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class PhiDecoderLayer(nn.Module):
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.self_attn = PhiAttention(config, layer_idx=layer_idx)
self.mlp = PhiMLP(config)
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | 9,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
attn_outputs, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output... | 9,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiRotaryEmbedding(nn.Module):
def __init__(self, config: PhiConfig, 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.get("rope_type", config... | 9,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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 ... | 9,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiPreTrainedModel(PreTrainedModel):
config_class = PhiConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PhiDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_f... | 9,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiModel(PhiPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
Args:
config: PhiConfig
"""
def __init__(self, config: PhiConfig):
super().__init__(config)
self.padding_idx = config.pad_token_i... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: ... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
inputs_embeds = self.embed_dropout(inputs_embeds) # diff with Llama
hidden_states = inputs_embeds
# create position embeddings to be shared across th... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.final_layernorm(hidden_states) # diff with Llama
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_state... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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,
... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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.
... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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(... | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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
) | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
return causal_mask | 9,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 9,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiForCausalLM(PhiPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = PhiModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.L... | 9,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
@add_start_docstrings_to_model_forward(PHI_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: Optional[t... | 9,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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
... | 9,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
... | 9,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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,
... | 9,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 9,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiForSequenceClassification(PhiPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PhiModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and app... | 9,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
@add_start_docstrings_to_model_forward(PHI_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[torch.Floa... | 9,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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 | 9,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiForTokenClassification(PhiPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PhiModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classifier_d... | 9,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
@add_start_docstrings_to_model_forward(PHI_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,
attent... | 9,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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).
"""
... | 9,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.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... | 9,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
Args:
vocab_size (`int`, *optional*, defaults to 51200):
Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`PhiModel`].
hidden_size (`int`, *optional*, defaults to 2048):
Dimension... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
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
accordingly.
Expected c... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
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 hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
partial_rotary_factor (`float`, *optional*, defaults to 0.5):
Percentage of the query and keys which will have rotary embedding.
qk_layernorm (`bool`, *optional*, defaults to `False`):
Whether or not to normalize the Queries and Keys after projecting the hidden states.
bos_token_... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
Example:
```python
>>> from transformers import PhiModel, PhiConfig
>>> # Initializing a Phi-1 style configuration
>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
>>> # Initializing a model from the configuration
>>> model = PhiModel(configuration)
>>> # Accessing the mo... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
def __init__(
self,
vocab_size=51200,
hidden_size=2048,
intermediate_size=8192,
num_hidden_layers=24,
num_attention_heads=32,
num_key_value_heads=None,
resid_pdrop=0.0,
embd_pdrop=0.0,
attention_dropout=0.0,
hidden_act="gelu_new",
... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
self.num_key_value_heads = num_key_value_heads
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attention_dropout = attention_dropout
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_r... | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 9,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py |
class PhiAttention(LlamaAttention):
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_h... | 9,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
) | 9,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torc... | 9,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
cos, sin = position_embeddings
# Partial rotary embedding
query_rot, query_pass = (
query_states[..., : self.rotary_ndims],
query_states[..., self.rotary_ndims :],
)
key_rot, key_pass = (
key_states[..., : self.rotary_ndims],
key_states[...... | 9,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 9,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
... | 9,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
class PhiMLP(CLIPMLP):
pass | 9,497 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
class PhiDecoderLayer(nn.Module):
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.self_attn = PhiAttention(config, layer_idx=layer_idx)
self.mlp = PhiMLP(config)
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | 9,498 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
attn_outputs, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output... | 9,498 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
class PhiModel(LlamaModel):
def __init__(self, config: PhiConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.embed_dropout = nn.Dropout(config.embd_pdrop)
self... | 9,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[b... | 9,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 9,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
inputs_embeds = self.embed_dropout(inputs_embeds) # diff with Llama
hidden_states = inputs_embeds
# create position embeddings to be shared across th... | 9,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 9,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.final_layernorm(hidden_states) # diff with Llama
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_state... | 9,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
class PhiForCausalLM(LlamaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True) | 9,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
class PhiForSequenceClassification(LlamaForSequenceClassification):
pass | 9,501 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
class PhiForTokenClassification(LlamaForTokenClassification):
pass | 9,502 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py |
class BertJapaneseTokenizer(PreTrainedTokenizer):
r"""
Construct a BERT tokenizer for Japanese text.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer
to: this superclass for more information regarding those methods. | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
Args:
vocab_file (`str`):
Path to a one-wordpiece-per-line vocabulary file.
spm_file (`str`, *optional*):
Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm or .model
extension) that contains the vocabulary.
do_lower_ca... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
Type of subword tokenizer. Choose from ["wordpiece", "character", "sentencepiece",].
mecab_kwargs (`dict`, *optional*):
Dictionary passed to the `MecabTokenizer` constructor.
sudachi_kwargs (`dict`, *optional*):
Dictionary passed to the `SudachiTokenizer` constructor.
jum... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
vocab_files_names = VOCAB_FILES_NAMES | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def __init__(
self,
vocab_file,
spm_file=None,
do_lower_case=False,
do_word_tokenize=True,
do_subword_tokenize=True,
word_tokenizer_type="basic",
subword_tokenizer_type="wordpiece",
never_split=None,
unk_token="[UNK]",
sep_token="[S... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google"
" pretrained model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
self.do_word_tokenize = do_word_tokenize
self.word_tokenizer_type = word_tokenizer_type
self.lower_case = do_lower_case
self.never_split = never_split
self.mecab_kwargs = copy.deepcopy(mecab_kwargs)
self.sudachi_kwargs = copy.deepcopy(sudachi_kwargs)
self.jumanpp_kwargs =... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
do_lower_case=do_lower_case, never_split=never_split, **(sudachi_kwargs or {})
)
elif word_tokenizer_type == "jumanpp":
self.word_tokenizer = JumanppTokenizer(
do_lower_case=do_lower_case, never_split=never_split, **(jumanpp_kwargs or {})
)... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
self.do_subword_tokenize = do_subword_tokenize
self.subword_tokenizer_type = subword_tokenizer_type
if do_subword_tokenize:
if subword_tokenizer_type == "wordpiece":
self.subword_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
elif subwo... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
do_lower_case=do_lower_case,
do_word_tokenize=do_word_tokenize,
do_subword_tokenize=do_subword_tokenize,
word_tokenizer_type=word_tokenizer_type,
subword_tokenizer_type=subword_tokenizer_type,
never_split=never_split,
mecab_kwargs=mecab_kwargs,
... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
@property
def do_lower_case(self):
return self.lower_case
def __getstate__(self):
state = dict(self.__dict__)
if self.word_tokenizer_type in ["mecab", "sudachi", "jumanpp"]:
del state["word_tokenizer"]
return state
def __setstate__(self, state):
self.__d... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def _tokenize(self, text):
if self.do_word_tokenize:
tokens = self.word_tokenizer.tokenize(text, never_split=self.all_special_tokens)
else:
tokens = [text]
if self.do_subword_tokenize:
split_tokens = [sub_token for token in tokens for sub_token in self.subwor... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if self.subword_tokenizer_type == "sentencepiece":
return self.subword_tokenizer.sp_model.PieceToId(token)
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_t... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.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... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.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 ... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.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 ... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.create_token_type_ids_from_sequences
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... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.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 + token_ids_0 + sep) * [0]
return len(cls + token_... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if os.path.isdir(save_directory):
if self.subword_tokenizer_type == "sentencepiece":
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
if self.subword_tokenizer_type == "sentencepiece":
with open(vocab_file, "wb") as writer:
content_spiece_model = self.subword_tokenizer.sp_model.serialized_model_proto()
writer.write(content_spiece_model)
else:
with open(vocab_file, "w", encoding="utf-8") ... | 9,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
class MecabTokenizer:
"""Runs basic tokenization with MeCab morphological parser."""
def __init__(
self,
do_lower_case=False,
never_split=None,
normalize_text=True,
mecab_dic: Optional[str] = "unidic_lite",
mecab_option: Optional[str] = None,
):
"""
... | 9,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
Args:
**do_lower_case**: (*optional*) boolean (default True)
Whether to lowercase the input.
**never_split**: (*optional*) list of str
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedToken... | 9,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
self.normalize_text = normalize_text | 9,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
try:
import fugashi
except ModuleNotFoundError as error:
raise error.__class__(
"You need to install fugashi to use MecabTokenizer. "
"See https://pypi.org/project/fugashi/ for installation."
)
mecab_option = mecab_option or ""
... | 9,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
elif mecab_dic == "unidic_lite":
try:
import unidic_lite
except ModuleNotFoundError as error:
raise error.__class__(
"The unidic_lite dictionary is not installed. "
"See https://github.com/polm/unidic... | 9,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
dic_dir = unidic.DICDIR
if not os.path.isdir(dic_dir):
raise RuntimeError(
"The unidic dictionary itself is not found. "
"See https://github.com/polm/unidic-py for installation."
)
else:
... | 9,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
if self.do_lower_case and token not in never_split:
token = token.lower()
tokens.append(token)
return tokens | 9,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
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