revert broadcast execution and fix batchnorm folding

microsoft · Apr 15, 2022 · f72b206 · f72b206
1 parent 50e2c59
commit f72b206
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Showing 2 changed files with 15 additions and 14 deletions.
diff --git a/src/nnfusion/engine/pass/codegen/cuda_codegen_pass.cpp b/src/nnfusion/engine/pass/codegen/cuda_codegen_pass.cpp
@@ -1133,6 +1133,7 @@ void CudaCodegenPass::create_main_file(std::shared_ptr<InterpreterContext> ctx,
             lu_main << get_d2hcopy(tu)->get_code();
             lu_main << get_sync()->get_code();
         }
+        /*
         for (size_t i = 0; i < tu->out.size(); i++)
         {
             auto& tensor = *tu->out[i];
@@ -1143,7 +1144,7 @@ void CudaCodegenPass::create_main_file(std::shared_ptr<InterpreterContext> ctx,
                     << "\nprintf(\" .. (size = " << tensor.get_tensor_layout()->get_size()
                     << ", ends with %e);\\n\", (float)" << tensor.get_name() << "_host["
                     << tensor.get_tensor_layout()->get_size() - 1 << "]);\n";
-        }
+        }*/
         lu_main.block_end();
 
         lu_main << "\n//GPU time measurement\n";

diff --git a/src/nnfusion/engine/pass/graph/batchnorm_inference_folding_pass.cpp b/src/nnfusion/engine/pass/graph/batchnorm_inference_folding_pass.cpp
@@ -622,10 +622,10 @@ class BatchNormInferenceOptimizer
             auto new_broadcast_gnode = m_graph->add_node_and_edge(
                 std::make_shared<op::Broadcast>(conv_output_shape, broadcast_axes),
                 {new_conv_bias_gnode});
-            shared_ptr<KernelContext> ke_ctx(new KernelContext(new_broadcast_gnode));
-            KernelEmitter::Pointer any_op_ke = std::make_shared<nnfusion::kernels::cuda::AnyOP>(ke_ctx);
-            any_op_ke->get_or_emit_source();
-            (*new_broadcast_gnode)["Kernel_Selection_Result"] = std::make_pair(NNFusion_DeviceType::CUDA_GPU, any_op_ke);
+            // shared_ptr<KernelContext> ke_ctx(new KernelContext(new_broadcast_gnode));
+            // KernelEmitter::Pointer any_op_ke = std::make_shared<nnfusion::kernels::cuda::AnyOP>(ke_ctx);
+            // any_op_ke->get_or_emit_source();
+            // (*new_broadcast_gnode)["Kernel_Selection_Result"] = std::make_pair(NNFusion_DeviceType::CUDA_GPU, any_op_ke);
 
             m_nodes.resize(m_graph->get_max_node_id());
             m_nodes[new_broadcast_gnode->get_id()] = std::make_shared<TaggedNode>();
@@ -796,10 +796,10 @@ class BatchNormInferenceOptimizer
             auto new_broadcast_gnode = m_graph->add_node_and_edge(
                 std::make_shared<op::Broadcast>(conv_output_shape, broadcast_axes),
                 {bn_node->get_in_edge(1)->get_src()});
-            shared_ptr<KernelContext> ke_ctx(new KernelContext(new_broadcast_gnode));
-            KernelEmitter::Pointer any_op_ke = std::make_shared<nnfusion::kernels::cuda::AnyOP>(ke_ctx);
-            any_op_ke->get_or_emit_source();
-            (*new_broadcast_gnode)["Kernel_Selection_Result"] = std::make_pair(NNFusion_DeviceType::CUDA_GPU, any_op_ke);;
+            // shared_ptr<KernelContext> ke_ctx(new KernelContext(new_broadcast_gnode));
+            // KernelEmitter::Pointer any_op_ke = std::make_shared<nnfusion::kernels::cuda::AnyOP>(ke_ctx);
+            // any_op_ke->get_or_emit_source();
+            // (*new_broadcast_gnode)["Kernel_Selection_Result"] = std::make_pair(NNFusion_DeviceType::CUDA_GPU, any_op_ke);;
             m_nodes.resize(m_graph->get_max_node_id());
             m_nodes[new_broadcast_gnode->get_id()] = std::make_shared<TaggedNode>();
             m_nodes[new_broadcast_gnode->get_id()]->node = new_broadcast_gnode;
@@ -903,11 +903,11 @@ bool BatchNormInferenceFoldingPass::run_on_graph(std::shared_ptr<nnfusion::graph
             BatchNormInferenceOptimizer optimizer(graph, pattern);
             optimizer.MatchAndFolding();
         }
-        if (FLAGS_fconst_folding_backend != "")
-        {
-            auto const_folding_optimizer = RuntimeConstantFoldingPass();
-            const_folding_optimizer.run_on_graph(graph);
-        }
+        // if (FLAGS_fconst_folding_backend != "")
+        // {
+        //     auto const_folding_optimizer = RuntimeConstantFoldingPass();
+        //     const_folding_optimizer.run_on_graph(graph);
+        // }
         NNFUSION_LOG(INFO) << "batchnorm inference folding Pass ends for Graph: "
                            << graph->get_name();
     }