Source code for transformers.generation_flax_utils

# coding=utf-8
# Copyright 2021 The Google AI Flax Team Authors, and The HuggingFace Inc. team.
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from functools import partial
from typing import Dict, Optional

import numpy as np

import flax
import jax
import jax.numpy as jnp
from jax import lax

from .file_utils import ModelOutput
from .generation_flax_logits_process import (
    FlaxForcedBOSTokenLogitsProcessor,
    FlaxForcedEOSTokenLogitsProcessor,
    FlaxLogitsProcessorList,
    FlaxMinLengthLogitsProcessor,
    FlaxTemperatureLogitsWarper,
    FlaxTopKLogitsWarper,
    FlaxTopPLogitsWarper,
)
from .utils import logging


logger = logging.get_logger(__name__)


[docs]@flax.struct.dataclass class FlaxGreedySearchOutput(ModelOutput): """ Flax Base class for outputs of decoder-only generation models using greedy search. Args: sequences (:obj:`jnp.ndarray` of shape :obj:`(batch_size, max_length)`): The generated sequences. """ sequences: jnp.ndarray = None
[docs]@flax.struct.dataclass class FlaxSampleOutput(ModelOutput): """ Flax Base class for outputs of decoder-only generation models using sampling. Args: sequences (:obj:`jnp.ndarray` of shape :obj:`(batch_size, max_length)`): The generated sequences. """ sequences: jnp.ndarray = None
@flax.struct.dataclass class FlaxBeamSearchOutput(ModelOutput): """ Flax Base class for outputs of decoder-only generation models using greedy search. Args: sequences (:obj:`jnp.ndarray` of shape :obj:`(batch_size, max_length)`): The generated sequences. scores (:obj:`jnp.ndarray` of shape :obj:`(batch_size,)`): The scores (log probabilites) of the generated sequences. """ sequences: jnp.ndarray = None scores: jnp.ndarray = None @flax.struct.dataclass class GreedyState: cur_len: jnp.ndarray sequences: jnp.ndarray running_token: jnp.ndarray is_sent_finished: jnp.ndarray model_kwargs: Dict[str, jnp.ndarray] @flax.struct.dataclass class SampleState: cur_len: jnp.ndarray sequences: jnp.ndarray running_token: jnp.ndarray is_sent_finished: jnp.ndarray prng_key: jnp.ndarray model_kwargs: Dict[str, jnp.ndarray] @flax.struct.dataclass class BeamSearchState: cur_len: jnp.ndarray running_sequences: jnp.ndarray running_scores: jnp.ndarray sequences: jnp.ndarray scores: jnp.ndarray is_sent_finished: jnp.ndarray model_kwargs: Dict[str, jnp.ndarray]
[docs]class FlaxGenerationMixin: """ A class containing all of the functions supporting generation, to be used as a mixin in :class:`~transformers.FlaxPreTrainedModel`. """ @staticmethod def _run_loop_in_debug(cond_fn, body_fn, init_state): """ Run generation in untraced mode. This should only be used for debugging purposes. """ state = init_state while cond_fn(state): state = body_fn(state) return state def _prepare_encoder_decoder_kwargs_for_generation(self, input_ids, params, model_kwargs): encoder_kwargs = { argument: value for argument, value in model_kwargs.items() if not (argument.startswith("decoder_") or argument.startswith("cross_attn")) } model_kwargs["encoder_outputs"] = self.encode(input_ids, params=params, return_dict=True, **encoder_kwargs) return model_kwargs @staticmethod def _expand_to_num_beams(tensor, num_beams): return jnp.broadcast_to(tensor[:, None], (tensor.shape[0], num_beams) + tensor.shape[1:]) def _adapt_logits_for_beam_search(self, logits): """ This function can be overwritten in the specific modeling_flax_<model-name>.py classes to allow for custom beam search behavior. Note that the only model that overwrites this method is :class:`~transformes.FlaxMarianMTModel`. """ return logits
[docs] def generate( self, input_ids: jnp.ndarray, max_length: Optional[int] = None, pad_token_id: Optional[int] = None, bos_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, decoder_start_token_id: Optional[int] = None, do_sample: Optional[bool] = None, prng_key: Optional[jnp.ndarray] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, temperature: Optional[float] = None, num_beams: Optional[int] = None, no_repeat_ngram_size: Optional[int] = None, min_length: Optional[int] = None, forced_bos_token_id: Optional[int] = None, forced_eos_token_id: Optional[int] = None, length_penalty: Optional[float] = None, early_stopping: Optional[bool] = None, trace: bool = True, params: Optional[Dict[str, jnp.ndarray]] = None, **model_kwargs, ): r""" Generates sequences for models with a language modeling head. The method currently supports greedy decoding, and, multinomial sampling. Apart from :obj:`input_ids`, all the arguments below will default to the value of the attribute of the same name inside the :class:`~transformers.PretrainedConfig` of the model. The default values indicated are the default values of those config. Most of these parameters are explained in more detail in `this blog post <https://huggingface.co/blog/how-to-generate>`__. Parameters: input_ids (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`): The sequence used as a prompt for the generation. max_length (:obj:`int`, `optional`, defaults to 20): The maximum length of the sequence to be generated. do_sample (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use sampling ; use greedy decoding otherwise. temperature (:obj:`float`, `optional`, defaults to 1.0): The value used to module the next token probabilities. top_k (:obj:`int`, `optional`, defaults to 50): The number of highest probability vocabulary tokens to keep for top-k-filtering. top_p (:obj:`float`, `optional`, defaults to 1.0): If set to float < 1, only the most probable tokens with probabilities that add up to :obj:`top_p` or higher are kept for generation. pad_token_id (:obj:`int`, `optional`): The id of the `padding` token. bos_token_id (:obj:`int`, `optional`): The id of the `beginning-of-sequence` token. eos_token_id (:obj:`int`, `optional`): The id of the `end-of-sequence` token. num_beams (:obj:`int`, `optional`, defaults to 1): Number of beams for beam search. 1 means no beam search. decoder_start_token_id (:obj:`int`, `optional`): If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token. trace (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to trace generation. Setting ``trace=False`` should only be used for debugging and will lead to a considerably slower runtime. params (:obj:`Dict[str, jnp.ndarray]`, `optional`): Optionally the model parameters can be passed. Can be useful for parallelized generation. model_kwargs: Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. Return: :class:`~transformers.file_utils.ModelOutput`. Examples:: >>> from transformers import AutoTokenizer, FlaxAutoModelForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2") >>> model = FlaxAutoModelForCausalLM.from_pretrained("distilgpt2") >>> input_context = "The dog" >>> # encode input context >>> input_ids = tokenizer(input_context, return_tensors="np").input_ids >>> # generate candidates using sampling >>> outputs = model.generate(input_ids=input_ids, max_length=20, top_k=30, do_sample=True) >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True)) """ # set init values max_length = max_length if max_length is not None else self.config.max_length bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id decoder_start_token_id = ( decoder_start_token_id if decoder_start_token_id else self.config.decoder_start_token_id ) prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0) if decoder_start_token_id is None and self.config.is_encoder_decoder: raise ValueError("`decoder_start_token_id` has to be defined for encoder-decoder generation.") if self.config.is_encoder_decoder: # add encoder_outputs to model_kwargs model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, params, model_kwargs) # prepare decoder_input_ids for generation input_ids = jnp.ones((input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id do_sample = do_sample if do_sample is not None else self.config.do_sample num_beams = num_beams if num_beams is not None else self.config.num_beams if not do_sample and num_beams == 1: logits_processor = self._get_logits_processor( no_repeat_ngram_size, min_length, max_length, eos_token_id, forced_bos_token_id, forced_eos_token_id ) return self._greedy_search( input_ids, max_length, pad_token_id, eos_token_id, logits_processor=logits_processor, trace=trace, params=params, model_kwargs=model_kwargs, ) elif do_sample and num_beams == 1: logits_warper = self._get_logits_warper(top_k=top_k, top_p=top_p, temperature=temperature) logits_processor = self._get_logits_processor( no_repeat_ngram_size, min_length, max_length, eos_token_id, forced_bos_token_id, forced_eos_token_id ) return self._sample( input_ids, max_length, pad_token_id, eos_token_id, prng_key, logits_warper=logits_warper, logits_processor=logits_processor, trace=trace, params=params, model_kwargs=model_kwargs, ) elif not do_sample and num_beams > 1: # broadcast input_ids & encoder_outputs input_ids = self._expand_to_num_beams(input_ids, num_beams=num_beams) if "encoder_outputs" in model_kwargs: model_kwargs["encoder_outputs"]["last_hidden_state"] = self._expand_to_num_beams( model_kwargs["encoder_outputs"]["last_hidden_state"], num_beams=num_beams ) if "attention_mask" in model_kwargs: model_kwargs["attention_mask"] = self._expand_to_num_beams( model_kwargs["attention_mask"], num_beams=num_beams ) logits_processor = self._get_logits_processor( no_repeat_ngram_size, min_length, max_length, eos_token_id, forced_bos_token_id, forced_eos_token_id ) return self._beam_search( input_ids, max_length, pad_token_id, eos_token_id, length_penalty=length_penalty, early_stopping=early_stopping, logits_processor=logits_processor, trace=trace, params=params, model_kwargs=model_kwargs, ) else: raise NotImplementedError("`Beam sampling is currently not implemented.")
def _get_logits_warper( self, top_k: int = None, top_p: float = None, temperature: float = None ) -> FlaxLogitsProcessorList: """ This class returns a :obj:`~transformers.FlaxLogitsProcessorList` list object that contains all relevant :obj:`~transformers.FlaxLogitsWarper` instances used for multinomial sampling. """ # init warp parameters top_k = top_k if top_k is not None else self.config.top_k top_p = top_p if top_p is not None else self.config.top_p temperature = temperature if temperature is not None else self.config.temperature # instantiate warpers list warpers = FlaxLogitsProcessorList() # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files # all samplers can be found in `generation_utils_samplers.py` if temperature is not None and temperature != 1.0: warpers.append(FlaxTemperatureLogitsWarper(temperature)) if top_k is not None and top_k != 0: warpers.append(FlaxTopKLogitsWarper(top_k=top_k, min_tokens_to_keep=1)) if top_p is not None and top_p < 1.0: warpers.append(FlaxTopPLogitsWarper(top_p=top_p, min_tokens_to_keep=1)) return warpers def _get_logits_processor( self, no_repeat_ngram_size: int, min_length: int, max_length: int, eos_token_id: int, forced_bos_token_id: int, forced_eos_token_id: int, ) -> FlaxLogitsProcessorList: """ This class returns a :obj:`~transformers.FlaxLogitsProcessorList` list object that contains all relevant :obj:`~transformers.FlaxLogitsProcessor` instances used to modify the scores of the language model head. """ processors = FlaxLogitsProcessorList() # init warp parameters no_repeat_ngram_size = ( no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size ) min_length = min_length if min_length is not None else self.config.min_length eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id forced_bos_token_id = ( forced_bos_token_id if forced_bos_token_id is not None else self.config.forced_bos_token_id ) forced_eos_token_id = ( forced_eos_token_id if forced_eos_token_id is not None else self.config.forced_eos_token_id ) # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files # all samplers can be found in `generation_utils_samplers.py` if min_length is not None and eos_token_id is not None and min_length > -1: processors.append(FlaxMinLengthLogitsProcessor(min_length, eos_token_id)) if forced_bos_token_id is not None: processors.append(FlaxForcedBOSTokenLogitsProcessor(forced_bos_token_id)) if forced_eos_token_id is not None: processors.append(FlaxForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id)) return processors def _greedy_search( self, input_ids: None, max_length: Optional[int] = None, pad_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, logits_processor: Optional[FlaxLogitsProcessorList] = None, trace: bool = True, params: Optional[Dict[str, jnp.ndarray]] = None, model_kwargs: Optional[Dict[str, jnp.ndarray]] = None, ): # init values max_length = max_length if max_length is not None else self.config.max_length pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id batch_size, cur_len = input_ids.shape eos_token_id = jnp.array(eos_token_id) pad_token_id = jnp.array(pad_token_id) cur_len = jnp.array(cur_len) # per batch-item holding current token in loop. sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32) sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0)) # per batch-item state bit indicating if sentence has finished. is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_) # For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop # and pass it the `encoder_outputs`, which are part of the `model_kwargs`. model = self.decode if self.config.is_encoder_decoder else self # initialize model specific kwargs model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs) # initialize state state = GreedyState( cur_len=cur_len, sequences=sequences, running_token=input_ids, is_sent_finished=is_sent_finished, model_kwargs=model_kwargs, ) def greedy_search_cond_fn(state): """state termination condition fn.""" has_reached_max_length = state.cur_len == max_length all_sequence_finished = jnp.all(state.is_sent_finished) finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished) return ~finish_generation def greedy_search_body_fn(state): """state update fn.""" model_outputs = model(state.running_token, params=params, **state.model_kwargs) logits = model_outputs.logits[:, -1] # apply min_length, ... logits = logits_processor(state.sequences, logits, state.cur_len) next_token = jnp.argmax(logits, axis=-1) next_token = next_token * ~state.is_sent_finished + pad_token_id * state.is_sent_finished next_is_sent_finished = state.is_sent_finished | (next_token == eos_token_id) next_token = next_token[:, None] next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len)) next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs) return GreedyState( cur_len=state.cur_len + 1, sequences=next_sequences, running_token=next_token, is_sent_finished=next_is_sent_finished, model_kwargs=next_model_kwargs, ) # The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU if input_ids.shape[1] > 1: state = greedy_search_body_fn(state) if not trace: state = self._run_loop_in_debug(greedy_search_cond_fn, greedy_search_body_fn, state) else: state = lax.while_loop(greedy_search_cond_fn, greedy_search_body_fn, state) return FlaxGreedySearchOutput(sequences=state.sequences) def _sample( self, input_ids: None, max_length: Optional[int] = None, pad_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, prng_key: Optional[jnp.ndarray] = None, logits_processor: Optional[FlaxLogitsProcessorList] = None, logits_warper: Optional[FlaxLogitsProcessorList] = None, trace: bool = True, params: Optional[Dict[str, jnp.ndarray]] = None, model_kwargs: Optional[Dict[str, jnp.ndarray]] = None, ): # init values max_length = max_length if max_length is not None else self.config.max_length pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0) batch_size, cur_len = input_ids.shape eos_token_id = jnp.array(eos_token_id) pad_token_id = jnp.array(pad_token_id) cur_len = jnp.array(cur_len) # per batch-item holding current token in loop. sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32) sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0)) # per batch-item state bit indicating if sentence has finished. is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_) # For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop # and pass it the `encoder_outputs`, which are part of the `model_kwargs`. model = self.decode if self.config.is_encoder_decoder else self # initialize model specific kwargs model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs) # initialize state state = SampleState( cur_len=cur_len, sequences=sequences, running_token=input_ids, is_sent_finished=is_sent_finished, prng_key=prng_key, model_kwargs=model_kwargs, ) def sample_search_cond_fn(state): """state termination condition fn.""" has_reached_max_length = state.cur_len == max_length all_sequence_finished = jnp.all(state.is_sent_finished) finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished) return ~finish_generation def sample_search_body_fn(state): """state update fn.""" prng_key, prng_key_next = jax.random.split(state.prng_key) model_outputs = model(state.running_token, params=params, **state.model_kwargs) logits = model_outputs.logits[:, -1] # apply min_length, ... logits = logits_processor(state.sequences, logits, state.cur_len) # apply top_k, top_k, temperature logits = logits_warper(logits, logits, state.cur_len) next_token = jax.random.categorical(prng_key, model_outputs.logits[:, -1], axis=-1) next_is_sent_finished = state.is_sent_finished | (next_token == eos_token_id) next_token = next_token * ~next_is_sent_finished + pad_token_id * next_is_sent_finished next_token = next_token[:, None] next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len)) next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs) return SampleState( cur_len=state.cur_len + 1, sequences=next_sequences, running_token=next_token, is_sent_finished=next_is_sent_finished, model_kwargs=next_model_kwargs, prng_key=prng_key_next, ) # The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU if input_ids.shape[1] > 1: state = sample_search_body_fn(state) if not trace: state = self._run_loop_in_debug(sample_search_cond_fn, sample_search_body_fn, state) else: state = lax.while_loop(sample_search_cond_fn, sample_search_body_fn, state) return FlaxSampleOutput(sequences=state.sequences) def _beam_search( self, input_ids: None, max_length: Optional[int] = None, pad_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, length_penalty: Optional[float] = None, early_stopping: Optional[bool] = None, logits_processor: Optional[FlaxLogitsProcessorList] = None, trace: bool = True, params: Optional[Dict[str, jnp.ndarray]] = None, model_kwargs: Optional[Dict[str, jnp.ndarray]] = None, ): """ This beam search function is heavily inspired by Flax's official example: https://github.com/google/flax/blob/master/examples/wmt/train.py#L254 """ def flatten_beam_dim(tensor): """Flattens the first two dimensions of a non-scalar array.""" # ignore scalars (e.g. cache index) if tensor.ndim == 0: return tensor return tensor.reshape((tensor.shape[0] * tensor.shape[1],) + tensor.shape[2:]) def unflatten_beam_dim(tensor, batch_size, num_beams): """Unflattens the first, flat batch*beam dimension of a non-scalar array.""" # ignore scalars (e.g. cache index) if tensor.ndim == 0: return tensor return tensor.reshape((batch_size, num_beams) + tensor.shape[1:]) def gather_beams(nested, beam_indices, batch_size, new_num_beams): """ Gathers the beam slices indexed by beam_indices into new beam array. """ batch_indices = jnp.reshape( jnp.arange(batch_size * new_num_beams) // new_num_beams, (batch_size, new_num_beams) ) def gather_fn(tensor): # ignore scalars (e.g. cache index) if tensor.ndim == 0: return tensor else: return tensor[batch_indices, beam_indices] return jax.tree_map(gather_fn, nested) # init values max_length = max_length if max_length is not None else self.config.max_length pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping batch_size, num_beams, cur_len = input_ids.shape eos_token_id = jnp.array(eos_token_id) pad_token_id = jnp.array(pad_token_id) cur_len = jnp.array(cur_len) # per batch,beam-item holding current token in loop. sequences = jnp.full((batch_size, num_beams, max_length), pad_token_id, dtype=jnp.int32) running_sequences = jnp.full((batch_size, num_beams, max_length), pad_token_id, dtype=jnp.int32) running_sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0, 0)) # per batch,beam-item state bit indicating if sentence has finished. is_sent_finished = jnp.zeros((batch_size, num_beams), dtype=jnp.bool_) # per batch,beam-item score, logprobs running_scores = jnp.tile(jnp.array([0.0] + [np.array(-1.0e7)] * (num_beams - 1)), [batch_size, 1]) scores = jnp.ones((batch_size, num_beams)) * np.array(-1.0e7) # For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop # and pass it the `encoder_outputs`, which are part of the `model_kwargs`. model = self.decode if self.config.is_encoder_decoder else self # flatten beam dim if "encoder_outputs" in model_kwargs: model_kwargs["encoder_outputs"]["last_hidden_state"] = flatten_beam_dim( model_kwargs["encoder_outputs"]["last_hidden_state"] ) if "attention_mask" in model_kwargs: model_kwargs["attention_mask"] = flatten_beam_dim(model_kwargs["attention_mask"]) # initialize model specific kwargs model_kwargs = self.prepare_inputs_for_generation(flatten_beam_dim(input_ids), max_length, **model_kwargs) # initialize state state = BeamSearchState( cur_len=cur_len, running_sequences=running_sequences, running_scores=running_scores, sequences=sequences, scores=scores, is_sent_finished=is_sent_finished, model_kwargs=model_kwargs, ) def beam_search_cond_fn(state): """beam search state termination condition fn.""" # 1. is less than max length? not_max_length_yet = state.cur_len < max_length # 2. can the new beams still improve? best_running_score = state.running_scores[:, -1:] / (max_length ** length_penalty) worst_finished_score = jnp.where( state.is_sent_finished, jnp.min(state.scores, axis=1, keepdims=True), np.array(-1.0e7) ) improvement_still_possible = jnp.all(worst_finished_score < best_running_score) # 3. is there still a beam that has not finished? still_open_beam = ~(jnp.all(state.is_sent_finished) & early_stopping) return not_max_length_yet & still_open_beam & improvement_still_possible def beam_search_body_fn(state, input_ids_length=1): """beam search state update fn.""" # 1. Forward current tokens # Collect the current position slice along length to feed the fast # autoregressive decoder model. Flatten the beam dimension into batch # dimension for feeding into the model. # unflatten beam dimension # Unflatten beam dimension in attention cache arrays input_token = flatten_beam_dim( lax.dynamic_slice( state.running_sequences, (0, 0, state.cur_len - input_ids_length), (batch_size, num_beams, input_ids_length), ) ) model_outputs = model(input_token, params=params, **state.model_kwargs) logits = unflatten_beam_dim(model_outputs.logits[:, -1], batch_size, num_beams) cache = jax.tree_map( lambda tensor: unflatten_beam_dim(tensor, batch_size, num_beams), model_outputs.past_key_values ) # adapt logits for FlaxMarianMTModel logits = self._adapt_logits_for_beam_search(logits) # 2. Compute log probs # get log probabilities from logits, # process logits with processors (*e.g.* min_length, ...), and # add new logprobs to existing running logprobs scores. log_probs = jax.nn.log_softmax(logits) log_probs = logits_processor( flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), state.cur_len ) log_probs = unflatten_beam_dim(log_probs, batch_size, num_beams) log_probs = log_probs + jnp.expand_dims(state.running_scores, axis=2) vocab_size = log_probs.shape[2] log_probs = log_probs.reshape((batch_size, num_beams * vocab_size)) # 3. Retrieve top-K # Each item in batch has num_beams * vocab_size candidate sequences. # For each item, get the top 2*k candidates with the highest log- # probabilities. We gather the top 2*K beams here so that even if the best # K sequences reach EOS simultaneously, we have another K sequences # remaining to continue the live beam search. # Gather the top 2*K scores from _all_ beams. # Gather 2*k top beams. # Recover the beam index by floor division. # Recover token id by modulo division and expand Id array for broadcasting. # Update sequences for the 2*K top-k new sequences. beams_to_keep = 2 * num_beams topk_log_probs, topk_indices = lax.top_k(log_probs, k=beams_to_keep) topk_beam_indices = topk_indices // vocab_size topk_running_sequences = gather_beams( state.running_sequences, topk_beam_indices, batch_size, beams_to_keep ) topk_ids = jnp.expand_dims(topk_indices % vocab_size, axis=2) topk_sequences = lax.dynamic_update_slice(topk_running_sequences, topk_ids, (0, 0, state.cur_len)) # 4. Check which sequences have ended # Update current sequences: # Did any of these sequences reach an end marker? # To prevent these just finished sequences from being added to the current sequences # set of active beam search sequences, set their log probs to a very large # negative value. did_topk_just_finished = topk_sequences[:, :, state.cur_len] == eos_token_id running_topk_log_probs = topk_log_probs + did_topk_just_finished * np.array(-1.0e7) # 5. Get running sequences scores for next # Determine the top k beam indices (from top 2*k beams) from log probs # and gather top k beams (from top 2*k beams). next_topk_indices = jnp.flip(lax.top_k(running_topk_log_probs, k=num_beams)[1], axis=1) next_running_sequences, next_running_scores = gather_beams( [topk_sequences, running_topk_log_probs], next_topk_indices, batch_size, num_beams ) # 6. Process topk logits # Further process log probs: # - add length penalty # - make sure no scores can be added anymore if beam is full # - make sure still running sequences cannot be chosen as finalized beam topk_log_probs = topk_log_probs / (state.cur_len ** length_penalty) beams_in_batch_are_full = ( jnp.broadcast_to(state.is_sent_finished.all(axis=-1, keepdims=True), did_topk_just_finished.shape) & early_stopping ) add_penalty = ~did_topk_just_finished | beams_in_batch_are_full topk_log_probs += add_penalty * np.array(-1.0e7) # 7. Get scores, sequences, is sentence finished for next. # Combine sequences, scores, and flags along the beam dimension and compare # new finished sequence scores to existing finished scores and select the # best from the new set of beams merged_sequences = jnp.concatenate([state.sequences, topk_sequences], axis=1) merged_scores = jnp.concatenate([state.scores, topk_log_probs], axis=1) merged_is_sent_finished = jnp.concatenate([state.is_sent_finished, did_topk_just_finished], axis=1) topk_merged_indices = jnp.flip(lax.top_k(merged_scores, k=num_beams)[1], axis=1) next_sequences, next_scores, next_is_sent_finished = gather_beams( [merged_sequences, merged_scores, merged_is_sent_finished], topk_merged_indices, batch_size, num_beams ) # 8. Update model kwargs. # Determine the top k beam indices from the original set of all beams. # With these, gather the top k beam-associated caches. next_running_indices = gather_beams(topk_beam_indices, next_topk_indices, batch_size, num_beams) next_cache = gather_beams(cache, next_running_indices, batch_size, num_beams) model_outputs["past_key_values"] = jax.tree_map(lambda x: flatten_beam_dim(x), next_cache) next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs) return BeamSearchState( cur_len=state.cur_len + 1, running_scores=next_running_scores, running_sequences=next_running_sequences, scores=next_scores, sequences=next_sequences, is_sent_finished=next_is_sent_finished, model_kwargs=next_model_kwargs, ) # The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU if input_ids.shape[-1] > 1: state = partial(beam_search_body_fn, input_ids_length=input_ids.shape[-1])(state) if not trace: state = self._run_loop_in_debug(beam_search_cond_fn, beam_search_body_fn, state) else: state = lax.while_loop(beam_search_cond_fn, beam_search_body_fn, state) # Account for the edge-case where there are no finished sequences for a # particular batch item. If so, return running sequences for that batch item. none_finished = jnp.any(state.is_sent_finished, axis=1) sequences = jnp.where(none_finished[:, None, None], state.sequences, state.running_sequences) scores = jnp.where(none_finished[:, None], state.scores, state.running_scores) # take best beam for each batch sequences = sequences[:, -1] scores = scores[:, -1] return FlaxBeamSearchOutput(sequences=sequences, scores=scores)