp"
    15 #include "ead/constant.hpp"
    16 #include "ead/variable.hpp"
    17 
    18 #ifndef EAD_SERIALIZE_HPP
    19 #define EAD_SERIALIZE_HPP
    20 
    21 namespace ead
    22 {
    23 
    24 static std::unordered_set<size_t> non_bijectives =
    25 {
    26  age::REDUCE_SUM,
    27  age::REDUCE_PROD,
    28  age::REDUCE_MIN,
    29  age::REDUCE_MAX,
    30  age::EXTEND,
    31 };
    32 
    33 static bool is_big_endian(void)
    34 {
    35  union
    36  {
    37  uint16_t _;
    38  char bytes[2];
    39  } twob = { 0x0001 };
    40 
    41  return twob.bytes[0] == 0;
    42 }
    43 
    44 struct EADSaver final : public pbm::iSaver
    45 {
    46  std::string save_leaf (bool& is_const, ade::iLeaf* leaf) override
    47  {
    48  char* data = (char*) leaf->data();
    49  size_t nelems = leaf->shape().n_elems();
    50  size_t nbytes = age::type_size((age::_GENERATED_DTYPE) leaf->type_code());
    51  if (is_big_endian() && nbytes > 1)
    52  {
    53  size_t totalbytes = nelems * nbytes;
    54  std::string out(totalbytes, '\0');
    55  for (size_t i = 0; i < totalbytes; ++i)
    56  {
    57  size_t elemi = i / nbytes;
    58  size_t outi = (elemi + 1) * nbytes - (i % nbytes);
    59  out[outi] = data[i];
    60  }
    61  return out;
    62  }
    63  return std::string(data, nelems * nbytes);
    64  }
    65 
    66  std::vector<double> save_shaper (const ade::CoordptrT& mapper) override
    67  {
    68  std::vector<double> out;
    69  mapper->access(
    70  [&out](const ade::MatrixT& mat)
    71  {
    72  for (ade::RankT i = 0; i < ade::mat_dim; ++i)
    73  {
    74  for (ade::RankT j = 0; j < ade::mat_dim; ++j)
    75  {
    76  out.push_back(mat[i][j]);
    77  }
    78  }
    79  });
    80  return out;
    81  }
    82 
    83  std::vector<double> save_coorder (const ade::CoordptrT& mapper) override
    84  {
    85  if (nullptr == mapper)
    86  {
    87  return std::vector<double>();
    88  }
    89  ade::CoordT coord;
    90  mapper->forward(coord.begin(), coord.begin());
    91  return std::vector<double>(coord.begin(), coord.end());
    92  }
    93 };
    94 
    95 #define _OUT_GENERIC(realtype)leaf = is_const?\
    96 make_constant<realtype>((realtype*) pb, shape)->get_tensor():\
    97 ade::TensptrT(Variable<realtype>::get((realtype*) pb, shape, label));
    98 
    100 struct EADLoader final : public pbm::iLoader
    101 {
    102  ade::TensptrT generate_leaf (const char* pb, ade::Shape shape,
    103  std::string typelabel, std::string label, bool is_const) override
    104  {
    105  ade::TensptrT leaf;
    106  age::_GENERATED_DTYPE gencode = age::get_type(typelabel);
    107  size_t nbytes = age::type_size(gencode);
    108  if (is_big_endian() && nbytes > 1)
    109  {
    110  size_t totalbytes = shape.n_elems() * nbytes;
    111  std::string out(totalbytes, '\0');
    112  for (size_t i = 0; i < totalbytes; ++i)
    113  {
    114  size_t elemi = i / nbytes;
    115  size_t outi = (elemi + 1) * nbytes - (i % nbytes);
    116  out[outi] = pb[i];
    117  }
    118  pb = out.c_str();
    119  TYPE_LOOKUP(_OUT_GENERIC, gencode)
    120  }
    121  else
    122  {
    123  TYPE_LOOKUP(_OUT_GENERIC, gencode)
    124  }
    125  return leaf;
    126  }
    127 
    128  ade::TensptrT generate_func (std::string opname, ade::ArgsT args) override
    129  {
    130  return ade::TensptrT(ade::Functor::get(ade::Opcode{opname, age::get_op(opname)}, args));
    131  }
    132 
    133  ade::CoordptrT generate_shaper (std::vector<double> coord) override
    134  {
    135  if (ade::mat_dim * ade::mat_dim != coord.size())
    136  {
    137  logs::fatal("cannot deserialize non-matrix shape map");
    138  }
    139  return std::make_shared<ade::CoordMap>(
    140  [&](ade::MatrixT fwd)
    141  {
    142  for (ade::RankT i = 0; i < ade::mat_dim; ++i)
    143  {
    144  for (ade::RankT j = 0; j < ade::mat_dim; ++j)
    145  {
    146  fwd[i][j] = coord[i * ade::mat_dim + j];
    147  }
    148  }
    149  });
    150  }
    151 
    152  ade::CoordptrT generate_coorder (
    153  std::string opname, std::vector<double> coord) override
    154  {
    155  if (0 == coord.size()) // is identity
    156  {
    157  return nullptr;
    158  }
    159  if (ade::rank_cap + 1 < coord.size())
    160  {
    161  logs::fatal("cannot deserialize non-vector coordinate map");
    162  }
    163  bool is_bijective = false == estd::has(non_bijectives, age::get_op(opname));
    164  ade::CoordT indices;
    165  auto cit = coord.begin();
    166  std::copy(cit, cit + ade::rank_cap, indices.begin());
    167  return std::make_shared<CoordMap>(indices, is_bijective);
    168  }
    169 };
    170 
    171 #undef _OUT_GENERIC
    172 
    173 }
    174 
    175 #endif // EAD_SERIALIZE_HPP
    csv_to_png.args
    args
    Definition: csv_to_png.py:105
    -
    ead::EADSaver::save_coorder
    std::vector< double > save_coorder(const ade::CoordptrT &mapper) override
    Definition: serialize.hpp:83
    -
    ade::Opcode
    Encoding of operation.
    Definition: ifunctor.hpp:18
    -
    ade::iTensor::shape
    virtual const Shape & shape(void) const =0
    Return the shape held by this tensor.
    -
    ead::EADLoader::generate_coorder
    ade::CoordptrT generate_coorder(std::string opname, std::vector< double > coord) override
    Definition: serialize.hpp:152
    - -
    ead
    Definition: constant.hpp:9
    -
    ade::iLeaf
    Leaf of the graph commonly representing the variable in an equation.
    Definition: ileaf.hpp:19
    -
    ead::EADLoader
    Unmarshal cortenn::Source as Variable containing context of source.
    Definition: serialize.hpp:100
    - -
    ade::CoordptrT
    std::shared_ptr< iCoordMap > CoordptrT
    Type of iCoordMap smartpointer.
    Definition: coord.hpp:106
    -
    ade::Shape::n_elems
    NElemT n_elems(void) const
    Return the total number of elements represented by the shape.
    Definition: shape.hpp:116
    -
    ead::EADLoader::generate_leaf
    ade::TensptrT generate_leaf(const char *pb, ade::Shape shape, std::string typelabel, std::string label, bool is_const) override
    Definition: serialize.hpp:102
    -
    ead::non_bijectives
    static std::unordered_set< size_t > non_bijectives
    Definition: serialize.hpp:24
    -
    ead::EADSaver::save_leaf
    std::string save_leaf(bool &is_const, ade::iLeaf *leaf) override
    Definition: serialize.hpp:46
    -
    ade::MatrixT
    double[mat_dim][mat_dim] MatrixT
    Coordinate transformation matrix (using homogeneous)
    Definition: matops.hpp:28
    - -
    ade::TensptrT
    std::shared_ptr< iTensor > TensptrT
    Tensor smart pointer.
    Definition: itensor.hpp:49
    -
    _OUT_GENERIC
    #define _OUT_GENERIC(realtype)
    Definition: serialize.hpp:95
    -
    ead::EADLoader::generate_func
    ade::TensptrT generate_func(std::string opname, ade::ArgsT args) override
    Definition: serialize.hpp:128
    -
    ead::is_big_endian
    static bool is_big_endian(void)
    Definition: serialize.hpp:33
    -
    ead::EADSaver::save_shaper
    std::vector< double > save_shaper(const ade::CoordptrT &mapper) override
    Definition: serialize.hpp:66
    -
    ade::mat_dim
    const RankT mat_dim
    Number of rows and columns for the homogeneous matrix.
    Definition: matops.hpp:22
    -
    ade::RankT
    uint8_t RankT
    Type used for shape rank.
    Definition: shape.hpp:23
    -
    ade::Functor::get
    static Functor * get(Opcode opcode, ArgsT args)
    Return a Functor with with input tensor and meta arguments.
    Definition: functor.hpp:21
    -
    ade::Shape
    Definition: shape.hpp:60
    -
    pbm::iLoader
    Definition: data.hpp:36
    -
    ade::CoordT
    std::array< CDimT, rank_cap > CoordT
    Definition: shape.hpp:54
    -
    ead::EADLoader::generate_shaper
    ade::CoordptrT generate_shaper(std::vector< double > coord) override
    Definition: serialize.hpp:133
    -
    ade::iData::data
    virtual void * data(void)=0
    Return pointer to internal data.
    -
    pbm::iSaver
    Definition: data.hpp:27
    -
    ade::iData::type_code
    virtual size_t type_code(void) const =0
    Return data type encoding.
    -
    ade::rank_cap
    const RankT rank_cap
    Number of dimsensions in a shape/coordinate.
    Definition: shape.hpp:45
    - -
    ead::EADSaver
    Definition: serialize.hpp:44
    -
    ade::ArgsT
    std::vector< FuncArg > ArgsT
    Type of functor arguments.
    Definition: funcarg.hpp:101
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    1 #include <list>
    2 #include <unordered_set>
    3 
    4 #include "ade/traveler.hpp"
    5 
    6 #include "opt/optimize.hpp"
    7 
    8 #include "ead/constant.hpp"
    9 #include "ead/functor.hpp"
    10 
    11 #ifndef EAD_SESSION_HPP
    12 #define EAD_SESSION_HPP
    13 
    14 namespace ead
    15 {
    16 
    17 using TensSetT = std::unordered_set<ade::iTensor*>;
    18 
    19 struct iSession
    20 {
    21  virtual ~iSession (void) = default;
    22 
    23  virtual void track (ade::TensT roots) = 0;
    24 
    28  virtual void update (TensSetT updated = {}, TensSetT ignores = {}) = 0;
    29 
    30  virtual void update_target (TensSetT target, TensSetT updated = {}) = 0;
    31 };
    32 
    33 struct SizeT
    34 {
    35  size_t d = 0;
    36 
    37  operator size_t() const { return d; }
    38 };
    39 
    40 // todo: give this more reasons for existence
    41 struct Traveler final : public ade::iTraveler
    42 {
    44  void visit (ade::iLeaf* leaf) override
    45  {
    46  visited_.emplace(leaf);
    47  }
    48 
    50  void visit (ade::iFunctor* func) override
    51  {
    52  if (false == estd::has(visited_, func))
    53  {
    54  visited_.emplace(func);
    55  auto& children = func->get_children();
    56  for (auto& child : children)
    57  {
    58  child.get_tensor()->accept(*this);
    59  }
    60  }
    61  }
    62 
    63  TensSetT visited_;
    64 };
    65 
    66 // for each leaf node, iteratively update the parents
    67 // don't update parent node if it is part of ignored set
    68 struct Session final : public iSession
    69 {
    70  void track (ade::TensT roots) override
    71  {
    72  tracked_.insert(roots.begin(), roots.end());
    73  ade::ParentFinder pfinder;
    74  for (ade::TensptrT& root : roots)
    75  {
    76  root->accept(pfinder);
    77  root->accept(stat_);
    78  }
    79  auto& statmap = stat_.graphsize_;
    80 
    81  std::list<ade::iOperableFunc*> all_ops;
    82  for (auto& statpair : statmap)
    83  {
    84  if (0 < statpair.second.upper_)
    85  {
    86  // ensure we only track operable functors
    87  auto op = dynamic_cast<ade::iOperableFunc*>(statpair.first);
    88  if (nullptr == op)
    89  {
    90  logs::fatalf("cannot track non-operable functor %s",
    91  statpair.first->to_string().c_str());
    92  }
    93  all_ops.push_back(op);
    94  }
    95  }
    96  all_ops.sort(
    97  [&statmap](ade::iOperableFunc* a, ade::iOperableFunc* b)
    98  {
    99  return statmap[a].upper_ < statmap[b].upper_;
    100  });
    101  requirements_.clear();
    102  for (ade::iOperableFunc* op : all_ops)
    103  {
    104  auto& args = op->get_children();
    105  TensSetT unique_children;
    106  for (const ade::FuncArg& arg : args)
    107  {
    108  auto tens = arg.get_tensor().get();
    109  if (0 < statmap[tens].upper_) // ignore leaves
    110  {
    111  unique_children.emplace(tens);
    112  }
    113  }
    114  requirements_.push_back({op, unique_children.size()});
    115  }
    116 
    117  for (auto& assocs : pfinder.parents_)
    118  {
    119  for (auto& parent_pair : assocs.second)
    120  {
    121  parents_[assocs.first].emplace(
    122  static_cast<ade::iOperableFunc*>(parent_pair.first));
    123  }
    124  }
    125  }
    126 
    127  void update (TensSetT updated = {}, TensSetT ignores = {}) override
    128  {
    129  std::unordered_map<ade::iOperableFunc*,SizeT> fulfilments;
    130  for (ade::iTensor* unodes : updated)
    131  {
    132  auto& node_parents = parents_[unodes];
    133  for (auto& node_parent : node_parents)
    134  {
    135  ++fulfilments[node_parent].d;
    136  }
    137  }
    138  // ignored nodes and its dependers will never fulfill requirement
    139  for (auto& op : requirements_)
    140  {
    141  // fulfilled and not ignored
    142  if (fulfilments[op.first].d >= op.second &&
    143  false == estd::has(ignores, op.first))
    144  {
    145  op.first->update();
    146  auto& op_parents = parents_[op.first];
    147  for (auto& op_parent : op_parents)
    148  {
    149  ++fulfilments[op_parent].d;
    150  }
    151  }
    152  }
    153  }
    154 
    155  void update_target (TensSetT target, TensSetT updated = {}) override
    156  {
    157  Traveler targetted;
    158  for (auto& tens : target)
    159  {
    160  tens->accept(targetted);
    161  }
    162  std::unordered_map<ade::iOperableFunc*,SizeT> fulfilments;
    163  for (ade::iTensor* unodes : updated)
    164  {
    165  auto& node_parents = parents_[unodes];
    166  for (auto& node_parent : node_parents)
    167  {
    168  ++fulfilments[node_parent].d;
    169  }
    170  }
    171  // ignored nodes and its dependers will never fulfill requirement
    172  for (auto& op : requirements_)
    173  {
    174  // is relevant to target, is fulfilled and not ignored
    175  if (estd::has(targetted.visited_, op.first) &&
    176  fulfilments[op.first].d >= op.second)
    177  {
    178  op.first->update();
    179  auto& op_parents = parents_[op.first];
    180  for (auto& op_parent : op_parents)
    181  {
    182  ++fulfilments[op_parent].d;
    183  }
    184  }
    185  }
    186  }
    187 
    188  void optimize (const opt::OptCtx& rules)
    189  {
    190  ade::TensT tracked(tracked_.begin(), tracked_.end());
    191  opt::optimize(tracked, rules);
    192  stat_.graphsize_.clear();
    193  parents_.clear();
    194  track(tracked);
    195  }
    196 
    197  std::unordered_set<ade::TensptrT> tracked_;
    198 
    199  ade::GraphStat stat_;
    200 
    201  std::unordered_map<ade::iTensor*,
    202  std::unordered_set<ade::iOperableFunc*>> parents_;
    203 
    204  std::vector<std::pair<ade::iOperableFunc*,size_t>> requirements_; // todo: test minimal requirements
    205 };
    206 
    207 }
    208 
    209 #endif // EAD_SESSION_HPP
    ead::Session::parents_
    std::unordered_map< ade::iTensor *, std::unordered_set< ade::iOperableFunc * > > parents_
    Definition: session.hpp:202
    -
    ade::iFunctor
    Interface of iOperation-defined operation node.
    Definition: ifunctor.hpp:28
    -
    ead::Session
    Definition: session.hpp:68
    -
    ead::Traveler::visit
    void visit(ade::iLeaf *leaf) override
    Implementation of iTraveler.
    Definition: session.hpp:44
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    ead::Session::optimize
    void optimize(const opt::OptCtx &rules)
    Definition: session.hpp:188
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    ade::GraphStat::graphsize_
    std::unordered_map< iTensor *, NumRange< size_t > > graphsize_
    Definition: traveler.hpp:89
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    void update(TensSetT updated={}, TensSetT ignores={}) override
    Definition: session.hpp:127
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    ead::Traveler
    Definition: session.hpp:41
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    ead::iSession::update_target
    virtual void update_target(TensSetT target, TensSetT updated={})=0
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    ead::TensSetT
    std::unordered_set< ade::iTensor * > TensSetT
    Definition: session.hpp:17
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    ead::iSession
    Definition: session.hpp:19
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    void visit(ade::iFunctor *func) override
    Implementation of iTraveler.
    Definition: session.hpp:50
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    ead
    Definition: constant.hpp:9
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    Leaf of the graph commonly representing the variable in an equation.
    Definition: ileaf.hpp:19
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    Traveler that maps each tensor to its subtree&#39;s maximum depth.
    Definition: traveler.hpp:37
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    opt::OptCtx
    Definition: optimize.hpp:12
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    ead::Traveler::visited_
    TensSetT visited_
    Definition: session.hpp:63
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    ead::iSession::update
    virtual void update(TensSetT updated={}, TensSetT ignores={})=0
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    ade::TensptrT
    std::shared_ptr< iTensor > TensptrT
    Tensor smart pointer.
    Definition: itensor.hpp:49
    -
    ead::Session::tracked_
    std::unordered_set< ade::TensptrT > tracked_
    Definition: session.hpp:197
    -
    ade::ParentFinder::parents_
    std::unordered_map< ade::iTensor *, ParentMapT > parents_
    Tracks child to parents relationship.
    Definition: traveler.hpp:174
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    ead::iSession::track
    virtual void track(ade::TensT roots)=0
    -
    ade::iTraveler
    Interface to travel through graph, treating iLeaf and iFunctor differently.
    Definition: itensor.hpp:22
    -
    ade::iTensor
    Interface of traversible and differentiable nodes with shape information.
    Definition: itensor.hpp:34
    -
    ade::iOperableFunc
    A functor node with direct access to evaluated data.
    Definition: iopfunc.hpp:20
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    ead::Session::track
    void track(ade::TensT roots) override
    Definition: session.hpp:70
    - -
    ade::iFunctor::get_children
    virtual const ArgsT & get_children(void) const =0
    Return children nodes as a vector of raw pointers.
    -
    ead::Session::requirements_
    std::vector< std::pair< ade::iOperableFunc *, size_t > > requirements_
    Definition: session.hpp:204
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    ead::SizeT
    Definition: session.hpp:33
    -
    ade::FuncArg
    Coordinate mapper and tensor pair.
    Definition: funcarg.hpp:21
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    ade::TensT
    std::vector< TensptrT > TensT
    Vector representation of ade tensor pointers.
    Definition: funcarg.hpp:104
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    ade::ParentFinder
    Traveler that for each child tracks the relationship to all parents.
    Definition: traveler.hpp:146
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    opt::optimize
    ade::TensT optimize(ade::TensT roots, const OptCtx &opts)
    -
    ead::Session::stat_
    ade::GraphStat stat_
    Definition: session.hpp:199
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    ead::Session::update_target
    void update_target(TensSetT target, TensSetT updated={}) override
    Definition: session.hpp:155
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    ead::SizeT::d
    size_t d
    Definition: session.hpp:35
    -
    ead::iSession::~iSession
    virtual ~iSession(void)=default
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    struct  ade::Shape
     
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     ade
     
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    using ade::RankT = uint8_t
     Type used for shape rank. More...
     
    using ade::DimT = uint16_t
     
    using ade::CDimT = double
     Type used for coordinate dimensions. More...
     
    using ade::NElemT = uint64_t
     
    using ade::ShapeT = std::array< DimT, rank_cap >
     Array type used to hold dimension info in Shape. More...
     
    using ade::CoordT = std::array< CDimT, rank_cap >
     
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    NElemT ade::index (Shape shape, CoordT coord)
     
    CoordT ade::coordinate (Shape shape, NElemT idx)
     
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    const RankT ade::rank_cap = 8
     Number of dimsensions in a shape/coordinate. More...
     
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    1 
    9 #include <array>
    10 #include <cmath>
    11 #include <numeric>
    12 #include <vector>
    13 
    14 #include "logs/logs.hpp"
    15 
    16 #ifndef ADE_SHAPE_HPP
    17 #define ADE_SHAPE_HPP
    18 
    19 namespace ade
    20 {
    21 
    23 using RankT = uint8_t;
    24 
    25 // /// Type used for shape dimension
    26 // #if !defined(SDIM_BYTES) || SDIM_BYTES <= 1
    27 // using DimT = uint8_t;
    28 // #elif SDIM_BYTES <= 2
    29 using DimT = uint16_t;
    30 // #elif SDIM_BYTES <= 4
    31 // using DimT = uint32_t;
    32 // #else
    33 // using DimT = uint64_t;
    34 // #endif
    35 
    37 using CDimT = double;
    38 
    42 using NElemT = uint64_t;
    43 
    45 const RankT rank_cap = 8;
    46 
    48 using ShapeT = std::array<DimT,rank_cap>;
    49 
    54 using CoordT = std::array<CDimT,rank_cap>;
    55 
    60 struct Shape final
    61 {
    63  using iterator = ShapeT::iterator;
    64 
    66  using const_iterator = ShapeT::const_iterator;
    67 
    68  Shape (void)
    69  {
    70  std::fill(dims_.begin(), dims_.end(), 1);
    71  }
    72 
    73  Shape (std::vector<DimT> dims)
    74  {
    75  vector_assign(dims);
    76  }
    77 
    78  Shape (const Shape& other) = default;
    79 
    80  Shape& operator = (const Shape& other) = default;
    81 
    82  Shape& operator = (const std::vector<DimT>& dims)
    83  {
    84  vector_assign(dims);
    85  return *this;
    86  }
    87 
    88  Shape (Shape&& other)
    89  {
    90  move_helper(std::move(other));
    91  }
    92 
    93  Shape& operator = (Shape&& other)
    94  {
    95  if (this != &other)
    96  {
    97  move_helper(std::move(other));
    98  }
    99  return *this;
    100  }
    101 
    102 
    103  // >>>> ACCESSORS <<<<
    104 
    106  DimT at (RankT idx) const
    107  {
    108  if (rank_cap <= idx)
    109  {
    110  logs::fatalf("cannot access out of bounds index %d", idx);
    111  }
    112  return dims_.at(idx);
    113  }
    114 
    116  NElemT n_elems (void) const
    117  {
    118  auto it = dims_.begin();
    119  return std::accumulate(it, it + rank_cap, (NElemT) 1,
    120  std::multiplies<NElemT>());
    121  }
    122 
    125  bool compatible_before (const Shape& other, RankT idx) const
    126  {
    127  auto it = dims_.begin();
    128  return std::equal(it, it + std::min(idx, rank_cap), other.dims_.begin());
    129  }
    130 
    134  bool compatible_after (const Shape& other, RankT idx) const
    135  {
    136  bool compatible = false;
    137  if (idx < rank_cap)
    138  {
    139  compatible = std::equal(dims_.begin() + idx,
    140  dims_.end(), other.dims_.begin() + idx);
    141  }
    142  return compatible;
    143  }
    144 
    146  std::string to_string (void) const
    147  {
    148  return fmts::to_string(begin(), end());
    149  }
    150 
    151  // >>>> INTERNAL CONTROL <<<<
    152 
    154  iterator begin (void)
    155  {
    156  return dims_.begin();
    157  }
    158 
    160  iterator end (void)
    161  {
    162  return dims_.end();
    163  }
    164 
    166  const_iterator begin (void) const
    167  {
    168  return dims_.begin();
    169  }
    170 
    172  const_iterator end (void) const
    173  {
    174  return dims_.end();
    175  }
    176 
    177 private:
    178  void vector_assign (const std::vector<DimT>& dims)
    179  {
    180  auto src = dims.begin();
    181  if (std::any_of(src, dims.end(),
    182  [](DimT d)
    183  {
    184  return d == 0;
    185  }))
    186  {
    187  logs::fatalf("cannot create shape with vector containing zero: %s",
    188  fmts::to_string(dims.begin(), dims.end()).c_str());
    189  }
    190  auto dest = dims_.begin();
    191  RankT rank = std::min((size_t) rank_cap, dims.size());
    192  std::copy(src, src + rank, dest);
    193  std::fill(dest + rank, dest + rank_cap, 1);
    194  }
    195 
    196  void move_helper (Shape&& other)
    197  {
    198  dims_ = std::move(other.dims_);
    199  std::fill(other.dims_.begin(), other.dims_.end(), 1);
    200  }
    201 
    203  ShapeT dims_;
    204 };
    205 
    212 NElemT index (Shape shape, CoordT coord);
    213 
    217 CoordT coordinate (Shape shape, NElemT idx);
    218 
    219 }
    220 
    221 #endif // ADE_SHAPE_HPP
    ade::NElemT
    uint64_t NElemT
    Definition: shape.hpp:42
    -
    ade::Shape::move_helper
    void move_helper(Shape &&other)
    Definition: shape.hpp:196
    -
    ade::ShapeT
    std::array< DimT, rank_cap > ShapeT
    Array type used to hold dimension info in Shape.
    Definition: shape.hpp:48
    -
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    DimT at(RankT idx) const
    Return DimT element at idx for any index in range [0:rank_cap)
    Definition: shape.hpp:106
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    const_iterator begin(void) const
    Return begin constant iterator of internal array.
    Definition: shape.hpp:166
    -
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    std::string to_string(void) const
    Return string representation of shape.
    Definition: shape.hpp:146
    -
    ade::Shape::operator=
    Shape & operator=(const Shape &other)=default
    -
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    iterator end(void)
    Return end iterator of internal array.
    Definition: shape.hpp:160
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    NElemT n_elems(void) const
    Return the total number of elements represented by the shape.
    Definition: shape.hpp:116
    -
    ade::DimT
    uint16_t DimT
    Definition: shape.hpp:29
    -
    ade::Shape::iterator
    ShapeT::iterator iterator
    Type of iterator used to iterate through internal array.
    Definition: shape.hpp:63
    -
    ead::min
    EigenptrT< T > min(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:893
    -
    ade::Shape::Shape
    Shape(Shape &&other)
    Definition: shape.hpp:88
    -
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    iterator begin(void)
    Return begin iterator of internal array.
    Definition: shape.hpp:154
    -
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    ShapeT::const_iterator const_iterator
    Type of constant iterator used to iterate through internal array.
    Definition: shape.hpp:66
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    ade
    Definition: coord.hpp:16
    -
    ade::CDimT
    double CDimT
    Type used for coordinate dimensions.
    Definition: shape.hpp:37
    -
    ade::Shape::compatible_before
    bool compatible_before(const Shape &other, RankT idx) const
    Definition: shape.hpp:125
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    void vector_assign(const std::vector< DimT > &dims)
    Definition: shape.hpp:178
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    Definition: shape.hpp:73
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    uint8_t RankT
    Type used for shape rank.
    Definition: shape.hpp:23
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    NElemT index(Shape shape, CoordT coord)
    -
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    ShapeT dims_
    Array of dimension values.
    Definition: shape.hpp:203
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    bool compatible_after(const Shape &other, RankT idx) const
    Definition: shape.hpp:134
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    Definition: shape.hpp:60
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    std::array< CDimT, rank_cap > CoordT
    Definition: shape.hpp:54
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    const_iterator end(void) const
    Return end constant iterator of internal array.
    Definition: shape.hpp:172
    -
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    CoordT coordinate(Shape shape, NElemT idx)
    -
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    Shape(void)
    Definition: shape.hpp:68
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    const RankT rank_cap
    Number of dimsensions in a shape/coordinate.
    Definition: shape.hpp:45
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    Return the string representation of input matrix.
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    1 #include "ade/ade.hpp"
    2 
    3 #include "tag/prop.hpp"
    4 
    5 #ifndef OPT_STATS_HPP
    6 #define OPT_STATS_HPP
    7 
    8 namespace opt
    9 {
    10 
    11 bool is_scalar (ade::iLeaf* leaf);
    12 
    13 // ==== CoordptrT stringification + comparators
    14 
    15 std::string to_string (ade::CoordptrT c);
    16 
    17 bool lt (ade::CoordptrT a, ade::CoordptrT b);
    18 
    19 bool is_equal (ade::CoordptrT a, ade::CoordptrT b);
    20 
    21 // ==== Leaf comparators
    22 
    23 bool lt (std::unordered_set<ade::iTensor*> priorities,
    24  ade::iLeaf* a, ade::iLeaf* b);
    25 
    26 // for any ileaf pair a-b, they are equivalent IFF they are both tagged immutable AND
    27 // share same shape and data values
    28 bool is_equal (ade::iLeaf* a, ade::iLeaf* b);
    29 
    30 // ==== Functor comparators
    31 
    32 bool lt (std::unordered_set<ade::iTensor*> priorities,
    33  ade::iFunctor* a, ade::iFunctor* b);
    34 
    35 // for any functors a-b, they are equivalent IFF a and b are the same opcode AND
    36 // share identical function arguments (same children, shapers, and coorders)
    37 // order matters UNLESS the op is tagged as commutative
    38 bool is_equal (ade::iFunctor* a, ade::iFunctor* b);
    39 
    40 }
    41 
    42 #endif // OPT_STATS_HPP
    ade::iFunctor
    Interface of iOperation-defined operation node.
    Definition: ifunctor.hpp:28
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    Type of iCoordMap smartpointer.
    Definition: coord.hpp:106
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     CSVEquation (GetTypeF get_ftype=[](ade::iFunctor *func) { return FUNCTOR;})
     
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     PrettyTree (std::function< std::vector< T >(T &)> traverser, std::function< void(std::ostream &, T &)> to_stream)
     
    void print (std::ostream &out, T root)
     
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    size_t branch_length_ = 2
     Horizontal length of the branch, by default the branch looks like `–. More...
     
    std::function< std::vector< T >T &)> traverser_
     Behavior of traversing through a structure. More...
     
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    void print_helper (std::ostream &out, T root, std::string prefix)
     
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    Detailed Description

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    template<typename T>
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    Generically draw a traversible structure Traversible structure is anything having a parent-children relationship Template argument T defines one node of the traversible structure

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     CoordMap (std::function< void(MatrixT)> init)
     
    iCoordMap * connect (const iCoordMap &rhs) const override
     Implementation of iCoordMap. More...
     
    void forward (CoordT::iterator out, CoordT::const_iterator in) const override
     Implementation of iCoordMap. More...
     
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     Implementation of iCoordMap. More...
     
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     Implementation of iCoordMap. More...
     
    void access (std::function< void(const MatrixT &)> cb) const override
     Implementation of iCoordMap. More...
     
    bool is_bijective (void) const override
     Implementation of iCoordMap. More...
     
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    MatrixT fwd_
     Forward transformation matrix. More...
     
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    Detailed Description

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    Coordinate transformation implementation using homogeneous matrices The transformation matrix must be inversible otherwise fatal on creation

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    Forward transformation matrix.

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    coorder_ade::FuncArgprivate
    FuncArg(TensptrT tensor, CoordptrT shaper)ade::FuncArginline
    FuncArg(TensptrT tensor, CoordptrT shaper, bool map_io, CoordptrT coorder)ade::FuncArginline
    get_coorder(void) constade::FuncArginline
    get_shaper(void) constade::FuncArginline
    get_tensor(void) constade::FuncArginline
    map_io(void) constade::FuncArginline
    map_io_ade::FuncArgprivate
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    shaper_ade::FuncArgprivate
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     FuncArg (TensptrT tensor, CoordptrT shaper)
     Construct FuncArg auto deducing coorder_ and map_io_ flag. More...
     
     FuncArg (TensptrT tensor, CoordptrT shaper, bool map_io, CoordptrT coorder)
     Construct FuncArg with specific coorder_ and map_io_ flag. More...
     
    Shape shape (void) const
     Return shape of tensor filtered through coordinate mapper. More...
     
    TensptrT get_tensor (void) const
     Return tensor being mapped. More...
     
    CoordptrT get_shaper (void) const
     Return shaper coord map. More...
     
    bool map_io (void) const
     
    CoordptrT get_coorder (void) const
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    TensptrT tensor_
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    CoordptrT shaper_
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    CoordptrT coorder_
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    Shape mapper.

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    Tensor reference.

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    accept(iTraveler &visiter) overrideade::iFunctorinlinevirtual
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     Functor (Opcode opcode, Shape shape, ArgsT args)
     
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    Opcode opcode_
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    ArgsT args_
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    std::unordered_map< iTensor *, estd::NumRange< size_t > > graphsize_
     
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    using ParentMapT = std::unordered_map< iTensor *, std::vector< size_t > >
     
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    ParentMapT typedefade::PathFinder
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    PathFinder(const iTensor *target)ade::PathFinderinline
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    using ParentMapT = std::unordered_map< iTensor *, std::unordered_set< size_t > >
     Type for mapping function nodes in path to boolean vector. More...
     
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     PathFinder (const iTensor *target)
     
    void visit (iLeaf *leaf) override
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    const iTensor * target_
     Target of tensor all paths are travelling to. More...
     
    ParentMapT parents_
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    Traveler that paints paths to a target tensor All nodes in the path are added as keys to the parents_ map with the values being a boolean vector denoting nodes leading to target For a boolean value x at index i in mapped vector, x is true if the ith child leads to target

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    ◆ parents_

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    Map of parent nodes in path.

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    at(RankT idx) constade::Shapeinline
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    const_iterator typedefade::Shape
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    iterator typedefade::Shape
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    operator=(const Shape &other)=defaultade::Shape
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    using iterator = ShapeT::iterator
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     Shape (void)
     
     Shape (std::vector< DimT > dims)
     
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    Shape & operator= (const std::vector< DimT > &dims)
     
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    DimT at (RankT idx) const
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     Return string representation of shape. More...
     
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    iterator end (void)
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    Models an aligned shape using an array of DimT values For each DimT at index i, DimT value is number of elements at dimension i For example, shape={3, 2} can model tensor [[x, y, z], [u, v, w]] (In cartesian coordinate, we treat values along the X-axis as dimension 0)

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    access(std::function< void(const MatrixT &)> cb) const =0ade::iCoordMappure virtual
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    virtual ~iCoordMap (void)=default
     
    virtual iCoordMap * connect (const iCoordMap &rhs) const =0
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    virtual void forward (CoordT::iterator out, CoordT::const_iterator in) const =0
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    virtual void access (std::function< void(const MatrixT &)> cb) const =0
     
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    get_children(void) const =0ade::iFunctorpure virtual
    get_opcode(void) const =0ade::iFunctorpure virtual
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    virtual ~iFunctor (void)=default
     
    void accept (iTraveler &visiter) override
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    virtual Opcode get_opcode (void) const =0
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    virtual const ArgsT & get_children (void) const =0
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    virtual ~iTensor (void)=default
     
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    add(TensptrT &lhs, TensptrT &rhs) const =0ade::iGradientBuilderpure virtual
    chain_rule(FuncptrT op, const TensptrT &local_der, TensptrT supcomp_grad, size_t arg_idx) const =0ade::iGradientBuilderpure virtual
    derive(TensptrT root, TensptrT target) constade::iGradientBuilderinline
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    ~iGradientBuilder(void)=defaultade::iGradientBuildervirtual
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    virtual ~iGradientBuilder (void)=default
     
    virtual TensptrT local_derivative (FuncptrT op, size_t arg_idx) const =0
     
    virtual TensptrT chain_rule (FuncptrT op, const TensptrT &local_der, TensptrT supcomp_grad, size_t arg_idx) const =0
     
    virtual TensptrT get_const_one (Shape shape) const =0
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     Return tensor representing 0 constant. More...
     
    virtual TensptrT add (TensptrT &lhs, TensptrT &rhs) const =0
     Return functor representing lhs + rhs. More...
     
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    Define manditory definitions required for tensor differentiation For some graph F(G(x)), chain rule for calculating dF/dx is defined in the following order:

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    accept(iTraveler &visiter) overrideade::iLeafinlinevirtual
    data(void)=0ade::iDatapure virtual
    data(void) const =0ade::iDatapure virtual
    nbytes(void) const =0ade::iDatapure virtual
    shape(void) const =0ade::iTensorpure virtual
    to_string(void) const =0ade::iTensorpure virtual
    type_code(void) const =0ade::iDatapure virtual
    type_label(void) const =0ade::iDatapure virtual
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    ~iLeaf(void)=defaultade::iLeafvirtual
    ~iTensor(void)=defaultade::iTensorvirtual
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    virtual ~iTensor (void)=default
     
    virtual const Shape & shape (void) const =0
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    virtual std::string to_string (void) const =0
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    - Public Member Functions inherited from ade::iData
    virtual ~iData (void)=default
     
    virtual void * data (void)=0
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    Leaf of the graph commonly representing the variable in an equation.

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    accept(iTraveler &visiter) overrideade::iFunctorinlinevirtual
    data(void)=0ade::iDatapure virtual
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    get_children(void) const =0ade::iFunctorpure virtual
    get_opcode(void) const =0ade::iFunctorpure virtual
    nbytes(void) const =0ade::iDatapure virtual
    shape(void) const =0ade::iTensorpure virtual
    to_string(void) const =0ade::iTensorpure virtual
    type_code(void) const =0ade::iDatapure virtual
    type_label(void) const =0ade::iDatapure virtual
    update(void)=0ade::iOperableFuncpure virtual
    update_child(FuncArg arg, size_t index)=0ade::iFunctorpure virtual
    ~iData(void)=defaultade::iDatavirtual
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    ~iTensor(void)=defaultade::iTensorvirtual
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    virtual ~iOperableFunc (void)=default
     
    virtual void update (void)=0
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    - Public Member Functions inherited from ade::iFunctor
    virtual ~iFunctor (void)=default
     
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    virtual Opcode get_opcode (void) const =0
     Return operation encoding. More...
     
    virtual const ArgsT & get_children (void) const =0
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    virtual void update_child (FuncArg arg, size_t index)=0
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    - Public Member Functions inherited from ade::iTensor
    virtual ~iTensor (void)=default
     
    virtual const Shape & shape (void) const =0
     Return the shape held by this tensor. More...
     
    virtual std::string to_string (void) const =0
     Return the string representation of the tensor. More...
     
    - Public Member Functions inherited from ade::iData
    virtual ~iData (void)=default
     
    virtual void * data (void)=0
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     Return const pointer to internal data. More...
     
    virtual size_t type_code (void) const =0
     Return data type encoding. More...
     
    virtual std::string type_label (void) const =0
     Return data type label (for better readability) More...
     
    virtual size_t nbytes (void) const =0
     Return number of bytes in the data. More...
     
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    A functor node with direct access to evaluated data.

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    ~iTensor(void)=defaultade::iTensorvirtual
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    virtual ~iTensor (void)=default
     
    virtual void accept (iTraveler &visiter)=0
     Obtain concrete information on either leaf or functor implementations. More...
     
    virtual const Shape & shape (void) const =0
     Return the shape held by this tensor. More...
     
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    virtual ~iTraveler (void)=default
     
    virtual void visit (iLeaf *leaf)=0
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    Interface to travel through graph, treating iLeaf and iFunctor differently.

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    ClientConfig(void)=defaultdbg::ClientConfig
    ClientConfig(std::chrono::duration< int64_t, std::milli > request_duration, std::chrono::duration< int64_t, std::milli > stream_duration)dbg::ClientConfiginline
    request_duration_dbg::ClientConfig
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     ClientConfig (void)=default
     
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     GraphEmitterClient (std::shared_ptr< grpc::ChannelInterface > channel, ClientConfig cfg)
     
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     InteractiveSession (std::shared_ptr< grpc::ChannelInterface > channel, ClientConfig client_cfg=ClientConfig(), tag::TagRegistry &registry=tag::get_reg())
     
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    AnyConvr(std::string any_id)ead::AnyConvr< T >inline
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     AnyConvr (std::string any_id)
     
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    std::string to_string (void) const override
     
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     BuilderArg (opt::ConvptrT arg, ade::CoordptrT shaper, CoordptrT coorder)
     
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    accept(iTraveler &visiter) overrideade::iLeafinlinevirtual
    Constant(const Constant< T > &other)=deleteead::Constant< T >
    Constant(Constant< T > &&other)=deleteead::Constant< T >
    Constant(T *data, ade::Shape shape)ead::Constant< T >inlineprivate
    data(void) overrideead::iLeaf< T >inlinevirtual
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    get(T *data, ade::Shape shape)ead::Constant< T >static
    get_scalar(T scalar, ade::Shape shape)ead::Constant< T >inlinestatic
    iLeaf(T *data, ade::Shape shape)ead::iLeaf< T >inlineprotected
    is_const(void) const overrideead::Constant< T >inlinevirtual
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    operator=(const Constant< T > &other)=deleteead::Constant< T >
    operator=(Constant< T > &&other)=deleteead::Constant< T >
    shape(void) const overrideead::iLeaf< T >inlinevirtual
    shape_ead::iLeaf< T >protected
    to_string(void) const overrideead::Constant< T >inlinevirtual
    type_code(void) const overrideead::iLeaf< T >inlinevirtual
    type_label(void) const overrideead::iLeaf< T >inlinevirtual
    ~iData(void)=defaultade::iDatavirtual
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     Constant (Constant< T > &&other)=delete
     
    Constant< T > & operator= (const Constant< T > &other)=delete
     
    Constant< T > & operator= (Constant< T > &&other)=delete
     
    std::string to_string (void) const override
     Implementation of iTensor. More...
     
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     Implementation of iData. More...
     
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    static Constant< T > * get (T *data, ade::Shape shape)
     
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     Data Source. More...
     
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    ConstantNode(std::shared_ptr< Constant< T >> cst)ead::ConstantNode< T >inline
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    shape(void)ead::iNode< T >inline
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    ~iNode(void)=defaultead::iNode< T >virtual
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    build(const ::Subgraph *sg, const opt::RulesContext &ctx) const overrideead::ConverterBuilder< T >inlinevirtual
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    opt::CstConvertF build_cconv (void) const override
     
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    CoordMap(ade::CoordT indices, bool bijective)ead::CoordMapinline
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     CoordMap (ade::CoordT indices, bool bijective)
     
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    void forward (ade::CoordT::iterator out, ade::CoordT::const_iterator in) const override
     
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     Return string representation of coordinate transformer. More...
     
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    virtual ~iCoordMap (void)=default
     
    virtual void forward (CoordT::iterator out, CoordT::const_iterator in) const =0
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    ade::TensptrT generate_leaf (const char *pb, ade::Shape shape, std::string typelabel, std::string label, bool is_const) override
     
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    EigenMatOp(DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)ead::EigenMatOp< T, EigenSource, EigenArgs >inline
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     EigenMatOp (DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)
     
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    EigenTensOp(DimensionsT dims, std::function< EigenSource(EigenArgs &)> make_base, EigenArgs args)ead::EigenTensOp< T, EigenSource, EigenArgs >inline
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    coorder_ead::FuncArg< T >private
    FuncArg(NodeptrT< T > node, ade::CoordptrT shaper, CoordptrT coorder)ead::FuncArg< T >inline
    get_coorder(void) constead::FuncArg< T >inline
    get_node(void) constead::FuncArg< T >inline
    get_shaper(void) constead::FuncArg< T >inline
    get_tensor(void) constead::FuncArg< T >inline
    map_io(void) constead::FuncArg< T >inline
    node_ead::FuncArg< T >private
    shape(void) constead::FuncArg< T >inline
    shaper_ead::FuncArg< T >private
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     FuncArg (NodeptrT< T > node, ade::CoordptrT shaper, CoordptrT coorder)
     Construct FuncArg with specific coorder_ and map_io_ flag. More...
     
    ade::Shape shape (void) const
     Return shape of tensor filtered through coordinate mapper. More...
     
    ade::TensptrT get_tensor (void) const
     Return tensor being mapped. More...
     
    NodeptrT< T > get_node (void) const
     
    ade::CoordptrT get_shaper (void) const
     Return shaper coord map. More...
     
    bool map_io (void) const
     
    CoordptrT get_coorder (void) const
     Return coord map for coordinates. More...
     
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    NodeptrT< T > node_
     Tensor reference. More...
     
    ade::CoordptrT shaper_
     Shape mapper. More...
     
    CoordptrT coorder_
     Coordinate mapper. More...
     
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    Construct FuncArg with specific coorder_ and map_io_ flag.

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    ◆ coorder_

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     FuncConvr (std::string op, BuilderArgsT args)
     
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    std::string to_string (void) const override
     
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    ade::Opcode opcode_
     
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    ◆ args_

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    Functor(const Functor< T > &other)=deleteead::Functor< T >
    Functor(ade::Opcode opcode, ade::Shape shape, ade::ArgsT args)ead::Functor< T >inlineprivate
    Functor(Functor< T > &&other)=defaultead::Functor< T >private
    get(ade::Opcode opcode, ArgsT< T > args)ead::Functor< T >static
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    initialize(void)ead::Functor< T >inline
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    operator=(Functor< T > &&other)=deleteead::Functor< T >
    out_ead::Functor< T >private
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    shape_ead::Functor< T >private
    to_string(void) const overrideead::Functor< T >inlinevirtual
    type_code(void) const overrideead::Functor< T >inlinevirtual
    type_label(void) const overrideead::Functor< T >inlinevirtual
    uninitialize(void)ead::Functor< T >inline
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    update_child(ade::FuncArg arg, size_t index) overrideead::Functor< T >inlinevirtual
    ~iData(void)=defaultade::iDatavirtual
    ~iFunctor(void)=defaultade::iFunctorvirtual
    ~iOperableFunc(void)=defaultade::iOperableFuncvirtual
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     Functor (const Functor< T > &other)=delete
     
    Functor< T > & operator= (const Functor< T > &other)=delete
     
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    const ade::Shape & shape (void) const override
     Implementation of iTensor. More...
     
    std::string to_string (void) const override
     Implementation of iTensor. More...
     
    ade::Opcode get_opcode (void) const override
     Implementation of iFunctor. More...
     
    const ade::ArgsT & get_children (void) const override
     Implementation of iFunctor. More...
     
    void update_child (ade::FuncArg arg, size_t index) override
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    void * data (void) override
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    const void * data (void) const override
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    size_t type_code (void) const override
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    bool is_uninit (void) const
     
    void uninitialize (void)
     
    void initialize (void)
     
    - Public Member Functions inherited from ade::iOperableFunc
    virtual ~iOperableFunc (void)=default
     
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    void accept (iTraveler &visiter) override
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    EigenptrT< T > out_ = nullptr
     
    ade::Opcode opcode_
     Operation encoding. More...
     
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    data(void) overrideead::FunctorNode< T >inlinevirtual
    func_ead::FunctorNode< T >private
    FunctorNode(std::shared_ptr< Functor< T >> f)ead::FunctorNode< T >inline
    get_tensor(void) overrideead::FunctorNode< T >inlinevirtual
    shape(void)ead::iNode< T >inline
    update(void) overrideead::FunctorNode< T >inlinevirtual
    ~iNode(void)=defaultead::iNode< T >virtual
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     FunctorNode (std::shared_ptr< Functor< T >> f)
     
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    add(ade::TensptrT &lhs, ade::TensptrT &rhs) const overrideead::GradientBuilder< T >inlinevirtual
    chain_rule(ade::FuncptrT op, const ade::TensptrT &local_der, ade::TensptrT supcomp_grad, size_t arg_idx) const overrideead::GradientBuilder< T >inlinevirtual
    derive(TensptrT root, TensptrT target) constade::iGradientBuilderinline
    get_const_one(ade::Shape shape) const overrideead::GradientBuilder< T >inlinevirtual
    get_const_zero(ade::Shape shape) const overrideead::GradientBuilder< T >inlinevirtual
    local_derivative(ade::FuncptrT op, size_t arg_idx) const overrideead::GradientBuilder< T >inlinevirtual
    ~iGradientBuilder(void)=defaultade::iGradientBuildervirtual
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    ade::TensptrT local_derivative (ade::FuncptrT op, size_t arg_idx) const override
     Implementation of iGradientBuilder. More...
     
    ade::TensptrT chain_rule (ade::FuncptrT op, const ade::TensptrT &local_der, ade::TensptrT supcomp_grad, size_t arg_idx) const override
     Implementation of iGradientBuilder. More...
     
    ade::TensptrT get_const_one (ade::Shape shape) const override
     Implementation of iGradientBuilder. More...
     
    ade::TensptrT get_const_zero (ade::Shape shape) const override
     Implementation of iGradientBuilder. More...
     
    ade::TensptrT add (ade::TensptrT &lhs, ade::TensptrT &rhs) const override
     Implementation of iGradientBuilder. More...
     
    - Public Member Functions inherited from ade::iGradientBuilder
    virtual ~iGradientBuilder (void)=default
     
    TensptrT derive (TensptrT root, TensptrT target) const
     Return derivative of root with respect to target. More...
     
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    build(const opt::ContexT &ctx, ade::Shape outshape) const overrideead::GroupConvr< T >inlinevirtual
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    GroupConvr(std::string group, BuilderArgsT args, std::string variadic)ead::GroupConvr< T >inline
    to_string(void) const overrideead::GroupConvr< T >inlinevirtual
    variadic_ead::GroupConvr< T >
    ~iConverter(void)=defaultopt::iConvertervirtual
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     GroupConvr (std::string group, BuilderArgsT args, std::string variadic)
     
    ade::TensptrT build (const opt::ContexT &ctx, ade::Shape outshape) const override
     
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    std::string group_
     
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    OpArg(T *data, ade::Shape shape, CoordMap *coorder)ead::OpArg< T >inline
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     OpArg (T *data, ade::Shape shape, CoordMap *coorder)
     
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    build(const opt::ContexT &ctx, ade::Shape outshape) const overrideead::ScalarConvr< T >inlinevirtual
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     operator size_t () const
     
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    visit(ade::iLeaf *leaf) overrideead::Travelerinlinevirtual
    visit(ade::iFunctor *func) overrideead::Travelerinlinevirtual
    visited_ead::Traveler
    ~iTraveler(void)=defaultade::iTravelervirtual
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    void visit (ade::iLeaf *leaf) override
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    accept(iTraveler &visiter) overrideade::iLeafinlinevirtual
    assign(void *input, age::_GENERATED_DTYPE dtype, ade::Shape shape)ead::Variable< T >inline
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    get(T *ptr, ade::Shape shape, std::string label="")ead::Variable< T >static
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    get(const Variable< T > &other)ead::Variable< T >inlinestatic
    get(Variable< T > &&other)ead::Variable< T >inlinestatic
    iLeaf(T *data, ade::Shape shape)ead::iLeaf< T >inlineprotected
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    operator=(const Variable< T > &other)=defaultead::Variable< T >
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    operator=(std::vector< T > input)ead::Variable< T >inline
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    Variable(T *data, ade::Shape shape, std::string label)ead::Variable< T >inlineprivate
    Variable(const Variable< T > &other)=defaultead::Variable< T >private
    Variable(Variable< T > &&other)=defaultead::Variable< T >private
    ~iData(void)=defaultade::iDatavirtual
    ~iLeaf(void)=defaultead::iLeaf< T >virtual
    ~iTensor(void)=defaultade::iTensorvirtual
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    Leaf node containing data. - More...

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    Variable< T > & operator= (const Variable< T > &other)=default
     
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    Variable< T > & operator= (std::vector< T > input)
     Assign vectorized data to data source. More...
     
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    void assign (void *input, age::_GENERATED_DTYPE dtype, ade::Shape shape)
     
    std::string to_string (void) const override
     Implementation of iTensor. More...
     
    bool is_const (void) const override
     
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    virtual ~iLeaf (void)=default
     
    const ade::Shape & shape (void) const override
     Implementation of iTensor. More...
     
    void * data (void) override
     Implementation of iData. More...
     
    const void * data (void) const override
     Implementation of iData. More...
     
    size_t type_code (void) const override
     Implementation of iData. More...
     
    std::string type_label (void) const override
     Implementation of iData. More...
     
    size_t nbytes (void) const override
     Implementation of iData. More...
     
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    void accept (iTraveler &visiter) override
     Implementation of iTensor. More...
     
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    virtual ~iData (void)=default
     
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    static Variable< T > * get (T *ptr, ade::Shape shape, std::string label="")
     
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    static Variable< T > * get (T scalar, ade::Shape shape, std::string label="")
     
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    std::string label_
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     Variable (T *data, ade::Shape shape, std::string label)
     
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     iLeaf (T *data, ade::Shape shape)
     
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    TensorT< T > data_
     Data Source. More...
     
    ade::Shape shape_
     Shape utility to avoid excessive conversion between data_.dimensions() More...
     
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    assign(T *input, ade::Shape shape)ead::VariableNode< T >inline
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    shape(void)ead::iNode< T >inline
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    ~iNode(void)=defaultead::iNode< T >virtual
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     VariableNode (std::shared_ptr< Variable< T >> var)
     
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    std::shared_ptr< Variable< T > > var_
     
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    ◆ VariableNode()

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    ◆ var_

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    assign(void)=0ead::iEigen< T >pure virtual
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    virtual ~iEigen (void)=default
     
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     iLeaf (T *data, ade::Shape shape)
     
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    ade::TensptrT tensor_
     
    CandsT candidates_
     
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    CommVoter(std::string label)opt::CommVoterinline
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     CommVoter (std::string label)
     
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     Matcher (void)=default
     
     Matcher (const VoterPool &voters)
     
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    VoterPool voters_
     
    std::unordered_map< ade::iTensor *, CandsT > candidates_
     
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    VoterPool voters_
     
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    VariadicVoter(std::string label, std::string variadic)opt::VariadicVoterinline
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     VariadicVoter (std::string label, std::string variadic)
     
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    std::string label_
     
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    VoterArg(std::string label, ade::CoordptrT shaper, ade::CoordptrT coorder, SUBGRAPH_TYPE type)opt::VoterArginline
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     VoterArg (std::string label, ade::CoordptrT shaper, ade::CoordptrT coorder, SUBGRAPH_TYPE type)
     
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    std::string label_
     
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    std::unordered_set< std::string > immutables_
     
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    build(const ContexT &ctx, ade::Shape outshape) const =0opt::iConverterpure virtual
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    Contains all information necessary to recreate labelled ADE graph. - More...

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    std::unordered_set< ade::TensptrT > roots_
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    save(cortenn::Graph &out, PathedMapT labels=PathedMapT())pbm::GraphSaver< SAVER, >inline
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    void save (cortenn::Graph &out, PathedMapT labels=PathedMapT())
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    SAVER saver_
     
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    template<typename SAVER, typename std::enable_if< std::is_base_of< iSaver, SAVER >::value >::type * = nullptr>
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    Graph serialization traveler.

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    get_labelled(StringsT path) constpbm::PathedTensinline
    get_labelled(StringsT::iterator path_begin, StringsT::iterator path_end) constpbm::PathedTensinline
    join(PathedTens *other)pbm::PathedTensinline
    set_labelled(StringsT path, ade::TensptrT tens)pbm::PathedTensinline
    set_labelled(StringsT::iterator path_begin, StringsT::iterator path_end, ade::TensptrT tens)pbm::PathedTensinline
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    ~PathedTens(void)pbm::PathedTensinline
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     ~PathedTens (void)
     
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    ade::TensptrT get_labelled (StringsT path) const
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    void set_labelled (StringsT path, ade::TensptrT tens)
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    ade::TensptrT get_labelled (StringsT::iterator path_begin, StringsT::iterator path_end) const
     
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    std::unordered_map< std::string, PathedTens * > children_
     Map of labels to branching nodes. More...
     
    std::unordered_map< std::string, ade::TensptrT > tens_
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    generate_coorder(std::string opname, std::vector< double > coord)=0pbm::iLoaderpure virtual
    generate_func(std::string opname, ade::ArgsT args)=0pbm::iLoaderpure virtual
    generate_leaf(const char *data, ade::Shape shape, std::string typelabel, std::string label, bool is_const)=0pbm::iLoaderpure virtual
    generate_shaper(std::vector< double > coord)=0pbm::iLoaderpure virtual
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    virtual std::string save_leaf (bool &is_const, ade::iLeaf *leaf)=0
     
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    group_tag(ade::TensrefT tens, std::string tag)tag::GroupRegistryinline
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    absorb(TagptrT &&other) overridetag::GroupTaginline
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     GroupTag (std::string init_label)
     
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    GroupTag define subgraphs/groups of nodes with a structural significance Groups are ordered tags, subsequent group tags (obtained through absorption) often denote supergraphs of prior groups e.g.: given tensor X, tag X with 'sum', then tag X with 'mlp', results in X having a collective [GroupTag:['sum','mlp']] ordered tag retains information that 'sum' is a subgraph of 'mlp'

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     PropTag (std::string init_label)
     
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     PropertyRegistry (TagRegistry &registry=get_reg())
     
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    static std::unordered_map< TensKey, TagCollective, TensKeyHash > registry
     
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     Subgraph (std::string group)
     
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    std::string group_
     
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     TagCollective (void)=default
     
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     TensKey (ade::TensrefT tens)
     
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    absorb(std::unique_ptr< iTag > &&other)=0tag::iTagpure virtual
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    #include <map>
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    struct  tag::iTag
     
    struct  tag::TagCollective
     
    struct  tag::TensKey
     
    struct  tag::TensKeyHash
     
    struct  tag::TagRegistry
     
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    using tag::TagRepsT = std::map< std::string, std::vector< std::string > >
     
    using tag::TagptrT = std::unique_ptr< iTag >
     
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    bool tag::operator== (const TensKey &lhs, const TensKey &rhs)
     
    TagRegistry & tag::get_reg (void)
     
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    tag.hpp
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    1 #include <map>
    2 #include <set>
    3 
    4 #include "ade/ade.hpp"
    5 
    6 #ifndef TAG_TAG_HPP
    7 #define TAG_TAG_HPP
    8 
    9 namespace tag
    10 {
    11 
    12 using TagRepsT = std::map<std::string,std::vector<std::string>>;
    13 
    14 // each tag instance is a set of tags with particular properties
    15 // iTag is the interface for such instances
    16 struct iTag
    17 {
    18  virtual ~iTag (void) = default;
    19 
    20  virtual size_t tag_id (void) const = 0;
    21 
    22  virtual void absorb (std::unique_ptr<iTag>&& other) = 0;
    23 
    24  virtual TagRepsT get_tags (void) const = 0;
    25 };
    26 
    27 using TagptrT = std::unique_ptr<iTag>;
    28 
    29 // TagCollective is a collective of generic iTag instances
    30 // only 1 instance of a particular type of iTag can be stored in an instance of
    31 // TagCollective, adding subsequent instances of the same type
    32 // absorbs new instances into the collective
    33 struct TagCollective final
    34 {
    35  TagCollective (void) = default;
    36 
    37  TagCollective (TagCollective&& other) : tags_(std::move(other.tags_)) {}
    38 
    39  TagCollective& operator = (TagCollective&& other)
    40  {
    41  if (this != &other)
    42  {
    43  tags_ = std::move(other.tags_);
    44  }
    45  return *this;
    46  }
    47 
    48  void absorb (TagCollective&& other)
    49  {
    50  for (auto& tagpair : other.tags_)
    51  {
    52  size_t tid = tagpair.first;
    53  auto it = tags_.find(tid);
    54  if (tags_.end() == it)
    55  {
    56  tags_.emplace(tid, std::move(tagpair.second));
    57  }
    58  else
    59  {
    60  it->second->absorb(std::move(tagpair.second));
    61  }
    62  }
    63  other.tags_.clear();
    64  }
    65 
    66  void add (TagptrT entry)
    67  {
    68  size_t tid = entry->tag_id();
    69  auto it = tags_.find(tid);
    70  if (tags_.end() == it)
    71  {
    72  tags_.emplace(tid, std::move(entry));
    73  }
    74  else
    75  {
    76  it->second->absorb(std::move(entry));
    77  }
    78  }
    79 
    80  TagRepsT get_tags (void) const
    81  {
    82  TagRepsT tags;
    83  for (auto& tpair : tags_)
    84  {
    85  auto temp = tpair.second->get_tags();
    86  tags.insert(temp.begin(), temp.end());
    87  }
    88  return tags;
    89  }
    90 
    91 private:
    92  std::unordered_map<size_t,TagptrT> tags_;
    93 };
    94 
    95 struct TensKey final
    96 {
    97  TensKey (ade::TensrefT tens) : val_(tens.lock().get()), ref_(tens) {}
    98 
    99  // used to match keys
    100  TensKey (ade::iTensor* tens) : val_(tens) {}
    101 
    102  TensKey (const ade::iTensor* tens) : val_(tens) {}
    103 
    104  operator const ade::iTensor*() const
    105  {
    106  return val_;
    107  }
    108 
    109  bool expired (void) const
    110  {
    111  return ref_.expired();
    112  }
    113 
    114  const ade::iTensor* val_;
    115 
    116  ade::TensrefT ref_;
    117 };
    118 
    119 struct TensKeyHash final
    120 {
    121  size_t operator() (const TensKey& key) const
    122  {
    123  return std::hash<const void*>()(key.val_);
    124  }
    125 };
    126 
    127 inline bool operator == (const TensKey& lhs, const TensKey& rhs)
    128 {
    129  TensKeyHash hasher;
    130  return hasher(lhs) == hasher(rhs);
    131 }
    132 
    133 // todo: move tag registry to some session that claims global context
    134 // todo: make an interface for this
    135 struct TagRegistry final
    136 {
    137  void add_tag (ade::TensrefT tens, TagptrT tag)
    138  {
    139  if (tens.expired())
    140  {
    141  logs::fatal("cannot tag with expired tensor ref");
    142  }
    143  auto it = registry_.find(TensKey(tens));
    144  // clear out previous entry that is expired
    145  if (registry_.end() != it && it->first.expired())
    146  {
    147  registry_.erase(tens.lock().get());
    148  }
    149  registry_[tens].add(std::move(tag));
    150  }
    151 
    152  TagRepsT get_tags (const ade::iTensor* tens)
    153  {
    154  auto it = registry_.find(TensKey(tens));
    155  if (registry_.end() == it || it->first.expired())
    156  {
    157  return {};
    158  }
    159  return it->second.get_tags();
    160  }
    161 
    162  void move_tags (ade::TensrefT dest, const ade::iTensor* source)
    163  {
    164  if (dest.expired())
    165  {
    166  logs::fatal("cannot move with expired destination tensor");
    167  }
    168  auto src_it = registry_.find(TensKey(source));
    169  auto dest_it = registry_.find(TensKey(dest));
    170  if (registry_.end() == src_it || src_it->first.expired())
    171  {
    172  return;
    173  }
    174 
    175  if (registry_.end() == dest_it || dest_it->first.expired())
    176  {
    177  registry_[dest] = std::move(src_it->second);
    178  }
    179  else
    180  {
    181  dest_it->second.absorb(std::move(src_it->second));
    182  }
    183  registry_.erase(TensKey(source));
    184  }
    185 
    186  std::unordered_map<TensKey,TagCollective,TensKeyHash> registry_;
    187 };
    188 
    189 TagRegistry& get_reg (void);
    190 
    191 }
    192 
    193 #endif // TAG_TAG_HPP
    tag::TensKeyHash
    Definition: tag.hpp:119
    -
    tag::TagRegistry::add_tag
    void add_tag(ade::TensrefT tens, TagptrT tag)
    Definition: tag.hpp:137
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    tag::TagCollective::get_tags
    TagRepsT get_tags(void) const
    Definition: tag.hpp:80
    -
    tag::iTag
    Definition: tag.hpp:16
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    tag::iTag::absorb
    virtual void absorb(std::unique_ptr< iTag > &&other)=0
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    tag::TensKey::TensKey
    TensKey(ade::iTensor *tens)
    Definition: tag.hpp:100
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    tag::TagRegistry::get_tags
    TagRepsT get_tags(const ade::iTensor *tens)
    Definition: tag.hpp:152
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    tag::TagRegistry
    Definition: tag.hpp:135
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    tag::iTag::get_tags
    virtual TagRepsT get_tags(void) const =0
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    tag
    Definition: group.hpp:10
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    tag::TagptrT
    std::unique_ptr< iTag > TagptrT
    Definition: tag.hpp:27
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    tag::TagCollective::TagCollective
    TagCollective(void)=default
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    tag::iTag::~iTag
    virtual ~iTag(void)=default
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    tag::TagRepsT
    std::map< std::string, std::vector< std::string > > TagRepsT
    Definition: tag.hpp:12
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    tag::TagRegistry::registry_
    std::unordered_map< TensKey, TagCollective, TensKeyHash > registry_
    Definition: tag.hpp:186
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    tag::TagRegistry::move_tags
    void move_tags(ade::TensrefT dest, const ade::iTensor *source)
    Definition: tag.hpp:162
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    tag::TagCollective
    Definition: tag.hpp:33
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    tag::TensKey::TensKey
    TensKey(const ade::iTensor *tens)
    Definition: tag.hpp:102
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    tag::TagCollective::add
    void add(TagptrT entry)
    Definition: tag.hpp:66
    -
    tag::operator==
    bool operator==(const TensKey &lhs, const TensKey &rhs)
    Definition: tag.hpp:127
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    tag::TensKey::ref_
    ade::TensrefT ref_
    Definition: tag.hpp:116
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    tag::TagCollective::tags_
    std::unordered_map< size_t, TagptrT > tags_
    Definition: tag.hpp:92
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    ade::iTensor
    Interface of traversible and differentiable nodes with shape information.
    Definition: itensor.hpp:34
    -
    tag::TensKey::TensKey
    TensKey(ade::TensrefT tens)
    Definition: tag.hpp:97
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    size_t operator()(const TensKey &key) const
    Definition: tag.hpp:121
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    tag::TensKey
    Definition: tag.hpp:95
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    tag::TagCollective::operator=
    TagCollective & operator=(TagCollective &&other)
    Definition: tag.hpp:39
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    tag::get_reg
    TagRegistry & get_reg(void)
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    tag::TensKey::expired
    bool expired(void) const
    Definition: tag.hpp:109
    -
    ade::TensrefT
    std::weak_ptr< iTensor > TensrefT
    Tensor weak pointers.
    Definition: itensor.hpp:52
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    tag::TagCollective::TagCollective
    TagCollective(TagCollective &&other)
    Definition: tag.hpp:37
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    tag::iTag::tag_id
    virtual size_t tag_id(void) const =0
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    tag::TensKey::val_
    const ade::iTensor * val_
    Definition: tag.hpp:114
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    tag::TagCollective::absorb
    void absorb(TagCollective &&other)
    Definition: tag.hpp:48
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    9 #include <iostream>
    10 #include <vector>
    11 
    12 #ifndef DBG_TENSOR_HPP
    13 #define DBG_TENSOR_HPP
    14 
    16 template <typename T>
    17 struct PrettyTensor final
    18 {
    25  PrettyTensor (std::vector<uint16_t> datalimit) : datalimit_(datalimit) {}
    26 
    31  void print (std::ostream& out, T* arr, std::vector<uint8_t> shape)
    32  {
    33  print_helper(out, shape, arr, shape.size() - 1);
    34  }
    35 
    37  std::vector<uint16_t> datalimit_;
    38 
    39 private:
    40  void print_helper (std::ostream& out, const std::vector<uint8_t>& shape,
    41  T* arr, uint8_t rank)
    42  {
    43  out << "[";
    44  uint16_t n = shape[rank];
    45  // apply limit only if limit is available
    46  if (rank < datalimit_.size())
    47  {
    48  n = std::min(n, datalimit_[rank]);
    49  }
    50  if (rank == 0)
    51  {
    52  out << arr[0];
    53  for (uint16_t i = 1; i < n; ++i)
    54  {
    55  out << "," << arr[i];
    56  }
    57  }
    58  else
    59  {
    60  auto it = shape.begin();
    61  size_t before = std::accumulate(it, it + rank, (size_t) 1,
    62  std::multiplies<size_t>());
    63  print_helper(out, shape, arr, rank - 1);
    64  for (uint16_t i = 1; i < n; ++i)
    65  {
    66  out << ",";
    67  print_helper(out, shape, arr + i * before, rank - 1);
    68  }
    69  }
    70  if (n < shape[rank])
    71  {
    72  out << "..";
    73  }
    74  out << "]";
    75  }
    76 };
    77 
    78 #endif // DBG_TENSOR_HPP
    PrettyTensor::print
    void print(std::ostream &out, T *arr, std::vector< uint8_t > shape)
    Definition: tensor.hpp:31
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    EigenptrT< T > min(ade::Shape &outshape, const OpArg< T > &a, const OpArg< T > &b)
    Definition: operator.hpp:893
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    Definition: tensor.hpp:40
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    PrettyTensor(std::vector< uint16_t > datalimit)
    Definition: tensor.hpp:25
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    Draw data as a multi-dimension array (similar to python) according to shape.
    Definition: tensor.hpp:17
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    Number of elements to show for each dimension.
    Definition: tensor.hpp:37
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    struct  ade::OnceTraveler
     Extremely generic traveler that visits every node in the graph once. More...
     
    struct  ade::GraphStat
     Traveler that maps each tensor to its subtree's maximum depth. More...
     
    struct  ade::PathFinder
     
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     Traveler that for each child tracks the relationship to all parents. More...
     
    struct  ade::HeightMatrix
     
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    1 
    9 #include "estd/estd.hpp"
    10 #include "estd/range.hpp"
    11 
    12 #include "ade/ileaf.hpp"
    13 #include "ade/ifunctor.hpp"
    14 
    15 #ifndef ADE_TRAVELER_HPP
    16 #define ADE_TRAVELER_HPP
    17 
    18 namespace ade
    19 {
    20 
    22 struct OnceTraveler : public iTraveler
    23 {
    24  virtual ~OnceTraveler (void) = default;
    25 
    27  void visit (iLeaf* leaf) override
    28  {
    29  if (false == estd::has(visited_, leaf))
    30  {
    31  visited_.emplace(leaf);
    32  visit_leaf(leaf);
    33  }
    34  }
    35 
    37  void visit (iFunctor* func) override
    38  {
    39  if (false == estd::has(visited_, func))
    40  {
    41  visited_.emplace(func);
    42  visit_func(func);
    43  }
    44  }
    45 
    46  virtual void visit_leaf (iLeaf* leaf) {} // do nothing
    47 
    48  virtual void visit_func (iFunctor* func)
    49  {
    50  auto& children = func->get_children();
    51  for (auto child : children)
    52  {
    53  child.get_tensor()->accept(*this);
    54  }
    55  }
    56 
    57  std::unordered_set<iTensor*> visited_;
    58 };
    59 
    61 struct GraphStat final : public iTraveler
    62 {
    64  void visit (iLeaf* leaf) override
    65  {
    66  graphsize_.emplace(leaf, estd::NumRange<size_t>());
    67  }
    68 
    70  void visit (iFunctor* func) override
    71  {
    72  if (false == estd::has(graphsize_, func))
    73  {
    74  ArgsT children = func->get_children();
    75  size_t nchildren = children.size();
    76  std::vector<size_t> max_heights;
    77  std::vector<size_t> min_heights;
    78  max_heights.reserve(nchildren);
    79  min_heights.reserve(nchildren);
    80  for (auto& child : children)
    81  {
    82  iTensor* tens = child.get_tensor().get();
    83  tens->accept(*this);
    84  estd::NumRange<size_t> range = estd::must_getf(graphsize_, tens,
    85  "GraphStat failed to visit child `%s` of functor `%s`",
    86  tens->to_string().c_str(), func->to_string().c_str());
    87  max_heights.push_back(range.upper_);
    88  min_heights.push_back(range.lower_);
    89  }
    90  size_t max_height = 1;
    91  size_t min_height = 1;
    92  auto max_it = std::max_element(
    93  max_heights.begin(), max_heights.end());
    94  auto min_it = std::min_element(
    95  min_heights.begin(), min_heights.end());
    96  if (max_heights.end() != max_it)
    97  {
    98  max_height += *max_it;
    99  }
    100  if (min_heights.end() != max_it)
    101  {
    102  min_height += *min_it;
    103  }
    104  graphsize_.emplace(func, estd::NumRange<size_t>(min_height, max_height));
    105  }
    106  }
    107 
    108  // Maximum depth of the subtree of mapped tensors
    109  std::unordered_map<iTensor*,estd::NumRange<size_t>> graphsize_;
    110 };
    111 
    117 struct PathFinder final : public iTraveler
    118 {
    120  using ParentMapT = std::unordered_map<iTensor*,std::unordered_set<size_t>>;
    121 
    122  PathFinder (const iTensor* target) : target_(target) {}
    123 
    125  void visit (iLeaf* leaf) override {}
    126 
    128  void visit (iFunctor* func) override
    129  {
    130  if (false == estd::has(parents_, func))
    131  {
    132  auto& children = func->get_children();
    133  size_t n = children.size();
    134  std::unordered_set<size_t> path;
    135  for (size_t i = 0; i < n; ++i)
    136  {
    137  TensptrT tens = children[i].get_tensor();
    138  if (tens.get() == target_)
    139  {
    140  path.emplace(i);
    141  }
    142  else
    143  {
    144  tens->accept(*this);
    145  if (estd::has(parents_, tens.get()))
    146  {
    147  path.emplace(i);
    148  }
    149  }
    150  }
    151  if (false == path.empty())
    152  {
    153  parents_[func] = path;
    154  }
    155  }
    156  }
    157 
    159  const iTensor* target_;
    160 
    162  ParentMapT parents_;
    163 };
    164 
    166 struct ParentFinder final : public iTraveler
    167 {
    168  using ParentMapT = std::unordered_map<iTensor*,std::vector<size_t>>;
    169 
    171  void visit (iLeaf* leaf) override
    172  {
    173  parents_.emplace(leaf, ParentMapT());
    174  }
    175 
    177  void visit (iFunctor* func) override
    178  {
    179  if (false == estd::has(parents_, func))
    180  {
    181  auto& children = func->get_children();
    182  for (size_t i = 0, n = children.size(); i < n; ++i)
    183  {
    184  auto& child = children[i];
    185  auto tens = child.get_tensor();
    186  tens->accept(*this);
    187  parents_[tens.get()][func].push_back(i);
    188  }
    189  parents_.emplace(func, ParentMapT());
    190  }
    191  }
    192 
    194  std::unordered_map<iTensor*,ParentMapT> parents_;
    195 };
    196 
    198 using OwnerMapT = std::unordered_map<iTensor*,TensrefT>;
    199 
    202 OwnerMapT track_owners (TensT roots);
    203 
    204 struct HeightMatrix
    205 {
    206  HeightMatrix (const ade::TensT& roots)
    207  {
    208  ade::GraphStat stat;
    209  for (ade::TensptrT root : roots)
    210  {
    211  root->accept(stat);
    212  }
    213 
    214  std::vector<size_t> root_heights;
    215  root_heights.reserve(roots.size());
    216  std::transform(roots.begin(), roots.end(),
    217  std::back_inserter(root_heights),
    218  [&stat](const ade::TensptrT& root)
    219  {
    220  return stat.graphsize_[root.get()].upper_;
    221  });
    222  // max of the maxheight of roots should be the maxheight of the whole graph
    223  size_t maxheight = *std::max_element(
    224  root_heights.begin(), root_heights.end());
    225  funcs_ = std::vector<std::unordered_set<ade::iFunctor*>>(maxheight);
    226 
    227  for (auto& gpair : stat.graphsize_)
    228  {
    229  auto tens = gpair.first;
    230  size_t height = gpair.second.upper_;
    231  if (0 == height)
    232  {
    233  leaves_.emplace(static_cast<ade::iLeaf*>(tens));
    234  }
    235  else
    236  {
    237  funcs_[height - 1].emplace(static_cast<ade::iFunctor*>(tens));
    238  }
    239  }
    240  }
    241 
    242  std::unordered_set<ade::iLeaf*> leaves_;
    243 
    244  std::vector<std::unordered_set<ade::iFunctor*>> funcs_;
    245 };
    246 
    247 }
    248 
    249 #endif // ADE_TRAVELER_HPP
    ade::iFunctor
    Interface of iOperation-defined operation node.
    Definition: ifunctor.hpp:28
    -
    ade::OnceTraveler
    Extremely generic traveler that visits every node in the graph once.
    Definition: traveler.hpp:22
    -
    ade::PathFinder::PathFinder
    PathFinder(const iTensor *target)
    Definition: traveler.hpp:122
    -
    ade::OnceTraveler::visit
    void visit(iLeaf *leaf) override
    Implementation of iTraveler.
    Definition: traveler.hpp:27
    -
    ade::ParentFinder::visit
    void visit(iFunctor *func) override
    Implementation of iTraveler.
    Definition: traveler.hpp:177
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    ade::OnceTraveler::visited_
    std::unordered_set< iTensor * > visited_
    Definition: traveler.hpp:57
    -
    ade::GraphStat::visit
    void visit(iLeaf *leaf) override
    Implementation of iTraveler.
    Definition: traveler.hpp:64
    -
    ade::iLeaf
    Leaf of the graph commonly representing the variable in an equation.
    Definition: ileaf.hpp:19
    -
    ade::ParentFinder::visit
    void visit(iLeaf *leaf) override
    Implementation of iTraveler.
    Definition: traveler.hpp:171
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    ade::HeightMatrix::HeightMatrix
    HeightMatrix(const ade::TensT &roots)
    Definition: traveler.hpp:206
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    ade::OnceTraveler::visit_leaf
    virtual void visit_leaf(iLeaf *leaf)
    Definition: traveler.hpp:46
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    ade::OwnerMapT
    std::unordered_map< iTensor *, TensrefT > OwnerMapT
    Map between tensor and its corresponding smart pointer.
    Definition: traveler.hpp:198
    -
    ade::ParentFinder::ParentMapT
    std::unordered_map< iTensor *, std::vector< size_t > > ParentMapT
    Definition: traveler.hpp:168
    -
    ade::GraphStat
    Traveler that maps each tensor to its subtree&#39;s maximum depth.
    Definition: traveler.hpp:61
    -
    ade::PathFinder::parents_
    ParentMapT parents_
    Map of parent nodes in path.
    Definition: traveler.hpp:162
    -
    ade::GraphStat::visit
    void visit(iFunctor *func) override
    Implementation of iTraveler.
    Definition: traveler.hpp:70
    -
    ade::iTensor::to_string
    virtual std::string to_string(void) const =0
    Return the string representation of the tensor.
    -
    ade::iTensor::accept
    virtual void accept(iTraveler &visiter)=0
    Obtain concrete information on either leaf or functor implementations.
    -
    ade::PathFinder::target_
    const iTensor * target_
    Target of tensor all paths are travelling to.
    Definition: traveler.hpp:159
    -
    ade::ParentFinder::parents_
    std::unordered_map< iTensor *, ParentMapT > parents_
    Tracks child to parents relationship.
    Definition: traveler.hpp:194
    -
    ade::PathFinder
    Definition: traveler.hpp:117
    -
    ade::TensptrT
    std::shared_ptr< iTensor > TensptrT
    Tensor smart pointer.
    Definition: itensor.hpp:49
    -
    ade
    Definition: coord.hpp:16
    -
    ade::OnceTraveler::visit_func
    virtual void visit_func(iFunctor *func)
    Definition: traveler.hpp:48
    -
    ade::HeightMatrix
    Definition: traveler.hpp:204
    -
    ade::iTraveler
    Interface to travel through graph, treating iLeaf and iFunctor differently.
    Definition: itensor.hpp:22
    -
    ade::iTensor
    Interface of traversible and differentiable nodes with shape information.
    Definition: itensor.hpp:34
    -
    ade::OnceTraveler::visit
    void visit(iFunctor *func) override
    Implementation of iTraveler.
    Definition: traveler.hpp:37
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    ade::iFunctor::get_children
    virtual const ArgsT & get_children(void) const =0
    Return children nodes as a vector of raw pointers.
    -
    ade::HeightMatrix::funcs_
    std::vector< std::unordered_set< ade::iFunctor * > > funcs_
    Definition: traveler.hpp:244
    -
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    void visit(iFunctor *func) override
    Implementation of iTraveler.
    Definition: traveler.hpp:128
    -
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    std::unordered_set< ade::iLeaf * > leaves_
    Definition: traveler.hpp:242
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    ade::TensT
    std::vector< TensptrT > TensT
    Vector representation of ade tensor pointers.
    Definition: funcarg.hpp:104
    -
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    Traveler that for each child tracks the relationship to all parents.
    Definition: traveler.hpp:166
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    std::unordered_map< iTensor *, std::unordered_set< size_t > > ParentMapT
    Type for mapping function nodes in path to boolean vector.
    Definition: traveler.hpp:120
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    virtual ~OnceTraveler(void)=default
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    9 #include <functional>
    10 #include <iostream>
    11 #include <string>
    12 
    13 #ifndef DBG_TREE_HPP
    14 #define DBG_TREE_HPP
    15 
    16 const char vert_branch = '-';
    17 
    21 template <typename T>
    22 struct PrettyTree final
    23 {
    26  PrettyTree (std::function<std::vector<T>(T&)> traverser,
    27  std::function<void(std::ostream&,T&)> to_stream) :
    28  traverser_(traverser), to_stream_(to_stream) {}
    29 
    33  void print (std::ostream& out, T root)
    34  {
    35  print_helper(out, root, "");
    36  }
    37 
    39  size_t branch_length_ = 2;
    40 
    42  std::function<std::vector<T>(T&)> traverser_;
    43 
    45  std::function<void(std::ostream&,T&)> to_stream_;
    46 
    47 private:
    48  void print_helper (std::ostream& out, T root, std::string prefix)
    49  {
    50  out << "(";
    51  to_stream_(out, root);
    52  out << ")\n";
    53  std::vector<T> children = traverser_(root);
    54  size_t nchildren = children.size();
    55  if (nchildren > 0)
    56  {
    57  std::string branch = prefix + " `" +
    58  std::string(branch_length_, vert_branch);
    59  for (size_t i = 0; i < nchildren - 1; ++i)
    60  {
    61  out << branch;
    62  this->print_helper(out, children[i],
    63  prefix + " |" + std::string(branch_length_, ' '));
    64  }
    65  out << branch;
    66  this->print_helper(out, children[nchildren - 1],
    67  prefix + std::string(2 + branch_length_, ' '));
    68  }
    69  }
    70 };
    71 
    72 #endif // DBG_TREE_HPP
    PrettyTree::to_stream_
    std::function< void(std::ostream &, T &)> to_stream_
    Behavior of displaying a node in the structure.
    Definition: tree.hpp:45
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    Definition: tree.hpp:16
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    Definition: tree.hpp:48
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    Definition: tree.hpp:33
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    Horizontal length of the branch, by default the branch looks like `–.
    Definition: tree.hpp:39
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    Definition: tree.hpp:22
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    Behavior of traversing through a structure.
    Definition: tree.hpp:42
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    1 
    10 #include "ead/ileaf.hpp"
    11 #include "ead/inode.hpp"
    12 
    13 #ifndef EAD_VARIABLE_HPP
    14 #define EAD_VARIABLE_HPP
    15 
    16 namespace ead
    17 {
    18 
    20 template <typename T>
    21 struct Variable final : public iLeaf<T>
    22 {
    23  static Variable<T>* get (T* ptr, ade::Shape shape, std::string label = "");
    24 
    25  static Variable<T>* get (ade::Shape shape, std::string label = "")
    26  {
    27  return Variable<T>::get(std::vector<T>(shape.n_elems(), 0),
    28  shape, label);
    29  }
    30 
    31  static Variable<T>* get (T scalar, ade::Shape shape, std::string label = "")
    32  {
    33  if (label.empty())
    34  {
    35  label = fmts::to_string(scalar);
    36  }
    37  return Variable<T>::get(std::vector<T>(shape.n_elems(),scalar),
    38  shape, label);
    39  }
    40 
    41  static Variable<T>* get (std::vector<T> data, ade::Shape shape,
    42  std::string label = "")
    43  {
    44  if (data.size() != shape.n_elems())
    45  {
    46  logs::fatalf("cannot create variable with data size %d "
    47  "against shape %s", data.size(), shape.to_string().c_str());
    48  }
    49  return Variable<T>::get(data.data(), shape, label);
    50  }
    51 
    52  static Variable<T>* get (const Variable<T>& other)
    53  {
    54  return new Variable<T>(other);
    55  }
    56 
    57  static Variable<T>* get (Variable<T>&& other)
    58  {
    59  return new Variable<T>(std::move(other));
    60  }
    61 
    62  Variable<T>& operator = (const Variable<T>& other) = default;
    63 
    64  Variable<T>& operator = (Variable<T>&& other) = default;
    65 
    67  Variable<T>& operator = (std::vector<T> input)
    68  {
    69  size_t ninput = input.size();
    70  if (this->shape_.n_elems() != ninput)
    71  {
    72  logs::fatalf("cannot assign vector of %d elements to "
    73  "internal data of shape %s", ninput,
    74  this->shape_.to_string().c_str());
    75  }
    76  std::memcpy(this->data_.data(), input.data(), ninput * sizeof(T));
    77  return *this;
    78  }
    79 
    80  Variable<T>& operator = (const TensorT<T>& input)
    81  {
    82  this->data_ = input;
    83  return *this;
    84  }
    85 
    86  void assign (void* input, age::_GENERATED_DTYPE dtype, ade::Shape shape)
    87  {
    88  std::vector<T> data;
    89  age::type_convert(data, input, dtype, shape.n_elems());
    90  this->data_ = make_tensmap<T>(data.data(), shape);;
    91  }
    92 
    94  std::string to_string (void) const override
    95  {
    96  return "variable:" + label_;
    97  }
    98 
    99  bool is_const (void) const override
    100  {
    101  return false;
    102  }
    103 
    105  std::string label_;
    106 
    107 private:
    108  Variable (T* data, ade::Shape shape, std::string label) :
    109  iLeaf<T>(data, shape), label_(label) {}
    110 
    111  Variable (const Variable<T>& other) = default;
    112 
    113  Variable (Variable<T>&& other) = default;
    114 
    115 };
    116 
    117 template <typename T>
    118 struct VariableNode final : public iNode<T>
    119 {
    120  VariableNode (std::shared_ptr<Variable<T>> var) : var_(var) {}
    121 
    122  T* data (void) override
    123  {
    124  return (T*) var_->data();
    125  }
    126 
    127  void update (void) override {}
    128 
    129  ade::TensptrT get_tensor (void) override
    130  {
    131  return var_;
    132  }
    133 
    134  void assign (T* input, ade::Shape shape)
    135  {
    136  var_->assign(input, age::get_type<T>(), shape);
    137  }
    138 
    139  void assign (TensMapT<T>* tensmap)
    140  {
    141  var_->assign(tensmap->data(), age::get_type<T>(), get_shape(*tensmap));
    142  }
    143 
    144  std::string get_label (void) const
    145  {
    146  return var_->label_;
    147  }
    148 
    149 private:
    150  std::shared_ptr<Variable<T>> var_;
    151 };
    152 
    153 template <typename T>
    154 Variable<T>* Variable<T>::get (T* ptr, ade::Shape shape, std::string label)
    155 {
    156  static bool registered = register_builder<Variable<T>,T>(
    157  [](ade::TensptrT tens)
    158  {
    159  return std::make_shared<VariableNode<T>>(
    160  std::static_pointer_cast<Variable<T>>(tens));
    161  });
    162  assert(registered);
    163 
    164  return new Variable<T>(ptr, shape, label);
    165 }
    166 
    167 template <typename T>
    168 using VarptrT = std::shared_ptr<VariableNode<T>>;
    169 
    170 template <typename T>
    171 NodeptrT<T> convert_to_node (VarptrT<T> var)
    172 {
    173  return std::static_pointer_cast<iNode<T>>(var);
    174 }
    175 
    176 template <typename T>
    177 VarptrT<T> make_variable_scalar (T scalar, ade::Shape shape, std::string label = "")
    178 {
    179  return std::make_shared<VariableNode<T>>(
    180  std::shared_ptr<Variable<T>>(Variable<T>::get(scalar, shape, label))
    181  );
    182 }
    183 
    184 template <typename T>
    185 VarptrT<T> make_variable (ade::Shape shape, std::string label = "")
    186 {
    187  return std::make_shared<VariableNode<T>>(
    188  std::shared_ptr<Variable<T>>(Variable<T>::get(shape, label))
    189  );
    190 }
    191 
    192 template <typename T>
    193 VarptrT<T> make_variable (T* data, ade::Shape shape, std::string label = "")
    194 {
    195  return std::make_shared<VariableNode<T>>(
    196  std::shared_ptr<Variable<T>>(Variable<T>::get(data, shape, label))
    197  );
    198 }
    199 
    200 }
    201 
    202 #endif // EAD_VARIABLE_HPP
    ead::VariableNode::VariableNode
    VariableNode(std::shared_ptr< Variable< T >> var)
    Definition: variable.hpp:120
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    ead::convert_to_node
    NodeptrT< T > convert_to_node(VarptrT< T > var)
    Definition: variable.hpp:171
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    ade::Shape get_shape(const TensorT< T > &tens)
    Definition: eigen.hpp:176
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    void assign(void *input, age::_GENERATED_DTYPE dtype, ade::Shape shape)
    Definition: variable.hpp:86
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    const ade::Shape & shape(void) const override
    Implementation of iTensor.
    Definition: ileaf.hpp:25
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    std::shared_ptr< iNode< T > > NodeptrT
    Definition: inode.hpp:31
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    Return string representation of shape.
    Definition: shape.hpp:146
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    Definition: variable.hpp:185
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    Definition: constant.hpp:9
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    Definition: variable.hpp:168
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    Definition: shape.hpp:116
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    ade::TensptrT get_tensor(void) override
    Definition: variable.hpp:129
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    Data Source.
    Definition: ileaf.hpp:69
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    Definition: eigen.hpp:25
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    Definition: variable.hpp:144
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    Definition: eigen.hpp:22
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    Definition: variable.hpp:177
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    Definition: variable.hpp:139
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    Definition: variable.hpp:105
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    Definition: variable.hpp:127
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    Definition: ileaf.hpp:31
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    Definition: variable.hpp:21
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    1 #include "opt/ivoter.hpp"
    2 
    3 #ifndef OPT_VOTER_HPP
    4 #define OPT_VOTER_HPP
    5 
    6 namespace opt
    7 {
    8 
    9 struct OrdrVoter final : public iVoter
    10 {
    11  OrdrVoter (std::string label) : label_(label) {}
    12 
    13  void emplace (VoterArgsT args, Symbol sym) override
    14  {
    15  args_.emplace(args, sym);
    16  }
    17 
    18  CandsT inspect (const CandArgsT& args) const override
    19  {
    20  CandsT out;
    21  out.reserve(args_.size());
    22  for (const auto& vpair : args_)
    23  {
    24  CtxsT ctxs;
    25  const VoterArgsT& vargs = vpair.first;
    26  if (vargs.size() != args.size())
    27  {
    28  continue;
    29  }
    30  if ([&]() -> bool
    31  {
    32  for (size_t i = 0, n = args.size(); i < n; ++i)
    33  {
    34  if (false == vargs[i].match(ctxs, args[i]))
    35  {
    36  return true;
    37  }
    38  }
    39  return false;
    40  }())
    41  {
    42  // failure to match one of the arguments
    43  continue;
    44  }
    45  out[vpair.second].insert(ctxs.begin(), ctxs.end());
    46  }
    47  return out;
    48  }
    49 
    50  std::string label_;
    51 
    52  std::unordered_map<VoterArgsT,Symbol,OrdrHasher> args_;
    53 };
    54 
    55 // todo: ensure comm voters are inspected after all available ordr voters are inspected (optimization)
    56 struct CommVoter final : public iVoter
    57 {
    58  CommVoter (std::string label) : label_(label) {}
    59 
    60  void emplace (VoterArgsT args, Symbol sym) override
    61  {
    62  // sort args
    63  SegVArgs segs;
    64  for (VoterArg& arg : args)
    65  {
    66  switch (arg.type_)
    67  {
    68  case ::SUBGRAPH_TYPE::SCALAR:
    69  segs.scalars_.push_back(arg);
    70  break;
    71  case ::SUBGRAPH_TYPE::BRANCH:
    72  segs.branches_.push_back(arg);
    73  break;
    74  case ::SUBGRAPH_TYPE::ANY:
    75  default:
    76  segs.anys_.push_back(arg);
    77  }
    78  }
    79  if (segs.branches_.size() > 1)
    80  {
    81  logs::fatal("implementation limit: "
    82  "cannot have more than 1 operator as an argument of the "
    83  "commutative operator for the source subgraph");
    84  }
    85  sort_vargs(segs.scalars_);
    86  sort_vargs(segs.anys_);
    87  args_.emplace(segs, sym);
    88  }
    89 
    90  CandsT inspect (const CandArgsT& args) const override
    91  {
    92  CandsT out;
    93  out.reserve(args_.size());
    94  for (const auto& vpair : args_)
    95  {
    96  const SegVArgs& vargs = vpair.first;
    97  if (vargs.size() != args.size())
    98  {
    99  continue;
    100  }
    101  CtxsT ctxs;
    102  std::list<CandArg> unmatched(args.begin(), args.end());
    103  // attempt matching scalars first
    104  bool match_failed = false;
    105  for (const VoterArg& sarg : vargs.scalars_)
    106  {
    107  match_failed = true;
    108  for (auto it = unmatched.begin(), et = unmatched.end();
    109  it != et; ++it)
    110  {
    111  if (sarg.match(ctxs, *it))
    112  {
    113  unmatched.erase(it);
    114  match_failed = false;
    115  break;
    116  }
    117  }
    118  if (match_failed)
    119  {
    120  break;
    121  }
    122  }
    123  // none of the unmatched args matched
    124  // a scalar voter argument
    125  if (match_failed)
    126  {
    127  continue;
    128  }
    129 
    130  for (const VoterArg& barg : vargs.branches_)
    131  {
    132  match_failed = true;
    133  for (auto it = unmatched.begin(), et = unmatched.end();
    134  it != et; ++it)
    135  {
    136  if (barg.match(ctxs, *it))
    137  {
    138  unmatched.erase(it);
    139  match_failed = false;
    140  break;
    141  }
    142  }
    143  if (match_failed)
    144  {
    145  break;
    146  }
    147  }
    148  // none of the unmatched args matched
    149  // a branch voter argument
    150  if (match_failed)
    151  {
    152  continue;
    153  }
    154 
    155  // create permutations of remaining against anys matches
    156  std::vector<CandArg> remaining(unmatched.begin(), unmatched.end());
    157  size_t nremaining = remaining.size();
    158  std::vector<size_t> indices(nremaining);
    159  std::iota(indices.begin(), indices.end(), 0);
    160  do
    161  {
    162  bool matched = true;
    163  CtxsT local_ctxs = ctxs;
    164  for (size_t i = 0; i < nremaining && matched; ++i)
    165  {
    166  matched = vargs.anys_[i].match(local_ctxs,
    167  remaining[indices[i]]);
    168  }
    169  if (false == matched)
    170  {
    171  continue;
    172  }
    173  out[vpair.second].insert(local_ctxs.begin(), local_ctxs.end());
    174  }
    175  while (std::next_permutation(indices.begin(), indices.end()));
    176  }
    177  return out;
    178  }
    179 
    180  std::string label_;
    181 
    182  std::unordered_map<SegVArgs,Symbol,CommHasher> args_;
    183 };
    184 
    185 struct VariadicVoter final : public iVoter
    186 {
    187  VariadicVoter (std::string label, std::string variadic) :
    188  label_(label), variadic_(variadic) {}
    189 
    190  void emplace (VoterArgsT args, Symbol sym) override
    191  {
    192  // sort args
    193  SegVArgs segs;
    194  for (VoterArg& arg : args)
    195  {
    196  switch (arg.type_)
    197  {
    198  case ::SUBGRAPH_TYPE::SCALAR:
    199  segs.scalars_.push_back(arg);
    200  break;
    201  case ::SUBGRAPH_TYPE::BRANCH:
    202  segs.branches_.push_back(arg);
    203  break;
    204  case ::SUBGRAPH_TYPE::ANY:
    205  default:
    206  segs.anys_.push_back(arg);
    207  }
    208  }
    209  if (segs.branches_.size() > 1)
    210  {
    211  logs::fatal("implementation limit: "
    212  "cannot have more than 1 operator as an argument of the "
    213  "commutative operator for the source subgraph");
    214  }
    215  sort_vargs(segs.scalars_);
    216  sort_vargs(segs.anys_);
    217  args_.emplace(segs, sym);
    218  }
    219 
    220  CandsT inspect (const CandArgsT& args) const override
    221  {
    222  CandsT out;
    223  out.reserve(args_.size());
    224  for (const auto& vpair : args_)
    225  {
    226  const SegVArgs& vargs = vpair.first;
    227  if (vargs.size() > args.size())
    228  {
    229  // not enough voter arguments to match candidate arguments
    230  continue;
    231  }
    232  CtxsT ctxs;
    233  std::list<CandArg> unmatched(args.begin(), args.end());
    234  // attempt matching scalars first
    235  bool match_failed = false;
    236  for (const VoterArg& sarg : vargs.scalars_)
    237  {
    238  match_failed = true;
    239  for (auto it = unmatched.begin(), et = unmatched.end();
    240  it != et; ++it)
    241  {
    242  if (sarg.match(ctxs, *it))
    243  {
    244  unmatched.erase(it);
    245  match_failed = false;
    246  break;
    247  }
    248  }
    249  if (match_failed)
    250  {
    251  break;
    252  }
    253  }
    254  // none of the unmatched args matched
    255  // a scalar voter argument
    256  if (match_failed)
    257  {
    258  continue;
    259  }
    260 
    261  for (const VoterArg& barg : vargs.branches_)
    262  {
    263  match_failed = true;
    264  for (auto it = unmatched.begin(), et = unmatched.end();
    265  it != et; ++it)
    266  {
    267  if (barg.match(ctxs, *it))
    268  {
    269  unmatched.erase(it);
    270  match_failed = false;
    271  break;
    272  }
    273  }
    274  if (match_failed)
    275  {
    276  break;
    277  }
    278  }
    279  // none of the unmatched args matched
    280  // a branch voter argument
    281  if (match_failed)
    282  {
    283  continue;
    284  }
    285 
    286  // create permutations of remaining against anys matches
    287  std::vector<CandArg> remaining(unmatched.begin(), unmatched.end());
    288  size_t nremaining = remaining.size();
    289  std::vector<size_t> indices(nremaining);
    290  std::iota(indices.begin(), indices.end(), 0);
    291 
    292  size_t nanys = vargs.anys_.size();
    293  do
    294  {
    295  // select first nanys indices,
    296  // and dump remaining as variadic
    297  bool matched = true;
    298  CtxsT local_ctxs = ctxs;
    299  for (size_t i = 0; i < nanys && matched; ++i)
    300  {
    301  matched = vargs.anys_[i].match(local_ctxs,
    302  remaining[indices[i]]);
    303  }
    304  if (false == matched)
    305  {
    306  continue;
    307  }
    308  CtxValT cvals;
    309  for (size_t i = nanys; i < nremaining; ++i)
    310  {
    311  // dump remaining[indices[i]] as variadic
    312  cvals.emplace(remaining[indices[i]].tensor_); // todo: also store coorder and shaper
    313  }
    314  CtxsT& out_ctxs = out[vpair.second];
    315  for (ContexT ctx : local_ctxs)
    316  {
    317  ctx.emplace(variadic_, cvals);
    318  out_ctxs.emplace(ctx);
    319  }
    320  }
    321  while (std::next_permutation(indices.begin(), indices.end()));
    322  }
    323  return out;
    324  }
    325 
    326  std::string label_;
    327 
    328  std::string variadic_;
    329 
    330  std::unordered_map<SegVArgs,Symbol,CommHasher> args_;
    331 };
    332 
    333 }
    334 
    335 #endif // OPT_VOTER_HPP
    opt::CtxValT
    std::set< ade::TensptrT > CtxValT
    Definition: candidate.hpp:14
    -
    opt::CommVoter::label_
    std::string label_
    Definition: voter.hpp:180
    -
    opt::VariadicVoter::inspect
    CandsT inspect(const CandArgsT &args) const override
    Definition: voter.hpp:220
    -
    csv_to_png.args
    args
    Definition: csv_to_png.py:105
    -
    opt::VariadicVoter::VariadicVoter
    VariadicVoter(std::string label, std::string variadic)
    Definition: voter.hpp:187
    -
    opt::SegVArgs::branches_
    VoterArgsT branches_
    Definition: ivoter.hpp:141
    -
    opt::CommVoter::args_
    std::unordered_map< SegVArgs, Symbol, CommHasher > args_
    Definition: voter.hpp:182
    -
    opt::OrdrVoter::label_
    std::string label_
    Definition: voter.hpp:50
    -
    opt::VariadicVoter::variadic_
    std::string variadic_
    Definition: voter.hpp:328
    -
    opt
    Definition: candidate.hpp:11
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    opt::OrdrVoter::args_
    std::unordered_map< VoterArgsT, Symbol, OrdrHasher > args_
    Definition: voter.hpp:52
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    opt::SegVArgs::anys_
    VoterArgsT anys_
    Definition: ivoter.hpp:143
    -
    BRANCH
    Definition: def.h:20
    -
    opt::VariadicVoter::emplace
    void emplace(VoterArgsT args, Symbol sym) override
    Definition: voter.hpp:190
    -
    opt::CtxsT
    std::unordered_set< ContexT, boost::hash< ContexT > > CtxsT
    Definition: candidate.hpp:18
    -
    opt::CandArgsT
    std::vector< CandArg > CandArgsT
    Definition: candidate.hpp:67
    -
    opt::CommVoter::emplace
    void emplace(VoterArgsT args, Symbol sym) override
    Definition: voter.hpp:60
    -
    opt::VariadicVoter
    Definition: voter.hpp:185
    -
    opt::OrdrVoter::emplace
    void emplace(VoterArgsT args, Symbol sym) override
    Definition: voter.hpp:13
    -
    opt::SegVArgs
    Definition: ivoter.hpp:132
    -
    opt::ContexT
    std::map< std::string, CtxValT > ContexT
    Definition: candidate.hpp:16
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    ANY
    Definition: def.h:19
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    opt::CandsT
    std::unordered_map< Symbol, CtxsT, SymbolHash > CandsT
    Definition: candidate.hpp:54
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    opt::SegVArgs::scalars_
    VoterArgsT scalars_
    Definition: ivoter.hpp:139
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    opt::sort_vargs
    void sort_vargs(VoterArgsT &args)
    -
    opt::iVoter
    Definition: ivoter.hpp:208
    -
    opt::CommVoter::CommVoter
    CommVoter(std::string label)
    Definition: voter.hpp:58
    -
    opt::VariadicVoter::label_
    std::string label_
    Definition: voter.hpp:326
    -
    opt::OrdrVoter::inspect
    CandsT inspect(const CandArgsT &args) const override
    Definition: voter.hpp:18
    -
    opt::Symbol
    Definition: candidate.hpp:28
    - -
    opt::OrdrVoter::OrdrVoter
    OrdrVoter(std::string label)
    Definition: voter.hpp:11
    -
    opt::SegVArgs::size
    size_t size(void) const
    Definition: ivoter.hpp:134
    -
    opt::VoterArgsT
    std::vector< VoterArg > VoterArgsT
    Definition: ivoter.hpp:130
    -
    opt::VariadicVoter::args_
    std::unordered_map< SegVArgs, Symbol, CommHasher > args_
    Definition: voter.hpp:330
    -
    opt::CommVoter::inspect
    CandsT inspect(const CandArgsT &args) const override
    Definition: voter.hpp:90
    -
    opt::OrdrVoter
    Definition: voter.hpp:9
    -
    SCALAR
    Definition: def.h:18
    -
    opt::CommVoter
    Definition: voter.hpp:56
    -
    opt::VoterArg
    Definition: ivoter.hpp:14
    -
    opt::VoterArg::match
    bool match(CtxsT &ctxs, const CandArg &arg) const
    Definition: ivoter.hpp:27
    -
    - -
    -Generated by   -doxygen - 1.8.14 -
    - - diff --git a/ead/BUILD.bazel b/ead/BUILD.bazel index ce0353ad5..b090a09f6 100644 --- a/ead/BUILD.bazel +++ b/ead/BUILD.bazel @@ -42,9 +42,19 @@ py_binary( deps = ["//gen:gen"], ) +config_setting( + name = "fast_build", + values = { + "define": "EAD_CFG=MIN", + }, +) + genrule( name = "generated_ead", - srcs = ["//cfg:ead"], + srcs = select({ + "//conditions:default": ["//cfg:ead"], + ":fast_build": ["//cfg:ead_min"] + }), outs = [ "generated/api.hpp", "generated/api.cpp", @@ -56,10 +66,17 @@ genrule( "generated/pyapi_tenncor.cpp", ], tools = ["//ead:agen"], - cmd = "$(location //ead:agen) " + + cmd = select({ + "//conditions:default": "$(location //ead:agen) " + "--cfg $(location //cfg:ead) " + "--out $(@D)/generated " + "--strip_prefix=$$(dirname $(@D))", + + ":fast_build": "$(location //ead:agen) " + + "--cfg $(location //cfg:ead_min) " + + "--out $(@D)/generated " + + "--strip_prefix=$$(dirname $(@D))", + }) ) cc_library( @@ -115,13 +132,14 @@ cc_test( "//ead:ead", "//dbg:stream_out", "@gtest//:gtest", + "@com_github_mingkaic_cppkg//diff:diff", "@com_github_mingkaic_cppkg//exam:exam", ], linkstatic = True, - data = ["//cfg:optimizations"] + glob([ - "data/*.pb", - "data/*.txt", - ]), + data = [ + "//:test_models", + "//cfg:optimizations", + ], ) py_test( diff --git a/ead/constant.hpp b/ead/constant.hpp index e34fe85b7..42e6fbd29 100644 --- a/ead/constant.hpp +++ b/ead/constant.hpp @@ -54,6 +54,7 @@ struct Constant final : public iLeaf return out; } + /// Implementation of iLeaf bool is_const (void) const override { return true; diff --git a/ead/ead.hpp b/ead/ead.hpp index a0adbdcc3..3796d4e13 100644 --- a/ead/ead.hpp +++ b/ead/ead.hpp @@ -1,3 +1,5 @@ #include "ead/grader.hpp" #include "ead/serialize.hpp" #include "ead/session.hpp" + +#include "ead/generated/api.hpp" diff --git a/ead/eigen.hpp b/ead/eigen.hpp index 7c7cc29e1..69af2ae0a 100644 --- a/ead/eigen.hpp +++ b/ead/eigen.hpp @@ -155,7 +155,7 @@ inline MatMapT make_matmap (T* data, const ade::Shape& shape) { if (nullptr == data) { - logs::fatal("cannot get matmap from nullptr");; + logs::fatal("cannot get matmap from nullptr"); } return MatMapT(data, shape.at(1), shape.at(0)); } @@ -167,7 +167,7 @@ inline TensMapT make_tensmap (T* data, const ade::Shape& shape) std::copy(shape.begin(), shape.end(), slist.begin()); if (nullptr == data) { - logs::fatal("cannot get tensmap from nullptr");; + logs::fatal("cannot get tensmap from nullptr"); } return TensMapT(data, slist); } diff --git a/ead/funcarg.hpp b/ead/funcarg.hpp index 55957157a..cdf14e32b 100644 --- a/ead/funcarg.hpp +++ b/ead/funcarg.hpp @@ -120,6 +120,63 @@ FuncArg permute_map (NodeptrT node, std::vector order) return FuncArg(node, ade::permute(order), permute(order)); } +template +FuncArg slice_map (NodeptrT node, ade::RankT offset, + ade::RankT extent, ade::RankT dimension) +{ + if (dimension >= ade::rank_cap) + { + logs::fatalf("cannot slice dimension %d beyond rank_cap %d", + dimension, ade::rank_cap); + } + ade::CoordT slicings; + std::fill(slicings.begin(), slicings.end(), ade::rank_cap); + slicings[0] = offset; + slicings[1] = extent; + slicings[2] = dimension; + return FuncArg(node, + std::make_shared( + [=](ade::MatrixT fwd) + { + for (ade::RankT i = 0; i < ade::rank_cap; ++i) + { + fwd[i][i] = 1; + } + fwd[ade::rank_cap][dimension] = + extent - node->shape().at(dimension); + }), + std::make_shared(slicings, false)); +} + +template +FuncArg pad_map (NodeptrT node, + const std::pair& padding, + ade::RankT dimension) +{ + if (dimension >= ade::rank_cap) + { + logs::fatalf("cannot pad dimension %d beyond rank_cap %d", + dimension, ade::rank_cap); + } + ade::CoordT paddings; + std::fill(paddings.begin(), paddings.end(), ade::rank_cap); + paddings[0] = padding.first; + paddings[1] = padding.second; + paddings[2] = dimension; + return FuncArg(node, + std::make_shared( + [=](ade::MatrixT fwd) + { + for (ade::RankT i = 0; i < ade::rank_cap; ++i) + { + fwd[i][i] = 1; + } + fwd[ade::rank_cap][dimension] = + padding.first + padding.second; + }), + std::make_shared(paddings, false)); +} + } #endif // EAD_FUNCARG_HPP diff --git a/ead/functor.hpp b/ead/functor.hpp index f154af92b..3588dc18c 100644 --- a/ead/functor.hpp +++ b/ead/functor.hpp @@ -202,7 +202,7 @@ Functor* Functor::get (ade::Opcode opcode, ArgsT args) size_t nargs = args.size(); if (0 == nargs) { - logs::fatalf("cannot perform %s with no arguments", + logs::fatalf("cannot perform `%s` with no arguments", opcode.name_.c_str()); } @@ -212,7 +212,7 @@ Functor* Functor::get (ade::Opcode opcode, ArgsT args) ade::Shape ishape = args[i].shape(); if (false == ishape.compatible_after(shape, 0)) { - logs::fatalf("cannot perform %s with incompatible shapes %s " + logs::fatalf("cannot perform `%s` with incompatible shapes %s " "and %s", opcode.name_.c_str(), shape.to_string().c_str(), ishape.to_string().c_str()); } diff --git a/ead/grader.hpp b/ead/grader.hpp index 31bc6b168..5c529c319 100644 --- a/ead/grader.hpp +++ b/ead/grader.hpp @@ -207,7 +207,7 @@ struct GradientBuilder final : public ade::iGradientBuilder case age::MAX: case age::MIN: out = tenncor::eq(NodeConverters::to_node(op), - NodeConverters::to_node(args[arg_idx].get_tensor()))->get_tensor();; + NodeConverters::to_node(args[arg_idx].get_tensor()))->get_tensor(); break; case age::POW: out = (arg_idx==0 ? @@ -224,7 +224,7 @@ struct GradientBuilder final : public ade::iGradientBuilder ) : tenncor::mul(tenncor::log(NodeConverters::to_node(args[0].get_tensor())), NodeConverters::to_node(op)) - )->get_tensor();; + )->get_tensor(); break; case age::SUB: out = make_constant_scalar(arg_idx == 0 ? @@ -414,6 +414,7 @@ struct GradientBuilder final : public ade::iGradientBuilder tenncor::slice(NodeConverters::to_node(supcomp_grad), offset, extent, dimension)); } + break; case age::SELECT: { if (0 == arg_idx) diff --git a/ead/ileaf.hpp b/ead/ileaf.hpp index d4d69d01a..ceeed87a6 100644 --- a/ead/ileaf.hpp +++ b/ead/ileaf.hpp @@ -57,9 +57,6 @@ struct iLeaf : public ade::iLeaf return sizeof(T) * shape_.n_elems(); } - // todo: deprecate (with is_mutable) - virtual bool is_const (void) const = 0; - protected: iLeaf (T* data, ade::Shape shape) : data_(make_tensmap(data, shape)), diff --git a/ead/inode.hpp b/ead/inode.hpp index 376e055c8..55fbc26be 100644 --- a/ead/inode.hpp +++ b/ead/inode.hpp @@ -45,7 +45,7 @@ struct NodeConverters final { const std::type_info& tp = typeid(*tens); return estd::must_getf(builders_, tp.hash_code(), - "unknown tensor type %s with %s dtype", + "unknown tensor type `%s` with `%s` dtype", tp.name(), age::name_type(age::get_type()).c_str())(tens); } diff --git a/ead/operator.hpp b/ead/operator.hpp index 054e6b908..222e1ad7d 100644 --- a/ead/operator.hpp +++ b/ead/operator.hpp @@ -120,6 +120,17 @@ EigenptrT permute (ade::Shape& outshape, const OpArg& in) assert(nullptr != in.coorder_); ade::CoordT reorder; in.coorder_->forward(reorder.begin(), reorder.begin()); + if (is_2d(outshape) && reorder[0] == 1 && reorder[1] == 0) + { + // use matrix when possible + return make_eigenmatrix>, + MatMapT>( + shape_convert(outshape), + [](MatMapT& in) + { + return in.transpose(); + }, make_matmap(in.data_, in.shape_)); + } return make_eigentensor>,TensMapT>( shape_convert(outshape), diff --git a/ead/parse.hpp b/ead/parse.hpp index 9bd0729f7..9e878b0d3 100644 --- a/ead/parse.hpp +++ b/ead/parse.hpp @@ -64,7 +64,7 @@ struct AnyConvr final : public opt::iConverter ade::Shape outshape) const override { const opt::CtxValT& val = estd::must_getf(ctx, any_id_, - "cannot find any id %s in conversion", any_id_.c_str()); + "cannot find any id `%s` in conversion", any_id_.c_str()); if (val.size() != 1) { logs::fatal("context value is not any"); @@ -250,7 +250,7 @@ struct ConverterBuilder final : public opt::iConverterBuilder std::string symbol(sg->val_.any_); if (false == estd::has(ctx.symbols_, symbol)) { - logs::fatalf("undeclared symbol '%s'", symbol.c_str()); + logs::fatalf("undeclared symbol `%s`", symbol.c_str()); } out = std::make_shared>(symbol); } diff --git a/ead/python/ead.cpp b/ead/python/ead.cpp index 70187e766..b9263a340 100644 --- a/ead/python/ead.cpp +++ b/ead/python/ead.cpp @@ -19,6 +19,7 @@ namespace py = pybind11; namespace pyead { +// todo: move these to a common file ade::Shape p2cshape (std::vector& pyshape) { return ade::Shape(std::vector( @@ -40,7 +41,7 @@ std::vector c2pshape (const ade::Shape& cshape) template py::array typedata_to_array (ead::iNode* tnode, py::dtype dtype) { - auto pshape = pyead::c2pshape(tnode->shape()); + auto pshape = c2pshape(tnode->shape()); return py::array(dtype, py::array::ShapeContainer(pshape.begin(), pshape.end()), tnode->data()); @@ -49,7 +50,7 @@ py::array typedata_to_array (ead::iNode* tnode, py::dtype dtype) std::vector arr2vec (ade::Shape& outshape, py::array data) { py::buffer_info info = data.request(); - outshape = pyead::p2cshape(info.shape); + outshape = p2cshape(info.shape); size_t n = outshape.n_elems(); auto dtype = data.dtype(); char kind = dtype.kind(); @@ -120,121 +121,124 @@ PYBIND11_MODULE(ead, m) m.doc() = "ead variables"; // ==== node ==== - py::object node = (py::object) + auto node = (py::class_,ead::NodeptrT>) py::module::import("ead.tenncor").attr("NodeptrT"); - ((py::class_,ead::NodeptrT>) node) + node .def("__str__", - [](py::object self) - { - auto dnode = self.cast*>(); - return dnode->get_tensor()->to_string(); - }, - "Return string representation of this tensor instance") + [](py::object self) + { + auto dnode = self.cast*>(); + return dnode->get_tensor()->to_string(); + }, + "Return string representation of this tensor instance") .def("shape", - [](py::object self) - { - auto dnode = self.cast*>(); - ade::Shape shape = dnode->get_tensor()->shape(); - auto pshape = pyead::c2pshape(shape); - std::vector ipshape(pshape.begin(), pshape.end()); - return py::array(ipshape.size(), ipshape.data()); - }, - "Return this instance's shape") + [](py::object self) + { + auto dnode = self.cast*>(); + ade::Shape shape = dnode->get_tensor()->shape(); + auto pshape = pyead::c2pshape(shape); + std::vector ipshape(pshape.begin(), pshape.end()); + return py::array(ipshape.size(), ipshape.data()); + }, + "Return this instance's shape") .def("children", - [](py::object self) - { - auto dnode = self.cast*>(); - std::vector tens; - if (auto f = dynamic_cast( - dnode->get_tensor().get())) + [](py::object self) { - auto args = f->get_children(); - std::transform(args.begin(), args.end(), - std::back_inserter(tens), - [](ade::FuncArg& mten) + auto dnode = self.cast*>(); + std::vector tens; + if (auto f = dynamic_cast( + dnode->get_tensor().get())) { - return mten.get_tensor(); - }); - } - return tens; - }) + auto args = f->get_children(); + std::transform(args.begin(), args.end(), + std::back_inserter(tens), + [](ade::FuncArg& mten) + { + return mten.get_tensor(); + }); + } + return tens; + }) .def("as_tens", - [](py::object self) - { - auto dnode = self.cast*>(); - return dnode->get_tensor(); - }) + [](py::object self) + { + auto dnode = self.cast*>(); + return dnode->get_tensor(); + }) .def("get", - [](py::object self) - { - auto dnode = self.cast*>(); - return pyead::typedata_to_array(dnode, - py::dtype::of()); - }); + [](py::object self) + { + auto dnode = self.cast*>(); + return pyead::typedata_to_array(dnode, + py::dtype::of()); + }); // ==== session ==== py::class_ isess(m, "iSession"); py::class_ session(m, "Session", isess); - py::implicitly_convertible(); - session - .def(py::init()) + isess .def("track", - [](py::object self, ead::NodesT roots) - { - auto sess = self.cast(); - ade::TensT troots; - troots.reserve(roots.size()); - std::transform(roots.begin(), roots.end(), - std::back_inserter(troots), - [](ead::NodeptrT& node) - { - return node->get_tensor(); - }); - sess->track(troots); - }, - "Track node") + [](py::object self, ead::NodesT roots) + { + auto sess = self.cast(); + ade::TensT troots; + troots.reserve(roots.size()); + std::transform(roots.begin(), roots.end(), + std::back_inserter(troots), + [](ead::NodeptrT& node) + { + return node->get_tensor(); + }); + sess->track(troots); + }) .def("update", - [](py::object self, std::vector> nodes) - { - auto sess = self.cast(); - std::unordered_set updates; - for (ead::NodeptrT& node : nodes) + [](py::object self, std::vector> nodes) { - updates.emplace(node->get_tensor().get()); - } - sess->update(updates); - }, - "Calculate every node in the graph given list of updated data nodes", - py::arg("nodes") = std::vector>{}) + auto sess = self.cast(); + std::unordered_set updates; + for (ead::NodeptrT& node : nodes) + { + updates.emplace(node->get_tensor().get()); + } + sess->update(updates); + }, + "Calculate every node in the graph given list of updated data nodes", + py::arg("nodes") = std::vector>{}) .def("update_target", - [](py::object self, std::vector> targeted, - std::vector> updated) - { - auto sess = self.cast(); - std::unordered_set targets; - std::unordered_set updates; - for (ead::NodeptrT& node : targeted) - { - targets.emplace(node->get_tensor().get()); - } - for (ead::NodeptrT& node : updated) + [](py::object self, std::vector> targeted, + std::vector> updated) { - updates.emplace(node->get_tensor().get()); - } - sess->update_target(targets, updates); - }, - "Calculate node relevant to targets in the graph given list of updated data", - py::arg("targets"), py::arg("updated") = std::vector>{}) + auto sess = self.cast(); + std::unordered_set targets; + std::unordered_set updates; + for (ead::NodeptrT& node : targeted) + { + targets.emplace(node->get_tensor().get()); + } + for (ead::NodeptrT& node : updated) + { + updates.emplace(node->get_tensor().get()); + } + sess->update_target(targets, updates); + }, + "Calculate node relevant to targets in the graph given list of updated data", + py::arg("targets"), + py::arg("updated") = std::vector>{}); + + py::implicitly_convertible(); + session + .def(py::init()) .def("optimize", - [](py::object self, std::string filename) - { - auto sess = self.cast(); - opt::OptCtx rules = ead::parse_file("cfg/optimizations.rules"); - sess->optimize(rules); - }, - "Optimize using rules for specified filename"); + [](py::object self, std::string filename) + { + auto sess = self.cast(); + opt::OptCtx rules = ead::parse_file(filename); + sess->optimize(rules); + }, + py::arg("filename") = "cfg/optimizations.rules", + "Optimize using rules for specified filename"); // ==== constant ==== py::class_,std::shared_ptr>> constant( @@ -250,56 +254,61 @@ PYBIND11_MODULE(ead, m) variable .def("assign", - [](py::object self, py::array data) - { - auto var = self.cast*>(); - ade::Shape shape; - std::vector vec = pyead::arr2vec(shape, data); - var->assign(vec.data(), shape); - }, - "Assign numpy data array to variable"); + [](py::object self, py::array data) + { + auto var = self.cast*>(); + ade::Shape shape; + std::vector vec = pyead::arr2vec(shape, data); + var->assign(vec.data(), shape); + }, + "Assign numpy data array to variable"); // ==== inline functions ==== - m.def("scalar_constant", - [](PybindT scalar, std::vector slist) - { - return ead::make_constant_scalar(scalar, - pyead::p2cshape(slist)); - }, - "Return scalar constant node"); - - m.def("constant", - [](py::array data) - { - ade::Shape shape; - std::vector vec = pyead::arr2vec(shape, data); - return ead::make_constant(vec.data(), shape); - }, "Return constant node with data"); - - m.def("scalar_variable", [](PybindT scalar, std::vector slist, std::string label) - { - return ead::make_variable_scalar(scalar, pyead::p2cshape(slist), label); - }, - "Return labelled variable containing numpy data array", - py::arg("scalar"), py::arg("slist"), py::arg("label") = ""); - - m.def("variable", - [](py::array data, std::string label) - { - ade::Shape shape; - std::vector vec = pyead::arr2vec(shape, data); - return ead::make_variable(vec.data(), shape, label); - }, - "Return labelled variable containing numpy data array", - py::arg("data"), py::arg("label") = ""); + m + // constant creation + .def("scalar_constant", + [](PybindT scalar, std::vector slist) + { + return ead::make_constant_scalar(scalar, + pyead::p2cshape(slist)); + }, + "Return scalar constant node") + .def("constant", + [](py::array data) + { + ade::Shape shape; + std::vector vec = pyead::arr2vec(shape, data); + return ead::make_constant(vec.data(), shape); + }, "Return constant node with data") - m.def("derive", &ead::derive, - "Return derivative of first tensor with respect to second tensor (deprecated)"); + // variable creation + .def("scalar_variable", + [](PybindT scalar, std::vector slist, std::string label) + { + return ead::make_variable_scalar(scalar, pyead::p2cshape(slist), label); + }, + "Return labelled variable containing numpy data array", + py::arg("scalar"), + py::arg("slist"), + py::arg("label") = "") + .def("variable", + [](py::array data, std::string label) + { + ade::Shape shape; + std::vector vec = pyead::arr2vec(shape, data); + return ead::make_variable(vec.data(), shape, label); + }, + "Return labelled variable containing numpy data array", + py::arg("data"), + py::arg("label") = "") - m.def("seed", - [](size_t seed) - { - ead::get_engine().seed(seed); - }, - "Seed internal RNG"); + // other stuff + .def("derive", &ead::derive, + "Return derivative of first tensor with respect to second tensor (deprecated)") + .def("seed", + [](size_t seed) + { + ead::get_engine().seed(seed); + }, + "Seed internal RNG"); } diff --git a/ead/random.hpp b/ead/random.hpp index 4a1a7b3f4..30ab3ebce 100644 --- a/ead/random.hpp +++ b/ead/random.hpp @@ -35,7 +35,7 @@ template ::value>::type* GenF unif_gen (const T& a, const T& b) { std::uniform_int_distribution dist(a, b); - return std::bind(dist, get_engine());; + return std::bind(dist, get_engine()); } template ::value>::type* = nullptr> diff --git a/ead/serialize.hpp b/ead/serialize.hpp index d25f47b72..8f6645a20 100644 --- a/ead/serialize.hpp +++ b/ead/serialize.hpp @@ -14,6 +14,7 @@ #include "ead/coord.hpp" #include "ead/constant.hpp" #include "ead/variable.hpp" +#include "ead/functor.hpp" #ifndef EAD_SERIALIZE_HPP #define EAD_SERIALIZE_HPP @@ -43,7 +44,7 @@ static bool is_big_endian(void) struct EADSaver final : public pbm::iSaver { - std::string save_leaf (bool& is_const, ade::iLeaf* leaf) override + std::string save_leaf (ade::iLeaf* leaf) override { char* data = (char*) leaf->data(); size_t nelems = leaf->shape().n_elems(); @@ -93,9 +94,21 @@ struct EADSaver final : public pbm::iSaver }; #define _OUT_GENERIC(realtype)leaf = is_const?\ -make_constant((realtype*) pb, shape)->get_tensor():\ +ade::TensptrT(Constant::get((realtype*) pb, shape)):\ ade::TensptrT(Variable::get((realtype*) pb, shape, label)); +#define _OUT_GENFUNC(realtype){\ +ArgsT eargs;eargs.reserve(args.size());\ +std::transform(args.begin(), args.end(), std::back_inserter(eargs),\ +[](ade::FuncArg arg){\ + return FuncArg(\ + NodeConverters::to_node(arg.get_tensor()),\ + arg.get_shaper(),\ + std::static_pointer_cast(arg.get_coorder()));\ +});\ +func = ade::TensptrT(\ +Functor::get(ade::Opcode{opname, age::get_op(opname)},eargs));} + /// Unmarshal cortenn::Source as Variable containing context of source struct EADLoader final : public pbm::iLoader { @@ -127,7 +140,28 @@ struct EADLoader final : public pbm::iLoader ade::TensptrT generate_func (std::string opname, ade::ArgsT args) override { - return ade::TensptrT(ade::Functor::get(ade::Opcode{opname, age::get_op(opname)}, args)); + if (args.empty()) + { + logs::fatalf("cannot generate func %s without args", opname.c_str()); + } + size_t gencode = age::BAD_TYPE; + auto arg = args[0].get_tensor().get(); + if (auto leaf = dynamic_cast(arg)) + { + gencode = leaf->type_code(); + } + else if (auto func = dynamic_cast(arg)) + { + gencode = func->type_code(); + } + else + { + logs::fatalf("cannot generate func from non-ead tensor arg %s", + arg->to_string().c_str()); + } + ade::TensptrT func = nullptr; + TYPE_LOOKUP(_OUT_GENFUNC, (age::_GENERATED_DTYPE) gencode); + return func; } ade::CoordptrT generate_shaper (std::vector coord) override diff --git a/ead/session.hpp b/ead/session.hpp index 6b073e3a4..69ec63231 100644 --- a/ead/session.hpp +++ b/ead/session.hpp @@ -30,7 +30,7 @@ struct iSession virtual void update_target (TensSetT target, TensSetT updated = {}) = 0; }; -struct SizeT +struct SizeT final { size_t d = 0; @@ -98,6 +98,7 @@ struct Session final : public iSession } } + // this function is expected to be called repeatedly during runtime void update (TensSetT updated = {}, TensSetT ignores = {}) override { std::unordered_map fulfilments; @@ -126,6 +127,7 @@ struct Session final : public iSession } } + // this function is expected to be called repeatedly during runtime void update_target (TensSetT target, TensSetT updated = {}) override { ade::OnceTraveler targetted; @@ -175,6 +177,7 @@ struct Session final : public iSession std::unordered_map> parents_; + // List of operatible nodes and its number of unique children ordered from leaf to root std::vector> requirements_; // todo: test minimal requirements }; diff --git a/ead/test/ptest.py b/ead/test/ptest.py index d7920141e..e66e1a729 100644 --- a/ead/test/ptest.py +++ b/ead/test/ptest.py @@ -102,7 +102,7 @@ def _common_unary_tf(self, shape, api, tf_op): tf_var = tf.Variable(data) tf_out = tf_op(tf_var) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) sess = ead.Session() @@ -177,7 +177,7 @@ def _common_reduce_1d(self, dim_reduce, tf_reduce): var = ead.variable(data, 'var') tf_var = tf.Variable(data) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) out = dim_reduce(var, 1) @@ -219,7 +219,7 @@ def _common_reduce(self, all_reduce, dim_reduce, tf_reduce): var = ead.variable(data, 'var') tf_var = tf.Variable(data) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) out = all_reduce(var) @@ -613,7 +613,7 @@ def test_rl2norm(self): var = ead.variable(data, 'var') tf_var = tf.Variable(data) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) out = tc.reduce_l2norm(var) @@ -674,7 +674,7 @@ def test_matmul(self): tf_var = tf.Variable(data) tf_var2 = tf.Variable(data2) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) @@ -777,7 +777,7 @@ def test_convolution(self): fout = out.get() - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_kernel.initializer) @@ -833,7 +833,7 @@ def test_conv2d(self): sess.track([out]) sess.update() - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfoutput = tf.nn.conv2d(tfimage, tfkernel, [1, 1, 1, 1], 'VALID') tfsess.run(tfimage.initializer) @@ -874,7 +874,7 @@ def test_grader_scenario1(self): # REDUCE -> MUL tf_var = tf.Variable(data) tf_var2 = tf.Variable(data2) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) @@ -910,7 +910,7 @@ def test_grader_scenario2(self): # EXTEND -> MUL tf_var = tf.Variable(data) tf_var2 = tf.Variable(data2) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) @@ -946,7 +946,7 @@ def test_grader_scenario3(self): # PERMUTE -> MUL tf_var = tf.Variable(data) tf_var2 = tf.Variable(data2) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) @@ -985,7 +985,7 @@ def test_grader_scenario4(self): # MATMUL -> MUL tf_var2 = tf.Variable(data2) tf_var3 = tf.Variable(data3) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) tfsess.run(tf_var3.initializer) @@ -1025,7 +1025,7 @@ def test_grader_scenario5(self): # MATMUL -> MATMUL tf_var2 = tf.Variable(data2) tf_var3 = tf.Variable(data3) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) tfsess.run(tf_var3.initializer) @@ -1062,7 +1062,7 @@ def test_grader_scenario6(self): # REDUCE -> MATMUL tf_var = tf.Variable(data) tf_var2 = tf.Variable(data2) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) @@ -1099,7 +1099,7 @@ def test_grader_scenario7(self): # EXTEND -> MATMUL tf_var = tf.Variable(data) tf_var2 = tf.Variable(data2) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) @@ -1137,7 +1137,7 @@ def test_grader_scenario8(self): var = ead.variable(data, 'var') tf_var = tf.Variable(data) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) out = tc.matmul( @@ -1196,7 +1196,7 @@ def test_grader_scenario9(self): dc = ead.derive(dest, c) tf_da, tf_db, tf_dc = tf.gradients(tf_dest, [tf_a, tf_b, tf_c]) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_a.initializer) tfsess.run(tf_b.initializer) tfsess.run(tf_c.initializer) diff --git a/ead/test/test_serialize.cpp b/ead/test/test_serialize.cpp index ef08dc8f0..ad75e465e 100644 --- a/ead/test/test_serialize.cpp +++ b/ead/test/test_serialize.cpp @@ -4,8 +4,12 @@ #include +#include + #include "gtest/gtest.h" +#include "diff/msg.hpp" + #include "exam/exam.hpp" #include "dbg/stream/ade.hpp" @@ -13,21 +17,21 @@ #include "pbm/save.hpp" #include "pbm/load.hpp" +#include "tag/prop.hpp" + #include "ead/serialize.hpp" #include "ead/ead.hpp" -const std::string testdir = "ead/data"; +const std::string testdir = "models/test"; TEST(SERIALIZE, SaveGraph) { - std::string expect_pbfile = testdir + "/graph.pb"; - std::string got_pbfile = "got_graph.pb"; + std::string expect_pbfile = testdir + "/ead_test.pbx"; + std::string got_pbfile = "got_ead_test.pbx"; cortenn::Graph graph; - std::vector roots; - pbm::PathedMapT labels; ade::Shape in_shape({10, 3}); ade::Shape weight0_shape({9, 10}); ade::Shape bias0_shape({9}); @@ -36,24 +40,31 @@ TEST(SERIALIZE, SaveGraph) ade::Shape out_shape({5,3}); ead::NodeptrT in = ead::make_variable( - std::vector(in_shape.n_elems()).data(), in_shape); + std::vector(in_shape.n_elems()).data(), + in_shape, "in"); ead::NodeptrT weight0 = ead::make_variable( - std::vector(weight0_shape.n_elems()).data(), weight0_shape); + std::vector(weight0_shape.n_elems()).data(), + weight0_shape, "weight0"); ead::NodeptrT bias0 = ead::make_variable( - std::vector(bias0_shape.n_elems()).data(), bias0_shape); + std::vector(bias0_shape.n_elems()).data(), + bias0_shape, "bias0"); ead::NodeptrT weight1 = ead::make_variable( - std::vector(weight1_shape.n_elems()).data(), weight1_shape); + std::vector(weight1_shape.n_elems()).data(), + weight1_shape, "weight1"); ead::NodeptrT bias1 = ead::make_variable( - std::vector(bias1_shape.n_elems()).data(), bias1_shape); + std::vector(bias1_shape.n_elems()).data(), + bias1_shape, "bias1"); ead::NodeptrT out = ead::make_variable( - std::vector(out_shape.n_elems()).data(), out_shape); + std::vector(out_shape.n_elems()).data(), + out_shape, "out"); - labels[in->get_tensor()] = {"global", "training", "in"}; - labels[weight0->get_tensor()] = {"global", "storage", "weight0"}; - labels[bias0->get_tensor()] = {"global", "storage", "bias0"}; - labels[weight1->get_tensor()] = {"global", "storage", "weight1"}; - labels[bias1->get_tensor()] = {"global", "storage", "bias1"}; - labels[out->get_tensor()] = {"global", "training", "out"}; + auto& preg = tag::get_property_reg(); + preg.property_tag(in->get_tensor(), "training_in"); + preg.property_tag(weight0->get_tensor(), "storage_weight0"); + preg.property_tag(bias0->get_tensor(), "storage_bias0"); + preg.property_tag(weight1->get_tensor(), "storage_weight1"); + preg.property_tag(bias1->get_tensor(), "storage_bias1"); + preg.property_tag(out->get_tensor(), "training_out"); auto layer0 = tenncor::add(tenncor::matmul(in, weight0), tenncor::extend(bias0, 1, {3})); auto sig0 = tenncor::div(ead::make_constant_scalar(1, ade::Shape({9, 3})), @@ -72,10 +83,10 @@ TEST(SERIALIZE, SaveGraph) auto dw1 = ead::derive(err, weight1); auto db1 = ead::derive(err, bias1); - labels[dw0->get_tensor()] = {"global", "dw0"}; - labels[db0->get_tensor()] = {"global", "db0"}; - labels[dw1->get_tensor()] = {"global", "dw1"}; - labels[db1->get_tensor()] = {"global", "db1"}; + preg.property_tag(dw0->get_tensor(), "derivative_dw0"); + preg.property_tag(db0->get_tensor(), "derivative_db0"); + preg.property_tag(dw1->get_tensor(), "derivative_dw1"); + preg.property_tag(db1->get_tensor(), "derivative_db1"); pbm::GraphSaver saver; dw0->get_tensor()->accept(saver); @@ -83,6 +94,7 @@ TEST(SERIALIZE, SaveGraph) dw1->get_tensor()->accept(saver); db1->get_tensor()->accept(saver); + pbm::PathedMapT labels; // todo: deprecate labels saver.save(graph, labels); { @@ -92,21 +104,21 @@ TEST(SERIALIZE, SaveGraph) ASSERT_TRUE(graph.SerializeToOstream(&gotstr)); } - std::fstream expect_ifs(expect_pbfile, std::ios::in | std::ios::binary); - std::fstream got_ifs(got_pbfile, std::ios::in | std::ios::binary); - ASSERT_TRUE(expect_ifs.is_open()); - ASSERT_TRUE(got_ifs.is_open()); - - std::string expect; - std::string got; - // skip the first line (it contains timestamp) - expect_ifs >> expect; - got_ifs >> got; - for (size_t lineno = 1; expect_ifs && got_ifs; ++lineno) { - expect_ifs >> expect; - got_ifs >> got; - EXPECT_STREQ(expect.c_str(), got.c_str()) << "line number " << lineno; + std::fstream expect_ifs(expect_pbfile, std::ios::in | std::ios::binary); + std::fstream got_ifs(got_pbfile, std::ios::in | std::ios::binary); + ASSERT_TRUE(expect_ifs.is_open()); + ASSERT_TRUE(got_ifs.is_open()); + + cortenn::Graph expect_graph; + cortenn::Graph got_graph; + ASSERT_TRUE(expect_graph.ParseFromIstream(&expect_ifs)); + ASSERT_TRUE(got_graph.ParseFromIstream(&got_ifs)); + + google::protobuf::util::MessageDifferencer differ; + std::string report; + differ.ReportDifferencesToString(&report); + EXPECT_TRUE(differ.Compare(expect_graph, got_graph)) << report; } } @@ -115,7 +127,7 @@ TEST(SERIALIZE, LoadGraph) { cortenn::Graph in; { - std::fstream inputstr(testdir + "/graph.pb", + std::fstream inputstr(testdir + "/ead_test.pbx", std::ios::in | std::ios::binary); ASSERT_TRUE(inputstr.is_open()); ASSERT_TRUE(in.ParseFromIstream(&inputstr)); @@ -123,45 +135,61 @@ TEST(SERIALIZE, LoadGraph) pbm::GraphInfo out; pbm::load_graph(out, in); - EXPECT_EQ(4, out.roots_.size()); - ASSERT_EQ(1, out.tens_.children_.size()); - ASSERT_EQ(0, out.tens_.tens_.size()); - ASSERT_HAS(out.tens_.children_, "global"); - - auto global = out.tens_.children_["global"]; - ASSERT_EQ(2, global->children_.size()); - ASSERT_EQ(4, global->tens_.size()); - ASSERT_HAS(global->children_, "training"); - ASSERT_HAS(global->children_, "storage"); - ASSERT_HAS(global->tens_, "dw0"); - ASSERT_HAS(global->tens_, "db0"); - ASSERT_HAS(global->tens_, "dw1"); - ASSERT_HAS(global->tens_, "db1"); - - auto training = global->children_["training"]; - auto storage = global->children_["storage"]; - ASSERT_EQ(0, training->children_.size()); - ASSERT_EQ(2, training->tens_.size()); - ASSERT_EQ(0, storage->children_.size()); - ASSERT_EQ(4, storage->tens_.size()); - ASSERT_HAS(training->tens_, "in"); - ASSERT_HAS(training->tens_, "out"); - ASSERT_HAS(storage->tens_, "weight0"); - ASSERT_HAS(storage->tens_, "bias0"); - ASSERT_HAS(storage->tens_, "weight1"); - ASSERT_HAS(storage->tens_, "bias1"); - - auto dw0 = global->tens_["dw0"]; - auto db0 = global->tens_["db0"]; - auto dw1 = global->tens_["dw1"]; - auto db1 = global->tens_["db1"]; - - ASSERT_ARRHAS(out.roots_, dw0); - ASSERT_ARRHAS(out.roots_, db0); - ASSERT_ARRHAS(out.roots_, dw1); - ASSERT_ARRHAS(out.roots_, db1); + auto& reg = tag::get_reg(); + tag::Query q; + + std::vector root_props; + std::unordered_map propdtens; + for (auto tens : out.roots_) + { + tens->accept(q); + auto tags = reg.get_tags(tens.get()); + ASSERT_HAS(tags, tag::props_key); + auto& props = tags[tag::props_key]; + for (auto& prop : props) + { + propdtens.emplace(prop, tens); + } + root_props.insert(root_props.end(), props.begin(), props.end()); + } + EXPECT_ARRHAS(root_props, "derivative_dw0"); + EXPECT_ARRHAS(root_props, "derivative_dw1"); + EXPECT_ARRHAS(root_props, "derivative_db0"); + EXPECT_ARRHAS(root_props, "derivative_db1"); + + ASSERT_HAS(q.labels_, tag::props_key); + auto& props = q.labels_[tag::props_key]; + ASSERT_HAS(props, "training_in"); + ASSERT_HAS(props, "storage_weight0"); + ASSERT_HAS(props, "storage_bias0"); + ASSERT_HAS(props, "storage_weight1"); + ASSERT_HAS(props, "storage_bias1"); + ASSERT_HAS(props, "training_out"); + + auto& tins = props["training_in"]; + auto& w0s = props["storage_weight0"]; + auto& b0s = props["storage_bias0"]; + auto& w1s = props["storage_weight1"]; + auto& b1s = props["storage_bias1"]; + auto& touts = props["training_out"]; + + ASSERT_EQ(1, tins.size()); + ASSERT_EQ(1, w0s.size()); + ASSERT_EQ(1, b0s.size()); + ASSERT_EQ(1, w1s.size()); + ASSERT_EQ(1, b1s.size()); + ASSERT_EQ(1, touts.size()); + + ASSERT_HAS(propdtens, "derivative_dw0"); + ASSERT_HAS(propdtens, "derivative_db0"); + ASSERT_HAS(propdtens, "derivative_dw1"); + ASSERT_HAS(propdtens, "derivative_db1"); + auto dw0 = propdtens["derivative_dw0"]; + auto db0 = propdtens["derivative_db0"]; + auto dw1 = propdtens["derivative_dw1"]; + auto db1 = propdtens["derivative_db1"]; ASSERT_NE(nullptr, dw0); ASSERT_NE(nullptr, db0); @@ -171,14 +199,16 @@ TEST(SERIALIZE, LoadGraph) std::string expect; std::string got; std::string line; - std::ifstream expectstr(testdir + "/graph.txt"); - ASSERT_TRUE(expectstr.is_open()); - while (std::getline(expectstr, line)) { - fmts::trim(line); - if (line.size() > 0) + std::ifstream expectstr(testdir + "/ead_test.txt"); + ASSERT_TRUE(expectstr.is_open()); + while (std::getline(expectstr, line)) { - expect += line + '\n'; + fmts::trim(line); + if (line.size() > 0) + { + expect += line + '\n'; + } } } diff --git a/ead/variable.hpp b/ead/variable.hpp index 46d2d6ea6..563b59ffd 100644 --- a/ead/variable.hpp +++ b/ead/variable.hpp @@ -87,22 +87,23 @@ struct Variable final : public iLeaf { std::vector data; age::type_convert(data, input, dtype, shape.n_elems()); - this->data_ = make_tensmap(data.data(), shape);; + this->data_ = make_tensmap(data.data(), shape); } /// Implementation of iTensor std::string to_string (void) const override { - return "variable:" + label_; + return label_; } + /// Implementation of iLeaf bool is_const (void) const override { return false; } /// Label for distinguishing variable nodes - std::string label_; + std::string label_; // todo: make private private: Variable (T* data, ade::Shape shape, std::string label) : @@ -143,7 +144,7 @@ struct VariableNode final : public iNode std::string get_label (void) const { - return var_->label_; + return var_->to_string(); } private: diff --git a/models/dqnmodel.json b/models/dqnmodel.json new file mode 100644 index 000000000..e155af453 --- /dev/null +++ b/models/dqnmodel.json @@ -0,0 +1,188 @@ +{ + "nodes": [ + { + "label": "weight", + "source": { + "shape": [ + "9", + "10", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "tsT1vlSvqD+qrIm+BhD5Px1aDj+g7TU/ATuUP9q/zj9HWJw/sdjQP4j0xj8DEwy/oiEgQHahaD0o72k/4zv5P7T0uD+E/TU/4pp+PyUivD/AyS6/S5MLQGhihT+U79Q/Jwj0P+jisT/vw/0+BdWSP2sM7D+05mq+k2AhQPppnz+5knc/Gp63P0mxuj9Qs30/mwuHP8R/H0A5uRe+DEMBQEN/aT8dpLE/DFwLQLFw1T98Wps/d6G3P3dxDEAMjpO8LpLqP38Plj993/Y/AbYVQJRcnT9j8eM/fuVCPwxY4j+/iTG/01obQLaQ1j8T3/Y+Hs8QQGIttD/IFqk/GgqIP/oeK0CHUxa+z6EAQLbn/D4m99s/eUYEQKn2jT8SnsI/LkcaPxB85j8Liru+l0ikP/enwT8sMwY/DYXnP7Qv7D9Hsu0/8aREP/iq8D88Xo6+UCs2QND5rj+R9AVANDsdQJ+hB0BCmwxA", + "typelabel": "FLOAT" + }, + "tags": { + "layer_dense": { + "labels": [ + "0::weight:0" + ] + }, + "layer_seqmodel": { + "labels": [ + "demo:layer_dense:0:0" + ] + } + } + }, + { + "label": "bias", + "source": { + "shape": [ + "9", + "1", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "4hp0P1oP9z8Li/u+6O0NQEgpeT+lu7Y/23H5P2lZyz/euKM/", + "typelabel": "FLOAT" + }, + "tags": { + "layer_seqmodel": { + "labels": [ + "demo:layer_dense:0:0" + ] + }, + "layer_dense": { + "labels": [ + "0::bias:0" + ] + } + } + }, + { + "source": { + "shape": [ + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "AAAAAA==", + "typelabel": "FLOAT", + "isConst": true + }, + "tags": { + "layer_sigmoid": { + "labels": [ + "sigmoid:::0" + ] + }, + "properties": { + "labels": [ + "immutable" + ] + }, + "layer_seqmodel": { + "labels": [ + "demo:layer_sigmoid:sigmoid:1" + ] + } + } + }, + { + "label": "weight", + "source": { + "shape": [ + "9", + "9", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": 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"1", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "AAAAAA==", + "typelabel": "FLOAT", + "isConst": true + }, + "tags": { + "layer_sigmoid": { + "labels": [ + "sigmoid:::0" + ] + }, + "properties": { + "labels": [ + "immutable" + ] + }, + "layer_seqmodel": { + "labels": [ + "demo:layer_sigmoid:sigmoid:3" + ] + } + } + } + ] +} diff --git a/models/dqnmodel.pbx b/models/dqnmodel.pbx new file mode 100644 index 000000000..0918f0367 Binary files /dev/null and b/models/dqnmodel.pbx differ diff --git a/models/gdmodel.json b/models/gdmodel.json new file mode 100644 index 000000000..1a9884782 --- /dev/null +++ b/models/gdmodel.json @@ -0,0 +1,188 @@ +{ + "nodes": [ + { + "label": "weight", + "source": { + "shape": [ + "9", + "10", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": 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+ }, + { + "source": { + "shape": [ + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "AAAAAA==", + "typelabel": "FLOAT", + "isConst": true + }, + "tags": { + "layer_sigmoid": { + "labels": [ + "sigmoid:::0" + ] + }, + "properties": { + "labels": [ + "immutable" + ] + }, + "layer_seqmodel": { + "labels": [ + "demo:layer_sigmoid:sigmoid:1" + ] + } + } + }, + { + "label": "weight", + "source": { + "shape": [ + "5", + "9", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "C8jlv2ZtlL+33Ui/aYF7v0bd3b+MzGW/yoJGwNAVjL25Y0E/08IwP4Sq7z5eW0I/rhAyQEeGyr8uNsC/U8QAPnYrTD91kkXA8BuzvmuSAMB3ofu/EV8UQNTU1DuyhvA+1zawP2TU5798CUe/i4wfP8mbyr48lGe/OA63P8kRAr8Kr0u/HgcpwLAlFkB95Yw/bKbOPgIvGD9SbCxAd9sEP5O2+L9NmcM+LCwzvun8Ib9ZPJ4/", + "typelabel": "FLOAT" + }, + "tags": { + "layer_seqmodel": { + "labels": [ + "demo:layer_dense:1:2" + ] + }, + "layer_dense": { + "labels": [ + "1::weight:0" + ] + } + } + }, + { + "label": "bias", + "source": { + "shape": [ + "5", + "1", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "aKYAQPhQlT4iW9A+5zWVP6YWbr4=", + "typelabel": "FLOAT" + }, + "tags": { + "layer_seqmodel": { + "labels": [ + "demo:layer_dense:1:2" + ] + }, + "layer_dense": { + "labels": [ + "1::bias:0" + ] + } + } + }, + { + "source": { + "shape": [ + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": "AAAAAA==", + "typelabel": "FLOAT", + "isConst": true + }, + "tags": { + "properties": { + "labels": [ + "immutable" + ] + }, + "layer_seqmodel": { + "labels": [ + "demo:layer_sigmoid:sigmoid:3" + ] + }, + "layer_sigmoid": { + "labels": [ + "sigmoid:::0" + ] + } + } + } + ] +} diff --git a/models/gdmodel.pbx b/models/gdmodel.pbx new file mode 100644 index 000000000..a93839fb1 Binary files /dev/null and b/models/gdmodel.pbx differ diff --git a/models/rbmmodel.json b/models/rbmmodel.json new file mode 100644 index 000000000..65f6b95e0 --- /dev/null +++ b/models/rbmmodel.json @@ -0,0 +1,240 @@ +{ + "nodes": [ + { + "label": "weight", + "source": { + "shape": [ + "64", + "784", + "1", + "1", + "1", + "1", + "1", + "1" + ], + "data": 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"fwd": true + }, + { + "idx": 145, + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ], + "fwd": true + } + ] + }, + "tags": { + "groups": { + "labels": [ + "prod" + ] + }, + "properties": { + "labels": [ + "commutative" + ] + } + }, + "label": "MUL" + }, + { + "functor": { + "opname": "EXTEND", + "args": [ + { + "idx": 146, + "coord": [ + 1, + 1, + 10, + 1, + 1, + 1, + 1, + 1 + ], + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 10, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ] + } + ] + }, + "label": "EXTEND" + }, + { + "functor": { + "opname": "REDUCE_SUM", + "args": [ + { + "idx": 147, + "coord": [ + 1, + 8, + 8, + 8, + 8, + 8, + 8, + 8 + ], + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.33333333333333331, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ] + } + ] + }, + "label": "REDUCE_SUM" + }, + { + "functor": { + "opname": "MUL", + "args": [ + { + "idx": 59, + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ], + "fwd": true + }, + { + "idx": 148, + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ], + "fwd": true + } + ] + }, + "tags": { + "groups": { + "labels": [ + "prod" + ] + }, + "properties": { + "labels": [ + "commutative" + ] + } + }, + "label": "MUL" + }, + { + "functor": { + "opname": "MUL", + "args": [ + { + "idx": 20, + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ], + "fwd": true + }, + { + "idx": 149, + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ], + "fwd": true + } + ] + }, + "tags": { + "groups": { + "labels": [ + "prod" + ] + }, + "properties": { + "labels": [ + "derivative_db0", + "commutative" + ] + } + }, + "label": "MUL" + }, + { + "functor": { + "opname": "PERMUTE", + "args": [ + { + "idx": 150, + "coord": [ + 0, + 2, + 1, + 3, + 4, + 5, + 6, + 7 + ], + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ], + "fwd": true + } + ] + }, + "label": "PERMUTE" + }, + { + "functor": { + "opname": "REDUCE_SUM", + "args": [ + { + "idx": 152, + "coord": [ + 2, + 8, + 8, + 8, + 8, + 8, + 8, + 8 + ], + "shaper": [ + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.33333333333333331, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1 + ] + } + ] + }, + "tags": { + "properties": { + "labels": [ + "derivative_dw0" + ] + } + }, + "label": "REDUCE_SUM" + } + ] +} diff --git a/ead/data/graph.pb b/models/test/ead_test.pbx similarity index 92% rename from ead/data/graph.pb rename to models/test/ead_test.pbx index 6571701d5..153f83352 100644 Binary files a/ead/data/graph.pb and b/models/test/ead_test.pbx differ diff --git a/ead/data/graph.txt b/models/test/ead_test.txt similarity index 89% rename from ead/data/graph.txt rename to models/test/ead_test.txt index 55eaac775..ab170c3ad 100644 --- a/ead/data/graph.txt +++ b/models/test/ead_test.txt @@ -6,9 +6,9 @@ | `--(variable:in) `--(EXTEND) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(EXP) | `--(NEG) @@ -19,14 +19,14 @@ | `--(EXTEND) | `--(variable:bias0) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) `--(DIV) | `--(DIV) | | `--(NEG) - | | | `--(variable:) + | | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) @@ -36,7 +36,7 @@ | | `--(EXTEND) | | `--(variable:bias0) | `--(ADD) - | `--(variable:) + | `--(constant:1) | `--(EXP) | `--(NEG) | `--(ADD) @@ -53,18 +53,18 @@ | `--(variable:weight1) `--(EXTEND) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -77,22 +77,22 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) `--(DIV) | `--(DIV) | | `--(NEG) - | | | `--(variable:) + | | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -105,15 +105,15 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(ADD) - | `--(variable:) + | `--(constant:1) | `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -126,25 +126,25 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(MUL) - | `--(variable:) + | `--(constant:2) | `--(POW) | `--(SUB) | | `--(variable:out) | | `--(DIV) - | | `--(variable:) + | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -157,16 +157,16 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(SUB) - | `--(variable:) - | `--(variable:) - `--(variable:) + | `--(constant:2) + | `--(constant:1) + `--(constant:1) (MUL) - `--(variable:) + `--(constant:1) `--(REDUCE_SUM) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(EXP) | `--(NEG) @@ -177,14 +177,14 @@ | `--(EXTEND) | `--(variable:bias0) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) `--(DIV) | `--(DIV) | | `--(NEG) - | | | `--(variable:) + | | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) @@ -194,7 +194,7 @@ | | `--(EXTEND) | | `--(variable:bias0) | `--(ADD) - | `--(variable:) + | `--(constant:1) | `--(EXP) | `--(NEG) | `--(ADD) @@ -211,18 +211,18 @@ | `--(variable:weight1) `--(EXTEND) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -235,22 +235,22 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) `--(DIV) | `--(DIV) | | `--(NEG) - | | | `--(variable:) + | | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -263,15 +263,15 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(ADD) - | `--(variable:) + | `--(constant:1) | `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -284,25 +284,25 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(MUL) - | `--(variable:) + | `--(constant:2) | `--(POW) | `--(SUB) | | `--(variable:out) | | `--(DIV) - | | `--(variable:) + | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -315,18 +315,18 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(SUB) - | `--(variable:) - | `--(variable:) - `--(variable:) + | `--(constant:2) + | `--(constant:1) + `--(constant:1) (REDUCE_SUM) `--(PERMUTE) `--(MUL) `--(PERMUTE) | `--(EXTEND) | `--(DIV) - | `--(variable:) + | `--(constant:1) | `--(ADD) - | `--(variable:) + | `--(constant:1) | `--(EXP) | `--(NEG) | `--(ADD) @@ -337,18 +337,18 @@ | `--(variable:bias0) `--(EXTEND) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -361,22 +361,22 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) `--(DIV) | `--(DIV) | | `--(NEG) - | | | `--(variable:) + | | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -389,15 +389,15 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(ADD) - | `--(variable:) + | `--(constant:1) | `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -410,25 +410,25 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(MUL) - | `--(variable:) + | `--(constant:2) | `--(POW) | `--(SUB) | | `--(variable:out) | | `--(DIV) - | | `--(variable:) + | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -441,25 +441,25 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(SUB) - | `--(variable:) - | `--(variable:) - `--(variable:) + | `--(constant:2) + | `--(constant:1) + `--(constant:1) (MUL) - `--(variable:) + `--(constant:1) `--(REDUCE_SUM) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -472,22 +472,22 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:1) `--(MUL) `--(DIV) | `--(DIV) | | `--(NEG) - | | | `--(variable:) + | | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -500,15 +500,15 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(ADD) - | `--(variable:) + | `--(constant:1) | `--(EXP) | `--(NEG) | `--(ADD) | `--(MATMUL) | | `--(DIV) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(ADD) - | | | `--(variable:) + | | | `--(constant:1) | | | `--(EXP) | | | `--(NEG) | | | `--(ADD) @@ -521,25 +521,25 @@ | `--(EXTEND) | `--(variable:bias1) `--(MUL) - `--(variable:) + `--(constant:-1) `--(MUL) `--(MUL) - | `--(variable:) + | `--(constant:2) | `--(POW) | `--(SUB) | | `--(variable:out) | | `--(DIV) - | | `--(variable:) + | | `--(constant:1) | | `--(ADD) - | | `--(variable:) + | | `--(constant:1) | | `--(EXP) | | `--(NEG) | | `--(ADD) | | `--(MATMUL) | | | `--(DIV) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(ADD) - | | | | `--(variable:) + | | | | `--(constant:1) | | | | `--(EXP) | | | | `--(NEG) | | | | `--(ADD) @@ -552,6 +552,6 @@ | | `--(EXTEND) | | `--(variable:bias1) | `--(SUB) - | `--(variable:) - | `--(variable:) - `--(variable:) + | `--(constant:2) + | `--(constant:1) + `--(constant:1) diff 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/dev/null +++ b/models/test/pbm_test.txt @@ -0,0 +1,20 @@ +(-) + `--(constant:[3\1\7\1\1\1\1\1]) + `--(@) + `--(/) + | `--(neg) + | | `--(constant:[3\7\1\1\1\1\1\1]) + | `--(+) + | `--(sin) + | | `--(constant:[3\7\1\1\1\1\1\1]) + | `--(constant:[3\7\1\1\1\1\1\1]) + `--(constant:[7\3\1\1\1\1\1\1]) +(-) + `--(constant:[3\3\1\1\1\1\1\1]) + `--(*) + `--(abs) + | `--(constant:[3\3\1\1\1\1\1\1]) + `--(exp) + | `--(constant:[3\3\1\1\1\1\1\1]) + `--(neg) + `--(constant:[3\3\1\1\1\1\1\1]) diff --git a/opt/src/parse.cpp b/opt/src/parse.cpp index fb2eb77a7..ccc05bdce 100644 --- a/opt/src/parse.cpp +++ b/opt/src/parse.cpp @@ -99,7 +99,7 @@ static std::string build_intermediate (VoterPool& voters, const ::Subgraph* sg, std::string symbol(sg->val_.any_); if (false == estd::has(ctx.symbols_, symbol)) { - logs::fatalf("undeclared symbol '%s'", symbol.c_str()); + logs::fatalf("undeclared symbol `%s`", symbol.c_str()); } out = symbol; } @@ -206,7 +206,7 @@ OptCtx process_stmts (::PtrList* stmts, const iConverterBuilder& builder) } else if (estd::has(it->second, property_tag)) { - logs::warnf("reassignment of property %s to %s", + logs::warnf("reassignment of property `%s` to `%s`", property_tag.c_str(), label.c_str()); } else diff --git a/opt/test/test_opt.cpp b/opt/test/test_opt.cpp index aef7e2ed6..26fadf351 100644 --- a/opt/test/test_opt.cpp +++ b/opt/test/test_opt.cpp @@ -49,7 +49,7 @@ TEST(OPTIMIZE, CalcConstants) ASSERT_EQ(1, opted.size()); // expect optimized cfunc to be sin(2) EXPECT_GRAPHEQ( - (fmts::sprintf("(%f[1\\1\\1\\1\\1\\1\\1\\1])", std::sin(2))), + (fmts::sprintf("(constant:%f[1\\1\\1\\1\\1\\1\\1\\1])", std::sin(2))), opted[0]); } @@ -66,8 +66,8 @@ TEST(OPTIMIZE, CalcConstants) " `--(ADD[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(SIN[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | `--(variable:special_var[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | `--(%g[1\\1\\1\\1\\1\\1\\1\\1])\n" - " `--(%g[1\\1\\1\\1\\1\\1\\1\\1])", + " | `--(constant:%g[1\\1\\1\\1\\1\\1\\1\\1])\n" + " `--(constant:%g[1\\1\\1\\1\\1\\1\\1\\1])", std::sin(2), std::pow(3, 4))), opted[0]); @@ -78,8 +78,8 @@ TEST(OPTIMIZE, CalcConstants) " `--(ADD[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(SIN[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | `--(variable:special_var[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | `--(%g[1\\1\\1\\1\\1\\1\\1\\1])\n" - " `--(%g[1\\1\\1\\1\\1\\1\\1\\1])", + " | `--(constant:%g[1\\1\\1\\1\\1\\1\\1\\1])\n" + " `--(constant:%g[1\\1\\1\\1\\1\\1\\1\\1])", std::sin(2), std::pow(3, 4))), adv_func->get_tensor()); } @@ -106,10 +106,10 @@ TEST(OPTIMIZE, PruneZeroSingles) }, rules); ASSERT_EQ(2, opted.size()); // expect both optimized wunfunc to be 1 - EXPECT_GRAPHEQ("(1[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); + EXPECT_GRAPHEQ("(constant:1[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); // expect both optimized zrofunc to be 0 - EXPECT_GRAPHEQ("(0[1\\1\\1\\1\\1\\1\\1\\1])", opted[1]); + EXPECT_GRAPHEQ("(constant:0[1\\1\\1\\1\\1\\1\\1\\1])", opted[1]); } { @@ -135,7 +135,7 @@ TEST(OPTIMIZE, PruneZeroSingles) }, rules); ASSERT_EQ(2, opted.size()); // expect both optimized l and r zeros to be 0 - std::string expect = "(0[1\\1\\1\\1\\1\\1\\1\\1])"; + std::string expect = "(constant:0[1\\1\\1\\1\\1\\1\\1\\1])"; EXPECT_GRAPHEQ(expect, opted[0]); EXPECT_GRAPHEQ(expect, opted[1]); } @@ -163,7 +163,7 @@ TEST(OPTIMIZE, PruneZeroSingles) auto opted = opt::optimize({divz->get_tensor()}, rules); ASSERT_EQ(1, opted.size()); // expect optimized divz to be zero - EXPECT_GRAPHEQ("(0[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); + EXPECT_GRAPHEQ("(constant:0[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); } { @@ -173,7 +173,7 @@ TEST(OPTIMIZE, PruneZeroSingles) // expect optimized not_opt to remain the same EXPECT_GRAPHEQ( "(MAX[1\\1\\1\\1\\1\\1\\1\\1])\n" - " `--(0[1\\1\\1\\1\\1\\1\\1\\1])\n" + " `--(constant:0[1\\1\\1\\1\\1\\1\\1\\1])\n" " `--(variable:special_var[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); } } @@ -211,13 +211,13 @@ TEST(OPTIMIZE, PruneZeroGraph) " | | `--(MAX[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | | `--(MAX[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | | | `--(variable:var2[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | | | | `--(0[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | | | `--(1[1\\1\\1\\1\\1\\1\\1\\1])\n" + " | | | | `--(constant:0[1\\1\\1\\1\\1\\1\\1\\1])\n" + " | | | `--(constant:1[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | `--(LT[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | `--(MAX[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | | `--(variable:var2[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | | | `--(0[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | | `--(1[1\\1\\1\\1\\1\\1\\1\\1])\n" + " | | | `--(constant:0[1\\1\\1\\1\\1\\1\\1\\1])\n" + " | | `--(constant:1[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(DIV[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(variable:var2[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(NEG[1\\1\\1\\1\\1\\1\\1\\1])\n" @@ -227,7 +227,7 @@ TEST(OPTIMIZE, PruneZeroGraph) auto got0 = tenncor::tan(zero); opted = opt::optimize({tenncor::pow(nocascades, got0)->get_tensor()}, rules); ASSERT_EQ(1, opted.size()); - EXPECT_GRAPHEQ("(1[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); + EXPECT_GRAPHEQ("(constant:1[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); } @@ -255,7 +255,7 @@ TEST(OPTIMIZE, PruneOneSingles) opted[0]); // expect optimized wunfunc to be 1 - EXPECT_GRAPHEQ("(1[1\\1\\1\\1\\1\\1\\1\\1])", opted[1]); + EXPECT_GRAPHEQ("(constant:1[1\\1\\1\\1\\1\\1\\1\\1])", opted[1]); } { @@ -286,7 +286,7 @@ TEST(OPTIMIZE, PruneOneSingles) auto opted = opt::optimize({wun->get_tensor()}, rules); ASSERT_EQ(1, opted.size()); // expect optimized wun to be 1 - EXPECT_GRAPHEQ("(1[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); + EXPECT_GRAPHEQ("(constant:1[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); } { @@ -295,7 +295,7 @@ TEST(OPTIMIZE, PruneOneSingles) // expect optimized no_opt to remain the same EXPECT_GRAPHEQ( "(MAX[1\\1\\1\\1\\1\\1\\1\\1])\n" - " `--(1[1\\1\\1\\1\\1\\1\\1\\1])\n" + " `--(constant:1[1\\1\\1\\1\\1\\1\\1\\1])\n" " `--(variable:special_var[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); } } @@ -330,13 +330,13 @@ TEST(OPTIMIZE, PruneOneGraph) "(SUB[1\\1\\1\\1\\1\\1\\1\\1])\n" " `--(POW[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(MIN[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | | `--(1[1\\1\\1\\1\\1\\1\\1\\1])\n" + " | | `--(constant:1[1\\1\\1\\1\\1\\1\\1\\1])\n" " | | `--(variable:var[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(DIV[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(variable:var[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(MAX[1\\1\\1\\1\\1\\1\\1\\1])\n" " | `--(variable:var[1\\1\\1\\1\\1\\1\\1\\1])\n" - " | `--(1[1\\1\\1\\1\\1\\1\\1\\1])\n" + " | `--(constant:1[1\\1\\1\\1\\1\\1\\1\\1])\n" " `--(variable:var[1\\1\\1\\1\\1\\1\\1\\1])", opted[0]); } @@ -596,44 +596,44 @@ TEST(OPTIMIZE, ReuseOpGraph) std::list expectlines = { "0:MUL,1:COS,0,white", - "1:COS,2:variable:0,0,white", "0:MUL,3:MAX,1,white", - "3:MAX,4:variable:2,0,white", - "3:MAX,2:variable:0,1,white", - "5:SUB,6:POW,0,white", - "6:POW,7:MIN,0,white", - "7:MIN,8:MIN,0,white", - "8:MIN,3:MAX,0,white", - "8:MIN,1:COS,1,white", - "7:MIN,9:MIN,1,white", - "9:MIN,10:ADD,0,white", - "10:ADD,2:variable:0,0,white", "10:ADD,0:MUL,1,white", - "9:MIN,11:POW,1,white", + "10:ADD,2:0,0,white", "11:POW,12:SUB,0,white", - "12:SUB,4:variable:2,0,white", - "12:SUB,2:variable:0,1,white", - "11:POW,2:variable:0,1,white", - "6:POW,13:DIV,1,white", + "11:POW,2:0,1,white", + "12:SUB,2:0,1,white", + "12:SUB,4:2,0,white", "13:DIV,14:ADD,0,white", - "14:ADD,15:ADD,0,white", - "15:ADD,16:variable:1,0,white", - "15:ADD,2:variable:0,1,white", - "14:ADD,4:variable:2,1,white", "13:DIV,17:SUB,1,white", - "17:SUB,2:variable:0,0,white", - "17:SUB,16:variable:1,1,white", - "5:SUB,12:SUB,1,white", + "14:ADD,15:ADD,0,white", + "14:ADD,4:2,1,white", + "15:ADD,16:1,0,white", + "15:ADD,2:0,1,white", + "17:SUB,16:1,1,white", + "17:SUB,2:0,0,white", "18:MUL,19:MUL,0,white", + "18:MUL,22:MUL,1,white", + "19:MUL,1:COS,1,white", "19:MUL,20:EQ,0,white", + "1:COS,2:0,0,white", + "20:EQ,17:SUB,1,white", "20:EQ,21:DIV,0,white", - "21:DIV,12:SUB,0,white", "21:DIV,0:MUL,1,white", - "20:EQ,17:SUB,1,white", - "19:MUL,1:COS,1,white", - "18:MUL,22:MUL,1,white", - "22:MUL,21:DIV,0,white", + "21:DIV,12:SUB,0,white", "22:MUL,14:ADD,1,white", + "22:MUL,21:DIV,0,white", + "3:MAX,2:0,1,white", + "3:MAX,4:2,0,white", + "5:SUB,12:SUB,1,white", + "5:SUB,6:POW,0,white", + "6:POW,13:DIV,1,white", + "6:POW,7:MIN,0,white", + "7:MIN,8:MIN,0,white", + "7:MIN,9:MIN,1,white", + "8:MIN,1:COS,1,white", + "8:MIN,3:MAX,0,white", + "9:MIN,10:ADD,0,white", + "9:MIN,11:POW,1,white", }; expectlines.sort(); std::list gotlines; diff --git a/pbm/BUILD.bazel b/pbm/BUILD.bazel index 6afc4f1db..5080fe2ea 100644 --- a/pbm/BUILD.bazel +++ b/pbm/BUILD.bazel @@ -49,11 +49,24 @@ cc_library( srcs = glob(["src/*.cpp"]), copts = ["-std=c++17"], deps = [ - "//ade:ade", + "//tag:tag", "//pbm:pbm_cc_proto", ], ) +######### TOOLS ######### + +cc_binary( + name = "inspector", + srcs = glob(["inspector.cpp"]), + copts = ["-std=c++17"], + deps = [ + "//pbm:pbm_cc_proto", + "@com_github_mingkaic_cppkg//estd:estd", + "@com_github_mingkaic_cppkg//flag:flag", + ], +) + ######### TEST ######### cc_test( @@ -67,8 +80,5 @@ cc_test( "@com_github_mingkaic_cppkg//exam:exam", ], copts = ["-std=c++17"], - data = glob([ - "data/*.pb", - "data/*.txt", - ]), + data = ["//:test_models"], ) diff --git a/pbm/data.hpp b/pbm/data.hpp index af33b516a..0ad99c77f 100644 --- a/pbm/data.hpp +++ b/pbm/data.hpp @@ -10,6 +10,8 @@ #include "ade/ade.hpp" +#include "tag/tag.hpp" + #include "pbm/graph.pb.h" #ifndef PBM_COMMON_HPP @@ -26,7 +28,7 @@ using StringsT = std::list; struct iSaver { - virtual std::string save_leaf (bool& is_const, ade::iLeaf* leaf) = 0; + virtual std::string save_leaf (ade::iLeaf* leaf) = 0; virtual std::vector save_shaper (const ade::CoordptrT& mapper) = 0; diff --git a/pbm/data/graph.txt b/pbm/data/graph.txt deleted file mode 100644 index 01a97bd07..000000000 --- a/pbm/data/graph.txt +++ /dev/null @@ -1,20 +0,0 @@ -(-) - `--([3\1\7\1\1\1\1\1]) - `--(@) - `--(/) - | `--(neg) - | | `--([3\7\1\1\1\1\1\1]) - | `--(+) - | `--(sin) - | | `--([3\7\1\1\1\1\1\1]) - | `--([3\7\1\1\1\1\1\1]) - `--([7\3\1\1\1\1\1\1]) -(-) - `--([3\3\1\1\1\1\1\1]) - `--(*) - `--(abs) - | `--([3\3\1\1\1\1\1\1]) - `--(exp) - | `--([3\3\1\1\1\1\1\1]) - `--(neg) - `--([3\3\1\1\1\1\1\1]) diff --git a/pbm/graph.proto b/pbm/graph.proto index f463889be..03ed85d06 100644 --- a/pbm/graph.proto +++ b/pbm/graph.proto @@ -5,14 +5,14 @@ option go_package = "github.com/mingkaic/tenncor/pbm"; package cortenn; message Source { - bytes shape = 1; + repeated uint64 shape = 1 [packed = true]; bytes data = 2; string typelabel = 3; bool is_const = 4; } message NodeArg { - uint32 idx = 1; + uint32 idx = 1; // todo: check if these are redundant repeated double coord = 2 [packed = true]; repeated double shaper = 3 [packed = true]; bool fwd = 4; @@ -24,53 +24,20 @@ message Functor { repeated NodeArg args = 2; } -message Node { +message Tag { repeated string labels = 1; +} + +message Node { + string label = 1; oneof detail { Source source = 2; Functor functor = 3; } + map tags = 4; } message Graph { string label = 1; repeated Node nodes = 2; } - -message ItTrainerState { - uint64 iterations = 1; -} - -message DQTrainerState { - message ExpBatch { - repeated float observation = 1; - - repeated float new_observation = 2; - - uint64 action_idx = 3; - - float reward = 4; - } - - Graph target_graph = 1; - - uint64 actions_executed = 2; - - uint64 trained_iteration = 3; - - uint64 ntrained_called = 4; - - uint64 nstored_called = 5; - - repeated ExpBatch experiences = 6; -} - -message Layer { - Graph graph = 1; - oneof layer_context { - ItTrainerState it_ctx = 19; - - // change to any once we have a lot of contexts - DQTrainerState dqn_ctx = 20; - } -} diff --git a/pbm/inspector.cpp b/pbm/inspector.cpp new file mode 100644 index 000000000..1e0111600 --- /dev/null +++ b/pbm/inspector.cpp @@ -0,0 +1,109 @@ +#include + +#include + +#include "flag/flag.hpp" +#include "fmts/fmts.hpp" +#include "estd/strs.hpp" + +#include "pbm/graph.pb.h" + +const std::string pbx_ext = ".pbx"; + +void write_json (std::string writepath, const std::string& jsonstr) +{ + if (estd::has_affix(writepath, pbx_ext)) + { + std::fstream writestr(writepath, std::ios::out | std::ios::trunc | std::ios::binary); + if (writestr.is_open()) + { + cortenn::Graph graph; + google::protobuf::util::JsonParseOptions options; + options.ignore_unknown_fields = true; + if (google::protobuf::util::Status::OK != + google::protobuf::util::JsonStringToMessage( + jsonstr, &graph, options)) + { + logs::fatal("failed to parse json graph"); + } + if (false == graph.SerializeToOstream(&writestr)) + { + logs::fatalf("failed to serialize protobuf to %s", + writepath.c_str()); + } + writestr.close(); + return; + } + } + else + { + std::ofstream writestr(writepath); + if (writestr.is_open()) + { + writestr << jsonstr; + writestr.flush(); + writestr.close(); + return; + } + } + // write to stdout when all options fail + std::cout << jsonstr << std::endl; +} + +int main (int argc, const char** argv) +{ + std::string readpath; + std::string writepath; + flag::FlagSet flags("inspector"); + flags.add_flags() + ("read", flag::opt::value(&readpath), + "filename of model to inspect") + ("write", flag::opt::value(&writepath), + "filename to write json format"); + + if (false == flags.parse(argc, argv)) + { + return 1; + } + + logs::get_logger().set_log_level(logs::INFO); + + std::ifstream readstr(readpath); + if (readstr.is_open()) + { + std::string jsonstr; + if (estd::has_affix(readpath, pbx_ext)) + { + cortenn::Graph graph; + if (false == graph.ParseFromIstream(&readstr)) + { + logs::fatalf("failed to parse from istream when read file %s", + readpath.c_str()); + } + google::protobuf::util::JsonPrintOptions options; + options.add_whitespace = true; + if (google::protobuf::util::Status::OK != + google::protobuf::util::MessageToJsonString( + graph, &jsonstr, options)) + { + logs::fatal("failed to parse pb graph"); + } + } + else + { + std::stringstream ss; + ss << readstr.rdbuf(); + jsonstr = ss.str(); + } + + write_json(writepath, jsonstr); + + readstr.close(); + } + else + { + logs::warnf("failed to read file `%s`", readpath.c_str()); + } + + return 0; +} diff --git a/pbm/load.hpp b/pbm/load.hpp index 15adccaef..b773ca943 100644 --- a/pbm/load.hpp +++ b/pbm/load.hpp @@ -158,27 +158,17 @@ void load_graph (GraphInfo& out, const cortenn::Graph& in) TensT invec; for (const cortenn::Node& node : nodes) { - auto pb_labels = node.labels(); + ade::TensptrT tens; if (node.has_source()) { - std::string src_label; - if (pb_labels.size() > 0) - { - src_label = *(pb_labels.rbegin()); - } const cortenn::Source& source = node.source(); - std::string sstr = source.shape(); - ade::Shape shape(std::vector(sstr.begin(), sstr.end())); + auto& slist = source.shape(); + ade::Shape shape(std::vector(slist.begin(), slist.end())); std::string data = source.data(); ade::TensptrT leaf = loader.generate_leaf(data.c_str(), - shape, source.typelabel(), src_label, source.is_const()); + shape, source.typelabel(), node.label(), source.is_const()); invec.push_back(leaf); - if (false == pb_labels.empty()) - { - StringsT labels(pb_labels.begin(), pb_labels.end()); - out.tens_.set_labelled(labels.begin(), labels.end(), leaf); - } - out.roots_.emplace(leaf); + tens = leaf; } else { @@ -201,13 +191,20 @@ void load_graph (GraphInfo& out, const cortenn::Graph& in) } ade::TensptrT f = loader.generate_func(opname, args); invec.push_back(f); - if (false == pb_labels.empty()) + tens = f; + } + auto& pb_tags = node.tags(); + for (auto& tagpair : pb_tags) + { + const std::string& tagkey = tagpair.first; + auto& taglabels = tagpair.second.labels(); + auto tagr = tag::get_reg().tagr_by_key(tagkey); + for (std::string taglabel : taglabels) { - StringsT labels(pb_labels.begin(), pb_labels.end()); - out.tens_.set_labelled(labels.begin(), labels.end(), f); + tagr(tens, taglabel); } - out.roots_.emplace(f); } + out.roots_.emplace(tens); } } diff --git a/pbm/save.hpp b/pbm/save.hpp index de3d2dca2..79b146129 100644 --- a/pbm/save.hpp +++ b/pbm/save.hpp @@ -29,6 +29,9 @@ template ::value>::type* = nullptr> struct GraphSaver final : public ade::iTraveler { + GraphSaver (tag::TagRegistry& registry = tag::get_reg()) : + registry_(registry) {} + /// Implementation of iTraveler void visit (ade::iLeaf* leaf) override { @@ -60,12 +63,6 @@ struct GraphSaver final : public ade::iTraveler /// Marshal all equation graphs in roots vector to protobuf object void save (cortenn::Graph& out, PathedMapT labels = PathedMapT()) { - std::unordered_map raw_labels; - for (auto lpair : labels) - { - raw_labels[lpair.first.get()] = lpair.second; - } - // sort functions from the root with the smallest subtree to the largest // this ensures every children of a node appears before the parent, // as is the order of node creations @@ -87,13 +84,8 @@ struct GraphSaver final : public ade::iTraveler ordermap[tens] = i; cortenn::Node* pb_node = out.add_nodes(); - auto it = raw_labels.find(tens); - if (raw_labels.end() != it) - { - google::protobuf::RepeatedPtrField vec( - it->second.begin(), it->second.end()); - pb_node->mutable_labels()->Swap(&vec); - } + pb_node->set_label(tens->to_string()); + tag_node(pb_node, tens, registry_); save_data(*pb_node->mutable_source(), tens); } for (size_t i = 0, n = funcs.size(); i < n; ++i) @@ -102,13 +94,8 @@ struct GraphSaver final : public ade::iTraveler ordermap[f] = nleaves + i; cortenn::Node* pb_node = out.add_nodes(); - auto it = raw_labels.find(f); - if (raw_labels.end() != it) - { - google::protobuf::RepeatedPtrField vec( - it->second.begin(), it->second.end()); - pb_node->mutable_labels()->Swap(&vec); - } + pb_node->set_label(f->to_string()); + tag_node(pb_node, f, registry_); cortenn::Functor* func = pb_node->mutable_functor(); ade::Opcode opcode = f->get_opcode(); func->set_opname(opcode.name_); @@ -149,14 +136,34 @@ struct GraphSaver final : public ade::iTraveler void save_data (cortenn::Source& out, ade::iLeaf* in) { const ade::Shape& shape = in->shape(); - bool is_const = false; - out.set_shape(std::string(shape.begin(), shape.end())); - out.set_data(saver_.save_leaf(is_const, in)); + google::protobuf::RepeatedField slist( + shape.begin(), shape.end()); + out.mutable_shape()->Swap(&slist); + out.set_data(saver_.save_leaf(in)); out.set_typelabel(in->type_label()); - out.set_is_const(is_const); + out.set_is_const(in->is_const()); + } + + void tag_node (cortenn::Node* node, + ade::iTensor* tens, tag::TagRegistry& registry) + { + google::protobuf::Map* tags = + node->mutable_tags(); + tag::TagRepsT reps = registry.get_tags(tens); + for (auto reppair : reps) + { + google::protobuf::RepeatedPtrField labels( + reppair.second.begin(), reppair.second.end()); + google::protobuf::MapPair tagpair( + reppair.first); + tagpair.second.mutable_labels()->Swap(&labels); + tags->insert(tagpair); + } } SAVER saver_; + + tag::TagRegistry& registry_; }; } diff --git a/pbm/test/common.hpp b/pbm/test/common.hpp index b2b203423..7e350517f 100644 --- a/pbm/test/common.hpp +++ b/pbm/test/common.hpp @@ -44,6 +44,11 @@ struct MockTensor final : public ade::iLeaf return 0; } + bool is_const (void) const override + { + return true; + } + ade::Shape shape_; }; diff --git a/pbm/test/test_load.cpp b/pbm/test/test_load.cpp index 4cbc972d7..2736befaf 100644 --- a/pbm/test/test_load.cpp +++ b/pbm/test/test_load.cpp @@ -10,12 +10,14 @@ #include "dbg/stream/ade.hpp" +#include "tag/prop.hpp" + #include "pbm/load.hpp" #include "pbm/test/common.hpp" -const std::string testdir = "pbm/data"; +const std::string testdir = "models/test"; struct TestLoader : public pbm::iLoader @@ -62,7 +64,7 @@ TEST(LOAD, LoadGraph) { cortenn::Graph graph; { - std::fstream inputstr(testdir + "/graph.pb", + std::fstream inputstr(testdir + "/pbm_test.pbx", std::ios::in | std::ios::binary); ASSERT_TRUE(inputstr.is_open()); ASSERT_TRUE(graph.ParseFromIstream(&inputstr)); @@ -70,26 +72,57 @@ TEST(LOAD, LoadGraph) pbm::GraphInfo graphinfo; pbm::load_graph(graphinfo, graph); - EXPECT_EQ(2, graphinfo.roots_.size()); - ASSERT_EQ(3, graphinfo.tens_.children_.size()); - ASSERT_EQ(0, graphinfo.tens_.tens_.size()); - - ASSERT_HAS(graphinfo.tens_.children_, "global"); - ASSERT_HAS(graphinfo.tens_.children_, "subtree"); - ASSERT_HAS(graphinfo.tens_.children_, "subtree2"); - - auto subtree = graphinfo.tens_.children_["subtree"]; - auto subtree2 = graphinfo.tens_.children_["subtree2"]; - ASSERT_EQ(3, subtree->tens_.size()); - ASSERT_EQ(4, subtree2->tens_.size()); + auto& reg = tag::get_reg(); + tag::Query q; - ASSERT_HAS(subtree->tens_, "dest"); - ASSERT_HAS(subtree2->tens_, "dest"); - - ade::TensptrT tree1 = graphinfo.tens_.get_labelled({"subtree", "dest"}); - ade::TensptrT tree2 = graphinfo.tens_.get_labelled({"subtree2", "dest"}); + std::vector root_props; + std::unordered_map propdtens; + for (auto tens : graphinfo.roots_) + { + tens->accept(q); + auto tags = reg.get_tags(tens.get()); + ASSERT_HAS(tags, tag::props_key); + auto& props = tags[tag::props_key]; + ASSERT_EQ(1, props.size()); + propdtens.emplace(props[0], tens); + root_props.insert(root_props.end(), props.begin(), props.end()); + } + EXPECT_ARRHAS(root_props, "subtree_dest"); + EXPECT_ARRHAS(root_props, "subtree2_dest"); + + ASSERT_HAS(q.labels_, tag::props_key); + auto& props = q.labels_[tag::props_key]; + + ASSERT_HAS(props, "subtree_src"); + ASSERT_HAS(props, "subtree_src2"); + ASSERT_HAS(props, "subtree2_src"); + ASSERT_HAS(props, "subtree2_src2"); + ASSERT_HAS(props, "subtree2_src3"); + ASSERT_HAS(props, "osrc"); + ASSERT_HAS(props, "osrc2"); + + auto& sts = props["subtree_src"]; + auto& sts2 = props["subtree_src2"]; + auto& st2s = props["subtree2_src"]; + auto& st2s2 = props["subtree2_src2"]; + auto& st2s3 = props["subtree2_src3"]; + auto& os = props["osrc"]; + auto& os2 = props["osrc2"]; + + ASSERT_EQ(1, sts.size()); + ASSERT_EQ(1, sts2.size()); + ASSERT_EQ(1, st2s.size()); + ASSERT_EQ(1, st2s2.size()); + ASSERT_EQ(1, st2s3.size()); + ASSERT_EQ(1, os.size()); + ASSERT_EQ(1, os2.size()); + + ASSERT_HAS(propdtens, "subtree_dest"); + ASSERT_HAS(propdtens, "subtree2_dest"); + ade::TensptrT tree1 = propdtens["subtree_dest"]; + ade::TensptrT tree2 = propdtens["subtree2_dest"]; ASSERT_NE(nullptr, tree1); ASSERT_NE(nullptr, tree2); @@ -97,7 +130,7 @@ TEST(LOAD, LoadGraph) std::string expect; std::string got; std::string line; - std::ifstream expectstr(testdir + "/graph.txt"); + std::ifstream expectstr(testdir + "/pbm_test.txt"); ASSERT_TRUE(expectstr.is_open()); while (std::getline(expectstr, line)) { diff --git a/pbm/test/test_save.cpp b/pbm/test/test_save.cpp index 0213371cd..a3918fda2 100644 --- a/pbm/test/test_save.cpp +++ b/pbm/test/test_save.cpp @@ -4,21 +4,25 @@ #include +#include + #include "gtest/gtest.h" #include "ade/functor.hpp" #include "pbm/save.hpp" +#include "tag/prop.hpp" + #include "pbm/test/common.hpp" -const std::string testdir = "pbm/data"; +const std::string testdir = "models/test"; struct TestSaver : public pbm::iSaver { - std::string save_leaf (bool& is_const, ade::iLeaf* leaf) override + std::string save_leaf (ade::iLeaf* leaf) override { return std::string(leaf->shape().n_elems(), 0); } @@ -49,113 +53,115 @@ struct TestSaver : public pbm::iSaver TEST(SAVE, SaveGraph) { - std::string expect_pbfile = testdir + "/graph.pb"; - std::string got_pbfile = "got_graph.pb"; - cortenn::Graph graph; - std::vector roots; - - pbm::PathedMapT labels; - // subtree one - ade::Shape shape({3, 7}); - ade::TensptrT osrc(new MockTensor(shape)); - - ade::Shape shape2({7, 3}); - ade::TensptrT osrc2(new MockTensor(shape2)); - - labels[osrc] = {"global", "osrc"}; - labels[osrc2] = {"global", "osrc2"}; + std::string expect_pbfile = testdir + "/pbm_test.pbx"; + std::string got_pbfile = "got_pbm_test.pbx"; { - ade::TensptrT src(new MockTensor(shape)); - - ade::Shape shape3({3, 1, 7}); - ade::TensptrT src2(new MockTensor(shape3)); - - ade::TensptrT dest(ade::Functor::get(ade::Opcode{"-", 0}, { - {src2, ade::identity}, - {ade::TensptrT(ade::Functor::get(ade::Opcode{"@", 1}, { - {ade::TensptrT(ade::Functor::get(ade::Opcode{"/", 2}, { + cortenn::Graph graph; + std::vector roots; + + // subtree one + ade::Shape shape({3, 7}); + ade::TensptrT osrc(new MockTensor(shape)); + + ade::Shape shape2({7, 3}); + ade::TensptrT osrc2(new MockTensor(shape2)); + + auto& preg = tag::get_property_reg(); + preg.property_tag(osrc, "osrc"); + preg.property_tag(osrc2, "osrc2"); + + { + ade::TensptrT src(new MockTensor(shape)); + + ade::Shape shape3({3, 1, 7}); + ade::TensptrT src2(new MockTensor(shape3)); + + ade::TensptrT dest(ade::Functor::get(ade::Opcode{"-", 0}, { + {src2, ade::identity}, + {ade::TensptrT(ade::Functor::get(ade::Opcode{"@", 1}, { + {ade::TensptrT(ade::Functor::get(ade::Opcode{"/", 2}, { + {ade::TensptrT(ade::Functor::get(ade::Opcode{"neg", 3}, { + {osrc, ade::identity}, + })), ade::identity}, + {ade::TensptrT(ade::Functor::get(ade::Opcode{"+", 4}, { + {ade::TensptrT( + ade::Functor::get(ade::Opcode{"sin", 5}, { + {src, ade::identity}})), ade::identity}, + {src, ade::identity}, + })), ade::identity} + })), ade::permute({1, 0})}, + {osrc2, ade::identity} + })), ade::permute({1, 2, 0})}, + })); + roots.push_back(dest); + + preg.property_tag(src, "subtree_src"); + preg.property_tag(src2, "subtree_src2"); + preg.property_tag(dest, "subtree_dest"); + } + + // subtree two + { + ade::Shape mshape({3, 3}); + ade::TensptrT src(new MockTensor(mshape)); + + ade::TensptrT src2(new MockTensor(mshape)); + + ade::TensptrT src3(new MockTensor(mshape)); + + ade::TensptrT dest(ade::Functor::get(ade::Opcode{"-", 0}, { + {src, ade::identity}, + {ade::TensptrT(ade::Functor::get(ade::Opcode{"*", 6}, { + {ade::TensptrT(ade::Functor::get(ade::Opcode{"abs", 7}, { + {src, ade::identity}, + })), ade::identity}, + {ade::TensptrT(ade::Functor::get(ade::Opcode{"exp", 8}, { + {src2, ade::identity}, + })), ade::identity}, {ade::TensptrT(ade::Functor::get(ade::Opcode{"neg", 3}, { - {osrc, ade::identity}, + {src3, ade::identity}, })), ade::identity}, - {ade::TensptrT(ade::Functor::get(ade::Opcode{"+", 4}, { - {ade::TensptrT( - ade::Functor::get(ade::Opcode{"sin", 5}, { - {src, ade::identity}})), ade::identity}, - {src, ade::identity}, - })), ade::identity} - })), ade::permute({1, 0})}, - {osrc2, ade::identity} - })), ade::permute({1, 2, 0})}, - })); - roots.push_back(dest); - - labels[src] = {"subtree", "src"}; - labels[src2] = {"subtree", "src2"}; - labels[dest] = {"subtree", "dest"}; - } - - // subtree two - { - ade::Shape mshape({3, 3}); - ade::TensptrT src(new MockTensor(mshape)); - - ade::TensptrT src2(new MockTensor(mshape)); - - ade::TensptrT src3(new MockTensor(mshape)); - - ade::TensptrT dest(ade::Functor::get(ade::Opcode{"-", 0}, { - {src, ade::identity}, - {ade::TensptrT(ade::Functor::get(ade::Opcode{"*", 6}, { - {ade::TensptrT(ade::Functor::get(ade::Opcode{"abs", 7}, { - {src, ade::identity}, - })), ade::identity}, - {ade::TensptrT(ade::Functor::get(ade::Opcode{"exp", 8}, { - {src2, ade::identity}, })), ade::identity}, - {ade::TensptrT(ade::Functor::get(ade::Opcode{"neg", 3}, { - {src3, ade::identity}, - })), ade::identity}, - })), ade::identity}, - })); - roots.push_back(dest); - - labels[src] = {"subtree2", "src"}; - labels[src2] = {"subtree2", "src2"}; - labels[src3] = {"subtree2", "src3"}; - labels[dest] = {"subtree2", "dest"}; - } + })); + roots.push_back(dest); - pbm::GraphSaver saver; - for (auto& root : roots) - { - root->accept(saver); - } + preg.property_tag(src, "subtree2_src"); + preg.property_tag(src2, "subtree2_src2"); + preg.property_tag(src3, "subtree2_src3"); + preg.property_tag(dest, "subtree2_dest"); + } - saver.save(graph, labels); + pbm::GraphSaver saver; + for (auto& root : roots) + { + root->accept(saver); + } + + pbm::PathedMapT labels; + saver.save(graph, labels); - { std::fstream gotstr(got_pbfile, std::ios::out | std::ios::trunc | std::ios::binary); ASSERT_TRUE(gotstr.is_open()); ASSERT_TRUE(graph.SerializeToOstream(&gotstr)); } - std::fstream expect_ifs(expect_pbfile, std::ios::in | std::ios::binary); - std::fstream got_ifs(got_pbfile, std::ios::in | std::ios::binary); - ASSERT_TRUE(expect_ifs.is_open()); - ASSERT_TRUE(got_ifs.is_open()); - - std::string expect; - std::string got; - // skip the first line (it contains timestamp) - expect_ifs >> expect; - got_ifs >> got; - for (size_t lineno = 1; expect_ifs && got_ifs; ++lineno) { - expect_ifs >> expect; - got_ifs >> got; - EXPECT_STREQ(expect.c_str(), got.c_str()) << "line number " << lineno; + std::fstream expect_ifs(expect_pbfile, std::ios::in | std::ios::binary); + std::fstream got_ifs(got_pbfile, std::ios::in | std::ios::binary); + ASSERT_TRUE(expect_ifs.is_open()); + ASSERT_TRUE(got_ifs.is_open()); + + cortenn::Graph expect_graph; + cortenn::Graph got_graph; + ASSERT_TRUE(expect_graph.ParseFromIstream(&expect_ifs)); + ASSERT_TRUE(got_graph.ParseFromIstream(&got_ifs)); + + google::protobuf::util::MessageDifferencer differ; + std::string report; + differ.ReportDifferencesToString(&report); + EXPECT_TRUE(differ.Compare(expect_graph, got_graph)) << report; } } diff --git a/perf/BUILD.bazel b/perf/BUILD.bazel new file mode 100644 index 000000000..da84e4c4a --- /dev/null +++ b/perf/BUILD.bazel @@ -0,0 +1,45 @@ +licenses(["notice"]) + +filegroup( + name = "srcs", + srcs = glob([ + "*.hpp", + "src/*.cpp", + ]) + ["BUILD.bazel"], + visibility = ["//visibility:public"], +) + +filegroup( + name = "test_srcs", + srcs = glob([ + "test/*.hpp", + "test/*.cpp", + ]), +) + +######### LIBRARY ######### + +cc_library( + name = "perf", + hdrs = glob(["*.hpp"]), + srcs = glob(["src/*.cpp"]), + copts = ["-std=c++17"], + deps = ["@com_github_mingkaic_cppkg//jobs:jobs"], + visibility = ["//visibility:public"], +) + +######### TEST ######### + +cc_test( + name = "test", + srcs = ["//perf:test_srcs"], + copts = ["-std=c++17"], + deps = [ + "//perf:perf", + "//dbg:stream_out", + "@gtest//:gtest", + "@com_github_mingkaic_cppkg//fmts:fmts", + "@com_github_mingkaic_cppkg//exam:exam", + ], + linkstatic = True, +) diff --git a/perf/measure.hpp b/perf/measure.hpp new file mode 100644 index 000000000..560a9d304 --- /dev/null +++ b/perf/measure.hpp @@ -0,0 +1,97 @@ +#include +#include +#include +#include + +#include "jobs/scope_guard.hpp" + +#ifndef PERF_MEASURE_HPP +#define PERF_MEASURE_HPP + +namespace perf +{ + +using TimeT = std::chrono::high_resolution_clock::time_point; + +using DurationT = std::chrono::duration; + +using MeanDurT = std::pair; + +struct PerfRecord final +{ + PerfRecord (void) = default; + + // initialize with some function that is called upon deletion + PerfRecord (std::function term) : term_(term) {} + + ~PerfRecord (void) + { + if (term_) + { + term_(*this); + } + } + + void to_csv (std::ostream& out) const + { + out << "function,mean duration(ns),total duration(ns),n occurrences\n"; + for (const auto& durs : durations_) + { + const auto& meandur = durs.second; + out << durs.first << "," << (meandur.first / meandur.second).count() << "," + << meandur.first.count() << "," << meandur.second << "\n"; + } + } + + void record_duration (std::string fname, DurationT duration) + { + auto it = durations_.find(fname); + if (durations_.end() == it) + { + durations_.emplace(fname, MeanDurT{duration, 1}); + } + else + { + auto& meandur = it->second; + meandur.first += duration; + ++meandur.second; + } + } + + bool empty (void) const + { + return durations_.empty(); + } + +private: + std::unordered_map durations_; + + std::function term_; +}; + +struct MeasureScope final : public jobs::ScopeGuard +{ + MeasureScope (PerfRecord* record, std::string fname) : + jobs::ScopeGuard([this, fname]() + { + this->record_->record_duration(fname, + std::chrono::duration_cast( + std::chrono::high_resolution_clock::now() - + this->measure_start_)); + }), + record_(record), + measure_start_(std::chrono::high_resolution_clock::now()) {} + +private: + PerfRecord* record_; + + TimeT measure_start_; +}; + +PerfRecord& get_global_record (void); + +#define MEASURE(NAME)perf::MeasureScope _defer(&perf::get_global_record(), NAME); + +} + +#endif // PERF_MEASURE_HPP diff --git a/perf/src/measure.cpp b/perf/src/measure.cpp new file mode 100644 index 000000000..a97effd7c --- /dev/null +++ b/perf/src/measure.cpp @@ -0,0 +1,27 @@ +#include + +#include "perf/measure.hpp" + +#ifdef PERF_MEASURE_HPP + +namespace perf +{ + +PerfRecord& get_global_record (void) +{ + static PerfRecord record( + [](PerfRecord& deletion) + { + if (false == deletion.empty()) + { + std::ofstream outf("/tmp/performance.csv"); + deletion.to_csv(outf); + outf.flush(); + } + }); + return record; +} + +} + +#endif diff --git a/perf/test/main.cpp b/perf/test/main.cpp new file mode 100644 index 000000000..6055a2cf3 --- /dev/null +++ b/perf/test/main.cpp @@ -0,0 +1,47 @@ + +#include + +#include "gtest/gtest.h" + +#include "fmts/fmts.hpp" + +#include "perf/measure.hpp" + +int main (int argc, char** argv) +{ + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} + + +static void mock_measure (perf::PerfRecord& record) +{ + perf::MeasureScope _defer(&record, "f1"); + + std::this_thread::sleep_for( + std::chrono::milliseconds(1000)); +} + + +TEST(PERFORMANCE, Measure) +{ + perf::PerfRecord record; + mock_measure(record); + + std::stringstream ss; + record.to_csv(ss); + auto got = ss.str(); + auto lines = fmts::split(got, "\n"); + EXPECT_EQ(3, lines.size()); + EXPECT_EQ(0, lines[2].size()); + ASSERT_NE(0, lines[1].size()); + ASSERT_NE(0, lines[0].size()); + + auto parts = fmts::split(lines[1], ","); + ASSERT_EQ(4, parts.size()); + EXPECT_STREQ("f1", parts[0].c_str()); + EXPECT_STREQ("1", parts[3].c_str()); + EXPECT_STREQ(parts[1].c_str(), parts[2].c_str()); + EXPECT_EQ(10, parts[1].size()); // expect duration to be in the billions + EXPECT_STREQ("function,mean duration(ns),total duration(ns),n occurrences", lines[0].c_str()); +} diff --git a/pll/BUILD.bazel b/pll/BUILD.bazel new file mode 100644 index 000000000..d5dd5196a --- /dev/null +++ b/pll/BUILD.bazel @@ -0,0 +1,103 @@ +licenses(["notice"]) + +load( + "@com_github_stackb_rules_proto//cpp:cpp_proto_library.bzl", + "cpp_proto_library" +) + +load("//third_party/drake_rules:pybind.bzl", "pybind_library") + +package( + default_visibility = ["//visibility:public"], +) + +filegroup( + name = "srcs", + srcs = glob([ + "*.hpp", + "src/*.cpp", + ]) + ["BUILD.bazel"], +) + +filegroup( + name = "protos", + srcs = glob(["*.proto"]), +) + +filegroup( + name = "test_srcs", + srcs = glob([ + "test/*.hpp", + "test/*.cpp", + ]), + visibility = ["//visibility:private"], +) + +######### LIBRARY ######### + +proto_library( + name = "weight_proto", + srcs = ["//pll:protos"], +) + +cpp_proto_library( + name = "weight_cc_proto", + deps = ["//pll:weight_proto"], +) + +cc_library( + name = "pll", + hdrs = glob(["*.hpp"]), + srcs = glob(["src/*.cpp"]), + copts = ["-std=c++17"], + deps = [ + "//ead:ead", + "@boost//:asio", + ], +) + +pybind_library( + name = "pll_py", + cc_srcs = ["//pll:python/pll.cpp"], + cc_deps = ["//pll:pll"], + py_deps = ["//ead:ead_py"], + visibility = ["//visibility:public"], +) + +######### TOOLS ######### + +cc_binary( + name = "inspector", + srcs = glob(["inspector.cpp"]), + copts = ["-std=c++17"], + deps = [ + "//pll:weight_cc_proto", + "@com_github_mingkaic_cppkg//flag:flag", + ], +) + +cc_binary( + name = "rt_anubis", + srcs = ["rtscale.cpp"], + copts = ["-std=c++17"], + deps = [ + "//ead:ead", + "//pll:weight_cc_proto", + "@com_github_mingkaic_cppkg//flag:flag", + ], +) + +######### TEST ######### + +cc_test( + name = "test", + srcs = ["//pll:test_srcs"], + copts = ["-std=c++17"], + deps = [ + "//dbg:stream_out", + "//pll:pll", + "@gtest//:gtest", + "@com_github_mingkaic_cppkg//exam:exam", + ], + linkstatic = True, +) diff --git a/pll/README_CCE.md b/pll/README_CCE.md new file mode 100644 index 000000000..f2ab69558 --- /dev/null +++ b/pll/README_CCE.md @@ -0,0 +1 @@ +# ConCurrent EAD diff --git a/pll/inspector.cpp b/pll/inspector.cpp new file mode 100644 index 000000000..49d6aa59b --- /dev/null +++ b/pll/inspector.cpp @@ -0,0 +1,67 @@ +#include + +#include + +#include "flag/flag.hpp" +#include "fmts/fmts.hpp" + +#include "pll/weights.pb.h" + +int main (int argc, const char** argv) +{ + std::string readpath; + std::string writepath; + flag::FlagSet flags("inspector"); + flags.add_flags() + ("read", flag::opt::value(&readpath), + "filename of model to inspect") + ("write", flag::opt::value(&writepath), + "filename to write json format"); + + if (false == flags.parse(argc, argv)) + { + return 1; + } + + logs::get_logger().set_log_level(logs::INFO); + + std::ifstream readstr(readpath); + if (readstr.is_open()) + { + weights::OpWeights ops; + if (false == ops.ParseFromIstream(&readstr)) + { + logs::fatalf("failed to parse from istream when read file %s", + readpath.c_str()); + } + std::string jsonstr; + google::protobuf::util::JsonPrintOptions options; + options.add_whitespace = true; + if (google::protobuf::util::Status::OK != + google::protobuf::util::MessageToJsonString( + ops, &jsonstr, options)) + { + logs::fatal("failed to parse op weights"); + } + + std::ofstream writestr(writepath); + if (writestr.is_open()) + { + writestr << jsonstr; + writestr.flush(); + writestr.close(); + } + else + { + std::cout << jsonstr << std::endl; + } + + readstr.close(); + } + else + { + logs::warnf("failed to read file `%s`", readpath.c_str()); + } + + return 0; +} diff --git a/pll/partition.hpp b/pll/partition.hpp new file mode 100644 index 000000000..45c6b2e8a --- /dev/null +++ b/pll/partition.hpp @@ -0,0 +1,17 @@ +#include "ade/traveler.hpp" + +#ifndef CCE_PARTITION_HPP +#define CCE_PARTITION_HPP + +namespace pll +{ + +using PartGroupsT = std::vector>; + +using OpWeightT = std::unordered_map; + +PartGroupsT k_partition (ade::TensT roots, size_t k, OpWeightT weights = OpWeightT()); + +} + +#endif // CCE_PARTITION_HPP diff --git a/pll/python/pll.cpp b/pll/python/pll.cpp new file mode 100644 index 000000000..207b61c18 --- /dev/null +++ b/pll/python/pll.cpp @@ -0,0 +1,35 @@ +#include "pybind11/pybind11.h" +#include "pybind11/numpy.h" +#include "pybind11/stl.h" + +#include "ead/generated/pyapi.hpp" +#include "ead/parse.hpp" + +#include "pll/session.hpp" + +namespace py = pybind11; + +PYBIND11_MODULE(pll, m) +{ + m.doc() = "pll session"; + + // ==== session ==== + auto isess = (py::class_) + py::module::import("ead.ead").attr("iSession"); + py::class_ session(m, "Session", isess); + + py::implicitly_convertible(); + session + .def(py::init(), + py::arg("nthread") = 2, + py::arg("weights") = pll::OpWeightT()) + .def("optimize", + [](py::object self, std::string filename) + { + auto sess = self.cast(); + opt::OptCtx rules = ead::parse_file(filename); + sess->optimize(rules); + }, + py::arg("filename") = "cfg/optimizations.rules", + "Optimize using rules for specified filename"); +} diff --git a/pll/rtscale.cpp b/pll/rtscale.cpp new file mode 100644 index 000000000..069713506 --- /dev/null +++ b/pll/rtscale.cpp @@ -0,0 +1,233 @@ +// Weigh the runtime of each opcode +#include +#include +#include + +#include "flag/flag.hpp" + +#include "ead/generated/api.hpp" +#include "ead/generated/opcode.hpp" +#include "ead/functor.hpp" + +#include "pll/weights.pb.h" + +#define TIME(action)\ +std::chrono::high_resolution_clock::time_point start =\ + std::chrono::high_resolution_clock::now();\ +action;\ +stat = std::chrono::duration_cast(\ + std::chrono::high_resolution_clock::now() - start).count(); + +double softplus (double x) +{ + return std::log(1 + std::exp(x)); +} + +int main (int argc, const char** argv) +{ + std::string writepath; + flag::FlagSet flags("rt_anubis"); + flags.add_flags() + ("target", flag::opt::value(&writepath), + "filename of json to write weights to"); + + if (false == flags.parse(argc, argv)) + { + return 1; + } + + logs::get_logger().set_log_level(logs::INFO); + + std::unordered_map stats; + size_t mean_stat = 0;//, + // max_stat = 0, + // min_stat = std::numeric_limits::max(); + for (size_t i = 0; i < age::_N_GENERATED_OPCODES; ++i) + { + size_t stat; + auto opcode = (age::_GENERATED_OPCODE) i; + ade::Opcode op{age::name_op(opcode), opcode}; + logs::infof("weighing operation %s", op.name_.c_str()); + switch (i) + { + // elementary unary + case age::ABS: + case age::NEG: + case age::SIN: + case age::COS: + case age::TAN: + case age::EXP: + case age::LOG: + case age::SQRT: + case age::ROUND: + case age::SIGMOID: + case age::SIGMOID_GRAD: + case age::TANH: + case age::SQUARE: + case age::CUBE: + { + auto var = ead::make_constant_scalar( + 0.5, ade::Shape({56, 57, 58})); + auto f = ead::make_functor(op, { + ead::identity_map(var)}); + TIME(f->update()) + } + break; + + // elementary binary + case age::POW: + case age::ADD: + case age::SUB: + case age::MUL: + case age::DIV: + case age::MIN: + case age::MAX: + case age::EQ: + case age::NEQ: + case age::LT: + case age::GT: + case age::RAND_UNIF: + { + auto var = ead::make_constant_scalar( + 0.5, ade::Shape({56, 57, 58})); + auto f = ead::make_functor(op, { + ead::identity_map(var), ead::identity_map(var)}); + TIME(f->update()) + } + break; + + // reductions + case age::REDUCE_SUM: + case age::REDUCE_PROD: + case age::REDUCE_MIN: + case age::REDUCE_MAX: + { + auto var = ead::make_constant_scalar( + 0.5, ade::Shape({56, 57, 58})); + auto f = ead::make_functor(op, { + ead::reduce_map(var, 1, 1)}); + TIME(f->update()) + } + break; + + // other stuff + case age::PERMUTE: + { + auto var = ead::make_constant_scalar( + 0.5, ade::Shape({56, 57, 58})); + auto f = ead::make_functor(op, { + ead::permute_map(var, {2, 0, 1})}); + TIME(f->update()) + } + break; + + case age::EXTEND: + { + auto var = ead::make_constant_scalar( + 0.5, ade::Shape({56, 58})); + auto f = ead::make_functor(op, { + ead::extend_map(var, 2, {57})}); + TIME(f->update()) + } + break; + + case age::SLICE: + { + auto var = ead::make_constant_scalar( + 0.5, ade::Shape({56, 57, 58})); + auto f = ead::make_functor(op, { + ead::slice_map(var, 2, 2, 2)}); + TIME(f->update()) + } + break; + + case age::MATMUL: + { + auto a = ead::make_constant_scalar( + 0.3, ade::Shape({253, 255})); + auto b = ead::make_constant_scalar( + 0.6, ade::Shape({254, 253})); + auto f = tenncor::matmul(a, b); + TIME(f->update()) + } + break; + + case age::CONV: + { + auto img = ead::make_constant_scalar( + 0.3, ade::Shape({254, 255})); + auto kern = ead::make_constant_scalar( + 0.6, ade::Shape({5, 7})); + auto f = tenncor::convolution(img, kern, {0, 1}); + TIME(f->update()) + } + break; + + case age::PAD: + { + auto var = ead::make_constant_scalar( + 0.5, ade::Shape({56, 57, 58})); + auto f = ead::make_functor(op, { + ead::pad_map(var, {3, 4}, 2)}); + TIME(f->update()) + } + break; + + case age::SELECT: + { + ade::Shape shape({56, 57, 58}); + size_t n = shape.n_elems(); + std::vector data; + data.reserve(n); + for (size_t i = 0; i < n; ++i) + { + data.push_back(i % 2); + } + auto cond = ead::make_constant(data.data(), shape); + auto a = ead::make_constant_scalar(0.3, shape); + auto b = ead::make_constant_scalar(0.6, shape); + auto f = tenncor::if_then_else(cond, a, b); + TIME(f->update()) + } + break; + + case age::CONV_IMG_GRAD: + case age::CONV_KRN_GRAD: + default: + continue; + } + mean_stat += stat; + // max_stat = std::max(max_stat, stat); + // min_stat = std::min(min_stat, stat); + stats.emplace(op.name_, stat); + } + + double mean = (double) mean_stat / stats.size(); + + // normalize stats by mean + weights::OpWeights opweights; + opweights.set_label("ead_weights"); + ::google::protobuf::Map< ::std::string,double>* weights = + opweights.mutable_weights(); + for (auto& op : stats) + { + double value = softplus((op.second - mean) / ( + mean + std::numeric_limits::epsilon())); + weights->insert({op.first, value}); + } + + logs::infof("writing to %s", writepath.c_str()); + std::fstream out(writepath, + std::ios::out | std::ios::trunc | std::ios::binary); + if (out.is_open()) + { + logs::infof("opened %s", writepath.c_str()); + if (opweights.SerializeToOstream(&out)) + { + logs::infof("done writing to %s", writepath.c_str()); + } + out.close(); + } + + return 0; +} diff --git a/pll/session.hpp b/pll/session.hpp new file mode 100644 index 000000000..aaf869515 --- /dev/null +++ b/pll/session.hpp @@ -0,0 +1,223 @@ +#include + +#include +#include + +#include "ead/session.hpp" + +#include "pll/partition.hpp" + +#ifndef CCE_ASESS_HPP +#define CCE_ASESS_HPP + +namespace pll +{ + +using SessReqsT = std::vector>; + +using AtomicFulfilMapT = std::unordered_map< + ade::iOperableFunc*,std::atomic>; + +struct Session final : public ead::iSession +{ + Session (size_t nthreads = 2, OpWeightT weights = OpWeightT()) : + nthreads_(nthreads), weights_(weights) {} + + std::unordered_set tracked_; + + void track (ade::TensT roots) override + { + tracked_.insert(roots.begin(), roots.end()); + + ade::GraphStat stat; + for (auto& trac : tracked_) + { + trac->accept(stat); + } + ade::ParentFinder pfinder; + for (ade::TensptrT& root : roots) + { + root->accept(pfinder); + } + + ade::TensT trackvecs(tracked_.begin(), tracked_.end()); + PartGroupsT groups = k_partition(trackvecs, nthreads_, weights_); + requirements_.clear(); + for (auto& group : groups) + { + SessReqsT reqs; + reqs.reserve(group.size()); + for (ade::iFunctor* func : group) + { + auto& args = func->get_children(); + ead::TensSetT unique_children; + for (const ade::FuncArg& arg : args) + { + auto tens = arg.get_tensor().get(); + if (0 < stat.graphsize_[tens].upper_) // ignore leaves + { + unique_children.emplace(tens); + } + } + reqs.push_back({ + static_cast(func), + unique_children.size() + }); + } + requirements_.push_back(reqs); + } + + for (auto& assocs : pfinder.parents_) + { + for (auto& parent_pair : assocs.second) + { + parents_[assocs.first].emplace( + static_cast(parent_pair.first)); + } + } + + ops_.clear(); + for (auto& tpair : stat.graphsize_) + { + if (tpair.second.upper_ > 0) + { + ops_.emplace(static_cast(tpair.first)); + } + } + } + + // this function is expected to be called repeatedly during runtime + void update (ead::TensSetT updated = {}, ead::TensSetT ignores = {}) override + { + AtomicFulfilMapT fulfilments; + for (auto op : ops_) + { + fulfilments.emplace(op, 0); + } + for (ade::iTensor* unodes : updated) + { + if (dynamic_cast(unodes)) + { + auto& node_parents = parents_[unodes]; + for (auto& node_parent : node_parents) + { + ++fulfilments[node_parent]; + } + } + } + // for each req in requirements distribute to thread + boost::asio::thread_pool pool(nthreads_); + for (auto& req : requirements_) + { + // add thread + boost::asio::post(pool, + [this, &req, &fulfilments, &ignores]() + { + for (auto& op : req) + { + // fulfilled and not ignored + auto& ff = fulfilments.at(op.first); + if (ff++ == op.second && + false == estd::has(ignores, op.first)) + { + op.first->update(); + std::unordered_set op_parents; + if (estd::get(op_parents, + this->parents_, op.first)) + { + for (auto& op_parent : op_parents) + { + ++fulfilments.at(op_parent); + } + } + ++ff; + } + --ff; + } + }); + } + pool.join(); + } + + // this function is expected to be called repeatedly during runtime + void update_target (ead::TensSetT target, ead::TensSetT updated = {}) override + { + ade::OnceTraveler targetted; + for (auto& tens : target) + { + tens->accept(targetted); + } + AtomicFulfilMapT fulfilments; + for (auto op : ops_) + { + fulfilments.emplace(op, 0); + } + for (ade::iTensor* unodes : updated) + { + if (dynamic_cast(unodes)) + { + auto& node_parents = parents_[unodes]; + for (auto& node_parent : node_parents) + { + ++fulfilments[node_parent]; + } + } + } + // for each req in requirements distribute to thread + boost::asio::thread_pool pool(nthreads_); + for (auto& req : requirements_) + { + // make thread + boost::asio::post(pool, + [this, &req, &fulfilments, &targetted]() + { + for (auto& op : req) + { + // is relevant to target, is fulfilled and not ignored + auto& ff = fulfilments.at(op.first); + if (ff++ == op.second && + estd::has(targetted.visited_, op.first)) + { + op.first->update(); + std::unordered_set op_parents; + if (estd::get(op_parents, + this->parents_, op.first)) + { + for (auto& op_parent : op_parents) + { + ++fulfilments.at(op_parent); + } + } + ++ff; + } + --ff; + } + }); + } + pool.join(); + } + + void optimize (const opt::OptCtx& rules) + { + ade::TensT tracked(tracked_.begin(), tracked_.end()); + opt::optimize(tracked, rules); + parents_.clear(); + track(tracked); + } + + std::vector requirements_; + + std::unordered_map> parents_; + +private: + size_t nthreads_; + + OpWeightT weights_; + + std::unordered_set ops_; +}; + +} + +#endif // CCE_ASESS_HPP diff --git a/pll/src/partition.cpp b/pll/src/partition.cpp new file mode 100644 index 000000000..29fe41459 --- /dev/null +++ b/pll/src/partition.cpp @@ -0,0 +1,185 @@ +#include + +#include "ade/iopfunc.hpp" + +#include "pll/partition.hpp" + +#ifdef CCE_PARTITION_HPP + +namespace pll +{ + +struct WeighedGroup final +{ + std::vector reps_; + + std::unordered_set ancestors_; + + double weight_; +}; + +PartGroupsT k_partition (ade::TensT roots, size_t k, OpWeightT weights) +{ + PartGroupsT groups; + + ade::GraphStat stat; + ade::ParentFinder pfinder; + for (auto root : roots) + { + root->accept(stat); + root->accept(pfinder); + } + + // partition by bases (the funcs right above variables) + std::vector bases; + for (auto& gpair : stat.graphsize_) + { + if (gpair.second.upper_ == 1) + { + bases.push_back(static_cast(gpair.first)); + } + } + + // partition bases by number of ancestor + std::unordered_map weight_map; + std::unordered_map> ancestors; + for (auto base : bases) + { + std::queue q; + q.push(base); + while (false == q.empty()) + { + auto tens = q.front(); + if (false == estd::has(weight_map, tens)) + { + double weight = 1; + if (auto op = dynamic_cast(tens)) + { + weight = estd::try_get(weights, op->type_code(), 1); + } + weight_map.emplace(tens, weight); + } + ade::ParentMapT parents; + if (estd::get(parents, pfinder.parents_, tens)) + { + for (auto& ppair : parents) + { + ancestors[base].emplace(ppair.first); + q.push(ppair.first); + } + } + q.pop(); + } + } + + size_t nbases = bases.size(); + if (nbases <= k) + { + groups = PartGroupsT(nbases); + for (size_t i = 0; i < nbases; ++i) + { + auto& ancs = ancestors[bases[i]]; + + auto& group = groups[i]; + group.reserve(ancs.size() + 1); + group.push_back(bases[i]); + for (auto anc : ancs) + { + group.push_back(static_cast(anc)); + } + } + } + else + { + // pick top K groups by minimizing sum weight of unique ancestors + std::vector base_groups; + base_groups.reserve(nbases); + for (auto& base : bases) + { + auto& ancs = ancestors.at(base); + double bweight = weight_map.at(base); + for (auto& anc : ancs) + { + bweight += weight_map.at(anc); + } + base_groups.push_back(WeighedGroup{{base}, ancs, bweight}); + } + auto group_cmp = + [](const WeighedGroup& a, const WeighedGroup& b) + { + return a.weight_ < b.weight_; + }; + auto group_mincmp = + [](const WeighedGroup& a, const WeighedGroup& b) + { + return a.weight_ > b.weight_; + }; + + std::make_heap(base_groups.begin(), base_groups.end(), group_cmp); + std::vector kgroups; + for (size_t i = 0; i < k; ++i) + { + kgroups.push_back(base_groups.front()); + std::push_heap(kgroups.begin(), kgroups.end(), group_mincmp); + + std::pop_heap(base_groups.begin(), base_groups.end(), group_cmp); + base_groups.pop_back(); + } + for (size_t i = k; i < nbases; ++i) + { + auto smallest_kgroup = kgroups.front(); + auto& smallest_bgroup = base_groups.front(); + // join the smallest groups + smallest_kgroup.weight_ += weight_map.at(smallest_bgroup.reps_[0]); + auto& bg_ancs = smallest_bgroup.ancestors_; + for (auto& bg_anc : bg_ancs) + { + if (false == estd::has(smallest_kgroup.ancestors_, bg_anc)) + { + smallest_kgroup.weight_ += weight_map.at(bg_anc); + smallest_kgroup.ancestors_.emplace(bg_anc); + } + } + smallest_kgroup.reps_.push_back(smallest_bgroup.reps_[0]); + + std::pop_heap(kgroups.begin(), kgroups.end(), group_mincmp); + kgroups[k - 1] = smallest_kgroup; + std::push_heap(kgroups.begin(), kgroups.end(), group_mincmp); + + std::pop_heap(base_groups.begin(), base_groups.end(), group_cmp); + base_groups.pop_back(); + } + groups.reserve(nbases); + for (auto& kgroup : kgroups) + { + std::vector group; + group.reserve(kgroup.reps_.size() + kgroup.ancestors_.size()); + for (auto& rep : kgroup.reps_) + { + group.push_back(rep); + } + for (auto& anc : kgroup.ancestors_) + { + group.push_back(static_cast(anc)); + } + groups.push_back(group); + } + } + + // for each group, order by upper height + for (auto& group : groups) + { + std::sort(group.begin(), group.end(), + [&stat](ade::iTensor* a, ade::iTensor* b) + { + return stat.graphsize_[a].upper_ < stat.graphsize_[b].upper_; + }); + } + + return groups; +} + +} + +#endif diff --git a/pll/test/main.cpp b/pll/test/main.cpp new file mode 100644 index 000000000..8f2718233 --- /dev/null +++ b/pll/test/main.cpp @@ -0,0 +1,12 @@ + +#include "gtest/gtest.h" + +#include "exam/exam.hpp" + +int main (int argc, char** argv) +{ + set_logger(std::static_pointer_cast(exam::tlogger)); + + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/pll/test/test_partition.cpp b/pll/test/test_partition.cpp new file mode 100644 index 000000000..0cee64b8e --- /dev/null +++ b/pll/test/test_partition.cpp @@ -0,0 +1,137 @@ + +#ifndef DISABLE_PARTITION_TEST + + +#include + +#include "gtest/gtest.h" + +#include "exam/exam.hpp" + +#include "ead/ead.hpp" + +#include "pll/partition.hpp" + + +TEST(PARTITION, Kpartition) +{ + ade::Shape in_shape({10, 3}); + ade::Shape weight0_shape({9, 10}); + ade::Shape bias0_shape({9}); + ade::Shape weight1_shape({5, 9}); + ade::Shape bias1_shape({5}); + ade::Shape out_shape({5,3}); + + std::vector in_data = { + 0.8575073725, 0.0910915775, 0.9133499042, + 0.0660953837, 0.2419061306, 0.3696410139, + 0.4013100896, 0.5172528430, 0.1323293907, + 0.4278464745, 0.0410668952, 0.1652450001, + 0.4190357348, 0.2008750679, 0.5067047954, + 0.0413809185, 0.3094381994, 0.2199267703, + 0.9221660000, 0.0781527711, 0.4804519704, + 0.1206410099, 0.1630566401, 0.6360934496, + 0.2976741228, 0.0709092288, 0.1560062294, + 0.2497900767, 0.2511943240, 0.5474749688, + }; + std::vector w0_data = { + 0.1613409462, 0.9457144276, 0.9495257985, + 0.2793930966, 0.2723075870, 0.3588235299, + 0.3938297525, 0.3393228095, 0.5716848928, + + 0.6339794570, 0.6139023931, 0.7724697132, + 0.0698909799, 0.6535996814, 0.2244414703, + 0.4194435958, 0.6321126915, 0.2891770970, + + 0.6457218252, 0.3446479912, 0.3171555503, + 0.2252455176, 0.7602351414, 0.9312997376, + 0.1333143817, 0.7155225995, 0.2032897111, + + 0.0224006501, 0.9908721456, 0.0319914474, + 0.9704203846, 0.5274515737, 0.3339836660, + 0.7091134065, 0.5576000673, 0.7501829168, + + 0.2442227058, 0.1842266311, 0.8504773433, + 0.3926588922, 0.2833117224, 0.9620642436, + 0.1147953593, 0.4177183136, 0.2914940248, + + 0.0219832027, 0.4042951820, 0.3837337063, + 0.5981982488, 0.1894350758, 0.6036559792, + 0.1345821880, 0.8417718235, 0.6846826161, + + 0.7122232912, 0.4294986009, 0.6729728379, + 0.4321375967, 0.0759832146, 0.6365364108, + 0.6262763516, 0.7468564758, 0.7312610352, + + 0.9549105342, 0.1993684076, 0.4657970235, + 0.7518583439, 0.2239421519, 0.2273247980, + 0.5971696669, 0.7370904837, 0.9237708470, + + 0.1546511078, 0.6033025992, 0.4691343777, + 0.1600327544, 0.4296355788, 0.6961808304, + 0.2007259045, 0.3526185965, 0.8712730678, + + 0.6285644271, 0.0312853544, 0.7161966751, + 0.1366034209, 0.0015131718, 0.0057481276, + 0.0878447392, 0.4122209597, 0.2014009404, + }; + std::vector b0_data = { + 0.8801580962, 0.5008790402, 0.8270796004, + 0.7715771391, 0.2662051941, 0.5704192232, + 0.5373307027, 0.1856321630, 0.5669738908, + }; + std::vector w1_data = { + 0.4260408543, 0.5722656129, 0.9604073010, 0.2924135371, 0.7859575638, + 0.7289488993, 0.9683322421, 0.3507929923, 0.6774173641, 0.6407122174, + 0.8975668345, 0.4348200381, 0.6121628654, 0.8736230885, 0.0833758337, + 0.8422647036, 0.3981612935, 0.0784194187, 0.7461062031, 0.4919135980, + 0.8157699378, 0.4931650049, 0.6282318830, 0.6176567461, 0.8502403216, + 0.9404269220, 0.6854869637, 0.9051396941, 0.2966845031, 0.2721141527, + 0.2877237941, 0.0590600035, 0.6288776397, 0.1353232608, 0.9594369234, + 0.5920096937, 0.1026460668, 0.9349781326, 0.2640904799, 0.6960341493, + 0.5056684425, 0.6169691389, 0.8741161106, 0.5260663550, 0.8161608103, + }; + std::vector b1_data = { + 0.1993173272, 0.6008457459, 0.3355862244, 0.1906307583, 0.3078908360, + }; + std::vector out_data = { + 0.5301287168, 0.0816631236, 0.4232512930, 0.1983706018, 0.2941365700, + 0.9427764606, 0.0267634765, 0.4367877602, 0.1155584527, 0.6275693090, + 0.4350741570, 0.3949956178, 0.2341486792, 0.1348473539, 0.8681677362, + }; + + ead::NodeptrT in = ead::make_variable(in_data.data(), in_shape); + ead::NodeptrT weight0 = ead::make_variable(w0_data.data(), weight0_shape); + ead::NodeptrT bias0 = ead::make_variable(b0_data.data(), bias0_shape); + ead::NodeptrT weight1 = ead::make_variable(w1_data.data(), weight1_shape); + ead::NodeptrT bias1 = ead::make_variable(b1_data.data(), bias1_shape); + ead::NodeptrT out = ead::make_variable(out_data.data(), out_shape); + + auto layer0 = tenncor::add(tenncor::matmul(in, weight0), tenncor::extend(bias0, 1, {3})); + auto sig0 = tenncor::sigmoid(layer0); + + auto layer1 = tenncor::add(tenncor::matmul(sig0, weight1), tenncor::extend(bias1, 1, {3})); + auto sig1 = tenncor::sigmoid(layer1); + + auto err = tenncor::pow(tenncor::sub(out, sig1), ead::make_constant_scalar(2, out_shape)); + + auto dw0 = ead::derive(err, weight0); + auto db0 = ead::derive(err, bias0); + auto dw1 = ead::derive(err, weight1); + auto db1 = ead::derive(err, bias1); + + auto groups = pll::k_partition({ + dw0->get_tensor(), + db0->get_tensor(), + dw1->get_tensor(), + db1->get_tensor(), + }, 2); + + ASSERT_EQ(2, groups.size()); + long ng0 = groups[0].size(); + long ng1 = groups[1].size(); + EXPECT_LE(1, std::abs(ng0 - ng1)); +} + + +#endif // DISABLE_PARTITION_TEST diff --git a/pll/test/test_session.cpp b/pll/test/test_session.cpp new file mode 100644 index 000000000..c11ddb390 --- /dev/null +++ b/pll/test/test_session.cpp @@ -0,0 +1,230 @@ + +#ifndef DISABLE_SESSION_TEST + + +#include + +#include "gtest/gtest.h" + +#include "exam/exam.hpp" + +#include "ead/ead.hpp" + +#include "pll/session.hpp" + + +TEST(SESSION, Update) +{ + ade::Shape in_shape({10, 3}); + ade::Shape weight0_shape({9, 10}); + ade::Shape bias0_shape({9}); + ade::Shape weight1_shape({5, 9}); + ade::Shape bias1_shape({5}); + ade::Shape out_shape({5,3}); + + std::vector in_data = { + 0.8575073725, 0.0910915775, 0.9133499042, + 0.0660953837, 0.2419061306, 0.3696410139, + 0.4013100896, 0.5172528430, 0.1323293907, + 0.4278464745, 0.0410668952, 0.1652450001, + 0.4190357348, 0.2008750679, 0.5067047954, + 0.0413809185, 0.3094381994, 0.2199267703, + 0.9221660000, 0.0781527711, 0.4804519704, + 0.1206410099, 0.1630566401, 0.6360934496, + 0.2976741228, 0.0709092288, 0.1560062294, + 0.2497900767, 0.2511943240, 0.5474749688, + }; + std::vector w0_data = { + 0.1613409462, 0.9457144276, 0.9495257985, + 0.2793930966, 0.2723075870, 0.3588235299, + 0.3938297525, 0.3393228095, 0.5716848928, + + 0.6339794570, 0.6139023931, 0.7724697132, + 0.0698909799, 0.6535996814, 0.2244414703, + 0.4194435958, 0.6321126915, 0.2891770970, + + 0.6457218252, 0.3446479912, 0.3171555503, + 0.2252455176, 0.7602351414, 0.9312997376, + 0.1333143817, 0.7155225995, 0.2032897111, + + 0.0224006501, 0.9908721456, 0.0319914474, + 0.9704203846, 0.5274515737, 0.3339836660, + 0.7091134065, 0.5576000673, 0.7501829168, + + 0.2442227058, 0.1842266311, 0.8504773433, + 0.3926588922, 0.2833117224, 0.9620642436, + 0.1147953593, 0.4177183136, 0.2914940248, + + 0.0219832027, 0.4042951820, 0.3837337063, + 0.5981982488, 0.1894350758, 0.6036559792, + 0.1345821880, 0.8417718235, 0.6846826161, + + 0.7122232912, 0.4294986009, 0.6729728379, + 0.4321375967, 0.0759832146, 0.6365364108, + 0.6262763516, 0.7468564758, 0.7312610352, + + 0.9549105342, 0.1993684076, 0.4657970235, + 0.7518583439, 0.2239421519, 0.2273247980, + 0.5971696669, 0.7370904837, 0.9237708470, + + 0.1546511078, 0.6033025992, 0.4691343777, + 0.1600327544, 0.4296355788, 0.6961808304, + 0.2007259045, 0.3526185965, 0.8712730678, + + 0.6285644271, 0.0312853544, 0.7161966751, + 0.1366034209, 0.0015131718, 0.0057481276, + 0.0878447392, 0.4122209597, 0.2014009404, + }; + std::vector b0_data = { + 0.8801580962, 0.5008790402, 0.8270796004, + 0.7715771391, 0.2662051941, 0.5704192232, + 0.5373307027, 0.1856321630, 0.5669738908, + }; + std::vector w1_data = { + 0.4260408543, 0.5722656129, 0.9604073010, 0.2924135371, 0.7859575638, + 0.7289488993, 0.9683322421, 0.3507929923, 0.6774173641, 0.6407122174, + 0.8975668345, 0.4348200381, 0.6121628654, 0.8736230885, 0.0833758337, + 0.8422647036, 0.3981612935, 0.0784194187, 0.7461062031, 0.4919135980, + 0.8157699378, 0.4931650049, 0.6282318830, 0.6176567461, 0.8502403216, + 0.9404269220, 0.6854869637, 0.9051396941, 0.2966845031, 0.2721141527, + 0.2877237941, 0.0590600035, 0.6288776397, 0.1353232608, 0.9594369234, + 0.5920096937, 0.1026460668, 0.9349781326, 0.2640904799, 0.6960341493, + 0.5056684425, 0.6169691389, 0.8741161106, 0.5260663550, 0.8161608103, + }; + std::vector b1_data = { + 0.1993173272, 0.6008457459, 0.3355862244, 0.1906307583, 0.3078908360, + }; + std::vector out_data = { + 0.5301287168, 0.0816631236, 0.4232512930, 0.1983706018, 0.2941365700, + 0.9427764606, 0.0267634765, 0.4367877602, 0.1155584527, 0.6275693090, + 0.4350741570, 0.3949956178, 0.2341486792, 0.1348473539, 0.8681677362, + }; + + ead::NodeptrT in = ead::make_variable(in_data.data(), in_shape); + ead::NodeptrT weight0 = ead::make_variable(w0_data.data(), weight0_shape); + ead::NodeptrT bias0 = ead::make_variable(b0_data.data(), bias0_shape); + ead::NodeptrT weight1 = ead::make_variable(w1_data.data(), weight1_shape); + ead::NodeptrT bias1 = ead::make_variable(b1_data.data(), bias1_shape); + ead::NodeptrT out = ead::make_variable(out_data.data(), out_shape); + + auto layer0 = tenncor::add(tenncor::matmul(in, weight0), tenncor::extend(bias0, 1, {3})); + auto sig0 = tenncor::sigmoid(layer0); + + auto layer1 = tenncor::add(tenncor::matmul(sig0, weight1), tenncor::extend(bias1, 1, {3})); + auto sig1 = tenncor::sigmoid(layer1); + + auto err = tenncor::pow(tenncor::sub(out, sig1), ead::make_constant_scalar(2, out_shape)); + + auto dw0 = ead::derive(err, weight0); + auto db0 = ead::derive(err, bias0); + auto dw1 = ead::derive(err, weight1); + auto db1 = ead::derive(err, bias1); + + pll::Session sess(4); + sess.track({ + dw0->get_tensor(), + db0->get_tensor(), + dw1->get_tensor(), + db1->get_tensor(), + }); + sess.update(); + + std::vector expect_gw0 = { + 0.0027019915285570115, 0.0049524065585107684, 0.0025163812594914499, + 0.0038923591714517891, 0.0069661439225563049, 0.0031510414764447736, + 0.0022478768029587262, 0.0022765639443853786, 0.0033410980166779791, + 0.00097522116465253051, 0.001693896620321596, 0.00095117349853452403, + 0.0013105640666332885, 0.0020640852743794621, 0.00092385650660917521, + 0.00066472294682227138, 0.00077684718047769464, 0.0010573870695921599, + 0.0030538233109959514, 0.0059914873371482626, 0.002816933963813279, + 0.0046344494070597901, 0.0074484900533361223, 0.002923178818749005, + 0.0025888494003570082, 0.0024167440905060772, 0.0036775897657920385, + 0.0024904082366803493, 0.0038341910866663683, 0.002500010407779862, + 0.0030142629092504893, 0.0052389646418249634, 0.0027829501832842408, + 0.0014995699089375711, 0.0020951478239990117, 0.002647907962560701, + 0.0027345254951523104, 0.0047865076822633029, 0.0026658727484682914, + 0.0036954188898844099, 0.0057282961097162239, 0.0025178275492600422, + 0.0018635506556079224, 0.0021607566147704336, 0.0029495543716158779, + 0.00085640005061550983, 0.0017314042599025487, 0.00076741704372808913, + 0.0013502530638826596, 0.0023208864089261359, 0.0009271185274612394, + 0.0008027150040004346, 0.00070015113143881663, 0.0011081335206311336, + 0.0019621893481767188, 0.0036606508512356819, 0.00185644674133355, + 0.0028297033136024576, 0.004484638344372795, 0.0018499041696268298, + 0.0015164101514628795, 0.0015527185924082477, 0.0022527047367589602, + 0.0021165392753594199, 0.003915006058678872, 0.0019922410790477645, + 0.0030454607213162344, 0.0050629793776993106, 0.00217522488536227, + 0.0016780267061119055, 0.0017163284163560558, 0.0024973871364443096, + 0.0037214774412162085, 0.0065510651741015865, 0.0036557068873296914, + 0.0050194415379978593, 0.0073000722708336623, 0.0030647941129167272, + 0.0024288766857152181, 0.0028581925623700367, 0.0038626543124159161, + 0.0023858654073475815, 0.0040005201672014658, 0.0023078024962114858, + 0.0031512536042022608, 0.0056436665737315383, 0.0027835035219529766, + 0.0017163829072048768, 0.0020227502089074081, 0.0027598072650753232, + }; + std::vector expect_gb0 = { + 0.0073014851440853963, 0.012795691944764515, 0.0070609853996545418, + 0.0099353821844054037, 0.016126120980560758, 0.0072594252580560938, + 0.0051855631031577599, 0.0058893997089519293, 0.0081361796595888665, + }; + std::vector expect_gw1 = { + 0.0075364033117421396, 0.050253916292103726, 0.012521300175850935, + 0.071258507740799906, 0.011490047203544582, 0.0074889441884519962, + 0.049364053114276017, 0.012400168850027316, 0.070298267826285218, + 0.011199991977589233, 0.0078271335097107944, 0.052010295641316096, + 0.013009172329239052, 0.073906224147165633, 0.011831323397315638, + 0.007474817281334166, 0.049098796177996611, 0.012354849203709242, + 0.069981094983390488, 0.011130069952177708, 0.006659969695296075, + 0.045029505644527811, 0.01116649756842061, 0.063711311399035839, + 0.010289373247410607, 0.0074443608399844983, 0.050801211812765354, + 0.012559014313408341, 0.071776564373706303, 0.011602207271183348, + 0.0070458198331052385, 0.046265739369542938, 0.011635104124310208, + 0.065919115499176104, 0.010502662637079581, 0.0073178590190504517, + 0.048699319909912865, 0.012117165013920111, 0.068991625331410358, + 0.011180624845463449, 0.0076281958672005084, 0.050999158876468637, + 0.012744598043270331, 0.072449921542875759, 0.011571221536496201, + }; + std::vector expect_gb1 = { + 0.0083642760546030238, 0.055794840260732122, 0.013998520595663699, 0.079441131671069379, 0.012580279526871282, + }; + + { + auto gotshape = dw0->shape(); + ASSERT_ARREQ(weight0_shape, gotshape); + } + double* gw0ptr = (double*) dw0->data(); + for (size_t i = 0, n = weight0_shape.n_elems(); i < n; ++i) + { + EXPECT_DOUBLE_EQ(expect_gw0[i], gw0ptr[i]); + } + { + auto gotshape = db0->shape(); + ASSERT_ARREQ(bias0_shape, gotshape); + } + double* gb0ptr = (double*) db0->data(); + for (size_t i = 0, n = bias0_shape.n_elems(); i < n; ++i) + { + EXPECT_DOUBLE_EQ(expect_gb0[i], gb0ptr[i]); + } + { + auto gotshape = dw1->shape(); + ASSERT_ARREQ(weight1_shape, gotshape); + } + + double* gw1ptr = (double*) dw1->data(); + for (size_t i = 0, n = weight1_shape.n_elems(); i < n; ++i) + { + EXPECT_DOUBLE_EQ(expect_gw1[i], gw1ptr[i]); + } + { + auto gotshape = db1->shape(); + ASSERT_ARREQ(bias1_shape, gotshape); + } + double* gb1ptr = (double*) db1->data(); + for (size_t i = 0, n = bias1_shape.n_elems(); i < n; ++i) + { + EXPECT_DOUBLE_EQ(expect_gb1[i], gb1ptr[i]); + } +} + + +#endif // DISABLE_SESSION_TEST diff --git a/pll/weights.proto b/pll/weights.proto new file mode 100644 index 000000000..9e7c7389a --- /dev/null +++ b/pll/weights.proto @@ -0,0 +1,11 @@ +syntax = "proto3"; + +option go_package = "github.com/mingkaic/tenncor/pll"; + +package weights; + +message OpWeights +{ + string label = 1; + map weights = 2; +} diff --git a/rocnnet/BUILD.bazel b/rocnnet/BUILD.bazel index bbf70bd67..9122c8786 100644 --- a/rocnnet/BUILD.bazel +++ b/rocnnet/BUILD.bazel @@ -42,6 +42,13 @@ py_binary( data = ["//cfg:optimizations"], ) +py_binary( + name = "comparison_mlp_grad_pll", + srcs = ["comparison/comparison_mlp_grad_pll.py"], + deps = [":rocnnet_py", "//pll:pll_py"], + data = ["//cfg:optimizations"], +) + ######### DEMOS ######### cc_binary( @@ -53,24 +60,21 @@ cc_binary( "//rocnnet/trainer:trainer", "@com_github_mingkaic_cppkg//flag:flag", ], - data = glob(["pretrained/*"]) + - ["//cfg:optimizations"], + data = ["//:models", "//cfg:optimizations"], ) py_binary( name = "gd_demo", srcs = ["demo/gd_demo.py"], deps = [":rocnnet_py"], - data = glob(["pretrained/*"]) + - ["//cfg:optimizations"], + data = ["//:models", "//cfg:optimizations"], ) py_binary( name = "dqn_demo", srcs = ["demo/dqn_demo.py"], deps = [":rocnnet_py"], - data = glob(["pretrained/*"]) + - ["//cfg:optimizations"], + data = ["//:models", "//cfg:optimizations"], ) py_binary( @@ -81,20 +85,8 @@ py_binary( ":rocnnet_py", "//dbg:grpc_dbg_py", ], - data = glob(["pretrained/*", "demo/MNIST_data/*"]) + - ["//cfg:optimizations"], -) - -py_binary( - name = "raw_rbm_demo", - srcs = glob(["demo/data/*.py"]) + - ["demo/raw_rbm_demo.py"], - deps = [ - ":rocnnet_py", - "//dbg:grpc_dbg_py", - ], - data = glob(["pretrained/*", "demo/MNIST_data/*"]) + - ["//cfg:optimizations"], + data = glob(["demo/MNIST_data/*"]) + + ["//:models", "//cfg:optimizations"], ) # cc_binary( @@ -107,5 +99,5 @@ py_binary( # "@com_github_mingkaic_cppkg//flag:flag", # "//dbg:dbg", # ], -# data = glob(["pretrained/*"]), +# data = glob([]), # ) diff --git a/rocnnet/comparison/comparison_matmul.py b/rocnnet/comparison/comparison_matmul.py index c165a98b4..c88b90fa8 100644 --- a/rocnnet/comparison/comparison_matmul.py +++ b/rocnnet/comparison/comparison_matmul.py @@ -37,7 +37,7 @@ tf_var = tf.Variable(data) tf_var2 = tf.Variable(data2) - tfsess = tf.Session() + tfsess = tf.compat.v1.Session() tfsess.run(tf_var.initializer) tfsess.run(tf_var2.initializer) diff --git a/rocnnet/comparison/comparison_mlp.py b/rocnnet/comparison/comparison_mlp.py index fd0dbe01c..8872cf19c 100644 --- a/rocnnet/comparison/comparison_mlp.py +++ b/rocnnet/comparison/comparison_mlp.py @@ -7,15 +7,23 @@ import ead.tenncor as tc import ead.ead as ead + import rocnnet.rocnnet as rcn matrix_dims = [ - 10, + 24, 50, + 74, 100, + 124, 150, + 174, 200, + 224, 250, + 512, + 1024, + 1500, ] def batch_generate(n, batchsize): @@ -39,22 +47,22 @@ def __init__(self, input_sizes, output_size, scope): self.output_size = output_size self.scope = scope or "Layer" - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): self.Ws = [] for input_idx, input_size in enumerate(input_sizes): W_name = "W_%d" % (input_idx,) W_initializer = tf.random_uniform_initializer( -1.0 / math.sqrt(input_size), 1.0 / math.sqrt(input_size)) - W_var = tf.get_variable(W_name, (input_size, output_size), initializer=W_initializer) + W_var = tf.compat.v1.get_variable(W_name, (input_size, output_size), initializer=W_initializer) self.Ws.append(W_var) - self.b = tf.get_variable("b", (output_size,), initializer=tf.constant_initializer(0)) + self.b = tf.compat.v1.get_variable("b", (output_size,), initializer=tf.constant_initializer(0)) def __call__(self, xs): if type(xs) != list: xs = [xs] assert len(xs) == len(self.Ws), \ "Expected %d input vectors, got %d" % (len(self.Ws), len(xs)) - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): return sum([tf.matmul(x, W) for x, W in zip(xs, self.Ws)]) + self.b def variables(self): @@ -63,9 +71,9 @@ def variables(self): def copy(self, scope=None): scope = scope or self.scope + "_copy" - with tf.variable_scope(scope) as sc: + with tf.compat.v1.variable_scope(scope) as sc: for v in self.variables(): - tf.get_variable(base_name(v), v.get_shape(), + tf.compat.v1.get_variable(base_name(v), v.get_shape(), initializer=lambda x,dtype=tf.float32,partition_info=None: v.initialized_value()) sc.reuse_variables() return Layer(self.input_sizes, self.output_size, scope=sc) @@ -80,7 +88,7 @@ def __init__(self, input_sizes, hiddens, nonlinearities, scope=None, given_layer assert len(hiddens) == len(nonlinearities), \ "Number of hiddens must be equal to number of nonlinearities" - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): if given_layers is not None: self.input_layer = given_layers[0] self.layers = given_layers[1:] @@ -94,7 +102,7 @@ def __init__(self, input_sizes, hiddens, nonlinearities, scope=None, given_layer def __call__(self, xs): if type(xs) != list: xs = [xs] - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): hidden = self.input_nonlinearity(self.input_layer(xs)) for layer, nonlinearity in zip(self.layers, self.layer_nonlinearities): hidden = nonlinearity(layer(hidden)) @@ -118,34 +126,38 @@ def copy(self, scope=None): for matrix_dim in matrix_dims: n_in = matrix_dim - n_out = n_in / 2 + n_out = int(n_in / 2) batch_size = 1 # regular mlp - nonlins = [tc.sigmoid, tc.sigmoid] - hiddens = [matrix_dim, n_out] - - brain = rcn.get_mlp(n_in, hiddens, 'brain_' + str(matrix_dim)) + brain = rcn.SequentialModel("comparison") + brain.add(rcn.Dense(matrix_dim, n_in, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="0")) + brain.add(rcn.sigmoid()) + brain.add(rcn.Dense(n_out, matrix_dim, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="1")) + brain.add(rcn.sigmoid()) invar = ead.variable(np.zeros([batch_size, n_in], dtype=float), 'in') - out = brain.forward(invar, nonlins) + out = brain.connect(invar) expected_out = ead.variable(np.zeros([batch_size, n_out], dtype=float), 'expected_out') err = tc.square(tc.sub(expected_out, out)) # tensorflow mlp tf_brain = MLP([n_in], [matrix_dim, n_out], [tf.sigmoid, tf.sigmoid], scope='brain_' + str(matrix_dim)) - tf_invar = tf.placeholder(tf.float32, [batch_size, n_in], name='tf_invar') + tf_invar = tf.compat.v1.placeholder(tf.float32, [batch_size, n_in], name='tf_invar') tf_out = tf_brain(tf_invar) - tf_expected_out = tf.placeholder(tf.float32, [batch_size, n_out], name='tf_expected_out') + tf_expected_out = tf.compat.v1.placeholder(tf.float32, [batch_size, n_out], name='tf_expected_out') tf_err = tf.square(tf_expected_out - tf_out) sess = ead.Session() - sess.track(err) - - tfsess = tf.Session() - tfsess.run(tf.global_variables_initializer()) + sess.track([err]) + tfsess = tf.compat.v1.Session() + tfsess.run(tf.compat.v1.global_variables_initializer()) test_batch = batch_generate(n_in, batch_size) test_batch_out = avgevry2(test_batch) diff --git a/rocnnet/comparison/comparison_mlp_grad.py b/rocnnet/comparison/comparison_mlp_grad.py index 8d7283a39..f6504ab46 100644 --- a/rocnnet/comparison/comparison_mlp_grad.py +++ b/rocnnet/comparison/comparison_mlp_grad.py @@ -7,15 +7,23 @@ import ead.tenncor as tc import ead.ead as ead + import rocnnet.rocnnet as rcn matrix_dims = [ - 10, + 24, 50, + 74, 100, + 124, 150, + 174, 200, + 224, 250, + 512, + 1024, + 1500, ] def batch_generate(n, batchsize): @@ -39,22 +47,22 @@ def __init__(self, input_sizes, output_size, scope): self.output_size = output_size self.scope = scope or "Layer" - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): self.Ws = [] for input_idx, input_size in enumerate(input_sizes): W_name = "W_%d" % (input_idx,) W_initializer = tf.random_uniform_initializer( -1.0 / math.sqrt(input_size), 1.0 / math.sqrt(input_size)) - W_var = tf.get_variable(W_name, (input_size, output_size), initializer=W_initializer) + W_var = tf.compat.v1.get_variable(W_name, (input_size, output_size), initializer=W_initializer) self.Ws.append(W_var) - self.b = tf.get_variable("b", (output_size,), initializer=tf.constant_initializer(0)) + self.b = tf.compat.v1.get_variable("b", (output_size,), initializer=tf.constant_initializer(0)) def __call__(self, xs): if type(xs) != list: xs = [xs] assert len(xs) == len(self.Ws), \ "Expected %d input vectors, got %d" % (len(self.Ws), len(xs)) - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): return sum([tf.matmul(x, W) for x, W in zip(xs, self.Ws)]) + self.b def variables(self): @@ -63,9 +71,9 @@ def variables(self): def copy(self, scope=None): scope = scope or self.scope + "_copy" - with tf.variable_scope(scope) as sc: + with tf.compat.v1.variable_scope(scope) as sc: for v in self.variables(): - tf.get_variable(base_name(v), v.get_shape(), + tf.compat.v1.get_variable(base_name(v), v.get_shape(), initializer=lambda x,dtype=tf.float32,partition_info=None: v.initialized_value()) sc.reuse_variables() return Layer(self.input_sizes, self.output_size, scope=sc) @@ -80,7 +88,7 @@ def __init__(self, input_sizes, hiddens, nonlinearities, scope=None, given_layer assert len(hiddens) == len(nonlinearities), \ "Number of hiddens must be equal to number of nonlinearities" - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): if given_layers is not None: self.input_layer = given_layers[0] self.layers = given_layers[1:] @@ -94,7 +102,7 @@ def __init__(self, input_sizes, hiddens, nonlinearities, scope=None, given_layer def __call__(self, xs): if type(xs) != list: xs = [xs] - with tf.variable_scope(self.scope): + with tf.compat.v1.variable_scope(self.scope): hidden = self.input_nonlinearity(self.input_layer(xs)) for layer, nonlinearity in zip(self.layers, self.layer_nonlinearities): hidden = nonlinearity(layer(hidden)) @@ -119,32 +127,36 @@ def copy(self, scope=None): for matrix_dim in matrix_dims: n_in = matrix_dim - n_out = n_in / 2 + n_out = int(n_in / 2) batch_size = 1 sess = ead.Session() - tfsess = tf.Session() - + tfsess = tf.compat.v1.Session() # regular mlp - nonlins = [tc.sigmoid, tc.sigmoid] - hiddens = [matrix_dim, n_out] - - brain = rcn.get_mlp(n_in, hiddens, 'brain_' + str(matrix_dim)) + brain = rcn.SequentialModel("comparison") + brain.add(rcn.Dense(matrix_dim, n_in, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="0")) + brain.add(rcn.sigmoid()) + brain.add(rcn.Dense(n_out, matrix_dim, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="1")) + brain.add(rcn.sigmoid()) invar = ead.variable(np.zeros([batch_size, n_in], dtype=float), 'in') - out = brain.forward(invar, nonlins) + out = brain.connect(invar) expected_out = ead.variable(np.zeros([batch_size, n_out], dtype=float), 'expected_out') err = tc.square(tc.sub(expected_out, out)) - trainer = rcn.MLPTrainer(brain, nonlins, sess, rcn.get_sgd(learning_rate), batch_size) + trainer = rcn.MLPTrainer(brain, sess, rcn.get_sgd(learning_rate), batch_size) # tensorflow mlp tf_brain = MLP([n_in], [matrix_dim, n_out], [tf.sigmoid, tf.sigmoid], scope='brain_' + str(matrix_dim)) - tf_invar = tf.placeholder(tf.float32, [batch_size, n_in], name='tf_invar') + tf_invar = tf.compat.v1.placeholder(tf.float32, [batch_size, n_in], name='tf_invar') tf_out = tf_brain(tf_invar) - tf_expected_out = tf.placeholder(tf.float32, [batch_size, n_out], name='tf_expected_out') + tf_expected_out = tf.compat.v1.placeholder(tf.float32, [batch_size, n_out], name='tf_expected_out') tf_err = tf.square(tf_expected_out - tf_out) layer0 = tf_brain.input_layer @@ -180,9 +192,8 @@ def calculate_update(batch, batch_out): return out - sess.track(err) - tfsess.run(tf.global_variables_initializer()) - + sess.track([err]) + tfsess.run(tf.compat.v1.global_variables_initializer()) test_batch = batch_generate(n_in, batch_size) test_batch_out = avgevry2(test_batch) diff --git a/rocnnet/comparison/comparison_mlp_grad_pll.py b/rocnnet/comparison/comparison_mlp_grad_pll.py new file mode 100644 index 000000000..3e012e575 --- /dev/null +++ b/rocnnet/comparison/comparison_mlp_grad_pll.py @@ -0,0 +1,221 @@ +import time +import math + +import numpy as np +import matplotlib.pyplot as plt +import tensorflow as tf + +import ead.tenncor as tc +import ead.ead as ead + +import pll.pll as pll + +import rocnnet.rocnnet as rcn + +matrix_dims = [ + 24, + 50, + 74, + 100, + 124, + 150, + 174, + 200, + 224, + 250, + 512, + 1024, + 1500, +] + +def batch_generate(n, batchsize): + total = n * batchsize + return np.random.rand(total) + +def avgevry2(indata): + return (indata[0::2] + indata[1::2]) / 2 + +def base_name(var): + """Extracts value passed to name= when creating a variable""" + return var.name.split('/')[-1].split(':')[0] + +class Layer(object): + def __init__(self, input_sizes, output_size, scope): + """Cretes a neural network layer.""" + if type(input_sizes) != list: + input_sizes = [input_sizes] + + self.input_sizes = input_sizes + self.output_size = output_size + self.scope = scope or "Layer" + + with tf.compat.v1.variable_scope(self.scope): + self.Ws = [] + for input_idx, input_size in enumerate(input_sizes): + W_name = "W_%d" % (input_idx,) + W_initializer = tf.random_uniform_initializer( + -1.0 / math.sqrt(input_size), 1.0 / math.sqrt(input_size)) + W_var = tf.compat.v1.get_variable(W_name, (input_size, output_size), initializer=W_initializer) + self.Ws.append(W_var) + self.b = tf.compat.v1.get_variable("b", (output_size,), initializer=tf.constant_initializer(0)) + + def __call__(self, xs): + if type(xs) != list: + xs = [xs] + assert len(xs) == len(self.Ws), \ + "Expected %d input vectors, got %d" % (len(self.Ws), len(xs)) + with tf.compat.v1.variable_scope(self.scope): + return sum([tf.matmul(x, W) for x, W in zip(xs, self.Ws)]) + self.b + + def variables(self): + return [self.b] + self.Ws + + def copy(self, scope=None): + scope = scope or self.scope + "_copy" + + with tf.compat.v1.variable_scope(scope) as sc: + for v in self.variables(): + tf.compat.v1.get_variable(base_name(v), v.get_shape(), + initializer=lambda x,dtype=tf.float32,partition_info=None: v.initialized_value()) + sc.reuse_variables() + return Layer(self.input_sizes, self.output_size, scope=sc) + +class MLP(object): + def __init__(self, input_sizes, hiddens, nonlinearities, scope=None, given_layers=None): + self.input_sizes = input_sizes + self.hiddens = hiddens + self.input_nonlinearity, self.layer_nonlinearities = nonlinearities[0], nonlinearities[1:] + self.scope = scope or "MLP" + + assert len(hiddens) == len(nonlinearities), \ + "Number of hiddens must be equal to number of nonlinearities" + + with tf.compat.v1.variable_scope(self.scope): + if given_layers is not None: + self.input_layer = given_layers[0] + self.layers = given_layers[1:] + else: + self.input_layer = Layer(input_sizes, hiddens[0], scope="input_layer") + self.layers = [] + + for l_idx, (h_from, h_to) in enumerate(zip(hiddens[:-1], hiddens[1:])): + self.layers.append(Layer(h_from, h_to, scope="hidden_layer_%d" % (l_idx,))) + + def __call__(self, xs): + if type(xs) != list: + xs = [xs] + with tf.compat.v1.variable_scope(self.scope): + hidden = self.input_nonlinearity(self.input_layer(xs)) + for layer, nonlinearity in zip(self.layers, self.layer_nonlinearities): + hidden = nonlinearity(layer(hidden)) + return hidden + + def variables(self): + res = self.input_layer.variables() + for layer in self.layers: + res.extend(layer.variables()) + return res + + def copy(self, scope=None): + scope = scope or self.scope + "_copy" + nonlinearities = [self.input_nonlinearity] + self.layer_nonlinearities + given_layers = [self.input_layer.copy()] + [layer.copy() for layer in self.layers] + return MLP(self.input_sizes, self.hiddens, nonlinearities, scope=scope, + given_layers=given_layers) + +ead_durs = [] +tf_durs = [] +learning_rate = 0.9 + +for matrix_dim in matrix_dims: + n_in = matrix_dim + n_out = int(n_in / 2) + batch_size = 1 + + sess = pll.Session(nthread=4) + tfsess = tf.compat.v1.Session() + + # regular mlp + brain = rcn.SequentialModel("comparison") + brain.add(rcn.Dense(matrix_dim, n_in, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="0")) + brain.add(rcn.sigmoid()) + brain.add(rcn.Dense(n_out, matrix_dim, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="1")) + brain.add(rcn.sigmoid()) + + invar = ead.variable(np.zeros([batch_size, n_in], dtype=float), 'in') + out = brain.connect(invar) + expected_out = ead.variable(np.zeros([batch_size, n_out], dtype=float), 'expected_out') + err = tc.square(tc.sub(expected_out, out)) + + trainer = rcn.MLPTrainer(brain, sess, rcn.get_sgd(learning_rate), batch_size) + + # tensorflow mlp + tf_brain = MLP([n_in], [matrix_dim, n_out], [tf.sigmoid, tf.sigmoid], scope='brain_' + str(matrix_dim)) + + tf_invar = tf.compat.v1.placeholder(tf.float32, [batch_size, n_in], name='tf_invar') + tf_out = tf_brain(tf_invar) + tf_expected_out = tf.compat.v1.placeholder(tf.float32, [batch_size, n_out], name='tf_expected_out') + tf_err = tf.square(tf_expected_out - tf_out) + + layer0 = tf_brain.input_layer + layer1 = tf_brain.layers[0] + + tf_w0 = layer0.Ws[0] + tf_b0 = layer0.b + tf_w1 = layer1.Ws[0] + tf_b1 = layer1.b + + tf_dw0, tf_db0, tf_dw1, tf_db1 = tf.gradients(tf_err, [ + tf_w0, + tf_b0, + tf_w1, + tf_b1, + ]) + w0_update = tf_w0.assign_sub(learning_rate * tf_dw0) + b0_update = tf_b0.assign_sub(learning_rate * tf_db0) + w1_update = tf_w1.assign_sub(learning_rate * tf_dw1) + b1_update = tf_b1.assign_sub(learning_rate * tf_db1) + + def calculate_update(batch, batch_out): + out, _, _, _, _ = tfsess.run([ + tf_err, + w0_update, + b0_update, + w1_update, + b1_update, + ], { + tf_invar: batch, + tf_expected_out: batch_out, + }) + + return out + + sess.track([err]) + tfsess.run(tf.compat.v1.global_variables_initializer()) + + test_batch = batch_generate(n_in, batch_size) + test_batch_out = avgevry2(test_batch) + tf_test_batch = test_batch.reshape([batch_size, n_in]) + tf_test_batch_out = test_batch_out.reshape([batch_size, n_out]) + + start = time.time() + trainer.train(test_batch, test_batch_out) + ead_dur = time.time() - start + + start = time.time() + calculate_update(tf_test_batch, tf_test_batch_out) + tf_dur = time.time() - start + + ead_durs.append(ead_dur) + tf_durs.append(tf_dur) + +print('ead durations: ', ead_durs) +print('tf durations: ', tf_durs) +ead_line = plt.plot(matrix_dims, ead_durs, 'r--', label='ead durations') +tf_line = plt.plot(matrix_dims, tf_durs, 'b--', label='tf durations') +plt.legend() +plt.show() diff --git a/rocnnet/demo/MNIST_data/t10k-images-idx3-ubyte.gz b/rocnnet/demo/MNIST_data/t10k-images-idx3-ubyte.gz deleted file mode 100644 index 5ace8ea93..000000000 Binary files a/rocnnet/demo/MNIST_data/t10k-images-idx3-ubyte.gz and /dev/null differ diff --git a/rocnnet/demo/MNIST_data/t10k-labels-idx1-ubyte.gz b/rocnnet/demo/MNIST_data/t10k-labels-idx1-ubyte.gz deleted file mode 100644 index a7e141541..000000000 Binary files a/rocnnet/demo/MNIST_data/t10k-labels-idx1-ubyte.gz and /dev/null differ diff --git a/rocnnet/demo/MNIST_data/train-images-idx3-ubyte.gz b/rocnnet/demo/MNIST_data/train-images-idx3-ubyte.gz deleted file mode 100644 index b50e4b6bc..000000000 Binary files a/rocnnet/demo/MNIST_data/train-images-idx3-ubyte.gz and /dev/null differ diff --git a/rocnnet/demo/MNIST_data/train-labels-idx1-ubyte.gz b/rocnnet/demo/MNIST_data/train-labels-idx1-ubyte.gz deleted file mode 100644 index 707a576bb..000000000 Binary files a/rocnnet/demo/MNIST_data/train-labels-idx1-ubyte.gz and /dev/null differ diff --git a/rocnnet/demo/dqn_demo.py b/rocnnet/demo/dqn_demo.py index 2ede1d3f1..d16646d0c 100644 --- a/rocnnet/demo/dqn_demo.py +++ b/rocnnet/demo/dqn_demo.py @@ -57,8 +57,8 @@ def main(args): help='Number of steps per episodes (default: 100)') parser.add_argument('--save', dest='save', nargs='?', default='', help='Filename to save model (default: )') - parser.add_argument('--load', dest='load', nargs='?', default='rocnnet/pretrained/dqnmodel.pbx', - help='Filename to load pretrained model (default: rocnnet/pretrained/dqnmodel.pbx)') + parser.add_argument('--load', dest='load', nargs='?', default='models/dqnmodel.pbx', + help='Filename to load pretrained model (default: models/dqnmodel.pbx)') args = parser.parse_args(args) episode_count = args.n_episodes @@ -71,18 +71,31 @@ def main(args): n_observations = 10 n_actions = 9 - n_outs = [9, n_actions] - nonlins = [tc.tanh, rcn.identity] - - brain = rcn.get_mlp(n_observations, n_outs, 'brain') - untrained_brain = brain.copy() + model = rcn.SequentialModel("demo") + model.add(rcn.Dense(9, n_observations, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="0")) + model.add(rcn.sigmoid()) + model.add(rcn.Dense(n_actions, 9, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="1")) + model.add(rcn.sigmoid()) + + untrained = model.clone() + try: + print('loading ' + args.load) + trained = rcn.load_file_seqmodel(args.load, "demo") + print('successfully loaded from ' + args.load) + except Exception as e: + print(e) + print('failed to load from "{}"'.format(args.load)) + trained = model.clone() bgd = rcn.get_rms_momentum( learning_rate = 0.1, - discount_factor = 0.5, - gradprocess = lambda x: tc.clip_by_l2norm(x, 5)) - param = rcn.get_dqninfo( + discount_factor = 0.5) + param = rcn.DQNInfo( mini_batch_size = 1, store_interval = 1, discount_rate = 0.99, @@ -90,20 +103,10 @@ def main(args): sess = ead.Session() - untrained_dqn = rcn.DQNTrainer(untrained_brain, nonlins, sess, bgd, param) - trained_dqn = rcn.DQNTrainer(brain, nonlins, sess, bgd, param) - try: - with open(args.load, 'rb') as f: - print('loading') - pretrained_dqn = rcn.load_dqntrainer(f.read(), - nonlins, sess, bgd, param) - print('successfully loaded from ' + args.load) - except Exception as e: - print(e) - print('failed to load from "{}"'.format(args.load)) - pretrained_brain = brain.copy() - pretrained_dqn = rcn.DQNTrainer(pretrained_brain, - nonlins, sess, bgd, param) + untrained_dqn = rcn.DQNTrainer(untrained, sess, bgd, param) + trained_dqn = rcn.DQNTrainer(model, sess, bgd, param, + gradprocess = lambda x: tc.clip_by_l2norm(x, 5)) + pretrained_dqn = rcn.DQNTrainer(trained, sess, bgd, param) sess.optimize("cfg/optimizations.rules") @@ -198,7 +201,7 @@ def main(args): if err_msg is None: try: print('saving') - if trained_dqn.serialize_to_file(args.save): + if model.save_file(args.save): print('successfully saved to {}'.format(args.save)) except Exception as e: print(e) diff --git a/rocnnet/demo/gd_demo.cpp b/rocnnet/demo/gd_demo.cpp index b51337af1..ebd545ae4 100644 --- a/rocnnet/demo/gd_demo.cpp +++ b/rocnnet/demo/gd_demo.cpp @@ -5,8 +5,6 @@ #include #include -#include "pbm/save.hpp" - #include "flag/flag.hpp" #include "ead/ead.hpp" @@ -14,6 +12,9 @@ #include "dbg/grpc/session.hpp" +#include "rocnnet/modl/model.hpp" +#include "rocnnet/modl/activations.hpp" + #include "rocnnet/trainer/mlp_trainer.hpp" static std::vector batch_generate (size_t n, size_t batchsize) @@ -68,7 +69,7 @@ int main (int argc, const char** argv) ("save", flag::opt::value(&savepath)->default_value(""), "filename to save model") ("load", flag::opt::value(&loadpath)->default_value( - "rocnnet/pretrained/gdmodel.pbx"), "filename to load pretrained model"); + "models/gdmodel.pbx"), "filename to load pretrained model"); int exit_status = 0; std::clock_t start; @@ -88,62 +89,51 @@ int main (int argc, const char** argv) uint8_t n_in = 10; uint8_t n_out = n_in / 2; std::vector n_outs = {9, n_out}; - modl::NonLinearsT nonlins = {tenncor::sigmoid, tenncor::sigmoid}; - auto brain = std::make_shared(n_in, n_outs, "brain"); - auto untrained_brain = std::make_shared(*brain); - modl::MLPptrT pretrained_brain; + modl::SequentialModel model("demo"); + model.push_back(std::make_shared(9, n_in, + eqns::unif_xavier_init(1), eqns::zero_init(), "0")); + model.push_back(modl::sigmoid()); + model.push_back(std::make_shared(n_out, 9, + eqns::unif_xavier_init(1), eqns::zero_init(), "1")); + model.push_back(modl::sigmoid()); + + modl::SequentialModel untrained_model(model); + modl::SeqModelptrT trained_model = nullptr; + std::ifstream loadstr(loadpath); if (loadstr.is_open()) { - cortenn::Layer layer; - layer.ParseFromIstream(&loadstr); - - // load graph to target - const cortenn::Graph& graph = layer.graph(); - pbm::GraphInfo info; - pbm::load_graph(info, graph); - - pretrained_brain = std::make_shared(info, "pretrained"); - - logs::infof("model successfully loaded from file '%s'", loadpath.c_str()); - if (cortenn::Layer::kItCtx != layer.layer_context_case()) - { - logs::warn("missing training context"); - } - else - { - auto& ctx = layer.it_ctx(); - logs::infof("loaded model trained for %d iterations", - ctx.iterations()); - } - + ade::TensT trained_roots; + trained_model = std::static_pointer_cast( + modl::load_layer(loadstr, trained_roots, modl::seq_model_key, "demo")); + logs::infof("model successfully loaded from file `%s`", loadpath.c_str()); loadstr.close(); } else { - logs::warnf("model failed to loaded from file '%s'", loadpath.c_str()); - pretrained_brain = std::make_shared(*brain); + logs::warnf("model failed to loaded from file `%s`", loadpath.c_str()); + trained_model = std::make_shared(model); } uint8_t n_batch = 3; size_t show_every_n = 500; - eqns::ApproxF approx = [](ead::NodeptrT& root, eqns::VariablesT leaves) + eqns::ApproxF approx = [](const eqns::VarErrsT& leaves) { - return eqns::sgd(root, leaves, 0.9); // learning rate = 0.9 + return eqns::sgd(leaves, 0.9); // learning rate = 0.9 }; dbg::InteractiveSession sess("localhost:50051"); - trainer::MLPTrainer trainer(brain, nonlins, sess, approx, n_batch); + trainer::MLPTrainer trainer(model, sess, approx, n_batch); ead::VarptrT testin = ead::make_variable_scalar( 0, ade::Shape({n_in}), "testin"); - auto untrained_out = (*untrained_brain)(testin, nonlins); - auto trained_out = (*brain)(testin, nonlins); - auto pretrained_out = (*pretrained_brain)(testin, nonlins); + auto untrained_out = untrained_model.connect(testin); + auto out = model.connect(testin); + auto trained_out = trained_model->connect(testin); sess.track({ untrained_out->get_tensor(), + out->get_tensor(), trained_out->get_tensor(), - pretrained_out->get_tensor(), }); opt::OptCtx rules = ead::parse_file("cfg/optimizations.rules"); @@ -175,8 +165,8 @@ int main (int argc, const char** argv) float pretrained_err = 0; float* untrained_res = untrained_out->data(); - float* trained_res = trained_out->data(); - float* pretrained_res = pretrained_out->data(); + float* trained_res = out->data(); + float* pretrained_res = trained_out->data(); for (size_t i = 0; i < n_test; i++) { if (i % show_every_n == show_every_n - 1) @@ -214,15 +204,15 @@ int main (int argc, const char** argv) std::ofstream savestr(savepath); if (savestr.is_open()) { - if (trainer.save(savestr)) + if (modl::save_layer(savestr, model, {})) { - logs::infof("successfully saved model to '%s'", savepath.c_str()); + logs::infof("successfully saved model to `%s`", savepath.c_str()); } savestr.close(); } else { - logs::warnf("failed to save model to '%s'", savepath.c_str()); + logs::warnf("failed to save model to `%s`", savepath.c_str()); } } diff --git a/rocnnet/demo/gd_demo.py b/rocnnet/demo/gd_demo.py index bb396c939..b111b86b4 100644 --- a/rocnnet/demo/gd_demo.py +++ b/rocnnet/demo/gd_demo.py @@ -44,8 +44,8 @@ def main(args): help='Number of times to test (default: 500)') parser.add_argument('--save', dest='save', nargs='?', default='', help='Filename to save model (default: )') - parser.add_argument('--load', dest='load', nargs='?', default='rocnnet/pretrained/gdmodel.pbx', - help='Filename to load pretrained model (default: rocnnet/pretrained/gdmodel.pbx)') + parser.add_argument('--load', dest='load', nargs='?', default='models/gdmodel.pbx', + help='Filename to load pretrained model (default: models/gdmodel.pbx)') args = parser.parse_args(args) if args.seed: @@ -54,33 +54,38 @@ def main(args): np.random.seed(args.seedval) n_in = 10 - n_out = n_in / 2 - n_outs = [9, n_out] - - nonlins = [tc.sigmoid, tc.sigmoid] - - brain = rcn.get_mlp(n_in, n_outs, 'brain') - untrained_brain = brain.copy() + n_out = int(n_in / 2) + + model = rcn.SequentialModel("demo") + model.add(rcn.Dense(9, n_in, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="0")) + model.add(rcn.sigmoid()) + model.add(rcn.Dense(n_out, 9, + weight_init=rcn.unif_xavier_init(), + bias_init=rcn.zero_init(), label="1")) + model.add(rcn.sigmoid()) + + untrained = model.clone() try: - with open(args.load, 'rb') as f: - print('loading') - pretrained_brain = brain.parse_from_string(f.read()) - print('successfully loaded from ' + args.load) + print('loading ' + args.load) + trained = rcn.load_file_seqmodel(args.load, "demo") + print('successfully loaded from ' + args.load) except Exception as e: print(e) print('failed to load from "{}"'.format(args.load)) - pretrained_brain = brain.copy() + trained = model.clone() sess = ead.Session() n_batch = args.n_batch show_every_n = 500 - trainer = rcn.MLPTrainer(brain, nonlins, sess, + trainer = rcn.MLPTrainer(model, sess, rcn.get_sgd(0.9), n_batch) testin = ead.variable(np.zeros([n_in], dtype=float), 'testin') - untrained_out = untrained_brain.forward(testin, nonlins) - trained_out = brain.forward(testin, nonlins) - pretrained_out = pretrained_brain.forward(testin, nonlins) + untrained_out = untrained.connect(testin) + trained_out = model.connect(testin) + pretrained_out = trained.connect(testin) sess.track([ untrained_out, trained_out, @@ -130,7 +135,7 @@ def main(args): try: print('saving') - if trainer.serialize_to_file(args.save): + if model.save_file(args.save): print('successfully saved to {}'.format(args.save)) except Exception as e: print(e) diff --git a/rocnnet/demo/raw_rbm_demo.py b/rocnnet/demo/raw_rbm_demo.py deleted file mode 100644 index 4c590b92a..000000000 --- a/rocnnet/demo/raw_rbm_demo.py +++ /dev/null @@ -1,319 +0,0 @@ -from __future__ import print_function - -import sys - -import matplotlib.pyplot as plt -import numpy as np -from tensorflow.examples.tutorials.mnist import input_data - -import ead.tenncor as tf -import ead.ead as ead -import rocnnet.rocnnet as rcn - - -def sample_bernoulli(probs): - return tf.random.rand_binom_one(probs) - -# def sample_gaussian(x, sigma): -# return x + tf.random_normal(tf.shape(x), mean=0.0, stddev=sigma, dtype=tf.float32) - - -# def cosine_errfunc(x, compute_visible): -# x1_norm = tf.nn.l2_normalize(x, 1) -# x2_norm = tf.nn.l2_normalize(compute_visible, 1) -# cos_val = tf.reduce_mean(tf.reduce_sum(tf.mul(x1_norm, x2_norm), 1)) -# return tf.acos(cos_val) / tf.constant(np.pi) - -def mse_errfunc(x, compute_visible): - return tf.reduce_mean(tf.square(tf.sub(x, compute_visible))) - - -class RBMConfig: - def __init__(self, - n_visible, - n_hidden, - learning_rate=0.01, - momentum=0.95, - xavier_const=1.0, - err_function=mse_errfunc, - tqdm=None): - if not 0.0 <= momentum <= 1.0: - raise ValueError('momentum should be in range [0, 1]') - - self.n_visible = n_visible - self.n_hidden = n_hidden - self.learning_rate=learning_rate - self.momentum=momentum - self.xavier_const=xavier_const - self.err_function=err_function - self.tqdm=tqdm - -class RBMInput: - def __init__(self, n_visible, n_hidden, n_batch): - self.x = ead.scalar_variable(0, [n_batch, n_visible]) - self.y = ead.scalar_variable(0, [n_batch, n_hidden]) - -class RBMStorage: - def __init__(self, config): - self.w = rcn.variable_from_init( - rcn.unif_xavier_init(config.xavier_const), - [config.n_visible, config.n_hidden]) - self.visible_bias = ead.scalar_variable(0, [config.n_visible]) - self.hidden_bias = ead.scalar_variable(0, [config.n_hidden]) - - self.delta_w = ead.scalar_variable(0, [config.n_visible, config.n_hidden]) - self.delta_visible_bias = ead.scalar_variable(0, [config.n_visible]) - self.delta_hidden_bias = ead.scalar_variable(0, [config.n_hidden]) - -class RBMOutput: - def __init__(self, update_weights, update_deltas, compute_hidden, compute_visible, compute_visible_from_hidden, compute_err): - self.update_weights = update_weights - self.update_deltas = update_deltas - self.compute_hidden = compute_hidden - self.compute_visible = compute_visible - self.compute_visible_from_hidden = compute_visible_from_hidden - self.compute_err = compute_err - assert self.update_weights is not None - assert self.update_deltas is not None - assert self.compute_hidden is not None - assert self.compute_visible is not None - assert self.compute_visible_from_hidden is not None - assert self.compute_err is not None - -def bb_init(rbm_in, rbm_storage, config): - # hidden = sigmoid(X @ W + hB) - # visible_rec = sigmoid(sample_bernoulli(hidden) @ W^T + vB) - # hidden_rec = sigmoid(visible_rec @ W + hB) - hidden_p = tf.sigmoid(tf.nn.fully_connect( - [rbm_in.x], [rbm_storage.w], - rbm_storage.hidden_bias)) - visible_recon_p = tf.sigmoid(tf.nn.fully_connect( - [sample_bernoulli(hidden_p)], [tf.transpose(rbm_storage.w)], - rbm_storage.visible_bias)) - hidden_recon_p = tf.sigmoid(tf.nn.fully_connect( - [visible_recon_p], [rbm_storage.w], - rbm_storage.hidden_bias)) - - # X^T @ sigmoid(X @ W + hB) - sigmoid(sample_bernoulli(sigmoid(X @ W + hB)) @ W^T + vB)^T @ sigmoid(sigmoid(sample_bernoulli(hidden) @ W^T + vB) @ W + hB) - grad = tf.sub( - tf.matmul( - tf.transpose(rbm_in.x), - hidden_p), - tf.matmul( - tf.transpose(visible_recon_p), - hidden_recon_p)) - # grad is derivative of ? with respect to rbm_in.w - - def f(x_old, x_new): - return tf.add( - tf.mul( - ead.scalar_constant(config.momentum, x_old.shape()), - x_old), - tf.prod([ - ead.scalar_constant(config.learning_rate, x_new.shape()), - x_new, - ead.scalar_constant((1 - config.momentum) / x_new.shape()[0], x_new.shape()) - ])) - - # momentum - delta_w_new = f(rbm_storage.delta_w, grad) - delta_visible_bias_new = f(rbm_storage.delta_visible_bias, tf.reduce_mean_1d(tf.sub(rbm_in.x, visible_recon_p), 1)) - delta_hidden_bias_new = f(rbm_storage.delta_hidden_bias, tf.reduce_mean_1d(tf.sub(hidden_p, hidden_recon_p), 1)) - - update_delta_w = (rbm_storage.delta_w, delta_w_new) - update_delta_visible_bias = (rbm_storage.delta_visible_bias, delta_visible_bias_new) - update_delta_hidden_bias = (rbm_storage.delta_hidden_bias, delta_hidden_bias_new) - - update_w = (rbm_storage.w, tf.add(rbm_storage.w, delta_w_new)) - update_visible_bias = (rbm_storage.visible_bias, - tf.add(rbm_storage.visible_bias, delta_visible_bias_new)) - update_hidden_bias = (rbm_storage.hidden_bias, - tf.add(rbm_storage.hidden_bias, delta_hidden_bias_new)) - - compute_hidden = tf.sigmoid(tf.nn.fully_connect( - [rbm_in.x], [rbm_storage.w], - rbm_storage.hidden_bias)) - compute_visible = tf.sigmoid(tf.nn.fully_connect( - [compute_hidden], [tf.transpose(rbm_storage.w)], - rbm_storage.visible_bias)) - return RBMOutput( - update_deltas=[update_delta_w, update_delta_visible_bias, update_delta_hidden_bias], - update_weights=[update_w, update_visible_bias, update_hidden_bias], - compute_hidden=compute_hidden, - compute_visible=compute_visible, - compute_visible_from_hidden=tf.sigmoid(tf.nn.fully_connect( - [rbm_in.y], [tf.transpose(rbm_storage.w)], - rbm_storage.visible_bias)), - compute_err=config.err_function(rbm_in.x, compute_visible)) - -# def gr_init(rbm_in, rbm_storage, config, sample_visible=False, sigma=1): -# hidden_p = tf.nn.sigmoid(tf.matmul(rbm_in.x, rbm_storage.w) + rbm_storage.hidden_bias) -# visible_recon_p = tf.matmul(sample_bernoulli(hidden_p), tf.transpose(rbm_storage.w)) + rbm_storage.visible_bias - -# if sample_visible: -# visible_recon_p = sample_gaussian(visible_recon_p, sigma) - -# hidden_recon_p = tf.nn.sigmoid(tf.matmul(visible_recon_p, rbm_storage.w) + rbm_storage.hidden_bias) - -# positive_grad = tf.matmul(tf.transpose(rbm_in.x), hidden_p) -# negative_grad = tf.matmul(tf.transpose(visible_recon_p), hidden_recon_p) - -# def f(x_old, x_new): -# return config.momentum * x_old +\ -# config.learning_rate * x_new * (1 - config.momentum) / tf.to_float(tf.shape(x_new)[0]) - -# delta_w_new = f(rbm_storage.delta_w, positive_grad - negative_grad) -# delta_visible_bias_new = f(rbm_storage.delta_visible_bias, tf.reduce_mean(rbm_in.x - visible_recon_p, 0)) -# delta_hidden_bias_new = f(rbm_storage.delta_hidden_bias, tf.reduce_mean(hidden_p - hidden_recon_p, 0)) - -# update_delta_w = rbm_storage.delta_w.assign(delta_w_new) -# update_delta_visible_bias = rbm_storage.delta_visible_bias.assign(delta_visible_bias_new) -# update_delta_hidden_bias = rbm_storage.delta_hidden_bias.assign(delta_hidden_bias_new) - -# update_w = rbm_storage.w.assign(rbm_storage.w + delta_w_new) -# update_visible_bias = rbm_storage.visible_bias.assign(rbm_storage.visible_bias + delta_visible_bias_new) -# update_hidden_bias = rbm_storage.hidden_bias.assign(rbm_storage.hidden_bias + delta_hidden_bias_new) - -# compute_hidden = tf.nn.sigmoid(tf.matmul(rbm_in.x, rbm_storage.w) + rbm_storage.hidden_bias) -# compute_visible = tf.matmul(compute_hidden, tf.transpose(rbm_storage.w)) + rbm_storage.visible_bias -# return RBMOutput( -# update_deltas=[update_delta_w, update_delta_visible_bias, update_delta_hidden_bias], -# update_weights=[update_w, update_visible_bias, update_hidden_bias], -# compute_hidden=compute_hidden, -# compute_visible=compute_visible, -# compute_visible_from_hidden=tf.matmul(rbm_in.y, tf.transpose(rbm_storage.w)) + rbm_storage.visible_bias, -# compute_err=config.err_function(rbm_in.x, compute_visible)) - -class RBM: - def __init__(self, config, initialize): - self.config = config - self.initialize = initialize - self.storage = RBMStorage(config) - - def __call__(self, inputs): - return self.initialize(inputs, self.storage, self.config) - -class RBMTrainee: - def __init__(self, rbm, n_batch, sess): - self.rbm = rbm - self.n_batch = n_batch - self.input = RBMInput(config.n_visible, config.n_hidden, n_batch=n_batch) - self.output = rbm(self.input) - self.sess = sess - - assigns = self.output.update_weights + self.output.update_deltas - sess.track([ - self.output.compute_visible, - self.output.compute_hidden, - self.output.compute_err] + [src for _, src in assigns]) - - def fit(self, - data_x, - n_epoches=10, - shuffle=True, - verbose=True): - assert n_epoches > 0 - - n_data = data_x.shape[0] - - if self.n_batch > 0: - n_batches = n_data // self.n_batch + (0 if n_data % self.n_batch == 0 else 1) - else: - n_batches = 1 - - if shuffle: - data_x_cpy = data_x.copy() - inds = np.arange(n_data) - else: - data_x_cpy = data_x - - errs = [] - assigns = self.output.update_weights + self.output.update_deltas - - for e in range(n_epoches): - epoch_errs = np.zeros((n_batches,)) - epoch_errs_ptr = 0 - - if shuffle: - np.random.shuffle(inds) - data_x_cpy = data_x_cpy[inds] - - r_batches = range(n_batches) - - if verbose: - if self.rbm.config.tqdm is not None: - r_batches = self.rbm.config.tqdm(r_batches, - desc='Epoch: {:d}'.format(e), - ascii=True, - file=sys.stdout) - else: - print('Epoch: {:d}'.format(e)) - - for b in r_batches: - batch_x = data_x_cpy[b * self.n_batch:(b + 1) * self.n_batch] - - self.input.x.assign(batch_x) - - self.sess.update_target([src for _, src in assigns], [self.input.x]) - for dest, src in assigns: - dest.assign(src.get()) - self.sess.update_target([self.output.compute_err], [dest for dest, _ in assigns]) - batch_err = self.output.compute_err.get() - - epoch_errs[epoch_errs_ptr] = batch_err - epoch_errs_ptr += 1 - - if verbose: - err_mean = epoch_errs.mean() - if self.rbm.config.tqdm is not None: - self.rbm.config.tqdm.write('Train error: {:.4f}'.format(err_mean)) - self.rbm.config.tqdm.write('') - else: - print('Train error: {:.4f}'.format(err_mean)) - print('') - sys.stdout.flush() - - errs = np.hstack([errs, epoch_errs]) - - return errs - -# ==================== demo ======================= -from tqdm import tqdm -mnist = input_data.read_data_sets('MNIST_data/', one_hot=True) -mnist_images = mnist.train.images - -IMAGE = 1 - -config = RBMConfig( - n_visible=784, - n_hidden=64, - learning_rate=0.01, - momentum=0.95, - tqdm=tqdm) - -rbm = RBM(config, bb_init) -sess = ead.Session() - -padewan = RBMTrainee(rbm=rbm, n_batch=10, sess=sess) - -master_inputs = RBMInput(config.n_visible, config.n_hidden, n_batch=1) -master = rbm(master_inputs) -image = mnist_images[IMAGE] -sess.track([master.compute_visible]) - -sess.optimize("cfg/optimizations.rules") - -errs = padewan.fit(mnist_images, n_epoches=30) -plt.plot(errs) -plt.show() - -def show_digit(x): - plt.imshow(x.reshape((28, 28)), cmap=plt.cm.gray) - plt.show() - -master_inputs.x.assign(image.reshape(1,-1)) -sess.update_target([master.compute_visible], [master_inputs.x]) -image_rec = master.compute_visible.get() - -show_digit(image) -show_digit(image_rec) diff --git a/rocnnet/demo/rbm_demo.py b/rocnnet/demo/rbm_demo.py index 31dec4005..edc497616 100644 --- a/rocnnet/demo/rbm_demo.py +++ b/rocnnet/demo/rbm_demo.py @@ -1,267 +1,191 @@ -from __future__ import print_function - import sys +import time +import random +import argparse +from tensorflow.examples.tutorials.mnist import input_data import matplotlib.pyplot as plt import numpy as np -from tensorflow.examples.tutorials.mnist import input_data -import ead.tenncor as tf +import ead.tenncor as tc import ead.ead as ead import rocnnet.rocnnet as rcn +prog_description = 'Demo rbm_trainer' -def mse_errfunc(x, compute_visible): - return tf.reduce_mean(tf.square(tf.sub(x, compute_visible))) - -class RBMConfig: - def __init__(self, - n_visible, - n_hidden, - learning_rate=0.01, - momentum=0.95, - xavier_const=1.0, - err_function=mse_errfunc, - tqdm=None): - if not 0.0 <= momentum <= 1.0: - raise ValueError('momentum should be in range [0, 1]') - - self.n_visible = n_visible - self.n_hidden = n_hidden - self.learning_rate=learning_rate - self.momentum=momentum - self.xavier_const=xavier_const - self.err_function=err_function - self.tqdm=tqdm - -class RBMInput: - def __init__(self, n_visible, n_hidden, n_batch): - self.x = ead.scalar_variable(0, [n_batch, n_visible]) - self.y = ead.scalar_variable(0, [n_batch, n_hidden]) - -class RBMStorage: - def __init__(self, config): - self.w = rcn.variable_from_init( - rcn.unif_xavier_init(config.xavier_const), - [config.n_visible, config.n_hidden]) - self.visible_bias = ead.scalar_variable(0, [config.n_visible]) - self.hidden_bias = ead.scalar_variable(0, [config.n_hidden]) - - self.delta_w = ead.scalar_variable(0, [config.n_visible, config.n_hidden]) - self.delta_visible_bias = ead.scalar_variable(0, [config.n_visible]) - self.delta_hidden_bias = ead.scalar_variable(0, [config.n_hidden]) - -class RBMOutput: - def __init__(self, update_weights, update_deltas, compute_hidden, compute_visible, compute_visible_from_hidden, compute_err): - self.update_weights = update_weights - self.update_deltas = update_deltas - self.compute_hidden = compute_hidden - self.compute_visible = compute_visible - self.compute_visible_from_hidden = compute_visible_from_hidden - self.compute_err = compute_err - assert self.update_weights is not None - assert self.update_deltas is not None - assert self.compute_hidden is not None - assert self.compute_visible is not None - assert self.compute_visible_from_hidden is not None - assert self.compute_err is not None - -def bb_init(rbm_in, rbm_storage, config): - # hidden = sigmoid(X @ W + hB) - # visible_rec = sigmoid(tf.random.rand_binom_one(hidden) @ W^T + vB) - # hidden_rec = sigmoid(visible_rec @ W + hB) - hidden_p = tf.sigmoid(tf.nn.fully_connect( - [rbm_in.x], [rbm_storage.w], - rbm_storage.hidden_bias)) - visible_recon_p = tf.sigmoid(tf.nn.fully_connect( - [tf.random.rand_binom_one(hidden_p)], [tf.transpose(rbm_storage.w)], - rbm_storage.visible_bias)) - hidden_recon_p = tf.sigmoid(tf.nn.fully_connect( - [visible_recon_p], [rbm_storage.w], - rbm_storage.hidden_bias)) - - # X^T @ sigmoid(X @ W + hB) - sigmoid(tf.random.rand_binom_one(sigmoid(X @ W + hB)) @ W^T + vB)^T @ sigmoid(sigmoid(tf.random.rand_binom_one(hidden) @ W^T + vB) @ W + hB) - grad = tf.sub( - tf.matmul( - tf.transpose(rbm_in.x), - hidden_p), - tf.matmul( - tf.transpose(visible_recon_p), - hidden_recon_p)) - # grad is derivative of ? with respect to rbm_in.w - - def f(x_old, x_new): - return tf.add( - tf.mul( - ead.scalar_constant(config.momentum, x_old.shape()), - x_old), - tf.prod([ - ead.scalar_constant(config.learning_rate, x_new.shape()), - x_new, - ead.scalar_constant((1 - config.momentum) / x_new.shape()[0], x_new.shape()) - ])) - - # momentum - delta_w_new = f(rbm_storage.delta_w, grad) - delta_visible_bias_new = f(rbm_storage.delta_visible_bias, tf.reduce_mean_1d(tf.sub(rbm_in.x, visible_recon_p), 1)) - delta_hidden_bias_new = f(rbm_storage.delta_hidden_bias, tf.reduce_mean_1d(tf.sub(hidden_p, hidden_recon_p), 1)) - - update_delta_w = (rbm_storage.delta_w, delta_w_new) - update_delta_visible_bias = (rbm_storage.delta_visible_bias, delta_visible_bias_new) - update_delta_hidden_bias = (rbm_storage.delta_hidden_bias, delta_hidden_bias_new) - - update_w = (rbm_storage.w, tf.add(rbm_storage.w, delta_w_new)) - update_visible_bias = (rbm_storage.visible_bias, - tf.add(rbm_storage.visible_bias, delta_visible_bias_new)) - update_hidden_bias = (rbm_storage.hidden_bias, - tf.add(rbm_storage.hidden_bias, delta_hidden_bias_new)) - - compute_hidden = tf.sigmoid(tf.nn.fully_connect( - [rbm_in.x], [rbm_storage.w], - rbm_storage.hidden_bias)) - compute_visible = tf.sigmoid(tf.nn.fully_connect( - [compute_hidden], [tf.transpose(rbm_storage.w)], - rbm_storage.visible_bias)) - return RBMOutput( - update_deltas=[update_delta_w, update_delta_visible_bias, update_delta_hidden_bias], - update_weights=[update_w, update_visible_bias, update_hidden_bias], - compute_hidden=compute_hidden, - compute_visible=compute_visible, - compute_visible_from_hidden=tf.sigmoid(tf.nn.fully_connect( - [rbm_in.y], [tf.transpose(rbm_storage.w)], - rbm_storage.visible_bias)), - compute_err=config.err_function(rbm_in.x, compute_visible)) - -class RBM: - def __init__(self, config, initialize): - self.config = config - self.initialize = initialize - self.storage = RBMStorage(config) - - def __call__(self, inputs): - return self.initialize(inputs, self.storage, self.config) - -class RBMTrainee: - def __init__(self, rbm, n_batch, sess): - self.rbm = rbm - self.n_batch = n_batch - self.input = RBMInput(config.n_visible, config.n_hidden, n_batch=n_batch) - self.output = rbm(self.input) - self.sess = sess - - assigns = self.output.update_weights + self.output.update_deltas - sess.track([ - self.output.compute_visible, - self.output.compute_hidden, - self.output.compute_err] + [src for _, src in assigns]) - - def fit(self, - data_x, - n_epoches=10, - shuffle=True, - verbose=True): - assert n_epoches > 0 - - n_data = data_x.shape[0] - - if self.n_batch > 0: - n_batches = n_data // self.n_batch + (0 if n_data % self.n_batch == 0 else 1) - else: - n_batches = 1 - - if shuffle: - data_x_cpy = data_x.copy() - inds = np.arange(n_data) - else: - data_x_cpy = data_x - - errs = [] - assigns = self.output.update_weights + self.output.update_deltas - - for e in range(n_epoches): - epoch_errs = np.zeros((n_batches,)) - epoch_errs_ptr = 0 - - if shuffle: - np.random.shuffle(inds) - data_x_cpy = data_x_cpy[inds] - - r_batches = range(n_batches) - - if verbose: - if self.rbm.config.tqdm is not None: - r_batches = self.rbm.config.tqdm(r_batches, - desc='Epoch: {:d}'.format(e), - ascii=True, - file=sys.stdout) - else: - print('Epoch: {:d}'.format(e)) - - for b in r_batches: - batch_x = data_x_cpy[b * self.n_batch:(b + 1) * self.n_batch] - - self.input.x.assign(batch_x) - - self.sess.update_target([src for _, src in assigns], [self.input.x]) - for dest, src in assigns: - dest.assign(src.get()) - self.sess.update_target([self.output.compute_err], [dest for dest, _ in assigns]) - batch_err = self.output.compute_err.get() - - epoch_errs[epoch_errs_ptr] = batch_err - epoch_errs_ptr += 1 - - if verbose: - err_mean = epoch_errs.mean() - if self.rbm.config.tqdm is not None: - self.rbm.config.tqdm.write('Train error: {:.4f}'.format(err_mean)) - self.rbm.config.tqdm.write('') - else: - print('Train error: {:.4f}'.format(err_mean)) - print('') - sys.stdout.flush() - - errs = np.hstack([errs, epoch_errs]) - - return errs - -# ==================== demo ======================= -from tqdm import tqdm mnist = input_data.read_data_sets('MNIST_data/', one_hot=True) -mnist_images = mnist.train.images - -IMAGE = 1 - -config = RBMConfig( - n_visible=784, - n_hidden=64, - learning_rate=0.01, - momentum=0.95, - tqdm=tqdm) -rbm = RBM(config, bb_init) -sess = ead.Session() +def mse_errfunc(x, visible_sample_): + return tc.reduce_mean(tc.square(tc.sub(x, visible_sample_))) -padewan = RBMTrainee(rbm=rbm, n_batch=10, sess=sess) - -master_inputs = RBMInput(config.n_visible, config.n_hidden, n_batch=1) -master = rbm(master_inputs) -image = mnist_images[IMAGE] -sess.track([master.compute_visible]) - -sess.optimize("cfg/optimizations.rules") - -errs = padewan.fit(mnist_images, n_epoches=30) -plt.plot(errs) -plt.show() - -def show_digit(x): +def show_digit(x, plt): plt.imshow(x.reshape((28, 28)), cmap=plt.cm.gray) plt.show() -master_inputs.x.assign(image.reshape(1,-1)) -sess.update_target([master.compute_visible], [master_inputs.x]) -image_rec = master.compute_visible.get() +def str2bool(opt): + optstr = opt.lower() + if optstr in ('yes', 'true', 't', 'y', '1'): + return True + elif optstr in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + +def main(args): + + default_ts = time.time() + + parser = argparse.ArgumentParser(description=prog_description) + parser.add_argument('--seed', dest='seed', + type=str2bool, nargs='?', const=False, default=True, + help='Whether to seed or not (default: True)') + parser.add_argument('--seedval', dest='seedval', type=int, nargs='?', default=int(default_ts), + help='Random seed value (default: )') + parser.add_argument('--init_const', dest='xavier_const', type=float, nargs='?', default=1.0, + help='Xavier constant for initializing weight (default: 1.0)') + parser.add_argument('--use_tqdm', dest='use_tqdm', + type=str2bool, nargs='?', const=False, default=True, + help='Whether to use tqdm (default: 100)') + parser.add_argument('--save', dest='save', nargs='?', default='', + help='Filename to save model (default: )') + parser.add_argument('--load', dest='load', nargs='?', default='models/rbmmodel.pbx', + help='Filename to load pretrained model (default: models/rbmmodel.pbx)') + args = parser.parse_args(args) + + if args.seed: + print('seeding {}'.format(args.seedval)) + ead.seed(args.seedval) + np.random.seed(args.seedval) + + if args.use_tqdm: + from tqdm import tqdm + tq = tqdm + else: + tq = None + + mnist_images = mnist.train.images + + n_visible = 784 + n_hidden = 64 + learning_rate = 0.01 + momentum = 0.95 + + model = rcn.RBM(n_hidden, n_visible, + weight_init=rcn.unif_xavier_init(args.xavier_const), + bias_init=rcn.zero_init(), + label="demo") + + untrained = model.clone() + try: + print('loading ' + args.load) + trained = rcn.load_file_rbmmodel(args.load, "demo") + print('successfully loaded from ' + args.load) + except Exception as e: + print(e) + print('failed to load from "{}"'.format(args.load)) + trained = model.clone() + + sess = ead.Session() + batch_size = 10 + + trainer = rcn.BernoulliRBMTrainer( + model=model, + sess=sess, + batch_size=batch_size, + learning_rate=learning_rate, + discount_factor=momentum, + err_func=mse_errfunc) + + x = ead.scalar_variable(0, [1, n_visible]) + genx = model.backward_connect( + tc.random.rand_binom_one(model.connect(x))) + + untrained_genx = untrained.backward_connect( + tc.random.rand_binom_one(untrained.connect(x))) + + trained_genx = trained.backward_connect( + tc.random.rand_binom_one(trained.connect(x))) + + image = random.choice(mnist_images) + sess.track([genx, trained_genx, untrained_genx]) + + sess.optimize("cfg/optimizations.rules") + + n_epoches = 30 + shuffle = True + verbose = True + n_data = mnist_images.shape[0] + + if batch_size > 0: + n_batches = n_data // batch_size + (0 if n_data % batch_size == 0 else 1) + else: + n_batches = 1 + + if shuffle: + data_x_cpy = mnist_images.copy() + inds = np.arange(n_data) + else: + data_x_cpy = mnist_images + + errs = [] + for e in range(n_epoches): + epoch_errs = np.zeros((n_batches,)) + epoch_errs_ptr = 0 + + if shuffle: + np.random.shuffle(inds) + data_x_cpy = data_x_cpy[inds] + + r_batches = range(n_batches) + + if verbose: + if tq is not None: + r_batches = tq(r_batches, + desc='Epoch: {:d}'.format(e), + ascii=True, + file=sys.stdout) + else: + print('Epoch: {:d}'.format(e)) + + for b in r_batches: + batch_x = data_x_cpy[b * batch_size:(b + 1) * batch_size] + + epoch_errs[epoch_errs_ptr] = trainer.train(batch_x) + epoch_errs_ptr += 1 + + if verbose: + err_mean = epoch_errs.mean() + if tq is not None: + tq.write('Train error: {:.4f}'.format(err_mean)) + tq.write('') + else: + print('Train error: {:.4f}'.format(err_mean)) + print('') + sys.stdout.flush() + + errs = np.hstack([errs, epoch_errs]) + + plt.plot(errs) + plt.show() -show_digit(image) -show_digit(image_rec) + x.assign(image.reshape(1,-1)) + sess.update_target([genx, trained_genx, untrained_genx], [x]) + image_rec = genx.get() + image_rec_trained = trained_genx.get() + image_rec_untrained = untrained_genx.get() + + show_digit(image, plt) + show_digit(image_rec, plt) + show_digit(image_rec_trained, plt) + show_digit(image_rec_untrained, plt) + + try: + print('saving') + if model.save_file(args.save): + print('successfully saved to {}'.format(args.save)) + except Exception as e: + print(e) + print('failed to write to "{}"'.format(args.save)) + +if '__main__' == __name__: + main(sys.argv[1:]) diff --git a/rocnnet/demo/tf_gd_demo.py b/rocnnet/demo/tf_gd_demo.py deleted file mode 100644 index 9d254552a..000000000 --- a/rocnnet/demo/tf_gd_demo.py +++ /dev/null @@ -1,229 +0,0 @@ -import sys -import time -import argparse -import math - -import numpy as np - -import tensorflow as tf - -prog_description = 'Demo mlp_trainer using sgd' - -def batch_generate(n, batchsize): - total = n * batchsize - return np.random.rand(total) - -def avgevry2(indata): - return (indata[0::2] + indata[1::2]) / 2 - -def str2bool(opt): - optstr = opt.lower() - if optstr in ('yes', 'true', 't', 'y', '1'): - return True - elif optstr in ('no', 'false', 'f', 'n', '0'): - return False - else: - raise argparse.ArgumentTypeError('Boolean value expected.') - -def base_name(var): - """Extracts value passed to name= when creating a variable""" - return var.name.split('/')[-1].split(':')[0] - -class Layer(object): - def __init__(self, input_sizes, output_size, scope): - """Cretes a neural network layer.""" - if type(input_sizes) != list: - input_sizes = [input_sizes] - - self.input_sizes = input_sizes - self.output_size = output_size - self.scope = scope or "Layer" - - with tf.variable_scope(self.scope): - self.Ws = [] - for input_idx, input_size in enumerate(input_sizes): - W_name = "W_%d" % (input_idx,) - W_initializer = tf.random_uniform_initializer( - -1.0 / math.sqrt(input_size), 1.0 / math.sqrt(input_size)) - W_var = tf.get_variable(W_name, (input_size, output_size), initializer=W_initializer) - self.Ws.append(W_var) - self.b = tf.get_variable("b", (output_size,), initializer=tf.constant_initializer(0)) - - def __call__(self, xs): - if type(xs) != list: - xs = [xs] - assert len(xs) == len(self.Ws), \ - "Expected %d input vectors, got %d" % (len(self.Ws), len(xs)) - with tf.variable_scope(self.scope): - return sum([tf.matmul(x, W) for x, W in zip(xs, self.Ws)]) + self.b - - def variables(self): - return [self.b] + self.Ws - - def copy(self, scope=None): - scope = scope or self.scope + "_copy" - - with tf.variable_scope(scope) as sc: - for v in self.variables(): - tf.get_variable(base_name(v), v.get_shape(), - initializer=lambda x,dtype=tf.float32,partition_info=None: v.initialized_value()) - sc.reuse_variables() - return Layer(self.input_sizes, self.output_size, scope=sc) - -class MLP(object): - def __init__(self, input_sizes, hiddens, nonlinearities, scope=None, given_layers=None): - self.input_sizes = input_sizes - self.hiddens = hiddens - self.input_nonlinearity, self.layer_nonlinearities = nonlinearities[0], nonlinearities[1:] - self.scope = scope or "MLP" - - assert len(hiddens) == len(nonlinearities), \ - "Number of hiddens must be equal to number of nonlinearities" - - with tf.variable_scope(self.scope): - if given_layers is not None: - self.input_layer = given_layers[0] - self.layers = given_layers[1:] - else: - self.input_layer = Layer(input_sizes, hiddens[0], scope="input_layer") - self.layers = [] - - for l_idx, (h_from, h_to) in enumerate(zip(hiddens[:-1], hiddens[1:])): - self.layers.append(Layer(h_from, h_to, scope="hidden_layer_%d" % (l_idx,))) - - def __call__(self, xs): - if type(xs) != list: - xs = [xs] - with tf.variable_scope(self.scope): - hidden = self.input_nonlinearity(self.input_layer(xs)) - for layer, nonlinearity in zip(self.layers, self.layer_nonlinearities): - hidden = nonlinearity(layer(hidden)) - return hidden - - def variables(self): - res = self.input_layer.variables() - for layer in self.layers: - res.extend(layer.variables()) - return res - - def copy(self, scope=None): - scope = scope or self.scope + "_copy" - nonlinearities = [self.input_nonlinearity] + self.layer_nonlinearities - given_layers = [self.input_layer.copy()] + [layer.copy() for layer in self.layers] - return MLP(self.input_sizes, self.hiddens, nonlinearities, scope=scope, - given_layers=given_layers) - -def main(args): - - default_ts = time.time() - - parser = argparse.ArgumentParser(description=prog_description) - parser.add_argument('--seed', dest='seed', - type=str2bool, nargs='?', const=False, default=True, - help='Whether to seed or not (default: True)') - parser.add_argument('--seedval', dest='seedval', type=int, nargs='?', default=int(default_ts), - help='Random seed value (default: )') - parser.add_argument('--n_batch', dest='n_batch', type=int, nargs='?', default=3, - help='Batch size when training (default: 3)') - parser.add_argument('--n_train', dest='n_train', type=int, nargs='?', default=3000, - help='Number of times to train (default: 3000)') - parser.add_argument('--n_test', dest='n_test', type=int, nargs='?', default=500, - help='Number of times to test (default: 500)') - args = parser.parse_args(args) - - if args.seed: - print('seeding {}'.format(args.seedval)) - np.random.seed(args.seedval) - - learning_rate = 0.9 - - n_in = 10 - n_out = n_in / 2 - - brain = MLP([n_in], [9, n_out], [tf.sigmoid, tf.sigmoid]) - - sess = tf.Session() - n_batch = args.n_batch - show_every_n = 500 - - trained_in = tf.placeholder(tf.float32, [None, n_in], name='trained_in') - trained_out = brain(trained_in) - expected_out = tf.placeholder(tf.float32, [n_batch, n_out], name='expected_out') - - error = tf.square(expected_out - trained_out) - - layer0 = brain.input_layer - layer1 = brain.layers[0] - - w0 = layer0.Ws[0] - b0 = layer0.b - w1 = layer1.Ws[0] - b1 = layer1.b - - dw0, db0, dw1, db1 = tf.gradients(error, [w0, b0, w1, b1]) - - w0_update = w0.assign_sub(learning_rate * dw0) - b0_update = b0.assign_sub(learning_rate * db0) - w1_update = w1.assign_sub(learning_rate * dw1) - b1_update = b1.assign_sub(learning_rate * db1) - - def calculate_update(batch, batch_out): - out, _, _, _, _ = sess.run([ - error, - w0_update, - b0_update, - w1_update, - b1_update, - ], { - trained_in: batch, - expected_out: batch_out, - }) - - return out - - # optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) - # gradients = optimizer.compute_gradients(error) - # train_op = optimizer.apply_gradients(gradients) - - sess.run(tf.global_variables_initializer()) - start = time.time() - for i in range(args.n_train): - batch = batch_generate(n_in, n_batch) - batch_out = avgevry2(batch) - batch = batch.reshape([n_batch, n_in]) - batch_out = batch_out.reshape([n_batch, n_out]) - # trained_derr, _ = sess.run([ - # error, - # train_op, - # ], { - # trained_in: batch, - # expected_out: batch_out - # }) - trained_derr = calculate_update(batch, batch_out) - if i % show_every_n == show_every_n - 1: - print('training {}\ntraining error:\n{}' - .format(i + 1, trained_derr)) - - print('training time: {} seconds'.format(time.time() - start)) - - trained_err = 0 - for i in range(args.n_test): - if i % show_every_n == show_every_n - 1: - print('testing {}'.format(i + 1)) - - test_batch = batch_generate(n_in, 1) - test_batch_out = avgevry2(test_batch) - test_batch = test_batch.reshape([1, n_in]) - test_batch_out = test_batch_out.reshape([1, n_out]) - - trained_data = sess.run(trained_out, { - trained_in: test_batch - }) - - trained_err += np.mean(abs(trained_data - test_batch_out)) - - trained_err /= args.n_test - print('trained mlp error rate: {}%'.format(trained_err * 100)) - -if '__main__' == __name__: - main(sys.argv[1:]) diff --git a/rocnnet/eqns/err_approx.hpp b/rocnnet/eqns/err_approx.hpp index 1bd25ec13..1166d7f68 100644 --- a/rocnnet/eqns/err_approx.hpp +++ b/rocnnet/eqns/err_approx.hpp @@ -12,7 +12,7 @@ namespace eqns { -using VariablesT = std::vector>; +using VarErrsT = std::vector,ead::NodeptrT>>; struct VarAssign { @@ -28,26 +28,32 @@ using AssignsT = std::list; using AssignGroupsT = std::list; // approximate error of sources given error of root -using ApproxF = std::function&,VariablesT)>; +using ApproxF = std::function; -using UpdateStepF = std::function&)>; +using UpdateStepF = std::function; using NodeUnarF = std::function(ead::NodeptrT)>; ead::NodeptrT identity (ead::NodeptrT node); // Stochastic Gradient Descent Approximation -AssignGroupsT sgd (ead::NodeptrT& root, VariablesT leaves, - PybindT learning_rate = 0.5, - NodeUnarF gradprocess = identity, - std::string root_label = ""); +// for each (x, err) in leaves +// x_next ~ x_curr - η * err, +// +// where η is the learning rate +AssignGroupsT sgd (const VarErrsT& leaves, + PybindT learning_rate = 0.5, std::string root_label = ""); // Momentum-based Root Mean Square Approximation -AssignGroupsT rms_momentum (ead::NodeptrT& root, VariablesT leaves, - PybindT learning_rate = 0.5, - PybindT discount_factor = 0.99, +// for each (x, err) in leaves +// momentum_next ~ χ * momentum_cur + (1 - χ) * err ^ 2 +// x_next ~ x_curr - (η * err) / (sqrt(ε + momentum_next)) +// +// where η is the learning rate, ε is epsilon, +// and χ is discount_factor +AssignGroupsT rms_momentum (const VarErrsT& leaves, + PybindT learning_rate = 0.5, PybindT discount_factor = 0.99, PybindT epsilon = std::numeric_limits::epsilon(), - NodeUnarF gradprocess = identity, std::string root_label = ""); void assign_groups (AssignGroupsT& groups, UpdateStepF update_step); diff --git a/rocnnet/eqns/init.hpp b/rocnnet/eqns/init.hpp index bbdd6fd08..bd759dc28 100644 --- a/rocnnet/eqns/init.hpp +++ b/rocnnet/eqns/init.hpp @@ -58,6 +58,16 @@ void truncated_normal (std::vector& out, ade::Shape shape, T mean, T stdev) } } +template +InitF zero_init (void) +{ + return + [](ade::Shape shape, std::string label) + { + return ead::make_variable_scalar(0, shape, label); + }; +} + template InitF variance_scaling_init (T factor, ShapeFactorF sfactor=fanavg) { diff --git a/rocnnet/eqns/src/err_approx.cpp b/rocnnet/eqns/src/err_approx.cpp index df3965853..f374942f7 100755 --- a/rocnnet/eqns/src/err_approx.cpp +++ b/rocnnet/eqns/src/err_approx.cpp @@ -1,7 +1,5 @@ #include "ead/generated/api.hpp" -#include "ead/grader.hpp" - #include "rocnnet/eqns/err_approx.hpp" #ifdef EQNS_ERR_APPROX_HPP @@ -14,71 +12,64 @@ ead::NodeptrT identity (ead::NodeptrT node) return node; } -AssignGroupsT sgd (ead::NodeptrT& root, VariablesT leaves, - PybindT learning_rate, NodeUnarF gradprocess, std::string root_label) +AssignGroupsT sgd (const VarErrsT& leaves, + PybindT learning_rate, std::string root_label) { AssignsT assignments; for (size_t i = 0, nleaves = leaves.size(); i < nleaves; ++i) { - auto leaf_node = ead::convert_to_node(leaves[i]); - auto grad = ead::derive(root, leaf_node); - // given root = f, err(x) ~ x - η * df(x), where η is the learning rate - ade::Shape gshape = grad->shape(); + auto leaf_node = ead::convert_to_node(leaves[i].first); + auto err = leaves[i].second; + ade::Shape eshape = err->shape(); auto next = tenncor::sub(leaf_node, - tenncor::mul(gradprocess(grad), - ead::make_constant_scalar(learning_rate, gshape))); + tenncor::mul(err, + ead::make_constant_scalar(learning_rate, eshape))); assignments.push_back(VarAssign{ fmts::sprintf("sgd::%s_grad_%s", - root_label.c_str(), leaves[i]->get_label().c_str()), - leaves[i], next}); + root_label.c_str(), leaves[i].first->get_label().c_str()), + leaves[i].first, next}); } return {assignments}; } -AssignGroupsT rms_momentum (ead::NodeptrT& root, VariablesT leaves, - PybindT learning_rate, PybindT discount_factor, PybindT epsilon, - NodeUnarF gradprocess, std::string root_label) +AssignGroupsT rms_momentum (const VarErrsT& leaves, PybindT learning_rate, + PybindT discount_factor, PybindT epsilon, std::string root_label) { // assign momentums before leaves AssignsT momentum_assigns; AssignsT leaf_assigns; for (size_t i = 0, nleaves = leaves.size(); i < nleaves; ++i) { - auto leaf_node = ead::convert_to_node(leaves[i]); - auto grad = ead::derive(root, leaf_node); - // next_x ~ x - (η * df(x)) / (sqrt(ε + momentum_next)) - // momentum_next ~ χ * momentum_prev + (1 - χ) * df(x) ^ 2 - // - // where root = f, η is the learning rate, ε is epsilon, - // and χ is discount_factor - ade::Shape gshape = grad->shape(); + auto leaf_node = ead::convert_to_node(leaves[i].first); + auto err = leaves[i].second; + ade::Shape eshape = err->shape(); ead::VarptrT momentum = - ead::make_variable_scalar(1, gshape, "momentum"); + ead::make_variable_scalar(1, eshape, "momentum"); auto momentum_node = ead::convert_to_node(momentum); ead::NodeptrT discount_node = - ead::make_constant_scalar(discount_factor, gshape); + ead::make_constant_scalar(discount_factor, eshape); ead::NodeptrT datcount_node = - ead::make_constant_scalar(1.0 - discount_factor, gshape); + ead::make_constant_scalar(1.0 - discount_factor, eshape); auto momentum_next = tenncor::add( tenncor::mul(discount_node, momentum_node), - tenncor::mul(datcount_node, tenncor::square(grad)) + tenncor::mul(datcount_node, tenncor::square(err)) ); auto leaf_next = tenncor::sub(leaf_node, tenncor::div( - tenncor::mul(gradprocess(grad), - ead::make_constant_scalar(learning_rate, gshape)), + tenncor::mul(err, + ead::make_constant_scalar(learning_rate, eshape)), tenncor::add(tenncor::sqrt(momentum_node), - ead::make_constant_scalar(epsilon, gshape)) + ead::make_constant_scalar(epsilon, eshape)) )); momentum_assigns.push_back(VarAssign{ fmts::sprintf("rms_momentum::%s_momentum_%s", - root_label.c_str(), leaves[i]->get_label().c_str()), + root_label.c_str(), leaves[i].first->get_label().c_str()), momentum, momentum_next}); leaf_assigns.push_back(VarAssign{ fmts::sprintf("rms_momentum::%s_grad_%s", - root_label.c_str(), leaves[i]->get_label().c_str()), - leaves[i], leaf_next}); + root_label.c_str(), leaves[i].first->get_label().c_str()), + leaves[i].first, leaf_next}); } return {momentum_assigns, leaf_assigns}; } @@ -87,7 +78,7 @@ void assign_groups (AssignGroupsT& groups, UpdateStepF update_step) { for (AssignsT& group : groups) { - std::unordered_set updated_var; + ead::TensSetT updated_var; for (eqns::VarAssign& assign : group) { updated_var.emplace(assign.target_->get_tensor().get()); diff --git a/rocnnet/modl/BUILD.bazel b/rocnnet/modl/BUILD.bazel index 1df71d5ff..abc815a37 100644 --- a/rocnnet/modl/BUILD.bazel +++ b/rocnnet/modl/BUILD.bazel @@ -19,5 +19,5 @@ cc_library( hdrs = glob(["*.hpp"]), srcs = glob(["src/*.cpp"]), copts = ["-std=c++17"], - deps = ["//ead:ead"], + deps = ["//ead:ead","//rocnnet/eqns:eqns"], ) diff --git a/rocnnet/modl/activations.hpp b/rocnnet/modl/activations.hpp new file mode 100644 index 000000000..66bd242fe --- /dev/null +++ b/rocnnet/modl/activations.hpp @@ -0,0 +1,147 @@ +#include "ead/generated/api.hpp" + +#include "rocnnet/modl/layer.hpp" + +#ifndef MODL_ACTIVATIONS_HPP +#define MODL_ACTIVATIONS_HPP + +namespace modl +{ + +struct ActivationBuilder final : public iLayerBuilder +{ + ActivationBuilder (std::string act_type, std::string label) : + act_type_(act_type), label_(label) {} + + void set_tensor (ade::TensptrT tens, std::string target) override {} + + void set_sublayer (LayerptrT layer) override {} // activation has no sublayer + + LayerptrT build (void) const override; + +private: + std::string act_type_; + + std::string label_; +}; + +const std::string sigmoid_layer_key = +get_layer_reg().register_tagr(layers_key_prefix + "sigmoid", +[](ade::TensrefT ref, std::string label) +{ + get_layer_reg().layer_tag(ref, sigmoid_layer_key, label); +}, +[](std::string label) -> LBuilderptrT +{ + return std::make_shared(sigmoid_layer_key, label); +}); + +const std::string tanh_layer_key = +get_layer_reg().register_tagr(layers_key_prefix + "tanh", +[](ade::TensrefT ref, std::string label) +{ + get_layer_reg().layer_tag(ref, tanh_layer_key, label); +}, +[](std::string label) -> LBuilderptrT +{ + return std::make_shared(tanh_layer_key, label); +}); + +const std::unordered_map activations = +{ + {sigmoid_layer_key, tenncor::sigmoid}, + {tanh_layer_key, tenncor::tanh}, +}; + +struct Activation final : public iLayer +{ + Activation (const std::string& label, const std::string& act_type) : + label_(label), + act_type_(act_type), + activation_(estd::must_getf(activations, act_type, + "failed to find activation `%s`", act_type.c_str())), + placeholder_(ead::make_constant_scalar(0, {})) + { + tag(placeholder_->get_tensor(), LayerId()); + } + + Activation (const Activation& other, + std::string label_prefix = "") : + label_(label_prefix + other.get_label()), + act_type_(other.act_type_), + activation_(other.activation_), + placeholder_(ead::make_constant_scalar(0, {})) + { + tag(placeholder_->get_tensor(), LayerId()); + } + + Activation& operator = (const Activation& other) = default; + + Activation (Activation&& other) = default; + + Activation& operator = (Activation&& other) = default; + + + Activation* clone (std::string label_prefix = "") const + { + return static_cast(this->clone_impl(label_prefix)); + } + + size_t get_ninput (void) const override + { + return 0; + } + + size_t get_noutput (void) const override + { + return 0; + } + + std::string get_ltype (void) const override + { + return act_type_; + } + + std::string get_label (void) const override + { + return label_; + } + + ead::NodeptrT connect (ead::NodeptrT input) const override + { + auto out = activation_(input); + recursive_tag(out->get_tensor(), { + input->get_tensor().get(), + }, LayerId()); + return out; + } + + ade::TensT get_contents (void) const override + { + return {placeholder_->get_tensor()}; + } + +private: + iLayer* clone_impl (std::string label_prefix) const override + { + return new Activation(*this, label_prefix); + } + + std::string label_; + + std::string act_type_; + + NonLinearF activation_; + + ead::NodeptrT placeholder_; +}; + +using ActivationptrT = std::shared_ptr; + +LayerptrT sigmoid (std::string label = "sigmoid"); + +LayerptrT tanh (std::string label = "tanh"); + +} + +#endif // MODL_ACTIVATIONS_HPP diff --git a/rocnnet/modl/dense.hpp b/rocnnet/modl/dense.hpp new file mode 100644 index 000000000..0aa018c24 --- /dev/null +++ b/rocnnet/modl/dense.hpp @@ -0,0 +1,192 @@ +#include "ead/generated/api.hpp" + +#include "rocnnet/eqns/init.hpp" + +#include "rocnnet/modl/layer.hpp" + +#ifndef MODL_DENSE_HPP +#define MODL_DENSE_HPP + +namespace modl +{ + +const std::string weight_key = "weight"; + +const std::string bias_key = "bias"; + +struct DenseBuilder final : public iLayerBuilder +{ + DenseBuilder (std::string label) : label_(label) {} + + void set_tensor (ade::TensptrT tens, std::string target) override + { + if (target == weight_key) + { + weight_ = ead::NodeConverters::to_node(tens); + return; + } + else if (target == bias_key) + { + bias_ = ead::NodeConverters::to_node(tens); + return; + } + logs::warnf("attempt to create dense layer " + "with unknown tensor `%s` with label `%s`", + tens->to_string().c_str(), target.c_str()); + } + + void set_sublayer (LayerptrT layer) override {} // dense has no sublayer + + LayerptrT build (void) const override; + +private: + ead::NodeptrT weight_ = nullptr; + + ead::NodeptrT bias_ = nullptr; + + std::string label_; +}; + +const std::string dense_layer_key = +get_layer_reg().register_tagr(layers_key_prefix + "dense", +[](ade::TensrefT ref, std::string label) +{ + get_layer_reg().layer_tag(ref, dense_layer_key, label); +}, +[](std::string label) -> LBuilderptrT +{ + return std::make_shared(label); +}); + +struct Dense final : public iLayer +{ + Dense (ade::DimT nunits, ade::DimT indim, + eqns::InitF weight_init, + eqns::InitF bias_init, + const std::string& label) : + label_(label), + weight_(weight_init(ade::Shape({nunits, indim}), weight_key)) + { + tag(weight_->get_tensor(), LayerId(weight_key)); + if (bias_init) + { + bias_ = bias_init(ade::Shape({nunits}), bias_key); + tag(bias_->get_tensor(), LayerId(bias_key)); + } + } + + Dense (ead::NodeptrT weight, + ead::NodeptrT bias, + std::string label) : + label_(label), + weight_(weight), + bias_(bias) + { + tag(weight_->get_tensor(), LayerId(weight_key)); + if (bias) + { + tag(bias_->get_tensor(), LayerId(bias_key)); + } + } + + Dense (const Dense& other, + std::string label_prefix = "") + { + copy_helper(other, label_prefix); + } + + Dense& operator = (const Dense& other) + { + if (this != &other) + { + copy_helper(other); + } + return *this; + } + + Dense (Dense&& other) = default; + + Dense& operator = (Dense&& other) = default; + + Dense* clone (std::string label_prefix = "") const + { + return static_cast(this->clone_impl(label_prefix)); + } + + size_t get_ninput (void) const override + { + return weight_->shape().at(1); + } + + size_t get_noutput (void) const override + { + return weight_->shape().at(0); + } + + std::string get_ltype (void) const override + { + return dense_layer_key; + } + + std::string get_label (void) const override + { + return label_; + } + + ead::NodeptrT connect (ead::NodeptrT input) const override + { + auto out = tenncor::nn::fully_connect({input}, {weight_}, bias_); + recursive_tag(out->get_tensor(), { + input->get_tensor().get(), + weight_->get_tensor().get(), + bias_->get_tensor().get(), + }, LayerId()); + return out; + } + + ade::TensT get_contents (void) const override + { + return { + weight_->get_tensor(), + nullptr == bias_ ? nullptr : bias_->get_tensor(), + }; + } + +private: + iLayer* clone_impl (std::string label_prefix) const override + { + return new Dense(*this, label_prefix); + } + + void copy_helper (const Dense& other, std::string label_prefix = "") + { + label_ = label_prefix + other.label_; + weight_ = std::make_shared>( + std::shared_ptr>( + ead::Variable::get( + *static_cast*>( + other.weight_->get_tensor().get())))); + tag(weight_->get_tensor(), LayerId(weight_key)); + if (other.bias_) + { + bias_ = std::make_shared>( + std::shared_ptr>( + ead::Variable::get( + *static_cast*>( + other.bias_->get_tensor().get())))); + tag(bias_->get_tensor(), LayerId(bias_key)); + } + } + + std::string label_; + + ead::NodeptrT weight_; + + ead::NodeptrT bias_; +}; + +using DenseptrT = std::shared_ptr; + +} + +#endif // MODL_DENSE_HPP diff --git a/rocnnet/modl/layer.hpp b/rocnnet/modl/layer.hpp new file mode 100644 index 000000000..0c80f7c93 --- /dev/null +++ b/rocnnet/modl/layer.hpp @@ -0,0 +1,196 @@ +#include "estd/estd.hpp" + +#include "tag/tag.hpp" + +#include "ead/constant.hpp" +#include "ead/variable.hpp" + +#include "ead/generated/pyapi.hpp" + +#ifndef MODL_LAYER_HPP +#define MODL_LAYER_HPP + +namespace modl +{ + +using NonLinearF = std::function(ead::NodeptrT)>; + +const std::string layers_key_prefix = "layer_"; + +const char llabel_sep = ':'; + +void validate_label (const std::string& label); + +struct LayerId final +{ + LayerId (void) = default; + + LayerId (std::string label) : label_(label) {} + + LayerId (std::string type, std::string label, size_t index) : + type_(type), label_(label), index_(index) {} + + std::string to_string (std::string label) const + { + return fmts::sprintf("%s%c%s%c%s%c%d", + label.c_str(), llabel_sep, + type_.c_str(), llabel_sep, + label_.c_str(), llabel_sep, + index_); + } + + std::string type_; + + std::string label_; + + size_t index_ = 0; +}; + +using LayerIdsT = std::vector; + +std::string layer_label_fmt (std::string label, LayerId subid); + +std::unordered_map unpack_labels ( + const std::vector& labels); + +struct LayerTag final : public tag::iTag +{ + LayerTag (std::string layer_type, std::string name) : + reps_({{layer_type, {name}}}) {} + + size_t tag_id (void) const override + { + return tag_id_; + } + + void absorb (tag::TagptrT&& other) override + { + LayersT& oreps = + static_cast(other.get())->reps_; + for (auto& reppair : oreps) + { + auto& names = reps_[reppair.first]; + names.insert(reppair.second.begin(), reppair.second.end()); + } + } + + tag::TagRepsT get_tags (void) const override + { + tag::TagRepsT out; + for (auto& layer : reps_) + { + out.emplace(layer.first, std::vector( + layer.second.begin(), layer.second.end())); + } + return out; + } + +private: + using LayersT = std::map>; + + LayersT reps_; + + static size_t tag_id_; +}; + +struct iLayer +{ + virtual ~iLayer (void) = default; + + iLayer* clone (std::string label_prefix = "") const + { + return this->clone_impl(label_prefix); + } + + virtual size_t get_ninput (void) const = 0; + + virtual size_t get_noutput (void) const = 0; + + virtual std::string get_ltype (void) const = 0; + + virtual std::string get_label (void) const = 0; + + virtual ead::NodeptrT connect ( + ead::NodeptrT input) const = 0; + + virtual ade::TensT get_contents (void) const = 0; + +protected: + virtual iLayer* clone_impl (std::string label_prefix) const = 0; + + void tag (ade::TensptrT tensor, LayerId subs) const; + + void recursive_tag (ade::TensptrT root, + std::unordered_set ignores, LayerId subs) const; +}; + +using LayerptrT = std::shared_ptr; + +struct iLayerBuilder +{ + virtual ~iLayerBuilder (void) = default; + + virtual void set_tensor (ade::TensptrT tens, std::string target) = 0; + + virtual void set_sublayer (LayerptrT layer) = 0; + + virtual LayerptrT build (void) const = 0; +}; + +using LBuilderptrT = std::shared_ptr; + +using LayerBuildF = std::function; + +struct LayerRegistry final +{ + LayerRegistry (tag::TagRegistry& registry = tag::get_reg()) : tag_reg_(registry) {} + + void layer_tag (ade::TensrefT tens, std::string layer_type, std::string name) + { + tag_reg_.add_tag(tens, tag::TagptrT(new LayerTag(layer_type, name))); + } + + std::string register_tagr (std::string key, tag::TagrF tagr, LayerBuildF builder) + { + lbuilders_.emplace(key, builder); + + return tag_reg_.register_tagr(key, + [this, key](ade::TensrefT ref, std::string label) + { + this->layer_tag(ref, key, label); + }); + } + + LayerBuildF get_builder (std::string layer_type) + { + return estd::must_getf(lbuilders_, layer_type, + "failed to find registered layer `%s`", layer_type.c_str()); + } + + tag::TagRegistry& get_tag_registry (void) + { + return tag_reg_; + } + +private: + std::unordered_map lbuilders_; + + tag::TagRegistry& tag_reg_; +}; + +LayerRegistry& get_layer_reg (void); + +void recursive_layer_tag (ade::TensptrT tens, std::string layer_type, + std::string name, std::unordered_set stops, + LayerRegistry& registry = get_layer_reg()); + +LayerptrT load_layer (std::istream& ins, ade::TensT& roots, + std::string ltype, std::string label, + LayerRegistry& registry = get_layer_reg()); + +bool save_layer (std::ostream& outs, const iLayer& layer, ade::TensT roots, + LayerRegistry& registry = get_layer_reg()); + +} + +#endif // MODL_LAYER_HPP diff --git a/rocnnet/modl/marshal.hpp b/rocnnet/modl/marshal.hpp deleted file mode 100644 index 7a1f33bef..000000000 --- a/rocnnet/modl/marshal.hpp +++ /dev/null @@ -1,227 +0,0 @@ -#include - -#include "pbm/save.hpp" -#include "pbm/load.hpp" - -#include "ead/constant.hpp" -#include "ead/variable.hpp" - -#include "ead/serialize.hpp" - -#include "ead/generated/pyapi.hpp" - -#ifndef MODL_MARSHAL_HPP -#define MODL_MARSHAL_HPP - -namespace modl -{ - -// iMarshaler is an interface for models to label -// and serialize variables -struct iMarshaler -{ - virtual ~iMarshaler (void) = default; - - iMarshaler* clone (void) const - { - return clone_impl(); - } - - virtual std::string get_label (void) const = 0; - - virtual pbm::PathedMapT list_bases (void) const = 0; - - /// Return all variables labelled specific to this model - virtual pbm::PathedTens* get_variables (void) const = 0; - - /// Set all the variables associated with this label in storage - virtual void set_variables (pbm::PathedTens* storage) = 0; - -protected: - virtual iMarshaler* clone_impl (void) const = 0; -}; - -using MarsptrT = std::shared_ptr; - -using MarsarrT = std::vector; - -struct iMarshalSet : public iMarshaler -{ - iMarshalSet (std::string label) : label_(label) {} - - iMarshalSet (const iMarshalSet& other) = default; - - iMarshalSet& operator = (const iMarshalSet& other) = default; - - iMarshalSet (iMarshalSet&& other) = default; - - iMarshalSet& operator = (iMarshalSet&& other) = default; - - virtual ~iMarshalSet (void) = default; - - std::string get_label (void) const override - { - return label_; - } - - pbm::PathedMapT list_bases (void) const override - { - pbm::PathedMapT out; - auto marshalers = get_subs(); - for (MarsptrT& marshal : marshalers) - { - auto temp = marshal->list_bases(); - for (auto& bpairs : temp) - { - bpairs.second.push_front(marshal->get_label()); - } - out.insert(temp.begin(), temp.end()); - } - return out; - } - - pbm::PathedTens* get_variables (void) const override - { - auto out = new pbm::PathedTens(); - auto marshalers = get_subs(); - for (MarsptrT& marshal : marshalers) - { - std::string child_label = marshal->get_label(); - auto child = marshal->get_variables(); - auto it = out->children_.find(child_label); - if (out->children_.end() != it) - { - it->second->join(child); - delete child; - } - else - { - out->children_.emplace(child_label, child); - } - } - return out; - } - - void set_variables (pbm::PathedTens* storage) override - { - auto marshalers = get_subs(); - for (MarsptrT& marshal : marshalers) - { - std::string child_label = marshal->get_label(); - auto it = storage->children_.find(child_label); - if (storage->children_.end() == it) - { - logs::warnf("label %s not found", child_label.c_str()); - continue; - } - marshal->set_variables(it->second); - } - } - - virtual MarsarrT get_subs (void) const = 0; - - std::string label_; -}; - -struct MarshalVar final : public iMarshaler -{ - MarshalVar (ead::VarptrT var) : var_(var) {} - - MarshalVar (const MarshalVar& other) - { - copy_helper(other); - } - - MarshalVar& operator = (const MarshalVar& other) - { - if (this != &other) - { - copy_helper(other); - } - return *this; - } - - MarshalVar (MarshalVar&& other) = default; - - MarshalVar& operator = (MarshalVar&& other) = default; - - std::string get_label (void) const override - { - return var_->get_label(); - } - - pbm::PathedMapT list_bases (void) const override - { - auto var = var_->get_tensor(); - return pbm::PathedMapT{ - std::pair{ - var, - pbm::StringsT{var_->get_label()} - } - }; - } - - pbm::PathedTens* get_variables (void) const override - { - auto out = new pbm::PathedTens(); - out->tens_.emplace(var_->get_label(), var_->get_tensor()); - return out; - } - - void set_variables (pbm::PathedTens* storage) override - { - auto it = storage->tens_.find(var_->get_label()); - if (storage->tens_.end() == it) - { - logs::warnf("variable %s not found", var_->get_tensor()->to_string().c_str()); - } - else - { - var_->assign(ead::NodeConverters::to_node(it->second)->data(), it->second->shape()); - } - } - - ead::VarptrT var_; - -private: - void copy_helper (const MarshalVar& other) - { - auto ov = static_cast*>( - other.var_->get_tensor().get()); - var_ = std::make_shared>( - std::shared_ptr>( - ead::Variable::get(*ov))); - } - - iMarshaler* clone_impl (void) const override - { - return new MarshalVar(*this); - } -}; - -// iTrainingContext is an interface describing how a model is trained -// (e.g.: how many iterations/how large the batch is) -struct iTrainingContext -{ - virtual ~iTrainingContext (void) = default; - - virtual void marshal_layer (cortenn::Layer& out_layer) const = 0; - - virtual void unmarshal_layer (const cortenn::Layer& in_layer) = 0; -}; - -using MarVarsptrT = std::shared_ptr; - -bool save (std::ostream& outs, ade::TensptrT source, - iMarshaler* source_graph, iTrainingContext* tctx = nullptr); - -void load (std::istream& ins, iMarshaler* target, - iTrainingContext* tctx = nullptr); - -using NonLinearF = std::function(ead::NodeptrT)>; - -using NonLinearsT = std::vector; - -} - -#endif // MODL_MARSHAL_HPP diff --git a/rocnnet/modl/mlp.hpp b/rocnnet/modl/mlp.hpp deleted file mode 100644 index 54851168d..000000000 --- a/rocnnet/modl/mlp.hpp +++ /dev/null @@ -1,205 +0,0 @@ -#include "ead/generated/api.hpp" - -#include "rocnnet/modl/marshal.hpp" - -#ifndef MODL_MLP_HPP -#define MODL_MLP_HPP - -namespace modl -{ - -static const std::string weight_fmt = "weight_%d"; - -static const std::string bias_fmt = "bias_%d"; - -struct MLP final : public iMarshalSet -{ - MLP (ade::DimT n_input, - std::vector layer_outs, std::string label) : - iMarshalSet(label) - { - if (layer_outs.empty()) - { - logs::fatal("cannot create MLP with no layers specified"); - } - - for (size_t i = 0, n = layer_outs.size(); i < n; ++i) - { - ade::DimT n_output = layer_outs[i]; - ade::Shape weight_shape({n_output, n_input}); - ade::NElemT nweight = weight_shape.n_elems(); - - PybindT bound = 1.0 / std::sqrt(n_input); - std::uniform_real_distribution dist(-bound, bound); - auto gen = [&dist]() - { - return dist(ead::get_engine()); - }; - std::vector wdata(nweight); - std::generate(wdata.begin(), wdata.end(), gen); - - ead::VarptrT weight = ead::make_variable( - wdata.data(), weight_shape, fmts::sprintf(weight_fmt, i)); - - ead::VarptrT bias = ead::make_variable_scalar( - 0.0, ade::Shape({n_output}), fmts::sprintf(bias_fmt, i)); - - layers_.push_back(HiddenLayer{ - std::make_shared(weight), - std::make_shared(bias) - }); - n_input = n_output; - } - } - - MLP (const pbm::GraphInfo& graph, std::string label) : - iMarshalSet(label) - { - const auto& children = graph.tens_.children_; - size_t nlayers = children.size() / 2; - if (nlayers == 0) - { - logs::fatal("expecting at least one layer"); - } - for (size_t i = 0; i < nlayers; ++i) - { - std::string weight_label = fmts::sprintf(weight_fmt, i); - auto wmarsh_it = children.find(weight_label); - if (children.end() == wmarsh_it) - { - logs::fatalf("cannot find weight_%d variable marshaller", i); - } - auto weight_it = wmarsh_it->second->tens_.find(weight_label); - if (wmarsh_it->second->tens_.end() == weight_it) - { - logs::fatalf("cannot find weight_%d variable", i); - } - std::string bias_label = fmts::sprintf(bias_fmt, i); - auto bmarsh_it = children.find(bias_label); - if (children.end() == bmarsh_it) - { - logs::fatalf("cannot find bias_%d variable marshaller", i); - } - auto bias_it = bmarsh_it->second->tens_.find(bias_label); - if (bmarsh_it->second->tens_.end() == bias_it) - { - logs::fatalf("cannot find bias_%d variable", i); - } - layers_.push_back(HiddenLayer{ - std::make_shared( - std::static_pointer_cast>( - ead::NodeConverters::to_node(weight_it->second))), - std::make_shared( - std::static_pointer_cast>( - ead::NodeConverters::to_node(bias_it->second))), - }); - } - } - - MLP (const MLP& other) : iMarshalSet(other) - { - copy_helper(other); - } - - MLP& operator = (const MLP& other) - { - if (this != &other) - { - iMarshalSet::operator = (other); - copy_helper(other); - } - return *this; - } - - MLP (MLP&& other) = default; - - MLP& operator = (MLP&& other) = default; - - - ead::NodeptrT operator () (ead::NodeptrT input, - NonLinearsT nonlinearities) - { - // sanity check - const ade::Shape& in_shape = input->shape(); - uint8_t ninput = get_ninput(); - if (in_shape.at(0) != ninput) - { - logs::fatalf("cannot generate mlp with input shape %s against n_input %d", - in_shape.to_string().c_str(), ninput); - } - - size_t nlayers = layers_.size(); - size_t nnlin = nonlinearities.size(); - if (nnlin != nlayers) - { - logs::fatalf( - "cannot generate mlp of %d layers with %d nonlinearities", - nlayers, nnlin); - } - - ead::NodeptrT out = input; - for (size_t i = 0; i < nlayers; ++i) - { - auto hypothesis = tenncor::nn::fully_connect({out}, - {ead::convert_to_node(layers_[i].weight_->var_)}, - ead::convert_to_node(layers_[i].bias_->var_)); - out = nonlinearities[i](hypothesis); - } - return out; - } - - ade::DimT get_ninput (void) const - { - return layers_.front().weight_->var_->shape().at(1); - } - - ade::DimT get_noutput (void) const - { - return layers_.back().weight_->var_->shape().at(0); - } - - MarsarrT get_subs (void) const override - { - MarsarrT out; - out.reserve(2 * layers_.size()); - for (const HiddenLayer& layer : layers_) - { - out.push_back(layer.weight_); - out.push_back(layer.bias_); - } - return out; - } - - struct HiddenLayer - { - MarVarsptrT weight_; - - MarVarsptrT bias_; - }; - - std::vector layers_; - -private: - void copy_helper (const MLP& other) - { - layers_.clear(); - for (const HiddenLayer& olayer : other.layers_) - { - layers_.push_back(HiddenLayer{ - std::make_shared(*olayer.weight_), - std::make_shared(*olayer.bias_), - }); - } - } - - iMarshaler* clone_impl (void) const override - { - return new MLP(*this); - } -}; - -using MLPptrT = std::shared_ptr; - -} - -#endif // MODL_MLP_HPP diff --git a/rocnnet/modl/model.hpp b/rocnnet/modl/model.hpp new file mode 100644 index 000000000..9eaec3900 --- /dev/null +++ b/rocnnet/modl/model.hpp @@ -0,0 +1,166 @@ +#include "rocnnet/modl/dense.hpp" + +#ifndef MODL_MODEL_HPP +#define MODL_MODEL_HPP + +namespace modl +{ + +struct SeqModelBuilder final : public iLayerBuilder +{ + SeqModelBuilder (std::string label) : label_(label) {} + + void set_tensor (ade::TensptrT tens, std::string target) override {} // seqmodel has no tensor + + void set_sublayer (LayerptrT layer) override + { + layers_.push_back(layer); + } + + LayerptrT build (void) const override; + +private: + std::string label_; + + std::vector layers_; +}; + +const std::string seq_model_key = +get_layer_reg().register_tagr(layers_key_prefix + "seqmodel", +[](ade::TensrefT ref, std::string label) +{ + get_layer_reg().layer_tag(ref, seq_model_key, label); +}, +[](std::string label) -> LBuilderptrT +{ + return std::make_shared(label); +}); + +struct SequentialModel final : public iLayer +{ + SequentialModel (const std::string& label) : + label_(label) {} + + SequentialModel (const SequentialModel& other, + std::string label_prefix = "") + { + copy_helper(other, label_prefix); + } + + SequentialModel& operator = (const SequentialModel& other) + { + if (this != &other) + { + copy_helper(other); + } + return *this; + } + + SequentialModel (SequentialModel&& other) = default; + + SequentialModel& operator = (SequentialModel&& other) = default; + + SequentialModel* clone (std::string label_prefix = "") const + { + return static_cast(this->clone_impl(label_prefix)); + } + + size_t get_ninput (void) const override + { + size_t input = 0; + for (auto it = layers_.begin(), et = layers_.end(); + it != et && 0 == input; ++it) + { + input = (*it)->get_ninput(); + } + return input; + } + + size_t get_noutput (void) const override + { + size_t output = 0; + for (auto it = layers_.rbegin(), et = layers_.rend(); + it != et && 0 == output; ++it) + { + output = (*it)->get_noutput(); + } + return output; + } + + std::string get_ltype (void) const override + { + return seq_model_key; + } + + std::string get_label (void) const override + { + return label_; + } + + ead::NodeptrT connect (ead::NodeptrT input) const override + { + ead::NodeptrT out; + for (size_t i = 0, n = layers_.size(); i < n; ++i) + { + auto& layer = layers_[i]; + out = layer->connect(input); + input = out; + recursive_tag(out->get_tensor(), { + input->get_tensor().get(), + }, LayerId(layer->get_ltype(), layer->get_label(), i)); + } + return out; + } + + ade::TensT get_contents (void) const override + { + ade::TensT out; + out.reserve(layers_.size()); + for (auto& layer : layers_) + { + auto tmp = layer->get_contents(); + out.insert(out.end(), tmp.begin(), tmp.end()); + } + return out; + } + + void push_back (LayerptrT layer) + { + // label layer content + auto subs = layer->get_contents(); + for (auto& sub : subs) + { + tag(sub, LayerId(layer->get_ltype(), + layer->get_label(), layers_.size())); + } + + layers_.push_back(layer); + } + +private: + iLayer* clone_impl (std::string label_prefix) const override + { + return new SequentialModel(*this, label_prefix); + } + + void copy_helper (const SequentialModel& other, std::string label_prefix = "") + { + label_ = label_prefix + other.label_; + layers_.clear(); + layers_.reserve(other.layers_.size()); + for (LayerptrT olayer : other.layers_) + { + push_back(LayerptrT(olayer->clone(label_prefix))); + } + } + + std::string label_; + + std::vector layers_; +}; + +using SeqModelptrT = std::shared_ptr; + +} + +#endif // MODL_MODEL_HPP diff --git a/rocnnet/modl/rbm.hpp b/rocnnet/modl/rbm.hpp index d68b57d19..520947426 100644 --- a/rocnnet/modl/rbm.hpp +++ b/rocnnet/modl/rbm.hpp @@ -1,6 +1,5 @@ -#include "ead/generated/api.hpp" - -#include "rocnnet/modl/marshal.hpp" +#include "rocnnet/modl/dense.hpp" +#include "rocnnet/modl/activations.hpp" #ifndef MODL_RBM_HPP #define MODL_RBM_HPP @@ -8,60 +7,86 @@ namespace modl { -struct RBM final : public iMarshalSet +const std::string hidden_key = "hidden"; + +const std::string visible_key = "visible"; + +struct RBMBuilder final : public iLayerBuilder { - RBM (ade::DimT n_input, - std::vector layer_outs, std::string label) : - iMarshalSet(label) + RBMBuilder (std::string label) : label_(label) {} + + void set_tensor (ade::TensptrT tens, std::string target) override {} // rbm has no tensor + + void set_sublayer (LayerptrT layer) override { - if (layer_outs.empty()) - { - logs::fatal("cannot create RBM with no layers specified"); - } + layers_.push_back(layer); + } + + LayerptrT build (void) const override; + +private: + std::vector layers_; + + std::string label_; +}; + +const std::string rbm_layer_key = +get_layer_reg().register_tagr(layers_key_prefix + "rbm", +[](ade::TensrefT ref, std::string label) +{ + get_layer_reg().layer_tag(ref, rbm_layer_key, label); +}, +[](std::string label) -> LBuilderptrT +{ + return std::make_shared(label); +}); - for (size_t i = 0, n = layer_outs.size(); i < n; ++i) +struct RBM final : public iLayer +{ + RBM (ade::DimT nhidden, ade::DimT nvisible, + ActivationptrT activation, + eqns::InitF weight_init, + eqns::InitF bias_init, + const std::string& label) : + label_(label), + hidden_(std::make_shared( + nhidden, nvisible, weight_init, bias_init, hidden_key)), + activation_(activation) + { + auto hidden_contents = hidden_->get_contents(); + auto weight = hidden_contents[0]; + auto hbias = hidden_contents[1]; + ead::NodeptrT vbias = nullptr; + + if (bias_init) { - ade::DimT n_output = layer_outs[i]; - ade::Shape weight_shape({n_output, n_input}); - ade::NElemT nweight = weight_shape.n_elems(); - - PybindT bound = 4 * std::sqrt(6.0 / (n_output + n_input)); - std::uniform_real_distribution dist(-bound, bound); - auto gen = [&dist]() - { - return dist(ead::get_engine()); - }; - std::vector wdata(nweight); - std::generate(wdata.begin(), wdata.end(), gen); - - ead::VarptrT weight = ead::make_variable( - wdata.data(), weight_shape, fmts::sprintf("weight_%d", i)); - - ead::VarptrT hbias = ead::make_variable_scalar( - 0.0, ade::Shape({n_output}), fmts::sprintf("hbias_%d", i)); - - ead::VarptrT vbias = ead::make_variable_scalar( - 0.0, ade::Shape({n_input}), fmts::sprintf("vbias_%d", i)); - - layers_.push_back(HiddenLayer{ - std::make_shared(weight), - std::make_shared(hbias), - std::make_shared(vbias), - }); - n_input = n_output; + vbias = bias_init(ade::Shape({nvisible}), bias_key); } + visible_ = std::make_shared(tenncor::transpose( + ead::NodeConverters::to_node(weight)), vbias, visible_key); + tag_sublayers(); + } + + RBM (DenseptrT hidden, DenseptrT visible, + ActivationptrT activation, std::string label) : + label_(label), + hidden_(hidden), + visible_(visible), + activation_(activation) + { + tag_sublayers(); } - RBM (const RBM& other) : iMarshalSet(other) + RBM (const RBM& other, + std::string label_prefix = "") { - copy_helper(other); + copy_helper(other, label_prefix); } RBM& operator = (const RBM& other) { if (this != &other) { - iMarshalSet::operator = (other); copy_helper(other); } return *this; @@ -71,136 +96,108 @@ struct RBM final : public iMarshalSet RBM& operator = (RBM&& other) = default; - - // input of shape - // propagate upwards (towards visibleness) - ead::NodeptrT operator () (ead::NodeptrT input, - NonLinearsT nonlinearities) + RBM* clone (std::string label_prefix = "") const { - // sanity check - const ade::Shape& in_shape = input->shape(); - uint8_t ninput = get_ninput(); - if (in_shape.at(0) != ninput) - { - logs::fatalf( - "cannot generate rbm with input shape %s against n_input %d", - in_shape.to_string().c_str(), ninput); - } - - size_t nlayers = layers_.size(); - size_t nnlin = nonlinearities.size(); - if (nnlin != nlayers) - { - logs::fatalf( - "cannot generate rbm of %d layers with %d nonlinearities", - nlayers, nnlin); - } - - ead::NodeptrT out = input; - for (size_t i = 0; i < nlayers; ++i) - { - // weight is - // in is - // out = in @ weight, so out is - auto hypothesis = tenncor::nn::fully_connect({out}, - {ead::convert_to_node(layers_[i].weight_->var_)}, - ead::convert_to_node(layers_[i].hbias_->var_)); - out = nonlinearities[i](hypothesis); - } - return out; + return static_cast(this->clone_impl(label_prefix)); } - // input of shape - ead::NodeptrT prop_down (ead::NodeptrT hidden, - NonLinearsT nonlinearities) + size_t get_ninput (void) const override { - // sanity check - const ade::Shape& out_shape = hidden->shape(); - uint8_t noutput = get_noutput(); - if (out_shape.at(0) != noutput) - { - logs::fatalf( - "cannot prop down rbm with output shape %s against n_output %d", - out_shape.to_string().c_str(), noutput); - } + return hidden_->get_ninput(); + } - size_t nlayers = layers_.size(); - size_t nnlin = nonlinearities.size(); - if (nnlin != nlayers) - { - logs::fatalf( - "cannot generate rbm of %d layers with %d nonlinearities", - nlayers, nnlin); - } + size_t get_noutput (void) const override + { + return hidden_->get_noutput(); + } - ead::NodeptrT out = hidden; - for (size_t i = 0; i < nlayers; ++i) - { - size_t index = nlayers - i - 1; - // weight is - // in is - // out = in @ weight.T, so out is - auto hypothesis = tenncor::nn::fully_connect({out}, - {tenncor::transpose( - ead::convert_to_node(layers_[index].weight_->var_))}, - ead::convert_to_node(layers_[index].vbias_->var_)); - out = nonlinearities[index](hypothesis); - } - return out; + std::string get_ltype (void) const override + { + return rbm_layer_key; } - uint8_t get_ninput (void) const + std::string get_label (void) const override { - return layers_.front().weight_->var_->shape().at(1); + return label_; } - uint8_t get_noutput (void) const + ead::NodeptrT connect (ead::NodeptrT visible) const override { - return layers_.back().weight_->var_->shape().at(0); + return activation_->connect(hidden_->connect(visible)); } - MarsarrT get_subs (void) const override + ade::TensT get_contents (void) const override { - MarsarrT out; - out.reserve(3 * layers_.size()); - for (const HiddenLayer& layer : layers_) - { - out.push_back(layer.weight_); - out.push_back(layer.hbias_); - out.push_back(layer.vbias_); - } + auto out = hidden_->get_contents(); + auto vis_contents = visible_->get_contents(); + auto act_contents = activation_->get_contents(); + out.insert(out.end(), vis_contents.begin(), vis_contents.end()); + out.insert(out.end(), act_contents.begin(), act_contents.end()); return out; } - struct HiddenLayer + ead::NodeptrT backward_connect (ead::NodeptrT hidden) const { - MarVarsptrT weight_; + return activation_->connect(visible_->connect(hidden)); + } - MarVarsptrT hbias_; +private: + iLayer* clone_impl (std::string label_prefix) const override + { + return new RBM(*this, label_prefix); + } - MarVarsptrT vbias_; - }; + void tag_sublayers (void) + { + auto hidden_subs = hidden_->get_contents(); + for (auto& sub : hidden_subs) + { + tag(sub, LayerId(hidden_->get_ltype(), + hidden_->get_label(), 0)); + } - std::vector layers_; + auto visible_subs = visible_->get_contents(); + for (auto& sub : visible_subs) + { + tag(sub, LayerId(visible_->get_ltype(), + visible_->get_label(), 1)); + } -private: - void copy_helper (const RBM& other) - { - layers_.clear(); - for (const HiddenLayer& olayer : other.layers_) + auto activation_subs = activation_->get_contents(); + for (auto& sub : activation_subs) { - layers_.push_back(HiddenLayer{ - std::make_shared(*olayer.weight_), - std::make_shared(*olayer.hbias_), - std::make_shared(*olayer.vbias_), - }); + tag(sub, LayerId(activation_->get_ltype(), + activation_->get_label(), 2)); } } - iMarshaler* clone_impl (void) const override + void copy_helper (const RBM& other, std::string label_prefix = "") { - return new RBM(*this); + label_ = label_prefix + other.label_; + hidden_ = DenseptrT(other.hidden_->clone(label_prefix)); + auto hidden_contents = hidden_->get_contents(); + auto vbias = other.visible_->get_contents()[1]; + if (nullptr != vbias) + { + vbias = ade::TensptrT(ead::Variable::get( + *static_cast*>(vbias.get()))); + } + visible_ = std::make_shared(tenncor::transpose( + ead::NodeConverters::to_node(hidden_contents[0])), + ead::NodeConverters::to_node(vbias), + label_prefix + visible_key); + + activation_ = ActivationptrT(other.activation_->clone(label_prefix)); + tag_sublayers(); } + + std::string label_; + + DenseptrT hidden_; + + DenseptrT visible_; + + ActivationptrT activation_; }; using RBMptrT = std::shared_ptr; diff --git a/rocnnet/modl/src/activations.cpp b/rocnnet/modl/src/activations.cpp new file mode 100644 index 000000000..5489f737a --- /dev/null +++ b/rocnnet/modl/src/activations.cpp @@ -0,0 +1,25 @@ +#include "rocnnet/modl/activations.hpp" + +#ifdef MODL_ACTIVATIONS_HPP + +namespace modl +{ + +LayerptrT ActivationBuilder::build (void) const +{ + return std::make_shared(label_, act_type_); +} + +LayerptrT sigmoid (std::string label) +{ + return std::make_shared(label, sigmoid_layer_key); +} + +LayerptrT tanh (std::string label) +{ + return std::make_shared(label, tanh_layer_key); +} + +} + +#endif diff --git a/rocnnet/modl/src/dense.cpp b/rocnnet/modl/src/dense.cpp new file mode 100644 index 000000000..1f092ecdc --- /dev/null +++ b/rocnnet/modl/src/dense.cpp @@ -0,0 +1,19 @@ +#include "rocnnet/modl/dense.hpp" + +#ifdef MODL_DENSE_HPP + +namespace modl +{ + +LayerptrT DenseBuilder::build (void) const +{ + if (nullptr == weight_) + { + logs::fatal("cannot build dense with null weight"); + } + return std::make_shared(weight_, bias_, label_); +} + +} + +#endif diff --git a/rocnnet/modl/src/layer.cpp b/rocnnet/modl/src/layer.cpp new file mode 100644 index 000000000..ff27d1c67 --- /dev/null +++ b/rocnnet/modl/src/layer.cpp @@ -0,0 +1,290 @@ +#include "pbm/save.hpp" +#include "pbm/load.hpp" + +#include "ead/serialize.hpp" + +#include "rocnnet/modl/layer.hpp" + +#ifdef MODL_LAYER_HPP + +namespace modl +{ + +size_t LayerTag::tag_id_ = typeid(LayerTag).hash_code(); + +void validate_label (const std::string& label) +{ + if (std::string::npos != label.find(llabel_sep)) + { + logs::fatalf("label `%s` cannot have reserved separator '%c'", + label.c_str(), llabel_sep); + } +} + +std::unordered_map unpack_labels ( + const std::vector& labels) +{ + std::unordered_map out; + for (std::string label : labels) + { + auto parts = fmts::split(label, std::string(&llabel_sep, 1)); + if (parts.size() < 4) + { + logs::errorf("invalid layer label %s", label.c_str()); + continue; + } + out[parts[0]].push_back(LayerId(parts[1], parts[2], std::stoul(parts[3]))); + } + return out; +} + +void iLayer::tag (ade::TensptrT tensor, LayerId subs) const +{ + get_layer_reg().layer_tag(tensor, get_ltype(), + subs.to_string(get_label())); +} + +void iLayer::recursive_tag (ade::TensptrT root, + std::unordered_set ignores, LayerId subs) const +{ + recursive_layer_tag(root, get_ltype(), + subs.to_string(get_label()), ignores); +} + +LayerRegistry& get_layer_reg (void) +{ + static LayerRegistry registry; + return registry; +} + +void recursive_layer_tag (ade::TensptrT tens, std::string layer_type, + std::string name, std::unordered_set stops, + LayerRegistry& registry) +{ + tag::recursive_tag(tens, stops, + [&](ade::TensrefT ref) + { + registry.layer_tag(ref, layer_type, name); + }); +} + +struct LayerNode; + +using LNodeptrT = std::shared_ptr; + +using LMatchesT = std::list>; + +struct LayerNode final +{ + LayerNode (const std::string& type, const std::string& label) : + type_(type), label_(label) {} + + // Return generated leaves after adding branches to the subtree from tag reps + LMatchesT match_layer (const tag::TagRepsT& reps) + { + LMatchesT matches; + std::vector labels; + if (estd::get(labels, reps, type_)) + { + auto layer_info = unpack_labels(labels); + LayerIdsT subids; + if (estd::get(subids, layer_info, label_)) + { + for (LayerId subid : subids) + { + if (subid.type_.empty()) + { + matches.push_back({this, subid.label_}); + } + else + { + size_t n = subs_.size(); + if (subid.index_ >= n) + { + subs_.insert(subs_.end(), + subid.index_ - n + 1, LNodeptrT()); + } + auto& sub = subs_[subid.index_]; + if (nullptr == sub) + { + sub = std::make_shared( + subid.type_, subid.label_); + } + auto tmp = sub->match_layer(reps); + matches.insert(matches.end(), tmp.begin(), tmp.end()); + } + } + } + } + return matches; + } + + std::string type_; + + std::string label_; + + std::vector subs_; +}; + +using SublayersT = std::unordered_map; + +using TensLablT = std::unordered_map; + +using TensLayerMapT = std::unordered_map; + +struct LayerDeserializer final : public ade::OnceTraveler +{ + LayerDeserializer (std::string key, std::string val) : + base_(std::make_shared(key, val)) {} + + /// Implementation of OnceTraveler + void visit_leaf (ade::iLeaf* leaf) override + { + tag::TagRepsT reps = + tag::get_reg().get_tags(leaf); + LMatchesT matches = base_->match_layer(reps); + if (false == matches.empty()) + { + sublayers_.emplace(leaf, matches); + roots_.emplace(leaf); + } + } + + /// Implementation of OnceTraveler + void visit_func (ade::iFunctor* func) override + { + auto& children = func->get_children(); + for (auto child : children) + { + child.get_tensor()->accept(*this); + } + + tag::TagRepsT reps = + tag::get_reg().get_tags(func); + LMatchesT matches = base_->match_layer(reps); + if (false == matches.empty()) + { + sublayers_.emplace(func, matches); + for (auto child : children) + { + roots_.erase(child.get_tensor().get()); + } + roots_.emplace(func); + } + } + + LayerptrT build_layer ( + LayerRegistry& registry, ade::OwnerMapT& owners) const + { + TensLayerMapT layer_tens; + for (auto& sublayer : sublayers_) + { + auto& tens = sublayer.first; + auto& matches = sublayer.second; + for (auto& match : matches) + { + layer_tens[match.first].emplace( + owners.at(tens).lock(), match.second); + } + } + return build_layer_helper(registry, layer_tens, base_.get()); + } + + std::unordered_set roots_; + +private: + LayerptrT build_layer_helper (LayerRegistry& registry, + const TensLayerMapT& layer_tens, LayerNode* lroot) const + { + if (nullptr == lroot) + { + logs::fatal("cannot builder layer from null layer node"); + } + auto builder = registry.get_builder(lroot->type_)(lroot->label_); + for (const LNodeptrT& sub : lroot->subs_) + { + builder->set_sublayer(build_layer_helper( + registry, layer_tens, sub.get())); + } + TensLablT tenslabels; + if (estd::get(tenslabels, layer_tens, lroot)) + { + for (auto& tenslabel : tenslabels) + { + builder->set_tensor(tenslabel.first, tenslabel.second); + } + } + return builder->build(); + } + + SublayersT sublayers_; + + LNodeptrT base_; +}; + +LayerptrT load_layer (std::istream& ins, ade::TensT& roots, + std::string ltype, std::string label, + LayerRegistry& registry) +{ + cortenn::Graph graph; + if (false == graph.ParseFromIstream(&ins)) + { + logs::fatalf("failed to parse from istream when loading %s", + ltype.c_str()); + } + pbm::GraphInfo info; + pbm::load_graph(info, graph); + + ade::OwnerMapT owners = ade::track_owners( + ade::TensT(info.roots_.begin(), info.roots_.end())); + + LayerDeserializer layd(ltype, label); + // get all layer labelled nodes in graph + for (ade::TensptrT tens : info.roots_) + { + tens->accept(layd); + } + + roots.reserve(layd.roots_.size()); + for (ade::iTensor* root : layd.roots_) + { + roots.push_back(owners.at(root).lock()); + } + + return layd.build_layer(registry, owners); +} + +bool save_layer (std::ostream& outs, const iLayer& layer, ade::TensT roots, + LayerRegistry& registry) +{ + pbm::GraphSaver saver(registry.get_tag_registry()); + for (auto& root : roots) + { + root->accept(saver); + } + + auto contents = layer.get_contents(); + auto owners = ade::track_owners(contents); + for (auto tens : contents) + { + tens->accept(saver); + } + + pbm::PathedMapT labels; + for (ade::iLeaf* leaf : saver.leaves_) + { + if (false == leaf->is_const()) + { + labels.emplace(owners.at(leaf).lock(), pbm::StringsT{leaf->to_string()}); + } + } + + // save graph from source + cortenn::Graph graph; + saver.save(graph, labels); + return graph.SerializeToOstream(&outs); +} + +} + +#endif // MODL_LAYER_HPP diff --git a/rocnnet/modl/src/marshal.cpp b/rocnnet/modl/src/marshal.cpp deleted file mode 100755 index 74eed3166..000000000 --- a/rocnnet/modl/src/marshal.cpp +++ /dev/null @@ -1,58 +0,0 @@ -#include "rocnnet/modl/marshal.hpp" - -#ifdef MODL_MARSHAL_HPP - -namespace modl -{ - -bool save (std::ostream& outs, ade::TensptrT source, - iMarshaler* source_graph, iTrainingContext* tctx) -{ - pbm::GraphSaver saver; - source->accept(saver); - - cortenn::Layer layer; - - // save graph from source - cortenn::Graph* graph = layer.mutable_graph(); - saver.save(*graph, source_graph->list_bases()); - - // save context if we have context - if (nullptr != tctx) - { - tctx->marshal_layer(layer); - } - - return layer.SerializeToOstream(&outs); -} - -void load (std::istream& ins, iMarshaler* target, - iTrainingContext* tctx) -{ - cortenn::Layer layer; - layer.ParseFromIstream(&ins); - - // load graph to target - const cortenn::Graph& graph = layer.graph(); - pbm::GraphInfo info; - pbm::load_graph(info, graph); - target->set_variables(&info.tens_); - - // load context to target - bool has_ctx = cortenn::Layer::LAYER_CONTEXT_NOT_SET != - layer.layer_context_case(); - bool has_training_ctx = nullptr != tctx; - if (has_ctx != has_training_ctx) - { - logs::warn("either missing training context or " - "missing layer context from protobuf"); - } - if (has_training_ctx) - { - tctx->unmarshal_layer(layer); - } -} - -} - -#endif diff --git a/rocnnet/modl/src/model.cpp b/rocnnet/modl/src/model.cpp new file mode 100644 index 000000000..de5610c2b --- /dev/null +++ b/rocnnet/modl/src/model.cpp @@ -0,0 +1,20 @@ +#include "rocnnet/modl/model.hpp" + +#ifdef MODL_MODEL_HPP + +namespace modl +{ + +LayerptrT SeqModelBuilder::build (void) const +{ + auto model = std::make_shared(label_); + for (auto& layer : layers_) + { + model->push_back(layer); + } + return model; +} + +} + +#endif diff --git a/rocnnet/modl/src/rbm.cpp b/rocnnet/modl/src/rbm.cpp new file mode 100644 index 000000000..aa1469f4c --- /dev/null +++ b/rocnnet/modl/src/rbm.cpp @@ -0,0 +1,25 @@ +#include "rocnnet/modl/rbm.hpp" + +#ifdef MODL_RBM_HPP + +namespace modl +{ + +LayerptrT RBMBuilder::build (void) const +{ + if (3 != layers_.size()) + { + logs::fatalf("cannot make rbm without hidden, visible, " + "and activation layer, got %d layers", + layers_.size()); + } + return std::make_shared( + std::static_pointer_cast(layers_[0]), + std::static_pointer_cast(layers_[1]), + std::static_pointer_cast(layers_[2]), + label_); +} + +} + +#endif diff --git a/rocnnet/notebooks/ead/__init__.py b/rocnnet/notebooks/ead/__init__.py index 1642e3625..1e99eb777 100644 --- a/rocnnet/notebooks/ead/__init__.py +++ b/rocnnet/notebooks/ead/__init__.py @@ -1,2 +1 @@ -import age import ead diff --git a/rocnnet/notebooks/karpathy_game.ipynb b/rocnnet/notebooks/karpathy_game.ipynb index 3f664851a..2f10000cb 100644 --- a/rocnnet/notebooks/karpathy_game.ipynb +++ b/rocnnet/notebooks/karpathy_game.ipynb @@ -22,7 +22,7 @@ "import numpy as np\n", "import tempfile\n", "\n", - "import ead.age as age\n", + "import ead.tenncor as tc\n", "import ead.ead as ead\n", "import rocnnet.rocnnet as rcn\n", "\n", @@ -40,7 +40,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/var/folders/fs/cfs_ql391csfpxfsdxhmktw00000gn/T/tmpEGIKU9\n" + "/var/folders/fs/cfs_ql391csfpxfsdxhmktw00000gn/T/tmpqmlst5rx\n" ] } ], @@ -134,8 +134,8 @@ " # Here it is a done using a multi layer perceptron with 2 hidden\n", " # layers\n", " hiddens = [200, 200, g.num_actions]\n", - " nonlins = [age.tanh, age.tanh, rcn.identity]\n", - " brain = rcn.get_mlp(g.observation_size, hiddens, 'brain')\n", + " nonlins = [tc.tanh, tc.tanh, rcn.identity]\n", + " brain = rcn.get_mlp(g.observation_size, hiddens, rcn.unif_xavier_init(), 'brain')\n", "\n", " learning_rate = 0.001\n", " bgd = rcn.get_rms_momentum(\n", @@ -151,7 +151,8 @@ " train_interval = 4)\n", " \n", " sess = ead.Session()\n", - " current_controller = DQNControllerWrapper(rcn.DQNTrainer(brain, nonlins, sess, bgd, param))" + " current_controller = DQNControllerWrapper(rcn.DQNTrainer(brain, nonlins, sess, bgd, param))\n", + " sess.optimize(\"cfg/optimizations.rules\")" ] }, { @@ -176,283 +177,283 @@ "\n", " width=\"700\" style=\"fill:none;\" />\n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", + "\n", + " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", + "\n", + " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", - "\n", - " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", - "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", " \n", "\n", - " fps = 2.9\n", + " fps = 0.9\n", "\n", " \n", "\n", " \n", "\n", - " nearest wall = 79.8\n", + " nearest wall = 54.7\n", "\n", " \n", "\n", @@ -464,7 +465,7 @@ "\n", " \n", "\n", - " objects eaten => enemy: 225, friend: 309\n", + " objects eaten => friend: 512, enemy: 324\n", "\n", " \n", "\n", @@ -472,7 +473,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -546,7 +547,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -584,27 +585,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[1.00 0.39 1.00 0.94 -0.67]\n", - " [1.00 1.00 1.00 0.00 0.00]\n", - " [1.00 1.00 1.00 0.00 0.00]\n", - " [1.00 1.00 1.00 0.00 0.00]\n", - " [1.00 1.00 1.00 0.00 0.00]\n", - " [1.00 0.56 1.00 -0.29 0.25]\n", - " [1.00 0.61 1.00 -0.29 0.25]\n", + "[[1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", + " [1.00 0.58 1.00 -0.52 -0.56]\n", + " [1.00 1.00 0.65 0.00 0.00]\n", + " [1.00 1.00 0.55 0.00 0.00]\n", + " [1.00 1.00 0.50 0.00 0.00]\n", + " [1.00 1.00 0.47 0.00 0.00]\n", + " [1.00 1.00 0.46 0.00 0.00]\n", + " [1.00 1.00 0.47 0.00 0.00]\n", + " [1.00 1.00 0.50 0.00 0.00]\n", + " [1.00 1.00 0.55 0.00 0.00]\n", + " [1.00 1.00 0.65 0.00 0.00]\n", + " [1.00 1.00 0.83 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", + " [0.38 1.00 1.00 -0.10 0.32]\n", " [1.00 1.00 1.00 0.00 0.00]\n", - " [0.59 1.00 1.00 0.73 -0.52]\n", " [1.00 1.00 1.00 0.00 0.00]\n", - " [0.55 1.00 1.00 0.37 0.82]\n", " [1.00 1.00 1.00 0.00 0.00]\n", - " [0.40 1.00 1.00 0.70 0.95]\n", - " [0.36 1.00 1.00 0.70 0.95]\n", " [1.00 1.00 1.00 0.00 0.00]\n", + " [1.00 0.71 1.00 0.64 0.36]\n", " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", @@ -612,11 +616,8 @@ " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", " [1.00 1.00 1.00 0.00 0.00]\n", - " [1.00 1.00 1.00 0.00 0.00]\n", - " [1.00 1.00 1.00 0.00 0.00]\n", - " [0.91 1.00 1.00 -0.63 0.11]\n", - " [1.00 0.41 1.00 0.94 -0.67]]\n", - "[0.12 1.87 -0.60 0.52]\n" + " [1.00 1.00 1.00 0.00 0.00]]\n", + "[-1.26 -0.57 -0.02 0.78]\n" ] }, { @@ -634,289 +635,289 @@ "\n", " width=\"700\" style=\"fill:none;\" />\n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " \n", "\n", - " \n", + " 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\n", "\n", - " \n", - "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", + " style=\"fill:green;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", - "\n", - " \n", "\n", - " \n", + " style=\"fill:red;\" />\n", "\n", - " \n", "\n", - " \n", "\n", - " \n", "\n", - " \n", + "\n", + " \n", "\n", " \n", "\n", - " nearest wall = 109.7\n", + " nearest wall = 45.2\n", "\n", " \n", "\n", " \n", "\n", - " reward = 0.0\n", + " reward = -0.0\n", "\n", " \n", "\n", " \n", "\n", - " objects eaten => enemy: 225, friend: 309\n", + " objects eaten => friend: 512, enemy: 325\n", "\n", " \n", "\n", @@ -924,7 +925,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -952,21 +953,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.16" + "pygments_lexer": "ipython3", + "version": "3.7.3" } }, "nbformat": 4, diff --git a/rocnnet/notebooks/karpathy_model.pbx b/rocnnet/notebooks/karpathy_model.pbx index 01bc012fd..1519e255e 100644 Binary files a/rocnnet/notebooks/karpathy_model.pbx and b/rocnnet/notebooks/karpathy_model.pbx differ diff --git a/rocnnet/pretrained/dqnmodel.pbx b/rocnnet/pretrained/dqnmodel.pbx deleted file mode 100644 index 3ca7fb30d..000000000 Binary files a/rocnnet/pretrained/dqnmodel.pbx and /dev/null differ diff --git a/rocnnet/pretrained/gdmodel.pbx b/rocnnet/pretrained/gdmodel.pbx deleted file mode 100644 index f64fbbc13..000000000 Binary files a/rocnnet/pretrained/gdmodel.pbx and /dev/null differ diff --git a/rocnnet/pretrained/rbmcache.pbx b/rocnnet/pretrained/rbmcache.pbx deleted file mode 100644 index a8372ddd5..000000000 Binary files a/rocnnet/pretrained/rbmcache.pbx and /dev/null differ diff --git a/rocnnet/python/rocnnet.cpp b/rocnnet/python/rocnnet.cpp index 394f0a44d..534ef5fd4 100644 --- a/rocnnet/python/rocnnet.cpp +++ b/rocnnet/python/rocnnet.cpp @@ -2,6 +2,7 @@ #include #include "pybind11/pybind11.h" +#include "pybind11/numpy.h" #include "pybind11/stl.h" #include "pybind11/functional.h" @@ -9,8 +10,10 @@ #include "rocnnet/eqns/init.hpp" -#include "rocnnet/modl/mlp.hpp" +#include "rocnnet/modl/activations.hpp" +#include "rocnnet/modl/dense.hpp" #include "rocnnet/modl/rbm.hpp" +#include "rocnnet/modl/model.hpp" // #include "rocnnet/modl/dbn.hpp" // #include "rocnnet/modl/conv.hpp" @@ -30,37 +33,71 @@ ade::Shape p2cshape (std::vector& pyshape) pyshape.rbegin(), pyshape.rend())); } -trainer::DQNInfo dqninfo_init (size_t train_interval = 5, - PybindT rand_action_prob = 0.05, - PybindT discount_rate = 0.95, - PybindT target_update_rate = 0.01, - PybindT exploration_period = 1000, - size_t store_interval = 5, - uint8_t mini_batch_size = 32, - size_t max_exp = 30000) +std::vector arr2vec (ade::Shape& outshape, py::array data) { - return trainer::DQNInfo{ - train_interval, - rand_action_prob, - discount_rate, - target_update_rate, - exploration_period, - store_interval, - mini_batch_size, - max_exp, - }; -} - -modl::MLPptrT mlp_init (size_t n_input, std::vector nouts, - std::string label) -{ - return std::make_shared(n_input, nouts, label); -} - -modl::RBMptrT rbm_init (size_t n_input, std::vector nouts, - std::string label) -{ - return std::make_shared(n_input, nouts, label); + py::buffer_info info = data.request(); + outshape = p2cshape(info.shape); + size_t n = outshape.n_elems(); + auto dtype = data.dtype(); + char kind = dtype.kind(); + py::ssize_t tbytes = dtype.itemsize(); + std::vector vec; + switch (kind) + { + case 'f': + switch (tbytes) + { + case 4: // float32 + { + float* dptr = static_cast(info.ptr); + vec = std::vector(dptr, dptr + n); + } + break; + case 8: // float64 + { + double* dptr = static_cast(info.ptr); + vec = std::vector(dptr, dptr + n); + } + break; + default: + logs::fatalf("unsupported float type with %d bytes", tbytes); + } + break; + case 'i': + switch (tbytes) + { + case 1: // int8 + { + int8_t* dptr = static_cast(info.ptr); + vec = std::vector(dptr, dptr + n); + } + break; + case 2: // int16 + { + int16_t* dptr = static_cast(info.ptr); + vec = std::vector(dptr, dptr + n); + } + break; + case 4: // int32 + { + int32_t* dptr = static_cast(info.ptr); + vec = std::vector(dptr, dptr + n); + } + break; + case 8: // int64 + { + int64_t* dptr = static_cast(info.ptr); + vec = std::vector(dptr, dptr + n); + } + break; + default: + logs::fatalf("unsupported integer type with %d bytes", tbytes); + } + break; + default: + logs::fatalf("unknown dtype %c", kind); + } + return vec; } // modl::DBNptrT dbn_init (size_t n_input, std::vector n_hiddens, @@ -70,21 +107,21 @@ modl::RBMptrT rbm_init (size_t n_input, std::vector nouts, // std::vector(n_hiddens.begin(), n_hiddens.end()), label); // } -eqns::ApproxF get_sgd (PybindT learning_rate, eqns::NodeUnarF gradprocess) +eqns::ApproxF get_sgd (PybindT learning_rate) { - return [=](ead::NodeptrT& root, eqns::VariablesT leaves) + return [=](const eqns::VarErrsT& leaves) { - return eqns::sgd(root, leaves, learning_rate, gradprocess); + return eqns::sgd(leaves, learning_rate); }; } eqns::ApproxF get_rms_momentum (PybindT learning_rate, - PybindT discount_factor, PybindT epsilon, eqns::NodeUnarF gradprocess) + PybindT discount_factor, PybindT epsilon) { - return [=](ead::NodeptrT& root, eqns::VariablesT leaves) + return [=](const eqns::VarErrsT& leaves) { - return eqns::rms_momentum(root, leaves, learning_rate, - discount_factor, epsilon, gradprocess); + return eqns::rms_momentum(leaves, learning_rate, + discount_factor, epsilon); }; } @@ -96,112 +133,129 @@ PYBIND11_MODULE(rocnnet, m) py::class_ shape(m, "Shape"); - // common parent - py::class_ marshaler(m, "Marshaler"); + // layers + py::class_ layer(m, "Layer"); + py::class_ activation(m, "Activation"); + py::class_ dense(m, "Dense"); + py::class_ rbm(m, "RBM"); + py::class_ seqmodel(m, "SequentialModel"); - // models - py::class_ mlp(m, "MLP"); - py::class_ rbm(m, "RBM"); - // py::class_ dbn(m, "DBN"); + // trainers + py::class_ mlptrainer(m, "MLPTrainer"); + py::class_ dqntrainer(m, "DQNTrainer"); + py::class_ brbmtrainer(m, "BernoulliRBMTrainer"); - // support classes + // supports py::class_ assigns(m, "VarAssign"); - py::class_ dqninfo(m, "DQNInfo"); - py::class_ mlptrainer(m, "MLPTrainer"); - py::class_ dqntrainer(m, "DQNTrainer"); - py::class_ rbmtrainer(m, "RBMTrainer"); + py::class_ trainingctx(m, "TrainingContext"); + py::class_ dqntrainingctx(m, "DQNTrainingContext"); - // marshaler - marshaler - .def("serialize_to_file", [](py::object self, ead::NodeptrT source, - std::string filename) - { - std::fstream output(filename, - std::ios::out | std::ios::trunc | std::ios::binary); - if (false == modl::save(output, source->get_tensor(), - self.cast())) + shape.def(py::init>()); + + // layer + layer + .def("connect", &modl::iLayer::connect) + .def("get_contents", + [](py::object self) -> ead::NodesT { - logs::errorf("cannot save to file %s", filename.c_str()); - return false; - } - return true; - }, "load a version of this instance from a data") - .def("serialize_to_string", [](py::object self, ead::NodeptrT source) - { - std::stringstream savestr; - modl::save(savestr, source->get_tensor(), - self.cast()); - return savestr.str(); - }, "load a version of this instance from a data") - .def("parse_from_string", [](py::object self, std::string data) - { - modl::MarsptrT out(self.cast()->clone()); - std::stringstream loadstr; - loadstr << data; - modl::load(loadstr, out.get()); - return out; - }, "load a version of this instance from a data") - .def("get_variables", [](py::object self) - { - std::unordered_map> out; - pbm::PathedMapT bases = self.cast()->list_bases(); - for (auto bpair : bases) + ade::TensT contents = self.cast()->get_contents(); + ead::NodesT nodes; + nodes.reserve(contents.size()); + std::transform(contents.begin(), contents.end(), + std::back_inserter(nodes), + ead::NodeConverters::to_node); + return nodes; + }) + .def("save_file", + [](py::object self, std::string filename) -> bool { - if (auto var = std::dynamic_pointer_cast< - ead::Variable>(bpair.first)) + modl::iLayer& me = *self.cast(); + std::fstream output(filename, + std::ios::out | std::ios::trunc | std::ios::binary); + if (false == modl::save_layer(output, me, me.get_contents())) { - std::string key = fmts::join("::", - bpair.second.begin(), bpair.second.end()); - out.emplace(key, - std::make_shared>(var)); + logs::errorf("cannot save to file %s", filename.c_str()); + return false; } - } - return out; - }, "return variables dict in this marshaler"); - - // mlp - m.def("get_mlp", &pyrocnnet::mlp_init); - mlp - .def("copy", [](py::object self) - { - return std::make_shared(*self.cast()); - }, "deep copy this instance") - .def("forward", [](py::object self, ead::NodeptrT input, - modl::NonLinearsT nonlins) + return true; + }) + .def("save_string", + [](py::object self) -> std::string + { + modl::iLayer& me = *self.cast(); + std::stringstream savestr; + modl::save_layer(savestr, me, me.get_contents()); + return savestr.str(); + }) + .def("get_ninput", &modl::iLayer::get_ninput) + .def("get_noutput", &modl::iLayer::get_noutput); + + // activation + activation + .def(py::init(), + py::arg("label"), + py::arg("activation_type") = "sigmoid") + .def("clone", &modl::Activation::clone, py::arg("prefix") = ""); + + // dense + m.def("create_dense", + [](ead::NodeptrT weight, + ead::NodeptrT bias, + std::string label) { - return (*self.cast())(input, nonlins); - }, "forward input tensor and returned connected output"); + return std::make_shared(weight, bias, label); + }, + py::arg("weight"), + py::arg("bias") = nullptr, + py::arg("label")); + dense + .def(py::init, + eqns::InitF, + const std::string&>(), + py::arg("nunits"), + py::arg("indim"), + py::arg("weight_init") = eqns::unif_xavier_init(1), + py::arg("bias_init") = eqns::zero_init(), + py::arg("label")) + .def("clone", &modl::Dense::clone, py::arg("prefix") = ""); // rbm - m.def("get_rbm", &pyrocnnet::rbm_init); - rbm - .def("copy", [](py::object self) - { - return std::make_shared(*self.cast()); - }, "deep copy this instance") - .def("forward", [](py::object self, ead::NodeptrT input, - modl::NonLinearsT nonlins) + m.def("create_rbm", + [](modl::DenseptrT hidden, + modl::DenseptrT visible, + modl::ActivationptrT activation, + std::string label) { - return (*self.cast())(input, nonlins); - }, "forward input tensor and returned connected output") - .def("backward", [](py::object self, ead::NodeptrT hidden, - modl::NonLinearsT nonlins) - { - return self.cast()->prop_down(hidden, nonlins); - }, "backward hidden tensor and returned connected output") - .def("reconstruct_visible", [](py::object self, - ead::NodeptrT input, modl::NonLinearsT nonlins) - { - return trainer::reconstruct_visible( - *self.cast(), input, nonlins); - }, "reconstruct input") - .def("reconstruct_hidden", [](py::object self, - ead::NodeptrT hidden, modl::NonLinearsT nonlins) - { - return trainer::reconstruct_hidden( - *self.cast(), hidden, nonlins); - }, "reconstruct output"); + return std::make_shared( + hidden, visible, activation, label); + }, + py::arg("hidden"), + py::arg("visible") = nullptr, + py::arg("activation") = nullptr, + py::arg("label")); + rbm + .def(py::init, + eqns::InitF, + const std::string&>(), + py::arg("nhidden"), + py::arg("nvisible"), + py::arg("activation") = modl::sigmoid(), + py::arg("weight_init") = eqns::unif_xavier_init(1), + py::arg("bias_init") = eqns::zero_init(), + py::arg("label")) + .def("clone", &modl::RBM::clone, py::arg("prefix") = "") + .def("backward_connect", &modl::RBM::backward_connect); + + // seqmodel + seqmodel + .def(py::init(), + py::arg("label")) + .def("clone", &modl::SequentialModel::clone, py::arg("prefix") = "") + .def("add", &modl::SequentialModel::push_back); // // dbn // m.def("get_dbn", &pyrocnnet::dbn_init); @@ -215,220 +269,157 @@ PYBIND11_MODULE(rocnnet, m) // return (*self.cast())(input); // }, "forward input tensor and returned connected output"); - // mlptrainer mlptrainer - .def(py::init()) + .def(py::init(), + py::arg("model"), py::arg("sess"), + py::arg("update"), py::arg("batch_size"), + py::arg("gradprocess") = eqns::NodeUnarF(eqns::identity), + py::arg("ctx") = trainer::TrainingContext()) .def("train", &trainer::MLPTrainer::train, "train internal variables") - .def("serialize_to_file", [](py::object self, std::string filename) - { - std::fstream output(filename, - std::ios::out | std::ios::trunc | std::ios::binary); - if (false == self.cast()->save(output)) + .def("train_in", + [](py::object self) { - logs::errorf("cannot save to file %s", filename.c_str()); - return false; - } - return true; - }, "load a version of this instance from a data") - .def("serialize_to_string", [](py::object self, - ead::NodeptrT source) -> std::string - { - std::stringstream savestr; - if (self.cast()->save(savestr)) + return self.cast()->train_in_; + }, + "get train_in variable") + .def("expected_out", + [](py::object self) { - return savestr.str(); - } - return ""; - }, "load a version of this instance from a data") - .def("train_in", [](py::object self) - { - return self.cast()->train_in_; - }, "get train_in variable") - .def("expected_out", [](py::object self) - { - return self.cast()->expected_out_; - }, "get expected_out variable") - .def("train_out", [](py::object self) - { - return self.cast()->train_out_; - }, "get training node") - .def("error", [](py::object self) - { - return self.cast()->error_; - }, "get error node") - .def("brain", [](py::object self) - { - return self.cast()->brain_; - }, "get mlp"); - m.def("load_mlptrainer", [](std::string data, modl::NonLinearsT nonlins, - ead::iSession& sess, eqns::ApproxF update, - uint8_t batch_size) -> trainer::MLPTrainer - { - cortenn::Layer layer; - if (false == layer.ParseFromString(data)) - { - logs::fatal("failed to parse string when loading mlptrainer"); - } - - // load graph to target - const cortenn::Graph& graph = layer.graph(); - pbm::GraphInfo info; - pbm::load_graph(info, graph); - - auto pretrained = std::make_shared(info, "pretrained"); - - if (cortenn::Layer::kItCtx != layer.layer_context_case()) - { - logs::fatal("missing training context"); - } - trainer::TrainingContext ctx; - ctx.unmarshal_layer(layer); - return trainer::MLPTrainer(pretrained, nonlins, - sess, update, batch_size, ctx); - }); + return self.cast()->expected_out_; + }, + "get expected_out variable") + .def("train_out", + [](py::object self) + { + return self.cast()->train_out_; + }, + "get training node") + .def("error", + [](py::object self) + { + return self.cast()->error_; + }, + "get error node"); // dqntrainer - m.def("get_dqninfo", &pyrocnnet::dqninfo_init, - py::arg("train_interval") = 5, - py::arg("rand_action_prob") = 0.05, - py::arg("discount_rate") = 0.95, - py::arg("target_update_rate") = 0.01, - py::arg("exploration_period") = 1000, - py::arg("store_interval") = 5, - py::arg("mini_batch_size") = 32, - py::arg("max_exp") = 30000); + dqninfo + .def(py::init(), + py::arg("train_interval") = 5, + py::arg("rand_action_prob") = 0.05, + py::arg("discount_rate") = 0.95, + py::arg("target_update_rate") = 0.01, + py::arg("exploration_period") = 1000, + py::arg("store_interval") = 5, + py::arg("mini_batch_size") = 32, + py::arg("max_exp") = 30000); dqntrainer - .def(py::init()) + .def(py::init(), + py::arg("model"), py::arg("sess"), + py::arg("update"), py::arg("param"), + py::arg("gradprocess") = eqns::NodeUnarF(eqns::identity), + py::arg("ctx") = trainer::DQNTrainingContext()) .def("action", &trainer::DQNTrainer::action, "get next action") .def("store", &trainer::DQNTrainer::store, "save observation, action, and reward") .def("train", &trainer::DQNTrainer::train, "train qnets") - .def("serialize_to_file", [](py::object self, std::string filename) - { - std::fstream output(filename, - std::ios::out | std::ios::trunc | std::ios::binary); - if (false == self.cast()->save(output)) - { - logs::errorf("cannot save to file %s", filename.c_str()); - return false; - } - return true; - }, "load a version of this instance from a data") - .def("serialize_to_string", [](py::object self, - ead::NodeptrT source) -> std::string - { - std::stringstream savestr; - if (self.cast()->save(savestr)) - { - return savestr.str(); - } - return ""; - }, "load a version of this instance from a data") .def("error", &trainer::DQNTrainer::get_error, "get prediction error") .def("ntrained", &trainer::DQNTrainer::get_numtrained, "get number of iterations trained") .def("train_out", [](py::object self) { return self.cast()->train_out_; }, "get training node"); - m.def("load_dqntrainer", [](std::string data, modl::NonLinearsT nonlins, - ead::iSession& sess, eqns::ApproxF update, - trainer::DQNInfo param) -> trainer::DQNTrainer - { - cortenn::Layer layer; - if (false == layer.ParseFromString(data)) - { - logs::fatal("failed to parse string when loading mlptrainer"); - } - // load graph to target - const cortenn::Graph& graph = layer.graph(); - pbm::GraphInfo info; - pbm::load_graph(info, graph); - - auto pretrained = std::make_shared(info, "pretrained"); - - if (cortenn::Layer::kDqnCtx != layer.layer_context_case()) - { - logs::fatal("missing training context"); - } - trainer::DQNTrainingContext ctx; - ctx.unmarshal_layer(layer); - return trainer::DQNTrainer(pretrained, nonlins, - sess, update, param, ctx); - }); - - // rbmtrainer - rbmtrainer + // brbmtrainer + brbmtrainer .def(py::init< - modl::RBMptrT, - modl::NonLinearsT, + modl::RBM&, ead::iSession&, - ead::VarptrT, - uint8_t, + ade::DimT, PybindT, - size_t, - ead::NodeptrT>(), - py::arg("brain"), - py::arg("nolins"), + PybindT, + trainer::ErrorF>(), + py::arg("model"), py::arg("sess"), - py::arg("persistent"), py::arg("batch_size"), - py::arg("learning_rate") = 1e-3, - py::arg("n_cont_div") = 1, - py::arg("train_in") = nullptr) - .def("train", &trainer::RBMTrainer::train, "train internal variables") - .def("train_in", [](py::object self) - { - return self.cast()->train_in_; - }, "get train_in variable") - .def("cost", [](py::object self) - { - return self.cast()->cost_; - }, "get cost node") - .def("monitoring_cost", [](py::object self) - { - return self.cast()->monitoring_cost_; - }, "get monitoring cost node") - .def("brain", [](py::object self) - { - return self.cast()->brain_; - }, "get rbm"); - + py::arg("learning_rate"), + py::arg("discount_factor"), + py::arg("err_func")) + .def("train", + [](py::object self, py::array data) + { + auto trainer = self.cast(); + ade::Shape shape; + std::vector vec = pyrocnnet::arr2vec(shape, data); + return trainer->train(vec); + }, "train internal variables"); // inlines - m.def("identity", &eqns::identity); - - m.def("get_sgd", &pyrocnnet::get_sgd, - py::arg("learning_rate") = 0.5, - py::arg("gradprocess") = eqns::NodeUnarF(eqns::identity)); - m.def("get_rms_momentum", &pyrocnnet::get_rms_momentum, - py::arg("learning_rate") = 0.5, - py::arg("discount_factor") = 0.99, - py::arg("epsilon") = std::numeric_limits::epsilon(), - py::arg("gradprocess") = eqns::NodeUnarF(eqns::identity)); - - m.def("variable_from_init", - [](eqns::InitF init, std::vector slist, std::string label) - { - return init(pyrocnnet::p2cshape(slist), label); - }, - "Return labelled variable containing data created from initializer", - py::arg("init"), py::arg("slist"), py::arg("label") = ""); - - m.def("variance_scaling_init", [](PybindT factor) - { - return eqns::variance_scaling_init(factor); - }, - "truncated_normal(shape, 0, sqrt(factor / ((fanin + fanout)/2))", - py::arg("factor")); - - m.def("unif_xavier_init", &eqns::unif_xavier_init, - "uniform xavier initializer", - py::arg("factor") = 1); - - m.def("norm_xavier_init", &eqns::norm_xavier_init, - "normal xavier initializer", - py::arg("factor") = 1); + m + // activations (no longer useful) + .def("identity", &eqns::identity) + + // optimizations + .def("get_sgd", &pyrocnnet::get_sgd, + py::arg("learning_rate") = 0.5) + .def("get_rms_momentum", &pyrocnnet::get_rms_momentum, + py::arg("learning_rate") = 0.5, + py::arg("discount_factor") = 0.99, + py::arg("epsilon") = std::numeric_limits::epsilon()) + + // inits + .def("variable_from_init", + [](eqns::InitF init, std::vector slist, std::string label) + { + return init(pyrocnnet::p2cshape(slist), label); + }, + "Return labelled variable containing data created from initializer", + py::arg("init"), py::arg("slist"), py::arg("label") = "") + .def("zero_init", eqns::zero_init) + .def("variance_scaling_init", + [](PybindT factor) + { + return eqns::variance_scaling_init(factor); + }, + "truncated_normal(shape, 0, sqrt(factor / ((fanin + fanout)/2))", + py::arg("factor")) + .def("unif_xavier_init", &eqns::unif_xavier_init, + "uniform xavier initializer", + py::arg("factor") = 1) + .def("norm_xavier_init", &eqns::norm_xavier_init, + "normal xavier initializer", + py::arg("factor") = 1) + + // layer creation + .def("sigmoid", modl::sigmoid, py::arg("label") = "sigmoid") + .def("tanh", modl::tanh, py::arg("label") = "tanh") + .def("load_file_seqmodel", + [](std::string filename, std::string layer_label) -> modl::SeqModelptrT + { + std::ifstream input(filename); + if (false == input.is_open()) + { + logs::fatalf("file %s not found", filename.c_str()); + } + ade::TensT trained_roots; + return std::static_pointer_cast( + modl::load_layer(input, trained_roots, modl::seq_model_key, layer_label)); + }) + .def("load_file_rbmmodel", + [](std::string filename, std::string layer_label) -> modl::RBMptrT + { + std::ifstream input(filename); + if (false == input.is_open()) + { + logs::fatalf("file %s not found", filename.c_str()); + } + ade::TensT trained_roots; + return std::static_pointer_cast( + modl::load_layer(input, trained_roots, modl::rbm_layer_key, layer_label)); + }); }; diff --git a/rocnnet/trainer/dqn_trainer.hpp b/rocnnet/trainer/dqn_trainer.hpp index a9d9cb6dd..bdfb9522b 100644 --- a/rocnnet/trainer/dqn_trainer.hpp +++ b/rocnnet/trainer/dqn_trainer.hpp @@ -1,6 +1,7 @@ #include "ead/parse.hpp" +#include "ead/grader.hpp" -#include "rocnnet/modl/mlp.hpp" +#include "rocnnet/modl/model.hpp" #ifndef MODL_DQN_TRAINER_HPP #define MODL_DQN_TRAINER_HPP @@ -8,72 +9,8 @@ namespace trainer { -struct DQNTrainingContext final : public modl::iTrainingContext +struct DQNTrainingContext final { - void marshal_layer (cortenn::Layer& out_layer) const override - { - assert(nullptr != next_output_); - cortenn::DQTrainerState* state = out_layer.mutable_dqn_ctx(); - - cortenn::Graph* target = state->mutable_target_graph(); - pbm::GraphSaver saver; - next_output_->get_tensor()->accept(saver); - saver.save(*target, target_qnet_->list_bases()); - - state->set_actions_executed(actions_executed_); - state->set_trained_iteration(iteration_); - state->set_ntrained_called(n_train_called_); - state->set_nstored_called(n_store_called_); - - for (const ExpBatch& exbatch : experiences_) - { - cortenn::DQTrainerState_ExpBatch* batch = - state->add_experiences(); - - google::protobuf::RepeatedField obs( - exbatch.observation_.begin(), exbatch.observation_.end()); - batch->mutable_observation()->Swap(&obs); - - google::protobuf::RepeatedField new_obs( - exbatch.new_observation_.begin(), - exbatch.new_observation_.end()); - batch->mutable_new_observation()->Swap(&new_obs); - - batch->set_action_idx(exbatch.action_idx_); - batch->set_reward(exbatch.reward_); - } - } - - void unmarshal_layer (const cortenn::Layer& in_layer) override - { - const cortenn::DQTrainerState& state = in_layer.dqn_ctx(); - - const cortenn::Graph& target_graph = state.target_graph(); - pbm::GraphInfo info; - pbm::load_graph(info, target_graph); - - target_qnet_ = std::make_shared(info, "target"); - - actions_executed_ = state.actions_executed(); - iteration_ = state.trained_iteration(); - n_train_called_ = state.ntrained_called(); - n_store_called_ = state.nstored_called(); - - auto& exps = state.experiences(); - for (const cortenn::DQTrainerState_ExpBatch& batch : exps) - { - auto obs = batch.observation(); - auto new_obs = batch.new_observation(); - - experiences_.push_back(ExpBatch{ - std::vector(obs.begin(), obs.end()), - batch.action_idx(), - batch.reward(), - std::vector(new_obs.begin(), new_obs.end()), - }); - } - } - // experience replay struct ExpBatch { @@ -94,13 +31,13 @@ struct DQNTrainingContext final : public modl::iTrainingContext std::vector experiences_; // target network - modl::MLPptrT target_qnet_ = nullptr; + modl::SeqModelptrT target_model_ = nullptr; // train fanout: shape ead::NodeptrT next_output_ = nullptr; }; -struct DQNInfo +struct DQNInfo final { DQNInfo (size_t train_interval = 5, PybindT rand_action_prob = 0.05, @@ -108,7 +45,7 @@ struct DQNInfo PybindT target_update_rate = 0.01, PybindT exploration_period = 1000, size_t store_interval = 5, - uint8_t mini_batch_size = 32, + ade::DimT mini_batch_size = 32, size_t max_exp = 30000) : train_interval_(train_interval), rand_action_prob_(rand_action_prob), @@ -126,37 +63,132 @@ struct DQNInfo PybindT exploration_period_ = 1000; // memory parameters size_t store_interval_ = 5; - uint8_t mini_batch_size_ = 32; + ade::DimT mini_batch_size_ = 32; size_t max_exp_ = 30000; }; -struct DQNTrainer +struct DQNTrainer final { - DQNTrainer (modl::MLPptrT brain, - modl::NonLinearsT nonlinearities, - ead::iSession& sess, - eqns::ApproxF update, DQNInfo param, - DQNTrainingContext ctx) : + DQNTrainer (modl::SequentialModel& model, + ead::iSession& sess, eqns::ApproxF update, DQNInfo param, + eqns::NodeUnarF gradprocess = eqns::NodeUnarF(eqns::identity), + DQNTrainingContext ctx = DQNTrainingContext()) : sess_(&sess), params_(param), - source_qnet_(brain), + source_model_(model), ctx_(ctx) { - initialize(nonlinearities, update); - } + if (nullptr == ctx_.target_model_) + { + ctx_.target_model_ = modl::SeqModelptrT(model.clone("target_")); + } - DQNTrainer (modl::MLPptrT brain, - modl::NonLinearsT nonlinearities, - ead::iSession& sess, - eqns::ApproxF update, DQNInfo param) : - sess_(&sess), - params_(param), - source_qnet_(brain) - { - ctx_.target_qnet_ = std::make_shared(*brain); - ctx_.target_qnet_->label_ += "_target"; + input_ = ead::make_variable_scalar(0.0, ade::Shape({ + (ade::DimT) source_model_.get_ninput()}), "observation"); + train_input_ = ead::make_variable_scalar(0.0, ade::Shape({ + (ade::DimT) source_model_.get_ninput(), + params_.mini_batch_size_}), "train_observation"); + next_input_ = ead::make_variable_scalar(0.0, ade::Shape({ + (ade::DimT) source_model_.get_ninput(), + params_.mini_batch_size_}), "next_observation"); + next_output_mask_ = ead::make_variable_scalar(0.0, + ade::Shape({params_.mini_batch_size_}), + "next_observation_mask"); + reward_ = ead::make_variable_scalar(0.0, + ade::Shape({params_.mini_batch_size_}), "rewards"); + output_mask_ = ead::make_variable_scalar(0.0, + ade::Shape({(ade::DimT) source_model_.get_noutput(), + params_.mini_batch_size_}), "action_mask"); + + // forward action score computation + output_ = source_model_.connect(ead::convert_to_node(input_)); + + train_out_ = source_model_.connect( + ead::convert_to_node(train_input_)); + + // predicting target future rewards + ctx_.next_output_ = ctx_.target_model_->connect( + ead::convert_to_node(next_input_)); + + auto target_values = tenncor::mul( + tenncor::reduce_max_1d(ctx_.next_output_, 0), + ead::convert_to_node(next_output_mask_)); + future_reward_ = tenncor::add(ead::convert_to_node(reward_), + tenncor::mul( + ead::make_constant_scalar(params_.discount_rate_, + target_values->shape()), + target_values)); // reward for each instance in batch + + // prediction error + auto masked_output_score = tenncor::reduce_sum_1d( + tenncor::mul(train_out_, ead::convert_to_node(output_mask_)), 0); + prediction_error_ = tenncor::reduce_mean(tenncor::square( + tenncor::sub(masked_output_score, future_reward_))); + + // updates for source network + ade::TensT source_contents = source_model_.get_contents(); + eqns::VarErrsT source_vars; + for (auto tens : source_contents) + { + if (auto var = std::dynamic_pointer_cast< + ead::Variable>(tens)) + { + auto varnode = std::make_shared>(var); + source_vars.push_back({ + varnode, + gradprocess(ead::derive(prediction_error_, ead::convert_to_node(varnode))) + }); + } + } + updates_ = update(source_vars); - initialize(nonlinearities, update); + // update target network + ade::TensT target_contents = ctx_.target_model_->get_contents(); + size_t nvars = source_vars.size(); + std::vector> target_vars; + target_vars.reserve(nvars); + for (auto tens : target_contents) + { + if (auto var = std::dynamic_pointer_cast< + ead::Variable>(tens)) + { + target_vars.push_back( + std::make_shared>(var)); + } + } + + eqns::AssignsT target_assigns; + for (size_t i = 0; i < nvars; i++) + { + // this is equivalent to target = (1-alpha) * target + alpha * source + auto target = ead::convert_to_node(target_vars[i]); + auto source = ead::convert_to_node(source_vars[i].first); + auto diff = tenncor::sub(target, source); + auto target_update_rate = ead::make_constant_scalar( + params_.target_update_rate_, diff->shape()); + + auto target_next = tenncor::sub(target, tenncor::mul( + target_update_rate, diff)); + target_assigns.push_back(eqns::VarAssign{ + fmts::sprintf("target_grad_%s", + target_vars[i]->get_label().c_str()), + target_vars[i], target_next}); + } + updates_.push_back(target_assigns); + + ade::TensT track_batch = { + prediction_error_->get_tensor(), + train_out_->get_tensor(), + output_->get_tensor(), + }; + for (eqns::AssignsT& assigns : updates_) + { + for (eqns::VarAssign& assign : assigns) + { + track_batch.push_back(assign.source_->get_tensor()); + } + } + sess_->track(track_batch); } uint8_t action (std::vector& input) @@ -166,7 +198,7 @@ struct DQNTrainer // perform random exploration action if (get_random() < exploration) { - return std::floor(get_random() * source_qnet_->get_noutput()); + return std::floor(get_random() * source_model_.get_noutput()); } input_->assign(input.data(), input_->shape()); sess_->update({input_->get_tensor().get()}); @@ -222,7 +254,7 @@ struct DQNTrainer batch.observation_.begin(), batch.observation_.end()); { std::vector local_act_mask( - source_qnet_->get_noutput(), 0); + source_model_.get_noutput(), 0); local_act_mask[batch.action_idx_] = 1.0; action_mask.insert(action_mask.end(), local_act_mask.begin(), local_act_mask.end()); @@ -231,7 +263,7 @@ struct DQNTrainer if (batch.new_observation_.empty()) { new_states.insert(new_states.end(), - source_qnet_->get_ninput(), 0); + source_model_.get_ninput(), 0); new_states_mask.push_back(0); } else @@ -277,12 +309,6 @@ struct DQNTrainer return ctx_.iteration_; } - bool save (std::ostream& outs) - { - return modl::save(outs, - output_->get_tensor(), source_qnet_.get(), &ctx_); - } - // === forward computation === // fanin: shape ead::VarptrT input_ = nullptr; @@ -303,120 +329,6 @@ struct DQNTrainer ead::iSession* sess_; private: - void initialize (modl::NonLinearsT nonlinearities, - eqns::ApproxF update) - { - input_ = ead::make_variable_scalar(0.0, - ade::Shape({source_qnet_->get_ninput()}), "observation"); - train_input_ = ead::make_variable_scalar(0.0, - ade::Shape({source_qnet_->get_ninput(), - params_.mini_batch_size_}), "train_observation"); - next_input_ = ead::make_variable_scalar(0.0, - ade::Shape({source_qnet_->get_ninput(), - params_.mini_batch_size_}), "next_observation"); - next_output_mask_ = ead::make_variable_scalar(0.0, - ade::Shape({params_.mini_batch_size_}), - "next_observation_mask"); - reward_ = ead::make_variable_scalar(0.0, - ade::Shape({params_.mini_batch_size_}), "rewards"); - output_mask_ = ead::make_variable_scalar(0.0, - ade::Shape({source_qnet_->get_noutput(), - params_.mini_batch_size_}), "action_mask"); - - // forward action score computation - output_ = (*source_qnet_)(ead::convert_to_node(input_), - nonlinearities); - - train_out_ = (*source_qnet_)( - ead::convert_to_node(train_input_), nonlinearities); - - // predicting target future rewards - ctx_.next_output_ = (*ctx_.target_qnet_)( - ead::convert_to_node(next_input_), nonlinearities); - - auto target_values = tenncor::mul( - tenncor::reduce_max_1d(ctx_.next_output_, 0), - ead::convert_to_node(next_output_mask_)); - future_reward_ = tenncor::add(ead::convert_to_node(reward_), - tenncor::mul( - ead::make_constant_scalar(params_.discount_rate_, - target_values->shape()), - target_values)); // reward for each instance in batch - - // prediction error - auto masked_output_score = tenncor::reduce_sum_1d( - tenncor::mul(train_out_, ead::convert_to_node(output_mask_)), 0); - prediction_error_ = tenncor::reduce_mean(tenncor::square( - tenncor::sub(masked_output_score, future_reward_))); - - // updates for source network - pbm::PathedMapT svmap = source_qnet_->list_bases(); - std::unordered_map labelled_indices; - eqns::VariablesT source_vars; - for (auto vpair : svmap) - { - if (auto var = std::dynamic_pointer_cast< - ead::Variable>(vpair.first)) - { - auto label = fmts::to_string( - vpair.second.begin(), vpair.second.end()); - labelled_indices.emplace(label, source_vars.size()); - - source_vars.push_back( - std::make_shared>(var)); - } - } - updates_ = update(prediction_error_, source_vars); - - // update target network - pbm::PathedMapT tvmap = ctx_.target_qnet_->list_bases(); - size_t nvars = source_vars.size(); - eqns::VariablesT target_vars(nvars); - for (auto vpair : tvmap) - { - if (auto var = std::dynamic_pointer_cast< - ead::Variable>(vpair.first)) - { - auto label = fmts::to_string( - vpair.second.begin(), vpair.second.end()); - target_vars[labelled_indices[label]] = - std::make_shared>(var); - } - } - eqns::AssignsT target_assigns; - for (size_t i = 0; i < nvars; i++) - { - // this is equivalent to target = (1-alpha) * target + alpha * source - auto target = ead::convert_to_node(target_vars[i]); - auto source = ead::convert_to_node(source_vars[i]); - auto diff = tenncor::sub(target, source); - auto target_update_rate = ead::make_constant_scalar( - params_.target_update_rate_, diff->shape()); - - auto target_next = tenncor::sub(target, tenncor::mul( - target_update_rate, diff)); - target_assigns.push_back(eqns::VarAssign{ - fmts::sprintf("target_grad_%s", - target_vars[i]->get_label().c_str()), - target_vars[i], target_next}); - } - updates_.push_back(target_assigns); - - ade::TensT track_batch = { - prediction_error_->get_tensor(), - train_out_->get_tensor(), - output_->get_tensor(), - }; - for (eqns::AssignsT& assigns : updates_) - { - for (eqns::VarAssign& assign : assigns) - { - track_batch.push_back(assign.source_->get_tensor()); - } - } - sess_->track(track_batch); - } - PybindT linear_annealing (PybindT initial_prob) const { if (ctx_.actions_executed_ >= params_.exploration_period_) @@ -449,7 +361,7 @@ struct DQNTrainer DQNInfo params_; // source network - modl::MLPptrT source_qnet_; + modl::SequentialModel& source_model_; // === prediction computation === // train_fanin: shape diff --git a/rocnnet/trainer/mlp_trainer.hpp b/rocnnet/trainer/mlp_trainer.hpp index 49a798474..5d759dc6d 100644 --- a/rocnnet/trainer/mlp_trainer.hpp +++ b/rocnnet/trainer/mlp_trainer.hpp @@ -1,4 +1,6 @@ -#include "rocnnet/modl/mlp.hpp" +#include "ead/grader.hpp" + +#include "rocnnet/modl/model.hpp" #include "rocnnet/eqns/err_approx.hpp" @@ -9,57 +11,47 @@ namespace trainer { // Normal default context that only stores the number of iterations -struct TrainingContext final : public modl::iTrainingContext +struct TrainingContext final { - void marshal_layer (cortenn::Layer& out_layer) const override - { - cortenn::ItTrainerState* state = out_layer.mutable_it_ctx(); - state->set_iterations(n_iterations_); - } - - void unmarshal_layer (const cortenn::Layer& in_layer) override - { - const cortenn::ItTrainerState& state = in_layer.it_ctx(); - n_iterations_ = state.iterations(); - } - size_t n_iterations_ = 0; }; // MLPTrainer does not own anything -struct MLPTrainer +struct MLPTrainer final { - MLPTrainer (modl::MLPptrT brain, - modl::NonLinearsT nonlinearities, - ead::iSession& sess, - eqns::ApproxF update, uint8_t batch_size, + MLPTrainer (modl::SequentialModel& model, + ead::iSession& sess, eqns::ApproxF update, ade::DimT batch_size, + eqns::NodeUnarF gradprocess = eqns::NodeUnarF(eqns::identity), TrainingContext ctx = TrainingContext()) : batch_size_(batch_size), - train_in_(ead::make_variable_scalar(0.0, - ade::Shape({brain->get_ninput(), batch_size}), "train_in")), - brain_(brain), + train_in_(ead::make_variable_scalar(0.0, ade::Shape({ + (ade::DimT) model.get_ninput(), batch_size}), "train_in")), + model_(model), sess_(&sess), ctx_(ctx) { - train_out_ = (*brain_)( - ead::convert_to_node(train_in_), nonlinearities); - expected_out_ = ead::make_variable_scalar(0.0, - ade::Shape({brain_->get_noutput(), batch_size}), "expected_out"); + train_out_ = model_.connect( + ead::convert_to_node(train_in_)); + expected_out_ = ead::make_variable_scalar(0.0, ade::Shape({ + (ade::DimT) model.get_noutput(), batch_size}), "expected_out"); error_ = tenncor::square( tenncor::sub(ead::convert_to_node(expected_out_), train_out_)); - pbm::PathedMapT vmap = brain_->list_bases(); - eqns::VariablesT vars; - for (auto vpair : vmap) + auto contents = model_.get_contents(); + eqns::VarErrsT vars; + for (auto tens : contents) { if (auto var = std::dynamic_pointer_cast< - ead::Variable>(vpair.first)) + ead::Variable>(tens)) { - vars.push_back( - std::make_shared>(var)); + auto varnode = std::make_shared>(var); + vars.push_back({ + varnode, + gradprocess(ead::derive(error_, ead::convert_to_node(varnode))) + }); } } - updates_ = update(error_, vars); + updates_ = update(vars); ade::TensT track_batch = { train_out_->get_tensor(), @@ -78,8 +70,8 @@ struct MLPTrainer void train (std::vector& train_in, std::vector& expected_out) { - size_t insize = brain_->get_ninput(); - size_t outsize = brain_->get_noutput(); + size_t insize = model_.get_ninput(); + size_t outsize = model_.get_noutput(); if (train_in.size() != insize * batch_size_) { logs::fatalf("training vector size (%d) does not match " @@ -107,16 +99,11 @@ struct MLPTrainer ++ctx_.n_iterations_; } - bool save (std::ostream& outs) - { - return modl::save(outs, - error_->get_tensor(), brain_.get(), &ctx_); - } + modl::SequentialModel& model_; uint8_t batch_size_; ead::VarptrT train_in_; ead::VarptrT expected_out_; - modl::MLPptrT brain_; ead::NodeptrT train_out_; ead::NodeptrT error_; diff --git a/rocnnet/trainer/old_rbm_trainer.hpp b/rocnnet/trainer/old_rbm_trainer.hpp new file mode 100644 index 000000000..d34635e63 --- /dev/null +++ b/rocnnet/trainer/old_rbm_trainer.hpp @@ -0,0 +1,228 @@ +#include "rocnnet/modl/rbm.hpp" + +#ifndef OLD_MODL_RBM_TRAINER_HPP +#define OLD_MODL_RBM_TRAINER_HPP + +namespace trainer +{ + +// recreate input using hidden distribution +// output shape of input->shape() +ead::NodeptrT reconstruct_visible (modl::RBM& rbm, + ead::NodeptrT input, modl::NonLinearsT nonlins) +{ + ead::NodeptrT hidden_dist = rbm(input, nonlins); + ead::NodeptrT hidden_sample = tenncor::random::rand_binom_one(hidden_dist); + return rbm.prop_down(hidden_sample, nonlins); +} + +ead::NodeptrT reconstruct_hidden (modl::RBM& rbm, + ead::NodeptrT hidden, modl::NonLinearsT nonlins) +{ + ead::NodeptrT visible_dist = rbm.prop_down(hidden, nonlins); + ead::NodeptrT visible_sample = tenncor::random::rand_binom_one(visible_dist); + return rbm(visible_sample, nonlins); +} + +struct RBMTrainer +{ + RBMTrainer (modl::RBMptrT brain, + modl::NonLinearsT nonlinearities, + ead::iSession& sess, + ead::VarptrT persistent, + uint8_t batch_size, + PybindT learning_rate = 1e-3, + size_t n_cont_div = 1, + ead::NodeptrT train_in = nullptr) : + brain_(brain), + sess_(&sess) + { + if (brain->layers_.size() != 1) + { + logs::error("rbm training only operates on the first layer"); + } + ead::VarptrT weight = brain_->layers_[0].weight_->var_; + ead::VarptrT hbias = brain_->layers_[0].hbias_->var_; + ead::VarptrT vbias = brain_->layers_[0].vbias_->var_; + + if (nullptr == train_in) + { + train_in_ = ead::convert_to_node( + ead::make_variable_scalar(0.0, ade::Shape({ + brain->get_ninput(), + batch_size, + }), "train_in")); + } + else + { + train_in_ = train_in; + } + // if persistent not available use Contrastive Divergence (CD) + if (nullptr == persistent) + { + persistent_ = tenncor::random::rand_binom_one( + (*brain)(train_in_, nonlinearities)); + } + // otherwise use Persistent CD + // (initialize from the old state of the chain) + else + { + persistent_ = persistent; + } + + // chain length is n_cont_div + auto chain_segment = tenncor::random::rand_binom_one( + reconstruct_hidden(*brain, persistent_, nonlinearities)); + assert(n_cont_div > 0); + for (size_t i = 0; i < n_cont_div - 1; ++i) + { + chain_segment = tenncor::random::rand_binom_one( + reconstruct_hidden(*brain, chain_segment, nonlinearities)); + } + + // use operational optimization to recover presig and vis nodes + auto presig_vis = tenncor::nn::fully_connect( + {chain_segment}, + {tenncor::transpose(ead::convert_to_node(weight))}, + ead::convert_to_node(vbias)); + auto final_visible_dist = tenncor::sigmoid(presig_vis); + auto chain_end = tenncor::random::rand_binom_one(final_visible_dist); + + cost_ = tenncor::sub(tenncor::reduce_mean(free_energy(train_in_)), + tenncor::reduce_mean(free_energy(chain_end))); + + auto dW = ead::derive(cost_, ead::convert_to_node(weight)); + auto dhb = ead::derive(cost_, ead::convert_to_node(hbias)); + auto dvb = ead::derive(cost_, ead::convert_to_node(vbias)); + + auto next_weight = tenncor::sub(ead::convert_to_node(weight), + tenncor::mul(ead::make_constant_scalar( + learning_rate, dW->shape()), dW)); + auto next_hbias = tenncor::sub(ead::convert_to_node(hbias), + tenncor::mul(ead::make_constant_scalar( + learning_rate, dhb->shape()), dhb)); + auto next_vbias = tenncor::sub(ead::convert_to_node(vbias), + tenncor::mul(ead::make_constant_scalar( + learning_rate, dvb->shape()), dvb)); + eqns::AssignsT assigns = { + eqns::VarAssign{"", weight, next_weight}, + eqns::VarAssign{"", hbias, next_hbias}, + eqns::VarAssign{"", vbias, next_vbias}, + }; + + if (nullptr == persistent) + { + // reconstruction cost + monitoring_cost_ = get_reconstruction_cost( + train_in_, final_visible_dist); + } + else + { + // pseudo-likelihood + auto next_persistent = tenncor::random::rand_binom_one( + reconstruct_hidden(*brain, chain_segment, nonlinearities)); + assigns.push_back( + eqns::VarAssign{"", persistent, next_persistent}); + + monitoring_cost_ = get_pseudo_likelihood_cost(train_in_); + } + updates_.push_back(assigns); + + ade::TensT track_batch; + track_batch.reserve(assigns.size()); + std::transform(assigns.begin(), assigns.end(), + std::back_inserter(track_batch), + [](eqns::VarAssign& assign) + { + return assign.source_->get_tensor(); + }); + sess_->track(track_batch); + } + + // input a 2-D vector of shape return monitor cost + PybindT train (std::vector& train_in) + { + auto var = dynamic_cast*>(train_in_.get()); + if (nullptr == var) + { + logs::fatal("cannot train RBM with non-native input"); + } + ade::Shape train_shape = var->shape(); + var->assign(train_in.data(), train_shape); + assign_groups(updates_, + [this](std::unordered_set& updated) + { + this->sess_->update(updated); + }); + + return monitoring_cost_->data()[0] / train_shape.at(1); + } + + ead::NodeptrT train_in_; + modl::RBMptrT brain_; + ead::NodeptrT cost_; + ead::NodeptrT monitoring_cost_; + + eqns::AssignGroupsT updates_; + ead::iSession* sess_; + +private: + ead::NodeptrT get_pseudo_likelihood_cost (ead::NodeptrT input) + { + const ade::Shape& shape = input->shape(); + std::vector zeros(shape.n_elems(), 0); + zeros[0] = 1; + auto one_i = ead::make_constant(zeros.data(), shape); + + ead::NodeptrT xi = tenncor::round(input); // xi = [0|1, ...] + ead::NodeptrT xi_flip = tenncor::sub(one_i, xi); + + ead::NodeptrT fe_xi = free_energy(xi); + ead::NodeptrT fe_xi_flip = free_energy(xi_flip); + + return tenncor::reduce_mean(tenncor::mul( + ead::make_constant_scalar(brain_->get_ninput(), fe_xi->shape()), + tenncor::log(tenncor::sigmoid(tenncor::sub(fe_xi_flip, fe_xi))))); + } + + ead::NodeptrT get_reconstruction_cost ( + ead::NodeptrT input, ead::NodeptrT visible_dist) + { + ead::NodeptrT p_success = tenncor::mul( + input, tenncor::log(visible_dist)); + ead::NodeptrT p_not = tenncor::mul( + tenncor::sub(ead::make_constant_scalar( + 1, input->shape()), input), + tenncor::log(tenncor::sub(ead::make_constant_scalar( + 1, visible_dist->shape()), visible_dist))); + return tenncor::reduce_mean(tenncor::reduce_sum_1d( + tenncor::transpose(tenncor::add(p_success, p_not)), 1)); + } + + ead::NodeptrT free_energy (ead::NodeptrT sample) + { + ead::VarptrT weight = brain_->layers_[0].weight_->var_; + ead::VarptrT hbias = brain_->layers_[0].hbias_->var_; + ead::VarptrT vbias = brain_->layers_[0].vbias_->var_; + + auto vbias_term = tenncor::matmul(sample, + tenncor::transpose(ead::convert_to_node(vbias))); + // @ + z -> + auto wx_b = tenncor::nn::fully_connect( + {sample}, + {ead::convert_to_node(weight)}, + ead::convert_to_node(hbias)); + auto hidden_term = tenncor::reduce_sum( + tenncor::log(tenncor::add( + ead::make_constant_scalar(1, wx_b->shape()), + tenncor::exp(wx_b) + )), 0, 1); + return tenncor::neg(tenncor::add(vbias_term, hidden_term)); + } + + ead::NodeptrT persistent_; +}; + +} + +#endif // OLD_MODL_RBM_TRAINER_HPP diff --git a/rocnnet/trainer/rbm_trainer.hpp b/rocnnet/trainer/rbm_trainer.hpp index 6a11e4ecd..fa4ab28db 100644 --- a/rocnnet/trainer/rbm_trainer.hpp +++ b/rocnnet/trainer/rbm_trainer.hpp @@ -1,226 +1,189 @@ #include "rocnnet/modl/rbm.hpp" +#include "rocnnet/eqns/err_approx.hpp" + #ifndef MODL_RBM_TRAINER_HPP #define MODL_RBM_TRAINER_HPP namespace trainer { -// recreate input using hidden distribution -// output shape of input->shape() -ead::NodeptrT reconstruct_visible (modl::RBM& rbm, - ead::NodeptrT input, modl::NonLinearsT nonlins) +// Bernoulli RBM "error approximation" +// for each (x, err) in leaves +// momentum_next ~ χ * momentum_cur + η * (1 - χ) / err.shape[0] * err +// x_next = x_curr + next_momentum +// +// where η is the learning rate, and χ is discount_factor +eqns::AssignGroupsT bbernoulli_approx (const eqns::VarErrsT& leaves, + PybindT learning_rate, PybindT discount_factor, + std::string root_label = "") { - ead::NodeptrT hidden_dist = rbm(input, nonlins); - ead::NodeptrT hidden_sample = tenncor::random::rand_binom_one(hidden_dist); - return rbm.prop_down(hidden_sample, nonlins); -} + // assign momentums before leaves + eqns::AssignsT assigns; + for (size_t i = 0, nleaves = leaves.size(); i < nleaves; ++i) + { + auto leaf_node = ead::convert_to_node(leaves[i].first); + auto err = leaves[i].second; -ead::NodeptrT reconstruct_hidden (modl::RBM& rbm, - ead::NodeptrT hidden, modl::NonLinearsT nonlins) -{ - ead::NodeptrT visible_dist = rbm.prop_down(hidden, nonlins); - ead::NodeptrT visible_sample = tenncor::random::rand_binom_one(visible_dist); - return rbm(visible_sample, nonlins); + auto shape = err->shape(); + std::vector slist(shape.begin(), shape.end()); + auto it = slist.rbegin(), et = slist.rend(); + while (it != et && *it == 1) + { + ++it; + } + ade::DimT shape_factor = it == et ? 1 : *it; + auto momentum = ead::make_variable_scalar(0, + err->shape(), leaves[i].first->get_label() + "_momentum"); + auto momentum_next = tenncor::add( + tenncor::mul( + ead::make_constant_scalar(discount_factor, momentum->shape()), + ead::convert_to_node(momentum)), + tenncor::mul( + ead::make_constant_scalar(learning_rate * + (1 - discount_factor) / shape_factor, err->shape()), + err)); + auto leaf_next = tenncor::add(leaf_node, momentum_next); + + assigns.push_back(eqns::VarAssign{ + fmts::sprintf("bbernoulli_momentum::%s_momentum_%s", + root_label.c_str(), leaves[i].first->get_label().c_str()), + momentum, momentum_next}); + assigns.push_back(eqns::VarAssign{ + fmts::sprintf("bbernoulli_momentum::%s_grad_%s", + root_label.c_str(), leaves[i].first->get_label().c_str()), + leaves[i].first, leaf_next}); + } + return {assigns}; } -struct RBMTrainer +using ErrorF = std::function(ead::NodeptrT,ead::NodeptrT)>; + +struct BernoulliRBMTrainer final { - RBMTrainer (modl::RBMptrT brain, - modl::NonLinearsT nonlinearities, + BernoulliRBMTrainer (modl::RBM& model, ead::iSession& sess, - ead::VarptrT persistent, - uint8_t batch_size, - PybindT learning_rate = 1e-3, - size_t n_cont_div = 1, - ead::NodeptrT train_in = nullptr) : - brain_(brain), - sess_(&sess) + ade::DimT batch_size, + PybindT learning_rate, + PybindT discount_factor, + ErrorF err_func = ErrorF()) : + model_(model), sess_(&sess), batch_size_(batch_size) { - if (brain->layers_.size() != 1) - { - logs::error("rbm training only operates on the first layer"); - } - ead::VarptrT weight = brain_->layers_[0].weight_->var_; - ead::VarptrT hbias = brain_->layers_[0].hbias_->var_; - ead::VarptrT vbias = brain_->layers_[0].vbias_->var_; - - if (nullptr == train_in) - { - train_in_ = ead::convert_to_node( - ead::make_variable_scalar(0.0, ade::Shape({ - brain->get_ninput(), - batch_size, - }), "train_in")); - } - else - { - train_in_ = train_in; - } - // if persistent not available use Contrastive Divergence (CD) - if (nullptr == persistent) - { - persistent_ = tenncor::random::rand_binom_one( - (*brain)(train_in_, nonlinearities)); - } - // otherwise use Persistent CD - // (initialize from the old state of the chain) - else - { - persistent_ = persistent; - } + visible_ = ead::make_variable_scalar(0, + ade::Shape({(ade::DimT) model.get_ninput(), batch_size})); + + hidden_sample_ = model.connect(visible_); + visible_sample_ = model.backward_connect( + tenncor::random::rand_binom_one(hidden_sample_)); + + auto hidden_reconp = model.connect(visible_sample_); + + auto grad_w = tenncor::sub( + tenncor::matmul(tenncor::transpose( + ead::convert_to_node(visible_)), hidden_sample_), + tenncor::matmul(tenncor::transpose( + visible_sample_), hidden_reconp)); + auto grad_hb = tenncor::reduce_mean_1d( + tenncor::sub(hidden_sample_, hidden_reconp), 1); + auto grad_vb = tenncor::reduce_mean_1d( + tenncor::sub(ead::convert_to_node(visible_), visible_sample_), 1); + + auto contents = model.get_contents(); + std::vector> vars; + vars.reserve(contents.size()); + std::transform(contents.begin(), contents.end(), + std::back_inserter(vars), + [](ade::TensptrT tens) + { + return std::make_shared>( + std::static_pointer_cast>(tens)); + }); + eqns::VarErrsT varerrs = { + {vars[0], grad_w}, + {vars[1], grad_hb}, + {vars[3], grad_vb}, + }; - // chain length is n_cont_div - auto chain_segment = tenncor::random::rand_binom_one( - reconstruct_hidden(*brain, persistent_, nonlinearities)); - assert(n_cont_div > 0); - for (size_t i = 0; i < n_cont_div - 1; ++i) - { - chain_segment = tenncor::random::rand_binom_one( - reconstruct_hidden(*brain, chain_segment, nonlinearities)); - } + updates_ = bbernoulli_approx(varerrs, learning_rate, discount_factor); - // use operational optimization to recover presig and vis nodes - auto presig_vis = tenncor::nn::fully_connect( - {chain_segment}, - {tenncor::transpose(ead::convert_to_node(weight))}, - ead::convert_to_node(vbias)); - auto final_visible_dist = tenncor::sigmoid(presig_vis); - auto chain_end = tenncor::random::rand_binom_one(final_visible_dist); - - cost_ = tenncor::sub(tenncor::reduce_mean(free_energy(train_in_)), - tenncor::reduce_mean(free_energy(chain_end))); - - auto dW = ead::derive(cost_, ead::convert_to_node(weight)); - auto dhb = ead::derive(cost_, ead::convert_to_node(hbias)); - auto dvb = ead::derive(cost_, ead::convert_to_node(vbias)); - - auto next_weight = tenncor::sub(ead::convert_to_node(weight), - tenncor::mul(ead::make_constant_scalar( - learning_rate, dW->shape()), dW)); - auto next_hbias = tenncor::sub(ead::convert_to_node(hbias), - tenncor::mul(ead::make_constant_scalar( - learning_rate, dhb->shape()), dhb)); - auto next_vbias = tenncor::sub(ead::convert_to_node(vbias), - tenncor::mul(ead::make_constant_scalar( - learning_rate, dvb->shape()), dvb)); - eqns::AssignsT assigns = { - eqns::VarAssign{"", weight, next_weight}, - eqns::VarAssign{"", hbias, next_hbias}, - eqns::VarAssign{"", vbias, next_vbias}, + ade::TensT to_track = { + hidden_sample_->get_tensor(), + visible_sample_->get_tensor(), }; - - if (nullptr == persistent) + to_track.reserve(updates_.size() + 1); + if (err_func) { - // reconstruction cost - monitoring_cost_ = get_reconstruction_cost( - train_in_, final_visible_dist); + error_ = err_func(ead::convert_to_node(visible_), visible_sample_); + to_track.push_back(error_->get_tensor()); } - else - { - // pseudo-likelihood - auto next_persistent = tenncor::random::rand_binom_one( - reconstruct_hidden(*brain, chain_segment, nonlinearities)); - assigns.push_back( - eqns::VarAssign{"", persistent, next_persistent}); - - monitoring_cost_ = get_pseudo_likelihood_cost(train_in_); - } - updates_.push_back(assigns); - ade::TensT track_batch; - track_batch.reserve(assigns.size()); - std::transform(assigns.begin(), assigns.end(), - std::back_inserter(track_batch), - [](eqns::VarAssign& assign) + for (auto& assigns : updates_) + { + for (auto& assign : assigns) { - return assign.source_->get_tensor(); - }); - sess_->track(track_batch); + auto source = assign.source_->get_tensor(); + assign_sources_.emplace(source.get()); + to_track.push_back(source); + } + } + sess.track(to_track); } - // input a 2-D vector of shape return monitor cost + // Return error after training with train_in + // if error is set, otherwise -1 PybindT train (std::vector& train_in) { - auto var = dynamic_cast*>(train_in_.get()); - if (nullptr == var) + size_t insize = model_.get_ninput(); + if (train_in.size() != insize * batch_size_) { - logs::fatal("cannot train RBM with non-native input"); + logs::fatalf("training vector size (%d) does not match " + "input size (%d) * batchsize (%d)", train_in.size(), + insize, batch_size_); } - ade::Shape train_shape = var->shape(); - var->assign(train_in.data(), train_shape); + visible_->assign(train_in.data(), visible_->shape()); + + sess_->update_target(assign_sources_, { + visible_->get_tensor().get(), + }); + + if (nullptr == error_) + { + assign_groups(updates_, + [this](ead::TensSetT& updated) + { + this->sess_->update(updated); + }); + return -1; + } + assign_groups(updates_, - [this](std::unordered_set& updated) + [this](ead::TensSetT& updated) { - this->sess_->update(updated); + this->sess_->update_target( + ead::TensSetT{this->error_->get_tensor().get()}, updated); }); - - return monitoring_cost_->data()[0] / train_shape.at(1); + return error_->data()[0]; } - ead::NodeptrT train_in_; - modl::RBMptrT brain_; - ead::NodeptrT cost_; - ead::NodeptrT monitoring_cost_; +private: + modl::RBM& model_; - eqns::AssignGroupsT updates_; - ead::iSession* sess_; + ead::VarptrT visible_ = nullptr; -private: - ead::NodeptrT get_pseudo_likelihood_cost (ead::NodeptrT input) - { - const ade::Shape& shape = input->shape(); - std::vector zeros(shape.n_elems(), 0); - zeros[0] = 1; - auto one_i = ead::make_constant(zeros.data(), shape); + ead::NodeptrT hidden_sample_ = nullptr; - ead::NodeptrT xi = tenncor::round(input); // xi = [0|1, ...] - ead::NodeptrT xi_flip = tenncor::sub(one_i, xi); + ead::NodeptrT visible_sample_ = nullptr; - ead::NodeptrT fe_xi = free_energy(xi); - ead::NodeptrT fe_xi_flip = free_energy(xi_flip); + ead::NodeptrT error_ = nullptr; - return tenncor::reduce_mean(tenncor::mul( - ead::make_constant_scalar(brain_->get_ninput(), fe_xi->shape()), - tenncor::log(tenncor::sigmoid(tenncor::sub(fe_xi_flip, fe_xi))))); - } + // === updates && optimizer === + eqns::AssignGroupsT updates_; - ead::NodeptrT get_reconstruction_cost ( - ead::NodeptrT input, ead::NodeptrT visible_dist) - { - ead::NodeptrT p_success = tenncor::mul( - input, tenncor::log(visible_dist)); - ead::NodeptrT p_not = tenncor::mul( - tenncor::sub(ead::make_constant_scalar( - 1, input->shape()), input), - tenncor::log(tenncor::sub(ead::make_constant_scalar( - 1, visible_dist->shape()), visible_dist))); - return tenncor::reduce_mean(tenncor::reduce_sum_1d( - tenncor::transpose(tenncor::add(p_success, p_not)), 1)); - } + ead::TensSetT assign_sources_; - ead::NodeptrT free_energy (ead::NodeptrT sample) - { - ead::VarptrT weight = brain_->layers_[0].weight_->var_; - ead::VarptrT hbias = brain_->layers_[0].hbias_->var_; - ead::VarptrT vbias = brain_->layers_[0].vbias_->var_; - - auto vbias_term = tenncor::matmul(sample, - tenncor::transpose(ead::convert_to_node(vbias))); - // @ + z -> - auto wx_b = tenncor::nn::fully_connect( - {sample}, - {ead::convert_to_node(weight)}, - ead::convert_to_node(hbias)); - auto hidden_term = tenncor::reduce_sum( - tenncor::log(tenncor::add( - ead::make_constant_scalar(1, wx_b->shape()), - tenncor::exp(wx_b) - )), 0, 1); - return tenncor::neg(tenncor::add(vbias_term, hidden_term)); - } + ead::iSession* sess_; - ead::NodeptrT persistent_; + size_t batch_size_; }; } diff --git a/tag/group.hpp b/tag/group.hpp index 70937e032..0547fb4cf 100644 --- a/tag/group.hpp +++ b/tag/group.hpp @@ -12,8 +12,6 @@ namespace tag using TensSetT = std::unordered_set; -const std::string groups_key = "groups"; - /// GroupTag define subgraphs/groups of nodes with a structural significance /// Groups are ordered tags, subsequent group tags /// (obtained through absorption) often denote supergraphs of prior groups @@ -36,13 +34,7 @@ struct GroupTag final : public iTag labels_.insert(olabels.begin(), olabels.end()); } - TagRepsT get_tags (void) const override - { - TagRepsT out; - out.emplace(groups_key, - std::vector(labels_.begin(), labels_.end())); - return out; - } + TagRepsT get_tags (void) const override; private: std::set labels_; @@ -75,7 +67,13 @@ struct GroupRegistry final GroupRegistry& get_group_reg (void); -void recursive_group_tag (ade::TensrefT tens, std::string group, +const std::string groups_key = get_reg().register_tagr("groups", +[](ade::TensrefT ref, std::string tag) +{ + get_group_reg().group_tag(ref, tag); +}); + +void recursive_group_tag (ade::TensptrT tens, std::string group, std::unordered_set stops, GroupRegistry& registry = get_group_reg()); diff --git a/tag/prop.hpp b/tag/prop.hpp index add5f2e8e..c4ad6628a 100644 --- a/tag/prop.hpp +++ b/tag/prop.hpp @@ -6,8 +6,6 @@ namespace tag { -const std::string props_key = "properties"; - // some property tags const std::string commutative_tag = "commutative"; @@ -30,13 +28,7 @@ struct PropTag final : public iTag labels_.insert(olabels.begin(), olabels.end()); } - TagRepsT get_tags (void) const override - { - TagRepsT out; - out.emplace(props_key, std::vector( - labels_.begin(), labels_.end())); - return out; - } + TagRepsT get_tags (void) const override; private: std::unordered_set labels_; @@ -54,22 +46,19 @@ struct PropertyRegistry final tag_reg_.add_tag(tens, TagptrT(new PropTag(property))); } - bool has_property (const ade::iTensor* tens, std::string property) - { - auto reps = tag_reg_.get_tags(tens); - auto it = reps.find(props_key); - if (reps.end() == it) - { - return false; - } - return estd::arr_has(it->second, property); - } + bool has_property (const ade::iTensor* tens, std::string property) const; TagRegistry& tag_reg_; }; PropertyRegistry& get_property_reg (void); +const std::string props_key = get_reg().register_tagr("properties", +[](ade::TensrefT ref, std::string property) +{ + get_property_reg().property_tag(ref, property); +}); + } #endif // TAG_PROP_HPP diff --git a/tag/src/group.cpp b/tag/src/group.cpp index 49b0cfa29..861148fc2 100644 --- a/tag/src/group.cpp +++ b/tag/src/group.cpp @@ -5,79 +5,30 @@ namespace tag { -using RefMapT = std::unordered_map; - size_t GroupTag::tag_id_ = typeid(GroupTag).hash_code(); +TagRepsT GroupTag::get_tags (void) const +{ + TagRepsT out; + out.emplace(groups_key, + std::vector(labels_.begin(), labels_.end())); + return out; +} + GroupRegistry& get_group_reg (void) { static GroupRegistry registry; return registry; } -struct Grouper final : public ade::iTraveler -{ - Grouper (std::string group, std::unordered_set stops, - GroupRegistry& registry) : - group_(group), - stops_(stops.begin(), stops.end()), - registry_(registry) {} - - /// Implementation of iTraveler - void visit (ade::iLeaf* leaf) override - { - if (false == estd::has(stops_, leaf)) - { - auto it = owners_.find(leaf); - if (owners_.end() == it) - { - logs::fatal("failed to get reference to leaf in group traveler"); - } - registry_.group_tag(it->second, group_); - } - } - - /// Implementation of iTraveler - void visit (ade::iFunctor* func) override - { - if (false == estd::has(stops_, func)) - { - auto it = owners_.find(func); - if (owners_.end() == it) - { - logs::fatal("failed to get reference to leaf in group traveler"); - } - auto& children = func->get_children(); - for (auto& child : children) - { - ade::TensptrT tens = child.get_tensor(); - owners_.emplace(tens.get(), tens); - tens->accept(*this); - } - registry_.group_tag(it->second, group_); - } - } - - RefMapT owners_; - - std::string group_; - - std::unordered_set stops_; - - GroupRegistry& registry_; -}; - -void recursive_group_tag (ade::TensrefT tens, std::string group, +void recursive_group_tag (ade::TensptrT tens, std::string group, std::unordered_set stops, GroupRegistry& registry) { - if (tens.expired()) - { - logs::fatal("cannot recursive group tag with expired tensor ref"); - } - Grouper trav(group, stops, registry); - auto tensor = tens.lock().get(); - trav.owners_.emplace(tensor, tens); - tensor->accept(trav); + recursive_tag(tens, stops, + [&](ade::TensrefT ref) + { + registry.group_tag(ref, group); + }); } void adjacencies (AdjMapT& out, ade::TensT roots, diff --git a/tag/src/prop.cpp b/tag/src/prop.cpp index c765bc97f..465d9093d 100644 --- a/tag/src/prop.cpp +++ b/tag/src/prop.cpp @@ -7,6 +7,25 @@ namespace tag size_t PropTag::tag_id_ = typeid(PropTag).hash_code(); +TagRepsT PropTag::get_tags (void) const +{ + TagRepsT out; + out.emplace(props_key, std::vector( + labels_.begin(), labels_.end())); + return out; +} + +bool PropertyRegistry::has_property (const ade::iTensor* tens, std::string property) const +{ + auto reps = tag_reg_.get_tags(tens); + auto it = reps.find(props_key); + if (reps.end() == it) + { + return false; + } + return estd::arr_has(it->second, property); +} + PropertyRegistry& get_property_reg (void) { static PropertyRegistry registry; diff --git a/tag/src/tag.cpp b/tag/src/tag.cpp index 1ab0d2ef1..a8c03f9f6 100644 --- a/tag/src/tag.cpp +++ b/tag/src/tag.cpp @@ -11,6 +11,65 @@ TagRegistry& get_reg (void) return registry; } +using RefMapT = std::unordered_map; + +struct Tagger final : public ade::iTraveler +{ + Tagger (std::unordered_set stops, + std::function tag_op) : + stops_(stops), tag_op_(tag_op) {} + + /// Implementation of iTraveler + void visit (ade::iLeaf* leaf) override + { + if (false == estd::has(stops_, leaf)) + { + auto it = owners_.find(leaf); + if (owners_.end() == it) + { + logs::fatal("failed to get reference to leaf in group traveler"); + } + tag_op_(it->second); + } + } + + /// Implementation of iTraveler + void visit (ade::iFunctor* func) override + { + if (false == estd::has(stops_, func)) + { + auto it = owners_.find(func); + if (owners_.end() == it) + { + logs::fatal("failed to get reference to leaf in group traveler"); + } + auto& children = func->get_children(); + for (auto& child : children) + { + ade::TensptrT tens = child.get_tensor(); + owners_.emplace(tens.get(), tens); + tens->accept(*this); + } + tag_op_(it->second); + } + } + + RefMapT owners_; + + std::unordered_set stops_; + + std::function tag_op_; +}; + +void recursive_tag (ade::TensptrT root, + std::unordered_set stops, + std::function tag_op) +{ + Tagger tagger(stops, tag_op); + tagger.owners_.emplace(root.get(), root); + root->accept(tagger); +} + } #endif diff --git a/tag/tag.hpp b/tag/tag.hpp index d8c361db5..e83d61638 100644 --- a/tag/tag.hpp +++ b/tag/tag.hpp @@ -130,6 +130,8 @@ inline bool operator == (const TensKey& lhs, const TensKey& rhs) return hasher(lhs) == hasher(rhs); } +using TagrF = std::function; + // todo: move tag registry to some session that claims global context // todo: make an interface for this struct TagRegistry final @@ -183,11 +185,75 @@ struct TagRegistry final registry_.erase(TensKey(source)); } + /// Return tagger associated to TagRepsT key + TagrF tagr_by_key (std::string tag_key) + { + return estd::must_getf(key_tagr_assoc_, tag_key, + "cannot find tagr associated with %s", tag_key.c_str()); + } + + std::string register_tagr (std::string tag_key, TagrF tagr) + { + key_tagr_assoc_.emplace(tag_key, tagr); + return tag_key; + } + std::unordered_map registry_; + +private: + std::unordered_map key_tagr_assoc_; }; TagRegistry& get_reg (void); +void recursive_tag (ade::TensptrT root, + std::unordered_set stops, + std::function tag_op); + +using LTensT = std::unordered_map>; + +using TTensT = std::unordered_map; + +struct Query final : public ade::OnceTraveler +{ + Query (TagRegistry& reg = get_reg()) : reg_(reg) {} + + void visit_leaf (ade::iLeaf* leaf) override + { + auto tags = reg_.get_tags(leaf); + save_tags(tags, leaf); + } + + void visit_func (ade::iFunctor* func) override + { + auto& children = func->get_children(); + for (auto child : children) + { + child.get_tensor()->accept(*this); + } + + auto tags = reg_.get_tags(func); + save_tags(tags, func); + } + + TTensT labels_; + + TagRegistry& reg_; + +private: + void save_tags (TagRepsT& tag, ade::iTensor* tens) + { + for (auto& tpair : tag) + { + auto& labs = labels_[tpair.first]; + for (auto lpair : tpair.second) + { + labs[lpair].push_back(tens); + } + } + } +}; + } #endif // TAG_TAG_HPP diff --git a/tag/test/common.hpp b/tag/test/common.hpp index a8e1a97db..34d012070 100644 --- a/tag/test/common.hpp +++ b/tag/test/common.hpp @@ -44,6 +44,11 @@ struct MockTensor final : public ade::iLeaf return 0; } + bool is_const (void) const override + { + return true; + } + ade::Shape shape_; }; diff --git a/tests.sh b/tests.sh index 9f5094780..589c6c7a6 100755 --- a/tests.sh +++ b/tests.sh @@ -13,35 +13,16 @@ free -m; # ===== Run Gtest ===== echo "===== TESTS ====="; -bazel test --config asan --config gtest --action_env="ASAN_OPTIONS=detect_leaks=0" //ade:test -bazel test --run_under='valgrind --leak-check=full' //ade:test +bazel test --config asan --config gtest --action_env="ASAN_OPTIONS=detect_leaks=0" --define EAD_CFG=MIN \ +//ade:test //tag:test //pbm:test //opt:test //opt/parse:test //ead:ctest //perf:test //pll:test -bazel test --config asan --config gtest --action_env="ASAN_OPTIONS=detect_leaks=0" //tag:test -bazel test --run_under='valgrind --leak-check=full' //tag:test - -bazel test --config asan --config gtest --action_env="ASAN_OPTIONS=detect_leaks=0" //pbm:test -bazel test --run_under='valgrind --leak-check=full' //pbm:test - -bazel test --config asan --config gtest --action_env="ASAN_OPTIONS=detect_leaks=0" //opt:test -bazel test --run_under='valgrind --leak-check=full' //opt:test - -bazel test --run_under='valgrind --leak-check=full' //gen:ptest - -bazel test --config asan --config gtest --action_env="ASAN_OPTIONS=detect_leaks=0" //ead:ctest -bazel test --run_under='valgrind --leak-check=full' //ead:ctest -bazel test --run_under='valgrind --leak-check=full' //ead:ptest - -# ===== Check Docs Directory ===== -echo "===== CHECK DOCUMENT EXISTENCE ====="; -if ! [ -d "$DOCS" ]; -then - echo "Documents not found. Please generate documents then try again" - exit 1; -fi +bazel test --run_under='valgrind --leak-check=full' --define EAD_CFG=MIN \ +//ade:test //gen:ptest //tag:test //pbm:test //opt:test //opt/parse:test //ead:ctest //ead:ptest //perf:test //pll:test # ===== Coverage Analysis ====== echo "===== STARTING COVERAGE ANALYSIS ====="; make lcov | grep -v '+' | grep -v 'Processing' + if ! [ -z "$COVERALLS_TOKEN" ]; then git rev-parse --abbrev-inode* HEAD; diff --git a/third_party/repos/cppkg.bzl b/third_party/repos/cppkg.bzl index 887a09fd7..e935edb32 100644 --- a/third_party/repos/cppkg.bzl +++ b/third_party/repos/cppkg.bzl @@ -4,5 +4,5 @@ def cppkg_repository(): git_repository( name = "com_github_mingkaic_cppkg", remote = "https://github.com/mingkaic/cppkg", - commit = "9eb6f9261c5f7407c586cdd6550b1c50ed943945", + commit = "c96b3c52e8d35a16a30c0ac94abe46a661760b78", ) diff --git a/third_party/repos/protobuf.bzl b/third_party/repos/protobuf.bzl index 2959d2dac..1a22c667e 100644 --- a/third_party/repos/protobuf.bzl +++ b/third_party/repos/protobuf.bzl @@ -3,7 +3,7 @@ load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") def pb_rules_repository(): http_archive( name = "com_github_stackb_rules_proto", - urls = ["https://github.com/stackb/rules_proto/archive/844077a71597f91c41b02d4509c5f79d51588552.tar.gz"], - sha256 = "867d09bf45515cb3ddeb06f7bdd2182eecf171ae3cd6b716b3b9d2fce50f292f", - strip_prefix = "rules_proto-844077a71597f91c41b02d4509c5f79d51588552", + urls = ["https://github.com/stackb/rules_proto/archive/b93b544f851fdcd3fc5c3d47aee3b7ca158a8841.tar.gz"], + sha256 = "c62f0b442e82a6152fcd5b1c0b7c4028233a9e314078952b6b04253421d56d61", + strip_prefix = "rules_proto-b93b544f851fdcd3fc5c3d47aee3b7ca158a8841", ) diff --git a/todo b/todo index 24051c428..da27aa7ce 100644 --- a/todo +++ b/todo @@ -6,27 +6,32 @@ P4 = "nice to have/need investigating" P5 = "get to it eventually" === feature === -P1 - make shape dimension size configurable +P2 - update API to simplify operator with constants e.g.: X + 1 instead of explicitly creating constant 1 +P2 - allow users to create ambiguous nodes holding shapes with ambiguous dimensions (denoted in python by None) +P2 - make shape dimension size configurable P2 - make constant pre-calculation configurable in opt module P2 - reimplement conv nn layer + dbn P3 - force dbn to conform to established trainer pattern (if no subgraph yet) P3 - implement control nodes/edges for assignments P4 - implement reduce_prod gradient using Eigen cumprod -=== transparency === -P2 - provide graph-setup time summary when failing to match shapes -P4 - add even more debugging tools -P4 - formalize codestyle -P4 - refactor, and rename variables to improve readability -P4 - conform to some bazel style/convention (or change to another build tool) -P4 - investigate adopting more of boost in place of cppkg +=== performance === +general performance: +P2 - ensure ade is thread-safe +P3 - benchmark test rocnnet against various other frameworks (CPU only) +P3 - convert as many shared_ptr to unique_ptr +P4 - remove unordered_map/unordered_set if we're not using them properly +P4 - define optimization conversion rules to increase data accuracy +P4 - more performance test -=== structure === -P4 - remove ead helper functions +run-time performance: +P3 - define dataflow to take advantage of parallel processing at node-level +P4 - implement GPU settings +P4 - improve session implementation +P4 - investigate remote sessions for distributed processing === correction === -P1 - create global session for all ade/ead/tag singletons, then make context configurable -P2 - test tag module (need ^) +P2 - centralize global context structures P2 - reorient tests so that tests cover its own immediate module P2 - fix rbm demo converging to infinity problem P2 - test dbg module @@ -35,20 +40,14 @@ P3 - test complicate equations (generate random valid equations) P3 - maximize coverage P3 - test rocnnet + eqns, modl, and trainer (or subgraph equivalent) +=== transparency === +P2 - provide graph-setup time summary when failing to match shapes +P4 - add even more debugging tools +P4 - formalize codestyle +P4 - refactor, and rename variables to improve readability +P4 - conform to some bazel style/convention (or change to another build tool) +P4 - investigate adopting more of boost in place of cppkg + === update === P2 - update all fmts usages P3 - address warnings - -=== performance === -general performance: -P3 - benchmark test rocnnet against various other frameworks (CPU only) -P3 - convert as many shared_ptr to unique_ptr -P4 - remove unordered_map/unordered_set if we're not using them properly -P4 - define optimization conversion rules to increase data accuracy -P4 - more performance test - -run-time performance: -P3 - define dataflow to take advantage of parallel processing at node-level -P4 - implement GPU settings -P4 - improve session implementation -P4 - investigate remote sessions for distributed processing