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Add sharding for speechlm and vlm (#11876)
* Add sharding for speechlm and vlm Signed-off-by: Boxiang Wang <[email protected]> * Add ci test for VLM Signed-off-by: Boxiang Wang <[email protected]> * Apply isort and black reformatting Signed-off-by: BoxiangW <[email protected]> --------- Signed-off-by: Boxiang Wang <[email protected]> Signed-off-by: BoxiangW <[email protected]> Co-authored-by: BoxiangW <[email protected]>
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| # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| from importlib.metadata import version | ||
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| import fiddle as fdl | ||
| import torch | ||
| from lightning.pytorch.loggers import WandbLogger | ||
| from packaging.version import Version as PkgVersion | ||
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| from nemo import lightning as nl | ||
| from nemo.collections import llm, vlm | ||
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| DATA_PATH = "/home/TestData/vlm/rdr-items" | ||
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| def get_torch_version_str(): | ||
| import torch | ||
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| if hasattr(torch, '__version__'): | ||
| return str(torch.__version__) | ||
| else: | ||
| return version("torch") | ||
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| def mk_hf_vlm_dataset(processor, mbs, gbs): | ||
| skipped_tokens = vlm.HFAutoModelForImageTextToText.extract_skipped_token_ids(processor) | ||
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| def collate_fn(examples, processor): | ||
| def fmt(sample): | ||
| instruction = "Describe accurately the given image." | ||
| conversation = [ | ||
| { | ||
| "role": "user", | ||
| "content": [{"type": "text", "text": instruction}, {"type": "image", "image": sample["image"]}], | ||
| }, | ||
| {"role": "assistant", "content": [{"type": "text", "text": sample["text"]}]}, | ||
| ] | ||
| return {"conversation": conversation, "images": [sample['image']]} | ||
|
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| text = [] | ||
| images = [] | ||
| for example in map(fmt, examples): | ||
| text.append( | ||
| processor.apply_chat_template( | ||
| example["conversation"], | ||
| tokenize=False, | ||
| add_generation_prompt=False, | ||
| ) | ||
| ) | ||
| images += example['images'] | ||
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| # Tokenize the text and process the images | ||
| batch = processor( | ||
| text=text, | ||
| images=images, | ||
| padding=True, | ||
| return_tensors="pt", | ||
| ) | ||
|
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| batch["pixel_values"] = batch["pixel_values"].to(torch.bfloat16) | ||
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| labels = batch["input_ids"].clone() | ||
| labels[torch.isin(labels, skipped_tokens)] = -100 | ||
| batch["labels"] = labels | ||
| return batch | ||
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| return vlm.HFDatasetDataModule( | ||
| DATA_PATH, | ||
| split="train[:10]", | ||
| micro_batch_size=mbs, | ||
| global_batch_size=gbs, | ||
| collate_fn=lambda x: collate_fn(x, processor=processor), | ||
| ) | ||
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| if __name__ == '__main__': | ||
| if PkgVersion(get_torch_version_str()) >= PkgVersion("2.4"): | ||
| import argparse | ||
|
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--model', default='Qwen/Qwen2-VL-2B-Instruct') | ||
| parser.add_argument('--devices', default=2) | ||
| parser.add_argument('--mbs', default=1) | ||
| parser.add_argument('--gbs', default=1) | ||
| parser.add_argument('--accelerator', default='gpu', choices=['gpu']) | ||
| parser.add_argument('--max-steps', type=int, default=100) | ||
| parser.add_argument('--wandb-project', type=str, default=None) | ||
| parser.add_argument('--disable-ckpt', action='store_false') | ||
| parser.add_argument('--use-4bit', help="Load model in 4bit", action="store_true") | ||
| args = parser.parse_args() | ||
|
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| wandb = None | ||
| if args.wandb_project is not None: | ||
| model = '_'.join(args.model.split('/')[-2:]) | ||
| wandb = WandbLogger( | ||
| project=args.wandb_project, | ||
| name=f'{model}_dev{args.devices}_strat_fsdp2', | ||
| ) | ||
| grad_clip = None | ||
| use_dist_samp = False | ||
| processor = vlm.HFAutoModelForImageTextToText.configure_processor(args.model) | ||
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| llm.api.finetune( | ||
| model=vlm.HFAutoModelForImageTextToText(args.model, load_in_4bit=args.use_4bit), | ||
| data=mk_hf_vlm_dataset(processor, args.mbs, args.gbs), | ||
| trainer=nl.Trainer( | ||
| devices=args.devices, | ||
| max_steps=args.max_steps, | ||
| accelerator=args.accelerator, | ||
| strategy=nl.FSDP2Strategy(data_parallel_size=2, tensor_parallel_size=1), | ||
| log_every_n_steps=1, | ||
| limit_val_batches=0.0, | ||
| num_sanity_val_steps=0, | ||
| accumulate_grad_batches=10, | ||
| gradient_clip_val=grad_clip, | ||
| use_distributed_sampler=use_dist_samp, | ||
| logger=wandb, | ||
| enable_checkpointing=args.disable_ckpt, | ||
| ), | ||
| optim=fdl.build(llm.adam.pytorch_adam_with_flat_lr(lr=1e-5)), | ||
| log=None, | ||
| ) |