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nudge hyperparameters of the base script with the results of the sweeps and miniseries. vocab size down to 32K. D:N ratio from 20 to 8. add miniseries script

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This commit is contained in:
Andrej Karpathy 2026-01-07 22:11:52 +00:00
parent 1b5de29e71
commit ccf4b7f9bf
9 changed files with 333 additions and 21 deletions
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13
.gitignore vendored
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@ -6,4 +6,15 @@ report.md
eval_bundle/
# Secrets
.env
.env
# Local setup
.claude
CLAUDE.md
wandb/
# Local experimentation
experiments/
ignore/
knowledge/
ideas/

89
miniseries.sh Normal file
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@ -0,0 +1,89 @@
#!/bin/bash
# See speedrun.sh for more comments
export OMP_NUM_THREADS=1
export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
mkdir -p $NANOCHAT_BASE_DIR
# uv
command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
[ -d ".venv" ] || uv venv
uv sync --extra gpu
source .venv/bin/activate
# Tokenizer
python -m nanochat.dataset -n 240
python -m scripts.tok_train --max_chars=2000000000 --vocab_size=32768
# Depths to train (the "miniseries")
DEPTHS=(10 11 12 13 14 15 16 17 18 19 20)
# Hardware
NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
# Logging
WANDB_RUN="${WANDB_RUN:-jan7_miniseries}"
RESULTS_DIR="$NANOCHAT_BASE_DIR/jan7_miniseries_results"
mkdir -p "$RESULTS_DIR"
RESULTS_FILE="$RESULTS_DIR/results.csv"
# Write CSV header
echo "depth,model_dim,num_params,num_scaling_params,num_iterations,tokens_trained,param_data_ratio,val_bpb,core_score,train_time_sec" > "$RESULTS_FILE"
log() {
echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
}
log "=============================================="
log "Jan 7 Miniseries Training"
log "=============================================="
for d in "${DEPTHS[@]}"; do
log "Training d=$d..."
TAG="jan7_miniseries_d${d}"
START_TIME=$(date +%s)
# Train the model with natural horizon (target_param_data_ratio default)
# No --target_flops, let it use the default ratio from base_train
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- \
--depth=$d \
--target_param_data_ratio=8 \
--run="${WANDB_RUN}_d${d}" \
--model_tag="${TAG}" \
--core_metric_every=999999 \
--core_metric_max_per_task=-1 \
--sample_every=-1 \
--save_every=-1 \
2>&1 | tee "$RESULTS_DIR/${TAG}_train.log"
END_TIME=$(date +%s)
TRAIN_TIME=$((END_TIME - START_TIME))
# Extract stats from log
LOG_FILE="$RESULTS_DIR/${TAG}_train.log"
NUM_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | head -1 | tr -d ',')
NUM_SCALING_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP 'scaling: [\d,]+' | grep -oP '[\d,]+' | tr -d ',')
NUM_ITERS=$(grep "Calculated number of iterations" "$LOG_FILE" | tail -1 | sed 's/.*: //' | tr -d ',')
TOKENS_TRAINED=$((NUM_ITERS * 524288))
PARAM_DATA_RATIO=$(python -c "print(f'{$TOKENS_TRAINED / $NUM_SCALING_PARAMS:.2f}')")
MODEL_DIM=$((d * 64))
VAL_BPB=$(grep "Validation bpb:" "$LOG_FILE" | tail -1 | grep -oP '[\d.]+$')
CORE_SCORE=$(grep "CORE metric:" "$LOG_FILE" | tail -1 | awk '{print $NF}')
if [ -z "$CORE_SCORE" ]; then
CORE_SCORE="0.0"
fi
log " d=$d: params=$NUM_PARAMS, scaling=$NUM_SCALING_PARAMS, ratio=$PARAM_DATA_RATIO, bpb=$VAL_BPB, CORE=$CORE_SCORE, time=${TRAIN_TIME}s"
# Append to CSV
echo "$d,$MODEL_DIM,$NUM_PARAMS,$NUM_SCALING_PARAMS,$NUM_ITERS,$TOKENS_TRAINED,$PARAM_DATA_RATIO,$VAL_BPB,$CORE_SCORE,$TRAIN_TIME" >> "$RESULTS_FILE"
done
log "=============================================="
log "Jan 7 Miniseries Complete!"
log "=============================================="
log "Results saved to: $RESULTS_FILE"
echo ""
echo "Results:"
column -t -s',' "$RESULTS_FILE"

27
nanochat/gpt.py
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@ -216,14 +216,35 @@ class GPT(nn.Module):
return self.transformer.wte.weight.device
def estimate_flops(self):
""" Return the estimated FLOPs per token for the model. Ref: https://arxiv.org/abs/2204.02311 """
"""
Return the estimated FLOPs per token for the model (forward + backward).
Each matmul weight parameter contributes 2 FLOPs (multiply *, accumulate +) in forward, and 2X that in backward => 2+4=6.
Cleanest explanation of this: https://medium.com/@dzmitrybahdanau/the-flops-calculus-of-language-model-training-3b19c1f025e4
On top of that, the term 12 * l * h * q * t accounts for key @ query matmul flops inside attention.
Ref: https://arxiv.org/abs/2204.02311 (PaLM paper).
This is ~1% off from the exact formulas of Chinchilla paper, the difference is:
- Chinchilla counts the embedding layer as flops (? weird, it's just a lookup => we ignore)
- Chinchilla counts exp/sum/divide in attention softmax as flops (a little sus and very tiny => we ignore)
"""
nparams = sum(p.numel() for p in self.parameters())
nparams_embedding = self.transformer.wte.weight.numel()
l, h, q, t = self.config.n_layer, self.config.n_head, self.config.n_embd // self.config.n_head, self.config.sequence_len
num_flops_per_token = 6 * (nparams - nparams_embedding) + 12 * l * h * q * t
return num_flops_per_token
def setup_optimizers(self, unembedding_lr=0.004, embedding_lr=0.2, matrix_lr=0.02, weight_decay=0.0):
def num_scaling_params(self):
"""
Return all of the parameters, same as Chinchilla paper.
Kaplan et al. did not include embedding parameters and said that this led to cleaner scaling laws.
But Kaplan et al. also had a bug in their results (as pointed out by Chinchilla).
My own experiments in nanochat confirm the Chinchilla approach gives the much cleaner scaling law.
Ref: https://arxiv.org/abs/2203.15556 (Chinchilla paper <- good).
Ref: https://arxiv.org/abs/2001.08361 (Kaplan et al. original scaling laws paper <- bad)
"""
nparams = sum(p.numel() for p in self.parameters())
return nparams
def setup_optimizers(self, unembedding_lr=0.004, embedding_lr=0.2, matrix_lr=0.02, weight_decay=0.0, adam_betas=(0.8, 0.95)):
model_dim = self.config.n_embd
ddp, rank, local_rank, world_size = get_dist_info()
# Separate out all parameters into 3 groups (matrix, embedding, lm_head)
@ -239,7 +260,7 @@ class GPT(nn.Module):
dict(params=lm_head_params, lr=unembedding_lr * dmodel_lr_scale),
dict(params=embedding_params, lr=embedding_lr * dmodel_lr_scale),
]
adamw_kwargs = dict(betas=(0.8, 0.95), eps=1e-10, weight_decay=weight_decay)
adamw_kwargs = dict(betas=adam_betas, eps=1e-10, weight_decay=weight_decay)
AdamWFactory = DistAdamW if ddp else partial(torch.optim.AdamW, fused=True)
adamw_optimizer = AdamWFactory(adam_groups, **adamw_kwargs)
# Create the Muon optimizer for the linear layers

2
pyproject.toml
View File

@ -13,7 +13,9 @@ dependencies = [
"python-dotenv>=1.2.1",
"regex>=2025.9.1",
"rustbpe>=0.1.0",
"scipy>=1.15.3",
"setuptools>=80.9.0",
"tabulate>=0.9.0",
"tiktoken>=0.11.0",
"tokenizers>=0.22.0",
"torch>=2.9.0",

4
run1000.sh
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@ -23,7 +23,7 @@ python -m nanochat.dataset -n 16
# start downloading the rest of the shards for a total of 800 (see below why 800)
python -m nanochat.dataset -n 800 &
# todo: download the rest of it
python -m scripts.tok_train --max_chars=4000000000
python -m scripts.tok_train --max_chars=4000000000 --vocab_size=65536
python -m scripts.tok_eval
# Documenting my process for determining the hyperparameters for this run1000.sh script:
@ -71,7 +71,7 @@ python -m scripts.tok_eval
# Number of processes/GPUs to use
NPROC_PER_NODE=8
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=32 --device_batch_size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=32 --target_param_data_ratio=20 --device_batch_size=8 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_loss
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_eval

47
scripts/base_train.py
View File

@ -1,11 +1,11 @@
"""
Train model. Run as:
Train model. From root directory of the project, run as:
python base_train.py
python -m scripts.base_train.py
or distributed as:
torchrun --nproc_per_node=8 base_train.py
torchrun --nproc_per_node=8 -m scripts.base_train.py
If you are only on CPU/Macbook, you'll want to train a much much smaller LLM. Example:
python -m scripts.base_train --depth=4 --max_seq_len=512 --device_batch_size=1 --eval_tokens=512 --core_metric_every=-1 --total_batch_size=512 --num_iterations=20
@ -39,11 +39,13 @@ parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('d
parser.add_argument("--device_type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
# Model architecture
parser.add_argument("--depth", type=int, default=20, help="depth of the Transformer model")
parser.add_argument("--aspect_ratio", type=int, default=64, help="model_dim = depth * aspect_ratio")
parser.add_argument("--head_dim", type=int, default=128, help="target head dimension for attention")
parser.add_argument("--max_seq_len", type=int, default=2048, help="max context length")
# Training horizon (only one used, in order of precedence)
parser.add_argument("--num_iterations", type=int, default=-1, help="explicit number of optimization steps (-1 = disable)")
parser.add_argument("--target_flops", type=float, default=-1.0, help="calculate num_iterations to reach target_flops (-1 = disable)")
parser.add_argument("--target_param_data_ratio", type=int, default=20, help="calculate num_iterations to maintain data:param ratio (Chinchilla=20, -1 = disable)")
parser.add_argument("--target_param_data_ratio", type=int, default=8, help="calculate num_iterations to maintain data:param ratio (Chinchilla=20, -1 = disable)")
# Optimization
parser.add_argument("--device_batch_size", type=int, default=32, help="per-device batch size")
parser.add_argument("--total_batch_size", type=int, default=524288, help="total batch size in tokens")
@ -51,6 +53,8 @@ parser.add_argument("--embedding_lr", type=float, default=0.3, help="learning ra
parser.add_argument("--unembedding_lr", type=float, default=0.004, help="learning rate for unembedding parameters (Adam)")
parser.add_argument("--weight_decay", type=float, default=0.0, help="weight decay for embedding/unembedding parameters (Adam)")
parser.add_argument("--matrix_lr", type=float, default=0.02, help="learning rate for matrix parameters (Muon)")
parser.add_argument("--adam_beta1", type=float, default=0.8, help="Adam beta1 for embedding/unembedding")
parser.add_argument("--adam_beta2", type=float, default=0.95, help="Adam beta2 for embedding/unembedding")
parser.add_argument("--grad_clip", type=float, default=1.0, help="gradient clipping value (0.0 = disabled)")
parser.add_argument("--warmup_ratio", type=float, default=0.0, help="ratio of iterations for LR warmup")
parser.add_argument("--warmdown_ratio", type=float, default=0.4, help="ratio of iterations for LR warmdown")
@ -89,8 +93,8 @@ print0(f"Vocab size: {vocab_size:,}")
# Model kwargs are derived from the desired depth of the model
num_layers = args.depth
model_dim = args.depth * 64 # aspect ratio 64 (usually this is varied from 64 -> 128 as model size increases)
def find_num_heads(model_dim, target_head_dim=128):
model_dim = args.depth * args.aspect_ratio
def find_num_heads(model_dim, target_head_dim):
# Find num_heads that divides model_dim evenly, with head_dim closest to target.
ideal = max(1, round(model_dim / target_head_dim))
for offset in range(model_dim):
@ -98,7 +102,7 @@ def find_num_heads(model_dim, target_head_dim=128):
if candidate > 0 and model_dim % candidate == 0:
return candidate
return 1
num_heads = find_num_heads(model_dim)
num_heads = find_num_heads(model_dim, args.head_dim)
num_kv_heads = num_heads # default is 1:1 GQA (Group Query Attention) ratio (i.e. GQA is disabled)
print0(f"num_layers: {num_layers}")
print0(f"model_dim: {model_dim}")
@ -115,6 +119,17 @@ print0(f"Tokens / micro-batch / rank: {args.device_batch_size} x {args.max_seq_l
print0(f"Tokens / micro-batch: {world_tokens_per_fwdbwd:,}")
print0(f"Total batch size {args.total_batch_size:,} => gradient accumulation steps: {grad_accum_steps}")
# Batch size scaling for learning rates (hyperparameters were tuned at reference batch size 2^19)
batch_lr_scale = 1.0
reference_batch_size = 2**19
batch_ratio = args.total_batch_size / reference_batch_size
if batch_ratio != 1.0:
# SGD: linear scaling with batch size is standard (not used in nanochat)
# AdamW: sqrt scaling is standard
# Muon: sqrt scaling is an assumption - not fully studied, but it's a second-order-ish optimizer
batch_lr_scale = batch_ratio ** 0.5
print0(f"Scaling LRs by {batch_lr_scale:.4f} for batch size {args.total_batch_size:,} (reference: {reference_batch_size:,})")
# -----------------------------------------------------------------------------
# Initialize the Model
@ -141,7 +156,8 @@ if resuming:
orig_model = model # original, uncompiled model, for saving raw model state_dict and for inference/evaluation (because the shapes may change shape)
model = torch.compile(model, dynamic=False) # the inputs to model will never change shape so dynamic=False is safe
num_params = sum(p.numel() for p in model.parameters())
print0(f"Number of parameters: {num_params:,}")
num_scaling_params = orig_model.num_scaling_params()
print0(f"Number of parameters: {num_params:,} (scaling: {num_scaling_params:,})")
num_flops_per_token = model.estimate_flops()
print0(f"Estimated FLOPs per token: {num_flops_per_token:e}")
@ -155,20 +171,27 @@ elif args.target_flops > 0:
num_iterations = round(args.target_flops / (num_flops_per_token * args.total_batch_size))
print0(f"Calculated number of iterations from target FLOPs: {num_iterations:,}")
elif args.target_param_data_ratio > 0:
# calculate the number of iterations from the target param data ratio
target_tokens = args.target_param_data_ratio * num_params
# calculate the number of iterations from the target param data ratio (use scaling params per Kaplan et al.)
target_tokens = args.target_param_data_ratio * num_scaling_params
num_iterations = target_tokens // args.total_batch_size
print0(f"Calculated number of iterations from target data:param ratio: {num_iterations:,}")
else:
raise ValueError("No training horizon specified")
total_tokens = args.total_batch_size * num_iterations
print0(f"Total number of training tokens: {total_tokens:,}")
print0(f"Tokens : Params ratio: {args.total_batch_size * num_iterations / num_params:.2f}") # Chinchilla is ~20
print0(f"Tokens : Params ratio: {args.total_batch_size * num_iterations / num_scaling_params:.2f}") # Chinchilla is ~20
print0(f"Total training FLOPs estimate: {num_flops_per_token * total_tokens:e}")
# -----------------------------------------------------------------------------
# Initialize the Optimizer (Muon for Linear layers, AdamW for embedding and lm_head)
optimizers = model.setup_optimizers(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=args.weight_decay)
adam_betas = (args.adam_beta1, args.adam_beta2)
optimizers = model.setup_optimizers(
unembedding_lr=args.unembedding_lr * batch_lr_scale,
embedding_lr=args.embedding_lr * batch_lr_scale,
matrix_lr=args.matrix_lr * batch_lr_scale,
weight_decay=args.weight_decay,
adam_betas=adam_betas,
)
adamw_optimizer, muon_optimizer = optimizers
if resuming:

2
scripts/tok_train.py
View File

@ -16,7 +16,7 @@ from nanochat.dataset import parquets_iter_batched
parser = argparse.ArgumentParser(description='Train a BPE tokenizer')
parser.add_argument('--max_chars', type=int, default=10_000_000_000, help='Maximum characters to train on (default: 10B)')
parser.add_argument('--doc_cap', type=int, default=10_000, help='Maximum characters per document (default: 10,000)')
parser.add_argument('--vocab_size', type=int, default=65536, help='Vocabulary size (default: 65536 = 2^16)')
parser.add_argument('--vocab_size', type=int, default=32768, help='Vocabulary size (default: 32768 = 2^15)')
args = parser.parse_args()
print(f"max_chars: {args.max_chars:,}")
print(f"doc_cap: {args.doc_cap:,}")

4
speedrun.sh
View File

@ -59,7 +59,7 @@ python -m nanochat.dataset -n 8
python -m nanochat.dataset -n 240 &
DATASET_DOWNLOAD_PID=$!
# train the tokenizer with vocab size 2**16 = 65536 on ~2B characters of data
python -m scripts.tok_train --max_chars=2000000000
python -m scripts.tok_train --max_chars=2000000000 --vocab_size=65536
# evaluate the tokenizer (report compression ratio etc.)
python -m scripts.tok_eval
@ -79,7 +79,7 @@ wait $DATASET_DOWNLOAD_PID
NPROC_PER_NODE=8
# pretrain the d20 model
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=20 --run=$WANDB_RUN
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=20 --target_param_data_ratio=20 --run=$WANDB_RUN
# evaluate the model on a larger chunk of train/val data and draw some samples
torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_loss
# evaluate the model on CORE tasks

166
uv.lock
View File

@ -1483,7 +1483,10 @@ dependencies = [
{ name = "python-dotenv" },
{ name = "regex" },
{ name = "rustbpe" },
{ name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
{ name = "scipy", version = "1.16.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
{ name = "setuptools" },
{ name = "tabulate" },
{ name = "tiktoken" },
{ name = "tokenizers" },
{ name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
@ -1520,7 +1523,9 @@ requires-dist = [
{ name = "python-dotenv", specifier = ">=1.2.1" },
{ name = "regex", specifier = ">=2025.9.1" },
{ name = "rustbpe", specifier = ">=0.1.0" },
{ name = "scipy", specifier = ">=1.15.3" },
{ name = "setuptools", specifier = ">=80.9.0" },
{ name = "tabulate", specifier = ">=0.9.0" },
{ name = "tiktoken", specifier = ">=0.11.0" },
{ name = "tokenizers", specifier = ">=0.22.0" },
{ name = "torch", specifier = ">=2.9.0" },
@ -2617,6 +2622,158 @@ wheels = [
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]
[[package]]
name = "scipy"
version = "1.15.3"
source = { registry = "https://pypi.org/simple" }
resolution-markers = [
"python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
"python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'",
]
dependencies = [
{ name = "numpy", marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
]
sdist = { url = "https://files.pythonhosted.org/packages/0f/37/6964b830433e654ec7485e45a00fc9a27cf868d622838f6b6d9c5ec0d532/scipy-1.15.3.tar.gz", hash = "sha256:eae3cf522bc7df64b42cad3925c876e1b0b6c35c1337c93e12c0f366f55b0eaf", size = 59419214, upload-time = "2025-05-08T16:13:05.955Z" }
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