PyTorch Multi-Process Inference Weight Sharing via Inter-Process Communication

PyTorch Multi-Process Inference Weight Sharing Via Inter-Process Communication - Lei Mao's Log Book

PyTorch Multi-Process Inference Weight Sharing Via Inter-Process Communication 07-31-2026 07-31-2026 blog 17 minutes read (About 2500 words) visits

Introduction PyTorch is probably the most popular deep learning framework for model training. Ideally, without any additional effort, PyTorch model could be used for inference seamlessly in Python. However, in practice, there are some concerns when using PyTorch for inference in Python, primarily because of the Global Interpreter Lock (GIL) in Python. The GIL prevents multiple threads from executing Python bytecodes at once, which can lead to performance bottlenecks in high-throughput applications. To overcome this limitation, PyTorch models can be run in multiple processes, where each process runs its own Python interpreter and has its own GIL. However, because model weights cannot be natively shared across processes in Python, multi-process inference on single-GPU can lead to duplicated model weights in GPU VRAM, which can quickly exhaust the available GPU memory. To mitigate this issue, model weights can be shared across processes using CUDA Inter-Process Communication (IPC).

It turns out that PyTorch multiprocessing module has abstracted away the details of CUDA IPC, and provides a simple API to share CUDA tensors across processes. In this blog post, I will demonstrate how to use PyTorch multiprocessing module to share model weights across multiple processes for inference, and avoid weight duplication in GPU VRAM.

PyTorch Multi-Process Inference Weight Sharing In the following example, I will demonstrate how to share model weights via CUDA IPC across multiple processes for PyTorch and AOTInductor inference using PyTorch multiprocessing module.

pytorch_inference_ipc.py1 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 import argparse import io import os import tempfile import time

import torch import torch.nn as nn import torch.multiprocessing as mp import torch._inductor

# ===================================================================== # 1. MODEL DEFINITION # ===================================================================== class SharedWeightInferenceEngine(nn.Module):

def __init__(self, in_features: int = 16384, hidden_dim: int = 16384, out_features: int = 12800): super().__init__() self.encoder = nn.Linear(in_features, hidden_dim) self.head = nn.Linear(hidden_dim, out_features)

def forward(self, x: torch.Tensor) -> torch.Tensor: x = torch.relu(self.encoder(x)) return self.head(x)

# ===================================================================== # 2. HELPER FUNCTIONS # ===================================================================== def get_device_vram_usage_mb(device_id: int = 0) -> float: """Return device-wide GPU memory usage in MB.""" torch.cuda.set_device(device_id) free_bytes, total_bytes = torch.cuda.mem_get_info(device_id) return (total_bytes - free_bytes) / (1024**2)

def load_aoti_runner(package_path: str, state_dict: dict, device_id: int): """Load an AOTI runner and bind it to the supplied model weights.""" runner = torch._inductor.aoti_load_package(package_path, device_index=device_id)

# To enable IPC weight sharing using the parent state dict, # uncomment the following lines: expected_keys = set(state_dict.keys()) if hasattr(runner, "get_constant_fqns"): assert set(runner.get_constant_fqns()) == expected_keys # Now the weight tensor pointers or references are pointing to the shared weights in the parent process. runner.load_constants(state_dict, check_full_update=True, user_managed=False)

torch.cuda.synchronize() time.sleep(0.2) return runner

# ===================================================================== # 3. WORKER PROCESS INFERENCE LOOPS #...

PyTorch Multi-Process Inference Weight Sharing via Inter-Process Communication

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