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paddlepaddle--paddle/paddle/phi/kernels/assign_kernel.cc
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2026-07-13 12:40:42 +08:00

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// Copyright (c) 2022 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/assign_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/utils/optional.h"
namespace phi {
template <typename Context>
void AssignKernel(const Context& dev_ctx,
const DenseTensor& x,
DenseTensor* out) {
phi::Copy(dev_ctx, x, x.place(), false, out);
}
template <typename Context>
void AssignRawKernel(const Context& dev_ctx,
const optional<DenseTensor>& x,
DenseTensor* out) {
if (x) {
if (!x->IsInitialized()) {
return;
}
auto& x_tensor = *x.get_ptr();
AssignKernel<Context>(dev_ctx, x_tensor, out);
}
}
// Note: use `const optional<std::vector<const DenseTensor*>&> x`
// as input if needed
template <typename Context>
void AssignArrayKernel(const Context& dev_ctx,
const TensorArray& x,
TensorArray* out) {
while (out->size() < x.size()) {
out->emplace_back();
}
for (size_t i = 0; i < x.size(); ++i) {
AssignKernel<Context>(dev_ctx, x[i], &out->at(i));
}
}
template <typename T, typename Context>
typename std::enable_if<std::is_same<T, bool>::value>::type CopyVectorToTensor(
const Context& dev_ctx,
const std::vector<Scalar>& values,
DenseTensor* out) {
// If attribute value dtype is vector<bool>, it will be converted to
// vector<int>. at the same time, we can not use vector<bool> to hold
// the value, because the c++ use bit value to replace byte value.
std::vector<int> assign_values;
assign_values.reserve(values.size());
for (const auto& val : values) {
assign_values.emplace_back(val.to<int>());
}
TensorFromVector(assign_values, dev_ctx, out);
// use the array to replace to vector
bool* array_ptr = new T[assign_values.size()];
for (unsigned int i = 0; i < assign_values.size(); i++) {
array_ptr[i] = static_cast<T>(assign_values[i]);
}
phi::TensorFromArray(array_ptr, assign_values.size(), dev_ctx, out);
delete[] array_ptr;
}
template <typename T, typename Context>
typename std::enable_if<!std::is_same<T, bool>::value>::type CopyVectorToTensor(
const Context& dev_ctx,
const std::vector<Scalar>& values,
DenseTensor* out) {
std::vector<T> assign_values;
assign_values.reserve(values.size());
for (const auto& val : values) {
assign_values.emplace_back(val.to<T>());
}
TensorFromVector(assign_values, dev_ctx, out);
}
template <typename T, typename Context>
void AssignValueKernel(const Context& dev_ctx,
const std::vector<int>& shape,
DataType dtype,
const std::vector<Scalar>& values,
DenseTensor* out) {
auto template_dtype = CppTypeToDataType<T>::Type();
PADDLE_ENFORCE_EQ(dtype,
template_dtype,
common::errors::InvalidArgument(
"Argument dtype mismatch for kernel dtype, "
"argument dtype is %s, kernel dtype is %s.",
dtype,
template_dtype));
CopyVectorToTensor<T>(dev_ctx, values, out);
out->Resize(shape);
}
#ifdef _WIN32
template PADDLE_API void AssignKernel<CPUContext>(const CPUContext& dev_ctx,
const DenseTensor& x,
DenseTensor* out);
#endif
} // namespace phi
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign,
CPU,
ALL_LAYOUT,
phi::AssignKernel<phi::CPUContext>) {}
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign_raw,
CPU,
ALL_LAYOUT,
phi::AssignRawKernel<phi::CPUContext>) {
kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign_array,
CPU,
ALL_LAYOUT,
phi::AssignArrayKernel<phi::CPUContext>) {
kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}
PD_REGISTER_KERNEL(assign_value,
CPU,
ALL_LAYOUT,
phi::AssignValueKernel,
bool,
int,
float,
double,
int8_t,
int64_t,
phi::float16,
phi::bfloat16,
phi::complex64,
phi::complex128) {}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign,
GPU,
ALL_LAYOUT,
phi::AssignKernel<phi::GPUContext>) {}
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign_raw,
GPU,
ALL_LAYOUT,
phi::AssignRawKernel<phi::GPUContext>) {
kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign_array,
GPU,
ALL_LAYOUT,
phi::AssignArrayKernel<phi::GPUContext>) {
kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}
PD_REGISTER_KERNEL(assign_value,
GPU,
ALL_LAYOUT,
phi::AssignValueKernel,
bool,
int,
float,
double,
int8_t,
int64_t,
phi::float16,
phi::bfloat16,
phi::complex64,
phi::complex128) {}
#endif
#ifdef PADDLE_WITH_XPU
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign,
XPU,
ALL_LAYOUT,
phi::AssignKernel<phi::XPUContext>) {}
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign_raw,
XPU,
ALL_LAYOUT,
phi::AssignRawKernel<phi::XPUContext>) {
kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}
PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign_array,
XPU,
ALL_LAYOUT,
phi::AssignArrayKernel<phi::XPUContext>) {
kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}
PD_REGISTER_KERNEL(assign_value,
XPU,
ALL_LAYOUT,
phi::AssignValueKernel,
bool,
int,
float,
phi::bfloat16,
phi::float16,
double,
int64_t,
phi::complex64,
phi::complex128) {}
#endif