diff --git a/docs/tutorial/optimizer/optimize.md b/docs/tutorial/optimizer/optimize.md
index 5ceb7dfb80..8ff36f4c67 100644
--- a/docs/tutorial/optimizer/optimize.md
+++ b/docs/tutorial/optimizer/optimize.md
@@ -15,6 +15,7 @@ onnxscript.optimizer.optimize(model)
 ```
 
 ### optimize API
+
 The `onnxscript.optimizer.optimize` call takes in several optional parameters that allows the caller to further fine-tune the process of optimization.
 
 ```{eval-rst}
@@ -24,12 +25,8 @@ The `onnxscript.optimizer.optimize` call takes in several optional parameters th
 
 ## Description of optimizations applied by `onnxscript.optimizer.optimize`
 
-:::{table}
-:widths: auto
-:align: center
-
-| Optimization 'onnxscript.optimizer.` + .. | Description |
-| - | - |
+| Optimization | Description |
+|-------------|-------------|
 | **Constant folding** <br>`constant_folding.fold_constants` | Applies constant folding optimization to the model. |
 | **Constant propagation** <br>`constant_folding.fold_constants` | Applies constant propagation optimization to the model. Applied as part of the constant folding optimization. |
 | **Sequence simplification** <br>`constant_folding.fold_constants` | Simplifies Sequence based ops (SequenceConstruct, ConcatFromSequence) present in the model. Applied as part of the constant folding optimization. |
@@ -37,17 +34,3 @@ The `onnxscript.optimizer.optimize` call takes in several optional parameters th
 | **Remove unused functions** <br>`remove_unused_function.remove_unused_functions` | Removes unused function protos from the model. |
 | **Inline functions with unused outputs** <br>`simple_function_folding.inline_functions_with_unused_outputs` | Inlines function nodes that have unused outputs. |
 | **Inline simple functions** <br>`simple_function_folding.inline_simple_functions` | Inlines simple functions based on a node count threshold. |
-:::
-
-## List of pattern rewrite rules applied by `onnxscript.optimizer.optimize`
-
-```{eval-rst}
-.. autosummary::
-    :nosignatures:
-
-    onnxscript.rewriter.broadcast_to_matmul
-    onnxscript.rewriter.cast_constant_of_shape
-    onnxscript.rewriter.gemm_to_matmul_add
-    onnxscript.rewriter.no_op
-
-```
diff --git a/onnxscript/rewriter/__init__.py b/onnxscript/rewriter/__init__.py
index cb0c1c70d6..fb7815bd1c 100644
--- a/onnxscript/rewriter/__init__.py
+++ b/onnxscript/rewriter/__init__.py
@@ -19,7 +19,6 @@
     broadcast_to_matmul,
     cast_constant_of_shape,
     collapse_slices,
-    gemm_to_matmul_add,
     no_op,
     pattern,
 )
@@ -28,7 +27,6 @@
 _DEFAULT_REWRITE_RULES: tuple[pattern.RewriteRule, ...] = (
     *no_op.rules.rules,  # TODO: merge this rule into constant folding?
     *broadcast_to_matmul.rules.rules,
-    gemm_to_matmul_add.rule,  # type: ignore[has-type]
     *cast_constant_of_shape.rules.rules,
     *collapse_slices.rules.rules,
     *basic_rules.basic_optimization_rules().rules,
diff --git a/onnxscript/rewriter/ort_fusions/_core.py b/onnxscript/rewriter/ort_fusions/_core.py
index e0d9331065..5fbdba42f9 100644
--- a/onnxscript/rewriter/ort_fusions/_core.py
+++ b/onnxscript/rewriter/ort_fusions/_core.py
@@ -7,9 +7,8 @@
 import onnxscript.rewriter.ort_fusions.fused_matmul_rule_sets as fused_matmul_rule_sets
 import onnxscript.rewriter.ort_fusions.shape_optimization as shape_optimization
 from onnxscript.optimizer import optimize
-from onnxscript.rewriter import rewrite
+from onnxscript.rewriter import gemm_to_matmul_add, rewrite
 from onnxscript.rewriter.ort_fusions import (
-    # group_normalization_merge_silu,
     instance_to_group_normalization,
     softmax,
 )
@@ -38,7 +37,7 @@
     *instance_to_group_normalization.rules.rules,
     # NOTE: group normalization merge silu should be applied after instance to group normalization
     # *group_normalization_merge_silu.rules.rules,
-    *fused_matmul_rule_sets.fused_matmul_rule_sets().rules,
+    *fused_matmul_rule_sets.fused_matmul_rule_sets(),
 ]
 
 
@@ -130,7 +129,7 @@ def optimize_for_ort(
         - The optimized `ir.Model` after applying transformer-specific fusions.
         - A dictionary with a count of each of the fusions applied.
     """
-
+    rewrite(model, [gemm_to_matmul_add.rule])
     model, fusion_count = fuse_xformers(
         model,
         debug=debug,