[torchlib] Fix calls to Unsqueeze to provide correct 1d axes (#2186)

Discovered in
onnx/onnx#6886 (comment), the
`axes` input in calls to unsqueeze are sometimes 0d. This is incorrect
according to the ONNX spec. The PR fixes the instances I could find.

Author: justinchuby

onnxscript/function_libs/torch_lib/ops/core.py

@@ -2991,8 +2991,8 @@ def _aten_embedding_bag_onnx(
     indices_1d = op.Reshape(indices, neg_1)
     # Get weight out according to indices_1d,
     new_weight = op.Gather(weight, indices_1d)
-    # This happends after first step of Gather. Because Shape(indices)==Shape(per_sample_weights)
-    new_weight = op.Mul(new_weight, op.Unsqueeze(per_sample_weights, axes=1))
+    # This happens after first step of Gather. Because Shape(indices)==Shape(per_sample_weights)
+    new_weight = op.Mul(new_weight, op.Unsqueeze(per_sample_weights, axes=[1]))
     weight_dim_1 = op.Reshape(op.Shape(weight, start=1), neg_1)
     indices_size = op.Shape(indices_1d)
 
@@ -3131,8 +3131,8 @@ def _aten_embedding_bag_1d_padding_idx_onnx(
     # Get weight out according to indices,
     # e.g. indices=[3,1,4,5,3] means get weight[[3,1,4,5,3]]
     indices_weight = op.Gather(weight, indices)
-    # This happends after first step of Gather. Because Shape(indices)==Shape(per_sample_weights)
-    indices_weight = op.Mul(indices_weight, op.Unsqueeze(per_sample_weights, axes=1))
+    # This happens after first step of Gather. Because Shape(indices)==Shape(per_sample_weights)
+    indices_weight = op.Mul(indices_weight, op.Unsqueeze(per_sample_weights, axes=[1]))
 
     # The element in sequence must be FLOAT32 dtype due to ORT bug
     indices_weight = op.Cast(indices_weight, to=FLOAT.dtype)
@@ -4145,7 +4145,6 @@ def _shape_of_broadcast_tensors(*args: TensorType) -> INT64:
     return op.Shape(broadcasted)
 
 
-@torch_op("aten::index.Tensor", private=True, trace_only=True)
 def _aten_index_onnx(
     self: TensorType,
     indices: Sequence[Optional[INT64]],
@@ -4173,7 +4172,7 @@ def _aten_index_onnx(
     not_none_indices = [idx for idx in indices if idx is not None]
     broadcast_shape = _shape_of_broadcast_tensors(*not_none_indices)
     final_index = op.Concat(
-        *(op.Unsqueeze(op.Expand(idx, broadcast_shape), -1) for idx in not_none_indices),
+        *(op.Unsqueeze(op.Expand(idx, broadcast_shape), [-1]) for idx in not_none_indices),
         axis=-1,
     )
 
@@ -7706,13 +7705,13 @@ def aten_select_backward(
     raise NotImplementedError()
 
 
-@torch_op("aten::select_scatter")
+@torch_op("aten::select_scatter", trace_only=True)
 def aten_select_scatter(self: TensorType, src: TensorType, dim: int, index: int) -> TensorType:
     """select_scatter(Tensor self, Tensor src, int dim, int index) -> Tensor"""
 
     # Change src rank to self rank according to dim
     # e.g. if self is [2,3,4], src is [2,4], dim=1, then update is [2,1,4]
-    update = op.Unsqueeze(src, axes=dim)
+    update = op.Unsqueeze(src, axes=[dim])
     # Change index rank to the same as 'update' [2,1,4]
     indices = op.Expand(index, op.Shape(update))
     return op.ScatterElements(self, indices, update, axis=dim, reduction="none")
@@ -7880,7 +7879,7 @@ def aten_slice_scatter(
         zero,
         op.Unsqueeze(step, zero),
     )
-    index_base = op.Unsqueeze(index_base, -1)
+    index_base = op.Unsqueeze(index_base, [-1])
 
     # Use trace only to construct the perm attribute in Transpose
     dims = None
@@ -8623,7 +8622,7 @@ def aten_unfold(self: TTensor, dimension: int, size: int, step: int) -> TTensor:
 
     self_rank = len(self.shape)
     if self_rank == 0:
-        result = op.Unsqueeze(self, 0)
+        result = op.Unsqueeze(self, [0])
     else:
         # Handle negative dimension
         if dimension < 0:
@@ -8792,8 +8791,7 @@ def aten_unsafe_split_with_sizes(
 def aten_unsqueeze(self: TTensor, dim: int) -> TTensor:
     """unsqueeze(Tensor(a) self, int dim) -> Tensor(a)"""
 
-    dim = op.Cast(dim, to=INT64.dtype)
-    return op.Unsqueeze(self, dim)
+    return op.Unsqueeze(self, [dim])
 
 
 def aten_unsqueeze_copy(self: TensorType, dim: int) -> TensorType:

onnxscript/function_libs/torch_lib/ops/nn.py

@@ -1002,7 +1002,7 @@ def _aten_max_pool_onnx(
 ) -> TFloatOrUInt8:
     self_rank_is_unbatched_rank = Rank(self) == unbatched_rank
     if self_rank_is_unbatched_rank:  # C,H,W -> N,C,H,W and N=1
-        self = op.Unsqueeze(self, op.Constant(value_ints=[0]))
+        self = op.Unsqueeze(self, [0])
 
     pool_result, _ = op.MaxPool(
         self,
@@ -1014,7 +1014,7 @@ def _aten_max_pool_onnx(
     )
 
     if self_rank_is_unbatched_rank:
-        pool_result = op.Squeeze(pool_result, op.Constant(value_ints=[0]))
+        pool_result = op.Squeeze(pool_result, [0])
 
     return pool_result
 
@@ -1136,7 +1136,7 @@ def _aten_max_pool_with_indices_onnx(
 ) -> Tuple[TFloatOrUInt8, INT64]:
     self_rank_is_unbatched_rank = Rank(self) == unbatched_rank
     if self_rank_is_unbatched_rank:
-        self = op.Unsqueeze(self, axes=0)
+        self = op.Unsqueeze(self, axes=[0])
 
     pool_result, indices = op.MaxPool(
         self,
@@ -1191,8 +1191,8 @@ def _aten_max_pool_with_indices_onnx(
     indices = op.Sub(indices, delta)
 
     if self_rank_is_unbatched_rank:
-        pool_result = op.Squeeze(pool_result, op.Constant(value_ints=[0]))
-        indices = op.Squeeze(indices, op.Constant(value_ints=[0]))
+        pool_result = op.Squeeze(pool_result, [0])
+        indices = op.Squeeze(indices, [0])
 
     return (pool_result, indices)
 
@@ -1365,11 +1365,11 @@ def aten_nll_loss(
 
     self_rank_is_1 = Rank(self) == 1
     if self_rank_is_1:  # self rank should be at least 2
-        self = op.Unsqueeze(self, op.Constant(value_ints=[0]))
+        self = op.Unsqueeze(self, [0])
 
     rank_target = Rank(target)
     if rank_target == 0:  # target rank should be at least 1
-        target = op.Unsqueeze(target, op.Constant(value_ints=[0]))
+        target = op.Unsqueeze(target, [0])
 
     if reduction == 0:
         reduction_str = "none"

onnxscript/function_libs/torch_lib/ops/special.py

@@ -219,7 +219,7 @@ def aten_special_log_softmax(self: TFloat, dim: int, dtype: int = -1) -> TFloat:
 
     self_is_scalar = len(self.shape) == 0
     if self_is_scalar:
-        self = op.Unsqueeze(self, op.Constant(value_ints=[0]))
+        self = op.Unsqueeze(self, [0])
     result = op.LogSoftmax(self, axis=dim)
     if dtype != -1:
         result = op.Cast(result, to=dtype)