From ccf4b7f9bf91a250aa398a0cecab270bcea56050 Mon Sep 17 00:00:00 2001 From: Andrej Karpathy Date: Wed, 7 Jan 2026 22:11:52 +0000 Subject: [PATCH] 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 --- .gitignore | 13 +++- miniseries.sh | 89 ++++++++++++++++++++++ nanochat/gpt.py | 27 ++++++- pyproject.toml | 2 + run1000.sh | 4 +- scripts/base_train.py | 47 +++++++++--- scripts/tok_train.py | 2 +- speedrun.sh | 4 +- uv.lock | 166 ++++++++++++++++++++++++++++++++++++++++++ 9 files changed, 333 insertions(+), 21 deletions(-) create mode 100644 miniseries.sh diff --git a/.gitignore b/.gitignore index 7f280bd..7950c9f 100644 --- a/.gitignore +++ b/.gitignore @@ -6,4 +6,15 @@ report.md eval_bundle/ # Secrets -.env \ No newline at end of file +.env + +# Local setup +.claude +CLAUDE.md +wandb/ + +# Local experimentation +experiments/ +ignore/ +knowledge/ +ideas/ diff --git a/miniseries.sh b/miniseries.sh new file mode 100644 index 0000000..9287def --- /dev/null +++ b/miniseries.sh @@ -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" diff --git a/nanochat/gpt.py b/nanochat/gpt.py index e6027a9..478f687 100644 --- a/nanochat/gpt.py +++ b/nanochat/gpt.py @@ -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 diff --git a/pyproject.toml b/pyproject.toml index 36cb7ce..0931ca6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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", diff --git a/run1000.sh b/run1000.sh index a0a6606..a7a3716 100644 --- a/run1000.sh +++ b/run1000.sh @@ -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 diff --git a/scripts/base_train.py b/scripts/base_train.py index c8345e0..de0321a 100644 --- a/scripts/base_train.py +++ b/scripts/base_train.py @@ -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: diff --git a/scripts/tok_train.py b/scripts/tok_train.py index e1b79ee..4ab995c 100644 --- a/scripts/tok_train.py +++ b/scripts/tok_train.py @@ -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:,}") diff --git a/speedrun.sh b/speedrun.sh index 8803dcb..f9be227 100644 --- a/speedrun.sh +++ b/speedrun.sh @@ -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 diff --git a/uv.lock b/uv.lock index 67ea035..63b2c01 100644 --- a/uv.lock +++ b/uv.lock @@ -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 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