New lightmapper

-Added LocalVector (needed it)
-Added stb_rect_pack (It's pretty cool, we could probably use it for other stuff too)
-Fixes and changes all around the place
-Added library for 128 bits fixed point (required for Delaunay3D)

1 files changed, 328 insertions, 0 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
new file mode 100644
index 0000000000..6f449a9016
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp
@@ -0,0 +1,328 @@
+/*******************************************************************************
+* 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