/* * Copyright (c) 2020 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "helpers_asymm.h" #if VEC_SIZE == 2 #define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 2) #define PERFORM_REDUCTION_IMPL(type) \ inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 2) sum) \ { \ sum.s0 += sum.s1; \ return sum.s0; \ } #elif VEC_SIZE == 4 #define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 4) #define PERFORM_REDUCTION_IMPL(type) \ inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 4) sum) \ { \ sum.s01 += sum.s23; \ sum.s0 += sum.s1; \ return sum.s0; \ } #elif VEC_SIZE == 8 #define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 8) #define PERFORM_REDUCTION_IMPL(type) \ inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 8) sum) \ { \ sum.s0123 += sum.s4567; \ sum.s01 += sum.s23; \ sum.s0 += sum.s1; \ return sum.s0; \ } #else /* VEC_SIZE DEFAULT */ #define VEC_SIZE 16 #define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 16) #define PERFORM_REDUCTION_IMPL(type) \ inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 16) sum) \ { \ sum.s01234567 += sum.s89abcdef; \ sum.s0123 += sum.s4567; \ sum.s01 += sum.s23; \ sum.s0 += sum.s1; \ return sum.s0; \ } #endif /* VEC_SIZE END */ #define PERFORM_REDUCTION_STR(input, type) perform_reduction_##type(input) #define PERFORM_REDUCTION(input, type) PERFORM_REDUCTION_STR(input, type) PERFORM_REDUCTION_IMPL(int) PERFORM_REDUCTION_IMPL(long) /** Compute quantized multiplier and shift for the inverse square root of input. * Using 3-bit fixed point and 5 iteration of Newton-Raphson method. * * @param[in] in Input to use * @param[in] reverse_shift -1 to reverse the shift direction * * @return: * .s0 Quantized multiplier for inverse square root * .s1 Shift for inverse square root * */ inline int2 get_invsqrt_quantized_multiplier_exp(int in, int reverse_shift) { int2 stddev_inv; int stddev_inv_multiplier = INT_MAX; int stddev_inv_shift = 0; int input = in; if(input <= 1) { stddev_inv.s0 = stddev_inv_multiplier; stddev_inv.s1 = stddev_inv_shift; return stddev_inv; } stddev_inv_shift = 11; while(input >= (1 << 29)) { input /= 4; ++stddev_inv_shift; } const unsigned int max_left_shift_bits = clz(input) - 1; const unsigned int max_left_shift_bits_pairs = max_left_shift_bits / 2; const unsigned int left_shift_bit_pairs = max_left_shift_bits_pairs - 1; stddev_inv_shift -= left_shift_bit_pairs; input <<= 2 * left_shift_bit_pairs; typedef int FixedPointRawType; const unsigned int fixedpoint_position = 3; const unsigned int fixedpoint_int_position = sizeof(FixedPointRawType) * 8 - 1 - fixedpoint_position; typedef FixedPointRawType FixedPoint3; typedef FixedPointRawType FixedPoint0; const FixedPoint3 fixedpoint_input = (input >> 1); const FixedPoint3 fixedpoint_half_input = ASYMM_ROUNDING_DIVIDE_BY_POW2(fixedpoint_input, 1, 1); const FixedPoint3 fixedpoint_half_three = (0x1 << fixedpoint_int_position) + (0x1 << (fixedpoint_int_position - 1)); FixedPoint3 x = 0x1 << fixedpoint_int_position; const int num_iteration = 5; for(int i = 0; i < num_iteration; i++) { int x3 = ASYMM_RESCALE(ASYMM_MULT(ASYMM_MULT(x, x, 1), x, 1), 9, fixedpoint_position, 1); x = ASYMM_RESCALE(ASYMM_MULT(fixedpoint_half_three, x, 1) - ASYMM_MULT(fixedpoint_half_input, x3, 1), 6, fixedpoint_position, 1); } const FixedPoint0 fixedpoint_half_sqrt_2 = 1518500250; x = ASYMM_MULT(fixedpoint_half_sqrt_2, x, 1); stddev_inv_multiplier = x; if(stddev_inv_shift < 0) { stddev_inv_multiplier <<= -stddev_inv_shift; stddev_inv_shift = 0; } stddev_inv_shift *= reverse_shift; stddev_inv.s0 = stddev_inv_multiplier; stddev_inv.s1 = stddev_inv_shift; return stddev_inv; } #if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(WIDTH) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT) /** This function implements QLSTM layer normalization. * * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float * @attention Width of the input tensor should be passed using the -DWIDTH compile flag, e.g. -DWIDTH=16 * * @param[in] input_ptr Pointer to the first source tensor. Supported data types: QSYMM16 * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes) * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes) * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor * @param[in] weight_ptr Pointer to the weight tensor. Supported data type: same as @p input_ptr * @param[in] weight_stride_x Stride of the weight tensor in X dimension (in bytes) * @param[in] weight_step_x weight_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weight_offset_first_element_in_bytes The offset of the first element in the weight tensor * @param[in] bias_ptr Pointer to the bias tensor. Supported data type: S32 * @param[in] bias_stride_x Stride of the bias tensor in X dimension (in bytes) * @param[in] bias_step_x bias_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] bias_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void qlstm_layer_normalization( IMAGE_DECLARATION(input), VECTOR_DECLARATION(weight), VECTOR_DECLARATION(bias), IMAGE_DECLARATION(output)) { // Get pixels pointer Image input = CONVERT_TO_IMAGE_STRUCT(input); Vector weight = CONVERT_TO_VECTOR_STRUCT(weight); Vector bias = CONVERT_TO_VECTOR_STRUCT(bias); Image output = CONVERT_TO_IMAGE_STRUCT(output); VEC_DATA_TYPE(int, VEC_SIZE) sum = 0; VEC_DATA_TYPE(long, VEC_SIZE) sum_sq = 0; // Calculate partial sum int i = 0; for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE) { // Load data VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&input, i, 0)); sum += CONVERT(data, VEC_DATA_TYPE(int, VEC_SIZE)); sum_sq += CONVERT(data, VEC_DATA_TYPE(long, VEC_SIZE)) * CONVERT(data, VEC_DATA_TYPE(long, VEC_SIZE)); } // Perform reduction sum.s0 = PERFORM_REDUCTION(sum, int); sum_sq.s0 = PERFORM_REDUCTION(sum_sq, long); // Left-overs loop for(; i < WIDTH; ++i) { DATA_TYPE data = *((__global DATA_TYPE *)offset(&input, i, 0)); sum.s0 += CONVERT(data, int); sum_sq.s0 += CONVERT(data, long) * CONVERT(data, long); } int temp = 0x100000 / WIDTH; int mean = (int)(sum.s0 * 1024 / WIDTH); int var2 = ((sum_sq.s0 * (long)temp) - ((long)mean * (long)mean)) / 0x100000; int2 stddev_inv = get_invsqrt_quantized_multiplier_exp(var2, -1); i = 0; for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE) { VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&input, i, 0)); VEC_DATA_TYPE(int, VEC_SIZE) res = CONVERT(data, VEC_DATA_TYPE(int, VEC_SIZE)) * 1024 - mean; res = multiply_by_quantized_multiplier(res, stddev_inv.s0, stddev_inv.s1); VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) w = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)vector_offset(&weight, i)); res = res * CONVERT(w, VEC_DATA_TYPE(int, VEC_SIZE)); res = res + VLOAD(VEC_SIZE)(0, (__global int *)vector_offset(&bias, i)); // Due to different rounding scheme, we might need to revisit in the future: res = select(res - 512, res + 512, res > 0) / 1024; res = (res + 512) >> 10; res = multiply_by_quantized_multiplier(res, OUTPUT_MULTIPLIER, OUTPUT_SHIFT + 12); #if defined(MIN_BOUND) res = max(res, (VEC_DATA_TYPE(int, VEC_SIZE))MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, (VEC_DATA_TYPE(int, VEC_SIZE))MAX_BOUND); #endif // defined(MAX_BOUND) VSTORE(VEC_SIZE) (CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), 0, (__global DATA_TYPE *)offset(&output, i, 0)); } for(; i < WIDTH; ++i) { DATA_TYPE data = *((__global DATA_TYPE *)offset(&input, i, 0)); int res = (int)data * 1024 - mean; res = MULTIPLY_BY_QUANTIZED_MULTIPLIER(res, stddev_inv.s0, stddev_inv.s1, 1); DATA_TYPE w = *((__global DATA_TYPE *)vector_offset(&weight, i)); res = res * (int)w; int b = *((__global int *)vector_offset(&bias, i)); res = res + b; // Due to different rounding scheme, we might need to revisit in the future: res = select(res - 512, res + 512, res > 0) / 1024; res = (res + 512) >> 10; res = MULTIPLY_BY_QUANTIZED_MULTIPLIER(res, OUTPUT_MULTIPLIER, OUTPUT_SHIFT + 12, 1); #if defined(MIN_BOUND) res = max(res, MIN_BOUND); #endif // defined(MIN_BOUND) #if defined(MAX_BOUND) res = min(res, MAX_BOUND); #endif // defined(MAX_BOUND) *((__global DATA_TYPE *)offset(&output, i, 0)) = (DATA_TYPE)res; } } #endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(WIDTH) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT) */