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4f34cd6d1e
Avoid exposing defines that conflict with google logging, since this blocks external usage of libtorch in certain cases. All the 'interesting' changes should be in these two files, and the rest should just be mechanical changes via sed. c10/util/logging_is_not_google_glog.h c10/util/logging_is_google_glog.h Fixes https://github.com/pytorch/pytorch/issues/81415 cc @miladm @malfet Pull Request resolved: https://github.com/pytorch/pytorch/pull/82032 Approved by: https://github.com/soumith, https://github.com/miladm
89 lines
2.9 KiB
C++
89 lines
2.9 KiB
C++
#pragma once
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#include "caffe2/core/operator.h"
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#include "caffe2/core/tensor_int8.h"
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#include "caffe2/quantization/server/caffe2_dnnlowp_utils.h"
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#include "caffe2/quantization/server/dnnlowp.h"
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namespace caffe2 {
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/**
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* Wrap a floating-point operator with quantized inputs with type T.
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* This class is to measure quantization error against fp32 reference.
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*/
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template <typename OpType, typename T>
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class OpWrapper {
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public:
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OpWrapper(OperatorBase* op, dnnlowp::QuantizationFactory* qfactory)
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: op_(op), qfactory_(qfactory) {
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for (auto name : op->debug_def().input()) {
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local_input_blobs_.push_back(local_ws_.CreateBlob(name));
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TORCH_CHECK_NOTNULL(local_input_blobs_.back());
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}
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OperatorDef def = op->debug_def();
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local_op_.reset(new OpType(def, &local_ws_));
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for (auto name : def.output()) {
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local_output_blobs_.push_back(local_ws_.GetBlob(name));
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TORCH_CHECK_NOTNULL(local_output_blobs_.back());
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}
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}
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void DequantizeInput() {
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const OperatorDef& def = op_->debug_def();
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CPUContext context(def.device_option());
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for (int i = 0; i < op_->InputSize(); ++i) {
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if (op_->InputIsType<int8::Int8TensorCPU>(i)) {
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const TensorCPU& qtensor = op_->Input<int8::Int8TensorCPU>(i).t;
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TensorCPU* float_tensor =
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BlobGetMutableTensor(local_input_blobs_[i], CPU);
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// FIXME: doesn't work for bias so we shouldn't quantize bias before
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// model loading when we're running a shadow operator in fp32 for
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// example for measuring quantization error.
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float_tensor->ResizeLike(qtensor);
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fbgemm::Dequantize<T>(
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qtensor.data<T>(),
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float_tensor->template mutable_data<float>(),
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qtensor.numel(),
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dnnlowp::GetInputTensorQuantizationParamsOf(op_, i, qfactory_));
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} else {
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local_input_blobs_[i]->ShareExternal(
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const_cast<void*>(op_->Inputs()[i]->GetRaw()),
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op_->Inputs()[i]->meta());
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}
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}
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}
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OpType* Get() {
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return local_op_.get();
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}
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dnnlowp::TensorQuantizationParams GetOutputQuantizationParams(
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dnnlowp::QuantizationFactory* qfactory,
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int index = 0) {
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using namespace dnnlowp;
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float min, max;
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auto& out_tensor = local_output_blobs_[index]->template Get<TensorCPU>();
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fbgemm::FindMinMax(
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out_tensor.template data<float>(), &min, &max, out_tensor.numel());
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if (op_->OperatorBase::GetSingleArgument<std::string>("followed_by", "") ==
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"Relu") {
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min = std::max(0.0f, min);
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max = std::max(0.0f, max);
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}
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return qfactory->ChooseQuantizationParams(min, max);
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}
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private:
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OperatorBase* op_; /* container quantized op */
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Workspace local_ws_;
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std::vector<Blob*> local_input_blobs_;
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std::vector<Blob*> local_output_blobs_;
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std::unique_ptr<OpType> local_op_; /* contained fp32 reference op */
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dnnlowp::QuantizationFactory* qfactory_;
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};
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} // namespace caffe2
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