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KoboldAI-Client
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Load model directly in fp16 if using GPU or breakmodel

This commit is contained in:
Gnome Ann 2021-11-26 10:55:52 -05:00
parent 95aff61781
commit a93a76eb01
1 changed files with 22 additions and 7 deletions
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29
aiserver.py
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@ -15,6 +15,7 @@ import json
import collections
import zipfile
import packaging
import contextlib
from typing import Any, Union, Dict, Set, List
import requests
@ -709,15 +710,26 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
print(f"\nWARNING: Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.", file=sys.stderr)
return {}
return {"low_cpu_mem_usage": True}
@contextlib.contextmanager
def maybe_use_float16(always_use=False):
if(always_use or (vars.hascuda and (vars.usegpu or vars.breakmodel))):
original_dtype = torch.get_default_dtype()
torch.set_default_dtype(torch.float16)
yield True
torch.set_default_dtype(original_dtype)
else:
yield False
# If custom GPT Neo model was chosen
if(vars.model == "NeoCustom"):
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
if("model_type" in js):
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
else:
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
with(maybe_use_float16()):
if("model_type" in js):
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
else:
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/")
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
@ -735,7 +747,8 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
elif(vars.model == "GPT2Custom"):
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
with(maybe_use_float16()):
model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
@ -750,12 +763,14 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
tokenizer = GPT2Tokenizer.from_pretrained(vars.model, cache_dir="cache/")
if(vars.hascuda):
if(vars.usegpu):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
with(maybe_use_float16()):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
model = model.half().to(0)
generator = model.generate
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
with(maybe_use_float16()):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
device_config(model)
else: