| /* |
| * 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) */ |