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NeMo-UX: Mistral/mixtral peft ci test (NVIDIA#11094)
* add mistral/mixtral peft ci test Signed-off-by: Alexandros Koumparoulis <[email protected]> * add mistral/mixtral peft ci test Signed-off-by: Alexandros Koumparoulis <[email protected]> * add mistral tp2 Signed-off-by: Alexandros Koumparoulis <[email protected]> * Apply isort and black reformatting Signed-off-by: akoumpa <[email protected]> * add tests to NEMO_CICD_Test Signed-off-by: Alexandros Koumparoulis <[email protected]> * Update .github/workflows/cicd-main.yml Co-authored-by: oliver könig <[email protected]> Signed-off-by: Alexandros Koumparoulis <[email protected]> * fix params Signed-off-by: Alexandros Koumparoulis <[email protected]> * rm devices arg Signed-off-by: Alexandros Koumparoulis <[email protected]> * add --dist-opt arg Signed-off-by: Alexandros Koumparoulis <[email protected]> * add tp=2 mixtral Signed-off-by: Alexandros Koumparoulis <[email protected]> * add ep test Signed-off-by: Alexandros Koumparoulis <[email protected]> * fix Signed-off-by: Alexandros Koumparoulis <[email protected]> --------- Signed-off-by: Alexandros Koumparoulis <[email protected]> Signed-off-by: akoumpa <[email protected]> Signed-off-by: Alexandros Koumparoulis <[email protected]> Co-authored-by: akoumpa <[email protected]> Co-authored-by: oliver könig <[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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| import argparse | ||
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| import pytorch_lightning as pl | ||
| import torch | ||
| from megatron.core.optimizer import OptimizerConfig | ||
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| from nemo import lightning as nl | ||
| from nemo.collections import llm | ||
| from nemo.lightning.io.mixin import track_io | ||
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| def get_args(): | ||
| parser = argparse.ArgumentParser(description='Finetune a small GPT model using NeMo 2.0') | ||
| parser.add_argument('--model', type=str.lower, choices=['mistral', 'mixtral'], help="model") | ||
| parser.add_argument('--max-steps', type=int, default=9, help="number of devices") | ||
| parser.add_argument('--mbs', type=int, default=2, help="micro batch size") | ||
| parser.add_argument('--gbs', type=int, default=4, help="global batch size") | ||
| parser.add_argument('--tp', type=int, default=1, help="tensor parallel size") | ||
| parser.add_argument('--ep', type=int, default=1, help="expert parallel size") | ||
| parser.add_argument('--dist-opt', action='store_true', help='use dist opt') | ||
| return parser.parse_args() | ||
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| def trainer(devices, tp, ep, sp, max_steps) -> nl.Trainer: | ||
| strategy = nl.MegatronStrategy( | ||
| tensor_model_parallel_size=tp, | ||
| expert_model_parallel_size=ep, | ||
| sequence_parallel=sp, | ||
| ) | ||
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| return nl.Trainer( | ||
| devices=max(ep, tp), | ||
| max_steps=max_steps, | ||
| accelerator="gpu", | ||
| strategy=strategy, | ||
| plugins=nl.MegatronMixedPrecision(precision="bf16-mixed"), | ||
| log_every_n_steps=1, | ||
| limit_val_batches=0, | ||
| val_check_interval=0, | ||
| num_sanity_val_steps=0, | ||
| ) | ||
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| @track_io | ||
| class OrdTokenizer: | ||
| def __init__(self, vocab_size=30_000, num_reserved_tokens=128, special_token_names=['bos_id', 'eos_id', 'pad_id']): | ||
| self.vocab_size = vocab_size | ||
| self.num_reserved_tokens = num_reserved_tokens | ||
| self.special_token_names = special_token_names | ||
| assert len(self.special_token_names) < num_reserved_tokens | ||
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| def __getattr__(self, name): | ||
| if name in self.__dict__.get('special_token_names', {}): | ||
| return self.__dict__['special_token_names'].index(name) | ||
| elif name in self.__dict__: | ||
| return self.__dict__[name] | ||
| else: | ||
| raise AttributeError | ||
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| def text_to_ids(self, text): | ||
| token_ids = list(map(lambda x: self.num_reserved_tokens + ord(x), list(text))) | ||
| assert max(token_ids) < self.vocab_size | ||
| return token_ids | ||
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| def logger() -> nl.NeMoLogger: | ||
| ckpt = nl.ModelCheckpoint( | ||
| save_last=True, | ||
| every_n_train_steps=10, | ||
| monitor="reduced_train_loss", | ||
| save_top_k=1, | ||
| save_on_train_epoch_end=True, | ||
| save_optim_on_train_end=True, | ||
| ) | ||
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| return nl.NeMoLogger( | ||
| name="nemo2_peft", | ||
| log_dir="/tmp/peft_logs", | ||
| use_datetime_version=False, # must be false if using auto resume | ||
| ckpt=ckpt, | ||
| wandb=None, | ||
| ) | ||
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| def squad(mbs, gbs) -> pl.LightningDataModule: | ||
| return llm.SquadDataModule(seq_length=2048, micro_batch_size=mbs, global_batch_size=gbs, num_workers=0) | ||
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| def mixtral_8x7b() -> pl.LightningModule: | ||
| tokenizer = OrdTokenizer() | ||
| model = llm.MixtralModel(llm.MixtralConfig8x7B(num_layers=2), tokenizer=tokenizer) | ||
| lora = llm.peft.LoRA() | ||
| return model, lora | ||
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| def mistral_7b() -> pl.LightningModule: | ||
| tokenizer = OrdTokenizer() | ||
| model = llm.MistralModel(llm.MistralConfig7B(num_layers=2), tokenizer=tokenizer) | ||
| lora = llm.peft.LoRA() | ||
| return model, lora | ||
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| if __name__ == '__main__': | ||
| args = get_args() | ||
| if args.model == 'mistral': | ||
| model, lora = mistral_7b() | ||
| else: | ||
| model, lora = mixtral_8x7b() | ||
| llm.finetune( | ||
| model=model, | ||
| data=squad(args.mbs, args.gbs), | ||
| trainer=trainer(args.tp, args.tp, args.ep, args.tp > 1, args.max_steps), | ||
| peft=lora, | ||
| log=logger(), | ||
| optim=nl.MegatronOptimizerModule( | ||
| config=OptimizerConfig( | ||
| optimizer="adam", | ||
| lr=0.0001, | ||
| adam_beta2=0.98, | ||
| use_distributed_optimizer=args.dist_opt, | ||
| clip_grad=1.0, | ||
| bf16=True, | ||
| ), | ||
| ), | ||
| ) |