File size: 8,168 Bytes
52e9e56 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import torch
import importlib.metadata
from packaging import version
from transformers import LlamaModel, LlamaForCausalLM, LlamaPreTrainedModel, LlamaConfig
from transformers.models.llama.modeling_llama import (
LlamaDecoderLayer,
LlamaAttention,
LlamaFlashAttention2,
LlamaSdpaAttention,
LlamaMLP,
LlamaRMSNorm,
LlamaRotaryEmbedding,
)
from torch import nn
from transformers.utils import logging
from transformers.utils.import_utils import _is_package_available
from transformers.cache_utils import Cache, StaticCache
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from peft import PeftModel
logger = logging.get_logger(__name__)
def is_transformers_attn_greater_or_equal_4_43_1():
if not _is_package_available("transformers"):
return False
return version.parse(importlib.metadata.version("transformers")) >= version.parse(
"4.43.1"
)
class ModifiedLlamaAttention(LlamaAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
class ModifiedLlamaFlashAttention2(LlamaFlashAttention2):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
class ModifiedLlamaSdpaAttention(LlamaSdpaAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
LLAMA_ATTENTION_CLASSES = {
"eager": ModifiedLlamaAttention,
"flash_attention_2": ModifiedLlamaFlashAttention2,
"sdpa": ModifiedLlamaSdpaAttention,
}
class ModifiedLlamaDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: LlamaConfig, layer_idx: int):
nn.Module.__init__(self)
self.hidden_size = config.hidden_size
self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](
config=config, layer_idx=layer_idx
)
self.mlp = LlamaMLP(config)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
class LlamaBiModel(LlamaModel):
_no_split_modules = ["ModifiedLlamaDecoderLayer"]
def __init__(self, config: LlamaConfig):
if not is_transformers_attn_greater_or_equal_4_43_1():
raise ValueError(
"The current implementation of LlamaEncoderModel follows modeling_llama.py of transformers version >= 4.43.1"
)
LlamaPreTrainedModel.__init__(self, config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(
config.vocab_size, config.hidden_size, self.padding_idx
)
self.layers = nn.ModuleList(
[
ModifiedLlamaDecoderLayer(config, layer_idx)
for layer_idx in range(config.num_hidden_layers)
]
)
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = LlamaRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def _update_causal_mask(
self,
attention_mask,
input_tensor,
cache_position,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = (
past_key_values.get_seq_length() if past_key_values is not None else 0
)
using_static_cache = isinstance(past_key_values, StaticCache)
# 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,
# inputs_embeds=input_tensor,
# past_key_values_length=past_seen_tokens,
# is_training=self.training,
# ):
# return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_length()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
causal_mask = torch.zeros(
(sequence_length, target_length), dtype=dtype, device=device
) # in original implementation - torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
# Commenting out next 2 lines to disable causal masking
# if sequence_length != 1:
# causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(
target_length, device=device
) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(
input_tensor.shape[0], 1, -1, -1
)
if attention_mask is not None:
causal_mask = (
causal_mask.clone()
) # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[
:, None, None, :
].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[
..., :mask_length
].masked_fill(padding_mask, min_dtype)
elif attention_mask.dim() == 4:
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
if attention_mask.shape[-2] < cache_position[0] + sequence_length:
offset = cache_position[0]
else:
offset = 0
mask_shape = attention_mask.shape
mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
causal_mask[
: mask_shape[0],
: mask_shape[1],
offset : mask_shape[2] + offset,
: mask_shape[3],
] = mask_slice
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
causal_mask = AttentionMaskConverter._unmask_unattended(
causal_mask, min_dtype
)
return causal_mask
class LlamaBiForMNTP(LlamaForCausalLM):
def __init__(self, config):
LlamaPreTrainedModel.__init__(self, config)
self.model = LlamaBiModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
# getter for PEFT model
def get_model_for_peft(self):
return self.model
# setter for PEFT model
def set_model_for_peft(self, model: PeftModel):
self.model = model
# save the PEFT model
def save_peft_model(self, path):
self.model.save_pretrained(path)
|