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from torchao.quantization.observer import MappingType, AffineQuantizedMinMaxObserver from torchao.quantization.granularity import PerAxis import torch o = AffineQuantizedMinMaxObserver(MappingType.ASYMMETRIC, torch.uint8, PerAxis(1)) _ = o(torch.tensor([[100, 120]])) _ = o(torch.arange(8).reshape(1, 2, 2, 2)) print(o.min_val) Expected: tensor([[0, 4]]) Actual: tensor([[100, 120]])
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/4189
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from torchao.quantization.observer import MappingType, AffineQuantizedMinMaxObserver
from torchao.quantization.granularity import PerAxis
import torch
o = AffineQuantizedMinMaxObserver(MappingType.ASYMMETRIC, torch.uint8, PerAxis(1))
_ = o(torch.tensor([[100, 120]]))
_ = o(torch.arange(8).reshape(1, 2, 2, 2))
print(o.min_val)
Expected:
tensor([[0, 4]])
Actual:
tensor([[100, 120]])