Support non-mkldnn fallback for Batchnorm training bprop in CPU backend (#3688)

* Support non-mkldnn fallback for Batchnorm training bprop in CPU backend

* Skip unit test on PlaidML backend

src/ngraph/runtime/cpu/cpu_emitter.cpp

@@ -825,46 +825,67 @@ namespace ngraph
             void CPU_Emitter::EMITTER_DECL(ngraph::op::BatchNormTrainingBackprop)
             {
                 writer.block_begin();
-                // define weights
-                writer << "std::vector<" << args[0].get_element_type().c_type_string()
-                       << ">bn_weights(2*" << args[0].get_size() << ");\n";
-                writer << "std::vector<" << args[0].get_element_type().c_type_string()
-                       << ">bn_dweights(2*" << args[0].get_size() << ");\n";
+                if (!mkldnn_utils::use_mkldnn_kernel(node))
+                {
+                    const ngraph::op::BatchNormTrainingBackprop* batchnorm =
+                        static_cast<const ngraph::op::BatchNormTrainingBackprop*>(node);
 
-                writer << "memcpy(&bn_weights[0], " << args[0].get_name() << ", "
-                       << args[0].get_size() * args[0].get_element_type().size() << ");\n";
-                writer << "memcpy(&bn_weights[0]+" << args[0].get_size() << ", "
-                       << args[1].get_name() << ", "
-                       << args[1].get_size() * args[1].get_element_type().size() << ");\n";
+                    writer << "reference::batch_norm_backprop(" << batchnorm->get_eps_value()
+                           << ",\n";
+                    writer << "            " << args[0].get_name() << ",\n";
+                    writer << "            " << args[1].get_name() << ",\n";
+                    writer << "            " << args[2].get_name() << ",\n";
+                    writer << "            " << args[3].get_name() << ",\n";
+                    writer << "            " << args[4].get_name() << ",\n";
+                    writer << "            " << args[5].get_name() << ",\n";
+                    writer << "            " << out[0].get_name() << ",\n";
+                    writer << "            " << out[1].get_name() << ",\n";
+                    writer << "            " << out[2].get_name() << ",\n";
+                    writer << "            {" << join(args[2].get_shape()) << "});\n";
+                }
+                else
+                {
+                    // define weights
+                    writer << "std::vector<" << args[0].get_element_type().c_type_string()
+                           << ">bn_weights(2*" << args[0].get_size() << ");\n";
+                    writer << "std::vector<" << args[0].get_element_type().c_type_string()
+                           << ">bn_dweights(2*" << args[0].get_size() << ");\n";
 
-                size_t batchnorm_index;
-                std::vector<std::size_t> deps;
-                emit_build_primitives(external_function, node, writer, batchnorm_index, deps);
+                    writer << "memcpy(&bn_weights[0], " << args[0].get_name() << ", "
+                           << args[0].get_size() * args[0].get_element_type().size() << ");\n";
+                    writer << "memcpy(&bn_weights[0]+" << args[0].get_size() << ", "
+                           << args[1].get_name() << ", "
+                           << args[1].get_size() * args[1].get_element_type().size() << ");\n";
 
-                writer << "cg_ctx->set_memory_ptr(" << to_string(deps[0])
-                       << ", bn_weights.data());\n";
-                writer << "cg_ctx->set_memory_ptr(" << to_string(deps[1]) << ", "
-                       << args[2].get_name() << ");\n";
-                writer << "cg_ctx->set_memory_ptr(" << to_string(deps[2]) << ", "
-                       << args[3].get_name() << ");\n";
-                writer << "cg_ctx->set_memory_ptr(" << to_string(deps[3]) << ", "
-                       << args[4].get_name() << ");\n";
-                writer << "cg_ctx->set_memory_ptr(" << to_string(deps[4]) << ", "
-                       << args[5].get_name() << ");\n";
-                writer << "cg_ctx->set_memory_ptr(" << to_string(deps[5]) << ", "
-                       << out[0].get_name() << ");\n";
-                writer << "cg_ctx->set_memory_ptr(" << to_string(deps[6])
-                       << ", bn_dweights.data());\n";
+                    size_t batchnorm_index;
+                    std::vector<std::size_t> deps;
+                    emit_build_primitives(external_function, node, writer, batchnorm_index, deps);
 
-                writer << "std::vector<size_t> deps{" << join(deps) << "};\n";
-                writer << "cg_ctx->mkldnn_invoke_primitive(" << to_string(batchnorm_index)
-                       << ", deps, OpType::BATCHNORMBACKPROP);\n";
+                    writer << "cg_ctx->set_memory_ptr(" << to_string(deps[0])
+                           << ", bn_weights.data());\n";
+                    writer << "cg_ctx->set_memory_ptr(" << to_string(deps[1]) << ", "
+                           << args[2].get_name() << ");\n";
+                    writer << "cg_ctx->set_memory_ptr(" << to_string(deps[2]) << ", "
+                           << args[3].get_name() << ");\n";
+                    writer << "cg_ctx->set_memory_ptr(" << to_string(deps[3]) << ", "
+                           << args[4].get_name() << ");\n";
+                    writer << "cg_ctx->set_memory_ptr(" << to_string(deps[4]) << ", "
+                           << args[5].get_name() << ");\n";
+                    writer << "cg_ctx->set_memory_ptr(" << to_string(deps[5]) << ", "
+                           << out[0].get_name() << ");\n";
+                    writer << "cg_ctx->set_memory_ptr(" << to_string(deps[6])
+                           << ", bn_dweights.data());\n";
 
-                writer << "memcpy(" << out[1].get_name() << ", &bn_dweights[0], "
-                       << args[0].get_size() * args[0].get_element_type().size() << ");\n";
-                writer << "memcpy(" << out[2].get_name() << ", &bn_dweights[0]+"
-                       << args[0].get_size() << ", "
-                       << args[1].get_size() * args[1].get_element_type().size() << ");\n";
+                    writer << "std::vector<size_t> deps{" << join(deps) << "};\n";
+                    writer << "cg_ctx->mkldnn_invoke_primitive(" << to_string(batchnorm_index)
+                           << ", deps, OpType::BATCHNORMBACKPROP);\n";
+
+                    writer << "memcpy(" << out[1].get_name() << ", &bn_dweights[0], "
+                           << args[0].get_size() * args[0].get_element_type().size() << ");\n";
+                    writer << "memcpy(" << out[2].get_name() << ", &bn_dweights[0]+"
+                           << args[0].get_size() << ", "
+                           << args[1].get_size() * args[1].get_element_type().size() << ");\n";
+                }
                 writer.block_end();
             }
 

src/ngraph/runtime/cpu/kernel/batchnorm.hpp

@@ -66,6 +66,32 @@ namespace ngraph
                                                     static_cast<ElementType*>(out0),
                                                     arg2_shape);
                 }
+
+                template <typename ElementType>
+                void batch_norm_backprop(double eps,
+                                         const void* arg0,
+                                         const void* arg1,
+                                         const void* arg2,
+                                         const void* arg3,
+                                         const void* arg4,
+                                         const void* arg5,
+                                         void* out0,
+                                         void* out1,
+                                         void* out2,
+                                         const Shape& arg2_shape)
+                {
+                    reference::batch_norm_backprop(eps,
+                                                   static_cast<const ElementType*>(arg0),
+                                                   static_cast<const ElementType*>(arg1),
+                                                   static_cast<const ElementType*>(arg2),
+                                                   static_cast<const ElementType*>(arg3),
+                                                   static_cast<const ElementType*>(arg4),
+                                                   static_cast<const ElementType*>(arg5),
+                                                   static_cast<ElementType*>(out0),
+                                                   static_cast<ElementType*>(out1),
+                                                   static_cast<ElementType*>(out2),
+                                                   arg2_shape);
+                }
             }
         }
     }

src/ngraph/runtime/cpu/pass/cpu_layout.cpp

@@ -2326,7 +2326,7 @@ namespace ngraph
                     }
                     else
                     {
-                        throw ngraph_error("Batchnorm Backprop only supported in MKLDNN for now");
+                        set_native_layouts(external_function, node);
                     }
                 }
 

src/ngraph/runtime/plaidml/unit_test.manifest

@@ -188,6 +188,7 @@ max_trivial_5d_int32
 floor_int32
 any_trivial
 any_2x2x3_eliminate_dim_0
+backwards_batch_norm_training_3d
 
 # unsupported op: `GenerateMask`
 generate_mask

test/backend/autodiff.in.cpp

@@ -1659,7 +1659,7 @@ NGRAPH_TEST(${BACKEND_NAME}, backwards_maxpool_n2c1h5w5_kh3kw3_sh2sw2)
     ASSERT_TRUE(read_vector<float>(output) == expected);
 }
 
-NGRAPH_TEST(${BACKEND_NAME}, backwards_batch_norm_training)
+NGRAPH_TEST(${BACKEND_NAME}, backwards_batch_norm_training_4d)
 {
     const Shape input_shape{10, 4, 5, 5};
     const Shape channel_shape{input_shape.at(1)};
@@ -1697,6 +1697,44 @@ NGRAPH_TEST(${BACKEND_NAME}, backwards_batch_norm_training)
         autodiff_numeric_compare<T>(backend.get(), make_graph, {input, gamma, beta}, .005, .005));
 }
 
+NGRAPH_TEST(${BACKEND_NAME}, backwards_batch_norm_training_3d)
+{
+    const Shape input_shape{10, 4, 5};
+    const Shape channel_shape{input_shape.at(1)};
+    const double eps = 1e-3;
+
+    // Need to keep the output elements for mean and variance from going out of scope
+    // and getting freed.
+    NodeVector goes;
+
+    auto make_graph = [&input_shape, &channel_shape, &eps, &goes] {
+        const element::Type& et = element::f32;
+        auto input = make_shared<op::Parameter>(et, input_shape);
+        auto gamma = make_shared<op::Parameter>(et, channel_shape);
+        auto beta = make_shared<op::Parameter>(et, channel_shape);
+        auto BN = make_shared<op::BatchNormTraining>(input, gamma, beta, eps);
+        auto normed_input = make_shared<op::Result>(make_shared<op::GetOutputElement>(BN, 0));
+        auto mean = make_shared<op::Result>(make_shared<op::GetOutputElement>(BN, 1));
+        auto variance = make_shared<op::Result>(make_shared<op::GetOutputElement>(BN, 2));
+        goes.push_back(mean);
+        goes.push_back(variance);
+        // TODO autodiff testing with more than one result
+        auto f =
+            make_shared<Function>(ResultVector{normed_input}, ParameterVector{input, gamma, beta});
+        return f;
+    };
+
+    auto backend = runtime::Backend::create("${BACKEND_NAME}");
+    using T = float;
+    test::Uniform<T> rng(-5.0, 2.0);
+    auto input = rng.initialize(backend->create_tensor<T>(input_shape));
+    auto gamma = rng.initialize(backend->create_tensor<T>(channel_shape));
+    auto beta = rng.initialize(backend->create_tensor<T>(channel_shape));
+
+    EXPECT_TRUE(
+        autodiff_numeric_compare<T>(backend.get(), make_graph, {input, gamma, beta}, .005, .005));
+}
+
 NGRAPH_TEST(${BACKEND_NAME}, backwards_reverse_sequence_n3_c2_h3)
 {
     auto backend = runtime::Backend::create("${BACKEND_NAME}");