text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all atten... | 10,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
attention_mask=(1 - attention_mask) * torch.finfo(hidden_states.dtype).min,
layer_head_mask=head_mask,
output_attentions=output_attentions,
)
if output_attentions:
attn_weights = (atten_out[1],)
else:
attn_weights = () | 10,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
attention_output = atten_out[0]
hidden = hidden_states + self.norm(attention_output)
if use_cache:
outputs = (hidden, atten_out[2]) # hidden, present, (attentions)
else:
outputs = (hidden,) # hidden, (attentions)
return outputs + attn_weights | 10,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseBlock(nn.Module):
"""
Self Attention and FFN Unit
"""
def __init__(self, config, ext_layer=False):
super().__init__()
self.self_attn = GPTSanJapaneseLayerSelfAttention(config)
self.feed_forward = GPTSanJapaneseLayerDenseFF(config) if ext_layer else GPTSanJapa... | 10,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
Args:
hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
past_key_va... | 10,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, ... | 10,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
head_mask (`numpy.ndarray` of shape `({0})`, `optional):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not ma... | 10,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
output_attentions (`bool`) :
output attention probabirities.
output_router_tuple:
... | 10,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if isinstance(self.feed_forward, GPTSanJapaneseLayerSparseFF):
sparse_out = self.feed_forward(attention_output, output_router_tuple)
if output_router_tuple:
hidden, router_tuple = sparse_out
else:
hidden = sparse_out
else:
hidden = ... | 10,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapanesePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTSanJapaneseConfig
base_model_prefix = "gptsan_japanese"
supports_gradient_checkpointing = ... | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_factor # Used for testing weights initialization
if isinstance(module, nn.LayerNorm):
module.weight.data.fill_(factor * 1.0)
module.bias.data.zero_()
elif isinstance(mo... | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
module.position_embeddings.weight.data.normal_(mean=0.0, std=factor * 1.0)
if hasattr(module, "extra_position_embeddings") and module.extra_position_embeddings is not None:
module.extra_position_embeddings.weight.data.normal_(mean=0.0, std=factor * 1.0)
elif isinstance(module, (GPTSa... | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
# and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_mod... | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
module.v_proj.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
module.q_proj.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
module.out_proj.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5... | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
module.experts[f"expert_{idx}"].wo.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
if decoder_start_token_id is None:
raise ValueError(
"self.model.config.decoder_start_token_id has to be defined. In T5 it is usuall... | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids | 10,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseModel(GPTSanJapanesePreTrainedModel):
def __init__(self, config: GPTSanJapaneseConfig):
super().__init__(config)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.d_model)
self.config = copy.deepcopy(config)
self.embed_tokens = nn.Embe... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if config.d_spout:
spouts = []
for _ in range(8):
spouts.append(nn.Linear(config.d_spout, config.d_spout, bias=False))
spouts.append(nn.Tanh())
spouts.append(nn.Linear(config.d_spout, config.num_layers * 2 * config.d_model, bias=False))
sel... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
@add_start_docstrings_to_model_forward(GPTSAN_JAPANESE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.FloatTensor] = None,
spout: Optional[torch.FloatTensor]... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
num_precontext (`torch.LongTensor` of shape `(batch_size,1)`):
length of `hybrid` input tokens in the input. Tokens up to this length refer to both front and back like
BERT, tokens after that refer only to front like GPT. see also:
https://github.com/tanreinama/GPTSAN/blob/main/repor... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
Returns:
`MoEModelOutputWithPastAndCrossAttentions` or `tuple` if `return_dict` returns
MoEModelOutputWithPastAndCrossAttentions insted of tuple
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
device = self.position_embeddings.wei... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# This controls the output by projecting embedded information such as the class of sentences during learning.
# It should passed instead of the first past_key_value.
# See the original GPTSAN repository for details
num_pasts_contexts += 1 | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# If there is an attention_mask, increase first one for spout
if self.config.d_spout and spout is not None and attention_mask is not None:
attention_mask_with_spout = torch.ones(num_batch, attention_mask.shape[1] + 1, device=device)
attention_mask_with_spout[:, 1:] -= 1 - attention_mask ... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if num_precontext is not None:
# `num_precontext` is the number of tokens that refer to each other in prefix-lm
# created per batch, so dimension of num_precontext should be [batch, 1]
if not (
len(num_precontext.shape) == 2 and num_precontext.shape[1] == 1
... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if past_key_values is not None:
pasts_or_spout_value = past_key_values
elif self.config.d_spout and spout is not None:
# Make vector from `spout` of GPTSAN to the same shape as past_key_values
pasts_or_spout_value = self.spout(spout) # projecting `spout` vector
p... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
)
else:
pasts_or_spout_value = [None] * self.config.num_layers | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# Token position considering spout and pasts
token_position = torch.arange(num_input_contexts).to(device) + num_pasts_contexts
if attention_mask is None:
attention_mask = torch.ones(num_batch, num_input_contexts, device=device)
# positions for get position_embeddings
gather... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# attention_mask is applied per batch
for i in range(num_batch):
hidden_states[i] += torch.gather(self.position_embeddings.weight, dim=0, index=gather_position[i])
# Create a mask to be used when making the prefix Input length of Prefix-LM variable
causal_mask = (
torch.... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# Prepare head mask if needed
if head_mask is not None:
head_mask = self.get_head_mask(
head_mask, self.config.num_switch_layers + self.config.num_ext_layers
) # n_layer x batch x n_heads x N x N
# outputs
present_key_value_states = () if self.config.use... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
for layer, past in enumerate(pasts_or_spout_value):
if layer == self.config.num_switch_layers:
if self.config.num_ext_layers > 0:
# extra_position_embeddings are extra position embeddings that are only created when extending the model with code from the original GPTSAN re... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
output_router_tuple = (
self.config.output_router_logits or output_router_logits
) and layer < self.config.num_switch_layers
block_output = self.blocks[layer](
hidden_states=hidden_states,
past_key_value=past,
attention_mask=extende... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
outpos = 0
hidden_states = block_output[outpos]
if self.config.output_hidden_states or output_hidden_states:
all_hidden_states += (hidden_states,)
if self.config.use_cache or use_cache:
outpos += 1
present = block_output[outpos]
... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
present_key_value_states,
all_hidden_states,
all_attentions,
all_router_probs,
]
i... | 10,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: GPTSanJapaneseConfig):
super().__init__(config)
self.model = GPTSanJapaneseModel(config)
self.register_buffer("final_logits_bias", torch.zeros(... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
@add_start_docstrings_to_model_forward(GPTSAN_JAPANESE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.FloatTensor] = None,
spout: Optional[torch.FloatTensor]... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification loss. Indices should be in `[-100, 0, ...,
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
labels in `[0, ..., conf... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
Returns:
`MoECausalLMOutputWithPast` or `tuple` if `return_dict` returns MoECausalLMOutputWithPast insted of tuple
Example:
Text Generation with regular LM Model
```python
>>> from transformers import AutoModel, AutoTokenizer, trainer_utils
>>> device = "cuda"
... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
>>> device = "cuda"
>>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device)
>>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> x_token = tokenizer("", prefix_text="織田信長は、", return_tensors="pt")
>>> trainer_utils.set_seed(30)
>>> input_i... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
>>> device = "cuda"
>>> model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device)
>>> tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> masked_sentence = "武田信玄は、<|inputmask|>時代ファンならぜひ押さえ<|inputmask|>きたい名将の一人。"
>>> x_token = tokenizer("", prefix_text=mas... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
>>> tokenizer.decode(out_lm_token[0][input_ids.shape[1] :])
"武田氏の三代に渡った武田家のひとり\n甲斐市に住む、日本史上最大の戦国大名。..."
```"""
SEG_TOKEN = self.config.separator_token_id
use_cache = use_cache or self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_retur... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
outputs = self.model(
input_ids,
attention_mask,
token_type_ids,
spout,
past_key_values,
head_mask,
use_cache,
inputs_embeds,
decoder_inputs_embeds,
output_attentions,
output_hidden_states... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if output_router_logits:
# Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder
router_logits, expert_indexes = self._unpack_router_logits(outputs.router_probs)
z_loss = router_z_loss_func(router_logits)
router_probs... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
return MoECausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_probs,
z_loss=z_loss,
a... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
attention_mask: torch.FloatTensor,
token_type_ids: Optional[torch.FloatTensor] = None,
spout: Optional[Union[List, torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] ... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
"token_type_ids": token_type_ids,
"spout": spout,
"past_key_values": None,
} | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self._shift_right(labels)
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def set_input_embeddings(self, new_embeddings):
self.model.set_input_embeddings(new_embeddings)
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def _unpack_router_logits(self, router_outputs):
... | 10,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
- Return token_type_ids for Prefix-LM model
The bagofword token represents a ... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> # You can confirm both 慶応 and 慶應 are encoded to 17750
>>> tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"]
[35993, 35998, 34347, 31459, 30647, 31448, 25, 30659, 35729, 35676, 32417, 30647, 17750, 35589, 17750, 35590, 321, ... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
>>> # Mask for Prefix-LM inputs
>>> tokenizer("実は慶応(慶應)大学出身", prefix_text="吾輩は猫である🐯。")["token_type_ids"]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
```
Example for batch encode:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseToken... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
emoji_file (`str`):
File containing the emoji.
unk_token (`str`, *optional*, defaults to `"<|nottoken|>"`):
The token used for unknown charactor
pad_token (`str`, *optional*, defaults to `"<|sep... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask", "token_type_ids"] | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
def __init__(
self,
vocab_file,
emoji_file,
unk_token="<|nottoken|>",
pad_token="<|separator|>",
bos_token="<|startoftext|>",
eos_token="<|endoftext|>",
sep_token="<|segmenter|>",
do_clean_text=False,
**kwargs,
):
if not os.path... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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
) | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
super().__init__(
unk_token=unk_token,
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
do_clean_text=do_clean_text,
**kwargs,
)
@property
def vocab_size(self):
# self.vocab co... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
words = []
byte_tokens = []
for word in tokens:
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2]))
else:
... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
elif word == "<U2000U2BFF>":
words.append("‖")
elif word == "<|bagoftoken|>":
if len(words) > 0:
words.append(words[-1])
words.append(words[-1])
words.append(words[-1])
elif wo... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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"]
... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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")
... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
# docstyle-ignore
"""
The tokenizer returns token_type_ids as separators between the Prefix part and the rest.
token_type_ids is 1 for the Prefix pa... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
>>> x_token = tokenizer("ｳｴ", prefix_text="ｱｲ")
>>> # input_ids: | SOT | ｱ | ｲ | SEG | ｳ | ｴ |
>>> # token_type_ids: | 1 | 1 | 1 | 0 | 0 | 0 |
```"""
prefix_len = 0
if self.sep_token in self.vocab:
segid = self.vocab[self.sep_token]
if segid in to... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
def prepare_for_tokenization(self, text, prefix_text=None, add_sep_token=None, **kwargs):
# GPTSAN inserts extra SEP tokens in Prefix-LM in addition to SOT for text generation.
# SOT at the beginning of the text, and SEP at the separator between the Prefix part and the rest.
if add_sep_token is ... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
trunca... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
# This tokenizer converts input text pairs into Prefix input and subsequent input
if isinstance(batch_text_or_text_pairs[0], tuple) or isinstance(tuple(batch_text_or_text_pairs[0]), list):
# As a single text with an explicit un-prefix position
batch_prefix_texts = []
for pref... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
return super()._batch_encode_plus(
batch_text_or_text_pairs,
add_special_tokens,
padding_strategy,
truncation_strategy,
max_length,
stride,
is_split_into_words,
pad_to_multiple_of,
return_tensors,
ret... | 10,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
class SubWordJapaneseTokenizer:
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
https://github.com/tanreinama/Japanese-BPEEncoder_V2 This tokenizer class is under MIT Lisence accor... | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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... | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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-... | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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 = "─━│┃┄... | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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... | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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(... | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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 | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_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:
... | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
result.append("<U2000U2BFF>")
else:
for i in wd.encode("utf-8"):
result.append("<|byte%d|>" % i)
pos = end
return result | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
def convert_id_to_token(self, index):
return self.ids_to_tokens[index][0] | 10,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py |
class GPTSanJapaneseConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTSanJapaneseModel`]. It is used to instantiate
a GPTSANJapanese model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defau... | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
Arguments:
vocab_size (`int`, *optional*, defaults to 36000):
Vocabulary size of the GPTSANJapanese model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`GPTSanJapaneseModel`].
max_position_embeddings (`int`, *optional*, d... | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
Number of layers in the Switch Transformer layer.
num_ext_layers (`int`, *optional*, defaults to 0):
Number of layers in the Extra-layers.
num_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_exp... | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
router_jitter_noise (`float`, *optional*, defaults to 0.0):
Amount of noise to add to the router. Set it to 0.0 during prediction or set small value (usually 1e-2)
during training.
router_dtype (`str`, *optional*, default to `"float32"`):
The `dtype` used for the routers. It ... | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
initializer_factor (`float`, *optional*, defaults to 0.002):
A factor for initializing all weight matrices.
output_router_logits (`bool`, *optional*, default to `False`):
Whether or not to return the router logits of all experts.
use_cache (`bool`, *optional*, defaults to `True`)... | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
model_type = "gptsan-japanese"
keys_to_ignore_at_inference = [
"past_key_values",
]
attribute_map = {
"hidden_size": "d_model",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
} | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
def __init__(
self,
vocab_size=36000,
max_position_embeddings=1280,
d_model=1024,
d_ff=8192,
d_ext=4096,
d_spout=128,
num_switch_layers=10,
num_ext_layers=0,
num_heads=16,
num_experts=16,
expert_capacity=128,
dropout... | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
self.num_switch_layers = num_switch_layers
self.num_ext_layers = num_ext_layers
self.num_layers = num_switch_layers + num_ext_layers
self.num_heads = num_heads
self.num_experts = num_experts
self.expert_capacity = expert_capacity
self.dropout_rate = dropout_rate
s... | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
super().__init__(
separator_token_id=separator_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
**kwargs,
) | 10,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py |
class XGLMTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" XGLM tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from [`RobertaTokenizer`]
and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
... | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip> | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequenc... | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
The token used for padding, for example when batching sequences of different lengths.
additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
Additional special tokens used by the tokenizer.
""" | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = XGLMTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s... | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
**kwargs,
)
... | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) wit... | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.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 zeros.
"""
sep = [self.sep_token_id]
if token_ids_1 i... | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
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 filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os... | 10,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py |
class FlaxXGLMAttention(nn.Module):
config: XGLMConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_hea... | 10,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py |
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
d... | 10,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py |
@nn.compact
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 to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
... | 10,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.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... | 10,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 10,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py |
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"... | 10,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.