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104 lines
3.5 KiB
104 lines
3.5 KiB
/* Copyright (c) 2020 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/dequantize_log_op.h"
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#include <math.h>
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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template <typename T>
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struct DequantizeFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& dev_ctx,
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const framework::Tensor* in, const framework::Tensor* dict,
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framework::Tensor* out) {
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const float* dict_data = dict->data<float>();
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const T* input_data = in->data<T>();
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float* output_data = out->mutable_data<float>(dev_ctx.GetPlace());
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int ind = in->numel();
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for (size_t i = 0; i < (unsigned)ind; i++) {
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if (input_data[i] < 0) {
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output_data[i] = -dict_data[input_data[i] + 128];
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} else {
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output_data[i] = dict_data[input_data[i]];
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}
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}
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}
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};
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template struct DequantizeFunctor<platform::CPUDeviceContext, int8_t>;
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class DequantizeLogOp : public framework::OperatorWithKernel {
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public:
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DequantizeLogOp(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_EQ(ctx->HasInput("X"), true,
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platform::errors::NotFound(
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"Input(X) of DequantizeLogOp is not found."));
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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platform::errors::NotFound(
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"Output(Out) of DequantizeLogOp is not found."));
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ctx->ShareDim("X", /*->*/ "Out");
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
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auto type = framework::OpKernelType(data_type, ctx.device_context());
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return type;
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}
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};
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class DequantizeLogOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(int8 Tensor) The input with int8 type is the "
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"low precision tensor.");
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AddInput("Dict", "(float) The Dict in quantization stage.");
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AddOutput("Out",
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"(float32 Tensor) The output is the dequantized high "
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"precision tensor.");
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AddComment(R"DOC(
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DequantizeLogOp operator.
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This calculation is an opposite operation of QuantizeLogOp:
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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(
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dequantize_log, ops::DequantizeLogOp, ops::DequantizeLogOpMaker,
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paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
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paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OP_CPU_KERNEL(dequantize_log, ops::DequantizeLogKernel<CPU, int8_t>);
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