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Merged
merged 1 commit into from
Nov 21, 2025
Merged

improve max_diff #315

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4 changes: 2 additions & 2 deletions _doc/technical/plot_gemm_or_matmul_add.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,8 +10,8 @@
What an operator Gemm in :epkg:`onnxruntime`, the most simple
way to represent a linear neural layer.

A model with three choices
==========================
A model with many choices
=========================
"""

import cpuinfo
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32 changes: 20 additions & 12 deletions onnx_diagnostic/helpers/helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1486,19 +1486,27 @@ def max_diff(
dev=dev,
)
if hist:
if isinstance(hist, bool):
hist = torch.tensor(
[0, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100], dtype=diff.dtype
)
hist = hist.to(diff.device)
ind = torch.bucketize(diff.reshape((-1,)), hist, right=False)
cou = torch.bincount(ind, minlength=ind.shape[0] + 1)
res["rep"] = dict(
zip(
[f">{x}" for x in hist],
[int(i) for i in (cou.sum() - torch.cumsum(cou, 0))],
if isinstance(hist, list) and len(hist) == 1:
res["rep"] = {f">{hist[0]}": (diff > hist[0]).sum().item()}
elif isinstance(hist, list) and len(hist) == 2:
res["rep"] = {
f">{hist[0]}": (diff > hist[0]).sum().item(),
f">{hist[1]}": (diff > hist[1]).sum().item(),
}
else:
if isinstance(hist, bool):
hist = torch.tensor(
[0, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100], dtype=diff.dtype
)
hist = torch.tensor(hist).to(diff.device)
ind = torch.bucketize(diff.reshape((-1,)), hist, right=False)
cou = torch.bincount(ind, minlength=ind.shape[0] + 1)
res["rep"] = dict(
zip(
[f">{x}" for x in hist],
[int(i) for i in (cou.sum() - torch.cumsum(cou, 0))],
)
)
)
return res # type: ignore

if isinstance(expected, int) and isinstance(got, torch.Tensor):
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