| /* |
| * Copyright (c) 2022-2023 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. |
| */ |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| |
| #include "src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h" |
| #include "src/core/utils/helpers/float_ops.h" |
| |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| void vector_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha) |
| { |
| const auto width_matrix_b = static_cast<int>(dst->info()->dimension(0)); |
| const auto in_b_stride = static_cast<int>(rhs->info()->strides_in_bytes()[1] / rhs->info()->element_size()); |
| const auto num_elems_vec_a = static_cast<int>(lhs->info()->dimension(0)); |
| |
| // The implementation computes 32 elements per iteration |
| const int window_start_x = 32 * info.thread_id; |
| const int window_step_x = 32 * info.num_threads; |
| const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x; |
| ARM_COMPUTE_ERROR_ON_MSG((window_end_x - window_start_x) % window_step_x, " (window_end_x - window_start_x) must be multiple of window_step_x"); |
| |
| Window win_out(window); |
| win_out.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| win_out.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| Window win_a(window); |
| win_a.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| win_a.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| |
| Window win_b; |
| // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 |
| // This scenario can happen when the the matrix multiplication is used to perform a convolution operation |
| if(rhs->info()->num_dimensions() >= 3) |
| { |
| win_b = window; |
| } |
| win_b.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| win_b.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| Iterator ina(lhs, win_a); |
| Iterator inb(rhs, win_b); |
| Iterator out(dst, win_out); |
| |
| const bool multiply_alpha = !(helpers::float_ops::is_one(alpha)); |
| |
| const float16x8_t alpha_f16 = vdupq_n_f16(alpha); |
| |
| execute_window_loop(win_out, [&](const Coordinates &) |
| { |
| int x = window_start_x; |
| // Here we don't check for x lower equal than (window_end_x - window_step_x) because of |
| // window_end_x is computed above which may cause out-of-bound writes to the dst. |
| for(; x < (window_end_x - window_step_x); x += window_step_x) |
| { |
| if(x > width_matrix_b) |
| { |
| return; |
| } |
| |
| auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x; |
| |
| float16x8_t acc0 = vdupq_n_f16(0.f); |
| float16x8_t acc1 = vdupq_n_f16(0.f); |
| float16x8_t acc2 = vdupq_n_f16(0.f); |
| float16x8_t acc3 = vdupq_n_f16(0.f); |
| |
| auto vec_a = reinterpret_cast<const float16_t *>(ina.ptr()); |
| const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a; |
| for(; vec_a <= (vec_a_end_addr - 4);) |
| { |
| const float16x4_t a0l = vld1_f16(vec_a); |
| |
| float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride); |
| float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride); |
| float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride); |
| float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride); |
| float16x8_t b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride); |
| float16x8_t b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride); |
| float16x8_t b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride); |
| float16x8_t b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride); |
| |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 0)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 0)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 0)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 0)); |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 1)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 1)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 1)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 1)); |
| |
| matrix_b += 2 * in_b_stride; |
| |
| b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride); |
| b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride); |
| b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride); |
| b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride); |
| b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride); |
| b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride); |
| b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride); |
| b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride); |
| |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 2)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 2)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 2)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 2)); |
| acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 3)); |
| acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 3)); |
| acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 3)); |
| acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 3)); |
| |
| vec_a += 4; |
| matrix_b += 2 * in_b_stride; |
| } |
| |
| for(; vec_a < vec_a_end_addr; ++vec_a) |
| { |
| const float16_t a0 = *vec_a; |
| const float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride); |
| const float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride); |
| const float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride); |
| const float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride); |
| |
| acc0 = vaddq_f16(acc0, vmulq_n_f16(b00, a0)); |
| acc1 = vaddq_f16(acc1, vmulq_n_f16(b01, a0)); |
| acc2 = vaddq_f16(acc2, vmulq_n_f16(b02, a0)); |
| acc3 = vaddq_f16(acc3, vmulq_n_f16(b03, a0)); |
| |
| matrix_b += in_b_stride; |
| } |
| |
| // Multiply by the weight of matrix product (alpha) |
| if(multiply_alpha) |
| { |
| acc0 = vmulq_f16(acc0, alpha_f16); |
| acc1 = vmulq_f16(acc1, alpha_f16); |
| acc2 = vmulq_f16(acc2, alpha_f16); |
| acc3 = vmulq_f16(acc3, alpha_f16); |
| } |
| |
| auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x; |
| |
| vst1q_f16(vec_out + 0, acc0); |
| vst1q_f16(vec_out + 8, acc1); |
| vst1q_f16(vec_out + 16, acc2); |
| vst1q_f16(vec_out + 24, acc3); |
| } |
| |
| for(; x < window_end_x; ++x) |
| { |
| if(x > width_matrix_b) |
| { |
| return; |
| } |
| |
| auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x; |
| |
| float16x4_t vacc = vdup_n_f16(0.f); |
| |
| auto vec_a = reinterpret_cast<const float16_t *>(ina.ptr()); |
| const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a; |
| for(; vec_a <= (vec_a_end_addr - 4); vec_a += 4) |
| { |
| const float16x4_t a0l = vld1_f16(vec_a); |
| |
| const float16x4_t b_col = |
| { |
| *(matrix_b + 0 * in_b_stride), |
| *(matrix_b + 1 * in_b_stride), |
| *(matrix_b + 2 * in_b_stride), |
| *(matrix_b + 3 * in_b_stride), |
| }; |
| |
| vacc = vadd_f16(vacc, vmul_f16(a0l, b_col)); |
| |
| matrix_b += 4 * in_b_stride; |
| } |
| |
| float16_t acc = vget_lane_f16(vacc, 0) + vget_lane_f16(vacc, 1) + vget_lane_f16(vacc, 2) + vget_lane_f16(vacc, 3); |
| |
| for(; vec_a < vec_a_end_addr; ++vec_a) |
| { |
| const float16_t a0 = *vec_a; |
| const float16_t b00 = *matrix_b; |
| |
| acc += b00 * a0; |
| |
| matrix_b += in_b_stride; |
| } |
| |
| // Multiply by the weight of matrix product (alpha) |
| if(multiply_alpha) |
| { |
| acc *= static_cast<float16_t>(alpha); |
| } |
| |
| auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x; |
| |
| *(vec_out) = acc; |
| } |
| }, |
| ina, inb, out); |
| } |
| |
| 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, 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 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); |
| } |
| |
| void neon_fp16_gemm_matrix_mul(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha, const bool is_dst_vector) |
| { |
| return (is_dst_vector) ? vector_matrix_multiply_f16(lhs, rhs, dst, window, info, alpha) : matrix_matrix_multiply_f16(lhs, rhs, dst, window, info, alpha); |
| } |
| } // namespce cpu |
| } // namespace arm_compute |
| #endif //__ARM_FEATURE_FP16_VECTOR_ARITHMETIC |