text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Examples:
``` python
>>> import torch
>>> from transformers import AutoProcessor, WhisperForConditionalGeneration, GenerationConfig
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditionalGeneration.from_pret... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> #Displaying timestamps
>>> generated_ids = model.generate(inputs=input_features, return_timestamps=True)
>>> transcription = processor.batch_decode(generated_ids, decode_with_timestamps=True)[0]
>>> print("Transcription:", transcription)
Transcription: <|startoftranscript|><|0.00|> He has grave doub... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> #No timestamps & change EOS:
>>> #This allows the user to select a specific token to terminate the sequence on, in this case it's the word "can"(460)
>>> model.generation_config.eos_token_id = 460
>>> generated_ids = model.generate(inputs=input_features,return_timestamps=False)
>>> transcription = p... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# this variable is mostly just used for testing
self._detect_timestamp_from_logprob = (
_detect_timestamp_from_logprob
if _detect_timestamp_from_logprob is not None
else getattr(generate_config, "_detect_timestamp_from_logprob", True)
)
num_forced_ids = (
... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# suppress <|notimestamps|> which is handled by without_timestamps
scores_processed = scores.clone()
scores_processed[:, self.no_timestamp... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
if last_was_timestamp:
if penultimate_was_timestamp: # has to be non-timestamp
scores_processed[k, self.timestamp_begin :] = -float("inf")
else: # cannot be normal text tokens
scores_processed[k, : self.eos_token_id] = -float("inf")
... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# apply the `max_initial_timestamp` option
if input_ids.shape[1] == self.begin_index:
scores_processed[:, : self.timestamp_begin] = -float("inf")
if self.max_initial_timestamp_index is not None:
last_allowed = self.timestamp_begin + self.max_initial_timestamp_index
... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class WhisperNoSpeechDetection(LogitsProcessor):
r"""This processor can be used to detect silence when using Whisper. It should take as input unprocessed logits to follow the original implementation"""
def __init__(self, no_speech_token: int, begin_index: int, scores_is_logprobs: bool = False):
self.no... | 10,690 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def set_inputs(self, inputs):
self.inputs = {**self.model.prepare_inputs_for_generation(**inputs), **inputs}
self.inputs["input_features"] = self.inputs.pop("inputs")
@property
def no_speech_prob(self):
return self._no_speech_prob
def set_begin_index(self, begin_index):
sel... | 10,690 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
if is_scores_logprobs:
probs = no_speech_scores.exp()
else:
probs = no_speech_scores.float().softmax(dim=-1)
self._no_speech_prob = probs[:, self.no_speech_token]
return scores | 10,690 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class ClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] for classifier free guidance (CFG). The scores are split over the batch dimension,
where the first half correspond to the conditional logits (predicted from the input prompt) and the second half
correspond to the unco... | 10,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
guidance_scale (float):
The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
Higher guidance scale encourages the model to generate samples that are more closely linked to the input
prompt, usually at the expense of poor... | 10,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, guidance_scale):
if guidance_scale > 1:
self.guidance_scale = guidance_scale
else:
raise ValueError(
"Require guidance scale >1 to use the classifier free guidance processor, got guidance scale "
f"{guidance_scale}."
... | 10,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# simple check to make sure we have compatible batch sizes between our
# logits scores (cond + uncond) and input ids (cond only)
if scores... | 10,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class AlternatingCodebooksLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing alternated generation between the two codebooks of Bark.
<Tip warning={true}>
This logits processor is exclusively compatible with
[Bark](https://huggingface.co/docs/transformers/en/model_doc/bark)'s fin... | 10,692 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
self.input_start_len = input_start_len
self.semantic_vocab_size = semantic_vocab_size
self.codebook_size = codebook_size
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
curr_len = input_ids.shape[-1]
# even -> first codebook, odd -> ... | 10,692 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class UnbatchedClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
r"""
Logits processor for Classifier-Free Guidance (CFG). The processors computes a weighted average across scores
from prompt conditional and prompt unconditional (or negative) logits, parameterized by the `guidance_scale`.
The unco... | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
guidance_scale (`float`):
The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale != 1`.
Higher guidance scale encourages the model to generate samples that are more closely linked to the input
prompt, usually at the expense of p... | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
unconditional_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Attention mask for unconditional_ids.
use_cache (`bool`, *optional*, defaults to `True`):
Whether to cache key/values during the negative prompt forward pass. | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> inputs = tokenizer(["Today, a dragon flew over Paris, France,"],... | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # with a positive prompt
>>> neg_inputs = tokenizer(["A very happy event happened,"], return_tensors="pt")
>>> out = model.generate(inputs["input_ids"], guidance_scale=0, negative_prompt_ids=neg_inputs["input_ids"])
>>> tokenizer.batch_decode(out, skip_special_tokens=True)[0]
"Today, a dragon flew o... | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def get_unconditional_logits(self, input_ids):
if self.unconditional_context["first_pass"]:
if self.unconditional_context["input_ids"] is None:
self.unconditional_context["input_ids"] = input_ids[:, -1:]
if self.unconditional_context["attention_mask"] is None:
... | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
input_ids = torch.cat([self.unconditional_context["input_ids"], input_ids[:, -1:]], dim=1)
else:
input_ids = input_ids[:, -1:]
self.unconditional_context["input_ids"] = input_ids
self.unconditional_context["attention_mask"] = attention_mask | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
out = self.model(
input_ids,
attention_mask=attention_mask,
use_cache=self.unconditional_context["use_cache"],
past_key_values=self.unconditional_context["past_key_values"],
)
self.unconditional_context["past_key_values"] = out.get("past_key_values", None)... | 10,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class BarkEosPrioritizerLogitsProcessor(LogitsProcessor):
r"""This processor ensures that the EOS token is selected if its probability is greater than the `min_eos_p`.
<Tip warning={true}>
This logits processor is exclusively compatible with
[Bark](https://huggingface.co/docs/transformers/en/model_doc... | 10,694 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
if torch.is_floating_point(eos_token_id) or (eos_token_id < 0).any():
raise ValueError(f"`eos_token_id` has to be a list of positive integers, but is {eos_token_id}")
if min_eos_p is not None and min_eos_p <= 0:
raise ValueError(f"`min_eos_p` has to be a positive float, but is {min_eos_... | 10,694 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
do_early_stop = probs[:, self.eos_token_id] > self.min_eos_p
do_early_stop = torch.any(do_early_stop, dim=1, keepdim=True)
scores_processed = torch.where(do_early_stop, early_stop_scores, scores)
return scores_processed | 10,694 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class WatermarkLogitsProcessor(LogitsProcessor):
r"""
Logits processor for watermarking generated text. The processor modifies model output scores by adding a small bias to
randomized set of "green" tokens before generating the next token. "Green" tokens selection process depends on the
`seeding_scheme`... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
vocab_size (`int`):
The model tokenizer's vocab_size. Used to calculate "green" tokens ratio.
device (`str`):
The device where model is allocated.
greenlist_ratio (`float`, optional, *optional*, defaults to 0.25):
The ratio of "green" tokens used to the ... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
The seeding scheme used for selecting "green" tokens. Accepts values:
- "lefthash" (default): "green" tokens selection depend on the last token (Algorithm 2 from paper)
- "selfhash": "green" tokens selection depends on the current token itself (Algorithm 3 from paper)
... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, WatermarkingConfig
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> inputs = tokenizer(["Alice and Bob are"], re... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # to detect watermarked text use the WatermarkDetector class
>>> from transformers import WatermarkDetector
>>> detector = WatermarkDetector(model_config=model.config, device="cpu", watermarking_config= watermarking_config)
>>> detection_preds = detector(out)
>>> detection_preds
array([ True])
... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
self.vocab_size = vocab_size
self.greenlist_size = int(self.vocab_size * greenlist_ratio)
self.bias = bias
self.seeding_scheme = seeding_scheme
self.rng = torch.Generator(device=device)
self.hash_key = hashing_key
self.context_width = context_width
self.rng.manua... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def _get_greenlist_ids(self, input_seq: torch.LongTensor) -> torch.LongTensor:
self.set_seed(input_seq)
vocab_permutation = torch.randperm(self.vocab_size, device=input_seq.device, generator=self.rng)
greenlist_ids = vocab_permutation[: self.greenlist_size]
return greenlist_ids
def ... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# 40 is an arbitrary number chosen to save compute and not run for long (taken from orig repo)
for i in range(40):
greenlist_ids = self._get_greenlist_ids(torch.cat([input_seq, greedy_predictions[i, None]], dim=-1))
if greedy_predictions[i] in greenlist_ids:
final_greenli... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
scores_processed = scores.clone()
for b_idx, input_seq in enumerate(input_ids):
if self.seeding_scheme == "selfhash":
greenlist_ids = self._score_rejection_sampling(input_seq, scores[b_idx])
else:
greenlist_ids = self._get_greenlist_ids(input_seq)
... | 10,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class SynthIDTextWatermarkState:
"""SynthID watermarking state."""
def __init__(
self,
batch_size: int,
ngram_len: int,
context_history_size: int,
device: torch.device,
):
"""Initializes the state.
Args:
batch_size (`int`): Batch size.
... | 10,696 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class SynthIDTextWatermarkLogitsProcessor(LogitsProcessor):
r"""
Logits processor that implements watermarking techniques for text generation models.
This class facilitates the application of SynthID text watermarking, a method for embedding imperceptible signals
into generated text to aid in detecting ... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
* **Score Adjustment:** Applies calculated g-values to modify token probabilities during generation, embedding the
watermark.
* **Context Repetition Handling:** Incorporates logic to avoid watermarking tokens in repeated contexts,
preserving naturalness.
* **EOS Token Masking:** Supports masking end-o... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
ngram_len (`int`):
Ngram length.
keys (`List[int]`):
A sequence of watermarking keys, one for each depth.
sampling_table_size (`int`):
Size of the sampling table.
sampling_table_seed (`int`):
Random seed to generate the sampling table... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')
>>> # SynthID Text configuration
>>> watermarking_config = SynthIDTextWatermarkingConfig(
... keys=[654, 400, 836, 123, 340, 443, 597, 160, 5... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(
self,
ngram_len: int,
keys: List[int],
sampling_table_size: int,
sampling_table_seed: int,
context_history_size: int,
device: torch.device,
skip_first_ngram_calls: bool = False,
debug_mode: bool = False,
):
self.ngram_len ... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
generator = torch.Generator(device=device).manual_seed(sampling_table_seed)
# A random sampling table is pre-computed and modulo table size is applied to map from a hash of ngram keys to
# g values, this is similar to the hashtable implementation used in
# https://github.com/facebookresearch/thr... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def _init_state(self, batch_size: int):
"""Initializes the state."""
self.state = SynthIDTextWatermarkState(
batch_size=batch_size,
ngram_len=self.ngram_len,
context_history_size=self.context_history_size,
device=self.device,
)
def update_scor... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
for i in range(depth):
g_values_at_depth = g_values[:, :, i]
g_mass_at_depth = (g_values_at_depth * probs).sum(axis=1, keepdims=True)
probs = probs * (1 + g_values_at_depth - g_mass_at_depth)
log_probs = torch.log(probs)
log_probs = torch.where(torch.isfinite(log_pro... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
if self.state is None:
# Initialize watermarking state if it does not exist.
self._init_state(batch_size)
else:
# Append last input id (which is the input id added in last call) to the
# previous context so we have the context to be used for current
# ... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# 2. Generate random keys for each ngram key combination.
ngram_keys, hash_result_with_just_context = self._compute_keys(self.state.context, all_indices)
# ngram_keys shape [batch_size, top_k, depth]
# 3. Sample g values.
g_values = self.sample_g_values(ngram_keys)
# g_values sh... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
updated_watermarked_scores = torch.where(
is_repeated_context,
input=scores,
other=updated_scores,
)
return updated_watermarked_scores
def accumulate_hash(
self,
current_hash: torch.LongTensor,
data: torch.LongTensor,
multiplier: i... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
current_hash (`torch.LongTensor`):
(shape,)
data (`torch.LongTensor`):
(shape, tensor_len)
multiplier (`int`, optional, *optional*, defaults to 6364136223846793005):
multiplier of linear congruential generator
incremen... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Returns:
ngram keys (batch_size, num_ngrams, depth).
"""
if len(ngrams.shape) != 3:
raise ValueError(
"Ngrams should be of shape (batch_size, num_ngrams, ngram_len), but" f" is {ngrams.shape}"
)
if ngrams.shape[2] != self.ngram_len:
... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
keys = self.keys[None, None, :, None]
# hash_result shape [batch_size, num_ngrams]
# keys shape [1, 1, depth, 1]
hash_result = torch.vmap(self.accumulate_hash, in_dims=(None, 2), out_dims=2)(hash_result, keys)
# hash_result shape [batch_size, num_ngrams, depth]
return hash_resul... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
hash_result = torch.ones(batch_size, device=self.device, dtype=torch.long)
# First hash n_minus_1 gram, for each batch entry we have a single
# n_minus_1 gram context.
# hash_result shape [batch_size]
# n_minus_1_gram shape [batch_size, ngram_len - 1]
hash_result_with_just_contex... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# [1, 1, depth, 1].
# So we can vmap over the depth dimension for compute_hash
keys = self.keys[None, None, :, None]
hash_result = torch.vmap(self.accumulate_hash, in_dims=(None, 2), out_dims=2)(hash_result, keys)
# hash_result shape should be [batch_size, num_indices, depth]
ret... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def sample_g_values(self, ngram_keys: torch.LongTensor) -> torch.LongTensor:
"""
Samples g values from Bernoulli distribution.
It is not possible to pass random keys in a vectorized way in torch. Instead
we pre-compute a random sampling table, and use apply modulo table size to
... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def _check_input_ids_shape(self, input_ids: torch.LongTensor):
"""Checks the shape of input ids."""
if len(input_ids.shape) != 2:
raise ValueError("Input ids should be of shape (batch_size, input_len), but is" f" {input_ids.shape}")
def compute_g_values(self, input_ids: torch.LongTensor... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
0 and 1 stand for repeated and not repeated context n-1 grams respectively.
Args:
input_ids (`torch.LongTensor`):
Input token ids (batch_size, input_len).
Returns:
Repetitions mask (batch_size, input_len - (ngram_len - 1)).
"""
self._check_input_... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
are_repeated_contexts = []
for i in range(num_contexts):
context = contexts[:, i, :]
hash_result = torch.ones(batch_size, device=self.device, dtype=torch.long)
context_hash = self.accumulate_hash(hash_result, context)[:, None]
is_repeated_context = (state.context_... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
input_ids (`torch.LongTensor`):
Input token ids (batch_size, input_len).
eos_token_id (`int`):
EOS token ID.
Returns:
EOS token mask (batch_size, input_len).
"""
self._check_input_ids_shape(input_ids)
noneos_masks... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
vocab_size (`int`):
The size of the vocabulary.
coinflip_prob arg_name (`float`, *optional*, defaults to 0.5):
Probability of 1 in boolean prf.
Returns:
The expected mean g-value for watermarked text.
"""
return coinflip_... | 10,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class TFLogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
"""TF method for processing logits."... | 10,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFLogitsWarper:
"""Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
"""TF method for... | 10,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFLogitsProcessorList(list):
"""
This class can be used to create a list of [`TFLogitsProcessor`] to subsequently process a `scores` input tensor.
This class inherits from list and adds a specific *__call__* method to apply each [`TFLogitsProcessor`] to the
inputs.
""" | 10,700 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int, **kwargs) -> tf.Tensor:
for processor in self:
function_args = inspect.signature(processor.__call__).parameters
if len(function_args) > 3:
... | 10,700 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFTemperatureLogitsWarper(TFLogitsWarper):
r"""
[`TFLogitsWarper`] for temperature (exponential scaling output probability distribution).
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(self, temperature: float):
if ... | 10,701 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFTopKLogitsWarper(TFLogitsWarper):
r"""
[`TFLogitsWarper`] 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`, *optional*,... | 10,702 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
top_k = min(self.top_k, scores.shape[-1]) # Safety check
# Boolean mask containing all tokens with a probability less than the last token of the top-k
indices_to_remove = scores < tf.math.top_k(scores, k=top... | 10,702 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFTopPLogitsWarper(TFLogitsWarper):
"""
[`TFLogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to <= 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_p` or
h... | 10,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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... | 10,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
# Also include the token that is higher than top_p (the first false = shift and insert a True on the left)
score_mask = tf.concat((tf.ones([score_mask.shape[0], 1], dtype=tf.bool), score_mask[:, :-1]), axis=-1)
# Ensure min tokens to keep
score_mask = tf.concat(
(
tf... | 10,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
# Undo the topk sorting: converts the 2D matrix of per-row original indices of shape (batch_size, vocab_size)
# to a 3D tensor of shape (batch_size, vocab_size, 2) containing the original score coordinate, from which we
# can scatter (i.e. `scatter_indices[row, col, :]` is a tensor containing `[row, top... | 10,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFMinLengthLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] 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`):
... | 10,704 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def _apply_eos_token_mask(self, scores: tf.Tensor) -> tf.Tensor:
eos_token_id_mask = tf.range(scores.shape[-1]) == self.eos_token_id
scores = tf.where(eos_token_id_mask, float("-inf"), scores)
return scores
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tens... | 10,704 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFRepetitionPenaltyLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] enforcing an exponential penalty on repeated sequences.
Args:
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/... | 10,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
# Gathers the penalties to apply
logit_penalties = tf.gather(logits, input_ids, axis=1, batch_dims=1)
logit_penalties = tf.where(logit_penalties > 0, 1 / self.penalty, logit_penalties)
logit_penalties = tf.where(logit_penalties < 0, self.penalty, logit_penalties)
# Scatters the penaltie... | 10,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
score_penalties = self._create_score_penalties(input_ids[:, :cur_len], scores)
scores = tf.math.multiply(scores, score_penalties)
return scores | 10,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFNoBadWordsLogitsProcessor(TFLogitsProcessor):
"""
[`TFLogitsProcessor`] that enforces that specified sequences will never be sampled.
Args:
bad_words_ids (`List[List[int]]`):
List of list of token ids that are not allowed to be generated. In order to get the tokens of the words
... | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def __init__(self, bad_words_ids: List[List[int]], eos_token_id: int):
if not isinstance(bad_words_ids, List) or len(bad_words_ids) == 0:
raise ValueError(f"`bad_words_ids` has to be a non-empty list, but is {bad_words_ids}.")
if any(not isinstance(bad_word_ids, list) for bad_word_ids in bad... | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
# stores the information about bad words in three tensors:
# 1. a rectangular tensor with the forbidden sequences (padded with `-1`), for full data comparisons
self.bad_word_seqs_ids = tf.ragged.constant(bad_words_ids).to_tensor(default_value=-1)
# 2. a tensor with the unpadded length of each fo... | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def _calc_row_banned_bad_tokens(self, row_input_ids: tf.Tensor) -> tf.Tensor:
def _tokens_match(bad_word_seq_number):
def _len_one():
# If the bad sequence only has one token, always mask it
return tf.cond(
tf.math.equal(self.bad_word_seqs_len[bad_... | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def _match_found():
# Finaly, runs the actual comparison. Can only be called if the previous comparisons do not yield
# an answer (otherwise we get indexing exceptions)
compare_len = self.bad_word_seqs_len[bad_word_seq_number] - 1
return tf.cond(
... | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
# Compares the current row against all bad word sequences, obtaining a mask with the matches.
match_mask = tf.map_fn(_tokens_match, tf.range(self.bad_word_seqs_ids.shape[0]), fn_output_signature=tf.bool)
row_banned_tokens = self.seq_forbidden_tokens[match_mask]
return row_banned_tokens | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
# We want to mask some banned tokens, at a score level. Since the banned tokens depend on the previous
# `input_ids`, they may have a different length for each row, and they may even be empty for some rows.
#... | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
row_score = tf.where(banned_tokens_mask, -float("inf"), row_score)
return row_score | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
scores = tf.map_fn(_get_row_updated_score, (input_ids, scores), fn_output_signature=tf.float32)
return scores | 10,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFNoRepeatNGramLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] 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`):
A... | 10,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def calc_banned_ngram_tokens(self, input_ids, num_hypos, cur_len):
# Copied from fairseq for no_repeat_ngram in beam_search
if cur_len + 1 < self.ngram_size:
# return no banned tokens if we haven't generated ngram_size tokens yet
return [[] for _ in range(num_hypos)]
gene... | 10,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - self.ngram_size
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].numpy().tolist())
return generated_ngrams[h... | 10,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
# create banned_tokens boolean mask
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = tf.where(tf.conver... | 10,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFForcedBOSTokenLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] 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: int):... | 10,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
if cur_len == 1:
batch_size, num_tokens = scores.shape
# sets the score to 0 in the bos_token_id column
scores = tf.zeros((batch_size, 1))
# sets the score to -inf everywhere e... | 10,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFForcedEOSTokenLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] 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`):
... | 10,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
if cur_len == self.max_length - 1:
batch_size, num_tokens = scores.shape
# sets the score to 0 in the eos_token_id column
scores = tf.zeros((batch_size, 1))
# sets the score to... | 10,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFSuppressTokensAtBeginLogitsProcessor(TFLogitsProcessor):
r"""
[`TFSuppressTokensAtBeginLogitsProcessor`] suppresses 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` at not
sampl... | 10,710 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
if len(suppressed_indices) > 0:
scores = tf.cond(
tf.equal(cur_len, self.begin_index),
lambda: tf.tensor_scatter_nd_update(
scores,
indices=suppressed_indices,
updates=[-float("inf") for _ in range(scores.shape[0] * ... | 10,710 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFSuppressTokensLogitsProcessor(TFLogitsProcessor):
r"""This processor can be used to suppress a list of tokens. The processor will set their log probs to `-inf` so that they
are not sampled."""
def __init__(self, suppress_tokens):
self.suppress_tokens = list(suppress_tokens)
def __call_... | 10,711 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class TFForceTokensLogitsProcessor(TFLogitsProcessor):
r"""This processor 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` so that they are sa... | 10,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
def __init__(self, force_token_map: List[List[int]]):
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 compatibilit... | 10,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
new_scores = tf.zeros_like(scores, dtype=scores.dtype) + tf.constant([scores.dtype.min])
indices = tf.stack((tf.range(batch_size), tf.tile([current_token], [batch_size])), axis=1)
updates = tf.zeros((batch_size,), dtype=scores.dtype)
new_scores = tf.tensor_scatter_nd_update(new_score... | 10,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
scores = tf.cond(
tf.greater_equal(cur_len, tf.shape(self.force_token_array)[0]),
# If the current length is geq than the length of force_token_array, the processor does nothing.
lambda: tf.identity(scores),
# Otherwise, it may force a certain token.
lambda: t... | 10,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py |
class StoppingCriteria(ABC):
"""Abstract base class for all stopping criteria that can be applied during generation.
If your stopping criteria depends on the `scores` input, make sure you pass `return_dict_in_generate=True,
output_scores=True` to `generate`.
"""
@add_start_docstrings(STOPPING_CRIT... | 10,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py |
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