custom_generate/generate.py

from typing import Union, Optional, TYPE_CHECKING
import torch
from transformers import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
from transformers.generation.utils import (
    GenerateBeamOutput,
    GenerationMixin,
    GenerateBeamDecoderOnlyOutput,
    GenerateBeamEncoderDecoderOutput,
)
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import logging

from .beam_constraints import DisjunctiveConstraint, PhrasalConstraint
from .beam_search import ConstrainedBeamSearchScorer

if TYPE_CHECKING:
    from transformers.generation.streamers import BaseStreamer

logger = logging.getLogger(__name__)

def _constrained_beam_search(
        model,
        input_ids: torch.LongTensor,
        logits_processor: LogitsProcessorList,
        stopping_criteria: StoppingCriteriaList,
        generation_config: GenerationConfig,
        synced_gpus: bool,
        streamer: Optional["BaseStreamer"] = None,
        **model_kwargs,
    ) -> Union[GenerateBeamOutput, torch.LongTensor]:
        r"""
        Generates sequences of token ids for models with a language modeling head using **constrained beam search
        decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.

        Parameters:
            input_ids (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`):
                The sequence used as a prompt for the generation.
            logits_processor (`LogitsProcessorList`):
                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
                used to modify the prediction scores of the language modeling head applied at each generation step.
            stopping_criteria (`StoppingCriteriaList`):
                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
                used to tell if the generation loop should stop.
            generation_config ([`~generation.GenerationConfig`]):
                The generation configuration to be used as parametrization of the decoding method.
            synced_gpus (`bool`):
                Whether to continue running the while loop until max_length (needed to avoid deadlocking with
                `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
            model_kwargs:
                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
                an encoder-decoder model the kwargs should include `encoder_outputs`.

        Return:
            [`~generation.GenerateBeamDecoderOnlyOutput`], [`~generation.GenerateBeamEncoderDecoderOutput`] or
            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
            [`~generation.GenerateBeamDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
            `return_dict_in_generate=True` or a [`~generation.GenerateBeamEncoderDecoderOutput`] if
            `model.config.is_encoder_decoder=True`.
        """
        if generation_config.constraints is not None or generation_config.force_words_ids is not None:
            constrained_wrong_parameter_msg = (
                "one of `constraints`, `force_words_ids` is not `None`, triggering constrained beam search. "
                "However, `{flag_name}` is set to `{flag_value}`, which is incompatible with this generation "
                "mode. Set `constraints` and `force_words_ids` to `None` or unset `{flag_name}` to continue."
            )
            if generation_config.do_sample is True:
                raise ValueError(
                    constrained_wrong_parameter_msg.format(flag_name="do_sample", flag_value=generation_config.do_sample)
                )
        
        final_constraints = []
        if generation_config.constraints is not None:
            final_constraints = generation_config.constraints

        if generation_config.force_words_ids is not None:

            def typeerror():
                raise ValueError(
                    "`force_words_ids` has to either be a `list[list[list[int]]]` or `list[list[int]]` "
                    f"of positive integers, but is {generation_config.force_words_ids}."
                )

            if (
                not isinstance(generation_config.force_words_ids, list)
                or len(generation_config.force_words_ids) == 0
            ):
                typeerror()

            for word_ids in generation_config.force_words_ids:
                if isinstance(word_ids[0], list):
                    if not isinstance(word_ids, list) or len(word_ids) == 0:
                        typeerror()
                    if any(not isinstance(token_ids, list) for token_ids in word_ids):
                        typeerror()
                    if any(
                        any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
                        for token_ids in word_ids
                    ):
                        typeerror()

                    constraint = DisjunctiveConstraint(word_ids)
                else:
                    if not isinstance(word_ids, list) or len(word_ids) == 0:
                        typeerror()
                    if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
                        typeerror()

                    constraint = PhrasalConstraint(word_ids)
                final_constraints.append(constraint)
        # define beam scorer
        constrained_beam_scorer = ConstrainedBeamSearchScorer(
                constraints=final_constraints,
                batch_size=input_ids.shape[0] // generation_config.num_beams,
                num_beams=generation_config.num_beams,
                device=input_ids.device,
                length_penalty=generation_config.length_penalty,
                do_early_stopping=generation_config.early_stopping,
                num_beam_hyps_to_keep=generation_config.num_return_sequences,
                max_length=generation_config.max_length,
            )
        # init values
        pad_token_id = generation_config._pad_token_tensor
        eos_token_id = generation_config._eos_token_tensor
        output_attentions = generation_config.output_attentions
        output_hidden_states = generation_config.output_hidden_states
        output_scores = generation_config.output_scores
        output_logits = generation_config.output_logits
        return_dict_in_generate = generation_config.return_dict_in_generate

        batch_size = len(constrained_beam_scorer._beam_hyps)
        num_beams = constrained_beam_scorer.num_beams

        batch_beam_size, cur_len = input_ids.shape[:2]
        model_kwargs = model._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)

        if num_beams * batch_size != batch_beam_size:
            raise ValueError(
                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
            )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        raw_logits = () if (return_dict_in_generate and output_logits) else None
        beam_indices = (
            tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
        )
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and model.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        # initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
        # of the first beam are considered to avoid sampling the exact same tokens across all beams.
        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
        beam_scores[:, 1:] = -1e9
        beam_scores = beam_scores.view((batch_size * num_beams,))

        this_peer_finished = False

        decoder_prompt_len = input_ids.shape[1]  # record the prompt length of decoder
        while model._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
            model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)

            # prepare variable output controls (note: some models won't accept all output controls)
            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})

            outputs = model(**model_inputs, return_dict=True)

            # synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
            model_kwargs = model._update_model_kwargs_for_generation(
                outputs,
                model_kwargs,
                is_encoder_decoder=model.config.is_encoder_decoder,
            )
            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue

            # Copy is needed to avoid keeping a hanging ref to outputs.logits which may be very large for first iteration
            # (the clone itself is always small)
            # .float() is needed to retain precision for later logits manipulations
            next_token_logits = outputs.logits[:, -1, :].to(copy=True, dtype=torch.float32, device=input_ids.device)
            next_token_scores = nn.functional.log_softmax(
                next_token_logits, dim=-1
            )  # (batch_size * num_beams, vocab_size)

            next_token_scores_processed = logits_processor(input_ids, next_token_scores)

            next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(
                next_token_scores_processed
            )

            scores_for_all_vocab = next_token_scores.clone()

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores,)
                if output_logits:
                    raw_logits += (next_token_logits,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if model.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if model.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if model.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # reshape for beam search
            vocab_size = next_token_scores.shape[-1]
            next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)

            # Sample 1 + len(eos_token_id) next tokens for each beam so we have at least 1 non eos token per beam.
            n_eos_tokens = eos_token_id.shape[0] if eos_token_id is not None else 0
            next_token_scores, next_tokens = torch.topk(
                next_token_scores, max(2, 1 + n_eos_tokens) * num_beams, dim=1, largest=True, sorted=True
            )

            next_indices = (next_tokens / vocab_size).long()
            next_tokens = next_tokens % vocab_size

            # stateless
            beam_outputs = constrained_beam_scorer.process(
                input_ids,
                next_token_scores,
                next_tokens,
                next_indices,
                scores_for_all_vocab,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                beam_indices=beam_indices,
                decoder_prompt_len=decoder_prompt_len,
            )
            beam_scores = beam_outputs["next_beam_scores"]
            beam_next_tokens = beam_outputs["next_beam_tokens"]
            beam_idx = beam_outputs["next_beam_indices"]

            input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)

            # This is needed to properly delete outputs.logits which may be very large for first iteration
            # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
            # IMPORTANT: Note that this should appear BEFORE the call to _reorder_cache() to save the maximum memory
            # (that way the memory peak does not include outputs.logits)
            del outputs

            # NOTE: we need to check if `model._reorder_cache` exists for special models like RAG, RecurrentGemma etc.
            if model_kwargs.get("past_key_values", None) is not None:
                if hasattr(model, "_reorder_cache"):
                    model_kwargs["past_key_values"] = model._reorder_cache(model_kwargs["past_key_values"], beam_idx)
                else:
                    model_kwargs["past_key_values"].reorder_cache(beam_idx)

            if return_dict_in_generate and output_scores:
                beam_indices = tuple(beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices)))

            # increase cur_len
            cur_len = cur_len + 1

            if constrained_beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
                this_peer_finished = True

        sequence_outputs = constrained_beam_scorer.finalize(
            input_ids,
            beam_scores,
            next_tokens,
            next_indices,
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            max_length=stopping_criteria.max_length,
            beam_indices=beam_indices,
            decoder_prompt_len=decoder_prompt_len,
        )

        if return_dict_in_generate:
            if not output_scores:
                sequence_outputs["sequence_scores"] = None
            if model.config.is_encoder_decoder:
                return GenerateBeamEncoderDecoderOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    logits=raw_logits,
                    beam_indices=sequence_outputs["beam_indices"],
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                    past_key_values=model_kwargs.get("past_key_values"),
                )
            else:
                return GenerateBeamDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    logits=raw_logits,
                    beam_indices=sequence_outputs["beam_indices"],
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                    past_key_values=model_kwargs.get("past_key_values"),
                )
        else:
            return sequence_outputs["sequences"]

def generate(model, *args, **kwargs):
    """Custom generate function for constrained beam search decoding.
    Args:
        model (`PreTrainedModel`):
            The model to generate from.
        num_beams (`int`): The number of beams to use for beam search.
        constraints (`list[Constraint]`, *optional*):
            Custom constraints that can be added to the generation to ensure that the output will contain the use of
            certain tokens as defined by `Constraint` objects, in the most sensible way possible.
        force_words_ids (`list[list[list[int]]]`): List of token ids that must be generated. If given a `list[list[int]]`, this is treated as a simple list of
            words that must be included, the opposite to `bad_words_ids`. If given `list[list[list[int]]]`, this
            triggers a [disjunctive constraint](https://github.com/huggingface/transformers/issues/14081), where one
            can allow different forms of each word.
        length_penalty (`float`): The length penalty to use for beam search.
        early_stopping (`bool`): Whether to stop beam search when sufficient beams have finished.
        num_return_sequences (`int`): The number of sequences to return.
        max_length (`int`): The maximum length of the generated sequence.
    """
    generation_outputs = GenerationMixin.generate(
        model, *args, custom_generate=_constrained_beam_search, **kwargs
    )
    return generation_outputs