text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
"""
# Let's assume that if one batch start with `bos`, all batched also do
if input_ids[0, 0] == self.bos_token_id:
input_ids = input_ids[:, 1:]
if input_ids.shape[-1] - self.processor.context_width < 1:
raise ValueError(
f"Must have at least `1` token t... | 10,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
return WatermarkDetectorOutput(
num_tokens_scored=num_tokens_scored,
num_green_tokens=green_token_count,
green_fraction=green_token_count / num_tokens_scored,
z_score=z_score,
p_value=p_value,
prediction=prediction,
... | 10,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
class BayesianDetectorConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BayesianDetectorModel`]. It is used to
instantiate a Bayesian Detector model according to the specified arguments.
Configuration objects inherit from [`PretrainedConfig`] and can be use... | 10,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
def set_detector_information(self, model_name, watermarking_config):
self.model_name = model_name
self.watermarking_config = watermarking_config | 10,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
class BayesianWatermarkDetectorModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if the text is watermarked.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
posterior_probabilities ... | 10,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
class BayesianDetectorWatermarkedLikelihood(nn.Module):
"""Watermarked likelihood model for binary-valued g-values.
This takes in g-values and returns p(g_values|watermarked).
"""
def __init__(self, watermarking_depth: int):
"""Initializes the model parameters."""
super().__init__()
... | 10,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
Returns:
p_one_unique_token and p_two_unique_tokens, both of shape
[batch_size, seq_len, watermarking_depth]. p_one_unique_token[i,t,l]
gives the probability of there being one unique token in a tournament
match on layer l, on timestep t, for batch item i.
p_o... | 10,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
# [batch_size, seq_len, watermarking_depth]
# (i, j, k, l) x (i, j, k, l) -> (i, j, k) einsum equivalent
logits = (self.delta[..., None, :] @ x.type(self.delta.dtype)[..., None]).squeeze() + self.beta
p_two_unique_tokens = torch.sigmoid(logits)
p_one_unique_token = 1 - p_two_unique_toke... | 10,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
# P(g_tl | watermarked) is equal to
# 0.5 * [ (g_tl+0.5) * p_two_unique_tokens + p_one_unique_token].
return 0.5 * ((g_values + 0.5) * p_two_unique_tokens + p_one_unique_token) | 10,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
class BayesianDetectorModel(PreTrainedModel):
r"""
Bayesian classifier for watermark detection.
This detector uses Bayes' rule to compute a watermarking score, which is the sigmoid of the log of ratio of the
posterior probabilities P(watermarked|g_values) and P(unwatermarked|g_values). Please see the s... | 10,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`BayesianDetectorConfig`]): Model co... | 10,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
self.watermarking_depth = config.watermarking_depth
self.base_rate = config.base_rate
self.likelihood_model_watermarked = BayesianDetectorWatermarkedLikelihood(
watermarking_depth=self.watermarking_depth
)
self.prior = torch.nn.Parameter(torch.tensor([self.base_rate]))
d... | 10,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
Args:
likelihoods_watermarked (`torch.Tensor` of shape `(batch, length, depth)`):
Likelihoods P(g_values|watermarked) of g-values under watermarked model.
likelihoods_unwatermarked (`torch.Tensor` of shape `(batch, length, depth)`):
Likelihoods P(g_values|unwaterm... | 10,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
Returns:
Posterior probability P(watermarked|g_values), shape [batch].
"""
mask = torch.unsqueeze(mask, dim=-1)
prior = torch.clamp(prior, min=1e-5, max=1 - 1e-5)
log_likelihoods_watermarked = torch.log(torch.clamp(likelihoods_watermarked, min=1e-30, max=float("inf")))
... | 10,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
# Compute the posterior probability P(w|g) = sigmoid(relative_surprisal).
return torch.sigmoid(relative_surprisal)
def forward(
self,
g_values: torch.Tensor,
mask: torch.Tensor,
labels: Optional[torch.Tensor] = None,
loss_batch_weight=1,
return_dict=False,
... | 10,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
likelihoods_watermarked = self.likelihood_model_watermarked(g_values)
likelihoods_unwatermarked = 0.5 * torch.ones_like(g_values)
out = self._compute_posterior(
likelihoods_watermarked=likelihoods_watermarked,
likelihoods_unwatermarked=likelihoods_unwatermarked,
mask=... | 10,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
class SynthIDTextWatermarkDetector:
r"""
SynthID text watermark detector class.
This class has to be initialized with the trained bayesian detector module check script
in examples/synthid_text/detector_training.py for example in training/saving/loading this
detector module. The folder also showcase... | 10,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
>>> # Load the detector. See examples/research_projects/synthid_text for training a detector.
>>> detector_model = BayesianDetectorModel.from_pretrained("joaogante/dummy_synthid_detector")
>>> logits_processor = SynthIDTextWatermarkLogitsProcessor(
... **detector_model.config.watermarking_config, device... | 10,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
def __init__(
self,
detector_module: BayesianDetectorModel,
logits_processor: SynthIDTextWatermarkLogitsProcessor,
tokenizer: Any,
):
self.detector_module = detector_module
self.logits_processor = logits_processor
self.tokenizer = tokenizer
def __call__(s... | 10,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
combined_mask = context_repetition_mask * eos_token_mask
g_values = self.logits_processor.compute_g_values(
input_ids=tokenized_outputs,
)
# g values shape [batch_size, output_len - (ngram_len - 1), depth]
return self.detector_module(g_values, combined_mask) | 10,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py |
class BeamScorer(ABC):
"""
Abstract base class for all beam scorers that are used for [`~PreTrainedModel.beam_search`] and
[`~PreTrainedModel.beam_sample`].
"""
@abstractmethod
@add_start_docstrings(PROCESS_INPUTS_DOCSTRING)
def process(
self,
input_ids: torch.LongTensor,
... | 10,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
class BeamSearchScorer(BeamScorer):
r"""
[`BeamScorer`] implementing standard beam search decoding.
Adapted in part from [Facebook's XLM beam search
code](https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529).
Reference for the diverse... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
Args:
batch_size (`int`):
Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
num_beams (`int`):
Number of beams for beam search.
device (`torch.device`):
Defines the device type (*e.g.*, `"cpu"` or `"cuda"`) on which this ins... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
`True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
heuristic is applied and the generation stops when is it very unlikely to find bet... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
The maximum length of the sequence to be generated.
""" | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
def __init__(
self,
batch_size: int,
num_beams: int,
device: torch.device,
length_penalty: Optional[float] = 1.0,
do_early_stopping: Optional[Union[bool, str]] = False,
num_beam_hyps_to_keep: Optional[int] = 1,
num_beam_groups: Optional[int] = 1,
m... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
self._is_init = False
# self._beam_hyps[i*self.num_beam_groups+j] is the beam_hyps of the j-th group in the i-th mini-batch.
# If group_beam_search is not used, the list consists of `batch_size` beam_hyps.
self._beam_hyps = [
BeamHypotheses(
num_beams=self.group_size,... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
if not isinstance(num_beams, int) or num_beams <= 1:
raise ValueError(
f"`num_beams` has to be an integer strictly greater than 1, but is {num_beams}. For `num_beams` == 1,"
" one should make use of `greedy_search` instead."
)
if not isinstance(num_beam_g... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
def process(
self,
input_ids: torch.LongTensor,
next_scores: torch.FloatTensor,
next_tokens: torch.LongTensor,
next_indices: torch.LongTensor,
pad_token_id: Optional[Union[int, torch.Tensor]] = None,
eos_token_id: Optional[Union[int, List[int], torch.Tensor]] = No... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
if not (batch_size == (input_ids.shape[0] // self.group_size)):
if self.num_beam_groups > 1:
raise ValueError(
f"A group beam size of {input_ids.shape[0]} is used as the input, but a group beam "
f"size of {self.group_size} is expected by the beam scor... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
if eos_token_id is not None and not isinstance(eos_token_id, torch.Tensor):
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
eos_token_id = torch.tensor(eos_token_id)
for batch_idx in range(batch_size):
batch_group_idx = batch_idx * self.num_be... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# next tokens for this sentence
beam_idx = 0
for beam_token_rank, (next_token, next_score, next_index) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx], next_indices[batch_idx])
):
batch_beam_idx = batch_idx * self.group_size + next_ind... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
beam_index = None | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
self._beam_hyps[batch_group_idx].add(
input_ids[batch_beam_idx].clone(),
next_score.item(),
beam_indices=beam_index,
generated_len=cur_len - decoder_prompt_len,
)
else:
... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
if beam_idx < self.group_size:
raise ValueError(
f"At most {self.group_size} tokens in {next_tokens[batch_idx]} can be equal to `eos_token_id:"
f" {eos_token_id}`. Make sure {next_tokens[batch_idx]} are corrected."
)
# Check if we are ... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
def finalize(
self,
input_ids: torch.LongTensor,
final_beam_scores: torch.FloatTensor,
final_beam_tokens: torch.LongTensor,
final_beam_indices: torch.LongTensor,
max_length: int,
pad_token_id: Optional[Union[int, torch.Tensor]] = None,
eos_token_id: Option... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# all open beam hypotheses are added to the beam hypothesis
# beam hypothesis class automatically keeps the best beams
for index_per_group in range(self.group_size):
batch_beam_idx = batch_group_idx * self.group_size + index_per_group
final_score = final_beam_scor... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# retrieve best hypotheses
for i in range(batch_size):
beam_hyps_in_batch = self._beam_hyps[i * self.num_beam_groups : (i + 1) * self.num_beam_groups]
candidate_beams = [beam for beam_hyp in beam_hyps_in_batch for beam in beam_hyp.beams]
sorted_hyps = sorted(candidate_beams, ... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# prepare for adding eos
sent_lengths_max = sent_lengths.max().item() + 1
sent_max_len = min(sent_lengths_max, max_length) if max_length is not None else sent_lengths_max
decoded: torch.LongTensor = input_ids.new(batch_size * self.num_beam_hyps_to_keep, sent_max_len)
if len(best_indices... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# fill with hypotheses and eos_token_id if the latter fits in
for i, (hypo, best_idx) in enumerate(zip(best, best_indices)):
decoded[i, : sent_lengths[i]] = hypo
if indices is not None:
indices[i, : len(best_idx)] = torch.tensor(best_idx)
if sent_lengths[i] ... | 10,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
class ConstrainedBeamSearchScorer(BeamScorer):
r"""
[`BeamScorer`] implementing constrained beam search decoding. | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
Args:
batch_size (`int`):
Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
num_beams (`int`):
Number of beams for beam search.
constraints (`List[Constraint]`):
A list of positive constraints represented as `Constraint` obj... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
`length_penalty` < 0.0 encourages shorter sequences.
do_early_stopping (`bool` or `str`, *optional*, defaults to `False`):
Controls the stopping condition for beam-based methods, like beam... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
max_length (`int`, *optional*):
The maximum length of the sequence to be generated.
""" | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
def __init__(
self,
batch_size: int,
num_beams: int,
constraints: List[Constraint],
device: torch.device,
length_penalty: Optional[float] = 1.0,
do_early_stopping: Optional[Union[bool, str]] = False,
num_beam_hyps_to_keep: Optional[int] = 1,
num_be... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
self._is_init = False
self._beam_hyps = [
BeamHypotheses(
num_beams=self.num_beams,
length_penalty=self.length_penalty,
early_stopping=self.do_early_stopping,
max_length=max_length,
)
for _ in range(batch_size)
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
if not isinstance(num_beam_groups, int) or (num_beam_groups > num_beams) or (num_beams % num_beam_groups != 0):
raise ValueError(
"`num_beam_groups` has to be an integer smaller or equal than `num_beams` and `num_beams` has to be"
f" divisible by `num_beam_groups`, but is {nu... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
def process(
self,
input_ids: torch.LongTensor,
next_scores: torch.FloatTensor,
next_tokens: torch.LongTensor,
next_indices: torch.LongTensor,
scores_for_all_vocab: torch.FloatTensor,
pad_token_id: Optional[Union[int, torch.Tensor]] = None,
eos_token_id: O... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
[What are input IDs?](../glossary#input-ids)
next_scores (`torch.FloatTensor` of shape `(batch_size, 2 * num_beams)`):
Current scores of the top `2 * num_beams` non-finished beam hypotheses.
next_tokens (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`):
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
beam_indices (`torch.LongTensor`, *optional*):
Beam indices indicating to which beam hypothesis each token correspond.
decoder_prompt_len (`int`, *optional*):
T... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
- **next_beam_scores** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Updated scores of
all
non-finished beams.
- **next_beam_tokens** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Next tokens to be
added
t... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# add up to the length which the next_scores is calculated on (including decoder prompt)
cur_len = input_ids.shape[-1] + 1
batch_size = len(self._beam_hyps)
if not (batch_size == (input_ids.shape[0] // self.group_size)):
if self.num_beam_groups > 1:
raise ValueError(
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
next_beam_scores = torch.zeros((batch_size, self.group_size), dtype=next_scores.dtype, device=device)
next_beam_tokens = torch.zeros((batch_size, self.group_size), dtype=next_tokens.dtype, device=device)
next_beam_indices = torch.zeros((batch_size, self.group_size), dtype=next_indices.dtype, device=devi... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
for batch_idx, beam_hyp in enumerate(self._beam_hyps):
if self._done[batch_idx]:
if self.num_beams < len(beam_hyp):
raise ValueError(f"Batch can only be done if at least {self.num_beams} beams have been generated")
if eos_token_id is None or pad_token_id i... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# next tokens for this sentence.
beam_idx = 0
for beam_token_rank, (next_token, next_score, next_index) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx], next_indices[batch_idx])
):
batch_beam_idx = batch_idx * self.group_size + next_in... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
completes_constraint = self.check_completes_constraints(input_ids[batch_beam_idx].cpu().tolist())
if completes_constraint:
if beam_indices is not None:
beam_index = beam_indices[batch_beam_idx]
beam_index = beam_index + ... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
beam_hyp.add(
input_ids[batch_beam_idx].clone(),
next_score.item(),
beam_indices=beam_index,
generated_len=cur_len - decoder_prompt_len,
)
else:
# a... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
new_scores, new_tokens, new_indices = self.step_sentence_constraint(
batch_idx,
input_ids,
scores_for_all_vocab,
next_beam_scores[batch_idx],
next_beam_tokens[batch_idx],
next_beam_indices[batch_idx],
)
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# Check if we are done so that we can save a pad step if all(done)
self._done[batch_idx] = self._done[batch_idx] or beam_hyp.is_done(
next_scores[batch_idx].max().item(), cur_len, decoder_prompt_len
)
return UserDict(
{
"next_beam_scores": nex... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# 1. Adding "advance_tokens"
# using ConstraintStateList.advance(), we propose new tokens to be added into this "candidate list" that will
# advance us in fulfilling the constraints.
# 2. Selecting best candidates such that we end up with highest probable candidates
# that f... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# need to make new hypothesis that advance the constraints
track_new = {
"new_seqs": full_hypotheses.tolist(),
"new_states": [],
"new_indices": [],
"new_tokens": [],
"new_scores": [],
}
for seq_idx, pre_seq in enumerate(this_batch_input... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
if not advance_state.completed:
advance_tokens = torch.LongTensor(advance_state.advance()).to(device)
for advance_token in advance_tokens:
# since adding each `advance_token` leads to a different hypothesis, create new state instance.
new_state = a... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
advance_seq = torch.cat((pre_seq, advance_token.unsqueeze(0)), -1).cpu().tolist()
if advance_seq not in track_new["new_seqs"]:
# prevent duplicates, which are basically bound to happen in this process.
track_new["new_seqs"].append(advance_seq)
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# pre_seq_1 = "The child loves pies and" pre_seq_2 = "The child plays in the playground and"
# Without this step, if `sent_beam_indices` is something like [1,1], then
# 1. `pre_seq_1` won't be added to the list of (topk) hypothesis since it's not in the indices and
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# Here, we basically take `pre_seq_1` and to "push" it into the considered list of hypotheses, by simply
# appending the next likely token in the vocabulary and adding it to the list of hypotheses.
new_score, new_token = torch.max(this_batch_token_scores[seq_idx], 0) # some next probab... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
advance_state.reset(advance_seq)
if advance_seq not in track_new["new_seqs"]:
# but still don't want to have duplicates
track_new["new_seqs"].append(advance_seq)
track_new["new_indices"].append(seq_idx)
track_new["new_tokens... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
all_states = topk_contraint_states + track_new["new_states"]
all_tokens = torch.cat((sent_beam_tokens, new_tokens), -1)
all_scores = torch.cat((sent_beam_scores, new_scores), -1)
all_banks = torch.tensor([one.get_bank() for one in all_states]).to(device)
zipped = all_ban... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
indices = indices[rearrangers][:orig_len]
sent_beam_scores = all_scores[indices]
sent_beam_tokens = all_tokens[indices]
sent_beam_indices = torch.cat((sent_beam_indices, new_indices))[indices]
return sent_beam_scores, sent_beam_tokens, sent_beam_indices
def finalize(
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
if eos_token_id is not None and not isinstance(eos_token_id, torch.Tensor):
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
eos_token_id = torch.tensor(eos_token_id)
# finalize all open beam hypotheses and add to generated hypotheses
for batch_idx... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
completes_constraint = self.check_completes_constraints(final_tokens.cpu().tolist())
if completes_constraint:
beam_index = beam_indices[batch_beam_idx] if beam_indices is not None else None
generated_len = final_tokens.shape[-1] - decoder_prompt_len
... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# due to overly complex constraints or other factors, sometimes we can't gaurantee a successful
# generation. In these cases we simply return the highest scoring outputs.
if len(ids_collect) < self.num_beam_hyps_to_keep:
for beam_id in range(self.num_beams):
i... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# select the best hypotheses
sent_lengths = input_ids.new(batch_size * self.num_beam_hyps_to_keep)
best = []
best_indices = []
best_scores = torch.zeros(batch_size * self.num_beam_hyps_to_keep, device=self.device, dtype=torch.float32)
# retrieve best hypotheses
for i, be... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# prepare for adding eos
sent_lengths_max = sent_lengths.max().item() + 1
sent_max_len = min(sent_lengths_max, max_length) if max_length is not None else sent_lengths_max
decoded: torch.LongTensor = input_ids.new(batch_size * self.num_beam_hyps_to_keep, sent_max_len)
if len(best_indice... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# fill with hypotheses and eos_token_id if the latter fits in
for i, (hypo, best_idx) in enumerate(zip(best, best_indices)):
decoded[i, : sent_lengths[i]] = hypo
if indices is not None:
indices[i, : len(best_idx)] = torch.tensor(best_idx)
if sent_lengths[i] ... | 10,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
class BeamHypotheses:
def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool, max_length: Optional[int] = None):
"""
Initialize n-best list of hypotheses.
"""
self.length_penalty = length_penalty
self.early_stopping = early_stopping
self.max_le... | 10,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
def add(
self,
hyp: torch.LongTensor,
sum_logprobs: float,
beam_indices: Optional[torch.LongTensor] = None,
generated_len: Optional[int] = None,
):
"""
Add a new hypothesis to the list.
"""
if generated_len is not None:
score = sum_... | 10,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
def is_done(self, best_sum_logprobs: float, cur_len: int, decoder_prompt_len: Optional[int] = 0) -> bool:
"""
If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst
one in the heap, then we are done with this sentence.
"""
... | 10,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# `True`: stop as soon as at least `num_beams` hypotheses are finished
if self.early_stopping is True:
return True
# `False`: heuristic -- compute best possible score from `cur_len`, even though it is not entirely accurate
# when `length_penalty` is positive. See the discussion belo... | 10,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
# its max this way
if self.length_penalty > 0.0:
if self.max_length <= decoder_prompt_len:
raise ValueError("max_length is not larger than decoder prompt length")
highest_attainable_score = (
best_sum_logprobs / (self.max_length - decod... | 10,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py |
class BaseStreamer:
"""
Base class from which `.generate()` streamers should inherit.
"""
def put(self, value):
"""Function that is called by `.generate()` to push new tokens"""
raise NotImplementedError()
def end(self):
"""Function that is called by `.generate()` to signal... | 10,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
class TextStreamer(BaseStreamer):
"""
Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenize... | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextStreamer(tok)
>>> # Despite returning the usual output, t... | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
def put(self, value):
"""
Receives tokens, decodes them, and prints them to stdout as soon as they form entire words.
"""
if len(value.shape) > 1 and value.shape[0] > 1:
raise ValueError("TextStreamer only supports batch size 1")
elif len(value.shape) > 1:
... | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
# After the symbol for a new line, we flush the cache.
if text.endswith("\n"):
printable_text = text[self.print_len :]
self.token_cache = []
self.print_len = 0
# If the last token is a CJK character, we print the characters.
elif len(text) > 0 and self._is_chi... | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
def end(self):
"""Flushes any remaining cache and prints a newline to stdout."""
# Flush the cache, if it exists
if len(self.token_cache) > 0:
text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)
printable_text = text[self.print_len :]
self.tok... | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
return False | 10,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
class TextIteratorStreamer(TextStreamer):
"""
Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is
useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive
Gradio demo).
<Tip warni... | 10,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout fo... | 10,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextIteratorStreamer(tok)
>>> # Run the generation in a separ... | 10,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
def __init__(
self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, timeout: Optional[float] = None, **decode_kwargs
):
super().__init__(tokenizer, skip_prompt, **decode_kwargs)
self.text_queue = Queue()
self.stop_signal = None
self.timeout = timeout
def on_finali... | 10,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
class AsyncTextIteratorStreamer(TextStreamer):
"""
Streamer that stores print-ready text in a queue, to be used by a downstream application as an async iterator.
This is useful for applications that benefit from acessing the generated text asynchronously (e.g. in an
interactive Gradio demo).
<Tip w... | 10,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout fo... | 10,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt") | 10,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
>>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
>>> async def main():
... # Important: AsyncTextIteratorStreamer must be initialized inside a coroutine!
... streamer = AsyncTextIteratorStreamer(tok)
... generation_... | 10,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
def __init__(
self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, timeout: Optional[float] = None, **decode_kwargs
):
super().__init__(tokenizer, skip_prompt, **decode_kwargs)
self.text_queue = asyncio.Queue()
self.stop_signal = None
self.timeout = timeout
se... | 10,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
async def __anext__(self):
try:
if self.has_asyncio_timeout:
async with asyncio.timeout(self.timeout):
value = await self.text_queue.get()
else:
value = await asyncio.wait_for(self.text_queue.get(), timeout=self.timeout)
except ... | 10,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py |
class GenerateDecoderOnlyOutput(ModelOutput):
"""
Outputs of decoder-only generation models, when using non-beam methods. | 10,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Args:
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *o... | 10,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.