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c03cbed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | from typing import Unpack
import torch
from transformers import (
DataCollatorWithFlattening,
ModernBertModel,
ModernBertConfig,
ModernBertForMaskedLM,
ModernBertForSequenceClassification,
ModernBertForTokenClassification,
ModernBertForQuestionAnswering,
ModernBertForMultipleChoice
)
from transformers.masking_utils import create_bidirectional_mask, create_bidirectional_sliding_window_mask
from transformers.modeling_outputs import BaseModelOutput
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 UnpadModernBertModel(ModernBertModel):
def __init__(self, config: ModernBertConfig):
super().__init__(config)
def forward(
self,
input_ids: torch.LongTensor | None = None,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
inputs_embeds: torch.Tensor | None = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutput:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
seq_len = inputs_embeds.shape[1] if inputs_embeds is not None else input_ids.shape[1]
batch_size = inputs_embeds.shape[0] if inputs_embeds is not None else input_ids.shape[0]
device = input_ids.device if input_ids is not None else inputs_embeds.device
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 position_ids is None:
position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
if not isinstance(attention_mask_mapping := attention_mask, dict):
mask_kwargs = {
"config": self.config,
"inputs_embeds": hidden_states,
"attention_mask": attention_mask,
}
attention_mask_mapping = {
"full_attention": create_bidirectional_mask(**mask_kwargs),
"sliding_attention": create_bidirectional_sliding_window_mask(**mask_kwargs),
}
position_embeddings = {}
for layer_type in self.config.layer_types:
position_embeddings[layer_type] = self.rotary_emb(hidden_states, position_ids, layer_type)
for encoder_layer in self.layers:
hidden_states = encoder_layer(
hidden_states,
attention_mask=attention_mask_mapping[encoder_layer.attention_type],
position_embeddings=position_embeddings[encoder_layer.attention_type],
**kwargs,
)
hidden_states = self.final_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_len
)
return BaseModelOutput(last_hidden_state=hidden_states)
class UnpadModernBertForMaskedLM(ModernBertForMaskedLM):
def __init__(self, config):
super().__init__(config)
self.model = UnpadModernBertModel(config)
self.post_init()
class UnpadModernBertForSequenceClassification(ModernBertForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.model = UnpadModernBertModel(config)
self.post_init()
class UnpadModernBertForTokenClassification(ModernBertForTokenClassification):
def __init__(self, config):
super().__init__(config)
self.model = UnpadModernBertModel(config)
self.post_init()
class UnpadModernBertForQuestionAnswering(ModernBertForQuestionAnswering):
def __init__(self, config):
super().__init__(config)
self.model = UnpadModernBertModel(config)
self.post_init()
class UnpadModernBertForMultipleChoice(ModernBertForMultipleChoice):
def __init__(self, config):
super().__init__(config)
self.model = UnpadModernBertModel(config)
self.post_init()
def enable_modernbert_unpadding():
ModernBertModel.forward = UnpadModernBertModel.forward
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