Backport processors: and types: to original cwrap #2
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duplicated of #10 |
zdevito
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zdevito
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* [bootcamp] Improve "Shape" operator to support axes specification To improve .shape operator of Caffe2 to support x.shape(tensor, axes), which takes an optional int array "axes" as input. For example, x.shape(tensor, [1, 0]) will return the dimension for axis 1 and 0 following the specified order. For current version, "axes" input allows duplications and can have arbitrary length. * Back out "Add barrier net that runs before training nets" Original commit changeset: b373fdc9c30f. Need additional changes to some callers to support barrier failures. * Change warning to verbose log to reduce log spam The `LOG(WARNING)` was a bit spammy for regular use so lets just make it a `VLOG`. * Extract the shared code from different caffe2_benchmark binaries The OSS benchmark and Internal benchmark will share most functions in the benchmark. * Support MFR in sequence training As titled. * Make knowledge distillation work with using logged prediction feature as teacher label. 1) Add loading raw dense feature as teacher label. 2) Optional calibration function for teacher label 3) Add teacher label into generic unit test 4) Deprecated TTSN workflow version using feature_options to config teacher label * [C2/CUDA]: unjoined cross entropy sigmoid as desc * Add async_scheduling executor into deferrable_net_exec_test Add async_scheduling into tests and fix some exception cases * Fix Event disabled error When disabling event in RNN ops make sure we don't call Finish on disabled event from op's RunAsync * cuda ensure cpu output op can handle both TensorCPU and TensorCUDA as desc. * [C2 Core] Infer input device option in C2 hypothesis_test checkers Improve how we default input blob device options. Previously it defaults as where op lives but it is not necessarily the case. For example: CopyCPUToGPU * [C2 Op]SplitByLengthsOp CPU/GPU implementation [C2 Op]SplitByLengthsOp CPU/GPU implementation * fix undefined symbol error not sure why we're getting undefined symbol even with link_whole = True Need to figure out why but need this workaround for now * Add tools in DAIPlayground platform to help debugging models Add additional tools to allow Plauground override individual method defined in AnyExp. This will allow user to create module that specificly change certain default method behavior. An example included in this diff is deactivating test model and checkpointing. When debugging any model problems, switching off components helps me quickly narrow down the location of the bug. The technique is extensively used in task T27038712 (Steady memory increase in EDPM, eventually resulting in gloo/cuda.cu:34: out of memory) * add shape and type inference for int8 conversion operator * Fix flaky test for group_norm Fix flaky test for group_norm * Fix group_norm_op_test flaky Fix group_norm_op_test flaky * Implementation of composite learning rate policy In many state-of-the-arts deep learning works, people use a simple trick to schedule the learning rate: use a fixed learning rate until error plateaus and then switch to a different fixed learning rate, and so on. In this diff, we implemented a simple version of the composite learning rate. The user gives a set of learning rates policies and corresponding iteration nums, and the optimizer will change the learning rate policy based on the number of iterations so far. For example, the user give two learning rate policies, one is FixedLearningRate and PolyLearningRate, with an iteration number of 1k. Then the first 1k iteration, we use FixedLearningRate. For the following iterations, we use PolyLearningRate. * Split two use cases of CachedReader into two classes, DBFileReader and CachedReader # Use Cases: 1). input: DB file -> output: DatasetReader. Use DBFileReader. 2). input: Reader -> build cache DB file -> output: DatasetReader. Use CachedReader. # Changes to CachedReader: 1). Move db_path to the constructor. Because in mock reader. cache will always be built ahead. # Changes to tests: 1). Make a separate TestCase class for CachedReader and DBFileReader. 2). Make it possible to add more test functions by adding setUp, tearDown and _make_temp_path. 3). Make delete db_path more general. `db_path` could be a file for `log_file_db`, but could also be a directory for `leveldb`. * Back out "On Mobile phones, call GlobalInit with no arguments in predictor in case we need to perform initialization" Original commit changeset: 4489c6133f11 * Fix LARS bug Fixed a bug in the LARS implementation which caused all subsequent blobs not using LARS to have the LARS learning rate multiplier applied to them. * [tum] support sparse init & add uniformFill option as title * Propagate exception for async nets Capture the exception when an exception is thrown in async nets and re-throw it after wait(). This allows exceptions to be propagated up to the caller. This diff was a part of D7752068. We split the diff so that C2 core files changes are in a separate diff. * Automatic update of fbcode/onnx to 69894f207dfcd72d1e70497d387201cec327efbc Previous import was 403ccfbd0161c38f0834413d790bad0874afbf9a Included changes: - **[69894f2](onnx/onnx@69894f2)**: Use op schema.all tensor types in random like definitions (pytorch#865) <Scott McKay> - **[b9d6b90](onnx/onnx@b9d6b90)**: Clarify random like operators (pytorch#846) <Scott McKay> - **[fc6b5fb](onnx/onnx@fc6b5fb)**: Refactor shape inference implementation (pytorch#855) <anderspapitto> - **[b7d8dc8](onnx/onnx@b7d8dc8)**: fix cmake warning message (pytorch#863) <Eric S. Yu> - **[f585c5d](onnx/onnx@f585c5d)**: add pytorch-operator test for tile (pytorch#831) <Wenhao Hu> - **[993fe70](onnx/onnx@993fe70)**: add install step (pytorch#832) <Eric S. Yu> - **[68bc26c](onnx/onnx@68bc26c)**: add type inference for traditional ml ops except classifier ops. (pytorch#857) <Ke Zhang> - **[9cc0cda](onnx/onnx@9cc0cda)**: fix string representation of scalar types (pytorch#858) <G. Ramalingam> - **[1078925](onnx/onnx@1078925)**: fix y in pow test case to scalar (pytorch#852) <Wenhao Hu> - **[c66fb6f](onnx/onnx@c66fb6f)**: Add some math function shape inference (pytorch#845) <anderspapitto> - **[ff667d1](onnx/onnx@ff667d1)**: Refactor return type and docs for ONNXIFI_BACKEND_DIRECTX_ID (pytorch#853) <Marat Dukhan> - **[11c6876](onnx/onnx@11c6876)**: clear initializer names when clear initializer (pytorch#849) <Wenhao Hu> - **[73c34ae](onnx/onnx@73c34ae)**: Clarify FeatureVectorizer description. (pytorch#843) <Scott McKay> - **[1befb9b](onnx/onnx@1befb9b)**: Remove useless text in docs (pytorch#850) <Lu Fang> - **[e84788f](onnx/onnx@e84788f)**: Fix SELU attributes' default values (pytorch#839) <Lu Fang> - **[ebac046](onnx/onnx@ebac046)**: Add tile test case (pytorch#823) <Wenhao Hu> - **[8b7a925](onnx/onnx@8b7a925)**: a few more shape inference functions (pytorch#772) <anderspapitto> - **[9718f42](onnx/onnx@9718f42)**: Make the coefficient non optional for LinearClassifier (pytorch#836) <Jaliya Ekanayake> - **[ef083d0](onnx/onnx@ef083d0)**: Add save_tensor and load_tensor functions for Protos (pytorch#770) <Lu Fang> - **[45ceb55](onnx/onnx@45ceb55)**: Check if CMAKE_BUILD_TYPE set before project(). (pytorch#812) <Sergii Dymchenko> - **[4b3d2b0](onnx/onnx@4b3d2b0)**: [WIP] reenable shape inference tests (pytorch#834) <anderspapitto> - **[22d17ee](onnx/onnx@22d17ee)**: RNN tests: LSTM, GRU, SimpleRNN (pytorch#739) <Peyman Manikashani> - **[de65b95](onnx/onnx@de65b95)**: dimension denotation (pytorch#443) <Tian Jin> - **[eccc76e](onnx/onnx@eccc76e)**: fix field number issue in onnx operator proto and enable its build (pytorch#829) <Ke Zhang> - **[d582beb](onnx/onnx@d582beb)**: disable shape inference test to unbreak ci (pytorch#830) <Lu Fang> - **[485b787](onnx/onnx@485b787)**: function proto for composite op. (pytorch#802) <Ke Zhang> - **[cd58928](onnx/onnx@cd58928)**: specify defaults for attributes of Affine op (pytorch#820) <G. Ramalingam> - **[7ee2cf9](onnx/onnx@7ee2cf9)**: merge the dummy backend back into the main one (pytorch#743) <anderspapitto> - **[1c03a5a](onnx/onnx@1c03a5a)**: [Proposal] ONNX Interface for Framework Integration (previously ONNX Backend API) header and docs (pytorch#551) <Marat Dukhan> - **[3769a98](onnx/onnx@3769a98)**: Rename real model test case from VGG-16 to ZFNet (pytorch#821) <Lu Fang> * [C2]ReluN Op relu n op. tf reference: https://www.tensorflow.org/api_docs/python/tf/nn/relu6 * Call destructor when assigning a blob value * Add executor overrides Add executor overrides flag to enable migration to async_scheduling executor * Add barrier net that runs before training nets - attempt #2 Add a synchonize barrier net that is run before training nets. With this net, shards that are faster will wait for other shards before start training. This reduce chances of the faster shards timing out during GLOO AllReduce. Removed explicit data_parallel_model.py.synchronize call in holmes workflow. This change was landed previously but caused errors for some EDPM workflows - See https://fb.facebook.com/groups/1426530000692545/permalink/1906766366002237/ - because EDPM assumes any call to CreateOrCloneCommonWorld and Gloo ops are wrapped in exception handlers but in this case exception thrown in the barrier init net is not handled. To address this issue, we add _CreateOrCloneCommonWorld to the param_init_net instead of a new barrier init net. Since errors for param_init_net run is handled gracefully and re-rendezvous, it should fixes the problem. * Handle empty nets in async_scheduling Make sure we don't get stuck on empty nets * use CUDA_ARCH for conditional compile * [C2 fix] infer function for ensure_cpu_output_op * Update group_norm test to reduce flaky test * Fix lr_multiplier for GPU
zdevito
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…orms we care about. (pytorch#11394) Summary: While the use of memcpy as part of the byte swapping sequence looks funky, all major compilers recognize and optimize this pattern reliably, resulting in essentially optimal code generation. For example, decodeUInt32LE goes from this on iOS arm64: > ldrb w8, [x0, #3] > ldrb w9, [x0, #2] > bfi w8, w9, #8, #8 > ldrb w9, [x0, #1] > bfi w8, w9, #16, #8 > ldrb w9, [x0] > bfi w8, w9, #24, #8 > mov x0, x8 > ret To this: > ldr w8, [x0] > rev w0, w8 > ret Pull Request resolved: pytorch#11394 Reviewed By: SsnL Differential Revision: D9728659 Pulled By: resistor fbshipit-source-id: 9afbd4adfad1d1fb7b01f1179e6707ee21fa726f
zdevito
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…ional to ATen/core" (pytorch#12568) Summary: Pull Request resolved: pytorch#12568 Second attempt at D10324615 Original commit changeset: b71eeec98dfe Original commit changeset #2: 1af6400ae0c1 Reviewed By: bwasti Differential Revision: D10338168 fbshipit-source-id: 04cb443a89a9cd1a174df6d5ac1a86c3d423d56b
zdevito
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Nov 19, 2018
Summary: hopefully this one doesn't break master. Pull Request resolved: pytorch#14053 Differential Revision: D13093406 Pulled By: suo fbshipit-source-id: 8fed44f1a3d463748726cb14acac2ea53dedf29b
zdevito
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Feb 8, 2019
…#16852) Summary: Add test/.hypothesis/ to .gitignore to pass git status --porcelain check in CI build Pull Request resolved: pytorch#16852 Differential Revision: D14000206 Pulled By: soumith fbshipit-source-id: 5da99a4bb242c12aa35776f7254f6399a7fa6d8c
zdevito
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Feb 22, 2019
…ytorch#17337) Summary: Attempt #2 (attempt 1 is pytorch#16705 and got reverted because of CI failures) Fixes pytorch#14805 Pull Request resolved: pytorch#17337 Differential Revision: D14175626 Pulled By: soumith fbshipit-source-id: 66f2e10e219a1bf88ed342ec5c89da6f2994d8eb
zdevito
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Summary: Currently there is a mismatch in naming between Python BatchNorm `running_var` and C++ BatchNorm `running_variance`, which causes JIT model parameters loading to fail (pytorch/vision#728 (comment)): ``` terminate called after throwing an instance of 'c10::Error' what(): No such serialized tensor 'running_variance' (read at /home/shahriar/Build/pytorch/torch/csrc/api/src/serialize/input-archive.cpp:27) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x85 (0x7f2d92d32f95 in /usr/local/lib/libc10.so) frame #1: torch::serialize::InputArchive::read(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, at::Tensor&, bool) + 0xdeb (0x7f2d938551ab in /usr/local/lib/libtorch.so.1) frame #2: torch::nn::Module::load(torch::serialize::InputArchive&) + 0x98 (0x7f2d9381cd08 in /usr/local/lib/libtorch.so.1) frame #3: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1) frame #4: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1) frame #5: torch::nn::operator>>(torch::serialize::InputArchive&, std::shared_ptr<torch::nn::Module> const&) + 0x32 (0x7f2d9381c7b2 in /usr/local/lib/libtorch.so.1) frame #6: <unknown function> + 0x2b16c (0x5645f4d1916c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #7: <unknown function> + 0x27a3c (0x5645f4d15a3c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #8: <unknown function> + 0x2165c (0x5645f4d0f65c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #9: <unknown function> + 0x1540b (0x5645f4d0340b in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) frame #10: __libc_start_main + 0xf3 (0x7f2d051dd223 in /usr/lib/libc.so.6) frame #11: <unknown function> + 0x1381e (0x5645f4d0181e in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest) ``` Renaming C++ BatchNorm `running_variance` to `running_var` should fix this problem. This is a BC-breaking change, but it should be easy for end user to rename `running_variance` to `running_var` in their call sites. Pull Request resolved: pytorch#17371 Reviewed By: goldsborough Differential Revision: D14172775 Pulled By: yf225 fbshipit-source-id: b9d3729ec79272a8084269756f28a8f7c4dd16b6
zdevito
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Mar 20, 2019
…fc3ff6 (pytorch#18028) Summary: Pull Request resolved: pytorch#18028 Previous import was 520e8e135f1ad75959bf9b5bd15c361b8caeb8d6 Included changes: - **[d1f45b1](houseroad/foxi@d1f45b1)**: update the gitignore (#6) <Lu Fang> - **[398135c](houseroad/foxi@398135c)**: Remove static variable in header (#3) <Lu Fang> - **[f817be1](houseroad/foxi@f817be1)**: sync to ONNX cb544d07cc022e3fe83622fda9b2b1fa00b75b89 (#2) <Lu Fang> Reviewed By: zrphercule Differential Revision: D14464213 fbshipit-source-id: b5d166f05f7fd503dec11d676e219cc6c6a373f9
ailzhang
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Apr 9, 2019
Summary: Tracing models which attempts to return this in-place value doesn't turn out well. I haven't run any tests to confirm the results to be honest, but regardless of the outcome, the operation happens in-place, so it should work as before. Sample output from traced model attempting to set `max_norm` on `Embedding`: ``` a leaf Variable that requires grad has been used in an in-place operation. (check_inplace at /pytorch/torch/csrc/autograd/VariableTypeUtils.h:49) frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f0ecc5cc021 in /usr/local/lib/python3.7/site-packages/torch/lib/libc10.so) frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f0ecc5cb8ea in /usr/local/lib/python3.7/site-packages/torch/lib/libc10.so) frame #2: <unknown function> + 0x38ab2f (0x7f0ecb55ab2f in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #3: torch::autograd::VariableType::embedding_renorm_(at::Tensor&, at::Tensor const&, double, double) const + 0x76 (0x7f0ecb5b5966 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #4: <unknown function> + 0x56c958 (0x7f0ecb73c958 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #5: <unknown function> + 0x672286 (0x7f0ecb842286 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #6: torch::jit::InterpreterState::run(std::vector<c10::IValue, std::allocator<c10::IValue> >&) + 0x22 (0x7f0ecb83d842 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #7: <unknown function> + 0x65c6ac (0x7f0ecb82c6ac in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1) frame #8: <unknown function> + 0x3c8ab4 (0x7f0f06bc0ab4 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) frame #9: <unknown function> + 0x3ad2c3 (0x7f0f06ba52c3 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) frame #10: <unknown function> + 0x11663e (0x7f0f0690e63e in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame pytorch#39: python_call + 0x11 (0x5563c3c521c1 in uwsgi) frame pytorch#40: uwsgi_request_wsgi + 0x100 (0x5563c3c54410 in uwsgi) frame pytorch#41: wsgi_req_recv + 0xac (0x5563c3becabc in uwsgi) frame pytorch#42: simple_loop_run + 0xc4 (0x5563c3c35be4 in uwsgi) frame pytorch#43: simple_loop + 0x10 (0x5563c3c35a00 in uwsgi) frame pytorch#44: uwsgi_ignition + 0x241 (0x5563c3c3a3a1 in uwsgi) frame pytorch#45: uwsgi_worker_run + 0x275 (0x5563c3c3ec35 in uwsgi) frame pytorch#46: <unknown function> + 0x8f22c (0x5563c3c3f22c in uwsgi) frame pytorch#47: <unknown function> + 0x3c13e (0x5563c3bec13e in uwsgi) frame pytorch#48: __libc_start_main + 0xf1 (0x7f0f138922e1 in /lib/x86_64-linux-gnu/libc.so.6) frame pytorch#49: _start + 0x2a (0x5563c3bec16a in uwsgi) : operation failed in interpreter: op_version_set = 0 def forward(self, input_1: Tensor) -> Tensor: _0 = torch.norm(self.item_embedding.weight, 2, 1, True) _1 = torch.div(self.item_embedding.weight, _0) m_weight = torch.t(_1) input_2 = torch.contiguous(input_1) weight_1 = torch.embedding_renorm_(self.item_embedding.weight, input_2, 1., 2.) ~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE x = torch.embedding(weight_1, input_2, -1, False, False) input_3 = torch.div(x, torch.norm(x, 2, 2, True)) max_batch_size = ops.prim.NumToTensor(torch.size(input_3, 0)) hx = torch.zeros([2, int(max_batch_size), 70], dtype=6, layout=0, device=torch.device("cpu")) _2 = [self.lstm_layer.weight_ih_l0, self.lstm_layer.weight_hh_l0, self.lstm_layer.weight_ih_l1, self.lstm_layer.weight_hh_l1] input_4, _3, _4 = torch.lstm(input_3, [hx, hx], _2, False, 2, 0.10000000000000001, False, False, True) input = torch.matmul(input_4, torch.t(self.rnn2item.weight)) tastevec = torch.div(input, torch.norm(input, 2, 2, True)) outputs = torch.matmul(tastevec, m_weight) ``` Pull Request resolved: pytorch#18684 Differential Revision: D14782041 Pulled By: ezyang fbshipit-source-id: 7b2fc19b7d5b6600263644498bb728319a19f39d
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removed defined_if that is related only to processors/types and processor_types_pairs. Write a plugin that generates defined_if from the processor/types info.
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