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Diffstat (limited to 'thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp')
| -rw-r--r-- | thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp | 186 |
1 files changed, 186 insertions, 0 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp new file mode 100644 index 0000000000..5cb74d8007 --- /dev/null +++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp @@ -0,0 +1,186 @@ +/******************************************************************************* +* 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. +*******************************************************************************/ + +#ifndef CPU_REF_SOFTMAX_HPP +#define CPU_REF_SOFTMAX_HPP + +#include <assert.h> + +#include "c_types_map.hpp" +#include "memory_tracking.hpp" +#include "type_helpers.hpp" +#include "utils.hpp" + +#include "cpu_softmax_pd.hpp" +#include "cpu_primitive.hpp" + +namespace mkldnn { +namespace impl { +namespace cpu { + +template <impl::data_type_t data_type> +struct ref_softmax_fwd_t: public cpu_primitive_t { + struct pd_t: public cpu_softmax_fwd_pd_t { + using cpu_softmax_fwd_pd_t::cpu_softmax_fwd_pd_t; + + DECLARE_COMMON_PD_T("ref:any", ref_softmax_fwd_t); + + status_t init() { + bool ok = true + && is_fwd() + && src_md()->data_type == data_type + && attr()->has_default_values(); + if (!ok) return status::unimplemented; + + init_scratchpad(); + + return status::success; + } + + private: + void init_scratchpad() { + const int inner_size = utils::array_product( + desc()->data_desc.dims + desc()->softmax_axis + 1, + desc()->data_desc.ndims - desc()->softmax_axis - 1); + + if (inner_size > 1) { + auto scratchpad = scratchpad_registry().registrar(); + scratchpad.book(memory_tracking::names::key_softmax_reduction, + sizeof(data_t) * 2 * inner_size); + } + } + }; + + ref_softmax_fwd_t(const pd_t *apd): cpu_primitive_t(apd) + { + auto ndims = pd()->desc()->data_desc.ndims; + auto dims = pd()->desc()->data_desc.dims; + auto axis = pd()->desc()->softmax_axis; + + outer_size_ = utils::array_product(dims, axis); + channels_ = dims[axis]; + inner_size_ = utils::array_product(dims + axis + 1, ndims - axis - 1); + + const memory_desc_wrapper data_d(pd()->src_md()); + + bool no_axis_blocking = true; + for (int iblk = 0; iblk < data_d.blocking_desc().inner_nblks; ++iblk) + if (data_d.blocking_desc().inner_idxs[iblk] == axis) + no_axis_blocking = false; + + use_dense_ = inner_size_ == 1 && data_d.is_dense() + && no_axis_blocking + && data_d.blocking_desc().strides[axis] == 1; + } + + typedef typename prec_traits<data_type>::type data_t; + + virtual status_t execute(const exec_ctx_t &ctx) const override { + if (use_dense_) + execute_forward_dense(ctx); + else + execute_forward_generic(ctx); + return status::success; + } + +private: + void execute_forward_dense(const exec_ctx_t &ctx) const; + void execute_forward_generic(const exec_ctx_t &ctx) const; + + void _max(int n, const data_t *x, data_t *max_data) const; + void _sub(int n, data_t alpha, const data_t *x, data_t *y) const; + void _exp(int n, const data_t *a, data_t *r) const; + void _sum(int n, const data_t *x, data_t *sum_data) const; + void _scal(int n, data_t alpha, data_t *x) const; + + const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); } + + bool use_dense_; + int outer_size_, channels_, inner_size_; +}; + +template <impl::data_type_t data_type> +struct ref_softmax_bwd_t: public cpu_primitive_t { + struct pd_t: public cpu_softmax_bwd_pd_t { + using cpu_softmax_bwd_pd_t::cpu_softmax_bwd_pd_t; + + DECLARE_COMMON_PD_T("ref:any", ref_softmax_bwd_t); + + status_t init() { + bool ok = true + && !is_fwd() + && utils::everyone_is(data_type, + dst_md()->data_type, + diff_src_md()->data_type) + && attr()->has_default_values(); + if (!ok) return status::unimplemented; + + return status::success; + } + }; + + ref_softmax_bwd_t(const pd_t *apd): cpu_primitive_t(apd) { + auto dims = pd()->desc()->diff_desc.dims; + auto axis = pd()->desc()->softmax_axis; + auto ndims = pd()->desc()->diff_desc.ndims; + + outer_size_ = utils::array_product(dims, axis); + channels_ = dims[axis]; + inner_size_ = utils::array_product(dims + axis + 1, ndims - axis - 1); + + const memory_desc_wrapper data_d(pd()->dst_md()); + const memory_desc_wrapper diff_d(pd()->diff_dst_md()); + + bool no_axis_blocking = true; + for (int iblk = 0; iblk < diff_d.blocking_desc().inner_nblks; ++iblk) + if (diff_d.blocking_desc().inner_idxs[iblk] == axis) + no_axis_blocking = false; + + use_dense_ = true + && inner_size_ == 1 + && diff_d == data_d + && diff_d.is_dense() + && no_axis_blocking + && diff_d.blocking_desc().strides[axis] == 1; + } + + typedef typename prec_traits<data_type>::type data_t; + + virtual status_t execute(const exec_ctx_t &ctx) const override { + if (use_dense_) + execute_backward_dense(ctx); + else + execute_backward_generic(ctx); + return status::success; + } + +private: + void execute_backward_dense(const exec_ctx_t &ctx) const; + void execute_backward_generic(const exec_ctx_t &ctx) const; + const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); } + + bool use_dense_; + int outer_size_, channels_, inner_size_; +}; + + +} +} +} + +#endif + +// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s |