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#ifndef CAFFE2_OPERATORS_INT8_SWISH_OP_H_
#define CAFFE2_OPERATORS_INT8_SWISH_OP_H_
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor_int8.h"
#include "caffe2/operators/quantized/int8_utils.h"
namespace caffe2 {
namespace int8 {
namespace {
using namespace std;
void SwishFakeInt8NNPI(
const uint8_t* in,
uint8_t* out,
const int64_t N,
const float X_scale,
const int32_t X_offset,
const float Y_scale,
const int32_t Y_offset) {
const uint8_t max_val = std::numeric_limits<uint8_t>::max();
const uint8_t min_val = std::numeric_limits<uint8_t>::min();
float X_scale_fp32 = 1.0f / X_scale;
float deq_val = 0.0f;
float deq_swish = 0.0f;
int32_t quant_val = 0;
uint8_t result = 0;
for (const auto i : c10::irange(N)) {
deq_val = (static_cast<uint8_t>(in[i]) - X_offset) / X_scale_fp32;
deq_swish = deq_val / (1 + exp(-deq_val));
quant_val = round(deq_swish / Y_scale + Y_offset);
result = quant_val;
if (quant_val > max_val) {
result = max_val;
}
if (quant_val < min_val) {
result = min_val;
}
out[i] = static_cast<uint8_t>(result);
}
}
} // namespace
class SwishInt8NNPIOp final : public Operator<CPUContext> {
public:
using Operator<CPUContext>::Operator;
template <class... Args>
explicit SwishInt8NNPIOp(Args&&... args)
: Operator<CPUContext>(std::forward<Args>(args)...) {}
bool RunOnDevice() override {
const auto& X = Inputs()[0]->template Get<Int8TensorCPU>();
auto* Y = Outputs()[0]->template GetMutable<Int8TensorCPU>();
Y->t.ResizeLike(X.t);
int32_t Y_offset_ =
this->template GetSingleArgument<int>("Y_zero_point", 0);
auto Y_scale_ = this->template GetSingleArgument<float>("Y_scale", 1);
Y->scale = Y_scale_;
Y->zero_point = Y_offset_;
SwishFakeInt8NNPI(
X.t.data<uint8_t>(),
Y->t.mutable_data<uint8_t>(),
X.t.numel(),
X.scale,
X.zero_point,
Y_scale_,
Y_offset_);
return true;
}
};
} // namespace int8
} // namespace caffe2
#endif // CAFFE2_OPERATORS_INT8_SWISH_OP_H_
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