diff options
Diffstat (limited to 'thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp')
| -rw-r--r-- | thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp | 328 |
1 files changed, 0 insertions, 328 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp deleted file mode 100644 index 6f449a9016..0000000000 --- a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp +++ /dev/null @@ -1,328 +0,0 @@ -/******************************************************************************* -* Copyright 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_RNN_HPP -#define CPU_REF_RNN_HPP - -#include <assert.h> - -#include "c_types_map.hpp" -#include "memory_tracking.hpp" -#include "type_helpers.hpp" -#include "utils.hpp" - -#include "../cpu_isa_traits.hpp" -#include "../gemm/os_blas.hpp" - -#include "cpu_rnn_pd.hpp" -#include "../cpu_primitive.hpp" -#include "rnn_utils.hpp" -#include "jit_uni_rnn_postgemm.hpp" - -namespace mkldnn { -namespace impl { -namespace cpu { - -template <alg_kind_t alg_kind, prop_kind_t prop_kind> -float activation(float s, float alpha, float cliping, float dd); - -template <prop_kind_t aprop, impl::data_type_t src_type, - impl::data_type_t weights_type> -struct _ref_rnn_common_t : public cpu_primitive_t { - typedef typename prec_traits<src_type>::type src_data_t; - typedef typename prec_traits<weights_type>::type weights_data_t; - typedef typename utils::conditional<src_type == data_type::u8, int32_t, - float>::type acc_data_t; - - using class_name = _ref_rnn_common_t<aprop, src_type, weights_type>; - - typedef rnn_elemwise_sig((class_name::*elemwise_f)); - typedef rnn_cell_execution_sig((class_name::*cell_execution_f)); - typedef rnn_grid_execution_sig((class_name::*grid_execution_f)); - - typedef rnn_gemm_sig((class_name::*gemm_t)); - typedef rnn_bias_prepare_sig((class_name::*bias_prepare_t)); - typedef rnn_bias_finalize_sig((class_name::*bias_finalize_t)); - typedef rnn_weights_assign_sig((class_name::*weights_assign_t)); - - using base_pd_t = - typename utils::conditional<false || aprop == prop_kind::forward, - cpu_rnn_fwd_pd_t, cpu_rnn_bwd_pd_t>::type; - - struct pd_t : public base_pd_t { - using base_pd_t::base_pd_t; - - DECLARE_COMMON_PD_T("ref:any", class_name); - - status_t init() { - using namespace prop_kind; - using namespace utils; - using namespace format_tag; - using namespace rnn_utils; - const alg_kind_t cell_kind = this->desc()->cell_desc.cell_kind; - - data_type_t src_layer_dt = this->desc()->src_layer_desc.data_type; - data_type_t weights_iter_dt - = this->desc()->weights_iter_desc.data_type; - data_type_t weights_layer_dt - = this->desc()->weights_layer_desc.data_type; - - bool ok = true - && one_of(cell_kind, alg_kind::vanilla_rnn, - alg_kind::vanilla_lstm, alg_kind::vanilla_gru, - alg_kind::gru_linear_before_reset) - && IMPLICATION(aprop == prop_kind::forward, - one_of(this->desc()->prop_kind, forward_training, - forward_inference)) - && IMPLICATION(aprop == backward, - one_of(this->desc()->prop_kind, backward)) - && src_layer_dt == src_type - && everyone_is( - weights_type, weights_iter_dt, weights_layer_dt) - && this->set_default_params() == status::success - && this->with_bias(); - if (!ok) - return status::unimplemented; - - init_conf(rnn_, *this->desc(), this->src_md(0), this->src_md(1), - this->weights_md(0), this->weights_md(1), this->dst_md(0)); - - if (rnn_.dt_conf == all_f32) - ok = ok && this->attr()->has_default_values(); - - // Set weights descriptors to desired format - memory_desc_t new_weights_layer_md = *this->weights_md(0); - CHECK(set_expected_desc(rnn_, new_weights_layer_md, false)); - if (this->weights_layer_md_.format_kind == format_kind::any) { - this->weights_layer_md_ = new_weights_layer_md; - } else if (this->weights_layer_md_.format_kind - == format_kind::rnn_packed) { - if (this->weights_layer_md_ != new_weights_layer_md) - return status::unimplemented; - } - - memory_desc_t new_weights_iter_md = *this->weights_md(1); - CHECK(set_expected_desc(rnn_, new_weights_iter_md, true)); - if (this->weights_iter_md_.format_kind == format_kind::any) { - this->weights_iter_md_ = new_weights_iter_md; - } else if (this->weights_iter_md_.format_kind - == format_kind::rnn_packed) { - if (this->weights_iter_md_ != new_weights_iter_md) - return status::unimplemented; - } - - CHECK(this->check_layout_consistency()); - - set_conf(rnn_, *this->desc(), this->weights_md(0), - this->weights_md(1), this->diff_weights_md(0), - this->diff_weights_md(1)); - - size_t scratchpad_sz{0}, ws_sz{0}; - get_scratchpad_and_workspace_sizes(rnn_, scratchpad_sz, ws_sz); - - // initialize the workspace if needed - if (rnn_.is_training) { - dims_t ws_dims = { (int)ws_sz }; - mkldnn_memory_desc_init_by_tag(&this->ws_md_, 1, ws_dims, - data_type::u8, format_tag::x); - } - - init_scratchpad(scratchpad_sz); - - return status::success; - } - - rnn_utils::rnn_conf_t rnn_; - - private: - void init_scratchpad(size_t scratchpad_sz) { - using namespace memory_tracking::names; - auto scratchpad = this->scratchpad_registry().registrar(); - scratchpad.book(key_rnn_space, sizeof(float) * scratchpad_sz, 4096); - - int max_nparts = this->cell_kind() == alg_kind::vanilla_gru ? 2 : 1; - int ptr_wei_sz = rnn_.n_layer * rnn_.n_dir * max_nparts; - scratchpad.book(key_rnn_ptrs_wei_layer, - sizeof(float *) * ptr_wei_sz); - scratchpad.book(key_rnn_ptrs_wei_iter, - sizeof(float *) * ptr_wei_sz); - scratchpad.book(key_rnn_ptrs_bia, - sizeof(float *) * ptr_wei_sz); - } - }; - - _ref_rnn_common_t(const pd_t *apd) - : cpu_primitive_t(apd, true), rnn_postgemm_(nullptr) { - /// @todo set max_feature_size assuming that we limit the number of - /// iterations and layer to one if slc != dic and sic != dic - /// respectively - - bias_preparation_func = &class_name::bias_prepare; - bias_finalization_func = &class_name::bias_finalize; - - auto set_gemm_funcs - = [](bool packed_gemm, gemm_t &g, weights_assign_t &a) { - if (packed_gemm) { - g = &class_name::packed_gemm; - a = &class_name::assign_packed_weights; - } else { - g = &class_name::gemm; - a = &class_name::assign_weights; - } - }; - set_gemm_funcs(pd()->rnn_.use_iter_packed_gemm, gemm_iter_func, - weights_iter_assign_func); - - set_gemm_funcs(pd()->rnn_.use_layer_packed_gemm, gemm_layer_func, - weights_layer_assign_func); - - switch (pd()->cell_kind()) { - case alg_kind::vanilla_lstm: - cell_func = &class_name::cell_execution; - if (aprop == prop_kind::forward) { - if (mayiuse(avx512_core)) - rnn_postgemm_ = new jit_uni_lstm_postgemm_kernel_fwd<avx512_core, src_type>( - pd()->rnn_, pd()->attr()); - else if (mayiuse(avx2)) - rnn_postgemm_ = new jit_uni_lstm_postgemm_kernel_fwd<avx2, src_type>( - pd()->rnn_, pd()->attr()); - else if (mayiuse(sse42)) - rnn_postgemm_ = new jit_uni_lstm_postgemm_kernel_fwd<sse42, src_type>( - pd()->rnn_, pd()->attr()); - assert(rnn_postgemm_ != nullptr); - rnn_postgemm_->init(); - } - elemwise_func = &class_name::lstm_elemwise; - break; - case alg_kind::vanilla_rnn: // @todo switch on cell kind - cell_func = &class_name::cell_execution; - elemwise_func = &class_name::rnn_elemwise; - switch (pd()->activation_kind()) { - case alg_kind::eltwise_relu: - activation_func = &activation<alg_kind::eltwise_relu, aprop>; - break; - case alg_kind::eltwise_tanh: - activation_func = &activation<alg_kind::eltwise_tanh, aprop>; - break; - case alg_kind::eltwise_logistic: - activation_func = &activation<alg_kind::eltwise_logistic, aprop>; - break; - default: break; - } - break; - case alg_kind::vanilla_gru: - cell_func = &class_name::cell_execution_gru; - break; - case alg_kind::gru_linear_before_reset: - cell_func = &class_name::cell_execution_gru_lbr; - elemwise_func = &class_name::gru_lbr_elemwise; - break; - default: break; - } - - grid_computation = &class_name::linear_execution; - - size_t scratchpad_size, workspace_size; - rnn_utils::set_offsets(pd()->rnn_, ws_gates_offset_, ws_states_offset_, - ws_c_states_offset_, ws_diff_states_offset_, - ws_grid_comp_offset_, ws_cell_comp_offset_, - ws_bias_offset_, scratchpad_size, workspace_size); - } - - ~_ref_rnn_common_t() {} - - // typedef typename prec_traits::type data_t; - - virtual status_t execute(const exec_ctx_t &ctx) const override { - execute_(ctx); - return status::success; - } - -private: - void execute_(const exec_ctx_t &ctx) const; - rnn_grid_execution_sig(linear_execution); - rnn_cell_execution_sig(cell_execution); - rnn_cell_execution_sig(cell_execution_gru); - rnn_cell_execution_sig(cell_execution_gru_lbr); - rnn_elemwise_sig(rnn_elemwise); - rnn_elemwise_sig(lstm_elemwise); - rnn_elemwise_sig(gru_lbr_elemwise); - rnn_gemm_sig(gemm); - rnn_gemm_sig(packed_gemm); - rnn_bias_prepare_sig(bias_prepare); - rnn_bias_finalize_sig(bias_finalize); - rnn_weights_assign_sig(assign_weights); - rnn_weights_assign_sig(assign_packed_weights); - - float (*activation_func)(float dd, float s, float alpha, float cliping); - - void copy_init_layer(const rnn_utils::rnn_conf_t &rnn, - src_data_t *ws_states_, float *ws_diff_states_, - const src_data_t *xt_, const float *diff_dst_layer) const; - - template <typename input_data_t> - void copy_init_iter(const rnn_utils::rnn_conf_t &rnn, - src_data_t *ws_states_, float *ws_c_states, float *ws_diff_states_, - const input_data_t *firstit_states_, - const float *diff_dst_iter) const; - - template <typename dst_data_t> - void copy_res_layer(const rnn_utils::rnn_conf_t &rnn, - dst_data_t *dst_layer_, float *diff_src_layer, - const src_data_t *ws_states_, const float *ws_diff_states_) const; - - template <typename output_data_t> - void copy_res_iter(const rnn_utils::rnn_conf_t &rnn, - output_data_t *dst_iter_, float *diff_src_iter, - const src_data_t *ws_states_, float *ws_c_states, - const float *ws_diff_states_) const; - - void gates_reduction(const rnn_utils::rnn_conf_t &rnn, - const acc_data_t *ws_gates_, float *diff_bias_) const; - - const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); } - - size_t ws_gates_offset_; - size_t ws_states_offset_; - size_t ws_c_states_offset_; - size_t ws_bias_offset_; - size_t ws_diff_states_offset_; - size_t ws_grid_comp_offset_; - size_t ws_cell_comp_offset_; - jit_uni_rnn_postgemm_kernel *rnn_postgemm_; - - grid_execution_f grid_computation; - cell_execution_f cell_func; - - bias_prepare_t bias_preparation_func; - bias_finalize_t bias_finalization_func; - weights_assign_t weights_layer_assign_func; - weights_assign_t weights_iter_assign_func; - - gemm_t gemm_layer_func; - gemm_t gemm_iter_func; - elemwise_f elemwise_func; -}; - -using ref_rnn_fwd_f32_t = _ref_rnn_common_t<prop_kind::forward, data_type::f32, data_type::f32>; -using ref_rnn_bwd_f32_t = _ref_rnn_common_t<prop_kind::backward, data_type::f32, data_type::f32>; -using ref_rnn_fwd_u8s8_t = _ref_rnn_common_t<prop_kind::forward, data_type::u8, data_type::s8>; -} -} -} -#endif - -// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s |