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# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dt... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
return causal_mask | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonForCausalLM(PersimmonPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->PERSIMMON,Llama->Persimmon
def __init__(self, config):
super().__init__(config)
self.mo... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
def get_output_embeddings(self):
return self.lm_head
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
s... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
@add_start_docstrings_to_model_forward(PERSIMMON_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Opti... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_si... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
'human: Hey, what should I eat for dinner?\n\ncat: 🐱\n\nhuman: 😐\n\n'
```"""
output_atten... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logi... | 9,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonForSequenceClassification(PersimmonPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PersimmonModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initiali... | 9,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
@add_start_docstrings_to_model_forward(PERSIMMON_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torc... | 9,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
o... | 9,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token foun... | 9,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_va... | 9,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonForTokenClassification(PersimmonPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PersimmonModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = c... | 9,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
@add_start_docstrings_to_model_forward(PERSIMMON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 9,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.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).
"""
... | 9,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden... | 9,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class GPTNeoXJapaneseTokenizer(PreTrainedTokenizer):
"""
This tokenizer inherits from [`PreTrainedTokenizer`] and is based on Japanese special Sub-Word-Encoding that is
used in this repository (https://github.com/tanreinama/Japanese-BPEEncoder_V2). Check the repository for details.
Japanese has a relati... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
- Conversion of heterographs to the same token_id
- Emoji and Emoticon are grouped into 12 types as special tags. | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
Example:
```python
>>> from transformers import GPTNeoXJapaneseTokenizer
>>> tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
>>> # You can confirm both 慶応 and 慶應 are encoded to 17749
>>> tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"]
[30014, 26883, 26638,... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
emoji_file (`str`):
File containing the emoji.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is ... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
def __init__(
self,
vocab_file,
emoji_file,
unk_token="<|endoftext|>",
pad_token="<|endoftext|>",
bos_token="<|startoftext|>",
eos_token="<|endoftext|>",
do_clean_text=False,
**kwargs,
):
if not os.path.isfile(vocab_file):
r... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
self.vocab, self.raw_vocab, self.ids_to_tokens, self.emoji = load_vocab_and_emoji(vocab_file, emoji_file)
self.subword_tokenizer = SubWordJapaneseTokenizer(
vocab=self.vocab, ids_to_tokens=self.ids_to_tokens, emoji=self.emoji
)
super().__init__(
unk_token=unk_token,
... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
@property
def vocab_size(self):
# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
return len(self.raw_vocab)
def get_vocab(self):
return dict(self.raw_vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
re... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(",".join(token) + "\n")
... | 9,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
class SubWordJapaneseTokenizer:
"""
https://github.com/tanreinama/Japanese-BPEEncoder_V2 This tokenizer class is under MIT Lisence according to the
original repository.
MIT License
Copyright (c) 2020 tanreinama
Permission is hereby granted, free of charge, to any person obtaining a copy of th... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHE... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
def __init__(self, vocab, ids_to_tokens, emoji):
self.vocab = vocab # same as swe
self.ids_to_tokens = ids_to_tokens # same as bpe
self.emoji = emoji
self.maxlen = np.max([len(w) for w in self.vocab.keys()])
self.content_repatter1 = re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
)
self.content_repatter6 = re.compile(
r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"
)
keisen = "─━│┃┄... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
def __len__(self):
return len(self.ids_to_tokens)
def clean_text(self, content):
content = self.content_repatter1.sub("<URL>", content)
content = self.content_repatter2.sub("<EMAIL>", content)
content = self.content_repatter3.sub("<TEL>", content)
content = self.content_repa... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
def tokenize(self, text, clean=False):
text = text.replace(" ", "<SP>")
text = text.replace(" ", "<SP>")
text = text.replace("\r\n", "<BR>")
text = text.replace("\n", "<BR>")
text = text.replace("\r", "<BR>")
text = text.replace("\t", "<TAB>")
text = text.replace(... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
def checku2e(x):
e = x.encode()
if len(x) == 1 and len(e) == 3:
c = (int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
if c >= 0xE28080 and c <= 0xE2B07F:
return True
return False | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
pos = 0
result = []
while pos < len(text):
end = min(len(text), pos + self.maxlen + 1) if text[pos] == "<" else pos + 3
candidates = [] # (token_id, token, pos)
for e in range(end, pos, -1):
wd = text[pos:e]
if wd in self.vocab:
... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
result.append("<U2000U2BFF>")
else:
for i in wd.encode("utf-8"):
result.append("<|byte%d|>" % i)
pos = end
return result | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
def convert_id_to_token(self, index, breakline="\n"):
words = []
byte_tokens = []
word = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2]))
else:
if len(byte_tokens) > 0:
words.ap... | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
if len(byte_tokens) > 0:
words.append(bytearray(byte_tokens).decode("utf-8", errors="replace"))
text = "".join(words)
return text | 9,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py |
class GPTNeoXJapaneseConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTNeoXModelJapanese`]. It is used to instantiate
a GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults w... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the GPTNeoXJapanese model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`GPTNeoXJapanese`].
hidden_size (`int`, *optional*, defaults to 2560):
... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
The non-linear activation function (function or string) in the encoder and pooler.
rotary_pct (`float`, *optional*, defaults to 1.00):
percentage of hidden dimensions to allocate to rotary embeddings
rotary_emb_base (`int`, *optional*, defaults to 10000)
base for computing rotary... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
relevant if `config.is_decoder=True`.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The s... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
hidden_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the hidden layer.
... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
```python
>>> from transformers import GPTNeoXJapaneseConfig, GPTNeoXJapaneseModel
>>> # Initializing a GPTNeoXJapanese gpt-neox-japanese-2.7b style configuration
>>> configuration = GPTNeoXJapaneseConfig()
>>> # Initializing a model (with random weights) from the gpt-neox-japanese-2.7b style configur... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
def __init__(
self,
vocab_size=32000,
hidden_size=2560,
num_hidden_layers=32,
num_attention_heads=32,
intermediate_multiple_size=4,
hidden_act="gelu",
rotary_pct=1.00,
rotary_emb_base=10000,
max_position_embeddings=2048,
initializer... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
self.rotary_pct = rotary_pct
self.partial_rotary_factor = rotary_pct
self.rotary_emb_base = rotary_emb_base
self.rope_theta = rotary_emb_base
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.rope_scali... | 9,569 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py |
class GPTNeoXJapanesePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTNeoXJapaneseConfig
base_model_prefix = "gpt_neox_japanese"
_no_split_modules = ["GPTNeoXJ... | 9,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embeddin... | 9,570 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
class GPTNeoXJapaneseAttention(nn.Module):
def __init__(self, config, use_bias=False, layer_idx=None):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_attention_heads
... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=False)
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
# Activate bias if the last layer
self.use_bias = use_bias
self.dense_bias = nn.Parameter(torch.zeros(config.hidden_size)) i... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
position_ids: torch.LongTensor,
head_mask: Optional[torch.FloatTensor] = None,
layer_past: Optional[Cache] = None,
use_cache: Optional[bool] = False,
output_attentions:... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3)
value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)
... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# Cache QKV values
if layer_past is not None:
cache_kwargs = {
"sin": sin,
"cos": cos,
"partial_rotation_size": self.rotary_ndims,
"cache_position": cache_position,
}
key, value = layer_past.update(key, value, se... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
@classmethod
def _split_heads(cls, tensor, num_attention_heads, attn_head_size):
"""
Splits hidden dim into attn_head_size and num_attention_heads
"""
# tensor: [bs, seq_len, hidden_size]
new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
# -> [bs,... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
@classmethod
def _merge_heads(cls, tensor, num_attention_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden dim
"""
# tensor [bs, num_attention_heads, seq_len, attn_head_size]
tensor = tensor.permute(0, 2, 1, 3).contiguous()
# -> ... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
query = query.view(batch_size * num_attention_heads, query_length, attn_head_size)
key = key.view(batch_size * num_attention_heads, key_length, attn_head_size)
# [batch_size * num_heads, q_length, kv_length]
attn_scores = torch.zeros(
batch_size * num_attention_heads,
qu... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
attn_weights = nn.functional.softmax(attention_scores, dim=-1)
attn_weights = self.attention_dropout(attn_weights)
attn_weights = attn_weights.to(value.dtype)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = ... | 9,571 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
class GPTNeoXJapaneseRotaryEmbedding(nn.Module):
def __init__(self, config: GPTNeoXJapaneseConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling... | 9,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 9,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 9,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 9,572 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
class GPTNeoXJapaneseMLP(nn.Module):
def __init__(self, config):
super().__init__()
intermediate_size = int(config.hidden_size * config.intermediate_multiple_size)
self.dense_h_to_4h = nn.Linear(config.hidden_size, intermediate_size, bias=False)
# Project back to h.
self.dens... | 9,573 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
class GPTNeoXJapaneseLayer(nn.Module):
def __init__(self, config, layer_number):
super().__init__()
self.layer_number = layer_number
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps... | 9,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
layer_past: Option... | 9,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
position_embeddings=position_embeddings,
)
attn_output = attention_layer_outputs[0] # output_attn: a, present, (attentions)
outputs = attention_layer_outputs[1:] | 9,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# attn_output = (atten_output + bias) + residual
attn_output = bias_dropout_add(
attn_output,
bias=attn_bias.expand_as(residual) if attn_bias is not None else attn_bias,
residual=residual,
prob=self.hidden_dropout,
training=self.training,
)
... | 9,574 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
class GPTNeoXJapaneseModel(GPTNeoXJapanesePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList(
[GPTNeoXJapaneseLayer(config=config, lay... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
@add_start_docstrings_to_model_forward(GPT_NEOX_JAPANESE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: ... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
```python
>>> from transformers import AutoTokenizer, GPTNeoXJapaneseModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
>>> model = GPTNeoXJapaneseModel.from_pretrained("abeja/gpt-neox-japanese-2.7b")
>>> inputs = tokenizer("日本語... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.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 inputs_embeds is None:
inputs_embeds = self.embed_in(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cac... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
seq_length = inputs_embeds.shape[1]
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device)
if position_ids is... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
next_decoder_cache = None
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, lay... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
outputs = layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
head_mask=head_mask[i],
layer_past=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutputWi... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dt... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
return causal_mask | 9,575 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
class GPTNeoXJapaneseForCausalLM(GPTNeoXJapanesePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["embed_out.weight"]
def __init__(self, config):
super().__init__(config)
self.config = config
self.gpt_neox_japanese = GPTNeoXJapaneseModel(config)
self.embed_out = nn.Linea... | 9,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
@add_start_docstrings_to_model_forward(GPT_NEOX_JAPANESE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: O... | 9,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
... | 9,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
>>> config = GPTNeoXJapaneseConfig.from_pretrained("... | 9,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
outputs = self.gpt_neox_japanese(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions... | 9,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
# we are doing next-token prediction; shift prediction scores and input ids by one
shift_logits = lm_logits[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.vi... | 9,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
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