text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.transform(hidden_states=hidden_states)
seq_length = shape_list(hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
hidden_states = tf.matmul(a=hidden_states, b=self... | 3,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaOnlyMLMHead(keras.layers.Layer):
def __init__(self, config: DebertaConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFDebertaLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Ten... | 3,961 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaMainLayer(keras.layers.Layer):
config_class = DebertaConfig
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFDebertaEmbeddings(config, name="embeddings")
self.encoder = TFDebertaEncoder(conf... | 3,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Ten... | 3,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
... | 3,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
return TFBaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(se... | 3,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DebertaConfig
base_model_prefix = "deberta" | 3,963 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaModel(TFDebertaPreTrainedModel):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.deberta = TFDebertaMainLayer(config, name="deberta") | 3,964 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_... | 3,964 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
) | 3,964 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "deberta", None) is not None:
with tf.name_scope(self.deberta.name):
self.deberta.build(None) | 3,964 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaForMaskedLM(TFDebertaPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFDebertaForMaskedLM` ... | 3,965 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_i... | 3,965 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
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]`
"""
... | 3,965 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs... | 3,965 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaForSequenceClassification(TFDebertaPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaMainLayer(config, name="de... | 3,966 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
... | 3,966 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.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).
"""
... | 3,966 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) | 3,966 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions... | 3,966 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "deberta", None) is not None:
with tf.name_scope(self.deberta.name):
self.deberta.build(None)
if getattr(self, "pooler", None) is not None:
with... | 3,966 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaForTokenClassification(TFDebertaPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaMainLayer(config, name="deberta"... | 3,967 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
... | 3,967 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
outputs = self.deberta(
input_ids=input_ids,
attention_mask=atten... | 3,967 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
... | 3,967 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaForQuestionAnswering(TFDebertaPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaMainLayer(config, name="deberta")
... | 3,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
... | 3,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence... | 3,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
logits = self.qa_outputs(inputs=sequence_output)
start_logits, end_logits = tf.split(value=logits... | 3,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
if not return_dict:
output = (start_log... | 3,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "deberta", None) is not None:
with tf.name_scope(self.deberta.name):
self.deberta.build(None)
if getattr(self, "qa_outputs", None) is not None:
... | 3,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class DebertaTokenizer(PreTrainedTokenizer):
"""
Construct a DeBERTa tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentenc... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.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.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token.... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Deberta tokenizer detect beginning of words by the preceding space).
add_bos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial <|endoftext|>... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask", "token_type_ids"] | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
bos_token="[CLS]",
eos_token="[SEP]",
sep_token="[SEP]",
cls_token="[CLS]",
unk_token="[UNK]",
pad_token="[PAD]",
mask_token="[MASK]",
add_prefix_space=False,
... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self.add_bos_token = add_bos_token
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.enc... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
super().__init__(
errors=errors,
bos_token=bos_token,
eos_toke... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = ... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) wit... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.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 ... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.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 DeBERTa
sequence pair mask has the following format:
... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize
def _tokenize(self, text):
"""Tokenize a string."""
bpe... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Toke... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a director... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Sa... | 3,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py |
class GraniteAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "... | 3,970 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
... | 3,970 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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: Unpack[FlashAttenti... | 3,970 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,970 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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,
)
... | 3,970 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GraniteRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GraniteRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 3,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GraniteMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
... | 3,972 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GraniteDecoderLayer(nn.Module):
def __init__(self, config: GraniteConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GraniteAttention(config=config, layer_idx=layer_idx)
self.mlp = GraniteMLP(config)
self.input_layernorm ... | 3,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are r... | 3,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states | 3,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
outpu... | 3,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GraniteRotaryEmbedding(nn.Module):
def __init__(self, config: GraniteConfig, 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"... | 3,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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 ... | 3,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GranitePreTrainedModel(PreTrainedModel):
config_class = GraniteConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GraniteDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | 3,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GraniteModel(GranitePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GraniteDecoderLayer`]
Args:
config: GraniteConfig
"""
def __init__(self, config: GraniteConfig):
super().__init__(config)
self.padding_idx ... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
@add_start_docstrings_to_model_forward(GRANITE_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_embe... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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 | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shar... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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,
... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
hidden_states = layer_outputs[0]
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_states:
all_hidden_states += (hidden_states,)
... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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,
... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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,
):
... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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.
... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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(... | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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
) | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
return causal_mask | 3,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GraniteForCausalLM(GranitePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = GraniteModel(config)
self.vocab_size = config.vocab_size
self.lm... | 3,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
@add_start_docstrings_to_model_forward(GRANITE_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: Option... | 3,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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
... | 3,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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... | 3,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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."
```"""
... | 3,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.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,
... | 3,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return Causal... | 3,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py |
class GraniteConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GraniteModel`]. It is used to instantiate an Granite
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a sim... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Granite model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GraniteModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Di... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`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 construc... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model s... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't upd... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier
logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output ... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
```python
>>> from transformers import GraniteModel, GraniteConfig
>>> # Initializing a Granite granite-3b style configuration
>>> configuration = GraniteConfig()
>>> # Initializing a model from the granite-7b style configuration
>>> model = GraniteModel(configuration)
>>> # Accessing the mod... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_no... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
self.num_attention_heads = num_attention_heads | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
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