[MLIR][TORCH] Add TorchToTosa lowering for aten.where.self op

e2e_testing/xfail_sets.py

@@ -455,6 +455,7 @@
     "ArgmaxModule_keepDim",
     "ArgmaxModule_with_dim",
     "_LogSoftmaxModuleStable_basic",
+    "ElementwiseAtenWhereSelfModule_basic",
     "LiftFreshCopyModule_basic",
     "ReduceSumDimIntListKeepDimNegativeDimStaticModule_basic",
     "ReduceSumDimIntListFloatModule_basic",

lib/Conversion/TorchToTosa/TorchToTosa.cpp

@@ -3004,6 +3004,30 @@ LogicalResult ConvertAtenOp<AtenBroadcastToOp>::matchAndRewrite(
       "unimplemented: broadcasts other than same rank or zero ranked tensor.");
 }
 
+
+template <>
+LogicalResult ConvertAtenOp<AtenWhereSelfOp>::matchAndRewrite(
+    AtenWhereSelfOp op, OpAdaptor adaptor,
+    ConversionPatternRewriter &rewriter) const {
+
+  // Not a tensor type.
+  auto selfType = adaptor.self().getType().dyn_cast<TensorType>();
+  if (!selfType)
+    return rewriter.notifyMatchFailure(
+        op, "Only tensor types input are currently supported");
+  auto condType = adaptor.condition().getType().dyn_cast<TensorType>();
+  if (!condType)
+    return rewriter.notifyMatchFailure(
+        op, "Only tensor types condition are currently supported");
+
+  auto outType = getTypeConverter()->convertType(op.getType());
+  rewriter.replaceOpWithNewOp<tosa::SelectOp>(op, outType, adaptor.condition(),
+                                              adaptor.self(), adaptor.other());
+
+  return success();
+}
+
+
 template <>
 LogicalResult ConvertAtenOp<AtenArangeStartStepOp>::matchAndRewrite(
     AtenArangeStartStepOp op, OpAdaptor adaptor,
@@ -3829,6 +3853,7 @@ class ConvertTorchToTosa : public ConvertTorchToTosaBase<ConvertTorchToTosa> {
     INSERT_ATENOP_PATTERN(AtenMaxDimOp);
     INSERT_ATENOP_PATTERN(AtenSliceTensorOp);
     INSERT_ATENOP_PATTERN(AtenBroadcastToOp);
+    INSERT_ATENOP_PATTERN(AtenWhereSelfOp);
     INSERT_ATENOP_PATTERN(AtenArangeStartStepOp);
     INSERT_ATENOP_PATTERN(PrimNumToTensorScalarOp);
     INSERT_ATENOP_PATTERN(ValsemVariantAtenCopyOp);

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -134,6 +134,30 @@ def ElementwiseTernaryModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseAtenWhereSelfModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([1, 1, 5, 5], torch.bool, True),
+        ([1, 12, 5, 5], torch.float32, True),
+        ([], torch.float32, True),
+    ])
+    def forward(self, a, b, c):
+        return torch.ops.aten.where(a, b, c)
+
+
+@register_test_case(module_factory=lambda: ElementwiseAtenWhereSelfModule())
+def ElementwiseAtenWhereSelfModule_basic(module, tu: TestUtils):
+    module.forward(torch.zeros(1, 1, 5, 5, dtype=torch.bool), torch.rand(1, 12, 5, 5), torch.rand(()))
+
+
+# ==============================================================================
+
+
 class ElementwiseWhereSelfModule(torch.nn.Module):
 
     def __init__(self):

test/Conversion/TorchToTosa/basic.mlir

@@ -913,3 +913,20 @@ func.func @torch.aten.to.dtype(%arg0: !torch.vtensor<[3,5],si64>) -> !torch.vten
   %0 = torch.aten.to.dtype %arg0, %int11, %false, %false, %none : !torch.vtensor<[3,5],si64>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[3,5],i1>
   return %0 : !torch.vtensor<[3,5],i1>
 }
+
+// -----
+// CHECK-LABEL:   func.func @torch.aten.where.self(
+// CHECK-SAME:                                     %[[VAL_0:.*]]: !torch.vtensor<[1,1,5,5],i1>,
+// CHECK-SAME:                                     %[[VAL_1:.*]]: !torch.vtensor<[1,12,5,5],f32>,
+// CHECK-SAME:                                     %[[VAL_2:.*]]: !torch.vtensor<[],f32>) -> !torch.vtensor<[1,12,5,5],f32> {
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[1,1,5,5],i1> -> tensor<1x1x5x5xi1>
+// CHECK:           %[[VAL_4:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[1,12,5,5],f32> -> tensor<1x12x5x5xf32>
+// CHECK:           %[[VAL_5:.*]] = torch_c.to_builtin_tensor %[[VAL_2]] : !torch.vtensor<[],f32> -> tensor<f32>
+// CHECK:           %[[VAL_6:.*]] = "tosa.select"(%[[VAL_3]], %[[VAL_4]], %[[VAL_5]]) : (tensor<1x1x5x5xi1>, tensor<1x12x5x5xf32>, tensor<f32>) -> tensor<1x12x5x5xf32>
+// CHECK:           %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<1x12x5x5xf32> -> !torch.vtensor<[1,12,5,5],f32>
+// CHECK:           return %[[VAL_7]] : !torch.vtensor<[1,12,5,5],f32>
+// CHECK:         }
+func.func @torch.aten.where.self(%arg0: !torch.vtensor<[1,1,5,5],i1>, %arg1: !torch.vtensor<[1,12,5,5],f32>, %arg2: !torch.vtensor<[],f32>) -> !torch.vtensor<[1,12,5,5],f32> {
+  %0 = torch.aten.where.self %arg0, %arg1, %arg2 : !torch.vtensor<[1,1,5,5],i1>, !torch.vtensor<[1,12,5,5],f32>, !torch.vtensor<[],f32> -> !torch.vtensor<[1,12,5,5],f32>
+  return %0 : !torch.vtensor<[1,12,5,5],f32>
+}