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824 lines
34 KiB
824 lines
34 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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#include "paddle/fluid/operators/fake_quantize_op.h"
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#include <algorithm>
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#include <string>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_version_registry.h"
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#include "paddle/fluid/operators/clip_op.h"
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#include "paddle/fluid/platform/transform.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct Compare {
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public:
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bool operator()(const T a, const T b) { return (std::abs(a) < std::abs(b)); }
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};
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template <typename T>
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struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx, const T* in,
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const int num, T* out) {
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*out = std::abs(*(std::max_element(in + 0, in + num, Compare<T>())));
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}
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};
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template struct FindAbsMaxFunctor<platform::CPUDeviceContext, float>;
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template <typename T>
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struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx,
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const framework::Tensor& in_tensor, const int quant_axis,
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T* out_abs_max) {
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// At present, channelwise quantization supports conv2d, depthwise_conv2d
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// conv2d_transpose and mul
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PADDLE_ENFORCE_EQ(
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quant_axis == 0 || quant_axis == 1, true,
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platform::errors::InvalidArgument("'quant_axis' should be 0 or 1, but "
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"the received is %d",
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quant_axis));
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auto* in_data = in_tensor.data<T>();
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auto in_dims = in_tensor.dims();
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const int64_t channel = in_dims[quant_axis];
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if (quant_axis == 0) {
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const int64_t channel_size = in_tensor.numel() / channel;
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for (int64_t i = 0; i < channel; i++) {
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auto* start = in_data + i * channel_size;
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auto* end = in_data + (i + 1) * channel_size;
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out_abs_max[i] =
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std::abs(*(std::max_element(start, end, Compare<T>())));
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}
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} else if (quant_axis == 1) {
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for (int64_t i = 0; i < channel; i++) {
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out_abs_max[i] = 0;
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}
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const int64_t step_i = in_tensor.numel() / in_dims[0];
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const int64_t step_j = in_tensor.numel() / (in_dims[0] * in_dims[1]);
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for (int64_t i = 0; i < in_dims[0]; i++) {
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for (int64_t j = 0; j < in_dims[1]; j++) {
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auto* start = in_data + i * step_i + j * step_j;
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auto* end = in_data + i * step_i + (j + 1) * step_j;
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T abs_max = std::abs(*(std::max_element(start, end, Compare<T>())));
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out_abs_max[j] = std::max(out_abs_max[j], abs_max);
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}
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}
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}
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}
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};
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template struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, float>;
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template <typename T>
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struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx,
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const framework::Tensor& in, const framework::Tensor& scale,
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const int bin_cnt, framework::Tensor* out) {
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T s = scale.data<T>()[0];
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T inv_s = inverse(s);
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platform::Transform<platform::CPUDeviceContext> trans;
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trans(ctx, in.data<T>(), in.data<T>() + in.numel(),
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out->mutable_data<T>(ctx.GetPlace()), ClipFunctor<T>(-s, s));
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auto out_e = framework::EigenVector<T>::Flatten(*out);
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out_e.device(*ctx.eigen_device()) = (bin_cnt * inv_s * out_e).round();
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}
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};
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template struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, float>;
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template <typename T>
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struct ClipAndFakeQuantDequantFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx,
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const framework::Tensor& in, const framework::Tensor& scale,
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const int bin_cnt, framework::Tensor* out) {
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T s = scale.data<T>()[0];
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T inv_s = inverse(s);
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platform::Transform<platform::CPUDeviceContext> trans;
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trans(ctx, in.data<T>(), in.data<T>() + in.numel(),
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out->mutable_data<T>(ctx.GetPlace()), ClipFunctor<T>(-s, s));
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auto out_e = framework::EigenVector<T>::Flatten(*out);
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out_e.device(*ctx.eigen_device()) =
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(bin_cnt * inv_s * out_e).round() * s / static_cast<T>(bin_cnt);
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}
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};
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template struct ClipAndFakeQuantDequantFunctor<platform::CPUDeviceContext,
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float>;
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template <typename T>
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struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx,
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const framework::Tensor& in, const framework::Tensor& scale,
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const int bin_cnt, const int quant_axis,
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framework::Tensor* out) {
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// At present, channelwise quantization supports conv2d, depthwise_conv2d
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// conv2d_transpose and mul
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PADDLE_ENFORCE_EQ(
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quant_axis == 0 || quant_axis == 1, true,
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platform::errors::InvalidArgument("'quant_axis' should be 0 or 1, but "
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"the received is %d",
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quant_axis));
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auto* scale_data = scale.data<T>();
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auto* in_data = in.data<T>();
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auto* out_data = out->mutable_data<T>(ctx.GetPlace());
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auto in_dims = in.dims();
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const int64_t channel = in_dims[quant_axis];
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platform::Transform<platform::CPUDeviceContext> trans;
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if (quant_axis == 0) {
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const int64_t channel_size = in.numel() / channel;
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for (int64_t i = 0; i < channel; i++) {
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T s = scale_data[i];
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auto* start = in_data + i * channel_size;
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auto* end = in_data + (i + 1) * channel_size;
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trans(ctx, start, end, out_data + i * channel_size,
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ClipFunctor<T>(-s, s));
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}
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for (int64_t i = 0; i < channel; i++) {
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T s = scale_data[i];
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T inv_s = inverse(s);
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framework::Tensor one_channel_out = out->Slice(i, i + 1);
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auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
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out_e.device(*ctx.eigen_device()) = (bin_cnt * inv_s * out_e).round();
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}
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} else if (quant_axis == 1) {
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const int64_t step_i = in.numel() / in_dims[0];
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const int64_t step_j = in.numel() / (in_dims[0] * in_dims[1]);
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for (int i = 0; i < in_dims[0]; i++) {
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for (int j = 0; j < in_dims[1]; j++) {
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T s = scale_data[j];
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T inv_s = inverse(s);
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auto* start = in_data + i * step_i + j * step_j;
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auto* end = in_data + i * step_i + (j + 1) * step_j;
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auto* cur_out_data = out_data + i * step_i + j * step_j;
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trans(ctx, start, end, cur_out_data, ClipFunctor<T>(-s, s));
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for (int k = 0; k < step_j; k++) {
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cur_out_data[k] = std::round(bin_cnt * inv_s * cur_out_data[k]);
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}
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}
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}
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}
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}
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};
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template struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext,
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float>;
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template <typename T>
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struct ChannelClipFakeQuantDequantFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx,
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const framework::Tensor& in, const framework::Tensor& scale,
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const int bin_cnt, const int quant_axis,
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framework::Tensor* out) {
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PADDLE_ENFORCE_EQ(
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quant_axis == 0 || quant_axis == 1, true,
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platform::errors::InvalidArgument("'quant_axis' should be 0 or 1, but "
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"the received is %d",
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quant_axis));
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auto* scale_data = scale.data<T>();
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auto* in_data = in.data<T>();
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auto* out_data = out->mutable_data<T>(ctx.GetPlace());
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auto in_dims = in.dims();
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const int64_t channel = in_dims[quant_axis];
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platform::Transform<platform::CPUDeviceContext> trans;
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if (quant_axis == 0) {
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const int64_t channel_size = in.numel() / channel;
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for (int i = 0; i < channel; i++) {
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T s = scale_data[i];
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auto* start = in_data + i * channel_size;
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auto* end = in_data + (i + 1) * channel_size;
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trans(ctx, start, end, out_data + i * channel_size,
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ClipFunctor<T>(-s, s));
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}
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for (int i = 0; i < channel; i++) {
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T s = scale_data[i];
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T inv_s = inverse(s);
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framework::Tensor one_channel_out = out->Slice(i, i + 1);
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auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
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out_e.device(*ctx.eigen_device()) =
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(bin_cnt * inv_s * out_e).round() * s / static_cast<T>(bin_cnt);
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}
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} else if (quant_axis == 1) {
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const int64_t step_i = in.numel() / in_dims[0];
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const int64_t step_j = in.numel() / (in_dims[0] * in_dims[1]);
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for (int i = 0; i < in_dims[0]; i++) {
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for (int j = 0; j < in_dims[1]; j++) {
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T s = scale_data[j];
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T inv_s = inverse(s);
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auto* start = in_data + i * step_i + j * step_j;
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auto* end = in_data + i * step_i + (j + 1) * step_j;
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auto* cur_out_data = out_data + i * step_i + j * step_j;
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trans(ctx, start, end, cur_out_data, ClipFunctor<T>(-s, s));
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for (int k = 0; k < step_j; k++) {
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cur_out_data[k] = std::round(bin_cnt * inv_s * cur_out_data[k]) *
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s / static_cast<T>(bin_cnt);
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}
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}
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}
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}
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}
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};
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template struct ChannelClipFakeQuantDequantFunctor<platform::CPUDeviceContext,
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float>;
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template <typename T>
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struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx,
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const framework::Tensor& cur_scale,
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const framework::Tensor& last_scale,
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const framework::Tensor& iter, const int window_size,
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framework::Tensor* scales_arr, framework::Tensor* out_scale) {
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T* scale_arr = scales_arr->mutable_data<T>(ctx.GetPlace());
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int64_t it = iter.data<int64_t>()[0];
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int idx = it % window_size;
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T removed = scale_arr[idx];
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T cur = cur_scale.data<T>()[0];
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scale_arr[idx] = cur;
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T max = last_scale.data<T>()[0];
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if (max < cur) {
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max = cur;
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} else if (fabs(removed - max) < 1e-6) {
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int size = (it > window_size) ? window_size : it;
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FindAbsMaxFunctor<platform::CPUDeviceContext, T>()(ctx, scale_arr, size,
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&max);
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}
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out_scale->mutable_data<T>(ctx.GetPlace())[0] = max;
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}
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};
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template struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, float>;
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template <typename T>
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struct FindMovingAverageAbsMaxFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& ctx,
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const framework::Tensor& in_accum,
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const framework::Tensor& in_state, const T* cur_scale,
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const float rate, framework::Tensor* out_state,
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framework::Tensor* out_accum, framework::Tensor* out_scale) {
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T accum = in_accum.data<T>()[0];
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T state = in_state.data<T>()[0];
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T scale = cur_scale[0];
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state = rate * state + 1;
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accum = rate * accum + scale;
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scale = accum / state;
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out_state->mutable_data<T>(ctx.GetPlace())[0] = state;
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out_accum->mutable_data<T>(ctx.GetPlace())[0] = accum;
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out_scale->mutable_data<T>(ctx.GetPlace())[0] = scale;
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}
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};
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template struct FindMovingAverageAbsMaxFunctor<platform::CPUDeviceContext,
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float>;
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class FakeQuantOrWithDequantAbsMaxOp : public framework::OperatorWithKernel {
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public:
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FakeQuantOrWithDequantAbsMaxOp(const std::string& type,
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const framework::VariableNameMap& inputs,
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const framework::VariableNameMap& outputs,
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const framework::AttributeMap& attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
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"FakeQuantOrWithDequantAbsMaxOp");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
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"FakeQuantOrWithDequantAbsMaxOp");
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OP_INOUT_CHECK(ctx->HasOutput("OutScale"), "Output", "OutScale",
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"FakeQuantOrWithDequantAbsMaxOp");
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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ctx->SetOutputDim("OutScale", {1});
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.device_context());
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}
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};
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class FakeQuantOrWithDequantAbsMaxOpMaker
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: public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor) Input is float data type.");
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AddOutput("Out",
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"(Tensor) Output of quantized low level tensor, "
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"but also saved as float data type.");
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AddOutput("OutScale", "(Tensor) Current scale");
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AddAttr<int>("bit_length", "(int, default 8)")
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.SetDefault(8)
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.AddCustomChecker([](const int& bit_length) {
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PADDLE_ENFORCE_EQ(bit_length >= 1 && bit_length <= 16, true,
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platform::errors::InvalidArgument(
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"'bit_length' should be between 1 and 16, but "
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"the received is %d",
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bit_length));
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});
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AddComment(R"DOC(
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This is a Base Op which supports FakeQuantAbsMaxOpMaker and FakeQuantDequantAbsMaxOpMaker.
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FakeQuantAbsMaxOp operator is used in the dynamic quantization.
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$$scale = max(abs(X))$$
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$$range = 2^{bit_length - 1} - 1$$
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$$Out = round(X/scale * range)$$
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FakeQuantDequantAbsMaxOp operator does the abs_max quantization and then dequantization.
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$$scale = max(abs(X))$$
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$$range = 2^{bit\_length - 1} - 1$$
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$$Out = round(X/scale * range) * scale / range$$
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)DOC");
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}
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};
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class FakeChannelWiseQuantizeAbsMaxOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
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"FakeChannelWiseQuantizeAbsMax");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
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"FakeChannelWiseQuantizeAbsMax");
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OP_INOUT_CHECK(ctx->HasOutput("OutScale"), "Output", "OutScale",
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"FakeChannelWiseQuantizeAbsMax");
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int quant_axis = ctx->Attrs().Get<int>("quant_axis");
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[quant_axis]});
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
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}
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};
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class FakeChannelWiseQuantizeAbsMaxOpMaker
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: public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor) Input is float data type.");
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AddOutput("Out",
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"(Tensor) Output of quantized low level tensor, "
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"but also saved as float data type.");
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AddOutput("OutScale", "(Tensor) Current channel wise scale");
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AddAttr<int>("quant_axis",
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"(int, default 0) The axis for quantization. "
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"For conv2d, depthwise_conv2d, conv2d_transpose "
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"and mul, the quant_axis is equal to the cout axis.")
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.SetDefault(0)
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.AddCustomChecker([](const int& quant_axis) {
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PADDLE_ENFORCE_EQ(quant_axis == 0 || quant_axis == 1, true,
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platform::errors::InvalidArgument(
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"'quant_axis' should be 0 or 1, but "
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"the received is %d",
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quant_axis));
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});
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AddAttr<int>("bit_length", "(int, default 8)")
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.SetDefault(8)
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.AddCustomChecker([](const int& bit_length) {
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PADDLE_ENFORCE_EQ(bit_length >= 1 && bit_length <= 16, true,
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platform::errors::InvalidArgument(
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"'bit_length' should be between 1 and 16, but "
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"the received is %d",
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bit_length));
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});
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AddAttr<bool>("is_test",
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"(bool, default false) Set to true for inference only, false "
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"for training. Some layers may run faster when this is true.")
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.SetDefault(false);
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AddComment(R"DOC(
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The scale of FakeChannelWiseQuantize operator is a vector.
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In detail, each channel of the input X has a scale value.
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$$scale_c = max(abs(X_c))$$
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$$range = 2^{bit\_length - 1} - 1$$
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$$Out_c = round(\frac{X_c * range} {scale_c})$$
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In above three formulas, the range value of c is as follow:
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$$0 \leq c \lt \ the\ channel\ number\ of\ X$$
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)DOC");
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}
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};
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class FakeChannelWiseQuantizeDequantizeAbsMaxOp
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: public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
|
|
"FakeChannelWiseQuantizeDequantizeAbsMax");
|
|
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
|
|
"FakeChannelWiseQuantizeDequantizeAbsMax");
|
|
OP_INOUT_CHECK(ctx->HasOutput("OutScale"), "Output", "OutScale",
|
|
"FakeChannelWiseQuantizeDequantizeAbsMax");
|
|
int quant_axis = ctx->Attrs().Get<int>("quant_axis");
|
|
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
|
|
ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[quant_axis]});
|
|
ctx->ShareLoD("X", /*->*/ "Out");
|
|
}
|
|
|
|
protected:
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(
|
|
OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
|
|
}
|
|
};
|
|
|
|
class FakeChannelWiseQuantizeDequantizeAbsMaxOpMaker
|
|
: public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X", "(Tensor) Input is float data type.");
|
|
AddOutput("Out",
|
|
"(Tensor) Output of quantized and dequantized low level tensor, "
|
|
"saved as float data type.");
|
|
AddOutput("OutScale", "(Tensor) Current channel wise scale");
|
|
AddAttr<int>("quant_axis",
|
|
"(int, default 0) The axis for quantization. "
|
|
"For conv2d, depthwise_conv2d, conv2d_transpose "
|
|
"and mul, the quant_axis is equal to the cout axis.")
|
|
.SetDefault(0)
|
|
.AddCustomChecker([](const int& quant_axis) {
|
|
PADDLE_ENFORCE_EQ(quant_axis == 0 || quant_axis == 1, true,
|
|
platform::errors::InvalidArgument(
|
|
"'quant_axis' should be 0 or 1, but "
|
|
"the received is %d",
|
|
quant_axis));
|
|
});
|
|
AddAttr<int>("bit_length", "(int, default 8)")
|
|
.SetDefault(8)
|
|
.AddCustomChecker([](const int& bit_length) {
|
|
PADDLE_ENFORCE_EQ(bit_length >= 1 && bit_length <= 16, true,
|
|
platform::errors::InvalidArgument(
|
|
"'bit_length' should be between 1 and 16, but "
|
|
"the received is %d",
|
|
bit_length));
|
|
});
|
|
AddComment(R"DOC(
|
|
The scale of FakeChannelWiseQuantize operator is a vector.
|
|
In detail, each channel of the input X has a scale value.
|
|
|
|
$$scale_c = max(abs(X_c))$$
|
|
$$range = 2^{bit\_length - 1} - 1$$
|
|
$$Out_c = round(\frac{X_c * range} {scale_c}) * \frac{scale_c} {range}$$
|
|
In above three formulas, the range value of c is as follow:
|
|
$$0 \leq c \lt \ the\ channel\ number\ of\ X$$
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class FakeQuantizeRangeAbsMaxOp : public framework::OperatorWithKernel {
|
|
public:
|
|
FakeQuantizeRangeAbsMaxOp(const std::string& type,
|
|
const framework::VariableNameMap& inputs,
|
|
const framework::VariableNameMap& outputs,
|
|
const framework::AttributeMap& attrs)
|
|
: OperatorWithKernel(type, inputs, outputs, attrs) {}
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "FakeQuantizeRangeAbsMax");
|
|
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
|
|
"FakeQuantizeRangeAbsMax");
|
|
OP_INOUT_CHECK(ctx->HasOutput("OutScale"), "Output", "OutScale",
|
|
"FakeQuantizeRangeAbsMax");
|
|
if (ctx->HasOutput("OutScales")) {
|
|
int window_size = ctx->Attrs().Get<int>("window_size");
|
|
ctx->SetOutputDim("OutScales", {window_size});
|
|
}
|
|
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
|
|
ctx->SetOutputDim("OutScale", {1});
|
|
ctx->ShareLoD("X", /*->*/ "Out");
|
|
}
|
|
|
|
protected:
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(
|
|
OperatorWithKernel::IndicateVarDataType(ctx, "X"),
|
|
ctx.device_context());
|
|
}
|
|
};
|
|
|
|
class FakeQuantizeRangeAbsMaxOpMaker
|
|
: public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X", "(Tensor) Input is float data type.");
|
|
AddInput("InScale", "Last scale.");
|
|
AddInput("Iter", "Global step iteration.").AsDispensable();
|
|
AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
|
|
AddOutput("OutScale", " Current scale");
|
|
AddOutput("OutScales", "(Tensor) scale buffer.").AsDispensable();
|
|
AddAttr<int>("window_size", "(int, default 10000) window range size.")
|
|
.SetDefault(10000);
|
|
AddAttr<int>("bit_length", "(int, default 8), quantization bit number.")
|
|
.SetDefault(8)
|
|
.AddCustomChecker([](const int& bit_length) {
|
|
PADDLE_ENFORCE_EQ(bit_length >= 1 && bit_length <= 16, true,
|
|
platform::errors::InvalidArgument(
|
|
"'bit_length' should be between 1 and 16, but "
|
|
"the received is %d",
|
|
bit_length));
|
|
});
|
|
AddAttr<bool>("is_test",
|
|
"(bool, default false) Set to true for inference only, false "
|
|
"for training. Some layers may run faster when this is true.")
|
|
.SetDefault(false);
|
|
AddComment(R"DOC(
|
|
FakeQuantize operator is used in static quantization.
|
|
|
|
$$scale = max(max(abs(x)), history_abs_max)$$
|
|
$$range = 2^{bit_length - 1} - 1$$
|
|
$$Out = round(X/scale * range)$$
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class FakeQuantOrWithDequantMovingAverageAbsMaxOp
|
|
: public framework::OperatorWithKernel {
|
|
public:
|
|
FakeQuantOrWithDequantMovingAverageAbsMaxOp(
|
|
const std::string& type, const framework::VariableNameMap& inputs,
|
|
const framework::VariableNameMap& outputs,
|
|
const framework::AttributeMap& attrs)
|
|
: OperatorWithKernel(type, inputs, outputs, attrs) {}
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
|
|
"FakeQuantOrWithDequantMovingAverageAbsMax");
|
|
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
|
|
"FakeQuantOrWithDequantMovingAverageAbsMax");
|
|
OP_INOUT_CHECK(ctx->HasOutput("OutScale"), "Output", "OutScale",
|
|
"FakeQuantOrWithDequantMovingAverageAbsMax");
|
|
if (ctx->HasOutput("OutState")) {
|
|
ctx->SetOutputDim("OutState", {1});
|
|
}
|
|
if (ctx->HasOutput("OutAccum")) {
|
|
ctx->SetOutputDim("OutAccum", {1});
|
|
}
|
|
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
|
|
ctx->SetOutputDim("OutScale", {1});
|
|
ctx->ShareLoD("X", /*->*/ "Out");
|
|
}
|
|
|
|
protected:
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(
|
|
OperatorWithKernel::IndicateVarDataType(ctx, "X"),
|
|
ctx.device_context());
|
|
}
|
|
};
|
|
|
|
class FakeQuantOrWithDequantMovingAverageAbsMaxOpMaker
|
|
: public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X", "(Tensor) Input is float data type.");
|
|
AddInput("InScale", "Last scale.");
|
|
AddInput("InAccum", "Last accum.").AsDispensable();
|
|
AddInput("InState", "Last state.").AsDispensable();
|
|
AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
|
|
AddOutput("OutScale", " Current scale");
|
|
AddOutput("OutState", "(Tensor) state buffer.").AsDispensable();
|
|
AddOutput("OutAccum", "(Tensor) accum buffer.").AsDispensable();
|
|
AddAttr<float>("moving_rate", "(float, default 0.9) moving rate.")
|
|
.SetDefault(0.9);
|
|
AddAttr<int>("bit_length", "(int, default 8), quantization bit number.")
|
|
.SetDefault(8)
|
|
.AddCustomChecker([](const int& bit_length) {
|
|
PADDLE_ENFORCE_EQ(bit_length >= 1 && bit_length <= 16, true,
|
|
platform::errors::InvalidArgument(
|
|
"'bit_length' should be between 1 and 16, but "
|
|
"the received is %d",
|
|
bit_length));
|
|
});
|
|
AddAttr<bool>("is_test",
|
|
"(bool, default false) Set to true for inference only, false "
|
|
"for training. Some layers may run faster when this is true.")
|
|
.SetDefault(false);
|
|
AddComment(R"DOC(
|
|
This is a Base Op which supports FakeQuantMovingAverageAbsMaxOp and FakeQuantDequantMovingAverageAbsMaxOp.
|
|
FakeQuantMovingAverageAbsMaxOp operator is used in the static quantization.
|
|
|
|
$$scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)$$
|
|
$$range = 2^{bit\_length - 1} - 1$$
|
|
$$Out = round(X/scale * range)$$
|
|
|
|
FakeQuantDequantMovingAverageAbsMaxOp operator does the moving_average_abs_max quant and then dequant.
|
|
|
|
$$scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)$$
|
|
$$range = 2^{bit\_length - 1} - 1$$
|
|
$$Out = round(X/scale * range) * scale / range$$
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class MovingAverageAbsMaxScaleOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
|
|
"MovingAverageAbsMaxScale");
|
|
OP_INOUT_CHECK(ctx->HasOutput("OutScale"), "Output", "OutScale",
|
|
"MovingAverageAbsMaxScale");
|
|
if (ctx->HasOutput("OutState")) {
|
|
ctx->SetOutputDim("OutState", {1});
|
|
}
|
|
if (ctx->HasOutput("OutAccum")) {
|
|
ctx->SetOutputDim("OutAccum", {1});
|
|
}
|
|
ctx->SetOutputDim("OutScale", {1});
|
|
}
|
|
|
|
protected:
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(
|
|
OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
|
|
}
|
|
};
|
|
|
|
class MovingAverageAbsMaxScaleOpMaker
|
|
: public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("X", "(Tensor) Input is float data type.");
|
|
AddInput("InAccum", "Last accum.").AsDispensable();
|
|
AddInput("InState", "Last state.").AsDispensable();
|
|
AddOutput("OutScale", " Current scale");
|
|
AddOutput("OutState", "(Tensor) state buffer.").AsDispensable();
|
|
AddOutput("OutAccum", "(Tensor) accum buffer.").AsDispensable();
|
|
AddAttr<float>("moving_rate", "(float, default 0.9) moving rate.")
|
|
.SetDefault(0.9);
|
|
AddAttr<bool>("is_test",
|
|
"(bool, default false) Set true for inference only and false "
|
|
"for training. Some layers may run faster when this is true.")
|
|
.SetDefault(false);
|
|
AddComment(R"DOC(
|
|
MovingAverageAbsMaxScale operator is only used for calculating the quantization scale.
|
|
And it will not quantize the input tensor.
|
|
|
|
$$scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)$$
|
|
$$Out = X$$
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class FakeQuantDequantGradOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
auto out_grad_name = framework::GradVarName("Out");
|
|
auto x_grad_name = framework::GradVarName("X");
|
|
OP_INOUT_CHECK(ctx->HasInput(out_grad_name), "Input", out_grad_name,
|
|
"FakeQuantDequantGradOp");
|
|
OP_INOUT_CHECK(ctx->HasOutput(x_grad_name), "Output", x_grad_name,
|
|
"FakeQuantDequantGradOp");
|
|
|
|
ctx->SetOutputDim(x_grad_name, ctx->GetInputDim(out_grad_name));
|
|
}
|
|
|
|
framework::OpKernelType GetExpectedKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
auto input_data_type = OperatorWithKernel::IndicateVarDataType(
|
|
ctx, framework::GradVarName("Out"));
|
|
return framework::OpKernelType(input_data_type, ctx.GetPlace());
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
class FakeQuantDequantGradMaker : public framework::SingleGradOpMaker<T> {
|
|
public:
|
|
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
|
|
|
|
protected:
|
|
void Apply(GradOpPtr<T> grad_op) const override {
|
|
grad_op->SetType("fake_quantize_dequantize_grad");
|
|
grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
|
|
grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
|
|
grad_op->SetAttrMap(this->Attrs());
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
using CPU = paddle::platform::CPUDeviceContext;
|
|
|
|
REGISTER_OPERATOR(
|
|
fake_quantize_abs_max, ops::FakeQuantOrWithDequantAbsMaxOp,
|
|
ops::FakeQuantOrWithDequantAbsMaxOpMaker,
|
|
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
|
|
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(fake_quantize_abs_max,
|
|
ops::FakeQuantizeAbsMaxKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(fake_quantize_dequantize_abs_max,
|
|
ops::FakeQuantOrWithDequantAbsMaxOp,
|
|
ops::FakeQuantOrWithDequantAbsMaxOpMaker,
|
|
ops::FakeQuantDequantGradMaker<paddle::framework::OpDesc>,
|
|
ops::FakeQuantDequantGradMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(fake_quantize_dequantize_abs_max,
|
|
ops::FakeQuantizeDequantizeAbsMaxKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(
|
|
fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxOp,
|
|
ops::FakeQuantizeRangeAbsMaxOpMaker,
|
|
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
|
|
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(fake_quantize_range_abs_max,
|
|
ops::FakeQuantizeRangeAbsMaxKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(
|
|
fake_quantize_moving_average_abs_max,
|
|
ops::FakeQuantOrWithDequantMovingAverageAbsMaxOp,
|
|
ops::FakeQuantOrWithDequantMovingAverageAbsMaxOpMaker,
|
|
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
|
|
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(fake_quantize_moving_average_abs_max,
|
|
ops::FakeQuantizeMovingAverageAbsMaxKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(fake_quantize_dequantize_moving_average_abs_max,
|
|
ops::FakeQuantOrWithDequantMovingAverageAbsMaxOp,
|
|
ops::FakeQuantOrWithDequantMovingAverageAbsMaxOpMaker,
|
|
ops::FakeQuantDequantGradMaker<paddle::framework::OpDesc>,
|
|
ops::FakeQuantDequantGradMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
fake_quantize_dequantize_moving_average_abs_max,
|
|
ops::FakeQuantizeDequantizeMovingAverageAbsMaxKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(
|
|
fake_channel_wise_quantize_abs_max, ops::FakeChannelWiseQuantizeAbsMaxOp,
|
|
ops::FakeChannelWiseQuantizeAbsMaxOpMaker,
|
|
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
|
|
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(fake_channel_wise_quantize_abs_max,
|
|
ops::FakeChannelWiseQuantizeAbsMaxKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(
|
|
moving_average_abs_max_scale, ops::MovingAverageAbsMaxScaleOp,
|
|
ops::MovingAverageAbsMaxScaleOpMaker,
|
|
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
|
|
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(moving_average_abs_max_scale,
|
|
ops::MovingAverageAbsMaxScaleKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(fake_quantize_dequantize_grad, ops::FakeQuantDequantGradOp);
|
|
REGISTER_OP_CPU_KERNEL(fake_quantize_dequantize_grad,
|
|
ops::FakeQuantDequantGradKernel<CPU, float>);
|
|
|
|
REGISTER_OPERATOR(fake_channel_wise_quantize_dequantize_abs_max,
|
|
ops::FakeChannelWiseQuantizeDequantizeAbsMaxOp,
|
|
ops::FakeChannelWiseQuantizeDequantizeAbsMaxOpMaker,
|
|
ops::FakeQuantDequantGradMaker<paddle::framework::OpDesc>,
|
|
ops::FakeQuantDequantGradMaker<paddle::imperative::OpBase>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
fake_channel_wise_quantize_dequantize_abs_max,
|
|
ops::FakeChannelWiseQuantizeDequantizeAbsMaxKernel<CPU, float>);
|
|
|
|
REGISTER_OP_VERSION(fake_channel_wise_quantize_abs_max)
|
|
.AddCheckpoint(
|
|
R"ROC(add new attributes [quant_axis] for applying per-channel "
|
|
"quantization to conv2d_tranpose and mul ops.)ROC",
|
|
paddle::framework::compatible::OpVersionDesc().NewAttr(
|
|
"quant_axis", "The axis for quantization.", 0));
|
|
REGISTER_OP_VERSION(moving_average_abs_max_scale)
|
|
.AddCheckpoint(
|
|
R"ROC(Incompatible upgrade of output [Out])ROC",
|
|
paddle::framework::compatible::OpVersionDesc().DeleteOutput(
|
|
"Out",
|
|
"Delete output in order to make the inference model not "
|
|
"save moving_average_abs_max_scale operator. This will "
|
|
"make the quantitative model be correctly applied in inference."));
|