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[bitsandbytes] improve dtype mismatch handling for bnb + lora. #11270

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Merged
merged 11 commits into from
Apr 17, 2025
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[bitsandbytes] improve dtype mismatch handling for bnb + lora. #11270

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5801679
improve dtype mismatch handling for bnb + lora.
sayakpaul Apr 10, 2025
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add a test
sayakpaul Apr 10, 2025
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Merge branch 'main' into improve-dtype-mismatch-bnb-lora
sayakpaul Apr 10, 2025
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Merge branch 'main' into improve-dtype-mismatch-bnb-lora
sayakpaul Apr 11, 2025
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Merge branch 'main' into improve-dtype-mismatch-bnb-lora
sayakpaul Apr 11, 2025
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Merge branch 'main' into improve-dtype-mismatch-bnb-lora
sayakpaul Apr 11, 2025
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Merge branch 'main' into improve-dtype-mismatch-bnb-lora
sayakpaul Apr 14, 2025
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fix and updates
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Merge branch 'main' into improve-dtype-mismatch-bnb-lora
sayakpaul Apr 17, 2025
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sayakpaul Apr 17, 2025
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Merge branch 'main' into improve-dtype-mismatch-bnb-lora
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5 changes: 5 additions & 0 deletions src/diffusers/loaders/lora_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,6 +95,11 @@ def _maybe_dequantize_weight_for_expanded_lora(model, module):
weight_on_cpu = True

if is_bnb_4bit_quantized:
if module.weight.quant_state.dtype != model.dtype:
raise ValueError(
f"Model is in {model.dtype} dtype while the current module weight will be dequantized to {module.weight.quant_state.dtype} dtype. "
f"Please pass {module.weight.quant_state.dtype} as `torch_dtype` in `from_pretrained()`."
)
module_weight = dequantize_bnb_weight(
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since we specified dtype = model.dtype in the dequantize_bnb_weight, won't the module_weights have the same dtype as model ?

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Yes it will. But the LoRA params would not be in that dtype as they are derived early from the module_weight data dtype. This is why in the error trace, the error happens in peft.

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@SunMarc SunMarc Apr 10, 2025 •
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To summarize, we have the following right ? :

  • changed loras params using dtype from module_weight (this is maybe where module.weight.quant_state.dtype was used)
  • dequantized module_weight using dtype from model.dtype (so we are not using module.weight.quant_state.dtype actually no ?). model.dtype value comes from torch_dtype.

-> dtype mismatch issue due to loras param not having the same dtype as module_weight

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Yeah. We don't really have any special treatment to handle LoRA param dtype. Ccing @BenjaminBossan here.

dequantized module_weight using dtype from model.dtype (so we are not using module.weight.quant_state.dtype actually no ?). model.dtype value comes from torch_dtype.

Well, we use the quant_state:

diffusers/src/diffusers/quantizers/bitsandbytes/utils.py

Line 173 in ea5a6a8

output_tensor = bnb.functional.dequantize_4bit(weight.data, weight.quant_state)

But then we also perform another type-casting:

diffusers/src/diffusers/quantizers/bitsandbytes/utils.py

Line 189 in ea5a6a8

if dtype:

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Just to clarify: This is unrelated to the LoRA parameters. Instead, what happens is that a PEFT LoraLayer wraps the base layer and calls self.base_layer(x), which should just be the result from the original layer. Due to the change in dtype, we will encounter the dtype mismatch there.

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from diffusers import DiffusionPipeline, FluxControlPipeline
from PIL import Image
import torch

pipe = FluxControlPipeline.from_pretrained("eramth/flux-4bit", torch_dtype=torch.bfloat16).to("cuda")

pipe("a dog", control_image=Image.new(mode="RGB", size=(256, 256)))

This works though.

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Yeah, it happens inside the LoRA layer, but what I mean is that the LoRA weights are not involved, it's the call to the base layer that is causing the issue.

module.weight.cuda() if weight_on_cpu else module.weight,
state=module.weight.quant_state,
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14 changes: 14 additions & 0 deletions tests/quantization/bnb/test_4bit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -752,6 +752,20 @@ def test_lora_loading(self):
max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
self.assertTrue(max_diff < 1e-3, msg=f"{out_slice=} != {expected_slice=}")

def test_loading_lora_with_incorrect_dtype_raises_error(self):
self.tearDown()
model_dtype = torch.bfloat16
# https://huggingface.co/eramth/flux-4bit/blob/main/transformer/config.json#L23
actual_dtype = torch.float16
self.pipeline_4bit = FluxControlPipeline.from_pretrained("eramth/flux-4bit", torch_dtype=torch.bfloat16)
self.pipeline_4bit.enable_model_cpu_offload()
with self.assertRaises(ValueError) as err_context:
self.pipeline_4bit.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora")
assert (
f"Model is in {model_dtype} dtype while the current module weight will be dequantized to {actual_dtype} dtype."
in str(err_context.exception)
)


@slow
class BaseBnb4BitSerializationTests(Base4bitTests):
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