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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
133 lines
3.7 KiB
C++
133 lines
3.7 KiB
C++
/**
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* Copyright (c) 2016-present, Facebook, Inc.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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*/
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#include "elementwise_linear_dnnlowp_op.h"
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namespace caffe2 {
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using namespace dnnlowp;
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// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
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template <typename T>
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ElementwiseLinearDNNLowPOp<T>::ElementwiseLinearDNNLowPOp(
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const OperatorDef& operator_def,
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Workspace* ws)
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: BaseType(operator_def, ws),
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axis_(this->template GetSingleArgument<int>("axis", 1)) {}
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template <typename T>
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bool ElementwiseLinearDNNLowPOp<T>::RunOnDevice() {
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if (!GetQuantizationParameters_()) {
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return false;
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}
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const auto& X = InputTensorCPU_(0);
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const auto& a = InputTensorCPU_(1);
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const auto& b = InputTensorCPU_(2);
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auto* Y = OutputTensorCPU_(0);
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const auto canonical_axis = X.canonical_axis_index(axis_);
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const int N = X.size_to_dim(canonical_axis);
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const int D = X.size_from_dim(canonical_axis);
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CAFFE_ENFORCE_EQ(a.ndim(), 1, a.ndim());
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CAFFE_ENFORCE_EQ(a.size(0), D, a.ndim());
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CAFFE_ENFORCE_EQ(b.ndim(), 1, b.ndim());
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CAFFE_ENFORCE_EQ(b.size(0), D, b.ndim());
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Y->ResizeLike(X);
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// Quantize X
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vector<T> X_temp;
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const T* X_quantized =
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QuantizeInputIfNeeded<T>(this, 0, in_qparams_[0], X_temp);
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// Quantize b
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vector<int32_t> b_quantized(b.numel());
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const float* b_data = b.template data<float>();
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#ifdef _OPENMP
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#pragma omp parallel for
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#endif
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for (int i = 0; i < b.numel(); ++i) {
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b_quantized[i] = fbgemm::Quantize<int32_t>(
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b_data[i],
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0,
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in_qparams_[0].scale * in_qparams_[1].scale,
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32,
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true /* signed */);
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}
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T* Y_quantized = GetQuantizedOutputData_();
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#ifdef _OPENMP
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#pragma omp parallel for
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#endif
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for (int n = 0; n < N; ++n) {
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for (int d = 0; d < D; ++d) {
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int32_t raw = (X_quantized[n * D + d] - in_qparams_[0].zero_point) *
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(a_quantized_[d] - in_qparams_[1].zero_point) +
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b_quantized[d];
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Y_quantized[n * D + d] =
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fbgemm::Requantize<T>(raw, requantization_params_);
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}
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}
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RunOnDeviceEpilogue_();
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return true;
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}
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template <typename T>
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bool ElementwiseLinearDNNLowPOp<T>::GetQuantizationParameters_() {
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using namespace dnnlowp;
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// Choose quantization for X
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in_qparams_[0] = GetInputTensorQuantizationParamsOf(this, 0, qfactory_.get());
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// Quantize a
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if (a_quantized_.empty()) {
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const auto& a = InputTensorCPU_(1);
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in_qparams_[1] = qfactory_->ChooseQuantizationParams(
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a.template data<float>(), a.numel(), true /*weight*/);
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a_quantized_.resize(a.numel());
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fbgemm::Quantize<T>(
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a.template data<float>(),
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a_quantized_.data(),
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a_quantized_.size(),
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in_qparams_[1]);
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}
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GetOutputQuantizationParams_();
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float real_multiplier =
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in_qparams_[0].scale * in_qparams_[1].scale / out_qparams_.scale;
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requantization_params_ =
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qfactory_->ChooseRequantizationMultiplier(real_multiplier, out_qparams_);
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return true;
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}
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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ElementwiseLinear,
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DNNLOWP,
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ElementwiseLinearDNNLowPOp<uint8_t>);
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REGISTER_CPU_OPERATOR_WITH_ENGINE(
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Int8ElementwiseLinear,
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DNNLOWP,
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ElementwiseLinearDNNLowPOp<uint8_t>);
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} // namespace caffe2
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