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Co-authored-by: Masahiro Tanaka <mtanaka@microsoft.com> Co-authored-by: chengming-zhang <chengming.zhang@anl.gov>
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# Sequence Parallelism | ||
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This folder contains examples that demonstrate how to use DeepSpeed's sequence parallelism. | ||
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## Setting Up the Environment for FlashAttention | ||
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DeepSpeed's sequence parallelism can be combined with the following types of attention. | ||
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- Classic attention | ||
- FlashAttention (enabled by `--use-flash-attn`) | ||
- FlashAttention + Triton (enabled by `--use-flash-attn-triton`) | ||
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For the best performance, we recommend using FlashAttention + Triton. Here are the installation steps and the versions we have tested. Note that FlashAttention is compatible only with Turing, Ampere, Ada, or Hopper GPUs. | ||
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```shell | ||
# install triton | ||
git clone -b legacy-backend https://github.com/openai/triton | ||
cd triton/python/ | ||
pip install cmake | ||
pip install . | ||
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# install | ||
cd ${WORK_DIR} | ||
git clone -b v1.0.4 https://github.com/HazyResearch/flash-attention | ||
cd flash-attention | ||
python setup.py install | ||
``` | ||
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## Enabling Sequence Parallelism | ||
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To enable sequence parallelism, set the degree of parallelism using the `--ds-sequence-parallel-size` argument. Ensure that the number of attention heads is divisible by this value. | ||
Ensure your model configuration is compliant with FlashAttention's requirements. For instance, to achieve optimal performance, the head size should be divisible by 8. Refer to the document of [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/v1.0.4) for more details. | ||
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Some working examples ([GPT1.3B](ds_pretrain_gpt_1.3B_seq_parallel_32k.sh), [GPT30B](ds_pretrain_gpt_30B_seq_parallel_32k.sh)), that enable sequence parallelism, are available in this foloder. | ||
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Please note that our sequence parallelism feature is currently incompatible with Megatron-LM's tensor or pipeline parallelism. |
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examples_deepspeed/sequence_parallel/ds_config_gpt_TEMPLATE.json
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{ | ||
"train_batch_size": GBSIZE, | ||
"train_micro_batch_size_per_gpu": MBSIZE, | ||
"steps_per_print": LOG_INTERVAL, | ||
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"zero_optimization": { | ||
"stage": ZERO_STAGE, | ||
"elastic_checkpoint": true | ||
}, | ||
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"gradient_clipping": 1.0, | ||
"prescale_gradients": PRESCALE_GRAD, | ||
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"fp16": { | ||
"enabled": true, | ||
"loss_scale": 0, | ||
"loss_scale_window": 500, | ||
"hysteresis": 2, | ||
"min_loss_scale": 1, | ||
"initial_scale_power": 11 | ||
}, | ||
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"wall_clock_breakdown" : false | ||
} |
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