Report a float compute dtype for quantized GGMLTensor#456
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A quantized GGMLTensor is backed by packed uint8 storage, so weight.dtype returns torch.uint8. Models that do x.to(weight.dtype) before a linear (Ideogram-4, Gemma-4, Qwen3.5, CogVideo, some VAEs) cast activations to uint8, corrupting them and breaking F.linear on MPS, which requires floating-point inputs. GGMLTensor.dtype now returns bfloat16 for quantized tensors; F16/F32 and unquantized tensors keep their real storage dtype. A loader-set compute_dtype takes precedence. Dequant paths read storage via .data and are unaffected. Regression-tested loading WAN 2.2 I2V A14B Q8_0 (1095 tensors) on MPS.
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A quantized
GGMLTensoris backed by packed uint8 storage, soweight.dtypereturnstorch.uint8. Models that cast activations to the weight dtype before a linear (Ideogram-4ldm/ideogram4/model.py, Gemma-4, Qwen3.5, CogVideo, some VAEs dox.to(weight.dtype)) therefore convert their activations to uint8. This corrupts them and breaksF.linearon MPS, which requires floating-point inputs:RuntimeError: MPS device does not support linear for non-float inputs.Change
GGMLTensor.dtypenow returns a floating-point compute dtype (bfloat16) for quantised tensors; F16, F32 and unquantised tensors keep their real storage dtype. Acompute_dtypeset by the loader takes precedence.bfloat16rather thanfloat16avoids downcast and saturation on GGML-BF16 models, which run bfloat16 natively (WAN, FLUX)..datarather than.dtype.Testing
The change mainly benefits MPS, where the non-float cast is fatal; CUDA tolerated the previous behaviour.
Part of #454.
Developed with AI assistance; the diff and test results above were verified on-device before submission.