Add aten.tensor.int and aten.tensor.float op lowerings.

Add the required lowerings and correct test cases.
These op produce zero-d tensors and it was incorrectly mentioned in
refine types to produce 1d tensor of size 1.

e2e_testing/torchscript/basic.py

@@ -835,6 +835,41 @@ def AddCDivModule_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
+class tensorIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,   
+    ])
+
+    def forward(self):
+        a = 1
+        return torch.tensor(a)
+
+@register_test_case(module_factory=lambda: tensorIntModule())
+def TensorIntModule_basic(module, tu: TestUtils):
+    module.forward()
+
+class tensorFloatModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,   
+    ])
+
+    def forward(self):
+        a = 1.0
+        return torch.tensor(a)
+
+@register_test_case(module_factory=lambda: tensorFloatModule())
+def TensorFloatModule_basic(module, tu: TestUtils):
+    module.forward()
+
+# ==============================================================================
 
 class DropoutModule(torch.nn.Module):
     def __init__(self):

lib/Conversion/TorchToLinalg/TorchToLinalg.cpp

@@ -3451,6 +3451,60 @@ public:
 };
 } // namespace
 
+namespace {
+struct ConvertAtenScalarToTensorLike : ConversionPattern {
+  ConvertAtenScalarToTensorLike(TypeConverter &typeConverter,
+                                MLIRContext *context)
+      : ConversionPattern(typeConverter, MatchAnyOpTypeTag(), /*benefit=*/1,
+                          context) {}
+  LogicalResult
+  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const override {
+    if (!isa<AtenTensorIntOp, AtenTensorFloatOp>(op))
+      return rewriter.notifyMatchFailure(
+          op, "not a supported Scalar to Tensor like op");
+
+    if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
+      return failure();
+    Location loc = op->getLoc();
+    Value elemVal, dtype, device, requires_grad;
+    if (AtenTensorIntOp tensorIntOp = dyn_cast<AtenTensorIntOp>(op)) {
+      AtenTensorIntOp::Adaptor adaptor(operands);
+      elemVal = adaptor.t();
+      dtype = tensorIntOp.dtype();
+      device = tensorIntOp.device();
+      requires_grad = tensorIntOp.requires_grad();
+    }
+    if (AtenTensorFloatOp tensorFloatOp = dyn_cast<AtenTensorFloatOp>(op)) {
+      AtenTensorFloatOp::Adaptor adaptor(operands);
+      elemVal = adaptor.t();
+      dtype = tensorFloatOp.dtype();
+      device = tensorFloatOp.device();
+      requires_grad = tensorFloatOp.requires_grad();
+    }
+    // TODO: Dtype conversion.
+    if (!dtype.getType().isa<Torch::NoneType>())
+      return rewriter.notifyMatchFailure(op, "Unimplemented non-None dtype");
+
+    // TODO: Device information.
+    if (!device.getType().isa<Torch::NoneType>())
+      return rewriter.notifyMatchFailure(
+          op, "Unimplemented non-None device information");
+
+    RankedTensorType resultType = getTypeConverter()
+                                      ->convertType(op->getResult(0).getType())
+                                      .cast<RankedTensorType>();
+    Type outElementType = resultType.getElementType();
+    Value elemValProm =
+        convertScalarToDtype(rewriter, loc, elemVal, outElementType);
+    Value zeroDTensor =
+        createInitTensor(rewriter, loc, {}, outElementType, elemValProm);
+    rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, zeroDTensor);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 // Converts constant tensor allocation like ops.
 template <typename OpTy>
@@ -3751,6 +3805,8 @@ public:
     patterns.add<ConvertAtenNllLossForwardOp>(typeConverter, context);
     target.addIllegalOp<AtenIndexSelectOp>();
     patterns.add<ConvertAtenIndexSelectOp>(typeConverter, context);
+    patterns.add<ConvertAtenScalarToTensorLike>(typeConverter, context);
+    target.addIllegalOp<AtenTensorIntOp, AtenTensorFloatOp>();
 
     if (failed(applyPartialConversion(getOperation(), target,
                                       std::move(patterns))))

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -238,11 +238,11 @@ public:
             AtenGeluBackwardOp, AtenBitwiseNotOp, AtenExpOp, AtenSinOp,
             AtenCosOp, AtenSigmoidOp, DerefineOp, AtenToPrimDeviceOp, AtenCpuOp,
             AtenContiguousOp, AtenFill_ScalarOp, AtenDetachOp,
-            AtenMaskedFill_ScalarOp, AtenCopy_Op, AtenIndexPut_Op, AtenCumsumOp,
-            AtenLayerNormOp, AtenClampOp, AtenLogOp, AtenNegOp, AtenSqrtOp,
-            AtenFloorOp, AtenLog2Op, Aten_SoftmaxBackwardDataOp, AtenRsqrtOp,
-            AtenDropoutOp, AtenTanhBackwardOp, Aten_LogSoftmaxBackwardDataOp,
-            AtenAddIntOp, AtenAbsOp, AtenReciprocalOp, AtenCeilOp>(op)) {
+            AtenMaskedFill_ScalarOp, AtenCopy_Op, AtenCumsumOp, AtenLayerNormOp,
+            AtenClampOp, AtenLogOp, AtenNegOp, AtenSqrtOp, AtenFloorOp,
+            AtenLog2Op, Aten_SoftmaxBackwardDataOp, AtenRsqrtOp, AtenDropoutOp,
+            AtenTanhBackwardOp, Aten_LogSoftmaxBackwardDataOp, AtenAddIntOp,
+            AtenAbsOp, AtenReciprocalOp, AtenCeilOp>(op)) {
       return getLatticeElement(op->getResult(0)).join(*operands[0]);
     }
 
@@ -1272,7 +1272,6 @@ ChangeResult TypeAnalyzer::visitScalarToTensorConversionOp(OpTy op) {
   Value t = op.t();
   Value dtype = op.dtype();
   knowledge.hasSizes = true;
-  knowledge.sizes.resize(1, 1);
   fillInDTypeGivenDTypeAndDataType(knowledge, dtype, t.getType());
   return getLatticeElement(op.getResult()).join(knowledge);
 }

test/Dialect/Torch/refine-types.mlir

@@ -593,8 +593,8 @@ builtin.func @prim.if$refined_type_conflicting(%none: !torch.none) -> !torch.ten
 // CHECK-SAME:                                          %[[t:.*]]: !torch.float) -> !torch.tensor {
 // CHECK:           %[[NONE:.*]] = torch.constant.none
 // CHECK:           %[[FALSE:.*]] = torch.constant.bool false
-// CHECK:           %[[RET:.*]] = torch.aten.tensor.float %[[t]], %[[NONE]], %[[NONE]], %[[FALSE]] : !torch.float, !torch.none, !torch.none, !torch.bool -> !torch.tensor<[1],f32>
-// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[RET]] : !torch.tensor<[1],f32> to !torch.tensor
+// CHECK:           %[[RET:.*]] = torch.aten.tensor.float %[[t]], %[[NONE]], %[[NONE]], %[[FALSE]] : !torch.float, !torch.none, !torch.none, !torch.bool -> !torch.tensor<[],f32>
+// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[RET]] : !torch.tensor<[],f32> to !torch.tensor
 // CHECK:           return %[[CAST]] : !torch.tensor
 
 builtin.func @torch.aten.tensor.float(%t: !torch.float) -> !torch.tensor {
@@ -611,8 +611,8 @@ builtin.func @torch.aten.tensor.float(%t: !torch.float) -> !torch.tensor {
 // CHECK:           %[[NONE:.*]] = torch.constant.none
 // CHECK:           %[[CST11:.*]] = torch.constant.int 11
 // CHECK:           %[[FALSE:.*]] = torch.constant.bool false
-// CHECK:           %[[RET:.*]] = torch.aten.tensor.float %[[t]], %[[CST11]], %[[NONE]], %[[FALSE]] : !torch.float, !torch.int, !torch.none, !torch.bool -> !torch.tensor<[1],i1>
-// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[RET]] : !torch.tensor<[1],i1> to !torch.tensor
+// CHECK:           %[[RET:.*]] = torch.aten.tensor.float %[[t]], %[[CST11]], %[[NONE]], %[[FALSE]] : !torch.float, !torch.int, !torch.none, !torch.bool -> !torch.tensor<[],i1>
+// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[RET]] : !torch.tensor<[],i1> to !torch.tensor
 // CHECK:           return %[[CAST]] : !torch.tensor
 
 builtin.func @torch.aten.tensor.float$specified_dtype(%t: !torch.float) -> !torch.tensor {