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Diffstat (limited to 'test/RequantizeOnlyTest.cc')
| -rw-r--r-- | test/RequantizeOnlyTest.cc | 169 |
1 files changed, 169 insertions, 0 deletions
diff --git a/test/RequantizeOnlyTest.cc b/test/RequantizeOnlyTest.cc new file mode 100644 index 0000000..2f73d49 --- /dev/null +++ b/test/RequantizeOnlyTest.cc @@ -0,0 +1,169 @@ +/* + * 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> qGranularityVals{ + 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(qGranularityVals))); // 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"; +} |