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Merged
merged 5 commits into from
Apr 24, 2025

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@shubhambhokare1 shubhambhokare1 commented Apr 23, 2025

This pull request introduces new fusion patterns and enhancements to the ONNXScript rewriter module, focusing on optimization and test coverage improvements. The key changes include adding support for BiasGelu and additional ErfGelu patterns, extending SkipLayerNormalization to handle bias addition, and updating test utilities for better accuracy validation.

New fusion patterns:

  • BiasGelu Fusion: Added a new fusion pattern for BiasGelu operations, including its implementation in onnxscript/rewriter/ort_fusions/bias_gelu.py and integration into the fuse_xformers pipeline. A corresponding unit test was added to validate the functionality. [1] [2] [3] [4]

  • ErfGelu Enhancements: Introduced a second pattern for ErfGelu fusion and refactored the corresponding implementation. The file was renamed from erfgelu.py to ort_fusions/erfgelu.py for consistency. [1] [2] [3] [4]

Enhancements to existing fusions:

  • SkipLayerNormalization with Bias: Extended the SkipLayerNormalization fusion to support an additional bias term. This includes new patterns and rewrite rules in onnxscript/rewriter/ort_fusions/skip_normalization.py.

Test utility updates:

  • Tolerance Adjustment: Increased the relative and absolute tolerances in assert_allclose to 1e-3 for better handling of numerical discrepancies in tests.

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codecov bot commented Apr 23, 2025

❌ 4 Tests Failed:

Tests completed Failed Passed Skipped
14440 4 14436 1879
View the top 3 failed test(s) by shortest run time
onnxscript.backend.onnx_export_test.TestOnnxBackEnd::test_export2python_produces_correct_onnx_script_model_0335_test_div_example
Stack Traces | 0.004s run time
onnxscript\backend\onnx_export_test.py:137: in extract_functions
    mod = importlib.import_module(import_name)
C:\hostedtoolcache\windows\Python\3.12.10\x64\Lib\importlib\__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
E   ModuleNotFoundError: No module named 'tests.onnx_backend_test_code.test_div_example'

The above exception was the direct cause of the following exception:
.nox\test\Lib\site-packages\parameterized\parameterized.py:620: in standalone_func
    return func(*(a + p.args), **p.kwargs, **kw)
onnxscript\backend\onnx_export_test.py:271: in test_export2python_produces_correct_onnx_script_model
    functions = extract_functions(backend_test.name, code, self.test_folder)
onnxscript\backend\onnx_export_test.py:139: in extract_functions
    raise AssertionError(
E   AssertionError: Unable to import 'tests.onnx_backend_test_code.test_div_example' (e=No module named 'tests.onnx_backend_test_code.test_div_example') (file: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_div_example.py', absolute path: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_div_example.py', current folder: D:\a\onnxscript\onnxscript
E   ---- CONTENT --
E   import numpy
E   from onnx import TensorProto
E   from onnx.helper import make_tensor
E   from onnxscript import script, external_tensor
E   from onnxscript.values import Opset
E   from onnxscript.onnx_types import FLOAT
E   from onnxscript.onnx_opset import opset14
E   
E   @script()
E   def bck_test_div_example(x: FLOAT[2], y: FLOAT[2]) -> (FLOAT[2]):
E       z = opset14.Div(x, y)
E       return z
onnxscript.backend.onnx_export_test.TestOnnxBackEnd::test_export2python_produces_correct_onnx_script_model_0418_test_greater_bcast
Stack Traces | 0.006s run time
onnxscript\backend\onnx_export_test.py:137: in extract_functions
    mod = importlib.import_module(import_name)
C:\hostedtoolcache\windows\Python\3.12.10\x64\Lib\importlib\__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
E   ModuleNotFoundError: No module named 'tests.onnx_backend_test_code.test_greater_bcast'

The above exception was the direct cause of the following exception:
.nox\test\Lib\site-packages\parameterized\parameterized.py:620: in standalone_func
    return func(*(a + p.args), **p.kwargs, **kw)
onnxscript\backend\onnx_export_test.py:271: in test_export2python_produces_correct_onnx_script_model
    functions = extract_functions(backend_test.name, code, self.test_folder)
onnxscript\backend\onnx_export_test.py:139: in extract_functions
    raise AssertionError(
E   AssertionError: Unable to import 'tests.onnx_backend_test_code.test_greater_bcast' (e=No module named 'tests.onnx_backend_test_code.test_greater_bcast') (file: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_greater_bcast.py', absolute path: 'D:\\a\\onnxscript\\onnxscript\\tests\\onnx_backend_test_code\\test_greater_bcast.py', current folder: D:\a\onnxscript\onnxscript
E   ---- CONTENT --
E   import numpy
E   from onnx import TensorProto
E   from onnx.helper import make_tensor
E   from onnxscript import script, external_tensor
E   from onnxscript.values import Opset
E   from onnxscript.onnx_types import BOOL, FLOAT
E   from onnxscript.onnx_opset import opset13
E   
E   @script()
E   def bck_test_greater_bcast(x: FLOAT[3,4,5], y: FLOAT[5]) -> (BOOL[3,4,5]):
E       greater = opset13.Greater(x, y)
E       return greater
onnxscript.backend.onnx_export_test.TestOnnxBackEnd::test_export2python_produces_correct_onnx_script_model_0125_test_ai_onnx_ml_tree_ensemble_set_membership
Stack Traces | 0.007s run time
onnxscript/converter.py:460: in _eval_constant_expr
    return eval(cpl, self.globals, locals)  # pylint: disable=eval-used
E   NameError: name 'nan' is not defined

The above exception was the direct cause of the following exception:
..../test_ort_nightly/lib/python3.11.../site-packages/parameterized/parameterized.py:620: in standalone_func
    return func(*(a + p.args), **p.kwargs, **kw)
onnxscript/backend/onnx_export_test.py:271: in test_export2python_produces_correct_onnx_script_model
    functions = extract_functions(backend_test.name, code, self.test_folder)
onnxscript/backend/onnx_export_test.py:137: in extract_functions
    mod = importlib.import_module(import_name)
.../Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/importlib/__init__.py:126: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1204: in _gcd_import
    ???
<frozen importlib._bootstrap>:1176: in _find_and_load
    ???
<frozen importlib._bootstrap>:1147: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:690: in _load_unlocked
    ???
..../test_ort_nightly/lib/python3.11.../_pytest/assertion/rewrite.py:185: in exec_module
    exec(co, module.__dict__)
tests/onnx_backend_test_code/test_ai_onnx_ml_tree_ensemble_set_membership.py:9: in <module>
    @script()
onnxscript/main.py:95: in transform
    result = script_check(f_ast, opset, env, src, default_opset=default_opset)
onnxscript/main.py:39: in script_check
    return convert.translate_function_def(f)
onnxscript/converter.py:1452: in translate_function_def
    fn_ir = self._translate_function_def_common(stmt)
onnxscript/converter.py:1439: in _translate_function_def_common
    self._translate_stmt(s, index_of_stmt=i)
onnxscript/converter.py:961: in _translate_stmt
    return self._translate_assign_stmt(node)
onnxscript/converter.py:1048: in _translate_assign_stmt
    assign(lhs, rhs)
onnxscript/converter.py:992: in assign
    t = self._translate_expr(rhs, lhs).name
onnxscript/converter.py:546: in _translate_expr
    r = self._translate_call_expr(node)
onnxscript/converter.py:825: in _translate_call_expr
    attrs = [
onnxscript/converter.py:826: in <listcomp>
    self._translate_attr(x, y, callee.op_schema.attributes[x])
onnxscript/converter.py:510: in _translate_attr
    val = self._eval_constant_expr(expr)
onnxscript/converter.py:462: in _eval_constant_expr
    raise NameError(
E   NameError: ERROR: Missing names, globals contains ['__name__', '__doc__', '__package__', '__loader__', '__spec__', '__file__', '__cached__', '__builtins__', '@py_builtins', '@pytest_ar', 'numpy', 'TensorProto', 'make_tensor', 'script', 'external_tensor', 'Opset', 'FLOAT', 'ai_onnx_ml5'], locals [].
E   at: Function 'bck_test_ai_onnx_ml_tree_ensemble_set_membership', line 3
E       Y = ai_onnx_ml5.TreeEnsemble(X, aggregate_function=1, leaf_targetids=[0, 1, 2, 3], leaf_weights=make_tensor("value", 1, dims=[4], vals=[1.0, 10.0, 1000.0, 100.0]), membership_values=make_tensor("value", 1, dims=[8], vals=[1.2000000476837158, 3.700000047683716, 8.0, 9.0, nan, 12.0, 7.0, nan]), n_targets=4, nodes_falseleafs=[1, 0, 1], nodes_falsenodeids=[2, 2, 3], nodes_featureids=[0, 0, 0], nodes_modes=make_tensor("value", 2, dims=[3], vals=[0, 6, 6]), nodes_splits=make_tensor("value", 1, dims=[3], vals=[11.0, 232344.0, nan]), nodes_trueleafs=[0, 1, 1], nodes_truenodeids=[1, 0, 1], post_transform=0, tree_roots=[0])
E                                                                                                                                                                                             ^

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@justinchuby justinchuby requested a review from Copilot April 23, 2025 21:23
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Pull Request Overview

This PR adds new fusion rules for BiasGelu, Erfgelu, and updates SkipLayerNormalization fusion to support an additional output. Key changes include:

  • Updating SkipLayerNormalization to return a new output (skip_sum) and adding a corresponding fusion rule for Add+SkipLayerNormalization.
  • Implementing a new BiasGelu fusion along with its test.
  • Updating Erfgelu rewriting with two pattern functions for better matching capability.

Reviewed Changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated no comments.

Show a summary per file
File Description
onnxscript/rewriter/ort_fusions/skip_normalization.py Modified SkipLayerNormalization to return an extra output and added support for bias fusion.
onnxscript/rewriter/ort_fusions/bias_gelu_test.py Added a unit test to verify the BiasGelu fusion.
onnxscript/rewriter/ort_fusions/bias_gelu.py Implemented the BiasGelu fusion rule.
onnxscript/rewriter/ort_fusions/_core.py Updated the fusion count to include BiasGelu fusion.
onnxscript/rewriter/erfgelu.py Introduced two Erfgelu pattern rules to enhance rewrite flexibility.
onnxscript/rewriter/init.py Updated to include the new Erfgelu fusion rule.
Comments suppressed due to low confidence (1)

onnxscript/rewriter/erfgelu.py:9

  • [nitpick] The current names 'erf_gelu_pattern_1' and 'erf_gelu_pattern_2' are not very descriptive. Consider renaming them to indicate the specific matching or transformation behavior they implement.
def erf_gelu_pattern_1(op, x):

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@gramalingam gramalingam left a comment

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Just a minor suggestion to use onnxscript for test-case model-proto

@shubhambhokare1 shubhambhokare1 force-pushed the sbhokare/gelu-skip-fusions branch from 82a891f to d65da68 Compare April 24, 2025 21:13
@shubhambhokare1 shubhambhokare1 merged commit f5327f8 into main Apr 24, 2025
25 of 29 checks passed
@shubhambhokare1 shubhambhokare1 deleted the sbhokare/gelu-skip-fusions branch April 24, 2025 22:15
shubhambhokare1 added a commit that referenced this pull request May 7, 2025
Add fusion rules to support the optimization of Whisper models.

Fusions added:

- Basic Fusions:
    * additional pattern for erfgelu [moved to #2222]
- SkipLayerNorm:
    * #2259 
    * Fusion patterns where skip_sum is also an output
    * Bias + SkipLayerNorm -> SkipLayerNorm (with bias) [moved to #2222]
- BiasGelu Fusion [moved to #2222]
- SDPA:
    * Support for pattern where only q is pre-scaled
- MHA:
    * Patterns with/without past/present keys/values
    * Patterns with non-rotary embeddings
    * Patterns with/without mask
    * Patterns with cross-attention (only for past key/value patterns)
 - MHA Bias Fusion:
* Bias was offloaded to Attention fusion previously, this fusion fixes
that
 - Attention:
    * Patterns where Q, K and V do not come from slicing

TODO:
- [x]  Fix SDPA singular prescale case, due to lost shape information
- [x] - Enable check conditions when #2210 is merged
- [x] - Improve/Rewrite whisper model test case to be similar to that of
smollm (for eg)
- [x] - Fix failing test cases to account for new patterns
- [x] - Add isolated test cases for new fusions like BiasGelu,
SkipLayerNorm etc
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3 participants