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* Supported Complex2Real Conversion for Eager Dygraph * Supported Complex2Real Conversion for Eager Dygraph * Enabled complex type promotion test for matmul_v2 * pylayer, test=develop * Fix CI issues * Support initializing specific grad tensors to zero for selected operators * finish forward, test=develop * create grad node finish, test=develop * Merged adj_edges_ with GradSlotMeta * Fixed monir issue * backward finish, start dbg, test=develop * Adjusted num runs * Recovered Eager performance tests configurations * Recovered Eager performance tests configurations * finish, test=develop * polish, test=develop * polish, test=develop * refine, test=develop * eager, test=develop * Adjusted performance tests configurations * Fixed Minor Issues with performance tests * [Phi] Fix macro name typo * support set_materialize_grads, test=develop * suppotr mark_non_differentiable, test=develop * support once_differentiable, test=develop * refine, test=develop * refine, test=develop * Moved out Edge from GradSlotMeta * Fixed issues from merge * Fixed typo * Addressed review comments * Fixed merge issues * Fixed minor issues * Fixed minor issue * refine, test=develop * refine, test=develop * refine, test=develop * Fixed major issues and enabled auto_prune test cases * Fixed issues from merge * refine, test=develop * refine, test=develop * refine, test=develop * refine, test=develop * refine, test=develop Co-authored-by: jim19930609 <jim19930609@gmail.com> Co-authored-by: Aurelius84 <zhangliujie@baidu.com>
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| cc_library(py_layer_node SRCS py_layer_node.cc DEPS phi phi_api grad_node_info) |
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| // Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
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| #include "paddle/fluid/eager/pylayer/py_layer_node.h" | ||
| #include "paddle/fluid/eager/eager_tensor.h" | ||
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| #include "paddle/phi/api/all.h" | ||
| #include "paddle/phi/core/dense_tensor.h" | ||
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| #include "paddle/fluid/platform/device_context.h" | ||
| #include "paddle/fluid/platform/enforce.h" | ||
| #include "paddle/fluid/platform/errors.h" | ||
| #include "paddle/fluid/pybind/eager.h" | ||
| #include "paddle/fluid/pybind/eager_utils.h" | ||
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| #include "glog/logging.h" | ||
| #pragma GCC diagnostic ignored "-Wattributes" | ||
| #include "pybind11/pytypes.h" | ||
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| namespace egr { | ||
| std::vector<std::vector<paddle::experimental::Tensor>> GradNodePyLayer:: | ||
| operator()( | ||
| std::vector<std::vector<paddle::experimental::Tensor>>& grads, // NOLINT | ||
| bool create_graph) { | ||
| VLOG(3) << "Running Eager Backward Node: " << name(); | ||
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| std::vector<std::vector<paddle::experimental::Tensor>> hooked_grads = | ||
| GradNodePyLayer::ApplyGradientHooks(grads); | ||
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| paddle::pybind::PyLayerObject* ctx = | ||
| reinterpret_cast<paddle::pybind::PyLayerObject*>(ctx_); | ||
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| PADDLE_ENFORCE_EQ(ctx->forward_output_tensor_is_duplicable.size(), | ||
| grads.size(), | ||
| paddle::platform::errors::InvalidArgument( | ||
| "%s's grad input size(%s) mast be equal with it's " | ||
| "forward's output size(%s).", | ||
| name(), grads.size(), | ||
| ctx->forward_output_tensor_is_duplicable.size())); | ||
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| auto backward_args = PyTuple_New(grads.size()); | ||
| for (size_t i = 0; i < grads.size(); i++) { | ||
| if (ctx->forward_output_tensor_is_duplicable[i]) { | ||
| PyObject* pylist = PyList_New((Py_ssize_t)grads[i].size()); | ||
| for (size_t j = 0; j < grads[i].size(); j++) { | ||
| if (ctx->materialize_grads && !grads[i][j].initialized()) { | ||
| paddle::experimental::Tensor tensor_tmp; | ||
| auto dense_tensor = std::make_shared<phi::DenseTensor>(); | ||
| dense_tensor->set_meta(forward_outputs_meta_[i][j]); | ||
| tensor_tmp.set_impl(dense_tensor); | ||
| PyList_SET_ITEM( | ||
| pylist, static_cast<Py_ssize_t>(i), | ||
| paddle::pybind::ToPyObject(paddle::experimental::zeros_like( | ||
| tensor_tmp, tensor_tmp.dtype(), | ||
| forward_outputs_place_[i][j]))); | ||
| } else { | ||
| PyList_SET_ITEM(pylist, static_cast<Py_ssize_t>(i), | ||
| paddle::pybind::ToPyObject(grads[i][0], true)); | ||
| } | ||
| } | ||
| PyTuple_SET_ITEM(backward_args, i, pylist); | ||
| } else { | ||
| if (ctx->materialize_grads && !grads[i][0].initialized()) { | ||
| paddle::experimental::Tensor tensor_tmp; | ||
| auto dense_tensor = std::make_shared<phi::DenseTensor>(); | ||
| dense_tensor->set_meta(forward_outputs_meta_[i][0]); | ||
| tensor_tmp.set_impl(dense_tensor); | ||
| PyTuple_SET_ITEM( | ||
| backward_args, i, | ||
| paddle::pybind::ToPyObject(paddle::experimental::zeros_like( | ||
| tensor_tmp, tensor_tmp.dtype(), forward_outputs_place_[i][0]))); | ||
| } else { | ||
| PyTuple_SET_ITEM(backward_args, i, | ||
| paddle::pybind::ToPyObject(grads[i][0], true)); | ||
| } | ||
| } | ||
| } | ||
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| VLOG(6) << "PyLayer backward args is ready, begin call user's backward " | ||
| "function..."; | ||
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| auto backward_fn = | ||
| PyObject_GetAttrString(reinterpret_cast<PyObject*>(ctx), "backward"); | ||
| if (!backward_fn) { | ||
| PADDLE_THROW(paddle::platform::errors::InvalidArgument( | ||
| "Get backward function faild.")); | ||
| } | ||
| auto outputs = PyObject_CallObject(backward_fn, backward_args); | ||
| if (!outputs) { | ||
| PADDLE_THROW(paddle::platform::errors::External( | ||
| pybind11::detail::error_string().c_str())); | ||
| } | ||
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| outputs_ = outputs; | ||
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| VLOG(6) << "PyLayer backward function finish..."; | ||
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| PyObject* outputs_tuple = nullptr; | ||
| if (PyTuple_Check(outputs)) { | ||
| outputs_tuple = outputs; | ||
| } else { | ||
| outputs_tuple = PyTuple_New(1); | ||
| Py_INCREF(outputs); | ||
| PyTuple_SET_ITEM(outputs_tuple, 0, outputs); | ||
| } | ||
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| size_t outputs_size = PyTuple_GET_SIZE(outputs_tuple); | ||
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| if (outputs_size > ctx->forward_input_tensor_is_duplicable.size()) { | ||
| PADDLE_THROW(paddle::platform::errors::InvalidArgument( | ||
| "The number of outputs of `PyLayer.backward` should be %d, but " | ||
| "received %d.", | ||
| ctx->forward_input_tensor_is_duplicable.size(), outputs_size)); | ||
| } | ||
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| std::vector<std::vector<paddle::experimental::Tensor>> grad_out; | ||
| grad_out.reserve(ctx->forward_input_tensor_is_duplicable.size()); | ||
| for (size_t i = 0; i < ctx->forward_input_tensor_is_duplicable.size(); i++) { | ||
| if (i < outputs_size) { | ||
| PyObject* obj = PyTuple_GET_ITEM(outputs_tuple, i); | ||
| if (this->OutputMeta()[i][0].IsStopGradient()) { | ||
| PADDLE_ENFORCE_EQ( | ||
| obj, Py_None, | ||
| paddle::platform::errors::InvalidArgument( | ||
| "%s's backward function should return None at %d position, " | ||
| "because it's forward Tensor's stopgradient is true.", | ||
| name(), i)); | ||
| grad_out.push_back({}); | ||
| } else { | ||
| if (ctx->forward_input_tensor_is_duplicable[i]) { | ||
| grad_out.push_back(paddle::pybind::GetTensorListFromPyObject(obj)); | ||
| } else { | ||
| grad_out.push_back({paddle::pybind::GetTensorFromPyObject(obj)}); | ||
| } | ||
| } | ||
| } else { | ||
| PADDLE_ENFORCE_EQ( | ||
| this->OutputMeta()[i][0].IsStopGradient(), true, | ||
| paddle::platform::errors::InvalidArgument( | ||
| "%s's backward function should not return empyt at %d position.", | ||
| name(), i)); | ||
| grad_out.push_back({}); | ||
| } | ||
| } | ||
|
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| return grad_out; | ||
| } | ||
| } // namespace egr |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,82 @@ | ||
| // Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #pragma once | ||
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| #include <Python.h> | ||
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| #include "paddle/fluid/eager/autograd_meta.h" | ||
| #include "paddle/fluid/eager/grad_node_info.h" | ||
| #include "paddle/fluid/eager/hooks.h" | ||
| #include "paddle/phi/core/compat/convert_utils.h" | ||
| #include "paddle/phi/core/tensor_meta.h" | ||
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| namespace egr { | ||
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| class GradNodePyLayer : public GradNodeBase { | ||
| public: | ||
| GradNodePyLayer(PyObject* ctx, size_t bwd_in_slot_num, | ||
| size_t bwd_out_slot_num) | ||
| : GradNodeBase(bwd_in_slot_num, bwd_out_slot_num) { | ||
| ctx_ = ctx; | ||
| } | ||
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| ~GradNodePyLayer() override { Py_DECREF(ctx_); }; | ||
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| virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()( | ||
| std::vector<std::vector<paddle::experimental::Tensor>>& grads, // NOLINT | ||
| bool create_graph = false) override; | ||
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| void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; } | ||
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| bool IsTensorWrappersCleared() override { | ||
| VLOG(6) << "Do nothing here now"; | ||
| return false; | ||
| } | ||
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| std::string name() { | ||
| return "GradNodePyLayer_" + std::string(Py_TYPE(ctx_)->tp_name); | ||
| } | ||
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| // for paddle.grad get result | ||
| PyObject* GetMutableOutputs() { return outputs_; } | ||
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| void SaveForwardOutputsMeta( | ||
| const std::vector<std::vector<paddle::experimental::Tensor*>>& | ||
| outputs_tensor) { | ||
| forward_outputs_meta_.resize(outputs_tensor.size()); | ||
| forward_outputs_place_.resize(outputs_tensor.size()); | ||
| for (size_t i = 0; i < outputs_tensor.size(); i++) { | ||
| forward_outputs_meta_[i].reserve(outputs_tensor[i].size()); | ||
| forward_outputs_place_[i].reserve(outputs_tensor[i].size()); | ||
| for (auto tensor : outputs_tensor[i]) { | ||
| if (tensor->is_dense_tensor()) { | ||
| forward_outputs_meta_[i].push_back( | ||
| static_cast<phi::DenseTensor*>(tensor->impl().get())->meta()); | ||
| } else { | ||
| forward_outputs_meta_[i].emplace_back(); | ||
| } | ||
| forward_outputs_place_[i].emplace_back(tensor->inner_place()); | ||
| } | ||
| } | ||
| } | ||
|
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| private: | ||
| PyObject* ctx_{nullptr}; | ||
| PyObject* outputs_{nullptr}; | ||
| std::vector<std::vector<phi::DenseTensorMeta>> forward_outputs_meta_; | ||
| std::vector<std::vector<paddle::platform::Place>> forward_outputs_place_; | ||
| }; | ||
|
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| } // namespace egr |
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This commit causes build error:
#41134