wehub-resource-sync
205 lines
7.2 KiB
C++
205 lines
7.2 KiB
C++
/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/strided_copy_kernel.h"
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#include <vector>
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#include "paddle/common/flags.h"
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/contiguous_kernel.h"
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#include "paddle/phi/kernels/funcs/dense_tensor_iterator.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/impl/transpose_grad_kernel_impl.h"
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COMMON_DECLARE_bool(use_stride_kernel);
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COMMON_DECLARE_bool(use_stride_compute_kernel);
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namespace phi {
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template <typename T, typename Context>
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void StridedCopyKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const std::vector<int64_t>& dims,
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const std::vector<int64_t>& out_stride,
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int64_t offset,
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DenseTensor* out) {
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#if defined(PADDLE_WITH_CUDA)
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// not support Windows
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#if !defined(_WIN32)
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if (FLAGS_use_stride_kernel &&
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input.place().GetType() == AllocationType::CPU &&
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out->place().GetType() == AllocationType::GPU &&
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input.dtype() == out->dtype() &&
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(!input.meta().is_contiguous() || !out->meta().is_contiguous())) {
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DenseTensor dst_gpu;
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if (out->meta().is_contiguous()) {
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dst_gpu = *out;
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} else {
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auto meta_dst = dst_gpu.meta();
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meta_dst.dims = out->dims();
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meta_dst.strides = meta_dst.calc_strides(out->dims());
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dst_gpu.set_meta(meta_dst);
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dev_ctx.Alloc(&dst_gpu, input.dtype());
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}
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auto src_cpu_place = input.place();
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auto dst_gpu_place = out->place();
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auto& pool = DeviceContextPool::Instance();
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auto* gpu_dev_ctx = static_cast<GPUContext*>(pool.Get(out->place()));
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auto stream = gpu_dev_ctx->stream();
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if (input.meta().is_contiguous()) {
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auto src_cpu_place = input.place();
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auto dst_gpu_place = out->place();
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auto size = SizeOf(input.dtype()) * input.numel();
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void* dst_ptr = gpu_dev_ctx->Alloc(
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&dst_gpu,
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dst_gpu.dtype(),
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0,
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dst_gpu_place.GetType() == AllocationType::GPUPINNED);
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memory_utils::Copy(
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dst_gpu_place, dst_ptr, src_cpu_place, input.data<T>(), size, stream);
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} else {
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DenseTensor cpu_out;
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ContiguousKernel<T, Context>(dev_ctx, input, &cpu_out);
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auto* src_ptr = cpu_out.data<T>();
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auto size = SizeOf(input.dtype()) * cpu_out.numel();
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void* dst_ptr = gpu_dev_ctx->Alloc(
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&dst_gpu,
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dst_gpu.dtype(),
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0,
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dst_gpu_place.GetType() == AllocationType::GPUPINNED);
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memory_utils::Copy(
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dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
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}
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if (out != &dst_gpu) {
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PD_VISIT_ALL_TYPES(
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out->dtype(), "StridedCopyKernel", ([&] {
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StridedCopyKernel<data_t, GPUContext>(
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reinterpret_cast<const GPUContext&>(*gpu_dev_ctx),
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dst_gpu,
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vectorize<int64_t>(out->dims()),
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vectorize<int64_t>(out->strides()),
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out->offset(),
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out);
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}));
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}
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return;
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}
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#endif
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#endif
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DenseTensorMeta meta = input.meta();
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meta.strides = make_ddim(out_stride);
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meta.dims = make_ddim(dims);
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meta.offset = offset;
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out->set_meta(meta);
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PADDLE_ENFORCE_EQ(input.dims(),
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out->dims(),
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common::errors::InvalidArgument(
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"Input shape(%s) must be equal with out shape(%s).",
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input.dims(),
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out->dims()));
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PADDLE_ENFORCE_EQ(input.numel(),
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out->numel(),
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common::errors::InvalidArgument(
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"Input numel(%d) must be equal with out numel(%d).",
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input.numel(),
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out->numel()));
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if (input.numel() <= 0) {
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return;
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}
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const T* input_data = input.data<T>();
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int input_rank = input.dims().size();
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const int64_t* input_dims = input.dims().Get();
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const int64_t* input_stride = input.strides().Get();
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T* output_data = out->data<T>();
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PADDLE_ENFORCE_NOT_NULL(output_data,
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common::errors::InvalidArgument(
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"StridedCopyKernel's out tensor must complete "
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"mutable data before call kernel."));
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int output_rank = meta.dims.size();
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const int64_t* output_dims = meta.dims.Get();
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const int64_t* output_stride = meta.strides.Get();
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auto numel = input.numel();
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for (int64_t i = 0; i < numel; i++) {
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int64_t input_offset = 0;
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int64_t index_tmp = i;
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for (int dim = input_rank - 1; dim >= 0; --dim) {
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input_offset += (index_tmp % input_dims[dim]) * input_stride[dim];
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index_tmp = index_tmp / input_dims[dim];
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}
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int64_t output_offset = 0;
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index_tmp = i;
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for (int dim = output_rank - 1; dim >= 0; --dim) {
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output_offset += (index_tmp % output_dims[dim]) * output_stride[dim];
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index_tmp = index_tmp / output_dims[dim];
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}
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output_data[output_offset] = input_data[input_offset];
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}
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}
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#ifdef _WIN32
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INSTANTIATE_STRIDEDCOPY_KERNEL(bool, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(uint8_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(uint16_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(uint32_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(uint64_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(int8_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(int16_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(int32_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(int64_t, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(float, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(double, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::float16, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::bfloat16, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::complex<float>, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::complex<double>, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::float8_e4m3fn, CPUContext)
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INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::float8_e5m2, CPUContext)
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#endif
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} // namespace phi
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PD_REGISTER_KERNEL(strided_copy,
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CPU,
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ALL_LAYOUT,
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phi::StridedCopyKernel,
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bool,
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uint8_t,
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uint16_t,
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uint32_t,
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uint64_t,
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int8_t,
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int16_t,
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int32_t,
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int64_t,
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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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phi::float8_e4m3fn,
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phi::float8_e5m2) {}
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