fix unittest

Author: xadupre

_unittests/ut_helpers/test_helper.py

@@ -584,7 +584,7 @@ def test_flatten_encoder_decoder_cache(self):
         self.assertIn("EncoderDecoderCache", s)
 
     def test_string_typeçconfig(self):
-        conf = get_pretrained_config("microsoft/phi-2")
+        conf = get_pretrained_config("microsoft/phi-2", use_only_preinstalled=True)
         s = string_type(conf)
         self.assertStartsWith("PhiConfig(**{", s)
 

_unittests/ut_torch_models/test_hghub_api.py

@@ -72,14 +72,16 @@ def test_task_from_id_long(self):
     @requires_torch("2.7")
     @hide_stdout()
     def test_get_pretrained_config(self):
-        conf = get_pretrained_config("microsoft/phi-2")
+        conf = get_pretrained_config("microsoft/phi-2", use_only_preinstalled=True)
         self.assertNotEmpty(conf)
 
     @requires_transformers("4.50")
     @requires_torch("2.7")
     @hide_stdout()
     def test_get_pretrained_config_options(self):
-        conf = get_pretrained_config("microsoft/phi-2", num_key_value_heads=16)
+        conf = get_pretrained_config(
+            "microsoft/phi-2", num_key_value_heads=16, use_only_preinstalled=True
+        )
         self.assertNotEmpty(conf)
         self.assertEqual(conf.num_key_value_heads, 16)
 

_unittests/ut_torch_models/test_hghub_model.py

@@ -129,11 +129,11 @@ def _diff(c1, c2):
                 try:
                     model(**inputs)
                 except Exception as e:
-                    diff = _diff(get_pretrained_config(mid), data["configuration"])
+                    cf = get_pretrained_config(mid, use_only_preinstalled=True)
+                    diff = _diff(cf, data["configuration"])
                     raise AssertionError(
                         f"Computation failed due to {e}.\n--- pretrained\n"
-                        f"{pprint.pformat(get_pretrained_config(mid))}\n"
-                        f"--- modified\n{data['configuration']}\n"
+                        f"{pprint.pformat(cf)}\n--- modified\n{data['configuration']}\n"
                         f"--- diff\n{diff}"
                     ) from e
                 # different expected value for different version of transformers

_unittests/ut_torch_models/test_validate_whole_models.py

@@ -283,7 +283,7 @@ def test_validate_phi35_mini_instruct(self):
     @ignore_warnings(FutureWarning)
     @requires_transformers("4.51")
     def test_validate_phi35_4k_mini_instruct(self):
-        mid = "microsoft/Phi-3.5-mini-4k-instruct"
+        mid = "microsoft/Phi-3-mini-4k-instruct"
         summary, data = validate_model(
             mid,
             do_run=True,

onnx_diagnostic/torch_models/hghub/hub_api.py

@@ -2,6 +2,7 @@
 import functools
 import json
 import os
+import pprint
 from typing import Any, Dict, List, Optional, Union
 import transformers
 from huggingface_hub import HfApi, model_info, hf_hub_download
@@ -33,10 +34,14 @@ def _retrieve_cached_configurations() -> Dict[str, transformers.PretrainedConfig
     return res
 
 
-def get_cached_configuration(name: str, **kwargs) -> Optional[transformers.PretrainedConfig]:
+def get_cached_configuration(
+    name: str, exc: bool = False, **kwargs
+) -> Optional[transformers.PretrainedConfig]:
     """
     Returns cached configuration to avoid having to many accesses to internet.
     It returns None if not Cache. The list of cached models follows.
+    If *exc* is True or if environment variable ``NOHTTP`` is defined,
+    the function raises an exception if *name* is not found.
 
     .. runpython::
 
@@ -54,8 +59,9 @@ def get_cached_configuration(name: str, **kwargs) -> Optional[transformers.Pretr
             conf = copy.deepcopy(conf)
             update_config(conf, kwargs)
         return conf
-    if os.environ.get("NOHTTP", ""):
-        raise AssertionError(f"Unable to find {name!r} in {sorted(cached)}")
+    assert not exc and not os.environ.get(
+        "NOHTTP", ""
+    ), f"Unable to find {name!r} in {pprint.pformat(sorted(cached))}"
     return None
 
 
@@ -64,6 +70,7 @@ def get_pretrained_config(
     trust_remote_code: bool = True,
     use_preinstalled: bool = True,
     subfolder: Optional[str] = None,
+    use_only_preinstalled: bool = False,
     **kwargs,
 ) -> Any:
     """
@@ -77,13 +84,20 @@ def get_pretrained_config(
         :func:`get_cached_configuration`, the cached list is mostly for
         unit tests
     :param subfolder: subfolder for the given model id
+    :param use_only_preinstalled: if True, raises an exception if not preinstalled
     :param kwargs: additional kwargs
     :return: a configuration
     """
     if use_preinstalled:
-        conf = get_cached_configuration(model_id, subfolder=subfolder, **kwargs)
+        conf = get_cached_configuration(
+            model_id, exc=use_only_preinstalled, subfolder=subfolder, **kwargs
+        )
         if conf is not None:
             return conf
+    assert not use_only_preinstalled, (
+        f"Inconsistencies: use_only_preinstalled={use_only_preinstalled}, "
+        f"use_preinstalled={use_preinstalled!r}"
+    )
     if subfolder:
         try:
             return transformers.AutoConfig.from_pretrained(

onnx_diagnostic/torch_models/hghub/model_inputs.py

@@ -26,6 +26,7 @@ def get_untrained_model_with_inputs(
     use_preinstalled: bool = True,
     add_second_input: bool = False,
     subfolder: Optional[str] = None,
+    use_only_preinstalled: bool = True,
 ) -> Dict[str, Any]:
     """
     Gets a non initialized model similar to the original model
@@ -46,6 +47,7 @@ def get_untrained_model_with_inputs(
     :param add_second_input: provides a second inputs to check a model
         supports different shapes
     :param subfolder: subfolder to use for this model id
+    :param use_only_preinstalled: use only preinstalled version
     :return: dictionary with a model, inputs, dynamic shapes, and the configuration,
         some necessary rewriting as well
 
@@ -74,6 +76,7 @@ def get_untrained_model_with_inputs(
         config = get_pretrained_config(
             model_id,
             use_preinstalled=use_preinstalled,
+            use_only_preinstalled=use_only_preinstalled,
             subfolder=subfolder,
             **(model_kwargs or {}),
         )