Add Op (aten::masked_scatter) | feat (torchlib) (#2112)

From Gemma3, the error lacks of support is raised.


https://github.com/huggingface/transformers/blob/7f5077e53682ca855afc826162b204ebf809f1f9/src/transformers/models/gemma3/modeling_gemma3.py#L1339

Author: titaiwangms

onnxscript/function_libs/torch_lib/ops/core.py

@@ -5202,10 +5202,26 @@ def aten_masked_fill(self: TTensor, mask: BOOL, value: TTensor) -> TTensor:
     return op.Where(mask, value_cast, self)
 
 
-def aten_masked_scatter(self: TensorType, mask: TensorType, source: TensorType) -> TensorType:
+@torch_op(("aten::masked_scatter"), trace_only=True)
+def aten_masked_scatter(self: TTensor, mask: TTensor, source: TTensor) -> TTensor:
     """masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor"""
 
-    raise NotImplementedError()
+    if len(mask.shape) < len(self.shape):
+        mask = op.Expand(mask, op.Shape(self))
+    else:
+        self = op.Expand(self, op.Shape(mask))
+    index = op.Transpose(op.NonZero(mask), perm=[1, 0])
+
+    # NOTE: source can have more elements than needed.
+    # It could also have arbitrary shape.
+    # This is not supported by ONNX::ScatterND, so we need to flatten and slice source tensor.
+    source = op.Reshape(source, op.Constant(value_ints=[-1]))
+    axes = op.Constant(value_ints=[0])
+    starts = op.Constant(value_ints=[0])
+    ends = op.Gather(op.Shape(index), op.Constant(value_ints=[0]), axis=0)
+    source = op.Slice(source, starts, ends, axes)
+
+    return op.ScatterND(self, index, source)
 
 
 def aten_masked_select(self: TensorType, mask: TensorType) -> TensorType:
@@ -6429,7 +6445,7 @@ def aten_nextafter(self: TensorType, other: TensorType) -> TensorType:
     raise NotImplementedError()
 
 
-@torch_op("aten::nonzero")
+@torch_op("aten::nonzero", trace_only=True)
 def aten_nonzero(self: TTensor) -> INT64:
     """nonzero(Tensor self) -> Tensor"""
     # NOTE: In torch the return shape is [n, d], while in onnx [d, n],

tests/function_libs/torch_lib/ops_test_data.py

@@ -932,6 +932,7 @@ def _where_input_wrangler(
         dtypes=(torch.bool,),
         reason="fixme: ORT does not have an implementation for Where with bool inputs.",
     ),
+    TorchLibOpInfo("masked_scatter", core_ops.aten_masked_scatter),
     TorchLibOpInfo(
         "matmul",
         core_ops.aten_matmul,