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# 10. go into different generation modes
if is_greedy_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
" greedy search."
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
" contrastive search."
)
# 11. run contrastive search
return self.contrastive_search(
input_ids,
top_k=generation_config.top_k,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 12. 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=generation_config.num_return_sequences,
is_encoder_decoder=self.config.is_encoder_d... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
elif is_beam_gen_mode:
if generation_config.num_beams < generation_config.num_return_sequences:
raise ValueError(
"Beam search decoding cannot return more sequences than it has beams. Please set num_beams >="
f" num_return_sequences, got {generation_co... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 12. run beam search
return self.beam_search(
input_ids,
max_length=generation_config.max_length,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
length_penalty=generation_config.length... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
elif is_beam_sample_gen_mode:
if generation_config.num_beams < generation_config.num_return_sequences:
raise ValueError(
"Beam search decoding cannot return more sequences than it has beams. Please set num_beams >="
f" num_return_sequences, got {genera... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 13. run beam sample (beam search with sampling)
return self.beam_search(
input_ids,
do_sample=True,
max_length=generation_config.max_length,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _prepare_attention_mask_for_generation(
self,
inputs: tf.Tensor,
pad_token_id: Optional[int],
eos_token_id: Optional[int],
) -> tf.Tensor:
is_input_ids = len(inputs.shape) == 2 and inputs.dtype in (tf.int32, tf.int64)
is_pad_token_in_inputs = (pad_token_id is not ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _prepare_encoder_decoder_kwargs_for_generation(
self, inputs_tensor: tf.Tensor, model_kwargs, model_input_name: Optional[str] = None
) -> Dict[str, Any]:
# 1. get encoder and store encoder outputs
encoder = self.get_encoder()
# 2. prepare encoder args and encoder kwargs from mod... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 3. vision models don't use `attention_mask`.
encoder_kwargs["return_dict"] = True
encoder_kwargs[model_input_name] = inputs_tensor
if model_input_name != self.main_input_name: # in Keras, the first input must always be passed
encoder_kwargs[self.main_input_name] = None
enc... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
model_input_name: str,
model_kwargs: Dict[str, tf.Tensor],
decoder_start_token_id: int = None,
bos_token_id: int = None,
) -> Tuple[tf.Tensor, Dict[str, tf.Tensor]]:
"""Prepares `decoder_inp... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
decoder_input_ids_start = tf.ones((batch_size, 1), dtype=tf.int32) * decoder_start_token_id | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# no user input -> use decoder_start_token_id as decoder_input_ids
if decoder_input_ids is None:
decoder_input_ids = decoder_input_ids_start
# user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
# decoder_attention_mask if provided)
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
@staticmethod
def _expand_inputs_for_generation(
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[tf.Tensor] = None,
expand_in_new_axis: bool = False,
**model_kwargs,
) -> Tuple[tf.Tensor, Dict[str, Any]]:
"""
Expands tensors fro... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _expand_dict_for_generation(dict_to_expand):
for key in dict_to_expand:
if dict_to_expand[key] is not None and isinstance(dict_to_expand[key], tf.Tensor):
dict_to_expand[key] = _expand_tensor(dict_to_expand[key])
return dict_to_expand
if input_ids... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _prepare_model_inputs(
self,
inputs: Optional[tf.Tensor] = None,
bos_token_id: Optional[int] = None,
model_kwargs: Optional[Dict[str, tf.Tensor]] = None,
) -> Tuple[tf.Tensor, Optional[str], Dict[str, tf.Tensor]]:
"""
This function extracts the model-specific `inp... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. check whether model_input_name is passed as kwarg
# if yes and `inputs` is None use kwarg inputs
inputs_kwarg = model_kwargs.pop(input_name, None)
if inputs_kwarg is not None and inputs is not None:
raise ValueError(
f"`inputs`: {inputs}` were passed alongside {i... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 3. In the presence of `inputs_embeds` for text models:
# - decoder-only models should complain if the user attempts to pass `inputs_embeds`, but the model
# doesn't have its forwarding implemented. `inputs_embeds` is kept in `model_kwargs` and can coexist with
# input_ids (`inputs_embeds` will... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
f"You passed `inputs_embeds` to `.generate()`, but the model class {self.__class__.__name__} "
"doesn't have its forwarding implemented. See the GPT2 implementation for an example "
"(https://github.com/huggingface/transformers/pull/21405), and feel free to open a PR with... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 4. if `inputs` is still None, try to create `input_ids` from BOS token
inputs = self._maybe_initialize_input_ids_for_generation(inputs, bos_token_id, model_kwargs)
return inputs, input_name, model_kwargs
def _maybe_initialize_input_ids_for_generation(
self,
inputs: Optional[tf.Te... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if bos_token_id is None:
raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
# If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
# soft-prompting or in multimodal implementations built on top of decoder-on... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _update_model_kwargs_for_generation(
self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
) -> Dict[str, Any]:
# update past_key_values
model_kwargs["past_key_values"] = self._extract_past_from_model_output(outputs)
# update attention mask
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _update_model_kwargs_for_xla_generation(
self,
model_outputs: ModelOutput,
model_kwargs: Dict[str, Any],
cur_len: int,
max_length: int,
batch_size: int,
is_encoder_decoder: bool = False,
batch_axis: int = 0,
):
def _initialize_attention(mod... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
axis=1,
)
mask = {"decoder_attention_mask": decoder_attention_mask}
else:
attention_mask = model_kwargs.pop("attention_mask")
# 0s for the currently-unfilled locations in the past_key_values tensor, 1s for the actual input_ids
a... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _update_attention(model_kwargs, new_past_index, is_encoder_decoder):
"""updates the appropriate attention mask -- encoder-decoder models use `decoder_attention_mask`"""
update_start = tf.constant([0, 1], dtype=tf.int32) * new_past_index
if is_encoder_decoder:
deco... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
attention_mask = dynamic_update_slice(attention_mask, attention_mask_update_slice, update_start)
mask = {"attention_mask": attention_mask}
return mask | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _initialize_past(past_key_values, num_padding_values, batch_axis):
"""initialize past_key_values with zeros -- the structure depends on `batch_axis`"""
if batch_axis == 0:
padding_values = tf.constant([[0, 0], [0, 0], [0, num_padding_values], [0, 0]], dtype=tf.int32)
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _update_past(past_key_values, new_past_index, batch_axis):
if batch_axis == 0:
slice_start_base = tf.constant([0, 0, 1, 0])
new_past = ()
for past_layer in past_key_values:
new_past_layer = list(past_layer)
for i in ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
for i in range(len(past_key_values)):
update_slice = past_key_values[i][:, :, :, -1:]
# Write the last slice to the first open location in the padded past_key_values array
# and then truncate the last slice off the array
new_past[i] = dynam... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
past_key_values = self._extract_past_from_model_output(model_outputs)
if past_key_values is None:
raise ValueError(
"No known `past_key_values variable` found in model outputs (model outputs keys:"
f" {list(model_outputs.keys())})"
)
is_past_initia... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if not is_past_initialized:
# The padded version of `past_key_values` has a length of `max_length - 1`, as `past_key_values` holds information relative to
# previous autoregressive generation steps (step 0 has no past_key_values, step 1 has 1 past_key_values value, ..., the last step
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
mask = _update_attention(model_kwargs, new_past_index, is_encoder_decoder)
new_past = _update_past(past_key_values, new_past_index, batch_axis) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# sets the updated variables (mask and past_key_values)
model_kwargs.update(mask)
model_kwargs["past_key_values"] = tuple(new_past)
return model_kwargs
def _get_logits_warper(
self,
generation_config: GenerationConfig,
) -> TFLogitsProcessorList:
"""
Thi... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# In beam methods, we need to keep at least one non-eos token to explore continuations that might have a
# better score (i.e. keep len(generation_config.eos_token_id) + 1)
if generation_config.num_beams > 1:
if isinstance(generation_config.eos_token_id, list):
min_tokens_to_k... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if generation_config.temperature is not None and generation_config.temperature != 1.0:
warpers.append(TFTemperatureLogitsWarper(generation_config.temperature))
if generation_config.top_k is not None and generation_config.top_k != 0:
warpers.append(TFTopKLogitsWarper(top_k=generation_conf... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _get_logits_processor(
self,
generation_config: GenerationConfig,
input_ids_seq_length: int,
logits_processor: Optional[TFLogitsProcessorList],
) -> TFLogitsProcessorList:
"""
This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# instantiate processors list
if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
processors.append(TFRepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
if generation_config.no_repeat_ngram_size is not None and gen... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if generation_config.forced_bos_token_id is not None:
processors.append(TFForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id))
if generation_config.forced_eos_token_id is not None:
processors.append(
TFForcedEOSTokenLogitsProcessor(generation_config.max_l... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
] # generation starts after the last token that is forced
processors.append(
TFSuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)
)
if generation_config.forced_decoder_ids is not None:
processors.append(TFForceTokensLog... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
processors = self._merge_criteria_processor_list(processors, logits_processor)
return processors | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def _merge_criteria_processor_list(
self,
default_list: TFLogitsProcessorList,
custom_list: TFLogitsProcessorList,
) -> TFLogitsProcessorList:
if len(custom_list) == 0:
return default_list
for default in default_list:
for custom in custom_list:
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
f" them as arguments to `generate` instead of using a custom {object_type}."
)
default_list.extend(custom_list)
return default_list | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def greedy_search(
self,
input_ids: tf.Tensor,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
output_attentions: Optional[bool] = None,
out... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived fro... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` und... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Return:
[`~generation.TFGreedySearchDecoderOnlyOutput`], [`~generation.TFGreedySearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFGreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
>>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
use_cache = model_kwargs.pop("use_cache", self.generation_config.use_cache)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
mod... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else Non... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define condition fn
def greedy_search_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state termination condition fn."""
return ~tf.reduce_all(finished_sequences) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# define condition fn
def greedy_search_body_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state update fn."""
if model_kwargs.get("past_key_values") is None or needs_full_input:
input_ids = generated[:, :cur_len]
else:
input_ids... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(next_tokens_scores)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.appen... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# argmax
next_tokens = tf.argmax(next_tokens_scores, axis=-1, output_type=tf.int32)
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_se... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1 | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
b... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past_key_values`
generated, finished_sequences, cur_len, model_kwargs = greedy_search_body_fn(
generated, finished_sequences, cur_len, model_kwar... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
return TFGreedySearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def sample(
self,
input_ids: tf.Tensor,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived fro... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
seed (`List[int]`, *optional*):
Random seed to control sampling, containing t... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Addit... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Return:
[`~generation.TFSampleDecoderOnlyOutput`], [`~generation.TFSampleEncoderDecoderOutput`] or `tf.Tensor`: A
`tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
>>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Today is a beautiful day, and I love my country. But when I look at Donald Trump,']
```"""
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
use_cache = model_kwargs.pop("use_cache", self.generation_config.use_cache)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
mod... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else Non... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 4. define "xla-compile-able" stop-condition and auto-regressive function
def sample_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
return ~tf.reduce_all(finished_sequences)
def sample_body_fn(generated, finished_sequences, cur_len, model_kwargs):
if model_kwarg... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# pre-process distribution
next_tokens_scores = logits_processor(generated, next_token_logits, cur_len)
next_tokens_scores = logits_warper(generated, next_tokens_scores, cur_len)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# sam... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pa... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
b... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past_key_values`
generated, finished_sequences, cur_len, model_kwargs = sample_body_fn(
generated, finished_sequences, cur_len, model_kwargs
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
return TFSampleEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cros... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def gather_fn(tensor):
if batch_axis > 0:
# pushes all dimentions before the batch to the end, so we get (batch, beam_id, ...)
perm = tf.concat((tf.range(tf.rank(tensor))[batch_axis:], tf.range(batch_axis)), axis=0)
tensor = tf.transpose(tensor, perm=perm)
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def beam_search(
self,
input_ids: tf.Tensor,
do_sample: bool = False,
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... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
a greedy approach, otherwise does multinomial sampling without replacement. | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
max_len... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
early_stopping (`bool` or `str`, *optional*, defaults to `False`):
Controls the stopping condition for beam-based m... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the predi... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Return:
[`~generation.TFBeamSearchDecoderOnlyOutput`], [`~generation.TFBeamSearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFBeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="tf").input_ids
>>> # lets run beam search using 3 beams
>>> num_beams = 3
>>> # define decoder start token ids
>>> input_ids = tf.ones((1,... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [TFMinLengthLogitsProcessor(5, eos_token_id=model.generation_config.eos_token_id)]
... )
>>> outputs = model.beam_search(input_ids, logits_processor=logits_processor, **model_kwargs)
>>> to... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def unflatten_beam_dim(tensor, num_beams, batch_axis=0):
"""Unflattens the first, flat batch*beam dimension of a non-scalar array."""
shape = shape_list(tensor)
return tf.reshape(tensor, shape[:batch_axis] + [-1, num_beams] + shape[batch_axis + 1 :])
# 1. init beam_search va... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
output_sco... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
use_cache = model_kwargs.pop("use_cache", self.generation_config.use_cache)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
mod... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. init `attentions`, `hidden_states`, and `scores` tuples
all_scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# per batch, beam-item holding current token in loop, pre-populated with `pad_token_id`
input_ids_padding = tf.ones((batch_size, num_beams, max_length - cur_len), dtype=tf.int32) * (
pad_token_id or 0
)
running_sequences = tf.concat([input_ids, input_ids_padding], axis=-1)
se... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# per batch beam indices
running_beam_indices = tf.ones((batch_size, num_beams, max_length - decoder_prompt_len), dtype=tf.int32) * -1
beam_indices = tf.ones((batch_size, num_beams, max_length - decoder_prompt_len), dtype=tf.int32) * -1
# flatten beam dim
if "encoder_outputs" in model_k... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define stop-condition and auto-regressive function
def beam_search_cond_fn(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
sc... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. can the new beams still improve?
# early_stopping == False -> apply heuristic = always get the best score from `cur_len - decoder_prompt_len`. See the discussion
# below for more details.
# https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
is_sent_finished, tf.math.reduce_min(scores, axis=1, keepdims=True), -1.0e9
)
improvement_still_possible = tf.math.reduce_any(best_running_score > worst_finished_score) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 3. is there still a beam that has not finished?
still_open_beam = ~(tf.math.reduce_all(is_sent_finished) & (early_stopping is True))
return not_max_length_yet & still_open_beam & improvement_still_possible | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def beam_search_body_fn(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
is_sent_finished,
decoder_prompt_len,
model_kwargs,
):
"... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
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