chore: import upstream snapshot with attribution
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wehub-resource-sync
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// 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_sample_kernel.h"
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#include <algorithm>
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#include <vector>
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#include "paddle/common/enforce.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_launch_config.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/funcs/math_function.h"
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namespace phi {
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namespace {
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#define PREDEFINED_BLOCK_SIZE_X 512
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#define PREDEFINED_BLOCK_SIZE 1024
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#define MIN(a, b) ((a) < (b) ? (a) : (b))
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#define UINT32_MAX std::numeric_limits<uint32_t>::max()
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} // namespace
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template <typename T, typename SampleIndexT = int, typename ElementIndexT>
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__global__ void IndexSampleForward(const SampleIndexT* index,
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const T* in_data,
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T* out_data,
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size_t index_length,
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size_t input_length,
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size_t batch_size) {
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ElementIndexT index_i = blockDim.x * blockIdx.x + threadIdx.x;
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ElementIndexT index_j = blockDim.y * blockIdx.y + threadIdx.y;
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for (; index_j < batch_size; index_j += blockDim.y * gridDim.y) {
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index_i = blockDim.x * blockIdx.x + threadIdx.x;
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for (; index_i < index_length; index_i += blockDim.x * gridDim.x) {
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ElementIndexT index_idx = index_j * index_length + index_i;
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ElementIndexT in_idx = index_j * input_length + index_i;
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SampleIndexT sample_idx = index[index_idx];
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out_data[index_idx] = in_data[in_idx - index_i + sample_idx];
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}
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}
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}
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template <typename T, typename Context>
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void IndexSampleKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& index,
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DenseTensor* out) {
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auto index_type = index.dtype();
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bool index_type_match =
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index_type == DataType::INT32 || index_type == DataType::INT64;
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PADDLE_ENFORCE_EQ(index_type_match,
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true,
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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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DataTypeToString(index_type),
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DataTypeToString(DataType::INT32),
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DataTypeToString(DataType::INT64)));
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const T* in_data = x.data<T>();
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T* out_data = dev_ctx.template Alloc<T>(out);
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if (out && out->numel() == 0) {
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return;
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}
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auto stream = reinterpret_cast<const GPUContext&>(dev_ctx).stream();
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auto input_dim = x.dims();
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auto index_dim = index.dims();
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size_t batch_size = input_dim[0];
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size_t input_length = input_dim[1];
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size_t index_length = index_dim[1];
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if (batch_size == 0 || input_length == 0 || index_length == 0) {
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return;
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}
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auto block_width = backends::gpu::RoundToPowerOfTwo(index_length);
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block_width = MIN(block_width, PREDEFINED_BLOCK_SIZE_X);
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int64_t block_height =
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backends::gpu::RoundToPowerOfTwo(index_length * batch_size) / block_width;
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block_height = MIN(block_height, PREDEFINED_BLOCK_SIZE / block_width);
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dim3 block_dim(block_width, block_height);
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const uint64_t grid_x = (index_length + block_dim.x - 1) / block_dim.x;
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const uint64_t grid_y = (batch_size + block_dim.y - 1) / block_dim.y;
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PADDLE_ENFORCE_LE_UINT32_MAX(grid_x, "grid.x");
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PADDLE_ENFORCE_LE_UINT32_MAX(grid_y, "grid.y");
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dim3 grid_dim(static_cast<uint32_t>(grid_x), static_cast<uint32_t>(grid_y));
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backends::gpu::LimitGridDim(dev_ctx, &grid_dim);
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// choose the element index type ; uint32 or int64 based on the tensor size
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bool use_uint32 = true;
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if (x.numel() > UINT32_MAX || out->numel() > UINT32_MAX) {
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use_uint32 = false;
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}
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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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if (use_uint32) {
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IndexSampleForward<T, int64_t, uint32_t>
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<<<grid_dim, block_dim, 0, stream>>>(index_data,
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in_data,
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out_data,
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index_length,
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input_length,
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batch_size);
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} else {
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IndexSampleForward<T, int64_t, int64_t>
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<<<grid_dim, block_dim, 0, stream>>>(index_data,
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in_data,
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out_data,
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index_length,
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input_length,
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batch_size);
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}
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} else if (index_type == DataType::INT32) {
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const int* index_data = index.data<int>();
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if (use_uint32) {
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IndexSampleForward<T, int, uint32_t>
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<<<grid_dim, block_dim, 0, stream>>>(index_data,
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in_data,
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out_data,
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index_length,
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input_length,
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batch_size);
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} else {
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IndexSampleForward<T, int, int64_t>
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<<<grid_dim, block_dim, 0, stream>>>(index_data,
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in_data,
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out_data,
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index_length,
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input_length,
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batch_size);
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(index_sample,
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GPU,
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ALL_LAYOUT,
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phi::IndexSampleKernel,
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phi::float16,
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phi::bfloat16,
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float,
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double,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {}
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