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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. | ||
|
|
||
| """ | ||
| Test fault tolerance with LLaMA3 recipe and a smaller model. | ||
| """ | ||
|
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||
| import argparse | ||
| import os | ||
|
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||
| import nemo_run as run | ||
|
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||
| from lightning.pytorch.callbacks import Callback | ||
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| from nemo.collections import llm | ||
| from nemo.collections.llm.recipes.callbacks.common import straggler_det_callback | ||
| from nemo.lightning.run.plugins import FaultTolerancePlugin | ||
| from nemo.utils.exp_manager import TimingCallback | ||
| from tests.collections.llm.common import small_llama_cfg, train_data | ||
|
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||
|
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| class CrashCallback(Callback): | ||
| def __init__(self, crash_step=16): | ||
| self.crash_step = crash_step | ||
| self.current_step = 0 | ||
| print(f"Setup to simulate a crash if step == {self.crash_step}") | ||
|
|
||
| def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): | ||
| self.current_step = self.current_step + 1 | ||
| if self.crash_step and self.current_step == self.crash_step: | ||
| raise Exception(f"Simulating a crash at step {self.crash_step}!") | ||
|
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||
|
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||
| def get_args(): | ||
| parser = argparse.ArgumentParser(prog="", description="") | ||
| parser.add_argument('--devices', type=int, required=True, help="Number of devices to use for training") | ||
| parser.add_argument( | ||
| '--crash-step', | ||
| type=int, | ||
| help="Step when a crash should be simulated", | ||
| ) | ||
| parser.add_argument( | ||
| '--check-report', type=bool, default=False, help="Check if StragglerDetection reports performance scores" | ||
| ) | ||
| parser.add_argument( | ||
| '--experiment-dir', type=str, required=True, help="directory to write results and checkpoints to" | ||
| ) | ||
| parser.add_argument( | ||
| '--data-path', type=str, default=None, help="Path to data file. If not specified, uses mock data." | ||
| ) | ||
| parser.add_argument( | ||
| '--tokenizer-path', | ||
| type=str, | ||
| default=None, | ||
| help="Path to a sentencepiece tokenizer model file. If not specified, uses mock data.", | ||
| ) | ||
| parser.add_argument('--index-mapping-dir', type=str, help="directory to write index mappings to") | ||
|
|
||
| return parser.parse_args() | ||
|
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|
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| def main(): | ||
| args = get_args() | ||
|
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||
| exp_name = "L2_llama3_small_pretrain_fault_tolerance_test" | ||
| pretrain_recipe = llm.llama3_8b.pretrain_recipe( | ||
| dir=args.experiment_dir, name=exp_name, num_gpus_per_node=args.devices | ||
| ) | ||
|
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||
| pretrain_recipe.model = run.Config(llm.LlamaModel, small_llama_cfg(1024)) | ||
|
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| if args.data_path and args.tokenizer_path: | ||
| pretrain_recipe.data = train_data( | ||
| data_path=args.data_path, | ||
| tokenizer_path=args.tokenizer_path, | ||
| index_mapping_dir=args.index_mapping_dir, | ||
| seq_length=1024, | ||
| ) | ||
|
|
||
| # Recipe Overrides | ||
| pretrain_recipe.trainer.max_steps = 20 | ||
| pretrain_recipe.trainer.log_every_n_steps = 1 | ||
| # Enable ckpt save so that after the simulated crash, training can resume from ckpt | ||
| pretrain_recipe.log.ckpt.every_n_train_steps = 10 | ||
| pretrain_recipe.log.ckpt.train_time_interval = None | ||
| # Disable async ckpt because the simulated crash happens during ckpt save | ||
| # So only an unfinished ckpt would be available for resume which can cause errors | ||
| pretrain_recipe.trainer.strategy.ckpt_async_save = False | ||
| pretrain_recipe.trainer.val_check_interval = 30 | ||
| pretrain_recipe.trainer.limit_val_batches = 2 | ||
|
|
||
| executor: run.SlurmExecutor = run.LocalExecutor(ntasks_per_node=args.devices, launcher="ft") | ||
| # Add the fault tolerance plugin which enables restart after a crash | ||
| run_plugins: list[run.Plugin] = [FaultTolerancePlugin(num_in_job_restarts=1, num_job_retries_on_failure=0)] | ||
| pretrain_recipe.trainer.callbacks = [ | ||
| run.Config(TimingCallback), | ||
| straggler_det_callback(straggler_report_time_interval=0.5), | ||
| ] | ||
|
|
||
| if args.crash_step: | ||
| pretrain_recipe.trainer.callbacks.append(run.Config(CrashCallback, crash_step=args.crash_step)) | ||
|
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| run.run(pretrain_recipe, plugins=run_plugins, executor=executor) | ||
|
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| # Assumes that NeMo logs are written into "run.log" | ||
| # When a crash a simulated, error shows up on the terminal but it is not written to a file | ||
| # So the test appends run output to run.log in the experiment-dir | ||
| log_content = None | ||
| with open(os.path.join(args.experiment_dir, "run.log")) as f: | ||
| log_content = f.read() | ||
|
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||
| if args.check_report: | ||
| assert "GPU relative performance" in log_content | ||
| assert "GPU individual performance" in log_content | ||
| assert "Straggler report processing time" in log_content | ||
| if args.crash_step: | ||
| assert f"Exception: Simulating a crash at step {args.crash_step}!" in log_content | ||
| assert "Restored all states from the checkpoint" in log_content | ||
| assert "`Trainer.fit` stopped: `max_steps=20` reached" in log_content | ||
|
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||
|
|
||
| if __name__ == '__main__': | ||
| main() |