Add convertScalarToDtype helper.

This is to facilitate scalar type conversion in the TorchToLinalg. As
part of adding the helper, this PR also:
- Updated `AtenAddTensorOp`, `AtenSubTensorOp` to use the helpers to
support more type variants.
- Added e2e type promotion testing.
- Added i32 memref return/arg type to support e2e testing.

e2e_testing/torchscript/main.py

@@ -33,6 +33,8 @@ from . import conv
 from . import batchnorm
 from . import quantized_models
 from . import elementwise
+from . import type_promotion
+from . import type_conversion
 from . import reduction
 from . import argmax
 from . import matmul

e2e_testing/torchscript/type_conversion.py

@@ -0,0 +1,77 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+import torch
+
+from torch_mlir_e2e_test.torchscript.framework import TestUtils
+from torch_mlir_e2e_test.torchscript.registry import register_test_case
+from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
+
+# ==============================================================================
+
+class TypeConversionF32ToF64Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True)
+    ])
+    def forward(self, x):
+        return x.to(torch.float64)
+
+@register_test_case(module_factory=lambda: TypeConversionF32ToF64Module())
+def TypeConversionF32ToF64Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 5))
+
+
+class TypeConversionF64ToF32Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float64, True)
+    ])
+    def forward(self, x):
+        return x.to(torch.float32)
+
+@register_test_case(module_factory=lambda: TypeConversionF64ToF32Module())
+def TypeConversionF64ToF32Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 5).type(torch.float64))
+
+class TypeConversionI32ToI64Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int32, True)
+    ])
+    def forward(self, x):
+        return x.to(torch.int64)
+
+@register_test_case(module_factory=lambda: TypeConversionI32ToI64Module())
+def TypeConversionI32ToI64Module_basic(module, tu: TestUtils):
+    module.forward(torch.randint(5, [2, 3]).type(torch.int32))
+
+class TypeConversionI64ToI32Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int64, True)
+    ])
+    def forward(self, x):
+        return x.to(torch.int32)
+
+@register_test_case(module_factory=lambda: TypeConversionI64ToI32Module())
+def TypeConversionI64ToI32Module_basic(module, tu: TestUtils):
+    module.forward(torch.randint(5, [2, 3]))

e2e_testing/torchscript/type_promotion.py

@@ -0,0 +1,113 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+import torch
+
+from torch_mlir_e2e_test.torchscript.framework import TestUtils
+from torch_mlir_e2e_test.torchscript.registry import register_test_case
+from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
+
+# ==============================================================================
+
+
+class TypePromotionSameCategoryDifferentWidthModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.int32, True),
+        ([-1], torch.int64, True),
+    ])
+    def forward(self, a, b):
+        return torch.add(a, b, alpha=3)
+
+
+@register_test_case(
+    module_factory=lambda: TypePromotionSameCategoryDifferentWidthModule())
+def TypePromotionSameCategoryDifferentWidthModule_basic(module, tu: TestUtils):
+    module.forward(
+        torch.randint(10, [4]).type(torch.int32),
+        torch.randint(10, [4]))
+
+
+class TypePromotionDifferentCategoryModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.int64, True),
+        ([-1], torch.float32, True),
+    ])
+    def forward(self, a, b):
+        return torch.add(a, b, alpha=3)
+
+
+@register_test_case(
+    module_factory=lambda: TypePromotionDifferentCategoryModule())
+def TypePromotionDifferentCategoryModule_basic(module, tu: TestUtils):
+    module.forward(torch.randint(10, [4]), torch.randn(4))
+
+
+class TypePromotionSameCategoryZeroRankWiderModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.float32, True),
+        ([], torch.float64, True),
+    ])
+    def forward(self, a, b):
+        return torch.add(a, b, alpha=2.3)
+
+
+@register_test_case(
+    module_factory=lambda: TypePromotionSameCategoryZeroRankWiderModule())
+def TypePromotionSameCategoryZeroRankWider_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4), tu.rand().type(torch.float64))
+
+
+class TypePromotionZeroRankHigherCategoryModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.int64, True),
+        ([], torch.float32, True),
+    ])
+    def forward(self, a, b):
+        return torch.add(a, b, alpha=2)
+
+
+@register_test_case(
+    module_factory=lambda: TypePromotionZeroRankHigherCategoryModule())
+def TypePromotionZeroRankHigherCategoryModule_basic(module, tu: TestUtils):
+    module.forward(torch.randint(10, [4]), tu.rand())
+
+
+class TypePromotionAlphaWiderModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.float32, True),
+        ([], torch.float32, True),
+    ])
+    def forward(self, a, b):
+        return torch.add(a, b, alpha=2.3)
+
+
+@register_test_case(module_factory=lambda: TypePromotionAlphaWiderModule())
+def TypePromotionAlphaWiderModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4), tu.rand())

lib/Conversion/TorchToLinalg/TorchToLinalg.cpp

@@ -1251,29 +1251,65 @@ public:
 };
 } // namespace
 
+// Convert a scalar value to the target type. The scalar value can be an element
+// from a tensor or a scalar in the pytorch dialect. Both the scalar and dtype
+// should be converted builtin types.
+static Value convertScalarToDtype(OpBuilder &b, Location loc, Value scalar,
+                                  Type dtype) {
+  Type scalarType = scalar.getType();
+  if (scalarType == dtype)
+    return scalar;
 
-static Value promoteScalarToDtype(OpBuilder &b, Location loc, Value scalar, Type dtype) {
+  // TODO: For the byte(ui8) or char(i8) case, we need the unconverted dtype to
-  // TODO: For the integer case, we probably need the unconverted dtype to
   // be able to know if we need signed or unsigned conversion.
-  if (dtype.isa<mlir::FloatType>()) {
+  auto isByteOrChar = [](Type type) {
-    if (scalar.getType().isa<mlir::FloatType>()) {
+    if (auto integerTy = type.dyn_cast<mlir::IntegerType>()) {
-      // `scalar` will always be f64 since that is what the TypeConverter
+      return integerTy.getWidth() == 8;
-      // converts !torch.float to.
-      return b.create<arith::TruncFOp>(loc, scalar, dtype);
-    } else {
-      assert(scalar.getType().isa<mlir::IntegerType>());
-      // `scalar` will always be i64 since that is what the TypeConverter
-      // converts !torch.int to.
-      return b.create<arith::SIToFPOp>(loc, scalar, dtype);
     }
+    return false;
+  };
+
+  if (isByteOrChar(scalarType) || isByteOrChar(dtype) ||
+      scalarType.isSignlessInteger(1) || dtype.isSignlessInteger(1)) {
+    // TODO: Handle bool type.
+    mlir::emitError(loc)
+        << "unsupported byte, char or bool type for convertScalarToDtype "
+        << scalarType << "(scalar type) -> " << dtype << "(dtype)";
+    return nullptr;
   }
-  mlir::emitError(loc) << "promoteScalarToDtype for dtype " << dtype;
+
-  return nullptr;
+  if (auto dtypeFloat = dtype.dyn_cast<mlir::FloatType>()) {
+    if (auto scalarFloat = scalarType.dyn_cast<mlir::FloatType>()) {
+      if (scalarFloat.getWidth() > dtypeFloat.getWidth())
+        return b.create<arith::TruncFOp>(loc, scalar, dtype);
+      // Only scalarFloat width < dtypeFloat width can reach here.
+      return b.create<arith::ExtFOp>(loc, scalar, dtype);
+    }
+    assert(scalarType.isa<mlir::IntegerType>());
+    // It's safe to use SIToFPOp because ui8/si8 are the only ones where
+    // unsigned handling is needed, and we checked for that case above.
+    return b.create<arith::SIToFPOp>(loc, scalar, dtype);
+  }
+
+  if (auto dtypeInteger = dtype.dyn_cast<mlir::IntegerType>()) {
+    if (auto scalarFloat = scalarType.dyn_cast<mlir::FloatType>())
+      return b.create<arith::FPToSIOp>(loc, scalar, dtype);
+    assert(scalarType.isa<mlir::IntegerType>());
+    auto scalarInteger = scalarType.cast<mlir::IntegerType>();
+    if (scalarInteger.getWidth() > dtypeInteger.getWidth())
+      return b.create<arith::TruncIOp>(loc, scalar, dtype);
+    // Only scalarInteger width < dtypeInteger width can reach here.
+    // It's safe to use ExtSIOp here because ui8/si8 are the only ones where
+    // unsigned handling is needed, and we checked for that case above.
+    return b.create<arith::ExtSIOp>(loc, scalar, dtype);
+  }
+
+  llvm_unreachable("convertScalarToDtype should handle all the types");
 }
 
 static Value createLinalgPayloadCalculationForElementwiseOp(
-    OpBuilder &b, Location loc, ValueRange payloadArgs, Operation *op,
+    OpBuilder &b, Location loc, TypeConverter *converter,
-    ArrayRef<Value> operands) {
+    ValueRange payloadArgs, Operation *op, ArrayRef<Value> operands) {
   if (isa<AtenTanhOp>(op))
     return b.create<math::TanhOp>(loc, payloadArgs[0]);
   if (isa<AtenExpOp>(op))
@@ -1322,40 +1358,35 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
   }
   if (auto add = dyn_cast<AtenAddTensorOp>(op)) {
     AtenAddTensorOp::Adaptor adaptor(operands);
-    if (add.alpha().getType().isa<Torch::FloatType>()) {
+    Type dtype = converter->convertType(add.getType())
-      add.emitError("unimplemented: !torch.float 'alpha'");
+                     .cast<RankedTensorType>()
-      return nullptr;
+                     .getElementType();
+    Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
+    Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
+    Value alpha = convertScalarToDtype(b, loc, adaptor.alpha(), dtype);
+    if (dtype.isa<mlir::FloatType>()) {
+      Value scaled = b.create<arith::MulFOp>(loc, rhs, alpha);
+      return b.create<arith::AddFOp>(loc, lhs, scaled);
+    } else {
+      Value scaled = b.create<arith::MulIOp>(loc, rhs, alpha);
+      return b.create<arith::AddIOp>(loc, lhs, scaled);
     }
-    if (!add.getType()
-             .cast<ValueTensorType>()
-             .getDtype()
-             .isa<mlir::FloatType>()) {
-      add.emitError("unimplemented: non-floating point dtype");
-      return nullptr;
-    }
-    Value alphaFloat = b.create<arith::SIToFPOp>(loc, payloadArgs[0].getType(),
-                                                 adaptor.alpha());
-    Value scaled = b.create<arith::MulFOp>(loc, payloadArgs[1], alphaFloat);
-    return b.create<arith::AddFOp>(loc, payloadArgs[0], scaled);
   }
   if (auto sub = dyn_cast<AtenSubTensorOp>(op)) {
     AtenSubTensorOp::Adaptor adaptor(operands);
-    if (sub.alpha().getType().isa<Torch::FloatType>()) {
+    Type dtype = converter->convertType(sub.getType())
-      sub.emitError("unimplemented: !torch.float 'alpha'");
+                     .cast<RankedTensorType>()
-      return nullptr;
+                     .getElementType();
+    Value lhs = convertScalarToDtype(b, loc, payloadArgs[0], dtype);
+    Value rhs = convertScalarToDtype(b, loc, payloadArgs[1], dtype);
+    Value alpha = convertScalarToDtype(b, loc, adaptor.alpha(), dtype);
+    if (dtype.isa<mlir::FloatType>()) {
+      Value scaled = b.create<arith::MulFOp>(loc, rhs, alpha);
+      return b.create<arith::SubFOp>(loc, lhs, scaled);
+    } else {
+      Value scaled = b.create<arith::MulIOp>(loc, rhs, alpha);
+      return b.create<arith::SubIOp>(loc, lhs, scaled);
     }
-    if (!sub.getType()
-             .cast<ValueTensorType>()
-             .getDtype()
-             .isa<mlir::FloatType>()) {
-      sub.emitError("unimplemented: non-floating point dtype");
-      return nullptr;
-    }
-    Value alphaFloat = b.create<arith::SIToFPOp>(loc, payloadArgs[0].getType(),
-                                                 adaptor.alpha());
-    Value scaled = b.create<arith::MulFOp>(loc, payloadArgs[1], alphaFloat);
-
-    return b.create<arith::SubFOp>(loc, payloadArgs[0], scaled);
   }
   if (auto mul = dyn_cast<AtenMulTensorOp>(op)) {
     if (!mul.getType()
@@ -1386,7 +1417,7 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
       return nullptr;
     }
     Type dtype = pow.self().getType().cast<ValueTensorType>().getDtype();
-    Value expPromoted = promoteScalarToDtype(b, loc, operands[1], dtype);
+    Value expPromoted = convertScalarToDtype(b, loc, operands[1], dtype);
     return b.create<math::PowFOp>(loc, payloadArgs[0], expPromoted);
   }
   if (auto lerp = dyn_cast<AtenLerpTensorOp>(op)) {
@@ -1430,7 +1461,9 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     return b.create<SelectOp>(loc, pred, payloadArgs[0], payloadArgs[1]);
   }
   if (auto clamp = dyn_cast<AtenClampOp>(op)) {
-    auto dtype = clamp.getType().cast<ValueTensorType>().getDtype();
+    Type dtype = converter->convertType(clamp.getType())
+                     .cast<RankedTensorType>()
+                     .getElementType();
     if (!dtype.isa<mlir::FloatType>()) {
       clamp.emitError("unimplemented: non-floating point dtype");
       return nullptr;
@@ -1445,13 +1478,13 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     }
     auto result = payloadArgs[0];
     if (!min.getType().isa<Torch::NoneType>()) {
-      auto minPromoted = promoteScalarToDtype(b, loc, min, dtype);
+      auto minPromoted = convertScalarToDtype(b, loc, min, dtype);
       auto pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULT,
                                           result, minPromoted);
       result = b.create<SelectOp>(loc, pred, minPromoted, result);
     }
     if (!max.getType().isa<Torch::NoneType>()) {
-      auto maxPromoted = promoteScalarToDtype(b, loc, max, dtype);
+      auto maxPromoted = convertScalarToDtype(b, loc, max, dtype);
       auto pred = b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::UGT,
                                           result, maxPromoted);
       result = b.create<SelectOp>(loc, pred, maxPromoted, result);
@@ -1459,36 +1492,25 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     return result;
   }
   if (auto rsub = dyn_cast<AtenRsubScalarOp>(op)) {
-    if (!rsub.getType()
+    Type dtype = converter->convertType(rsub.getType())
-             .cast<ValueTensorType>()
+                     .cast<RankedTensorType>()
-             .getDtype()
+                     .getElementType();
-             .isa<mlir::FloatType>()) {
+    if (!dtype.isa<mlir::FloatType>()) {
       rsub.emitError("unimplemented: non-floating point dtype");
       return nullptr;
     }
     Value self = payloadArgs[0];
-    Value other = promoteScalarToDtype(b, loc, operands[1], self.getType());
+    Value other = convertScalarToDtype(b, loc, operands[1], dtype);
-    Value alpha = promoteScalarToDtype(b, loc, operands[2], self.getType());
+    Value alpha = convertScalarToDtype(b, loc, operands[2], dtype);
     Value mult = b.create<arith::MulFOp>(loc, self, alpha);
     return b.create<arith::SubFOp>(loc, other, mult);
   }
   if (auto atenToDtype = dyn_cast<AtenToDtypeOp>(op)) {
     Value input = payloadArgs[0];
-    Type inType = input.getType();
+    Type dtype = converter->convertType(atenToDtype.getType())
-    Type outType = atenToDtype.getType().cast<ValueTensorType>().getDtype();
+                     .cast<RankedTensorType>()
-    Value result;
+                     .getElementType();
-    if (!inType.isF32()) {
+    Value result = convertScalarToDtype(b, loc, input, dtype);
-      atenToDtype.emitError("unimplemented: non-floating point dtype");
-      return nullptr;
-    }
-    if (inType == outType)
-      result = input;
-    else if (outType.isInteger(64))
-      result = b.create<arith::FPToSIOp>(loc, b.getI64Type(), input);
-    else if (outType.isInteger(1))
-      result = b.create<arith::FPToSIOp>(loc, b.getI1Type(), input);
-    else
-      atenToDtype.emitError("unimplemented: unsupported target dtype");
     return result;
   }
 
@@ -1808,7 +1830,7 @@ struct ConvertElementwiseOp : ConversionPattern {
         /*iteratorTypes=*/iteratorTypes,
         [&](OpBuilder &b, Location loc, ValueRange payloadArgs) {
           Value result = createLinalgPayloadCalculationForElementwiseOp(
-              b, loc, payloadArgs, op, operands);
+              b, loc, getTypeConverter(), payloadArgs, op, operands);
           if (!result) {
             hadErrorCreatingPayload = true;
             return;
@@ -2161,7 +2183,7 @@ public:
     }
     SmallVector<Value> expectedSize = getTypeConvertedValues(
         rewriter, loc, typeConverter, expectedSizeTorchInt);
-    if (expectedSize.size() != resultRank) {
+    if (resultRank != (int64_t)expectedSize.size()) {
       return rewriter.notifyMatchFailure(
           op, "desired size list length mismatches with the result type rank");
     }

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -229,8 +229,8 @@ public:
             AtenExpOp, AtenSinOp, AtenCosOp, AtenSigmoidOp, DerefineOp,
             AtenToPrimDeviceOp, AtenCpuOp, AtenContiguousOp, AtenFill_ScalarOp,
             AtenDetachOp, AtenMaskedFill_ScalarOp, AtenCopy_Op, AtenIndexPut_Op,
-            AtenCopy_Op, AtenCumsumOp, AtenLayerNormOp, AtenClampOp,
+            AtenCumsumOp, AtenLayerNormOp, AtenClampOp, AtenLogOp, AtenSqrtOp,
-            AtenRsubScalarOp, AtenLogOp, AtenSqrtOp, AtenFloorOp, AtenLog2Op>(op)) {
+            AtenFloorOp, AtenLog2Op>(op)) {
       return getLatticeElement(op->getResult(0)).join(*operands[0]);
     }
 
@@ -285,7 +285,7 @@ public:
       return visitAtenAdaptiveAvgPool2dOp(avgPool2d, operands);
     } else if (isa<AtenAddScalarOp, AtenSubScalarOp, AtenMulScalarOp,
                    AtenDivScalarOp, AtenFmodScalarOp, AtenFloorDivideScalarOp,
-                   AtenPowTensorScalarOp>(op)) {
+                   AtenPowTensorScalarOp, AtenRsubScalarOp>(op)) {
       return visitBinaryTensorScalarOp(op, operands);
     } else if (isa<AtenAddTensorOp, AtenSubTensorOp, AtenMulTensorOp,
                    AtenDivTensorOp, Aten__And__TensorOp, AtenEqTensorOp,

lib/RefBackend/RefBackend.cpp

@@ -51,6 +51,8 @@ static bool isArgMemRefTypeValid(Type type) {
     } else if (auto integerTy = elemTy.dyn_cast<IntegerType>()) {
       if (integerTy.isSignlessInteger(64))
         return true;
+      if (integerTy.isSignlessInteger(32))
+        return true;
     }
   }
   return false;
@@ -109,7 +111,7 @@ static LogicalResult mungeFunction(
     auto type = arg.getType();
     if (!isArgMemRefTypeValid(type))
       return emitError(arg.getLoc(),
-                       "argument must be a memref of f32, f64, i64");
+                       "argument must be a memref of f32, f64, i32, i64");
     auto cast = b.create<memref::CastOp>(arg.getLoc(), arg, type);
     arg.replaceAllUsesExcept(cast, cast);
     arg.setType(getAbiTypeForMemRef(type));
@@ -175,6 +177,8 @@ class MungeCallingConventions
     };
 
     // Memref return types.
+    createConsumeFuncReturnFunc(UnrankedMemRefType::get(b.getI32Type(), 0),
+                                "refbackend_consume_memref_int32_func_return");
     createConsumeFuncReturnFunc(UnrankedMemRefType::get(b.getI64Type(), 0),
                                 "refbackend_consume_memref_int64_func_return");
     createConsumeFuncReturnFunc(

python/torch_mlir_e2e_test/linalg_on_tensors_backends/refbackend.py

@@ -24,7 +24,7 @@ __all__ = [
 
 
 def checkArgTypeIsSupported(ty):
-    SUPPORTED = [np.float32, np.float64, np.int64]
+    SUPPORTED = [np.float32, np.float64, np.int32, np.int64]
     assert ty in SUPPORTED, f"Only numpy arrays with dtypes in {SUPPORTED} are supported"
 
 class RefBackendInvoker:
@@ -32,6 +32,10 @@ class RefBackendInvoker:
         self.ee = ExecutionEngine(module)
         self.result = None
 
+        @ctypes.CFUNCTYPE(None, ctypes.POINTER(UnrankedMemRefDescriptor))
+        def consume_memref_i32_return(a):
+            self.result = unranked_memref_to_numpy(a, np.int32)
+
         @ctypes.CFUNCTYPE(None, ctypes.POINTER(UnrankedMemRefDescriptor))
         def consume_memref_i64_return(a):
             self.result = unranked_memref_to_numpy(a, np.int64)
@@ -56,6 +60,9 @@ class RefBackendInvoker:
         def consume_f64_return(a):
             self.result = a
 
+        self.ee.register_runtime("refbackend_consume_memref_int32_func_return",
+                                 consume_memref_i32_return)
+
         self.ee.register_runtime("refbackend_consume_memref_int64_func_return",
                                  consume_memref_i64_return)
 

python/torch_mlir_e2e_test/torchscript/reporting.py

@@ -20,9 +20,9 @@ from .framework import TestResult, TraceItem
 class TensorSummary:
     """A summary of a tensor's contents."""
     def __init__(self, tensor):
-        self.min = torch.min(tensor)
+        self.min = torch.min(tensor.type(torch.float64))
-        self.max = torch.max(tensor)
+        self.max = torch.max(tensor.type(torch.float64))
-        self.mean = torch.mean(tensor)
+        self.mean = torch.mean(tensor.type(torch.float64))
         self.shape = list(tensor.shape)
 
     def __str__(self):
@@ -148,10 +148,15 @@ class ValueReport:
         if isinstance(golden, torch.Tensor):
             if not isinstance(value, torch.Tensor):
                 return self._record_mismatch_type_failure('torch.Tensor', value)
+
             if value.shape != golden.shape:
                 return self._record_failure(
                     f'shape ({value.shape}) is not equal to golden shape ({golden.shape})'
                 )
+            if value.dtype != golden.dtype:
+                return self._record_failure(
+                    f'shape ({value.dtype}) is not equal to golden dtype ({golden.dtype})'
+                )
             if not torch.allclose(value, golden, rtol=1e-03, atol=1e-07, equal_nan=True):
                 return self._record_failure(
                     f'value ({TensorSummary(value)}) is not close to golden value ({TensorSummary(golden)})'

test/Dialect/Torch/refine-types.mlir

@@ -980,12 +980,11 @@ func @torch.aten.softmax.int$specified_dtype(%t: !torch.tensor<[2,3],f32>, %dim:
 
 // ----
 // CHECK-LABEL:  func @torch.aten.Matmul.Broadcast.Matrix(
-// CHECK-SAME:   %[[LHS:.*]]: !torch.vtensor<[?,?,?,?,?],f32>, 
+// CHECK-SAME:                                            %[[LHS:.*]]: !torch.vtensor<[?,?,?,?,?],f32>,
-// CHECK-SAME:   %[[RHS:.*]]: !torch.vtensor<[?,?,?],f32>)
+// CHECK-SAME:                                            %[[RHS:.*]]: !torch.vtensor<[?,?,?],f32>) -> !torch.tensor {
-// CHECK-SAME:   -> !torch.tensor {
+// CHECK:           %[[MUL:.*]] = torch.aten.matmul %[[LHS]], %[[RHS]] : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?,?,?],f32> -> !torch.tensor<[?,?,?,?,?],f32>
-// CHECK:        %[[MUL:.*]] = torch.aten.matmul %[[LHS]], %[[RHS]] : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?,?,?],f32> -> !torch.tensor<[?,?,?,?,?],f32>
+// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[MUL]] : !torch.tensor<[?,?,?,?,?],f32> to !torch.tensor
-// CHECK:        %[[CAST:.*]] = torch.tensor_static_info_cast %[[MUL]] : !torch.tensor<[?,?,?,?,?],f32> to !torch.tensor
+// CHECK:           return %[[CAST]] : !torch.tensor
-// CHECK:        return %[[CAST]] : !torch.tensor
 func @torch.aten.Matmul.Broadcast.Matrix(%arg0: !torch.vtensor<[?,?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?],f32>) -> !torch.tensor {
   %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?,?,?],f32> -> !torch.tensor
   return %0 : !torch.tensor
@@ -994,27 +993,25 @@ func @torch.aten.Matmul.Broadcast.Matrix(%arg0: !torch.vtensor<[?,?,?,?,?],f32>,
 
 // ----
 // CHECK-LABEL:  func @torch.aten.Matmul.Broadcast.Vector(
-// CHECK-SAME:   %[[LHS:.*]]: !torch.vtensor<[?,?,?,?,?],f32>, 
+// CHECK-SAME:                                            %[[LHS:.*]]: !torch.vtensor<[?,?,?,?,?],f32>,
-// CHECK-SAME:   %[[RHS:.*]]: !torch.vtensor<[?],f32>)
+// CHECK-SAME:                                            %[[RHS:.*]]: !torch.vtensor<[?],f32>) -> !torch.tensor {
-// CHECK-SAME:   -> !torch.tensor {
+// CHECK:           %[[MUL:.*]] = torch.aten.matmul %[[LHS]], %[[RHS]] : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?],f32> -> !torch.tensor<[?,?,?,?],f32>
-// CHECK:        %[[MUL:.*]] = torch.aten.matmul %[[LHS]], %[[RHS]] : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?],f32> -> !torch.tensor<[?,?,?,?],f32>
+// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[MUL]] : !torch.tensor<[?,?,?,?],f32> to !torch.tensor
-// CHECK:        %[[CAST:.*]] = torch.tensor_static_info_cast %[[MUL]] : !torch.tensor<[?,?,?,?],f32> to !torch.tensor
+// CHECK:           return %[[CAST]] : !torch.tensor
-// CHECK:        return %[[CAST]] : !torch.tensor
 func @torch.aten.Matmul.Broadcast.Vector(%arg0: !torch.vtensor<[?,?,?,?,?],f32>, %arg1: !torch.vtensor<[?],f32>) -> !torch.tensor {
   %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?],f32> -> !torch.tensor
   return %0 : !torch.tensor
 }
 
 // -----
-// CHECK-LABEL: func @torch.aten.to.dtype
+// CHECK-LABEL: func @torch.aten.to.dtype(
-// CHECK-SAME:  (%[[ARG:.*]]: !torch.tensor<[?,?],f32>) -> !torch.tensor
+// CHECK-SAME:                            %[[ARG:.*]]: !torch.tensor<[?,?],f32>) -> !torch.tensor
 // CHECK:           %[[TODTYPE:.*]] = torch.aten.to.dtype
 // CHECK-SAME:          %[[ARG]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}} :
 // CHECK-SAME:          !torch.tensor<[?,?],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none
 // CHECK-SAME:          -> !torch.tensor<[?,?],si64>
 // CHECK-NEXT:      %[[RES:.*]] = torch.tensor_static_info_cast %[[TODTYPE]] : !torch.tensor<[?,?],si64> to !torch.tensor
 // CHECK-NEXT:      return %[[RES]] : !torch.tensor
-
 func @torch.aten.to.dtype(%arg0: !torch.tensor<[?,?],f32>) -> !torch.tensor{
     %none = torch.constant.none
     %false = torch.constant.bool false
@@ -1025,12 +1022,10 @@ func @torch.aten.to.dtype(%arg0: !torch.tensor<[?,?],f32>) -> !torch.tensor{
 
 // ----
 // CHECK-LABEL:  func @torch.prim.NumToTensor.Scalar(
-// CHECK-SAME:   %[[SELF:.*]]: !torch.int)
+// CHECK-SAME:                                       %[[SELF:.*]]: !torch.int) -> !torch.tensor {
-// CHECK-SAME:   -> !torch.tensor {
+// CHECK:           %[[NTT:.*]] = torch.prim.NumToTensor.Scalar %[[SELF]] : !torch.int -> !torch.tensor<[],si64>
-// CHECK:        %[[NTT:.*]] = torch.prim.NumToTensor.Scalar %[[SELF]] : !torch.int -> !torch.tensor<[],si64>
+// CHECK:           %[[CAST:.*]] = torch.tensor_static_info_cast %[[NTT]] : !torch.tensor<[],si64> to !torch.tensor
-// CHECK:        %[[CAST:.*]] = torch.tensor_static_info_cast %[[NTT]] : !torch.tensor<[],si64> to !torch.tensor
+// CHECK:           return %[[CAST]] : !torch.tensor
-// CHECK:        return %[[CAST]] : !torch.tensor
-
 func @torch.prim.NumToTensor.Scalar(%arg0: !torch.int) -> !torch.tensor {
   %0 = torch.prim.NumToTensor.Scalar %arg0: !torch.int -> !torch.tensor
   return %0: !torch.tensor