text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_sta... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: C... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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,
... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dt... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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.
... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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(... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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
) | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
return causal_mask | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronForCausalLM(NemotronPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = NemotronModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, c... | 9,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
@add_start_docstrings_to_model_forward(NEMOTRON_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
# Ignore copy (doc string different)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Te... | 9,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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
... | 9,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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."
```"""
... | 9,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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,
... | 9,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronForSequenceClassification(NemotronPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = NemotronModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize ... | 9,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
@add_start_docstrings_to_model_forward(NEMOTRON_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... | 9,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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 | 9,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronForQuestionAnswering(NemotronPreTrainedModel):
base_model_prefix = "transformer"
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Nemotron
def __init__(self, config):
super().__init__(config)
self.transformer = NemotronModel... | 9,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
@add_start_docstrings_to_model_forward(NEMOTRON_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[... | 9,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled spa... | 9,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_state... | 9,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronForTokenClassification(NemotronPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = NemotronModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = conf... | 9,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
@add_start_docstrings_to_model_forward(NEMOTRON_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,
a... | 9,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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).
"""
... | 9,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class FNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | 9,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py |
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`].
hidden_size (`int`, *optional*, defaults to 768):
... | 9,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 ... | 9,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py |
Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used.
tpu_short_seq_length (`int`, *optional*, defaults to 512):
The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT
matrix only when *use_tpu_fourier_optimiza... | 9,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py |
Example:
```python
>>> from transformers import FNetConfig, FNetModel
>>> # Initializing a FNet fnet-base style configuration
>>> configuration = FNetConfig()
>>> # Initializing a model (with random weights) from the fnet-base style configuration
>>> model = FNetModel(configuration)
>>> ... | 9,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py |
def __init__(
self,
vocab_size=32000,
hidden_size=768,
num_hidden_layers=12,
intermediate_size=3072,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=4,
initializer_range=0.02,
layer_norm_... | 9,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py |
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_... | 9,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py |
class FNetTokenizer(PreTrainedTokenizer):
"""
Construct an FNet tokenizer. Adapted from [`AlbertTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`]
which contains most of the main methods. Users should refer to this supercl... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to low... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
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 sequence built with special tokens.
pad_token (`str`, *optional*... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set: | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming tha... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
keep_accents=True,
unk_token="<unk>",
sep_token="[SEP]",
pad_token="<pad>",
cls_token="[CLS]",
mask_token="[MASK]",
sp_model_kwargs: Optional[Dict[str, Any]] = ... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
super().__init__(
do_lower_cas... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
def _tokenize(self, text: str) -> List[str]:
"""Tokenize a string."""
text = self.preprocess_text(text)
pieces = self.sp_model.encode(text, out_type=str)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_model.IdToPiece(index) | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.convert_tokens_to_string
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
f... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
spaces_between_special_tokens: bool = False,
**kwargs,
) -> str:
text = super()._decode(
token_ids=token_ids,
skip_sp... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
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 for sequence classification tasks by concatenating and
adding special tokens. An FNet sequence has ... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens ... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.seri... | 9,739 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py |
class FNetEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddi... | 9,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.register_buffer(
"token_type_ids", torch.zeros(self.posit... | 9,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 9,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.projection(embeddings)
embeddings = self.dropout(embeddings)
return embeddings | 9,740 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetBasicFourierTransform(nn.Module):
def __init__(self, config):
super().__init__()
self._init_fourier_transform(config) | 9,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
def _init_fourier_transform(self, config):
if not config.use_tpu_fourier_optimizations:
self.fourier_transform = partial(torch.fft.fftn, dim=(1, 2))
elif config.max_position_embeddings <= 4096:
if is_scipy_available():
self.register_buffer(
"df... | 9,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
self.fourier_transform = fftn
else:
self.fourier_transform = fftn | 9,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
def forward(self, hidden_states):
# NOTE: We do not use torch.vmap as it is not integrated into PyTorch stable versions.
# Interested users can modify the code to use vmap from the nightly versions, getting the vmap from here:
# https://pytorch.org/docs/master/generated/torch.vmap.html. Note tha... | 9,741 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetBasicOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states, input_tensor):
hidden_states = self.LayerNorm(input_tensor + hidden_states)
return hidde... | 9,742 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetFourierTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.self = FNetBasicFourierTransform(config)
self.output = FNetBasicOutput(config)
def forward(self, hidden_states):
self_outputs = self.self(hidden_states)
fourier_output = self.outpu... | 9,743 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interm... | 9,744 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def... | 9,745 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1 # The dimension which has the sequence length
self.fourier = FNetFourierTransform(config)
self.intermediate = FNetInt... | 9,746 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([FNetLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(self, hidden_states, output_hidden_states=Fa... | 9,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states) | 9,747 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidde... | 9,748 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.tran... | 9,749 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = FNetPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(c... | 9,750 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = FNetLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | 9,751 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score | 9,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = FNetLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(... | 9,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FNetConfig
base_model_prefix = "fnet"
supports_gradient_checkpointing = True | 9,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# 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.... | 9,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForPreTrainingOutput(ModelOutput):
"""
Output type of [`FNetForPreTraining`]. | 9,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, seque... | 9,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
""" | 9,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
loss: Optional[torch.FloatTensor] = None
prediction_logits: torch.FloatTensor = None
seq_relationship_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | 9,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetModel(FNetPreTrainedModel):
"""
The model can behave as an encoder, following the architecture described in [FNet: Mixing Tokens with Fourier
Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
"""
def __init__(self, config,... | 9,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.L... | 9,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.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:
input_shape = input_ids.size()
batch_size, seq_length = input_shape
elif inputs_embeds is not N... | 9,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffer... | 9,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if not return_dict:
return (sequence_output, pooler_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooler_output,
hidden_states=encoder_outputs.hidden_states,
) | 9,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForPreTraining(FNetPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetPreTrainingHeads(config)
# Initialize weight... | 9,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=FNetForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.T... | 9,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computin... | 9,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
kwargs (`Dict[str, any]`, *optional*, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Returns:
Example:
```python
>>> fr... | 9,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:... | 9,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return FNetForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_rel... | 9,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForMaskedLM(FNetPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetOnlyMLMHead(config)
# Initialize weights and ap... | 9,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Te... | 9,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
predi... | 9,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForNextSentencePrediction(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init() | 9,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
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