text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# same for min length
if generation_config.min_new_tokens is not None:
if not has_default_min_length:
logger.warning(
f"Both `min_new_tokens` (={generation_config.min_new_tokens}) and `min_length`(="
f"{generation_config.min_length}) seem to ha... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _prepare_generation_config(
self, generation_config: Optional[GenerationConfig], **kwargs: Dict
) -> Tuple[GenerationConfig, Dict]:
"""
Prepares the base generation config, then applies any generation configuration options from kwargs. This
function handles retrocompatibility wit... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
using_model_generation_config = False
if generation_config is None:
# legacy: users may modify the model configuration to control generation. To trigger this legacy behavior,
# th... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
and self.generation_config._original_object_hash == hash(self.generation_config) # 2)
and len(self.config._get_non_default_generation_parameters()) > 0 # 3)
):
new_generation_config = GenerationConfig.from_model_config(self.config)
if new_generation_config !... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
generation_config = self.generation_config
using_model_generation_config = True | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# `torch.compile` can't compile `copy.deepcopy`, arguments in `kwargs` that are part of `generation_config`
# will mutate the object with `.update`. As such, passing these arguments through `kwargs` is disabled -- an
# exception will be raised in `_validate_model_kwargs`
if not is_torchdynamo_co... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
generation_config.pad_token_id = self.generation_config.pad_token_id
if generation_config.decoder_start_token_id is None:
generation_config.decoder_start_token_id = self.generation_config.decoder_start_token_id
else:
model_kwargs = kwargs | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
return generation_config, model_kwargs
def _get_initial_cache_position(self, input_ids, model_kwargs):
"""Calculates `cache_position` for the pre-fill stage based on `input_ids` and optionally past length"""
# `torch.compile`-friendly `torch.arange` from a shape -- the lines below are equivalent to... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
past_length = 0
if model_kwargs.get("past_key_values") is not None:
cache = model_kwargs["past_key_values"]
past_length = 0
if not isinstance(cache, Cache):
past_length = cache[0][0].shape[2]
elif hasattr(cache, "get_seq_length") and cache.get_seq_... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _get_cache(
self, cache_implementation: str, batch_size: int, max_cache_len: int, device: torch.device, model_kwargs
) -> Cache:
"""
Sets a cache for `generate`, that will persist across calls. A new cache will only be initialized a
new `generate` call requires a larger cache or ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
need_new_cache = (
not hasattr(self, "_cache")
or (not isinstance(cache_to_check, cache_cls))
or cache_to_check.max_batch_size != batch_size
)
if cache_implementation != "mamba":
need_new_cache = need_new_cache or cache_to_check.max_cache_len < max_cache_l... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if need_new_cache:
if hasattr(self.config, "_pre_quantization_dtype"):
cache_dtype = self.config._pre_quantization_dtype
else:
if not is_torchdynamo_compiling():
cache_dtype = self.dtype
else:
# NOTE: self.dt... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def get_layer_device_map(execution_device_map: Optional[dict] = None):
num_hidden_layers = self.config.get_text_config().num_hidden_layers
if execution_device_map is None:
return None
elif len(execution_device_map) == 1 and "" in execution_device_map:
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
execution_device_map = None
# Taken from dispatch_model from accelerate.
# This is needed here if we don't want to make changes in accelerate in order to save execution_device
# For offloaded case, we need to get the execution device, not just the device where it is offloaded
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
cache_kwargs = {
"config": self.config.get_text_config(),
"max_batch_size": batch_size,
"max_cache_len": max_cache_len,
"device": device,
"dtype": cache_dtype,
"layer_device_map": layer_device_map,
}
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _supports_default_dynamic_cache(self) -> bool:
"""
Return `True` if current model can use a `DynamicCache` instance when initializing the `past_key_values`.
This is mostly the same as `_supports_cache_class` attribute, but add exception for `Jamba` model which
uses its own `HybridMam... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _prepare_cache_for_generation(
self,
generation_config: GenerationConfig,
model_kwargs: Dict,
assistant_model: "PreTrainedModel",
batch_size: int,
max_cache_length: int,
device: torch.device,
) -> bool:
"""
Prepares the cache for generation... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Quick escape route 1: if the user specifies a cache, we only need to:
# a) check for conflicting `generate` arguments
# b) convert to the new cache format (if the user passes a legacy cache and model supports it)
user_defined_cache = model_kwargs.get(cache_name)
if user_defined_cache i... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
)
return | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Quick escape route 2: if the user specifies no cache is to be used. (conflicting arguments are handled in
# `generation_config.validate()`)
if generation_config.use_cache is False:
return
# Quick escape route 3: model that only supports legacy caches = nothing to prepare
i... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# TODO(joao): support static caches in assisted generation. assisted generation needs to roll back caches,
# which is only supported in dynamic caches atm
if assistant_model is not None and generation_config.cache_implementation is not None:
logger.warning_once(
"An assistant... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if generation_config.cache_implementation is not None:
if generation_config.cache_implementation in NEED_SETUP_CACHE_CLASSES_MAPPING:
if generation_config.cache_implementation == "static" and not self._supports_static_cache:
raise ValueError(
"This... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if not self._supports_quantized_cache:
raise ValueError(
"This model does not support the quantized cache. If you want your model to support quantized "
"cache, please open an issue and tag @zucchini-nlp."
) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
cache_config = (
generation_config.cache_config
if generation_config.cache_config is not None
else QuantizedCacheConfig()
)
cache_class = QUANT_BACKEND_CLASSES_MAPPING[cache_config.backend]
if cache_config.backe... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
model_kwargs[cache_name] = cache_class(cache_config)
elif generation_config.cache_implementation == "offloaded":
model_kwargs[cache_name] = OffloadedCache()
# Use DynamicCache() instance by default. This will avoid back and forth from legacy format that
# keeps copying the c... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _prepare_special_tokens(
self,
generation_config: GenerationConfig,
kwargs_has_attention_mask: Optional[bool] = None,
device: Optional[Union[torch.device, str]] = None,
):
"""
Prepares the special tokens for generation, overwriting the generation config with their... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
device = device if device is not None else self.device
if isinstance(token, torch.Tensor):
return token.to(device)
return torch.tensor(token, device=device, dtype=torch.long)
bos_token_tensor = _tensor_or_none(generation_config.bos_token_id, device=device)
eos_to... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# We can have more than one eos token. Always treat it as a 1D tensor (when it exists).
if eos_token_tensor is not None and eos_token_tensor.ndim == 0:
eos_token_tensor = eos_token_tensor.unsqueeze(0)
# Set pad token if unset (and there are conditions to do so)
if pad_token_tensor i... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Sanity checks/warnings
if self.config.is_encoder_decoder and decoder_start_token_tensor is None:
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
)
if not is_torchdynamo_compiling(): # Checks that ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if eos_token_tensor is not None and (
torch.is_floating_point(eos_token_tensor) or (eos_token_tensor < 0).any()
):
logger.warning(
f"`eos_token_id` should consist of positive integers, but is {eos_token_tensor}. Your generation "
"will ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Update generation config with the updated special tokens tensors
# NOTE: this must be written into a different attribute name than the one holding the original special tokens
# (in their non-tensor form), in order to enable end-to-end compilation. See
# https://pytorch.org/docs/stable/torch.co... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
model's default generation configuration. You can override any `generation_config` by passing the corresponding
parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
method initializes it with `bos_token_id` and a batch size of 1. For decoder-only mode... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
generation config an error is thrown. If your stopping criteria depends on the `scores` input, make
sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is
intended for advanced users.
prefix_allowed_tokens_fn (`Callable[[int, torch.Ten... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
synced_gpus (`bool`, *optional*):
Whether to continue running the while loop until max_length. Unless overridden, this flag will be set
to `True` if using `FullyShardedDataParallel` or DeepSpeed ZeRO Stage 3 with multiple GPUs to avoid
deadlocking if one GPU finishes gene... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
negative_prompt_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
The negative prompt needed for some processors such as CFG. The batch size must match the input batch
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Return:
[`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
or when `config.return_dict_in_generate=True`) or a `torch.LongTensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class()
tokenizer = kwargs.pop("tokenizer", None) # Pull this out first, we only use it for stopping criteria
assistant_tokenizer = kwargs.pop("assistant_tokenizer", None) ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
r... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# decoder-only models must use left-padding for batched generation.
if not self.config.is_encoder_decoder and not is_torchdynamo_compiling():
# If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
# Note: If using, `inputs_embeds` this check does not work, bec... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 4. Define other model kwargs
# decoder-only models with inputs_embeds forwarding must use caching (otherwise we can't detect whether we are
# generating the first new token or not, and we only want to use the embeddings for the first new token)
if not self.config.is_encoder_decoder and model_i... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
# if model is encoder decoder encoder_outputs are created and added to `model_kwargs`
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
inputs_tensor, model_kwargs, model_input_name, ge... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if generation_config.token_healing:
input_ids = self.heal_tokens(input_ids, tokenizer)
if streamer is not None:
streamer.put(input_ids.cpu())
# 6. Prepare `max_length` depending on other stopping criteria.
input_ids_length = input_ids.shape[-1]
has_default_max_l... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# If the model supports `num_logits_to_keep` in forward(), set it to 1 to avoid computing the whole
# logit matrix. This can save a lot of memory during the first forward pass. Note that assisted decoding
# dynamically overrides this value as it can need more than the last token logits
if self._... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 7. Prepare the cache.
# - `model_kwargs` may be updated in place with a cache as defined by the parameters in `generation_config`.
# - different models have a different cache name expected by the model (default = "past_key_values")
# - `max_length`, prepared above, is used to determine the max... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if streamer is not None and (generation_config.num_beams > 1):
raise ValueError(
"`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
)
if not is_torchdynamo_compiling() and self.device.type != input_ids.device.type:
wa... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 9. prepare logits processors and stopping criteria
prepared_logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_length,
encoder_input_ids=inputs_tensor,
prefix_allowed_tokens_fn=prefix_allowed_tokens... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 10. go into different generation modes
if generation_mode == GenerationMode.ASSISTED_GENERATION:
if generation_config.num_return_sequences > 1:
raise ValueError(
"num_return_sequences has to be 1 when doing assisted generate, "
f"but is {gene... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# which is not possible with stateful models (they can't reset to a previous subset of generated text)
raise ValueError(
f"assisted generation is not supported with stateful models, such as {self.__class__.__name__}"
) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 11. Get the candidate generator, given the parameterization
candidate_generator = self._get_candidate_generator(
generation_config=generation_config,
input_ids=input_ids,
inputs_tensor=inputs_tensor,
assistant_model=assistant_model,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 12. run assisted generate
result = self._assisted_decoding(
input_ids,
candidate_generator=candidate_generator,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
generation_config=genera... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
stopping_criteria=prepared_stopping_criteria,
generation_config=generation_config,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs,
) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
elif generation_mode == GenerationMode.CONTRASTIVE_SEARCH:
if not model_kwargs["use_cache"]:
raise ValueError("Contrastive search requires `use_cache=True`")
if self._is_stateful:
# Just like assisted generation, we need to be able to rollback to a previous state ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
elif generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
# 11. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generati... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
elif generation_mode in (GenerationMode.BEAM_SAMPLE, GenerationMode.BEAM_SEARCH):
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 13. run beam sample
result = self._beam_search(
input_ids,
beam_scorer,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
generation_config=generation_config,
synced_gpus... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
elif generation_mode == GenerationMode.GROUP_BEAM_SEARCH:
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=gen... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 13. run beam search
result = self._group_beam_search(
input_ids,
beam_scorer,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
generation_config=generation_config,
synce... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
elif generation_mode == GenerationMode.CONSTRAINED_BEAM_SEARCH:
final_constraints = []
if generation_config.constraints is not None:
final_constraints = generation_config.constraints
if generation_config.force_words_ids is not None:
def typeerror():
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
for word_ids in generation_config.force_words_ids:
if isinstance(word_ids[0], list):
if not isinstance(word_ids, list) or len(word_ids) == 0:
typeerror()
if any(not isinstance(token_ids, list) for token_ids in word_ids):
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
constraint = PhrasalConstraint(word_ids)
final_constraints.append(constraint) | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 11. prepare beam search scorer
constrained_beam_scorer = ConstrainedBeamSearchScorer(
constraints=final_constraints,
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=gen... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
result = self._constrained_beam_search(
input_ids,
constrained_beam_scorer=constrained_beam_scorer,
logits_processor=prepared_logits_processor,
stopping_criteria=prepared_stopping_criteria,
generation_config=generation_config,
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Convert to legacy cache format if requested
if (
generation_config.return_legacy_cache is True
and not is_torchdynamo_compiling()
and hasattr(result, "past_key_values")
and getattr(result.past_key_values, "to_legacy_cache") is not None
):
res... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _has_unfinished_sequences(
self,
this_peer_finished: bool,
synced_gpus: bool,
device: torch.device,
cur_len: Optional[int] = None,
max_length: Optional[int] = None,
) -> bool:
"""
Returns whether there are still unfinished sequences in the device. ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(device)
#... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def heal_tokens(
self, input_ids: torch.LongTensor, tokenizer: Optional["PreTrainedTokenizerBase"] = None
) -> torch.LongTensor:
r"""
Generates sequences of token ids for models with a language modeling head.
Parameters:
input_ids (`torch.LongTensor`): The sequence used a... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
bos_token_id, pad_token_id = tokenizer.bos_token_id, tokenizer.pad_token_id
vocab_trie = ExtensionsTrie(tokenizer.get_vocab())
generation_config = GenerationConfig(max_new_tokens=1, pad_token_id=pad_token_id)
# assumption: leading/trailing whitespace is not meaningful, so the prompts are
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
tail_ids = input_ids[:, -1].tolist()
space_tok = tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids(" "))[0]
# tail tokens are used for a prefix search, thus, whitespaces are replaced with
# their tokenization (e.g. 'Ġ') to enable search for tokens prefixed with a whitespace
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# apply bias for alternatives (extensions) to the tail token
"""
seq_bias key has to be tuple with int so have to use
tokenizer function to convert str to int
"""
seq_bias = {
(tokenizer.convert_tokens_to_ids(alt_tok),): 10.0 for alt_tok in vocab_trie.e... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# if the prompt is a single (non-pad) token, regenerate from bos
if len(batch_ids[batch_ids != pad_token_id]) == 1:
trimmed_ids[-1] = bos_token_id
input_ids[batch_idx] = self.generate(trimmed_ids.unsqueeze(0), generation_config=generation_config)
return input_ids | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
def _dola_decoding(
self,
input_ids: torch.LongTensor,
dola_layers: Union[str, List[int]],
logits_processor: LogitsProcessorList,
stopping_criteria: StoppingCriteriaList,
generation_config: GenerationConfig,
synced_gpus: bool,
streamer: "BaseStreamer",
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
dola_layers (`Union[str, List[int]]`):
The candidate layers used in contrasting layers of DoLa. It can be either 1) 'low' or 'hig... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
generation_config ([`~generation.GenerationConfig`]):
The generation configuration to be used as parametrization of the decod... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
If model is an encoder-decoder model the kwargs should include `encoder_outputs`. | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Return:
[`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`]
or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` an... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
pad_token_id = generation_config._pad_token_tensor
output_attentions = generation_config.output_attentions
output_hidden_states = generation_config.output_hidden_states
output_scores = generation_config.output_scores
output_logits = generation_config.output_logits
return_dict_in_... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
raw_logits = () if (return_dict_in_generate and output_logits) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attent... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# prepare layers for DoLa decoding
final_layer = self.config.get_text_config().num_hidden_layers
# if the model has tied word embeddings, we skip the word embeddings (0-th) layer and start from the 2nd layer,
# as the early exit from word embeddings will become identity function
# if the... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# For `N`-layer models with `N <= 40` layers, the layers of `range(0, N // 2, 2)` and `range(N // 2, N, 2)`
# are used for `'low'` and `'high'` layers, respectively.
# For models with `N > 40` layers, the layers of `range(0, 20, 2)` and `range(N - 20, N, 2)` are used for
# `'low'` and `'high'` l... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
else list(range(final_layer - 20, final_layer, 2))
)
# Set the `dola_layers` to a list of integers for layer indices to contrast manually specified layers.
elif isinstance(dola_layers, list):
candidate_premature_layers = [i for i in dola_layers if i < final_layer]
else:
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
lm_head = self.get_output_embeddings()
if lm_head is None:
raise ValueError("DoLa is not supported for models that don't have output embeddings.")
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
# prepare model inputs
m... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# .float() is needed to retain precision for later logits manipulations
final_layer_next_token_logits = outputs.logits[:, -1, :].detach().clone().float()
final_logits = outputs.logits[:, -1, :].float()
candidate_premature_logits = {}
for candidate_premature_layer in candi... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
next_token_logits = _dola_select_contrast(
candidate_premature_layers, candidate_premature_logits, final_logits
)
next_token_logits = next_token_logits.to(input_ids.device)
# pre-process distribution
next_token_scores = logits_processor(input_ids, next_tok... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
if do_sample: # sample
prob... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
if streamer is not None:
streamer.put(next_tokens.cpu())
# stop when each sentence is finished
unfinished_sequences = unfinishe... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
@torch.no_grad()
def _contrastive_search(
self,
input_ids: torch.LongTensor,
logits_processor: LogitsProcessorList,
stopping_criteria: StoppingCriteriaList,
generation_config: GenerationConfig,
synced_gpus: bool,
streamer: Optional["BaseStreamer"],
**m... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`LogitsProcessorList`):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`Logit... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
`FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further pr... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
Return:
[`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`]
or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` an... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
return_dict_in_generate = generation_config.return_dict_in_generate
sequential = generation_config.low_memory | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# init attention / hidden states / scores tuples
raw_logits = () if (return_dict_in_generate and output_logits) else None
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attent... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# keep track of which sequences are already finished
batch_size = input_ids.shape[0]
unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
# Create cosine_matrix_mask based on the... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
# 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 mode... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
# the `encoder_outputs`
outputs = self(
**model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# next logit for contrastive search to select top-k candidate tokens
# Clone is needed to avoid keeping a hanging ref to outputs.logits which may be very large for this first iteration
# (the clone itself is always small)
# .float() is needed to retain precision for later... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if not sequential:
# Expands model inputs top_k times, for batched forward passes (akin to beam search).
_, model_kwargs = self._expand_inputs_for_generation(
expand_size=top_k, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/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."
)
... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
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