fake_dequantize_op.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 <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"

namespace paddle {
namespace operators {

template <typename DeviceContext, typename T>
struct DequantizeFunctor {
  void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
                  const framework::Tensor* scale, T max_range,
                  framework::Tensor* out);
};

template <typename DeviceContext, typename T>
class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
 public:
  virtual void Compute(const framework::ExecutionContext& ctx) const {
    auto* in = ctx.Input<framework::Tensor>("X");
    auto* scale = ctx.Input<framework::Tensor>("Scale");
    auto* out = ctx.Output<framework::Tensor>("Out");

    float max_range = ctx.Attr<float>("max_range");

    auto& dev_ctx = ctx.template device_context<DeviceContext>();
    out->mutable_data<T>(dev_ctx.GetPlace());

    DequantizeFunctor<DeviceContext, T>()(dev_ctx, in, scale,
                                          static_cast<T>(max_range), out);
  }
};

template <typename DeviceContext, typename T>
class FakeChannelWiseDequantizeMaxAbsKernel : public framework::OpKernel<T> {
 public:
  virtual void Compute(const framework::ExecutionContext& ctx) const {
    auto* in = ctx.Input<framework::Tensor>("X");
    auto scales = ctx.MultiInput<framework::Tensor>("Scales");
    auto* out = ctx.Output<framework::Tensor>("Out");

    PADDLE_ENFORCE_EQ(scales[0]->numel(), in->dims()[0],
                      "The number of first scale values must be the same with "
                      "first dimension value of Input(X).");

    auto quant_bits = ctx.Attr<std::vector<int>>("quant_bits");
    int max_range = std::pow(2, quant_bits[0] - 1) - 1;

    auto& dev_ctx = ctx.template device_context<DeviceContext>();
    out->mutable_data<T>(dev_ctx.GetPlace());

    auto dequant = DequantizeFunctor<DeviceContext, T>();
    if (scales.size() == 2) {
      PADDLE_ENFORCE_EQ(
          scales[1]->numel(), 1,
          "The second scale tensor should only have one value at now.");
      for (int64_t i = 0; i < in->dims()[0]; i++) {
        framework::Tensor one_channel_in = in->Slice(i, i + 1);
        framework::Tensor one_channel_out = out->Slice(i, i + 1);
        framework::Tensor one_channel_scale = scales[0]->Slice(i, i + 1);
        max_range *= (std::pow(2, quant_bits[1] - 1) - 1);
        dequant(dev_ctx, &one_channel_in, &one_channel_scale,
                static_cast<T>(max_range), &one_channel_out);
      }
      dequant(dev_ctx, out, scales[1], static_cast<T>(1), out);
    } else {
      for (int64_t i = 0; i < in->dims()[0]; i++) {
        framework::Tensor one_channel_in = in->Slice(i, i + 1);
        framework::Tensor one_channel_out = out->Slice(i, i + 1);
        framework::Tensor one_channel_scale = scales[0]->Slice(i, i + 1);
        dequant(dev_ctx, &one_channel_in, &one_channel_scale,
                static_cast<T>(max_range), &one_channel_out);
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle