[stablehlo] support aten.view.dtype lowering

lib/Conversion/TorchToStablehlo/ViewLike.cpp

@@ -161,20 +161,77 @@ class ConvertAtenViewOp : public ConvertAtenOp<AtenOpT> {
   using ConvertAtenOp<AtenOpT>::ConvertAtenOp;
   using OpAdaptor = typename AtenOpT::Adaptor;
 
+  unsigned getBitWidth(Type type) const {
+    if (auto complexTy = dyn_cast<ComplexType>(type))
+      return 2 * getBitWidth(complexTy.getElementType());
+    return type.getIntOrFloatBitWidth();
+  }
+
   LogicalResult
   matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     auto rankType = dyn_cast<RankedTensorType>(adaptor.getSelf().getType());
     if (!rankType)
-      return op.emitError("Only ranked tensor types are currently supported");
+      return op.emitError("Only ranked tensor types are currently supported.");
+    auto loc = op.getLoc();
+
+    // support AtenViewDtypeOp
+    if (isa<AtenViewDtypeOp>(op)) {
+      auto self = adaptor.getSelf();
+      auto baseResultTy = dyn_cast<BaseTensorType>(op.getType());
+
+      // infer the result shape
+      auto operandElt = rankType.getElementType();
+      auto targetElt = baseResultTy.getDtype();
+      auto operandEltBitWidth = getBitWidth(operandElt);
+      auto targetEltBitWidth = getBitWidth(targetElt);
+      auto operandSizes = rankType.getShape();
+      SmallVector<int64_t> castShape(operandSizes);
+      if (operandEltBitWidth > targetEltBitWidth) {
+        int64_t last_size = operandEltBitWidth / targetEltBitWidth;
+        castShape.push_back(last_size);
+      } else if (operandEltBitWidth < targetEltBitWidth) {
+        int64_t last_size = targetEltBitWidth / operandEltBitWidth;
+        if (!ShapedType::isDynamic(castShape.back()) and
+            last_size != castShape.back()) {
+          return rewriter.notifyMatchFailure(
+              op, "The last dim size is not equal to targetEltBitWidth / "
+                  "operandEltBitWidth.");
+        } else {
+          castShape.pop_back();
+        }
+      }
+
+      auto resultType =
+          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
+              baseResultTy);
+      if (!dyn_cast<ShapedType>(resultType).hasStaticShape()) {
+        return rewriter.notifyMatchFailure(
+            op, "Currently only support static output shape.");
+      }
+
+      auto castType =
+          baseResultTy.getWithSizesAndDtype(castShape, baseResultTy.getDtype());
+      auto cast = rewriter.create<stablehlo::BitcastConvertOp>(
+          loc,
+          OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
+              castType),
+          self);
+
+      auto reshape =
+          rewriter.create<stablehlo::ReshapeOp>(loc, resultType, cast);
+
+      rewriter.replaceOp(op, reshape);
+
+      return success();
+    }
 
     // collect Value of dims
     SmallVector<Value, 4> dimSizes;
     if (!getAtenViewOpSizes(op, adaptor, rewriter, dimSizes)) {
       return op.emitError("Dims size must be a list of Scalar");
     }
 
-    auto loc = op.getLoc();
     if (dimSizes.size() == 0 || rankType.getRank() == 0) {
       rewriter.replaceOpWithNewOp<stablehlo::ReshapeOp>(
           op,
@@ -236,6 +293,13 @@ class ConvertAtenViewOp : public ConvertAtenOp<AtenOpT> {
                           SmallVector<Value, 4> &dimSizes) const;
 };
 
+template <>
+bool ConvertAtenViewOp<AtenViewDtypeOp>::getAtenViewOpSizes(
+    AtenViewDtypeOp op, OpAdaptor adaptor, ConversionPatternRewriter &rewriter,
+    SmallVector<Value, 4> &dimSizes) const {
+  return false;
+}
+
 template <>
 bool ConvertAtenViewOp<AtenViewOp>::getAtenViewOpSizes(
     AtenViewOp op, OpAdaptor adaptor, ConversionPatternRewriter &rewriter,
@@ -496,6 +560,7 @@ void mlir::torch::torch_to_stablehlo::populateViewLikeOpPatternsAndLegality(
 #define INSERT_VIEW_OP_PATTERN(AtenOp)                                         \
   target.addIllegalOp<AtenOp>();                                               \
   patterns.add<ConvertAtenViewOp<AtenOp>>(typeConverter, context, options)
+  INSERT_VIEW_OP_PATTERN(AtenViewDtypeOp);
   INSERT_VIEW_OP_PATTERN(AtenViewOp);
   INSERT_VIEW_OP_PATTERN(AtenReshapeOp);
 #undef INSERT_VIEW_OP_PATTERN

projects/pt1/e2e_testing/xfail_sets.py

@@ -506,6 +506,7 @@
     "UpSampleNearest2dDynamicFactor_basic",
     "ViewCollapseDynamicWithAtenSizeIntModule_basic",
     "ViewSizeFromOtherTensor_basic",
+    "ViewDtypeStaticModule_basic",
     "WeightNormInterfaceModule_basic",
     # Error: `aten.as_strided` op is not supported
     "ChunkListUnpackDynamic_Module_basic",
@@ -3167,6 +3168,7 @@
     "Unfold_Module_Rank_Zero_basic",
     "Unfold_Module_Rank_Zero_Size_Zero_basic",
     "Unfold_Module_Dynamic_basic",
+    "ViewDtypeStaticModule_basic",
 }
 
 if torch_version_for_comparison() < version.parse("2.3.0.dev"):

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

@@ -1174,6 +1174,30 @@ def ReshapeDynamicModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ViewDtypeStaticModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([12, 1], torch.float32, True),
+        ]
+    )
+    def forward(self, a):
+        res = a.view(torch.int8)
+        return res
+
+
+@register_test_case(module_factory=lambda: ViewDtypeStaticModule())
+def ViewDtypeStaticModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(12, 1))
+
+
+# ==============================================================================
+
+
 class ReshapeAliasCollapseModule(torch.nn.Module):
     def __init__(self):
         super().__init__()