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113 lines
4.1 KiB
113 lines
4.1 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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namespace paddle {
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namespace operators {
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class FakeQuantizeOp : public framework::OperatorWithKernel {
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public:
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FakeQuantizeOp(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("OutMovingScale"),
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"OutMovingScale(Out) of FakeQuantizeOp should not be null");
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// if (ctx->HasInput("InMovingScale")) {
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ctx->SetOutputDim("OutMovingScale", ctx->GetInputDim("InMovingScale"));
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//}
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// if (ctx->HasInput("InScales")) {
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PADDLE_ENFORCE(ctx->HasOutput("OutScales"),
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"OutScales(Out) of FakeQuantizeOp should not be null");
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ctx->SetOutputDim("OutScales", ctx->GetInputDim("InScales"));
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// PADDLE_ENFORCE_EQ(ctx->Inputs("InScales")[0],
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// ctx->Outputs("OutScales")[0],
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// "Mean and MeanOut should share the same memory");
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//}
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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class FakeQuantizeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor) Input tensor of scale operator.");
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AddInput("InScales", "(Tensor) scale buffer, used in static quantization.")
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.AsDispensable();
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AddInput("InMovingScale", "Last scale, used in static quantization.")
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.AsDispensable();
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AddInput("InCurrentIter",
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"Last iteration number, used in static quantization.")
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.AsDispensable();
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AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
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AddOutput("OutScales",
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"(Tensor) scale buffer, used in static quantization.")
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.AsDispensable();
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AddOutput("OutMovingScale", " Current scale");
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AddOutput("OutCurrentIter", "Current iteration number.").AsDispensable();
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AddAttr<std::string>("quantize_type",
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"(string, default abs_max)"
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"The scaling tpe of the quantize operator.")
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.SetDefault("abs_max");
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AddAttr<int>("window_size", "(int, default 10000)").SetDefault(10000);
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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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AddAttr<bool>("is_test", "").SetDefault(false);
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AddComment(R"DOC(
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FakeQuantize operator
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quantize_type = abs_max:
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$$scale = max(abs(x))$$
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quantize_type = range_abs_max:
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$$scale = max(max(abs(x)), history_abs_max)$$
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quantize_type = moving_average_abs_max:
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$$scale = 0.1*scale+0.9*new_abs_max)$$
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$$Out = scale*X$$
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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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REGISTER_OPERATOR(fake_quantize, ops::FakeQuantizeOp, ops::FakeQuantizeOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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fake_quantize,
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ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, float>,
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ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, double>);
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