[torchlib] Fix names for registered functions (#1606)

Fix the name for `getitem` and `adaptive*` ops.

onnxscript/function_libs/torch_lib/ops/core.py

@@ -3560,7 +3560,7 @@ def aten_full(size: INT64, fill_value: FLOAT, dtype: int = FLOAT.dtype):
 
 
 @torch_op("aten::full_like")
-def aten_full_like(self, fill_value: TTensor) -> TTensor:
+def aten_full_like(self: TTensor, fill_value: TTensor) -> TTensor:
     """full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor"""
 
     fill_value = op.CastLike(fill_value, self)
@@ -3570,7 +3570,7 @@ def aten_full_like(self, fill_value: TTensor) -> TTensor:
 
 
 @torch_op("aten::full_like")
-def aten_full_like_dtype(self, fill_value: TTensor, dtype: int) -> TTensor:
+def aten_full_like_dtype(self: TTensor, fill_value: TTensor, dtype: int) -> TTensor:
     """full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor"""
 
     fill_value = op.Cast(fill_value, to=dtype)
@@ -3669,8 +3669,7 @@ def aten_ger(self: TensorType, vec2: TensorType) -> TensorType:
     raise NotImplementedError()
 
 
-# NOTE: The name is made up for `getitem` to be included in the registry
-@torch_op("aten::getitem")
+@torch_op("_operator::getitem")
 def aten_getitem(self: Sequence[TTensor], i: INT64) -> TTensor:
     return op.SequenceAt(self, i)
 
@@ -8174,7 +8173,7 @@ def aten_trace_backward(grad: TensorType, sizes: INT64) -> TensorType:
 
 
 @torch_op(("aten::transpose", "aten::transpose.int"), trace_only=True)
-def aten_transpose(self, dim0: int, dim1: int):
+def aten_transpose(self: TTensor, dim0: int, dim1: int) -> TTensor:
     """transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)"""
 
     # Use trace only to construct the prem attribute in Transpose
@@ -8194,7 +8193,7 @@ def aten_transpose(self, dim0: int, dim1: int):
 
 
 @torch_op(("aten::transpose", "aten::transpose.int"), trace_only=True, complex=True)
-def aten_transpose_complex(self, dim0: int, dim1: int):
+def aten_transpose_complex(self: TTensor, dim0: int, dim1: int) -> TTensor:
     """transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)"""
 
     # Use trace only to construct the prem attribute in Transpose

onnxscript/function_libs/torch_lib/ops/nn.py

@@ -40,7 +40,7 @@
 TFloatUnlessFloat32 = TypeVar("TFloatUnlessFloat32", bound=Union[BFLOAT16, FLOAT16, DOUBLE])
 
 
-@torch_op("aten::aten_adaptive_avg_pool1d", traceable=True)
+@torch_op("aten::adaptive_avg_pool1d", traceable=True)
 def aten_adaptive_avg_pool1d(self: TFloat, output_size: INT64[1]) -> TFloat:
     """adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor"""
 
@@ -58,7 +58,7 @@ def aten_adaptive_avg_pool1d(self: TFloat, output_size: INT64[1]) -> TFloat:
     return result
 
 
-@torch_op("aten::aten_adaptive_avg_pool2d", traceable=True)
+@torch_op("aten::adaptive_avg_pool2d", traceable=True)
 def aten_adaptive_avg_pool2d(self: TFloat, output_size: INT64[2]) -> TFloat:
     """adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor"""
 
@@ -76,7 +76,7 @@ def aten_adaptive_avg_pool2d(self: TFloat, output_size: INT64[2]) -> TFloat:
     return result
 
 
-@torch_op("aten::aten_adaptive_avg_pool3d", traceable=True)
+@torch_op("aten::adaptive_avg_pool3d", traceable=True)
 def aten_adaptive_avg_pool3d(self: TFloat, output_size: INT64[3]) -> TFloat:
     """adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor"""