diff options
Diffstat (limited to 'src/core/CL/cl_kernels/qlstm_layer_normalization.cl')
-rw-r--r-- | src/core/CL/cl_kernels/qlstm_layer_normalization.cl | 260 |
1 files changed, 0 insertions, 260 deletions
diff --git a/src/core/CL/cl_kernels/qlstm_layer_normalization.cl b/src/core/CL/cl_kernels/qlstm_layer_normalization.cl deleted file mode 100644 index 08f0b53632..0000000000 --- a/src/core/CL/cl_kernels/qlstm_layer_normalization.cl +++ /dev/null @@ -1,260 +0,0 @@ -/* - * 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) */
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