Mirrors/text-generation-webui
1
0
Fork 0
mirror of https://github.com/oobabooga/text-generation-webui.git synced 2024-11-25 17:29:22 +01:00
Code Issues Packages Projects Releases Wiki Activity

Add support for the latest GPTQ models with group-size (#530)

Browse Source 
**Warning: old 4-bit weights will not work anymore!**

See here how to get up to date weights: https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model#step-2-get-the-pre-converted-weights
This commit is contained in:
oobabooga 2023-03-26 00:11:33 -03:00 committed by GitHub
parent 8c8e8b4450
commit 49c10c5570
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 64 additions and 43 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
README.md
View File

@ -176,10 +176,10 @@ Optionally, you can use the following command-line flags:
| `--cai-chat` | Launch the web UI in chat mode with a style similar to Character.AI's. If the file `img_bot.png` or `img_bot.jpg` exists in the same folder as server.py, this image will be used as the bot's profile picture. Similarly, `img_me.png` or `img_me.jpg` will be used as your profile picture. | | `--cai-chat` | Launch the web UI in chat mode with a style similar to Character.AI's. If the file `img_bot.png` or `img_bot.jpg` exists in the same folder as server.py, this image will be used as the bot's profile picture. Similarly, `img_me.png` or `img_me.jpg` will be used as your profile picture. |
| `--cpu` | Use the CPU to generate text.| | `--cpu` | Use the CPU to generate text.|
| `--load-in-8bit` | Load the model with 8-bit precision.| | `--load-in-8bit` | Load the model with 8-bit precision.|
| `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. | | `--wbits WBITS` | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
| `--gptq-bits GPTQ_BITS` | GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. | | `--model_type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported. |
| `--gptq-model-type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported. | | `--groupsize GROUPSIZE` | GPTQ: Group size. |
| `--gptq-pre-layer GPTQ_PRE_LAYER` | GPTQ: The number of layers to preload. | | `--pre_layer PRE_LAYER` | GPTQ: The number of layers to preload. |
| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. | | `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.| | `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. | | `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |

52
modules/GPTQ_loader.py
View File

@ -14,18 +14,21 @@ import opt
def load_quantized(model_name): def load_quantized(model_name):
if not shared.args.gptq_model_type: if not shared.args.model_type:
# Try to determine model type from model name # Try to determine model type from model name
model_type = model_name.split('-')[0].lower() if model_name.lower().startswith(('llama', 'alpaca')):
if model_type not in ('llama', 'opt'): model_type = 'llama'
print("Can't determine model type from model name. Please specify it manually using --gptq-model-type " elif model_name.lower().startswith(('opt', 'galactica')):
model_type = 'opt'
else:
print("Can't determine model type from model name. Please specify it manually using --model_type "
"argument") "argument")
exit() exit()
else: else:
model_type = shared.args.gptq_model_type.lower() model_type = shared.args.model_type.lower()
if model_type == 'llama': if model_type == 'llama':
if not shared.args.gptq_pre_layer: if not shared.args.pre_layer:
load_quant = llama.load_quant load_quant = llama.load_quant
else: else:
load_quant = llama_inference_offload.load_quant load_quant = llama_inference_offload.load_quant
@ -35,20 +38,29 @@ def load_quantized(model_name):
print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported") print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
exit() exit()
# Now we are going to try to locate the quantized model file.
path_to_model = Path(f'models/{model_name}') path_to_model = Path(f'models/{model_name}')
if path_to_model.name.lower().startswith('llama-7b'): found_pts = list(path_to_model.glob("*.pt"))
pt_model = f'llama-7b-{shared.args.gptq_bits}bit' found_safetensors = list(path_to_model.glob("*.safetensors"))
elif path_to_model.name.lower().startswith('llama-13b'): pt_path = None
pt_model = f'llama-13b-{shared.args.gptq_bits}bit'
elif path_to_model.name.lower().startswith('llama-30b'): if len(found_pts) == 1:
pt_model = f'llama-30b-{shared.args.gptq_bits}bit' pt_path = found_pts[0]
elif path_to_model.name.lower().startswith('llama-65b'): elif len(found_safetensors) == 1:
pt_model = f'llama-65b-{shared.args.gptq_bits}bit' pt_path = found_safetensors[0]
else: else:
pt_model = f'{model_name}-{shared.args.gptq_bits}bit' if path_to_model.name.lower().startswith('llama-7b'):
pt_model = f'llama-7b-{shared.args.wbits}bit'
elif path_to_model.name.lower().startswith('llama-13b'):
pt_model = f'llama-13b-{shared.args.wbits}bit'
elif path_to_model.name.lower().startswith('llama-30b'):
pt_model = f'llama-30b-{shared.args.wbits}bit'
elif path_to_model.name.lower().startswith('llama-65b'):
pt_model = f'llama-65b-{shared.args.wbits}bit'
else:
pt_model = f'{model_name}-{shared.args.wbits}bit'
# Try to find the .safetensors or .pt both in models/ and in the subfolder # Try to find the .safetensors or .pt both in models/ and in the subfolder
pt_path = None
for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]: for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
if path.exists(): if path.exists():
print(f"Found {path}") print(f"Found {path}")
@ -56,14 +68,14 @@ def load_quantized(model_name):
break break
if not pt_path: if not pt_path:
print(f"Could not find {pt_model}, exiting...") print("Could not find the quantized model in .pt or .safetensors format, exiting...")
exit() exit()
# qwopqwop200's offload # qwopqwop200's offload
if shared.args.gptq_pre_layer: if shared.args.pre_layer:
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits, shared.args.gptq_pre_layer) model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer)
else: else:
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits) model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize)
# accelerate offload (doesn't work properly) # accelerate offload (doesn't work properly)
if shared.args.gpu_memory: if shared.args.gpu_memory:

4
modules/models.py
View File

@ -44,7 +44,7 @@ def load_model(model_name):
shared.is_RWKV = model_name.lower().startswith('rwkv-') shared.is_RWKV = model_name.lower().startswith('rwkv-')
# Default settings # Default settings
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]): if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')): if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True) model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
else: else:
@ -95,7 +95,7 @@ def load_model(model_name):
return model, tokenizer return model, tokenizer
# Quantized model # Quantized model
elif shared.args.gptq_bits > 0: elif shared.args.wbits > 0:
from modules.GPTQ_loader import load_quantized from modules.GPTQ_loader import load_quantized
model = load_quantized(model_name) model = load_quantized(model_name)

24
modules/shared.py
View File

@ -52,7 +52,8 @@ settings = {
'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:', 'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
'^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n', '^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n',
'(rosey|chip|joi)_.*_instruct.*': 'User: \n', '(rosey|chip|joi)_.*_instruct.*': 'User: \n',
'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>' 'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>',
'alpaca-*': "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction:\nWrite a poem about the transformers Python library. \nMention the word \"large language models\" in that poem.\n### Response:\n",
}, },
'lora_prompts': { 'lora_prompts': {
'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:', 'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
@ -78,10 +79,15 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch
parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.') parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.') parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.') parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
parser.add_argument('--gptq-bits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.') parser.add_argument('--gptq-bits', type=int, default=0, help='DEPRECATED: use --wbits instead.')
parser.add_argument('--gptq-model-type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported.') parser.add_argument('--gptq-model-type', type=str, help='DEPRECATED: use --model_type instead.')
parser.add_argument('--gptq-pre-layer', type=int, default=0, help='GPTQ: The number of layers to preload.') parser.add_argument('--gptq-pre-layer', type=int, default=0, help='DEPRECATED: use --pre_layer instead.')
parser.add_argument('--wbits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
parser.add_argument('--model_type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported.')
parser.add_argument('--groupsize', type=int, default=-1, help='GPTQ: Group size.')
parser.add_argument('--pre_layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.') parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.') parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
@ -109,6 +115,8 @@ parser.add_argument('--verbose', action='store_true', help='Print the prompts to
args = parser.parse_args() args = parser.parse_args()
# Provisional, this will be deleted later # Provisional, this will be deleted later
if args.load_in_4bit: deprecated_dict = {'gptq_bits': ['wbits', 0], 'gptq_model_type': ['model_type', None], 'gptq_pre_layer': ['prelayer', 0]}
print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n") for k in deprecated_dict:
args.gptq_bits = 4 if eval(f"args.{k}") != deprecated_dict[k][1]:
print(f"Warning: --{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
exec(f"args.{deprecated_dict[k][0]} = args.{k}")

3
server.py
View File

@ -237,8 +237,9 @@ if shared.args.lora:
# Default UI settings # Default UI settings
default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')] default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
if shared.lora_name != "None":
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')] default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
if default_text == '': else:
default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')] default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
title ='Text generation web UI' title ='Text generation web UI'
description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n' description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n'