/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #ifdef RELU_OP #pragma once #include #include "operators/op_param.h" #if defined(__ARM_NEON__) || defined(__ARM_NEON) #include #endif namespace paddle_mobile { namespace operators { template struct ReluFunctor { inline T operator()(T in) const { return in > 0 ? in : 0; } }; /* * @b 特化到具体平台的实现, param 从 op 层传入 * */ template void ReluCompute(const ReluParam ¶m) { const auto *input_x = param.InputX(); auto *input_x_ptr = input_x->data(); auto *out = param.Out(); auto *out_ptr = out->mutable_data(); int numel = input_x->numel(); #if defined(__ARM_NEON__) || defined(__ARM_NEON) #if __aarch64__ if (numel > 0) { int loop = numel >> 0x4; int remain = numel & 0xF; float32x4_t zero = vdupq_n_f32(0.f); for (int i = 0; i < loop; ++i) { float32x4_t r0 = vld1q_f32(input_x_ptr); float32x4_t r1 = vld1q_f32(input_x_ptr + 4); float32x4_t r2 = vld1q_f32(input_x_ptr + 8); float32x4_t r3 = vld1q_f32(input_x_ptr + 12); r0 = vmaxq_f32(r0, zero); r1 = vmaxq_f32(r1, zero); r2 = vmaxq_f32(r2, zero); r3 = vmaxq_f32(r3, zero); vst1q_f32(out_ptr, r0); vst1q_f32(out_ptr + 4, r1); vst1q_f32(out_ptr + 8, r2); vst1q_f32(out_ptr + 12, r3); input_x_ptr += 16; out_ptr += 16; } for (int i = 0; i < remain; ++i) { out_ptr[i] = (input_x_ptr[i] > 0) * input_x_ptr[i]; } #else if (numel > 64) { asm volatile( "pld [%[input_x_ptr], #0] \n\t" "vmov.f32 q8, #0.0 \n\t" "subs %[num], %[num], #32 \n\t" "blt end_num_%= \n\t" "loop_num_%=: \n\t" "pld [%[input_x_ptr], #1024] \n\t" "vld1.32 {q0, q1}, [%[input_x_ptr]]! \n\t" "vld1.32 {q2, q3}, [%[input_x_ptr]]! \n\t" "vld1.32 {q4, q5}, [%[input_x_ptr]]! \n\t" "vld1.32 {q6, q7}, [%[input_x_ptr]]! \n\t" "vmax.f32 q0, q0, q8 \n\t" "vmax.f32 q1, q1, q8 \n\t" "vmax.f32 q2, q2, q8 \n\t" "vmax.f32 q3, q3, q8 \n\t" "vmax.f32 q4, q4, q8 \n\t" "vmax.f32 q5, q5, q8 \n\t" "vmax.f32 q6, q6, q8 \n\t" "vmax.f32 q7, q7, q8 \n\t" "vst1.32 {q0, q1}, [%[out_ptr]]! \n\t" "vst1.32 {q2, q3}, [%[out_ptr]]! \n\t" "vst1.32 {q4, q5}, [%[out_ptr]]! \n\t" "vst1.32 {q6, q7}, [%[out_ptr]]! \n\t" "subs %[num], %[num], #32 \n\t" "bge loop_num_%= \n\t" "end_num_%=: \n\t" "cmp %[num], #0 \n\t" "bge end_%= \n\t" "mov r6, #4 \n\t" "mul r5, %[num], r6 \n\t" "add %[input_x_ptr], %[input_x_ptr], r5 \n\t" "vld1.32 {q0, q1}, [%[input_x_ptr]]! \n\t" "vld1.32 {q2, q3}, [%[input_x_ptr]]! \n\t" "vld1.32 {q4, q5}, [%[input_x_ptr]]! \n\t" "vld1.32 {q6, q7}, [%[input_x_ptr]]! \n\t" "vmax.f32 q0, q0, q8 \n\t" "vmax.f32 q1, q1, q8 \n\t" "vmax.f32 q2, q2, q8 \n\t" "vmax.f32 q3, q3, q8 \n\t" "vmax.f32 q4, q4, q8 \n\t" "vmax.f32 q5, q5, q8 \n\t" "vmax.f32 q6, q6, q8 \n\t" "vmax.f32 q7, q7, q8 \n\t" "add %[out_ptr], %[out_ptr], r5 \n\t" "vst1.32 {q0, q1}, [%[out_ptr]]! \n\t" "vst1.32 {q2, q3}, [%[out_ptr]]! \n\t" "vst1.32 {q4, q5}, [%[out_ptr]]! \n\t" "vst1.32 {q6, q7}, [%[out_ptr]]! \n\t" "end_%=: \n\t" : : [out_ptr] "r"(out_ptr), [input_x_ptr] "r"(input_x_ptr), [num] "r"(numel) : "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "r5", "r6"); #endif } else { #endif ReluFunctor func_; math::Transform trans; trans(input_x_ptr, input_x_ptr + numel, out_ptr, func_); #if defined(__ARM_NEON__) || defined(__ARM_NEON) } #endif } } // namespace operators } // namespace paddle_mobile #endif