File size: 5,484 Bytes
d02c2a6 | 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 | import torch
from .manager_modules import LinearLayerMemoryManager, ConvLayerMemoryManager
import random
LINEAR_MODULES = [
"Linear",
"LoRACompatibleLinear",
"QLinear",
]
CONV_MODULES = [
"Conv2d",
"LoRACompatibleConv",
"QConv2d",
]
UNMANAGED_MODULES = [
"LayerNorm",
"BatchNorm1d",
"BatchNorm2d",
"BatchNorm3d",
"GroupNorm",
"InstanceNorm1d",
"InstanceNorm2d",
"InstanceNorm3d",
"Embedding",
"EmbeddingBag",
"RNNBase",
"LSTM",
"GRU",
"RNN",
"Conv3d"
]
UNMANAGED_MODULES_INCLUDES = ["RotaryEmbedding", "Norm", "RotaryPosEmbed"]
class MemoryManager:
def __init__(
self,
module: torch.nn.Module,
process_device: torch.device = torch.device("cpu"),
):
self.module: torch.nn.Module = module
self.process_device: torch.device = process_device
self.unmanaged_modules: list[torch.nn.Module] = []
def memory_managed_to(self, *args, **kwargs):
# first move all the unmanaged modules
for module in self.unmanaged_modules:
if isinstance(module, torch.nn.Parameter):
# Parameter cannot move this way
module.data = module.data.to(*args, **kwargs)
else:
module.to(*args, **kwargs)
# check for a dtype argument
dtype = None
if "dtype" in kwargs:
dtype = kwargs["dtype"]
elif len(args) > 0:
for i, arg in enumerate(args):
if isinstance(arg, torch.dtype):
dtype = arg
break
if dtype is not None:
return self.module._mm_to(dtype=dtype)
return self.module
@classmethod
def attach(
cls,
module: torch.nn.Module,
device: torch.device,
offload_percent: float = 1.0,
ignore_modules: list[torch.nn.Module] = []
):
if hasattr(module, "_memory_manager"):
# already attached
return
module._memory_manager = cls(module, device)
# override the to method to handle memory management
module._mm_to = module.to
module.to = module._memory_manager.memory_managed_to
# add ignore modules to unmanaged list
for im in ignore_modules:
module._memory_manager.unmanaged_modules.append(im)
# count ignore modules as processed
modules_processed = [x for x in ignore_modules]
# attach to all modules
for name, sub_module in module.named_modules():
for child_name, child_module in sub_module.named_modules():
if (
child_module.__class__.__name__ in LINEAR_MODULES
and child_module not in modules_processed
):
skip = False
if offload_percent < 1.0:
# randomly skip some modules
if random.random() > offload_percent:
skip = True
if skip:
module._memory_manager.unmanaged_modules.append(child_module)
else:
# linear
LinearLayerMemoryManager.attach(
child_module, module._memory_manager
)
# attach to ARA as well
if hasattr(child_module, "ara_lora_ref"):
ara = child_module.ara_lora_ref()
if ara not in modules_processed:
MemoryManager.attach(
ara,
device,
)
modules_processed.append(child_module)
elif (
child_module.__class__.__name__ in CONV_MODULES
and child_module not in modules_processed
):
skip = False
if offload_percent < 1.0:
# randomly skip some modules
if random.random() > offload_percent:
skip = True
if skip:
module._memory_manager.unmanaged_modules.append(child_module)
else:
# conv
ConvLayerMemoryManager.attach(
child_module, module._memory_manager
)
# attach to ARA as well
if hasattr(child_module, "ara_lora_ref"):
ara = child_module.ara_lora_ref()
if ara not in modules_processed:
MemoryManager.attach(
ara,
device,
)
modules_processed.append(ara)
modules_processed.append(child_module)
elif child_module.__class__.__name__ in UNMANAGED_MODULES or any(
inc in child_module.__class__.__name__
for inc in UNMANAGED_MODULES_INCLUDES
):
# unmanaged
module._memory_manager.unmanaged_modules.append(child_module)
else:
continue
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