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18d81cd c870645 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | from typing import Unpack
import torch
from transformers import (
Cache,
EncoderDecoderCache,
DynamicCache,
DataCollatorWithFlattening,
XLMRobertaModel,
XLMRobertaForSequenceClassification,
XLMRobertaForMaskedLM,
XLMRobertaForCausalLM,
XLMRobertaForTokenClassification,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering
)
from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
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 UnpadXLMRobertaModel(XLMRobertaModel):
_no_split_modules = ["XLMRobertaEmbeddings", "XLMRobertaLayer"]
def __init__(self, config, add_pooling_layer=True):
super().__init__(config, add_pooling_layer=add_pooling_layer)
def forward(
self,
input_ids: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
token_type_ids: torch.Tensor | None = None,
position_ids: torch.Tensor | None = None,
inputs_embeds: torch.Tensor | None = None,
encoder_hidden_states: torch.Tensor | None = None,
encoder_attention_mask: torch.Tensor | None = None,
past_key_values: Cache | None = None,
use_cache: bool | None = None,
cache_position: torch.Tensor | None = None,
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor] | BaseModelOutputWithPoolingAndCrossAttentions:
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.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if use_cache and past_key_values is None:
past_key_values = (
EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
if encoder_hidden_states is not None or self.config.is_encoder_decoder
else DynamicCache(config=self.config)
)
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if input_ids is not None:
device = input_ids.device
seq_length = input_ids.shape[1]
batch_size = input_ids.shape[0]
else:
device = inputs_embeds.device
seq_length = inputs_embeds.shape[1]
batch_size = inputs_embeds.shape[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)
# roberta requires shifting position_ids by 2
position_ids = (features["position_ids"] + 2).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"]
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
attention_mask, encoder_attention_mask = self._create_attention_masks(
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
embedding_output=embedding_output,
encoder_hidden_states=encoder_hidden_states,
past_key_values=past_key_values,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
position_ids=position_ids,
**kwargs,
)
sequence_output = encoder_outputs.last_hidden_state
if self.config._attn_implementation.startswith("flash_attention"):
sequence_output = _pad_output(
inputs=sequence_output, indices=indices, batch=batch_size, seqlen=seq_length
)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
)
class UnpadXLMRobertaForCausalLM(XLMRobertaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.roberta = UnpadXLMRobertaModel(config, add_pooling_layer=False)
self.post_init()
class UnpadXLMRobertaForMaskedLM(XLMRobertaForMaskedLM):
def __init__(self, config):
super().__init__(config)
self.roberta = UnpadXLMRobertaModel(config, add_pooling_layer=False)
self.post_init()
class UnpadXLMRobertaForSequenceClassification(XLMRobertaForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.roberta = UnpadXLMRobertaModel(config, add_pooling_layer=False)
self.post_init()
class UnpadXLMRobertaForTokenClassification(XLMRobertaForTokenClassification):
def __init__(self, config):
super().__init__(config)
self.roberta = UnpadXLMRobertaModel(config, add_pooling_layer=False)
self.post_init()
class UnpadXLMRobertaForMultipleChoice(XLMRobertaForMultipleChoice):
def __init__(self, config):
super().__init__(config)
self.roberta = UnpadXLMRobertaModel(config)
self.post_init()
class UnpadXLMRobertaForQuestionAnswering(XLMRobertaForQuestionAnswering):
def __init__(self, config):
super().__init__(config)
self.roberta = UnpadXLMRobertaModel(config, add_pooling_layer=False)
self.post_init()
def enable_xlm_roberta_unpadding():
XLMRobertaModel.forward = UnpadXLMRobertaModel.forward |