/* 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 BATCHNORM_OP #pragma once #include #include "operators/op_param.h" namespace paddle_mobile { namespace operators { template void BatchnormCompute(const BatchNormParam ¶m) { const Tensor *input_x = param.InputX(); auto input_x_ptr = input_x->data(); const auto &x_dims = input_x->dims(); const int N = x_dims[0]; const int C = x_dims[1]; const int H = x_dims[2]; const int W = x_dims[3]; const int stride0 = C * H * W; const int stride1 = H * W; const int stride2 = W; Tensor *out = param.OutputY(); auto out_ptr = out->mutable_data(); const float epsilon = param.Epsilon(); const Tensor *mean = param.InputMean(); const Tensor *variance = param.InputVariance(); const Tensor *scale = param.InputScale(); const Tensor *bias = param.InputBias(); auto mean_ptr = mean->data(); auto variance_ptr = variance->data(); auto scale_ptr = scale->data(); auto bias_ptr = bias->data(); // Tensor inv_std; // auto inv_std_ptr = inv_std.mutable_data(make_ddim({C})); PADDLE_MOBILE_ENFORCE(C == variance->numel(), "C must equal to variance.numel()"); int HXW = H * W; #ifdef ARMV7 if (HXW > 32) { int NXC = N * C; float *inv_std_ptr = new float[NXC * 4]; float *volatile new_scale_ptr = new float[NXC * 4]; float *volatile new_bias_ptr = new float[NXC * 4]; /// std = (var + epsilon).sqrt(); /// inv_std = 1 / std; for (int i = 0; i < C * 4; i += 4) { int index = i / 4; inv_std_ptr[i] = 1 / static_cast(pow((variance_ptr[index] + epsilon), 0.5)); inv_std_ptr[i + 1] = inv_std_ptr[i]; inv_std_ptr[i + 2] = inv_std_ptr[i]; inv_std_ptr[i + 3] = inv_std_ptr[i]; new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[index]; new_scale_ptr[i + 1] = new_scale_ptr[i]; new_scale_ptr[i + 2] = new_scale_ptr[i]; new_scale_ptr[i + 3] = new_scale_ptr[i]; new_bias_ptr[i] = bias_ptr[index] - mean_ptr[index] * inv_std_ptr[i] * scale_ptr[index]; new_bias_ptr[i + 1] = new_bias_ptr[i]; new_bias_ptr[i + 2] = new_bias_ptr[i]; new_bias_ptr[i + 3] = new_bias_ptr[i]; } for (int j = C * 4; j < NXC * 4; ++j) { new_scale_ptr[j] = new_scale_ptr[j - C * 4]; new_bias_ptr[j] = new_bias_ptr[j - C * 4]; } asm volatile( "subs %[N], %[N], #1 \n\t" "blt end_n_%= \n\t" "loop_n_%=: \n\t" "subs %[C], %[C], #1 \n\t" "blt end_c_%= \n\t" "loop_c_%=: \n\t" "vld1.32 {q9}, [%[new_scale_ptr]]! \n\t" "vld1.32 {q10}, [%[new_bias_ptr]]! \n\t" "mov r6, %[HXW] \n\t" "subs r6, r6, #32 \n\t" "blt end_hw_%= \n\t" "loop_hw_%=: \n\t" "vld1.32 {q1, q2}, [%[input_x_ptr]]! \n\t" "vld1.32 {q3, q4}, [%[input_x_ptr]]! \n\t" "vld1.32 {q5, q6}, [%[input_x_ptr]]! \n\t" "vld1.32 {q7, q8}, [%[input_x_ptr]]! \n\t" "vmul.f32 q1, q1, q9 \n\t" "vmul.f32 q2, q2, q9 \n\t" "vmul.f32 q3, q3, q9 \n\t" "vmul.f32 q4, q4, q9 \n\t" "vmul.f32 q5, q5, q9 \n\t" "vmul.f32 q6, q6, q9 \n\t" "vmul.f32 q7, q7, q9 \n\t" "vmul.f32 q8, q8, q9 \n\t" "vadd.f32 q1, q1, q10 \n\t" "vadd.f32 q2, q2, q10 \n\t" "vadd.f32 q3, q3, q10 \n\t" "vadd.f32 q4, q4, q10 \n\t" "vadd.f32 q5, q5, q10 \n\t" "vadd.f32 q6, q6, q10 \n\t" "vadd.f32 q7, q7, q10 \n\t" "vadd.f32 q8, q8, q10 \n\t" "vst1.32 {q1, q2}, [%[out_ptr]]! \n\t" "vst1.32 {q3, q4}, [%[out_ptr]]! \n\t" "vst1.32 {q5, q6}, [%[out_ptr]]! \n\t" "vst1.32 {q7, q8}, [%[out_ptr]]! \n\t" "subs r6, r6, #32 \n\t" "bge loop_hw_%= \n\t" "end_hw_%=: \n\t" "cmp r6, #0 \n\t" "bge end_remainder_%= \n\t" "mov r5, #4 \n\t" "mul r6, r6, r5 \n\t" "add %[input_x_ptr], %[input_x_ptr], r6 \n\t" "vld1.32 {q1, q2}, [%[input_x_ptr]]! \n\t" "vld1.32 {q3, q4}, [%[input_x_ptr]]! \n\t" "vld1.32 {q5, q6}, [%[input_x_ptr]]! \n\t" "vld1.32 {q7, q8}, [%[input_x_ptr]]! \n\t" "vmul.f32 q1, q1, q9 \n\t" "vmul.f32 q2, q2, q9 \n\t" "vmul.f32 q3, q3, q9 \n\t" "vmul.f32 q4, q4, q9 \n\t" "vmul.f32 q5, q5, q9 \n\t" "vmul.f32 q6, q6, q9 \n\t" "vmul.f32 q7, q7, q9 \n\t" "vmul.f32 q8, q8, q9 \n\t" "vadd.f32 q1, q1, q10 \n\t" "vadd.f32 q2, q2, q10 \n\t" "vadd.f32 q3, q3, q10 \n\t" "vadd.f32 q4, q4, q10 \n\t" "vadd.f32 q5, q5, q10 \n\t" "vadd.f32 q6, q6, q10 \n\t" "vadd.f32 q7, q7, q10 \n\t" "vadd.f32 q8, q8, q10 \n\t" "add %[out_ptr], %[out_ptr], r6 \n\t" "vst1.32 {q1, q2}, [%[out_ptr]]! \n\t" "vst1.32 {q3, q4}, [%[out_ptr]]! \n\t" "vst1.32 {q5, q6}, [%[out_ptr]]! \n\t" "vst1.32 {q7, q8}, [%[out_ptr]]! \n\t" "end_remainder_%=: \n\t" "subs %[C], %[C], #1 \n\t" "bge loop_c_%= \n\t" "end_c_%=: \n\t" "subs %[N], %[N], #1 \n\t" "bge loop_n_%= \n\t" "end_n_%=: \n\t" : : [input_x_ptr] "r"(input_x_ptr), [out_ptr] "r"(out_ptr), [new_scale_ptr] "r"(new_scale_ptr), [new_bias_ptr] "r"(new_bias_ptr), [N] "r"(N), [C] "r"(C), [HXW] "r"(HXW) : "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9", "q10", "r5", "r6"); delete[] inv_std_ptr; delete[] new_scale_ptr; delete[] new_bias_ptr; } else { float *inv_std_ptr = new float[C]; for (int i = 0; i < C; i++) { inv_std_ptr[i] = 1 / static_cast(pow((variance_ptr[i] + epsilon), 0.5)); } Tensor new_scale; auto new_scale_ptr = new_scale.mutable_data(framework::make_ddim({C})); Tensor new_bias; auto new_bias_ptr = new_bias.mutable_data(framework::make_ddim({C})); /// ((x - est_mean) * (inv_var) * scale + bias equal to /// (x * inv_var * scale) + (bias - est_mean * inv_var * scale) for (int i = 0; i < C; i++) { new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i]; new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i]; { for (int n = 0; n < N; n++) { for (int h = 0; h < H; h++) { int tmp_index = n * stride0 + i * stride1 + h * stride2; for (int w = 0; w < W; w++) { int index = tmp_index + w; out_ptr[index] = input_x_ptr[index] * new_scale_ptr[i] + new_bias_ptr[i]; } } } } } delete[] inv_std_ptr; } #else float *inv_std_ptr = new float[C]; for (int i = 0; i < C; i++) { inv_std_ptr[i] = 1 / static_cast(pow((variance_ptr[i] + epsilon), 0.5)); } Tensor new_scale; auto new_scale_ptr = new_scale.mutable_data(framework::make_ddim({C})); Tensor new_bias; auto new_bias_ptr = new_bias.mutable_data(framework::make_ddim({C})); /// ((x - est_mean) * (inv_var) * scale + bias equal to /// (x * inv_var * scale) + (bias - est_mean * inv_var * scale) for (int i = 0; i < C; i++) { new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i]; new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i]; { for (int n = 0; n < N; n++) { for (int h = 0; h < H; h++) { int tmp_index = n * stride0 + i * stride1 + h * stride2; for (int w = 0; w < W; w++) { int index = tmp_index + w; out_ptr[index] = input_x_ptr[index] * new_scale_ptr[i] + new_bias_ptr[i]; } } } } } delete[] inv_std_ptr; #endif } } // namespace operators } // namespace paddle_mobile #endif