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, 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