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