mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2025-02-17 04:00:44 +01:00
Disk cache support in CPU-only mode
This commit is contained in:
Gnome Ann
parent
af07d7a15f
commit
90fd8b1845
25
aiserver.py
25
aiserver.py
@ -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)
|
||||
|
||||
@ -4,7 +4,7 @@ https://github.com/arrmansa/Basic-UI-for-GPT-J-6B-with-low-vram/blob/main/GPT-J-
|
||||
The ORIGINAL version of the patch is released under the Apache License 2.0
|
||||
Copyright 2021 arrmansa
|
||||
Copyright 2021 finetuneanon
|
||||
Copyright 2018 The Hugging Face team
|
||||
Copyright 2018, 2022 The Hugging Face team
|
||||
|
||||
|
||||
Apache License
|
||||
@ -216,11 +216,13 @@ from torch import nn
|
||||
import torch.cuda.comm
|
||||
import copy
|
||||
import gc
|
||||
import os
|
||||
import sys
|
||||
import itertools
|
||||
import bisect
|
||||
import random
|
||||
from typing import Optional
|
||||
import utils
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions
|
||||
|
||||
@ -234,6 +236,98 @@ disk_blocks = 0
|
||||
primary_device = 0 if torch.cuda.device_count() > 0 else "cpu"
|
||||
|
||||
|
||||
if utils.HAS_ACCELERATE:
|
||||
from accelerate.hooks import attach_align_device_hook_on_blocks
|
||||
from accelerate.utils import OffloadedWeightsLoader, check_device_map, extract_submodules_state_dict, offload_state_dict
|
||||
from accelerate import dispatch_model
|
||||
|
||||
def dispatch_model_ex(
|
||||
model: nn.Module,
|
||||
device_map: Dict[str, Union[str, int, torch.device]],
|
||||
main_device: Optional[torch.device] = None,
|
||||
state_dict: Optional[Dict[str, torch.Tensor]] = None,
|
||||
offload_dir: Union[str, os.PathLike] = None,
|
||||
offload_buffers: bool = False,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
This is a modified version of
|
||||
https://github.com/huggingface/accelerate/blob/eeaba598f455fbd2c48661d7e816d3ff25ab050b/src/accelerate/big_modeling.py#L130
|
||||
that still works when the main device is the CPU.
|
||||
|
||||
Dispatches a model according to a given device map. Layers of the model might be spread across GPUs, offloaded on
|
||||
the CPU or even the disk.
|
||||
|
||||
Args:
|
||||
model (`torch.nn.Module`):
|
||||
The model to dispatch.
|
||||
device_map (`Dict[str, Union[str, int, torch.device]]`):
|
||||
A dictionary mapping module names in the models `state_dict` to the device they should go to. Note that
|
||||
`"disk"` is accepted even if it's not a proper value for `torch.device`.
|
||||
main_device (`str`, `int` or `torch.device`, *optional*):
|
||||
The main execution device. Will default to the first device in the `device_map` different from `"cpu"` or
|
||||
`"disk"`.
|
||||
state_dict (`Dict[str, torch.Tensor]`, *optional*):
|
||||
The state dict of the part of the model that will be kept on CPU.
|
||||
offload_dir (`str` or `os.PathLike`):
|
||||
The folder in which to offload the model weights (or where the model weights are already offloaded).
|
||||
offload_buffers (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to offload the buffers with the model parameters.
|
||||
preload_module_classes (`List[str]`, *optional*):
|
||||
A list of classes whose instances should load all their weights (even in the submodules) at the beginning
|
||||
of the forward. This should only be used for classes that have submodules which are registered but not
|
||||
called directly during the forward, for instance if a `dense` linear layer is registered, but at forward,
|
||||
`dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly.
|
||||
"""
|
||||
if main_device != "cpu":
|
||||
return dispatch_model(model, device_map, main_device, state_dict, offload_dir=offload_dir, offload_buffers=offload_buffers, **kwargs)
|
||||
|
||||
# Error early if the device map is incomplete.
|
||||
check_device_map(model, device_map)
|
||||
|
||||
offload_devices = ["cpu", "disk"] if main_device != "cpu" else ["disk"]
|
||||
|
||||
if main_device is None:
|
||||
main_device = [d for d in device_map.values() if d not in offload_devices][0]
|
||||
|
||||
cpu_modules = [name for name, device in device_map.items() if device == "cpu"] if main_device != "cpu" else []
|
||||
if state_dict is None and len(cpu_modules) > 0:
|
||||
state_dict = extract_submodules_state_dict(model.state_dict(), cpu_modules)
|
||||
|
||||
disk_modules = [name for name, device in device_map.items() if device == "disk"]
|
||||
if offload_dir is None and len(disk_modules) > 0:
|
||||
raise ValueError(
|
||||
"We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules "
|
||||
f"need to be offloaded: {', '.join(disk_modules)}."
|
||||
)
|
||||
if len(disk_modules) > 0 and (
|
||||
not os.path.isdir(offload_dir) or not os.path.isfile(os.path.join(offload_dir, "index.json"))
|
||||
):
|
||||
disk_state_dict = extract_submodules_state_dict(model.state_dict(), disk_modules)
|
||||
offload_state_dict(offload_dir, disk_state_dict)
|
||||
|
||||
execution_device = {
|
||||
name: main_device if device in offload_devices else device for name, device in device_map.items()
|
||||
}
|
||||
offload = {name: device in offload_devices for name, device in device_map.items()}
|
||||
save_folder = offload_dir if len(disk_modules) > 0 else None
|
||||
if state_dict is not None or save_folder is not None:
|
||||
weights_map = OffloadedWeightsLoader(state_dict=state_dict, save_folder=save_folder)
|
||||
else:
|
||||
weights_map = None
|
||||
|
||||
attach_align_device_hook_on_blocks(
|
||||
model,
|
||||
execution_device=execution_device,
|
||||
offload=offload,
|
||||
offload_buffers=offload_buffers,
|
||||
weights_map=weights_map,
|
||||
**kwargs,
|
||||
)
|
||||
model.hf_device_map = device_map
|
||||
return model
|
||||
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
||||
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
||||
"""
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user