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232 lines
8.9 KiB
232 lines
8.9 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 <string>
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#include "paddle/fluid/framework/eigen.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, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVectorArrayMap =
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Eigen::TensorMap<Eigen::Tensor<T, 1, MajorType, IndexType>>;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using ConstEigenVectorArrayMap =
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Eigen::TensorMap<const Eigen::Tensor<T, 1, MajorType, IndexType>>;
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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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Eigen::DSizes<Eigen::DenseIndex, 1> idim(num);
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Eigen::DSizes<Eigen::DenseIndex, 1> odim(1);
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Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor>> in_e(in, idim);
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Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>> out_e(out, odim);
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out_e = in_e.abs().maximum();
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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 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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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 in_e = framework::EigenVector<T>::Flatten(in);
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auto out_e = framework::EigenVector<T>::Flatten(*out);
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out_e.device(*ctx.eigen_device()) = (bin_cnt / s * in_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 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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class FakeQuantizeAbsMaxOp : public framework::OperatorWithKernel {
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public:
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FakeQuantizeAbsMaxOp(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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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of FakeQuantizeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of FakeQuantizeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("OutScale"),
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"Output(Scale) of FakeQuantizeOp should not be null.");
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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(ctx.Input<framework::LoDTensor>("X")->type(),
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ctx.device_context());
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}
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};
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class FakeQuantizeAbsMaxOpMaker : 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(bit_length >= 1 && bit_length <= 16,
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"'bit_length' should be between 1 and 16.");
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});
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AddComment(R"DOC(
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FakeQuantize operator
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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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)DOC");
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}
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};
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class FakeQuantizeRangeAbsMaxOp : public framework::OperatorWithKernel {
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public:
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FakeQuantizeRangeAbsMaxOp(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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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of FakeQuantizeRangeAbsMaxOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("Out"),
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"Output(Out) of FakeQuantizeRangeAbsMaxOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("OutScale"),
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"Output(OutScale) of FakeQuantizeRangeAbsMaxOp should not be null");
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if (ctx->HasOutput("OutScales")) {
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int window_size = ctx->Attrs().Get<int>("window_size");
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ctx->SetOutputDim("OutScales", {window_size});
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}
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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(ctx.Input<framework::LoDTensor>("X")->type(),
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ctx.device_context());
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}
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};
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class FakeQuantizeRangeAbsMaxOpMaker
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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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AddInput("InScale", "Last scale.");
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AddInput("Iter", "Global step iteration.").AsDispensable();
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AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
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AddOutput("OutScale", " Current scale");
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AddOutput("OutScales", "(Tensor) scale buffer.").AsDispensable();
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AddAttr<int>("window_size", "(int, default 10000) window range size.")
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.SetDefault(10000);
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AddAttr<int>("bit_length", "(int, default 8), quantization bit number.")
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.SetDefault(8)
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.AddCustomChecker([](const int& bit_length) {
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PADDLE_ENFORCE(bit_length >= 1 && bit_length <= 16,
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"'bit_length' should be between 1 and 16.");
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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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FakeQuantize operator is used in static quantization.
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$$scale = max(max(abs(x)), history_abs_max)$$
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$$range = 2^{bit_length - 1} - 1$$
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$$Out = round(X/scale * range)$$
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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using CPU = paddle::platform::CPUDeviceContext;
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REGISTER_OPERATOR(fake_quantize_abs_max, ops::FakeQuantizeAbsMaxOp,
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ops::FakeQuantizeAbsMaxOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(fake_quantize_abs_max,
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ops::FakeQuantizeAbsMaxKernel<CPU, float>);
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REGISTER_OPERATOR(fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxOp,
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ops::FakeQuantizeRangeAbsMaxOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(fake_quantize_range_abs_max,
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ops::FakeQuantizeRangeAbsMaxKernel<CPU, float>);
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