Decouple CpuGemmMatrixMultiplyKernel and CpuGemmMatrixAdditionKernel
Resolves COMPMID-4629, COMPMID-4631
Change-Id: Idceafc84735116ef63ec13a202895f954b87e32f
Signed-off-by: Dana Zlotnik <dana.zlotnik@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7095
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp b/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
index 93ae904..03b372e 100644
--- a/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
+++ b/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,11 +29,10 @@
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "src/core/CPP/Validate.h"
+#include "src/core/common/Registrars.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>
+#include "src/cpu/kernels/gemm_matrix_mul/list.h"
namespace arm_compute
{
@@ -43,985 +42,25 @@
{
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)
+static const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> available_kernels =
{
- 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)
+ "neon_fp32_gemm_matrix_mul",
+ [](const DataTypeISASelectorData & data)
{
- 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;
- }
+ return (data.dt == DataType::F32);
+ },
+ REGISTER_FP32_NEON(neon_fp32_gemm_matrix_mul)
},
- 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)
+ "neon_fp16_gemm_matrix_mul",
+ [](const DataTypeISASelectorData & data)
{
- 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;
- }
+ return (data.dt == DataType::F16) && data.isa.fp16;
+ },
+ REGISTER_FP16_NEON(neon_fp16_gemm_matrix_mul)
},
- 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)
{
@@ -1083,6 +122,7 @@
return Status{};
}
+
} // namespace
void CpuGemmMatrixMultiplyKernel::configure(const ITensorInfo *lhs, const ITensorInfo *rhs, ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
@@ -1120,26 +160,10 @@
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;
- }
- }
+ const auto uk = CpuGemmMatrixMultiplyKernel::get_implementation(DataTypeISASelectorData{ lhs->data_type(), CPUInfo::get().get_isa() });
+ ARM_COMPUTE_ERROR_ON_NULLPTR(uk);
+ _func = uk->ukernel;
+
ICPPKernel::configure(win);
}
@@ -1162,13 +186,19 @@
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 bool is_dst_vector = (dst->info()->dimension(1) == 1);
+ (*_func)(lhs, rhs, dst, window, info, _alpha, is_dst_vector);
}
const char *CpuGemmMatrixMultiplyKernel::name() const
{
return "CpuGemmMatrixMultiplyKernel";
}
+
+const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> &CpuGemmMatrixMultiplyKernel::get_available_kernels()
+{
+ return available_kernels;
+}
} // namespace kernels
} // namespace cpu
} // namespace arm_compute