This is a self-sufficient repository for pretraining experiments.
conda create --name das_standard python=3.12 -c conda-forge --override-channels -y
conda activate das_standardcd /tmp
git clone [email protected]:IST-DASLab/DAS-Standard.git
cd /tmp/DAS-Standard
pip install -r requirements.txtcd /tmp/DAS-Standard
python download_hf_data.py # modify `local_dir` in this file if needed
cd /tmp/DAS-Standard/datasets
cat chunk_* > train.bin # merge training chunks into a single training filecd /tmp/DAS-Standard
python example_scripts/ex_gridsearcher.py # edit `dataset_path` argument or `script_path` in GridSearcher constructorGridSearcher PID: 1736237
Commands: Runnable: 1 Finished: 0 Total: 1
Waiting 5 seconds before running GridSearcher...
=== Using FSDP2 backend ===
==================================================
EXPERIMENT CONFIGURATION
==================================================
[DATASET]
dataset_path : /tmp/DAS-Standard/datasets/
dataset_name : c4
[MODEL]
dtype : torch.bfloat16
attn_type : mhsa
attn_gqa_kvg : 8
qk_norm : 0
rope_theta : 10_000.0
vocab_size : 32_000
num_layers : 8
num_heads : 8
embed_size : 512
seq_len : 1_024
dropout : 0.0
bias : 0
tie_embeds : 1
gpt_mlp_exp_factor : 4
llama_mlp_size : 1_536
[OPTIMIZER]
optimizer_name : adamw
weight_decay : 0.0
grad_clipping : 1.0
lr_sched_name : cosine
lr_sched_warmup : 0.1
lr_sched_start_ratio : 0.01
lr_sched_end_ratio : 0.1
adamw_lr : 0.001
adamw_beta1 : 0.9
adamw_beta2 : 0.95
adamw_eps : 1e-08
muon_lr : 0.04
muon_momentum : 0.95
microadam_m : 10
microadam_q_block : 100_000
microadam_k : 0.01
[QAT]
qat_method : quartet2
hadamard_size : 128
[TRAINING]
model_name_size : llama2-44m
seed : 42
num_tokens : 545_423_380
chinchilla_token_ratio : 1
local_batch_size : 16
grad_acc_steps : 4
max_iters : 1_041
backend : fsdp2
amp_dtype : bf16
compile_model : 1
eval_interval : 10
eval_batches : 32
output_folder : /tmp/DAS-Standard/results/qat=quartet2_H=128_llama2-44m_adamw_wd=0.0_b=0.9-0.95_e=1e-8_lr=1e-3_seed=42
[WANDB]
entity : ist
project : das-standard
group : qat=quartet2_H=128_llama2-44m_adamw_wd=0.0_b=0.9-0.95_e=1e-8
job_type : lr=1e-3
run_name : qat=quartet2_H=128_llama2-44m_adamw_wd=0.0_b=0.9-0.95_e=1e-8_lr=1e-3_seed=42
==================================================
Model:
Llama(
(token_embed): Embedding(32000, 512)
(blocks): ModuleList(
(0-7): 8 x LlamaBlock(
(attn): MultiHeadSelfAttention(
(Wq): Quartet_II_Linear(in_features=512, out_features=512, bias=False)
(Wk): Quartet_II_Linear(in_features=512, out_features=512, bias=False)
(Wv): Quartet_II_Linear(in_features=512, out_features=512, bias=False)
(Wo): Quartet_II_Linear(in_features=512, out_features=512, bias=False)
(dropout_attn): Dropout(p=0.0, inplace=False)
(dropout_out): Dropout(p=0.0, inplace=False)
)
(mlp): LlamaFeedForward(
(fc1): Quartet_II_Linear(in_features=512, out_features=1536, bias=False)
(fc2): Quartet_II_Linear(in_features=512, out_features=1536, bias=False)
(fc3): Quartet_II_Linear(in_features=1536, out_features=512, bias=False)
(silu): SiLU()
)
(norm1): RMSNorm()
(norm2): RMSNorm()
)
)
(norm): RMSNorm()
(lm_head): Linear(in_features=512, out_features=32000, bias=False)
)
==================================================
Model summary:
Input Embedding params: 16_384_000 (tied: True)
Output Embedding params: 16_384_000 (tied: True)
Linear params: 27_262_976
Layer norms params: 8_704
All params: 43_655_680
==================================================
Total tokens & training steps:
Linear params (P): 27_262_976
Norm params (N): 8_704
Total params for tokens (P + N): 27_271_680
Chinchilla Token Ratio (CTR): 20x
Tokens (T = CTR * (P + N)): 545_433_600
Sequence Length (S): 1024
Device Batch Size (B): 16
Grad Acc Steps (A): 4
World Size (W): 8
Steps (T / (B * S * A * W)): 1_041
==================================================
Dataset tokens: train=197_980_429_805 | val=99_126_266
Training: 0%| | 0/1041 [00:00<?, ?it/s]
Evaluation: step=0 | num_batches=32 | loss=10.5625 | ppl=38657.6514
Training: 1%|█▎ | 9/1041 [00:46<1:26:21, 5.02s/it, loss=9.8594]
Evaluation: step=10 | num_batches=32 | loss=9.6348 | ppl=15287.1154
Training: 2%|██▋ | 19/1041 [01:05<56:13, 3.30s/it, loss=8.9688]
Evaluation: step=20 | num_batches=32 | loss=8.7344 | ppl=6212.8500
Training: 3%|████ | 29/1041 [01:24<46:42, 2.77s/it, loss=7.9688]
Evaluation: step=30 | num_batches=32 | loss=7.8027 | ppl=2447.2846
Training: 4%|█████▍ | 39/1041 [01:43<41:52, 2.51s/it, loss=7.4531]
Evaluation: step=40 | num_batches=32 | loss=7.4219 | ppl=1672.1659
Training: 5%|██████▊ | 49/1041 [02:02<38:59, 2.36s/it, loss=7.2891]
Evaluation: step=50 | num_batches=32 | loss=7.2588 | ppl=1420.5353
Training: 6%|████████▎ | 59/1041 [02:21<36:58, 2.26s/it, loss=7.1406]
Evaluation: step=60 | num_batches=32 | loss=7.0498 | ppl=1152.6336
Training: 7%|█████████▋ | 69/1041 [02:40<35:30, 2.19s/it, loss=6.9297]
Evaluation: step=70 | num_batches=32 | loss=6.8682 | ppl=961.1823
Training: 8%|███████████ | 79/1041 [02:59<34:17, 2.14s/it, loss=6.6328]
Evaluation: step=80 | num_batches=32 | loss=6.5508 | ppl=699.7907
Training: 9%|████████████▍ | 89/1041 [03:18<33:21, 2.10s/it, loss=6.3906]
Evaluation: step=90 | num_batches=32 | loss=6.3525 | ppl=573.9482
Training: 10%|█████████████▉ | 99/1041 [03:37<32:30, 2.07s/it, loss=6.2656]
Evaluation: step=100 | num_batches=32 | loss=6.1982 | ppl=491.8836
Training: 10%|███████████████▏ | 109/1041 [03:56<31:47, 2.05s/it, loss=6.1250]
Evaluation: step=110 | num_batches=32 | loss=6.1074 | ppl=449.1792
Training: 11%|████████████████▌ | 119/1041 [04:15<31:07, 2.03s/it, loss=6.0000]
Evaluation: step=120 | num_batches=32 | loss=5.9727 | ppl=392.5470
Training: 12%|█████████████████▉ | 129/1041 [04:34<30:31, 2.01s/it, loss=5.8516]
Evaluation: step=130 | num_batches=32 | loss=5.8584 | ppl=350.1629
Training: 13%|███████████████████▎ | 139/1041 [04:54<30:00, 2.00s/it, loss=5.8047]
Evaluation: step=140 | num_batches=32 | loss=5.7832 | ppl=324.7979
Training: 14%|████████████████████▊ | 149/1041 [05:13<29:31, 1.99s/it, loss=5.7344]
Evaluation: step=150 | num_batches=32 | loss=5.7002 | ppl=298.9258
Training: 15%|██████████████████████▏ | 159/1041 [05:32<29:02, 1.98s/it, loss=5.6172]
Evaluation: step=160 | num_batches=32 | loss=5.6104 | ppl=273.2403
Training: 16%|██████████████████████▌ | 162/1041 [05:39<29:06, 1.99s/it, loss=5.6172]