[rewriter] Decouple llama rule sets and make API explicit (#2388)

This PR addresses the misleading naming and tangled organization of
rewrite rules by decoupling the `llama_rule_sets.py` module and creating
a more explicit API.

## Problem

The original `llama_rule_sets.py` contained general optimization rules
that weren't specific to Llama models, making the naming misleading. The
API didn't explicitly specify what rules were being applied, making it
unclear what optimizations were happening.

```python
# Before: Unclear what this does
from onnxscript.rewriter import llama_rule_sets
rules = llama_rule_sets.llama_p0_rule_set()  # What rules? Why "llama"? What's "p0"?
```

## Solution

### 1. Created `basic_rules.py` with explicit naming
- Moved all general optimization rules to a new `basic_rules.py` module
- Used descriptive function name: `basic_optimization_rules()`
- Added comprehensive documentation for each rule

### 2. Made API explicit for fine-grained control
```python
# New explicit API - users know exactly what they're getting
from onnxscript.rewriter import basic_rules

# Use all basic optimizations (recommended default)
rules = basic_rules.basic_optimization_rules()

# Or use specific individual rules
transpose_rule = basic_rules.transpose_identity_rule
cast_rule = basic_rules.cast_identity_rule

# Or create custom rule combinations
custom_rules = basic_rules.orp.RewriteRuleSet([
    basic_rules.transpose_identity_rule,
    basic_rules.cast_identity_rule,
])
```

### 3. Updated default rewriter to be explicit
```python
# Before (in rewriter/__init__.py)
*llama_rule_sets.llama_p0_rule_set().rules,

# After - much clearer what's being applied
*basic_rules.basic_optimization_rules().rules,
```

### 4. Maintained backward compatibility
- `llama_rule_sets.py` now serves as a compatibility wrapper
- All existing APIs continue to work with deprecation warnings
- Existing tests pass unchanged

## Available Rules

The new API provides access to these optimization rules:
- `cast_cast_rule` - Eliminates consecutive casts
- `cast_identity_rule` - Removes redundant casts
- `expand_identity_rule` - Removes no-op expands
- `reshape_reshape_rule` - Combines consecutive reshapes
- `slice_split_rule` - Converts slices to splits when beneficial
- `transpose_identity_rule` - Removes identity transposes
- `transpose_transpose_rule` - Combines consecutive transposes
- `unsqueeze_unsqueeze_rule` - Combines consecutive unsqueezes
- `squeeze_reshape_1d_rule` - Optimizes 1D squeeze+reshape patterns

## Migration

```python
# OLD (deprecated but still works)
from onnxscript.rewriter import llama_rule_sets
rules = llama_rule_sets.llama_p0_rule_set()

# NEW (recommended)
from onnxscript.rewriter import basic_rules
rules = basic_rules.basic_optimization_rules()
```

This change resolves the core issue by making the optimizer API
explicitly specify what rules are being applied, while providing users
with fine-grained control over optimization behavior.

Fixes #2128.

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---------

Signed-off-by: Justin Chu <[email protected]>
Co-authored-by: copilot-swe-agent[bot] <[email protected]>
Co-authored-by: justinchuby <[email protected]>
Co-authored-by: Justin Chu <[email protected]>

Author: Copilot

onnxscript/rewriter/__init__.py

@@ -15,11 +15,11 @@
 import onnxscript.ir.passes.common as common_passes
 from onnxscript import ir
 from onnxscript.rewriter import (
+    basic_rules,
     broadcast_to_matmul,
     cast_constant_of_shape,
     collapse_slices,
     gemm_to_matmul_add,
-    llama_rule_sets,
     no_op,
     pattern,
 )
@@ -31,7 +31,7 @@
     gemm_to_matmul_add.rule,  # type: ignore[has-type]
     *cast_constant_of_shape.rules.rules,
     *collapse_slices.rules.rules,
-    *llama_rule_sets.llama_p0_rule_set().rules,
+    *basic_rules.basic_optimization_rules().rules,
 )
 
 

onnxscript/rewriter/llama_rule_sets.py renamed to onnxscript/rewriter/basic_rules.py

@@ -1,5 +1,12 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+"""Basic rewrite rules for general optimization patterns.
+
+This module contains fundamental optimization rules that are generally applicable
+to most ONNX models, including cast elimination, transpose simplification,
+shape operation fusion, and other common patterns.
+"""
+
 from __future__ import annotations
 
 from typing import ClassVar, Sequence
@@ -271,6 +278,7 @@ def check(self, context, x, axes1, axes2) -> orp.MatchResult:
         return check_result
 
 
+# Create rule instances
 cast_cast_rule = CastCast.rule()
 cast_identity_rule = CastIdentity.rule()
 expand_identity_rule = ExpandIdentity.rule()
@@ -282,21 +290,28 @@ def check(self, context, x, axes1, axes2) -> orp.MatchResult:
 squeeze_reshape_1d_rule = SqueezeReshape.rule()
 
 
-def llama_p0_rule_set() -> orp.RewriteRuleSet:
-    """Returns a set of rules which should be applied
-    before any other one as they usually remove unnecessary computation
-    such as the multiplication by 1 or two consecutive transpose.
+def basic_optimization_rules() -> orp.RewriteRuleSet:
+    """Returns a set of basic optimization rules.
+
+    These rules perform fundamental optimizations such as:
+    - Eliminating redundant cast operations
+    - Simplifying consecutive operations of the same type
+    - Removing identity operations
+    - Optimizing shape manipulation operations
+
+    These rules are generally safe to apply as a first optimization pass
+    before other more specialized optimizations.
 
     Returns:
-        RewriteRuleSet
+        RewriteRuleSet: A collection of basic optimization rules
     """
     return orp.RewriteRuleSet(
         [
             cast_cast_rule,
             cast_identity_rule,
             expand_identity_rule,
             reshape_reshape_rule,
-            slice_split_rule,  # Affect collapse slices rules?
+            slice_split_rule,
             transpose_identity_rule,
             transpose_transpose_rule,
             unsqueeze_unsqueeze_rule,

onnxscript/rewriter/llama_rule_sets_test.py renamed to onnxscript/rewriter/basic_rules_test.py

@@ -12,7 +12,7 @@
 
 import onnxscript
 import onnxscript.onnx_types as ot
-import onnxscript.rewriter.llama_rule_sets as llama_rule_sets
+import onnxscript.rewriter.basic_rules as basic_rules
 from onnxscript import ir
 from onnxscript.onnx_opset import opset18
 
@@ -29,7 +29,7 @@ def _make_model(*args, **kwargs) -> ir.Model:
     return ir.serde.deserialize_model(onnx.helper.make_model(*args, **kwargs))
 
 
-class LlamaRuleSetsTest(unittest.TestCase):
+class BasicRulesTest(unittest.TestCase):
     def _get_random_inputs(self, model: onnx.ModelProto) -> dict[str, Any]:
         feeds: dict[str, Any] = {}
         for i in model.graph.input:
@@ -97,8 +97,8 @@ def _check_model(
             ),
         ]
     )
-    def test_llama_p0_rule_set_identity(self, _: str, model: ir.Model):
-        rule_set = llama_rule_sets.llama_p0_rule_set()
+    def test_basic_optimization_rules_identity(self, _: str, model: ir.Model):
+        rule_set = basic_rules.basic_optimization_rules()
         model_proto = ir.serde.serialize_model(model)
         rule_set.apply_to_model(model)
         rewritten_model = ir.serde.serialize_model(model)
@@ -125,8 +125,8 @@ def test_llama_p0_rule_set_identity(self, _: str, model: ir.Model):
             ),
         ]
     )
-    def test_llama_p0_rule_set_transpose_transpose(self, _: str, model: ir.Model):
-        rule_set = llama_rule_sets.llama_p0_rule_set()
+    def test_basic_optimization_rules_transpose_transpose(self, _: str, model: ir.Model):
+        rule_set = basic_rules.basic_optimization_rules()
         model_proto = ir.serde.serialize_model(model)
         rule_set.apply_to_model(model)
         rewritten_model = ir.serde.serialize_model(model)
@@ -152,17 +152,16 @@ def cast_cast_model(x):
             ("float16_float_float16", ot.FLOAT16, ot.FLOAT, ot.FLOAT16),
         ]
     )
-    def test_llama_p0_rule_set_cast_cast(self, _: str, type1, type2, type3):
-        rule_set = llama_rule_sets.cast_cast_rule
+    def test_cast_cast_rule(self, _: str, type1, type2, type3):
+        rule = basic_rules.cast_cast_rule
         model_proto = self._double_cast_model(type1, type2, type3)
         model = ir.serde.deserialize_model(model_proto)
-        rule_set.apply_to_model(model)
-        rewritten_model = ir.serde.serialize_model(model)
+        rule.apply_to_model(model)
+        _rewritten_model = ir.serde.serialize_model(model)
 
         self.assertEqual(["Cast"], [n.op_type for n in model.graph])
         # TODO: (random) fp16 inputs
         # self._check_model(model_proto, rewritten_model, atol=1e-2)
-        del rewritten_model  # to avoid unused variable warning
 
     @parameterized.parameterized.expand(
         [
@@ -172,8 +171,8 @@ def test_llama_p0_rule_set_cast_cast(self, _: str, type1, type2, type3):
             ),
         ]
     )
-    def test_llama_p0_rule_set_cast_identity(self, _: str, model: ir.Model):
-        rule_set = llama_rule_sets.llama_p0_rule_set()
+    def test_cast_identity_rule(self, _: str, model: ir.Model):
+        rule_set = basic_rules.basic_optimization_rules()
         model_proto = ir.serde.serialize_model(model)
         rule_set.apply_to_model(model)
         rewritten_model = ir.serde.serialize_model(model)
@@ -226,10 +225,10 @@ def test_llama_p0_rule_set_cast_identity(self, _: str, model: ir.Model):
             ),
         ]
     )
-    def test_llama_p0_rule_set_expand_identity(
+    def test_expand_identity_rule(
         self, _: str, model: ir.Model, expected_nodes: tuple[str, ...]
     ):
-        rule_set = llama_rule_sets.llama_p0_rule_set()
+        rule_set = basic_rules.basic_optimization_rules()
         model_proto = ir.serde.serialize_model(model)
         rule_set.apply_to_model(model)
         rewritten_model = ir.serde.serialize_model(model)
@@ -310,8 +309,8 @@ def test_llama_p0_rule_set_expand_identity(
             ),
         ]
     )
-    def test_llama_p0_rule_set_unsqueeze_unsqueeze(self, _: str, model: ir.Model):
-        rule_set = llama_rule_sets.llama_p0_rule_set()
+    def test_unsqueeze_unsqueeze_rule(self, _: str, model: ir.Model):
+        rule_set = basic_rules.basic_optimization_rules()
         model_proto = ir.serde.serialize_model(model)
         rule_set.apply_to_model(model)
         rewritten_model = ir.serde.serialize_model(model)
@@ -369,8 +368,8 @@ def test_llama_p0_rule_set_unsqueeze_unsqueeze(self, _: str, model: ir.Model):
             ),
         ]
     )
-    def test_llama_p0_rule_set_reshape_reshape(self, _: str, model: ir.Model):
-        rule_set = llama_rule_sets.llama_p0_rule_set()
+    def test_reshape_reshape_rule(self, _: str, model: ir.Model):
+        rule_set = basic_rules.basic_optimization_rules()
         model_proto = ir.serde.serialize_model(model)
         rule_set.apply_to_model(model)
         rewritten_model = ir.serde.serialize_model(model)
@@ -379,7 +378,7 @@ def test_llama_p0_rule_set_reshape_reshape(self, _: str, model: ir.Model):
         self._check_model(model_proto, rewritten_model)
 
     @classmethod
-    def _slides_split_models(cls):
+    def _slices_split_models(cls):
         models = [
             _make_model(
                 onnx.helper.make_graph(
@@ -418,18 +417,18 @@ 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():
+    def test_slices_split_rule(self):
+        for model_proto in self._slices_split_models():
             ir_model = ir.serde.deserialize_model(model_proto)
-            rule_set = llama_rule_sets.llama_p0_rule_set()
+            rule_set = basic_rules.basic_optimization_rules()
             rule_set.apply_to_model(ir_model)
             rewritten_model = ir.serde.serialize_model(ir_model)
 
             self.assertEqual(["Split"], [n.op_type for n in rewritten_model.graph.node])
             self._check_model(model_proto, rewritten_model)
 
-    def test_squeeze_reshape_1d_test(self):
-        rule = llama_rule_sets.squeeze_reshape_1d_rule
+    def test_squeeze_reshape_1d_rule(self):
+        rule = basic_rules.squeeze_reshape_1d_rule
 
         def check(model_script, expected_count) -> None:
             model_proto = model_script.to_model_proto()