Generate: add model class validation

src/transformers/generation_flax_utils.py

@@ -36,6 +36,11 @@
     FlaxTopKLogitsWarper,
     FlaxTopPLogitsWarper,
 )
+from .models.auto import (
+    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
+    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
+)
 from .utils import ModelOutput, logging
 
 
@@ -161,6 +166,30 @@ def _adapt_logits_for_beam_search(self, logits):
         """
         return logits
 
+    def _validate_model_class(self):
+        """
+        Confirms that the model class is compatible with generation. If not, raises an exception that points to the
+        right class to use.
+        """
+        if not hasattr(self, "prepare_inputs_for_generation"):
+            generate_compatible_mappings = [
+                FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
+                FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
+                FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+            ]
+            generate_compatible_classes = set()
+            for model_mapping in generate_compatible_mappings:
+                supported_models = model_mapping.get(type(self.config), default=None)
+                if supported_models is not None:
+                    generate_compatible_classes.add(supported_models.__name__)
+            exception_message = (
+                f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
+                "it doesn't have a language model head."
+            )
+            if generate_compatible_classes:
+                exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
+            raise TypeError(exception_message)
+
     def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
         """Validates model kwargs for generation. Generate argument typos will also be caught here."""
         unused_model_args = []
@@ -281,7 +310,8 @@ def generate(
         >>> outputs = model.generate(input_ids=input_ids, max_length=20, top_k=30, do_sample=True)
         >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
         ```"""
-        # Validate model kwargs
+        # Validate the `.generate()` call
+        self._validate_model_class()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # set init values

src/transformers/generation_tf_utils.py

@@ -35,6 +35,12 @@
     TFTopKLogitsWarper,
     TFTopPLogitsWarper,
 )
+from .models.auto import (
+    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
+    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
+    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
+)
 from .tf_utils import shape_list, stable_softmax
 from .utils import ModelOutput, logging
 
@@ -357,12 +363,6 @@ def seed_generator(self):
 
     supports_xla_generation = True
 
-    def prepare_inputs_for_generation(self, inputs, **kwargs):
-        """
-        Implement in subclasses of [`TFPreTrainedModel`] for custom behavior to prepare inputs in the generate method.
-        """
-        return {"input_ids": inputs}
-
     def _use_cache(self, outputs, use_cache):
         """During generation, decide whether to pass the `past` variable to the next forward pass."""
         use_cache = getattr(self.config, "use_cache", False)
@@ -1290,6 +1290,31 @@ def adjust_logits_during_generation(
         else:
             return logits
 
+    def _validate_model_class(self):
+        """
+        Confirms that the model class is compatible with generation. If not, raises an exception that points to the
+        right class to use.
+        """
+        if not hasattr(self, "prepare_inputs_for_generation"):
+            generate_compatible_mappings = [
+                TF_MODEL_FOR_CAUSAL_LM_MAPPING,
+                TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
+                TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+                TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
+            ]
+            generate_compatible_classes = set()
+            for model_mapping in generate_compatible_mappings:
+                supported_models = model_mapping.get(type(self.config), default=None)
+                if supported_models is not None:
+                    generate_compatible_classes.add(supported_models.__name__)
+            exception_message = (
+                f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
+                "it doesn't have a language model head."
+            )
+            if generate_compatible_classes:
+                exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
+            raise TypeError(exception_message)
+
     def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
         """Validates model kwargs for generation. Generate argument typos will also be caught here."""
         # Excludes arguments that are handled before calling any model function
@@ -1508,7 +1533,8 @@ def _generate(
         # generate sequences without allowing bad_words to be generated
         outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)
         ```"""
-        # 0. Validate model kwargs
+        # 0. Validate the `.generate()` call
+        self._validate_model_class()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # 1. Set generation parameters if not already defined

src/transformers/generation_utils.py

@@ -51,6 +51,13 @@
     StoppingCriteriaList,
     validate_stopping_criteria,
 )
+from .models.auto import (
+    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
+    MODEL_FOR_CAUSAL_LM_MAPPING,
+    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
+    MODEL_FOR_VISION_2_SEQ_MAPPING,
+)
 from .pytorch_utils import torch_int_div
 from .utils import ModelOutput, logging
 
@@ -463,12 +470,6 @@ def _can_retrieve_inputs_from_name(
 
         return can_retrieve_inputs
 
-    def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> Dict[str, Any]:
-        """
-        Implement in subclasses of [`PreTrainedModel`] for custom behavior to prepare inputs in the generate method.
-        """
-        return {"input_ids": input_ids}
-
     def adjust_logits_during_generation(self, logits: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
         """
         Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
@@ -841,6 +842,32 @@ def compute_transition_beam_scores(
 
         return transition_scores
 
+    def _validate_model_class(self):
+        """
+        Confirms that the model class is compatible with generation. If not, raises an exception that points to the
+        right class to use.
+        """
+        if not hasattr(self, "prepare_inputs_for_generation"):
+            generate_compatible_mappings = [
+                MODEL_FOR_CAUSAL_LM_MAPPING,
+                MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
+                MODEL_FOR_VISION_2_SEQ_MAPPING,
+                MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+                MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
+            ]
+            generate_compatible_classes = set()
+            for model_mapping in generate_compatible_mappings:
+                supported_models = model_mapping.get(type(self.config), default=None)
+                if supported_models is not None:
+                    generate_compatible_classes.add(supported_models.__name__)
+            exception_message = (
+                f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
+                "it doesn't have a language model head."
+            )
+            if generate_compatible_classes:
+                exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
+            raise TypeError(exception_message)
+
     def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
         """Validates model kwargs for generation. Generate argument typos will also be caught here."""
         # Excludes arguments that are handled before calling any model function
@@ -1143,7 +1170,8 @@ def generate(
         >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
         ['Paris ist eines der dichtesten besiedelten Gebiete Europas.']
         ```"""
-        # 0. Validate model kwargs
+        # 0. Validate the `.generate()` call
+        self._validate_model_class()
         self._validate_model_kwargs(model_kwargs.copy())
 
         # 1. Set generation parameters if not already defined

src/transformers/models/openai/modeling_openai.py

@@ -20,7 +20,7 @@
 import math
 import os
 from dataclasses import dataclass
-from typing import Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 from torch import nn
@@ -607,6 +607,9 @@ def forward(
             attentions=transformer_outputs.attentions,
         )
 
+    def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> Dict[str, Any]:
+        return {"input_ids": input_ids}
+
 
 @add_start_docstrings(
     """

src/transformers/models/openai/modeling_tf_openai.py

@@ -633,6 +633,9 @@ def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
 
         return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
 
+    def prepare_inputs_for_generation(self, inputs, **kwargs):
+        return {"input_ids": inputs}
+
 
 @add_start_docstrings(
     """