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Diffstat (limited to 'src/core/CL/cl_kernels/common/qlstm_layer_normalization.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/common/qlstm_layer_normalization.cl | 260 |
1 files changed, 260 insertions, 0 deletions
diff --git a/src/core/CL/cl_kernels/common/qlstm_layer_normalization.cl b/src/core/CL/cl_kernels/common/qlstm_layer_normalization.cl new file mode 100644 index 0000000000..4494dd8cec --- /dev/null +++ b/src/core/CL/cl_kernels/common/qlstm_layer_normalization.cl @@ -0,0 +1,260 @@ +/* + * Copyright (c) 2020-2021 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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