Generate: store special token tensors under a unique variable name (#31980)

* rename stuff

* english; this one shouldn't be changed

* add a _ to the new var names

* musicgen

* derp

src/transformers/generation/utils.py

@@ -754,12 +754,12 @@ class GenerationMixin:
         warpers = LogitsProcessorList()
 
         # In beam methods, we need to keep at least one non-eos token to explore continuations that might have a
-        # better score (i.e. keep len(list(generation_config.eos_token_id)) + 1)
+        # better score (i.e. keep len(list(generation_config._eos_token_tensor)) + 1)
         if generation_config.num_beams > 1:
-            if isinstance(generation_config.eos_token_id, list):
-                min_tokens_to_keep = len(generation_config.eos_token_id) + 1
-            elif isinstance(generation_config.eos_token_id, torch.Tensor):
-                min_tokens_to_keep = generation_config.eos_token_id.shape[0] + 1
+            if isinstance(generation_config._eos_token_tensor, list):
+                min_tokens_to_keep = len(generation_config._eos_token_tensor) + 1
+            elif isinstance(generation_config._eos_token_tensor, torch.Tensor):
+                min_tokens_to_keep = generation_config._eos_token_tensor.shape[0] + 1
             else:
                 min_tokens_to_keep = 2
         else:
@@ -863,31 +863,31 @@ class GenerationMixin:
             processors.append(
                 NoBadWordsLogitsProcessor(
                     generation_config.bad_words_ids,
-                    generation_config.eos_token_id,
+                    generation_config._eos_token_tensor,
                 )
             )
         if (
             generation_config.min_length is not None
-            and generation_config.eos_token_id is not None
+            and generation_config._eos_token_tensor is not None
             and generation_config.min_length > 0
         ):
             processors.append(
                 MinLengthLogitsProcessor(
                     generation_config.min_length,
-                    generation_config.eos_token_id,
+                    generation_config._eos_token_tensor,
                     device=device,
                 )
             )
         if (
             generation_config.min_new_tokens is not None
-            and generation_config.eos_token_id is not None
+            and generation_config._eos_token_tensor is not None
             and generation_config.min_new_tokens > 0
         ):
             processors.append(
                 MinNewTokensLengthLogitsProcessor(
                     input_ids_seq_length,
                     generation_config.min_new_tokens,
-                    generation_config.eos_token_id,
+                    generation_config._eos_token_tensor,
                     device=device,
                 )
             )
@@ -918,7 +918,7 @@ class GenerationMixin:
             processors.append(
                 ExponentialDecayLengthPenalty(
                     generation_config.exponential_decay_length_penalty,
-                    generation_config.eos_token_id,
+                    generation_config._eos_token_tensor,
                     input_ids_seq_length,
                 )
             )
@@ -997,8 +997,8 @@ class GenerationMixin:
                     "stop strings, you must pass the model's tokenizer to the `tokenizer` argument of `generate`."
                 )
             criteria.append(StopStringCriteria(stop_strings=generation_config.stop_strings, tokenizer=tokenizer))
-        if generation_config.eos_token_id is not None:
-            criteria.append(EosTokenCriteria(eos_token_id=generation_config.eos_token_id))
+        if generation_config._eos_token_tensor is not None:
+            criteria.append(EosTokenCriteria(eos_token_id=generation_config._eos_token_tensor))
         criteria = self._merge_criteria_processor_list(criteria, stopping_criteria)
         return criteria
 
@@ -1349,13 +1349,15 @@ class GenerationMixin:
         self, generation_config: Optional[GenerationConfig], **kwargs: Dict
     ) -> Tuple[GenerationConfig, Dict]:
         """
-        Prepares the base generation config, then applies any generation configuration options from kwargs.
+        Prepares the base generation config, then applies any generation configuration options from kwargs. This
+        function handles retrocompatibility with respect to configuration files.
         """
         # TODO joao: when we can detect `fullgraph=True` in `torch.compile` (https://github.com/pytorch/pytorch/pull/120400)
         # replace `is_torchdynamo_compiling` by the corresponding check. As it is, we are being too restrictive with
         # the parameterization in `fullgraph=False` so as to enable `fullgraph=True`.
 
         # priority: `generation_config` argument > `model.generation_config` (the default generation config)
+        using_model_generation_config = False
         if generation_config is None:
             # legacy: users may modify the model configuration to control generation. To trigger this legacy behavior,
             # three conditions must be met
@@ -1378,6 +1380,7 @@ class GenerationMixin:
                         " https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )"
                     )
                     self.generation_config = new_generation_config
+            using_model_generation_config = True
             generation_config = self.generation_config
 
         # `torch.compile` can't compile `copy.deepcopy`, arguments in `kwargs` that are part of `generation_config`
@@ -1395,6 +1398,16 @@ class GenerationMixin:
         else:
             generation_config = copy.deepcopy(generation_config)
             model_kwargs = generation_config.update(**kwargs)
+            # If `generation_config` is provided, let's fallback ALL special tokens to the default values for the model
+            if not using_model_generation_config:
+                if generation_config.bos_token_id is None:
+                    generation_config.bos_token_id = self.generation_config.bos_token_id
+                if generation_config.eos_token_id is None:
+                    generation_config.eos_token_id = self.generation_config.eos_token_id
+                if generation_config.pad_token_id is None:
+                    generation_config.pad_token_id = self.generation_config.pad_token_id
+                if generation_config.decoder_start_token_id is None:
+                    generation_config.decoder_start_token_id = self.generation_config.decoder_start_token_id
 
         return generation_config, model_kwargs
 
@@ -1493,52 +1506,46 @@ class GenerationMixin:
         function). However, if called outside `generate`, consider creating a copy of `generation_config` first.
         """
 
-        # Convert special tokens to tensors (if they exist either in kwargs or in self.config)
-        def _tensor_or_none(token_kwargs, token_self, device=None):
-            if device is None:
-                device = self.device
-
-            token = token_kwargs if token_kwargs is not None else token_self
+        # Convert special tokens to tensors
+        def _tensor_or_none(token, device=None):
             if token is None:
                 return token
-            elif isinstance(token, torch.Tensor):
-                return token.to(device)
 
+            device = device if device is not None else self.device
+            if isinstance(token, torch.Tensor):
+                return token.to(device)
             return torch.tensor(token, device=device, dtype=torch.long)
 
-        bos_token_id = _tensor_or_none(
-            generation_config.bos_token_id, self.generation_config.bos_token_id, device=device
-        )
-        eos_token_id = _tensor_or_none(
-            generation_config.eos_token_id, self.generation_config.eos_token_id, device=device
-        )
-        pad_token_id = _tensor_or_none(
-            generation_config.pad_token_id, self.generation_config.pad_token_id, device=device
-        )
-        decoder_start_token_id = _tensor_or_none(
-            generation_config.decoder_start_token_id, self.generation_config.decoder_start_token_id, device=device
-        )
+        bos_token_tensor = _tensor_or_none(generation_config.bos_token_id, device=device)
+        eos_token_tensor = _tensor_or_none(generation_config.eos_token_id, device=device)
+        pad_token_tensor = _tensor_or_none(generation_config.pad_token_id, device=device)
+        decoder_start_token_tensor = _tensor_or_none(generation_config.decoder_start_token_id, device=device)
 
         # for BC we also try to get `decoder_start_token_id` or `bos_token_id` (#30892)
         if self.config.is_encoder_decoder:
-            decoder_start_token_id = decoder_start_token_id if decoder_start_token_id is not None else bos_token_id
+            decoder_start_token_tensor = (
+                decoder_start_token_tensor if decoder_start_token_tensor is not None else bos_token_tensor
+            )
 
         # We can have more than one eos token. Always treat it as a 1D tensor (when it exists).
-        if eos_token_id is not None and eos_token_id.ndim == 0:
-            eos_token_id = eos_token_id.unsqueeze(0)
+        if eos_token_tensor is not None and eos_token_tensor.ndim == 0:
+            eos_token_tensor = eos_token_tensor.unsqueeze(0)
 
         # Set pad token if unset (and there are conditions to do so)
-        if pad_token_id is None and eos_token_id is not None:
+        if pad_token_tensor is None and eos_token_tensor is not None:
             if kwargs_has_attention_mask is not None and not kwargs_has_attention_mask:
                 logger.warning(
                     "The attention mask and the pad token id were not set. As a consequence, you may observe "
                     "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
                 )
-            pad_token_id = eos_token_id[0]
-            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{pad_token_id} for open-end generation.")
+            pad_token_tensor = eos_token_tensor[0]
+            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{pad_token_tensor} for open-end generation.")
 
         # we can't infer attn mask if pad token is set to be eos token in model's generation config
-        if eos_token_id is not None and torch.isin(elements=eos_token_id, test_elements=pad_token_id).any():
+        if (
+            eos_token_tensor is not None
+            and torch.isin(elements=eos_token_tensor, test_elements=pad_token_tensor).any()
+        ):
             if kwargs_has_attention_mask is not None and not kwargs_has_attention_mask:
                 logger.warning_once(
                     "The attention mask is not set and cannot be inferred from input because pad token is same as eos token."
@@ -1547,21 +1554,26 @@ class GenerationMixin:
                 )
 
         # Sanity checks/warnings
-        if self.config.is_encoder_decoder and decoder_start_token_id is None:
+        if self.config.is_encoder_decoder and decoder_start_token_tensor is None:
             raise ValueError(
                 "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
             )
-        if eos_token_id is not None and (torch.is_floating_point(eos_token_id) or (eos_token_id < 0).any()):
+        if eos_token_tensor is not None and (
+            torch.is_floating_point(eos_token_tensor) or (eos_token_tensor < 0).any()
+        ):
             logger.warning(
-                f"`eos_token_id` should consist of positive integers, but is {eos_token_id}. Your generation will not "
+                f"`eos_token_id` should consist of positive integers, but is {eos_token_tensor}. Your generation will not "
                 "stop until the maximum length is reached. Depending on other flags, it may even crash."
             )
 
         # Update generation config with the updated special tokens tensors
-        generation_config.bos_token_id = bos_token_id
-        generation_config.eos_token_id = eos_token_id
-        generation_config.pad_token_id = pad_token_id
-        generation_config.decoder_start_token_id = decoder_start_token_id
+        # NOTE: this must be written into a different attribute name than the one holding the original special tokens
+        # (in their non-tensor form), in order to enable end-to-end compilation. See
+        # https://pytorch.org/docs/stable/torch.compiler_cudagraph_trees.html#limitations
+        generation_config._bos_token_tensor = bos_token_tensor
+        generation_config._eos_token_tensor = eos_token_tensor
+        generation_config._pad_token_tensor = pad_token_tensor
+        generation_config._decoder_start_token_tensor = decoder_start_token_tensor
 
     @torch.no_grad()
     def generate(
@@ -1696,10 +1708,10 @@ class GenerationMixin:
             # If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
             # Note: If using, `inputs_embeds` this check does not work, because we want to be more hands-off.
             if (
-                generation_config.pad_token_id is not None
+                generation_config._pad_token_tensor is not None
                 and batch_size > 1
                 and len(inputs_tensor.shape) == 2
-                and torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id) > 0
+                and torch.sum(inputs_tensor[:, -1] == generation_config._pad_token_tensor) > 0
             ):
                 logger.warning(
                     "A decoder-only architecture is being used, but right-padding was detected! For correct "
@@ -1716,7 +1728,7 @@ class GenerationMixin:
 
         if not kwargs_has_attention_mask and requires_attention_mask and accepts_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
+                inputs_tensor, generation_config._pad_token_tensor, generation_config._eos_token_tensor
             )
 
         if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
@@ -1731,7 +1743,7 @@ class GenerationMixin:
                 batch_size=batch_size,
                 model_input_name=model_input_name,
                 model_kwargs=model_kwargs,
-                decoder_start_token_id=generation_config.decoder_start_token_id,
+                decoder_start_token_id=generation_config._decoder_start_token_tensor,
                 device=inputs_tensor.device,
             )
         else:
@@ -2279,7 +2291,7 @@ class GenerationMixin:
             raise ValueError("DoLa decoding is only available for decoder-only models.")
         # init values
 
-        pad_token_id = generation_config.pad_token_id
+        pad_token_id = generation_config._pad_token_tensor
         output_attentions = generation_config.output_attentions
         output_hidden_states = generation_config.output_hidden_states
         output_scores = generation_config.output_scores
@@ -2486,7 +2498,7 @@ class GenerationMixin:
         has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
         top_k = generation_config.top_k
         penalty_alpha = generation_config.penalty_alpha
-        pad_token_id = generation_config.pad_token_id
+        pad_token_id = generation_config._pad_token_tensor
         output_attentions = generation_config.output_attentions
         output_hidden_states = generation_config.output_hidden_states
         output_scores = generation_config.output_scores
@@ -2877,7 +2889,7 @@ class GenerationMixin:
             `model.config.is_encoder_decoder=True`.
         """
         # init values
-        pad_token_id = generation_config.pad_token_id
+        pad_token_id = generation_config._pad_token_tensor
         output_attentions = generation_config.output_attentions
         output_hidden_states = generation_config.output_hidden_states
         output_scores = generation_config.output_scores
@@ -3084,8 +3096,8 @@ class GenerationMixin:
             `model.config.is_encoder_decoder=True`.
         """
         # init values
-        pad_token_id = generation_config.pad_token_id
-        eos_token_id = generation_config.eos_token_id
+        pad_token_id = generation_config._pad_token_tensor
+        eos_token_id = generation_config._eos_token_tensor
         output_attentions = generation_config.output_attentions
         output_hidden_states = generation_config.output_hidden_states
         output_scores = generation_config.output_scores
@@ -3366,8 +3378,8 @@ class GenerationMixin:
             `model.config.is_encoder_decoder=True`.
         """
         # init values
-        pad_token_id = generation_config.pad_token_id
-        eos_token_id = generation_config.eos_token_id
+        pad_token_id = generation_config._pad_token_tensor
+        eos_token_id = generation_config._eos_token_tensor
         output_attentions = generation_config.output_attentions
         output_hidden_states = generation_config.output_hidden_states
         output_scores = generation_config.output_scores
@@ -3658,8 +3670,8 @@ class GenerationMixin:
             `model.config.is_encoder_decoder=True`.
         """
         # init values
-        pad_token_id = generation_config.pad_token_id
-        eos_token_id = generation_config.eos_token_id
+        pad_token_id = generation_config._pad_token_tensor
+        eos_token_id = generation_config._eos_token_tensor
         output_attentions = generation_config.output_attentions
         output_hidden_states = generation_config.output_hidden_states
         output_scores = generation_config.output_scores

src/transformers/models/musicgen/modeling_musicgen.py

@@ -1539,75 +1539,43 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
 
-        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
-            if model_kwargs.get("attention_mask", None) is None:
-                logger.warning(
-                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
-                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
-                )
-            eos_token_id = generation_config.eos_token_id
-            if isinstance(eos_token_id, list):
-                eos_token_id = eos_token_id[0]
-            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
-            generation_config.pad_token_id = eos_token_id
+        requires_attention_mask = "encoder_outputs" not in model_kwargs
+        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
 
-        # 3. Define model inputs
-        # inputs_tensor has to be defined
-        # model_input_name is defined if model-specific keyword input is passed
-        # otherwise model_input_name is None
-        # all model-specific keyword inputs are removed from `model_kwargs`
+        # 3. Define model inputs`
         input_ids, model_input_name, model_kwargs = self._prepare_model_inputs(
             inputs, generation_config.bos_token_id, model_kwargs
         )
         batch_size = input_ids.shape[0] // self.num_codebooks
-        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
         self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device)
 
         # 4. Define other model kwargs
         model_kwargs["use_cache"] = generation_config.use_cache
         model_kwargs["guidance_scale"] = generation_config.guidance_scale
 
-        requires_attention_mask = "encoder_outputs" not in model_kwargs
         if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                input_ids, generation_config.pad_token_id, generation_config.eos_token_id
+                input_ids, generation_config._pad_token_tensor, generation_config._eos_token_tensor
             )
 
         # 5. Prepare `max_length` depending on other stopping criteria.
-        input_ids_seq_length = input_ids.shape[-1]
+        input_ids_length = input_ids.shape[-1]
         has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
-            logger.warning(
-                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
-                "to control the generation length.  recommend setting `max_new_tokens` to control the maximum length of the generation."
-            )
-        elif generation_config.max_new_tokens is not None:
-            if not has_default_max_length:
-                logger.warning(
-                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
-                )
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
-
-        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
-            raise ValueError(
-                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
-                f" the maximum length ({generation_config.max_length})"
-            )
-        if input_ids_seq_length >= generation_config.max_length:
-            logger.warning(
-                f"Input length of decoder_input_ids is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
+        has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+        generation_config = self._prepare_generated_length(
+            generation_config=generation_config,
+            has_default_max_length=has_default_max_length,
+            has_default_min_length=has_default_min_length,
+            model_input_name=model_input_name,
+            inputs_tensor=input_ids,
+            input_ids_length=input_ids_length,
+        )
 
         # 6. Prepare `input_ids` which will be used for auto-regressive generation
         # Build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
         input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
             input_ids,
-            pad_token_id=generation_config.decoder_start_token_id,
+            pad_token_id=generation_config._decoder_start_token_tensor,
             max_length=generation_config.max_length,
         )
 
@@ -1628,7 +1596,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
         # 9. prepare distribution pre_processing samplers
         logits_processor = self._get_logits_processor(
             generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
+            input_ids_seq_length=input_ids_length,
             encoder_input_ids=input_ids,
             prefix_allowed_tokens_fn=None,
             logits_processor=logits_processor,
@@ -1682,7 +1650,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
         output_ids = self.apply_delay_pattern_mask(output_ids, model_kwargs["delay_pattern_mask"])
 
         # revert the pattern delay mask by filtering the pad token id
-        output_ids = output_ids[output_ids != generation_config.pad_token_id].reshape(
+        output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
             batch_size, self.num_codebooks, -1
         )
 
@@ -2590,39 +2558,23 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
 
-        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
-            if model_kwargs.get("attention_mask", None) is None:
-                logger.warning(
-                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
-                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
-                )
-            eos_token_id = generation_config.eos_token_id
-            if isinstance(eos_token_id, list):
-                eos_token_id = eos_token_id[0]
-            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
-            generation_config.pad_token_id = eos_token_id
+        requires_attention_mask = "encoder_outputs" not in model_kwargs
+        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
 
         # 3. Define model inputs
-        # inputs_tensor has to be defined
-        # model_input_name is defined if model-specific keyword input is passed
-        # otherwise model_input_name is None
-        # all model-specific keyword inputs are removed from `model_kwargs`
         inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
             inputs, generation_config.bos_token_id, model_kwargs
         )
         batch_size = inputs_tensor.shape[0]
-        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
         self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=inputs_tensor.device)
 
         # 4. Define other model kwargs
         model_kwargs["use_cache"] = generation_config.use_cache
         model_kwargs["guidance_scale"] = generation_config.guidance_scale
 
-        requires_attention_mask = "encoder_outputs" not in model_kwargs
-
         if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
+                inputs_tensor, generation_config._pad_token_tensor, generation_config._eos_token_tensor
             )
 
         if "encoder_outputs" not in model_kwargs:
@@ -2642,45 +2594,28 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
             batch_size=batch_size,
             model_input_name=model_input_name,
             model_kwargs=model_kwargs,
-            decoder_start_token_id=generation_config.decoder_start_token_id,
-            bos_token_id=generation_config.bos_token_id,
+            decoder_start_token_id=generation_config._decoder_start_token_tensor,
+            bos_token_id=generation_config._bos_token_tensor,
             device=inputs_tensor.device,
         )
 
         # 6. Prepare `max_length` depending on other stopping criteria.
-        input_ids_seq_length = input_ids.shape[-1]
+        input_ids_length = input_ids.shape[-1]
         has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None:
-            logger.warning(
-                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
-                "to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation."
-            )
-        elif generation_config.max_new_tokens is not None:
-            if not has_default_max_length:
-                logger.warning(
-                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
-                )
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
-
-        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
-            raise ValueError(
-                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
-                f" the maximum length ({generation_config.max_length})"
-            )
-        if input_ids_seq_length >= generation_config.max_length:
-            logger.warning(
-                f"Input length of decoder_input_ids is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
+        has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+        generation_config = self._prepare_generated_length(
+            generation_config=generation_config,
+            has_default_max_length=has_default_max_length,
+            has_default_min_length=has_default_min_length,
+            model_input_name=model_input_name,
+            inputs_tensor=inputs_tensor,
+            input_ids_length=input_ids_length,
+        )
 
         # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
         input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
             input_ids,
-            pad_token_id=generation_config.decoder_start_token_id,
+            pad_token_id=generation_config._decoder_start_token_tensor,
             max_length=generation_config.max_length,
         )
         # stash the delay mask so that we don't have to recompute in each forward pass
@@ -2701,7 +2636,7 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
         # 9. prepare distribution pre_processing samplers
         logits_processor = self._get_logits_processor(
             generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
+            input_ids_seq_length=input_ids_length,
             encoder_input_ids=inputs_tensor,
             prefix_allowed_tokens_fn=None,
             logits_processor=logits_processor,
@@ -2756,7 +2691,7 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
         output_ids = self.decoder.apply_delay_pattern_mask(output_ids, model_kwargs["decoder_delay_pattern_mask"])
 
         # revert the pattern delay mask by filtering the pad token id
-        output_ids = output_ids[output_ids != generation_config.pad_token_id].reshape(
+        output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
             batch_size, self.decoder.num_codebooks, -1
         )
 

src/transformers/models/musicgen_melody/modeling_musicgen_melody.py

@@ -1375,6 +1375,7 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
         return input_ids
 
     @torch.no_grad()
+    # Ignore copy
     def generate(
         self,
         inputs: Optional[torch.Tensor] = None,
@@ -1460,75 +1461,43 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
 
-        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
-            if model_kwargs.get("attention_mask", None) is None:
-                logger.warning(
-                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
-                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
-                )
-            eos_token_id = generation_config.eos_token_id
-            if isinstance(eos_token_id, list):
-                eos_token_id = eos_token_id[0]
-            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
-            generation_config.pad_token_id = eos_token_id
+        requires_attention_mask = "encoder_outputs" not in model_kwargs
+        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
 
-        # 3. Define model inputs
-        # inputs_tensor has to be defined
-        # model_input_name is defined if model-specific keyword input is passed
-        # otherwise model_input_name is None
-        # all model-specific keyword inputs are removed from `model_kwargs`
+        # 3. Define model inputs`
         input_ids, model_input_name, model_kwargs = self._prepare_model_inputs(
             inputs, generation_config.bos_token_id, model_kwargs
         )
         batch_size = input_ids.shape[0] // self.num_codebooks
-        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
         self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device)
 
         # 4. Define other model kwargs
         model_kwargs["use_cache"] = generation_config.use_cache
         model_kwargs["guidance_scale"] = generation_config.guidance_scale
 
-        # Ignore copy
-        if model_kwargs.get("attention_mask", None) is None:
+        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                input_ids, generation_config.pad_token_id, generation_config.eos_token_id
+                input_ids, generation_config._pad_token_tensor, generation_config._eos_token_tensor
             )
 
         # 5. Prepare `max_length` depending on other stopping criteria.
-        input_ids_seq_length = input_ids.shape[-1]
+        input_ids_length = input_ids.shape[-1]
         has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
-            logger.warning(
-                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
-                "to control the generation length.  recommend setting `max_new_tokens` to control the maximum length of the generation."
-            )
-        elif generation_config.max_new_tokens is not None:
-            if not has_default_max_length:
-                logger.warning(
-                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
-                )
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
-
-        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
-            raise ValueError(
-                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
-                f" the maximum length ({generation_config.max_length})"
-            )
-        if input_ids_seq_length >= generation_config.max_length:
-            logger.warning(
-                f"Input length of decoder_input_ids is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
+        has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+        generation_config = self._prepare_generated_length(
+            generation_config=generation_config,
+            has_default_max_length=has_default_max_length,
+            has_default_min_length=has_default_min_length,
+            model_input_name=model_input_name,
+            inputs_tensor=input_ids,
+            input_ids_length=input_ids_length,
+        )
 
         # 6. Prepare `input_ids` which will be used for auto-regressive generation
-        # Build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
+        # Build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to Musicgen)
         input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
             input_ids,
-            pad_token_id=generation_config.decoder_start_token_id,
+            pad_token_id=generation_config._decoder_start_token_tensor,
             max_length=generation_config.max_length,
         )
 
@@ -1549,7 +1518,7 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
         # 9. prepare distribution pre_processing samplers
         logits_processor = self._get_logits_processor(
             generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
+            input_ids_seq_length=input_ids_length,
             encoder_input_ids=input_ids,
             prefix_allowed_tokens_fn=None,
             logits_processor=logits_processor,
@@ -1603,7 +1572,7 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
         output_ids = self.apply_delay_pattern_mask(output_ids, model_kwargs["delay_pattern_mask"])
 
         # revert the pattern delay mask by filtering the pad token id
-        output_ids = output_ids[output_ids != generation_config.pad_token_id].reshape(
+        output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
             batch_size, self.num_codebooks, -1
         )
 
@@ -2397,7 +2366,7 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
                 Custom stopping criteria that complement the default stopping criteria built from arguments and a
                 generation config. If a stopping criteria is passed that is already created with the arguments or a
                 generation config an error is thrown. This feature is intended for advanced users.
-            synced_gpus (`bool`, *optional*):
+            synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
             streamer (`BaseStreamer`, *optional*):
                 Streamer object that will be used to stream the generated sequences. Generated tokens are passed
@@ -2414,18 +2383,14 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
                 If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
                 [`~utils.ModelOutput`] types are:
 
-                    - [`~generation.GreedySearchDecoderOnlyOutput`],
-                    - [`~generation.SampleDecoderOnlyOutput`],
-                    - [`~generation.BeamSearchDecoderOnlyOutput`],
-                    - [`~generation.BeamSampleDecoderOnlyOutput`]
+                    - [`~generation.GenerateDecoderOnlyOutput`],
+                    - [`~generation.GenerateBeamDecoderOnlyOutput`]
 
                 If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
                 [`~utils.ModelOutput`] types are:
 
-                    - [`~generation.GreedySearchEncoderDecoderOutput`],
-                    - [`~generation.SampleEncoderDecoderOutput`],
-                    - [`~generation.BeamSearchEncoderDecoderOutput`],
-                    - [`~generation.BeamSampleEncoderDecoderOutput`]
+                    - [`~generation.GenerateEncoderDecoderOutput`],
+                    - [`~generation.GenerateBeamEncoderDecoderOutput`]
         """
         # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects
         if generation_config is None:
@@ -2440,37 +2405,23 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
 
-        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
-            if model_kwargs.get("attention_mask", None) is None:
-                logger.warning(
-                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
-                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
-                )
-            eos_token_id = generation_config.eos_token_id
-            if isinstance(eos_token_id, list):
-                eos_token_id = eos_token_id[0]
-            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
-            generation_config.pad_token_id = eos_token_id
+        requires_attention_mask = "encoder_outputs" not in model_kwargs
+        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
 
         # 3. Define model inputs
-        # inputs_tensor has to be defined
-        # model_input_name is defined if model-specific keyword input is passed
-        # otherwise model_input_name is None
-        # all model-specific keyword inputs are removed from `model_kwargs`
         inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
             inputs, generation_config.bos_token_id, model_kwargs
         )
         batch_size = inputs_tensor.shape[0]
-        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
         self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=inputs_tensor.device)
 
         # 4. Define other model kwargs
         model_kwargs["use_cache"] = generation_config.use_cache
         model_kwargs["guidance_scale"] = generation_config.guidance_scale
 
-        if model_kwargs.get("attention_mask", None) is None:
+        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
+                inputs_tensor, generation_config._pad_token_tensor, generation_config._eos_token_tensor
             )
 
         if "encoder_hidden_states" not in model_kwargs:
@@ -2484,46 +2435,28 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
             batch_size=batch_size,
             model_input_name=model_input_name,
             model_kwargs=model_kwargs,
-            decoder_start_token_id=generation_config.decoder_start_token_id,
-            bos_token_id=generation_config.bos_token_id,
+            decoder_start_token_id=generation_config._decoder_start_token_tensor,
+            bos_token_id=generation_config._bos_token_tensor,
             device=inputs_tensor.device,
         )
 
         # 6. Prepare `max_length` depending on other stopping criteria.
-        input_ids_seq_length = input_ids.shape[-1]
-
+        input_ids_length = input_ids.shape[-1]
         has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None:
-            logger.warning(
-                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
-                "to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation."
-            )
-        elif generation_config.max_new_tokens is not None:
-            if not has_default_max_length:
-                logger.warning(
-                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
-                )
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
+        has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+        generation_config = self._prepare_generated_length(
+            generation_config=generation_config,
+            has_default_max_length=has_default_max_length,
+            has_default_min_length=has_default_min_length,
+            model_input_name=model_input_name,
+            inputs_tensor=inputs_tensor,
+            input_ids_length=input_ids_length,
+        )
 
-        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
-            raise ValueError(
-                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
-                f" the maximum length ({generation_config.max_length})"
-            )
-        if input_ids_seq_length >= generation_config.max_length:
-            logger.warning(
-                f"Input length of decoder_input_ids is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
-
-        # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to Musicgen Melody)
+        # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
         input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
             input_ids,
-            pad_token_id=generation_config.decoder_start_token_id,
+            pad_token_id=generation_config._decoder_start_token_tensor,
             max_length=generation_config.max_length,
         )
         # stash the delay mask so that we don't have to recompute in each forward pass
@@ -2544,7 +2477,7 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
         # 9. prepare distribution pre_processing samplers
         logits_processor = self._get_logits_processor(
             generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
+            input_ids_seq_length=input_ids_length,
             encoder_input_ids=inputs_tensor,
             prefix_allowed_tokens_fn=None,
             logits_processor=logits_processor,
@@ -2599,7 +2532,7 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
         output_ids = self.decoder.apply_delay_pattern_mask(output_ids, model_kwargs["decoder_delay_pattern_mask"])
 
         # revert the pattern delay mask by filtering the pad token id
-        output_ids = output_ids[output_ids != generation_config.pad_token_id].reshape(
+        output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
             batch_size, self.decoder.num_codebooks, -1
         )
 

tests/generation/test_utils.py

@@ -3196,6 +3196,40 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
         )
         self.assertTrue(input_length <= out.shape[-1] <= input_length + 20)
 
+    def test_special_tokens_fall_back_to_model_default(self):
+        # PT-only test: TF doesn't support assisted decoding yet.
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM").to(
+            torch_device
+        )
+        test_bos_id = 50
+
+        # Sanity-check: the model has a BOS token set, and the first generated token is a BOS token
+        gen_output = model.generate()
+        self.assertTrue(model.generation_config.bos_token_id is not None)
+        self.assertTrue(model.generation_config.bos_token_id == gen_output[0, 0])
+
+        # If we pass a generation config **with** a BOS token, `generate` will use it
+        generation_config = GenerationConfig(bos_token_id=test_bos_id)
+        gen_output = model.generate(generation_config=generation_config)
+        self.assertFalse(model.generation_config.bos_token_id == gen_output[0, 0])
+        self.assertTrue(generation_config.bos_token_id == gen_output[0, 0])
+        self.assertTrue(test_bos_id == gen_output[0, 0])
+
+        # If we pass a generation config **without** a BOS token, `generate` will fetch the BOS token from
+        # `model.generation_config`
+        generation_config = GenerationConfig(bos_token_id=None)
+        gen_output = model.generate(generation_config=generation_config)
+        self.assertTrue(model.generation_config.bos_token_id == gen_output[0, 0])
+        self.assertFalse(test_bos_id == gen_output[0, 0])
+        self.assertTrue(generation_config.bos_token_id is None)
+
+        # Changing `model.generation_config` will affect fallback behavior
+        model.generation_config.bos_token_id = test_bos_id
+        gen_output = model.generate(generation_config=generation_config)
+        self.assertTrue(model.generation_config.bos_token_id == gen_output[0, 0])
+        self.assertTrue(test_bos_id == gen_output[0, 0])
+        self.assertTrue(generation_config.bos_token_id is None)
+
 
 @require_torch
 class TokenHealingTestCase(unittest.TestCase):