Generate: unset GenerationConfig parameters do not raise warning (#29119

)

src/transformers/generation/configuration_utils.py

@@ -271,7 +271,6 @@ class GenerationConfig(PushToHubMixin):
 
     def __init__(self, **kwargs):
         # Parameters that control the length of the output
-        # if the default `max_length` is updated here, make sure to update the `generate` tests following https://github.com/huggingface/transformers/pull/25030
         self.max_length = kwargs.pop("max_length", 20)
         self.max_new_tokens = kwargs.pop("max_new_tokens", None)
         self.min_length = kwargs.pop("min_length", 0)
@@ -407,32 +406,34 @@ def validate(self, is_init=False):
                 "used in sample-based generation modes. You should set `do_sample=True` or unset `{flag_name}`."
                 + fix_location
             )
-            if self.temperature != 1.0:
+            if self.temperature is not None and self.temperature != 1.0:
                 warnings.warn(
                     greedy_wrong_parameter_msg.format(flag_name="temperature", flag_value=self.temperature),
                     UserWarning,
                 )
-            if self.top_p != 1.0:
+            if self.top_p is not None and self.top_p != 1.0:
                 warnings.warn(
                     greedy_wrong_parameter_msg.format(flag_name="top_p", flag_value=self.top_p),
                     UserWarning,
                 )
-            if self.typical_p != 1.0:
+            if self.typical_p is not None and self.typical_p != 1.0:
                 warnings.warn(
                     greedy_wrong_parameter_msg.format(flag_name="typical_p", flag_value=self.typical_p),
                     UserWarning,
                 )
-            if self.top_k != 50 and self.penalty_alpha is None:  # contrastive search uses top_k
+            if (
+                self.top_k is not None and self.top_k != 50 and self.penalty_alpha is None
+            ):  # contrastive search uses top_k
                 warnings.warn(
                     greedy_wrong_parameter_msg.format(flag_name="top_k", flag_value=self.top_k),
                     UserWarning,
                 )
-            if self.epsilon_cutoff != 0.0:
+            if self.epsilon_cutoff is not None and self.epsilon_cutoff != 0.0:
                 warnings.warn(
                     greedy_wrong_parameter_msg.format(flag_name="epsilon_cutoff", flag_value=self.epsilon_cutoff),
                     UserWarning,
                 )
-            if self.eta_cutoff != 0.0:
+            if self.eta_cutoff is not None and self.eta_cutoff != 0.0:
                 warnings.warn(
                     greedy_wrong_parameter_msg.format(flag_name="eta_cutoff", flag_value=self.eta_cutoff),
                     UserWarning,
@@ -453,21 +454,21 @@ def validate(self, is_init=False):
                     single_beam_wrong_parameter_msg.format(flag_name="early_stopping", flag_value=self.early_stopping),
                     UserWarning,
                 )
-            if self.num_beam_groups != 1:
+            if self.num_beam_groups is not None and self.num_beam_groups != 1:
                 warnings.warn(
                     single_beam_wrong_parameter_msg.format(
                         flag_name="num_beam_groups", flag_value=self.num_beam_groups
                     ),
                     UserWarning,
                 )
-            if self.diversity_penalty != 0.0:
+            if self.diversity_penalty is not None and self.diversity_penalty != 0.0:
                 warnings.warn(
                     single_beam_wrong_parameter_msg.format(
                         flag_name="diversity_penalty", flag_value=self.diversity_penalty
                     ),
                     UserWarning,
                 )
-            if self.length_penalty != 1.0:
+            if self.length_penalty is not None and self.length_penalty != 1.0:
                 warnings.warn(
                     single_beam_wrong_parameter_msg.format(flag_name="length_penalty", flag_value=self.length_penalty),
                     UserWarning,
@@ -491,7 +492,7 @@ def validate(self, is_init=False):
                     raise ValueError(
                         constrained_wrong_parameter_msg.format(flag_name="do_sample", flag_value=self.do_sample)
                     )
-                if self.num_beam_groups != 1:
+                if self.num_beam_groups is not None and self.num_beam_groups != 1:
                     raise ValueError(
                         constrained_wrong_parameter_msg.format(
                             flag_name="num_beam_groups", flag_value=self.num_beam_groups
@@ -1000,6 +1001,9 @@ def update(self, **kwargs):
                 setattr(self, key, value)
                 to_remove.append(key)
 
-        # remove all the attributes that were updated, without modifying the input dict
+        # Confirm that the updated instance is still valid
+        self.validate()
+
+        # Remove all the attributes that were updated, without modifying the input dict
         unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
         return unused_kwargs

src/transformers/generation/flax_utils.py

@@ -330,7 +330,6 @@ def generate(
 
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
-        generation_config.validate()
         self._validate_model_kwargs(model_kwargs.copy())
 
         logits_processor = logits_processor if logits_processor is not None else FlaxLogitsProcessorList()

src/transformers/generation/tf_utils.py

@@ -736,7 +736,6 @@ def generate(
 
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
-        generation_config.validate()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # 2. Cast input dtypes to tf.int32 unless they're floats (which happens for some image models)

src/transformers/generation/utils.py

@@ -1347,7 +1347,6 @@ def generate(
 
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
-        generation_config.validate()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # 2. Set generation parameters if not already defined

src/transformers/utils/quantization_config.py

@@ -152,7 +152,6 @@ def to_json_string(self, use_diff: bool = True) -> str:
             config_dict = self.to_dict()
         return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
 
-    # Copied from transformers.generation.configuration_utils.GenerationConfig.update
     def update(self, **kwargs):
         """
         Updates attributes of this class instance with attributes from `kwargs` if they match existing atributtes,
@@ -171,7 +170,7 @@ def update(self, **kwargs):
                 setattr(self, key, value)
                 to_remove.append(key)
 
-        # remove all the attributes that were updated, without modifying the input dict
+        # Remove all the attributes that were updated, without modifying the input dict
         unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
         return unused_kwargs
 

tests/generation/test_configuration_utils.py

@@ -124,26 +124,44 @@ def test_validate(self):
         """
         Tests that the `validate` method is working as expected. Note that `validate` is called at initialization time
         """
-        # Case 1: A correct configuration will not throw any warning
+        # A correct configuration will not throw any warning
         with warnings.catch_warnings(record=True) as captured_warnings:
             GenerationConfig()
         self.assertEqual(len(captured_warnings), 0)
 
-        # Case 2: Inconsequent but technically wrong configuration will throw a warning (e.g. setting sampling
+        # Inconsequent but technically wrong configuration will throw a warning (e.g. setting sampling
         # parameters with `do_sample=False`). May be escalated to an error in the future.
         with warnings.catch_warnings(record=True) as captured_warnings:
-            GenerationConfig(temperature=0.5)
+            GenerationConfig(do_sample=False, temperature=0.5)
         self.assertEqual(len(captured_warnings), 1)
 
-        # Case 3: Impossible sets of contraints/parameters will raise an exception
+        # Expanding on the case above, we can update a bad configuration to get rid of the warning. Ideally,
+        # that is done by unsetting the parameter (i.e. setting it to None)
+        generation_config_bad_temperature = GenerationConfig(do_sample=False, temperature=0.5)
+        with warnings.catch_warnings(record=True) as captured_warnings:
+            # BAD - 0.9 means it is still set, we should warn
+            generation_config_bad_temperature.update(temperature=0.9)
+        self.assertEqual(len(captured_warnings), 1)
+        generation_config_bad_temperature = GenerationConfig(do_sample=False, temperature=0.5)
+        with warnings.catch_warnings(record=True) as captured_warnings:
+            # CORNER CASE - 1.0 is the default, we can't detect whether it is set by the user or not, we shouldn't warn
+            generation_config_bad_temperature.update(temperature=1.0)
+        self.assertEqual(len(captured_warnings), 0)
+        generation_config_bad_temperature = GenerationConfig(do_sample=False, temperature=0.5)
+        with warnings.catch_warnings(record=True) as captured_warnings:
+            # OK - None means it is unset, nothing to warn about
+            generation_config_bad_temperature.update(temperature=None)
+        self.assertEqual(len(captured_warnings), 0)
+
+        # Impossible sets of contraints/parameters will raise an exception
         with self.assertRaises(ValueError):
-            GenerationConfig(num_return_sequences=2)
+            GenerationConfig(do_sample=False, num_beams=1, num_return_sequences=2)
 
-        # Case 4: Passing `generate()`-only flags to `validate` will raise an exception
+        # Passing `generate()`-only flags to `validate` will raise an exception
         with self.assertRaises(ValueError):
             GenerationConfig(logits_processor="foo")
 
-        # Case 5: Model-specific parameters will NOT raise an exception or a warning
+        # Model-specific parameters will NOT raise an exception or a warning
         with warnings.catch_warnings(record=True) as captured_warnings:
             GenerationConfig(foo="bar")
         self.assertEqual(len(captured_warnings), 0)