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>>> # However, with temperature close to 0, it approximates greedy decoding strategies (invariant)
>>> generate_kwargs["temperature"] = 0.0001
>>> outputs = model.generate(**inputs, **generate_kwargs)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['Hugging Face Company is a compan... | 10,667 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
self.temperature = temperature
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
scores_processed = scores / self.temperature
return scores_processed | 10,667 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that prevents the repetition of previous tokens through a penalty. This penalty is applied at
most once per token. Note that, for decoder-only models like most LLMs, the considered tokens include the prompt.
In the origin... | 10,668 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
```py
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> # Initializing the model and tokenizer for it
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer(["I'm not ... | 10,668 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, penalty: float):
if not isinstance(penalty, float) or not (penalty > 0):
raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
self.penalty = penalty
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_... | 10,668 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class EncoderRepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that works similarly to [`RepetitionPenaltyLogitsProcessor`], but with an *inverse* penalty
that is applied to the tokens present in the prompt. In other words, a penalty above 1.0 increases the odds of
selecting to... | 10,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
>>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
>>> inputs = tokenizer(["Alice and Bob. The third member's name was"], return_tensors="pt")
>>> gen_out = model.generate(**inputs)
>>> print(tokenizer.batch... | 10,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, penalty: float, encoder_input_ids: torch.LongTensor):
if not isinstance(penalty, float) or not (penalty > 0):
raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
self.penalty = 1 / penalty
self.encoder_input_ids = encoder_input_ids... | 10,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class TopPLogitsWarper(LogitsProcessor):
"""
[`LogitsProcessor`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
Often used together with [`TemperatureLogitsWarper`] and [`TopKLogitsWarper`].
Args:
top_p (`float`):
If set to < 1, only the... | 10,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> inputs = tokenizer("A sequence: 1, 2", return_tensors="pt")
>>> # With sampling, the output is unexpected -- sometimes too unexpected.
>>> outputs = model.generate(**inputs, do_sample=True)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
A sequence: 1, 2, 3 | < 4 (left-hand ... | 10,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, top_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
top_p = float(top_p)
if 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_tokens_t... | 10,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs <= (1 - self.top_p)
# Keep at least min_tokens_to_keep
sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0
# scatter sorted tensors to original indexin... | 10,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class TopKLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs top-k, i.e. restricting to the k highest probability elements. Often used
together with [`TemperatureLogitsWarper`] and [`TopPLogitsWarper`].
Args:
top_k (`int`):
The number of highest probability vocabu... | 10,671 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # With sampling, the output is unexpected -- sometimes too unexpected.
>>> outputs = model.generate(**inputs, do_sample=True)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
A sequence: A, B, C, D, E — S — O, P — R
>>> # With `top_k` sampling, the output gets restricted the ... | 10,671 | /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:
top_k = min(self.top_k, scores.size(-1)) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indi... | 10,671 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class MinPLogitsWarper(LogitsProcessor):
"""
[`LogitsProcessor`] that performs min-p, i.e. keeps all tokens that are above a minimum probability, scaled by the
probability of the most likely token. As a result, the filter becomes more agressive in the presence of
high-probability tokens, which is a sign... | 10,672 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
min_p (`float`):
Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
value between 0 and 1. Typical values are in the 0.01-0.2 range, comparably selective as setting `top_p` in
the 0.99-0.8 range (use the opposite of... | 10,672 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # With sampling, the output is unexpected -- sometimes too unexpected.
>>> outputs = model.generate(**inputs, do_sample=True)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
A sequence: 1, 2, 3 | < 4 (left-hand pointer) ;
<BLANKLINE>
<BLANKLINE>
>>> # With `min_p` sa... | 10,672 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, min_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if not (0 <= min_p <= 1.0):
raise ValueError(f"`min_p` has to be a float in the [0, 1] interval, but is {min_p}")
if not isinstance(min_tokens_to_keep, int) or (min_tokens_to_keep < 1):
... | 10,672 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# Convert logits to probabilities
probs = torch.softmax(scores, dim=-1)
# Get the probability of the top token for each sequence in the batch
top_probs, _ = probs.max(dim=-1, keepdim=True)
... | 10,672 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
scores_processed = scores.masked_fill(indices_to_remove, self.filter_value)
return scores_processed | 10,672 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class TypicalLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs typical decoding. Inspired on how humans use language, it prioritizes tokens
whose log probability is close to the entropy of the token probability distribution. This means that the most
likely tokens may be discarded in ... | 10,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
>>> inputs = tokenizer("1, 2, 3", return_tensors="pt")
>>> # We can see that greedy decoding produces a sequence of numbers
>>> outputs = model.generate(**... | 10,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # With `typical_p` set, the most obvious sequence is no longer produced, which may be good for your problem
>>> set_seed(18)
>>> outputs = model.generate(
... **inputs, do_sample=True, typical_p=0.1, return_dict_in_generate=True, output_scores=True
... )
>>> print(tokenizer.batch_decode(outp... | 10,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, mass: float = 0.9, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
mass = float(mass)
if not (mass > 0 and mass < 1):
raise ValueError(f"`typical_p` has to be a float > 0 and < 1, but is {mass}")
if not isinstance(min_tokens_to_keep, int) or (min... | 10,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# shift and sort
shifted_scores = torch.abs((-normalized) - ent)
sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False)
sorted_logits = scores.gather(-1, sorted_indices)
cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
# Remove tokens with cu... | 10,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class EpsilonLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs epsilon-sampling, i.e. restricting to tokens with `prob >= epsilon`. Takes the
largest min_tokens_to_keep tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.... | 10,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> set_seed(1)
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer("A sequence: 1, 2", return_tensors="pt")
>>> # With sampling, the output is unexpected -- sometimes too unexpected.
... | 10,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # With epsilon sampling, the output gets restricted to high-probability tokens. Note that this is similar to
>>> # Top P sampling, which restricts tokens based on their cumulative probability.
>>> # Pro tip: The paper recomends using `epsilon_cutoff` values between 3e-4 and 9e-4
>>> outputs = model.gene... | 10,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
min_tokens_to_keep = int(min_tokens_to_keep)
if min_tokens_to_keep < 1:
raise ValueError(
f"`min_tokens_to_keep` has to be a strictly positive integer, but is {min_tokens_to_keep}"
)
self.epsilon = epsilon
self.filter_value = filter_value
self.min... | 10,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
scores_processed = scores.masked_fill(indices_to_remove, self.filter_value)
return scores_processed | 10,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class EtaLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs eta-sampling, a technique to filter out tokens with probabilities below a dynamic
cutoff value, `eta`, which is calculated based on a combination of the hyperparameter `epsilon` and the entropy of
the token probabilities, i.e... | 10,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
epsilon (`float`):
A float value in the range (0, 1). Hyperparameter used to calculate the dynamic cutoff value, `eta`. The
suggested values from the paper ranges from 3e-4 to 4e-3 depending on the size of the model.
filter_value (`float`, *optional*, defaults to -inf):
... | 10,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
device (`str`, *optional*, defaults to `"cpu"`):
The device to allocate the tensors. | 10,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
>>> set_seed(1)
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
>>> inputs = tokenizer("A sequence: 1,... | 10,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # With eta sampling, the output gets restricted to high-probability tokens. You can see it as a dynamic form of
>>> # epsilon sampling that adapts its cutoff probability based on the entropy (high entropy = lower cutoff).
>>> # Pro tip: The paper recomends using `eta_cutoff` values between 3e-4 to 4e-3
... | 10,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
min_tokens_to_keep = int(min_tokens_to_keep)
if min_tokens_to_keep < 1:
raise ValueError(
f"`min_tokens_to_keep` has to be a strictly positive integer, but is {min_tokens_to_keep}"
)
self.epsilon = torch.tensor(epsilon, device=device)
self.filter_value = ... | 10,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# Keep the words with the 'min_tokens_to_keep'-highest probabilities
top_k = min(self.min_tokens_to_keep, scores.size(-1)) # Safety check
indices_to_remove = indices_to_remove & (scores < torch.topk(scores, top_k)[0][..., -1, None])
scores_processed = scores.masked_fill(indices_to_remove, self... | 10,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class NoRepeatNGramLogitsProcessor(LogitsProcessor):
r"""
N-grams are groups of "n" consecutive words, characters, or tokens taken from a sequence of text. Given the
sentence: "She runs fast", the bi-grams (n=2) would be ("she", "runs") and ("runs", "fast"). In text generation,
avoiding repetitions of w... | 10,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Use n-gram penalties with care. For instance, penalizing 2-grams (bigrams) in an article about the city of New York
might lead to undesirable outcomes where the city's name appears only once in the entire text.
[Reference](https://huggingface.co/blog/how-to-generate)
</Tip>
Args:
ngram_size (`... | 10,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # Now let's add ngram size using `no_repeat_ngram_size`. This stops the repetitions ("I’m") in the output.
>>> output = model.generate(**inputs, no_repeat_ngram_size=2)
>>> print(tokenizer.decode(output[0], skip_special_tokens=True))
Today I’m not sure if I can get a better understanding of the nature o... | 10,676 | /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:
num_batch_hypotheses = scores.shape[0]
cur_len = input_ids.shape[-1]
scores_processed = scores.clone()
banned_batch_tokens = _calc... | 10,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class EncoderNoRepeatNGramLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that works similarly to [`NoRepeatNGramLogitsProcessor`], but applied exclusively to prevent
the repetition of n-grams present in the prompt.
It was designed to promote chattiness in a language model, by preventing the... | 10,677 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # With greedy decoding, we see Bob repeating Alice's opinion. If Bob was a chatbot, it would be a poor one.
>>> outputs = model.generate(**inputs)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
Alice: I love cats. What do you love?
Bob: I love cats. What do you
>>> # Wi... | 10,677 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, encoder_ngram_size: int, encoder_input_ids: torch.LongTensor):
if not isinstance(encoder_ngram_size, int) or encoder_ngram_size <= 0:
raise ValueError(
f"`encoder_ngram_size` has to be a strictly positive integer, but is {encoder_ngram_size}"
)
... | 10,677 | /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:
# B x num_beams
num_hypos = scores.shape[0]
num_beams = num_hypos // self.batch_size
cur_len = input_ids.shape[-1]
scores_... | 10,677 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class SequenceBiasLogitsProcessor(LogitsProcessor):
"""
[`LogitsProcessor`] that applies an additive bias on sequences. The bias is applied to the last token of a sequence
when the next generated token can complete it. Consequently, to take the most of biasing sequences with more than
one token, conside... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
sequence_bias (`List[List[Union[List[int], float]]]`):
List of lists that maps a sequence of tokens to its bias term (e.g. `[[[10, 45], -2.0],
[[64], -7.5]]`). Positive biases increase the odds of the
sequence being selected, while negative biases do the opposite. If a ... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> summary_ids = model.generate(inputs["input_ids"], max_new_tokens=4)
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0])
The full name of Donald is Donald J. Trump Jr
>>> # Now let's control generation through a bias. Please note that the tokenizer is initialized differently!
... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
>>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
The full name of Donald is Donald Rumsfeld,
>>> # We can also add a positive bias to nudge the model towards specific t... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# Bias variables that will be populated on the first call (for retrocompatibility purposes, the vocabulary size
# is infered in the first usage, which inhibits initializing here)
self.length_1_bias = None
self.prepared_bias_variables = False
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOC... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# 4 - include the bias from length > 1, after determining which biased sequences may be completed.
for sequence_ids, sequence_bias in self.sequence_bias.items():
if len(sequence_ids) == 1: # the sequence is of length 1, already applied
continue
if len(sequence_ids) > inp... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# 5 - apply the bias to the scores
scores_processed = scores + bias
return scores_processed
def _prepare_bias_variables(self, scores: torch.FloatTensor):
vocabulary_size = scores.shape[-1]
# Check biased tokens out of bounds
invalid_biases = []
for sequence_ids in s... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# Precompute the bias tensors to be applied. Sequences of length 1 are kept separately, as they can be applied
# with simpler logic.
self.length_1_bias = torch.zeros((vocabulary_size,), dtype=torch.float).to(scores.device)
for sequence_ids, bias in self.sequence_bias.items():
if len(... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def _validate_arguments(self):
sequence_bias = self.sequence_bias
if not isinstance(sequence_bias, dict) and not isinstance(sequence_bias, list) or len(sequence_bias) == 0:
raise ValueError(
f"`sequence_bias` has to be a non-empty dictionary, or non-empty list of lists but is... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
f"{sequence_bias}."
) | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def all_token_bias_pairs_are_valid(sequence):
return (
isinstance(sequence[0], list)
and all(isinstance(token_id, (int, np.integer)) and token_id > 0 for token_id in sequence[0])
and isinstance(sequence[1], float)
)
if isinstance(sequence_... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def _convert_list_arguments_into_dict(self):
"""BC: we used to accept `dict{tuple of tokens: float}` directly, now we expect a list"""
if isinstance(self.sequence_bias, list):
temp_sequence = self.sequence_bias
self.sequence_bias = {tuple(sublist[0]): sublist[1] for sublist in te... | 10,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class NoBadWordsLogitsProcessor(SequenceBiasLogitsProcessor):
"""
[`LogitsProcessor`] that enforces that specified sequences will never be selected.
<Tip>
In order to get the token ids of the words that should not appear in the generated text, make sure to set
`add_prefix_space=True` when initiali... | 10,679 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> inputs = tokenizer(["In a word, the cake is a"], return_tensors="pt")
>>> output_ids = model.generate(inputs["input_ids"], max_new_tokens=5, pad_token_id=toke... | 10,679 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> def get_tokens_as_list(word_list):
... "Converts a sequence of words into a list of tokens"
... tokens_list = []
... for word in word_list:
... tokenized_word = tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0]
... tokens_list.append(tokenized... | 10,679 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
# Filter EOS token from bad_words_ids
if eos_token_id is not None:
if 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)
bad_words_ids =... | 10,679 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def _validate_arguments(self):
bad_words_ids = self.bad_word_ids
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 b... | 10,679 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class PrefixConstrainedLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces constrained generation and is useful for prefix-conditioned constrained
generation. See [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) for more information.
Args:
prefix_allowed_... | 10,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> inputs = tokenizer("Alice and Bob", return_tensors="pt")
>>> # By default, it continues generating according to the model's logits
>>> outputs = model.generate(**inputs, max_new_tokens=5)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
Alice and Bob are friends | 10,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # We can contrain it with `prefix_allowed_tokens_fn` to force a certain behavior based on a prefix.
>>> # For instance, we can force an entire entity to be generated when its beginning is detected.
>>> entity = tokenizer(" Bob Marley", return_tensors="pt").input_ids[0] # 3 tokens
>>> def prefix_allowed... | 10,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> outputs = model.generate(**inputs, max_new_tokens=5, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
Alice and Bob Marley
```
"""
def __init__(self, prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]], num... | 10,680 | /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:
mask = torch.full_like(scores, -math.inf)
for batch_id, beam_sent in enumerate(input_ids.view(-1, self._num_beams, input_ids.shape[-1])):
... | 10,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class HammingDiversityLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces diverse beam search.
Note that this logits processor is only effective for [`PreTrainedModel.group_beam_search`]. See [Diverse Beam
Search: Decoding Diverse Solutions from Neural Sequence Models](https://arxi... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
diversity_penalty (`float`):
This value is subtracted from a beam's score if it generates a token same as any beam from other group at a
particular time. A higher `diversity_penalty` will enforce greater diversity among the beams. Adjusting
this value can help strike a ... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # A long text about the solar system
>>> text = (
... "The Solar System is a gravitationally bound system comprising the Sun and the objects that orbit it, "
... "either directly or indirectly. Of the objects that orbit the Sun directly, the largest are the eight "
... "planets, with the... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # Generate non-diverse summary
>>> outputs_non_diverse = model.generate(
... **inputs,
... max_length=100,
... num_beams=4,
... num_return_sequences=2,
... )
>>> summary_non_diverse = tokenizer.batch_decode(outputs_non_diverse, skip_special_tokens=True)
>>> # With `d... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> print(summaries_diverse)
['the solar system formed 4.6 billion years ago from the collapse of a giant interstellar molecular cloud. of the objects that orbit the Sun directly, the largest are the eight planets.',
'the solar system formed 4.6 billion years ago from the collapse of a giant interstellar molecu... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, diversity_penalty: float, num_beams: int, num_beam_groups: int):
if not isinstance(diversity_penalty, float) or (not diversity_penalty > 0.0):
raise ValueError("`diversity_penalty` should be a float strictly larger than 0.")
self._diversity_penalty = diversity_penalty
... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
current_tokens: torch.LongTensor,
beam_group_idx: int,
) -> torch.FloatTensor:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
The index of the beam group currently being processed. | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Return:
`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`:
The processed prediction scores.
"""
# hamming diversity: penalise using same token in current group which was used in previous groups at
# the same time step
batch_size = current_tokens.s... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
scores_processed = scores.clone()
for batch_idx in range(batch_size):
# predicted tokens of last time step of previous groups
previous_group_tokens = current_tokens[
batch_idx * self._num_beams : batch_idx * self._num_beams + group_start_idx
]
toke... | 10,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class ForcedBOSTokenLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces the specified token as the first generated token. Used with encoder-decoder
models.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
Examples:
... | 10,682 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # We can use `forced_bos_token_id` to force the start of generation with an encoder-decoder model
>>> # (including forcing it to end straight away with an EOS token)
>>> outputs = model.generate(**inputs, max_new_tokens=10, forced_bos_token_id=tokenizer.eos_token_id)
>>> print(tokenizer.batch_decode(out... | 10,682 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class ForcedEOSTokenLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] 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 (`Union[int, List... | 10,683 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # By default, it continues generating according to the model's logits
>>> outputs = model.generate(**inputs, max_new_tokens=10)
>>> print(tokenizer.batch_decode(outputs)[0])
A sequence: 1, 2, 3, 4, 5, 6, 7, 8
>>> # `forced_eos_token_id` ensures the generation ends with a EOS token
>>> outputs =... | 10,683 | /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}")
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) ... | 10,683 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class InfNanRemoveLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that removes all `nan` and `inf` values to avoid the generation method to fail. Note that using
the logits processor should only be used if necessary since it can slow down the generation method.
This logits processor has no `... | 10,684 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class ExponentialDecayLengthPenalty(LogitsProcessor):
r"""
[`LogitsProcessor`] that exponentially increases the score of the `eos_token_id` after `start_index` has been
reached. This allows generating shorter sequences without having a hard cutoff, allowing the `eos_token` to be
predicted in a meaningfu... | 10,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> text = "Just wanted to let you know, I"
>>> inputs = tokenizer(text, return_tensors="pt")
>>> # Let's consider that we want short sentences, so we limit ... | 10,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # To promote the appearance of the EOS token at the right time, we add the `exponential_decay_length_penalty =
>>> # (start_index, decay_factor)`. Instead of cutting at max_tokens, the output comes to an end before and usually
>>> # with more meaning. What happens is that starting from `start_index` the EOS... | 10,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # With a small decay factor, you will have a higher chance of getting a meaningful sequence.
>>> set_seed(1)
>>> outputs = model.generate(
... **inputs,
... do_sample=True,
... temperature=0.9,
... max_length=30,
... pad_token_id=50256,
... exponential_decay_l... | 10,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
if 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)
self.eos_token_id = eos_token_id
if torch.is_floating_point(eos_token_id) or (eos_token_id < 0).any():
... | 10,685 | /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:
cur_len = input_ids.shape[-1]
self.eos_token_id = self.eos_token_id.to(scores.device)
penalties = torch.zeros_like(scores)
scores_... | 10,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class LogitNormalization(LogitsProcessor):
r"""
[`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
the scores during beam search, after applying the logits processors or warpers, since the search algorithm used in
this library doesn't do it (it only does it bef... | 10,686 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # By default, the scores are not normalized -- the sum of their exponentials is NOT a normalized probability
>>> # distribution, summing to 1
>>> outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
>>> print(torch.allclose(torch.sum(torch.exp(outputs.scores[-1])), torch.... | 10,686 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class SuppressTokensAtBeginLogitsProcessor(LogitsProcessor):
r"""
[`SuppressTokensAtBeginLogitsProcessor`] supresses 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 generated a... | 10,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # Whisper has `begin_suppress_tokens` set by default (= `[220, 50256]`). 50256 is the EOS token, so this means
>>> # it can't generate and EOS token in the first iteration, but it can in the others.
>>> outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
>>> print(output... | 10,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def set_begin_index(self, begin_index):
self.begin_index = begin_index
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
vocab_tensor = torch.arange(scores.shape[-1], device=scores.device)
... | 10,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class SuppressTokensLogitsProcessor(LogitsProcessor):
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 generated. Originally created for
[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper).
Examples... | 10,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> # Whisper has a long list of suppressed tokens. For instance, in this case, the token 1 is suppressed by default.
>>> outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
>>> print(outputs.scores[1][0, 1]) # 1 (and not 0) is the first freely generated token
tensor(-inf)
... | 10,688 | /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:
vocab_tensor = torch.arange(scores.shape[-1], device=scores.device)
suppress_token_mask = isin_mps_friendly(vocab_tensor, self.suppress_tokens)
... | 10,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class WhisperTimeStampLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that modifies the logits for the generation of timestamps in the transcription. When the input
tokens are at a specific threshold, the processor sets the scores to negative infinity. The processor makes sure
that timestamp... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
Args:
generate_config (`GenerateConfig`):
The generate config used to generate the output. The following parameters are required:
eos_token_id (`int`, *optional*, defaults to 50257):
The id of the *end-of-sequence* token.
no_timestamps_token_id (`i... | 10,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
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