wehub-resource-sync
225 lines
7.8 KiB
C++
225 lines
7.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/embedding_grad_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/common/data_type.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/embedding_util.h"
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namespace phi {
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template <typename T, typename Context>
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struct EmbeddingGradCPUFunctor {
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EmbeddingGradCPUFunctor(const Context& dev_ctx,
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const DenseTensor& input,
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const DenseTensor& weight,
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const DenseTensor& out_grad,
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int64_t padding_idx,
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DenseTensor* weight_grad)
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: dev_ctx_(dev_ctx),
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input_(input),
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weight_(weight),
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out_grad_(out_grad),
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weight_grad_(weight_grad),
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padding_idx_(padding_idx) {}
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template <typename IdT>
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void apply() {
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DDim table_dim = weight_.dims();
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auto ids = CopyIdsToVector<IdT, int64_t>(input_);
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auto ids_num = static_cast<int64_t>(ids.size());
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// Since paddings are not trainable and fixed in forward, the gradient of
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// paddings makes no sense and we don't deal with it in backward.
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{
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auto* d_output = &out_grad_;
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auto* ids_data = ids.data();
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int64_t N = table_dim[0];
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int64_t D = table_dim[1];
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auto* d_output_data = d_output->template data<T>();
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dev_ctx_.template Alloc<T>(weight_grad_);
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auto* d_table_data = weight_grad_->data<T>();
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memset(d_table_data, 0, weight_grad_->numel() * sizeof(T));
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for (int64_t i = 0; i < ids_num; ++i) {
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if (padding_idx_ != kNoPadding && ids_data[i] == padding_idx_) {
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// the gradient of padding_idx should be 0, already done by memset, so
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// do nothing.
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} else {
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PADDLE_ENFORCE_LT(
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ids_data[i],
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N,
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common::errors::InvalidArgument(
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"Variable value (input) of "
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"OP(paddle.nn.functional.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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N,
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ids_data[i]));
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PADDLE_ENFORCE_GE(
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ids_data[i],
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0,
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common::errors::InvalidArgument(
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"Variable value (input) of "
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"OP(paddle.nn.functional.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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N,
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ids_data[i]));
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for (int j = 0; j < D; ++j) {
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d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
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}
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}
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}
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}
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}
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private:
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const Context& dev_ctx_;
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const DenseTensor& input_;
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const DenseTensor& weight_;
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const DenseTensor& out_grad_;
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DenseTensor* weight_grad_;
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int64_t padding_idx_;
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};
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template <typename T, typename Context>
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void EmbeddingGradKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const DenseTensor& weight,
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const DenseTensor& out_grad,
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int64_t padding_idx,
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DenseTensor* weight_grad) {
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EmbeddingGradCPUFunctor<T, Context> functor(
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dev_ctx, input, weight, out_grad, padding_idx, weight_grad);
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if (input.dtype() == DataType::INT32) {
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functor.template apply<int>();
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} else if (input.dtype() == DataType::INT64) {
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functor.template apply<int64_t>();
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} else {
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PADDLE_THROW(common::errors::Unimplemented(
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"embedding input only support int32 and int64"));
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}
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}
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template <typename T, typename Context>
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struct EmbeddingSparseGradCPUFunctor {
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EmbeddingSparseGradCPUFunctor(const Context& dev_ctx,
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const DenseTensor& input,
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const DenseTensor& weight,
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const DenseTensor& out_grad,
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int64_t padding_idx,
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SelectedRows* weight_grad)
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: dev_ctx_(dev_ctx),
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input_(input),
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weight_(weight),
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out_grad_(out_grad),
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weight_grad_(weight_grad),
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padding_idx_(padding_idx) {}
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template <typename IdT>
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void apply() {
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DDim table_dim = weight_.dims();
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auto ids = CopyIdsToVector<IdT, int64_t>(input_);
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auto ids_num = static_cast<int64_t>(ids.size());
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// Since paddings are not trainable and fixed in forward, the gradient of
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// paddings makes no sense and we don't deal with it in backward.
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auto* d_table = weight_grad_;
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auto* d_output = &out_grad_;
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d_table->set_rows(ids);
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auto* d_table_value = d_table->mutable_value();
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d_table_value->Resize({ids_num, table_dim[1]});
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dev_ctx_.template Alloc<T>(d_table_value);
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d_table->set_height(table_dim[0]);
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auto* d_output_data = d_output->template data<T>();
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auto* d_table_data = d_table_value->template data<T>();
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auto d_output_dims = d_output->dims();
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auto d_output_dims_2d =
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flatten_to_2d(d_output_dims, d_output_dims.size() - 1);
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PADDLE_ENFORCE_EQ(d_table_value->dims(),
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d_output_dims_2d,
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common::errors::InvalidArgument(
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"ShapeError: The shape of lookup_table@Grad and "
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"output@Grad should be same. "
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"But received lookup_table@Grad's shape = [%s], "
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"output@Grad's shape = [%s].",
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d_table_value->dims(),
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d_output_dims_2d));
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memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
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}
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private:
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const Context& dev_ctx_;
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const DenseTensor& input_;
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const DenseTensor& weight_;
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const DenseTensor& out_grad_;
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SelectedRows* weight_grad_;
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int64_t padding_idx_;
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};
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template <typename T, typename Context>
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void EmbeddingSparseGradKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const DenseTensor& weight,
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const DenseTensor& out_grad,
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int64_t padding_idx,
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SelectedRows* weight_grad) {
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EmbeddingSparseGradCPUFunctor<T, Context> functor(
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dev_ctx, input, weight, out_grad, padding_idx, weight_grad);
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if (input.dtype() == DataType::INT32) {
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functor.template apply<int>();
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} else if (input.dtype() == DataType::INT64) {
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functor.template apply<int64_t>();
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} else {
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PADDLE_THROW(common::errors::Unimplemented(
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"embedding input only support int32 and int64"));
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(embedding_grad,
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CPU,
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ALL_LAYOUT,
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phi::EmbeddingGradKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_KERNEL(embedding_sparse_grad,
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CPU,
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ALL_LAYOUT,
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phi::EmbeddingSparseGradKernel,
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float,
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double,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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