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Shared utilities for nanochat sweep scripts.
Extracted from lr_sweep.py / warmup_sweep.py / research_compare.py /
actual_lr_research_sweep.py to eliminate copy-paste.
"""
from __future__ import annotations
import os
import shutil
import subprocess
import sys
import torch
# ---------------------------------------------------------------------------
# DDP runner
# ---------------------------------------------------------------------------
def resolve_runner() -> list[str]:
"""Return torchrun command prefix, capped to available GPUs.
Reads NPROC_PER_NODE (set by shell scripts) or NANOCHAT_NPROC as the
requested worker count and caps it to the number of visible CUDA devices.
"""
nproc_requested = int(os.environ.get("NPROC_PER_NODE", os.environ.get("NANOCHAT_NPROC", 8)))
gpu_count = max(torch.cuda.device_count(), 1)
nproc = min(nproc_requested, gpu_count)
if nproc < nproc_requested:
print(
f"[sweep] Requested {nproc_requested} DDP workers but only "
f"{gpu_count} GPU(s) available — using {nproc}."
)
torchrun = shutil.which("torchrun")
if torchrun:
return [torchrun, "--standalone", f"--nproc_per_node={nproc}"]
return [
sys.executable, "-m", "torch.distributed.run",
"--standalone", f"--nproc_per_node={nproc}",
]
# ---------------------------------------------------------------------------
# Architecture sizing
# ---------------------------------------------------------------------------
def model_dims(depth: int, aspect_ratio: int = 0) -> tuple[int, int, int, int]:
"""Return (aspect_ratio, head_dim, model_dim, research_dim) for a given depth.
research_dim is ~1/8th of model_dim, rounded up to the nearest head_dim
multiple, capped at model_dim.
"""
if aspect_ratio <= 0:
aspect_ratio = 57 if depth == 9 else 64
head_dim = 128
base_dim = depth * aspect_ratio
model_dim = ((base_dim + head_dim - 1) // head_dim) * head_dim
raw_research_dim = max(model_dim // 8, 1)
research_dim = ((raw_research_dim + head_dim - 1) // head_dim) * head_dim
research_dim = min(research_dim, model_dim)
return aspect_ratio, head_dim, model_dim, research_dim
def estimate_tokens_from_base(
depth: int,
target_ratio: float = 10.5,
tokenizer_dir: str | None = None,
) -> int:
"""Chinchilla-style optimal token count: target_ratio × scaling_params.
Mirrors base_train.py exactly (transformer_matrices + lm_head).
"""
from nanochat.gpt import GPT, GPTConfig
vocab_size = 32768
try:
from nanochat.tokenizer import get_tokenizer
tokenizer = get_tokenizer(tokenizer_dir=tokenizer_dir)
vocab_size = tokenizer.get_vocab_size()
except Exception:
pass
_, head_dim, model_dim, _ = model_dims(depth)
num_heads = model_dim // head_dim
config = GPTConfig(
sequence_len=2048, vocab_size=vocab_size,
n_layer=depth, n_head=num_heads, n_kv_head=num_heads, n_embd=model_dim,
)
with torch.device("meta"):
model = GPT(config)
counts = model.num_scaling_params()
scaling_params = counts["transformer_matrices"] + counts["lm_head"]
return int(scaling_params * target_ratio)
# ---------------------------------------------------------------------------
# Environment helpers
# ---------------------------------------------------------------------------
def check_and_prepare_env(args, label: str = "sweep") -> None:
"""Ensure data shards and tokenizer exist, downloading/training if needed."""
from nanochat.common import get_base_dir
from nanochat.dataset import resolve_data_dir, list_parquet_files
data_dir = getattr(args, "data_dir", None) or resolve_data_dir()
tokenizer_dir = getattr(args, "tokenizer_dir", None) or os.path.join(get_base_dir(), "tokenizer")
os.makedirs(data_dir, exist_ok=True)
os.makedirs(tokenizer_dir, exist_ok=True)
shards = list_parquet_files(data_dir=data_dir)
if not shards:
max_shards = getattr(args, "max_shards", -1)
num_files = max_shards if max_shards and max_shards > 0 else 2
cmd = [sys.executable, "-m", "nanochat.dataset", "-n", str(num_files), "--data-dir", data_dir]
print(f"[{label}] Downloading data: {' '.join(cmd)}")
subprocess.run(cmd, check=True)
tokenizer_pkl = os.path.join(tokenizer_dir, "tokenizer.pkl")
if not os.path.exists(tokenizer_pkl):
cmd = [
sys.executable, "-m", "scripts.tok_train",
"--max-chars", "10000000",
"--data-dir", data_dir,
"--tokenizer-dir", tokenizer_dir,
]
print(f"[{label}] Training tokenizer: {' '.join(cmd)}")
subprocess.run(cmd, check=True)
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