/* 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. */

#include <iostream>
#include "mkldnn.hpp"
#include "paddle/fluid/operators/softmax_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"

namespace paddle {
namespace operators {

using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc;

using mkldnn::memory;  // Note: paddle has also "memory" namespace
using mkldnn::primitive;
using mkldnn::softmax_forward;
using mkldnn::prop_kind;
using mkldnn::stream;

template <typename T>
class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
 public:
  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
                   "It must use CPUPlace.");
    auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
    auto mkldnn_engine = dev_ctx.GetEngine();
    const Tensor* input = ctx.Input<Tensor>("X");
    Tensor* output = ctx.Output<Tensor>("Out");
    PADDLE_ENFORCE(input->dims().size() == 2UL,
                   "The input of softmax op must be a 2D matrix.");
    const T* input_data = input->data<T>();
    // allocate memory for output
    T* output_data = output->mutable_data<T>(ctx.GetPlace());
    std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
    std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
    // MKL-DNN does support softmax over selected axis. Having 2D Tensor,
    // we will make normalization after final eg. axis: 1
    PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
                   "Softmax input and output dimensions should match");
    // Same memory descriptor to be used for input and output
    memory::dims softmax_tz = {src_tz[0], src_tz[1]};
    // Currently only supports NC data format
    // TODO(jczaja-intel): support more formats
    auto softmax_md =
        MKLDNNMemDesc({softmax_tz}, memory::f32, memory::format::nc);
    // Normalization is made after innermost dimension eg. C out of NC
    auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
                                              softmax_md, 1 /*dim: C*/);
    // create memory primitives
    auto softmax_src_memory =
        memory({softmax_md, mkldnn_engine},
               static_cast<void*>(const_cast<T*>(input_data)));
    auto softmax_dst_memory =
        memory({softmax_md, mkldnn_engine},
               static_cast<void*>(const_cast<T*>(output_data)));
    auto softmax_prim_desc =
        softmax_forward::primitive_desc(softmax_desc, mkldnn_engine);
    auto softmax = softmax_forward(softmax_prim_desc, softmax_src_memory,
                                   softmax_dst_memory);
    std::vector<primitive> pipeline{softmax};
    stream(stream::kind::eager).submit(pipeline).wait();

    const bool is_test = ctx.Attr<bool>("is_test");
    if (!is_test) {
      T threshold = exp(-64);
      for (size_t i = 0; i < dst_tz[0] * dst_tz[1]; ++i) {
        output_data[i] =
            output_data[i] < threshold ? threshold : output_data[i];
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_KERNEL(softmax, MKLDNN, ::paddle::platform::CPUPlace,
                   ops::SoftmaxMKLDNNKernel<float>);