mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
Merge remote-tracking branch 'refs/remotes/origin/dev' into dev
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oobabooga
commit
e0b43102e6
@ -7,7 +7,7 @@ HOST = 'localhost:5000'
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URI = f'http://{HOST}/api/v1/chat'
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# For reverse-proxied streaming, the remote will likely host with ssl - https://
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# URI = 'https://your-uri-here.trycloudflare.com/api/v1/generate'
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# URI = 'https://your-uri-here.trycloudflare.com/api/v1/chat'
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def run(user_input, history):
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176
api-examples/api-example-model.py
Executable file
176
api-examples/api-example-model.py
Executable file
@ -0,0 +1,176 @@
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#!/usr/bin/env python3
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import requests
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HOST = '0.0.0.0:5000'
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def generate(prompt, tokens = 200):
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request = { 'prompt': prompt, 'max_new_tokens': tokens }
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response = requests.post(f'http://{HOST}/api/v1/generate', json=request)
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if response.status_code == 200:
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return response.json()['results'][0]['text']
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def model_api(request):
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response = requests.post(f'http://{HOST}/api/v1/model', json=request)
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return response.json()
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# print some common settings
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def print_basic_model_info(response):
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basic_settings = ['truncation_length', 'instruction_template']
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print("Model: ", response['result']['model_name'])
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print("Lora(s): ", response['result']['lora_names'])
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for setting in basic_settings:
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print(setting, "=", response['result']['shared.settings'][setting])
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# model info
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def model_info():
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response = model_api({'action': 'info'})
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print_basic_model_info(response)
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# simple loader
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def model_load(model_name):
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return model_api({'action': 'load', 'model_name': model_name})
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# complex loader
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def complex_model_load(model):
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def guess_groupsize(model_name):
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if '1024g' in model_name:
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return 1024
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elif '128g' in model_name:
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return 128
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elif '32g' in model_name:
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return 32
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else:
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return -1
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req = {
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'action': 'load',
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'model_name': model,
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'args': {
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'gptq_for_llama': False, # Use AutoGPTQ by default, set to True for gptq-for-llama
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'bf16': False,
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'load_in_8bit': False,
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'groupsize': 0,
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'wbits': 0,
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# llama.cpp
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'threads': 0,
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'n_batch': 512,
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'no_mmap': False,
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'mlock': False,
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'cache_capacity': None,
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'n_gpu_layers': 0,
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'n_ctx': 2048,
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# RWKV
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'rwkv_strategy': None,
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'rwkv_cuda_on': False,
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# b&b 4-bit
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#'load_in_4bit': False,
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#'compute_dtype': 'float16',
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#'quant_type': 'nf4',
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#'use_double_quant': False,
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#"cpu": false,
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#"auto_devices": false,
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#"gpu_memory": null,
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#"cpu_memory": null,
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#"disk": false,
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#"disk_cache_dir": "cache",
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},
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}
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model = model.lower()
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if '4bit' in model or 'gptq' in model or 'int4' in model:
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req['args']['wbits'] = 4
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req['args']['groupsize'] = guess_groupsize(model)
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elif '3bit' in model:
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req['args']['wbits'] = 3
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req['args']['groupsize'] = guess_groupsize(model)
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else:
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req['args']['gptq_for_llama'] = False
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if '8bit' in model:
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req['args']['load_in_8bit'] = True
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elif '-hf' in model or 'fp16' in model:
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if '7b' in model:
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req['args']['bf16'] = True # for 24GB
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elif '13b' in model:
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req['args']['load_in_8bit'] = True # for 24GB
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elif 'ggml' in model:
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#req['args']['threads'] = 16
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if '7b' in model:
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req['args']['n_gpu_layers'] = 100
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elif '13b' in model:
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req['args']['n_gpu_layers'] = 100
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elif '30b' in model or '33b' in model:
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req['args']['n_gpu_layers'] = 59 # 24GB
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elif '65b' in model:
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req['args']['n_gpu_layers'] = 42 # 24GB
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elif 'rwkv' in model:
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req['args']['rwkv_cuda_on'] = True
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if '14b' in model:
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req['args']['rwkv_strategy'] = 'cuda f16i8' # 24GB
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else:
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req['args']['rwkv_strategy'] = 'cuda f16' # 24GB
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return model_api(req)
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if __name__ == '__main__':
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for model in model_api({'action': 'list'})['result']:
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try:
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resp = complex_model_load(model)
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if 'error' in resp:
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print (f"❌ {model} FAIL Error: {resp['error']['message']}")
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continue
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else:
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print_basic_model_info(resp)
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ans = generate("0,1,1,2,3,5,8,13,", tokens=2)
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if '21' in ans:
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print (f"✅ {model} PASS ({ans})")
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else:
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print (f"❌ {model} FAIL ({ans})")
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except Exception as e:
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print (f"❌ {model} FAIL Exception: {repr(e)}")
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# 0,1,1,2,3,5,8,13, is the fibonacci sequence, the next number is 21.
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# Some results below.
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""" $ ./model-api-example.py
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Model: 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda
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Lora(s): []
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truncation_length = 2048
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instruction_template = Alpaca
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✅ 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda PASS (21)
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Model: 4bit_WizardLM-13B-Uncensored-4bit-128g
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Lora(s): []
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truncation_length = 2048
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instruction_template = WizardLM
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✅ 4bit_WizardLM-13B-Uncensored-4bit-128g PASS (21)
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Model: Aeala_VicUnlocked-alpaca-30b-4bit
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Lora(s): []
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truncation_length = 2048
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instruction_template = Alpaca
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✅ Aeala_VicUnlocked-alpaca-30b-4bit PASS (21)
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Model: alpaca-30b-4bit
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Lora(s): []
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truncation_length = 2048
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instruction_template = Alpaca
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✅ alpaca-30b-4bit PASS (21)
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"""
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@ -30,7 +30,15 @@ pip install protobuf==3.20.1
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2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link:
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### [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
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### Convert LLaMA to HuggingFace format
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If you have `transformers` installed in place
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```
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python -m transformers.models.llama.convert_llama_weights_to_hf --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
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```
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Otherwise download script [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
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```
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python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
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@ -6,7 +6,19 @@ from extensions.api.util import build_parameters, try_start_cloudflared
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from modules import shared
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from modules.chat import generate_chat_reply
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from modules.text_generation import encode, generate_reply, stop_everything_event
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from modules.models import load_model, unload_model
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from modules.LoRA import add_lora_to_model
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from modules.utils import get_available_models
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from server import get_model_specific_settings, update_model_parameters
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def get_model_info():
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return {
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'model_name': shared.model_name,
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'lora_names': shared.lora_names,
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# dump
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'shared.settings': shared.settings,
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'shared.args': vars(shared.args),
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}
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class Handler(BaseHTTPRequestHandler):
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def do_GET(self):
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@ -91,6 +103,67 @@ class Handler(BaseHTTPRequestHandler):
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self.wfile.write(response.encode('utf-8'))
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elif self.path == '/api/v1/model':
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self.send_response(200)
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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# by default return the same as the GET interface
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result = shared.model_name
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# Actions: info, load, list, unload
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action = body.get('action', '')
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if action == 'load':
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model_name = body['model_name']
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args = body.get('args', {})
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print('args', args)
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for k in args:
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setattr(shared.args, k, args[k])
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shared.model_name = model_name
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unload_model()
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model_settings = get_model_specific_settings(shared.model_name)
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shared.settings.update(model_settings)
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update_model_parameters(model_settings, initial=True)
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if shared.settings['mode'] != 'instruct':
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shared.settings['instruction_template'] = None
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try:
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shared.model, shared.tokenizer = load_model(shared.model_name)
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if shared.args.lora:
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add_lora_to_model(shared.args.lora) # list
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except Exception as e:
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response = json.dumps({'error': { 'message': repr(e) } })
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self.wfile.write(response.encode('utf-8'))
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raise e
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shared.args.model = shared.model_name
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result = get_model_info()
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elif action == 'unload':
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unload_model()
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shared.model_name = None
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shared.args.model = None
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result = get_model_info()
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elif action == 'list':
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result = get_available_models()
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elif action == 'info':
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result = get_model_info()
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response = json.dumps({
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'result': result,
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})
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self.wfile.write(response.encode('utf-8'))
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elif self.path == '/api/v1/token-count':
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self.send_response(200)
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self.send_header('Content-Type', 'application/json')
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