wehub-resource-sync
110 lines
4.0 KiB
C++
110 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/kernels/index_select_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/common/memory_utils.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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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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dim = dim >= 0 ? dim : dim + input_dim.size();
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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::INT32 || index_type == DataType::INT64;
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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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using XPUType = typename XPUTypeTrait<T>::Type;
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auto* in_data = x.data<T>();
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std::vector<int64_t> in_shape = vectorize<int64_t>(input_dim);
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int64_t index_len = output->dims()[dim];
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dev_ctx.template Alloc<T>(output);
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int r = 0;
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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int8_t* index_ptr = nullptr; // temp xpu buffer
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int byte_times = SizeOf(index_type);
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if (index.place() == CPUPlace()) {
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index_ptr = RAII_GUARD.alloc_l3_or_gm<int8_t>(byte_times * index.numel());
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PADDLE_ENFORCE_XDNN_NOT_NULL(index_ptr);
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const void* cpu_idx_data = nullptr;
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if (index_type == DataType::INT64) {
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cpu_idx_data = reinterpret_cast<const void*>(index.data<int64_t>());
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} else if (index_type == DataType::INT32) {
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cpu_idx_data = reinterpret_cast<const void*>(index.data<int>());
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}
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memory_utils::Copy(dev_ctx.GetPlace(),
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reinterpret_cast<void*>(index_ptr),
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CPUPlace(),
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cpu_idx_data,
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byte_times * index.numel());
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}
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if (index_type == DataType::INT64) {
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const int64_t* index_data =
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index_ptr ? reinterpret_cast<const int64_t*>(index_ptr)
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: index.template data<int64_t>();
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r = xpu::index_select<XPUType, int64_t>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(in_data),
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reinterpret_cast<const int64_t*>(index_data),
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reinterpret_cast<XPUType*>(output->data<T>()),
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in_shape,
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index_len,
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dim);
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} else {
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const int* index_data = index_ptr ? reinterpret_cast<const int*>(index_ptr)
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: index.template data<int>();
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r = xpu::index_select<XPUType, int>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(in_data),
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reinterpret_cast<const int*>(index_data),
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reinterpret_cast<XPUType*>(output->data<T>()),
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in_shape,
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index_len,
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dim);
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}
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "index_select");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(index_select,
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XPU,
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ALL_LAYOUT,
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phi::IndexSelectKernel,
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float,
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phi::float16,
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phi::bfloat16,
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int,
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int64_t) {}
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