diff options
Diffstat (limited to 'src/cpu/kernels/fuse_batch_normalization')
-rw-r--r-- | src/cpu/kernels/fuse_batch_normalization/generic/impl.cpp | 135 | ||||
-rw-r--r-- | src/cpu/kernels/fuse_batch_normalization/generic/impl.h | 98 |
2 files changed, 95 insertions, 138 deletions
diff --git a/src/cpu/kernels/fuse_batch_normalization/generic/impl.cpp b/src/cpu/kernels/fuse_batch_normalization/generic/impl.cpp deleted file mode 100644 index 3c6a2069ee..0000000000 --- a/src/cpu/kernels/fuse_batch_normalization/generic/impl.cpp +++ /dev/null @@ -1,135 +0,0 @@ -/* - * Copyright (c) 2018-2022 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/fuse_batch_normalization/generic/impl.h" - -namespace arm_compute -{ -namespace cpu -{ -template <typename T> -void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias, - const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window) -{ - using ScalarType = T; - const int size = 16 / conv_weights->info()->element_size(); - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; - - const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights); - const bool run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias); - - // Set build options - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - const int window_step_x = size; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - Iterator conv_w_in(conv_weights, win); - Iterator conv_w_out(run_in_place_weights ? conv_weights : fused_weights, win); - - const auto conv_bias_in = (conv_bias != nullptr ? reinterpret_cast<ScalarType *>(conv_bias->ptr_to_element(Coordinates(0, 0))) : nullptr); - auto conv_bias_out = (run_in_place_bias ? conv_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0)))); - - const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; - - auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); - auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); - auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{}); - auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); - auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); - const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{}); - - auto mean = ScalarType(0.0); - auto var = ScalarType(0.0); - auto gamma = ScalarType(1.0); - auto beta = ScalarType(0.0); - auto conv_bias_in_scalar = ScalarType(0.0); - execute_window_loop(win, [&](const Coordinates & id) - { - var = input_var[id[3]]; - if(input_gamma != nullptr) - { - gamma = input_gamma[id[3]]; - } - - if((id[0] == 0) && (id[1] == 0) && (id[2] == 0)) - { - if(input_beta != nullptr) - { - beta = input_beta[id[3]]; - beta_vec = wrapper::vdup_n(beta, ExactTagType{}); - } - - // Construct vectors - mean = input_mean[id[3]]; - mean_vec = wrapper::vdup_n(mean, ExactTagType{}); - - if(conv_bias_in != nullptr) - { - conv_bias_in_scalar = conv_bias_in[id[3]]; - } - auto conv_bias_tmp_scalar = (conv_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon)); - conv_bias_out[id[3]] = (conv_bias_tmp_scalar * gamma) + beta; - } - - int x = window_start_x; - auto conv_w_in_ptr = reinterpret_cast<const ScalarType *>(conv_w_in.ptr()); - auto conv_w_out_ptr = reinterpret_cast<ScalarType *>(conv_w_out.ptr()); - var_vec = wrapper::vdup_n(var, ExactTagType{}); - gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); - rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); - - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - auto wn = wrapper::vloadq(conv_w_in_ptr + x); - wn = wrapper::vmul(wn, rvar_vec); - wn = wrapper::vmul(wn, gamma_vec); - - // Store results - wrapper::vstore(conv_w_out_ptr + x, wn); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - *(conv_w_out_ptr + x) = *(conv_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma; - } - }, - conv_w_in, conv_w_out); -} - -template void fused_batch_normalization_conv<float32_t>(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias, - const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window); - -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) -template void fused_batch_normalization_conv<float16_t>(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias, - const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, 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/fuse_batch_normalization/generic/impl.h b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h index 979ea13842..b9017600d6 100644 --- a/src/cpu/kernels/fuse_batch_normalization/generic/impl.h +++ b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2021-2022 Arm Limited. + * Copyright (c) 2021-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -33,7 +33,99 @@ namespace cpu { template <typename T> void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias, - const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window); + const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window) +{ + using ScalarType = T; + const int size = 16 / conv_weights->info()->element_size(); + using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; + + const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights); + const bool run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias); + + // Set build options + Window win = window; + win.set(Window::DimX, Window::Dimension(0, 1, 1)); + + const int window_step_x = size; + const auto window_start_x = static_cast<int>(window.x().start()); + const auto window_end_x = static_cast<int>(window.x().end()); + + Iterator conv_w_in(conv_weights, win); + Iterator conv_w_out(run_in_place_weights ? conv_weights : fused_weights, win); + + const auto conv_bias_in = (conv_bias != nullptr ? reinterpret_cast<ScalarType *>(conv_bias->ptr_to_element(Coordinates(0, 0))) : nullptr); + auto conv_bias_out = (run_in_place_bias ? conv_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0)))); + + const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0))); + const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; + + auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); + auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); + auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{}); + auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); + auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); + const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{}); + + auto mean = ScalarType(0.0); + auto var = ScalarType(0.0); + auto gamma = ScalarType(1.0); + auto beta = ScalarType(0.0); + auto conv_bias_in_scalar = ScalarType(0.0); + execute_window_loop(win, [&](const Coordinates & id) + { + var = input_var[id[3]]; + if(input_gamma != nullptr) + { + gamma = input_gamma[id[3]]; + } + + if((id[0] == 0) && (id[1] == 0) && (id[2] == 0)) + { + if(input_beta != nullptr) + { + beta = input_beta[id[3]]; + beta_vec = wrapper::vdup_n(beta, ExactTagType{}); + } + + // Construct vectors + mean = input_mean[id[3]]; + mean_vec = wrapper::vdup_n(mean, ExactTagType{}); + + if(conv_bias_in != nullptr) + { + conv_bias_in_scalar = conv_bias_in[id[3]]; + } + auto conv_bias_tmp_scalar = (conv_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon)); + conv_bias_out[id[3]] = (conv_bias_tmp_scalar * gamma) + beta; + } + + int x = window_start_x; + auto conv_w_in_ptr = reinterpret_cast<const ScalarType *>(conv_w_in.ptr()); + auto conv_w_out_ptr = reinterpret_cast<ScalarType *>(conv_w_out.ptr()); + var_vec = wrapper::vdup_n(var, ExactTagType{}); + gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); + rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); + + for(; x <= (window_end_x - window_step_x); x += window_step_x) + { + auto wn = wrapper::vloadq(conv_w_in_ptr + x); + wn = wrapper::vmul(wn, rvar_vec); + wn = wrapper::vmul(wn, gamma_vec); + + // Store results + wrapper::vstore(conv_w_out_ptr + x, wn); + } + + // Compute left-over elements + for(; x < window_end_x; ++x) + { + *(conv_w_out_ptr + x) = *(conv_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma; + } + }, + conv_w_in, conv_w_out); +} } } -#endif //SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
\ No newline at end of file +#endif //SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H |