rmsprop_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 <gflags/gflags.h>
#include <iostream>
#include "paddle/fluid/framework/op_registry.h"

namespace paddle {
namespace operators {

static inline float GetAttrFromTensor(const framework::Tensor* tensor) {
  const float* tensor_data = tensor->data<float>();
  framework::Tensor cpu_tensor;
  if (platform::is_gpu_place(tensor->place()) ||
      platform::is_xpu_place(tensor->place())) {
    paddle::framework::TensorCopySync(*tensor, platform::CPUPlace(),
                                      &cpu_tensor);
    tensor_data = cpu_tensor.data<float>();
  }
  return tensor_data[0];
}

using framework::OpKernelType;
using framework::Tensor;

template <typename DeviceContext, typename T>
class RmspropOpXPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    /*** TODO wait XDNN new interface
        using paddle::framework::LoDTensor;

        // check Param & Grad tensor type
        const auto* param_var = ctx.InputVar("Param");
        PADDLE_ENFORCE_EQ(param_var->IsType<LoDTensor>(), true,
                          platform::errors::InvalidArgument(
                              "Tensor holds the wrong type，Expected Var(%s)'s "
                              "type is LoDTensor, "
                              "but the received is %s",
                              ctx.InputNames("Param").front(),
                              framework::ToTypeName(param_var->Type())));

        const auto* grad_var = ctx.InputVar("Grad");
        PADDLE_ENFORCE_EQ(grad_var->IsType<LoDTensor>(), true,
                          platform::errors::InvalidArgument(
                              "Tensor holds the wrong type，Expected Var(%s)'s "
                              "type is LoDTensor, "
                              "but the received is %s",
                              ctx.InputNames("Grad").front(),
                              framework::ToTypeName(grad_var->Type())));

        // inputs
        auto& param = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Param"), "Input",
                                      "Param", "Rmsprop");
        auto& meanSquare = GET_DATA_SAFELY(ctx.Input<LoDTensor>("MeanSquare"),
                                           "Input", "MeanSquare", "Rmsprop");
        auto& grad = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Grad"), "Input",
    "Grad",
                                     "Rmsprop");
        auto& mom = GET_DATA_SAFELY(ctx.Input<LoDTensor>("Moment"), "Input",
                                    "Moment", "Rmsprop");

        auto* learning_rate = ctx.Input<Tensor>("LearningRate");
        PADDLE_ENFORCE_EQ(learning_rate->dims().size(), 1,
                          platform::errors::InvalidArgument(
                              "learining rate should have dimension = 1."
                              " But received learning rate dim [%s] ",
                              learning_rate->dims().size()));
        T lr = static_cast<T>(GetAttrFromTensor(learning_rate));

        // constants
        T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
        T decay = static_cast<T>(ctx.Attr<float>("decay"));
        T momentum = static_cast<T>(ctx.Attr<float>("momentum"));

        // outputs
        auto& param_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("ParamOut"),
                                          "Output", "ParamOut", "Rmsprop");
        auto& mom_out = GET_DATA_SAFELY(ctx.Output<LoDTensor>("MomentOut"),
                                        "Output", "MomentOut", "Rmsprop");
        auto& mom_sqrt_out =
    GET_DATA_SAFELY(ctx.Output<LoDTensor>("MeanSquareOut"),
                                             "Output", "MeanSquareOut",
    "Rmsprop");
        auto& dev_ctx = ctx.template device_context<DeviceContext>();

        ///// rmsprop优化算法
        ///
        /// ms_out[i] = rho * ms[i] + (1 - rho) * (g[i] * g[i]);
        ///
        /// mom_out[i] = momentum * mom[i] + lr *
        /// (g[i] / ((float)sqrt(ms_out[i] + epsilon)));
        ///
        /// p_out[i] = p[i] - mom_out[i];
        /// DLL_EXPORT int rmsprop(Context* ctx, const float* p,
        /// const float* ms, const float* g, const float* mom,
        /// float epsilon, float rho, float momentum, float lr,
        /// float *ms_out, float *mom_out, float *p_out, int n)
        int r = xpu::rmsprop(dev_ctx.x_context(), param.template data<T>(),
                             meanSquare.template data<T>(), grad.template
    data<T>(),
                             mom.template data<T>(), epsilon, decay, momentum,
    lr,
                             mom_sqrt_out.template
    mutable_data<T>(ctx.GetPlace()),
                             mom_out.template mutable_data<T>(ctx.GetPlace()),
                             param_out.template mutable_data<T>(ctx.GetPlace()),
                             param.numel());

        if (r == xpu::Error_t::INVALID_PARAM) {
          PADDLE_ENFORCE_EQ(
              r, xpu::Error_t::SUCCESS,
              platform::errors::InvalidArgument(
                  "XPU kernel error of RmspropOp, error message: INVALID_PARAM,
    "
                  "please check your input & output."));
        } else if (r == xpu::Error_t::RUNTIME_ERROR) {
          PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                            platform::errors::Unavailable(
                                "XPU kernel error of RmspropOp, error message: "
                                "RUNTIME_ERROR, please check whether Baidu "
                                "Kunlun Card is properly installed."));
        } else if (r == xpu::Error_t::NO_ENOUGH_WORKSPACE) {
          PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                            platform::errors::ResourceExhausted(
                                "XPU kernel error of RmspropOp, error "
                                "message: NO_ENOUGH_WORKSPACE, XPU "
                                "has no enough memory."));
        } else {
          PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
                            platform::errors::ResourceExhausted(
                                "XPU kernel error of RmspropOp, error "
                                "message: OTHER "
                                "XPU API returns error code: %d.",
                                r));
        }
    ***/
  }
};

}  // namespace operators
}  // namespace paddle

// namespace ops = paddle::operators;
// REGISTER_OP_XPU_KERNEL(
//     rmsprop,
//     ops::RmspropOpXPUKernel<paddle::platform::XPUDeviceContext, float>);
#endif