Unofficial documentation for the HuggingFace🤗 generation pipeline

by Manuel de Prada Corral

6 min read

While implementing a new generation strategy for Transformer models, I found myself delving deep into the HuggingFace library. The documentation is clear with respect to the usage, but not so much with respect to the implementation details.

Here is a collection of notes I've compiled from my dive into the codebase. This may prove beneficial for anyone looking to understand or extend HuggingFace's generation pipeline.

The overall class structure

HuggingFace Transformer models have all one common ancestor: PreTrainedModel. This class is defined in transformers/ It is a subclass of torch.nn.Module, ModuleUtilsMixin, GenerationMixin and PushToHubMixin.

graph TD;

The generation pipeline for all Transformer models is centralized in GenerationMixin. This class is defined in transformers/generation/, and all models must implement prepare_inputs_for_generation. Additionally, models can implement adjust_logits_during_generation and _reorder_cache.

The main method in GenerationMixin is generate, which orchestrates the generation process and then calls the different specialized methods such as contrastive_search, greedy_search, sample, beam_search, beam_sample, group_beam_search, constrained_beam_search and assisted_decoding.

The generation pipeline

Let's break down the generation pipeline into its different steps. Note that these steps are written with the same numbers into the code comments.This is a permalink to the generate method being analyzed in this post (note that HF is a fast moving target, so some details may be outdated soon).

Another vital point to note is that the generation happens in batches, meaning that the input_ids have a shape of (batch_size, seq_len, embed_dim). This is to allow, for example, to translate multiple sentences at once.

%%{init: { 'themeVariables': {'fontSize': '24px'} } }%%
    1. Prepare generation_config : Merge model and users gen config
    2. Set generation parameters" : Prepare logits processors and stopping criteria
    3. Define model_inputs : Get encoder inputs if needed
    4. Define other model kwargs
    5. Prepare input_ids which for the decoder : Initialize with <bos> if needed
%%{init: { 'themeVariables': {'fontSize': '23px'} } }%%
    6. Prepare `max_length` depending on stopping criteria
    7. Determine generation mode : Set is_greedy, is_sample, is_beam, ... : check if arguments are consistent
    8. Prepare distribution pre_processing samplers : Prepare logits_processor
    9. Prepare stopping criteria
    10. Go into different generation modes

The logits_processor is a list of functions that are applied to the logits before selecting or sampling the next token. There is also a logits_warper that is applied to the logits after the logits_processor but only in stochastic generation modes (sample, beam_sample, assisted_decoding, constraint_beam_search and contrastive_search). Also, in beam_sample mode, logits_processor is applied to the logits, but then the logits are integrated into the beam search scores, and the logits_warper is applied to the beam search scores.

The beam_search generation mode

    11. Prepare beam search scorer : initialize beam hypotheses
    12. Interleave input_ids with n_beams additional sequences : tensor of shape [batch_size, seq_len, embed_dim] -> [batch_size*n_beams, seq_len, embed_dim]
    13. Run beam search : call beam_search method

The beam search generation mode has two main components:

  • The beam_search method, found in GenerationMixin, handles the primary decoding loop, maintains the beam scores and calls the model (referenced in step 13 of generate).
  • In transformers/generation/, BeamSearchScorer has one BeamHypotheses object for each sequence in the batch. It is a general construction that makes sense for generalizing beam search to diverse_beam_search (keep different groups of beams to ensure diversity).
    • The BeamHypotheses keeps the list of the n_beams best hypotheses for each sequence in the batch, with its beam scores and beam indices.

The beam_search method

  1. Initialize the beam_scores to 0 as a tensor of dimension (batch_size, n_beams).
  2. Set beam_scores to -\infty for all beams except the first one (beam_scores[:,1:] = -1e9).
  3. View beam_scores as a 1D tensor of dimension (batch_size*n_beams).
  4. Generation loop:
    1. Run the model, get outputs for the next token over all beams of all sequences in the batch.
    2. Locally normalize the output (apply log_softmax).
    3. Apply the logits_processor to the logits.
    4. Append the new logits to the running beam scores. Note that now we have a tensor of dimension (batch_size*n_beams, vocab_size).
    5. To form the next_token_scores, view as a tensor of dimension (batch_size, n_beams*vocab_size).
    6. Get the 2*n_beams best scores from next_token_scores by applying torch.topk. Derive the beam indices and token indices.
    7. Call beam_scorer.process to update the beam hypotheses. Get the new beam scores, indices and next_tokens for each beam. Update input_ids with the new tokens.
    8. If all beams are finished or the stopping criteria are met, break the loop.

The BeamScorer process method

This method is defined in transformers/generation/ and takes as output the 2*n_beams topk elements and indexes calculated above. The beam search scorer is initialized with a BeamHypotheses object for each sequence in the batch.

  1. Create new tensors for the next scores, tokens and indices of dimension (batch_size, group_size) (this is because of diverse beam search, we know group_size=n_beams for normal beam search. In this case, the tensors have dimension (batch_size, n_beams)).
  2. For each beam hypotheses object in the scorer (i.e. for each sentence in the batch):
    1. If the sentence is finished, do nothing and continue to the next sentence.
    2. For each (token, score, index) in the top 2*n_beams next scores among the n_beams*vocab_size scores:
      1. If the token is the EOS token, check if the beam is still among the n_beams best beams. If so, add the beam to the list of hypotheses of the sentence. The beam_score for this beam would be 0, since it moves from the running beams to the finished beams.
      2. If the token is not the EOS token, add the token, score and beam_index to the next scores, tokens and indices tensors. If we have already all the running beams, break the loop (remember that we started from the top scores, so we only want to keep the n_beams best finished beams and the n_beams best running beams).

We can see how the beam_hypotheses keep the n_beams best finished beams, while the n_beams best running beams are kept in the next_scores, next_tokens and next_indices tensors, which are sent back and forth between the beam_search method and the process method, as the main loop from the beam_search progresses through the running beams.

The interesting (and obscure) bits

Why do we need to select the 2*n_beams best beams? It is something strange at first look. From a theoretical point of view, each new generation step will always make the sequence probabilities smaller, so the first n_beams that reach <EOS> will always be higher probability than any possible continuation. However, there is two empirical reasons to keep more beams alive.

First, in closed-vocabulary models, we might encounter that <UNK> is the best token at some point. Most beam search implementations will fall back to the next best token in this case, hence needing n_beams+1 tokens. Second, beam search is commonly used with length normalization, which allows longer sequences to have a higher probability as they grow longer. This means that we need to store separately the best finished beams and the best running beams, and only make the comparison between them when they are finished (thanks Clara for helping me figure this out!).

This is why HF's beam_search saves 2*n_beams beams. We might encounter situations where all the alive n_beams sequences reach <EOS>, leaving no live sequences to continue. With 2*n_beams, we are guaranteed to have at least one non-EOS token for each beam hypothesis.

On top of this, without length normalization, we can stop generation when n_beams sequences reach <EOS>. This is achieved in HF by setting early_stopping=True. When early_stopping is set to False or "never", HF will use two different non-satisfactory heuristics to stop generation whenever the best running beam is thought to be worse than the worst finished beam. Surprisingly, no setting of early_stopping will effectively stop early stopping and let the generation continue until all beams are finished or the maximum length is reached. To be fair, this would probably cause OOM problems.

Interestingly, the beam search in HuggingFace was adapted from facebookresearch/XLM. You can check out the original 2019 commit here. Early days when Thomas Wolf was coding and HuggingFace was still a chatbot for teenagers!

The different scores and how to interpret them

During beam search, we keep track of the following scores:

  • beam_scores: The running scores of the beams. This is the sum of the log probabilities of the tokens generated so far for each beam. It is a tensor of dimension (batch_size * n_beams). They model logits may have been modified by the logit processors or by the length penalty. Optionally, also:
  • scores: The word-per-word scores of the beams, this is, the log probabilities for every token in the vocabulary at each generation step. It is a tuple of size seq_len of tensors of dimension (batch_size * n_beams, vocab_size). Beam indices are needed to recover the scores for each selected token.
  • beam_indices: The indices of the beams that generated the scores at each time step. I believe here beam_indices are referred to the indices of the n_beams * vocab_size scores of the previous timestep torch.topk call. However, I am not sure, and the indices may maintain coherence across timesteps. TODO: investigate this.