dequantize_mkldnn_op.cc

/* 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 "dnnl.hpp"
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/dequantize_op.h"
#include "paddle/fluid/platform/errors.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"

namespace paddle {
namespace operators {

using dnnl::memory;
using dnnl::primitive;
using dnnl::reorder;
using platform::to_void_cast;
using Tensor = framework::Tensor;
using framework::DataLayout;
using dnnl::stream;
using platform::GetMKLDNNFormat;

template <typename T>
class DeQuantOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* input = ctx.Input<Tensor>("Input");
    auto scale_data = ctx.Attr<float>("Scale");
    auto scale_shift = ctx.Attr<float>("Shift");
    bool with_shift = scale_shift != 0.0f;
    auto* output = ctx.Output<Tensor>("Output");

    PADDLE_ENFORCE_NE(scale_data, 0.0f,
                      platform::errors::InvalidArgument(
                          "Dequantization scale cannot be 0.0"));
    PADDLE_ENFORCE_GE(scale_shift, 0,
                      platform::errors::Unimplemented(
                          "Dequantization shift must be nonnegative."));
    PADDLE_ENFORCE_LE(
        scale_shift, 255,
        platform::errors::Unimplemented(
            "Dequantization shift must be less than or equal to 255."));

    auto& dev_ctx =
        ctx.template device_context<platform::MKLDNNDeviceContext>();
    const auto& engine = dev_ctx.GetEngine();

    const T* input_data = input->data<T>();
    float* output_data = output->mutable_data<float>(ctx.GetPlace());

    float reorder_shift = -scale_shift / scale_data;

    auto src_tz = paddle::framework::vectorize<int64_t>(input->dims());
    auto dst_tz = paddle::framework::vectorize<int64_t>(output->dims());
    dnnl::memory::data_type src_dt = paddle::framework::ToMKLDNNDataType(
        framework::TransToProtoVarType(input->dtype()));
    MKLDNNMemoryFormat src_fmt = input->format();

    std::string key =
        platform::CreateKey(dev_ctx, src_dt, src_tz, ctx.OutputName("Output"));
    key = platform::ExtendKeyWithThreadInfoIfNeeded(dev_ctx, key);

    const std::string key_prim = key + "@r";
    const std::string key_src_mem = key + "@s";
    const std::string key_dst_mem = key + "@d";

    std::shared_ptr<dnnl::memory> src_memory;
    std::shared_ptr<dnnl::memory> dst_memory;
    std::shared_ptr<reorder> reorder_p;
    reorder_p = std::static_pointer_cast<reorder>(dev_ctx.GetBlob(key_prim));

    if (reorder_p == nullptr) {
      dnnl::primitive_attr attri;
      int mask = 0;
      float reorder_scale = 1. / scale_data;
      attri.set_output_scales(mask, {reorder_scale});

      if (with_shift) {
        dnnl::post_ops post_operations;
        post_operations.append_sum();
        attri.set_post_ops(post_operations);
        std::fill(output_data, output_data + output->numel(), reorder_shift);
      }

      auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt);
      src_memory = std::make_shared<dnnl::memory>(src_md, engine,
                                                  to_void_cast<T>(input_data));

      auto dst_md =
          platform::MKLDNNMemDesc({dst_tz}, memory::data_type::f32,
                                  platform::MKLDNNFormatForSize(
                                      dst_tz.size(), MKLDNNMemoryFormat::nchw));

      dst_memory = std::make_shared<dnnl::memory>(
          dst_md, engine, to_void_cast<float>(output_data));

      auto reorder_pd = std::shared_ptr<reorder::primitive_desc>(
          new reorder::primitive_desc(*src_memory, *dst_memory, attri));
      reorder_p = std::shared_ptr<reorder>(new reorder(*reorder_pd));
      dev_ctx.SetBlob(key_prim, reorder_p);
      dev_ctx.SetBlob(key_src_mem, src_memory);
      dev_ctx.SetBlob(key_dst_mem, dst_memory);
    } else {
      src_memory =
          std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(key_src_mem));
      src_memory->set_data_handle(to_void_cast<T>(input_data));

      dst_memory =
          std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(key_dst_mem));
      if (with_shift)
        std::fill(output_data, output_data + output->numel(), reorder_shift);
      dst_memory->set_data_handle(output->mutable_data<float>(ctx.GetPlace()));
    }

    auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
    reorder_p->execute(astream, *src_memory, *dst_memory);
    astream.wait();

    output->set_layout(DataLayout::kMKLDNN);
    output->set_format(GetMKLDNNFormat(*dst_memory));
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_KERNEL(dequantize, MKLDNN, ::paddle::platform::CPUPlace,
                   ops::DeQuantOpKernel<uint8_t>, ops::DeQuantOpKernel<int8_t>,
                   ops::DeQuantOpKernel<paddle::platform::bfloat16>);