activation_op_xpu.cc

/* Copyright (c) 2020 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. */

#ifdef PADDLE_WITH_XPU

#include "paddle/fluid/operators/activation_op.h"
#include <string>
#include "paddle/fluid/platform/device/xpu/xpu_header.h"

namespace paddle {
namespace operators {

using paddle::framework::Tensor;

template <typename Functor>
class XPUActivationKernel
    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
 public:
  void Compute(const framework::ExecutionContext &context) const override {
    Functor functor;

    auto attrs = functor.GetAttrs();
    for (auto &attr : attrs) {
      *attr.second = context.Attr<float>(attr.first);
    }
    functor(context);
  }
};

template <typename Functor>
class XPUActivationGradKernel
    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
 public:
  void Compute(const framework::ExecutionContext &context) const override {
    Functor functor;

    auto attrs = functor.GetAttrs();
    for (auto &attr : attrs) {
      *attr.second = context.Attr<float>(attr.first);
    }
    functor(context);
  }
};

template <typename DeviceContext, typename T, typename XPUT>
void xpu_activation_forward(
    const framework::ExecutionContext &ctx,
    std::function<int(xpu::Context *, const XPUT *, XPUT *, int)> func) {
  const auto *x = ctx.Input<Tensor>("X");
  auto *y = ctx.Output<Tensor>("Out");
  const XPUT *x_data = reinterpret_cast<const XPUT *>(x->data<T>());
  XPUT *y_data = reinterpret_cast<XPUT *>(y->mutable_data<T>(ctx.GetPlace()));

  auto xpu_context = ctx.device_context<DeviceContext>().x_context();
  int r = func(xpu_context, x_data, y_data, x->numel());
  PADDLE_ENFORCE_EQ(
      r, xpu::Error_t::SUCCESS,
      platform::errors::External("XPU activation op return wrong value[%d %s].",
                                 r, XPUAPIErrorMsg[r]));
}

template <typename DeviceContext, typename T, typename XPUT>
void xpu_activation_backward(
    const framework::ExecutionContext &ctx,
    std::function<int(xpu::Context *, const XPUT *, const XPUT *, const XPUT *,
                      XPUT *, int)>
        func) {
  /* TODO: relu tanh sigmoid are inplace */
  const auto *x = ctx.Input<Tensor>("X");
  auto *y = ctx.Input<Tensor>("Out");
  auto *dOut = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
  auto *dX = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
  const XPUT *x_data = nullptr;
  const XPUT *y_data = nullptr;
  const XPUT *y_grad = nullptr;
  if (x != nullptr) x_data = reinterpret_cast<const XPUT *>(x->data<T>());
  if (y != nullptr) y_data = reinterpret_cast<const XPUT *>(y->data<T>());
  if (dOut != nullptr) y_grad = reinterpret_cast<const XPUT *>(dOut->data<T>());
  XPUT *x_grad = reinterpret_cast<XPUT *>(dX->mutable_data<T>(ctx.GetPlace()));
  auto xpu_context = ctx.device_context<DeviceContext>().x_context();

  int r = func(xpu_context, x_data, y_data, y_grad, x_grad, dX->numel());
  PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                    platform::errors::External(
                        "XPU activation grad op return wrong value[%d %s].", r,
                        XPUAPIErrorMsg[r]));
}

template <typename T>
struct XPUReluFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::relu<XPUType>);
  }
};

template <typename T>
struct XPUSigmoidFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::sigmoid<XPUType>);
  }
};

template <typename T>
struct XPUTanhFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::tanh<XPUType>);
  }
};

template <typename T>
struct XPULogFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::log<XPUType>);
  }
};

template <typename T>
struct XPUSquareFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::square<XPUType>);
  }
};

template <typename T>
struct XPUSqrtFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::sqrt<XPUType>);
  }
};

template <typename T>
struct XPUAbsFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::abs<XPUType>);
  }
};

template <typename T>
struct XPUPowFunctor : public BaseActivationFunctor<T> {
  void operator()(const framework::ExecutionContext &ctx) const {
    const auto *x = ctx.Input<Tensor>("X");
    auto *y = ctx.Output<Tensor>("Out");
    auto pow_factor = ctx.Attr<float>("factor");
    const T *x_data = x->data<T>();
    T *y_data = y->mutable_data<T>(ctx.GetPlace());
    T *factor_data = nullptr;

    auto xpu_context =
        ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
    PADDLE_ENFORCE_EQ(xpu_malloc(reinterpret_cast<void **>(&factor_data),
                                 x->numel() * sizeof(T)),
                      XPU_SUCCESS, platform::errors::ResourceExhausted(
                                       "XPU has no enough memory"));
    int r = xpu::constant<T>(xpu_context, factor_data, x->numel(), pow_factor);
    PADDLE_ENFORCE_EQ(
        r, xpu::Error_t::SUCCESS,
        platform::errors::External("XPU constant op return"
                                   " wrong value[%d %s] in pow op.",
                                   r, XPUAPIErrorMsg[r]));
    r = xpu::pow(xpu_context, x_data, factor_data, y_data, x->numel());
    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                      platform::errors::External("XPU pow op return"
                                                 " wrong value[%d %s].",
                                                 r, XPUAPIErrorMsg[r]));
    if (xpu_context->xpu_stream != nullptr) {
      xpu_wait(xpu_context->xpu_stream);
    }
    xpu_free(factor_data);
  }
};

template <typename T>
struct XPUHardSwishFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    float threshold = ctx.Attr<float>("threshold");
    float scale = ctx.Attr<float>("scale");
    float offset = ctx.Attr<float>("offset");
    PADDLE_ENFORCE_EQ(threshold, 6.0f,
                      platform::errors::External(
                          "Not support threshold [%f] in XPU", threshold));
    PADDLE_ENFORCE_EQ(scale, 6.0f, platform::errors::External(
                                       "Not support scale [%f] in XPU", scale));
    PADDLE_ENFORCE_EQ(
        offset, 3.0f,
        platform::errors::External("Not support offset [%f] in XPU", offset));
    xpu_activation_forward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::hard_swish<XPUType>);
  }
};

template <typename T>
struct XPUReluGradFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_backward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::relu_grad<XPUType>);
  }
};

template <typename T>
struct XPUTanhGradFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_backward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::tanh_grad<XPUType>);
  }
};

template <typename T>
struct XPUSigmoidGradFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_backward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::sigmoid_grad<XPUType>);
  }
};

template <typename T>
struct XPUSqrtGradFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_backward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::sqrt_grad<XPUType>);
  }
};

template <typename T>
struct XPUSquareGradFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    xpu_activation_backward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::square_grad<XPUType>);
  }
};

template <typename T>
struct XPUHardSwishGradFunctor : public BaseActivationFunctor<T> {
  using XPUType = typename XPUTypeTrait<T>::Type;
  void operator()(const framework::ExecutionContext &ctx) const {
    float threshold = ctx.Attr<float>("threshold");
    float scale = ctx.Attr<float>("scale");
    float offset = ctx.Attr<float>("offset");
    PADDLE_ENFORCE_EQ(threshold, 6.0f,
                      platform::errors::External(
                          "Not support threshold [%f] in XPU", threshold));
    PADDLE_ENFORCE_EQ(scale, 6.0f, platform::errors::External(
                                       "Not support scale [%f] in XPU", scale));
    PADDLE_ENFORCE_EQ(
        offset, 3.0f,
        platform::errors::External("Not support offset [%f] in XPU", offset));
    xpu_activation_backward<paddle::platform::XPUDeviceContext, T, XPUType>(
        ctx, xpu::hard_swish_grad<XPUType>);
  }
};

template <typename T>
struct XPULeakyReluFunctor : public BaseActivationFunctor<T> {
  void operator()(const framework::ExecutionContext &ctx) const {
    const auto *x = ctx.Input<Tensor>("X");
    auto *y = ctx.Output<Tensor>("Out");
    float alpha = ctx.Attr<float>("alpha");
    const T *x_data = x->data<T>();
    T *y_data = y->mutable_data<T>(ctx.GetPlace());

    auto xpu_context =
        ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();
    int r = xpu::leaky_relu(xpu_context, x_data, y_data, x->numel(), alpha);
    PADDLE_ENFORCE_EQ(
        r, xpu::Error_t::SUCCESS,
        platform::errors::External("XPU leaky_relu return wrong value[%d %s].",
                                   r, XPUAPIErrorMsg[r]));
  }
};

template <typename T>
struct XPULeakyReluGradFunctor : public BaseActivationFunctor<T> {
  void operator()(const framework::ExecutionContext &ctx) const {
    const auto *x = ctx.Input<Tensor>("X");
    auto *dOut = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
    auto *dX = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
    float alpha = ctx.Attr<float>("alpha");
    const T *x_data = nullptr;
    const T *y_grad = nullptr;
    if (x != nullptr) x_data = x->data<T>();
    if (dOut != nullptr) y_grad = dOut->data<T>();
    T *x_grad = dX->mutable_data<T>(ctx.GetPlace());
    auto xpu_context =
        ctx.device_context<paddle::platform::XPUDeviceContext>().x_context();

    // The signs of x and y are the same,
    // y == nullptr here,
    // so we give 2 x to the api
    int r = xpu::leaky_relu_grad(
        xpu_context, reinterpret_cast<const float *>(x_data),
        reinterpret_cast<const float *>(x_data),
        reinterpret_cast<const float *>(y_grad),
        reinterpret_cast<float *>(x_grad), dX->numel(), alpha);
    PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                      platform::errors::External(
                          "XPU leaky_relu_grad return wrong value[%d %s].", r,
                          XPUAPIErrorMsg[r]));
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

#define REGISTER_ACTIVATION_XPU_KERNEL(act_type, functor, grad_functor)  \
  REGISTER_OP_XPU_KERNEL(act_type,                                       \
                         ops::XPUActivationKernel<ops::functor<float>>); \
  REGISTER_OP_XPU_KERNEL(                                                \
      act_type##_grad,                                                   \
      ops::XPUActivationGradKernel<ops::grad_functor<float>>);

REGISTER_ACTIVATION_XPU_KERNEL(relu, XPUReluFunctor, XPUReluGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(sigmoid, XPUSigmoidFunctor,
                               XPUSigmoidGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(sqrt, XPUSqrtFunctor, XPUSqrtGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(square, XPUSquareFunctor, XPUSquareGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(hard_swish, XPUHardSwishFunctor,
                               XPUHardSwishGradFunctor)
REGISTER_ACTIVATION_XPU_KERNEL(leaky_relu, XPULeakyReluFunctor,
                               XPULeakyReluGradFunctor)

REGISTER_OP_XPU_KERNEL(
    tanh, ops::XPUActivationKernel<ops::XPUTanhFunctor<float>>,
    ops::XPUActivationKernel<ops::XPUTanhFunctor<paddle::platform::float16>>);
REGISTER_OP_XPU_KERNEL(
    tanh_grad, ops::XPUActivationGradKernel<ops::XPUTanhGradFunctor<float>>,
    ops::XPUActivationGradKernel<
        ops::XPUTanhGradFunctor<paddle::platform::float16>>);

REGISTER_OP_XPU_KERNEL(log,
                       ops::XPUActivationKernel<ops::XPULogFunctor<float>>);
REGISTER_OP_XPU_KERNEL(pow,
                       ops::XPUActivationKernel<ops::XPUPowFunctor<float>>);
REGISTER_OP_XPU_KERNEL(abs,
                       ops::XPUActivationKernel<ops::XPUAbsFunctor<float>>);

#endif  // PADDLE_WITH_XPU