Config: unified logic to retrieve text config (#33219)

Author: gante

.circleci/parse_test_outputs.py

@@ -67,4 +67,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

src/transformers/configuration_utils.py

@@ -1019,17 +1019,17 @@ def _get_non_default_generation_parameters(self) -> Dict[str, Any]:
         """
         non_default_generation_parameters = {}
         decoder_attribute_name = None
-        default_config = None
 
         # Composite models don't have a default config, use their decoder config as a fallback for default values
         # If no known pattern is matched, then `default_config = None` -> check against the global generation defaults
         try:
             default_config = self.__class__()
         except ValueError:
-            for decoder_attribute_name in ("decoder", "generator", "text_config"):
-                if hasattr(self, decoder_attribute_name):
-                    default_config = getattr(self, decoder_attribute_name).__class__()
-                    break
+            decoder_config = self.get_text_config(decoder=True)
+            if decoder_config is not self:
+                default_config = decoder_config.__class__()
+            else:
+                decoder_config = None
 
         # If it is a composite model, we want to check the subconfig that will be used for generation
         self_decoder_config = self if decoder_attribute_name is None else getattr(self, decoder_attribute_name)
@@ -1057,6 +1057,36 @@ def _get_non_default_generation_parameters(self) -> Dict[str, Any]:
 
         return non_default_generation_parameters
 
+    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+        """
+        Returns the config that is meant to be used with text IO. On most models, it is the original config instance
+        itself. On specific composite models, it is under a set of valid names.
+
+        If `decoder` is set to `True`, then only search for decoder config names.
+        """
+        decoder_possible_text_config_names = ("decoder", "generator", "text_config")
+        encoder_possible_text_config_names = ("text_encoder",)
+        if decoder:
+            possible_text_config_names = decoder_possible_text_config_names
+        else:
+            possible_text_config_names = encoder_possible_text_config_names + decoder_possible_text_config_names
+
+        valid_text_config_names = []
+        for text_config_name in possible_text_config_names:
+            if hasattr(self, text_config_name):
+                text_config = getattr(self, text_config_name, None)
+                if text_config is not None:
+                    valid_text_config_names += [text_config_name]
+
+        if len(valid_text_config_names) > 1:
+            raise ValueError(
+                f"Multiple valid text configs were found in the model config: {valid_text_config_names}. In this "
+                "case, using `get_text_config()` would be ambiguous. Please specify the desied text config directly."
+            )
+        elif len(valid_text_config_names) == 1:
+            return getattr(self, valid_text_config_names[0])
+        return self
+
 
 def get_configuration_file(configuration_files: List[str]) -> str:
     """

src/transformers/generation/configuration_utils.py

@@ -1192,25 +1192,30 @@ def from_model_config(cls, model_config: PretrainedConfig) -> "GenerationConfig"
         """
         config_dict = model_config.to_dict()
         config_dict.pop("_from_model_config", None)
-        config = cls.from_dict(config_dict, return_unused_kwargs=False, _from_model_config=True)
+        generation_config = cls.from_dict(config_dict, return_unused_kwargs=False, _from_model_config=True)
 
         # Special case: some models have generation attributes set in the decoder. Use them if still unset in the
-        # generation config.
-        for decoder_name in ("decoder", "generator", "text_config"):
-            if decoder_name in config_dict:
-                default_generation_config = GenerationConfig()
-                decoder_config = config_dict[decoder_name]
-                for attr in config.to_dict().keys():
-                    if attr in decoder_config and getattr(config, attr) == getattr(default_generation_config, attr):
-                        setattr(config, attr, decoder_config[attr])
+        # generation config (which in turn is defined from the outer attributes of model config).
+        decoder_config = model_config.get_text_config(decoder=True)
+        if decoder_config is not model_config:
+            default_generation_config = GenerationConfig()
+            decoder_config_dict = decoder_config.to_dict()
+            for attr in generation_config.to_dict().keys():
+                is_unset = getattr(generation_config, attr) == getattr(default_generation_config, attr)
+                if attr in decoder_config_dict and is_unset:
+                    setattr(generation_config, attr, decoder_config_dict[attr])
 
         # If any `output_...` flag is set to `True`, we ensure `return_dict_in_generate` is set to `True`.
-        if config.return_dict_in_generate is False:
-            if any(getattr(config, extra_output_flag, False) for extra_output_flag in config.extra_output_flags):
-                config.return_dict_in_generate = True
-
-        config._original_object_hash = hash(config)  # Hash to detect whether the instance was modified
-        return config
+        if generation_config.return_dict_in_generate is False:
+            if any(
+                getattr(generation_config, extra_output_flag, False)
+                for extra_output_flag in generation_config.extra_output_flags
+            ):
+                generation_config.return_dict_in_generate = True
+
+        # Hash to detect whether the instance was modified
+        generation_config._original_object_hash = hash(generation_config)
+        return generation_config
 
     def update(self, **kwargs):
         """

src/transformers/integrations/awq.py

@@ -209,10 +209,7 @@ def get_modules_to_fuse(model, quantization_config):
         current_fused_mapping = AWQ_FUSED_MAPPINGS[model.config.model_type]
 
         # Properly deal with the case where we have a multi-modal model as well (e.g. Llava)
-        if not hasattr(model.config, "text_config"):
-            config = model.config
-        else:
-            config = model.config.text_config
+        config = model.config.get_text_config(decoder=True)
 
         # Handle hidden_size, num_attention_heads, num_key_value_heads on our own.
         hidden_size = config.hidden_size
@@ -345,11 +342,8 @@ def _fuse_awq_mlp(model, current_module_name, fuse_module_names, module, target_
         previous_device = gate_proj.qweight.device
 
         # Deal also with the case model has `text_config` attribute
-        hidden_act = (
-            model.config.hidden_act
-            if not hasattr(model.config, "text_config")
-            else model.config.text_config.hidden_act
-        )
+        config = model.config.get_text_config(decoder=True)
+        hidden_act = config.hidden_act
         activation_fn = ACT2FN[hidden_act]
         new_module = target_cls(gate_proj, down_proj, up_proj, activation_fn)
 

src/transformers/modeling_utils.py

@@ -2025,11 +2025,8 @@ def resize_token_embeddings(
         else:
             vocab_size = model_embeds.weight.shape[0]
 
-        # Update base model and current model config
-        if hasattr(self.config, "text_config"):
-            self.config.text_config.vocab_size = vocab_size
-        else:
-            self.config.vocab_size = vocab_size
+        # Update base model and current model config.
+        self.config.get_text_config().vocab_size = vocab_size
         self.vocab_size = vocab_size
 
         # Tie weights again if needed

src/transformers/models/clvp/modeling_clvp.py

@@ -735,7 +735,7 @@ def _init_weights(self, module):
             nn.init.normal_(module.fc1.proj.weight if getattr(module.fc1, "proj") else module.fc1.weight, std=fc_std)
             nn.init.normal_(module.fc2.weight, std=in_proj_std)
         elif isinstance(module, ClvpEncoder):
-            config = self.config.text_config if hasattr(self.config, "text_config") else self.config
+            config = self.config.get_text_config()
             factor = config.initializer_factor
             module.projection.weight.data.normal_(mean=0.0, std=factor * (config.hidden_size**-0.5))
         elif isinstance(module, ClvpConditioningEncoder):

src/transformers/models/olmoe/modeling_olmoe.py

@@ -1330,7 +1330,7 @@ def prepare_inputs_for_generation(
         cache_position=None,
         position_ids=None,
         use_cache=True,
-        num_logits_to_keep=0,
+        num_logits_to_keep=None,
         **kwargs,
     ):
         # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
@@ -1381,14 +1381,16 @@ def prepare_inputs_for_generation(
                 batch_size=batch_size,
             )
 
+        if num_logits_to_keep is not None:
+            model_inputs["num_logits_to_keep"] = num_logits_to_keep
+
         model_inputs.update(
             {
                 "position_ids": position_ids,
                 "cache_position": cache_position,
                 "past_key_values": past_key_values,
                 "use_cache": use_cache,
                 "attention_mask": attention_mask,
-                "num_logits_to_keep": num_logits_to_keep,
             }
         )
         return model_inputs

tests/generation/test_utils.py

@@ -831,7 +831,7 @@ def test_constrained_beam_search_generate(self):
 
             # Sample constraints
             min_id = 3
-            max_id = config.vocab_size
+            max_id = config.get_text_config(decoder=True).vocab_size
 
             force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
             constraints = [
@@ -889,7 +889,7 @@ def test_constrained_beam_search_generate_dict_output(self):
 
             # Sample constraints
             min_id = 3
-            max_id = model.config.vocab_size
+            max_id = model.config.get_text_config(decoder=True).vocab_size
             force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
             constraints = [
                 PhrasalConstraint(force_tokens),
@@ -2012,18 +2012,20 @@ def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_
                 self.assertTrue(output.past_key_values is None)
 
     def _check_scores(self, batch_size, scores, length, config):
-        expected_shape = (batch_size, config.vocab_size)
+        vocab_size = config.get_text_config(decoder=True).vocab_size
+        expected_shape = (batch_size, vocab_size)
         self.assertIsInstance(scores, tuple)
         self.assertEqual(len(scores), length)
         self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
 
     def _check_logits(self, batch_size, scores, config):
+        vocab_size = config.get_text_config(decoder=True).vocab_size
         self.assertIsInstance(scores, tuple)
         self.assertListEqual([iter_scores.shape[0] for iter_scores in scores], [batch_size] * len(scores))
         # vocabulary difference equal to one (imagegptmodel?) or zero (all other models)
-        vocab_diff = config.vocab_size - scores[0].shape[-1]
+        vocab_diff = vocab_size - scores[0].shape[-1]
         self.assertTrue(vocab_diff in [0, 1])
-        self.assertListEqual([config.vocab_size - score.shape[-1] for score in scores], [vocab_diff] * len(scores))
+        self.assertListEqual([vocab_size - score.shape[-1] for score in scores], [vocab_diff] * len(scores))
 
     def _check_attentions_for_generate(
         self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1

tests/test_modeling_common.py

@@ -1747,12 +1747,13 @@ def test_resize_position_vector_embeddings(self):
             self.assertTrue(models_equal)
 
     def test_resize_tokens_embeddings(self):
+        if not self.test_resize_embeddings:
+            self.skipTest(reason="test_resize_embeddings is set to `False`")
+
         (
             original_config,
             inputs_dict,
         ) = self.model_tester.prepare_config_and_inputs_for_common()
-        if not self.test_resize_embeddings:
-            self.skipTest(reason="test_resize_embeddings is set to `False`")
 
         for model_class in self.all_model_classes:
             config = copy.deepcopy(original_config)
@@ -1764,18 +1765,15 @@ def test_resize_tokens_embeddings(self):
             if self.model_tester.is_training is False:
                 model.eval()
 
-            model_vocab_size = config.text_config.vocab_size if hasattr(config, "text_config") else config.vocab_size
+            model_vocab_size = config.get_text_config().vocab_size
             # Retrieve the embeddings and clone theme
             model_embed = model.resize_token_embeddings(model_vocab_size)
             cloned_embeddings = model_embed.weight.clone()
 
             # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
             model_embed = model.resize_token_embeddings(model_vocab_size + 10)
-            new_model_vocab_size = (
-                model.config.text_config.vocab_size
-                if hasattr(model.config, "text_config")
-                else model.config.vocab_size
-            )
+            new_model_vocab_size = model.config.get_text_config().vocab_size
+
             self.assertEqual(new_model_vocab_size, model_vocab_size + 10)
             # Check that it actually resizes the embeddings matrix
             self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
@@ -1787,11 +1785,7 @@ def test_resize_tokens_embeddings(self):
 
             # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
             model_embed = model.resize_token_embeddings(model_vocab_size - 15)
-            new_model_vocab_size = (
-                model.config.text_config.vocab_size
-                if hasattr(model.config, "text_config")
-                else model.config.vocab_size
-            )
+            new_model_vocab_size = model.config.get_text_config().vocab_size
             self.assertEqual(new_model_vocab_size, model_vocab_size - 15)
             # Check that it actually resizes the embeddings matrix
             self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
@@ -1817,21 +1811,13 @@ def test_resize_tokens_embeddings(self):
             model = model_class(config)
             model.to(torch_device)
 
-            model_vocab_size = config.text_config.vocab_size if hasattr(config, "text_config") else config.vocab_size
+            model_vocab_size = config.get_text_config().vocab_size
             model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1)
-            new_model_vocab_size = (
-                model.config.text_config.vocab_size
-                if hasattr(model.config, "text_config")
-                else model.config.vocab_size
-            )
+            new_model_vocab_size = model.config.get_text_config().vocab_size
             self.assertTrue(new_model_vocab_size + 10, model_vocab_size)
 
             model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64)
-            new_model_vocab_size = (
-                model.config.text_config.vocab_size
-                if hasattr(model.config, "text_config")
-                else model.config.vocab_size
-            )
+            new_model_vocab_size = model.config.get_text_config().vocab_size
             self.assertTrue(model_embed.weight.shape[0] // 64, 0)
 
             self.assertTrue(model_embed.weight.shape[0], new_model_vocab_size)
@@ -1852,13 +1838,10 @@ def test_resize_tokens_embeddings(self):
                 model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3)
 
     def test_resize_embeddings_untied(self):
-        (
-            original_config,
-            inputs_dict,
-        ) = self.model_tester.prepare_config_and_inputs_for_common()
         if not self.test_resize_embeddings:
             self.skipTest(reason="test_resize_embeddings is set to `False`")
 
+        original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         original_config.tie_word_embeddings = False
 
         # if model cannot untied embeddings -> leave test
@@ -1874,13 +1857,9 @@ def test_resize_embeddings_untied(self):
                 continue
 
             # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
-            model_vocab_size = config.text_config.vocab_size if hasattr(config, "text_config") else config.vocab_size
+            model_vocab_size = config.get_text_config().vocab_size
             model.resize_token_embeddings(model_vocab_size + 10)
-            new_model_vocab_size = (
-                model.config.text_config.vocab_size
-                if hasattr(model.config, "text_config")
-                else model.config.vocab_size
-            )
+            new_model_vocab_size = model.config.get_text_config().vocab_size
             self.assertEqual(new_model_vocab_size, model_vocab_size + 10)
             output_embeds = model.get_output_embeddings()
             self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
@@ -1892,11 +1871,7 @@ def test_resize_embeddings_untied(self):
 
             # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
             model.resize_token_embeddings(model_vocab_size - 15)
-            new_model_vocab_size = (
-                model.config.text_config.vocab_size
-                if hasattr(model.config, "text_config")
-                else model.config.vocab_size
-            )
+            new_model_vocab_size = model.config.get_text_config().vocab_size
             self.assertEqual(new_model_vocab_size, model_vocab_size - 15)
             # Check that it actually resizes the embeddings matrix
             output_embeds = model.get_output_embeddings()
@@ -1988,7 +1963,7 @@ def check_same_values(layer_1, layer_2):
             # self.assertTrue(check_same_values(embeddings, decoding))
 
             # Check that after resize they remain tied.
-            vocab_size = config.text_config.vocab_size if hasattr(config, "text_config") else config.vocab_size
+            vocab_size = config.get_text_config().vocab_size
             model_tied.resize_token_embeddings(vocab_size + 10)
             params_tied_2 = list(model_tied.parameters())
             self.assertEqual(len(params_tied_2), len(params_tied))
@@ -4831,7 +4806,7 @@ def test_forward_with_num_logits_to_keep(self):
 
             config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
             batch_size, sequence_length = inputs["input_ids"].shape
-            vocab_size = config.vocab_size
+            vocab_size = config.get_text_config().vocab_size
             model = model_class(config).to(device=torch_device).eval()
 
             # num_logits_to_keep=0 is a special case meaning "keep all logits"

tests/test_pipeline_mixin.py

@@ -675,14 +675,12 @@ def validate_test_components(test_case, task, model, tokenizer, processor):
     # Avoid `IndexError` in embedding layers
     CONFIG_WITHOUT_VOCAB_SIZE = ["CanineConfig"]
     if tokenizer is not None:
-        config_vocab_size = getattr(model.config, "vocab_size", None)
+        # Removing `decoder=True` in `get_text_config` can lead to conflicting values e.g. in MusicGen
+        config_vocab_size = getattr(model.config.get_text_config(decoder=True), "vocab_size", None)
         # For CLIP-like models
         if config_vocab_size is None:
-            if hasattr(model.config, "text_config"):
+            if hasattr(model.config, "text_encoder"):
                 config_vocab_size = getattr(model.config.text_config, "vocab_size", None)
-            elif hasattr(model.config, "text_encoder"):
-                config_vocab_size = getattr(model.config.text_encoder, "vocab_size", None)
-
         if config_vocab_size is None and model.config.__class__.__name__ not in CONFIG_WITHOUT_VOCAB_SIZE:
             raise ValueError(
                 "Could not determine `vocab_size` from model configuration while `tokenizer` is not `None`."