File size: 4,323 Bytes
e2bfccc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | #!/usr/bin/env bash
set -euo pipefail
DATA_PATH="${DATA_PATH:-/home/student/Data/TaoData/pretrain.jsonl}"
TOKENIZER_PATH="${TOKENIZER_PATH:-/home/student/YouZheng/tokenizers/taodata_pilot_8k/tokenizer.model}"
SSM_REPO_PATH="${SSM_REPO_PATH:-/home/student/YouZheng/gamma_ssm_repo}"
PYTHON_BIN="${PYTHON_BIN:-/home/student/.venv/bin/python}"
REMOTE_REPO="${REMOTE_REPO:-$(pwd)}"
OUTPUT_BASE="${REPOBRIDGE_OUTPUT_DIR:-$REMOTE_REPO/results/200m-base-suite}"
CHECKPOINT_BASE="${TAOTERN_CHECKPOINT_DIR:-$OUTPUT_BASE/checkpoints}"
# Stage-1 defaults are intentionally modest. Increase these through environment
# variables after the 200M shapes are stable on the RTX5090.
MAX_TOKENS="${MAX_TOKENS:-50000000}"
MAX_RECORDS="${MAX_RECORDS:-100000}"
TRAIN_STEPS="${TRAIN_STEPS:-200}"
EVAL_BATCHES="${EVAL_BATCHES:-16}"
BATCH_SIZES="${BATCH_SIZES:-4,8}"
SEQ_LEN="${SEQ_LEN:-512}"
LEARNING_RATE="${LEARNING_RATE:-0.0006}"
WEIGHT_DECAY="${WEIGHT_DECAY:-0.01}"
DRY_RUN="${DRY_RUN:-0}"
export PYTHONPATH="$REMOTE_REPO/src:$SSM_REPO_PATH"
mkdir -p "$OUTPUT_BASE" "$CHECKPOINT_BASE"
run_variant() {
local variant="$1"
shift
local output_dir="$OUTPUT_BASE/$variant"
local checkpoint_dir="$CHECKPOINT_BASE/$variant"
mkdir -p "$output_dir" "$checkpoint_dir"
local cmd="$PYTHON_BIN scripts/benchmark_taonet_real_tokens.py \
--data-path $DATA_PATH \
--text-field text \
--tokenizer-type sentencepiece \
--tokenizer-path $TOKENIZER_PATH \
--max-records $MAX_RECORDS \
--max-tokens $MAX_TOKENS \
--eval-fraction 0.1 \
--batch-sizes $BATCH_SIZES \
--seq-len $SEQ_LEN \
--dtype bf16 \
--device cuda \
--warmup 1 \
--repeats 2 \
--backward \
--train-steps $TRAIN_STEPS \
--learning-rate $LEARNING_RATE \
--weight-decay $WEIGHT_DECAY \
--eval-batches $EVAL_BATCHES \
--output-dir $output_dir \
--resume-completed \
--incremental-output \
--save-case-checkpoints \
--checkpoint-dir $checkpoint_dir \
$*"
printf '\n=== 200M variant: %s ===\n' "$variant"
printf '%s\n' "$cmd"
if [ "$DRY_RUN" = "1" ]; then
return 0
fi
eval "$cmd"
}
run_variant attention_196m \
--architectures taonet \
--hidden-dim 960 \
--num-layers 16 \
--num-heads 8 \
--d-latent-kv 720 \
--d-rope 120 \
--hidden-dim-ff 2880
run_variant pure_ssm_196m_hadamard \
--architectures taonet_ssm \
--hidden-dim 1024 \
--num-layers 18 \
--num-heads 8 \
--d-latent-kv 768 \
--d-rope 128 \
--hidden-dim-ff 3072 \
--ssm-core dplr \
--ssm-hidden-dims 16 \
--ssm-mixer-dims 256 \
--ssm-num-lanes-list 2 \
--ssm-lane-combine channel \
--ssm-lane-modes split \
--ssm-split-mixes hadamard \
--ssm-rank 1 \
--ssm-kernel-mode conv \
--no-ssm-finite-tail-correction \
--ssm-gate-types channel \
--ssm-local-shift \
--ssm-local-shift-per-channel \
--ssm-local-shift-init 0.1
run_variant pure_ssm_196m_nomix \
--architectures taonet_ssm \
--hidden-dim 1024 \
--num-layers 18 \
--num-heads 8 \
--d-latent-kv 768 \
--d-rope 128 \
--hidden-dim-ff 3072 \
--ssm-core dplr \
--ssm-hidden-dims 16 \
--ssm-mixer-dims 256 \
--ssm-num-lanes-list 2 \
--ssm-lane-combine channel \
--ssm-lane-modes split \
--ssm-split-mixes none \
--ssm-rank 1 \
--ssm-kernel-mode conv \
--no-ssm-finite-tail-correction \
--ssm-gate-types channel \
--ssm-local-shift \
--ssm-local-shift-per-channel \
--ssm-local-shift-init 0.1
run_variant hybrid_ssm_first_199m \
--architectures taonet_hybrid \
--hidden-dim 1024 \
--num-layers 16 \
--num-heads 8 \
--d-latent-kv 768 \
--d-rope 128 \
--hidden-dim-ff 3072 \
--ssm-core dplr \
--ssm-hidden-dims 32 \
--ssm-mixer-dims 256 \
--ssm-num-lanes-list 2 \
--ssm-lane-combine channel \
--ssm-lane-modes split \
--ssm-split-mixes hadamard \
--ssm-rank 1 \
--ssm-kernel-mode conv \
--no-ssm-finite-tail-correction \
--ssm-gate-types channel \
--hybrid-patterns ssm_first \
--ssm-local-shift \
--ssm-local-shift-per-channel \
--ssm-local-shift-init 0.1
if [ "$DRY_RUN" != "1" ]; then
"$PYTHON_BIN" scripts/summarize_taonet_benchmark_suite.py --suite-dir "$OUTPUT_BASE"
fi
|