Merge branch 'main' of https://github.com/microsoft/onnxscript into bbb

onnxscript/function_libs/torch_lib/ops/core.py

@@ -308,7 +308,7 @@ def aten_affine_grid_generator_backward(
 def aten_alias(self: TTensor) -> TTensor:
     """alias(Tensor(a) self) -> Tensor(a)"""
 
-    return self
+    return op.Identity(self)
 
 
 def aten_alias_copy(self: TensorType) -> TensorType:
@@ -374,7 +374,7 @@ def aten_all_dims(self: TTensor, dim: Sequence[int] = (), keepdim: bool = False)
         self = aten_all_dim(self, d, keepdim=True)
     if not keepdim:
         self = op.Squeeze(self, list(dim))
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::all.dims", traceable=True)
@@ -499,7 +499,7 @@ def aten_any_dims(self: TTensor, dim: Sequence[int] = (), keepdim: bool = False)
         self = aten_any_dim(self, d, keepdim=True)
     if not keepdim:
         self = op.Squeeze(self, list(dim))
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::any.dims", traceable=True)
@@ -940,7 +940,7 @@ def aten_atleast_1d(self: TTensor) -> TTensor:
 
     if IsScalar(self):
         self = op.Reshape(self, op.Constant(value_ints=[1]))
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::atleast_1d.Sequence")
@@ -964,7 +964,7 @@ def aten_atleast_2d(self: TTensor) -> TTensor:
 
     if Rank(self) <= 1:
         self = op.Reshape(self, op.Constant(value_ints=[1, -1]))
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::atleast_2d.Sequence")
@@ -991,7 +991,7 @@ def aten_atleast_3d(self: TTensor) -> TTensor:
         self = op.Reshape(self, op.Constant(value_ints=[1, -1, 1]))
     elif rank == 2:
         self = op.Unsqueeze(self, op.Constant(value_ints=[-1]))
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::atleast_3d.Sequence")
@@ -1691,7 +1691,7 @@ def aten_clone(
 ) -> TTensor:
     """clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor"""
 
-    return self
+    return op.Identity(self)
 
 
 def aten_coalesce(self: TensorType) -> TensorType:
@@ -1749,7 +1749,7 @@ def aten_complex(real: TFloat, imag: TFloat) -> TFloat:
 def aten_conj(self: TTensor) -> TTensor:
     """conj(Tensor(a) self) -> Tensor(a)"""
 
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::conj", complex=True, private=True)
@@ -1825,7 +1825,7 @@ def aten_contiguous(
     """contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)"""
 
     # ONNX does not have the notion of memory_format. It is always treated as a no-op.
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::conv1d", trace_only=True)
@@ -2168,7 +2168,7 @@ def aten__to_copy(
     """_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor"""
 
     if dtype == -1:
-        return self
+        return op.Identity(self)
     else:
         return common_ops.cast_to(self, dtype=dtype)
 
@@ -2493,7 +2493,7 @@ def aten_dense_dim(self: TensorType) -> int:
 def aten_detach(self: TensorType) -> TensorType:
     """detach(Tensor(a) self) -> Tensor(a)"""
 
-    return self
+    return op.Identity(self)
 
 
 def aten_detach_copy(self: TensorType) -> TensorType:
@@ -4061,7 +4061,7 @@ def _aten_index_onnx(
     if _has_none_in_middle(indices):
         # If there is None in the middle, Advanced Indexing cannot decide where to put
         # the new dimensions. So it places them in the front, like GatherND does.
-        return self
+        return op.Identity(self)
 
     # When the indices are consecutive, Advanced Indexing will place the new dimensions
     # (aka. the broadcasted shape) in the middle, replacing the original [x1, ..., xk] axes.
@@ -4227,7 +4227,7 @@ def aten_index_put_bool(
     index = op.SequenceAt(indices, 0)  # assume indices only have 1 element
     # FIXME: ORT ArgMax fails on INT64 input even though ONNX allows it
     index_int = op.Cast(index, to=INT32.dtype)
-    # if all False, return self
+    # if all False, return op.Identity(self)
     if op.ReduceSum(index_int) == 0:
         result = self
     else:
@@ -4700,7 +4700,7 @@ def aten_lift_fresh(self: TensorType) -> TensorType:
 def aten_lift_fresh_copy(self: TensorType) -> TensorType:
     """lift_fresh_copy(Tensor self) -> Tensor"""
 
-    return self
+    return op.Identity(self)
 
 
 def aten_linear_backward(
@@ -7082,14 +7082,14 @@ def aten_reshape_as(self: TensorType, other: TensorType) -> TensorType:
 def aten_resolve_conj(self: TTensor) -> TTensor:
     """resolve_conj(Tensor(a) self) -> Tensor(a)"""
 
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::resolve_neg", trace_only=True)
 def aten_resolve_neg(self: TTensor) -> TTensor:
     """resolve_neg(Tensor(a) self) -> Tensor(a)"""
 
-    return self
+    return op.Identity(self)
 
 
 def aten_result_type(tensor: TensorType, other: TensorType) -> int:
@@ -7142,9 +7142,9 @@ def aten_roll(self: TTensor, shifts: INT64, dims: Sequence[int] = ()) -> TTensor
 
     self_rank = len(self.shape)
     if self_rank == 0:
-        return self
+        return op.Identity(self)
     elif self.shape[0] == 0:  # empty tensor
-        return self
+        return op.Identity(self)
     else:
         # NOTE: In pytorch, default value of dims is an empty list.
         if len(dims) == 0:  # Empty sequence
@@ -7166,10 +7166,10 @@ def aten_roll_complex(self: TTensor, shifts: INT64, dims: Sequence[int] = ()) ->
 
     self_rank = len(self.shape)
     if self_rank == 1:
-        return self
+        return op.Identity(self)
 
     if self.shape[0] == 0:  # empty tensor
-        return self
+        return op.Identity(self)
 
     self_real = op.Slice(self, [0], [1], axes=[-1])
     self_imag = op.Slice(self, [1], [2], axes=[-1])
@@ -7819,7 +7819,7 @@ def _add_batch_dimension(self: TFloatOrBFloat16) -> Tuple[TFloatOrBFloat16, INT6
     if signal_rank == 1:
         # Add a batch dimension
         self = op.Unsqueeze(self, op.Constant(value_ints=[0]))
-    return self, signal_rank
+    return op.Identity(self), signal_rank
 
 
 @torch_op("aten::stft", private=True)
@@ -8768,7 +8768,7 @@ def aten_view_as_complex(self: TTensor) -> TTensor:
 
     # We always operate on the real representation of a complex number in torchlib
     # So this is a no-op
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::view_as_complex_copy", trace_only=True)
@@ -8777,7 +8777,7 @@ def aten_view_as_complex_copy(self: TTensor) -> TTensor:
 
     # We always operate on the real representation of a complex number in torchlib
     # So this is a no-op
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::view_as_real", complex=True, trace_only=True)
@@ -8786,7 +8786,7 @@ def aten_view_as_real(self: TTensor) -> TTensor:
 
     # We always operate on the real representation of a complex number in torchlib
     # So this is a no-op
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::view_as_real_copy", complex=True, trace_only=True)
@@ -8795,7 +8795,7 @@ def aten_view_as_real_copy(self: TTensor) -> TTensor:
 
     # We always operate on the real representation of a complex number in torchlib
     # So this is a no-op
-    return self
+    return op.Identity(self)
 
 
 @torch_op("aten::view_copy")

onnxscript/rewriter/llama_rule_sets_test.py

@@ -170,7 +170,7 @@ def test_llama_p0_rule_set_cast_cast(self):
             rewritten_model = ir.serde.serialize_model(ir_model)
 
             self.assertEqual(["Cast"], [n.op_type for n in rewritten_model.graph.node])
-            self._check_model(model_proto, rewritten_model, atol=1e-3)
+            self._check_model(model_proto, rewritten_model, atol=1e-2)
 
     @classmethod
     def _cast_identity_models(cls):
@@ -376,6 +376,7 @@ def _slides_split_models(cls):
         ]
         return models
 
+    @unittest.skipIf(True, reason="see https://github.com/microsoft/onnxscript/issues/1642")
     def test_llama_p0_rule_set_slice_split(self):
         for model_proto in self._slides_split_models():
             ir_model = ir.serde.deserialize_model(model_proto)