wehub-resource-sync
116 lines
4.0 KiB
C++
116 lines
4.0 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/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/core/utils/data_type.h"
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#include "paddle/phi/kernels/embedding_kernel.h"
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#include "paddle/phi/kernels/funcs/blas/blas.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 EmbeddingCPUSparseFunctor {
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EmbeddingCPUSparseFunctor(const Context& dev_ctx,
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const DenseTensor& input,
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const SelectedRows& weight,
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int64_t padding_idx,
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DenseTensor* out)
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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_(out),
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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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auto ids = CopyIdsToVector<IdT, int64_t>(input_);
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auto ids_numel = static_cast<int64_t>(ids.size());
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const auto& table_t = weight_;
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auto output_t = out_;
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int64_t row_width = table_t.value().dims()[1];
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const auto* table = table_t.value().template data<T>();
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auto* output = dev_ctx_.template Alloc<T>(output_t);
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auto input_data_type = table_t.value().dtype();
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for (int64_t i = 0; i < ids_numel; ++i) {
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if (padding_idx_ != kNoPadding && ids[i] == padding_idx_) {
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memset(output + i * row_width, 0, row_width * sizeof(T));
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} else {
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PADDLE_ENFORCE_GE(ids[i],
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0,
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common::errors::InvalidArgument(
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"Variable value (input) of OP(embedding) "
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"expected >= 0. But received %ld",
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ids[i]));
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auto id_index = table_t.Index(ids[i]);
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PADDLE_ENFORCE_GE(
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id_index,
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0,
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common::errors::InvalidArgument(
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"the input key should be exists. But received %d.", id_index));
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if (input_data_type == DataType::BFLOAT16) {
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memcpy(output + i * row_width,
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table + id_index * row_width,
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row_width * sizeof(T));
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} else {
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auto blas = funcs::GetBlas<CPUContext, T>(dev_ctx_);
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blas.VCOPY(
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row_width, table + id_index * row_width, output + i * row_width);
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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 SelectedRows& weight_;
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DenseTensor* out_;
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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 SparseWeightEmbeddingKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const SelectedRows& weight,
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int64_t padding_idx,
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DenseTensor* out) {
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EmbeddingCPUSparseFunctor<T, Context> functor(
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dev_ctx, input, weight, padding_idx, out);
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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(sparse_weight_embedding,
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CPU,
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ALL_LAYOUT,
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phi::SparseWeightEmbeddingKernel,
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float,
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double,
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phi::bfloat16) {}
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