Decouple CpuActivationKernel
1- Data types were already decoupled. This commit arrange the folder struct of the activation kernel.
2- Refactor NEON CpuActivationKernel for floating-point cases.
Resolves COMPMID-4636
Change-Id: Ia4527244c84260dce1dd1d4bd4a9e3cfe2486d85
Signed-off-by: Dana Zlotnik <dana.zlotnik@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6739
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/src/cpu/kernels/activation/generic/neon/impl.h b/src/cpu/kernels/activation/generic/neon/impl.h
new file mode 100644
index 0000000..2dd239e
--- /dev/null
+++ b/src/cpu/kernels/activation/generic/neon/impl.h
@@ -0,0 +1,212 @@
+/*
+ * Copyright (c) 2020-2021 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/Helpers.h"
+#include "arm_compute/core/Window.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+namespace arm_compute
+{
+namespace cpu
+{
+/** Constant parameters needed by the activation implementation.
+ * These parameters differ for each floating type
+ *
+ * @note This are passed as a struct as C++ does not allow float as a template parameter until C++20
+ **/
+struct ActFpImplParams
+{
+ float delta; /**< Minimum delta needed to avoid NaN on corner-cases of elementary functions */
+ int step_x; /**< Window step at the x dimension */
+};
+
+#ifndef __aarch64__
+inline float32x4_t mask_float_vector(const float32x4_t &in, const uint32x4_t &mask)
+{
+ auto int_in = vreinterpretq_u32_f32(in);
+ return vreinterpretq_f32_u32(wrapper::vand(int_in, mask));
+}
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
+inline float16x8_t mask_float_vector(const float16x8_t &in, const uint16x8_t &mask)
+{
+ auto int_in = vreinterpretq_u16_f16(in);
+ return vreinterpretq_f16_u16(wrapper::vand(int_in, mask));
+}
+#endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
+#endif /* __aarch64__ */
+
+template <typename T, const ActFpImplParams &P>
+void fp_neon_activation_impl(const ITensor *src, ITensor *dst, const ActivationLayerInfo &act_info, const Window &window)
+{
+ /** SIMD vector tag type. */
+ using ExactTagType = typename arm_compute::wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
+ constexpr int window_step_x = P.step_x;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+ const ActivationLayerInfo::ActivationFunction act = act_info.activation();
+ Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+ win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+ Iterator input(src, win_collapsed);
+ Iterator output(dst, win_collapsed);
+ // In case of non-aarch64, a small delta value is added to the input
+ // to prevent NAN values caused by zeros in inputs to SQRT.
+ // In case of aarh64, we call vsqrt directly, so we don't use delta.
+#ifndef __aarch64__
+ const auto delta = wrapper::vdup_n(static_cast<T>(P.delta), ExactTagType {});
+#endif /* __aarch64__ */
+ const auto const_1 = wrapper::vdup_n(static_cast<T>(1.f), ExactTagType {});
+ const auto const_0 = wrapper::vdup_n(static_cast<T>(0.f), ExactTagType{});
+ const auto const_6 = wrapper::vdup_n(static_cast<T>(6.f), ExactTagType{});
+ const auto const_3 = wrapper::vdup_n(static_cast<T>(3.f), ExactTagType{});
+ const auto const_inv_6 = wrapper::vdup_n(static_cast<T>(0.166666667f), ExactTagType{});
+ constexpr float soft_relu_thresh = 12.f;
+ const auto vsoft_relu_thresh = wrapper::vdup_n(static_cast<T>(soft_relu_thresh), ExactTagType{});
+ const auto va = wrapper::vdup_n(static_cast<T>(act_info.a()), ExactTagType{});
+ const auto vb = wrapper::vdup_n(static_cast<T>(act_info.b()), ExactTagType{});
+ const auto a = static_cast<T>(act_info.a());
+ const auto b = static_cast<T>(act_info.b());
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ const auto input_ptr = reinterpret_cast<const T *>(input.ptr());
+ const auto output_ptr = reinterpret_cast<T *>(output.ptr());
+ wrapper::traits::neon_bitvector_t<T, wrapper::traits::BitWidth::W128> tmp;
+ // Compute S elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto vin = wrapper::vloadq(input_ptr + x);
+ switch(act)
+ {
+ case ActivationLayerInfo::ActivationFunction::ABS:
+ tmp = wrapper::vabs(vin);
+ break;
+ case ActivationLayerInfo::ActivationFunction::LINEAR:
+ tmp = wrapper::vmla(vb, va, vin);
+ break;
+ case ActivationLayerInfo::ActivationFunction::LOGISTIC:
+ tmp = wrapper::vinv(wrapper::vadd(const_1, wrapper::vexpq(wrapper::vneg(vin))));
+ break;
+ case ActivationLayerInfo::ActivationFunction::RELU:
+ tmp = wrapper::vmax(const_0, vin);
+ break;
+ case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
+ tmp = wrapper::vmin(va, wrapper::vmax(const_0, vin));
+ break;
+ case ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU:
+ tmp = wrapper::vmin(va, wrapper::vmax(vb, vin));
+ break;
+ case ActivationLayerInfo::ActivationFunction::LEAKY_RELU:
+ tmp = wrapper::vbsl(wrapper::vcgt(vin, const_0), vin, wrapper::vmul(va, vin));
+ break;
+ case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
+ tmp = wrapper::vbsl(wrapper::vcgt(vin, vsoft_relu_thresh), vin, wrapper::vlog(wrapper::vadd(const_1, wrapper::vexpq(vin))));
+ break;
+ case ActivationLayerInfo::ActivationFunction::ELU:
+ tmp = wrapper::vbsl(wrapper::vcge(vin, const_0), vin, wrapper::vmul(va, wrapper::vsub(wrapper::vexpq(vin), const_1)));
+ break;
+ case ActivationLayerInfo::ActivationFunction::SQRT:
+#ifdef __aarch64__
+ tmp = wrapper::vsqrt(vin);
+#else /* __aarch64__ */
+ {
+ const auto bitmask = wrapper::vceq(vin, wrapper::vdup_n(0.f, ExactTagType{}));
+ tmp = wrapper::vinv(wrapper::vinvsqrt(wrapper::vadd(vin, mask_float_vector(delta, bitmask))));
+ tmp = mask_float_vector(tmp, wrapper::vnot(bitmask));
+ }
+#endif /* __aarch64__ */
+ break;
+ case ActivationLayerInfo::ActivationFunction::SQUARE:
+ tmp = wrapper::vmul(vin, vin);
+ break;
+ case ActivationLayerInfo::ActivationFunction::TANH:
+ tmp = wrapper::vmul(va, wrapper::vtanh(wrapper::vmul(vb, vin)));
+ break;
+ case ActivationLayerInfo::ActivationFunction::IDENTITY:
+ tmp = vin;
+ break;
+ case ActivationLayerInfo::ActivationFunction::HARD_SWISH:
+ tmp = wrapper::vmul(vin, wrapper::vmul(const_inv_6, wrapper::vmin(const_6, wrapper::vmax(const_0, wrapper::vadd(vin, const_3)))));
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported activation function");
+ }
+ wrapper::vstore(output_ptr + x, tmp);
+ }
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const T in = *(reinterpret_cast<const T *>(input_ptr + x));
+ T tmp;
+ switch(act)
+ {
+ case ActivationLayerInfo::ActivationFunction::ABS:
+ tmp = std::abs(in);
+ break;
+ case ActivationLayerInfo::ActivationFunction::LINEAR:
+ tmp = a * in + b;
+ break;
+ case ActivationLayerInfo::ActivationFunction::LOGISTIC:
+ tmp = static_cast<T>(1) / (static_cast<T>(1) + std::exp(-in));
+ break;
+ case ActivationLayerInfo::ActivationFunction::RELU:
+ tmp = std::max<T>(static_cast<T>(0), in);
+ break;
+ case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
+ tmp = std::min<T>(a, std::max(static_cast<T>(0), in));
+ break;
+ case ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU:
+ tmp = std::min<T>(a, std::max<T>(b, in));
+ break;
+ case ActivationLayerInfo::ActivationFunction::LEAKY_RELU:
+ tmp = (in > 0) ? in : a * in;
+ break;
+ case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
+ tmp = (in > soft_relu_thresh) ? in : std::log(static_cast<T>(1) + std::exp(in));
+ break;
+ case ActivationLayerInfo::ActivationFunction::ELU:
+ tmp = (in >= 0) ? in : a * (std::exp(in) - 1);
+ break;
+ case ActivationLayerInfo::ActivationFunction::SQRT:
+ tmp = std::sqrt(in);
+ break;
+ case ActivationLayerInfo::ActivationFunction::SQUARE:
+ tmp = in * in;
+ break;
+ case ActivationLayerInfo::ActivationFunction::TANH:
+ tmp = a * std::tanh(b * in);
+ break;
+ case ActivationLayerInfo::ActivationFunction::IDENTITY:
+ tmp = in;
+ break;
+ case ActivationLayerInfo::ActivationFunction::HARD_SWISH:
+ tmp = in * ((std::min(std::max((in + 3), 0.0f), 6.0f)) * 0.166666667f);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported activation function");
+ }
+ *(output_ptr + x) = tmp;
+ }
+ },
+ input, output);
+}
+} // namespace cpu
+} // namespace arm_compute