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/*
* 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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