Tanh backpropagation mkldnn implementation (#308)

* libnd4j first step of tanh_bp operation implementation on mkldnn

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

* libnd4j optimize several places and added test case for tanh_bp

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

* libnd4j minor corrections and renaming, added one more test case

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

* libnd4j missed mkldnn data format definition

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

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

@@ -94,6 +94,8 @@ namespace sd {
 
             DECLARE_PLATFORM(tanh, ENGINE_CPU);
 
+            DECLARE_PLATFORM(tanh_bp, ENGINE_CPU);
+
         }
     }
 

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

@@ -109,12 +109,126 @@ namespace sd {
                 const DataType zType = z->dataType();
 
                 const int xRank = x->rankOf();
-                bool bSupportedRanks = xRank < 7;
+                bool bSupportedRanks = !x->isEmpty() && xRank < 7 && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
                 /*
                 Source     Destination
                 f32 	    f32
                 */
-                return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
+                return  block.isUseMKLDNN() && bSupportedRanks;
+            }
+
+
+            //////////////////////////////////////////////////////////////////////
+            static void tanhBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx) {
+
+                const auto xRank = x->rankOf();
+                dnnl::memory::dims xShape, dLdzShape, dLdxShape;
+
+                mkldnnUtils::getDims(x, xRank, xShape);
+                mkldnnUtils::getDims(dLdz, xRank, dLdzShape);
+                mkldnnUtils::getDims(dLdx, xRank, dLdxShape);
+
+                dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
+
+                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);
+                mkldnnUtils::setBlockStrides(x, xRank, x_user_md);
+
+                // dLdz
+                dnnl::memory::desc dLdz_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
+                dnnl::memory::desc dLdz_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
+                mkldnnUtils::setBlockStrides(dLdz, xRank, dLdz_user_md);
+  
+                // dLdx
+                dnnl::memory::desc dLdx_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
+                dnnl::memory::desc dLdx_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
+                mkldnnUtils::setBlockStrides(dLdx, xRank, dLdx_user_md);
+
+                auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
+
+                // arguments (memory buffers) necessary for calculations
+                std::unordered_map<int, dnnl::memory> args;
+
+                dnnl::stream stream(engine);
+
+                // operation primitive description
+                // forward
+                dnnl::eltwise_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0);
+                dnnl::eltwise_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
+
+                // backward description
+                dnnl::eltwise_backward::desc op_desc(algorithm::eltwise_tanh, dLdz_mkl_md, x_mkl_md, 0, 0);
+                dnnl::eltwise_backward::primitive_desc op_prim_desc(op_desc, engine, op_ff_prim_desc);
+
+                // provide memory buffers and check whether reorder is required for forward
+                // input
+                mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC);
+
+                // dLdz
+                mkldnnUtils::loadDataToMklStream(dLdz, engine, stream, args, dLdz_user_md, op_prim_desc.diff_dst_desc(), DNNL_ARG_DIFF_DST);
+
+                // dLdx
+                auto dLdx_user_mem = dnnl::memory(dLdx_user_md, engine, dLdx->getBuffer());
+                const bool dLdxReorder = op_prim_desc.diff_src_desc() != dLdx_user_mem.get_desc();
+                auto dLdx_mkl_mem = dLdxReorder ? dnnl::memory(op_prim_desc.diff_src_desc(), engine) : dLdx_user_mem;
+                args[DNNL_ARG_DIFF_SRC] = dLdx_mkl_mem;
+
+                // run calculations backward
+                dnnl::eltwise_backward(op_prim_desc).execute(stream, args);
+
+                // reorder outputs if necessary
+                if (dLdxReorder)
+                    dnnl::reorder(dLdx_mkl_mem, dLdx_user_mem).execute(stream, dLdx_mkl_mem, dLdx_user_mem);
+
+                stream.wait();
+            }
+
+
+            PLATFORM_IMPL(tanh_bp, ENGINE_CPU) {
+
+                auto input = INPUT_VARIABLE(0);
+                auto dLdz = INPUT_VARIABLE(1);
+                auto dLdx = OUTPUT_VARIABLE(0);
+
+                const int rank = input->rankOf();
+                const int dLdzRank = dLdz->rankOf();
+
+                REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0, "TANH_BP_MKLDNN OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and dLdz rank rank = %i instead !", rank, dLdzRank);
+
+                // mkldnnSoftMax
+                tanhBpMKLDNN(input, dLdz, dLdx);
+
+                return Status::OK();
+            }
+
+            PLATFORM_CHECK(tanh_bp, ENGINE_CPU) {
+
+                auto x = INPUT_VARIABLE(0);
+                auto dLdz = INPUT_VARIABLE(1);
+                auto dLdx = OUTPUT_VARIABLE(0);
+
+                const DataType xType = x->dataType();
+                const DataType dLdzType = dLdz->dataType();
+                const DataType dLdxType = dLdx->dataType();
+
+                const int xRank = x->rankOf();
+                const int dLdzRank = dLdz->rankOf();
+
+                bool bSupportedRanks = xRank < 7 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty());
+                bSupportedRanks &= (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32);
+
+                if (bSupportedRanks) {
+                    for (int i = 0; i < xRank; i++) {
+                        if (x->sizeAt(i) != dLdz->sizeAt(i)) {
+                            bSupportedRanks = false;
+                            break;
+                        }
+                    }
+                }
+
+                //Source     Destination
+                //f32 	    f32
+                return block.isUseMKLDNN() && bSupportedRanks;
             }
 
         }

libnd4j/tests_cpu/layers_tests/MklDnnTests.cpp

@@ -75,6 +75,9 @@ TEST_F(MklDnnTests, helpers_includer) {
 
     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, &softmax_bp, &tanh });
+    sd::ops::platforms::PLATFORM_tanh_ENGINE_CPU tanh_bp;
+
+    printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul, &softmax, &softmax_bp, &tanh, &tanh_bp });
+
 #endif
 }