[Torch] emit aten.dot and canonicalize it to aten.matmul (#3361)

* canonicalize `aten.dot` to `aten.matmul`

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -5955,6 +5955,31 @@ def Torch_AtenMvOp : Torch_Op<"aten.mv", [
   }];
 }
 
+def Torch_AtenDotOp : Torch_Op<"aten.dot", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::dot : (Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$tensor
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenDotOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenDotOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+  let hasCanonicalizer = 1;
+}
+
 def Torch_AtenCosineSimilarityOp : Torch_Op<"aten.cosine_similarity", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -548,6 +548,24 @@ void PrimIfOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
   });
 }
 
+//===----------------------------------------------------------------------===//
+// AtenDotOp
+//===----------------------------------------------------------------------===//
+
+void AtenDotOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
+                                            MLIRContext *context) {
+  patterns.add(+[](AtenDotOp op, PatternRewriter &rewriter) {
+    auto ty = dyn_cast<ValueTensorType>(op.getResult().getType());
+    if (!ty || !ty.hasSizes() || !ty.hasDtype()) {
+      return failure();
+    }
+
+    rewriter.replaceOpWithNewOp<AtenMatmulOp>(op, op.getResult().getType(),
+                                              op.getSelf(), op.getTensor());
+    return success();
+  });
+}
+
 //===----------------------------------------------------------------------===//
 // RuntimeAssertOp
 //===----------------------------------------------------------------------===//

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -7351,6 +7351,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    } : (!torch.int, !torch.bool) -> ()\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.dot\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = torch.prim.ListConstruct  : () -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.matmul\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.matmul(%arg0, %arg1) : (!torch.list<int>, !torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -11437,6 +11441,20 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %2 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.dot\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %0:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %2 = torch.aten.eq.int %1#1, %0#1 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %2 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    return %1#1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.matmul\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"

projects/pt1/e2e_testing/xfail_sets.py

@@ -822,6 +822,7 @@ FX_IMPORTER_STABLEHLO_CRASHING_SET = {
 }
 
 STABLEHLO_PASS_SET = {
+    "AtenDotModule_basic",
     "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
     "AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
     "AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
@@ -1452,6 +1453,7 @@ STABLEHLO_CRASHING_SET = {
 # Write the TOSA set as a "passing" set as it is very early in development
 # and very few tests work yet.
 TOSA_PASS_SET = {
+    "AtenDotModule_basic",
     "ElementwiseFloatTensorGtIntScalarModule_basic",
     "ElementwiseLogSigmoidModule_basic",
     "ElementwiseTruncModule_basic",

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -724,6 +724,10 @@ def aten〇numpy_T〡shape(self: List[int]) -> List[int]:
         result_shape.insert(0, i)
     return result_shape
 
+@check_shape_function([Invocation(TensorOfShape(3), TensorOfShape(3))])
+def aten〇dot〡shape(self: List[int], tensor: List[int]) -> List[int]:
+    return []
+
 def aten〇matmul〡shape(self: List[int], other: List[int]) -> List[int]:
     return upstream_shape_functions.matmul(self, other)
 
@@ -3303,6 +3307,13 @@ def aten〇div〇Scalar_mode〡dtype(self_rank_dtype: Tuple[int, int], other: Un
     else:
         return torch.float32
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(4,), (4,)]))
+def aten〇dot〡dtype(self_rank_dtype: Tuple[int, int], tensor_rank_dtype: Tuple[int, int]) -> int:
+    other_rank, other_dtype = tensor_rank_dtype
+    self_rank, self_dtype = self_rank_dtype
+    assert self_dtype == other_dtype
+    return self_dtype
+
 @check_dtype_function(
     _check_tensors_with_the_same_dtype(tensor_shapes=[(2, 3, 4), (2, 4, 3)]) +
     # Different width

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py

@@ -532,6 +532,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::addmm : (Tensor, Tensor, Tensor, Scalar, Scalar) -> (Tensor)")
     emit("aten::matmul : (Tensor, Tensor) -> (Tensor)")
     emit("aten::mv : (Tensor, Tensor) -> (Tensor)")
+    emit("aten::dot : (Tensor, Tensor) -> (Tensor)", has_canonicalizer=True)
     emit("aten::cosine_similarity : (Tensor, Tensor, int, float) -> (Tensor)")
     emit(
         "aten::conv3d : (Tensor, Tensor, Tensor?, int[], int[], int[], int) -> (Tensor)"

projects/pt1/python/torch_mlir_e2e_test/test_suite/matmul.py

@@ -12,6 +12,30 @@ from torch_mlir_e2e_test.annotations import annotate_args, export
 # ==============================================================================
 
 
+class AtenDotModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1], torch.float32, True),
+            ([-1], torch.float32, True),
+        ]
+    )
+    def forward(self, lhs, rhs):
+        return torch.dot(lhs, rhs)
+
+
+@register_test_case(module_factory=lambda: AtenDotModule())
+def AtenDotModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4), tu.rand(4))
+
+
+# ==============================================================================
+
+
 class MatmulDot(torch.nn.Module):
     def __init__(self):
         super().__init__()