chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,204 @@
|
||||
/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#include "paddle/phi/kernels/strided_copy_kernel.h"
|
||||
|
||||
#include <vector>
|
||||
|
||||
#include "paddle/common/flags.h"
|
||||
#include "paddle/phi/backends/context_pool.h"
|
||||
#include "paddle/phi/backends/cpu/cpu_context.h"
|
||||
#include "paddle/phi/core/kernel_registry.h"
|
||||
#include "paddle/phi/kernels/contiguous_kernel.h"
|
||||
#include "paddle/phi/kernels/funcs/dense_tensor_iterator.h"
|
||||
#include "paddle/phi/kernels/funcs/math_function.h"
|
||||
#include "paddle/phi/kernels/impl/transpose_grad_kernel_impl.h"
|
||||
|
||||
COMMON_DECLARE_bool(use_stride_kernel);
|
||||
COMMON_DECLARE_bool(use_stride_compute_kernel);
|
||||
|
||||
namespace phi {
|
||||
|
||||
template <typename T, typename Context>
|
||||
void StridedCopyKernel(const Context& dev_ctx,
|
||||
const DenseTensor& input,
|
||||
const std::vector<int64_t>& dims,
|
||||
const std::vector<int64_t>& out_stride,
|
||||
int64_t offset,
|
||||
DenseTensor* out) {
|
||||
#if defined(PADDLE_WITH_CUDA)
|
||||
// not support Windows
|
||||
#if !defined(_WIN32)
|
||||
if (FLAGS_use_stride_kernel &&
|
||||
input.place().GetType() == AllocationType::CPU &&
|
||||
out->place().GetType() == AllocationType::GPU &&
|
||||
input.dtype() == out->dtype() &&
|
||||
(!input.meta().is_contiguous() || !out->meta().is_contiguous())) {
|
||||
DenseTensor dst_gpu;
|
||||
if (out->meta().is_contiguous()) {
|
||||
dst_gpu = *out;
|
||||
} else {
|
||||
auto meta_dst = dst_gpu.meta();
|
||||
meta_dst.dims = out->dims();
|
||||
meta_dst.strides = meta_dst.calc_strides(out->dims());
|
||||
dst_gpu.set_meta(meta_dst);
|
||||
dev_ctx.Alloc(&dst_gpu, input.dtype());
|
||||
}
|
||||
|
||||
auto src_cpu_place = input.place();
|
||||
auto dst_gpu_place = out->place();
|
||||
auto& pool = DeviceContextPool::Instance();
|
||||
auto* gpu_dev_ctx = static_cast<GPUContext*>(pool.Get(out->place()));
|
||||
auto stream = gpu_dev_ctx->stream();
|
||||
|
||||
if (input.meta().is_contiguous()) {
|
||||
auto src_cpu_place = input.place();
|
||||
auto dst_gpu_place = out->place();
|
||||
auto size = SizeOf(input.dtype()) * input.numel();
|
||||
void* dst_ptr = gpu_dev_ctx->Alloc(
|
||||
&dst_gpu,
|
||||
dst_gpu.dtype(),
|
||||
0,
|
||||
dst_gpu_place.GetType() == AllocationType::GPUPINNED);
|
||||
|
||||
memory_utils::Copy(
|
||||
dst_gpu_place, dst_ptr, src_cpu_place, input.data<T>(), size, stream);
|
||||
|
||||
} else {
|
||||
DenseTensor cpu_out;
|
||||
ContiguousKernel<T, Context>(dev_ctx, input, &cpu_out);
|
||||
auto* src_ptr = cpu_out.data<T>();
|
||||
auto size = SizeOf(input.dtype()) * cpu_out.numel();
|
||||
void* dst_ptr = gpu_dev_ctx->Alloc(
|
||||
&dst_gpu,
|
||||
dst_gpu.dtype(),
|
||||
0,
|
||||
dst_gpu_place.GetType() == AllocationType::GPUPINNED);
|
||||
|
||||
memory_utils::Copy(
|
||||
dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
|
||||
}
|
||||
if (out != &dst_gpu) {
|
||||
PD_VISIT_ALL_TYPES(
|
||||
out->dtype(), "StridedCopyKernel", ([&] {
|
||||
StridedCopyKernel<data_t, GPUContext>(
|
||||
reinterpret_cast<const GPUContext&>(*gpu_dev_ctx),
|
||||
dst_gpu,
|
||||
vectorize<int64_t>(out->dims()),
|
||||
vectorize<int64_t>(out->strides()),
|
||||
out->offset(),
|
||||
out);
|
||||
}));
|
||||
}
|
||||
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
#endif
|
||||
|
||||
DenseTensorMeta meta = input.meta();
|
||||
meta.strides = make_ddim(out_stride);
|
||||
meta.dims = make_ddim(dims);
|
||||
meta.offset = offset;
|
||||
out->set_meta(meta);
|
||||
|
||||
PADDLE_ENFORCE_EQ(input.dims(),
|
||||
out->dims(),
|
||||
common::errors::InvalidArgument(
|
||||
"Input shape(%s) must be equal with out shape(%s).",
|
||||
input.dims(),
|
||||
out->dims()));
|
||||
|
||||
PADDLE_ENFORCE_EQ(input.numel(),
|
||||
out->numel(),
|
||||
common::errors::InvalidArgument(
|
||||
"Input numel(%d) must be equal with out numel(%d).",
|
||||
input.numel(),
|
||||
out->numel()));
|
||||
|
||||
if (input.numel() <= 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
const T* input_data = input.data<T>();
|
||||
int input_rank = input.dims().size();
|
||||
const int64_t* input_dims = input.dims().Get();
|
||||
const int64_t* input_stride = input.strides().Get();
|
||||
|
||||
T* output_data = out->data<T>();
|
||||
PADDLE_ENFORCE_NOT_NULL(output_data,
|
||||
common::errors::InvalidArgument(
|
||||
"StridedCopyKernel's out tensor must complete "
|
||||
"mutable data before call kernel."));
|
||||
int output_rank = meta.dims.size();
|
||||
const int64_t* output_dims = meta.dims.Get();
|
||||
const int64_t* output_stride = meta.strides.Get();
|
||||
|
||||
auto numel = input.numel();
|
||||
|
||||
for (int64_t i = 0; i < numel; i++) {
|
||||
int64_t input_offset = 0;
|
||||
int64_t index_tmp = i;
|
||||
for (int dim = input_rank - 1; dim >= 0; --dim) {
|
||||
input_offset += (index_tmp % input_dims[dim]) * input_stride[dim];
|
||||
index_tmp = index_tmp / input_dims[dim];
|
||||
}
|
||||
int64_t output_offset = 0;
|
||||
index_tmp = i;
|
||||
for (int dim = output_rank - 1; dim >= 0; --dim) {
|
||||
output_offset += (index_tmp % output_dims[dim]) * output_stride[dim];
|
||||
index_tmp = index_tmp / output_dims[dim];
|
||||
}
|
||||
output_data[output_offset] = input_data[input_offset];
|
||||
}
|
||||
}
|
||||
#ifdef _WIN32
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(bool, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(uint8_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(uint16_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(uint32_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(uint64_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(int8_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(int16_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(int32_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(int64_t, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(float, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(double, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::float16, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::bfloat16, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::complex<float>, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::complex<double>, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::float8_e4m3fn, CPUContext)
|
||||
INSTANTIATE_STRIDEDCOPY_KERNEL(dtype::float8_e5m2, CPUContext)
|
||||
#endif
|
||||
} // namespace phi
|
||||
|
||||
PD_REGISTER_KERNEL(strided_copy,
|
||||
CPU,
|
||||
ALL_LAYOUT,
|
||||
phi::StridedCopyKernel,
|
||||
bool,
|
||||
uint8_t,
|
||||
uint16_t,
|
||||
uint32_t,
|
||||
uint64_t,
|
||||
int8_t,
|
||||
int16_t,
|
||||
int32_t,
|
||||
int64_t,
|
||||
float,
|
||||
double,
|
||||
phi::float16,
|
||||
phi::bfloat16,
|
||||
phi::complex64,
|
||||
phi::complex128,
|
||||
phi::float8_e4m3fn,
|
||||
phi::float8_e5m2) {}
|
||||
Reference in New Issue
Block a user