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9
README.md
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@ -60,7 +60,9 @@ pip3 install torch torchvision torchaudio --extra-index-url https://download.pyt
conda install pytorch torchvision torchaudio git -c pytorch conda install pytorch torchvision torchaudio git -c pytorch
``` ```
See also: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings). > **Note**
> 1. If you are on Windows, it may be easier to run the commands above in a WSL environment. The performance may also be better.
> 2. For a more detailed, user-contributed guide, see: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
## Installation option 2: one-click installers ## Installation option 2: one-click installers
@ -140,8 +142,9 @@ 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` | Load the model with 4-bit precision. Currently only works with LLaMA.| | `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. |
| `--gptq-bits GPTQ_BITS` | Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA. | | `--gptq-bits GPTQ_BITS` | Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. |
| `--gptq-model-type MODEL_TYPE` | Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
| `--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. |

38
modules/quantized_LLaMA.py → modules/GPTQ_loader.py
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@ -7,28 +7,40 @@ import torch
import modules.shared as shared import modules.shared as shared
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa"))) sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
from llama import load_quant import llama
import opt
# 4-bit LLaMA def load_quantized(model_name):
def load_quantized_LLaMA(model_name): if not shared.args.gptq_model_type:
if shared.args.load_in_4bit: # Try to determine model type from model name
bits = 4 model_type = model_name.split('-')[0].lower()
if model_type not in ('llama', 'opt'):
print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
"argument")
exit()
else: else:
bits = shared.args.gptq_bits model_type = shared.args.gptq_model_type.lower()
if model_type == 'llama':
load_quant = llama.load_quant
elif model_type == 'opt':
load_quant = opt.load_quant
else:
print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
exit()
path_to_model = Path(f'models/{model_name}') path_to_model = Path(f'models/{model_name}')
pt_model = ''
if path_to_model.name.lower().startswith('llama-7b'): if path_to_model.name.lower().startswith('llama-7b'):
pt_model = f'llama-7b-{bits}bit.pt' pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-13b'): elif path_to_model.name.lower().startswith('llama-13b'):
pt_model = f'llama-13b-{bits}bit.pt' pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-30b'): elif path_to_model.name.lower().startswith('llama-30b'):
pt_model = f'llama-30b-{bits}bit.pt' pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-65b'): elif path_to_model.name.lower().startswith('llama-65b'):
pt_model = f'llama-65b-{bits}bit.pt' pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt'
else: else:
pt_model = f'{model_name}-{bits}bit.pt' pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt'
# Try to find the .pt both in models/ and in the subfolder # Try to find the .pt both in models/ and in the subfolder
pt_path = None pt_path = None
@ -40,7 +52,7 @@ def load_quantized_LLaMA(model_name):
print(f"Could not find {pt_model}, exiting...") print(f"Could not find {pt_model}, exiting...")
exit() exit()
model = load_quant(str(path_to_model), str(pt_path), bits) model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
# Multiple GPUs or GPU+CPU # Multiple GPUs or GPU+CPU
if shared.args.gpu_memory: if shared.args.gpu_memory:

12
modules/models.py
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@ -1,6 +1,5 @@
import json import json
import os import os
import sys
import time import time
import zipfile import zipfile
from pathlib import Path from pathlib import Path
@ -35,6 +34,7 @@ if shared.args.deepspeed:
ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir) ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
def load_model(model_name): def load_model(model_name):
print(f"Loading {model_name}...") print(f"Loading {model_name}...")
t0 = time.time() t0 = time.time()
@ -42,7 +42,7 @@ 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.load_in_4bit, shared.args.gptq_bits > 0, 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.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 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:
@ -87,11 +87,11 @@ def load_model(model_name):
return model, tokenizer return model, tokenizer
# 4-bit LLaMA # Quantized model
elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit: elif shared.args.gptq_bits > 0:
from modules.quantized_LLaMA import load_quantized_LLaMA from modules.GPTQ_loader import load_quantized
model = load_quantized_LLaMA(model_name) model = load_quantized(model_name)
# Custom # Custom
else: else:

10
modules/shared.py
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@ -69,8 +69,9 @@ 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='Load the model with 4-bit precision. Currently only works with LLaMA.') 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='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA.') parser.add_argument('--gptq-bits', type=int, default=0, help='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-model-type', type=str, help='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
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.')
@ -95,3 +96,8 @@ parser.add_argument('--share', action='store_true', help='Create a public URL. T
parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.') parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
args = parser.parse_args() args = parser.parse_args()
# Provisional, this will be deleted later
if args.load_in_4bit:
print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n")
args.gptq_bits = 4

65
modules/text_generation.py
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@ -122,7 +122,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
input_ids = encode(question, max_new_tokens) input_ids = encode(question, max_new_tokens)
original_input_ids = input_ids original_input_ids = input_ids
output = input_ids[0] output = input_ids[0]
cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()" cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else [] eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
if eos_token is not None: if eos_token is not None:
eos_token_ids.append(int(encode(eos_token)[0][-1])) eos_token_ids.append(int(encode(eos_token)[0][-1]))
@ -132,45 +132,48 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
t = encode(stopping_string, 0, add_special_tokens=False) t = encode(stopping_string, 0, add_special_tokens=False)
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0]))) stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
generate_params = {}
if not shared.args.flexgen: if not shared.args.flexgen:
generate_params = [ generate_params.update({
f"max_new_tokens=max_new_tokens", "max_new_tokens": max_new_tokens,
f"eos_token_id={eos_token_ids}", "eos_token_id": eos_token_ids,
f"stopping_criteria=stopping_criteria_list", "stopping_criteria": stopping_criteria_list,
f"do_sample={do_sample}", "do_sample": do_sample,
f"temperature={temperature}", "temperature": temperature,
f"top_p={top_p}", "top_p": top_p,
f"typical_p={typical_p}", "typical_p": typical_p,
f"repetition_penalty={repetition_penalty}", "repetition_penalty": repetition_penalty,
f"top_k={top_k}", "top_k": top_k,
f"min_length={min_length if shared.args.no_stream else 0}", "min_length": min_length if shared.args.no_stream else 0,
f"no_repeat_ngram_size={no_repeat_ngram_size}", "no_repeat_ngram_size": no_repeat_ngram_size,
f"num_beams={num_beams}", "num_beams": num_beams,
f"penalty_alpha={penalty_alpha}", "penalty_alpha": penalty_alpha,
f"length_penalty={length_penalty}", "length_penalty": length_penalty,
f"early_stopping={early_stopping}", "early_stopping": early_stopping,
] })
else: else:
generate_params = [ generate_params.update({
f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}", "max_new_tokens": max_new_tokens if shared.args.no_stream else 8,
f"do_sample={do_sample}", "do_sample": do_sample,
f"temperature={temperature}", "temperature": temperature,
f"stop={eos_token_ids[-1]}", "stop": eos_token_ids[-1],
] })
if shared.args.deepspeed: if shared.args.deepspeed:
generate_params.append("synced_gpus=True") generate_params.update({"synced_gpus": True})
if shared.soft_prompt: if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.insert(0, "inputs_embeds=inputs_embeds") generate_params.update({"inputs_embeds": inputs_embeds})
generate_params.insert(0, "inputs=filler_input_ids") generate_params.update({"inputs": filler_input_ids})
else: else:
generate_params.insert(0, "inputs=input_ids") generate_params.update({"inputs": input_ids})
try: try:
# Generate the entire reply at once. # Generate the entire reply at once.
if shared.args.no_stream: if shared.args.no_stream:
with torch.no_grad(): with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0] output = shared.model.generate(**generate_params)[0]
if cuda:
output = output.cuda()
if shared.soft_prompt: if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@ -194,7 +197,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
return Iteratorize(generate_with_callback, kwargs, callback=None) return Iteratorize(generate_with_callback, kwargs, callback=None)
yield formatted_outputs(original_question, shared.model_name) yield formatted_outputs(original_question, shared.model_name)
with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator: with generate_with_streaming(**generate_params) as generator:
for output in generator: for output in generator:
if shared.soft_prompt: if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@ -214,7 +217,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
for i in range(max_new_tokens//8+1): for i in range(max_new_tokens//8+1):
clear_torch_cache() clear_torch_cache()
with torch.no_grad(): with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)})")[0] output = shared.model.generate(**generate_params)[0]
if shared.soft_prompt: if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output) reply = decode(output)

2
server.py
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@ -269,7 +269,7 @@ if shared.args.chat or shared.args.cai_chat:
function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper' function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream, api_name='textgen')) gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))