Generate: v4.38 removals and related updates (#29171)

src/transformers/generation/__init__.py

@@ -40,6 +40,11 @@ else:
         "BeamSearchScorer",
         "ConstrainedBeamSearchScorer",
     ]
+    _import_structure["candidate_generator"] = [
+        "AssistedCandidateGenerator",
+        "CandidateGenerator",
+        "PromptLookupCandidateGenerator",
+    ]
     _import_structure["logits_process"] = [
         "AlternatingCodebooksLogitsProcessor",
         "ClassifierFreeGuidanceLogitsProcessor",
@@ -178,6 +183,7 @@ if TYPE_CHECKING:
     else:
         from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
         from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
+        from .candidate_generator import AssistedCandidateGenerator, CandidateGenerator, PromptLookupCandidateGenerator
         from .logits_process import (
             AlternatingCodebooksLogitsProcessor,
             ClassifierFreeGuidanceLogitsProcessor,

src/transformers/generation/candidate_generator.py

@@ -99,6 +99,7 @@ class AssistedCandidateGenerator(CandidateGenerator):
         # Make sure all data at the same device as assistant model
         device = assistant_model.device
         input_ids = input_ids.to(device)
+        if inputs_tensor is not None:
             inputs_tensor = inputs_tensor.to(device)
 
         # Prepare the assistant and the starting number of candidate tokens

src/transformers/generation/utils.py

@@ -4319,7 +4319,6 @@ class GenerationMixin:
     def assisted_decoding(
         self,
         input_ids: torch.LongTensor,
-        assistant_model: Optional["PreTrainedModel"] = None,
         candidate_generator: Optional["CandidateGenerator"] = None,
         do_sample: bool = False,
         logits_processor: Optional[LogitsProcessorList] = None,
@@ -4355,12 +4354,7 @@ class GenerationMixin:
                 The sequence used as a prompt for the generation.
             candidate_generator (`CandidateGenerator`, *optional*):
                 A derived instance of [`CandidateGenerator`] that defines how candidate sequences are generated. For
-                more information, the documentation of [`CandidateGenerator`] should be read. Only one of `assistant_model` or `candidate_generator` should be passed as input to this function.
-            assistant_model (`PreTrainedModel`, *optional*):
-                An assistant model that can be used to accelerate generation. The assistant model must have the exact
-                same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistent model
-                is much faster than running generation with the model you're calling generate from. As such, the
-                assistant model should be much smaller.
+                more information, the documentation of [`CandidateGenerator`] should be read.
             do_sample (`bool`, *optional*, defaults to `False`):
                 Whether or not to use sampling ; use greedy decoding otherwise.
             logits_processor (`LogitsProcessorList`, *optional*):
@@ -4417,6 +4411,7 @@ class GenerationMixin:
         ...     StoppingCriteriaList,
         ...     MaxLengthCriteria,
         ... )
+        >>> from transformers.generation import AssistedCandidateGenerator
 
         >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
         >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
@@ -4432,33 +4427,22 @@ class GenerationMixin:
         ...     ]
         ... )
         >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
+        >>> candidate_generator = AssistedCandidateGenerator(
+        ...     input_ids=input_ids,
+        ...     assistant_model=assistant_model,
+        ...     generation_config=model.generation_config,
+        ...     logits_processor=logits_processor,
+        ...     model_kwargs={},
+        ... )
         >>> outputs = model.assisted_decoding(
         ...     input_ids,
-        ...     assistant_model=assistant_model,
+        ...     candidate_generator=candidate_generator,
         ...     logits_processor=logits_processor,
         ...     stopping_criteria=stopping_criteria,
         ... )
         >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
         ["It might be possible to get a better understanding of the nature of the problem, but it's not"]
         ```"""
-        # handling deprecated arguments
-        if (assistant_model is None) == (candidate_generator is None):
-            raise ValueError("One (and only one) of `assistant_model` and `candidate_generator` should be defined.")
-
-        if assistant_model is not None:
-            candidate_generator = AssistedCandidateGenerator(
-                input_ids=input_ids,
-                assistant_model=assistant_model,
-                logits_processor=logits_processor,
-                model_kwargs=model_kwargs,
-                eos_token_id=eos_token_id,
-            )
-            warnings.warn(
-                "Passing `assistant_model` to `assisted_decoding` is deprecated and will be removed in v4.38. "
-                "Pass the `candidate_generator` argument instead.",
-                FutureWarning,
-            )
-
         # init values
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
         logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()

src/transformers/models/opt/modeling_opt.py

@@ -129,8 +129,8 @@ class OPTAttention(nn.Module):
             val = None
             if fn_arg_name in kwargs:
                 logging.warning(
-                    "Passing in {} to {self.__class__.__name__} is deprecated and won't be supported from v4.38."
-                    " Please set it in the config instead"
+                    "Passing in {fn_arg_name} to {self.__class__.__name__} is deprecated and won't be supported from "
+                    "v4.39. Please set it in the config instead"
                 )
                 val = kwargs.pop(fn_arg_name)
             else:

src/transformers/utils/__init__.py

@@ -120,7 +120,6 @@ from .import_utils import (
     is_essentia_available,
     is_faiss_available,
     is_flash_attn_2_available,
-    is_flash_attn_available,
     is_flash_attn_greater_or_equal_2_10,
     is_flax_available,
     is_fsdp_available,

src/transformers/utils/import_utils.py

@@ -665,14 +665,6 @@ def is_flash_attn_greater_or_equal_2_10():
     return version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.1.0")
 
 
-def is_flash_attn_available():
-    logger.warning(
-        "Using `is_flash_attn_available` is deprecated and will be removed in v4.38. "
-        "Please use `is_flash_attn_2_available` instead."
-    )
-    return is_flash_attn_2_available()
-
-
 def is_torchdistx_available():
     return _torchdistx_available