self-gen data misaligned fixed + niah reproducible + vllm-tokenizers version combo

scripts/niah/1-train.sh

@@ -1,22 +1,40 @@
-WANDB_MODE=disabled uv run train.py configs/toy_exp/ctx_magic_number_32_256.yaml \
---model_name_or_path=google/gemma-2-2b-it \
---num_train_epochs=1 \
---per_device_train_batch_size=-1 \
---gradient_accumulation_steps=32 \
---per_device_eval_batch_size=16 \
---exp_setup=hyper_lora \
---aggregator_type=perceiver --target_modules=down_proj \
---num_blocks=8 --num_self_attn_per_block=0 \
---num_pre_head_layers=1 --lora_r=8 \
---eval_steps=100 --logging_steps=10 \
---save_steps=1000 --learning_rate=4e-5 \
---lora_dropout=0.0 --neftune_noise_alpha=0 \
---per_rank_gen=True --per_layer_processing=True \
---gen_lora_l1_reg_coef=1 --use_sequence_packing=True \
---max_packed_inp_len=2048 --max_packed_ctx_len=2048 \
---dataloader_num_workers=0 --dataloader_prefetch_factor=None \
---ctx_encoder_type=early_exit --n_latent_queries=208 \
---eval_on_start=True --lora_r=8 \
---max_ctx_chunk_len=512 --min_ctx_chunk_len=25 \
---num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
---max_val_samples_per_ds=100 --seed=1 --use_per_ctx_average_loss=True
+ WANDB_MODE=disabled run uv run train.py \
+ configs/niah_exp/ctx_magic_number_32_256.yaml \
+ --model_name_or_path=google/gemma-2-2b-it \
+ --num_train_epochs=1 \
+ --per_device_train_batch_size=-1 \
+ --gradient_accumulation_steps=16 \
+ --per_device_eval_batch_size=16 \
+ --exp_setup=hyper_lora \
+ --aggregator_type=perceiver \
+ --target_modules=down_proj \
+ --num_blocks=8 \
+ --num_self_attn_per_block=0 \
+ --num_pre_head_layers=1 \
+ --lora_r=8 \
+ --eval_steps=100 \
+ --logging_steps=10 \
+ --save_steps=1000 \
+ --learning_rate=4e-5 \
+ --lora_dropout=0.0 \
+ --neftune_noise_alpha=0 \
+ --per_rank_gen=True \
+ --per_layer_processing=True \
+ --gen_lora_l1_reg_coef=1.5 \
+ --use_sequence_packing=True \
+ --max_packed_inp_len=4096 \
+ --max_packed_ctx_len=4096 \
+ --dataloader_num_workers=0 \
+ --dataloader_prefetch_factor=None \
+ --eval_on_start=False \
+ --ctx_encoder_type=early_exit \
+ --n_latent_queries=208 \
+ --use_kl_loss=False \
+ --eval_on_start=True \
+ --max_ctx_chunk_len=512 \
+ --min_ctx_chunk_len=25 \
+ --num_chunk_probs='{"1":"0.5", "2":"0.125", "3":"0.0625", "4":"0.0625", "5":"0.0625", "6":"0.0625", "7":"0.0625", "8":"0.0625"}' \
+ --max_val_samples_per_ds=100 \
+ --seed=1 \
+ --use_per_ctx_average_loss=True \
+ --torch_empty_cache_steps=10

scripts/niah/2-eval-test.sh

@@ -0,0 +1 @@
+WANDB_MODE=disabled uv run run_eval.py --checkpoint_path CHECKPOINT_PATH --datasets ctx_magic_number_32_1024 ctx_magic_number_1024_2048 ctx_magic_number_3072_4096 ctx_magic_number_7168_8192 ctx_magic_number_15360_16384 ctx_magic_number_28672_32768 ctx_magic_number_57344_65536 ctx_magic_number_122880_131072 --max_ctx_chunk_len=1024 --split test --eval_batch_size_gen=4

scripts/niah/README.md

@@ -0,0 +1,8 @@
+# NIAH experiment
+```bash
+# run the scripts in this order
+# data generation is only needed to be run once
+uv run bash scripts/niah/0-gen_data.sh
+uv run bash scripts/niah/1-train.sh
+uv run bash scripts/niah/2-eval.sh
+```