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# contrastive_search main logic start:
# contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
# degeneration penalty
processed_logit_for_next_step = logits_processor(input_ids, logit_for_next_step)
next_probs = nn.functi... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_logits:
raw_logits += (logit_for_next_step,)
if output_scores:
scores += (processed_logit_for_next_step,)
if output_attenti... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# This is needed to properly delete outputs.logits which may be very large for this first iteration
# Otherwise a reference to outputs.logits is kept all along until after the next call to self.forward()
del outputs | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if not sequential:
# Replicates the new past_key_values to match the `top_k` candidates
past = model_kwargs["past_key_values"]
# If it is a static cache, modify it in-place layer after layer to save memory
if isinstance(past, DynamicCache) or (
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if sequential:
all_outputs = []
for i in range(top_k):
# compute the candidate tokens by the language model and collect their hidden_states
next_model_inputs = self.prepare_inputs_for_generation(top_k_ids[:, i].view(-1, 1), **model_kwargs) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
outputs = self(
**next_model_inputs,
return_dict=True,
output_hidden_states=True,
output_attentions=output_attentions,
)
if isinstance(outputs["past_key_values"], DynamicCache) or (
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
else:
# compute the candidate tokens by the language model and collect their hidden_states
# assembles top_k_ids into batch of size k
next_model_inputs = self.prepare_inputs_for_generation(top_k_ids.view(-1, 1), **model_kwargs)
outputs = self(
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# name is different for encoder-decoder and decoder-only models
if self.config.is_encoder_decoder:
next_hidden = outputs.decoder_hidden_states[-1]
full_hidden_states = outputs.decoder_hidden_states
else:
next_hidden = outputs.hidden_states[-1]
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# compute the degeneration penalty and re-rank the candidates based on the degeneration penalty and the
# model confidence. Keeping `selected_idx` on CPU enables multi-device contrastive search and doesn't
# introduce (noticeable) slowdowns on single-device runs.
selected_idx = _rank... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
# the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
# (model confidence minus degeneration penalty); (6) decoder hidden_states
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# generate past_key_values cache of only the selected token
if sequential:
next_model_input = self.prepare_inputs_for_generation(
top_k_ids[:, selected_idx].view(-1, 1), **model_kwargs
)
selected_outputs = self(
**next_... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
else:
_, next_past_key_values = self._extract_past_from_model_output(outputs)
# Do it in-place layer per layer to save memory
if isinstance(next_past_key_values, DynamicCache) or (
isinstance(next_past_key_values, EncoderDecoderCache)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
logit_for_next_step = torch.stack(torch.split(logits, top_k))[range(batch_size), selected_idx, :]
logit_for_next_step = logit_for_next_step.to(input_ids.device) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
if self.config.is_encoder_decoder:
next_step_cross_attentions = ()
next_step_decoder_attentions = ()
if output_attentions:
for layer in ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
decoder_attentions=next_step_decoder_attentions or None,
cross_attentions=next_step_cross_attentions or None,
)
else:
next_step_attentions = ()
if output_attentions:
for layer in outputs.attentions:
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
model_kwargs = self._update_model_kwargs_for_generation(
outputs,
model_kwargs,
is_encoder_decoder=self.config.is_encoder_decoder,
)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# stop when each sentence is finished
unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
this_peer_finished = unfinished_sequences.max() == 0
if streamer is not None:
streamer.end() | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if return_dict_in_generate:
# Contrastive search works by forward looking at the next token, so we need to exclude it from
# `past_key_values` to be consistent with the other decoding methods
if model_kwargs.get("past_key_values") is not None:
if isinstance(model_kwar... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
model_kwargs["past_key_values"] = tuple(past_key_values) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if self.config.is_encoder_decoder:
return GenerateEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
logits=raw_logits,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_s... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
return input_ids | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _sample(
self,
input_ids: torch.LongTensor,
logits_processor: LogitsProcessorList,
stopping_criteria: StoppingCriteriaList,
generation_config: GenerationConfig,
synced_gpus: bool,
streamer: Optional["BaseStreamer"],
**model_kwargs,
) -> Union[Gener... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, 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 [`Logit... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
`FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further pr... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Return:
[`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or `torch.LongTensor`:
A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` an... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
do_sample = generation_config.do_sample | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attent... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# keep track of which sequences are already finished
batch_size, cur_len = input_ids.shape
this_peer_finished = False
unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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 {})
if i... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Clone 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)
next_token_logits = outputs.logits[:, -1, :].clone().float()
next_token_logits = next_token_logits.to(input_ids.device)
# p... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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:
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# token selection
if do_sample:
probs = nn.functional.softmax(next_token_scores, dim=-1)
# TODO (joao): this OP throws "skipping cudagraphs due to ['incompatible ops']", find solution
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
this_peer_finished = unfinished_sequences.max() == 0
cur_len += 1
# This is needed to properly delete outputs.logits which may be very large for first iteration
# Otherwise a reference to... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if return_dict_in_generate:
if self.config.is_encoder_decoder:
return GenerateEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
logits=raw_logits,
encoder_attentions=encoder_attentions,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
)
else:
return input_ids | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _temporary_reorder_cache(self, past_key_values, beam_idx):
"""
Temporary function to handle the different types of cache reordering processes while we roll out `Cache`. | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
TODO: standardize cache formats and make all models compatible with `Cache`. It would remove the need
for this function, with `Cache.reorder_cache` being the sole remaining code path
"""
model_class = self.__class__.__name__.lower()
# Exception 1: code path for models using the legacy ca... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
past_key_values = self._reorder_cache(past_key_values, beam_idx)
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
# Standard code path: use the `Cache.reorder_cache`
else:
past_key_values.reorder_cache(beam_idx)
return past_key_values | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _beam_search(
self,
input_ids: torch.LongTensor,
beam_scorer: BeamScorer,
logits_processor: LogitsProcessorList,
stopping_criteria: StoppingCriteriaList,
generation_config: GenerationConfig,
synced_gpus: bool,
**model_kwargs,
) -> Union[GenerateBea... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
beam_scorer (`BeamScorer`):
An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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
`FullyS... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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_decode... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
do_sample = generation_config.do_sample | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
batch_size = len(beam_scorer._beam_hyps)
num_beams = beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
if num_beams * batch_size != batch_beam_size:
raise ValueError(
f"B... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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 ou... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# prepare variable output controls (note: some models won't accept all output controls)
model_inputs.update({"... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# if sequential is True, split the input to batches of batch_size and run sequentially
if sequential:
if any(
model_name in self.__class__.__name__.lower()
for model_name in [
"fsmt",
"reformer",
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
inputs_per_sub_batches = _split_model_inputs(
model_inputs,
split_size=batch_size,
full_batch_size=batch_beam_size,
config=self.config.get_text_config(),
)
outputs_per_sub_batch = [
self(*... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
model_kwargs = self._update_model_kwargs_for_generation(
outputs,
model_kwargs,
is_encoder_decoder=self.config.is_encoder_decoder,
)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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
) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (next_token_scores_processed,)
if output_logits:
raw_logits += (next_token_logits,)
if output_attentions:... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# reshape for beam search
vocab_size = next_token_scores.shape[-1]
next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Beam token selection: pick 1 + eos_token_id.shape[0] 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
n_tokens_to_keep = max(2, 1 + n_eos_tokens) * num_beams
if do_sample:
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
next_tokens = next_tokens % vocab_size
# stateless
beam_outputs = beam_scorer.process(
input_ids,
next_token_scores,
next_tokens,
next_indices,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
this_peer_finished = True
sequence_outputs = beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if self.config.is_encoder_decoder:
return GenerateBeamEncoderDecoderOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
logits=raw_logits,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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 se... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _group_beam_search(
self,
input_ids: torch.LongTensor,
beam_scorer: BeamScorer,
logits_processor: LogitsProcessorList,
stopping_criteria: StoppingCriteriaList,
generation_config: GenerationConfig,
synced_gpus: bool,
**model_kwargs,
):
r"""
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
beam_scorer (`BeamScorer`):
An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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
`FullyS... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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_decod... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
num_beams = beam_scorer.num_beams
num_beam_groups = beam_scorer.num_beam_groups
num_sub_beams = num_beams // num_beam_groups
batch_size = len(beam_scorer._beam_hyps) // num_beam_groups
device = input_ids.device
batch_beam_size, cur_len = input_ids.shape
model_kwargs = se... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attent... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# initialise score of first beam of each group with 0 and the rest with -1e9. This ensures that the beams in
# the same group don't produce same tokens every time.
beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
beam_scores[:, ::num_sub_beams] = 0
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# do one decoder step on all beams of all sentences in batch
model_inputs = self.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_attentio... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if output_scores:
processed_score = torch.zeros_like(outputs.logits[:, -1, :])
if output_logits:
# Clone 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)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
for batch_idx in range(batch_size):
batch_group_indices.extend(
[batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
)
group_input_ids = input_ids[batch_group_indices]
# select outputs of beams of ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
next_token_scores_processed = logits_processor(
group_input_ids, next_token_scores, current_tokens=current_tokens, beam_group_idx=beam_group_idx
)
next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
next_tok... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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) * gr... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# stateless
process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
beam_outputs = beam_scorer.process(
group_input_ids,
next_token_scores,
next_tokens,
next_indices,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if return_dict_in_generate and output_scores:
beam_indices[beam_group_idx] = tuple(
beam_indices[beam_group_idx][beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices[0]))
)
input_ids[batch_group_indices] = group_input_ids[beam_idx... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (processed_score,)
if output_logits:
raw_logits += (raw_logit_score,)
if output_attentions:
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
sequence_outputs = beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
m... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if self.config.is_encoder_decoder:
return GenerateBeamEncoderDecoderOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
logits=raw_logits,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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 se... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _constrained_beam_search(
self,
input_ids: torch.LongTensor,
constrained_beam_scorer: ConstrainedBeamSearchScorer,
logits_processor: LogitsProcessorList,
stopping_criteria: StoppingCriteriaList,
generation_config: GenerationConfig,
synced_gpus: bool,
*... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
constrained_beam_scorer (`ConstrainedBeamSearchScorer`):
A derived instance of [`BeamScorer`] that defines how beam hypotheses ar... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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_len... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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_decod... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
batch_size = len(constrained_beam_scorer._beam_hyps)
num_beams = constrained_beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
if num_beams * batch_size != batch_beam_size:
raise ValueEr... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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 ou... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
decoder_prompt_len = input_ids.shape[-1] # record the prompt length of decoder
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# prepare variable output controls... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
model_kwargs = self._update_model_kwargs_for_generation(
outputs,
model_kwargs,
is_encoder_decoder=self.config.is_encoder_decoder,
)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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,... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# reshape for beam search
vocab_... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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_i... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if return_dict_in_generate:
if not output_scores:
sequence_outputs["sequence_scores"] = None
if self.config.is_encoder_decoder:
return GenerateBeamEncoderDecoderOutput(
sequences=sequence_outputs["sequences"],
sequences_scor... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
logits=raw_logits,
beam_indices=sequence_outputs["beam_indices"],
attentions=decoder_attentions,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _assisted_decoding(
self,
input_ids: torch.LongTensor,
candidate_generator: CandidateGenerator,
logits_processor: LogitsProcessorList,
stopping_criteria: StoppingCriteriaList,
generation_config: GenerationConfig,
synced_gpus: bool,
streamer: Optional["... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
candidate_generator (`CandidateGenerator`):
A derived instance of [`CandidateGenerator`] that defines how candidate sequences are... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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
`FullyS... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Return:
[`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` an... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 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
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attent... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# keep track of which sequences are already finished
batch_size = input_ids.shape[0]
unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
this_peer_finished = False
is_fi... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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