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author | Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com> | 2023-09-27 17:46:17 +0100 |
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committer | felixjohnny.thomasmathibalan <felixjohnny.thomasmathibalan@arm.com> | 2023-09-28 12:08:05 +0000 |
commit | afd38f0c617d6f89b2b4532c6c44f116617e2b6f (patch) | |
tree | 03bc7d5a762099989b16a656fa8d397b490ed70e /src/cpu/kernels/fuse_batch_normalization/generic/impl.h | |
parent | bdcb4c148ee2fdeaaddf4cf1e57bbb0de02bb894 (diff) | |
download | ComputeLibrary-afd38f0c617d6f89b2b4532c6c44f116617e2b6f.tar.gz |
Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Diffstat (limited to 'src/cpu/kernels/fuse_batch_normalization/generic/impl.h')
-rw-r--r-- | src/cpu/kernels/fuse_batch_normalization/generic/impl.h | 120 |
1 files changed, 69 insertions, 51 deletions
diff --git a/src/cpu/kernels/fuse_batch_normalization/generic/impl.h b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h index b9017600d6..6fa843263a 100644 --- a/src/cpu/kernels/fuse_batch_normalization/generic/impl.h +++ b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h @@ -25,6 +25,7 @@ #define SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H #include "arm_compute/core/Helpers.h" + #include "src/core/NEON/wrapper/wrapper.h" namespace arm_compute @@ -32,8 +33,16 @@ 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) +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(); @@ -53,13 +62,20 @@ void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor * 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 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; + 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{}); @@ -73,59 +89,61 @@ void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor * 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) + execute_window_loop( + win, + [&](const Coordinates &id) { - gamma = input_gamma[id[3]]; - } + 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) + if ((id[0] == 0) && (id[1] == 0) && (id[2] == 0)) { - beta = input_beta[id[3]]; - beta_vec = wrapper::vdup_n(beta, ExactTagType{}); + 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; } - // Construct vectors - mean = input_mean[id[3]]; - mean_vec = wrapper::vdup_n(mean, ExactTagType{}); + 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)); - if(conv_bias_in != nullptr) + for (; x <= (window_end_x - window_step_x); x += window_step_x) { - 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); + 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); - } + // 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); -} -} + // 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); } +} // namespace cpu +} // namespace arm_compute #endif //SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H |