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88 lines
3.2 KiB
88 lines
3.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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struct DequantizeFunctor {
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void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
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const framework::Tensor* scale, T max_range,
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framework::Tensor* out);
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};
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template <typename DeviceContext, typename T>
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class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
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public:
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virtual void Compute(const framework::ExecutionContext& ctx) const {
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auto* in = ctx.Input<framework::Tensor>("X");
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auto* scale = ctx.Input<framework::Tensor>("Scale");
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auto* out = ctx.Output<framework::Tensor>("Out");
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float max_range = ctx.Attr<float>("max_range");
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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out->mutable_data<T>(dev_ctx.GetPlace());
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DequantizeFunctor<DeviceContext, T>()(dev_ctx, in, scale,
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static_cast<T>(max_range), out);
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}
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};
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template <typename DeviceContext, typename T>
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class FakeChannelWiseDequantizeMaxAbsKernel : public framework::OpKernel<T> {
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public:
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virtual void Compute(const framework::ExecutionContext& ctx) const {
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auto* in = ctx.Input<framework::Tensor>("X");
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auto scales = ctx.MultiInput<framework::Tensor>("Scales");
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auto* out = ctx.Output<framework::Tensor>("Out");
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PADDLE_ENFORCE_EQ(scales[0]->numel(), in->dims()[0],
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"The number of first scale values must be the same with "
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"first dimension value of Input(X).");
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auto quant_bits = ctx.Attr<std::vector<int>>("quant_bits");
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int max_range = std::pow(2, quant_bits[0] - 1) - 1;
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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out->mutable_data<T>(dev_ctx.GetPlace());
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auto dequant = DequantizeFunctor<DeviceContext, T>();
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for (int64_t i = 0; i < in->dims()[0]; i++) {
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framework::Tensor one_channel_in = in->Slice(i, i + 1);
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framework::Tensor one_channel_out = out->Slice(i, i + 1);
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framework::Tensor one_channel_scale = scales[0]->Slice(i, i + 1);
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dequant(dev_ctx, &one_channel_in, &one_channel_scale,
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static_cast<T>(max_range), &one_channel_out);
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}
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if (scales.size() == 2) {
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PADDLE_ENFORCE_EQ(
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scales[1]->numel(), 1,
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"The second scale tensor should only have one value at now.");
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max_range = std::pow(2, quant_bits[1] - 1) - 1;
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dequant(dev_ctx, out, scales[1], static_cast<T>(max_range), out);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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