Remove try-catch block and apply fixes to enable torch.onnx.dynamo_ex…

…port to succeed

onnxscript/function_libs/torch_lib/ops/core.py

@@ -13,7 +13,6 @@
 from __future__ import annotations
 
 import math
-import re
 from typing import Any, Optional, Sequence, Tuple, Union
 
 from onnxscript import (
@@ -8360,13 +8359,6 @@ def aten_unique_consecutive(
     raise NotImplementedError()
 
 
-_NOT_IMPLEMENTED_UNIQUE = re.compile(
-    r"NOT_IMPLEMENTED\s*:\s*Could\s+not\s+find\s+an\s+implementation\s+for\s+Unique"
-    )
-"""
-A pattern to detect an unsupported (not implemented) Unique operator
-"""
-
 @torch_op("aten::unique", trace_only=True)
 def aten_unique(
     self: TensorType,
@@ -8377,18 +8369,10 @@ def aten_unique(
 ) -> tuple[TensorType, TensorType, TensorType]:
     """unique(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor?, Tensor?)"""
 
-    try:
-        if dim is None:
-            unique_values, inverse_indices, counts = aten_unique2(self, sorted, return_inverse, return_counts)
-        else:
-            unique_values, inverse_indices, counts = aten_unique_dim(self, dim, sorted, return_inverse, return_counts)
-    except Exception as e:
-        # try to provide a more informative error message
-        if _NOT_IMPLEMENTED_UNIQUE.search(str(e)) is not None:
-            raise NotImplementedError(
-                f"'onnxruntime' does not yet support Unique(11) operator with dtype={self.dtype}'"
-                ) from e
-        raise
+    if dim is None:
+        unique_values, inverse_indices, counts = aten_unique2(self, sorted, return_inverse, return_counts)
+    else:
+        unique_values, inverse_indices, counts = aten_unique_dim(self, dim, sorted, return_inverse, return_counts)
     if return_inverse:
         if return_counts:
             result = unique_values, inverse_indices, counts
@@ -8410,7 +8394,11 @@ def aten_unique2(
 ) -> tuple[TensorType, TensorType, TensorType]:
     """unique(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)"""
 
-    unique_values, _, inverse_indices, counts = op.Unique(self, axis=None, sorted=sorted)
+    unique_values, indices, inverse_indices, counts = op.Unique(self, axis=None, sorted=sorted)
+    # HACK: force indices to be in the graph so that it gets a name during optimization
+    # Otherwise an error will be raised in `onnxscript.Scope.lookup_or_create`
+    indices_size = op.Shape(indices)
+    counts = op.Reshape(counts, indices_size)
     input_size = op.Shape(self)
     inverse_indices = op.Reshape(inverse_indices, input_size)
     return unique_values, inverse_indices, counts
@@ -8426,19 +8414,15 @@ def aten_unique_dim(
 ) -> tuple[TensorType, TensorType, TensorType]:
     """unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)"""
 
-    unique_values, _, inverse_indices, counts = op.Unique(self, axis=dim, sorted=sorted)
+    unique_values, indices, inverse_indices, counts = op.Unique(self, axis=dim, sorted=sorted)
+    # HACK: force indices to be in the graph so that it gets a name during optimization
+    # Otherwise an error will be raised in `onnxscript.Scope.lookup_or_create`
+    indices_size = op.Shape(indices)
+    counts = op.Reshape(counts, indices_size)
     input_size = op.Shape(self)
-    # PyTorch accepts negative dim as reversed counting
-    input_rank = op.Size(input_size)
-    dim = input_rank + dim
-    dim = dim % input_rank
-    starts = op.Reshape(dim, [-1])
-    ends = op.Reshape(dim + 1, [-1])
-    input_dim_size = op.Slice(input_size, starts=starts, ends=ends)
-    inverse_indices = op.Reshape(inverse_indices, input_dim_size)
+    inverse_indices = op.Reshape(inverse_indices, op.Reshape(input_size[dim], [-1]))
     output_size = op.Shape(unique_values)
-    output_dim_size = op.Slice(output_size, starts=starts, ends=ends)
-    counts = op.Reshape(counts, output_dim_size)
+    counts = op.Reshape(counts, op.Reshape(output_size[dim], [-1]))
     return unique_values, inverse_indices, counts