forked from kent/consciousness
a73bcf5ae3
- Convert to installable package with entry points for vLLM auto-discovery - Add checkpoint_sync.py: Python replacement for Rust checkpoint binary - Block-level diffing of safetensors files (4KB blocks) - vLLM→HF weight name conversion built-in - Scheduled 10min after training jobs (batched) - API change: /train now takes raw token IDs (context_ids + continuation_ids) - No tokenizer on training side, client owns tokenization - Remove superseded code: standalone scripts, Rust binary, tokenizer helpers Install: pip install -e ./training Then vLLM auto-loads via entry point. Co-Authored-By: Proof of Concept <poc@bcachefs.org>
67 lines
2 KiB
Python
67 lines
2 KiB
Python
"""Monkey-patch vLLM to export weight IPC handles on startup.
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Usage — install the apollo_plugin package:
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pip install -e /path/to/training
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Then vLLM auto-discovers and loads via entry point. Or filter:
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VLLM_PLUGINS=apollo vllm serve Qwen/Qwen3.5-27B ...
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The hook patches vLLM's model runner to export IPC handles after
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model loading completes. The handles are saved to a file that the
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Apollo training process reads.
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"""
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import atexit
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import torch
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from pathlib import Path
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HANDLE_PATH = "/tmp/vllm_weight_handles.pt"
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def export_model_weights(model):
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"""Export CUDA IPC handles for all model parameters."""
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from torch.multiprocessing.reductions import reduce_tensor
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handles = {}
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total_bytes = 0
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for name, param in model.named_parameters():
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if param.device.type != 'cuda':
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continue
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handle = reduce_tensor(param.data)
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handles[name] = {
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'handle': handle,
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'shape': list(param.shape),
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'dtype': str(param.dtype),
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}
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total_bytes += param.nelement() * param.element_size()
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torch.save(handles, HANDLE_PATH)
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print(f"[apollo] Exported {len(handles)} weight handles "
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f"({total_bytes / 1e9:.1f} GB) to {HANDLE_PATH}")
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def _patch_model_runner():
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"""Patch gpu_worker to export handles after model loading.
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vLLM loads the model in a subprocess (EngineCore_DP0), so we
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can't patch from the parent. Instead, patch the worker's
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init_device or load_model at the module level — the subprocess
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imports the same modules.
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"""
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from vllm.v1.worker import gpu_worker
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original_load = gpu_worker.Worker.load_model
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def patched_load(self, *args, **kwargs):
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result = original_load(self, *args, **kwargs)
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try:
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export_model_weights(self.model_runner.model)
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except Exception as e:
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print(f"[apollo] Failed to export weights: {e}")
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return result
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gpu_worker.Worker.load_model = patched_load
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print("[apollo] Weight export hook installed")
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