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1c44b60e3e
Co-authored-by: Benjamin Feuer <penfever@gmail.com> Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>
51 lines
1.2 KiB
YAML
51 lines
1.2 KiB
YAML
# FP8 training example with FSDP
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# This config demonstrates FP8 mixed precision training using HuggingFace Accelerate
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# with FSDP for distributed training and float8 all-gather optimization
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### Model configuration
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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trust_remote_code: true
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### Method configuration
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stage: sft
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do_train: true
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finetuning_type: full
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### Dataset configuration
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dataset: identity
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template: llama3
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cutoff_len: 1024
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max_samples: 1000
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overwrite_cache: true
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preprocessing_num_workers: 16
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### Output configuration
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output_dir: saves/llama3-8b/fp8-fsdp/sft
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logging_steps: 10
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save_steps: 500
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plot_loss: true
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overwrite_output_dir: true
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### Training configuration
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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learning_rate: 5.0e-5
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num_train_epochs: 3.0
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lr_scheduler_type: cosine
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warmup_ratio: 0.1
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bf16: true
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### FP8 configuration
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fp8: true
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fp8_backend: torchao # Use TorchAO backend for FP8
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fp8_enable_fsdp_float8_all_gather: true # Enable FSDP2 float8 all-gather optimization
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### FSDP configuration (using training arguments - no separate FSDP config file)
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fsdp:
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- full_shard
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- auto_wrap
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fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
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### Logging configuration
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report_to: wandb
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run_name: llama3_fp8_fsdp_sft |