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#include "concat_dnnlowp_op.h"
#ifdef _OPENMP
#include <omp.h>
#endif
#include "dnnlowp_partition.h"
namespace caffe2 {
using namespace std;
template <typename T>
ConcatDNNLowPOp<T>::ConcatDNNLowPOp(
const OperatorDef& operator_def,
Workspace* ws)
: BaseType(operator_def, ws) {
if (HasArgument("axis")) {
axis_ = this->template GetSingleArgument<int>("axis", -1);
add_axis_ = this->template GetSingleArgument<int>("add_axis", 0);
} else {
axis_ = GetDimFromOrderString(
this->template GetSingleArgument<string>("order", "NCHW"));
add_axis_ = 0;
}
CAFFE_ENFORCE_GE(axis_, 0);
requantization_params_.resize(InputSize());
}
template <typename T>
bool ConcatDNNLowPOp<T>::RunOnDevice() {
GetQuantizationParameters_();
auto* output = OutputTensorCPU_(0);
Tensor* split = nullptr;
int* axis_data = nullptr;
if (OutputSize() >= 2) {
split = this->template Output<Tensor>(1, CPU);
split->Resize(vector<int64_t>(1, InputSize()));
axis_data = split->template mutable_data<int>();
}
auto& input_zero = InputTensorCPU_(0);
CAFFE_ENFORCE_LT(
axis_,
input_zero.ndim() + (add_axis_ ? 1 : 0),
"Axis not in input ndim range.");
for (int i = 1; i < InputSize(); ++i) {
CAFFE_ENFORCE(
InputTensorCPU_(i).dtype() == input_zero.dtype(),
"All inputs must have the same type, expected: ",
input_zero.dtype().name(),
" but got: ",
InputTensorCPU_(i).dtype().name(),
" for input: ",
i);
}
int before = 1, after = 1;
vector<int64_t> output_dims(input_zero.sizes().vec());
for (int i = 0; i < input_zero.ndim(); ++i) {
if (i == axis_ && !add_axis_) {
continue;
}
int dim = input_zero.dim32(i);
if (i < axis_) {
before *= dim;
} else { // i > axis_ || i == axis_ && add_axis_
after *= dim;
}
// check the input dims are compatible.
for (int j = 1; j < InputSize(); ++j) {
int dim_j = InputTensorCPU_(j).dim32(i);
CAFFE_ENFORCE(
dim == dim_j,
"Expect dimension = ",
dim,
" got ",
dim_j,
" at axis = ",
i,
" for input: ",
j,
". The input tensors can only have different dimensions "
"when arg 'add_axis' = 0 and along the axis = ",
axis_,
" <",
InputTensorCPU_(0).sizes(),
"> vs <",
InputTensorCPU_(j).sizes(),
">.");
}
}
int output_channels = 0;
for (int i = 0; i < InputSize(); ++i) {
auto dim = add_axis_ ? 1 : InputTensorCPU_(i).dim32(axis_);
if (axis_data) {
axis_data[i] = dim;
}
output_channels += dim;
}
if (add_axis_) {
output_dims.insert(output_dims.begin() + axis_, output_channels);
} else {
output_dims[axis_] = output_channels;
}
output->Resize(output_dims);
size_t output_offset = 0;
char* output_data = reinterpret_cast<char*>(GetQuantizedOutputData_());
for (int i = 0; i < InputSize(); ++i) {
auto& input = InputTensorCPU_(i);
auto axis_dim = add_axis_ ? 1 : input.dim32(axis_);
vector<T> input_temp(input.numel());
#ifdef _OPENMP
#pragma omp parallel
#endif
{
int nthreads = dnnlowp_get_num_threads();
int tid = dnnlowp_get_thread_num();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int before_begin, before_end;
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int after_begin, after_end;
Get1DPartitionOf2D(
before,
axis_dim * after,
nthreads,
tid,
&before_begin,
&before_end,
&after_begin,
&after_end);
int j_begin = before_begin * axis_dim * after + after_begin;
int j_end = (before_end - 1) * axis_dim * after + after_end;
if (InputTensorCPU_(i).template IsType<T>()) {
const T* input_data = input.template data<T>();
for (int j = j_begin; j < j_end; ++j) {
input_temp[j] = fbgemm::Requantize<T>(
input_data[j] - in_qparams_[i].zero_point,
requantization_params_[i]);
}
} else {
fbgemm::Quantize<T>(
input.template data<float>() + j_begin,
input_temp.data() + j_begin,
j_end - j_begin,
out_qparams_);
}
math::CopyMatrix<CPUContext>(
sizeof(T),
before_end - before_begin,
after_end - after_begin,
input_temp.data() + before_begin * axis_dim * after + after_begin,
axis_dim * after,
output_data + output_offset + before_begin * output_channels * after +
after_begin * sizeof(T),
output_channels * after,
&context_,
input_zero.dtype().copy());
}
output_offset += axis_dim * after * sizeof(T);
}
RunOnDeviceEpilogue_();
return true;
}
template <typename T>
void ConcatDNNLowPOp<T>::GetQuantizationParameters_() {
using namespace dnnlowp;
for (int i = 0; i < InputSize(); ++i) {
in_qparams_[i] =
GetInputTensorQuantizationParamsOf(this, i, qfactory_.get());
}
GetOutputQuantizationParams_();
for (int i = 0; i < InputSize(); ++i) {
float real_multiplier = in_qparams_[i].scale / out_qparams_.scale;
requantization_params_[i] = qfactory_->ChooseRequantizationMultiplier(
real_multiplier, out_qparams_);
}
}
REGISTER_CPU_OPERATOR_WITH_ENGINE(Concat, DNNLOWP, ConcatDNNLowPOp<uint8_t>);
REGISTER_CPU_OPERATOR_WITH_ENGINE(
Int8Concat,
DNNLOWP,
ConcatDNNLowPOp<uint8_t>);
} // namespace caffe2
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