Spaces:
Runtime error
Runtime error
File size: 11,803 Bytes
476ac07 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import types
import torch
import transformers
from mmengine.config.lazy import LazyObject
from mmengine.utils import digit_version
from transformers.utils.import_utils import is_flash_attn_2_available
TRANSFORMERS_VERSION = digit_version(transformers.__version__)
IS_LOW_VERSION_TRANSFORMERS = TRANSFORMERS_VERSION < digit_version('4.38')
# Transformers requires torch version >= 2.1.1 when using Torch SDPA.
# Refer to https://github.com/huggingface/transformers/blob/caa5c65db1f4db617cdac2ad667ba62edf94dd98/src/transformers/modeling_utils.py#L1611 # noqa: E501
SUPPORT_FLASH1 = digit_version(torch.__version__) >= digit_version('2.1.1')
SUPPORT_FLASH2 = is_flash_attn_2_available()
SUPPORT_FLASH = SUPPORT_FLASH1 or SUPPORT_FLASH2
USE_TRITON_KERNEL = bool(os.getenv('USE_TRITON_KERNEL', default=0))
SUPPORT_TRITON = False
try:
import triton # pre-check # noqa: F401
import triton.language as tl # pre-check # noqa: F401
SUPPORT_TRITON = True
except ImportError:
if USE_TRITON_KERNEL:
raise RuntimeError(
'USE_TRITON_KERNEL is set to 1, but triton has not been installed.'
' Run `pip install triton==2.1.0` to install triton.')
NO_ATTN_WEIGHTS_MSG = (
'Due to the implementation of the PyTorch version of flash attention, '
'even when the `output_attentions` flag is set to True, it is not '
'possible to return the `attn_weights`.')
LOWEST_TRANSFORMERS_VERSION = dict(
InternLM2ForCausalLM=digit_version('4.36'),
InternLMForCausalLM=digit_version('4.36'),
LlamaForCausalLM=digit_version('4.36'),
Phi3ForCausalLM=digit_version('4.39'),
MistralForCausalLM=digit_version('4.36'),
# Training mixtral with lower version may lead to nccl timeout
# Refer to https://github.com/microsoft/DeepSpeed/issues/5066
MixtralForCausalLM=digit_version('4.40'),
CohereForCausalLM=digit_version('4.40'),
Qwen2ForCausalLM=digit_version('4.39'),
Qwen2MoeForCausalLM=digit_version('4.40'),
DeepseekV2ForCausalLM=digit_version('4.40'),
)
ATTN_DISPATCH_MAPPING = dict(
InternLM2FlashAttention2=LazyObject(
'xtuner.model.modules.dispatch.internlm2', 'internlm2_attn_forward'),
InternLMAttention=LazyObject('xtuner.model.modules.dispatch.internlm',
'internlm_attn_forward'),
LlamaFlashAttention2=LazyObject('xtuner.model.modules.dispatch.llama',
'llama_attn_forward'),
Phi3FlashAttention2=LazyObject('xtuner.model.modules.dispatch.phi3',
'phi3_attn_forward'),
MistralFlashAttention2=LazyObject('xtuner.model.modules.dispatch.mistral',
'mistral_attn_forward'),
MixtralFlashAttention2=LazyObject('xtuner.model.modules.dispatch.mistral',
'mistral_attn_forward'),
CohereFlashAttention2=LazyObject('xtuner.model.modules.dispatch.cohere',
'cohere_attn_forward'),
Qwen2FlashAttention2=LazyObject('xtuner.model.modules.dispatch.qwen2',
'qwen2_attn_forward'),
Qwen2MoeFlashAttention2=LazyObject('xtuner.model.modules.dispatch.qwen2',
'qwen2_attn_forward'),
DeepseekV2FlashAttention2=LazyObject(
'xtuner.model.modules.dispatch.deepseek_v2', 'deepseek_attn_forward'),
)
ATTN_LEGACY_DISPATCH_MAPPING = dict(
LlamaFlashAttention2=LazyObject('xtuner.model.modules.dispatch.llama',
'llama_attn_forward_legacy'), )
VARLEN_ATTN_DISPATCH_MAPPING = dict(
InternLM2FlashAttention2=LazyObject(
'xtuner.model.modules.dispatch.internlm2',
'internlm2_varlen_attn_forward'),
InternLMAttention=LazyObject('xtuner.model.modules.dispatch.internlm',
'internlm_varlen_attn_forward'),
LlamaFlashAttention2=LazyObject('xtuner.model.modules.dispatch.llama',
'llama_varlen_attn_forward'),
Phi3FlashAttention2=LazyObject('xtuner.model.modules.dispatch.phi3',
'phi3_varlen_attn_forward'),
MistralFlashAttention2=LazyObject('xtuner.model.modules.dispatch.mistral',
'mistral_varlen_attn_forward'),
MixtralFlashAttention2=LazyObject('xtuner.model.modules.dispatch.mistral',
'mistral_varlen_attn_forward'),
CohereFlashAttention2=None,
Qwen2FlashAttention2=LazyObject('xtuner.model.modules.dispatch.qwen2',
'qwen2_varlen_attn_forward'),
Qwen2MoeFlashAttention2=LazyObject('xtuner.model.modules.dispatch.qwen2',
'qwen2_varlen_attn_forward'),
DeepseekV2FlashAttention2=LazyObject(
'xtuner.model.modules.dispatch.deepseek_v2',
'deepseek_varlen_attn_forward'),
)
VARLEN_ATTN_LEGACY_DISPATCH_MAPPING = dict(
LlamaFlashAttention2=LazyObject('xtuner.model.modules.dispatch.llama',
'llama_varlen_attn_forward_legacy'), )
RMS_DISPATCH_MAPPING = dict(
InternLM2RMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
InternLMRMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
LlamaRMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
Phi3RMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
MistralRMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
MixtralRMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
CohereLayerNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'layer_norm_forward'),
Qwen2RMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
Qwen2MoeRMSNorm=LazyObject('xtuner.model.modules.dispatch.triton_kernels',
'rms_norm_forward'),
)
ROTE_DISPATCH_MAPPING = dict(
InternLM2RotaryEmbedding=LazyObject(
'xtuner.model.modules.dispatch.internlm2', 'InternLM2RotaryEmbedding'),
InternLMRotaryEmbedding=LazyObject(
'xtuner.model.modules.dispatch.internlm', 'InternLMRotaryEmbedding'),
MistralRotaryEmbedding=LazyObject('xtuner.model.modules.dispatch.mistral',
'MistralRotaryEmbedding'),
MixtralRotaryEmbedding=LazyObject('xtuner.model.modules.dispatch.mistral',
'MistralRotaryEmbedding'),
)
def log_once(func):
logged = False
def wrapper(*args, **kwargs):
nonlocal logged
if not logged:
logged = True
func(*args, **kwargs)
return
return wrapper
def dispatch_attn_forward(model):
if not SUPPORT_FLASH2:
return
from mmengine import print_log
print_log = log_once(print_log)
attn_forward = None
for module in model.modules():
name = type(module).__name__
if (IS_LOW_VERSION_TRANSFORMERS
and name in ATTN_LEGACY_DISPATCH_MAPPING):
if attn_forward is None:
attn_forward = ATTN_LEGACY_DISPATCH_MAPPING[name]
attn_forward = attn_forward.build()
print_log(f'Dispatch {name} legacy forward. {NO_ATTN_WEIGHTS_MSG}',
'current')
module.forward = types.MethodType(attn_forward, module)
elif name in ATTN_DISPATCH_MAPPING:
if attn_forward is None:
attn_forward = ATTN_DISPATCH_MAPPING[name]
attn_forward = attn_forward.build()
print_log(f'Dispatch {name} forward. {NO_ATTN_WEIGHTS_MSG}',
'current')
module.forward = types.MethodType(attn_forward, module)
def dispatch_varlen_attn_forward(model):
if not SUPPORT_FLASH2:
return
from mmengine import print_log
print_log = log_once(print_log)
varlen_attn_forward = None
for module in model.modules():
name = type(module).__name__
if (IS_LOW_VERSION_TRANSFORMERS
and name in VARLEN_ATTN_LEGACY_DISPATCH_MAPPING):
if varlen_attn_forward is None:
varlen_attn_forward = VARLEN_ATTN_LEGACY_DISPATCH_MAPPING[name]
varlen_attn_forward = varlen_attn_forward.build()
print_log(
f'Dispatch legacy {name} varlen forward. '
f'{NO_ATTN_WEIGHTS_MSG}', 'current')
module.forward = types.MethodType(varlen_attn_forward, module)
elif name in VARLEN_ATTN_DISPATCH_MAPPING:
if varlen_attn_forward is None:
varlen_attn_forward = VARLEN_ATTN_DISPATCH_MAPPING[name]
varlen_attn_forward = varlen_attn_forward.build()
print_log(f'Dispatch {name} varlen forward. {NO_ATTN_WEIGHTS_MSG}',
'current')
module.forward = types.MethodType(varlen_attn_forward, module)
def dispatch_rmsnorm_forward(model):
if (not SUPPORT_TRITON) or (not USE_TRITON_KERNEL):
return
from mmengine import print_log
print_log = log_once(print_log)
rms_forward = None
for module in model.modules():
name = type(module).__name__
if name in RMS_DISPATCH_MAPPING:
if rms_forward is None:
rms_forward = RMS_DISPATCH_MAPPING[name]
rms_forward = rms_forward.build()
print_log(f'Dispatch {name} forward.', 'current')
module.forward = types.MethodType(rms_forward, module)
def replace_rote(model):
from mmengine import print_log
print_log = log_once(print_log)
assert hasattr(model.config, 'rope_theta'), \
'`rope_theta` should be in the model config.'
rope_theta = model.config.rope_theta
def traverse(module):
for name, child in module.named_children():
cls_name = type(child).__name__
if cls_name in ROTE_DISPATCH_MAPPING:
rote = ROTE_DISPATCH_MAPPING[cls_name]
rote = rote.build()
print_log(f'replace {cls_name}', 'current')
dim_model = child.inv_freq.shape[0] * 2
child_new = rote(dim_model, child.max_seq_len_cached,
rope_theta).to(
device=child.inv_freq.device,
dtype=child.inv_freq.dtype)
setattr(module, name, child_new)
else:
traverse(child)
traverse(model)
def dispatch_modules(model, use_varlen_attn=False):
def check(model_name):
if 'ForCausalLM' not in model_name and model_name.endswith('Model'):
# a walkaround for reward model
model_name = model_name[:-5] + 'ForCausalLM'
msg = '{} requires transformers version at least {}, but got {}'
assert TRANSFORMERS_VERSION >= LOWEST_TRANSFORMERS_VERSION[
model_name], msg.format(model_name,
LOWEST_TRANSFORMERS_VERSION[model_name],
TRANSFORMERS_VERSION)
check(type(model).__name__)
if use_varlen_attn:
dispatch_varlen_attn_forward(model)
else:
dispatch_attn_forward(model)
dispatch_rmsnorm_forward(model)
replace_rote(model)
__all__ = ['dispatch_modules']
|