[docs] Quantization + torch.compile + offloading #11703
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
Code: https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d Worth mentioning:
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| </hfoption> | ||
| <hfoption id="group offloading"> |
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Do you think it might be better demonstrated with a more compute heavy model like Wan? This way, we can show the actual benefits of group offloading.
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Sounds good, could you get me the updated numbers for Wan with quantization/group offloading/torch.compile please?
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I think it's okay to have the Flux numbers but for the sake of code and discussions, we could have Wan.
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Ah ok, don't worry about getting the Wan numbers then!
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| Offloading strategies move not currently active layers or models to the CPU to avoid increasing GPU memory. These strategies can be combined with quantization and torch.compile to balance inference speed and memory usage. | ||
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| Refer to the [Compile and offloading quantized models](./speed-memory-optims) guide for more details. |
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I think #11731 can be resolved in this PR where I make a note that offloading can be combined with quantization and torch.compile
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Added your layerwise casting note in here as well :)
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Yeah feel free to close those :)
| pipeline.transformer.enable_group_offload( | ||
| onload_device=onload_device, | ||
| offload_device=offload_device, | ||
| offload_type="block_level", | ||
| num_blocks_per_group=4 | ||
| ) |
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We should use these args when a component is quantized with bitsandbytes to mitigate device mismatch issues:
But I am curious. Were you able to run the code?
| from diffusers import DiffusionPipeline | ||
| from diffusers.quantizers import PipelineQuantizationConfig | ||
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| torch._dynamo.config.cache_size_limit = 1000 |
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| torch._dynamo.config.cache_size_limit = 1000 | |
| torch._dynamo.config.cache_size_limit = 1000 | |
| torch._dynamo.config.capture_dynamic_output_shape_ops = True | |
| from diffusers.quantizers import PipelineQuantizationConfig | ||
| from transformers import UMT5EncoderModel | ||
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| torch._dynamo.config.cache_size_limit = 1000 |
Follows up on #11670 and #11672 to document combinations of quantization, torch.compile, and offloading.