[GitHub] [incubator-tvm] zhiics commented on a change in pull request #4741: [External codegen] Add test cases for fused ops with manual annotation

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zhiics commented on a change in pull request #4741: [External codegen] Add test cases for fused ops with manual annotation
URL: https://github.com/apache/incubator-tvm/pull/4741#discussion_r368308285
 
 

 ##########
 File path: src/relay/backend/contrib/dnnl/codegen.cc
 ##########
 @@ -50,82 +51,109 @@ class CodegenDNNL : public ExprVisitor, public CodegenCBase {
     out_.push_back({node->name_hint(), 0});
   }
 
-  void VisitExpr_(const TupleGetItemNode* op) final {
-    // Do nothing
-  }
-
   void VisitExpr_(const CallNode* call) final {
-    std::ostringstream decl_stream;
-    std::ostringstream buf_stream;
-    // Args: ID
-    std::vector<std::string> args;
+    struct Output {
+      std::string decl, buf;
+      int out_size = 1;
+      std::string out;
+    };
+
+    auto generate_body = [=](const CallNode* root_call, const std::string& func_name,
+                             const std::vector<std::string>& args,
+                             const std::vector<std::string>& fused_func_args) {
+      // Make function call with input buffers when visiting arguments
+      bool first = true;
+      std::ostringstream arg_stream;
+      arg_stream << "(";
+      for (size_t i = 0; i < root_call->args.size(); ++i) {
+        VisitExpr(root_call->args[i]);
+        for (auto out : out_) {
+          if (!first) {
+            arg_stream << ", ";
+          }
+          first = false;
+          arg_stream << out.first;
+        }
+      }
+
+      for (auto arg_name : fused_func_args) {
+        arg_stream << ", " << arg_name;
+      }
+
+      // Analyze the output buffer
+      auto type_node = root_call->checked_type().as<TensorTypeNode>();
+      CHECK(type_node != nullptr && runtime::TypeMatch(type_node->dtype, kDLFloat, 32))
+          << "Only support single output tensor with float type";
+
+      auto out_shape = GetShape(root_call->checked_type());
+
+      Output ret;
+      ret.out = "buf_" + std::to_string(buf_idx_++);
+      ret.out_size = std::accumulate(out_shape.begin(), out_shape.end(), 1, std::multiplies<int>());
+
+      this->PrintIndents();
+
+      std::ostringstream buf_stream;
+      buf_stream << "float* " << ret.out << " = (float*)std::malloc(4 * " << ret.out_size << ");";
+      ret.buf = buf_stream.str();
 
-    // Get the arguments for various DNNL kernels.
-    if (IsOp(call, "nn.conv2d")) {
-      decl_stream << "dnnl_conv2d";
-      args = Conv2d(call);
+      arg_stream << ", " << ret.out;
+      // Attach attribute arguments
+      for (size_t i = 0; i < args.size(); ++i) {
+        arg_stream << ", " << args[i];
+      }
+      arg_stream << ");";
+      ret.decl = func_name + arg_stream.str();
+
+      return ret;
+    };
+
+    Output ret;
+    if (auto conv_call = DetectFusedConv2DBiasReLU(call)) {
 
 Review comment:
   I am not sure if we really want to handle fused op from relay for external codegen. This looks quite ad-hoc to me. You may have countless combinations.

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