[MLIR][TORCH] Add support for conversion to int8 dtype

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>

e2e_testing/xfail_sets.py

@@ -288,6 +288,9 @@ TORCHDYNAMO_XFAIL_SET = {
 
     # AssertionError: Unregistered operation: torch.aten._embedding_bag_forward_only
     "AtenEmbeddingBagStaticModule_basic",
+
+    # Lowering not present for this case
+    "ElementwiseToDtypeI64ToUI8Module_basic",
 }
 
 if torch_version_for_comparison() < version.parse("2.1.0.dev"):

include/torch-mlir/Conversion/Utils/Utils.h

@@ -87,7 +87,8 @@ mlir::RankedTensorType GetTypeFromTensorShape(llvm::ArrayRef<int64_t> shape,
 // from a tensor or a scalar in the pytorch dialect. Both the scalar and dtype
 // should be converted builtin types.
 Value convertScalarToDtype(OpBuilder &b, Location loc, Value scalar, Type dtype,
-                           std::optional<Type> srcOriginalDtype = std::nullopt);
+                           std::optional<Type> srcOriginalDtype = std::nullopt,
+                           std::optional<Type> dstOriginalDtype = std::nullopt);
 
 Value toPositiveValidDim(ConversionPatternRewriter &rewriter, Location loc,
                          Value torchOptionalInt, Value builtinInt,

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -988,7 +988,23 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     Type dtype = converter->convertType(atenToDtype.getType())
                      .cast<RankedTensorType>()
                      .getElementType();
-    Value result = convertScalarToDtype(b, loc, input, dtype);
+    Type resultElementType;
+    int64_t dtypeInt;
+    if (!matchPattern(atenToDtype.getDtype(), m_TorchConstantInt(&dtypeInt))) {
+      atenToDtype.emitError("unimplemented: dtype must be a constant integer");
+      return nullptr;
+    }
+    FailureOr<Type> maybeResultElementType = getTypeForScalarType(
+        atenToDtype->getContext(), (torch_upstream::ScalarType)dtypeInt,
+        IntegerType::Signless);
+    if (failed(maybeResultElementType)) {
+      atenToDtype.emitError("unable to convert `dtypeInt` to builtin type");
+      return nullptr;
+    }
+    resultElementType = *maybeResultElementType;
+    Value result = convertScalarToDtype(b, loc, input, dtype,
+                                        /*srcOriginalDtype=*/std::nullopt,
+                                        /*dstOriginalDtype=*/resultElementType);
     return result;
   }
   if (auto divScalar = dyn_cast<AtenDivScalarOp>(op)) {

lib/Conversion/Utils/Utils.cpp

@@ -249,7 +249,8 @@ mlir::RankedTensorType GetTypeFromTensorShape(llvm::ArrayRef<int64_t> shape,
 // from a tensor or a scalar in the pytorch dialect. Both the scalar and dtype
 // should be converted builtin types.
 Value convertScalarToDtype(OpBuilder &b, Location loc, Value scalar, Type dtype,
-                           std::optional<Type> srcOriginalDtype) {
+                           std::optional<Type> srcOriginalDtype,
+                           std::optional<Type> dstOriginalDtype) {
   Type scalarType = scalar.getType();
   if (scalarType == dtype)
     return scalar;
@@ -261,14 +262,20 @@ Value convertScalarToDtype(OpBuilder &b, Location loc, Value scalar, Type dtype,
     return false;
   };
 
-  // We only support conversion from Byte or Char scalarType not to Byte or Char
-  // dtype.
+  // We don't support conversion to Byte dtype.
   if (isByteOrChar(dtype)) {
-    mlir::emitError(loc) << "unsupported: conversion to byte or char type for "
-                            "convertScalarToDtype "
-                         << scalarType << "(scalar type) -> " << dtype
-                         << "(dtype)";
-    return nullptr;
+    if (!dstOriginalDtype.has_value()) {
+      mlir::emitError(loc)
+          << "unimplemented: for conversion to byte or char type "
+             "dstOriginalDtype has to be passed to convertScalarToDtype";
+      return nullptr;
+    }
+    if (dstOriginalDtype->isUnsignedInteger()) {
+      mlir::emitError(loc)
+          << "unsupported: conversion to byte type for convertScalarToDtype "
+          << scalarType << "(scalar type) -> " << dtype << "(dtype)";
+      return nullptr;
+    }
   }
 
   // If the dtype is i1, i.e., a boolean type.

python/torch_mlir_e2e_test/test_suite/__init__.py

@@ -14,6 +14,7 @@ COMMON_TORCH_MLIR_LOWERING_XFAILS = {
     "NativeGroupNormBackwardModule_basic",
     "QuantizedMLP_basic",
     "ReduceMaxAlongDimUnsignedInt_basic",
+    "ElementwiseToDtypeI64ToUI8Module_basic",
 }
 
 # TODO: Delete once torch 2.1.0 is released

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -1642,6 +1642,44 @@ def ElementwiseToDtypeIdentityModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseToDtypeI64ToI8Module(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.int8)
+
+
+@register_test_case(module_factory=lambda: ElementwiseToDtypeI64ToI8Module())
+def ElementwiseToDtypeI64ToI8Module_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=-100, high=100))
+
+
+# ==============================================================================
+
+
+class ElementwiseToDtypeI64ToUI8Module(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.uint8)
+
+
+@register_test_case(module_factory=lambda: ElementwiseToDtypeI64ToUI8Module())
+def ElementwiseToDtypeI64ToUI8Module_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=-100, high=100))
+
+
+# ==============================================================================
+
+
 class ElementwiseLog2Module(torch.nn.Module):
 
     def __init__(self):