text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
logits = unflatten_beam_dim(model_outputs.logits[:, -1], num_beams) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 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 = tf.nn.log_softmax(logits)
log_probs = logits_processor(flatten_beam_dim(running_seq... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
all_scores.append(
logits_warper(
flatten_beam_dim(running_sequences),
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 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... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
topk_log_probs, topk_indices = tf.math.top_k(log_probs, k=beams_to_keep)
topk_current_beam_indices = topk_indices // vocab_size
topk_running_beam_indices = self._gather_beams(running_beam_indices, topk_current_beam_indices)
topk_running_sequences = self._gather_beams(running_sequence... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# writes the new token
indices_batch = tf.repeat(tf.range(batch_size), [beams_to_keep])
indices_beam = tf.tile(tf.range(beams_to_keep), [batch_size])
update_indices = tf.stack(
[indices_batch, indices_beam, tf.broadcast_to(cur_len, [batch_size * beams_to_keep])], axis... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# we want to store the beam indices with batch information -> real beam index = beam index % num beams
batch_modified_indices = topk_current_beam_indices + tf.broadcast_to(
tf.expand_dims(tf.range(batch_size) * num_beams, axis=1), topk_current_beam_indices.shape
)
upd... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 4. Check which sequences have ended
# Update current sequences: Did the top `num_beams` 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 neg... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
tf.concat((tf.ones((num_beams), dtype=tf.bool), tf.zeros((num_beams), dtype=tf.bool)), axis=0),
shape_list(eos_in_next_token),
) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# non-top `num_beams` eos tokens can't be used to finish a beam, but the others can't be used in the next
# running sentences either
running_topk_log_probs = topk_log_probs + tf.cast(eos_in_next_token, tf.float32) * -1.0e9
# 5. Get running sequences scores for next
# Det... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 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 / (
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 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 = tf.concat([sequences, top... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 8. Prepare data for the next iteration
# Determine the top k beam indices from the original set of all beams. With these, gather the top k
# beam-associated caches.
cur_len = cur_len + 1
if "past_key_values" in model_outputs:
cache = tf.nest.map_structur... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if use_xla:
next_model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=(batch_size * num_b... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
return (
cur_len,
next_running_sequences,
next_running_scores,
next_running_beam_indices,
next_sequences,
next_scores,
next_beam_indices,
next_is_sent_finished,
decoder_prompt_... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 5. run generation
# 1st generation step has to be run before to initialize `past_key_values` (if active)
(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2-to-n generation steps can then be run in autoregressive fashion (only in case 1st generation step does
# NOT yield EOS token though)
maximum_iterations = max_length - cur_len
(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 6. prepare outputs
# 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 = tf.math.reduce_any(is_sent_finished, axis=1)
sequences = tf.where(none_finished[:, None, None], seque... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# Take best beams for each batch (the score is sorted in descending order)
sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
scores = flatten_beam_dim(scores[:, :num_return_sequences])
beam_indices = flatten_beam_dim(beam_indices[:, :num_return_sequences, :])
if not u... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
output_cls = TFBeamSampleEncoderDecoderOutput if do_sample else TFBeamSearchEncoderDecoderOutput
return output_cls(
sequences=sequences,
sequences_scores=scores,
scores=all_scores,
beam_indices=beam_indices,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequences | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def contrastive_search(
self,
input_ids: tf.Tensor,
top_k: Optional[int] = 1,
penalty_alpha: Optional[float] = 0,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
top_k (`int`, *optional*, defaults to 1):
The size of the candidate set that is used to re-rank for contrastive search
penal... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *opti... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to re... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
`model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a
[`~generation.TFContrastiveSearchEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import AutoTokenizer, TFAutoModelForCausalLM | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
>>> model = TFAutoModelForCausalLM.from_pretrained("facebook/opt-125m")
>>> # set pad_token_id to eos_token_id because OPT does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prom... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def gather_best_candidate(nested, selected_idx_stacked, batch_axis=0):
"""Gathers the slices indexed by selected_idx_stacked from a potentially nested structure of tensors."""
def gather_fn(tensor):
gathered_tensor = tf.gather(params=tensor, indices=selected_idx_stacked, axis=ba... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.generation_config.ma... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# initialize `generated` (`input_ids` padded with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,)... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
# (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
if model_kwargs.get("past_key_values") is None:
# prepare inputs
model_inputs = sel... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# last decoder hidden states will be used to compute the degeneration penalty (cosine similarity with
# previous tokens)
if self.config.is_encoder_decoder:
last_hidden_states = outputs.decoder_hidden_states[-1]
else:
last_hidden_sta... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
past_key_values = model_kwargs.get("past_key_values")
if past_key_values is None:
raise ValueError(
f"{self.__class__.__name__} does not support caching and therefore **can't** be used "
"for contrastive search."
)
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
outputs = next_step_cached_variables["outputs"] | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# contrastive_search main logic start:
# contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
# degeneration penalty
logit_for_next_step = logits_processor(generated, logit_for_next_step, cur_len)
logit_for_next_step = ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(logit_for_next_step)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.appe... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# Replicates the new past_key_values to match the `top_k` candidates
model_kwargs["past_key_values"] = tf.nest.map_structure(
lambda tensor: tf.repeat(tensor, top_k, axis=cache_batch_axis), model_kwargs["past_key_values"]
)
# compute the candidate tokens by the langu... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
logits = outputs.logits[:, -1, :]
# 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:
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# converts indices to a dimension of top_k to the stacked top_k * batch_size dimension, for indexing
# without a need to reshape on tensors that have these two dimensions stacked
selected_idx_stacked = selected_idx + tf.range(selected_idx.shape[0], dtype=tf.int64) * top_k
# prepare ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# XLA: last_hidden_states normally grows at each step, but in XLA it is padded so as to be used across
# iterations (with fixed shapes)
if use_xla:
last_hidden_states = dynamic_update_slice(last_hidden_states, next_hidden, [0, cur_len, 0])
else:
last_h... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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:
next_step_cro... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if output_attentions:
next_step_attentions = gather_best_candidate(outputs.attentions, selected_idx_stacked)
outputs = TFCausalLMOutputWithPast(
past_key_values=next_past_key_values,
hidden_states=next_decoder_hidden_states,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pa... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if use_xla:
# NOTE: 1) relative to other generation strategies, contrastive search is always running forward
# passes one step ahead -- hence the `cur_len=cur_len + 1`; 2) the attention mask here is expanded from
# [batch_size, ...] to [batch_size*top_k, ...] -- hence the... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
next_step_cached_variables = {
"logit_for_next_step": logit_for_next_step,
"last_hidden_states": last_hidden_states,
"outputs": outputs,
}
return generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
# 5. run gen... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
maximum_iterations = max_length - cur_len
generated, _, cur_len, _, _ = tf.while_loop(
contrastive_search_cond_fn,
contrastive_search_bod... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
return TFContrastiveSearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class FlaxLogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray) -> jnp.ndarray:
"""Flax method for processing logits."""
... | 10,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxLogitsWarper:
"""Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray) -> jnp.ndarray:
"""Flax method for warpin... | 10,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxLogitsProcessorList(list):
"""
This class can be used to create a list of [`FlaxLogitsProcessor`] or [`FlaxLogitsWarper`] to subsequently process
a `scores` input tensor. This class inherits from list and adds a specific *__call__* method to apply each
[`FlaxLogitsProcessor`] or [`FlaxLogitsWa... | 10,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int, **kwargs) -> jnp.ndarray:
for processor in self:
function_args = inspect.signature(processor.__call__).parameters
if len(function_args) > 3:
... | 10,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxTemperatureLogitsWarper(FlaxLogitsWarper):
r"""
[`FlaxLogitsWarper`] for temperature (exponential scaling output probability distribution).
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(self, temperature: float):
... | 10,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxTopPLogitsWarper(FlaxLogitsWarper):
"""
[`FlaxLogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
Args:
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_... | 10,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def __init__(self, top_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if not isinstance(top_p, float) or (top_p < 0 or top_p > 1.0):
raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}")
if not isinstance(min_tokens_to_keep, int) or (min_tok... | 10,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
# include the token that is higher than top_p as well
score_mask = jnp.roll(score_mask, 1)
score_mask |= score_mask.at[:, 0].set(True)
# min tokens to keep
score_mask = score_mask.at[:, : self.min_tokens_to_keep].set(True)
topk_next_scores = jnp.where(score_mask, topk_scores, m... | 10,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxTopKLogitsWarper(FlaxLogitsWarper):
r"""
[`FlaxLogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
Args:
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
filter_value (`float`, *opti... | 10,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
batch_size, vocab_size = scores.shape
next_scores_flat = jnp.full(batch_size * vocab_size, self.filter_value)
topk = min(self.top_k, scores.shape[-1]) # Safety check
topk_scores, topk_indices ... | 10,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxForcedBOSTokenLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces the specified token as the first generated token.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
"""
def __init__(self, bos_token_id:... | 10,762 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxForcedEOSTokenLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`int... | 10,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxMinLengthLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] enforcing a min-length by setting EOS probability to 0.
Args:
min_length (`int`):
The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`int`):
... | 10,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
# create boolean flag to decide if min length penalty should be applied
apply_penalty = 1 - jnp.clip(cur_len - self.min_length, 0, 1)
scores = jnp.where(apply_penalty, scores.at[:, self.eos_token_id].s... | 10,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxSuppressTokensAtBeginLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] supressing a list of tokens as soon as the `generate` function starts generating using
`begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` are not sampled at the
begi... | 10,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxSuppressTokensLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] suppressing a list of tokens at each decoding step. The processor will set their log probs
to be `-inf` so they are not sampled.
Args:
suppress_tokens (`list`):
Tokens to not sample.
"""
... | 10,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxForceTokensLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that takes a list of pairs of integers which indicates a mapping from generation indices to
token indices that will be forced before sampling. The processor will set their log probs to 0 and all other tokens
to `-inf... | 10,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def __init__(self, force_token_map):
force_token_map = dict(force_token_map)
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Inde... | 10,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
new_scores = jnp.ones_like(scores, dtype=scores.dtype) * -float("inf")
updates = jnp.zeros((batch_size, 1), dtype=scores.dtype)
new_scores = lax.dynamic_update_slice(new_scores, updates, (0, current_token))
return new_scores
scores = lax.cond(
cur_len >= self.for... | 10,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxWhisperTimeStampLogitsProcessor(FlaxLogitsProcessor):
r"""
Whisper specific Processor. This processor can be used to force a list of tokens. The processor will set their log
probs to `inf` so that they are sampled at their corresponding index.
Args:
generate_config (`GenerateConfig`):... | 10,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def __init__(self, generate_config, model_config, decoder_input_length):
self.eos_token_id = generate_config.eos_token_id
self.no_timestamps_token_id = generate_config.no_timestamps_token_id
self.timestamp_begin = generate_config.no_timestamps_token_id + 1
self.begin_index = decoder_inp... | 10,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def __call__(self, input_ids, scores, cur_len):
# suppress <|notimestamps|> which is handled by without_timestamps
scores = scores.at[:, self.no_timestamps_token_id].set(-float("inf"))
def handle_pairs(input_ids_k, scores_k):
last_was_timestamp = jnp.where((cur_len - self.begin_inde... | 10,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
return jnp.where(
last_was_timestamp,
jnp.where(
penultimate_was_timestamp > 0,
scores_k.at[self.timestamp_begin :].set(-float("inf")),
scores_k.at[: self.eos_token_id].set(-float("inf")),
),
scor... | 10,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
# if sum of probability over timestamps is above any other token, sample timestamp
logprobs = jax.nn.log_softmax(scores, axis=-1)
def handle_cumulative_probs(logprobs_k, scores_k):
timestamp_logprob = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :], axis=-1)
max_text_token_l... | 10,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class FlaxNoRepeatNGramLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces no repetition of n-grams. See
[Fairseq](https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345).
Args:
ngram_size (`int`):
... | 10,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def get_previous_ngrams(self, input_ids: jnp.ndarray, vocab_size: int, cur_len: int):
"""
get a matrix of size (batch_size,) + (vocab_size,)*n (for n-grams) that
represent the n-grams that occurred previously.
The BCOO representation allow to store only the few non-zero entries, instead ... | 10,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
shape = (batch_size * seq_ngrams, self.ngram_size + 1)
all_update_indices = jax.lax.fori_loop(
0, batch_size * cur_ngrams, body_fun, jnp.zeros(shape, dtype=input_ids.dtype)
)
# ignore the n-grams not yet generated
data = (jnp.arange(batch_size * seq_ngrams) < batch_size * cu... | 10,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
def true_fn():
_, vocab_size = scores.shape
# store the previously seen n-grams
previous_ngrams = self.get_previous_ngrams(input_ids, vocab_size, cur_len)
# get the n-1 ... | 10,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
# compute the banned tokens, ie all the tokens that when added to the latest tokens lead to a n-gram that was previously generated
banned_tokens_indices_mask = self.get_banned_tokens_mask(latest_tokens, previous_ngrams).astype("bool")
return jnp.where(banned_tokens_indices_mask, -float("inf"), s... | 10,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py |
class Constraint(ABC):
r"""Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.
All classes that inherit Constraint must follow the requirement that
```py
completed = False
while not completed:
_, completed =... | 10,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
stepped, completed, reset = self.update(advance)
counter += 1
if counter > 10000:
raise Exception("update() does not fulfill the constraint.")
if self.remaining() != 0:
raise Exception("Custom Constraint is not defined correctly.")
@abstractmethod
d... | 10,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
@abstractmethod
def does_advance(self, token_id: int):
"""
Reads in a token and returns whether it creates progress.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
@abstractmethod
... | 10,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
Args:
token_id(`int`):
The id of a newly generated token in the beam search.
Return:
stepped(`bool`):
Whether this constraint has become one step closer to being fulfuilled.
completed(`bool`):
Whether this constraint has been co... | 10,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
@abstractmethod
def reset(self):
"""
Resets the state of this constraint to its initialization. We would call this in cases where the fulfillment of
a constraint is abrupted by an unwanted token.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract cla... | 10,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
Return:
constraint(`Constraint`): The same constraint as the one being called from.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
) | 10,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
class PhrasalConstraint(Constraint):
r"""
[`Constraint`] enforcing that an ordered sequence of tokens is included in the output.
Args:
token_ids (`List[int]`):
The id of the token that must be generated by the output.
"""
def __init__(self, token_ids: List[int]):
super(... | 10,771 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
def advance(self):
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def does_advance(self, token_id: int):
if not isinstance(token_id, int):
raise TypeError(f"`token_id` has to be an `int`, but is {token_id} of type {type(token_id)}")
... | 10,771 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
if self.does_advance(token_id):
self.fulfilled_idx += 1
stepped = True
if self.fulfilled_idx == (self.seqlen - 1):
completed = True
self.completed = completed
else:
# failed to make progress.
reset = True
self.re... | 10,771 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
class DisjunctiveTrie:
def __init__(self, nested_token_ids: List[List[int]], no_subsets=True):
r"""
A helper class that builds a trie with the words represented in `nested_token_ids`.
"""
self.max_height = max([len(one) for one in nested_token_ids])
root = {}
for tok... | 10,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
def next_tokens(self, current_seq):
"""
The next possible tokens that will progress the trie, given the current sequence of tokens in `current_seq`.
"""
start = self.trie
for current_token in current_seq:
start = start[current_token]
next_tokens = list(start... | 10,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
class DisjunctiveConstraint(Constraint):
r"""
A special [`Constraint`] that is fulfilled by fulfilling just one of several constraints.
Args:
nested_token_ids (`List[List[int]]`):
A list of words, where each word is a list of ids. This constraint is fulfilled by generating just one from... | 10,773 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
if not isinstance(nested_token_ids, list) or len(nested_token_ids) == 0:
raise ValueError(f"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.")
if any(not isinstance(token_ids, list) for token_ids in nested_token_ids):
raise ValueError(f"`nested_token_ids` has to ... | 10,773 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
if len(token_list) == 0:
return None
else:
return token_list
def does_advance(self, token_id: int):
if not isinstance(token_id, int):
raise TypeError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(token_id)}")
next_tokens = se... | 10,773 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
def reset(self):
self.completed = False
self.current_seq = []
def remaining(self):
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq)
def copy(self, stateful=... | 10,773 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
class ConstraintListState:
r"""
A class for beam scorers to track its progress through a list of constraints.
Args:
constraints (`List[Constraint]`):
A list of [`Constraint`] objects that must be fulfilled by the beam scorer.
"""
def __init__(self, constraints: List[Constraint]... | 10,774 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
def get_bank(self):
add = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints) * self.max_seqlen) + add
def advance(self):
... | 10,774 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
Though we don't care which constraint is fulfilled first, if we are in the progress of fulfilling a constraint,
that's the only one we'll return.
"""
token_list = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfille... | 10,774 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
def reset(self, token_ids: Optional[List[int]]):
"""
token_ids: the tokens generated thus far to reset the state of the progress through constraints.
"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** ... | 10,774 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py |
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