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1433160a36
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: malfet Differential Revision: D31705366 fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
54 lines
1.2 KiB
C++
54 lines
1.2 KiB
C++
#ifndef CAFFE2_OPERATORS_INT8_DEQUANTIZE_OP_H_
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#define CAFFE2_OPERATORS_INT8_DEQUANTIZE_OP_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/core/tensor_int8.h"
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#include "caffe2/operators/quantized/int8_utils.h"
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#include <c10/util/irange.h>
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namespace caffe2 {
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namespace int8 {
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namespace {
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void Int8Dequantize(
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const uint8_t* in,
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float* out,
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const int64_t N,
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const float X_scale,
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const int32_t X_offset) {
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for (const auto i : c10::irange(N)) {
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out[i] = (static_cast<int32_t>(in[i]) - X_offset) * X_scale;
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}
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}
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} // namespace
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class Int8DequantizeOp final : public Operator<CPUContext> {
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public:
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using Operator<CPUContext>::Operator;
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bool RunOnDevice() override {
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const auto& X = Inputs()[0]->template Get<Int8TensorCPU>();
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auto* Y = Output(0, X.t.sizes(), at::dtype<float>());
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int32_t X_offset = X.zero_point;
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auto X_scale = X.scale;
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Int8Dequantize(
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X.t.data<uint8_t>(),
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Y->mutable_data<float>(),
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X.t.numel(),
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X_scale,
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X_offset);
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return true;
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}
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};
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} // namespace int8
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_INT8_DEQUANTIZE_OP_H_
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