text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.Tensor]... | 9,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). | 9,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModelForSequenceClassifi... | 9,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
outputs = self.lilt(
input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 9,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
... | 9,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 9,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltForTokenClassification(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.lilt =... | 9,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor... | 9,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. | 9,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForTokenClassification
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModelForTokenClassification... | 9,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
outputs = self.lilt(
input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 9,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dr... | 9,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LiltForQuestionAnswering(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.lilt = Lil... | 9,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
@add_start_docstrings_to_model_forward(LILT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.Lon... | 9,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
... | 9,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModelForQuestionAnswering.fro... | 9,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
>>> predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1]
>>> predicted_answer = tokenizer.decode(predict_answer_tokens)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.lilt(
input... | 9,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 9,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 9,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py |
class LlamaTokenizer(PreTrainedTokenizer):
"""
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model. | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
Args:
vocab_file (`str`):
Path to the vocabulary file.
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
b... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set: | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming tha... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
add_bos_token (`bool`, *optional*, defaults to `True`):
Whether or not to add an `bos_token` at the start of sequences.
add_eos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an `eos_token` at the end of sequences.
clean_up_tokenization_spaces (`bool`, *op... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
and #25224 which includes fixes to properly handle tokens that appear after special tokens.
Make sure to also set `from_slow` to `True`.
A simple example: | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
- `legacy=True`:
```python
>>> from transformers import LlamaTokenizerFast
>>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=True, from_slow=True)
>>> tokenizer.encode("Hello <s>.") # 869 is '▁.'
[1, 15043, 29871, 1, 869]
... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
>>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=False, from_slow=True)
>>> tokenizer.encode("Hello <s>.") # 29889 is '.'
[1, 15043, 29871, 1, 29889]
```
Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) f... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token=None,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
clean_up_tokenization_spaces=False,
use... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
if legacy is None:
logger.warning_once(
f"You are using the default legacy behaviour of the {self.__class__}. This is"
" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
" If you want to use the new beh... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
self.legacy = legacy
self.vocab_file = vocab_file
self.add_bos_token = add_bos_token
self.add_eos_token = add_eos_token
self.use_default_system_prompt = use_default_system_prompt
self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
self.add_prefix_space ... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
@property
def unk_token_length(self):
return len(self.sp_model.encode(str(self.unk_token)))
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
def get_spm_processor(self, from_slow=False):
tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
state["sp_model_proto"] = self.sp_model.serialized_model_proto()
return state
def __setstate__(self, d):
self.__dict__.update(d)
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_k... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
"""
Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
first token is special.
"""
... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and stri... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.sp_model.IdToPiece(index)
... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
current_sub_tokens = []
out_string = ""
prev_is_special = False
for i, token in enumerate(tokens):
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special and i != 0 and self.leg... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.seri... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens ... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=Tru... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
... | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
if token_ids_1 is not None:
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
return output | 9,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py |
class LlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LlamaModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimens... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be construc... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, ... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to ap... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the line... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
head_dim (`int`, *optional*):
The attention head dimension. If None, it will default to hidden_size // num_attention_heads | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
```python
>>> from transformers import LlamaModel, LlamaConfig
>>> # Initializing a LLaMA llama-7b style configuration
>>> configuration = LlamaConfig()
>>> # Initializing a model from the llama-7b style configuration
>>> model = LlamaModel(configuration)
>>> # Accessing the model configurati... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_no... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling =... | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 9,865 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py |
class FlaxLlamaRMSNorm(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.epsilon = self.config.rms_norm_eps
self.weight = self.param("weight", lambda _, shape: jnp.ones(shape), self.config.hidden_size)
def __call__(self, hidden_states):
varian... | 9,866 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaRotaryEmbedding(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
head_dim = self.config.hidden_size // self.config.num_attention_heads
self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim)
def __call__(sel... | 9,867 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaAttention(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.hea... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
self.q_proj = dense(self.num_heads * self.head_dim)
self.k_proj = dense(self.num_key_value_heads * self.head_dim)
self.v_proj = dense(self.num_key_value_heads * self.head_dim)
self.o_proj = dense(self.embed_dim)
self.causal_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddi... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
@nn.compact
# Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoSelfAttention._concatenate_to_cache
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states ... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
def __call__(
self,
hidden_states,
attention_mask,
position_ids,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_deco... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.has_variable("cache", "cached_key") or init_cache:
key, value, attention_mask = self._concatenate_to_cache(key, value, query, attention_mask)
key = jnp.repeat... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
# usual dot product attention
attention_dtype = jnp.float32 if self.attention_softmax_in_fp32 else self.dtype
attn_weights = dot_product_attention_weights(
query,
key,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.config.atten... | 9,868 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaMLP(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
inner_dim = self.config.intermediate_size if self.config.intermediate_size is not None else 4 * embed_dim
kernel_init = jax.nn.initializers.normal(... | 9,869 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaDecoderLayer(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.input_layernorm = FlaxLlamaRMSNorm(self.config, dtype=self.dtype)
self.self_attn = FlaxLlamaAttention(self.config, dtype=self.dtype)
self.post_attention_layernorm = FlaxL... | 9,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
def __call__(
self,
hidden_states,
attention_mask=None,
position_ids=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
... | 9,870 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LlamaConfig
base_model_prefix = "model"
module_class: nn.Module = None
def __init__(
... | 9,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids)... | 9,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decodin... | 9,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def __call__(
self,
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
past_key_values: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
... | 9,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
if position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
if attention_mask is Non... | 9,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
not train,
False,
output_attentions,
output_hidden_states,
return... | 9,871 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaLayerCollection(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxLlamaDecoderLayer(self.config, dtype=self.dtype, name=str(i))
for i in range(self.config.num_hidden_layers)
]
def __call__(
... | 9,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
for block in self.blocks:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
deterministic=deterministic,
... | 9,872 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaModule(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.hidden_size = self.config.hidden_size
embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range)
self.embed_tokens = nn.Embed(
self.config.vo... | 9,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
outputs = self.layers(
input_embeds,
position_ids=position_ids,
attention_mask=attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 9,873 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaModel(FlaxLlamaPreTrainedModel):
module_class = FlaxLlamaModule | 9,874 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaForCausalLMModule(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxLlamaModule(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,... | 9,875 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
def __call__(
self,
input_ids,
attention_mask=None,
position_ids=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
outputs = sel... | 9,875 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class FlaxLlamaForCausalLM(FlaxLlamaPreTrainedModel):
module_class = FlaxLlamaForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape | 9,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
past_key_values = self.init_cache(batch_size, max_length)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since Llama uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention... | 9,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
return model_kwargs | 9,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py |
class Llama3Converter(TikTokenConverter):
def __init__(self, vocab_file, special_tokens=None, instruct=False, llama_version="3.2", **kwargs):
super().__init__(vocab_file, additional_special_tokens=special_tokens, **kwargs)
tokenizer = self.converted()
# References for chat templates in inst... | 9,877 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py |
# Add chat_template only if instruct is True.
# Prevents a null chat_template, which triggers
# a parsing warning in the Hub.
additional_kwargs = {}
if instruct or llama_version in ["Guard-3"]:
model_id, revision = templates_for_version.get(llama_version, (None, None))
... | 9,877 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py |
self.converted_tokenizer = PreTrainedTokenizerFast(
tokenizer_object=tokenizer,
bos_token="<|begin_of_text|>",
eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>",
model_input_names=["input_ids", "attention_mask"],
model_max_length=CONTEXT_LENGTH_FOR... | 9,877 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py |
# We can't do this while building the tokenizer because we have no easy access to the bos token id
def update_post_processor(self, tokenizer):
tokenizer._tokenizer.post_processor = processors.Sequence(
[
processors.ByteLevel(trim_offsets=False),
processors.Templat... | 9,877 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py |
class LlamaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no normalization.
```python
>>> from transformers import LlamaTokenizerFast
>>> tokenizer = LlamaTokenizerFast.from_pretrained("hf-int... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
Args:
vocab_file (`str`, *optional*):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
contains the vocabulary necessary to instantiate a tokenizer.
tokenizer_file (`str`, *optional*):
[tokenizers](https://github.co... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
The end of sequence token.
add_bos_token (`bool`, *optional*, defaults to `True`):
Whether or... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
A simple example: | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
- `legacy=True`:
```python
>>> from transformers import LlamaTokenizerFast
>>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=True, from_slow=True)
>>> tokenizer.encode("Hello <s>.") # 869 is '▁.'
[1, 15043, 29871, 1, 869]
... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = LlamaTokenizer
padding_side = "left"
model_input_names = ["input_ids", "attention_mask"] | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
clean_up_tokenization_spaces=False,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
add_bos_token=True,
add_eos_token=False,
use_default_system_prompt=False,
legacy=Non... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
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