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import torch
from transformers import DataCollatorWithFlattening
from transformers.masking_utils import create_bidirectional_mask
from transformers.modeling_outputs import BaseModelOutput
from transformers.models.eurobert import (
EuroBertForMaskedLM,
EuroBertModel,
EuroBertForSequenceClassification,
EuroBertForTokenClassification
)
from transformers.utils import TransformersKwargs
def _unpad_input(input_ids: torch.Tensor, attention_mask: torch.Tensor):
collator = DataCollatorWithFlattening(return_flash_attn_kwargs=True)
features = collator([{"input_ids": i[a.bool()].tolist()} for i, a in zip(input_ids, attention_mask)])
return features
def _pad_output(inputs: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int,) -> torch.Tensor:
if inputs.dim() == 3:
inputs = inputs.squeeze()
if inputs.dim() == 1:
output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
output[indices] = inputs
padded_inputs = output.view(batch, seqlen)
else:
_, *rest = inputs.shape
output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
output[indices] = inputs
padded_inputs = output.view(batch, seqlen, *rest)
return padded_inputs
class UnpadEuroBertModel(EuroBertModel):
def __init__(self, config):
super().__init__(config)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
inputs_embeds: torch.FloatTensor | None = None,
**kwargs: Unpack[TransformersKwargs],
) -> tuple | BaseModelOutput:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if input_ids is not None:
device = input_ids.device
seq_length = input_ids.shape[1]
batch_size = input_ids.size(0)
else:
device = inputs_embeds.device
seq_length = inputs_embeds.shape[1]
batch_size = inputs_embeds.size(0)
indices = None
if self.config._attn_implementation.startswith("flash_attention"):
if input_ids is None or attention_mask is None:
raise ValueError("Unpadding requires both input_ids and attention_mask")
with torch.no_grad():
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
features = _unpad_input(input_ids, attention_mask)
input_ids = features["input_ids"].to(device=device)
position_ids = features["position_ids"].to(device=device)
attention_mask = None
kwargs["cu_seq_lens_k"] = features["cu_seq_lens_k"].to(device=device)
kwargs["cu_seq_lens_q"] = features["cu_seq_lens_q"].to(device=device)
kwargs["max_length_k"] = features["max_length_k"]
kwargs["max_length_q"] = features["max_length_q"]
if inputs_embeds is None:
inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
if position_ids is None:
position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0)
bidirectional_mask = create_bidirectional_mask(
config=self.config,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
)
hidden_states = inputs_embeds
position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
for encoder_layer in self.layers[: self.config.num_hidden_layers]:
hidden_states = encoder_layer(
hidden_states,
attention_mask=bidirectional_mask,
position_embeddings=position_embeddings,
position_ids=position_ids,
**kwargs,
)
hidden_states = self.norm(hidden_states)
if self.config._attn_implementation.startswith("flash_attention"):
hidden_states = _pad_output(
inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_length
)
return BaseModelOutput(
last_hidden_state=hidden_states,
)
class UnpadEuroBertForMaskedLM(EuroBertForMaskedLM):
def __init__(self, config):
super().__init__(config)
self.model = UnpadEuroBertModel(config)
self.post_init()
class UnpadEuroBertForSequenceClassification(EuroBertForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.model = UnpadEuroBertModel(config)
self.post_init()
class UnpadEuroBertForTokenClassification(EuroBertForTokenClassification):
def __init__(self, config):
super().__init__(config)
self.model = UnpadEuroBertModel(config)
self.post_init()
def enable_eurobert_unpadding():
EuroBertModel.forward = UnpadEuroBertModel.forward
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