fix(index_put): handle None cases

Author: AyoubMDL

onnxscript/function_libs/torch_lib/ops/core.py

@@ -4298,14 +4298,70 @@ def aten_index_put(
     <https://github.com/pytorch/pytorch/blob/main/torch/onnx/symbolic_opset11.py#L212>`_.
     """
 
-    # TODO(justinchuby): Handle when indicies has more than one element
-    index = indices[0]
-    new_index = op.Unsqueeze(index, [-1])
+    def _make_reshape_list_broadcastable(reshape_list, values_shape):
+        # Remove ones until the rank of reshape_list matches values_shape.
+        while len(reshape_list) > len(values_shape) and 1 in reshape_list:
+            reshape_list.remove(1)
+
+        # Now ensure each dimension is broadcastable:
+        # This is mandatory when mixing basic and advanced indexing
+        # Example: data((10, 3, 4)), indices([[0, 1], :, [0, 1]]) values(2, 3)
+        # the reshape list should be : [[2, 1], [1, 3], [2, 1]]
+        for i, r in enumerate(reshape_list):
+            if r != 1 and r != values_shape[i]:
+                one_index = reshape_list.index(1)
+                # Swap elements
+                # For the example above the current reshape list is [1, 2] for last dim,
+                # to make it broadcastable, we swap the elements
+                reshape_list[one_index], reshape_list[i] = reshape_list[i], 1
+
+        return reshape_list
+
+    # Ensure the number of indices matches the tensor rank.
+    self_rank = len(self.shape)
+    if len(indices) < self_rank:
+        indices = list(indices) + [None] * (self_rank - len(indices))
+
+    # Get values shape (we use .numpy to make it hashable)
+    values_shape = values.shape.numpy()
+
+    index_vectors = []
+    for i in range(self_rank):
+        if indices[i] is None:
+            # For a full slice along dim i, create a range index [0, self.shape[i]).
+            idx = op.Range(start=0, limit=self.shape[i], delta=1)
+            reshape_update = self.shape[i]
+        else:
+            idx = indices[i]
+            reshape_update = indices[i].shape[0]
+
+        # Create a reshape pattern: one value per index dimension,
+        # with the current dimension set to the update size.
+        reshape_list = [1] * len(indices)
+        reshape_list[i] = reshape_update
+
+        # Adjust the reshape list to match the values shape.
+        reshape_list = _make_reshape_list_broadcastable(reshape_list, values_shape)
+
+        # Reshape and expand the index.
+        idx = op.Reshape(idx, reshape_list)
+        idx = op.Expand(idx, values_shape)
+
+        # Flatten the index to 1D and unsqueeze to form a column vector.
+        idx = op.Reshape(idx, [-1])
+        idx = op.Unsqueeze(idx, axes=[1])
+        index_vectors.append(idx)
+
+    # Concatenate the index vectors along axis=1 to form the final indices.
+    new_index = op.Concat(*index_vectors, axis=1)
+
+    # Flatten values to match the indices
+    flat_values = op.Reshape(values, [-1])
 
     if accumulate:
-        result = op.ScatterND(self, new_index, values, reduction="add")
+        result = op.ScatterND(self, new_index, flat_values, reduction="add")
     else:
-        result = op.ScatterND(self, new_index, values)
+        result = op.ScatterND(self, new_index, flat_values)
 
     return result
 

tests/function_libs/torch_lib/extra_opinfo.py

@@ -790,20 +790,57 @@ def sample_inputs_index_put(op_info, device, dtype, requires_grad, **kwargs):
     del op_info
     del kwargs
 
-    data = torch_testing.make_tensor(
-        (10, 3),
-        device=device,
-        dtype=dtype,
-        requires_grad=requires_grad,
-    )
-    indices = [torch.arange(8, dtype=torch.int64, device=device).reshape((-1, 4))]
-    values = torch_testing.make_tensor(
-        (2, 4, 3),
-        device=device,
-        dtype=dtype,
-        requires_grad=requires_grad,
+    make_arg = functools.partial(
+        torch_testing.make_tensor, device=device, dtype=dtype, requires_grad=requires_grad
     )
-    yield opinfo_core.SampleInput(data, indices, values)
+
+    cases = [
+        # Cases: one None
+        ((1, 3, 4), [None, torch.arange(2, device=device), None], (1, 2, 4)),
+        ((10, 3, 4), [torch.arange(5, device=device), None, None], (5, 3, 4)),
+        ((10, 3, 4, 6), [None, None, None, torch.arange(3, device=device)], (10, 3, 4, 3)),
+        # Cases: two None
+        (
+            (10, 3, 4),
+            [None, torch.arange(3, device=device), torch.arange(3, device=device)],
+            (10, 3),
+        ),
+        (
+            (10, 3, 4, 6),
+            [
+                torch.arange(2, device=device),
+                None,
+                torch.arange(2, device=device),
+                torch.arange(2, device=device),
+            ],
+            (2, 3),
+        ),
+        (
+            (10, 3, 4),
+            [torch.arange(2, device=device), torch.arange(2, device=device), None],
+            (2, 4),
+        ),
+        # Cases: Single indexing
+        ((10, 3, 4), [None, None, torch.tensor([0], device=device)], (10, 3, 1)),
+        ((10, 3, 4), [torch.tensor([0], device=device), None, None], (1, 3, 4)),
+        ((10, 3, 4, 6), [None, torch.tensor([0], device=device), None, None], (10, 1, 4, 6)),
+        # Cases: Single element
+        (
+            (10, 3, 4),
+            [
+                torch.tensor([0], device=device),
+                torch.tensor([0], device=device),
+                torch.tensor([0], device=device),
+            ],
+            (1,),
+        ),
+    ]
+
+    for data_shape, indices, values_shape in cases:  # type: ignore[misc]
+        data = make_arg(data_shape)
+        values = make_arg(values_shape)  # type: ignore[has-type]
+
+        yield opinfo_core.SampleInput(data, indices, values)
 
 
 def sample_inputs_layer_norm(op_info, device, dtype, requires_grad, **kwargs):