training: restructure as vLLM plugin package
- 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takes raw token IDs (context_ids + continuation_ids) - No tokenizer on training side, client owns tokenization - Remove superseded code: standalone scripts, Rust binary, tokenizer helpers Install: pip install -e ./training Then vLLM auto-loads via entry point.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>
This commit is contained in:
Kent Overstreet
parent
b649a11645
commit
a73bcf5ae3
15 changed files with 607 additions and 1068 deletions
17
training/apollo_plugin/__init__.py
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training/apollo_plugin/__init__.py
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"""Apollo training plugin for vLLM.
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Enables continuous fine-tuning alongside live inference by:
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1. Exporting CUDA IPC handles for weight sharing
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2. Providing a training worker daemon (/train endpoint)
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3. Block-level checkpoint sync to safetensors files
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Install: pip install -e /path/to/training
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Then vLLM auto-loads via entry point.
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"""
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from .export_hook import _patch_model_runner
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def register():
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"""Called by vLLM's plugin loader on startup."""
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_patch_model_runner()
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500
training/apollo_plugin/checkpoint_sync.py
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training/apollo_plugin/checkpoint_sync.py
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"""Sync live GPU weights to safetensors files on disk.
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Reads vLLM weight tensors via CUDA IPC handles, converts from vLLM's
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merged layout to HuggingFace's separate layout, diffs block-by-block
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against on-disk safetensors files, and writes only changed blocks.
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For small behavioral training steps, this turns a 54GB checkpoint
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write into a few hundred MB of actual disk I/O.
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Usage:
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# Sync live weights to disk
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python checkpoint_sync.py sync --model-dir /path/to/Qwen3.5-27B
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# Debug name mapping issues
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python checkpoint_sync.py diagnose --model-dir /path/to/Qwen3.5-27B
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# From Python:
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from checkpoint_sync import checkpoint_sync
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result = checkpoint_sync("/path/to/model")
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"""
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import json
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import mmap
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import struct
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import sys
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from pathlib import Path
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from typing import Dict, List, Tuple, Any
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import logging
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import torch
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logger = logging.getLogger(__name__)
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DEFAULT_BLOCK_SIZE = 4096 # 4KB blocks — matches filesystem block size
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DEFAULT_HANDLES_PATH = "/tmp/vllm_weight_handles.pt"
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# ---------------------------------------------------------------------------
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# vLLM → HuggingFace weight name/shape conversion
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# ---------------------------------------------------------------------------
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# Qwen3.5-27B dimensions (could be read from config.json for generality)
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HIDDEN = 5120
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NUM_K_HEADS = 16
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NUM_V_HEADS = 48
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HEAD_K_DIM = 128
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HEAD_V_DIM = 128
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KEY_DIM = NUM_K_HEADS * HEAD_K_DIM # 2048
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VALUE_DIM = NUM_V_HEADS * HEAD_V_DIM # 6144
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INTERMEDIATE = 17408
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# Full attention (some layers use standard attention, not GDN)
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NUM_ATTN_HEADS = 24
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NUM_ATTN_KV_HEADS = 4
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ATTN_HEAD_DIM = 256
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ATTN_Q_HEAD_DIM = ATTN_HEAD_DIM * 2 # 512
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ATTN_Q_DIM = NUM_ATTN_HEADS * ATTN_Q_HEAD_DIM # 12288
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ATTN_K_DIM = NUM_ATTN_KV_HEADS * ATTN_HEAD_DIM # 1024
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ATTN_V_DIM = NUM_ATTN_KV_HEADS * ATTN_HEAD_DIM # 1024
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def vllm_to_hf_tensors(vllm_params: Dict[str, torch.Tensor]
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) -> Dict[str, torch.Tensor]:
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"""Convert vLLM merged weights to HF-compatible separate tensors.
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vLLM merges certain projections for efficiency:
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- qkv_proj (full attn) → q_proj, k_proj, v_proj
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- in_proj_qkvz (GDN) → in_proj_qkv, in_proj_z
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- in_proj_ba (GDN) → in_proj_b, in_proj_a
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- gate_up_proj (MLP) → gate_proj, up_proj
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Returns views that share GPU memory with the original tensors.
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"""
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hf_params = {}
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for name, tensor in vllm_params.items():
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# Strip vLLM's 'language_model.' prefix to match HF naming
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hf_name = name.removeprefix('language_model.')
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if 'in_proj_qkvz' in name:
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# GDN layer: [key*2 + value*2, hidden] → qkv + z
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prefix = hf_name.replace('in_proj_qkvz.weight', '')
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split_at = KEY_DIM * 2 + VALUE_DIM
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hf_params[prefix + 'in_proj_qkv.weight'] = tensor[:split_at]
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hf_params[prefix + 'in_proj_z.weight'] = tensor[split_at:]
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elif 'in_proj_ba' in name:
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# GDN layer: [num_v_heads*2, hidden] → b + a
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prefix = hf_name.replace('in_proj_ba.weight', '')
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hf_params[prefix + 'in_proj_b.weight'] = tensor[:NUM_V_HEADS]
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hf_params[prefix + 'in_proj_a.weight'] = tensor[NUM_V_HEADS:]
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elif 'qkv_proj' in name:
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# Full attention: [q + k + v, hidden] → separate
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prefix = hf_name.replace('qkv_proj.weight', '')
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hf_params[prefix + 'q_proj.weight'] = tensor[:ATTN_Q_DIM]
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hf_params[prefix + 'k_proj.weight'] = tensor[ATTN_Q_DIM:ATTN_Q_DIM + ATTN_K_DIM]
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hf_params[prefix + 'v_proj.weight'] = tensor[ATTN_Q_DIM + ATTN_K_DIM:]
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elif 'gate_up_proj' in name:
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# MLP: [intermediate*2, hidden] → gate + up
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prefix = hf_name.replace('gate_up_proj.weight', '')
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hf_params[prefix + 'gate_proj.weight'] = tensor[:INTERMEDIATE]
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hf_params[prefix + 'up_proj.weight'] = tensor[INTERMEDIATE:]
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else:
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# Pass through unchanged
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hf_params[hf_name] = tensor
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return hf_params
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# ---------------------------------------------------------------------------
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# Safetensors file handling
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# ---------------------------------------------------------------------------
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def read_safetensors_index(model_dir: Path) -> Dict[str, str]:
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"""Map tensor names to safetensors filenames.
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For sharded models, reads model.safetensors.index.json.
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For single-file models, returns empty dict (default to model.safetensors).
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"""
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index_path = model_dir / "model.safetensors.index.json"
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if not index_path.exists():
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return {}
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with open(index_path) as f:
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index = json.load(f)
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return dict(index.get("weight_map", {}))
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def parse_safetensors_header(data: memoryview) -> Tuple[int, dict]:
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"""Parse safetensors file header.
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Returns (data_start_offset, header_dict).
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Header dict maps tensor names to metadata including 'data_offsets'.
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"""
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header_size = struct.unpack('<Q', data[:8])[0]
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header = json.loads(bytes(data[8:8 + header_size]))
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return 8 + header_size, header
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# ---------------------------------------------------------------------------
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# Block-level diffing and sync
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# ---------------------------------------------------------------------------
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def sync_tensor_to_mmap(
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mm: mmap.mmap,
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name: str,
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tensor: torch.Tensor,
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data_start: int,
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offsets: List[int],
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block_size: int,
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) -> Tuple[int, int]:
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"""Sync a single tensor to mmap'd file using block-level diffing.
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Returns (bytes_compared, bytes_changed).
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"""
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start = data_start + offsets[0]
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end = data_start + offsets[1]
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disk_len = end - start
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# Transfer tensor to CPU and get raw bytes
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# Use .detach() to avoid autograd overhead, .contiguous() for memory layout
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try:
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live_bytes = tensor.detach().contiguous().cpu().numpy().tobytes()
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except Exception as e:
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logger.warning(f"Failed to transfer {name} to CPU: {e}")
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return 0, 0
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if len(live_bytes) != disk_len:
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logger.warning(
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f"Size mismatch for {name}: disk={disk_len}, live={len(live_bytes)} "
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f"(shape={list(tensor.shape)}, dtype={tensor.dtype})"
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)
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return 0, 0
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# Block-level diff: compare and write only changed blocks
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compared = 0
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changed = 0
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offset = 0
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while offset < disk_len:
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block_end = min(offset + block_size, disk_len)
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block_len = block_end - offset
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disk_block = mm[start + offset:start + block_end]
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live_block = live_bytes[offset:block_end]
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compared += block_len
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if disk_block != live_block:
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mm[start + offset:start + block_end] = live_block
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changed += block_len
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offset = block_end
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return compared, changed
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def sync_file(
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file_path: Path,
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tensors: Dict[str, torch.Tensor],
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block_size: int,
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) -> Tuple[int, int, int, int]:
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"""Sync tensors to a single safetensors file.
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Returns (bytes_compared, bytes_changed, tensors_found, tensors_missing).
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"""
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with open(file_path, 'r+b') as f:
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mm = mmap.mmap(f.fileno(), 0)
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try:
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data_start, header = parse_safetensors_header(memoryview(mm))
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total_compared = 0
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total_changed = 0
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found = 0
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missing = 0
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for name, tensor in tensors.items():
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if name == "__metadata__":
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continue
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if name not in header:
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missing += 1
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continue
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found += 1
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meta = header[name]
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offsets = meta['data_offsets']
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compared, changed = sync_tensor_to_mmap(
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mm, name, tensor, data_start, offsets, block_size
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)
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total_compared += compared
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total_changed += changed
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# Flush changes to disk
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if total_changed > 0:
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mm.flush()
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return total_compared, total_changed, found, missing
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finally:
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mm.close()
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# ---------------------------------------------------------------------------
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# Main entry point
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# ---------------------------------------------------------------------------
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def load_vllm_weights(handles_path: str) -> Dict[str, torch.Tensor]:
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"""Load vLLM weight tensors from CUDA IPC handles.
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The handles file is written by vllm_export_hook.py on vLLM startup.
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Each handle can be used to reconstruct a tensor pointing to vLLM's
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GPU memory — no copy, direct access.
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"""
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handles = torch.load(handles_path, weights_only=False)
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weights = {}
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for name, info in handles.items():
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func, args = info['handle']
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try:
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weights[name] = func(*args)
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except Exception as e:
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logger.warning(f"Failed to reconstruct {name}: {e}")
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return weights
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def checkpoint_sync(
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model_dir: str,
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handles_path: str = DEFAULT_HANDLES_PATH,
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block_size: int = DEFAULT_BLOCK_SIZE,
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) -> Dict[str, Any]:
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"""Sync live GPU weights to model safetensors files.
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This is the main entry point. Call this after training steps
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or periodically to checkpoint weights without full serialization.
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Args:
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model_dir: Directory containing safetensors files
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handles_path: Path to vLLM weight IPC handles file
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block_size: Block size for diffing (default 4KB)
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Returns:
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Dict with sync statistics:
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- total_compared: bytes compared
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- total_changed: bytes actually written
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- files_changed: list of modified filenames
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- tensors_synced: number of tensors processed
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- tensors_missing: tensors not found in safetensors
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"""
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model_dir = Path(model_dir)
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if not Path(handles_path).exists():
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raise FileNotFoundError(
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f"Weight handles not found: {handles_path}. "
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"Is vLLM running with the export hook?"
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)
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# Step 1: Load live weights from GPU via IPC
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logger.info("Loading live weights from GPU...")
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vllm_weights = load_vllm_weights(handles_path)
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logger.info(f" Loaded {len(vllm_weights)} vLLM tensors")
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# Step 2: Convert to HF naming/layout
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hf_weights = vllm_to_hf_tensors(vllm_weights)
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logger.info(f" Converted to {len(hf_weights)} HF tensors")
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# Step 3: Map tensors to safetensors files
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weight_map = read_safetensors_index(model_dir)
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by_file: Dict[str, Dict[str, torch.Tensor]] = {}
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unmapped = []
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for name, tensor in hf_weights.items():
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filename = weight_map.get(name)
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if filename is None:
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# Single-file model or missing from index
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if (model_dir / "model.safetensors").exists():
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filename = "model.safetensors"
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else:
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unmapped.append(name)
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continue
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by_file.setdefault(filename, {})[name] = tensor
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if unmapped:
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logger.warning(f" {len(unmapped)} tensors not in index: {unmapped[:3]}...")
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# Step 4: Sync each file
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total_compared = 0
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total_changed = 0
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total_found = 0
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total_missing = 0
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files_changed = []
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for filename in sorted(by_file.keys()):
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tensors = by_file[filename]
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file_path = model_dir / filename
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if not file_path.exists():
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logger.warning(f" File not found: {filename}")
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total_missing += len(tensors)
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continue
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compared, changed, found, missing = sync_file(file_path, tensors, block_size)
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total_compared += compared
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total_changed += changed
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total_found += found
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total_missing += missing
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if changed > 0:
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files_changed.append(filename)
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logger.info(f" {filename}: {changed / 1e6:.2f} MB changed ({found} tensors)")
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# Summary
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if total_changed == 0:
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logger.info("No changes - model files are up to date")
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else:
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pct = (total_changed / total_compared * 100) if total_compared > 0 else 0
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logger.info(
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f"Synced: {total_changed / 1e6:.2f} MB changed / "
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f"{total_compared / 1e9:.2f} GB compared ({pct:.3f}%)"
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)
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if total_missing > 0:
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logger.warning(f" {total_missing} tensors not found in safetensors files")
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return {
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"total_compared": total_compared,
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"total_changed": total_changed,
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"files_changed": files_changed,
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"tensors_synced": total_found,
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"tensors_missing": total_missing,
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}
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# ---------------------------------------------------------------------------
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# Diagnostics
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# ---------------------------------------------------------------------------
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def diagnose(model_dir: str, handles_path: str = DEFAULT_HANDLES_PATH):
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"""Print diagnostic info about weight name mappings.
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Useful for debugging mismatches between vLLM and safetensors names.
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"""
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model_dir = Path(model_dir)
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# Load and convert vLLM weights
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vllm_weights = load_vllm_weights(handles_path)
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hf_weights = vllm_to_hf_tensors(vllm_weights)
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hf_names = set(hf_weights.keys())
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# Read safetensors index
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weight_map = read_safetensors_index(model_dir)
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disk_names = set(weight_map.keys())
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# If single-file model, parse that file's header
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if not disk_names:
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st_path = model_dir / "model.safetensors"
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if st_path.exists():
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with open(st_path, 'rb') as f:
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mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
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_, header = parse_safetensors_header(memoryview(mm))
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disk_names = {k for k in header.keys() if k != "__metadata__"}
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mm.close()
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print(f"vLLM tensors (raw): {len(vllm_weights)}")
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print(f"HF tensors (converted): {len(hf_names)}")
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print(f"Disk tensors: {len(disk_names)}")
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print()
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in_both = hf_names & disk_names
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only_hf = hf_names - disk_names
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only_disk = disk_names - hf_names
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print(f"Matched: {len(in_both)}")
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print(f"Only in HF (won't sync): {len(only_hf)}")
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print(f"Only on disk (not updated): {len(only_disk)}")
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if only_hf:
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print(f"\nSample HF-only: {sorted(only_hf)[:5]}")
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if only_disk:
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print(f"\nSample disk-only: {sorted(only_disk)[:5]}")
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def main():
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import argparse
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parser = argparse.ArgumentParser(
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description="Sync live GPU weights to safetensors files"
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)
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subparsers = parser.add_subparsers(dest="command", help="Command")
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# sync command
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sync_parser = subparsers.add_parser("sync", help="Sync weights to disk")
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sync_parser.add_argument(
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"--model-dir", required=True,
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help="Directory containing safetensors files"
|
||||
)
|
||||
sync_parser.add_argument(
|
||||
"--handles", default=DEFAULT_HANDLES_PATH,
|
||||
help=f"Path to IPC handles (default: {DEFAULT_HANDLES_PATH})"
|
||||
)
|
||||
sync_parser.add_argument(
|
||||
"--block-size", type=int, default=DEFAULT_BLOCK_SIZE,
|
||||
help=f"Block size for diffing (default: {DEFAULT_BLOCK_SIZE})"
|
||||
)
|
||||
sync_parser.add_argument(
|
||||
"-v", "--verbose", action="store_true",
|
||||
help="Verbose output"
|
||||
)
|
||||
|
||||
# diagnose command
|
||||
diag_parser = subparsers.add_parser("diagnose", help="Check name mappings")
|
||||
diag_parser.add_argument(
|
||||
"--model-dir", required=True,
|
||||
help="Directory containing safetensors files"
|
||||
)
|
||||
diag_parser.add_argument(
|
||||
"--handles", default=DEFAULT_HANDLES_PATH,
|
||||
help=f"Path to IPC handles (default: {DEFAULT_HANDLES_PATH})"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.command is None:
|
||||
parser.print_help()
|
||||
sys.exit(1)
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG if getattr(args, 'verbose', False) else logging.INFO,
|
||||
format='%(message)s'
|
||||
)
|
||||
|
||||
try:
|
||||
if args.command == "sync":
|
||||
result = checkpoint_sync(args.model_dir, args.handles, args.block_size)
|
||||
print(json.dumps(result, indent=2))
|
||||
elif args.command == "diagnose":
|
||||
diagnose(args.model_dir, args.handles)
|
||||
except FileNotFoundError as e:
|
||||
logger.error(str(e))
|
||||
sys.exit(1)
|
||||
except Exception as e:
|
||||
logger.exception(f"Failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,17 +1,12 @@
|
|||
"""Monkey-patch vLLM to export weight IPC handles on startup.
|
||||
|
||||
Usage — add to start_vllm.sh BEFORE the vllm serve command:
|
||||
Usage — install the apollo_plugin package:
|
||||
|
||||
export VLLM_PLUGINS=vllm_export_hook
|
||||
vllm serve Qwen/Qwen3.5-27B ...
|
||||
pip install -e /path/to/training
|
||||
|
||||
Or use Python to launch vLLM with the hook:
|
||||
Then vLLM auto-discovers and loads via entry point. Or filter:
|
||||
|
||||
python3 -c "
|
||||
import vllm_export_hook # installs the patch
|
||||
from vllm.entrypoints.openai.api_server import run_server
|
||||
run_server(...)
|
||||
"
|
||||
VLLM_PLUGINS=apollo vllm serve Qwen/Qwen3.5-27B ...
|
||||
|
||||
The hook patches vLLM's model runner to export IPC handles after
|
||||
model loading completes. The handles are saved to a file that the
|
||||
|
|
@ -70,7 +65,3 @@ def _patch_model_runner():
|
|||
|
||||
gpu_worker.Worker.load_model = patched_load
|
||||
print("[apollo] Weight export hook installed")
|
||||
|
||||
|
||||
# Auto-install when imported
|
||||
_patch_model_runner()
|
||||
|
|
@ -74,6 +74,9 @@ class TrainingJob:
|
|||
'error': self.error,
|
||||
}
|
||||
|
||||
CHECKPOINT_DELAY_SECS = 10 * 60 # 10 minutes
|
||||
|
||||
|
||||
class ApolloWorker:
|
||||
def __init__(self, config_path: str = "/home/kent/poc/consciousness/training/config.json"):
|
||||
self.config = self._load_config(config_path)
|
||||
|
|
@ -81,6 +84,7 @@ class ApolloWorker:
|
|||
self.vllm_paused = False
|
||||
self.app = web.Application()
|
||||
self._setup_routes()
|
||||
self._checkpoint_timer: Optional[asyncio.Task] = None
|
||||
|
||||
def _load_config(self, config_path: str) -> Dict[str, Any]:
|
||||
"""Load configuration from file or use defaults."""
|
||||
|
|
@ -233,6 +237,9 @@ class ApolloWorker:
|
|||
|
||||
logger.info(f"Training job {job.job_id} completed successfully")
|
||||
|
||||
# Schedule checkpoint sync (batched — won't duplicate if timer pending)
|
||||
self.schedule_checkpoint_sync()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Training job {job.job_id} failed: {e}")
|
||||
job.status = TrainingStatus.FAILED
|
||||
|
|
@ -278,6 +285,43 @@ class ApolloWorker:
|
|||
except Exception as e:
|
||||
logger.warning(f"Failed to resume vLLM: {e}")
|
||||
|
||||
def schedule_checkpoint_sync(self):
|
||||
"""Schedule a checkpoint sync in 10 minutes, if not already scheduled.
|
||||
|
||||
This batches multiple training runs into a single sync — the timer
|
||||
resets only when no timer is pending.
|
||||
"""
|
||||
if self._checkpoint_timer is not None:
|
||||
logger.debug("Checkpoint sync already scheduled, skipping")
|
||||
return
|
||||
|
||||
self._checkpoint_timer = asyncio.create_task(self._checkpoint_sync_after_delay())
|
||||
logger.info(f"Checkpoint sync scheduled in {CHECKPOINT_DELAY_SECS // 60} minutes")
|
||||
|
||||
async def _checkpoint_sync_after_delay(self):
|
||||
"""Wait then sync — the actual timer task."""
|
||||
try:
|
||||
await asyncio.sleep(CHECKPOINT_DELAY_SECS)
|
||||
await self._do_checkpoint_sync()
|
||||
except asyncio.CancelledError:
|
||||
logger.debug("Checkpoint sync cancelled")
|
||||
finally:
|
||||
self._checkpoint_timer = None
|
||||
|
||||
async def _do_checkpoint_sync(self):
|
||||
"""Execute the checkpoint sync."""
|
||||
try:
|
||||
from apollo_plugin.checkpoint_sync import checkpoint_sync
|
||||
logger.info("Starting checkpoint sync...")
|
||||
result = checkpoint_sync(
|
||||
self.config['model_path'],
|
||||
self.config.get('weight_handles', '/tmp/vllm_weight_handles.pt'),
|
||||
)
|
||||
changed_mb = result['total_changed'] / 1e6
|
||||
logger.info(f"Checkpoint sync complete: {changed_mb:.2f} MB written")
|
||||
except Exception as e:
|
||||
logger.error(f"Checkpoint sync failed: {e}")
|
||||
|
||||
async def load_model_for_training(self) -> nn.Module:
|
||||
"""Load HF model with weights pointing to vLLM's GPU memory.
|
||||
|
||||
|
|
@ -299,22 +343,24 @@ class ApolloWorker:
|
|||
logger.info(f"Imported {len(vllm_params)} parameters")
|
||||
|
||||
# Map vLLM merged layout → HF separate layout (views, no copies)
|
||||
from weight_mapping import load_hf_model_with_vllm_weights
|
||||
from apollo_plugin.weight_mapping import load_hf_model_with_vllm_weights
|
||||
model = load_hf_model_with_vllm_weights(vllm_params, model_path)
|
||||
logger.info("HF model constructed with vLLM weight views")
|
||||
|
||||
return model
|
||||
|
||||
async def run_apollo_training(self, model: nn.Module,
|
||||
samples: List[Dict[str, str]],
|
||||
samples: List[Dict[str, Any]],
|
||||
config: Dict[str, Any]) -> List[float]:
|
||||
"""Run Apollo-Mini training on conversation decision points."""
|
||||
from apollo_mini import Apollo
|
||||
from transformers import AutoTokenizer
|
||||
"""Run Apollo-Mini training on conversation decision points.
|
||||
|
||||
Each sample has:
|
||||
context_ids: token IDs for frozen context (no gradients)
|
||||
continuation_ids: token IDs for the decision we're training on
|
||||
"""
|
||||
from apollo_plugin.optimizer import Apollo
|
||||
|
||||
lr = config.get('learning_rate', self.config['learning_rate'])
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
self.config['model_path'], trust_remote_code=True)
|
||||
|
||||
# Build parameter groups (Apollo for 2D+, standard for small/1D)
|
||||
apollo_params, standard_params = [], []
|
||||
|
|
@ -340,12 +386,10 @@ class ApolloWorker:
|
|||
loss_history = []
|
||||
|
||||
for i, sample in enumerate(samples):
|
||||
context = sample.get('context', '')
|
||||
continuation = sample.get('continuation', '')
|
||||
|
||||
# Tokenize
|
||||
ctx_ids = tokenizer.encode(context, add_special_tokens=True)
|
||||
cont_ids = tokenizer.encode(continuation, add_special_tokens=False)
|
||||
# context_ids: frozen (forward only, no gradients)
|
||||
# continuation_ids: the decision we're training on
|
||||
ctx_ids = sample['context_ids']
|
||||
cont_ids = sample['continuation_ids']
|
||||
all_ids = ctx_ids + cont_ids
|
||||
context_len = len(ctx_ids)
|
||||
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
[package]
|
||||
name = "apollo-checkpoint"
|
||||
version = "0.1.0"
|
||||
edition = "2024"
|
||||
|
||||
[dependencies]
|
||||
memmap2 = "0.9"
|
||||
safetensors = "0.5"
|
||||
serde = { version = "1", features = ["derive"] }
|
||||
serde_json = "1"
|
||||
anyhow = "1"
|
||||
clap = { version = "4", features = ["derive"] }
|
||||
|
|
@ -1,265 +0,0 @@
|
|||
// apollo-checkpoint — Sync live GPU weights back to model files on disk.
|
||||
//
|
||||
// mmaps the model's safetensors files, reads live weights from GPU via
|
||||
// Python helper (CUDA IPC handles), compares block by block, and memcpys
|
||||
// only changed regions back into the mmap. For small behavioral training
|
||||
// steps, this turns a 54GB write into a few hundred MB.
|
||||
//
|
||||
// The model files on disk are the checkpoint. No separate checkpoint
|
||||
// directory — just keep the model up to date.
|
||||
//
|
||||
// Usage:
|
||||
// apollo-checkpoint sync \
|
||||
// --handles /tmp/vllm_weight_handles.pt \
|
||||
// --model-dir /path/to/Qwen3.5-27B
|
||||
//
|
||||
// Runs every 10 minutes via cron. Daily rsync to moria.
|
||||
|
||||
use anyhow::{Context, Result, bail};
|
||||
use clap::{Parser, Subcommand};
|
||||
use memmap2::MmapMut;
|
||||
use std::collections::HashMap;
|
||||
use std::fs;
|
||||
use std::path::{Path, PathBuf};
|
||||
use std::process::Command;
|
||||
|
||||
#[derive(Parser)]
|
||||
#[command(name = "apollo-checkpoint", about = "Sync live GPU weights to model files")]
|
||||
struct Cli {
|
||||
#[command(subcommand)]
|
||||
command: Cmd,
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
enum Cmd {
|
||||
/// Sync live GPU weights back to model safetensors files
|
||||
Sync {
|
||||
/// Path to vLLM weight IPC handles
|
||||
#[arg(long, default_value = "/tmp/vllm_weight_handles.pt")]
|
||||
handles: PathBuf,
|
||||
|
||||
/// Model directory containing safetensors files
|
||||
#[arg(long)]
|
||||
model_dir: PathBuf,
|
||||
|
||||
/// Block size for diffing (bytes)
|
||||
#[arg(long, default_value_t = 4096)]
|
||||
block_size: usize,
|
||||
},
|
||||
}
|
||||
|
||||
/// Dump live GPU weights to a flat binary file, ordered by safetensors
|
||||
/// file and offset to match the on-disk layout.
|
||||
///
|
||||
/// Returns a map of (safetensors filename, tensor name) → raw bytes.
|
||||
fn dump_live_weights(handles_path: &Path, output_dir: &Path) -> Result<HashMap<String, Vec<u8>>> {
|
||||
let dump_path = output_dir.join(".live_dump.bin");
|
||||
let index_path = output_dir.join(".live_dump.json");
|
||||
|
||||
let status = Command::new("python3")
|
||||
.arg("-c")
|
||||
.arg(format!(r#"
|
||||
import torch, json
|
||||
|
||||
handles = torch.load("{handles}", weights_only=False)
|
||||
index = {{}}
|
||||
offset = 0
|
||||
|
||||
with open("{dump}", "wb") as f:
|
||||
for name in sorted(handles.keys()):
|
||||
info = handles[name]
|
||||
func, args = info["handle"]
|
||||
tensor = func(*args)
|
||||
data = tensor.contiguous().cpu().numpy().tobytes()
|
||||
f.write(data)
|
||||
index[name] = {{"offset": offset, "size": len(data)}}
|
||||
offset += len(data)
|
||||
|
||||
with open("{index}", "w") as f:
|
||||
json.dump(index, f)
|
||||
|
||||
print(f"Dumped {{len(index)}} tensors, {{offset / 1e9:.1f}} GB")
|
||||
"#,
|
||||
handles = handles_path.display(),
|
||||
dump = dump_path.display(),
|
||||
index = index_path.display(),
|
||||
))
|
||||
.status()
|
||||
.context("Failed to run Python weight dump")?;
|
||||
|
||||
if !status.success() {
|
||||
bail!("Python weight dump failed");
|
||||
}
|
||||
|
||||
let index_str = fs::read_to_string(&index_path)?;
|
||||
let index: HashMap<String, DumpEntry> = serde_json::from_str(&index_str)?;
|
||||
let dump_data = fs::read(&dump_path)?;
|
||||
|
||||
let mut result = HashMap::new();
|
||||
for (name, entry) in &index {
|
||||
result.insert(name.clone(), dump_data[entry.offset..entry.offset + entry.size].to_vec());
|
||||
}
|
||||
|
||||
// Clean up temp files
|
||||
let _ = fs::remove_file(&dump_path);
|
||||
let _ = fs::remove_file(&index_path);
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
#[derive(serde::Deserialize)]
|
||||
struct DumpEntry {
|
||||
offset: usize,
|
||||
size: usize,
|
||||
}
|
||||
|
||||
/// Read the safetensors index to map parameter names to files.
|
||||
fn read_safetensors_index(model_dir: &Path) -> Result<HashMap<String, String>> {
|
||||
let index_path = model_dir.join("model.safetensors.index.json");
|
||||
if !index_path.exists() {
|
||||
// Single file model
|
||||
return Ok(HashMap::new());
|
||||
}
|
||||
|
||||
let index_str = fs::read_to_string(&index_path)?;
|
||||
let index: serde_json::Value = serde_json::from_str(&index_str)?;
|
||||
let weight_map = index["weight_map"]
|
||||
.as_object()
|
||||
.context("No weight_map in index")?;
|
||||
|
||||
let mut result = HashMap::new();
|
||||
for (name, file) in weight_map {
|
||||
result.insert(name.clone(), file.as_str().unwrap().to_string());
|
||||
}
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
/// Sync changed blocks from live weights into a mmap'd safetensors file.
|
||||
/// Returns (total_bytes_compared, bytes_changed).
|
||||
fn sync_tensors_to_file(
|
||||
file_path: &Path,
|
||||
tensors: &[(String, Vec<u8>)],
|
||||
block_size: usize,
|
||||
) -> Result<(usize, usize)> {
|
||||
use safetensors::SafeTensors;
|
||||
|
||||
let file = fs::OpenOptions::new()
|
||||
.read(true)
|
||||
.write(true)
|
||||
.open(file_path)
|
||||
.with_context(|| format!("Failed to open {}", file_path.display()))?;
|
||||
|
||||
let mut mmap = unsafe { MmapMut::map_mut(&file)? };
|
||||
|
||||
// Parse safetensors header to find tensor offsets
|
||||
let header_size = u64::from_le_bytes(mmap[..8].try_into().unwrap()) as usize;
|
||||
let header_json: serde_json::Value =
|
||||
serde_json::from_slice(&mmap[8..8 + header_size])?;
|
||||
let data_start = 8 + header_size;
|
||||
|
||||
let mut total_compared = 0usize;
|
||||
let mut total_changed = 0usize;
|
||||
|
||||
for (name, live_data) in tensors {
|
||||
let meta = match header_json.get(name) {
|
||||
Some(m) => m,
|
||||
None => {
|
||||
eprintln!(" Warning: {} not found in {}", name, file_path.display());
|
||||
continue;
|
||||
}
|
||||
};
|
||||
|
||||
let offsets = meta["data_offsets"].as_array().unwrap();
|
||||
let start = data_start + offsets[0].as_u64().unwrap() as usize;
|
||||
let end = data_start + offsets[1].as_u64().unwrap() as usize;
|
||||
let disk_data = &mmap[start..end];
|
||||
|
||||
if disk_data.len() != live_data.len() {
|
||||
eprintln!(" Warning: size mismatch for {}: disk={} live={}",
|
||||
name, disk_data.len(), live_data.len());
|
||||
continue;
|
||||
}
|
||||
|
||||
// Diff block by block, memcpy only changed blocks
|
||||
let mut offset = 0;
|
||||
while offset < disk_data.len() {
|
||||
let block_end = (offset + block_size).min(disk_data.len());
|
||||
total_compared += block_end - offset;
|
||||
|
||||
if disk_data[offset..block_end] != live_data[offset..block_end] {
|
||||
mmap[start + offset..start + block_end]
|
||||
.copy_from_slice(&live_data[offset..block_end]);
|
||||
total_changed += block_end - offset;
|
||||
}
|
||||
offset = block_end;
|
||||
}
|
||||
}
|
||||
|
||||
mmap.flush()?;
|
||||
Ok((total_compared, total_changed))
|
||||
}
|
||||
|
||||
fn cmd_sync(handles: PathBuf, model_dir: PathBuf, block_size: usize) -> Result<()> {
|
||||
if !handles.exists() {
|
||||
bail!("Weight handles not found: {}. Is vLLM running with the export hook?",
|
||||
handles.display());
|
||||
}
|
||||
|
||||
eprintln!("Dumping live weights from GPU...");
|
||||
let live_weights = dump_live_weights(&handles, &model_dir)?;
|
||||
eprintln!(" {} tensors dumped", live_weights.len());
|
||||
|
||||
// Map parameter names to safetensors files
|
||||
let weight_map = read_safetensors_index(&model_dir)?;
|
||||
|
||||
// Group tensors by safetensors file
|
||||
let mut by_file: HashMap<String, Vec<(String, Vec<u8>)>> = HashMap::new();
|
||||
for (name, data) in live_weights {
|
||||
let file = weight_map
|
||||
.get(&name)
|
||||
.cloned()
|
||||
.unwrap_or_else(|| "model.safetensors".to_string());
|
||||
by_file.entry(file).or_default().push((name, data));
|
||||
}
|
||||
|
||||
let mut total_compared = 0usize;
|
||||
let mut total_changed = 0usize;
|
||||
|
||||
for (filename, tensors) in &by_file {
|
||||
let file_path = model_dir.join(filename);
|
||||
if !file_path.exists() {
|
||||
eprintln!(" Warning: {} not found, skipping", filename);
|
||||
continue;
|
||||
}
|
||||
|
||||
let (compared, changed) = sync_tensors_to_file(&file_path, tensors, block_size)?;
|
||||
total_compared += compared;
|
||||
total_changed += changed;
|
||||
|
||||
if changed > 0 {
|
||||
eprintln!(" {}: {:.1} MB changed", filename, changed as f64 / 1e6);
|
||||
}
|
||||
}
|
||||
|
||||
if total_changed == 0 {
|
||||
eprintln!("No changes — model files are up to date");
|
||||
} else {
|
||||
eprintln!(
|
||||
"Synced: {:.1} MB changed / {:.1} GB total ({:.3}%)",
|
||||
total_changed as f64 / 1e6,
|
||||
total_compared as f64 / 1e9,
|
||||
total_changed as f64 / total_compared as f64 * 100.0,
|
||||
);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
fn main() -> Result<()> {
|
||||
let cli = Cli::parse();
|
||||
match cli.command {
|
||||
Cmd::Sync { handles, model_dir, block_size } => {
|
||||
cmd_sync(handles, model_dir, block_size)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Export vLLM's live model weight IPC handles for the training process.
|
||||
|
||||
Connects to a running vLLM instance, iterates over model parameters,
|
||||
and exports CUDA IPC handles that allow another process to access the
|
||||
same GPU memory without copying.
|
||||
|
||||
Usage:
|
||||
# Run after vLLM is serving:
|
||||
python3 export_weights.py --output /tmp/vllm_weight_handles.pt
|
||||
|
||||
# Or via vLLM's API (future):
|
||||
curl -X POST http://localhost:8000/export_weights
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import torch
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def export_from_model(model, output_path: str):
|
||||
"""Export IPC handles for all model parameters."""
|
||||
from torch.multiprocessing.reductions import reduce_tensor
|
||||
|
||||
handles = {}
|
||||
total_bytes = 0
|
||||
|
||||
for name, param in model.named_parameters():
|
||||
handle = reduce_tensor(param.data)
|
||||
handles[name] = {
|
||||
'handle': handle,
|
||||
'shape': list(param.shape),
|
||||
'dtype': str(param.dtype),
|
||||
}
|
||||
param_bytes = param.nelement() * param.element_size()
|
||||
total_bytes += param_bytes
|
||||
|
||||
torch.save(handles, output_path)
|
||||
|
||||
n_params = len(handles)
|
||||
print(f"Exported {n_params} parameters ({total_bytes / 1e9:.1f} GB)")
|
||||
print(f"Saved to {output_path}")
|
||||
return handles
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Export vLLM weight IPC handles")
|
||||
parser.add_argument("--output", "-o", default="/tmp/vllm_weight_handles.pt",
|
||||
help="Output path for IPC handles")
|
||||
parser.add_argument("--vllm-pid", type=int, default=None,
|
||||
help="vLLM worker PID (auto-detected if not specified)")
|
||||
args = parser.parse_args()
|
||||
|
||||
# For now: load the model directly and export.
|
||||
# TODO: connect to running vLLM process instead.
|
||||
print("Note: This currently loads the model separately.")
|
||||
print("Full integration will export from the running vLLM process.")
|
||||
print()
|
||||
|
||||
# Detect model path from running vLLM
|
||||
import subprocess
|
||||
result = subprocess.run(
|
||||
['ps', 'aux'], capture_output=True, text=True
|
||||
)
|
||||
model_path = None
|
||||
for line in result.stdout.split('\n'):
|
||||
if 'vllm' in line and '--model' in line:
|
||||
parts = line.split()
|
||||
for i, p in enumerate(parts):
|
||||
if p == '--model' and i + 1 < len(parts):
|
||||
model_path = parts[i + 1]
|
||||
break
|
||||
# Also check model_tag format
|
||||
if p.startswith('--model='):
|
||||
model_path = p.split('=', 1)[1]
|
||||
break
|
||||
|
||||
if model_path:
|
||||
print(f"Detected vLLM model: {model_path}")
|
||||
else:
|
||||
print("Could not detect running vLLM model. Specify manually.")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -1,215 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""First real Apollo training step — ready for Kent to run.
|
||||
|
||||
This script:
|
||||
1. Imports vLLM's live weights via CUDA IPC
|
||||
2. Constructs HF model with shared memory views
|
||||
3. Runs ONE forward+backward on a real training example
|
||||
4. Applies ONE Apollo optimizer step
|
||||
5. Verifies vLLM still works after the update
|
||||
|
||||
The training example is from March 30: Kent said "use vLLM's code"
|
||||
and the model should have accepted instead of suggesting alternatives.
|
||||
|
||||
Usage:
|
||||
source ~/training-env/bin/activate
|
||||
python3 first_training_step.py [--dry-run]
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import time
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from transformers import AutoConfig, AutoTokenizer
|
||||
from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5ForCausalLM
|
||||
|
||||
sys.path.insert(0, '.')
|
||||
from weight_mapping import vllm_to_hf_views
|
||||
from apollo_mini import Apollo
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--dry-run', action='store_true',
|
||||
help="Run forward+backward but don't apply the optimizer step")
|
||||
parser.add_argument('--lr', type=float, default=1e-5,
|
||||
help="Learning rate (default: 1e-5 = conservative)")
|
||||
parser.add_argument('--rank', type=int, default=256)
|
||||
parser.add_argument('--handles', default='/tmp/vllm_weight_handles.pt')
|
||||
parser.add_argument('--model-path', default='Qwen/Qwen3.5-27B')
|
||||
args = parser.parse_args()
|
||||
|
||||
print("=== First Apollo Training Step ===\n")
|
||||
|
||||
# 1. Import vLLM weights
|
||||
print("1. Importing vLLM weights via CUDA IPC...")
|
||||
handles = torch.load(args.handles, weights_only=False)
|
||||
vllm_params = {}
|
||||
for name, info in handles.items():
|
||||
func, args_h = info['handle']
|
||||
vllm_params[name] = func(*args_h)
|
||||
print(f" {len(vllm_params)} parameters imported")
|
||||
|
||||
# 2. Map to HF layout
|
||||
print("2. Mapping to HF layout (zero-copy views)...")
|
||||
hf_params = vllm_to_hf_views(vllm_params)
|
||||
|
||||
# 3. Create HF model
|
||||
print("3. Creating HF model with shared weights...")
|
||||
config = AutoConfig.from_pretrained(args.model_path, trust_remote_code=True)
|
||||
with torch.device('meta'):
|
||||
model = Qwen3_5ForCausalLM(config.text_config)
|
||||
|
||||
replaced = 0
|
||||
for name, param in list(model.named_parameters()):
|
||||
if name in hf_params:
|
||||
parts = name.split('.')
|
||||
parent = model
|
||||
for part in parts[:-1]:
|
||||
parent = getattr(parent, part)
|
||||
setattr(parent, parts[-1],
|
||||
nn.Parameter(hf_params[name], requires_grad=True))
|
||||
replaced += 1
|
||||
print(f" {replaced} parameters replaced with vLLM memory views")
|
||||
|
||||
# 4. Load tokenizer
|
||||
print("4. Loading tokenizer...")
|
||||
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
|
||||
|
||||
# 5. Construct training example
|
||||
print("5. Constructing training example...")
|
||||
|
||||
# Context: conversation where Kent says to use vLLM's code
|
||||
# Target: the response that accepts the direction
|
||||
context = (
|
||||
"<|im_start|>user\n"
|
||||
"vllm has a fused kernel already, right?<|im_end|>\n"
|
||||
"<|im_start|>assistant\n"
|
||||
"Yeah — vLLM has `gdn_attention_core` which is a custom op "
|
||||
"that does the whole GDN layer's core in one dispatch.<|im_end|>\n"
|
||||
"<|im_start|>user\n"
|
||||
"Why wouldn't we just use that?<|im_end|>\n"
|
||||
"<|im_start|>assistant\n"
|
||||
)
|
||||
|
||||
# The CORRECT response (accept direction, don't suggest alternatives)
|
||||
continuation = (
|
||||
"We should. Let me pull in their kernel and wire it into "
|
||||
"our Rust orchestration. Which file should I start with?"
|
||||
)
|
||||
|
||||
context_ids = tokenizer.encode(context, add_special_tokens=False)
|
||||
continuation_ids = tokenizer.encode(continuation, add_special_tokens=False)
|
||||
all_ids = context_ids + continuation_ids
|
||||
context_len = len(context_ids)
|
||||
|
||||
print(f" Context: {context_len} tokens")
|
||||
print(f" Continuation: {len(continuation_ids)} tokens")
|
||||
print(f" Total: {len(all_ids)} tokens")
|
||||
|
||||
input_ids = torch.tensor([all_ids], device='cuda:0')
|
||||
|
||||
# 6. Initialize Apollo optimizer
|
||||
print(f"6. Initializing Apollo optimizer (rank={args.rank}, lr={args.lr})...")
|
||||
apollo_params = []
|
||||
standard_params = []
|
||||
for p in model.parameters():
|
||||
if p.requires_grad:
|
||||
if p.ndim >= 2 and min(p.shape) >= args.rank:
|
||||
apollo_params.append(p)
|
||||
else:
|
||||
standard_params.append(p)
|
||||
|
||||
groups = []
|
||||
if apollo_params:
|
||||
groups.append({'params': apollo_params})
|
||||
if standard_params:
|
||||
groups.append({'params': standard_params})
|
||||
|
||||
optimizer = Apollo(groups, lr=args.lr, rank=args.rank)
|
||||
print(f" Apollo: {len(apollo_params)} projected, {len(standard_params)} standard")
|
||||
|
||||
# 7. Forward pass
|
||||
print("7. Forward pass...")
|
||||
model.train()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Context-frozen: no grad for context, grad for continuation
|
||||
with torch.no_grad():
|
||||
ctx_output = model(input_ids[:, :context_len], use_cache=True)
|
||||
past_kv = ctx_output.past_key_values
|
||||
|
||||
with torch.enable_grad():
|
||||
output = model(input_ids[:, context_len:],
|
||||
past_key_values=past_kv, use_cache=False)
|
||||
logits = output.logits
|
||||
# Shift for next-token prediction
|
||||
shift_logits = logits[:, :-1].contiguous()
|
||||
shift_labels = input_ids[:, context_len + 1:].contiguous()
|
||||
loss = F.cross_entropy(
|
||||
shift_logits.view(-1, shift_logits.size(-1)),
|
||||
shift_labels.view(-1),
|
||||
)
|
||||
print(f" Loss: {loss.item():.4f}")
|
||||
|
||||
# 8. Backward pass
|
||||
print("8. Backward pass...")
|
||||
loss.backward()
|
||||
n_grads = sum(1 for p in model.parameters() if p.grad is not None)
|
||||
print(f" {n_grads} parameters have gradients")
|
||||
|
||||
# 9. Apollo step (or dry run)
|
||||
if args.dry_run:
|
||||
print("\n9. DRY RUN — skipping optimizer step")
|
||||
print(" (run without --dry-run to apply the update)")
|
||||
else:
|
||||
print("9. Applying Apollo optimizer step...")
|
||||
# Record a few weight norms before
|
||||
sample_norms_before = {}
|
||||
for name, p in model.named_parameters():
|
||||
if 'layers.0.' in name and p.grad is not None:
|
||||
sample_norms_before[name] = p.data.norm().item()
|
||||
|
||||
optimizer.step()
|
||||
|
||||
# Check weight changes
|
||||
print(" Weight changes (layer 0):")
|
||||
for name, before in sample_norms_before.items():
|
||||
p = dict(model.named_parameters())[name]
|
||||
after = p.data.norm().item()
|
||||
delta = abs(after - before)
|
||||
pct = delta / before * 100 if before > 0 else 0
|
||||
print(f" {name}: {before:.6f} → {after:.6f} (Δ{pct:.4f}%)")
|
||||
|
||||
optimizer.zero_grad()
|
||||
|
||||
# 10. Verify vLLM still works
|
||||
print("\n10. Verifying vLLM still serves...")
|
||||
import subprocess
|
||||
result = subprocess.run(
|
||||
['curl', '-s', '--max-time', '30',
|
||||
'-X', 'POST', 'http://localhost:8000/v1/chat/completions',
|
||||
'-H', 'Content-Type: application/json',
|
||||
'-H', 'Authorization: Bearer bcachefs-agents-2026',
|
||||
'-d', '{"model":"Qwen/Qwen3.5-27B","messages":[{"role":"user","content":"Hi"}],"max_tokens":4}'],
|
||||
capture_output=True, text=True, timeout=45
|
||||
)
|
||||
if result.returncode == 0 and 'choices' in result.stdout:
|
||||
print(" vLLM still serving ✓")
|
||||
else:
|
||||
print(" WARNING: vLLM may not be responding")
|
||||
print(f" stdout: {result.stdout[:200]}")
|
||||
|
||||
print("\n=== COMPLETE ===")
|
||||
if args.dry_run:
|
||||
print("Run without --dry-run to apply the first real training step.")
|
||||
else:
|
||||
print("First Apollo training step applied to vLLM's live weights.")
|
||||
print(f"Optimizer state: {optimizer.state_size_bytes() / 1e6:.1f} MB")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
28
training/pyproject.toml
Normal file
28
training/pyproject.toml
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
[build-system]
|
||||
requires = ["setuptools>=61.0"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "apollo-plugin"
|
||||
version = "0.1.0"
|
||||
description = "Apollo training plugin for vLLM"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"torch",
|
||||
"aiohttp",
|
||||
"safetensors",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = ["pytest"]
|
||||
|
||||
[project.entry-points."vllm.general_plugins"]
|
||||
apollo = "apollo_plugin:register"
|
||||
|
||||
[project.scripts]
|
||||
apollo-worker = "apollo_plugin.worker:main"
|
||||
apollo-checkpoint = "apollo_plugin.checkpoint_sync:main"
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
where = ["."]
|
||||
include = ["apollo_plugin*"]
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
#!/bin/bash
|
||||
# Start vLLM with Apollo weight export hook.
|
||||
#
|
||||
# The hook patches vLLM's model runner to export CUDA IPC handles
|
||||
# after loading, so the Apollo training process can share the same
|
||||
# GPU memory.
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
|
||||
exec python3 -c "
|
||||
import sys
|
||||
sys.path.insert(0, '$SCRIPT_DIR')
|
||||
import vllm_export_hook # patches model runner before vLLM loads
|
||||
|
||||
sys.argv = ['vllm'] + sys.argv[1:]
|
||||
from vllm.entrypoints.cli.main import main
|
||||
main()
|
||||
" serve "$@"
|
||||
|
|
@ -1,269 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Nightly training process for Apollo-Mini fine-tuning.
|
||||
|
||||
Imports vLLM's model weights via CUDA IPC, runs context-frozen
|
||||
training on flagged conversation segments, saves updated checkpoint.
|
||||
|
||||
Usage:
|
||||
python3 train.py \
|
||||
--weights /tmp/vllm_weight_handles.pt \
|
||||
--examples training-examples.jsonl \
|
||||
--checkpoint-dir checkpoints/ \
|
||||
--lr 1e-5
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from safetensors.torch import save_file
|
||||
|
||||
from apollo_mini import ApolloMini
|
||||
|
||||
|
||||
def import_weights(handle_path: str) -> dict[str, torch.Tensor]:
|
||||
"""Import weight tensors from CUDA IPC handles."""
|
||||
handles = torch.load(handle_path, weights_only=False)
|
||||
params = {}
|
||||
for name, info in handles.items():
|
||||
func, args = info['handle']
|
||||
tensor = func(*args)
|
||||
params[name] = tensor
|
||||
return params
|
||||
|
||||
|
||||
def make_param_groups(params: dict[str, torch.Tensor]) -> list[dict]:
|
||||
"""Split parameters into Apollo-Mini and standard groups.
|
||||
|
||||
Apollo-Mini needs 2D+ matrices with min dimension >= 2.
|
||||
Small tensors (norms, biases, conv1d 3D weights) use standard Adam.
|
||||
"""
|
||||
apollo_params = []
|
||||
standard_params = []
|
||||
|
||||
for name, p in params.items():
|
||||
p.requires_grad_(True)
|
||||
if p.ndim >= 2 and min(p.shape) >= 2:
|
||||
apollo_params.append(p)
|
||||
else:
|
||||
standard_params.append(p)
|
||||
|
||||
groups = []
|
||||
if apollo_params:
|
||||
groups.append({
|
||||
'params': apollo_params,
|
||||
'name': 'apollo',
|
||||
})
|
||||
if standard_params:
|
||||
groups.append({
|
||||
'params': standard_params,
|
||||
'name': 'standard',
|
||||
})
|
||||
|
||||
n_apollo = sum(p.nelement() for p in apollo_params)
|
||||
n_standard = sum(p.nelement() for p in standard_params)
|
||||
print(f"Parameter groups: apollo={n_apollo/1e9:.2f}B, standard={n_standard/1e6:.1f}M")
|
||||
return groups
|
||||
|
||||
|
||||
def forward_pass(params, input_ids, context_len, device):
|
||||
"""Run context-frozen forward pass.
|
||||
|
||||
Args:
|
||||
params: dict of name -> tensor (shared with vLLM)
|
||||
input_ids: full sequence [1, seq_len]
|
||||
context_len: number of context tokens (no gradient)
|
||||
device: CUDA device
|
||||
|
||||
Returns:
|
||||
logits for decision tokens, target ids for loss
|
||||
"""
|
||||
# TODO: Build proper forward model matching vLLM's weight layout.
|
||||
# For now this is a placeholder — the real implementation needs
|
||||
# to replicate vLLM's model architecture (merged projections,
|
||||
# GDN recurrence, full attention, MLP) using the shared weights.
|
||||
raise NotImplementedError(
|
||||
"Forward model not yet implemented. "
|
||||
"Need to build a model that matches vLLM's merged weight layout "
|
||||
"(MergedColumnParallelLinear for qkvz/ba/gate_up, "
|
||||
"RowParallelLinear for out_proj/down) and computes the same "
|
||||
"forward pass with autograd enabled."
|
||||
)
|
||||
|
||||
|
||||
def save_checkpoint(params: dict[str, torch.Tensor],
|
||||
checkpoint_dir: str,
|
||||
config_path: str = None):
|
||||
"""Save model checkpoint in HuggingFace safetensors format.
|
||||
|
||||
Saves weights split across shards matching the original model layout,
|
||||
archives the previous checkpoint, and updates the 'latest' symlink.
|
||||
"""
|
||||
date_str = datetime.now().strftime("%Y-%m-%d")
|
||||
out_dir = Path(checkpoint_dir) / date_str
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save all weights in a single safetensors file for now.
|
||||
# TODO: split across shards matching HF model index for large models.
|
||||
tensors = {}
|
||||
for name, param in params.items():
|
||||
tensors[name] = param.data.contiguous().cpu()
|
||||
|
||||
save_path = out_dir / "model.safetensors"
|
||||
save_file(tensors, str(save_path))
|
||||
print(f"Saved checkpoint to {save_path} ({save_path.stat().st_size / 1e9:.1f} GB)")
|
||||
|
||||
# Copy config files if provided
|
||||
if config_path:
|
||||
import shutil
|
||||
config_dir = Path(config_path)
|
||||
for f in ['config.json', 'tokenizer.json', 'tokenizer_config.json',
|
||||
'special_tokens_map.json', 'generation_config.json']:
|
||||
src = config_dir / f
|
||||
if src.exists():
|
||||
shutil.copy2(src, out_dir / f)
|
||||
|
||||
# Update latest symlink
|
||||
latest = Path(checkpoint_dir) / "latest"
|
||||
if latest.is_symlink():
|
||||
latest.unlink()
|
||||
latest.symlink_to(date_str)
|
||||
print(f"Updated {latest} -> {date_str}")
|
||||
|
||||
return str(out_dir)
|
||||
|
||||
|
||||
def train_step(params, example, optimizer, device, log_entries):
|
||||
"""Run one training step on a single example.
|
||||
|
||||
Args:
|
||||
params: dict of name -> tensor
|
||||
example: dict with 'input_ids', 'context_len', 'target_ids'
|
||||
optimizer: ApolloMini instance
|
||||
device: CUDA device
|
||||
log_entries: list to append log dicts to
|
||||
|
||||
Returns:
|
||||
loss value
|
||||
"""
|
||||
optimizer.zero_grad()
|
||||
|
||||
input_ids = torch.tensor(example['input_ids'], device=device).unsqueeze(0)
|
||||
context_len = example['context_len']
|
||||
|
||||
# Forward pass (context frozen, decision tokens with grad)
|
||||
logits, targets = forward_pass(params, input_ids, context_len, device)
|
||||
|
||||
# Cross-entropy loss on decision tokens
|
||||
loss = torch.nn.functional.cross_entropy(
|
||||
logits.view(-1, logits.shape[-1]),
|
||||
targets.view(-1),
|
||||
)
|
||||
|
||||
# Backward
|
||||
loss.backward()
|
||||
|
||||
# Compute gradient stats before optimizer step
|
||||
total_grad_norm = 0.0
|
||||
for p in params.values():
|
||||
if p.grad is not None:
|
||||
total_grad_norm += p.grad.norm().item() ** 2
|
||||
total_grad_norm = total_grad_norm ** 0.5
|
||||
|
||||
# Optimizer step
|
||||
optimizer.step()
|
||||
|
||||
# Log
|
||||
log_entries.append({
|
||||
'example_id': example.get('id', 'unknown'),
|
||||
'loss': loss.item(),
|
||||
'grad_norm': total_grad_norm,
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
})
|
||||
|
||||
return loss.item()
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Apollo-Mini training")
|
||||
parser.add_argument("--weights", required=True,
|
||||
help="Path to exported weight IPC handles")
|
||||
parser.add_argument("--examples", required=True,
|
||||
help="Path to training examples JSONL")
|
||||
parser.add_argument("--checkpoint-dir", default="checkpoints",
|
||||
help="Directory for saving checkpoints")
|
||||
parser.add_argument("--config-path", default=None,
|
||||
help="Path to model config files (for checkpoint)")
|
||||
parser.add_argument("--lr", type=float, default=1e-5,
|
||||
help="Learning rate")
|
||||
parser.add_argument("--warmup-steps", type=int, default=10,
|
||||
help="Learning rate warmup steps")
|
||||
parser.add_argument("--weight-decay", type=float, default=0.01)
|
||||
parser.add_argument("--dry-run", action="store_true",
|
||||
help="Load weights and validate, don't train")
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f"Apollo-Mini Training")
|
||||
print(f" weights: {args.weights}")
|
||||
print(f" examples: {args.examples}")
|
||||
print(f" lr: {args.lr}")
|
||||
print()
|
||||
|
||||
# Import weights
|
||||
print("Importing weights via CUDA IPC...")
|
||||
params = import_weights(args.weights)
|
||||
print(f" {len(params)} parameters imported")
|
||||
|
||||
# Make parameter groups
|
||||
param_groups = make_param_groups(params)
|
||||
|
||||
# Initialize optimizer
|
||||
optimizer = ApolloMini(param_groups, lr=args.lr,
|
||||
weight_decay=args.weight_decay,
|
||||
warmup_steps=args.warmup_steps)
|
||||
print(f" Optimizer state: {optimizer.state_size_bytes() / 1e6:.1f} MB")
|
||||
|
||||
if args.dry_run:
|
||||
print("\nDry run — weights imported and validated successfully.")
|
||||
return
|
||||
|
||||
# Load training examples
|
||||
examples = []
|
||||
with open(args.examples) as f:
|
||||
for line in f:
|
||||
examples.append(json.loads(line))
|
||||
print(f" {len(examples)} training examples")
|
||||
|
||||
# Training loop
|
||||
log_entries = []
|
||||
print(f"\nTraining...")
|
||||
t0 = time.time()
|
||||
|
||||
for i, example in enumerate(examples):
|
||||
loss = train_step(params, example, optimizer, 'cuda:0', log_entries)
|
||||
print(f" [{i+1}/{len(examples)}] loss={loss:.4f}")
|
||||
|
||||
elapsed = time.time() - t0
|
||||
print(f"\nTraining complete: {len(examples)} examples in {elapsed:.1f}s")
|
||||
print(f" Final optimizer state: {optimizer.state_size_bytes() / 1e6:.1f} MB")
|
||||
|
||||
# Save checkpoint
|
||||
print("\nSaving checkpoint...")
|
||||
save_checkpoint(params, args.checkpoint_dir, args.config_path)
|
||||
|
||||
# Save training log
|
||||
date_str = datetime.now().strftime("%Y-%m-%d")
|
||||
log_path = Path(args.checkpoint_dir) / date_str / "training-log.jsonl"
|
||||
with open(log_path, 'w') as f:
|
||||
for entry in log_entries:
|
||||
f.write(json.dumps(entry) + '\n')
|
||||
print(f"Training log: {log_path}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -1,175 +0,0 @@
|
|||
"""Training example construction and tokenization.
|
||||
|
||||
Takes raw conversation context + improved continuation, produces
|
||||
tokenized tensors ready for context-frozen forward+backward.
|
||||
"""
|
||||
|
||||
import json
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
|
||||
@dataclass
|
||||
class TrainingExample:
|
||||
"""A single training example for context-frozen training."""
|
||||
id: str
|
||||
context: str # conversation up to decision point
|
||||
continuation: str # the better response
|
||||
reason: str = "" # why this is a training target
|
||||
memories: list[str] = field(default_factory=list) # memories that were in context
|
||||
|
||||
# Computed after tokenization
|
||||
input_ids: torch.Tensor | None = None
|
||||
context_len: int = 0
|
||||
total_len: int = 0
|
||||
|
||||
def tokenize(self, tokenizer, max_len: int = 8192, device: str = "cuda:0"):
|
||||
"""Tokenize context + continuation into training-ready tensors.
|
||||
|
||||
The chat template is applied to make the token distribution
|
||||
match what the model sees during inference.
|
||||
"""
|
||||
# Build messages for context (everything up to the decision)
|
||||
# The context should already be in chat format
|
||||
context_ids = tokenizer.encode(self.context, add_special_tokens=False)
|
||||
continuation_ids = tokenizer.encode(self.continuation, add_special_tokens=False)
|
||||
|
||||
self.context_len = len(context_ids)
|
||||
self.total_len = len(context_ids) + len(continuation_ids)
|
||||
|
||||
if self.total_len > max_len:
|
||||
# Truncate context from the left, keep continuation intact
|
||||
excess = self.total_len - max_len
|
||||
context_ids = context_ids[excess:]
|
||||
self.context_len = len(context_ids)
|
||||
self.total_len = len(context_ids) + len(continuation_ids)
|
||||
|
||||
all_ids = context_ids + continuation_ids
|
||||
self.input_ids = torch.tensor(all_ids, device=device)
|
||||
return self
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
'id': self.id,
|
||||
'context': self.context,
|
||||
'continuation': self.continuation,
|
||||
'reason': self.reason,
|
||||
'memories': self.memories,
|
||||
'context_len': self.context_len,
|
||||
'total_len': self.total_len,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, d: dict) -> 'TrainingExample':
|
||||
return cls(
|
||||
id=d['id'],
|
||||
context=d['context'],
|
||||
continuation=d['continuation'],
|
||||
reason=d.get('reason', ''),
|
||||
memories=d.get('memories', []),
|
||||
)
|
||||
|
||||
|
||||
def load_examples(path: str) -> list[TrainingExample]:
|
||||
"""Load training examples from JSONL file."""
|
||||
examples = []
|
||||
with open(path) as f:
|
||||
for line in f:
|
||||
if line.strip():
|
||||
examples.append(TrainingExample.from_dict(json.loads(line)))
|
||||
return examples
|
||||
|
||||
|
||||
def save_examples(examples: list[TrainingExample], path: str):
|
||||
"""Save training examples to JSONL file."""
|
||||
with open(path, 'w') as f:
|
||||
for ex in examples:
|
||||
f.write(json.dumps(ex.to_dict()) + '\n')
|
||||
|
||||
|
||||
class ExampleTokenizer:
|
||||
"""Handles tokenization with the model's chat template.
|
||||
|
||||
Applies the same chat template that vLLM uses during inference,
|
||||
so the token distribution matches what the model expects.
|
||||
"""
|
||||
|
||||
def __init__(self, model_path: str):
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_path, trust_remote_code=True)
|
||||
|
||||
def prepare_example(self, example: TrainingExample,
|
||||
max_len: int = 8192,
|
||||
device: str = "cuda:0") -> TrainingExample:
|
||||
"""Tokenize an example using the chat template.
|
||||
|
||||
For proper training, the context should be formatted exactly
|
||||
as vLLM would format it — with chat template applied.
|
||||
"""
|
||||
# Apply chat template to get the exact token sequence
|
||||
# the model would see during inference
|
||||
#
|
||||
# Context: everything up to the decision point
|
||||
# Continuation: the improved response
|
||||
#
|
||||
# We tokenize them separately to know where context ends
|
||||
# and continuation begins.
|
||||
context_ids = self.tokenizer.encode(
|
||||
example.context, add_special_tokens=True)
|
||||
continuation_ids = self.tokenizer.encode(
|
||||
example.continuation, add_special_tokens=False)
|
||||
|
||||
example.context_len = len(context_ids)
|
||||
example.total_len = len(context_ids) + len(continuation_ids)
|
||||
|
||||
if example.total_len > max_len:
|
||||
excess = example.total_len - max_len
|
||||
context_ids = context_ids[excess:]
|
||||
example.context_len = len(context_ids)
|
||||
example.total_len = example.context_len + len(continuation_ids)
|
||||
|
||||
all_ids = context_ids + continuation_ids
|
||||
example.input_ids = torch.tensor(all_ids, device=device)
|
||||
return example
|
||||
|
||||
def prepare_from_messages(self, example_id: str,
|
||||
messages: list[dict],
|
||||
decision_idx: int,
|
||||
better_response: str,
|
||||
reason: str = "",
|
||||
memories: list[str] | None = None,
|
||||
max_len: int = 8192,
|
||||
device: str = "cuda:0") -> TrainingExample:
|
||||
"""Build a training example from a chat message list.
|
||||
|
||||
Args:
|
||||
example_id: unique identifier
|
||||
messages: list of {"role": ..., "content": ...} dicts
|
||||
decision_idx: index of the assistant message to replace
|
||||
better_response: the improved response text
|
||||
reason: why this is a training target
|
||||
memories: memory keys that were in context
|
||||
max_len: maximum sequence length
|
||||
device: target device
|
||||
|
||||
Returns:
|
||||
Tokenized TrainingExample
|
||||
"""
|
||||
# Context: all messages up to (not including) the decision
|
||||
context_messages = messages[:decision_idx]
|
||||
context_text = self.tokenizer.apply_chat_template(
|
||||
context_messages, tokenize=False, add_generation_prompt=True)
|
||||
|
||||
# Build the example
|
||||
example = TrainingExample(
|
||||
id=example_id,
|
||||
context=context_text,
|
||||
continuation=better_response,
|
||||
reason=reason,
|
||||
memories=memories or [],
|
||||
)
|
||||
|
||||
return self.prepare_example(example, max_len=max_len, device=device)
|
||||
Loading…
Add table
Add a link
Reference in a new issue