Merge pull request llvm#33 from clang-ykt/fix-gemm

Fix Gemm translation to ONNX dialect.

Author: doru1004

src/dialect/onnx/onnx.td

@@ -90,17 +90,17 @@ 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:$bias_in);
-  let results = (outs AnyTensor);
+  let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$lhs_in, AnyTypeOf<[AnyMemRef, AnyTensor]>:$rhs_in);
+  let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
 }
 
 def ONNXConv1Op:ONNX_Op<"Conv1", 

src/dialect/onnx/onnx_ops.cpp

@@ -347,9 +347,9 @@ 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 @@ class ShapeInferencePass : public mlir::FunctionPass<ShapeInferencePass> {
         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>) -> ()