consciousness/training/vllm_export_hook.py

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"""Monkey-patch vLLM to export weight IPC handles on startup.
Usage add to start_vllm.sh BEFORE the vllm serve command:
export VLLM_PLUGINS=vllm_export_hook
vllm serve Qwen/Qwen3.5-27B ...
Or use Python to launch vLLM with the hook:
python3 -c "
import vllm_export_hook # installs the patch
from vllm.entrypoints.openai.api_server import run_server
run_server(...)
"
The hook patches vLLM's model runner to export IPC handles after
model loading completes. The handles are saved to a file that the
Apollo training process reads.
"""
import atexit
import torch
from pathlib import Path
HANDLE_PATH = "/tmp/vllm_weight_handles.pt"
def export_model_weights(model):
"""Export CUDA IPC handles for all model parameters."""
from torch.multiprocessing.reductions import reduce_tensor
handles = {}
total_bytes = 0
for name, param in model.named_parameters():
if param.device.type != 'cuda':
continue
handle = reduce_tensor(param.data)
handles[name] = {
'handle': handle,
'shape': list(param.shape),
'dtype': str(param.dtype),
}
total_bytes += param.nelement() * param.element_size()
torch.save(handles, HANDLE_PATH)
print(f"[apollo] Exported {len(handles)} weight handles "
f"({total_bytes / 1e9:.1f} GB) to {HANDLE_PATH}")
def _patch_model_runner():
"""Patch gpu_worker to export handles after model loading.
vLLM loads the model in a subprocess (EngineCore_DP0), so we
can't patch from the parent. Instead, patch the worker's
init_device or load_model at the module level the subprocess
imports the same modules.
"""
from vllm.v1.worker import gpu_worker
original_load = gpu_worker.Worker.load_model
def patched_load(self, *args, **kwargs):
result = original_load(self, *args, **kwargs)
try:
export_model_weights(self.model_runner.model)
except Exception as e:
print(f"[apollo] Failed to export weights: {e}")
return result
gpu_worker.Worker.load_model = patched_load
print("[apollo] Weight export hook installed")
# Auto-install when imported
_patch_model_runner()