mirror of
https://github.com/karpathy/nanochat.git
synced 2026-01-24 04:14:27 +00:00
116 lines
4.2 KiB
Bash
116 lines
4.2 KiB
Bash
#!/bin/bash
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FLOPS_BUDGETS=(
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1e18
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3e18
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6e18
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)
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DEPTHS=(8 10 12 14 16 18 20)
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NPROC_PER_NODE="${NPROC_PER_NODE:-8}"
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WANDB_RUN="${WANDB_RUN:-scaling}"
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EVAL_TOKENS=$((100 * 524288)) # ~100M tokens for final eval (default is ~10M)
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export OMP_NUM_THREADS=1
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export NANOCHAT_BASE_DIR="${NANOCHAT_BASE_DIR:-$HOME/.cache/nanochat}"
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source .venv/bin/activate
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RESULTS_DIR="$NANOCHAT_BASE_DIR/scaling_laws_results"
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mkdir -p "$RESULTS_DIR"
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RESULTS_FILE="$RESULTS_DIR/results.csv"
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# Write CSV header only if file doesn't exist
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if [ ! -f "$RESULTS_FILE" ]; then
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echo "flops_budget,depth,model_dim,num_params,num_scaling_params,num_iterations,tokens_trained,param_data_ratio,val_bpb,core_score,train_time_sec" > "$RESULTS_FILE"
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fi
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log() {
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echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
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}
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# Check if a run already exists in results
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run_exists() {
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local flops=$1
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local depth=$2
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grep -q "^${flops},${depth}," "$RESULTS_FILE" 2>/dev/null
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}
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# =============================================================================
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# Main Loop
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# =============================================================================
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for flops in "${FLOPS_BUDGETS[@]}"; do
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log "=============================================="
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log "Compute budget: $flops FLOPs"
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log "=============================================="
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for d in "${DEPTHS[@]}"; do
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# Skip if already completed
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if run_exists "$flops" "$d"; then
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log "Skipping d=$d at $flops FLOPs (already in results)"
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continue
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fi
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log "Training d=$d at $flops FLOPs..."
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# Unique tag for this run
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TAG="scaling_${flops}_d${d}"
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# Record start time
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START_TIME=$(date +%s)
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# Train the model with fixed flops budget
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# The script will auto-calculate num_iterations to hit target_flops
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# CORE eval happens once at the end (999999 ensures only final step)
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torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- \
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--depth=$d \
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--target-flops=$flops \
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--target-param-data-ratio=-1 \
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--run="${WANDB_RUN}_${TAG}" \
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--model-tag="${TAG}" \
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--eval-tokens=$EVAL_TOKENS \
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--core-metric-every=999999 \
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--core-metric-max-per-task=-1 \
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--sample-every=-1 \
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--save-every=-1 \
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2>&1 | tee "$RESULTS_DIR/${TAG}_train.log"
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END_TIME=$(date +%s)
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TRAIN_TIME=$((END_TIME - START_TIME))
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# Extract training stats from the log
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LOG_FILE="$RESULTS_DIR/${TAG}_train.log"
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NUM_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP '[\d,]+' | head -1 | tr -d ',')
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NUM_SCALING_PARAMS=$(grep "Number of parameters:" "$LOG_FILE" | tail -1 | grep -oP 'scaling: [\d,]+' | grep -oP '[\d,]+' | tr -d ',')
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NUM_ITERS=$(grep "Calculated number of iterations" "$LOG_FILE" | tail -1 | sed 's/.*: //' | tr -d ',')
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# Calculate tokens trained (iterations * batch_size, default 524288)
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TOKENS_TRAINED=$((NUM_ITERS * 524288))
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# Param:data ratio (using scaling params per Kaplan et al.)
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PARAM_DATA_RATIO=$(python -c "print(f'{$TOKENS_TRAINED / $NUM_SCALING_PARAMS:.2f}')")
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# Model dim
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MODEL_DIM=$((d * 64))
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# Val BPB from final eval
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VAL_BPB=$(grep "Validation bpb:" "$LOG_FILE" | tail -1 | grep -oP '[\d.]+$')
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# Extract CORE score from training log (evaluated on final step)
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CORE_SCORE=$(grep "CORE metric:" "$LOG_FILE" | tail -1 | awk '{print $NF}')
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if [ -z "$CORE_SCORE" ]; then
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log "WARNING: Could not extract CORE score for d=$d"
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CORE_SCORE="0.0"
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fi
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log " Params: $NUM_PARAMS, Iters: $NUM_ITERS, Ratio: $PARAM_DATA_RATIO, Val BPB: $VAL_BPB, CORE: $CORE_SCORE"
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# Append to CSV
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echo "$flops,$d,$MODEL_DIM,$NUM_PARAMS,$NUM_SCALING_PARAMS,$NUM_ITERS,$TOKENS_TRAINED,$PARAM_DATA_RATIO,$VAL_BPB,$CORE_SCORE,$TRAIN_TIME" >> "$RESULTS_FILE"
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done
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done
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log "=============================================="
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log "Scaling Laws Sweep Complete"
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log "=============================================="
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log "Results saved to: $RESULTS_FILE"
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echo ""
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echo "Results:"
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column -t -s',' "$RESULTS_FILE"
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