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/*
* Copyright (c) Facebook, Inc. and its affiliates.
* All rights reserved.
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <algorithm>
#include <functional>
#include <iostream>
#include <random>
#include <stdexcept>
#include <string>
#include <gtest/gtest.h>
#include "TestUtils.h"
#include "bench/BenchUtils.h"
#include "fbgemm/Fbgemm.h"
using namespace std;
using namespace fbgemm;
vector<QuantizationGranularity> qGranularityValsLocal{
QuantizationGranularity::TENSOR,
QuantizationGranularity::OUT_CHANNEL};
namespace {
// tuple represents #rows, #cols, fuse_relu, quantization_granularity, bias_type
class FloatRequantizeTest
: public testing::TestWithParam<
tuple<int, int, bool, QuantizationGranularity>> {};
}; // namespace
INSTANTIATE_TEST_CASE_P(
InstantiationName,
FloatRequantizeTest,
::testing::Combine(
::testing::ValuesIn({1, 2, 3, 4}), // number of rows
::testing::ValuesIn(
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16, 20, 32}), // number of
// cols
::testing::Bool(), // fuse relu
::testing::ValuesIn(qGranularityValsLocal))); // requantization granularity
/**
* Test for float bias
*/
TEST_P(FloatRequantizeTest, floatBiasTest) {
int rows, cols;
bool fuse_relu;
QuantizationGranularity q_gran;
tie(rows, cols, fuse_relu, q_gran) = GetParam();
int numElements = rows * cols;
aligned_vector<float> act_times_w_scale(cols);
randFill<float>(act_times_w_scale, -8, 8);
float out_scale = 2.0f;
aligned_vector<float> C_multiplier(cols);
transform(
act_times_w_scale.begin(),
act_times_w_scale.end(),
C_multiplier.begin(),
[&out_scale](float i) { return i / out_scale; });
aligned_vector<int32_t> Bint8_zero_point(cols);
randFill<int32_t>(Bint8_zero_point, -8, 8);
aligned_vector<int32_t> row_offset_buf(rows);
randFill<int32_t>(row_offset_buf, -8, 8);
aligned_vector<int32_t> col_offsets(cols);
randFill<int32_t>(col_offsets, -8, 8);
// quantized bias
aligned_vector<int32_t> bias_q(cols);
randFill<int32_t>(bias_q, -8, 8);
// floating point bias
aligned_vector<float> bias_f(cols);
if (q_gran == QuantizationGranularity::TENSOR) {
transform(
bias_q.begin(),
bias_q.end(),
bias_f.begin(),
[&act_times_w_scale](float i) { return i * act_times_w_scale[0]; });
} else if (q_gran == QuantizationGranularity::OUT_CHANNEL) {
transform(
act_times_w_scale.begin(),
act_times_w_scale.end(),
bias_q.begin(),
bias_f.begin(),
multiplies<float>());
} else {
FAIL();
}
aligned_vector<int32_t> input(numElements);
randFill<int32_t>(input, -8, 8);
aligned_vector<uint8_t> output_q_bias(numElements);
aligned_vector<uint8_t> output_f_bias(numElements);
int32_t C_zero_point = 3;
int32_t Aint8_zero_point = 3;
block_type_t block{0, rows, 0, cols};
DoNothing<> doNothingObj{};
#define TESTCODE(FUSE_RELU, Q_GRAN) \
ReQuantizeOutput<FUSE_RELU, Q_GRAN> reqObj_q( \
doNothingObj, \
C_multiplier.data(), \
C_zero_point, \
Aint8_zero_point, \
Bint8_zero_point.data(), \
row_offset_buf.data(), \
col_offsets.data(), \
bias_q.data(), \
cols); \
ReQuantizeOutput<FUSE_RELU, Q_GRAN, float> reqObj_f( \
doNothingObj, \
C_multiplier.data(), \
C_zero_point, \
Aint8_zero_point, \
Bint8_zero_point.data(), \
row_offset_buf.data(), \
col_offsets.data(), \
bias_f.data(), \
cols, \
1, \
act_times_w_scale.data()); \
reqObj_q.f<inst_set_t::avx2>( \
output_q_bias.data(), input.data(), block, cols, cols); \
reqObj_f.f<inst_set_t::avx2>( \
output_f_bias.data(), input.data(), block, cols, cols);
if (fuse_relu) {
if (q_gran == QuantizationGranularity::TENSOR) {
TESTCODE(true, QuantizationGranularity::TENSOR)
} else if (q_gran == QuantizationGranularity::OUT_CHANNEL) {
TESTCODE(true, QuantizationGranularity::OUT_CHANNEL)
} else {
FAIL();
}
} else {
if (q_gran == QuantizationGranularity::TENSOR) {
TESTCODE(false, QuantizationGranularity::TENSOR)
} else if (q_gran == QuantizationGranularity::OUT_CHANNEL) {
TESTCODE(false, QuantizationGranularity::OUT_CHANNEL)
} else {
FAIL();
}
}
#undef TESTCODE
ASSERT_EQ(output_q_bias, output_f_bias)
<< "Requantization with quantized bias and float bias differs";
}
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