From f57d6ec5ff4305d2e388730f6dad004908e6e97a Mon Sep 17 00:00:00 2001 From: Pablo Marquez Tello Date: Tue, 19 Sep 2023 14:46:07 +0100 Subject: Gemm changes to enable fp16 in armv8a multi_isa builds * Code guarded with __ARM_FEATURE_FP16_VECTOR_ARITHMETIC needs to be moved to an fp16.cpp file to allow compilation with -march=armv8.2-a+fp16 * fp16.cpp needs to use the templates vector_matrix_multiply_f16() and matrix_matrix_multiply_f16 which had to be moved from impl.cpp to fp16.cpp * Partially resolves MLCE-1102 Change-Id: Ic87440797d6f1653c815ab6565972206f5afd0ad Signed-off-by: Pablo Marquez Tello Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10345 Benchmark: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Viet-Hoa Do Comments-Addressed: Arm Jenkins --- .../kernels/gemm_matrix_mul/generic/neon/fp16.cpp | 376 +++++++++++++++++++- .../kernels/gemm_matrix_mul/generic/neon/impl.cpp | 378 +-------------------- .../kernels/gemm_matrix_mul/generic/neon/impl.h | 9 +- 3 files changed, 377 insertions(+), 386 deletions(-) (limited to 'src/cpu/kernels/gemm_matrix_mul') diff --git a/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp b/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp index 1bd5a57fab..fae26a5dd6 100644 --- a/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp +++ b/src/cpu/kernels/gemm_matrix_mul/generic/neon/fp16.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022 Arm Limited. + * Copyright (c) 2022-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -24,11 +24,385 @@ #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 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(dst->info()->dimension(0)); + const auto in_b_stride = static_cast(rhs->info()->strides_in_bytes()[1] / rhs->info()->element_size()); + const auto num_elems_vec_a = static_cast(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(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(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(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(inb.ptr()) + x; + + float16x4_t vacc = vdup_n_f16(0.f); + + auto vec_a = reinterpret_cast(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(alpha); + } + + auto vec_out = reinterpret_cast(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(dst->info()->dimension(0)); + const int out_height = static_cast(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(ina.ptr()); + const auto *mtx_b0 = reinterpret_cast(inb.ptr()); + auto *mtx_out = reinterpret_cast(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); diff --git a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp index 300dc3ffc7..0051d3d9dc 100644 --- a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp +++ b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2022 Arm Limited. + * Copyright (c) 2017-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -31,202 +31,6 @@ namespace arm_compute { namespace cpu { -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -void vector_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha) -{ - const auto width_matrix_b = static_cast(dst->info()->dimension(0)); - const auto in_b_stride = static_cast(rhs->info()->strides_in_bytes()[1] / rhs->info()->element_size()); - const auto num_elems_vec_a = static_cast(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(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(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(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(inb.ptr()) + x; - - float16x4_t vacc = vdup_n_f16(0.f); - - auto vec_a = reinterpret_cast(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(alpha); - } - - auto vec_out = reinterpret_cast(out.ptr()) + x; - - *(vec_out) = acc; - } - }, - ina, inb, out); -} -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - void vector_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha) { const auto width_matrix_b = static_cast(dst->info()->dimension(0)); @@ -831,186 +635,6 @@ void matrix_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor }, 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(dst->info()->dimension(0)); - const int out_height = static_cast(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(ina.ptr()); - const auto *mtx_b0 = reinterpret_cast(inb.ptr()); - auto *mtx_out = reinterpret_cast(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 */ - } // namespace cpu } // namespace arm_compute diff --git a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h index 6bf865a624..f9f1f247ac 100644 --- a/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h +++ b/src/cpu/kernels/gemm_matrix_mul/generic/neon/impl.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022 Arm Limited. + * Copyright (c) 2022-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -30,13 +30,6 @@ namespace arm_compute { namespace cpu { -#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); - -void matrix_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha); - -#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); void matrix_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha); -- cgit v1.2.1