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Disk cache support in CPU-only mode

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Gnome Ann
2022-06-20 16:06:09 -04:00
parent af07d7a15f
commit 90fd8b1845
2 changed files with 111 additions and 12 deletions
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25
aiserver.py
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@ -516,10 +516,11 @@ def device_config(config):
import breakmodel
n_layers = utils.num_layers(config)
if(args.breakmodel_gpulayers is not None or (utils.HAS_ACCELERATE and args.breakmodel_disklayers is not None)):
if(args.breakmodel_gpulayers is None):
args.breakmodel_gpulayers = ",".join(["0"] * torch.cuda.device_count())
try:
breakmodel.gpu_blocks = list(map(int, args.breakmodel_gpulayers.split(',')))
if(not args.breakmodel_gpulayers):
breakmodel.gpu_blocks = []
else:
breakmodel.gpu_blocks = list(map(int, args.breakmodel_gpulayers.split(',')))
assert len(breakmodel.gpu_blocks) <= torch.cuda.device_count()
s = n_layers
for i in range(len(breakmodel.gpu_blocks)):
@ -622,7 +623,7 @@ def device_config(config):
def move_model_to_devices(model):
global generator
if(not vars.breakmodel):
if(not utils.HAS_ACCELERATE and not vars.breakmodel):
if(vars.usegpu):
model = model.half().to(vars.gpu_device)
else:
@ -630,11 +631,8 @@ def move_model_to_devices(model):
generator = model.generate
return
model.half()
gc.collect()
if(utils.HAS_ACCELERATE):
import accelerate
import breakmodel
disk_blocks = breakmodel.disk_blocks
gpu_blocks = breakmodel.gpu_blocks
ram_blocks = len(vars.layers_module_names) - sum(gpu_blocks)
@ -646,11 +644,14 @@ def move_model_to_devices(model):
device_map[name] = device
for name in utils.get_missing_module_names(model, list(device_map.keys())):
device_map[name] = breakmodel.primary_device
accelerate.dispatch_model(model, device_map, main_device=breakmodel.primary_device, offload_buffers=True, offload_dir="accelerate-disk-cache")
breakmodel.dispatch_model_ex(model, device_map, main_device=breakmodel.primary_device, offload_buffers=True, offload_dir="accelerate-disk-cache")
gc.collect()
generator = model.generate
return
model.half()
gc.collect()
if(hasattr(model, "transformer")):
model.transformer.wte.to(breakmodel.primary_device)
model.transformer.ln_f.to(breakmodel.primary_device)
@ -1874,7 +1875,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
# If we're using torch_lazy_loader, we need to get breakmodel config
# early so that it knows where to load the individual model tensors
if(vars.lazy_load and vars.hascuda and vars.breakmodel):
if(utils.HAS_ACCELERATE or vars.lazy_load and vars.hascuda and vars.breakmodel):
device_config(model_config)
# Download model from Huggingface if it does not exist, otherwise load locally
@ -2003,6 +2004,10 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
if(not vars.lazy_load):
device_config(model.config)
move_model_to_devices(model)
elif(utils.HAS_ACCELERATE):
move_model_to_devices(model)
vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate
else:
model = model.to('cpu').float()
vars.modeldim = get_hidden_size_from_model(model)