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af9c3a3652
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37260 List of supported types here: https://en.cppreference.com/w/cpp/numeric/random/uniform_int_distribution Test Plan: CircleCI green, test compiles and passes on msvc. Reviewed By: malfet Differential Revision: D21237280 fbshipit-source-id: 51b09b87511e35bfe8a57ecd48ed772d587dba9b
120 lines
3.8 KiB
C++
120 lines
3.8 KiB
C++
#ifndef CAFFE2_INT8_TEST_UTILS_H_
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#define CAFFE2_INT8_TEST_UTILS_H_
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#include "caffe2/core/common.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.h"
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#include "caffe2/core/tensor_int8.h"
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#include <array>
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#include <cmath>
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#include <random>
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#include "gtest/gtest.h"
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namespace caffe2 {
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// for quantized Add, the error shouldn't exceed 2 * scale
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inline float addErrorTolerance(float scale) {
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return 2 * scale;
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}
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inline std::unique_ptr<int8::Int8TensorCPU> q(
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const std::vector<int64_t>& dims) {
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auto r = std::make_unique<int8::Int8TensorCPU>();
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r->scale = 0.01;
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r->zero_point = static_cast<int32_t>(std::numeric_limits<uint8_t>::max()) / 2;
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ReinitializeTensor(&r->t, dims, at::dtype<uint8_t>().device(CPU));
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std::random_device rd;
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std::mt19937 gen(rd());
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std::uniform_int_distribution<uint32_t> dis{
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0, std::numeric_limits<uint8_t>::max()};
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for (auto i = 0; i < r->t.numel(); ++i) {
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r->t.mutable_data<uint8_t>()[i] = dis(gen);
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}
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return r;
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}
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inline std::unique_ptr<int8::Int8TensorCPU> biasq(
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const std::vector<int64_t>& dims,
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double scale) {
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auto r = std::make_unique<int8::Int8TensorCPU>();
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r->scale = scale;
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r->zero_point = 0;
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r->t.Resize(dims);
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std::random_device rd;
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std::mt19937 gen(rd());
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std::uniform_real_distribution<float> dis(-1, 1);
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for (auto i = 0; i < r->t.numel(); ++i) {
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r->t.mutable_data<int32_t>()[i] =
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static_cast<int32_t>(dis(gen) / scale + r->zero_point);
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}
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return r;
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}
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inline std::unique_ptr<TensorCPU> dq(const int8::Int8TensorCPU& XQ) {
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auto r = std::make_unique<Tensor>(CPU);
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r->Resize(XQ.t.sizes());
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for (auto i = 0; i < r->numel(); ++i) {
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r->mutable_data<float>()[i] =
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(static_cast<int32_t>(XQ.t.data<uint8_t>()[i]) - XQ.zero_point) *
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XQ.scale;
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}
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return r;
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}
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inline std::unique_ptr<TensorCPU> biasdq(const int8::Int8TensorCPU& XQ) {
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auto r = std::make_unique<Tensor>(CPU);
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r->Resize(XQ.t.sizes());
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for (auto i = 0; i < r->numel(); ++i) {
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r->mutable_data<float>()[i] =
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(XQ.t.data<int32_t>()[i] - XQ.zero_point) * XQ.scale;
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}
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return r;
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}
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#define EXPECT_TENSOR_EQ(_YA, _YE) \
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do { \
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EXPECT_TRUE((_YA).sizes() == (_YE).sizes()); \
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for (auto i = 0; i < (_YA).numel(); ++i) { \
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EXPECT_FLOAT_EQ((_YA).data<float>()[i], (_YE).data<float>()[i]); \
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} \
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} while (0);
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#define EXPECT_TENSOR_APPROX_EQ(_YA, _YE, _tol) \
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do { \
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EXPECT_TRUE((_YA).sizes() == (_YE).sizes()); \
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for (auto i = 0; i < (_YA).numel(); ++i) { \
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EXPECT_NEAR((_YA).data<float>()[i], (_YE).data<float>()[i], (_tol)); \
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} \
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} while (0);
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inline void int8Copy(int8::Int8TensorCPU* dst, const int8::Int8TensorCPU& src) {
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dst->zero_point = src.zero_point;
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dst->scale = src.scale;
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dst->t.CopyFrom(src.t);
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}
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inline void add_input(
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const vector<int64_t>& shape,
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const vector<float>& values,
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const string& name,
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Workspace* ws) {
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// auto* t = ws->CreateBlob(name)->GetMutable<TensorCPU>();
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auto t = std::make_unique<Tensor>(CPU);
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t->Resize(shape);
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std::copy(values.begin(), values.end(), t->mutable_data<float>());
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BlobGetMutableTensor(ws->CreateBlob(name), CPU)->CopyFrom(*t);
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}
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inline int randomInt(int a, int b) {
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static std::random_device rd;
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static std::mt19937 gen(rd());
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return std::uniform_int_distribution<int>(a, b)(gen);
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}
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} // namespace caffe2
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#endif // CAFFE2_INT8_TEST_UTILS_H_
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