1/* 2 * Copyright (c) 2020-2021 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24#include "helpers_asymm.h" 25 26#if VEC_SIZE == 2 27#define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 2) 28#define PERFORM_REDUCTION_IMPL(type) \ 29 inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 2) sum) \ 30 { \ 31 sum.s0 += sum.s1; \ 32 return sum.s0; \ 33 } 34#elif VEC_SIZE == 4 35#define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 4) 36#define PERFORM_REDUCTION_IMPL(type) \ 37 inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 4) sum) \ 38 { \ 39 sum.s01 += sum.s23; \ 40 sum.s0 += sum.s1; \ 41 return sum.s0; \ 42 } 43#elif VEC_SIZE == 8 44#define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 8) 45#define PERFORM_REDUCTION_IMPL(type) \ 46 inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 8) sum) \ 47 { \ 48 sum.s0123 += sum.s4567; \ 49 sum.s01 += sum.s23; \ 50 sum.s0 += sum.s1; \ 51 return sum.s0; \ 52 } 53#else /* VEC_SIZE DEFAULT */ 54#define VEC_SIZE 16 55#define multiply_by_quantized_multiplier(input, qmul, shift) MULTIPLY_BY_QUANTIZED_MULTIPLIER(input, qmul, shift, 16) 56#define PERFORM_REDUCTION_IMPL(type) \ 57 inline VEC_DATA_TYPE(type, 1) perform_reduction_##type(VEC_DATA_TYPE(type, 16) sum) \ 58 { \ 59 sum.s01234567 += sum.s89abcdef; \ 60 sum.s0123 += sum.s4567; \ 61 sum.s01 += sum.s23; \ 62 sum.s0 += sum.s1; \ 63 return sum.s0; \ 64 } 65#endif /* VEC_SIZE END */ 66 67#define PERFORM_REDUCTION_STR(input, type) perform_reduction_##type(input) 68#define PERFORM_REDUCTION(input, type) PERFORM_REDUCTION_STR(input, type) 69 70PERFORM_REDUCTION_IMPL(int) 71PERFORM_REDUCTION_IMPL(long) 72 73/** Compute quantized multiplier and shift for the inverse square root of input. 74 * Using 3-bit fixed point and 5 iteration of Newton-Raphson method. 75 * 76 * @param[in] in Input to use 77 * @param[in] reverse_shift -1 to reverse the shift direction 78 * 79 * @return: 80 * .s0 Quantized multiplier for inverse square root 81 * .s1 Shift for inverse square root 82 * 83 */ 84inline int2 get_invsqrt_quantized_multiplier_exp(int in, int reverse_shift) 85{ 86 int2 stddev_inv; 87 int stddev_inv_multiplier = INT_MAX; 88 int stddev_inv_shift = 0; 89 int input = in; 90 if(input <= 1) 91 { 92 stddev_inv.s0 = stddev_inv_multiplier; 93 stddev_inv.s1 = stddev_inv_shift; 94 return stddev_inv; 95 } 96 97 stddev_inv_shift = 11; 98 while(input >= (1 << 29)) 99 { 100 input /= 4; 101 ++stddev_inv_shift; 102 } 103 104 const unsigned int max_left_shift_bits = clz(input) - 1; 105 const unsigned int max_left_shift_bits_pairs = max_left_shift_bits / 2; 106 const unsigned int left_shift_bit_pairs = max_left_shift_bits_pairs - 1; 107 stddev_inv_shift -= left_shift_bit_pairs; 108 input <<= 2 * left_shift_bit_pairs; 109 110 typedef int FixedPointRawType; 111 const unsigned int fixedpoint_position = 3; 112 const unsigned int fixedpoint_int_position = sizeof(FixedPointRawType) * 8 - 1 - fixedpoint_position; 113 typedef FixedPointRawType FixedPoint3; 114 typedef FixedPointRawType FixedPoint0; 115 116 const FixedPoint3 fixedpoint_input = (input >> 1); 117 const FixedPoint3 fixedpoint_half_input = ASYMM_ROUNDING_DIVIDE_BY_POW2(fixedpoint_input, 1, 1); 118 const FixedPoint3 fixedpoint_half_three = (0x1 << fixedpoint_int_position) + (0x1 << (fixedpoint_int_position - 1)); 119 FixedPoint3 x = 0x1 << fixedpoint_int_position; 120 121 const int num_iteration = 5; 122 for(int i = 0; i < num_iteration; i++) 123 { 124 int x3 = ASYMM_RESCALE(ASYMM_MULT(ASYMM_MULT(x, x, 1), x, 1), 9, fixedpoint_position, 1); 125 x = ASYMM_RESCALE(ASYMM_MULT(fixedpoint_half_three, x, 1) - ASYMM_MULT(fixedpoint_half_input, x3, 1), 6, fixedpoint_position, 1); 126 } 127 const FixedPoint0 fixedpoint_half_sqrt_2 = 1518500250; 128 x = ASYMM_MULT(fixedpoint_half_sqrt_2, x, 1); 129 stddev_inv_multiplier = x; 130 if(stddev_inv_shift < 0) 131 { 132 stddev_inv_multiplier <<= -stddev_inv_shift; 133 stddev_inv_shift = 0; 134 } 135 stddev_inv_shift *= reverse_shift; 136 137 stddev_inv.s0 = stddev_inv_multiplier; 138 stddev_inv.s1 = stddev_inv_shift; 139 return stddev_inv; 140} 141 142#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(WIDTH) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT) 143/** This function implements QLSTM layer normalization. 144 * 145 * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 146 * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float 147 * @attention Width of the input tensor should be passed using the -DWIDTH compile flag, e.g. -DWIDTH=16 148 * 149 * @param[in] input_ptr Pointer to the first source tensor. Supported data types: QSYMM16 150 * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes) 151 * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) 152 * @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes) 153 * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) 154 * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor 155 * @param[in] weight_ptr Pointer to the weight tensor. Supported data type: same as @p input_ptr 156 * @param[in] weight_stride_x Stride of the weight tensor in X dimension (in bytes) 157 * @param[in] weight_step_x weight_stride_x * number of elements along X processed per workitem(in bytes) 158 * @param[in] weight_offset_first_element_in_bytes The offset of the first element in the weight tensor 159 * @param[in] bias_ptr Pointer to the bias tensor. Supported data type: S32 160 * @param[in] bias_stride_x Stride of the bias tensor in X dimension (in bytes) 161 * @param[in] bias_step_x bias_stride_x * number of elements along X processed per workitem(in bytes) 162 * @param[in] bias_offset_first_element_in_bytes The offset of the first element in the biases tensor 163 * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr 164 * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) 165 * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) 166 * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) 167 * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) 168 * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor 169 */ 170__kernel void qlstm_layer_normalization( 171 IMAGE_DECLARATION(input), 172 VECTOR_DECLARATION(weight), 173 VECTOR_DECLARATION(bias), 174 IMAGE_DECLARATION(output)) 175{ 176 // Get pixels pointer 177 Image input = CONVERT_TO_IMAGE_STRUCT(input); 178 Vector weight = CONVERT_TO_VECTOR_STRUCT(weight); 179 Vector bias = CONVERT_TO_VECTOR_STRUCT(bias); 180 Image output = CONVERT_TO_IMAGE_STRUCT(output); 181 182 VEC_DATA_TYPE(int, VEC_SIZE) 183 sum = 0; 184 VEC_DATA_TYPE(long, VEC_SIZE) 185 sum_sq = 0; 186 // Calculate partial sum 187 int i = 0; 188 for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE) 189 { 190 // Load data 191 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) 192 data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&input, i, 0)); 193 194 sum += CONVERT(data, VEC_DATA_TYPE(int, VEC_SIZE)); 195 sum_sq += CONVERT(data, VEC_DATA_TYPE(long, VEC_SIZE)) * CONVERT(data, VEC_DATA_TYPE(long, VEC_SIZE)); 196 } 197 // Perform reduction 198 sum.s0 = PERFORM_REDUCTION(sum, int); 199 sum_sq.s0 = PERFORM_REDUCTION(sum_sq, long); 200 201 // Left-overs loop 202 for(; i < WIDTH; ++i) 203 { 204 DATA_TYPE data = *((__global DATA_TYPE *)offset(&input, i, 0)); 205 206 sum.s0 += CONVERT(data, int); 207 sum_sq.s0 += CONVERT(data, long) * CONVERT(data, long); 208 } 209 210 int temp = 0x100000 / WIDTH; 211 int mean = (int)(sum.s0 * 1024 / WIDTH); 212 int var2 = ((sum_sq.s0 * (long)temp) - ((long)mean * (long)mean)) / 0x100000; 213 int2 stddev_inv = get_invsqrt_quantized_multiplier_exp(var2, -1); 214 215 i = 0; 216 for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE) 217 { 218 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) 219 data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&input, i, 0)); 220 VEC_DATA_TYPE(int, VEC_SIZE) 221 res = CONVERT(data, VEC_DATA_TYPE(int, VEC_SIZE)) * 1024 - mean; 222 res = multiply_by_quantized_multiplier(res, stddev_inv.s0, stddev_inv.s1); 223 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) 224 w = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)vector_offset(&weight, i)); 225 res = res * CONVERT(w, VEC_DATA_TYPE(int, VEC_SIZE)); 226 res = res + VLOAD(VEC_SIZE)(0, (__global int *)vector_offset(&bias, i)); 227 // Due to different rounding scheme, we might need to revisit in the future: res = select(res - 512, res + 512, res > 0) / 1024; 228 res = (res + 512) >> 10; 229 res = multiply_by_quantized_multiplier(res, OUTPUT_MULTIPLIER, OUTPUT_SHIFT + 12); 230#if defined(MIN_BOUND) 231 res = max(res, (VEC_DATA_TYPE(int, VEC_SIZE))MIN_BOUND); 232#endif // defined(MIN_BOUND) 233#if defined(MAX_BOUND) 234 res = min(res, (VEC_DATA_TYPE(int, VEC_SIZE))MAX_BOUND); 235#endif // defined(MAX_BOUND) 236 VSTORE(VEC_SIZE) 237 (CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), 0, (__global DATA_TYPE *)offset(&output, i, 0)); 238 } 239 for(; i < WIDTH; ++i) 240 { 241 DATA_TYPE data = *((__global DATA_TYPE *)offset(&input, i, 0)); 242 int res = (int)data * 1024 - mean; 243 res = MULTIPLY_BY_QUANTIZED_MULTIPLIER(res, stddev_inv.s0, stddev_inv.s1, 1); 244 DATA_TYPE w = *((__global DATA_TYPE *)vector_offset(&weight, i)); 245 res = res * (int)w; 246 int b = *((__global int *)vector_offset(&bias, i)); 247 res = res + b; 248 // Due to different rounding scheme, we might need to revisit in the future: res = select(res - 512, res + 512, res > 0) / 1024; 249 res = (res + 512) >> 10; 250 res = MULTIPLY_BY_QUANTIZED_MULTIPLIER(res, OUTPUT_MULTIPLIER, OUTPUT_SHIFT + 12, 1); 251#if defined(MIN_BOUND) 252 res = max(res, MIN_BOUND); 253#endif // defined(MIN_BOUND) 254#if defined(MAX_BOUND) 255 res = min(res, MAX_BOUND); 256#endif // defined(MAX_BOUND) 257 *((__global DATA_TYPE *)offset(&output, i, 0)) = (DATA_TYPE)res; 258 } 259} 260#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(WIDTH) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT) */