text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class WatermarkingConfig(BaseWatermarkingConfig):
"""
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://arxiv.org/abs/2306.04634) for more details on the arguments. | 10,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
Accepts the following keys:
- greenlist_ratio (`float`):
Used for watermarking. The ratio of "green" tokens used to the vocabulary size. Defaults to 0.25.
- bias (`float`):
Used with watermarking. The bias added to the selected "green" tokens' logits. Defaults to 2.0.
- h... | 10,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
The context length of previous tokens to use in seeding. Higher context length makes watermarking more robust.
""" | 10,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
def __init__(
self,
greenlist_ratio: Optional[float] = 0.25,
bias: Optional[float] = 2.0,
hashing_key: Optional[int] = 15485863,
seeding_scheme: Optional[str] = "lefthash",
context_width: Optional[int] = 1,
):
self.greenlist_ratio = greenlist_ratio
sel... | 10,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
def validate(self):
watermark_missing_arg_msg = (
"Some of the keys in `watermarking_config` are defined incorrectly. `{key}` should be {correct_value}` "
"but found {found_value}"
)
if self.seeding_scheme not in ["selfhash", "lefthash"]:
raise ValueError(
... | 10,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
key="context_width",
correct_value="a positive integer",
found_value=self.context_width,
),
) | 10,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
def construct_processor(self, vocab_size: int, device) -> "WatermarkLogitsProcessor":
return WatermarkLogitsProcessor(
vocab_size=vocab_size,
device=device,
greenlist_ratio=self.greenlist_ratio,
bias=self.bias,
hashing_key=self.hashing_key,
... | 10,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
"""
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://www.nature.com/articles/s41586-024-08025-4) for more details on the arguments. | 10,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
Args:
ngram_len (`int`):
Ngram length.
keys (`List[int]`):
A sequence of watermarking keys, one for each depth.
context_history_size (`int`, *optional*, defaults to 1024):
Size of the tensor to keep track of seen contexts.
sampling_table_seed (`int`, *... | 10,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.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... | 10,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
def __init__(
self,
ngram_len: int,
keys: List[int],
context_history_size: int = 1024,
sampling_table_seed: int = 0,
sampling_table_size: int = 2**16,
skip_first_ngram_calls: bool = False,
debug_mode: bool = False,
):
self.ngram_len = ngram_len... | 10,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
def validate(self):
watermark_missing_arg_msg = (
"Some of the keys in `watermarking_config` are defined incorrectly. `{key}` should be {correct_value}` "
"but found {found_value}"
)
if self.sampling_table_size > 2**24:
raise ValueError(
waterm... | 10,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
def construct_processor(self, vocab_size: int, device) -> "WatermarkLogitsProcessor":
return SynthIDTextWatermarkLogitsProcessor(
ngram_len=self.ngram_len,
keys=self.keys,
sampling_table_size=self.sampling_table_size,
sampling_table_seed=self.sampling_table_seed,
... | 10,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
class CompileConfig(object):
"""
Class that holds arguments relative to `torch.compile` behavior, when using automatic compilation in `generate`.
See [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for more details on the arguments.
Args:
fullgraph (`bool`, *opti... | 10,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b')
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b').cuda()
>>> # Automatic compile configuration, used with static cache
>>> compile_config = CompileConfig(dynamic=True)
>>> # Generation with static cache and compile ... | 10,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py |
class FlaxGreedySearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None | 10,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
class FlaxSampleOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None | 10,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
class FlaxBeamSearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
scores (`jnp.ndarray` of shape `(batch_size,)`):
... | 10,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
class GreedyState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray] | 10,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
class SampleState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
prng_key: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray] | 10,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
class BeamSearchState:
cur_len: jnp.ndarray
running_sequences: jnp.ndarray
running_scores: jnp.ndarray
sequences: jnp.ndarray
scores: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray] | 10,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
class FlaxGenerationMixin:
"""
A class containing all functions for auto-regressive text generation, to be used as a mixin in
[`FlaxPreTrainedModel`].
The class exposes [`~generation.FlaxGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.FlaxGene... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def prepare_inputs_for_generation(self, *args, **kwargs):
raise NotImplementedError(
"A model class needs to define a `prepare_inputs_for_generation` method in order to use `generate`."
)
@staticmethod
def _run_loop_in_debug(cond_fn, body_fn, init_state):
"""
Run gen... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
decoder_start_token_id: int = None,
bos_token_id: int = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
) -> jnp.ndarray:
if model_kwargs is not None and "decoder_input_ids" in model_kw... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
# retrieve decoder_start_token_id for encoder-decoder models
# fall back to bos_token_id if necessary
decoder_start_token_id = (
decoder_start_token_id
if decoder_s... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
and hasattr(self.config.decoder, "bos_token_id")
and self.config.decoder.bos_token_id is not None
):
return self.config.decoder.bos_token_id
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
@staticmethod
def _expand_to_num_beams(tensor, num_beams):
return jnp.broadcast_to(tensor[:, None], (tensor.shape[0], num_beams) + tensor.shape[1:])
def _adapt_logits_for_beam_search(self, logits):
"""
This function can be overwritten in the specific modeling_flax_<model-name>.py classe... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _validate_model_class(self):
"""
Confirms that the model class is compatible with generation. If not, raises an exception that points to the
right class to use.
"""
if not self.can_generate():
generate_compatible_mappings = [
FLAX_MODEL_FOR_CAUSAL_... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwarg... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
def generate(
self,
input_ids: jnp.ndarray,
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
Parameters:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for th... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
Whether to trace generation. Setting `trace=False` should only be used for debugging and will lead to a
considerably slower runtime.
params (`Dict[str, jnp.ndarray]`, *optional*):
Optionally the model parameters can be passed. Can be useful for parallelized generation.
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
Return:
[`~utils.ModelOutput`].
"""
# Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class() | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
if generation_config is None:
# legacy: users may modify the model configuration to control generation. To trigger this legacy behavior,
# two conditions must be met
# 1) the ... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
" deprecated strategy to control generation and will be removed soon, in a future version."
" Please use and modify the model generation configuration (see"
" https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )"
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
self._validate_model_kwargs(model_kwargs.copy())
logits_processor = logits_processor if logits_processor is not None else FlaxLogitsProcessorList()
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
if model_kwargs.get("attention_mask") is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpec... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# decoder-only models should use left-padding for generation (can't be checked with `trace=True`)
if not self.config.is_encoder_decoder and not trace:
if (
generation_config.pad_token_id is not None
and jnp.sum(input_ids[:, -1] == generation_config.pad_token_id) > 0
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if self.config.is_encoder_decoder:
# add encoder_outputs to model_kwargs
if model_kwargs.get("encoder_outputs") is None:
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, params, model_kwargs)
# prepare decoder_input_ids for generation
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None and generation_con... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
generation_... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({generation_config.min_length}) is larger than"
f" the maximum length ({generation_config.max_le... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
logits_processor=logits_processor,
) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if not generation_config.do_sample and generation_config.num_beams == 1:
return self._greedy_search(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
logits_processor=logit... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
model_kwargs=model_kwargs,
)
elif not generation_config.do_sample and generation_config.num_beams > 1:
# broadcast input_ids & encoder_outputs
input_ids = self._expand_to_num_beams(input_ids, num_beams=generation_config.num_beams) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = self._expand_to_num_beams(
model_kwargs["encoder_outputs"]["last_hidden_state"], num_beams=generation_config.num_beams
)
for kwarg in ["attention_mask", "decoder_... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
return self._beam_search(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
length_penalty=generation_config.length_penalty,
early_stopping=generation_config.early_stopp... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if generation_config.temperature is not None and generation_config.temperature != 1.0:
warpers.append(FlaxTemperatureLogitsWarper(generation_config.temperature))
if generation_config.top_k is not None and generation_config.top_k != 0:
warpers.append(FlaxTopKLogitsWarper(top_k=generation_... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _get_logits_processor(
self,
generation_config: GenerationConfig,
input_ids_seq_length: int,
logits_processor: Optional[FlaxLogitsProcessorList],
) -> FlaxLogitsProcessorList:
"""
This class returns a [`FlaxLogitsProcessorList`] list object that contains all relev... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
if (
generation_config.min_length is not None
and generation_config.eos_token_id is not None
and generation_config.min_length > -1
):
processors.append(
FlaxMinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id)
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
begin_index = (
begin_index
if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
else begin_index + 1
)
if generation_config.forced_decoder_ids is not None and len(generation_config.forced_decoder_ids) > 0:
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
processors.append(FlaxNoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
processors = self._merge_criteria_processor_list(processors, logits_processor) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
return processors | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _merge_criteria_processor_list(
self,
default_list: FlaxLogitsProcessorList,
custom_list: FlaxLogitsProcessorList,
) -> FlaxLogitsProcessorList:
if len(custom_list) == 0:
return default_list
for default in default_list:
for custom in custom_list:
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
f" them as arguments to `generate` instead of using a custom {object_type}."
)
default_list.extend(custom_list)
return default_list | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _greedy_search(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
trace: bool = True,
params: Optional[Dict[str, ... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# per batch-item holding current token in loop.
sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32)
sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0))
# per batch-item state bit indicating if sentence has finished.
is_sent_finished = jnp.zeros((... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def greedy_search_cond_fn(state):
"""state termination condition fn."""
has_reached_max_length = state.cur_len == max_length
all_sequence_finished = jnp.all(state.is_sent_finished)
finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished)
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
return GreedyState(
cur_len=state.cur_len + 1,
sequences=next_sequences,
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _sample(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
prng_key: Optional[jnp.ndarray] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
logits_warper:... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# per batch-item holding current token in loop.
sequences = jnp.full((batch_size, max_length), pad_token_id,... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# initialize state
state = SampleState(
cur_len=cur_len,
sequences=sequences,
running_token=input_ids,
is_sent_finished=is_sent_finished,
prng_key=prng_key,
model_kwargs=model_kwargs,
)
def sample_search_cond_fn(state):
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# apply min_length, ...
logits = logits_processor(state.sequences, logits, state.cur_len)
# apply top_p, top_k, temperature
logits = logits_warper(logits, logits, state.cur_len)
next_token = jax.random.categorical(prng_key, logits, axis=-1)
next_token = next... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
return SampleState(
cur_len=state.cur_len + 1,
sequences=next_sequences,
running_token=next_token,
is_sent_finished=next_is_sent_finished,
model_kwargs=next_model_kwargs,
prng_key=prng_key_next,
)
# The ... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def _beam_search(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
length_penalty: Optional[float] = None,
early_stopping: Optional[Union[bool, str]] = None,
logits_processor: ... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def flatten_beam_dim(tensor):
"""Flattens the first two dimensions of a non-scalar array."""
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
return tensor.reshape((tensor.shape[0] * tensor.shape[1],) + tensor.shape[2:])
def ... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def gather_fn(tensor):
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
else:
return tensor[batch_indices, beam_indices]
return jax.tree_util.tree_map(gather_fn, nested)
# init values
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
batch_size, num_beams, cur_len = input_ids.shape
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# record the prompt length of decoder
decoder_prom... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# per batch,beam-item score, logprobs
running_scores = jnp.tile(jnp.array([0.0] + [np.array(-1.0e7)] * (num_beams - 1)), [batch_size, 1])
scores = jnp.ones((batch_size, num_beams)) * np.array(-1.0e7)
# For Seq2Seq generation, we only need to use the decoder instead of the whole model in generat... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# initialize model specific kwargs
model_kwargs = self.prepare_inputs_for_generation(flatten_beam_dim(input_ids), max_length, **model_kwargs)
# initialize state
state = BeamSearchState(
cur_len=cur_len,
running_sequences=running_sequences,
running_scores=runn... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 2. can the new beams still improve?
# early_stopping == False -> apply heuristic = always get the best score from `cur_len`. See the discussion
# below for more details.
# https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
# early_stopping ==... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
state.is_sent_finished, jnp.min(state.scores, axis=1, keepdims=True), np.array(-1.0e7)
)
improvement_still_possible = jnp.any(best_running_score > worst_finished_score) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 3. is there still a beam that has not finished?
still_open_beam = ~(jnp.all(state.is_sent_finished) & (early_stopping is True))
return not_max_length_yet & still_open_beam & improvement_still_possible | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
def beam_search_body_fn(state, input_ids_length=1):
"""beam search state update fn."""
# 1. Forward current tokens
# Collect the current position slice along length to feed the fast
# autoregressive decoder model. Flatten the beam dimension into batch
# dimen... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
logits = unflatten_beam_dim(model_outputs.logits[:, -1], batch_size, num_beams)
cache = jax.tree_util.tree_map(
lambda tensor: unflatten_beam_dim(tensor, batch_size, num_beams), model_outputs.past_key_values
)
# adapt logits for FlaxMarianMTModel
logits =... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 2. Compute log probs
# get log probabilities from logits,
# process logits with processors (*e.g.* min_length, ...), and
# add new logprobs to existing running logprobs scores.
log_probs = jax.nn.log_softmax(logits)
log_probs = logits_processor(
... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 3. Retrieve top-K
# Each item in batch has num_beams * vocab_size candidate sequences.
# For each item, get the top 2*k candidates with the highest log-
# probabilities. We gather the top 2*K beams here so that even if the best
# K sequences reach EOS simultaneously, we... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
state.running_sequences, topk_beam_indices, batch_size, beams_to_keep
)
topk_ids = jnp.expand_dims(topk_indices % vocab_size, axis=2)
topk_sequences = lax.dynamic_update_slice(topk_running_sequences, topk_ids, (0, 0, state.cur_len)) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 4. Check which sequences have ended
# Update current sequences:
# Did any of these sequences reach an end marker?
# To prevent these just finished sequences from being added to the current sequences
# set of active beam search sequences, set their log probs to a very la... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 6. Process topk logits
# Further process log probs:
# - add length penalty
# - make sure no scores can be added anymore if beam is full
# - make sure still running sequences cannot be chosen as finalized beam
topk_log_probs = topk_log_probs / ((state.cur_len... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 7. Get scores, sequences, is sentence finished for next.
# Combine sequences, scores, and flags along the beam dimension and compare
# new finished sequence scores to existing finished scores and select the
# best from the new set of beams
merged_sequences = jnp.concate... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# 8. Update model kwargs.
# Determine the top k beam indices from the original set of all beams.
# With these, gather the top k beam-associated caches.
next_running_indices = gather_beams(topk_beam_indices, next_topk_indices, batch_size, num_beams)
next_cache = gather_bea... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# Always run first iteration outside of `lax.while_loop` to avoid calling `beam_search_cond_fn`
# when `state.cur_len` equals `decoder_prompt_len`. This also helps to comply with TPU when
# the very first prompt has sequence length > 1.
state = partial(beam_search_body_fn, input_ids_length=input... | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
# Take best beams for each batch (the score is sorted in descending order)
sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
scores = flatten_beam_dim(scores[:, :num_return_sequences])
return FlaxBeamSearchOutput(sequences=sequences, scores=scores) | 10,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py |
class LogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
raise NotImplementedError(
... | 10,663 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class LogitsProcessorList(list):
"""
This class can be used to create a list of [`LogitsProcessor`] to subsequently process a `scores` input tensor.
This class inherits from list and adds a specific *__call__* method to apply each [`LogitsProcessor`] to the
inputs.
""" | 10,664 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary... | 10,664 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
"""
for processor in self:
function_args = inspect.signature(processor.__call__).parameters
if len(function_args) > 2:
if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
raise ValueError(
f"Make sure that all t... | 10,664 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class MinLengthLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing a min-length by setting EOS probability to 0. Note that, for decoder-only models
like most LLMs, the length includes the prompt.
Args:
min_length (`int`):
The minimum length below which the score of ... | 10,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> inputs = tokenizer("A number:", return_tensors="pt")
>>> gen_out = model.generate(**inputs)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
A number: one
>>> # setting `min_length` to a value smaller than the uncontrolled output length has no impact
>>> gen_out = model.g... | 10,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(self, min_length: int, eos_token_id: Union[int, List[int], torch.Tensor], device: str = "cpu"):
if not isinstance(min_length, int) or min_length < 0:
raise ValueError(f"`min_length` has to be a non-negative integer, but is {min_length}")
if not isinstance(eos_token_id, torch.Te... | 10,665 | /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)
eos_token_mask = isin_mps_friendly(vocab_tensor, self.eos_token_id)
sc... | 10,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class MinNewTokensLengthLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing a min-length of new tokens by setting EOS (End-Of-Sequence) token probability to 0.
Contrarily to [`MinLengthLogitsProcessor`], this processor ignores the prompt.
Args:
prompt_length_to_skip (`int`):
... | 10,666 | /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(["A number:"], return_tensors="pt")
>>> gen_out = model.generate(**inputs)
>>> print(tokenizer.batch_decode(gen_out, skip_special_tok... | 10,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
def __init__(
self,
prompt_length_to_skip: int,
min_new_tokens: int,
eos_token_id: Union[int, List[int], torch.Tensor],
device: str = "cpu",
):
for arg_name, arg_value in [
("prompt_length_to_skip", prompt_length_to_skip),
("min_new_tokens", mi... | 10,666 | /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:
new_tokens_length = input_ids.shape[-1] - self.prompt_length_to_skip
scores_processed = scores.clone()
vocab_tensor = torch.arange(scores.... | 10,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
class TemperatureLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] for temperature (exponential scaling output probability distribution), which effectively means
that it can control the randomness of the predicted tokens. Often used together with [`TopPLogitsWarper`] and
[`TopKLogitsWarper`].
... | 10,667 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> model.config.pad_token_id = model.config.eos_token_id
>>> inputs = tokenizer(["Hugging Face Company is"], return_tensors="pt")
>>> # With temperature=1.0,... | 10,667 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py |
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