L2Norm 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 template l2_normalize_x() and
l2_normalize_yz which had to be moved from impl.cpp to impl.h
* Removed impl.cpp
* Partially resolves MLCE-1102
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Change-Id: Id00a823730108293fc712295a178dad80588af30
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10344
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/Android.bp b/Android.bp
index 1603739..696942c 100644
--- a/Android.bp
+++ b/Android.bp
@@ -531,7 +531,6 @@
"src/cpu/kernels/internal/CpuPool2dAssemblyWrapperKernel.cpp",
"src/cpu/kernels/l2normlayer/generic/neon/fp16.cpp",
"src/cpu/kernels/l2normlayer/generic/neon/fp32.cpp",
- "src/cpu/kernels/l2normlayer/generic/neon/impl.cpp",
"src/cpu/kernels/lut/generic/neon/u8.cpp",
"src/cpu/kernels/maxunpool/generic/neon/fp16.cpp",
"src/cpu/kernels/maxunpool/generic/neon/fp32.cpp",
diff --git a/filelist.json b/filelist.json
index e4627f8..215b363 100644
--- a/filelist.json
+++ b/filelist.json
@@ -1792,7 +1792,6 @@
"src/runtime/NEON/functions/NEL2NormalizeLayer.cpp"
],
"neon":{
- "common":["src/cpu/kernels/l2normlayer/generic/neon/impl.cpp"],
"fp32":["src/cpu/kernels/l2normlayer/generic/neon/fp32.cpp"],
"fp16":["src/cpu/kernels/l2normlayer/generic/neon/fp16.cpp"]
}
diff --git a/src/BUILD.bazel b/src/BUILD.bazel
index 3b42839..d4d1fc8 100644
--- a/src/BUILD.bazel
+++ b/src/BUILD.bazel
@@ -779,7 +779,6 @@
"cpu/kernels/internal/CpuPool2dAssemblyWrapperKernel.cpp",
"cpu/kernels/l2normlayer/generic/neon/fp16.cpp",
"cpu/kernels/l2normlayer/generic/neon/fp32.cpp",
- "cpu/kernels/l2normlayer/generic/neon/impl.cpp",
"cpu/kernels/lut/generic/neon/u8.cpp",
"cpu/kernels/maxunpool/generic/neon/fp16.cpp",
"cpu/kernels/maxunpool/generic/neon/fp32.cpp",
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 0b3da44..ee1ff47 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -771,7 +771,6 @@
cpu/kernels/internal/CpuPool2dAssemblyWrapperKernel.cpp
cpu/kernels/l2normlayer/generic/neon/fp16.cpp
cpu/kernels/l2normlayer/generic/neon/fp32.cpp
- cpu/kernels/l2normlayer/generic/neon/impl.cpp
cpu/kernels/lut/generic/neon/u8.cpp
cpu/kernels/maxunpool/generic/neon/fp16.cpp
cpu/kernels/maxunpool/generic/neon/fp32.cpp
diff --git a/src/cpu/kernels/l2normlayer/generic/neon/impl.cpp b/src/cpu/kernels/l2normlayer/generic/neon/impl.cpp
deleted file mode 100644
index 2886537..0000000
--- a/src/cpu/kernels/l2normlayer/generic/neon/impl.cpp
+++ /dev/null
@@ -1,131 +0,0 @@
-/*
- * Copyright (c) 2017-2022 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 "src/cpu/kernels/l2normlayer/generic/neon/impl.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "src/core/NEON/wrapper/wrapper.h"
-#include "src/core/common/Registrars.h"
-
-#include <cstddef>
-
-namespace arm_compute
-{
-namespace cpu
-{
-template <typename T, int S>
-void l2_normalize_x(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window)
-{
- using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
-
- const int window_step_x = 16 / data_size_from_type(in->info()->data_type());
- const auto window_start_x = static_cast<int>(window.x().start());
- const auto window_end_x = static_cast<int>(window.x().end());
-
- Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
- win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator input_it(in, win_collapsed);
- Iterator sum_it(sum, win_collapsed);
- Iterator output_it(out, win_collapsed);
-
- execute_window_loop(win_collapsed, [&](const Coordinates &)
- {
- const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr());
- const auto out_ptr = reinterpret_cast<T *>(output_it.ptr());
-
- const T sum_value = *reinterpret_cast<const T *>(sum_it.ptr());
- const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_value, static_cast<T>(epsilon)));
- const auto vec_norm_value = wrapper::vdup_n(norm_value, ExactTagType{});
-
- // Compute elements over vector steps
- int x = window_start_x;
- for(; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value));
- }
-
- // Compute left-over elements
- for(; x < window_end_x; ++x)
- {
- out_ptr[x] = in_ptr[x] * norm_value;
- }
- },
- input_it, sum_it, output_it);
-}
-
-template <typename T, int S>
-void l2_normalize_yz(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis)
-{
- using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
-
- const int window_step_x = 16 / data_size_from_type(in->info()->data_type());
- 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;
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Window window_sum(win);
- window_sum.set(axis, Window::Dimension(0, 0, 0));
-
- Iterator input_it(in, win);
- Iterator sum_it(sum, window_sum);
- Iterator output_it(out, win);
-
- const auto vec_eps = wrapper::vdup_n(static_cast<T>(epsilon), ExactTagType{});
-
- execute_window_loop(win, [&](const Coordinates &)
- {
- const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr());
- const auto sum_ptr = reinterpret_cast<const T *>(sum_it.ptr());
- const auto out_ptr = reinterpret_cast<T *>(output_it.ptr());
-
- // Compute elements over vector steps
- int x = window_start_x;
- for(; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const auto vec_norm_value = wrapper::vinvsqrt(wrapper::vmax(wrapper::vloadq(sum_ptr + x), vec_eps));
- wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value));
- }
-
- // Compute left-over elements
- for(; x < window_end_x; ++x)
- {
- const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_ptr[x], static_cast<T>(epsilon)));
- out_ptr[x] = in_ptr[x] * norm_value;
- }
- },
- input_it, sum_it, output_it);
-}
-
-template void l2_normalize_yz<float, 4>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis);
-template void l2_normalize_x<float, 4>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window);
-
-#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
-template void l2_normalize_yz<float16_t, 8>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis);
-template void l2_normalize_x<float16_t, 8>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window);
-#endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
-} // namespace cpu
-} // namespace arm_compute
diff --git a/src/cpu/kernels/l2normlayer/generic/neon/impl.h b/src/cpu/kernels/l2normlayer/generic/neon/impl.h
index 98391fb..a06cdd3 100644
--- a/src/cpu/kernels/l2normlayer/generic/neon/impl.h
+++ b/src/cpu/kernels/l2normlayer/generic/neon/impl.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2017-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,21 +24,102 @@
#ifndef SRC_CORE_NEON_KERNELS_L2NORMLAYER_LIST_H
#define SRC_CORE_NEON_KERNELS_L2NORMLAYER_LIST_H
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/common/Registrars.h"
+
#include <cstddef>
namespace arm_compute
{
-class ITensor;
-class Window;
-
namespace cpu
{
template <typename T, int S>
-void l2_normalize_x(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window);
+void l2_normalize_x(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window)
+{
+ using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
+
+ const int window_step_x = 16 / data_size_from_type(in->info()->data_type());
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+ win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input_it(in, win_collapsed);
+ Iterator sum_it(sum, win_collapsed);
+ Iterator output_it(out, win_collapsed);
+
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr());
+ const auto out_ptr = reinterpret_cast<T *>(output_it.ptr());
+
+ const T sum_value = *reinterpret_cast<const T *>(sum_it.ptr());
+ const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_value, static_cast<T>(epsilon)));
+ const auto vec_norm_value = wrapper::vdup_n(norm_value, ExactTagType{});
+
+ // Compute elements over vector steps
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ out_ptr[x] = in_ptr[x] * norm_value;
+ }
+ },
+ input_it, sum_it, output_it);
+}
template <typename T, int S>
-void l2_normalize_yz(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis);
+void l2_normalize_yz(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis)
+{
+ using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type;
+ const int window_step_x = 16 / data_size_from_type(in->info()->data_type());
+ 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;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Window window_sum(win);
+ window_sum.set(axis, Window::Dimension(0, 0, 0));
+
+ Iterator input_it(in, win);
+ Iterator sum_it(sum, window_sum);
+ Iterator output_it(out, win);
+
+ const auto vec_eps = wrapper::vdup_n(static_cast<T>(epsilon), ExactTagType{});
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr());
+ const auto sum_ptr = reinterpret_cast<const T *>(sum_it.ptr());
+ const auto out_ptr = reinterpret_cast<T *>(output_it.ptr());
+
+ // Compute elements over vector steps
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto vec_norm_value = wrapper::vinvsqrt(wrapper::vmax(wrapper::vloadq(sum_ptr + x), vec_eps));
+ wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_ptr[x], static_cast<T>(epsilon)));
+ out_ptr[x] = in_ptr[x] * norm_value;
+ }
+ },
+ input_it, sum_it, output_it);
+}
} // namespace cpu
} // namespace arm_compute
#endif //SRC_CORE_NEON_KERNELS_L2NORMLAYER_LIST_H