Merge branch 'master' into matmul-shape

src/builder/frontend_dialect_transformer.cpp

@@ -611,7 +611,7 @@ private:
    * a specialized function is used
    */
   void ImportNodeConv(
-      onnx::NodeProto node, int nIn, int nOut,
+      onnx::NodeProto node, int nOut,
       std::initializer_list<std::tuple<std::string, std::string, std::string>>
           attrs) {
     // Conv has attribute dilations, kernel_shape, pads, the default value of
@@ -624,12 +624,12 @@ private:
     // similar situation for pads, strides in AveragePool
     // axes of ReduceSum,  pads, strides, dilations and kernel_shape of MaxPool
     // TODO: fix this after type inference
+    int nOps = node.input().size();
 
-    if (node.input().size() == 1) {
+    if (nOps == 2)
-      ImportNodeOneOut<mlir::ONNXConv1Op>(node, nIn, nOut, attrs);
+      ImportNodeOneOut<mlir::ONNXConvNoBiasOp>(node, nOps, nOut, attrs);
-    } else {
+    else
-      ImportNodeOneOut<mlir::ONNXConv3Op>(node, nIn, nOut, attrs);
+      ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut, attrs);
-    }
   }
 
   /*!

src/builder/op_build_table.inc

@@ -87,7 +87,7 @@
          {"value","", ""}
       });
     }else if (OpName == "Conv") {
-       ImportNodeConv(node, 3, 1, {
+       ImportNodeConv(node, 1, {
          {"auto_pad","str","NOTSET"}
         ,{"dilations","", ""}
         ,{"group","int", "1"}

src/dialect/onnx/onnx.td

@@ -90,39 +90,28 @@ def ONNXEntryPointOp: ONNX_Op<"EntryPoint"> {
 // or outputs. This decision affects only ONNX operations with optional
 // arguments not ONNX operations with variadic operands.
 
-def ONNXFullGemmOp: ONNX_Op<"FullGemm",
+def ONNXGemmNoBiasOp: ONNX_Op<"GemmNoBias",
     [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
-  let summary = "ONNX general matrix multiply operation";
+  let summary = "ONNX general matrix multiply operation without bias.";
   let description = [{
 
-    The "onnx.gemm" generic matrix multiplication with bias.
+    The "onnx.Gemm" generic matrix multiplication without bias.
 
   }];
 
-  let arguments = (ins AnyTensor:$lhs_in, AnyTensor:$rhs_in, AnyTensor:$bias_in);
+  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$lhs_in, AnyTypeOf<[AnyMemRef, AnyTensor]>:$rhs_in);
-  let results = (outs AnyTensor);
+  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
 }
 
-def ONNXConv1Op:ONNX_Op<"Conv1", 
+def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
     [NoSideEffect]> {
-  let summary = "ONNX Conv operation";
+  let summary = "ONNX Conv operation with no Bias operand.";
   let description = [{
     "The convolution operator consumes an input tensor and a filter, and"
     "computes the output."
   }];
-  let arguments = (ins AnyTensor:$X);
+  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W);
-  let results = (outs AnyTensor);
+  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
-}
-
-def ONNXConv3Op:ONNX_Op<"Conv3", 
-    [NoSideEffect]> {
-  let summary = "ONNX Conv operation";
-  let description = [{
-    "The convolution operator consumes an input tensor and a filter, and"
-    "computes the output."
-  }];
-  let arguments = (ins AnyTensor:$X, AnyTensor:$W, AnyTensor:$B);
-  let results = (outs AnyTensor);
 }
 
 def ONNXMaxPoolSingleOutOp: ONNX_Op<"MaxPoolSingleOut",

src/dialect/onnx/onnx_ops.cpp

@@ -424,9 +424,9 @@ void ONNXGemmOp::inferShapes() {
   getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
 }
 
-// FullGemm
+// GemmNoBias
 
-void ONNXFullGemmOp::inferShapes() {
+void ONNXGemmNoBiasOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())

src/pass/onnx_combine.td

@@ -30,9 +30,9 @@ def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;
 // Pattern-Match and Rewrite
 //===----------------------------------------------------------------------===//
 
-// onnx.add(onnx.matmul(%X, %Y), %Z) = onnx.FullGemm(%X, %Y, %Z)
+// onnx.add(onnx.matmul(%X, %Y), %Z) = onnx.Gemm(%X, %Y, %Z)
 def MulAddToGemmOptPattern : Pat<(ONNXAddOp (ONNXMatMulOp:$res $m1, $m2), $m3),
-                                 (ONNXFullGemmOp $m1, $m2, $m3),
+                                 (ONNXGemmOp $m1, $m2, $m3),
 				 [(HasOneUse $res)]>;
 
 // ONNX_Op (onnx.Identity (%X)) = ONNX_Op (%X)

src/pass/shape_inference_pass.cpp

@@ -114,7 +114,7 @@ public:
         op->getName().getStringRef() != "onnx.Identity" &&
         op->getName().getStringRef() != "onnx.MatMul" &&
         op->getName().getStringRef() != "onnx.Gemm" &&
-        op->getName().getStringRef() != "onnx.FullGemm" &&
+        op->getName().getStringRef() != "onnx.GemmNoBias" &&
         op->getName().getStringRef() != "onnx.Reshape" &&
         op->getName().getStringRef() != "onnx.Transpose")
       return false;

test/mlir/onnx/onnx_canonicalization.mlir

@@ -2,7 +2,7 @@
 
 func @test_matmul_add_simplification(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
   // CHECK-LABEL: test_matmul_add_simplification
-  // CHECK: %{{[0-9]+}} = "onnx.FullGemm"(%{{.*}}, %{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
+  // CHECK: %{{[0-9]+}} = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
   %0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
   %1 = "onnx.Add"(%0, %a2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
   "std.return"(%1) : (tensor<10x10xf32>) -> ()