text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.outp... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_st... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
return {
"input_features": None, # needs to be passed to make Keras.layer.__call__ happy
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask":... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def tf_to_pt_weight_rename(self, tf_weight):
if tf_weight == "lm_head.weight":
return tf_weight, "model.decoder.embed_tokens.weight"
else:
return (tf_weight,) | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class Speech2TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Speech2TextModel`]. It is used to instantiate a
Speech2Text model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
Args:
vocab_size (`int`, *optional*, defaults to 10000):
Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`Speech2TextModel`]
encoder_layers (`int`, *optional*, defaults to 12):
... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
Number of attention heads for each attention layer in the Transformer decoder.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](https://arxiv.org/abs/1909.11556) for
more details.
decoder_layerdrop (`fl... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
d_model (`int`, *optional*, defaults to 256):
Dimensionality of the layers and the pooler layer.
dropout (`float`, *optional*... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
The initial token ID of the decoder when decoding sequences.
scale_embedding (`bool`, *optional*, defaults to `True`):
Whether the embeddings are scaled by the square root of `d_model`.
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
Number of 1D convolutional layers in the conv module.
conv_kernel_sizes (`Tuple[int]`, *optional*, defaults to `(5, 5)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the conv module. The length
of `conv_kernel_sizes` has to match `num_conv_layers`.
... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
Example:
```python
>>> from transformers import Speech2TextConfig, Speech2TextModel
>>> # Initializing a Speech2Text s2t_transformer_s style configuration
>>> configuration = Speech2TextConfig()
>>> # Initializing a model (with random weights) from the s2t_transformer_s style configuration
>>... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
def __init__(
self,
vocab_size=10000,
encoder_layers=12,
encoder_ffn_dim=2048,
encoder_attention_heads=4,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=4,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
use_cache=Tru... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = a... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
self.conv_channels = conv_channels
self.input_feat_per_channel = input_feat_per_channel
self.input_channels = input_channels | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
if len(self.conv_kernel_sizes) != self.num_conv_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
"It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` "
f"but is `len(config.conv_kernel_sizes) = {len(... | 3,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py |
class Speech2TextProcessor(ProcessorMixin):
r"""
Constructs a Speech2Text processor which wraps a Speech2Text feature extractor and a Speech2Text tokenizer into a
single processor.
[`Speech2TextProcessor`] offers all the functionalities of [`Speech2TextFeatureExtractor`] and
[`Speech2TextTokenizer`... | 3,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/processing_speech_to_text.py |
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards al... | 3,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/processing_speech_to_text.py |
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
audio = kwargs.pop("raw_speech")
else:
audio = kwargs.pop("audio", None)
sampling_rate = kwargs.pop("sampling_rate", None)
text = kwargs.po... | 3,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/processing_speech_to_text.py |
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Speech2TextTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def... | 3,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/processing_speech_to_text.py |
@contextmanager
def as_target_processor(self):
"""
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
Speech2Text.
"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transforme... | 3,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/processing_speech_to_text.py |
class GemmaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
intermediate_size (`int`, *optional*, defaults to 24576):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number o... | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
T... | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
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`, *optional*, defaults to `True`):
Whether or not the model s... | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
... | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
model_type = "gemma"
keys_to_ignore_at_inference = ["past_key_values"] | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
def __init__(
self,
vocab_size=256000,
hidden_size=3072,
intermediate_size=24576,
num_hidden_layers=28,
num_attention_heads=16,
num_key_value_heads=16,
head_dim=256,
hidden_act="gelu_pytorch_tanh",
hidden_activation=None,
max_positi... | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.hidden_activation = hidden_activation
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attentio... | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.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,
) | 3,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaTokenizer(LlamaTokenizer, PreTrainedTokenizer):
"""
Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model. | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.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... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.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: | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.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... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.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... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<bos>",
eos_token="<eos>",
pad_token="<pad>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
clean_up_tokenization_spaces=False,
... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
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 = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
PreTrainedToken... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
def unk_token_length(self):
raise AttributeError("Not needed for Gemma")
def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
"""
Args:
text: TextInput
Simply calls PreTrainedTokenizer's method
"""
return PreTrainedTokenizer.tokenize(self, text, ... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
spaces_between_special_tokens: bool = False,
**kwargs,
) -> str:
sub_texts = []
current_sub_text = []
for ids in token_ids:
if skip_special_tokens and ids in self.a... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self._ad... | 3,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
... | 3,303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaMLP(LlamaMLP):
def __init__(self, config):
super().__init__()
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, ... | 3,304 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaModel(LlamaModel):
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds:... | 3,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 3,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
# embed positions
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self... | 3,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 3,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
output = BaseModelOutputWithPast(
... | 3,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaForCausalLM(LlamaForCausalLM):
def forward(**super_kwargs):
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
... | 3,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
```python
>>> from transformers import AutoTokenizer, GemmaForCausalLM
>>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
>>> prompt = "What is your favorite condiment?"
>>> inputs = tokenizer(prompt,... | 3,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaForSequenceClassification(LlamaForSequenceClassification):
pass | 3,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaForTokenClassification(LlamaForTokenClassification):
pass | 3,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py |
class GemmaTokenizer(PreTrainedTokenizer):
"""
Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model. | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.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... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.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: | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.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... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.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... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
def __init__(
self,
vocab_file,
unk_token="<unk>",
bos_token="<bos>",
eos_token="<eos>",
pad_token="<pad>",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
clean_up_tokenization_spaces=False,
... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
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 = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
super().__init_... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.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... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
def _tokenize(self, text, **kwargs):
"""
Args:
text: TextInput
Returns a tokenized string. The Gemma tokenizer never adds a prefix space.
"""
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) ... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self._ad... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
Returns:
`Tuple(str)`: Paths to the files saved.
"""
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if fil... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = bos_token_id + token_ids_0 + eos_token_id
if token_ids_1 is not None:
... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is ... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
if token_ids_1 is None:
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
return (
bos_token_id
+ ([0] * len(token_ids_0))
+ eos_token_id
+ bos_token_id
+ ([0] * len(token_ids_1))
+ eos_token_id
)
def cr... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
spaces_between_special_tokens: bool = False,
**kwargs,
) -> str:
sub_texts = []
current_sub_text = []
for ids in token_ids:
if skip_special_tokens and ids in self.a... | 3,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py |
class GemmaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
intermediate_size (`int`, *optional*, defaults to 24576):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number o... | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
T... | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
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`, *optional*, defaults to `True`):
Whether or not the model s... | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
... | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
model_type = "gemma"
keys_to_ignore_at_inference = ["past_key_values"] | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
def __init__(
self,
vocab_size=256000,
hidden_size=3072,
intermediate_size=24576,
num_hidden_layers=28,
num_attention_heads=16,
num_key_value_heads=16,
head_dim=256,
hidden_act="gelu_pytorch_tanh",
hidden_activation=None,
max_positi... | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.hidden_activation = hidden_activation
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attentio... | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.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,
) | 3,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py |
class GemmaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a Gemma tokenizer fast. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no prefix space. Normalization is applied to replace `" "` with `"▁"`
```python
>>> from transformers import GemmaTokenizerFast
... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_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. | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_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... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_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 `"<eos>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The padding... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = GemmaTokenizer
padding_side = "left"
model_input_names = ["input_ids", "attention_mask"] | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py |
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
clean_up_tokenization_spaces=False,
unk_token="<unk>",
bos_token="<bos>",
eos_token="<eos>",
pad_token="<pad>",
add_bos_token=True,
add_eos_token=False,
**kwargs,
):
... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py |
@property
def can_save_slow_tokenizer(self) -> bool:
return os.path.isfile(self.vocab_file) if self.vocab_file else False
# Copied from transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast.update_post_processor
def update_post_processor(self):
"""
Updates the underly... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py |
special_tokens = []
if self.add_bos_token:
special_tokens.append((bos, bos_token_id))
if self.add_eos_token:
special_tokens.append((eos, eos_token_id))
self._tokenizer.post_processor = processors.TemplateProcessing(
single=single, pair=pair, special_tokens=spe... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py |
# Copied from transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py |
# Copied from transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast.build_inputs_with_special_tokens
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.a... | 3,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py |
class FlaxGemmaRMSNorm(nn.Module):
config: GemmaConfig
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... | 3,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaRotaryEmbedding(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
# Ignore copy
def setup(self):
head_dim = self.config.head_dim
self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim)
def __call__(self, key, query, posit... | 3,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaAttention(nn.Module):
config: GemmaConfig
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... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
kernel = jax.nn.initializers.normal(self.config.initializer_range)
self.q_proj = nn.Dense(
self.num_heads * self.head_dim, use_bias=config.attention_bias, dtype=self.dtype, kernel_init=kernel
)
self.k_proj = nn.Dense(
self.num_key_value_heads * self.head_dim,
... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
def _split_heads(self, hidden_states, num_heads):
return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads * self.head_dim,)) | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.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 ... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.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... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.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.... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.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... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.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)
# transform bool... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.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... | 3,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaMLP(nn.Module):
config: GemmaConfig
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 | 3,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
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