paddlepaddle--paddle/paddle/phi/kernels/xpu/full_kernel.cc

// 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/full_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/common/scalar.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/visit_type.h"
#include "paddle/phi/kernels/complex_kernel.h"
#include "paddle/phi/kernels/impl/full_with_tensor_kernel_impl.h"

namespace phi {

template <typename T, typename Context>
void FullKernel(const Context& dev_ctx,
                const IntArray& shape,
                const Scalar& val,
                DataType dtype,
                DenseTensor* out) {
  using XPUInTDType = typename XPUTypeTrait<T>::Type;
  out->Resize(shape.GetData());
  dev_ctx.template Alloc<T>(out);
  if (out->numel() > 0) {
    auto out_data = reinterpret_cast<XPUInTDType*>(out->data<T>());
    int r = xpu::constant(dev_ctx.x_context(),
                          out_data,
                          out->numel(),
                          static_cast<XPUInTDType>(val.to<T>()));
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
  }
}

#ifdef PADDLE_WITH_XPU_FFT
template <>
void FullKernel<phi::complex64, XPUContext>(const XPUContext& dev_ctx,
                                            const IntArray& shape,
                                            const Scalar& val,
                                            DataType dtype,
                                            DenseTensor* out) {
  using T = phi::complex64;
  out->Resize(shape.GetData());
  dev_ctx.template Alloc<T>(out);

  T complex_val = val.to<T>();
  float real_part = complex_val.real;
  float imag_part = complex_val.imag;

  // The current complex number implementation uses separate real/imaginary
  // parts,resulting in redundant operations and performance
  // penalties.Optimization should address this in future iterations.
  DenseTensor real_out, imag_out;
  real_out.Resize(out->dims());
  imag_out.Resize(out->dims());
  dev_ctx.template Alloc<float>(&real_out);
  dev_ctx.template Alloc<float>(&imag_out);

  if (out->numel() > 0) {
    int r = xpu::constant(
        dev_ctx.x_context(), real_out.data<float>(), out->numel(), real_part);
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
    r = xpu::constant(
        dev_ctx.x_context(), imag_out.data<float>(), out->numel(), imag_part);
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
    phi::ComplexKernel<float>(dev_ctx, real_out, imag_out, out);
  }
}
#endif

template <typename T, typename Context>
void FullLikeKernel(const Context& dev_ctx,
                    const DenseTensor& x,
                    const Scalar& val,
                    DataType dtype,
                    DenseTensor* out) {
  dev_ctx.template Alloc<T>(out);
  if (out->numel() > 0) {
    if (!std::is_same<T, int64_t>::value) {
      auto value = val.to<double>();
      using XPUInTDType = typename XPUTypeTrait<T>::Type;
      using CommonType = typename std::common_type<
          float,
          typename std::conditional<std::is_same<T, phi::float16>::value,
                                    float,
                                    T>::type>::type;

      auto common_type_value = static_cast<CommonType>(value);
      bool is_out_range = true;
      if (std::isinf(value) || std::isnan(value)) {
        is_out_range = false;
      }
      if ((common_type_value >=
           static_cast<CommonType>(std::numeric_limits<T>::lowest())) &&
          (common_type_value <=
           static_cast<CommonType>(std::numeric_limits<T>::max()))) {
        is_out_range = false;
      }

      PADDLE_ENFORCE_EQ(
          is_out_range,
          false,
          common::errors::InvalidArgument(
              "The filled value is out of range for target type, "
              "current kernel type is %s, the range should between %f "
              "and %f, but now value is %f.",
              typeid(T).name(),
              static_cast<CommonType>(std::numeric_limits<T>::lowest()),
              static_cast<CommonType>(std::numeric_limits<T>::max()),
              static_cast<float>(value)));

      auto out_data = reinterpret_cast<XPUInTDType*>(out->data<T>());
      int r = xpu::constant(dev_ctx.x_context(),
                            out_data,
                            out->numel(),
                            static_cast<XPUInTDType>(value));
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
    } else {
      using XPUInTDType = typename XPUTypeTrait<T>::Type;
      auto out_data = reinterpret_cast<XPUInTDType*>(out->data<T>());
      int r = xpu::constant(dev_ctx.x_context(),
                            out_data,
                            out->numel(),
                            static_cast<XPUInTDType>(val.to<T>()));
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
    }
  }
}

template <typename T, typename Context>
void FullBatchSizeLikeKernel(const Context& dev_ctx,
                             const DenseTensor& x,
                             const std::vector<int>& shape,
                             const Scalar& val,
                             DataType dtype,
                             int x_batch_size_dim,
                             int out_batch_size_dim,
                             DenseTensor* out) {
  if (x.lod().size() && x_batch_size_dim == 0) {
    // set the correct batch size for the DenseTensor.
    auto odims = out->dims();
    odims[out_batch_size_dim] = x.lod().back().size() - 1;
    FullKernel<T, Context>(dev_ctx, vectorize(odims), val, dtype, out);
  }
  FullLikeKernel<T, Context>(dev_ctx, x, val, dtype, out);
}
}  // namespace phi

PD_REGISTER_KERNEL(full,
                   XPU,
                   ALL_LAYOUT,
                   phi::FullKernel,
                   float,
                   double,
                   int8_t,
                   uint8_t,
                   int16_t,
                   int,
                   int64_t,
                   bool,
                   phi::float16,
                   phi::bfloat16) {}

PD_REGISTER_KERNEL(full_like,
                   XPU,
                   ALL_LAYOUT,
                   phi::FullLikeKernel,
                   float,
                   double,
                   uint8_t,
                   int8_t,
                   int16_t,
                   int,
                   int64_t,
                   bool,
                   phi::float16,
                   phi::bfloat16) {
  kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}

PD_REGISTER_KERNEL(full_batch_size_like,
                   XPU,
                   ALL_LAYOUT,
                   phi::FullBatchSizeLikeKernel,
                   float,
                   int,
                   int64_t,
                   bool,
                   phi::float16,
                   phi::bfloat16) {
  kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
}

PD_REGISTER_KERNEL(full_with_tensor,
                   XPU,
                   ALL_LAYOUT,
                   phi::FullWithTensorKernel,
                   float,
                   int8_t,
                   uint8_t,
                   int16_t,
                   int,
                   int64_t,
                   bool,
                   phi::float16,
                   phi::bfloat16) {
  kernel->InputAt(0).SetBackend(phi::Backend::CPU);
}