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#include "utility_dnnlowp_ops.h"
#include <array>
#include <tuple>
#include <type_traits>
#include <vector>
// #define DNNLOWP_MEASURE_TIME_BREAKDOWN
#ifdef DNNLOWP_MEASURE_TIME_BREAKDOWN
#include <chrono>
#endif
#include "dnnlowp_partition.h"
namespace caffe2 {
using namespace std;
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
template <typename T, bool ReluFused>
SumDNNLowPOp<T, ReluFused>::SumDNNLowPOp(
const OperatorDef& operator_def,
Workspace* ws)
: BaseType(operator_def, ws) {}
template <typename T, bool ReluFused>
bool SumDNNLowPOp<T, ReluFused>::RunOnDevice() {
if (!this->arguments_parsed_) {
dnnlowp::ParseDNNLowPOperatorArguments(
this, &dequantize_output_, &measure_quantization_error_, &followed_by_);
if (ReluFused) {
// It's actually fused with Relu not followed by but setting this to make
// sure quantization error is correctly measured in
// this->MeasureQuantizationError_
followed_by_ = "Relu";
dnnlowp::AdjustOutputTensorQuantizationParamsWithFollowedBy(
this, followed_by_);
}
this->arguments_parsed_ = true;
}
#ifdef DNNLOWP_MEASURE_TIME_BREAKDOWN
chrono::time_point<chrono::system_clock> t_begin, t_end;
t_begin = chrono::system_clock::now();
#endif
if (!GetQuantizationParameters_()) {
return false;
}
#ifdef DNNLOWP_MEASURE_TIME_BREAKDOWN
t_end = chrono::system_clock::now();
double dt = chrono::duration<double>(t_end - t_begin).count();
LOG(INFO) << "this=" << this << " get_quant_params: " << dt * 1e3 << " ms";
t_begin = chrono::system_clock::now();
#endif
using namespace dnnlowp;
// Quantize inputs
int len = InputTensorCPU_(0).size();
// Element-wise sum
int32_t intermediate_zero_point =
intermediate_qparams_.zero_point * InputSize();
auto* output = OutputTensorCPU_(0);
output->ResizeLike(InputTensorCPU_(0));
T* output_data = GetQuantizedOutputData_();
if (InputTensorCPU_(0).template IsType<T>()) {
if (InputSize() == 2 && is_same<T, uint8_t>::value && GetCpuId().avx2() &&
GetCpuId().fma()) {
// fast path when we have 2 uint8_t inputs with AVX2 / FMA support
// NOTE: this path does addition in floating point unlike slow path that
// does everything in fixed-point. So they are numerically different.
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
array<const T*, 2> input_data;
for (int i = 0; i < 2; ++i) {
input_data[i] = InputTensorCPU_(i).template data<T>();
}
#ifdef _OPENMP
#pragma omp parallel
#endif
{
constexpr int VLEN = 8;
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int j_begin, j_end;
tie(j_begin, j_end) = Get1DPartition(
len, dnnlowp_get_num_threads(), dnnlowp_get_thread_num(), VLEN);
internal::ElementWiseSumAVX2<T, ReluFused>(
input_data[0] + j_begin,
input_data[1] + j_begin,
output_data + j_begin,
j_end - j_begin,
in_qparams_[0].scale,
in_qparams_[0].zero_point,
in_qparams_[1].scale,
in_qparams_[1].zero_point,
out_qparams_.scale,
out_qparams_.zero_point);
} // omp parallel
} else {
vector<RequantizationParams> in_requantization_params(InputSize());
vector<T*> input_data(InputSize());
for (int i = 0; i < InputSize(); ++i) {
float real_multiplier =
in_qparams_[i].scale / intermediate_qparams_.scale;
in_requantization_params[i] = qfactory_->ChooseRequantizationMultiplier(
real_multiplier, intermediate_qparams_);
input_data[i] = InputTensorCPU_(i).template data<T>();
}
#ifdef _OPENMP
#pragma omp parallel
#endif
{
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int j_begin, j_end;
tie(j_begin, j_end) = Get1DPartition(
len, dnnlowp_get_num_threads(), dnnlowp_get_thread_num());
for (int j = j_begin; j < j_end; ++j) {
int32_t acc = 0;
for (int i = 0; i < InputSize(); ++i) {
acc += fbgemm::Requantize<int32_t>(
input_data[i][j] - in_qparams_[i].zero_point,
in_requantization_params[i]);
}
int32_t raw = acc - intermediate_zero_point;
if (ReluFused) {
raw = std::max(0, raw);
}
output_data[j] =
fbgemm::Requantize<T>(raw, out_requantization_params_);
}
}
}
} else { // InputTensorCPU_(0).template IsType<T>()
vector<float*> input_data(InputSize());
for (int i = 0; i < InputSize(); ++i) {
input_data[i] = InputTensorCPU_(i).template data<float>();
}
#ifdef _OPENMP
#pragma omp parallel
#endif
{
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int j_begin, j_end;
tie(j_begin, j_end) = Get1DPartition(
len, dnnlowp_get_num_threads(), dnnlowp_get_thread_num());
for (int j = j_begin; j < j_end; ++j) {
int32_t acc = 0;
for (int i = 0; i < InputSize(); ++i) {
acc += fbgemm::Quantize<int32_t>(
input_data[i][j],
intermediate_qparams_.zero_point,
intermediate_qparams_.scale,
qfactory_->GetEltwiseQuantizePrecision());
}
int32_t raw = acc - intermediate_zero_point;
if (ReluFused) {
raw = std::max(0, raw);
}
output_data[j] = fbgemm::Requantize<T>(raw, out_requantization_params_);
}
}
} // !InputTensorCPU_(0).template IsType<T>()
#ifdef DNNLOWP_MEASURE_TIME_BREAKDOWN
t_end = chrono::system_clock::now();
dt = chrono::duration<double>(t_end - t_begin).count();
LOG(INFO) << "this=" << this << " requantize inputs: " << dt * 1e3 << " ms";
t_begin = chrono::system_clock::now();
#endif
RunOnDeviceEpilogue_();
#ifdef DNNLOWP_MEASURE_TIME_BREAKDOWN
t_end = chrono::system_clock::now();
dt = chrono::duration<double>(t_end - t_begin).count();
LOG(INFO) << "this=" << this << " prologue: " << dt * 1e3 << " ms";
t_begin = chrono::system_clock::now();
#endif
return true;
} // DoRunQuantizedWithType_
template <typename T, bool ReluFused>
bool SumDNNLowPOp<T, ReluFused>::GetQuantizationParameters_() {
using namespace dnnlowp;
// Find global min and max of all inputs
float global_min = numeric_limits<float>::max(),
global_max = numeric_limits<float>::lowest();
for (int i = 0; i < InputSize(); ++i) {
in_qparams_[i] =
GetInputTensorQuantizationParamsOf(this, i, qfactory_.get());
global_min = std::min(global_min, in_qparams_[i].Min());
global_max = std::max(global_max, in_qparams_[i].Max());
}
intermediate_qparams_ = qfactory_->ChooseQuantizationParams(
global_min,
global_max,
qfactory_->GetEltwiseQuantizePrecision(),
qfactory_->GetPreserveActivationSparsity());
GetOutputQuantizationParams_();
// requantize from the intermediate precision to the final precision
float real_multiplier = intermediate_qparams_.scale / out_qparams_.scale;
out_requantization_params_ =
qfactory_->ChooseRequantizationMultiplier(real_multiplier, out_qparams_);
return true;
}
OPERATOR_SCHEMA(SumRelu)
.NumInputs(1, INT_MAX)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.InputsCanCrossDevices()
.IdenticalTypeAndShapeOfInput(0)
.Input(0, "data_0", "First of the input tensors. Can be inplace.")
.Output(0, "sum", "Output tensor. Same dimension as inputs.");
REGISTER_CPU_OPERATOR_WITH_ENGINE(Sum, DNNLOWP, SumDNNLowPOp<uint8_t, false>);
REGISTER_CPU_OPERATOR_WITH_ENGINE(
SumRelu,
DNNLOWP,
SumDNNLowPOp<uint8_t, true>);
REGISTER_CPU_OPERATOR_WITH_ENGINE(
Int8Sum,
DNNLOWP,
SumDNNLowPOp<uint8_t, false>);
REGISTER_CPU_OPERATOR_WITH_ENGINE(
Int8SumRelu,
DNNLOWP,
SumDNNLowPOp<uint8_t, true>);
REGISTER_CPU_OPERATOR_WITH_ENGINE(
Sum,
DNNLOWP_16,
SumDNNLowPOp<uint16_t, false>);
REGISTER_CPU_OPERATOR_WITH_ENGINE(
SumRelu,
DNNLOWP_16,
SumDNNLowPOp<uint16_t, true>);
} // namespace caffe2
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