mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-31 22:50:15 +01:00
203 lines
7.5 KiB
Python
203 lines
7.5 KiB
Python
import inspect
|
|
import logging
|
|
import re
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import accelerate
|
|
import torch
|
|
import transformers
|
|
from transformers import AutoConfig, AutoModelForCausalLM
|
|
|
|
import modules.shared as shared
|
|
|
|
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
|
|
|
|
try:
|
|
import llama_inference_offload
|
|
except ImportError:
|
|
logging.error('Failed to load GPTQ-for-LLaMa')
|
|
logging.error('See https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md')
|
|
sys.exit(-1)
|
|
|
|
try:
|
|
from modelutils import find_layers
|
|
except ImportError:
|
|
from utils import find_layers
|
|
|
|
try:
|
|
from quant import make_quant
|
|
is_triton = False
|
|
except ImportError:
|
|
import quant
|
|
is_triton = True
|
|
|
|
|
|
# This function is a replacement for the load_quant function in the
|
|
# GPTQ-for_LLaMa repository. It supports more models and branches.
|
|
def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True):
|
|
exclude_layers = exclude_layers or ['lm_head']
|
|
|
|
def noop(*args, **kwargs):
|
|
pass
|
|
|
|
config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code)
|
|
torch.nn.init.kaiming_uniform_ = noop
|
|
torch.nn.init.uniform_ = noop
|
|
torch.nn.init.normal_ = noop
|
|
|
|
torch.set_default_dtype(torch.half)
|
|
transformers.modeling_utils._init_weights = False
|
|
torch.set_default_dtype(torch.half)
|
|
model = AutoModelForCausalLM.from_config(config, trust_remote_code=shared.args.trust_remote_code)
|
|
torch.set_default_dtype(torch.float)
|
|
if eval:
|
|
model = model.eval()
|
|
|
|
layers = find_layers(model)
|
|
for name in exclude_layers:
|
|
if name in layers:
|
|
del layers[name]
|
|
|
|
if not is_triton:
|
|
gptq_args = inspect.getfullargspec(make_quant).args
|
|
|
|
make_quant_kwargs = {
|
|
'module': model,
|
|
'names': layers,
|
|
'bits': wbits,
|
|
}
|
|
if 'groupsize' in gptq_args:
|
|
make_quant_kwargs['groupsize'] = groupsize
|
|
if 'faster' in gptq_args:
|
|
make_quant_kwargs['faster'] = faster_kernel
|
|
if 'kernel_switch_threshold' in gptq_args:
|
|
make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold
|
|
|
|
make_quant(**make_quant_kwargs)
|
|
else:
|
|
quant.make_quant_linear(model, layers, wbits, groupsize)
|
|
|
|
del layers
|
|
if checkpoint.endswith('.safetensors'):
|
|
from safetensors.torch import load_file as safe_load
|
|
model.load_state_dict(safe_load(checkpoint), strict=False)
|
|
else:
|
|
model.load_state_dict(torch.load(checkpoint), strict=False)
|
|
|
|
if is_triton:
|
|
if shared.args.quant_attn:
|
|
quant.make_quant_attn(model)
|
|
|
|
if eval and shared.args.fused_mlp:
|
|
quant.make_fused_mlp(model)
|
|
|
|
if shared.args.warmup_autotune:
|
|
quant.autotune_warmup_linear(model, transpose=not eval)
|
|
if eval and shared.args.fused_mlp:
|
|
quant.autotune_warmup_fused(model)
|
|
|
|
model.seqlen = 2048
|
|
return model
|
|
|
|
|
|
# Used to locate the .pt/.safetensors quantized file
|
|
def find_quantized_model_file(model_name):
|
|
if shared.args.checkpoint:
|
|
return Path(shared.args.checkpoint)
|
|
|
|
path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
|
|
pt_path = None
|
|
priority_name_list = [
|
|
Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}')
|
|
for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else [''])
|
|
for ext in ['.safetensors', '.pt']
|
|
for hyphen in ['-', f'/{model_name}-', '/']
|
|
]
|
|
|
|
for path in priority_name_list:
|
|
if path.exists():
|
|
pt_path = path
|
|
break
|
|
|
|
# If the model hasn't been found with a well-behaved name, pick the last .pt
|
|
# or the last .safetensors found in its folder as a last resort
|
|
if not pt_path:
|
|
found_pts = list(path_to_model.glob("*.pt"))
|
|
found_safetensors = list(path_to_model.glob("*.safetensors"))
|
|
pt_path = None
|
|
|
|
if len(found_pts) > 0:
|
|
if len(found_pts) > 1:
|
|
logging.warning('More than one .pt model has been found. The last one will be selected. It could be wrong.')
|
|
|
|
pt_path = found_pts[-1]
|
|
elif len(found_safetensors) > 0:
|
|
if len(found_pts) > 1:
|
|
logging.warning('More than one .safetensors model has been found. The last one will be selected. It could be wrong.')
|
|
|
|
pt_path = found_safetensors[-1]
|
|
|
|
return pt_path
|
|
|
|
|
|
# The function that loads the model in modules/models.py
|
|
def load_quantized(model_name):
|
|
if shared.args.model_type is None:
|
|
logging.error("The model could not be loaded because its type could not be inferred from its name.")
|
|
logging.error("Please specify the type manually using the --model_type argument.")
|
|
return
|
|
|
|
# Select the appropriate load_quant function
|
|
model_type = shared.args.model_type.lower()
|
|
if shared.args.pre_layer and model_type == 'llama':
|
|
load_quant = llama_inference_offload.load_quant
|
|
elif model_type in ('llama', 'opt', 'gptj'):
|
|
if shared.args.pre_layer:
|
|
logging.warning("Ignoring --pre_layer because it only works for llama model type.")
|
|
|
|
load_quant = _load_quant
|
|
else:
|
|
logging.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
|
|
exit()
|
|
|
|
# Find the quantized model weights file (.pt/.safetensors)
|
|
path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
|
|
pt_path = find_quantized_model_file(model_name)
|
|
if not pt_path:
|
|
logging.error("Could not find the quantized model in .pt or .safetensors format, exiting...")
|
|
exit()
|
|
else:
|
|
logging.info(f"Found the following quantized model: {pt_path}")
|
|
|
|
# qwopqwop200's offload
|
|
if model_type == 'llama' and shared.args.pre_layer:
|
|
model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer)
|
|
else:
|
|
threshold = False if model_type == 'gptj' else 128
|
|
model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold)
|
|
|
|
# accelerate offload (doesn't work properly)
|
|
if shared.args.gpu_memory or torch.cuda.device_count() > 1:
|
|
if shared.args.gpu_memory:
|
|
memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
|
|
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
|
|
max_memory = {}
|
|
for i in range(len(memory_map)):
|
|
max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
|
|
|
|
max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
|
|
else:
|
|
max_memory = accelerate.utils.get_balanced_memory(model)
|
|
|
|
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
|
|
logging.info("Using the following device map for the quantized model:", device_map)
|
|
# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
|
|
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)
|
|
|
|
# No offload
|
|
elif not shared.args.cpu:
|
|
model = model.to(torch.device('cuda:0'))
|
|
|
|
return model
|