text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
query = self.q_attn(hidden_states)
key_value = self.c_attn(encoder_hidden_states)
attention_mask = encoder_attention_mask
elif self.multi_query:
query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
else:
# Note: We ... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
key, value = key_value.split((self.head_dim, self.head_dim), dim=-1) | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if not output_attentions and head_mask is None:
# Difference with the original implementation: there is no need to transpose the key here,
# as SDPA expects seq_length to be at index -2 for the key as well
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
attn_output, attn_weights = super()._attn(query, key.transpose(-1, -2), value, attention_mask, head_mask) | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if not self.multi_query:
attn_output = attn_output.transpose(1, 2).reshape(hidden_states.shape)
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
if self.multi_query:
... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = nn.Linear(embed_dim, intermediate_size)
self.c_proj = nn.Linear(intermediate_size, embed_dim)
self.act = ACT2FN[config.activation_funct... | 9,431 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
... | 9,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
def forward(
self,
hidden_states: Optional[Tuple[torch.Tensor]],
layer_past: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_atten... | 9,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual | 9,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attentio... | 9,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights | 9,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
if use_cache:
outputs = (hidden_states,) + outputs
else:
... | 9,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTBigCodeConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no... | 9,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (GPTBigCodeMLP, GPTBigCodeAttention)):
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residua... | 9,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
... | 9,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeModel(GPTBigCodePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.multi_query = config.multi_query
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_posi... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
@add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0].size(-2)
if attention_mask is not None and len(attention_mask.shape) == 2 and position_ids is None:
# create position_ids on t... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# Self-attention mask.
query_length = input_shape[-1]
key_length = past_length + query_length
self_attention_mask = self.bias[None, key_length - query_length : key_length, :key_length]
if self._use_flash_attention_2:
# 2d mask is passed through the layers
attenti... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# MQA models: (batch_size, query_length, n_heads, key_length)
# MHA models: (batch_size, n_heads, query_length, key_length)
self_attention_mask = self_attention_mask.unsqueeze(2 if self.multi_query else 1)
if self._use_sdpa and head_mask is None and not output_attentions:
... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# output_attentions=True can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
if self.multi_query:
# gpt_bigcode using MQA has the bad taste to use a causal mask with shape
... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if query_length > 1 and attention_mask is not None and attention_mask.device.type == "cuda":
# From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
# produces nans if sequences are completely unattended in the attention mask. Detail... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if (
self.config.add_cross_attention
and encoder_hidden_states is not None
and encoder_attention_m... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + ... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
hidden_states = outputs[0]
if use_cache:
presents.append(outputs[1])
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if self.config.add_cross_attention:
all_cross_attentions =... | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
) | 9,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeForCausalLM(GPTBigCodePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPTBigCodeModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
token_type_ids = kwargs.get("token_type_ids", None)
# Omit tokens covered by past_key_values
if past_key_values:
if self.config.multi_query:
past_length = past_key_values[0].shape[1]
else:
past_length = past_key_values[0].shape[2]
# So... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
)
r... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
def _get_initial_cache_position(self, input_ids, model_kwargs):
"""
Calculates `cache_position` for the pre-fill stage based on `input_ids` and optionally past length.
Since gpt bigcode is special, the method is overridden here, other models use it from `generation.utils.py`.
"""
... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
@add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
e... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
# Flatten the tokens
loss_fct = CrossEntropyLoss()
... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is call... | 9,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeForSequenceClassification(GPTBigCodePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTBigCodeModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Init... | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
@add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor... | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
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).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
u... | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
assert (
self.config.pad_token_id is not None or batch_size == 1
), "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 fou... | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_ty... | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_... | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 9,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeForTokenClassification(GPTBigCodePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTBigCodeModel(config)
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
... | 9,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
@add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor... | 9,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
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).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
u... | 9,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 9,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`GPTBigCodeModel`]. It is used to instantiate a
GPTBigCode model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yiel... | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
Args:
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTBigCodeModel`].
n_positions (`int`, *optional*, defaults to 1024):
... | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
activation_function (`str`, *optional*, defaults to `"gelu_pytorch_tanh"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new",
"gelu_pytorch_tanh"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all... | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
attention_softmax_in_fp32 (`bool`, *optional*, defaults to `True`):
Wheth... | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
```python
>>> from transformers import GPTBigCodeConfig, GPTBigCodeModel
>>> # Initializing a GPTBigCode configuration
>>> configuration = GPTBigCodeConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = GPTBigCodeModel(configuration)
>>> # Accessing the ... | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
def __init__(
self,
vocab_size=50257,
n_positions=1024,
n_embd=768,
n_layer=12,
n_head=12,
n_inner=None,
activation_function="gelu_pytorch_tanh",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
... | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.attention_softmax_in_fp32 = attention_softmax_in_fp32
self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32
self.multi_query = multi_query | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | 9,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py |
class FunnelTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" Funnel Transformer tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this super... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is no... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used f... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
bos_token (`str`, `optional`, defaults to `"<s>"`):
The beginning of sentence token.
eos_token (`str`, `optional`, defaults to `"</s>"`):
The end of sentence token.
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specifie... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = FunnelTokenizer
cls_token_type_id: int = 2 | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="<unk>",
sep_token="<sep>",
pad_token="<pad>",
cls_token="<cls>",
mask_token="<mask>",
bos_token="<s>",
eos_token="</s>",
clean_text=Tru... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
) | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", toke... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
# Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.build_inputs_with_special_tokens with BERT->Funnel
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
if token_ids_1 is not None:
output += token_ids_1 + [self.sep_token_id]
return output
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to b... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls) * [self.cls_token_type_id] + len(token_ids_0 + sep) * [0]... | 9,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py |
class FunnelTokenizer(PreTrainedTokenizer):
r"""
Construct a Funnel Transformer tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic toke... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengt... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sentence token.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="<unk>",
sep_token="<sep>",
pad_token="<pad>",
cls_token="<cls>",
mask_token="<mask>",
bos_token="<s>",
eos_token="</s>"... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, u... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_vocab
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer._tokenize
def _tokenize(self, text, split_special_tokens=False):
... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_token_to_id
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
# Copied from transformers.models.bert.tokenizat... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.build_inputs_with_special_tokens
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls ... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.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 ... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Funnel
Transformer sequence pair mask has the following... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls) * [self.cls_token_type_id] + len(token_ids_0 + sep) * [0]... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.save_vocabulary
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_director... | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
writer.write(token + "\n")
index += 1
return (vocab_file,) | 9,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two s... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
#... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split)) | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 9,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py |
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