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init.cpp
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init.cpp
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#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/python_arg_parser.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/backends/backend_init.h>
#include <torch/csrc/jit/codegen/cuda/interface.h>
#include <torch/csrc/jit/codegen/fuser/interface.h>
#include <torch/csrc/jit/codegen/fuser/kernel_cache.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/passes/canonicalize.h>
#include <torch/csrc/jit/passes/canonicalize_graph_fuser_ops.h>
#include <torch/csrc/jit/passes/common_subexpression_elimination.h>
#include <torch/csrc/jit/passes/constant_pooling.h>
#include <torch/csrc/jit/passes/constant_propagation.h>
#include <torch/csrc/jit/passes/create_autodiff_subgraphs.h>
#include <torch/csrc/jit/passes/create_functional_graphs.h>
#include <torch/csrc/jit/passes/cuda_graph_fuser.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/decompose_ops.h>
#include <torch/csrc/jit/passes/erase_number_types.h>
#include <torch/csrc/jit/passes/fold_conv_bn.h>
#include <torch/csrc/jit/passes/freeze_module.h>
#include <torch/csrc/jit/passes/fuse_linear.h>
#include <torch/csrc/jit/passes/fuse_relu.h>
#include <torch/csrc/jit/passes/graph_fuser.h>
#include <torch/csrc/jit/passes/inline_fork_wait.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/passes/loop_unrolling.h>
#include <torch/csrc/jit/passes/lower_graph.h>
#include <torch/csrc/jit/passes/lower_tuples.h>
#include <torch/csrc/jit/passes/metal_rewrite.h>
#include <torch/csrc/jit/passes/normalize_ops.h>
#include <torch/csrc/jit/passes/onnx.h>
#include <torch/csrc/jit/passes/onnx/cast_all_constant_to_floating.h>
#include <torch/csrc/jit/passes/onnx/constant_fold.h>
#include <torch/csrc/jit/passes/onnx/eliminate_unused_items.h>
#include <torch/csrc/jit/passes/onnx/eval_peephole.h>
#include <torch/csrc/jit/passes/onnx/fixup_onnx_controlflow.h>
#include <torch/csrc/jit/passes/onnx/function_substitution.h>
#include <torch/csrc/jit/passes/onnx/list_model_parameters.h>
#include <torch/csrc/jit/passes/onnx/peephole.h>
#include <torch/csrc/jit/passes/onnx/prepare_division_for_onnx.h>
#include <torch/csrc/jit/passes/onnx/preprocess_for_onnx.h>
#include <torch/csrc/jit/passes/onnx/remove_inplace_ops_for_onnx.h>
#include <torch/csrc/jit/passes/onnx/scalar_type_analysis.h>
#include <torch/csrc/jit/passes/onnx/shape_type_inference.h>
#include <torch/csrc/jit/passes/onnx/unpack_quantized_weights.h>
#include <torch/csrc/jit/passes/peephole.h>
#include <torch/csrc/jit/passes/quantization/dedup_module_uses.h>
#include <torch/csrc/jit/passes/quantization/finalize.h>
#include <torch/csrc/jit/passes/quantization/fusion_passes.h>
#include <torch/csrc/jit/passes/quantization/insert_observers.h>
#include <torch/csrc/jit/passes/quantization/insert_quant_dequant.h>
#include <torch/csrc/jit/passes/quantization/quantization_type.h>
#include <torch/csrc/jit/passes/reconstruct_scopes.h>
#include <torch/csrc/jit/passes/remove_dropout.h>
#include <torch/csrc/jit/passes/remove_expands.h>
#include <torch/csrc/jit/passes/remove_inplace_ops.h>
#include <torch/csrc/jit/passes/remove_mutation.h>
#include <torch/csrc/jit/passes/shape_analysis.h>
#include <torch/csrc/jit/passes/specialize_autogradzero.h>
#include <torch/csrc/jit/passes/subgraph_rewrite.h>
#include <torch/csrc/jit/passes/tensorexpr_fuser.h>
#include <torch/csrc/jit/passes/utils/check_alias_annotation.h>
#include <torch/csrc/jit/passes/vulkan_rewrite.h>
#include <torch/csrc/jit/passes/xnnpack_rewrite.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/jit/python/python_arg_flatten.h>
#include <torch/csrc/jit/python/python_custom_class.h>
#include <torch/csrc/jit/python/python_ir.h>
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/jit/python/python_tree_views.h>
#include <torch/csrc/jit/python/script_init.h>
#include <torch/csrc/jit/runtime/argument_spec.h>
#include <torch/csrc/jit/runtime/autodiff.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <torch/csrc/jit/runtime/jit_exception.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/runtime/print_handler.h>
#include <torch/csrc/jit/runtime/static/init.h>
#include <torch/csrc/jit/serialization/export.h>
#include <torch/csrc/jit/serialization/import.h>
#include <torch/csrc/jit/tensorexpr/execution_counter.h>
#include <torch/csrc/jit/tensorexpr/kernel.h>
#include <c10/macros/Export.h>
#include <caffe2/serialize/inline_container.h>
#include <ATen/core/function_schema.h>
#include <pybind11/functional.h>
#include <pybind11/iostream.h>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include <tuple>
#include <utility>
namespace torch {
namespace jit {
using ::c10::Argument;
using ::c10::FunctionSchema;
using caffe2::serialize::PyTorchStreamReader;
using caffe2::serialize::PyTorchStreamWriter;
namespace {
using autograd::variable_list;
bool loadPythonClasses() {
// Leaving this code here, because it will likely be useful at some point
// PyObject *jit_module = PyImport_ImportModule("torch.jit");
// THPUtils_assert(jit_module, "class loader couldn't access "
//"torch.jit module");
// PyObject *jit_dict = PyModule_GetDict(jit_module);
return true;
}
} // anonymous namespace
#if !defined(__HIP_PLATFORM_HCC__)
TORCH_API void runJITCPPTests();
#endif
void initJITBindings(PyObject* module) {
auto m = py::handle(module).cast<py::module>();
py::register_exception<JITException>(m, "JITException");
py::class_<python::IODescriptor> iodescriptor(
m, "IODescriptor"); // NOLINT(bugprone-unused-raii)
m.def("_jit_init", loadPythonClasses)
.def(
"_jit_debug_fuser_num_cached_kernel_specs",
torch::jit::fuser::debugNumCachedKernelSpecs)
.def("_jit_pass_onnx_remove_print", RemovePrintOps)
.def("_jit_pass_onnx_preprocess_caffe2", PreprocessCaffe2Ops)
.def("_jit_pass_onnx", ToONNX)
.def(
"_jit_pass_onnx_assign_output_shape",
[](std::shared_ptr<Graph>& graph,
const std::vector<at::Tensor>& tensors,
bool onnx_shape_inference = false) {
ONNXAssignOutputShape(graph, tensors, onnx_shape_inference);
})
.def("_jit_pass_lower_all_tuples", LowerAllTuples)
.def("_jit_pass_onnx_function_substitution", ONNXFunctionCallSubstitution)
.def(
"_jit_pass_onnx_peephole",
[](std::shared_ptr<Graph>& graph,
int opset_version,
bool fixed_batch_size) {
return PeepholeOptimizeONNX(graph, opset_version, fixed_batch_size);
})
.def("_jit_pass_onnx_preprocess", PreprocessForONNX)
.def(
"_jit_pass_onnx_eval_peephole",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, IValue>& paramsDict) {
EvalPeepholeONNX(graph->block(), paramsDict);
return paramsDict;
},
pybind11::return_value_policy::move)
.def(
"_jit_pass_onnx_cast_all_constant_to_floating",
CastAllConstantToFloating)
.def(
"_jit_pass_onnx_constant_fold",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, IValue>& paramsDict,
int opset_version) {
ConstantFoldONNX(
graph->block(),
paramsDict,
opset_version); // overload resolution
return paramsDict;
},
pybind11::return_value_policy::move)
.def(
"_jit_pass_onnx_eliminate_unused_items",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, IValue>& paramsDict) {
EliminateUnusedItemsONNX(
graph->block(),
paramsDict); // overload resolution
return paramsDict;
},
pybind11::return_value_policy::move)
.def("_jit_pass_onnx_scalar_type_analysis", ScalarTypeAnalysisForONNX)
.def(
"_jit_pass_onnx_remove_inplace_ops_for_onnx", RemoveInplaceOpsForONNX)
.def(
"_jit_pass_onnx_prepare_inplace_ops_for_onnx",
PrepareInplaceOpsForONNX)
.def(
"_jit_pass_onnx_node_shape_type_inference",
[](Node* n, int opset_version) {
ONNXShapeTypeInference(n, opset_version);
})
.def(
"_jit_pass_onnx_graph_shape_type_inference",
[](std::shared_ptr<Graph>& graph, int opset_version) {
ONNXShapeTypeInference(graph, opset_version);
})
.def("_jit_pass_onnx_set_dynamic_input_shape", ONNXSetDynamicInputShape)
.def("_jit_pass_fuse", FuseGraph)
.def(
"_jit_pass_dce",
[](std::shared_ptr<Graph>& g) {
return EliminateDeadCode(g->block()); // overload resolution
})
.def(
"_jit_pass_dce_allow_deleting_nodes_with_side_effects",
[](std::shared_ptr<Graph>& g) {
return EliminateDeadCode(
g->block(),
true,
DCESideEffectPolicy::
ALLOW_DELETING_NODES_WITH_SIDE_EFFECTS); // overload
// resolution
})
.def(
"_jit_pass_cse",
[](std::shared_ptr<Graph>& g) {
return EliminateCommonSubexpression(g); // overload resolution
})
.def(
"_jit_pass_fuse_quantized_add_relu",
[](std::shared_ptr<Graph>& g) {
return FuseQuantizedAddRelu(g); // overload resolution
})
.def(
"_jit_pass_insert_observers",
[](Module& module,
const std::string& method_name,
const py::dict& qconfig_dict,
bool inplace,
int quant_type_int) {
auto dict = py::cast<std::unordered_map<
std::string,
c10::optional<std::tuple<Module, Module>>>>(qconfig_dict);
auto quant_type = static_cast<QuantType>(quant_type_int);
return InsertObservers(
module, method_name, dict, inplace, quant_type);
},
py::arg("module"),
py::arg("method_name"),
py::arg("qconfig_dict"),
py::arg("inplace"),
py::arg("quant_type_int") = 1)
.def(
"_jit_pass_insert_quant_dequant",
[](Module& module,
const std::string& method_name,
bool inplace,
bool debug,
int quant_type_int) {
auto quant_type = static_cast<QuantType>(quant_type_int);
return InsertQuantDeQuant(
module, method_name, inplace, debug, quant_type);
},
py::arg("module"),
py::arg("method_name"),
py::arg("inplace"),
py::arg("debug"),
py::arg("quant_type_int") = 1)
.def(
"_jit_pass_insert_prepack_unpack",
[](std::shared_ptr<Graph>& g) { return InsertPrepackUnpack(g); })
.def(
"_jit_pass_insert_prepack_unpack",
[](Module& module) { return InsertPrepackUnpack(module); })
.def(
"_jit_pass_quant_fusion",
[](std::shared_ptr<Graph>& g) { return QuantFusion(g); })
.def("_jit_pass_fold_convbn", &FoldConvBatchNorm)
.def(
"_jit_onnx_list_model_parameters",
[](Module& module) { return list_module_parameters(module); })
.def(
"_freeze_module",
[](Module& module,
std::vector<std::string>& preservedAttrs,
bool freezeInterfaces,
bool preserveParameters) {
return freeze_module(
module, preservedAttrs, freezeInterfaces, preserveParameters);
},
py::arg("module"),
py::arg("preservedAttrs") = std::vector<std::string>(),
py::arg("freezeInterfaces") = true,
py::arg("preserveParameters") = false)
.def("_jit_pass_fuse_linear", &FuseLinear)
.def(
"_jit_pass_fuse_add_relu",
[](std::shared_ptr<Graph>& graph) { FuseAddRelu(graph); })
.def("_jit_pass_dedup_module_uses", &DedupModuleUses)
.def("_jit_pass_replicate_dequantize", &ReplicateDeQuant)
.def(
"_jit_pass_swap_functional_linear",
[](std::shared_ptr<Graph>& graph) { SwapFunctionalLinear(graph); })
.def(
"_jit_pass_swap_functional_linear",
[](Module& module) { SwapFunctionalLinear(module); })
.def(
"_jit_pass_quant_finalize",
[](Module& module,
int quant_type_int,
const std::vector<std::string>& preserved_attrs) {
auto quant_type = static_cast<QuantType>(quant_type_int);
return Finalize(module, quant_type, preserved_attrs);
},
py::arg("module"),
py::arg("quant_type_int") = 1,
py::arg("preserved_attrs") = std::vector<std::string>())
.def(
"_jit_pass_pattern_based_rewrite",
[](const Module& m) { return PatternBasedRewrite(m); })
.def(
"_jit_pass_custom_pattern_based_rewrite",
[](const std::string& pattern,
const std::string& fused_node_name,
const Module& m) {
SubgraphRewriter subgraph_rewriter;
subgraph_rewriter.RegisterRewritePattern(pattern, fused_node_name);
subgraph_rewriter.runOnModule(m);
})
.def(
"_jit_pass_custom_pattern_based_rewrite_graph",
[](const std::string& pattern,
const std::string& fused_node_name,
std::shared_ptr<Graph> g) {
SubgraphRewriter subgraph_rewriter;
subgraph_rewriter.RegisterRewritePattern(pattern, fused_node_name);
subgraph_rewriter.runOnGraph(g);
})
.def(
"_jit_pass_reconstruct_scopes",
[](script::Module& module,
std::shared_ptr<Graph>& g,
const std::string& prefix) {
ReconstructScopes(module, *g, prefix);
},
py::arg("module"),
py::arg("graph"),
py::arg("prefix") = "top")
.def(
"_jit_pass_remove_inplace_ops",
[](std::shared_ptr<Graph> g) { return RemoveInplaceOps(g); })
.def("_jit_pass_constant_pooling", ConstantPooling)
.def(
"_jit_pass_create_functional_graphs",
[](std::shared_ptr<Graph>& g) { return CreateFunctionalGraphs(g); })
.def(
"_jit_pass_remove_mutation",
[](std::shared_ptr<Graph>& g) {
RemoveListMutation(g);
return RemoveTensorMutation(g);
})
.def(
"_jit_pass_inline_functional_graphs",
[](std::shared_ptr<Graph>& g) { return InlineFunctionalGraphs(g); })
.def(
"_jit_pass_peephole",
[](const std::shared_ptr<Graph>& g, bool addmm_fusion_enabled) {
return PeepholeOptimize(g, addmm_fusion_enabled);
},
py::arg("graph"),
py::arg("addmm_fusion_enabled") = false)
.def(
"_jit_pass_fuse_addmm",
[](std::shared_ptr<Graph>& g) { return FuseAddMM(g); })
.def(
"_jit_pass_canonicalize",
[](const std::shared_ptr<Graph>& g) { return Canonicalize(g); })
.def("_jit_pass_lint", LintGraph)
.def(
"_jit_pass_complete_shape_analysis",
[](std::shared_ptr<Graph> graph, py::tuple inputs, bool with_grad) {
ArgumentSpecCreator arg_spec_creator(*graph);
Stack stack;
stack.reserve(inputs.size()); // captures?
for (auto& obj : inputs) {
stack.push_back(toTypeInferredIValue(obj));
}
ArgumentSpec spec = arg_spec_creator.create(with_grad, stack);
arg_spec_creator.specializeTypes(*graph, spec);
// We only get partial specialization from the arg_spec_creator, but
// we want full shape specialization. The alternative would be to
// have a "complete type inference" function in ArguemntSpecCreator.
auto g_inputs = graph->inputs();
for (size_t i = 0; i < inputs.size(); ++i) {
if (stack[i].isTensor()) {
g_inputs[i]->setType(stack[i].type());
}
}
PropagateInputShapes(graph);
})
.def(
"_jit_interpret_graph",
[](std::shared_ptr<Graph>& graph, py::tuple inputs) {
Stack stack;
stack.reserve(inputs.size()); // captures?
for (auto& obj : inputs) {
stack.push_back(toTypeInferredIValue(obj));
}
auto g_inputs = graph->inputs();
for (size_t i = 0; i < inputs.size(); ++i) {
if (stack[i].isTensor()) {
g_inputs[i]->setType(stack[i].type());
}
}
Code code(graph, "<on-demand-func>");
InterpreterState(code).run(stack);
return createPyObjectForStack(std::move(stack));
},
py::doc(
"Interpret a JIT graph with given inputs without running any optimization passes on it"))
.def("_jit_pass_remove_expands", RemoveExpands)
.def("_jit_pass_erase_number_types", EraseNumberTypes)
.def("_jit_pass_inline_fork_wait", InlineForkWait)
.def("_jit_pass_inline", Inline)
.def("_jit_pass_prepare_division_for_onnx", PrepareDivisionForONNX)
.def(
"_jit_pass_lower_graph",
[](std::shared_ptr<Graph>& graph, const Module& self) {
return LowerGraph(*graph, self._ivalue());
})
.def("_jit_pass_loop_unrolling", UnrollLoops)
.def(
"_jit_pass_constant_propagation_immutable_types",
[](std::shared_ptr<Graph>& g) {
return ConstantPropagationImmutableTypes(g);
})
.def(
"_jit_pass_constant_propagation",
[](std::shared_ptr<Graph>& g) { return ConstantPropagation(g); })
.def("_jit_pass_erase_shape_information", EraseShapeInformation)
.def(
"_jit_pass_create_autodiff_subgraphs",
[](std::shared_ptr<Graph> graph) { CreateAutodiffSubgraphs(graph); })
#if defined(BUILDING_TESTS) && !defined(__HIP_PLATFORM_HCC__)
.def(
"_jit_run_cpp_tests",
[]() {
// We have to release the GIL inside this method, because if we
// happen to initialize the autograd engine in these tests, the
// newly spawned worker threads will try to initialize their
// PyThreadState*, and they need the GIL for this.
pybind11::gil_scoped_release _no_gil;
return runJITCPPTests();
})
.def("_jit_has_cpp_tests", []() { return true; })
.def("_has_tensorexpr_cpp_tests", []() { return true; })
#else
.def("_jit_run_cpp_tests", []() { throw std::exception(); })
.def("_jit_has_cpp_tests", []() { return false; })
.def("_run_tensorexpr_cpp_tests", []() { throw std::exception(); })
.def("_has_tensorexpr_cpp_tests", []() { return false; })
#endif
.def(
"_jit_flatten",
[](py::handle& obj) {
auto res = python::flatten(obj);
return std::make_pair(res.vars, res.desc);
})
.def(
"_jit_unflatten",
[](autograd::variable_list vars, python::IODescriptor& desc) {
return py::reinterpret_steal<py::object>(
python::unflatten(vars, desc));
})
.def("_jit_pass_onnx_block", BlockToONNX)
.def("_jit_pass_fixup_onnx_controlflow_node", FixupONNXControlflowNode)
.def("_jit_pass_fixup_onnx_loop_node_inputs", FixupONNXLoopNodeInputs)
.def("_jit_pass_canonicalize_graph_fuser_ops", CanonicalizeOps)
.def("_jit_pass_decompose_ops", DecomposeOps)
.def("_jit_pass_specialize_autogradzero", specializeAutogradZero)
.def("_jit_override_can_fuse_on_cpu", &overrideCanFuseOnCPU)
.def("_jit_override_can_fuse_on_gpu", &overrideCanFuseOnGPU)
.def("_jit_can_fuse_on_cpu", &canFuseOnCPU)
.def("_jit_can_fuse_on_gpu", &canFuseOnGPU)
.def(
"_jit_differentiate",
[](Graph& g) {
// the python binding slightly differs in semantics
// it makes a copy of the input Graph, and works on that
// jit::differentiate mutates the input Graph
auto g_clone = g.copy();
return differentiate(g_clone);
})
.def(
"_jit_check_alias_annotation",
[](std::shared_ptr<Graph> g,
py::tuple args,
const std::string& unqualified_op_name) {
auto stack = toTraceableStack(args);
checkAliasAnnotation(g, std::move(stack), unqualified_op_name);
})
.def("_jit_set_nvfuser_enabled", &RegisterCudaFuseGraph::registerPass)
.def(
"_jit_set_nvfuser_guard_mode",
[](bool profiling_flag) {
bool oldState = fuser::cuda::getCudaFusionGuardMode();
fuser::cuda::getCudaFusionGuardMode() = profiling_flag;
return oldState;
})
.def("_jit_nvfuser_enabled", &RegisterCudaFuseGraph::isRegistered)
.def(
"_jit_set_profiling_mode",
[](bool profiling_flag) {
bool oldState = getProfilingMode();
getProfilingMode() = profiling_flag;
return oldState;
})
.def(
"_jit_set_profiling_executor",
[](bool profiling_flag) {
bool oldState = getExecutorMode();
getExecutorMode() = profiling_flag;
return oldState;
})
.def(
"_jit_set_num_profiled_runs",
[](size_t num) {
size_t old_num = getNumProfiledRuns();
getNumProfiledRuns() = num;
return old_num;
})
.def(
"_jit_get_num_profiled_runs",
[] {
// pybind can't automatically bind to atomic size_t
size_t num_runs = getNumProfiledRuns();
return num_runs;
})
.def(
"_jit_set_bailout_depth",
[](size_t depth) {
size_t old_depth = getBailoutDepth();
getBailoutDepth() = depth;
return old_depth;
})
.def(
"_jit_set_inline_everything_mode",
[](bool enabled) { getInlineEverythingMode() = enabled; })
.def(
"_jit_get_inline_everything_mode",
[]() { return getInlineEverythingMode(); })
.def(
"_jit_try_infer_type",
[](py::object obj) -> TypePtr {
auto match = tryToInferType(obj);
if (match.success()) {
return match.type();
}
return nullptr;
})
.def(
"_jit_get_trigger_value",
[](const std::string& trigger_name) -> int {
using namespace torch::jit::tensorexpr;
ExecutionTrigger* trigger =
ExecutionTriggerList::GetInstance().FindByName(trigger_name);
return trigger->value();
})
.def(
"_jit_get_te_cuda_pointwise_loop_levels",
[]() -> int {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseLoopLevels();
})
.def(
"_jit_set_te_cuda_pointwise_loop_levels",
[](int level) {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseLoopLevels() = level;
})
.def(
"_jit_get_te_cuda_pointwise_block_count",
[]() -> int {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockCount();
})
.def(
"_jit_set_te_cuda_pointwise_block_count",
[](int block_count) {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockCount() = block_count;
})
.def(
"_jit_get_te_cuda_pointwise_block_size",
[]() -> int {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockSize();
})
.def(
"_jit_set_te_cuda_pointwise_block_size",
[](int block_size) {
using namespace torch::jit::tensorexpr;
return getTECudaPointwiseBlockSize() = block_size;
})
.def("_jit_set_texpr_fuser_enabled", &setTensorExprFuserEnabled)
.def("_jit_texpr_fuser_enabled", &tensorExprFuserEnabled)
.def("_jit_texpr_fallback_allowed", &tensorexpr::fallbackAllowed)
.def("_jit_texpr_set_fallback_allowed", &tensorexpr::setFallbackAllowed)
.def("_jit_set_texpr_reductions_enabled", &setTexprReductionsEnabled)
.def("_jit_texpr_reductions_enabled", &texprReductionsEnabled)
.def(
"_jit_set_te_generate_block_code",
[](bool gen_block_code) {
using namespace torch::jit::tensorexpr;
return getTEGenerateBlockCode() = gen_block_code;
})
.def(
"_jit_get_te_generate_block_code",
[]() -> bool {
using namespace torch::jit::tensorexpr;
return getTEGenerateBlockCode();
})
.def(
"_jit_get_te_must_use_llvm_cpu",
[]() -> bool {
using namespace torch::jit::tensorexpr;
return getTEMustUseLLVMOnCPU();
})
.def(
"_jit_set_te_must_use_llvm_cpu",
[](bool use_llvm) {
using namespace torch::jit::tensorexpr;
getTEMustUseLLVMOnCPU() = use_llvm;
})
.def(
"_llvm_enabled",
[]() {
#ifdef TORCH_ENABLE_LLVM
return true;
#else
return false;
#endif
})
.def(
"_jit_pass_fuse_tensorexprs",
[](std::shared_ptr<Graph>& g) { return FuseTensorExprs(g); })
.def(
"_jit_fuser_get_fused_kernel_code",
[](Graph& g, std::vector<at::Tensor> inps) {
return debugGetFusedKernelCode(g, inps);
})
.def(
"_jit_pass_remove_dropout",
[](script::Module& module) { return removeDropout(module); })
.def(
"_jit_pass_transform_conv1d_to_conv2d",
[](std::shared_ptr<Graph>& graph) {
return transformConv1dToConv2d(graph);
})
.def(
"_jit_pass_transform_conv1d_to_conv2d",
[](script::Module& module) {
return transformConv1dToConv2d(module);
})
.def(
"_jit_pass_insert_prepacked_ops",
[](std::shared_ptr<Graph>& graph) {
return insertPrePackedOps(graph);
})
.def(
"_jit_pass_insert_prepacked_ops",
[](script::Module& module) { return insertPrePackedOps(module); })
.def(
"_jit_pass_fuse_clamp_w_prepacked_linear_conv",
[](script::Module& module) {
return fusePrePackedLinearConvWithClamp(module);
})
.def(
"_jit_pass_fold_prepacking_ops",
[](script::Module& module) { return FoldPrePackingOps(module); })
.def(
"_jit_pass_optimize_for_mobile",
[](script::Module& module,
std::set<MobileOptimizerType>& optimization_blocklist,
std::vector<std::string>& preserved_methods) {
return optimizeForMobile(
module, optimization_blocklist, preserved_methods);
})
.def(
"_jit_pass_vulkan_insert_prepacked_ops",
[](std::shared_ptr<Graph>& graph) {
return vulkanInsertPrePackedOps(graph);
})
.def(
"_jit_pass_vulkan_insert_prepacked_ops",
[](script::Module& module) {
return vulkanInsertPrePackedOps(module);
})
.def(
"_jit_pass_vulkan_fuse_clamp_w_prepacked_conv",
[](script::Module& module) {
return vulkanFusePrePackedConvWithClamp(module);
})
.def(
"_jit_pass_vulkan_fold_prepacking_ops",
[](script::Module& module) {
return vulkanFoldPrePackingOps(module);
})
.def(
"_jit_pass_vulkan_optimize_for_mobile",
[](script::Module& module,
std::vector<std::string>& preserved_methods) {
return vulkanOptimizeForMobile(module, preserved_methods);
})
.def(
"_jit_pass_metal_insert_prepacked_ops",
[](std::shared_ptr<Graph>& graph) {
return metalInsertPrePackedOps(graph);
})
.def(
"_jit_pass_metal_insert_prepacked_ops",
[](script::Module& module) {
return metalInsertPrePackedOps(module);
})
.def(
"_jit_pass_metal_fuse_clamp_w_prepacked_conv",
[](script::Module& module) {
return metalFusePrePackedConvWithClamp(module);
})
.def(
"_jit_pass_metal_fold_prepacking_ops",
[](script::Module& module) { return metalFoldPrePackingOps(module); })
.def(
"_jit_pass_metal_optimize_for_mobile",
[](script::Module& module,
std::vector<std::string>& preserved_methods) {
return metalOptimizeForMobile(module, preserved_methods);
})
.def(
"_jit_pass_onnx_unpack_quantized_weights",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, IValue>& paramsDict) {
UnpackQuantizedWeights(graph, paramsDict);
return paramsDict;
},
pybind11::return_value_policy::move)
.def(
"_jit_pass_onnx_quantization_insert_permutes",
[](std::shared_ptr<Graph>& graph,
std::map<std::string, IValue>& paramsDict) {
insertPermutes(graph, paramsDict);
return paramsDict;
},
pybind11::return_value_policy::move)
.def(
"_jit_pass_filter_non_tensor_arguments",
[](std::map<std::string, IValue> params) {
std::map<std::string, at::Tensor> retval;
for (auto& kv : params) {
if (kv.second.isTensor()) {
retval[kv.first] = std::move(kv.second).toTensor();
}
}
return retval;
})
.def("_jit_decay_packed_param_input_types", [](Graph& g) {
for (Value* i : g.inputs()) {
if (i->type() ==
getCustomClass(
"__torch__.torch.classes.quantized.Conv2dPackedParamsBase") ||
i->type() ==
getCustomClass(
"__torch__.torch.classes.quantized.Conv3dPackedParamsBase") ||
i->type() ==
getCustomClass(
"__torch__.torch.classes.quantized.LinearPackedParamsBase")) {
// Dummy CompleteTensorType to appease ONNX validator.
i->setType(TensorType::create(
at::kQInt8,
c10::kCPU,
std::vector<int64_t>{1},
std::vector<int64_t>{1},
c10::nullopt));
}
}
});
// NOLINTNEXTLINE(bugprone-unused-raii)
py::class_<CompleteArgumentSpec>(m, "CompleteArgumentSpec")
.def("__repr__", [](CompleteArgumentSpec& self) {
std::ostringstream s;
s << self;
return s.str();
});
// NOLINTNEXTLINE(bugprone-unused-raii)
py::class_<ArgumentSpec>(m, "ArgumentSpec");
py::class_<Code>(m, "Code")
.def(
"grad_executor_states",
[](Code& c) {
std::vector<GraphExecutorState> states;
for (auto& e : c.grad_executors()) {
states.emplace_back(e->getDebugState());
}
return states;
})
.def("num_bailouts", [](Code& c) { return c.num_bailouts(); })
.def("request_bailout", [](Code& c, size_t index) {
c.request_bailout(index);
});
py::class_<ExecutionPlan>(m, "ExecutionPlan")
.def_property_readonly("graph", [](ExecutionPlan& s) { return s.graph; })
.def_property_readonly("code", [](ExecutionPlan& s) { return s.code; });
py::class_<Gradient>(m, "Gradient")
.def_property_readonly("f", [](Gradient& m) { return m.f; })
.def_property_readonly("df", [](Gradient& m) { return m.df; })
.def_property_readonly(
"f_real_outputs", [](Gradient& m) { return m.f_real_outputs; })
.def_property_readonly(
"df_input_vjps", [](Gradient& m) { return m.df_input_vjps; })
.def_property_readonly(
"df_input_captured_inputs",
[](Gradient& m) { return m.df_input_captured_inputs; })
.def_property_readonly(
"df_input_captured_outputs",
[](Gradient& m) { return m.df_input_captured_outputs; })
.def_property_readonly(
"df_output_vjps", [](Gradient& m) { return m.df_output_vjps; });
py::class_<GraphExecutorState>(m, "GraphExecutorState")
.def_property_readonly(
"graph", [](GraphExecutorState& s) { return s.graph; })
.def_property_readonly(
"execution_plans",
[](GraphExecutorState& s) { return s.execution_plans; })
.def_property_readonly(
"fallback", [](GraphExecutorState& s) { return s.fallback; });
py::class_<PyTorchStreamWriter>(m, "PyTorchFileWriter")
.def(py::init<std::string>())
.def(py::init([](const py::object& buffer) {
auto writer_func = [=](const void* data, size_t size) {
auto bytes = py::bytes(reinterpret_cast<const char*>(data), size);
buffer.attr("write")(std::move(bytes));
return size;
};
return std::make_unique<PyTorchStreamWriter>(std::move(writer_func));
}))
.def(py::init<const std::function<size_t(const void*, size_t)>&>())
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
const char* data,
size_t size) { return self.writeRecord(name, data, size); })
.def("write_end_of_file", &PyTorchStreamWriter::writeEndOfFile)
.def(
"write_record",
[](PyTorchStreamWriter& self,
const std::string& name,
uintptr_t data,
size_t size) {
return self.writeRecord(
name, reinterpret_cast<const char*>(data), size);
});
py::enum_<MobileOptimizerType>(m, "MobileOptimizerType")
.value("CONV_BN_FUSION", MobileOptimizerType::CONV_BN_FUSION)
.value(
"INSERT_FOLD_PREPACK_OPS",
MobileOptimizerType::INSERT_FOLD_PREPACK_OPS)
.value("REMOVE_DROPOUT", MobileOptimizerType::REMOVE_DROPOUT)
.value("FUSE_ADD_RELU", MobileOptimizerType::FUSE_ADD_RELU)
.value(
"HOIST_CONV_PACKED_PARAMS",
MobileOptimizerType::HOIST_CONV_PACKED_PARAMS)
.export_values();
// This allows PyTorchStreamReader to read from a Python buffer. It requires
// that the buffer implement `seek()`, `tell()`, and `read()`.
class BufferAdapter : public caffe2::serialize::ReadAdapterInterface {
public:
BufferAdapter(const py::object& buffer) : buffer_(buffer) {
// Jump to the end of the buffer to get its size
auto current = buffer.attr("tell")();
start_offset_ = py::cast<size_t>(current);
buffer.attr("seek")(current, py::module::import("os").attr("SEEK_END"));
size_ = py::cast<size_t>(buffer.attr("tell")()) - start_offset_;
buffer.attr("seek")(current);
// If we can read directly into a buffer, do that instead of an extra copy
use_readinto_ = py::hasattr(buffer, "readinto");
}
size_t size() const override {
return size_;
}
THPObjectPtr getMemview(void* buf, size_t n) const {
THPObjectPtr memview(PyMemoryView_FromMemory(
reinterpret_cast<char*>(buf), n, PyBUF_WRITE));
if (!memview) {
throw python_error();
}
return memview;
}
size_t read(uint64_t pos, void* buf, size_t n, const char* what)
const override {
// Seek to desired position (NB: this has to be a Py_ssize_t or Python
// throws a weird error)
Py_ssize_t absolute_pos = start_offset_ + pos;
buffer_.attr("seek")(absolute_pos);
if (use_readinto_) {
auto memview = getMemview(buf, n);
auto res =
PyObject_CallMethod(buffer_.ptr(), "readinto", "O", memview.get());
if (res) {
int64_t i = static_cast<int64_t>(PyLong_AsLongLong(res));
if (i > 0) {
return i;
}
}
}
// Read bytes into `buf` from the buffer
std::string bytes = py::cast<std::string>(buffer_.attr("read")(n));
std::copy(
bytes.data(),
bytes.data() + bytes.size(),
reinterpret_cast<char*>(buf));
return bytes.size();
}
py::object buffer_;
size_t size_;
size_t start_offset_;
bool use_readinto_;
};
py::class_<PyTorchStreamReader>(m, "PyTorchFileReader")
.def(py::init<std::string>())
.def(py::init([](const py::object& buffer) {
auto adapter = std::make_unique<BufferAdapter>(std::move(buffer));
return std::make_unique<PyTorchStreamReader>(std::move(adapter));
}))
.def(
"get_record",
[](PyTorchStreamReader& self, const std::string& key) {
at::DataPtr data;
size_t size;
std::tie(data, size) = self.getRecord(key);
return py::bytes(reinterpret_cast<const char*>(data.get()), size);
})
.def(
"get_storage_from_record",
[](PyTorchStreamReader& self,
const std::string& key,
size_t numel,
py::object data_type_obj) {
at::DataPtr data(std::get<0>(self.getRecord(key)));
auto scalar_type =
reinterpret_cast<THPDtype*>(data_type_obj.ptr())->scalar_type;
c10::Storage storage(
c10::Storage::use_byte_size_t(),
numel * elementSize(scalar_type),
std::move(data),
/*allocator=*/nullptr,
/*resizable=*/false);
auto ptr =
c10::make_intrusive<at::TensorImpl, at::UndefinedTensorImpl>(
std::move(storage),
at::DispatchKeySet(),
at::CPU(scalar_type).typeMeta());
return at::Tensor(std::move(ptr));
})
.def("get_all_records", [](PyTorchStreamReader& self) {
return self.getAllRecords();
});
m.def(
"_jit_get_operation",
[](const std::string& op_name) {
try {
auto symbol = Symbol::fromQualString(op_name);
auto operations = getAllOperatorsFor(symbol);
TORCH_CHECK(!operations.empty(), "No such operator ", op_name);
std::ostringstream docstring;
docstring << "Automatically bound operator '" << op_name
<< "' with schema(s):\n";
for (const auto& op : operations) {