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 <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10345
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp b/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp
index 2d61b72..505a371 100644
--- a/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp
+++ b/src/cpu/kernels/gemm_matrix_add/generic/neon/fp16.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -25,10 +25,55 @@
#include "src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h"
+#include <arm_neon.h>
+
namespace arm_compute
{
namespace cpu
{
+namespace
+{
+void matrix_addition_f16(const ITensor *src, ITensor *dst, const Window &window, float beta)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ const float16x8_t beta_f16 = vdupq_n_f16(beta);
+
+ constexpr int window_step_x = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ Window win = window.collapse_if_possible(window, Window::DimZ);
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(src, win);
+ Iterator out(dst, win);
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto in_ptr = reinterpret_cast<const float16_t *>(in.ptr());
+ const auto out_ptr = reinterpret_cast<float16_t *>(out.ptr());
+
+ int x = window_start_x;
+ for(; x < (window_end_x - window_step_x); x += window_step_x)
+ {
+ float16x8x2_t alpha_ab = vld2q_f16(out_ptr + x);
+ const float16x8x2_t c = vld2q_f16(in_ptr + x);
+ // Multiply matrix C by its weight and accumulate
+ alpha_ab.val[0] = vaddq_f16(alpha_ab.val[0], vmulq_f16(c.val[0], beta_f16));
+ alpha_ab.val[1] = vaddq_f16(alpha_ab.val[1], vmulq_f16(c.val[1], beta_f16));
+
+ vst2q_f16(out_ptr + x, alpha_ab);
+ }
+
+ // Left-over loop
+ for(; x < window_end_x; ++x)
+ {
+ *(out_ptr + x) += *(in_ptr + x) * static_cast<float16_t>(beta);
+ }
+ },
+ in, out);
+}
+} // namespace
void neon_fp16_gemm_matrix_add(const ITensor *src, ITensor *dst, const Window &window, float beta)
{
return matrix_addition_f16(src, dst, window, beta);
diff --git a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp
index 675ed1b..dd0384c 100644
--- a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp
+++ b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016-2022 Arm Limited.
+ * Copyright (c) 2016-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -72,48 +72,5 @@
},
in, out);
}
-
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void matrix_addition_f16(const ITensor *src, ITensor *dst, const Window &window, float beta)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
- const float16x8_t beta_f16 = vdupq_n_f16(beta);
-
- constexpr int window_step_x = 16;
- const auto window_start_x = static_cast<int>(window.x().start());
- const auto window_end_x = static_cast<int>(window.x().end());
-
- Window win = window.collapse_if_possible(window, Window::DimZ);
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator in(src, win);
- Iterator out(dst, win);
-
- execute_window_loop(win, [&](const Coordinates &)
- {
- const auto in_ptr = reinterpret_cast<const float16_t *>(in.ptr());
- const auto out_ptr = reinterpret_cast<float16_t *>(out.ptr());
-
- int x = window_start_x;
- for(; x < (window_end_x - window_step_x); x += window_step_x)
- {
- float16x8x2_t alpha_ab = vld2q_f16(out_ptr + x);
- const float16x8x2_t c = vld2q_f16(in_ptr + x);
- // Multiply matrix C by its weight and accumulate
- alpha_ab.val[0] = vaddq_f16(alpha_ab.val[0], vmulq_f16(c.val[0], beta_f16));
- alpha_ab.val[1] = vaddq_f16(alpha_ab.val[1], vmulq_f16(c.val[1], beta_f16));
-
- vst2q_f16(out_ptr + x, alpha_ab);
- }
-
- // Left-over loop
- for(; x < window_end_x; ++x)
- {
- *(out_ptr + x) += *(in_ptr + x) * static_cast<float16_t>(beta);
- }
- },
- in, out);
-}
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
} // namespace cpu
} // namespace arm_compute
diff --git a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h
index ff35f28..26ac99b 100644
--- a/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h
+++ b/src/cpu/kernels/gemm_matrix_add/generic/neon/impl.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -30,10 +30,6 @@
{
namespace cpu
{
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void matrix_addition_f16(const ITensor *src, ITensor *dst, const Window &window, float beta);
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-
void matrix_addition_f32(const ITensor *src, ITensor *dst, const Window &window, float beta);
} // namespace cpu
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 1bd5a57..fae26a5 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 <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);
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 300dc3f..0051d3d 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 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<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);
-}
-#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));
@@ -831,186 +635,6 @@
},
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, 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);
-}
-#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 6bf865a..f9f1f24 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 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);