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update autotuner input tensor random range #2116

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yzh119 merged 3 commits into from
Nov 22, 2025
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update autotuner input tensor random range #2116

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9d7dd0e
uddapte autotuner input random tensor
jiahanc Nov 19, 2025
cad6e85
rever unrelated change
jiahanc Nov 19, 2025
3e9bc87
per code rabbit
jiahanc Nov 20, 2025
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10 changes: 5 additions & 5 deletions flashinfer/autotuner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,9 +61,9 @@ def __post_init__(self):
# Set default tensor_initializers if not provided
if self.tensor_initializers is None:
self.tensor_initializers = [
lambda shapes, dtype, device: torch.randn(shapes, device=device).to(
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@yzh119 yzh119 Nov 20, 2025

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randn is gaussian distribution, which is different from your desciption:

input tensor random range from [0,1) to [-5,5) for larger range

where [0, 1) is a uniform distribution.

I have no idea about the real data distribution tbh and changing it to [-5, 5) seems fine. Just a heads up to make sure it's not a typo.

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@jiahanc jiahanc Nov 20, 2025 •
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thanks for pointing out. Is there a reason why randn here but rand in the other place?
Reason to change to [-5,5) is @rosenrodt did some work on MXFP4 tuning experiment and found this range can get better autotuner config than [0,1)

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@rosenrodt rosenrodt Nov 20, 2025

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I speculated [-5, 5) is than [0, 1) because the latter could truncate to 0s, thus affecting the power profile during autotune and less representative of the power profile of the actual workload.

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@yzh119 yzh119 Nov 22, 2025

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Yes I think if [-5, 5) is better let's use it, data distribution affects kernel execution time.

dtype
)
lambda shapes, dtype, device: (
torch.rand(shapes, device=device) * 10 - 5
).to(dtype)
for _ in range(len(self.input_idx))
]

Expand Down Expand Up @@ -761,8 +761,8 @@ def _create_tensor_like(
def _prepare_input_tensors(
self, profile: OptimizationProfile, inputs: List[torch.Tensor]
) -> List[torch.Tensor]:
default_initializer = lambda shapes, dtype, device: torch.rand(
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@yzh119 yzh119 Nov 20, 2025

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this is indeed uniform distribution [0, 1)

shapes, device=device
default_initializer = lambda shapes, dtype, device: (
torch.rand(shapes, device=device) * 10 - 5
).to(dtype)
tensors = []
for i, p in enumerate(profile.shapes):
Expand Down