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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "elementwise_linear_dnnlowp_op.h"
namespace caffe2 {
using namespace dnnlowp;
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
template <typename T>
ElementwiseLinearDNNLowPOp<T>::ElementwiseLinearDNNLowPOp(
const OperatorDef& operator_def,
Workspace* ws)
: BaseType(operator_def, ws),
axis_(this->template GetSingleArgument<int>("axis", 1)) {}
template <typename T>
bool ElementwiseLinearDNNLowPOp<T>::RunOnDevice() {
if (!GetQuantizationParameters_()) {
return false;
}
const auto& X = InputTensorCPU_(0);
const auto& a = InputTensorCPU_(1);
const auto& b = InputTensorCPU_(2);
auto* Y = OutputTensorCPU_(0);
const auto canonical_axis = X.canonical_axis_index(axis_);
const int N = X.size_to_dim(canonical_axis);
const int D = X.size_from_dim(canonical_axis);
CAFFE_ENFORCE_EQ(a.ndim(), 1, a.ndim());
CAFFE_ENFORCE_EQ(a.size(0), D, a.ndim());
CAFFE_ENFORCE_EQ(b.ndim(), 1, b.ndim());
CAFFE_ENFORCE_EQ(b.size(0), D, b.ndim());
Y->ResizeLike(X);
// Quantize X
vector<T> X_temp;
const T* X_quantized =
QuantizeInputIfNeeded<T>(this, 0, in_qparams_[0], X_temp);
// Quantize b
vector<int32_t> b_quantized(b.numel());
const float* b_data = b.template data<float>();
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (int i = 0; i < b.numel(); ++i) {
b_quantized[i] = fbgemm::Quantize<int32_t>(
b_data[i],
0,
in_qparams_[0].scale * in_qparams_[1].scale,
32,
true /* signed */);
}
T* Y_quantized = GetQuantizedOutputData_();
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (int n = 0; n < N; ++n) {
for (int d = 0; d < D; ++d) {
int32_t raw = (X_quantized[n * D + d] - in_qparams_[0].zero_point) *
(a_quantized_[d] - in_qparams_[1].zero_point) +
b_quantized[d];
Y_quantized[n * D + d] =
fbgemm::Requantize<T>(raw, requantization_params_);
}
}
RunOnDeviceEpilogue_();
return true;
}
template <typename T>
bool ElementwiseLinearDNNLowPOp<T>::GetQuantizationParameters_() {
using namespace dnnlowp;
// Choose quantization for X
in_qparams_[0] = GetInputTensorQuantizationParamsOf(this, 0, qfactory_.get());
// Quantize a
if (a_quantized_.empty()) {
const auto& a = InputTensorCPU_(1);
in_qparams_[1] = qfactory_->ChooseQuantizationParams(
a.template data<float>(), a.numel(), true /*weight*/);
a_quantized_.resize(a.numel());
fbgemm::Quantize<T>(
a.template data<float>(),
a_quantized_.data(),
a_quantized_.size(),
in_qparams_[1]);
}
GetOutputQuantizationParams_();
float real_multiplier =
in_qparams_[0].scale * in_qparams_[1].scale / out_qparams_.scale;
requantization_params_ =
qfactory_->ChooseRequantizationMultiplier(real_multiplier, out_qparams_);
return true;
}
REGISTER_CPU_OPERATOR_WITH_ENGINE(
ElementwiseLinear,
DNNLOWP,
ElementwiseLinearDNNLowPOp<uint8_t>);
REGISTER_CPU_OPERATOR_WITH_ENGINE(
Int8ElementwiseLinear,
DNNLOWP,
ElementwiseLinearDNNLowPOp<uint8_t>);
} // namespace caffe2
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