// 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. #include "paddle/fluid/eager/grad_tensor_holder.h" #include "paddle/fluid/eager/api/generated/eager_generated/forwards/dygraph_functions.h" #include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/var_type.h" #include "paddle/fluid/imperative/gradient_accumulator.h" #include "paddle/phi/core/sparse_coo_tensor.h" #include "paddle/phi/kernels/funcs/math_function.h" namespace egr { void GradTensorHolder::SetBufferSlotRankZeros(size_t slot_id, size_t rank) { buffer_[slot_id][rank] = paddle::experimental::zeros_like(buffer_[slot_id][rank]); } void GradTensorHolder::CopyValueFromTensor( size_t slot_id, size_t rank, const paddle::experimental::Tensor& t, bool fill_one) { // TODO(jiabin): We need to deal with empty input_buffer with slot size not // empty; PADDLE_ENFORCE(slot_id < buffer_.size(), paddle::platform::errors::Fatal( "Invalid slot_id for GradTensorHolder::add() " "which exceeds size of buffer")); VLOG(6) << "Add Tensor for buffer_ slot: " << slot_id << ", size: " << buffer_[slot_id].size(); if (buffer_[slot_id].empty()) { VLOG(6) << "Pass add Tensor for buffer_ slot: " << slot_id << " since its buffer_ is empty "; return; } PADDLE_ENFORCE( rank < buffer_[slot_id].size(), paddle::platform::errors::Fatal( "Invalid rank for GradTensorHolder::add() which exceeds size " "of buffer slot %d, got slot size is: %d rank is: %d", slot_id, buffer_[slot_id].size(), rank)); if (!fill_one) { paddle::experimental::Tensor& buffer_tensor = buffer_[slot_id][rank]; if ((!buffer_tensor.defined() || !buffer_tensor.initialized())) { // Perform deep copy here buffer_tensor.copy_(t, t.place(), false); buffer_tensor.set_autograd_meta(t.mutable_autograd_meta()); } else { PADDLE_THROW(paddle::platform::errors::Fatal( "Cannot copy grad_tensors' value to grad tensor holders," "input buffer has already been initialized.")); } } else { // Create new tensor->impl and fill it with 1.0 if (t.defined()) { // Fill 1.0, use full to support complex, one_like don't support it. buffer_[slot_id][rank] = paddle::experimental::full(t.shape(), 1, t.dtype(), t.place()); } } } void GradTensorHolder::add(size_t slot_id, size_t rank, const paddle::experimental::Tensor& t, bool create_graph) { // TODO(jiabin): We need to deal with empty input_buffer with slot size not // empty; PADDLE_ENFORCE(slot_id < buffer_.size(), paddle::platform::errors::Fatal( "Invalid slot_id for GradTensorHolder::add() " "which exceeds size of buffer")); VLOG(6) << "Add Tensor for buffer_ slot: " << slot_id << ", size: " << buffer_[slot_id].size(); if (buffer_[slot_id].empty()) { VLOG(6) << "Pass add Tensor for buffer_ slot: " << slot_id << " since its buffer_ is empty "; return; } PADDLE_ENFORCE( rank < buffer_[slot_id].size(), paddle::platform::errors::Fatal( "Invalid rank for GradTensorHolder::add() which exceeds size " "of buffer slot %d, got slot size is: %d rank is: %d", slot_id, buffer_[slot_id].size(), rank)); paddle::experimental::Tensor& buffer_tensor = buffer_[slot_id][rank]; // TODO(jiabin): Code bellow is ugly to divide which inner var we used, // remove framework::Variable // related code later. // This if statement is trying to test neither phi::Tensor nor // framework::Variable is initialized. if ((!buffer_tensor.defined() || !buffer_tensor.initialized())) { // Simply copy tensor->impl buffer_tensor = t; } else { // Accumulation PADDLE_ENFORCE_EQ(t.initialized(), true, paddle::platform::errors::Fatal( "We can only accumulate initialized tensor, but we " "got tensor: %s is empty please check you network " "and make sure it creates grads.", t.name())); if (t.is_dense_tensor()) { if (buffer_tensor.is_dense_tensor()) { if (create_graph) { buffer_tensor = add_final_state_dygraph_function(t, buffer_tensor); } else { paddle::imperative::TensorAdd( t, &buffer_tensor); } } else { // TODO(jiabin): Support Other TensorBase later // TODO(zhanlve): Replace SelectedRowsAddTensor with // add_dygraph_function once it's supported paddle::experimental::Tensor new_buffer( std::make_shared(), "tmp_accumulator"); paddle::imperative::SelectedRowsAddTensor(buffer_tensor, t, &new_buffer); buffer_tensor.set_impl(new_buffer.impl()); } } else if (t.is_sparse_coo_tensor()) { auto t_sparse = std::dynamic_pointer_cast(t.impl()); paddle::experimental::Tensor t_values( std::make_shared(t_sparse->non_zero_elements())); // In fact, the gradient of SparseTensor is still a SparseTensor if (buffer_tensor.is_sparse_coo_tensor()) { auto buffer_sparse = std::dynamic_pointer_cast( buffer_tensor.impl()); paddle::experimental::Tensor buffer_values( std::make_shared( buffer_sparse->non_zero_elements())); if (create_graph) { buffer_values = add_final_state_dygraph_function(t_values, buffer_values); } else { paddle::imperative::TensorAdd( t_values, &buffer_values); } } } else { // TODO(jiabin): Support Other TensorBase later // TODO(zhanlve): Replace SelectedRowsAddTensor with add_dygraph_function // once it's supported if (buffer_tensor.is_dense_tensor()) { paddle::imperative::SelectedRowsAddToTensor(t, &buffer_tensor); } else { buffer_tensor = std::move(*paddle::imperative::SelectedRowsMerge< paddle::experimental::Tensor>(t, buffer_tensor)); } } } } } // namespace egr