Fix failing tests

docs/rewriter/examples/broadcast_matmul.py

@@ -32,8 +32,8 @@ def original_model(A: FLOAT[1, 4, 512, 512], B: FLOAT[1, 4, 512, 64]) -> FLOAT[1
     return result
 
 
-model = original_model.to_model_proto()
-onnx.checker.check_model(model)
+_model = original_model.to_model_proto()
+onnx.checker.check_model(_model)
 
 
 ####################################
@@ -55,7 +55,7 @@ def two_reshapes_matmul_reshape_pattern(input_a, input_b, shape_a, shape_b, shap
 # =====================
 
 
-def matmul(op, input_a: ir.Value, input_b: ir.Value, **_):
+def matmul_pattern(op, input_a: ir.Value, input_b: ir.Value, **_):
     return op.MatMul(input_a, input_b)
 
 
@@ -181,7 +181,7 @@ def apply_rewrite(model):
     # Create rewrite rules
     two_reshapes_matmul_reshape_rule = pattern.RewriteRule(
         two_reshapes_matmul_reshape_pattern,  # target pattern
-        matmul,  # replacement pattern
+        matmul_pattern,  # replacement pattern
         check_if_need_reshape,  # match_condition function
     )
     # Create a Rewrite Rule Set
@@ -194,5 +194,5 @@ def apply_rewrite(model):
     return model_with_rewrite
 
 
-model_with_rewrite = apply_rewrite(model)
-onnx.checker.check_model(model_with_rewrite)
+_model_with_rewrite = apply_rewrite(_model)
+onnx.checker.check_model(_model_with_rewrite)

docs/rewriter/examples/erfgelu.py

@@ -28,8 +28,8 @@ def original_model(X: FLOAT[64, 128], Y: FLOAT[64, 128]) -> FLOAT[64, 128]:
     return result
 
 
-model = original_model.to_model_proto()
-onnx.checker.check_model(model)
+_model = original_model.to_model_proto()
+onnx.checker.check_model(_model)
 
 
 ####################################
@@ -144,7 +144,7 @@ def apply_rewrite_with_commute(model):
 
 
 # Rewrite-Simple
-model_with_rewrite = apply_rewrite(model)
+model_with_rewrite = apply_rewrite(_model)
 onnx.checker.check_model(model_with_rewrite)
 
 # Rewrite-Single-Patterns

docs/rewriter/rewrite_patterns.md

@@ -94,7 +94,7 @@ Extending the previous [simple example](heading-target-simple), assumming a scen
 
 In this graph, there exist two node pattern that constitute a `GELU` op. However, there is a subtle difference between the two. Focusing on the parent `Mul` nodes in either patterns, the order of the input values being multiplied is switched.
 
-![gelu_pattern_1](examples/img/erfgelu_04_commute.png){width=330px align=left} ![gelu_pattern_2](examples/img/erfgelu_05_commute.png){width=330px align=center} 
+![gelu_pattern_1](examples/img/erfgelu_04_commute.png){width=330px align=left} ![gelu_pattern_2](examples/img/erfgelu_05_commute.png){width=330px align=center}
 
 
 If we utilize the same `target_pattern` created for the earlier [simple example](heading-target-simple) (shown below), only one of two `GELU` pattern will be matched.

onnxscript/rewriter/pattern_test.py

@@ -245,7 +245,7 @@ def identity(op, x, newshape):
             del newshape  # Unused
             return op.Identity(x)
 
-        def _check_for_redundant_reshape(x, newshape):
+        def check_for_redundant_reshape(x, newshape):
             oldshape = x.shape
             newshape = _ir_utils.propagate_const_value(newshape)
             newshape = _ir_utils.get_numpy_from_ir_value(newshape)
@@ -257,9 +257,6 @@ def _check_for_redundant_reshape(x, newshape):
                 return False
             return all(not (d1 != d2 and d2 != -1) for d1, d2 in zip(oldshape, newshape))  # pylint: disable=consider-using-in
 
-        def check_for_redundant_reshape(bindings):
-            return _check_for_redundant_reshape(**bindings)
-
         rule = pattern.RewriteRule(reshape, identity, check_for_redundant_reshape)
 
         model_proto = onnx.parser.parse_model(