1 files changed, 91 insertions, 0 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.cpp
new file mode 100644
index 0000000000..f0947573a9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.cpp
@@ -0,0 +1,91 @@
+/*******************************************************************************
+* Copyright 2017-2018 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 "mkldnn_thread.hpp"
+
+#include "simple_sum.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t data_type>
+status_t simple_sum_t<data_type>::execute(const exec_ctx_t &ctx) const {
+    auto output = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper o_d(pd()->dst_md());
+    output += o_d.blk_off(0);
+
+    const int num_arrs = pd()->n_inputs();
+    const data_t *input_ptrs[max_num_arrs];
+    const size_t nelems = o_d.nelems();
+
+    for (int a = 0; a < num_arrs; ++a) {
+        const memory_desc_wrapper i_d(pd()->src_md(a));
+        input_ptrs[a] = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MULTIPLE_SRC + a)
+            + i_d.blk_off(0);
+    }
+
+    const size_t block_size = 16 * 1024 / sizeof(data_type);
+    const size_t blocks_number = nelems / block_size;
+    const size_t tail = nelems % block_size;
+
+    const auto scales = pd()->scales();
+    parallel(0, [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+        balance211(blocks_number, nthr, ithr, start, end);
+
+        for (size_t nb = start; nb < end; ++nb) {
+            size_t start_e = nb * block_size;
+            size_t end_e = start_e + block_size;
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = start_e; e < end_e; e++) {
+                output[e] = data_t(scales[0] * input_ptrs[0][e]);
+            }
+            for (int a = 1; a < num_arrs; a++) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] += data_t(scales[a] * input_ptrs[a][e]);
+                }
+            }
+        }
+
+        if (tail != 0 && ithr == nthr - 1) {
+            size_t start_e = nelems - tail;
+            size_t end_e = nelems;
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = start_e; e < end_e; e++) {
+                output[e] = data_t(scales[0] * input_ptrs[0][e]);
+            }
+            for (int a = 1; a < num_arrs; a++) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] += data_t(scales[a] * input_ptrs[a][e]);
+                }
+            }
+        }
+    });
+
+    return status::success;
+}
+
+template struct simple_sum_t<data_type::f32>;
+
+}
+}
+}