Issue
I was running this conformance test for NVIDIA's TensorRT-RTX EP with ORT backend and encountered a failure.
Input:
bnInput shape [3,1,2] float32 = [-1, 0, 1, 2, 3, 4]
bnMean shape [3] float32 = [0, 3, 6]
bnVariance shape [3] float32 = [1.0, 1.5, 2.0]
Graph:
batchNormalization(input=bnInput, mean=bnMean, variance=bnVariance, {axis: 0}) -> bnOutput
gelu(bnOutput) -> output
Expected output: shape [3,1,2] float32
[-0.15865567326545715, 0, -0.08366739749908447,
-0.16910307109355927, -0.03595117852091789, -0.1112278625369072]
Error happens at index 4
actual -0.035951320081949234 should be close enough to expected -0.03595117852091789 by ULP distance: expected <= 24 but got 38
Observations
I tested this with other execution providers in ORT and other backends and encountered similar or same error.
| Backend |
Actual |
ULP Difference |
| ORT CPU EP |
−0.035951320081949234 |
38 |
| TFLite |
−0.035951387137174606 |
56 |
| LiteRT (GPU) |
−0.035951387137174606 |
56 |
Question for the Working Group
- What is the expected behavior for this test ?
- Should the discrepancy between the actual and expected outputs be investigated, or is the current error tolerance too strict and in need of adjustment?
Issue
I was running this conformance test for NVIDIA's TensorRT-RTX EP with ORT backend and encountered a failure.
Input:
Graph:
Expected output: shape [3,1,2] float32
Error happens at index 4
Observations
I tested this with other execution providers in ORT and other backends and encountered similar or same error.
Question for the Working Group