5410195e0b
* guard return if ring attn alrady registered * add docs link, bits in multi-gpu docs, remove save model callback (subsumed by HF trainers) * configurable heads_k_stride from ring-flash-attn hf adapter
128 lines
3.0 KiB
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128 lines
3.0 KiB
Plaintext
---
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title: "Multi-GPU"
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format:
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html:
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toc: true
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toc-depth: 3
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number-sections: true
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code-tools: true
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execute:
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enabled: false
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---
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This guide covers advanced training configurations for multi-GPU setups using Axolotl.
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## Overview {#sec-overview}
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Axolotl supports several methods for multi-GPU training:
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- DeepSpeed (recommended)
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- FSDP (Fully Sharded Data Parallel)
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- Sequence parallelism
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- FSDP + QLoRA
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## DeepSpeed {#sec-deepspeed}
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DeepSpeed is the recommended approach for multi-GPU training due to its stability and performance. It provides various optimization levels through ZeRO stages.
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### Configuration {#sec-deepspeed-config}
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Add to your YAML config:
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```{.yaml}
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deepspeed: deepspeed_configs/zero1.json
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```
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### Usage {#sec-deepspeed-usage}
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```{.bash}
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# Passing arg via config
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axolotl train config.yml
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# Passing arg via cli
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axolotl train config.yml --deepspeed deepspeed_configs/zero1.json
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```
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### ZeRO Stages {#sec-zero-stages}
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We provide default configurations for:
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- ZeRO Stage 1 (`zero1.json`)
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- ZeRO Stage 2 (`zero2.json`)
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- ZeRO Stage 3 (`zero3.json`)
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Choose based on your memory requirements and performance needs.
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## FSDP {#sec-fsdp}
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### Basic FSDP Configuration {#sec-fsdp-config}
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```{.yaml}
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fsdp:
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- full_shard
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- auto_wrap
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fsdp_config:
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fsdp_offload_params: true
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fsdp_state_dict_type: FULL_STATE_DICT
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fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
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```
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## Sequence parallelism {#sec-sequence-parallelism}
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We support sequence parallelism (SP) via the
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[ring-flash-attention](https://github.com/zhuzilin/ring-flash-attention) project. This
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allows one to split up sequences across GPUs, which is useful in the event that a
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single sequence causes OOM errors during model training.
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First, install `ring-flash-attn`, recommended via `pip install axolotl[ring-flash-attn]`,
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or from source with `pip install .[ring-flash-attn]`.
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Your Axolotl YAML config should contain the following lines:
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```{.yaml}
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sequence_parallel_degree: 4 # Split each sequence into 4 parts, one per GPU
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flash_attention: true # Required with sequence parallelism
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# Optional; strides across the key dimension. Larger values use more memory but will make training faster.
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heads_k_stride: 1
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```
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See our [dedicated guide](sequence_parallelism.qmd) for more details.
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### FSDP + QLoRA {#sec-fsdp-qlora}
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For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).
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## Performance Optimization {#sec-performance}
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### Liger Kernel Integration {#sec-liger}
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Please see [docs](custom_integrations.qmd#liger) for more info.
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## Troubleshooting {#sec-troubleshooting}
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### NCCL Issues {#sec-nccl}
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For NCCL-related problems, see our [NCCL troubleshooting guide](nccl.qmd).
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### Common Problems {#sec-common-problems}
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::: {.panel-tabset}
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## Memory Issues
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- Reduce `micro_batch_size`
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- Reduce `eval_batch_size`
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- Adjust `gradient_accumulation_steps`
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- Consider using a higher ZeRO stage
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## Training Instability
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- Start with DeepSpeed ZeRO-2
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- Monitor loss values
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- Check learning rates
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:::
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For more detailed troubleshooting, see our [debugging guide](debugging.qmd).
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