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101 lines
4.0 KiB
101 lines
4.0 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 "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* weight_scales = ctx.Input<framework::Tensor>("WeightScales");
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auto* out = ctx.Output<framework::Tensor>("Out");
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PADDLE_ENFORCE_EQ(weight_scales->numel(), in->dims()[0],
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"The weight uses the per-channel quantization type, so "
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"the number of weight scale values must be the same with "
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"first dimension value of Input(X).");
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int ativation_bits = ctx.Attr<int>("activation_bits");
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int weight_bits = ctx.Attr<int>("weight_bits");
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int range = std::pow(2, weight_bits - 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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if (ctx.HasInput("ActivationScale")) {
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auto* activation_scale = ctx.Input<framework::Tensor>("ActivationScale");
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PADDLE_ENFORCE_EQ(activation_scale->numel(), 1,
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"The activation uses per-layer quantization type, so "
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"it must have only one value.");
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framework::Tensor cpu_weigth_scales;
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framework::TensorCopy(*weight_scales, platform::CPUPlace(),
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&cpu_weigth_scales);
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dev_ctx.Wait();
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const T* weight_scales_data = cpu_weigth_scales.data<T>();
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range *= (std::pow(2, ativation_bits - 1) - 1);
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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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auto max_range = range / weight_scales_data[i];
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dequant(dev_ctx, &one_channel_in, activation_scale,
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static_cast<T>(max_range), &one_channel_out);
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}
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} else {
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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 = weight_scales->Slice(i, i + 1);
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dequant(dev_ctx, &one_channel_in, &one_channel_scale,
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static_cast<T>(range), &one_channel_out);
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}
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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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