From 4370cffc7fb0da7fb486b9d06d24e16169521876 Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Fri, 7 Feb 2020 16:31:59 +0000 Subject: COMPMID-3034: Add NERequantizationLayerKernel Change-Id: I3f098c3c2c2031d8cbe7326eab88a4e78bda867f Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2704 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Sang-Hoon Park --- arm_compute/core/NEON/NEMath.h | 11 +++++- arm_compute/core/NEON/NEMath.inl | 14 ++++++- .../core/NEON/kernels/NEQuantizationLayerKernel.h | 12 +++--- arm_compute/core/QuantizationInfo.h | 44 ++++++++++++++++++++++ 4 files changed, 73 insertions(+), 8 deletions(-) (limited to 'arm_compute/core') diff --git a/arm_compute/core/NEON/NEMath.h b/arm_compute/core/NEON/NEMath.h index 54f8252250..3905f67e29 100644 --- a/arm_compute/core/NEON/NEMath.h +++ b/arm_compute/core/NEON/NEMath.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2019 ARM Limited. + * Copyright (c) 2016-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -173,6 +173,15 @@ float32x4x4_t convert_uint8x16_to_float32x4x4(const uint8x16_t &in); */ float32x4x4_t convert_int8x16_to_float32x4x4(const int8x16_t &in); +/** Converts to float32x4x4_t from the specified templated 16 elements vectors + * + * @param[in] in Vector of float to be converted + * + * @return Converted vector of float + */ +template +float32x4x4_t convert_to_float32x4x4(const T &in); + /** Converts from two float32x4x3_t to just one uint8x8x3_t * * @param[in] in1 First input vector of float to be converted diff --git a/arm_compute/core/NEON/NEMath.inl b/arm_compute/core/NEON/NEMath.inl index 5d8b82c281..49870d06a8 100644 --- a/arm_compute/core/NEON/NEMath.inl +++ b/arm_compute/core/NEON/NEMath.inl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2019 ARM Limited. + * Copyright (c) 2016-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -345,6 +345,18 @@ inline float32x4x4_t convert_int8x16_to_float32x4x4(const int8x16_t &in) return out; } +template <> +inline float32x4x4_t convert_to_float32x4x4(const uint8x16_t &in) +{ + return convert_uint8x16_to_float32x4x4(in); +} + +template <> +inline float32x4x4_t convert_to_float32x4x4(const int8x16_t &in) +{ + return convert_int8x16_to_float32x4x4(in); +} + inline void convert_float32x4x3_to_uint8x8x3(const float32x4x3_t &in1, const float32x4x3_t &in2, uint8x8x3_t &out) { out.val[0] = vqmovn_u16(vcombine_u16(vqmovn_u32(vcvtq_u32_f32(in1.val[0])), diff --git a/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h b/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h index 1a9b533640..087e767b73 100644 --- a/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -56,16 +56,16 @@ public: ~NEQuantizationLayerKernel() = default; /** Set the input, output. * - * @param[in] input Source tensor. The dimensions over the third will be interpreted as batches. Data types supported: F32/F16. - * @param[out] output Destination tensor with the same dimensions of input. Data types supported: QASYMM8/QASYMM16. + * @param[in] input Source tensor. The dimensions over the third will be interpreted as batches. Data types supported: QASYMM8/QASYMM8_SIGNED/F32/F16. + * @param[out] output Destination tensor with the same dimensions of input. Data types supported: QASYMM8/QASYMM8_SIGNED/QASYMM16. * * @note Output auto initialization is not supported by this kernel */ void configure(const ITensor *input, ITensor *output); /** Static function to check if given info will lead to a valid configuration of @ref NEQuantizationLayerKernel * - * @param[in] input Input tensor info. Data types supported: F32/F16. - * @param[in] output Output tensor info. Data types supported: QASYMM8/QASYMM16. + * @param[in] input Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F32/F16. + * @param[in] output Output tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/QASYMM16. * * @return a status */ @@ -80,7 +80,7 @@ private: * @param[in] window Region on which to execute the kernel. */ using QuantizationFunctionExecutorPtr = void (NEQuantizationLayerKernel::*)(const Window &window); - /** Function to apply QASYMM8 quantization on a tensor. + /** Function to apply QASYMM8 or QASYMM8_SIGNED quantization on a tensor. * * @param[in] window Region on which to execute the kernel. */ diff --git a/arm_compute/core/QuantizationInfo.h b/arm_compute/core/QuantizationInfo.h index 06ba665c6b..f859beb87a 100644 --- a/arm_compute/core/QuantizationInfo.h +++ b/arm_compute/core/QuantizationInfo.h @@ -516,5 +516,49 @@ inline float dequantize_qasymm16(uint16_t value, const QuantizationInfo &qinfo) { return dequantize_qasymm16(value, qinfo.uniform()); } + +/* + * In case of requantization of a quantized input tensor to an output tensor with another quantization + * instead of applying dequantization and then a quantization functions, we just compute new scale and + * offset. + * + * Assuming: + * - q_i as input quantized value + * - q_o as output quantized value + * - z_i as input quantization offset value + * - z_o as output quantization offset value + * - s_i as input quantization scale value + * - s_o as output quantization scale value + * - z_n as new quantization offset value + * - s_n as new quantization scale value + * + * q_o = ( q_i - z_i ) * s_i / s_o + z_o + * + * We can rewrite the formula as: + * + * q_o = ( q_i * s_i / s_o ) - z_i * s_i / s_o + z_o + * + * q_o = q_i / s_n + z_n + * + * Where: + * + * s_n = s_o / s_i + * + * z_n = - z_i * s_i / s_o + z_o + * + */ +inline UniformQuantizationInfo compute_requantization_scale_offset(const UniformQuantizationInfo &uqinfo_in, const UniformQuantizationInfo &uqinfo_out) +{ + float scale_to_apply = uqinfo_out.scale; + int32_t offset_to_apply = uqinfo_out.offset; + + scale_to_apply /= uqinfo_in.scale; + // In order to minimize flooring we convert the offset to a float, + // then compute the new offset in the float domain, + // finally we convert it back as int32_t + offset_to_apply -= static_cast(static_cast(uqinfo_in.offset) * uqinfo_in.scale / uqinfo_out.scale); + return UniformQuantizationInfo(scale_to_apply, offset_to_apply); +} + } // namespace arm_compute #endif /* ARM_COMPUTE_QUANTIZATION_INFO_H */ -- cgit v1.2.1