Spaces:
Runtime error
Runtime error
Upload model_server.py
Browse files- model_server.py +144 -0
model_server.py
ADDED
@@ -0,0 +1,144 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
A model worker with transformers libs executes the model.
|
3 |
+
|
4 |
+
Run BF16 inference with:
|
5 |
+
|
6 |
+
python model_server.py --host localhost --model-path THUDM/glm-4-voice-9b --port 10000 --dtype bfloat16 --device cuda:0
|
7 |
+
|
8 |
+
Run Int4 inference with:
|
9 |
+
|
10 |
+
python model_server.py --host localhost --model-path THUDM/glm-4-voice-9b --port 10000 --dtype int4 --device cuda:0
|
11 |
+
|
12 |
+
"""
|
13 |
+
import argparse
|
14 |
+
import json
|
15 |
+
|
16 |
+
from fastapi import FastAPI, Request
|
17 |
+
from fastapi.responses import StreamingResponse
|
18 |
+
from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
|
19 |
+
from transformers.generation.streamers import BaseStreamer
|
20 |
+
import torch
|
21 |
+
import uvicorn
|
22 |
+
|
23 |
+
from threading import Thread
|
24 |
+
from queue import Queue
|
25 |
+
|
26 |
+
|
27 |
+
class TokenStreamer(BaseStreamer):
|
28 |
+
def __init__(self, skip_prompt: bool = False, timeout=None):
|
29 |
+
self.skip_prompt = skip_prompt
|
30 |
+
|
31 |
+
# variables used in the streaming process
|
32 |
+
self.token_queue = Queue()
|
33 |
+
self.stop_signal = None
|
34 |
+
self.next_tokens_are_prompt = True
|
35 |
+
self.timeout = timeout
|
36 |
+
|
37 |
+
def put(self, value):
|
38 |
+
if len(value.shape) > 1 and value.shape[0] > 1:
|
39 |
+
raise ValueError("TextStreamer only supports batch size 1")
|
40 |
+
elif len(value.shape) > 1:
|
41 |
+
value = value[0]
|
42 |
+
|
43 |
+
if self.skip_prompt and self.next_tokens_are_prompt:
|
44 |
+
self.next_tokens_are_prompt = False
|
45 |
+
return
|
46 |
+
|
47 |
+
for token in value.tolist():
|
48 |
+
self.token_queue.put(token)
|
49 |
+
|
50 |
+
def end(self):
|
51 |
+
self.token_queue.put(self.stop_signal)
|
52 |
+
|
53 |
+
def __iter__(self):
|
54 |
+
return self
|
55 |
+
|
56 |
+
def __next__(self):
|
57 |
+
value = self.token_queue.get(timeout=self.timeout)
|
58 |
+
if value == self.stop_signal:
|
59 |
+
raise StopIteration()
|
60 |
+
else:
|
61 |
+
return value
|
62 |
+
|
63 |
+
|
64 |
+
class ModelWorker:
|
65 |
+
def __init__(self, model_path, dtype="bfloat16", device='cuda'):
|
66 |
+
self.device = device
|
67 |
+
self.bnb_config = BitsAndBytesConfig(
|
68 |
+
load_in_4bit=True,
|
69 |
+
bnb_4bit_use_double_quant=True,
|
70 |
+
bnb_4bit_quant_type="nf4",
|
71 |
+
bnb_4bit_compute_dtype=torch.bfloat16
|
72 |
+
) if dtype == "int4" else None
|
73 |
+
|
74 |
+
self.glm_model = AutoModel.from_pretrained(
|
75 |
+
model_path,
|
76 |
+
trust_remote_code=True,
|
77 |
+
quantization_config=self.bnb_config if self.bnb_config else None,
|
78 |
+
device_map={"": 0}
|
79 |
+
).eval()
|
80 |
+
self.glm_tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
81 |
+
|
82 |
+
@torch.inference_mode()
|
83 |
+
def generate_stream(self, params):
|
84 |
+
tokenizer, model = self.glm_tokenizer, self.glm_model
|
85 |
+
|
86 |
+
prompt = params["prompt"]
|
87 |
+
|
88 |
+
temperature = float(params.get("temperature", 1.0))
|
89 |
+
top_p = float(params.get("top_p", 1.0))
|
90 |
+
max_new_tokens = int(params.get("max_new_tokens", 256))
|
91 |
+
|
92 |
+
inputs = tokenizer([prompt], return_tensors="pt")
|
93 |
+
inputs = inputs.to(self.device)
|
94 |
+
streamer = TokenStreamer(skip_prompt=True)
|
95 |
+
thread = Thread(
|
96 |
+
target=model.generate,
|
97 |
+
kwargs=dict(
|
98 |
+
**inputs,
|
99 |
+
max_new_tokens=int(max_new_tokens),
|
100 |
+
temperature=float(temperature),
|
101 |
+
top_p=float(top_p),
|
102 |
+
streamer=streamer
|
103 |
+
)
|
104 |
+
)
|
105 |
+
thread.start()
|
106 |
+
for token_id in streamer:
|
107 |
+
yield (json.dumps({"token_id": token_id, "error_code": 0}) + "\n").encode()
|
108 |
+
|
109 |
+
def generate_stream_gate(self, params):
|
110 |
+
try:
|
111 |
+
for x in self.generate_stream(params):
|
112 |
+
yield x
|
113 |
+
except Exception as e:
|
114 |
+
print("Caught Unknown Error", e)
|
115 |
+
ret = {
|
116 |
+
"text": "Server Error",
|
117 |
+
"error_code": 1,
|
118 |
+
}
|
119 |
+
yield (json.dumps(ret) + "\n").encode()
|
120 |
+
|
121 |
+
|
122 |
+
app = FastAPI()
|
123 |
+
|
124 |
+
|
125 |
+
@app.post("/generate_stream")
|
126 |
+
async def generate_stream(request: Request):
|
127 |
+
params = await request.json()
|
128 |
+
|
129 |
+
generator = worker.generate_stream_gate(params)
|
130 |
+
return StreamingResponse(generator)
|
131 |
+
|
132 |
+
|
133 |
+
if __name__ == "__main__":
|
134 |
+
parser = argparse.ArgumentParser()
|
135 |
+
|
136 |
+
parser.add_argument("--host", type=str, default="localhost")
|
137 |
+
parser.add_argument("--dtype", type=str, default="bfloat16")
|
138 |
+
parser.add_argument("--device", type=str, default="cuda:0")
|
139 |
+
parser.add_argument("--port", type=int, default=10000)
|
140 |
+
parser.add_argument("--model-path", type=str, default="THUDM/glm-4-voice-9b")
|
141 |
+
args = parser.parse_args()
|
142 |
+
|
143 |
+
worker = ModelWorker(args.model_path, args.dtype, args.device)
|
144 |
+
uvicorn.run(app, host=args.host, port=args.port, log_level="info")
|