Tanh mkldnn implementation (#296)

* libnd4j first step of softmax mkldnn implementation

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j raw implementation of mkldnn softmax

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j merge master and added softmax to MklDnnTests

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j some corrections for softmax mkldnn

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j merge branch, fixed problem with negative axis, fixed dnnl::memory::format_tag selection, test cases added

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j minor corrections to avoid risk connected with negative axis usage

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j fixed windows builds, added switcher to use mkldnn sofmax version only for 3D, 4D, 5D, 6D arrays

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j fixed dataType selection per request

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j fix for mac and windows builds

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j builds fix

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j first spet of elementwize tanh implementation on mkldnn

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j fixed typo in error message for softmax MKLDNN, test case added, implementation of tanh on MKLDNN, need supported DataType testing

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j several fixes for tanh and temporary performance test added

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j fixed mkldnn platform loader for tanh

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j MklDnn tanh removed unsupported data types, removed performance test case, added more appropriate equivalence test case, code clean up

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* libnd4j fixed problem with empty input case for MklDnn tanh and softmax

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.h

@@ -90,6 +90,8 @@ namespace sd {
 
             DECLARE_PLATFORM(softmax, ENGINE_CPU);
 
+            DECLARE_PLATFORM(tanh, ENGINE_CPU);
+
         }
     }
 

libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp

@@ -152,7 +152,7 @@ namespace sd {
 
                 REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim);
 
-                REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 4, but got rank = %i instead !", rank);
+                REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank);
 
                 // mkldnnSoftMax
                 softmaxMKLDNN(input, output, dim);
@@ -174,7 +174,7 @@ namespace sd {
                 Source     Destination
                 f32 	    f32
                 */
-                return  block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
+                return  !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
 
             }
 

libnd4j/include/ops/declarable/platform/mkldnn/tanh.cpp

@@ -0,0 +1,158 @@
+/*******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://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.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+ //
+ //  @author Oleg Semeniv <oleg.semeniv@gmail.com>
+ //
+ //
+
+#include <ops/declarable/PlatformHelper.h>
+#include <ops/declarable/OpRegistrator.h>
+#include <system/platform_boilerplate.h>
+#include <helpers/MKLDNNStream.h>
+#include "mkldnnUtils.h"
+
+using namespace dnnl;
+
+namespace sd {
+    namespace ops {
+        namespace platforms {
+
+            //////////////////////////////////////////////////////////////////////
+            static void tanhMKLDNN(const NDArray* x, NDArray* z) {
+
+                const auto xRank = x->rankOf();
+
+                std::vector<int64_t> dimsX(xRank), dimsZ(xRank);
+                for (auto i = 0; i < xRank; i++) {
+                    dimsX[i] = x->sizeAt(i);
+                    dimsZ[i] = z->sizeAt(i);
+                }
+
+                dnnl::memory::dims xShape = dnnl::memory::dims(dimsX);
+                dnnl::memory::dims zShape = dnnl::memory::dims(dimsZ);
+
+                dnnl::memory::format_tag format = dnnl::memory::format_tag::a;
+                if (2 == xRank) {
+                    format = dnnl::memory::format_tag::ab;
+                }
+                else if (3 == xRank) {
+                    format = dnnl::memory::format_tag::abc;
+                }
+                else if (4 == xRank) {
+                    format = dnnl::memory::format_tag::abcd;
+                }
+                else if (5 == xRank) {
+                    format = dnnl::memory::format_tag::abcde;
+                }
+                else if (6 == xRank) {
+                    format = dnnl::memory::format_tag::abcdef;
+                }
+
+                dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
+                dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
+
+                if (x->ews() != 1 || x->ordering() != 'c') {
+                    x_user_md.data.format_kind = dnnl_blocked;    // overrides format
+                    for (auto i = 0; i < xRank; ++i) {
+                        x_user_md.data.format_desc.blocking.strides[i] = x->strideAt(i);
+                    }
+                }
+
+                // z
+                dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format);
+                dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format);
+                if (z->ews() != 1 || z->ordering() != 'c') {
+                    z_user_md.data.format_kind = dnnl_blocked;    // overrides format
+                    for (auto i = 0; i < xRank; ++i) {
+                        z_user_md.data.format_desc.blocking.strides[i] = z->strideAt(i);
+                    }
+                }
+
+                auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
+
+                // Create attributes (to handle alpha and beta if necessary)
+                dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0)
+
+                // operation primitive description
+                dnnl::eltwise_forward::desc op_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0);
+
+                dnnl::eltwise_forward::primitive_desc op_prim_desc(op_desc, attr, engine);
+
+                // arguments (memory buffers) necessary for calculations
+                std::unordered_map<int, dnnl::memory> args;
+
+                dnnl::stream stream(engine);
+
+                // provide memory buffers and check whether reorder is required
+                // input
+                auto x_user_mem = dnnl::memory(x_user_md, engine, x->getBuffer());
+                const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc();
+                auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem;
+                if (xReorder)
+                    dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
+                args[DNNL_ARG_SRC] = x_mkl_mem;
+
+                // z
+                auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer());
+                const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
+                auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
+                args[DNNL_ARG_DST] = z_mkl_mem;
+
+                // run calculations
+                dnnl::eltwise_forward(op_prim_desc).execute(stream, args);
+
+                // reorder outputs if necessary
+                if (zReorder)
+                    dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
+
+                stream.wait();
+            }
+
+
+            PLATFORM_IMPL(tanh, ENGINE_CPU) {
+
+                auto input = INPUT_VARIABLE(0);
+                auto output = OUTPUT_VARIABLE(0);
+                const int rank = input->rankOf();
+                REQUIRE_TRUE(rank <= 6, 0, "TANH_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank);
+
+                // mkldnnTanh
+                tanhMKLDNN(input, output);
+
+                return Status::OK();
+            }
+
+            PLATFORM_CHECK(tanh, ENGINE_CPU) {
+
+                auto x = INPUT_VARIABLE(0);
+                auto z = OUTPUT_VARIABLE(0);
+
+                const DataType xType = x->dataType();
+                const DataType zType = z->dataType();
+
+                const int xRank = x->rankOf();
+                bool bSupportedRanks = xRank < 7;
+                /*
+                Source     Destination
+                f32 	    f32
+                */
+                return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
+            }
+
+        }
+    }
+}

libnd4j/tests_cpu/layers_tests/MklDnnTests.cpp

@@ -71,6 +71,8 @@ TEST_F(MklDnnTests, helpers_includer) {
 
     sd::ops::platforms::PLATFORM_softmax_ENGINE_CPU softmax;
 
-    printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul, &softmax });
+    sd::ops::platforms::PLATFORM_tanh_ENGINE_CPU tanh;
+
+    printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul, &softmax, &tanh });
 #endif
 }