Merge branch 'main' of https://github.com/sdpython/onnx-diagnostic into exl

Author: xadupre

.github/workflows/check-release.yml

@@ -16,8 +16,8 @@ jobs:
       matrix:
         os: [ubuntu-latest, macOS-latest, windows-latest]
         python: ['3.11', '3.12']
-        transformers: ['4.48.3', '4.51.3', 'main']
-        torch: ['2.6', 'main']
+        transformers: ['4.48.3', '4.52.4', 'main']
+        torch: ['2.6', '2.7', 'main']
 
     steps:
 

.github/workflows/ci.yml

@@ -16,19 +16,19 @@ jobs:
       matrix:
         os: [ubuntu-latest]
         python: ['3.10', '3.11', '3.12', '3.13']
-        transformers: ['4.48.3', '4.51.3', '4.52.3', 'main']
+        transformers: ['4.48.3', '4.51.3', '4.52.4', 'main']
         torch: ['2.7', 'main']
         exclude:
           - python: '3.10'
             torch: 'main'
           - python: '3.11'
             torch: 'main'
           - python: '3.10'
-            transformers: '4.52.3'
+            transformers: '4.52.4'
           - python: '3.10'
             transformers: 'main'
           - python: '3.11'
-            transformers: '4.52.3'
+            transformers: '4.52.4'
           - python: '3.11'
             transformers: 'main'
           - python: '3.13'

_doc/api/reference/torch_ops/index.rst

@@ -18,10 +18,10 @@ onnx_diagnostic.reference.torch_ops
     shape_ops
     unary_ops
 
-OpRun
-+++++
+OpRunKernel
++++++++++++
 
-.. autoclass:: onnx_diagnostic.reference.torch_ops.OpRun
+.. autoclass:: onnx_diagnostic.reference.torch_ops.OpRunKernel
     :members:
 
 OpRunTensor

_doc/index.rst

@@ -206,6 +206,7 @@ The function replaces dynamic dimensions defined as strings by
 Older versions
 ++++++++++++++
 
+* `0.6.2 <../v0.6.2/index.html>`_
 * `0.6.1 <../v0.6.1/index.html>`_
 * `0.6.0 <../v0.6.0/index.html>`_
 * `0.5.0 <../v0.5.0/index.html>`_

_unittests/ut_reference/test_torch_onnx_evaluator.py

@@ -8,7 +8,7 @@
 from onnx_diagnostic.helpers.onnx_helper import from_array_extended
 from onnx_diagnostic.helpers.torch_helper import onnx_dtype_to_torch_dtype
 from onnx_diagnostic.reference import ExtendedReferenceEvaluator, TorchOnnxEvaluator
-from onnx_diagnostic.reference.torch_ops import OpRun, OpRunTensor
+from onnx_diagnostic.reference.torch_ops import OpRunKernel, OpRunTensor
 from onnx_diagnostic.reference.torch_evaluator import get_kernels
 
 
@@ -1373,7 +1373,7 @@ def test_tile(self):
         )
 
     def test_custom_kernels(self):
-        class LayerNormalizationOrt(OpRun):
+        class LayerNormalizationOrt(OpRunKernel):
             "LayerNormalization"
 
             _shared = [0]

onnx_diagnostic/__init__.py

@@ -3,5 +3,5 @@
 Functions, classes to dig into a model when this one is right, slow, wrong...
 """
 
-__version__ = "0.6.1"
+__version__ = "0.6.2"
 __author__ = "Xavier Dupré"

onnx_diagnostic/reference/torch_evaluator.py

@@ -9,7 +9,7 @@
 
 
 @functools.lru_cache
-def get_kernels() -> Dict[Tuple[str, str, int], type[torch_ops.OpRun]]:
+def get_kernels() -> Dict[Tuple[str, str, int], type[torch_ops.OpRunKernel]]:
     """
     Retrieves all the available kernels class :class:`TorchOnnxEvaluator`
     can use. The full list is the following.
@@ -28,7 +28,7 @@ def get_kernels() -> Dict[Tuple[str, str, int], type[torch_ops.OpRun]]:
     """
     res = {}
     for _k, v in torch_ops.__dict__.items():
-        if isinstance(v, type) and issubclass(v, torch_ops.OpRun) and "_" in v.__name__:
+        if isinstance(v, type) and issubclass(v, torch_ops.OpRunKernel) and "_" in v.__name__:
             name, version = v.__name__.split("_")
             domain = getattr(v, "domain", "")
             res[domain, name, int(version)] = v
@@ -161,11 +161,11 @@ class TorchOnnxEvaluator:
         from onnx_diagnostic.helpers import string_type
         from onnx_diagnostic.helpers.torch_helper import onnx_dtype_to_torch_dtype
         from onnx_diagnostic.reference import TorchOnnxEvaluator
-        from onnx_diagnostic.reference.torch_ops import OpRun, OpRunTensor
+        from onnx_diagnostic.reference.torch_ops import OpRunKernel, OpRunTensor
 
         TFLOAT16 = onnx.TensorProto.FLOAT16
 
-        class LayerNormalizationOrt(OpRun):
+        class LayerNormalizationOrt(OpRunKernel):
             "LayerNormalization based on onnxruntime"
 
             def __init__(self, node: onnx.NodeProto, version=None):
@@ -284,11 +284,11 @@ def __init__(
         opsets: Optional[Dict[str, int]] = None,
         local_functions: Optional[Dict[Tuple[str, str], "TorchOnnxEvaluator"]] = None,
         verbose: int = 0,
-        custom_kernels: Optional[Dict[Tuple[str, str], type[torch_ops.OpRun]]] = None,
+        custom_kernels: Optional[Dict[Tuple[str, str], type[torch_ops.OpRunKernel]]] = None,
     ):
         self.providers = providers
         self.constants: Dict[str, torch.Tensor] = {}
-        self.kernels: List[Optional[torch_ops.OpRun]] = []
+        self.kernels: List[Optional[torch_ops.OpRunKernel]] = []
         self.functions = local_functions.copy() if local_functions else {}
         self.CPU = torch.tensor([0]).to("cpu").device
         self.verbose = verbose

onnx_diagnostic/reference/torch_ops/__init__.py

@@ -1,4 +1,4 @@
-from ._op_run import OpRun, OpRunFunction, OpRunSequence, OpRunTensor, OpRunValue
+from ._op_run import OpRunKernel, OpRunFunction, OpRunSequence, OpRunTensor, OpRunValue
 from .access_ops import Gather_1, ScatterND_16, Slice_13
 from .binary_ops import (
     And_1,

onnx_diagnostic/reference/torch_ops/_op_run.py

@@ -167,7 +167,7 @@ def string_type(self) -> str:
         return string_type(self.sequence, with_shape=True)
 
 
-class OpRun:
+class OpRunKernel:
     """
     Main class. Every kernel should inherit from it.
     It does not copy the proto.
@@ -305,7 +305,7 @@ def same_device(self, *tensors: torch.Tensor) -> Tuple[torch.Tensor, ...]:
         return tuple(t.to(device) for t in tensors)
 
 
-class OpRunFunction(OpRun):
+class OpRunFunction(OpRunKernel):
     """
     Defines a kernel based on a local functions.
     """

onnx_diagnostic/reference/torch_ops/access_ops.py

@@ -1,10 +1,10 @@
 from typing import Optional
 import onnx
 import torch
-from . import OpRun, OpRunTensor
+from . import OpRunKernel, OpRunTensor
 
 
-class Gather_1(OpRun):
+class Gather_1(OpRunKernel):
     "Gather"
 
     def __init__(self, node: onnx.NodeProto, version: Optional[int] = None):
@@ -21,7 +21,7 @@ def run(self, x, indices):
         return OpRunTensor(x.tensor[tuple(ind)])
 
 
-class ScatterND_16(OpRun):
+class ScatterND_16(OpRunKernel):
     "ScatterND"
 
     def __init__(self, node: onnx.NodeProto, version: Optional[int] = None):
@@ -54,7 +54,7 @@ def run(
         return OpRunTensor(output)
 
 
-class Slice_13(OpRun):
+class Slice_13(OpRunKernel):
     "Slice"
 
     def run(