Merge branch 'master' into fix-conv

src/builder/frontend_dialect_transformer.cpp

@@ -632,6 +632,21 @@ private:
       ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut, attrs);
   }
 
+  /*!
+   * Special handle for MaxPool operations.
+   */
+  void ImportNodeMaxPool(
+      onnx::NodeProto node, int nIn,
+      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+          attrs) {
+    int nOuts = node.output().size();
+    if (nOuts == 1) {
+      ImportNodeOneOut<mlir::ONNXMaxPoolSingleOutOp>(node, nIn, nOuts, attrs);
+    } else {
+      ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(node, nIn, nOuts, attrs);
+    }
+  }
+
   void ImportNode(onnx::NodeProto node) {
     std::vector<mlir::Value> inputs;
     for (auto item : node.input()) {

src/builder/op_build_table.inc

@@ -303,7 +303,7 @@
        ImportNodeOneOut<mlir::ONNXMaxOp>(node, 1, 1, {
       });
     }else if (OpName == "MaxPool") {
-       ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(node, 1, 2, {
+       ImportNodeMaxPool(node, 1, {
          {"auto_pad","str","NOTSET"}
         ,{"ceil_mode","int","0"}
         ,{"dilations","", ""}

src/dialect/onnx/gen_doc.py

@@ -365,6 +365,7 @@ special cases:
 def gen_code(schema,fefile) :
     special_handler = dict([
         ("Conv", "ImportNodeConv"),
+        ("MaxPool", "ImportNodeMaxPool"),
         #("Transpose", "ImportNodeTranspose")
         ])
     special_type = dict([

src/dialect/onnx/onnx.td

@@ -78,6 +78,17 @@ def ONNXEntryPointOp: ONNX_Op<"EntryPoint"> {
   }];
 }
 
+//===----------------------------------------------------------------------===//
+// ONNX Operations for handling optional arguments
+//===----------------------------------------------------------------------===//
+
+// To allow pattern matching on operations with optional arguments/outputs we
+// implement variants of the original ONNX dialect operations. The ONNX
+// operations automatically generated by the `gen_doc.py` script and included
+// in the `onnxop.inc` file have all optional arguments and outputs present.
+// In the operations below we include the variants with missing operands
+// or outputs. This decision affects only ONNX operations with optional
+// arguments not ONNX operations with variadic operands.
 
 def ONNXFullGemmOp: ONNX_Op<"FullGemm",
     [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
@@ -103,4 +114,15 @@ def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
 }
 
+def ONNXMaxPoolSingleOutOp: ONNX_Op<"MaxPoolSingleOut",
+    [NoSideEffect]> {
+  let summary = "ONNX MaxPool operation with a single output.";
+  let description = [{
+    "ONNX MaxPool operation with a single output."
+    "See ONNXMaxPoolOp for a full description of the MaxPool semantics."
+  }];
+  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
+  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
+}
+
 #endif // ONNX_OPS