wehub-resource-sync
97 lines
3.5 KiB
Plaintext
97 lines
3.5 KiB
Plaintext
// 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/index_select_kernel.h"
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#include "paddle/common/enforce.h"
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#include "paddle/phi/backends/gpu/gpu_info.h"
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#include "paddle/phi/backends/gpu/gpu_launch_config.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.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/gpu/index_select_impl.h"
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namespace phi {
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template <typename T, typename Context>
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void IndexSelectKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& index,
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int dim,
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DenseTensor* output) {
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if (output && output->numel() == 0) {
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dev_ctx.template Alloc<T>(output);
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return;
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}
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auto input_dim = x.dims();
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auto output_dim = output->dims();
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dim = dim >= 0 ? dim : dim + input_dim.size();
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auto stride_dim = common::stride(input_dim);
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int64_t stride = stride_dim[dim];
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int64_t size = output_dim[dim];
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int64_t delta = input_dim[dim] - size;
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int64_t dim_size = input_dim[dim];
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const auto& index_type = index.dtype();
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bool index_type_match =
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index_type == DataType::INT64 || index_type == DataType::INT32;
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PADDLE_ENFORCE_EQ(index_type_match,
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true,
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common::errors::InvalidArgument(
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"Input(Index) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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index_type,
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DataType::INT32,
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DataType::INT64));
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auto* in_data = x.data<T>();
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T* out_data = dev_ctx.template Alloc<T>(output);
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int64_t numel = output->numel();
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auto stream = dev_ctx.stream();
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unsigned int block_dim = PADDLE_CUDA_NUM_THREADS;
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const uint64_t grid_x = (numel + block_dim - 1) / block_dim;
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PADDLE_ENFORCE_LE_UINT32_MAX(grid_x, "grid.x");
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dim3 grid_dim = dim3(static_cast<uint32_t>(grid_x));
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backends::gpu::LimitGridDim(dev_ctx, &grid_dim);
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if (index_type == DataType::INT64) {
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const int64_t* index_data = index.data<int64_t>();
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index_select_cuda_kernel<T, int64_t><<<grid_dim, block_dim, 0, stream>>>(
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in_data, out_data, index_data, numel, stride, size, delta, dim_size);
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} else {
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const int* index_data = index.data<int>();
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index_select_cuda_kernel<T, int><<<grid_dim, block_dim, 0, stream>>>(
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in_data, out_data, index_data, numel, stride, size, delta, dim_size);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(index_select,
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GPU,
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ALL_LAYOUT,
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phi::IndexSelectKernel,
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float,
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double,
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phi::float8_e4m3fn,
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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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int,
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int64_t,
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bool) {}
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