File size: 3,976 Bytes
1146a67 | 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 | from ..vram.initialization import skip_model_initialization
from ..vram.disk_map import DiskMap
from ..vram.layers import enable_vram_management
from .file import load_state_dict
import torch
def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None):
config = {} if config is None else config
# Why do we use `skip_model_initialization`?
# It skips the random initialization of model parameters,
# thereby speeding up model loading and avoiding excessive memory usage.
with skip_model_initialization():
model = model_class(**config)
# What is `module_map`?
# This is a module mapping table for VRAM management.
if module_map is not None:
devices = [vram_config["offload_device"], vram_config["onload_device"], vram_config["preparing_device"], vram_config["computation_device"]]
device = [d for d in devices if d != "disk"][0]
dtypes = [vram_config["offload_dtype"], vram_config["onload_dtype"], vram_config["preparing_dtype"], vram_config["computation_dtype"]]
dtype = [d for d in dtypes if d != "disk"][0]
if vram_config["offload_device"] != "disk":
state_dict = DiskMap(path, device, torch_dtype=dtype)
if state_dict_converter is not None:
state_dict = state_dict_converter(state_dict)
else:
state_dict = {i: state_dict[i] for i in state_dict}
model.load_state_dict(state_dict, assign=True)
model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=None, vram_limit=vram_limit)
else:
disk_map = DiskMap(path, device, state_dict_converter=state_dict_converter)
model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map, vram_limit=vram_limit)
else:
# Why do we use `DiskMap`?
# Sometimes a model file contains multiple models,
# and DiskMap can load only the parameters of a single model,
# avoiding the need to load all parameters in the file.
if use_disk_map:
state_dict = DiskMap(path, device, torch_dtype=torch_dtype)
else:
state_dict = load_state_dict(path, torch_dtype, device)
# Why do we use `state_dict_converter`?
# Some models are saved in complex formats,
# and we need to convert the state dict into the appropriate format.
if state_dict_converter is not None:
state_dict = state_dict_converter(state_dict)
else:
state_dict = {i: state_dict[i] for i in state_dict}
model.load_state_dict(state_dict, assign=True)
# Why do we call `to()`?
# Because some models override the behavior of `to()`,
# especially those from libraries like Transformers.
model = model.to(dtype=torch_dtype, device=device)
if hasattr(model, "eval"):
model = model.eval()
return model
def load_model_with_disk_offload(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, module_map=None):
if isinstance(path, str):
path = [path]
config = {} if config is None else config
with skip_model_initialization():
model = model_class(**config)
if hasattr(model, "eval"):
model = model.eval()
disk_map = DiskMap(path, device, state_dict_converter=state_dict_converter)
vram_config = {
"offload_dtype": "disk",
"offload_device": "disk",
"onload_dtype": "disk",
"onload_device": "disk",
"preparing_dtype": torch.float8_e4m3fn,
"preparing_device": device,
"computation_dtype": torch_dtype,
"computation_device": device,
}
enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map, vram_limit=80)
return model
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