1 files changed, 0 insertions, 264 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.cpp
deleted file mode 100644
index 36d5237f56..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.cpp
+++ /dev/null
@@ -1,264 +0,0 @@
-/*******************************************************************************
-* 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.
-*******************************************************************************/
-
-#include <assert.h>
-#include <float.h>
-#include <math.h>
-
-#include "c_types_map.hpp"
-#include "mkldnn_thread.hpp"
-#include "type_helpers.hpp"
-
-#include "ref_softmax.hpp"
-#include "gemm/os_blas.hpp"
-
-#ifdef USE_MKL
-#include "mkl_vml_functions.h"
-#endif
-
-namespace mkldnn {
-namespace impl {
-namespace cpu {
-
-template <impl::data_type_t data_type>
-void ref_softmax_fwd_t<data_type>::execute_forward_dense(
-        const exec_ctx_t &ctx) const {
-    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
-    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
-
-    parallel_nd(outer_size_, [&](int ou) {
-        const data_t *src_data = src + ou * channels_;
-        data_t *dst_data = dst + ou * channels_;
-        data_t scalar = 0;
-
-        _max(channels_, src_data, &scalar);
-        _sub(channels_, scalar, src_data, dst_data);
-        _exp(channels_, dst_data, dst_data);
-        _sum(channels_, dst_data, &scalar);
-        _scal(channels_, data_t(1)/scalar, dst_data);
-    });
-}
-
-template <impl::data_type_t data_type>
-void ref_softmax_fwd_t<data_type>::execute_forward_generic(
-        const exec_ctx_t &ctx) const {
-    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
-    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
-
-    data_t space_max_val = 0, space_denom_val = 0;
-    data_t *space_max = &space_max_val, *space_denom = &space_denom_val;
-    if (inner_size_ > 1) {
-        using namespace memory_tracking::names;
-        space_max = scratchpad(ctx).template get<data_t>(key_softmax_reduction);
-        space_denom = space_max + inner_size_;
-    }
-
-    const memory_desc_wrapper data_d(pd()->src_md());
-    const size_t dim = channels_ * inner_size_;
-
-    for (int ou = 0; ou < outer_size_; ou++) {
-        utils::array_set(space_max, -FLT_MAX, inner_size_);
-        utils::array_set(space_denom, 0, inner_size_);
-
-        for (int c = 0; c < channels_; c++) {
-            for(int in = 0; in < inner_size_; in++) {
-                size_t off = data_d.off_l(ou * dim + c * inner_size_ + in);
-                space_max[in] = nstl::max(space_max[in], src[off]);
-            }
-        }
-
-        for (int c = 0; c < channels_; c++) {
-            for(int in = 0; in < inner_size_; in++) {
-                size_t off = data_d.off_l(ou * dim + c * inner_size_ + in);
-                space_denom[in] += dst[off] = exp(src[off] - space_max[in]);
-            }
-        }
-
-        for (int c = 0; c < channels_; c++) {
-            for (int in = 0; in < inner_size_; in++) {
-                size_t off = data_d.off_l(ou * dim + c * inner_size_ + in);
-                dst[off] /= space_denom[in];
-            }
-        }
-    }
-}
-
-template <impl::data_type_t data_type>
-void ref_softmax_fwd_t<data_type>::_max(int n, const data_t *x,
-        data_t *max_data) const {
-// Intel(R) C++ Compiler generates the maxps + shuffle pattern
-// for the max search which works faster
-#if !defined(__INTEL_COMPILER)
-    // The code below makes a compiler to generate maxps instruction
-    // rather than maxss, which is generated for the 'else' code path
-    auto max_wrapper = [](data_t a, data_t b) { return nstl::max(a, b); };
-    auto min_wrapper = [](int a, int b) { return nstl::min(a, b); };
-
-    constexpr int unroll_factor = 32;
-    data_t max_values[unroll_factor];
-
-    if (n < unroll_factor) {
-        data_t max_val = x[0];
-        for (int i = 1; i < n; i++) {
-            max_val = max_wrapper(max_val, x[i]);
-        }
-        max_data[0] = max_val;
-        return;
-    }
-    for (int i = 0; i < unroll_factor; i++) {
-        max_values[i] = x[i];
-    }
-    for (int i = unroll_factor; i < n; i += unroll_factor) {
-        int offset = min_wrapper(i, n - unroll_factor);
-        for (int j = 0; j < unroll_factor; j++) {
-            max_values[j] = max_wrapper(max_values[j], x[offset + j]);
-        }
-    }
-    data_t max_val = max_values[0];
-    for (int i = 1; i < unroll_factor; i++) {
-        max_val = max_wrapper(max_val, max_values[i]);
-    }
-    max_data[0] = max_val;
-#else
-    max_data[0] = x[0];
-    for (int c = 1; c < n; ++c)
-        max_data[0] = nstl::max(max_data[0], x[c]);
-#endif
-}
-
-template <impl::data_type_t data_type>
-void ref_softmax_fwd_t<data_type>::_sub(int n, data_t alpha, const data_t *x,
-        data_t *y) const {
-    constexpr int unroll_factor = 32;
-    int tail = n % unroll_factor;
-    for (int i = 0; i < n - tail; i += unroll_factor) {
-        PRAGMA_OMP_SIMD()
-        for (int j = 0; j < unroll_factor; j++) {
-            y[i + j] = x[i + j] - alpha;
-        }
-    }
-    PRAGMA_OMP_SIMD()
-    for (int i = n - tail; i < n; i++) {
-        y[i] = x[i] - alpha;
-    }
-}
-
-template <impl::data_type_t data_type>
-void ref_softmax_fwd_t<data_type>::_exp(int n, const data_t *a,
-        data_t *r) const {
-#ifdef USE_MKL
-    if (data_type == data_type::f32) {
-        vsExp(n, a, r);
-        return;
-    }
-#endif
-    parallel_nd(n, [&](int c) { r[c] = expf(a[c]); });
-}
-
-template <impl::data_type_t data_type>
-void ref_softmax_fwd_t<data_type>::_sum(int n, const data_t *x,
-        data_t *sum_data) const {
-#ifdef USE_CBLAS
-    // Here we are summing x's eg. e^z , which are positives
-    // so we can use BLAS ASUM
-    if (data_type == data_type::f32) {
-        sum_data[0] = cblas_sasum(n, x, 1);
-        return;
-    }
-#endif
-    data_t tsum = static_cast<data_t>(0);
-    PRAGMA_OMP_SIMD(reduction(+ : tsum))
-    for (int c = 0; c < n; ++c)
-        tsum += x[c];
-    sum_data[0] = tsum;
-}
-
-template <impl::data_type_t data_type>
-void ref_softmax_fwd_t<data_type>::_scal(int n, data_t alpha, data_t *x) const {
-#ifdef USE_CBLAS
-    if (data_type == data_type::f32) {
-        cblas_sscal(n, alpha, x, 1);
-        return;
-    }
-#endif
-    parallel_nd(n, [&](int c) { x[c] *= alpha; });
-}
-
-template struct ref_softmax_fwd_t<data_type::f32>;
-
-
-// NC/NCHW softmax for along final axe (1 for NC, 3 for NCHW)
-template <impl::data_type_t data_type>
-void ref_softmax_bwd_t<data_type>::execute_backward_dense(
-        const exec_ctx_t &ctx) const {
-    auto dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DST);
-    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
-    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
-
-    parallel_nd(outer_size_, [&](int ou) {
-        data_t sbr = 0;
-        size_t off = channels_*ou;
-        for (int c = 0; c < channels_; c++) {
-            size_t loff = off + c;
-            data_t ldata = dst[loff];
-            sbr += diff_dst[loff]*ldata;
-            diff_src[loff] = ldata;
-        }
-
-        for(int c=0; c < channels_ ; ++c) {
-          size_t loff = off + c;
-          diff_src[loff] *= (diff_dst[loff] - sbr);
-        }
-    });
-}
-
-template <impl::data_type_t data_type>
-void ref_softmax_bwd_t<data_type>::execute_backward_generic(
-        const exec_ctx_t &ctx) const {
-    auto dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DST);
-    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
-    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
-
-    const memory_desc_wrapper diff_d(pd()->diff_src_md());
-    const memory_desc_wrapper data_d(pd()->dst_md());
-
-    const size_t dim = channels_ * inner_size_;
-
-    parallel_nd(outer_size_, [&](int ou) {
-        for (int in = 0; in < inner_size_; in++) {
-            data_t sbr = 0;
-            for (int c = 0; c < channels_; c++) {
-                size_t off_diff = diff_d.off_l(ou * dim + c * inner_size_ + in);
-                size_t off_data = diff_d.off_l(ou * dim + c * inner_size_ + in);
-                sbr += diff_dst[off_diff] * dst[off_data];
-            }
-
-            for(int c=0; c < channels_ ; ++c) {
-              size_t off_diff = diff_d.off_l(ou * dim + c * inner_size_ + in);
-              size_t off_data = data_d.off_l(ou * dim + c * inner_size_ + in);
-              diff_src[off_diff] = dst[off_data] * (diff_dst[off_diff] - sbr);
-            }
-        }
-    });
-}
-
-template struct ref_softmax_bwd_t<data_type::f32>;
-
-}
-}
-}
-
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s