File size: 8,682 Bytes
fedd8d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 | # MedGemma vLLM 模型运行器
# 实现基于 vLLM 的推理引擎
import logging
from typing import Any, Dict, List, Optional, Set, Mapping
import numpy as np
logger = logging.getLogger(__name__)
class VLLMModelRunner:
"""
基于 vLLM 的 MedGemma 模型运行器
兼容 MedGemma serving_framework 的 ModelRunner 接口
"""
def __init__(
self,
model_path: str,
tokenizer_path: Optional[str] = None,
gpu_memory_utilization: float = 0.9,
max_model_len: Optional[int] = None,
tensor_parallel_size: int = 1,
trust_remote_code: bool = True,
):
"""
初始化 vLLM 模型运行器
Args:
model_path: 模型权重路径
tokenizer_path: Tokenizer 路径（默认与 model_path 相同）
gpu_memory_utilization: GPU 内存使用率
max_model_len: 最大序列长度
tensor_parallel_size: Tensor 并行大小
trust_remote_code: 是否信任远程代码
"""
self.model_path = model_path
self.tokenizer_path = tokenizer_path or model_path
try:
from vllm import LLM
self.vllm_available = True
except ImportError:
logger.warning("vLLM not available. Install with: pip install vllm")
self.vllm_available = False
self.llm = None
return
logger.info(f"Initializing vLLM with model: {model_path}")
self.llm = LLM(
model=self.model_path,
tokenizer=self.tokenizer_path,
gpu_memory_utilization=gpu_memory_utilization,
max_model_len=max_model_len,
tensor_parallel_size=tensor_parallel_size,
trust_remote_code=trust_remote_code,
)
logger.info("vLLM model loaded successfully")
def run_model_multiple_output(
self,
model_input: Mapping[str, np.ndarray] | np.ndarray,
model_name: str = "default",
model_version: Optional[int] = None,
model_output_keys: Optional[Set[str]] = None,
parameters: Optional[Mapping[str, Any]] = None,
) -> Mapping[str, np.ndarray]:
"""
运行推理（兼容 MedGemma ModelRunner 接口）
Args:
model_input: 输入数据（字典或 numpy 数组）
model_name: 模型名称
model_version: 模型版本
model_output_keys: 输出键集合
parameters: 推理参数
Returns:
输出字典
"""
if not self.vllm_available or self.llm is None:
raise RuntimeError("vLLM is not available")
from vllm import SamplingParams
parameters = parameters or {}
model_output_keys = model_output_keys or {"text_output"}
# 提取 prompt
if isinstance(model_input, dict):
prompt = model_input.get("prompt", "")
if isinstance(prompt, np.ndarray):
prompt = prompt.tobytes().decode('utf-8')
else:
# 如果是 numpy 数组，解码为字符串
prompt = model_input.tobytes().decode('utf-8')
# 创建采样参数
sampling_params = SamplingParams(
max_tokens=parameters.get("max_tokens", 500),
temperature=parameters.get("temperature", 0.7),
top_p=parameters.get("top_p", 0.9),
top_k=parameters.get("top_k", -1),
n=parameters.get("n", 1),
)
# 运行推理
outputs = self.llm.generate([prompt], sampling_params)
# 格式化输出
result = {}
if "text_output" in model_output_keys:
result["text_output"] = np.array([
output.text.encode('utf-8') for output in outputs[0].outputs
])
if "num_input_tokens" in model_output_keys:
result["num_input_tokens"] = np.array([len(outputs[0].prompt_token_ids)])
if "num_output_tokens" in model_output_keys:
result["num_output_tokens"] = np.array([
len(output.token_ids) for output in outputs[0].outputs
])
return result
def generate(
self,
prompts: List[str],
max_tokens: int = 500,
temperature: float = 0.7,
top_p: float = 0.9,
top_k: int = -1,
n: int = 1,
) -> List[Dict[str, Any]]:
"""
简化的生成接口
Args:
prompts: 输入提示列表
max_tokens: 最大生成 token 数
temperature: 采样温度
top_p: Nucleus 采样参数
top_k: Top-K 采样参数
n: 生成数量
Returns:
生成结果列表
"""
if not self.vllm_available or self.llm is None:
raise RuntimeError("vLLM is not available")
from vllm import SamplingParams
sampling_params = SamplingParams(
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
n=n,
)
outputs = self.llm.generate(prompts, sampling_params)
results = []
for output in outputs:
result = {
"prompt": output.prompt,
"outputs": [
{
"text": o.text,
"token_ids": o.token_ids,
"cumulative_logprob": o.cumulative_logprob,
"finish_reason": o.finish_reason,
}
for o in output.outputs
],
"num_input_tokens": len(output.prompt_token_ids),
}
results.append(result)
return results
class TransformersModelRunner:
"""
基于 Transformers 的备用模型运行器
当 vLLM 不可用时使用
"""
def __init__(
self,
model_path: str,
tokenizer_path: Optional[str] = None,
device: str = "cuda",
torch_dtype: str = "auto",
):
"""
初始化 Transformers 模型运行器
Args:
model_path: 模型路径
tokenizer_path: Tokenizer 路径
device: 设备（cuda 或 cpu）
torch_dtype: 数据类型
"""
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
self.device = device
self.tokenizer_path = tokenizer_path or model_path
logger.info(f"Loading model with transformers: {model_path}")
self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_path)
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch_dtype if torch_dtype != "auto" else "auto",
device_map=device,
)
logger.info("Model loaded successfully")
def generate(
self,
prompts: List[str],
max_tokens: int = 500,
temperature: float = 0.7,
top_p: float = 0.9,
top_k: int = 50,
n: int = 1,
) -> List[Dict[str, Any]]:
"""
生成文本
Args:
prompts: 输入提示列表
max_tokens: 最大生成 token 数
temperature: 采样温度
top_p: Nucleus 采样参数
top_k: Top-K 采样参数
n: 生成数量
Returns:
生成结果列表
"""
import torch
results = []
for prompt in prompts:
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_return_sequences=n,
do_sample=temperature > 0,
)
generated_texts = [
self.tokenizer.decode(output, skip_special_tokens=True)
for output in outputs
]
result = {
"prompt": prompt,
"outputs": [
{
"text": text,
"token_ids": None,
"cumulative_logprob": None,
"finish_reason": "stop",
}
for text in generated_texts
],
"num_input_tokens": len(inputs["input_ids"][0]),
}
results.append(result)
return results
|