paddlepaddle--paddle/paddle/phi/kernels/funcs/math_function.h

/* Copyright (c) 2016 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. */

#pragma once
#include <cmath>
#include <memory>
#include <vector>

#include "paddle/phi/backends/all_context.h"
#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/utils/data_type.h"
#ifdef PADDLE_WITH_XPU
#include <type_traits>
#include "paddle/phi/backends/context_pool.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_header.h"
#endif

namespace phi {
namespace funcs {

#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
template <typename T>
void BatchTranspose(T* output,
                    const T* input,
                    int64_t batch,
                    int64_t m,
                    int64_t n,
                    const GPUContext* dev_ctx);
#endif
template <typename DeviceContext, typename T>
struct TransposeNormal {
  // for dims >= 7 situation
  void operator()(const DeviceContext& dev_ctx,
                  const DenseTensor& in,
                  DenseTensor* out,
                  const std::vector<int>& axis);
};

template <typename DeviceContext, typename T, int Rank>
struct Transpose {
  void operator()(const DeviceContext& dev_ctx,
                  const DenseTensor& in,
                  DenseTensor* out,
                  const std::vector<int>& axis);
};

template <typename DeviceContext, typename T>
struct PADDLE_API SetConstant {
  void operator()(const DeviceContext& dev_ctx, DenseTensor* tensor, T num);
};

#ifdef PADDLE_WITH_XPU
template <typename T>
struct SetConstant<XPUContext, T> {
  void operator()(const XPUContext& dev_ctx, DenseTensor* tensor, T num);
};
#endif

template <typename Place>
void set_constant_with_place(const DeviceContext& dev_ctx,
                             DenseTensor* tensor,
                             float value);

PADDLE_API void set_constant(const DeviceContext& dev_ctx,
                             DenseTensor* tensor,
                             float value);

template <typename DeviceContext, typename T>
struct RowwiseAdd {
  void operator()(const DeviceContext& dev_ctx,
                  const DenseTensor& input,
                  const DenseTensor& vec,
                  DenseTensor* output);
};

template <typename DeviceContext, typename T>
struct ColwiseSum {
  void operator()(const DeviceContext& dev_ctx,
                  const DenseTensor& input,
                  DenseTensor* vec);
};

template <typename DeviceContext, typename T>
struct RowwiseSum {
  void operator()(const DeviceContext& dev_ctx,
                  const DenseTensor& input,
                  DenseTensor* vec);
};

template <typename DeviceContext, typename T>
struct RowwiseMean {
  void operator()(const DeviceContext& dev_ctx,
                  const DenseTensor& input,
                  DenseTensor* vec);
};

#ifdef PADDLE_WITH_XPU
template <typename U>
struct TensorSetConstantXPU {
  TensorSetConstantXPU(DenseTensor* tensor, U value, phi::Place place)
      : tensor_(tensor), value_(value), place_(place) {}
  template <typename T>
  void apply() const {
    auto* dev_ctx = DeviceContextPool::Instance().Get(place_);
    auto begin = dev_ctx->Alloc<T>(tensor_);
    int64_t numel = tensor_->numel();
    if (std::is_same<T, phi::complex64>::value ||
        std::is_same<T, phi::complex128>::value) {
      std::unique_ptr<T[]> data_cpu(new T[numel]);
      std::fill(data_cpu.get(), data_cpu.get() + numel, static_cast<T>(value_));
      memory_utils::Copy(place_,
                         begin,
                         CPUPlace(),
                         static_cast<void*>(data_cpu.get()),
                         numel * sizeof(T));
    } else if (std::is_same<T, phi::float8_e4m3fn>::value ||
               std::is_same<T, phi::float8_e5m2>::value) {
      PADDLE_THROW(common::errors::Fatal("XPU does not support fp8"));
    } else {
      auto* dev_ctx2 = static_cast<XPUContext*>(dev_ctx);
      using XPUType = typename XPUTypeTrait<T>::Type;
      T val = static_cast<T>(value_);
      int r = xpu::constant<XPUType>(dev_ctx2->x_context(),
                                     reinterpret_cast<XPUType*>(begin),
                                     numel,
                                     static_cast<XPUType>(val));
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
    }
  }
  DenseTensor* tensor_;
  U value_;
  phi::Place place_;
};
#endif

template <typename Context, typename T>
inline void TransCompute(const int dim,
                         const Context& dev_ctx,
                         const DenseTensor& in,
                         DenseTensor* out,
                         const std::vector<int>& axis) {
  switch (dim) {
    case 1:
      Transpose<Context, T, 1> trans1;
      trans1(dev_ctx, in, out, axis);
      break;
    case 2:
      Transpose<Context, T, 2> trans2;
      trans2(dev_ctx, in, out, axis);
      break;
    case 3:
      Transpose<Context, T, 3> trans3;
      trans3(dev_ctx, in, out, axis);
      break;
    case 4:
      Transpose<Context, T, 4> trans4;
      trans4(dev_ctx, in, out, axis);
      break;
    case 5:
      Transpose<Context, T, 5> trans5;
      trans5(dev_ctx, in, out, axis);
      break;
    case 6:
      Transpose<Context, T, 6> trans6;
      trans6(dev_ctx, in, out, axis);
      break;
    default:
      // for dim >= 7 situation
      TransposeNormal<Context, T> trans_normal;
      trans_normal(dev_ctx, in, out, axis);
  }
}

}  // namespace funcs
}  // namespace phi