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import torch
def load_model(model, model_path):
"""
Load model from saved weights.
"""
# import ipdb
# ipdb.set_trace()
if '.pt' in model_path:
if hasattr(model, "module"):
model.module.load_state_dict(torch.load(model_path, map_location="cpu")['module'], strict=False)
else:
model.load_state_dict(torch.load(model_path, map_location="cpu")['module'], strict=False)
else:
if hasattr(model, "module"):
model.module.load_state_dict(torch.load(model_path, map_location="cpu"), strict=False)
else:
model.load_state_dict(torch.load(model_path, map_location="cpu"), strict=False)
return model
def _load_state_dict_into_model(model_to_load, model_path, start_prefix="", lora_pretrained_model_path=None):
# Convert old format to new format if needed from a PyTorch state_dict
# copy state_dict so _load_from_state_dict can modify it
state_dict = torch.load(model_path, map_location="cpu")
metadata = getattr(state_dict, "_metadata", None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
if lora_pretrained_model_path is not None:
lora_state_dict = torch.load(lora_pretrained_model_path, map_location="cpu")
lora_metadata = getattr(lora_state_dict, "_metadata", None)
lora_state_dict = lora_state_dict.copy()
if lora_metadata is not None:
lora_state_dict._metadata = lora_metadata
error_msgs = []
# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
# so we need to apply the function recursively.
def load(module, state_dict, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
# Parameters of module and children will start with prefix. We can exit early if there are none in this
# state_dict
if len([key for key in state_dict if key.startswith(prefix)]) > 0:
import deepspeed
# In sharded models, each shard has only part of the full state_dict, so only gather
# parameters that are in the current state_dict.
named_parameters = dict(module.named_parameters(prefix=prefix[:-1], recurse=False))
params_to_gather = [named_parameters[k] for k in state_dict.keys() if k in named_parameters]
if len(params_to_gather) > 0:
# because zero3 puts placeholders in model params, this context
# manager gathers (unpartitions) the params of the current layer, then loads from
# the state dict and then re-partitions them again
with deepspeed.zero.GatheredParameters(params_to_gather, modifier_rank=0):
if torch.distributed.get_rank() == 0:
module._load_from_state_dict(*args)
for name, child in module._modules.items():
if child is not None:
load(child, state_dict, prefix + name + ".")
load(model_to_load, state_dict, prefix=start_prefix)
if lora_pretrained_model_path is not None:
load(model_to_load, lora_state_dict, prefix=start_prefix)
del lora_state_dict
# Delete `state_dict` so it could be collected by GC earlier. Note that `state_dict` is a copy of the argument, so
# it's safe to delete it.
del state_dict
return model_to_load |