fix: unscale fp16 gradient problem & potential error (#6086) #6231
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sayakpaul
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Dec 20, 2023
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| # Make sure vae.dtype is consistent with the unet.dtype | ||
| if args.mixed_precision == "fp16": | ||
| vae.to(weight_dtype) |
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This is not needed in my opinion. We already set the torch_dtype in the pipeline during loading it.
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If no other pretrained_vae_model_name_or_path is set, then the vae is set to float32,
if args.pretrained_vae_model_name_or_path is None:
vae.to(accelerator.device, dtype=torch.float32)
and the pipeline here does not reload vae. So vae.dtype float32 != unet.dtype fp16, which in my tests causes RuntimeError: Input type (c10::Half) and bias type (float) should be the same
sayakpaul
reviewed
Dec 20, 2023
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| # Make sure the trainable params are in float32. | ||
| if args.mixed_precision == "fp16": | ||
| models = [unet] | ||
| if args.train_text_encoder: | ||
| models.extend([text_encoder_one, text_encoder_two]) | ||
| for model in models: | ||
| for param in model.parameters(): | ||
| # only upcast trainable parameters (LoRA) into fp32 | ||
| if param.requires_grad: | ||
| param.data = param.to(torch.float32) |
|
Thank you for your contributions! |
donhardman
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Dec 29, 2023
… (huggingface#6231) Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
AmericanPresidentJimmyCarter
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Apr 26, 2024
… (huggingface#6231) Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
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Referring to commit ,
Fixes # (6086) in
train_text_to_image_lora_sdxl.py, and Fixes # (4619) intrain_text_to_image_lora_sdxl.pyduring fixing the former error.