diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2019-11-21 14:10:25 +0000 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2019-11-27 10:56:10 +0000 |
commit | 448a81fcec04333364a1e3266d5081596d3a0477 (patch) | |
tree | bd5382a58fae39a8014157423a8ff339d39e14b9 /tests/validation/reference | |
parent | 449cbf9c20287fca9a56898cdc5821c061a66ce3 (diff) | |
download | ComputeLibrary-448a81fcec04333364a1e3266d5081596d3a0477.tar.gz |
COMPMID-2805: Add QASYMM8_SIGNED support in NEGEMMLowpOutputStage
Add support from requantizing down from S32 to Int8 with fixed point
requantization. This involves the following:
- Compute fixed point multiplication between each entry of input by
result_fixedpoint_multiplier
- Add bias to final result if bias tensor is not a nullptr
- Round to nearest division by a power-of-two using result_shift
- Add offset to each result
- Clamp the value between the specified min and max bounds
- Cast to int8 data type
Change-Id: I641b3fac0833c568d8565ccb859bbc561a24c17d
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2340
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/reference')
-rw-r--r-- | tests/validation/reference/GEMMLowp.cpp | 135 | ||||
-rw-r--r-- | tests/validation/reference/GEMMLowp.h | 19 |
2 files changed, 63 insertions, 91 deletions
diff --git a/tests/validation/reference/GEMMLowp.cpp b/tests/validation/reference/GEMMLowp.cpp index 08be4a5182..4529b91a48 100644 --- a/tests/validation/reference/GEMMLowp.cpp +++ b/tests/validation/reference/GEMMLowp.cpp @@ -39,6 +39,28 @@ namespace reference namespace { template <typename T> +struct DataTypeExtractor +{ + static DataType data_type() + { + DataType data_type = DataType::UNKNOWN; + if(std::is_same<T, int8_t>::value) + { + data_type = DataType::QASYMM8_SIGNED; + } + else if(std::is_same<T, uint8_t>::value) + { + data_type = DataType::QASYMM8; + } + else if(std::is_same<T, int16_t>::value) + { + data_type = DataType::QSYMM16; + } + return data_type; + } +}; + +template <typename T> void quantize_down_int32_to_uint8_scale(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<uint8_t> *dst, int32_t result_offset, std::vector<int32_t> result_mult_int, std::vector<int32_t> result_shift, int32_t min, int32_t max) { @@ -68,16 +90,16 @@ void quantize_down_int32_to_uint8_scale(const SimpleTensor<T> *in, const SimpleT } } -template <typename T> -void quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<uint8_t> *dst, std::vector<int32_t> result_fixedpoint_multiplier, - std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max) +template <typename TIn, typename TOut> +void quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max) { const int cols_in = in->shape().x(); const bool is_per_channel = result_fixedpoint_multiplier.size() > 1; for(int i = 0; i < in->num_elements(); ++i) { - int32_t result = (*in)[i]; + TIn result = (*in)[i]; if(bias != nullptr) { @@ -88,43 +110,15 @@ void quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> *in, const int32_t multiplier = (is_per_channel) ? result_fixedpoint_multiplier[i % cols_in] : result_fixedpoint_multiplier[0]; const int32_t shift = (is_per_channel) ? result_shift[i % cols_in] : result_shift[0]; - result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift); - result += result_offset_after_shift; - - // Bounded ReLu - if(min != max) + if(shift < 0) { - result = std::max(min, std::min(max, result)); - } - - (*dst)[i] = static_cast<uint8_t>(std::max(0, std::min(255, result))); - } -} - -template <typename T> -void quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<int16_t> *dst, int32_t result_fixedpoint_multiplier, int32_t result_shift, - int32_t min, int32_t max) -{ - const int cols_in = in->shape().x(); - - for(int i = 0; i < in->num_elements(); ++i) - { - int32_t result = (*in)[i]; - - if(bias != nullptr) - { - result += (*bias)[i % cols_in]; - } - - // Fixed point multiplication - if(result_shift < 0) - { - result = asymm_int_mult(result * (1 << (-result_shift)), result_fixedpoint_multiplier); + result = asymm_int_mult(result * (1 << (-shift)), multiplier); } else { - result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, result_fixedpoint_multiplier), result_shift); + result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift); } + result += result_offset_after_shift; // Bounded ReLu if(min != max) @@ -132,7 +126,8 @@ void quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> *in, result = std::max(min, std::min(max, result)); } - (*dst)[i] = static_cast<int16_t>(std::max(-32768, std::min(32767, result))); + (*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(), + std::min<TIn>(std::numeric_limits<TOut>::max(), result))); } } } // namespace @@ -219,59 +214,43 @@ SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTe return dst; } -template <typename T> -SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift, - int32_t result_offset_after_shift, int32_t min, int32_t max) +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift, + int32_t result_offset_after_shift, int32_t min, int32_t max) { - SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8); + SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type()); - quantize_down_int32_to_uint8_scale_by_fixedpoint<T>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); + quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); return dst; } -template <typename T> -SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, std::vector<int32_t> result_fixedpoint_multiplier, - std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max) -{ - SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8); - - quantize_down_int32_to_uint8_scale_by_fixedpoint<T>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); - - return dst; -} - -template <typename T> -SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min, - int32_t max) -{ - SimpleTensor<int16_t> dst(in.shape(), DataType::QSYMM16); - - quantize_down_int32_to_int16_scale_by_fixedpoint<T>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, min, max); - - return dst; -} - -template <typename T> -SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_fixedpoint_multiplier, int32_t result_shift, - int32_t min, int32_t max) +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max) { - SimpleTensor<int16_t> dst(in.shape(), DataType::QSYMM16); + SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type()); - quantize_down_int32_to_int16_scale_by_fixedpoint<T>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, min, max); + quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max); return dst; } -template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, - std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); -template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, - std::vector<int32_t> result_fixedpoint_multiplier, - std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); -template SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, int32_t result_fixedpoint_multiplier, int32_t result_shift, - int32_t min, int32_t max); -template SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_fixedpoint_multiplier, - int32_t result_shift, int32_t min, int32_t max); +template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); +template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, + std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); +template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); +template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, + std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); +template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); +template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, + std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max); template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int, std::vector<int32_t> result_shift, int32_t min, int32_t max); template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int, diff --git a/tests/validation/reference/GEMMLowp.h b/tests/validation/reference/GEMMLowp.h index 815527e1b7..7ff01ef611 100644 --- a/tests/validation/reference/GEMMLowp.h +++ b/tests/validation/reference/GEMMLowp.h @@ -52,20 +52,13 @@ template <typename T> SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, std::vector<int32_t> result_mult_int, std::vector<int32_t> result_shift, int32_t min = 0, int32_t max = 0); -template <typename T> -SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift, - int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0); - -template <typename T> -SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, std::vector<int32_t> result_fixedpoint_multiplier, - std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0); +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift, + int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0); -template <typename T> -SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, int32_t result_fixedpoint_multiplier, int32_t result_shift, - int32_t min, int32_t max); -template <typename T> -SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_fixedpoint_multiplier, - int32_t result_shift, int32_t min, int32_t max); +template <typename TIn, typename TOut> +SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier, + std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0); } // namespace reference } // namespace validation } // namespace test |