/
grad_tensor_holder.cc
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/
grad_tensor_holder.cc
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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.
#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.
if (t.is_dense_tensor()) {
buffer_[slot_id][rank] =
paddle::experimental::full(t.shape(), 1, t.dtype(), t.place());
} else if (t.is_sparse_csr_tensor() || t.is_sparse_coo_tensor()) {
buffer_[slot_id][rank] =
paddle::experimental::sparse::full_like(t, 1, t.dtype());
} else {
PADDLE_THROW(paddle::platform::errors::Fatal(
"Only Support DENSE_TENSOR, SPARSE_COO_TENSOR, SPARSE_CSR_TENSOR "
"now."));
}
egr::EagerUtils::autograd_meta(&(buffer_[slot_id][rank]))
->SetStopGradient(false);
}
}
}
void GradTensorHolder::add(size_t slot_id, size_t rank,
const paddle::experimental::Tensor& t,
bool create_graph) {
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<paddle::experimental::Tensor>(
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<phi::DenseTensor>(), "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<phi::SparseCooTensor>(t.impl());
paddle::experimental::Tensor t_values(
std::make_shared<phi::DenseTensor>(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<phi::SparseCooTensor>(
buffer_tensor.impl());
paddle::experimental::Tensor buffer_values(
std::make_shared<phi::DenseTensor>(
buffer_sparse->non_zero_elements()));
if (create_graph) {
buffer_values =
add_final_state_dygraph_function(t_values, buffer_values);
} else {
paddle::imperative::TensorAdd<paddle::experimental::Tensor>(
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