rixty
/
KoboldAI-Client
mirror of https://github.com/KoboldAI/KoboldAI-Client.git
1
1
Fork 0
Code Issues Projects Releases Wiki Activity

more init messages

This commit is contained in:
Divided by Zer0 2022-09-12 01:00:03 +02:00
parent 4817a27552
commit 48f6b5a939
2 changed files with 15 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
aiserver.py
View File

@ -805,7 +805,7 @@ def device_config(config):
breakmodel.gpu_blocks = [n_layers - max(0, min(n_layers, args.breakmodel_layers))]
n_layers -= sum(breakmodel.gpu_blocks)
elif(args.model is not None):
print("Breakmodel not specified, assuming GPU 0")
logger.info("Breakmodel not specified, assuming GPU 0")
breakmodel.gpu_blocks = [n_layers]
n_layers = 0
else:
@ -864,7 +864,7 @@ def device_config(config):
else:
print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}")
print(colors.PURPLE + "\nFinal device configuration:")
logger.init_ok("Final device configuration:", status="Info")
device_list(n_layers)
# If all layers are on the same device, use the old GPU generation mode
@ -2193,18 +2193,18 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
vars.hascuda = torch.cuda.is_available()
vars.bmsupported = (utils.HAS_ACCELERATE or vars.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not vars.nobreakmodel
if(args.breakmodel is not None and args.breakmodel):
print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).", file=sys.stderr)
logger.warning("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).")
if(args.breakmodel_layers is not None):
print("WARNING: --breakmodel_layers is deprecated. Use --breakmodel_gpulayers instead (see --help for details).", file=sys.stderr)
logger.warning("WARNING: --breakmodel_layers is deprecated. Use --breakmodel_gpulayers instead (see --help for details).")
if(args.model and vars.bmsupported and not args.breakmodel_gpulayers and not args.breakmodel_layers and (not utils.HAS_ACCELERATE or not args.breakmodel_disklayers)):
print("WARNING: Model launched without the --breakmodel_gpulayers argument, defaulting to GPU only mode.", file=sys.stderr)
logger.warning("WARNING: Model launched without the --breakmodel_gpulayers argument, defaulting to GPU only mode.")
vars.bmsupported = False
if(not vars.bmsupported and (args.breakmodel_gpulayers is not None or args.breakmodel_layers is not None or args.breakmodel_disklayers is not None)):
print("WARNING: This model does not support hybrid generation. --breakmodel_gpulayers will be ignored.", file=sys.stderr)
logger.warning("WARNING: This model does not support hybrid generation. --breakmodel_gpulayers will be ignored.")
if(vars.hascuda):
logger.init_ok("GPU support", status="<yellow>Found</yellow>")
logger.init_ok("GPU support", status="Found")
else:
logger.init_warn("GPU support", status="<yellow>Not Found</>")
logger.init_warn("GPU support", status="Not Found")
if args.cpu:
vars.usegpu = False
@ -2241,7 +2241,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
# Start transformers and create pipeline
if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
if(not vars.noai):
print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
logger.init("Transformers", status='Starting')
for m in ("GPTJModel", "XGLMModel"):
try:
globals()[m] = getattr(__import__("transformers"), m)
@ -2306,7 +2306,6 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs))
else:
num_tensors = len(device_map)
print(flush=True)
utils.bar = tqdm(total=num_tensors, desc="Loading model tensors", file=Send_to_socketio())
with zipfile.ZipFile(f, "r") as z:
@ -2377,7 +2376,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
def maybe_low_cpu_mem_usage() -> Dict[str, Any]:
if(packaging.version.parse(transformers_version) < packaging.version.parse("4.11.0")):
print(f"\nWARNING: Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.", file=sys.stderr)
logger.warning(f"Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.")
return {}
return {"low_cpu_mem_usage": True}
@ -2434,7 +2433,6 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
if os.path.isdir(vars.model.replace('/', '_')):
import shutil
shutil.move(vars.model.replace('/', '_'), "models/{}".format(vars.model.replace('/', '_')))
print("\n", flush=True)
if(vars.lazy_load): # If we're using lazy loader, we need to figure out what the model's hidden layers are called
with torch_lazy_loader.use_lazy_torch_load(dematerialized_modules=True, use_accelerate_init_empty_weights=True):
try:
@ -2556,7 +2554,6 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
vars.modeldim = get_hidden_size_from_model(model)
if(not vars.lazy_load):
print(2)
device_config(model.config)
move_model_to_devices(model)
elif(utils.HAS_ACCELERATE and __import__("breakmodel").disk_blocks > 0):
@ -2583,7 +2580,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
#for key in vars.badwords:
# vars.badwordsids.append([vocab[key]])
print("{0}OK! {1} pipeline created!{2}".format(colors.GREEN, vars.model, colors.END))
logger.info(f"Pipeline created: {vars.model}")
else:
from transformers import GPT2TokenizerFast

8
logger.py
View File

@ -33,10 +33,10 @@ msgfmt = "<level>{level: <10}</level> | <level>{message}</level>"
logger.level("GENERATION", no=24, color="<cyan>")
logger.level("PROMPT", no=23, color="<yellow>")
logger.level("INIT", no=21, color="<white>")
logger.level("INIT_OK", no=21, color="<green>")
logger.level("INIT_WARN", no=21, color="<yellow>")
logger.level("INIT_ERR", no=21, color="<red>")
logger.level("INIT", no=31, color="<white>")
logger.level("INIT_OK", no=31, color="<green>")
logger.level("INIT_WARN", no=31, color="<yellow>")
logger.level("INIT_ERR", no=31, color="<red>")
logger.level("MESSAGE", no=20, color="<green>")
logger.__class__.generation = partialmethod(logger.__class__.log, "GENERATION")