diff options
Diffstat (limited to 'src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp')
-rw-r--r-- | src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp | 1054 |
1 files changed, 0 insertions, 1054 deletions
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp deleted file mode 100644 index e3508a1234..0000000000 --- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp +++ /dev/null @@ -1,1054 +0,0 @@ -/* - * Copyright (c) 2017-2019 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 "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/CPP/Validate.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/NEON/NEFixedPoint.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/helpers/float_ops.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" - -#include <arm_neon.h> -#include <cstddef> -#include <cstdint> -#include <tuple> - -using namespace arm_compute; - -namespace arm_compute -{ -class Coordinates; -} // namespace arm_compute - -namespace -{ -template <bool multiply_alpha> -void vector_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info, float alpha) -{ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - const auto width_matrix_b = static_cast<int>(output->info()->dimension(0)); - const auto in_b_stride = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type())); - const auto num_elems_vec_a = static_cast<int>(input0->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(window_start_x, window_end_x, window_step_x)); - 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(input1->info()->num_dimensions() >= 3) - { - win_b = window; - } - win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); - win_b.set(Window::DimY, Window::Dimension(0, 1, 1)); - - Iterator ina(input0, win_a); - Iterator inb(input1, win_b); - Iterator out(output, win_out); - - const float16x8_t alpha_f16 = vdupq_n_f16(alpha); - ARM_COMPUTE_UNUSED(alpha_f16); - - execute_window_loop(win_out, [&](const Coordinates & id) - { - if(id.x() > width_matrix_b) - { - return; - } - - 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()); - auto matrix_b = reinterpret_cast<const float16_t *>(inb.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;) - { - 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)); - - vec_a += 1; - 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); - } - - const auto vec_out = reinterpret_cast<float16_t *>(out.ptr()); - - vst1q_f16(vec_out + 0, acc0); - vst1q_f16(vec_out + 8, acc1); - vst1q_f16(vec_out + 16, acc2); - vst1q_f16(vec_out + 24, acc3); - - }, - ina, inb, out); -#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - ARM_COMPUTE_UNUSED(input0); - ARM_COMPUTE_UNUSED(input1); - ARM_COMPUTE_UNUSED(output); - ARM_COMPUTE_UNUSED(window); - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_UNUSED(alpha); - ARM_COMPUTE_ERROR("Not implemented"); -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -} - -template <bool multiply_alpha> -void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info, float alpha) -{ - const auto width_matrix_b = static_cast<int>(output->info()->dimension(0)); - const auto in_b_stride = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type())); - const auto num_elems_vec_a = static_cast<int>(input0->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(window_start_x, window_end_x, window_step_x)); - 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(input1->info()->num_dimensions() >= 3) - { - win_b = window; - } - win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); - win_b.set(Window::DimY, Window::Dimension(0, 1, 1)); - - Iterator ina(input0, win_a); - Iterator inb(input1, win_b); - Iterator out(output, win_out); - - execute_window_loop(win_out, [&](const Coordinates & id) - { - if(id.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()); - -#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;) - { - 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); - - vec_a += 1; - matrix_b += in_b_stride; - } - - // Multiply by the weight of matrix product (alpha) - if(multiply_alpha) - { - const float32x4_t alpha_f32 = vdupq_n_f32(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()); - - vst1q_f32(vec_out + 0, acc0); - vst1q_f32(vec_out + 4, acc1); - vst1q_f32(vec_out + 8, acc2); - vst1q_f32(vec_out + 12, acc3); - }, - ina, inb, out); -} - -template <bool multiply_alpha> -void matrix_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha) -{ - const size_t in_b_stride = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()); - const size_t out_stride1 = output->info()->strides_in_bytes()[1] / data_size_from_type(output->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 = input1->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 output 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(input1->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 output 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(input0, win_a); - Iterator inb(input1, win_b); - Iterator out(output, window); - - // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW - // 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 &) - { - 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) - { - const float32x4_t alpha_f32 = vdupq_n_f32(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; - - // Store the 4 blocks - vst1q_f32(mtx_out0, acc00); - vst1q_f32(mtx_out1, acc01); - vst1q_f32(mtx_out0 + out_stride1, acc10); - vst1q_f32(mtx_out1 + out_stride1, acc11); - vst1q_f32(mtx_out0 + out_stride2, acc20); - vst1q_f32(mtx_out1 + out_stride2, acc21); - vst1q_f32(mtx_out0 + out_stride3, acc30); - vst1q_f32(mtx_out1 + out_stride3, acc31); - }, - ina, inb, out); -} - -template <bool multiply_alpha> -void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha) -{ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - const size_t in_b_stride = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()); - const size_t out_stride = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type()); - const int num_elems_matrix_b_x = input1->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 output 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(input1->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 output 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(input0, win_a); - Iterator inb(input1, win_b); - Iterator out(output, window); - - const float16x8_t alpha_f16 = vdupq_n_f16(alpha); - - execute_window_loop(window, [&](const Coordinates &) - { - 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 output 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 output 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); - } - - vst1q_f16(mtx_out + 0 * out_stride, c.val[0]); - vst1q_f16(mtx_out + 1 * out_stride, c.val[1]); - vst1q_f16(mtx_out + 2 * out_stride, c.val[2]); - vst1q_f16(mtx_out + 3 * out_stride, c.val[3]); - }, - ina, inb, out); -#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - ARM_COMPUTE_UNUSED(input0); - ARM_COMPUTE_UNUSED(input1); - ARM_COMPUTE_UNUSED(output); - ARM_COMPUTE_UNUSED(window); - ARM_COMPUTE_UNUSED(alpha); - ARM_COMPUTE_ERROR("Not implemented"); -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -} - -inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info) -{ - ARM_COMPUTE_UNUSED(alpha); - - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input0); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output); - - if(!is_interleaved) - { - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1)); - - if(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); - } - } - 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{ input0->tensor_shape() }; - tensor_shape0.set(0, k); - tensor_shape0.set(1, m); - - const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0); - const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(misc::shape_calculator::compute_interleaved_shape(tensor_info0, mult_interleave4x4_height)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0); - - if(n != 0) /* Transpose */ - { - TensorShape tensor_shape1{ input1->tensor_shape() }; - tensor_shape1.set(0, n); - tensor_shape1.set(1, k); - - const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); - const TensorInfo tensor_info_reshaped1 = input1->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(input1, &tensor_info_reshaped1); - } - - if(output->total_size() != 0) - { - if(n != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n)); - } - ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); - } - } - - return Status{}; -} - -inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output) -{ - bool window_changed{}; - Window win{}; - - unsigned int num_elems_processed_per_iteration_x = 0; - const unsigned int num_elems_processed_per_iteration_y = 4; - - // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication - if((output->dimension(1) == 1)) - { - switch(input0->data_type()) - { - case DataType::F32: - { - num_elems_processed_per_iteration_x = 16; - break; - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - { - num_elems_processed_per_iteration_x = 32; - break; - } -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - default: - { - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - - // Configure kernel window - win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x)); - - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_x); - - window_changed = update_window_and_padding(win, - AccessWindowStatic(input0, 0, 0, input0->tensor_shape().x(), 1), - AccessWindowHorizontal(input1, 0, num_elems_processed_per_iteration_x), - output_access); - - Coordinates coord; - coord.set_num_dimensions(output->num_dimensions()); - output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape())); - } - else - { - switch(input0->data_type()) - { - case DataType::F32: - { - num_elems_processed_per_iteration_x = 8; - break; - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - { - num_elems_processed_per_iteration_x = 8; - break; - } -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - default: - { - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - - // Configure kernel window - win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - - AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); - - window_changed = update_window_and_padding(win, - AccessWindowRectangle(input0, 0, 0, 4, 1, 1.f, 0.25f), - AccessWindowStatic(input1, 0, 0, input1->tensor_shape().x(), ceil_to_multiple(input1->tensor_shape().y(), 4)), - output_access); - - output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape())); - } - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -NEGEMMMatrixMultiplyKernel::NEGEMMMatrixMultiplyKernel() - : _input0(nullptr), _input1(nullptr), _output(nullptr), _alpha(1.0f) -{ -} - -void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); - - // Output tensor auto inizialitation if not yet initialized - TensorShape tensor_shape{ input0->info()->tensor_shape() }; - tensor_shape.set(0, is_interleaved ? reshape_info.n() : input1->info()->dimension(0)); - tensor_shape.set(1, is_interleaved ? reshape_info.m() : input0->info()->dimension(1)); - - auto_init_if_empty(*output->info(), input0->info()->clone()->set_tensor_shape(tensor_shape)); - - // Perform validate step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, is_interleaved, reshape_info)); - - _input0 = input0; - _input1 = input1; - _output = output; - _alpha = alpha; - - // Configure kernel window - auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -Status NEGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved, - const GEMMReshapeInfo &reshape_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, is_interleaved, reshape_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()).first); - - return Status{}; -} - -void NEGEMMMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - const bool multiply_alpha = !(helpers::float_ops::is_one(_alpha)); - - // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication - if((_output->info()->dimension(1) == 1)) - { - switch(_input0->info()->data_type()) - { - case DataType::F32: - { - multiply_alpha ? vector_matrix_multiply_f32<true>(_input0, _input1, _output, window, info, _alpha) : - vector_matrix_multiply_f32<false>(_input0, _input1, _output, window, info, _alpha); - break; - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - { - multiply_alpha ? vector_matrix_multiply_f16<true>(_input0, _input1, _output, window, info, _alpha) : - vector_matrix_multiply_f16<false>(_input0, _input1, _output, window, info, _alpha); - break; - } -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - default: - { - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - } - else - { - switch(_input0->info()->data_type()) - { - case DataType::F32: - { - multiply_alpha ? matrix_matrix_multiply_f32<true>(_input0, _input1, _output, window, _alpha) : - matrix_matrix_multiply_f32<false>(_input0, _input1, _output, window, _alpha); - break; - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - { - multiply_alpha ? matrix_matrix_multiply_f16<true>(_input0, _input1, _output, window, _alpha) : - matrix_matrix_multiply_f16<false>(_input0, _input1, _output, window, _alpha); - break; - } -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - default: - { - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - } -} |