BART can only generate a maximum of 20 tokens

[ayaka14732]

Environment info

• transformers version: 4.18.0.dev0
• Platform: Linux-5.11.0-1018-gcp-x86_64-with-glibc2.31
• Python version: 3.10.4
• Huggingface_hub version: 0.4.0
• PyTorch version (GPU?): 1.11.0+cu102 (False)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): 0.4.1 (tpu)
• Jax version: 0.3.4
• JaxLib version: 0.3.2
• Using GPU in script?: No
• Using distributed or parallel set-up in script?: No

Who can help

@patil-suraj @patrickvonplaten

Information

Model I am using: BART

To reproduce

Steps to reproduce the behavior:

from transformers import BartTokenizer, BartForConditionalGeneration

tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-base')

sentences = ['At the launch of the latest report by the Intergovernmental Panel on Climate Change, on the mitigation of climate change, the UN Secretary-General called for an urgent shift of investments and subsidies from fossil fuels to renewable energy, warning that investing in new fossil fuels infrastructure is moral and economic madness.']

inputs = tokenizer(sentences, return_tensors='pt')
print('Input shape:', inputs.input_ids.shape)

generate_ids = model.generate(inputs.input_ids, num_beams=5, min_length=50)
print('Generated shape:', generate_ids.shape)

print(tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])

Output:

Input shape: torch.Size([1, 60])
Generated shape: torch.Size([1, 20])
At the launch of the latest report by the Intergovernmental Panel on Climate Change, on

Expected behavior

The output should not be truncated.

Actual behavior

The output is truncated.

Note that the output is truncated even if min_length=50 is specified.

[gante]

Hi @ayaka14732 👋 That happens because the stopping conditions take precedence over anything else. The default for max_length is 20, so that's why you see 20 generated tokens. In your example, if you rewrite the generate line into generate_ids = model.generate(inputs.input_ids, num_beams=5, min_length=50, max_length=100), you'll get the results you expect.

@patrickvonplaten @patil-suraj should we raise an exception in this case? (min_length > max_length)

[patrickvonplaten]

@gante, yes this would work for me! Let's maybe do this in generate() before we just into the sub-generation methods

[gante]

@ayaka14732 if you pull from master (or install transformers==4.19.0.dev0), you shall see an informative Exception if you try to run your original script.

Thank you for reporting this issue :D