fix: use accelerate device_map for proper MoE quantization
This commit is contained in:
Christian Medina
@@ -1,15 +1,11 @@
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#!/usr/bin/env python3
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"""True streaming 4-bit NF4 quantization - one shard at a time."""
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"""Streaming NF4 quantization using accelerate device_map."""
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import argparse
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import gc
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import torch
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import concurrent.futures
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from pathlib import Path
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from safetensors.torch import load_file, save_file
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from bitsandbytes.nn import Linear4bit
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from torch import nn
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from transformers import AutoConfig
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig
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def quantize_weight_nf4(weight: torch.Tensor):
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@@ -36,139 +32,36 @@ def streaming_quantize(model_path: str, output_path: str):
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output_path = Path(output_path)
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output_path.mkdir(parents=True, exist_ok=True)
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import glob
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shards = sorted(glob.glob(f"{model_path}/*.safetensors"))
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# Rename existing 0-indexed files to 1-indexed
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for existing in output_path.glob("*.safetensors"):
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parts = existing.name.split("-")
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if len(parts) >= 3 and existing.name.startswith("model-00000-"):
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num = int(parts[1])
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new_name = f"model-{num+1:05d}-of-{len(shards):05d}.safetensors"
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new_path = output_path / new_name
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existing.rename(new_path)
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print(f"Renamed: {existing.name} -> {new_name}")
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print(f"Found {len(shards)} shards\n")
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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)
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# Process continuously: max 4 shards per GPU, add to whichever GPU has room
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max_per_gpu = 4
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num_gpus = 2
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def process_shard(idx, shard_file, gpu_id):
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"""Process a single shard (called per-GPU)."""
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shard_name = f"model-{idx+1:05d}-of-{len(shards):05d}.safetensors"
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if (output_path / shard_name).exists():
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return
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print(f"[{idx+1}/{len(shards)}] {Path(shard_file).name} (GPU {gpu_id})")
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# Load to assigned GPU
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state_dict = load_file(shard_file, device=f"cuda:{gpu_id}")
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weight_keys = [
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k for k, v in state_dict.items()
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if "weight" in k and isinstance(v, torch.Tensor) and v.dim() == 2
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]
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print(f" Quantizing {len(weight_keys)} tensors...")
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for key in weight_keys:
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try:
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weight = state_dict[key]
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qweight, qstate = quantize_weight_nf4(weight)
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state_dict[key] = qweight
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del weight, qweight, qstate
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gc.collect()
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torch.cuda.empty_cache()
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except Exception as e:
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print(f" Warning: Failed on {key}: {e}")
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gc.collect()
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torch.cuda.empty_cache()
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# Move to CPU and save
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state_dict = {
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k: v.cpu() if isinstance(v, torch.Tensor) else v
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for k, v in state_dict.items()
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}
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save_file(state_dict, output_path / shard_name)
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print(f" ✓ Saved {shard_name}")
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del state_dict
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gc.collect()
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torch.cuda.empty_cache()
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# Find first unsaved shard
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start_idx = 0
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for idx in range(len(shards)):
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shard_name = f"model-{idx+1:05d}-of-{len(shards):05d}.safetensors"
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if not (output_path / shard_name).exists():
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start_idx = idx
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break
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print(f"Starting from shard {start_idx+1}/16\n")
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# Process continuously: submit to GPU with fewer active tasks
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import threading
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gpu_locks = [threading.Lock() for _ in range(num_gpus)]
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gpu_counts = [0] * num_gpus
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def get_next_gpu():
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"""Get the GPU with fewest active tasks (max 4 per GPU)."""
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with gpu_locks[0]:
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with gpu_locks[1]:
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if gpu_counts[0] <= gpu_counts[1] and gpu_counts[0] < max_per_gpu:
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return 0
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elif gpu_counts[1] < max_per_gpu:
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return 1
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else:
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return None # Both GPUs at max
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# Create a queue of unprocessed shards
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from collections import deque
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remaining = deque(range(start_idx, len(shards)))
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_per_gpu * num_gpus) as executor:
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futures = []
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def submit_next():
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"""Submit next shard to available GPU."""
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gpu_id = get_next_gpu()
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if gpu_id is None or not remaining:
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return
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idx = remaining.popleft()
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shard_file = shards[idx]
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with gpu_locks[gpu_id]:
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gpu_counts[gpu_id] += 1
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future = executor.submit(process_shard, idx, shard_file, gpu_id)
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futures.append(future)
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# When this future completes, release GPU slot and submit next
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def on_complete(f):
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with gpu_locks[gpu_id]:
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gpu_counts[gpu_id] -= 1
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submit_next() # Try to submit next shard
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future.add_done_callback(on_complete)
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# Start submitting
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while remaining:
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gpu_id = get_next_gpu()
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if gpu_id is None:
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break
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submit_next()
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# Wait for all to complete
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concurrent.futures.wait(futures)
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print("Loading model with BnB 4-bit (shards streamed to GPUs)...")
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print(" This will distribute across both GPUs\n")
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config.save_pretrained(output_path)
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print(f"\n✅ Done → {output_path}")
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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quantization_config=bnb_config,
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device_map="auto",
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max_memory={0: "28GiB", 1: "28GiB", "cpu": "120GiB"},
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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)
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# Count quantized parameters
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total_params = sum(p.numel() for p in model.parameters())
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bnb_params = sum(p.numel() for p in model.parameters() if hasattr(p, 'quant_state') and p.quant_state is not None)
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print(f"✓ Model loaded and quantized")
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print(f" Total: {total_params / 1e9:.2f}B parameters")
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print(f" Quantized: {bnb_params / 1e9:.2f}B parameters ({bnb_params/total_params*100:.1f}%)\n")
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print(f"Saving quantized model to: {output_path}")
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model.save_pretrained(output_path)
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print(f"\n✅ Quantized model saved to: {output_path}")
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if __name__ == "__main__":
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