Unable to use model.generate for Vision encoder decoder model

Beginners
1

Here is my model configuration

from transformers import ViTImageProcessor, BertTokenizer, VisionEncoderDecoderModel
from datasets import load_dataset

model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
    "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased"
)

image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")

model.config.decoder_start_token_id = tokenizer.cls_token_id
model.config.pad_token_id = tokenizer.pad_token_id


# print(model.config.decoder_start_token_id) --> returns 0

After finetuning, I tried model.generate() but I am getting the below error

ValueError                                Traceback (most recent call last)
Cell In[69], line 1
----> 1 model.generate(s)

File /opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
    112 @functools.wraps(func)
    113 def decorate_context(*args, **kwargs):
    114     with ctx_factory():
--> 115         return func(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:1419, in GenerationMixin.generate(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)
   1417 # 5. Prepare `input_ids` which will be used for auto-regressive generation
   1418 if self.config.is_encoder_decoder:
-> 1419     input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
   1420         batch_size=batch_size,
   1421         model_input_name=model_input_name,
   1422         model_kwargs=model_kwargs,
   1423         decoder_start_token_id=generation_config.decoder_start_token_id,
   1424         bos_token_id=generation_config.bos_token_id,
   1425         device=inputs_tensor.device,
   1426     )
   1427 else:
   1428     input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")

File /opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:542, in GenerationMixin._prepare_decoder_input_ids_for_generation(self, batch_size, model_input_name, model_kwargs, decoder_start_token_id, bos_token_id, device)
    539     decoder_input_ids = None
    541 # 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
--> 542 decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
    543 if device is None:
    544     device = self.device

File /opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:599, in GenerationMixin._get_decoder_start_token_id(self, decoder_start_token_id, bos_token_id)
    597 elif bos_token_id is not None:
    598     return bos_token_id
--> 599 raise ValueError(
    600     "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
    601 )

ValueError: `decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation.

In spite of setting decoder_start_token_id, why am I getting the ValueError?

1 Like
2

Got the same error here

3

@hwaseem04 I got the solution for ValueError: 'decoder_start_token_id' or 'bos_token_id' has to be defined for encoder-decoder generation.

I set up the generation_config by doing model.generation_config.decoder_start_token_id = tokenizer.cls_token_id

4

cc @joaogante there might be a regression here

e.g. this blog post should work OOTB: Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models