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Basic LORA trainer, doesn't seem to make any speed difference

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This commit is contained in:
Jake Poznanski 2024-09-23 09:08:00 -07:00
parent 3ed14a9ea5
commit ab9458b913
2 changed files with 45 additions and 19 deletions
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12
pdelfin/train/config/qwen2vl-2b.yaml
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@ -3,8 +3,8 @@ model:
arch: causal arch: causal
wandb: wandb:
project: refine project: pdelfin
entity: pdf-qwen2vl entity: ai2-llm
# TODO This is not used # TODO This is not used
format: format:
@ -93,10 +93,10 @@ hparams:
gradient_checkpointing: true gradient_checkpointing: true
clip_grad_norm: 1.0 clip_grad_norm: 1.0
learning_rate: 3e-4 learning_rate: 3e-4
max_steps: 10000 max_steps: 200
pad_multiple_of: 16 pad_multiple_of: 16
log_every_steps: 5 log_every_steps: 5
eval_every_steps: 250 eval_every_steps: 100
optim: adamw_torch optim: adamw_torch
lr_scheduler: cosine lr_scheduler: cosine
weight_decay: 0.01 weight_decay: 0.01
@ -118,7 +118,7 @@ lora:
- down_proj - down_proj
save: save:
path: s3://ai2-tylerm-experimental/experiments/rephrase/v1/models/lucas path: s3://ai2-oe-data/jakep/experiments/qwen2vl-pdf/v1/models/
save_every_steps: 500 save_every_steps: 100
max_workers: 1 max_workers: 1

52
pdelfin/train/train.py
View File

@ -123,6 +123,8 @@ def run_train(config: TrainConfig):
accelerator = accelerate.Accelerator() accelerator = accelerate.Accelerator()
setup_environment(aws_config=config.aws, wandb_config=config.wandb, WANDB_RUN_GROUP=run_name.group)
train_ds = build_batch_query_response_vision_dataset( train_ds = build_batch_query_response_vision_dataset(
query_glob_path="s3://ai2-oe-data/jakep/openai_batch_data_v2_mini/*.jsonl", query_glob_path="s3://ai2-oe-data/jakep/openai_batch_data_v2_mini/*.jsonl",
response_glob_path="s3://ai2-oe-data/jakep/openai_batch_done_v2_mini/*.json", response_glob_path="s3://ai2-oe-data/jakep/openai_batch_done_v2_mini/*.json",
@ -133,10 +135,25 @@ def run_train(config: TrainConfig):
) )
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
if config.lora is not None:
peft_config = LoraConfig(
r=config.lora.rank,
lora_alpha=config.lora.alpha,
lora_dropout=config.lora.dropout,
bias=config.lora.bias, # pyright: ignore
task_type=config.lora.task_type,
target_modules=list(config.lora.target_modules),
)
model = get_peft_model(model=model, peft_config=peft_config)
log_trainable_parameters(model=model, logger=logger)
train_ds = train_ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor)) train_ds = train_ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor))
print(train_ds) print(train_ds)
print("---------------") print("---------------")
save_path = join_path("", config.save.path, run_name.run)
save_config(config, join_path("", save_path, "config.yaml")) # pyright: ignore
with TemporaryDirectory() as output_dir: with TemporaryDirectory() as output_dir:
@ -177,22 +194,31 @@ def run_train(config: TrainConfig):
# Set the collator # Set the collator
collator = partial(packing_collator, pad_multiple_of=config.hparams.pad_multiple_of, do_shrink=False) collator = partial(packing_collator, pad_multiple_of=config.hparams.pad_multiple_of, do_shrink=False)
#checkpoint_callback = CheckpointUploadCallback(save_path=save_path, logger=logger) checkpoint_callback = CheckpointUploadCallback(save_path=save_path, logger=logger)
# # Initialize Trainer # Initialize Trainer
# trainer = Trainer( trainer = Trainer(
# model=model, model=model,
# args=training_args, args=training_args,
# train_dataset=train_ds, train_dataset=train_ds,
# #eval_dataset=formatted_dataset["validation"], # pyright: ignore #eval_dataset=formatted_dataset["validation"], # pyright: ignore
# tokenizer=processor.tokenizer, tokenizer=processor.tokenizer,
# #data_collator=collator, #data_collator=collator,
# #callbacks=[checkpoint_callback], #callbacks=[checkpoint_callback],
# ) )
# Could not get this to work
# if get_rank() == 0:
# # this is a hack to add script and peft config to wandb config
# update_wandb_config(config, trainer, model)
# # Train the model # Train the model
# trainer.train() # pyright: ignore trainer.train() # pyright: ignore
with get_local_dir(join_path("", save_path, "best")) as best_dir:
model.save_pretrained(best_dir)
tokenizer.tokenizer.save_pretrained(best_dir)
logger.info("Saved best model to %s", best_dir)
# Uncomment to test speed of data loader # Uncomment to test speed of data loader
# train_dataloader = DataLoader(train_ds, batch_size=1, num_workers=2, shuffle=False) # train_dataloader = DataLoader(train_ds, batch_size=1, num_workers=2, shuffle=False)