diff options
Diffstat (limited to 'src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp | 1024 |
1 files changed, 27 insertions, 997 deletions
diff --git a/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp b/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp index 93ae90436a..03b372efd4 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 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) +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) - { - 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) + "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; - - 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) - { - 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) + "neon_fp16_gemm_matrix_mul", + [](const DataTypeISASelectorData & data) { - 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 @@ inline Status validate_arguments(const ITensorInfo *lhs, const ITensorInfo *rhs, 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 @@ void CpuGemmMatrixMultiplyKernel::configure(const ITensorInfo *lhs, const ITenso 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 @@ void CpuGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &win 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 |