stream_preprocess.py

#!/usr/bin/env python3
"""
Stream-preprocess: download one shard at a time, process it, delete it.
Avoids needing 17 GB free for the full model download.

Usage:
    python3 stream_preprocess.py
"""

import os
import sys
import json
import time
import gc
import shutil

sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))

import numpy as np
import mlx.core as mx

REPO = "mlx-community/Qwen3-30B-A3B-4bit"
OUTPUT_DIR = os.path.expanduser("~/models/qwen3-30b")
PAGE_SIZE = 16384

TENSOR_NAMES = [
    "gate_proj.weight", "gate_proj.scales", "gate_proj.biases",
    "up_proj.weight", "up_proj.scales", "up_proj.biases",
    "down_proj.weight", "down_proj.scales", "down_proj.biases",
]


def convert_layer_to_bin(layer_data, layer_idx, num_experts, output_dir):
    tensor_info = {}
    expert_block_size = 0
    for name in TENSOR_NAMES:
        t = layer_data[name]
        per_expert_shape = list(t.shape[1:])
        if t.dtype == mx.uint32:
            elem_size = 4
        elif t.dtype in (mx.bfloat16, mx.float16):
            elem_size = 2
        else:
            elem_size = 4
        nbytes = 1
        for s in per_expert_shape:
            nbytes *= s
        nbytes *= elem_size
        tensor_info[name] = {
            "shape_per_expert": per_expert_shape,
            "dtype": str(t.dtype).replace("mlx.core.", ""),
            "nbytes": nbytes,
            "inner_offset": expert_block_size,
        }
        expert_block_size += nbytes

    header = {
        "layer_idx": layer_idx,
        "num_experts": num_experts,
        "layout": {
            "expert_block_size": expert_block_size,
            "data_start": PAGE_SIZE,
            "tensors": tensor_info,
        }
    }
    header_bytes = json.dumps(header, indent=2).encode()
    assert len(header_bytes) < PAGE_SIZE
    header_bytes += b"\x00" * (PAGE_SIZE - len(header_bytes))

    out_path = os.path.join(output_dir, "bin", f"moe_layer_{layer_idx:02d}.bin")
    with open(out_path, "wb") as f:
        f.write(header_bytes)
        for expert_id in range(num_experts):
            for name in TENSOR_NAMES:
                t = layer_data[name][expert_id]
                if t.dtype == mx.bfloat16:
                    raw = np.array(t.astype(mx.float16)).astype(np.float16).tobytes()
                elif t.dtype == mx.uint32:
                    raw = np.array(t).astype(np.uint32).tobytes()
                else:
                    raw = np.array(t).tobytes()
                f.write(raw)

    return os.path.getsize(out_path)


def main():
    from huggingface_hub import hf_hub_download, HfApi

    print("=" * 55)
    print("  Stream Preprocess — one shard at a time")
    print(f"  Model:  {REPO}")
    print(f"  Output: {OUTPUT_DIR}")
    print("=" * 55)

    os.makedirs(os.path.join(OUTPUT_DIR, "bin"), exist_ok=True)

    # Download config + tokenizer files (small)
    for fname in ["config.json", "tokenizer.json", "tokenizer_config.json",
                   "special_tokens_map.json"]:
        try:
            path = hf_hub_download(REPO, fname, local_dir="/tmp/sniper_dl")
            shutil.copy(path, os.path.join(OUTPUT_DIR, fname))
            print(f"  Downloaded {fname}")
        except Exception as e:
            print(f"  Skipped {fname}: {e}")

    # Get shard list
    with open(os.path.join(OUTPUT_DIR, "config.json")) as f:
        config = json.load(f)
    num_layers = config.get("num_hidden_layers", 48)

    # Download the index to find shard names
    idx_path = hf_hub_download(REPO, "model.safetensors.index.json",
                                local_dir="/tmp/sniper_dl")
    with open(idx_path) as f:
        idx = json.load(f)
    shards = sorted(set(idx["weight_map"].values()))
    print(f"\n  {len(shards)} shards to process")

    # Check which layers already done
    existing = set()
    for f in os.listdir(os.path.join(OUTPUT_DIR, "bin")):
        if f.startswith("moe_layer_") and f.endswith(".bin"):
            existing.add(int(f.split("_")[2].split(".")[0]))
    if existing:
        print(f"  Already done: layers {sorted(existing)}")

    pinned = {}
    layers_done = set(existing)

    # Track partial layers that span shards
    partial_layers = {}

    for si, shard_name in enumerate(shards):
        print(f"\n  [{si+1}/{len(shards)}] Downloading {shard_name}...")
        t0 = time.time()

        shard_path = hf_hub_download(REPO, shard_name, local_dir="/tmp/sniper_dl")
        dl_time = time.time() - t0
        shard_size = os.path.getsize(shard_path) / 1e9
        print(f"    Downloaded {shard_size:.1f} GB in {dl_time:.0f}s")

        print(f"    Loading tensors...")
        data = mx.load(shard_path)
        print(f"    {len(data)} tensors")

        # Classify
        layer_experts = {}
        for key, tensor in data.items():
            if "switch_mlp" in key:
                layer = int(key.split(".layers.")[1].split(".")[0])
                short = key.split(".switch_mlp.")[1]
                layer_experts.setdefault(layer, {})[short] = tensor
            else:
                pinned[key] = tensor

        # Convert complete expert layers
        for layer_idx, tensors in layer_experts.items():
            if layer_idx in layers_done:
                continue

            # Merge with partial data from previous shards
            if layer_idx in partial_layers:
                partial_layers[layer_idx].update(tensors)
                tensors = partial_layers[layer_idx]

            if len(tensors) < 9:
                # Partial — save for later
                partial_layers[layer_idx] = tensors
                print(f"    Layer {layer_idx}: partial ({len(tensors)}/9 tensors)")
                continue

            num_experts = tensors[list(tensors.keys())[0]].shape[0]
            sz = convert_layer_to_bin(tensors, layer_idx, num_experts, OUTPUT_DIR)
            layers_done.add(layer_idx)
            if layer_idx in partial_layers:
                del partial_layers[layer_idx]
            print(f"    Layer {layer_idx}: {sz/1e6:.0f} MB")

        del data, layer_experts
        gc.collect()
        mx.clear_cache()

        # Delete the downloaded shard to free disk
        try:
            os.remove(shard_path)
            print(f"    Deleted shard ({shard_size:.1f} GB freed)")
        except:
            pass

    # Handle remaining partial layers
    if partial_layers:
        print(f"\n  {len(partial_layers)} partial layers remain — re-downloading...")
        for layer_idx, tensors in partial_layers.items():
            if layer_idx in layers_done:
                continue
            if len(tensors) >= 9:
                num_experts = tensors[list(tensors.keys())[0]].shape[0]
                sz = convert_layer_to_bin(tensors, layer_idx, num_experts, OUTPUT_DIR)
                layers_done.add(layer_idx)
                print(f"    Layer {layer_idx}: {sz/1e6:.0f} MB (merged)")

    # Save pinned if we don't already have it or if it's stale
    pinned_path = os.path.join(OUTPUT_DIR, "pinned.safetensors")
    if pinned:
        print(f"\n  Saving pinned ({len(pinned)} tensors)...")
        mx.save_safetensors(pinned_path, pinned)
        psz = os.path.getsize(pinned_path) / 1e9
        print(f"    Pinned: {psz:.2f} GB")
    else:
        psz = os.path.getsize(pinned_path) / 1e9 if os.path.exists(pinned_path) else 0

    # Clean up temp downloads
    shutil.rmtree("/tmp/sniper_dl", ignore_errors=True)

    # Summary
    import glob
    bin_files = sorted(glob.glob(os.path.join(OUTPUT_DIR, "bin", "moe_layer_*.bin")))
    total = sum(os.path.getsize(f) for f in bin_files)
    print(f"\n  Expert layers: {len(bin_files)}/{num_layers}")
    print(f"  Expert total:  {total/1e9:.2f} GB")
    print(f"  Pinned:        {psz:.2f} GB")
    print(f"  Total:         {(total/1e9 + psz):.2f} GB")

    missing = set(range(num_layers)) - layers_done
    if missing:
        print(f"\n  WARNING: Missing layers: {sorted(missing)}")
    else:
        print(f"\n  All {num_layers} layers converted!")
        print(f"\n  Test with:")
        print(f"    mlx-sniper run {OUTPUT_DIR} -p 'What is 2+2?' -v")


if __name__ == "__main__":
    main()