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Diffstat (limited to 'thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp')
| -rw-r--r-- | thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp | 265 |
1 files changed, 0 insertions, 265 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp deleted file mode 100644 index d79b1a034b..0000000000 --- a/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp +++ /dev/null @@ -1,265 +0,0 @@ -/******************************************************************************* -* Copyright 2016-2018 Intel Corporation -* -* 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. -*******************************************************************************/ - -#include <assert.h> -#include <math.h> - -#include "c_types_map.hpp" -#include "type_helpers.hpp" -#include "mkldnn_thread.hpp" -#include "simple_q10n.hpp" - -#include "ref_batch_normalization.hpp" - -namespace mkldnn { -namespace impl { -namespace cpu { - -template <impl::data_type_t data_type> -void ref_batch_normalization_fwd_t<data_type>::execute_forward( - const exec_ctx_t &ctx) const { - /* fast return */ - if (this->pd()->has_zero_dim_memory()) return; - - auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC); - auto scaleshift = CTX_IN_MEM(const float *, MKLDNN_ARG_SCALE_SHIFT); - - auto mean = pd()->stats_is_src() - ? const_cast<float *>(CTX_IN_MEM(const float *, MKLDNN_ARG_MEAN)) - : CTX_OUT_MEM(float *, MKLDNN_ARG_MEAN); - auto variance = pd()->stats_is_src() - ? const_cast<float *>(CTX_IN_MEM(const float *, MKLDNN_ARG_VARIANCE)) - : CTX_OUT_MEM(float *, MKLDNN_ARG_VARIANCE); - - auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST); - auto ws = CTX_OUT_MEM(uint8_t *, MKLDNN_ARG_WORKSPACE); - - const memory_desc_wrapper data_d(pd()->src_md()); - const memory_desc_wrapper scaleshift_d(pd()->weights_md()); - - const dim_t N = pd()->MB(); - const dim_t C = pd()->C(); - dim_t H = 1, W = 1, D = 1; - const bool has_spatial = utils::one_of(data_d.ndims(), 4, 5); - if (has_spatial) { - D = pd()->D(); - H = pd()->H(); - W = pd()->W(); - } - - const float eps = pd()->desc()->batch_norm_epsilon; - const bool use_scaleshift = pd()->use_scaleshift();; - const bool save_stats = pd()->is_training(); - const bool is_training = pd()->is_training(); - const bool fuse_bn_relu = pd()->fuse_bn_relu(); - const bool calculate_stats = !pd()->stats_is_src(); - - const bool with_relu = pd()->with_relu_post_op(); - auto maybe_post_op = [&](float res) { - return (with_relu && res < 0.0f) ? 0.0f : res; - }; - const bool is_3d = data_d.ndims() == 5; - - auto data_offset = [&](const memory_desc_wrapper &data_d, dim_t n, dim_t c, - dim_t d, dim_t h, dim_t w) { - if (has_spatial) { - if (is_3d) - return data_d.off(n, c, d, h, w); - else - return data_d.off(n, c, h, w); - } else - return data_d.off(n, c); - }; - - parallel_nd(C, [&](dim_t c) { - float v_mean = calculate_stats ? 0 : mean[c]; - float v_variance = calculate_stats ? 0 : variance[c]; - - if (calculate_stats) { - for (dim_t n = 0; n < N; ++n) - for (dim_t d = 0; d < D; ++d) - for (dim_t h = 0; h < H; ++h) - for (dim_t w = 0; w < W; ++w) - v_mean += src[data_offset(data_d, n, c, d, h, w)]; - v_mean /= W*N*H*D; - - for (dim_t n = 0; n < N; ++n) - for (dim_t d = 0; d < D; ++d) - for (dim_t h = 0; h < H; ++h) - for (dim_t w = 0; w < W; ++w) { - float m = src[data_offset(data_d, n, c, d, h, w)] - v_mean; - v_variance += m*m; - } - v_variance /= W*H*N*D; - } - - float sqrt_variance = sqrtf(v_variance + eps); - float sm = (use_scaleshift - ? scaleshift[scaleshift_d.off(0, c)] - : 1.0f) / sqrt_variance; - float sv = use_scaleshift ? scaleshift[scaleshift_d.off(1, c)] : 0; - - for (dim_t n = 0; n < N; ++n) - for (dim_t d = 0; d < D; ++d) - for (dim_t h = 0; h < H; ++h) - for (dim_t w = 0; w < W; ++w) { - auto d_off = data_offset(data_d,n,c,d,h,w); - float bn_res = sm * ((float)src[d_off] - v_mean) + sv; - if (fuse_bn_relu) { - if (bn_res <= 0) { - bn_res = 0; - if (is_training) - ws[d_off] = 0; - } else { - if (is_training) - ws[d_off] = 1; - } - } - if (data_type == data_type::s8) { - dst[d_off] = qz_a1b0<float, data_t>()(maybe_post_op(bn_res)); - } else { - dst[d_off] = static_cast<data_t>(maybe_post_op(bn_res)); - } - } - - if (calculate_stats) { - if (save_stats) { - mean[c] = v_mean; - variance[c] = v_variance; - } - } - }); -} - -template struct ref_batch_normalization_fwd_t<data_type::f32>; -template struct ref_batch_normalization_fwd_t<data_type::s8>; - -template <impl::data_type_t data_type> -void ref_batch_normalization_bwd_t<data_type>::execute_backward( - const exec_ctx_t &ctx) const { - auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC); - auto mean = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN); - auto variance = CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE); - auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST); - auto scaleshift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT); - auto ws = CTX_IN_MEM(const uint8_t *, MKLDNN_ARG_WORKSPACE); - - auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC); - auto diff_scaleshift = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SCALE_SHIFT); - - const memory_desc_wrapper data_d(pd()->src_md()); - const memory_desc_wrapper diff_data_d(pd()->diff_src_md()); - const memory_desc_wrapper scaleshift_d(pd()->weights_md()); - const memory_desc_wrapper diff_scaleshift_d(pd()->diff_weights_md()); - - const dim_t C = pd()->C(); - - /* fast return */ - if (this->pd()->has_zero_dim_memory()) { - if (diff_scaleshift) { - for (dim_t c = 0; c < C; ++c) { - diff_scaleshift[diff_scaleshift_d.off(0, c)] = 0; - diff_scaleshift[diff_scaleshift_d.off(1, c)] = 0; - } - } - return; - } - - const dim_t N = pd()->MB(); - dim_t H = 1, W = 1, D = 1; - const bool has_spatial = utils::one_of(data_d.ndims(), 4, 5); - if (has_spatial) { - D = pd()->D(); - H = pd()->H(); - W = pd()->W(); - } - - const float eps = pd()->desc()->batch_norm_epsilon; - const bool use_scaleshift = pd()->use_scaleshift(); - const bool calculate_diff_stats = !pd()->use_global_stats(); - const bool fuse_bn_relu = pd()->fuse_bn_relu(); - - const bool is_3d = data_d.ndims() == 5; - - auto data_offset = [&](const memory_desc_wrapper &data_d, dim_t n, dim_t c, - dim_t d, dim_t h, dim_t w) { - if (has_spatial) { - if (is_3d) - return data_d.off(n, c, d, h, w); - else - return data_d.off(n, c, h, w); - } else - return data_d.off(n, c); - }; - - parallel_nd(C, [&](dim_t c) { - data_t v_mean = mean[c]; - data_t v_variance = variance[c]; - data_t sqrt_variance = static_cast<data_t>(1.0f / sqrtf(v_variance + eps)); - data_t gamma = use_scaleshift ? scaleshift[scaleshift_d.off(0, c)] : 1; - data_t diff_gamma = data_t(0); - data_t diff_beta = data_t(0); - diff_gamma = 0.0; - diff_beta = 0.0; - - for (dim_t n = 0; n < N; ++n) - for (dim_t d = 0; d < D; ++d) - for (dim_t h = 0; h < H; ++h) - for (dim_t w = 0; w < W; ++w) { - const size_t s_off = data_offset(data_d, n, c, d, h, w); - data_t dd = diff_dst[data_offset(diff_data_d, n, c, d, h, w)]; - if (fuse_bn_relu && !ws[s_off]) - dd = 0; - - diff_gamma += (src[s_off] - v_mean) * dd; - diff_beta += dd; - } - diff_gamma *= sqrt_variance; - - if (diff_scaleshift) { - diff_scaleshift[diff_scaleshift_d.off(0, c)] = diff_gamma; - diff_scaleshift[diff_scaleshift_d.off(1, c)] = diff_beta; - } - - for (dim_t n = 0; n < N; ++n) - for (dim_t d = 0; d < D; ++d) - for (dim_t h = 0; h < H; ++h) - for (dim_t w = 0; w < W; ++w) { - const size_t s_off = data_offset(data_d, n, c, d, h, w); - const size_t dd_off = data_offset(diff_data_d, n, c, d, h, w); - data_t dd = diff_dst[dd_off]; - if (fuse_bn_relu && !ws[s_off]) - dd = 0; - - data_t v_diff_src = dd; - if (calculate_diff_stats) { - v_diff_src -= diff_beta/(D*W*H*N) + - (src[s_off] - v_mean) * - diff_gamma*sqrt_variance/(D*W*H*N); - } - v_diff_src *= gamma*sqrt_variance; - diff_src[dd_off] = v_diff_src; - } - }); -} - -template struct ref_batch_normalization_bwd_t<data_type::f32>; - -} -} -} - -// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s |