CpuMul changes to enable fp16 in armv8a multi_isa builds
* Moved fp16 and fp32 to their corresponding files
src/cpu/kernels/mul/generic/neon/fp16.cpp and
src/cpu/kernels/mul/generic/neon/fp32.cpp
* Changes in filelist.json: added a new fp16.cpp file for the float16_t kernels
* Partially resolves MLCE-1102
Change-Id: I88f24cf034c11b55ff84644b182ba76c7cb94296
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10778
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
diff --git a/Android.bp b/Android.bp
index c4bf740..31ec9b2 100644
--- a/Android.bp
+++ b/Android.bp
@@ -543,6 +543,8 @@
"src/cpu/kernels/meanstddevnorm/generic/neon/fp32.cpp",
"src/cpu/kernels/meanstddevnorm/generic/neon/impl.cpp",
"src/cpu/kernels/meanstddevnorm/generic/neon/qasymm8.cpp",
+ "src/cpu/kernels/mul/generic/neon/fp16.cpp",
+ "src/cpu/kernels/mul/generic/neon/fp32.cpp",
"src/cpu/kernels/norm_layer/generic/neon/fp16.cpp",
"src/cpu/kernels/norm_layer/generic/neon/fp32.cpp",
"src/cpu/kernels/pool2d/neon/fp16.cpp",
diff --git a/filelist.json b/filelist.json
index ca8b18c..a84db71 100644
--- a/filelist.json
+++ b/filelist.json
@@ -1904,7 +1904,11 @@
"src/cpu/operators/CpuMul.cpp",
"src/cpu/kernels/CpuMulKernel.cpp",
"src/runtime/NEON/functions/NEPixelWiseMultiplication.cpp"
- ]
+ ],
+ "neon":{
+ "fp16":["src/cpu/kernels/mul/generic/neon/fp16.cpp"],
+ "fp32":["src/cpu/kernels/mul/generic/neon/fp32.cpp"]
+ }
}
},
"Normalize": {
diff --git a/src/BUILD.bazel b/src/BUILD.bazel
index 6ffc2eb..42841fe 100644
--- a/src/BUILD.bazel
+++ b/src/BUILD.bazel
@@ -794,6 +794,8 @@
"cpu/kernels/meanstddevnorm/generic/neon/fp32.cpp",
"cpu/kernels/meanstddevnorm/generic/neon/impl.cpp",
"cpu/kernels/meanstddevnorm/generic/neon/qasymm8.cpp",
+ "cpu/kernels/mul/generic/neon/fp16.cpp",
+ "cpu/kernels/mul/generic/neon/fp32.cpp",
"cpu/kernels/norm_layer/generic/neon/fp16.cpp",
"cpu/kernels/norm_layer/generic/neon/fp32.cpp",
"cpu/kernels/pool2d/neon/fp16.cpp",
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 55169b6..1de9e63 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -785,6 +785,8 @@
cpu/kernels/meanstddevnorm/generic/neon/fp32.cpp
cpu/kernels/meanstddevnorm/generic/neon/impl.cpp
cpu/kernels/meanstddevnorm/generic/neon/qasymm8.cpp
+ cpu/kernels/mul/generic/neon/fp16.cpp
+ cpu/kernels/mul/generic/neon/fp32.cpp
cpu/kernels/norm_layer/generic/neon/fp16.cpp
cpu/kernels/norm_layer/generic/neon/fp32.cpp
cpu/kernels/pool2d/neon/fp16.cpp
diff --git a/src/cpu/kernels/CpuMulKernel.cpp b/src/cpu/kernels/CpuMulKernel.cpp
index ba086e3..8001482 100644
--- a/src/cpu/kernels/CpuMulKernel.cpp
+++ b/src/cpu/kernels/CpuMulKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016-2022 Arm Limited.
+ * Copyright (c) 2016-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -26,12 +26,14 @@
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/TensorInfo.h"
+#include "src/core/common/Registrars.h"
#include "src/core/CPP/Validate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/core/NEON/NEAsymm.h"
#include "src/core/NEON/NESymm.h"
#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/cpu/kernels/mul/generic/neon/list.h"
#include <arm_neon.h>
@@ -1170,108 +1172,6 @@
}
}
-void mul_F32_F32_F32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale)
-{
- // Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape());
-
- // Clear X Dimension on execution window as we handle manually
- Window win = window;
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- constexpr int window_step_x = 16 / sizeof(float);
- const auto window_start_x = static_cast<int>(window.x().start());
- const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x();
-
- using ExactTagType = typename wrapper::traits::neon_vector<float, window_step_x>::tag_type;
-
- if (is_broadcast_across_x)
- {
- const bool is_broadcast_input_2 = input2_win.x().step() == 0;
- Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
- Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1;
-
- // Clear X Dimension on execution window as we handle manually
- non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator broadcast_input(broadcast_tensor, broadcast_win);
- Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator dst(out, win);
-
- execute_window_loop(
- win,
- [&](const Coordinates &)
- {
- const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr());
- const auto output_ptr = reinterpret_cast<float *>(dst.ptr());
-
- const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr());
- const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{});
- const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
-
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x);
- auto res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec);
- wrapper::vstore(output_ptr + x, res);
- }
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
- *(output_ptr + x) = broadcast_value * non_broadcast_v * scale;
- }
- },
- broadcast_input, non_broadcast_input, dst);
- }
- else
- {
- // Clear X Dimension on execution window as we handle manually
- input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator input1(src1, input1_win);
- Iterator input2(src2, input2_win);
- Iterator dst(out, win);
-
- execute_window_loop(
- win,
- [&](const Coordinates &)
- {
- const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr());
- const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr());
- const auto output_ptr = reinterpret_cast<float *>(dst.ptr());
-
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const auto ta1 = wrapper::vloadq(input1_ptr + x);
- const auto ta2 = wrapper::vloadq(input2_ptr + x);
- const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
- const auto res = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec);
- wrapper::vstore(output_ptr + x, res);
- }
-
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- const auto ta1 = *(input1_ptr + x);
- const auto ta2 = *(input2_ptr + x);
- *(output_ptr + x) = ta1 * ta2 * scale;
- }
- },
- input1, input2, dst);
- }
-}
-
void c_mul_F32_F32_F32_n(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window)
{
// Create input windows
@@ -1409,115 +1309,6 @@
}
}
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void mul_F16_F16_F16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale)
-{
- // Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape());
-
- // Clear X Dimension on execution window as we handle manually
- Window win = window;
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
- 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());
- const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x();
- if (is_broadcast_across_x)
- {
- const bool is_broadcast_input_2 = input2_win.x().step() == 0;
- Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
- Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1;
- // Clear X Dimension on execution window as we handle manually
- non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator broadcast_input(broadcast_tensor, broadcast_win);
- Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator dst(out, win);
- execute_window_loop(
- win,
- [&](const Coordinates &)
- {
- const auto non_broadcast_input_ptr = reinterpret_cast<const float16_t *>(non_broadcast_input.ptr());
- const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr());
- const auto broadcast_value = *reinterpret_cast<const float16_t *>(broadcast_input.ptr());
- const float16x8x2_t broadcast_value_vec = {{
- vdupq_n_f16(broadcast_value),
- vdupq_n_f16(broadcast_value),
- }};
- const auto scale_vec = vdupq_n_f16(scale);
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const float16x8x2_t non_broadcast_v = {{
- vld1q_f16(non_broadcast_input_ptr + x),
- vld1q_f16(non_broadcast_input_ptr + x + 8),
- }};
- const float16x8x2_t result = {{
- vmulq_f16(vmulq_f16(broadcast_value_vec.val[0], non_broadcast_v.val[0]), scale_vec),
- vmulq_f16(vmulq_f16(broadcast_value_vec.val[1], non_broadcast_v.val[1]), scale_vec),
- }};
- vst1q_f16(output_ptr + x, result.val[0]);
- vst1q_f16(output_ptr + x + 8, result.val[1]);
- }
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
- *(output_ptr + x) = broadcast_value * non_broadcast_v * scale;
- }
- },
- broadcast_input, non_broadcast_input, dst);
- }
- else
- {
- input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(src1, input1_win);
- Iterator input2(src2, input2_win);
- Iterator dst(out, win);
- execute_window_loop(
- win,
- [&](const Coordinates &)
- {
- const auto input1_ptr = reinterpret_cast<const float16_t *>(input1.ptr());
- const auto input2_ptr = reinterpret_cast<const float16_t *>(input2.ptr());
- const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr());
- // Compute window_step_x elements per iteration
- int x = window_start_x;
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const float16x8x2_t ta1 = {{
- vld1q_f16(input1_ptr + x),
- vld1q_f16(input1_ptr + x + 8),
- }};
- const float16x8x2_t ta2 = {{
- vld1q_f16(input2_ptr + x),
- vld1q_f16(input2_ptr + x + 8),
- }};
- const float16x8_t scale_vec = vdupq_n_f16(scale);
- const float16x8x2_t result = {{
- vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec),
- vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec),
- }};
- vst1q_f16(output_ptr + x, result.val[0]);
- vst1q_f16(output_ptr + x + 8, result.val[1]);
- }
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- const auto ta1 = *(input1_ptr + x);
- const auto ta2 = *(input2_ptr + x);
- *(output_ptr + x) = ta1 * ta2 * scale;
- }
- },
- input1, input2, dst);
- }
-}
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
template <bool is_scale255, bool is_sat>
void mul_U8_U8_S16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n)
{
@@ -1857,13 +1648,11 @@
}
}
break;
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
- _func_float = &mul_F16_F16_F16;
+ _func_float = REGISTER_FP16_NEON(cpu::mul_F16_F16_F16);
break;
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
case DataType::F32:
- _func_float = &mul_F32_F32_F32;
+ _func_float = REGISTER_FP32_NEON(cpu::mul_F32_F32_F32);
break;
default:
ARM_COMPUTE_ERROR("You called with the wrong img formats");
diff --git a/src/cpu/kernels/mul/generic/neon/fp16.cpp b/src/cpu/kernels/mul/generic/neon/fp16.cpp
new file mode 100644
index 0000000..920f298
--- /dev/null
+++ b/src/cpu/kernels/mul/generic/neon/fp16.cpp
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 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.
+ */
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
+
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+
+#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/cpu/CpuTypes.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void mul_F16_F16_F16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale)
+{
+ // Create input windows
+ Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape());
+
+ // Clear X Dimension on execution window as we handle manually
+ Window win = window;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ 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());
+ const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x();
+ if (is_broadcast_across_x)
+ {
+ const bool is_broadcast_input_2 = input2_win.x().step() == 0;
+ Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
+ Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1;
+ // Clear X Dimension on execution window as we handle manually
+ non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ Iterator broadcast_input(broadcast_tensor, broadcast_win);
+ Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
+ Iterator dst(out, win);
+ execute_window_loop(
+ win,
+ [&](const Coordinates &)
+ {
+ const auto non_broadcast_input_ptr = reinterpret_cast<const float16_t *>(non_broadcast_input.ptr());
+ const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr());
+ const auto broadcast_value = *reinterpret_cast<const float16_t *>(broadcast_input.ptr());
+ const float16x8x2_t broadcast_value_vec = {{
+ vdupq_n_f16(broadcast_value),
+ vdupq_n_f16(broadcast_value),
+ }};
+ const auto scale_vec = vdupq_n_f16(scale);
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for (; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const float16x8x2_t non_broadcast_v = {{
+ vld1q_f16(non_broadcast_input_ptr + x),
+ vld1q_f16(non_broadcast_input_ptr + x + 8),
+ }};
+ const float16x8x2_t result = {{
+ vmulq_f16(vmulq_f16(broadcast_value_vec.val[0], non_broadcast_v.val[0]), scale_vec),
+ vmulq_f16(vmulq_f16(broadcast_value_vec.val[1], non_broadcast_v.val[1]), scale_vec),
+ }};
+ vst1q_f16(output_ptr + x, result.val[0]);
+ vst1q_f16(output_ptr + x + 8, result.val[1]);
+ }
+ // Compute left-over elements
+ for (; x < window_end_x; ++x)
+ {
+ const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
+ *(output_ptr + x) = broadcast_value * non_broadcast_v * scale;
+ }
+ },
+ broadcast_input, non_broadcast_input, dst);
+ }
+ else
+ {
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ Iterator input1(src1, input1_win);
+ Iterator input2(src2, input2_win);
+ Iterator dst(out, win);
+ execute_window_loop(
+ win,
+ [&](const Coordinates &)
+ {
+ const auto input1_ptr = reinterpret_cast<const float16_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const float16_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr());
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for (; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const float16x8x2_t ta1 = {{
+ vld1q_f16(input1_ptr + x),
+ vld1q_f16(input1_ptr + x + 8),
+ }};
+ const float16x8x2_t ta2 = {{
+ vld1q_f16(input2_ptr + x),
+ vld1q_f16(input2_ptr + x + 8),
+ }};
+ const float16x8_t scale_vec = vdupq_n_f16(scale);
+ const float16x8x2_t result = {{
+ vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec),
+ vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec),
+ }};
+ vst1q_f16(output_ptr + x, result.val[0]);
+ vst1q_f16(output_ptr + x + 8, result.val[1]);
+ }
+ // Compute left-over elements
+ for (; x < window_end_x; ++x)
+ {
+ const auto ta1 = *(input1_ptr + x);
+ const auto ta2 = *(input2_ptr + x);
+ *(output_ptr + x) = ta1 * ta2 * scale;
+ }
+ },
+ input1, input2, dst);
+ }
+}
+} // namespace cpu
+} // namespace arm_compute
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
diff --git a/src/cpu/kernels/mul/generic/neon/fp32.cpp b/src/cpu/kernels/mul/generic/neon/fp32.cpp
new file mode 100644
index 0000000..3001eb5
--- /dev/null
+++ b/src/cpu/kernels/mul/generic/neon/fp32.cpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 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.
+ */
+
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+
+#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/cpu/CpuTypes.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void mul_F32_F32_F32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale)
+{
+ // Create input windows
+ Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape());
+
+ // Clear X Dimension on execution window as we handle manually
+ Window win = window;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ constexpr int window_step_x = 16 / sizeof(float);
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+ const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x();
+
+ using ExactTagType = typename wrapper::traits::neon_vector<float, window_step_x>::tag_type;
+
+ if (is_broadcast_across_x)
+ {
+ const bool is_broadcast_input_2 = input2_win.x().step() == 0;
+ Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
+ Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1;
+
+ // Clear X Dimension on execution window as we handle manually
+ non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator broadcast_input(broadcast_tensor, broadcast_win);
+ Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
+ Iterator dst(out, win);
+
+ execute_window_loop(
+ win,
+ [&](const Coordinates &)
+ {
+ const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr());
+ const auto output_ptr = reinterpret_cast<float *>(dst.ptr());
+
+ const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr());
+ const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{});
+ const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for (; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x);
+ auto res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec);
+ wrapper::vstore(output_ptr + x, res);
+ }
+
+ // Compute left-over elements
+ for (; x < window_end_x; ++x)
+ {
+ const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
+ *(output_ptr + x) = broadcast_value * non_broadcast_v * scale;
+ }
+ },
+ broadcast_input, non_broadcast_input, dst);
+ }
+ else
+ {
+ // Clear X Dimension on execution window as we handle manually
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input1(src1, input1_win);
+ Iterator input2(src2, input2_win);
+ Iterator dst(out, win);
+
+ execute_window_loop(
+ win,
+ [&](const Coordinates &)
+ {
+ const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<float *>(dst.ptr());
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for (; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto ta1 = wrapper::vloadq(input1_ptr + x);
+ const auto ta2 = wrapper::vloadq(input2_ptr + x);
+ const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
+ const auto res = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec);
+ wrapper::vstore(output_ptr + x, res);
+ }
+
+ // Compute left-over elements
+ for (; x < window_end_x; ++x)
+ {
+ const auto ta1 = *(input1_ptr + x);
+ const auto ta2 = *(input2_ptr + x);
+ *(output_ptr + x) = ta1 * ta2 * scale;
+ }
+ },
+ input1, input2, dst);
+ }
+}
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/cpu/kernels/mul/generic/neon/list.h b/src/cpu/kernels/mul/generic/neon/list.h
new file mode 100644
index 0000000..710cb68
--- /dev/null
+++ b/src/cpu/kernels/mul/generic/neon/list.h
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 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.
+ */
+#ifndef ACL_SRC_CPU_KERNELS_MUL_GENERIC_NEON_LIST_H
+#define ACL_SRC_CPU_KERNELS_MUL_GENERIC_NEON_LIST_H
+namespace arm_compute
+{
+namespace cpu
+{
+#define DECLARE_MUL_KERNEL(func_name) \
+ void func_name(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale)
+
+DECLARE_MUL_KERNEL(mul_F32_F32_F32);
+DECLARE_MUL_KERNEL(mul_F16_F16_F16);
+#undef DECLARE_MUL_KERNEL
+} // namespace cpu
+} // namespace arm_compute
+#endif // ACL_SRC_CPU_KERNELS_MUL_GENERIC_NEON_LIST_H