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* add pretrain tests Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * Apply isort and black reformatting Signed-off-by: akoumpa <akoumpa@users.noreply.github.com> * fix Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * add max_steps Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> --------- Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: akoumpa <akoumpa@users.noreply.github.com> Co-authored-by: akoumpa <akoumpa@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
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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. | ||
|
|
||
| import fiddle as fdl | ||
| from lightning.pytorch.loggers import WandbLogger | ||
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| from nemo import lightning as nl | ||
| from nemo.collections import llm | ||
| from nemo.lightning.pytorch.accelerate.transformer_engine import is_te_accelerated | ||
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| DATA_PATH = '/home/TestData/lite/hf_cache/squad/' | ||
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| def make_squad_hf_dataset(data_path, tokenizer): | ||
| EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN | ||
|
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| def formatting_prompts_func(examples): | ||
| alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. | ||
| ### Instruction: | ||
| {} | ||
| ### Input: | ||
| {} | ||
| ### Response: | ||
| {}""" | ||
| instruction = examples["context"] | ||
| input = examples["question"] | ||
| output = examples["answers"]['text'] | ||
| if isinstance(output, list): | ||
| output = output[0] | ||
| text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN | ||
| ans = tokenizer(text) | ||
| ans['labels'] = ans['input_ids'] | ||
| return ans | ||
|
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| tokenizer = getattr(tokenizer, 'tokenizer', tokenizer) | ||
| datamodule = llm.HFDatasetDataModule(data_path, split="train[:100]", pad_token_id=tokenizer.eos_token_id) | ||
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| datamodule.map( | ||
| formatting_prompts_func, | ||
| batched=False, | ||
| batch_size=2, | ||
| remove_columns=["id", "title", "context", "question", 'answers'], | ||
| ) | ||
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| return datamodule | ||
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| if __name__ == '__main__': | ||
| import argparse | ||
|
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--model', default='meta-llama/Llama-3.2-1B') | ||
| parser.add_argument('--strategy', type=str, default='auto', choices=['auto', 'ddp', 'fsdp']) | ||
| parser.add_argument('--devices', default=1) | ||
| parser.add_argument('--accelerator', default='gpu', choices=['gpu']) | ||
| parser.add_argument('--model-accelerator', default=None, choices=['te']) | ||
| parser.add_argument('--max-steps', type=int, default=100) | ||
| parser.add_argument("--fp8-autocast", default=False, action='store_true') | ||
| parser.add_argument('--wandb-project', type=str, default=None) | ||
| parser.add_argument('--model-save-path', type=str, default=None) | ||
| 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_{args.strategy}', | ||
| ) | ||
| grad_clip = 0.5 | ||
| if args.strategy == 'fsdp': | ||
| # See: https://github.com/Lightning-AI/pytorch-lightning/blob/8ad3e29816a63d8ce5c00ac104b14729a4176f4f/src/lightning/pytorch/plugins/precision/fsdp.py#L81 | ||
| grad_clip = None | ||
| use_dist_samp = False | ||
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| model_accelerator = None | ||
| if args.model_accelerator == "te": | ||
| from functools import partial | ||
| from nemo.lightning.pytorch.accelerate.transformer_engine import te_accelerate | ||
|
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| model_accelerator = partial(te_accelerate, fp8_autocast=args.fp8_autocast) | ||
|
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| from nemo.lightning.pytorch.accelerate.transformer_engine import te_accelerate | ||
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| model = llm.HFAutoModelForCausalLM( | ||
| model_name=args.model, model_accelerator=model_accelerator, load_pretrained_weights=False | ||
| ) | ||
| tokenizer = model.tokenizer | ||
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| llm.api.finetune( | ||
| model=model, | ||
| data=make_squad_hf_dataset(DATA_PATH, tokenizer), | ||
| trainer=nl.Trainer( | ||
| devices=args.devices, | ||
| max_steps=args.max_steps, | ||
| accelerator=args.accelerator, | ||
| strategy=args.strategy, | ||
| 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, | ||
| callbacks=[], | ||
| logger=wandb, | ||
| ), | ||
| optim=fdl.build(llm.adam.pytorch_adam_with_flat_lr(lr=1e-5)), | ||
| log=None, | ||
| ) | ||
|
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| if args.model_accelerator: | ||
| if args.model_accelerator == "te": | ||
| te_acc = is_te_accelerated(model.model) | ||
| assert te_acc, "Transformer Engine acceleration was unsuccessful" | ||
| print("TE Accelerated: ", te_acc) | ||
|
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| if args.model_save_path is not None: | ||
| model.save_pretrained(args.model_save_path) |
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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 nemo_run as run | ||
|
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| from nemo.collections import llm | ||
| from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer | ||
| from nemo.collections.llm.gpt.data.hf_dataset import SquadHFDataModule | ||
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| DATA_PATH = '/home/TestData/lite/hf_cache/squad/' | ||
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| def local_executor_torchrun(nodes: int = 1, devices: int = 2) -> run.LocalExecutor: | ||
| # Env vars for jobs are configured here | ||
| env_vars = { | ||
| "TORCH_NCCL_AVOID_RECORD_STREAMS": "1", | ||
| "NCCL_NVLS_ENABLE": "0", | ||
| "NVTE_DP_AMAX_REDUCE_INTERVAL": "0", | ||
| "NVTE_ASYNC_AMAX_REDUCTION": "1", | ||
| "NVTE_FUSED_ATTN": "0", | ||
| } | ||
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| executor = run.LocalExecutor(ntasks_per_node=devices, launcher="torchrun", env_vars=env_vars) | ||
|
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| return executor | ||
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|
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| if __name__ == '__main__': | ||
| import argparse | ||
|
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--model', default='meta-llama/Llama-3.2-1B') | ||
| parser.add_argument('--strategy', type=str, default='auto', choices=['auto', 'ddp', 'fsdp']) | ||
| parser.add_argument('--devices', default=1) | ||
| parser.add_argument('--accelerator', default='gpu', choices=['gpu']) | ||
| parser.add_argument('--max-steps', type=int, default=100) | ||
| args = parser.parse_args() | ||
|
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| recipe = llm.hf_auto_model_for_causal_lm.pretrain_recipe( | ||
| model_name=args.model, | ||
| name="pt", | ||
| num_nodes=1, | ||
| num_gpus_per_node=args.devices, | ||
| max_steps=args.max_steps, | ||
| ) | ||
| recipe.trainer.val_check_interval = 50 | ||
|
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| tokenizer = llm.HFAutoModelForCausalLM.configure_tokenizer(args.model) | ||
| recipe.data = run.Config( | ||
| SquadHFDataModule, | ||
| path_or_dataset=DATA_PATH, | ||
| split="train[:100]", | ||
| pad_token_id=tokenizer.tokenizer.eos_token_id, | ||
| tokenizer=run.Config(AutoTokenizer, pretrained_model_name=args.model), | ||
| ) | ||
| executor = local_executor_torchrun(nodes=recipe.trainer.num_nodes, devices=recipe.trainer.devices) | ||
| run.run(recipe, executor=executor) |