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review.mlplatform.org / ml / ComputeLibrary / 7891a73ef36f4ad7b71069b3c57694f85bb79454 / . / src / cpu / kernels / CpuGemmMatrixMultiplyKernel.cpp
blob: 93ae90436a7858537f78a398aecae3f2622074a1 [file] [log] [blame]
/*
* Copyright (c) 2017-2021 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 "src/cpu/kernels/CpuGemmMatrixMultiplyKernel.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "src/core/CPP/Validate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/core/utils/helpers/float_ops.h"
#include <arm_neon.h>
namespace arm_compute
{
namespace cpu
{
namespace kernels
{
namespace
{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
void vector_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
{
const auto width_matrix_b = static_cast<int>(dst->info()->dimension(0));
const auto in_b_stride = static_cast<int>(rhs->info()->strides_in_bytes()[1] / rhs->info()->element_size());
const auto num_elems_vec_a = static_cast<int>(lhs->info()->dimension(0));
// The implementation computes 32 elements per iteration
const int window_start_x = 32 * info.thread_id;
const int window_step_x = 32 * info.num_threads;
const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
ARM_COMPUTE_ERROR_ON_MSG((window_end_x - window_start_x) % window_step_x, " (window_end_x - window_start_x) must be multiple of window_step_x");
Window win_out(window);
win_out.set(Window::DimX, Window::Dimension(0, 1, 1));
win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
Window win_a(window);
win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
Window win_b;
// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
if(rhs->info()->num_dimensions() >= 3)
{
win_b = window;
}
win_b.set(Window::DimX, Window::Dimension(0, 1, 1));
win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
Iterator ina(lhs, win_a);
Iterator inb(rhs, win_b);
Iterator out(dst, win_out);
const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
execute_window_loop(win_out, [&](const Coordinates &)
{
int x = window_start_x;
// Here we don't check for x lower equal than (window_end_x - window_step_x) because of
// window_end_x is computed above which may cause out-of-bound writes to the dst.
for(; x < (window_end_x - window_step_x); x += window_step_x)
{
if(x > width_matrix_b)
{
return;
}
auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
float16x8_t acc0 = vdupq_n_f16(0.f);
float16x8_t acc1 = vdupq_n_f16(0.f);
float16x8_t acc2 = vdupq_n_f16(0.f);
float16x8_t acc3 = vdupq_n_f16(0.f);
auto vec_a = reinterpret_cast<const float16_t *>(ina.ptr());
const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
for(; vec_a <= (vec_a_end_addr - 4);)
{
const float16x4_t a0l = vld1_f16(vec_a);
float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
float16x8_t b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
float16x8_t b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
float16x8_t b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
float16x8_t b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 0));
acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 0));
acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 0));
acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 0));
acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 1));
acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 1));
acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 1));
acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 1));
matrix_b += 2 * in_b_stride;
b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 2));
acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 2));
acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 2));
acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 2));
acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 3));
acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 3));
acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 3));
acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 3));
vec_a += 4;
matrix_b += 2 * in_b_stride;
}
for(; vec_a < vec_a_end_addr; ++vec_a)
{
const float16_t a0 = *vec_a;
const float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
const float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
const float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
const float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
acc0 = vaddq_f16(acc0, vmulq_n_f16(b00, a0));
acc1 = vaddq_f16(acc1, vmulq_n_f16(b01, a0));
acc2 = vaddq_f16(acc2, vmulq_n_f16(b02, a0));
acc3 = vaddq_f16(acc3, vmulq_n_f16(b03, a0));
matrix_b += in_b_stride;
}
// Multiply by the weight of matrix product (alpha)
if(multiply_alpha)
{
acc0 = vmulq_f16(acc0, alpha_f16);
acc1 = vmulq_f16(acc1, alpha_f16);
acc2 = vmulq_f16(acc2, alpha_f16);
acc3 = vmulq_f16(acc3, alpha_f16);
}
auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
vst1q_f16(vec_out + 0, acc0);
vst1q_f16(vec_out + 8, acc1);
vst1q_f16(vec_out + 16, acc2);
vst1q_f16(vec_out + 24, acc3);
}
for(; x < window_end_x; ++x)
{
if(x > width_matrix_b)
{
return;
}
auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
float16x4_t vacc = vdup_n_f16(0.f);
auto vec_a = reinterpret_cast<const float16_t *>(ina.ptr());
const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
for(; vec_a <= (vec_a_end_addr - 4); vec_a += 4)
{
const float16x4_t a0l = vld1_f16(vec_a);
const float16x4_t b_col =
{
*(matrix_b + 0 * in_b_stride),
*(matrix_b + 1 * in_b_stride),
*(matrix_b + 2 * in_b_stride),
*(matrix_b + 3 * in_b_stride),
};
vacc = vadd_f16(vacc, vmul_f16(a0l, b_col));
matrix_b += 4 * in_b_stride;
}
float16_t acc = vget_lane_f16(vacc, 0) + vget_lane_f16(vacc, 1) + vget_lane_f16(vacc, 2) + vget_lane_f16(vacc, 3);
for(; vec_a < vec_a_end_addr; ++vec_a)
{
const float16_t a0 = *vec_a;
const float16_t b00 = *matrix_b;
acc += b00 * a0;
matrix_b += in_b_stride;
}
// Multiply by the weight of matrix product (alpha)
if(multiply_alpha)
{
acc *= static_cast<float16_t>(alpha);
}
auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
*(vec_out) = acc;
}
},
ina, inb, out);
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
void vector_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
{
const auto width_matrix_b = static_cast<int>(dst->info()->dimension(0));
const auto in_b_stride = static_cast<int>(rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type()));
const auto num_elems_vec_a = static_cast<int>(lhs->info()->dimension(0));
// The implementation computes 16 elements per iteration
const int window_start_x = 16 * info.thread_id;
const int window_step_x = 16 * info.num_threads;
// Make sure (window_end_x - window_start_x) is a multiple of window_step_x
const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
Window win_out(window);
win_out.set(Window::DimX, Window::Dimension(0, 1, 1));
win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
Window win_a(window);
win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
Window win_b;
// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
if(rhs->info()->num_dimensions() >= 3)
{
win_b = window;
}
win_b.set(Window::DimX, Window::Dimension(0, 1, 1));
win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
Iterator ina(lhs, win_a);
Iterator inb(rhs, win_b);
Iterator out(dst, win_out);
const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
execute_window_loop(win_out, [&](const Coordinates &)
{
int x = window_start_x;
// Here we don't check for x lower equal than (window_end_x - window_step_x) because of
// window_end_x is computed above which may cause out-of-bound writes to the dst.
for(; x < (window_end_x - window_step_x); x += window_step_x)
{
if(x > width_matrix_b)
{
return;
}
float32x4_t acc0 = vdupq_n_f32(0.f);
float32x4_t acc1 = vdupq_n_f32(0.f);
float32x4_t acc2 = vdupq_n_f32(0.f);
float32x4_t acc3 = vdupq_n_f32(0.f);
auto vec_a = reinterpret_cast<const float *>(ina.ptr());
auto matrix_b = reinterpret_cast<const float *>(inb.ptr()) + x;
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + in_b_stride)));
#endif /* __arm__ */
auto vec_a_end_addr = vec_a + num_elems_vec_a;
for(; vec_a <= (vec_a_end_addr - 4);)
{
float32x2_t a0l = vld1_f32(vec_a);
float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
float32x4_t b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
float32x4_t b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
float32x4_t b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
float32x4_t b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 1 * in_b_stride)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 2 * in_b_stride)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 3 * in_b_stride)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 4 * in_b_stride)));
#endif /* __arm__ */
acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);
acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);
vec_a += 2;
matrix_b += 2 * in_b_stride;
a0l = vld1_f32(vec_a);
b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);
acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);
acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);
vec_a += 2;
matrix_b += 2 * in_b_stride;
}
for(; vec_a < vec_a_end_addr; ++vec_a)
{
const float a0 = *vec_a;
const float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
const float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
const float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
const float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);
acc0 = vmlaq_n_f32(acc0, b00, a0);
acc1 = vmlaq_n_f32(acc1, b01, a0);
acc2 = vmlaq_n_f32(acc2, b02, a0);
acc3 = vmlaq_n_f32(acc3, b03, a0);
matrix_b += in_b_stride;
}
// Multiply by the weight of matrix product (alpha)
if(multiply_alpha)
{
acc0 = vmulq_f32(acc0, alpha_f32);
acc1 = vmulq_f32(acc1, alpha_f32);
acc2 = vmulq_f32(acc2, alpha_f32);
acc3 = vmulq_f32(acc3, alpha_f32);
}
const auto vec_out = reinterpret_cast<float *>(out.ptr()) + x;
vst1q_f32(vec_out + 0, acc0);
vst1q_f32(vec_out + 4, acc1);
vst1q_f32(vec_out + 8, acc2);
vst1q_f32(vec_out + 12, acc3);
}
// Left-over loop
for(; x < window_end_x; ++x)
{
if(x > width_matrix_b)
{
return;
}
float32x4_t vacc = vdupq_n_f32(0.f);
auto vec_a = reinterpret_cast<const float *>(ina.ptr());
auto matrix_b = reinterpret_cast<const float *>(inb.ptr()) + x;
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + in_b_stride)));
#endif /* __arm__ */
auto vec_a_end_addr = vec_a + num_elems_vec_a;
for(; vec_a <= (vec_a_end_addr - 4); vec_a += 4)
{
const float32x4_t a0l = vld1q_f32(vec_a);
const float32x4_t b_col =
{
*(matrix_b + 0 * in_b_stride),
*(matrix_b + 1 * in_b_stride),
*(matrix_b + 2 * in_b_stride),
*(matrix_b + 3 * in_b_stride),
};
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 1 * in_b_stride)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 2 * in_b_stride)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 3 * in_b_stride)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 4 * in_b_stride)));
#endif /* __arm__ */
vacc = vmlaq_f32(vacc, b_col, a0l);
matrix_b += 4 * in_b_stride;
}
float acc = vgetq_lane_f32(vacc, 0) + vgetq_lane_f32(vacc, 1) + vgetq_lane_f32(vacc, 2) + vgetq_lane_f32(vacc, 3);
for(; vec_a < vec_a_end_addr; ++vec_a)
{
const float a0 = *vec_a;
const float b00 = *matrix_b;
acc += b00 * a0;
matrix_b += in_b_stride;
}
// Multiply by the weight of matrix product (alpha)
if(multiply_alpha)
{
acc *= alpha;
}
const auto vec_out = reinterpret_cast<float *>(out.ptr()) + x;
*vec_out = acc;
}
},
ina, inb, out);
}
void matrix_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
{
ARM_COMPUTE_UNUSED(info);
const int out_width = static_cast<int>(dst->info()->dimension(0));
const int out_height = static_cast<int>(dst->info()->dimension(1));
const size_t in_b_stride = rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type());
const size_t out_stride1 = dst->info()->strides_in_bytes()[1] / data_size_from_type(dst->info()->data_type());
const size_t out_stride2 = out_stride1 * 2;
const size_t out_stride3 = out_stride1 * 3;
const int num_elems_matrix_b_x = rhs->info()->dimension(0);
// Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the dst matrix
Window win_a(window);
win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
Window win_b;
// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
if(rhs->info()->num_dimensions() >= 3)
{
win_b = window;
}
// Set step_x and step_y for matrix B. Scale by a factor of 4 the X range as the input transposed matrix A has 4 times less the cols of the dst matrix
// The step along the x direction is 2 times the in_b_stride because for each iteration we compute 2 blocks of size 4x4
win_b.set(Window::DimX, Window::Dimension(window.x().start() / 4, window.x().end() / 4, 2 * in_b_stride));
win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
Iterator ina(lhs, win_a);
Iterator inb(rhs, win_b);
Iterator out(dst, window);
const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
// The implementation assumes that the matrix A and Matrix B have been reshaped respectively with CpuGemmInterleave4x4 and CpuGemmTranspose1xW
// The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
// All the values needed for computing a single 4x4 block will be read from consecutive memory positions
execute_window_loop(window, [&](const Coordinates & id)
{
auto mtx_a0 = reinterpret_cast<const float *>(ina.ptr());
auto mtx_b0 = reinterpret_cast<const float *>(inb.ptr());
auto mtx_b1 = mtx_b0 + in_b_stride;
float32x4_t acc00 = vdupq_n_f32(0.f);
float32x4_t acc10 = vdupq_n_f32(0.f);
float32x4_t acc20 = vdupq_n_f32(0.f);
float32x4_t acc30 = vdupq_n_f32(0.f);
float32x4_t acc01 = vdupq_n_f32(0.f);
float32x4_t acc11 = vdupq_n_f32(0.f);
float32x4_t acc21 = vdupq_n_f32(0.f);
float32x4_t acc31 = vdupq_n_f32(0.f);
#if __arm__
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif /* __arm__ */
auto mtx_b0_end_addr = mtx_b0 + num_elems_matrix_b_x;
for(; mtx_b0 <= (mtx_b0_end_addr - 32);)
{
float32x4_t a0 = vld1q_dup_f32(mtx_a0 + 0);
float32x4_t a1 = vld1q_dup_f32(mtx_a0 + 1);
float32x4_t a2 = vld1q_dup_f32(mtx_a0 + 2);
float32x4_t a3 = vld1q_dup_f32(mtx_a0 + 3);
float32x4_t b00 = vld1q_f32(mtx_b0);
float32x4_t b10 = vld1q_f32(mtx_b1);
float32x4_t b01 = vld1q_f32(mtx_b0 + 4);
float32x4_t b11 = vld1q_f32(mtx_b1 + 4);
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif /* __arm__ */
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b00, a0);
acc10 = vmlaq_f32(acc10, b00, a1);
acc20 = vmlaq_f32(acc20, b00, a2);
acc30 = vmlaq_f32(acc30, b00, a3);
float32x4_t a4 = vld1q_dup_f32(mtx_a0 + 4);
float32x4_t a5 = vld1q_dup_f32(mtx_a0 + 5);
float32x4_t a6 = vld1q_dup_f32(mtx_a0 + 6);
float32x4_t a7 = vld1q_dup_f32(mtx_a0 + 7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b10, a0);
acc11 = vmlaq_f32(acc11, b10, a1);
acc21 = vmlaq_f32(acc21, b10, a2);
acc31 = vmlaq_f32(acc31, b10, a3);
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b01, a4);
acc10 = vmlaq_f32(acc10, b01, a5);
acc20 = vmlaq_f32(acc20, b01, a6);
acc30 = vmlaq_f32(acc30, b01, a7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b11, a4);
acc11 = vmlaq_f32(acc11, b11, a5);
acc21 = vmlaq_f32(acc21, b11, a6);
acc31 = vmlaq_f32(acc31, b11, a7);
mtx_a0 += 8;
mtx_b0 += 8;
mtx_b1 += 8;
a0 = vld1q_dup_f32(mtx_a0 + 0);
a1 = vld1q_dup_f32(mtx_a0 + 1);
a2 = vld1q_dup_f32(mtx_a0 + 2);
a3 = vld1q_dup_f32(mtx_a0 + 3);
b00 = vld1q_f32(mtx_b0);
b10 = vld1q_f32(mtx_b1);
b01 = vld1q_f32(mtx_b0 + 4);
b11 = vld1q_f32(mtx_b1 + 4);
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b00, a0);
acc10 = vmlaq_f32(acc10, b00, a1);
acc20 = vmlaq_f32(acc20, b00, a2);
acc30 = vmlaq_f32(acc30, b00, a3);
a4 = vld1q_dup_f32(mtx_a0 + 4);
a5 = vld1q_dup_f32(mtx_a0 + 5);
a6 = vld1q_dup_f32(mtx_a0 + 6);
a7 = vld1q_dup_f32(mtx_a0 + 7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b10, a0);
acc11 = vmlaq_f32(acc11, b10, a1);
acc21 = vmlaq_f32(acc21, b10, a2);
acc31 = vmlaq_f32(acc31, b10, a3);
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b01, a4);
acc10 = vmlaq_f32(acc10, b01, a5);
acc20 = vmlaq_f32(acc20, b01, a6);
acc30 = vmlaq_f32(acc30, b01, a7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b11, a4);
acc11 = vmlaq_f32(acc11, b11, a5);
acc21 = vmlaq_f32(acc21, b11, a6);
acc31 = vmlaq_f32(acc31, b11, a7);
mtx_a0 += 8;
mtx_b0 += 8;
mtx_b1 += 8;
a0 = vld1q_dup_f32(mtx_a0 + 0);
a1 = vld1q_dup_f32(mtx_a0 + 1);
a2 = vld1q_dup_f32(mtx_a0 + 2);
a3 = vld1q_dup_f32(mtx_a0 + 3);
b00 = vld1q_f32(mtx_b0);
b10 = vld1q_f32(mtx_b1);
b01 = vld1q_f32(mtx_b0 + 4);
b11 = vld1q_f32(mtx_b1 + 4);
#if __arm__
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif /* __arm__ */
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b00, a0);
acc10 = vmlaq_f32(acc10, b00, a1);
acc20 = vmlaq_f32(acc20, b00, a2);
acc30 = vmlaq_f32(acc30, b00, a3);
a4 = vld1q_dup_f32(mtx_a0 + 4);
a5 = vld1q_dup_f32(mtx_a0 + 5);
a6 = vld1q_dup_f32(mtx_a0 + 6);
a7 = vld1q_dup_f32(mtx_a0 + 7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b10, a0);
acc11 = vmlaq_f32(acc11, b10, a1);
acc21 = vmlaq_f32(acc21, b10, a2);
acc31 = vmlaq_f32(acc31, b10, a3);
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b01, a4);
acc10 = vmlaq_f32(acc10, b01, a5);
acc20 = vmlaq_f32(acc20, b01, a6);
acc30 = vmlaq_f32(acc30, b01, a7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b11, a4);
acc11 = vmlaq_f32(acc11, b11, a5);
acc21 = vmlaq_f32(acc21, b11, a6);
acc31 = vmlaq_f32(acc31, b11, a7);
mtx_a0 += 8;
mtx_b0 += 8;
mtx_b1 += 8;
a0 = vld1q_dup_f32(mtx_a0 + 0);
a1 = vld1q_dup_f32(mtx_a0 + 1);
a2 = vld1q_dup_f32(mtx_a0 + 2);
a3 = vld1q_dup_f32(mtx_a0 + 3);
b00 = vld1q_f32(mtx_b0);
b10 = vld1q_f32(mtx_b1);
b01 = vld1q_f32(mtx_b0 + 4);
b11 = vld1q_f32(mtx_b1 + 4);
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b00, a0);
acc10 = vmlaq_f32(acc10, b00, a1);
acc20 = vmlaq_f32(acc20, b00, a2);
acc30 = vmlaq_f32(acc30, b00, a3);
a4 = vld1q_dup_f32(mtx_a0 + 4);
a5 = vld1q_dup_f32(mtx_a0 + 5);
a6 = vld1q_dup_f32(mtx_a0 + 6);
a7 = vld1q_dup_f32(mtx_a0 + 7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b10, a0);
acc11 = vmlaq_f32(acc11, b10, a1);
acc21 = vmlaq_f32(acc21, b10, a2);
acc31 = vmlaq_f32(acc31, b10, a3);
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b01, a4);
acc10 = vmlaq_f32(acc10, b01, a5);
acc20 = vmlaq_f32(acc20, b01, a6);
acc30 = vmlaq_f32(acc30, b01, a7);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b11, a4);
acc11 = vmlaq_f32(acc11, b11, a5);
acc21 = vmlaq_f32(acc21, b11, a6);
acc31 = vmlaq_f32(acc31, b11, a7);
mtx_a0 += 8;
mtx_b0 += 8;
mtx_b1 += 8;
}
for(; mtx_b0 < mtx_b0_end_addr;)
{
float32x4_t a0 = vld1q_dup_f32(mtx_a0 + 0);
float32x4_t a1 = vld1q_dup_f32(mtx_a0 + 1);
float32x4_t a2 = vld1q_dup_f32(mtx_a0 + 2);
float32x4_t a3 = vld1q_dup_f32(mtx_a0 + 3);
float32x4_t b00 = vld1q_f32(mtx_b0);
float32x4_t b10 = vld1q_f32(mtx_b1);
#if __arm__
asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif /* __arm__ */
// 4x4 block 0
acc00 = vmlaq_f32(acc00, b00, a0);
acc10 = vmlaq_f32(acc10, b00, a1);
acc20 = vmlaq_f32(acc20, b00, a2);
acc30 = vmlaq_f32(acc30, b00, a3);
// 4x4 block 1
acc01 = vmlaq_f32(acc01, b10, a0);
acc11 = vmlaq_f32(acc11, b10, a1);
acc21 = vmlaq_f32(acc21, b10, a2);
acc31 = vmlaq_f32(acc31, b10, a3);
mtx_a0 += 4;
mtx_b0 += 4;
mtx_b1 += 4;
}
// Multiply by the weight of matrix product (alpha)
if(multiply_alpha)
{
acc00 = vmulq_f32(acc00, alpha_f32);
acc10 = vmulq_f32(acc10, alpha_f32);
acc20 = vmulq_f32(acc20, alpha_f32);
acc30 = vmulq_f32(acc30, alpha_f32);
acc01 = vmulq_f32(acc01, alpha_f32);
acc11 = vmulq_f32(acc11, alpha_f32);
acc21 = vmulq_f32(acc21, alpha_f32);
acc31 = vmulq_f32(acc31, alpha_f32);
}
const auto mtx_out0 = reinterpret_cast<float *>(out.ptr());
const auto mtx_out1 = mtx_out0 + 4;
if(id.x() < (out_width - 8))
{
vst1q_f32(mtx_out0, acc00);
vst1q_f32(mtx_out1, acc01);
if(id.y() + 1 < out_height)
{
vst1q_f32(mtx_out0 + out_stride1, acc10);
vst1q_f32(mtx_out1 + out_stride1, acc11);
if(id.y() + 2 < out_height)
{
vst1q_f32(mtx_out0 + out_stride2, acc20);
vst1q_f32(mtx_out1 + out_stride2, acc21);
if(id.y() + 3 < out_height)
{
vst1q_f32(mtx_out0 + out_stride3, acc30);
vst1q_f32(mtx_out1 + out_stride3, acc31);
}
}
}
}
else if(id.x() < (out_width - 4))
{
vst1q_f32(mtx_out0, acc00);
if(id.y() + 1 < out_height)
{
vst1q_f32(mtx_out0 + out_stride1, acc10);
if(id.y() + 2 < out_height)
{
vst1q_f32(mtx_out0 + out_stride2, acc20);
if(id.y() + 3 < out_height)
{
vst1q_f32(mtx_out0 + out_stride3, acc30);
}
}
}
// Left-over columns
const int columns_left = out_width - id.x() - 4;
for(auto x = 0; x < columns_left; ++x)
{
*(mtx_out1 + x) = acc01[x];
if(id.y() + 1 < out_height)
{
*(mtx_out1 + x + out_stride1) = acc11[x];
if(id.y() + 2 < out_height)
{
*(mtx_out1 + x + out_stride2) = acc21[x];
if(id.y() + 3 < out_height)
{
*(mtx_out1 + x + out_stride3) = acc31[x];
}
}
}
}
}
else
{
// Left-over columns
const int columns_left = out_width - id.x();
for(int x = 0; x < columns_left; ++x)
{
*(mtx_out0 + x) = acc00[x];
if(id.y() + 1 < out_height)
{
*(mtx_out0 + x + out_stride1) = acc10[x];
if(id.y() + 2 < out_height)
{
*(mtx_out0 + x + out_stride2) = acc20[x];
if(id.y() + 3 < out_height)
{
*(mtx_out0 + x + out_stride3) = acc30[x];
}
}
}
}
}
},
ina, inb, out);
}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
void matrix_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
{
ARM_COMPUTE_UNUSED(info);
const int out_width = static_cast<int>(dst->info()->dimension(0));
const int out_height = static_cast<int>(dst->info()->dimension(1));
const size_t in_b_stride = rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type());
const size_t out_stride = dst->info()->strides_in_bytes()[1] / data_size_from_type(dst->info()->data_type());
const int num_elems_matrix_b_x = rhs->info()->dimension(0);
// Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the dst matrix
Window win_a(window);
win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
Window win_b;
// Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
// This scenario can happen when the the matrix multiplication is used to perform a convolution operation
if(rhs->info()->num_dimensions() >= 3)
{
win_b = window;
}
// Set step_x and step_y for matrix B. Scale by a factor of 8 the X range as the input transposed matrix A has 8 times less the cols of the dst matrix
win_b.set(Window::DimX, Window::Dimension(window.x().start() / 8, window.x().end() / 8, in_b_stride));
win_b.set(Window::DimY, Window::Dimension(0, 1, 0));
Iterator ina(lhs, win_a);
Iterator inb(rhs, win_b);
Iterator out(dst, window);
const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
execute_window_loop(window, [&](const Coordinates & id)
{
const auto *mtx_a0 = reinterpret_cast<const float16_t *>(ina.ptr());
const auto *mtx_b0 = reinterpret_cast<const float16_t *>(inb.ptr());
auto *mtx_out = reinterpret_cast<float16_t *>(out.ptr());
float16x8x4_t c =
{
{
vdupq_n_f16(0.f),
vdupq_n_f16(0.f),
vdupq_n_f16(0.f),
vdupq_n_f16(0.f)
}
};
/*
This kernel puts the values in a 4x4 block of Matrix A on the same row (Interleaved values)
|a00 a01 a02 a03 | a04 a05 a06 a07|
|a10 a11 a12 a13 | a14 a15 a16 a17|
|a20 a21 a22 a23 | a24 a25 a26 a27| = | a00 a10 a20 a30 || a01 a11 a21 a31 || a02 a12 a22 a32 || a03 a13 a23 a33 | a40 a50 a60 a70 | ...
|a30 a31 a32 a33 | a34 a35 a36 a37| | a04 a14 a24 a34 || a05 a15 a25 a35 || a06 a15 a26 a36 || a07 a17 a27 a37 | a44 a54 a64 a74 | ...
|a40 a41 a42 a43 | a44 a45 a46 a47|
|a50 a51 a52 a53 | a54 a55 a56 a57|
|a60 a61 a62 a63 | a64 a65 a66 a67|
|a70 a71 a72 a73 | a74 a75 a76 a77|
After this operation, the dst matrix will have the following shape: [ height * 4, width / 4 ]
B Matrix has been transposed as shown below
|b00 b01 b02 b03 b04 b05 b06 b07|
|b10 b11 b12 b13 b14 b15 b16 b17|
|b20 b21 b22 b23 b24 b25 b26 b27|
|b30 b31 b32 b33 b34 b35 b36 b37|
------------------->
|b00 b01 b02 b03 b04 b05 b06 b07||b10 b11 b12 b13 b14 b15 b16 b17||b20 b21 b22 b23 b24 b25 b26 b27||b30 b31 b32 b33 b34 b35 b36 b37|
c.val[0][0] = a00*b00 + a01*b10 + a02*b20 + a03*b30
c.val[0][1] = a00*b01 + a01*b11 + a02*b21 + a03*b31
The size of the dst tensor's XY-plane must be the following shape [ width * 8, height / 8 ]. All other dimensions must have the same size.
*/
const float16_t *mtx_b0_end_addr = mtx_b0 + num_elems_matrix_b_x;
for(; mtx_b0 <= (mtx_b0_end_addr - 32);)
{
const float16x8_t p00 = vld1q_f16(mtx_a0);
const float16x8_t p02 = vld1q_f16(mtx_a0 + 8);
const float16x8_t q00 = vld1q_f16(mtx_b0);
const float16x8_t q02 = vld1q_f16(mtx_b0 + 8);
const float16x8_t q04 = vld1q_f16(mtx_b0 + 16);
const float16x8_t q06 = vld1q_f16(mtx_b0 + 24);
c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vgetq_lane_f16(p00, 0)));
c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vgetq_lane_f16(p00, 1)));
c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vgetq_lane_f16(p00, 2)));
c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vgetq_lane_f16(p00, 3)));
c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q02, vgetq_lane_f16(p00, 4)));
c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q02, vgetq_lane_f16(p00, 5)));
c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q02, vgetq_lane_f16(p00, 6)));
c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q02, vgetq_lane_f16(p00, 7)));
c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q04, vgetq_lane_f16(p02, 0)));
c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q04, vgetq_lane_f16(p02, 1)));
c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q04, vgetq_lane_f16(p02, 2)));
c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q04, vgetq_lane_f16(p02, 3)));
c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q06, vgetq_lane_f16(p02, 4)));
c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q06, vgetq_lane_f16(p02, 5)));
c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q06, vgetq_lane_f16(p02, 6)));
c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q06, vgetq_lane_f16(p02, 7)));
mtx_a0 += 16;
mtx_b0 += 32;
}
for(; mtx_b0 < mtx_b0_end_addr;)
{
const float16x4_t p00 = vld1_f16(mtx_a0);
const float16x8_t q00 = vld1q_f16(mtx_b0);
c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vget_lane_f16(p00, 0)));
c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vget_lane_f16(p00, 1)));
c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vget_lane_f16(p00, 2)));
c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vget_lane_f16(p00, 3)));
mtx_a0 += 4;
mtx_b0 += 8;
}
if(multiply_alpha)
{
c.val[0] = vmulq_f16(c.val[0], alpha_f16);
c.val[1] = vmulq_f16(c.val[1], alpha_f16);
c.val[2] = vmulq_f16(c.val[2], alpha_f16);
c.val[3] = vmulq_f16(c.val[3], alpha_f16);
}
if(id.x() < (out_width - 8))
{
vst1q_f16(mtx_out, c.val[0]);
if(id.y() + 1 < out_height)
{
vst1q_f16(mtx_out + 1 * out_stride, c.val[1]);
if(id.y() + 2 < out_height)
{
vst1q_f16(mtx_out + 2 * out_stride, c.val[2]);
if(id.y() + 3 < out_height)
{
vst1q_f16(mtx_out + 3 * out_stride, c.val[3]);
}
}
}
}
else
{
// Left-over columns
const int columns_left = out_width - id.x();
for(int x = 0; x < columns_left; ++x)
{
*(mtx_out + x) = c.val[0][x];
if(id.y() + 1 < out_height)
{
*(mtx_out + x + 1 * out_stride) = c.val[1][x];
if(id.y() + 2 < out_height)
{
*(mtx_out + x + 2 * out_stride) = c.val[2][x];
if(id.y() + 3 < out_height)
{
*(mtx_out + x + 3 * out_stride) = c.val[3][x];
}
}
}
}
}
},
ina, inb, out);
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
inline Status validate_arguments(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
{
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(lhs);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs, dst);
if(!is_interleaved)
{
ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(0) != rhs->dimension(1));
if(dst->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON(rhs->dimension(0) != dst->dimension(0));
ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(1) != dst->dimension(1));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
}
else
{
const int m = reshape_info.m();
const int n = reshape_info.n();
const int k = reshape_info.k();
const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
/* Interleave */
TensorShape tensor_shape0{ lhs->tensor_shape() };
tensor_shape0.set(0, k);
tensor_shape0.set(1, m);
const TensorInfo tensor_info0 = lhs->clone()->set_tensor_shape(tensor_shape0);
const TensorInfo tensor_info_reshaped0 = lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lhs, &tensor_info_reshaped0);
if(n != 0) /* Transpose */
{
TensorShape tensor_shape1{ rhs->tensor_shape() };
tensor_shape1.set(0, n);
tensor_shape1.set(1, k);
const TensorInfo tensor_info1 = rhs->clone()->set_tensor_shape(tensor_shape1);
const TensorInfo tensor_info_reshaped1 = rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(rhs, &tensor_info_reshaped1);
}
if(dst->total_size() != 0)
{
if(n != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(0) != static_cast<size_t>(n));
}
ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(1) != static_cast<size_t>(m));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
}
return Status{};
}
} // namespace
void CpuGemmMatrixMultiplyKernel::configure(const ITensorInfo *lhs, const ITensorInfo *rhs, ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
// dst tensor auto inizialitation if not yet initialized
TensorShape tensor_shape{ lhs->tensor_shape() };
tensor_shape.set(0, is_interleaved ? reshape_info.n() : rhs->dimension(0));
tensor_shape.set(1, is_interleaved ? reshape_info.m() : lhs->dimension(1));
auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(tensor_shape));
// Perform validate step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info));
_alpha = alpha;
// Configure kernel window
Window win{};
// Check if the dst tensor is a vector. If so,the kernel runs the vector-matrix multiplication
const bool is_dst_vector = (dst->dimension(1) == 1);
if(is_dst_vector)
{
const unsigned int num_elems_processed_per_iteration_x = (lhs->data_type() == DataType::F32) ? 16 : 32;
win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x));
}
else
{
constexpr unsigned int num_elems_processed_per_iteration_x = 8;
constexpr unsigned int num_elems_processed_per_iteration_y = 4;
win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
}
switch(lhs->data_type())
{
case DataType::F32:
{
_func = (is_dst_vector) ? vector_matrix_multiply_f32 : matrix_matrix_multiply_f32;
break;
}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
{
_func = (is_dst_vector) ? vector_matrix_multiply_f16 : matrix_matrix_multiply_f16;
break;
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
default:
{
ARM_COMPUTE_ERROR("Data type not supported");
break;
}
}
ICPPKernel::configure(win);
}
Status CpuGemmMatrixMultiplyKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, float alpha, bool is_interleaved,
const GEMMReshapeInfo &reshape_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info));
return Status{};
}
void CpuGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
ARM_COMPUTE_ERROR_ON(tensors.empty());
ARM_COMPUTE_ERROR_ON(_func == nullptr);
const ITensor *lhs = tensors.get_const_tensor(TensorType::ACL_SRC_0);
const ITensor *rhs = tensors.get_const_tensor(TensorType::ACL_SRC_1);
ITensor *dst = tensors.get_tensor(TensorType::ACL_DST);
(*_func)(lhs, rhs, dst, window, info, _alpha);
}
const char *CpuGemmMatrixMultiplyKernel::name() const
{
return "CpuGemmMatrixMultiplyKernel";
}
} // namespace kernels
} // namespace cpu
} // namespace arm_compute