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https://github.com/zebrajr/pytorch.git
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a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
107 lines
2.7 KiB
C++
107 lines
2.7 KiB
C++
#include "quantize_dnnlowp_op.h"
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#include "dnnlowp_op.h"
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#include "caffe2/core/tensor_int8.h"
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#include "caffe2/quantization/server/int8_gen_quant_params.h"
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#include "caffe2_dnnlowp_utils.h"
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#include "dnnlowp_partition.h"
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namespace caffe2 {
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using namespace std;
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template <typename T>
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QuantizeDNNLowPOp<T>::QuantizeDNNLowPOp(
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const OperatorDef& operator_def,
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Workspace* ws)
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: Operator<CPUContext>(operator_def, ws),
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qfactory_(dnnlowp::GetQuantizationFactoryOf(this)) {}
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template <typename T>
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bool QuantizeDNNLowPOp<T>::RunOnDevice() {
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using namespace dnnlowp;
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if (!arguments_parsed_) {
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dnnlowp::ParseDNNLowPOperatorArguments(this);
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arguments_parsed_ = true;
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}
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CAFFE_ENFORCE(InputSize() <= 2);
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CAFFE_ENFORCE(Input(0).template IsType<float>());
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bool use_input_qparam = false;
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float in_scale = 0;
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int in_zero_point = 0;
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if (InputSize() == 2) {
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use_input_qparam = true;
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const auto* input_qparam_blob =
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Input<caffe2::unique_ptr<Int8QuantParamsBlob>>(1).get();
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CAFFE_ENFORCE(input_qparam_blob);
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in_scale = input_qparam_blob->qparam.scale;
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in_zero_point = input_qparam_blob->qparam.zero_point;
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}
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// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
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TensorQuantizationParams in_qparams;
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if (use_input_qparam) {
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in_qparams.scale = in_scale;
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in_qparams.zero_point = in_zero_point;
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in_qparams.precision = qfactory_->GetActivationPrecision();
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} else {
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if (HasStaticQuantization(this)) {
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in_qparams = GetStaticQuantizationParamsOf(this, 0);
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} else {
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in_qparams = GetInputTensorQuantizationParamsOf(this, 0, qfactory_.get());
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}
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}
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int8::Int8TensorCPU* output =
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Outputs()[0]->template GetMutable<int8::Int8TensorCPU>();
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output->t.ResizeLike(Input(0));
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const float* in_data = Input(0).template data<float>();
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T* out_data = output->t.template mutable_data<T>();
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fbgemm::Quantize<T>(in_data, out_data, Input(0).numel(), in_qparams);
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PropagateOutputTensorQuantizationParams(this, 0, in_qparams);
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return true;
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}
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OPERATOR_SCHEMA(Quantize)
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.NumInputs(1, 2)
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.NumOutputs(1)
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.IdenticalTypeAndShapeOfInput(0);
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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Quantize,
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DNNLOWP,
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QuantizeDNNLowPOp<uint8_t>);
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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Quantize,
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DNNLOWP_ROWWISE,
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QuantizeDNNLowPOp<uint8_t>);
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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Quantize,
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DNNLOWP_16,
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QuantizeDNNLowPOp<uint16_t>);
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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Quantize,
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DNNLOWP_ROWWISE_16,
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QuantizeDNNLowPOp<uint16_t>);
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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Int8Quantize,
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DNNLOWP,
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QuantizeDNNLowPOp<uint8_t>);
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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Int8Quantize,
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DNNLOWP_ROWWISE,
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QuantizeDNNLowPOp<uint8_t>);
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} // namespace caffe2
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