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candidate_length = candidate_input_ids.shape[1] - input_ids.shape[1]
is_done_candidate = stopping_criteria(candidate_input_ids, None)
# 2. Use the original model to obtain the next token logits given the candidate sequence. We obtain
# `candidate_length + 1` relevant logits from thi... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 2.1. Prepare the model inputs
candidate_kwargs = copy.copy(model_kwargs)
candidate_kwargs = _prepare_attention_mask(
candidate_kwargs, candidate_input_ids.shape[1], self.config.is_encoder_decoder
)
candidate_kwargs = _prepare_token_type_ids(candidate_kwa... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 2.2. Run a forward pass on the candidate sequence
# prepare variable output controls (note: some models won't accept all output controls)
model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
model_inputs.update({"output_hidden_states": output_h... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 3. Select the accepted tokens. There are two possible cases:
# Case 1: `do_sample=True` and we have logits for the candidates (originally from speculative decoding)
# 👉 Apply algorithm 1 from the speculative decoding paper (https://arxiv.org/pdf/2211.17192.pdf).
if do_sample and c... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Case 2: all other cases (originally from assisted generation) 👉 Compare the tokens selected from the
# original model logits with the candidate tokens. We can keep the candidate tokens until the first
# mismatch, or until the max length is reached.
else:
if do_samp... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 4. Update variables according to the number of matching assistant tokens. Remember: the token generated
# by the model after the last candidate match is also valid, as it is generated from a correct sequence.
# Because of this last token, assisted generation search reduces to a normal greedy s... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# 5. Update the candidate generation strategy if needed
candidate_generator.update_candidate_strategy(input_ids, new_logits, n_matches)
# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
model_kwargs = self._update_model_kwarg... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
# Store scores, attentions and hidden_states when required
# Assistant: modified to append one tuple element per token, as in the other generation methods.
if return_dict_in_generate:
newly_added_length = n_matches + 1
if output_scores:
scores ... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
newly_added_length = new_cur_len if is_first_iteration else newly_added_length
if output_attentions:
if self.config.is_encoder_decoder:
cross_attentions = _split_model_outputs(
cross_attentions, outputs.cross_attentions, cur_len, ne... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
outputs.attentions,
cur_len,
newly_added_length,
is_decoder_attention=True,
)
if output_hidden_states:
if self.config.is_encoder_decoder:
decoder_hidden... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
this_peer_finished = unfinished_sequences.max() == 0
is_first_iteration = False
if streamer is not None:
streamer.end() | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
if (
hasattr(candidate_generator, "assistant_model")
and candidate_generator.assistant_model.generation_config.num_assistant_tokens_schedule == "heuristic"
):
candidate_generator.assistant_model.generation_config.num_assistant_tokens = (
candidate_generator.nu... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
else:
return GenerateDecoderOnlyOutput(
sequences=input_ids,
scores=scores,
logits=raw_logits,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
past_key_values=model... | 10,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py |
class TFGreedySearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using greedy search. | 10,745 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, retur... | 10,745 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one elemen... | 10,745 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None | 10,745 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFGreedySearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (r... | 10,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, retur... | 10,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size,... | 10,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (on... | 10,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None... | 10,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using sampling. | 10,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tenso... | 10,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True... | 10,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None | 10,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively... | 10,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tenso... | 10,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size*num_return_sequences,
num_heads, sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
... | 10,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, gener... | 10,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None... | 10,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFBeamSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam search. | 10,749 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.T... | 10,749 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
... | 10,749 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
""" | 10,749 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None | 10,749 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFBeamSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respe... | 10,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.T... | 10,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
`(batch_size*n... | 10,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
`(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each l... | 10,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
""" | 10,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[... | 10,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFBeamSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam sample. | 10,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.T... | 10,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
... | 10,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
""" | 10,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None | 10,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFBeamSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (res... | 10,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_beams, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of s... | 10,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
`(batch_size*n... | 10,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_... | 10,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
""" | 10,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[... | 10,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFContrastiveSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using contrastive search. | 10,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, retur... | 10,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one elemen... | 10,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None | 10,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFContrastiveSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using contrastive search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states att... | 10,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, retur... | 10,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size,... | 10,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (on... | 10,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None... | 10,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
class TFGenerationMixin:
"""
A class containing all of the functions supporting generation, to be used as a mixin in [`TFPreTrainedModel`].
The class exposes [`~generation.TFGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_sear... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
@property
def seed_generator(self):
warnings.warn("`seed_generator` is deprecated and will be removed in a future version.", UserWarning)
if self._seed_generator is None:
self._seed_generator = tf.random.Generator.from_non_deterministic_state()
return self._seed_generator
su... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
def compute_transition_scores(
self,
sequences: tf.Tensor,
scores: Tuple[tf.Tensor],
beam_indices: Optional[tf.Tensor] = None,
normalize_logits: bool = False,
) -> tf.Tensor:
"""
Computes the transition scores of sequences given the generation scores (and beam... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Parameters:
sequences (`tf.Tensor`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or
shorter if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)`):
Transition scores ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
normalize_logits (`bool`, *optional*, defaults to `False`):
Whether to normalize the logits (which, for legacy reasons, may be unnormalized). | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Return:
`tf.Tensor`: A `tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing
the transition scores (logits)
Examples:
```python
>>> from transformers import GPT2Tokenizer, TFAutoModelForCausalLM
>>> import numpy as np
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> # Example 1: Print the scores for each token generated with Greedy Search
>>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
>>> transition_scores = model.compute_transition_scores(
... outputs.sequences, outputs.scores, normalize_l... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
| 1110 | day | -2.609 | 7.36%
| 618 | when | -2.010 | 13.40%
| 356 | we | -1.859 | 15.58%
| 460 | can | -2.508 | 8.14% | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> # Example 2: Reconstruct the sequence scores from Beam Search
>>> outputs = model.generate(
... **inputs,
... max_new_tokens=5,
... num_beams=4,
... num_return_sequences=4,
... return_dict_in_generate=True,
... output_scores=True,
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
>>> reconstructed_scores = np.sum(transition_scores, axis=1) / (output_length**length_penalty)
>>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
True
```"""
# 1. In absence of `beam_indices`, we can assume that we come from e.g. greedy search, which is equivalent
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. reshape scores as [batch_size, vocab_size, # generation steps] with # generation steps being
# seq_len - input_length
scores = tf.transpose(tf.reshape(tf.stack(scores), (len(scores), -1)), (1, 0))
scores = tf.reshape(scores, (-1, self.config.vocab_size, scores.shape[-1]))
# 3. Opti... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 6. Define which indices contributed to scores
cut_idx = sequences.shape[-1] - max_beam_length
token_indices = sequences[:, cut_idx:]
gen_step_idx = tf.broadcast_to(tf.range(scores.shape[-1]), token_indices.shape)
indices = tf.stack([beam_indices, token_indices, gen_step_idx], axis=-1)
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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 = [
TF_MODEL_FOR_CAUSAL_LM... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
"it doesn't have a language model head."
)
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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."""
# Excludes arguments that are handled before calling any model function
if self.config.is_encoder_decoder:
for key in ["decod... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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,
inputs: Optional[tf.Tensor] ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
For an overview of generation strategies and code examples, check out the [following
guide](../generation_strategies).
</Tip> | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Parameters:
inputs (`tf.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 models ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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.
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
Return:
[`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a `tf.Tensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
- [`~generation.TFGreedySearchEncoderDecoderOutput`],
- [`~generation.TFSampleEncoderDecoderOutput`],
- [`~generation.TFBeamSearchEncoderDecoderOutput`],
- [`~generation.TFBeamSampleEncoderDecoderOutput`]
"""
# 1. Handle `generation_config` a... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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 ... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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 )"
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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()) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 2. Cast input dtypes to tf.int32 unless they're floats (which happens for some image models)
if inputs is not None:
if isinstance(inputs, tf.Tensor) and inputs.dtype.is_floating:
pass
elif isinstance(inputs, np.ndarray) and np.issubdtype(inputs.dtype, np.floating):
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
else:
model_kwargs["decoder_input_ids"] = tf.cast(model_kwargs["decoder_input_ids"], tf.int32) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 3. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
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:
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
use_xla = not tf.executing_eagerly()
if use_xla and not self.supports_xla_generation:
raise ValueError(
"The selected model does not support Graph mode nor XLA generation (e.g. from tf.function())"
)
# 4. Define model inputs
inputs_tensor, model_input_nam... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder:
if generation_config.pad_token_id is not None and tf.math.reduce_any(
inputs_tensor[:, -1] == generation_config.pad_token_id
):
logger.warning(
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 6. Prepare model inputs which will be used for auto-regressive generation
if self.config.is_encoder_decoder:
input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
batch_size=batch_size,
model_input_name=model_input_name,
model_kwa... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 7. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = shape_list(input_ids)[-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 gener... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_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_... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# If the input length is a tensor (i.e. dynamic length), skip length checks
if not isinstance(input_ids_seq_length, tf.Tensor):
if (
generation_config.min_length is not None
and generation_config.min_length > generation_config.max_length
):
... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
" increasing`max_new_tokens`."
) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 8. determine generation mode
is_contrastive_search_gen_mode = (
generation_config.top_k is not None
and generation_config.top_k > 1
and generation_config.do_sample is False
and generation_config.penalty_alpha is not None
and generation_config.penalty... | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
# 9. prepare distribution pre_processing samplers
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
logits_processor=logits_processor,
) | 10,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py |
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