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Fix accuracy bitwise_alignment bug #600

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Merged
DanielSun11 merged 3 commits into from
Dec 25, 2025
Merged

Fix accuracy bitwise_alignment bug #600

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efccc53
fix bitwise not enable
DanielSun11 Dec 25, 2025
8ad67c2
rm lerp case
DanielSun11 Dec 25, 2025
c7bb2fe
engineV2 支持 exit_on_error
DanielSun11 Dec 25, 2025
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4 changes: 2 additions & 2 deletions engineV2-README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -88,8 +88,8 @@
| `--random_seed` | int | numpy random的随机种子(默认为0，此时不会显式设置numpy random的seed) |
| `--custom_device_vs_gpu` | bool | 启用自定义设备与GPU的精度对比测试模式（默认 False） |
| `--custom_device_vs_gpu_mode` | str | 自定义设备与GPU对比的模式：`upload` 或 `download`（默认 `upload`） |
| `--bitwise_alignment` | bool | 是否进行诸位对齐对比，开启后所有的api的精度对比都按照atol=0.0,rtol = 0.0的精度对比结果|

| `--bitwise_alignment` | bool | 是否进行诸位对齐对比，开启后所有的api的精度对比都按照atol=0.0,rtol = 0.0的精度对比结果(默认False)|
| `--exit_on_error` | bool | 是否在精度测试出现`paddle_error`或者 `accuracy_error` 错误时立即退出测试进程(exit code 为1)。默认为False，测试进程会继续执行 |

### 示例命令

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17 changes: 17 additions & 0 deletions engineV2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,8 @@
"random_seed",
"bos_conf_path",
"bcecmd_path",
"bitwise_alignment",
"exit_on_error",
}

DEVICE_TYPE = None
Expand Down Expand Up @@ -496,6 +498,8 @@ def run_test_case(api_config_str, options):
os._exit(99)
if "CUDA out of memory" in str(err) or "Out of memory error" in str(err):
os._exit(98)
if "AssertionError" in str(err) or "Tensor-likes are not equal" in str(err):
os._exit(1)
# if not fatal error, subprocess will be alive and report error
print(f"[test error] {api_config_str}: {err}", flush=True)
raise
Expand Down Expand Up @@ -680,6 +684,12 @@ def main():
default=False,
help="Whether to using bitwise alignment when run accuracy test",
)
parser.add_argument(
"--exit_on_error",
type=parse_bool,
default=False,
help="Whether to exit the process when a paddle_error occurs.",
)

options = parser.parse_args()
print(f"Options: {vars(options)}", flush=True)
Expand Down Expand Up @@ -821,12 +831,19 @@ def main():
rtol=options.rtol,
test_tol=options.test_tol,
bitwise_alignment=options.bitwise_alignment,
exit_on_error=options.exit_on_error,
)
else:
case = test_class(api_config, test_amp=options.test_amp)
try:
case.test()
except Exception as err:
if (
"Tensor-likes are not equal" in str(err)
or "Mismatched elements" in str(err)
or "Tensor-likes are not equal" in str(err)
):
exit(1)
print(f"[test error] {options.api_config}: {err}", flush=True)
finally:
case.clear_tensor()
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30 changes: 0 additions & 30 deletions tester/api_config/monitor_config/accuracy/GPU/monitoring_configs_4.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -233951,48 +233951,18 @@ paddle.ldexp(Tensor([247],"float64"), Tensor([247],"int32"), )
paddle.ldexp(Tensor([5, 6, 6],"float16"), Tensor([6],"int32"), )
paddle.ldexp(Tensor([5, 6, 6],"float64"), Tensor([6],"int32"), )
paddle.ldexp(Tensor([],"float32"), Tensor([],"float32"), )
paddle.lerp(Tensor([1, 1, 1],"float32"), Tensor([3, 28, 28],"float32"), 0.36, )
paddle.lerp(Tensor([1, 1, 1],"float32"), Tensor([3, 8, 8],"float32"), 0.3, )
paddle.lerp(Tensor([1, 28, 28],"float32"), Tensor([3, 28, 28],"float32"), 1.0, )
paddle.lerp(Tensor([1, 3, 3],"float32"), Tensor([1, 3, 3],"float32"), Tensor([1, 3, 3],"float32"), )
paddle.lerp(Tensor([1, 3, 3],"float64"), Tensor([1, 3, 3],"float64"), Tensor([1, 3, 3],"float64"), )
paddle.lerp(Tensor([1, 3, 4],"float64"), Tensor([1, 3, 4],"float64"), Tensor([1, 3, 4],"float64"), )
paddle.lerp(Tensor([1, 3],"float64"), Tensor([1, 3],"float64"), Tensor([1, 3],"float64"), )
paddle.lerp(Tensor([1, 8, 8],"float32"), Tensor([3, 8, 8],"float32"), 1.1, )
paddle.lerp(Tensor([10, 1, 10, 5, 5],"float32"), Tensor([10, 5, 1, 5, 5],"float32"), Tensor([1],"float32"), )
paddle.lerp(Tensor([10, 10, 5, 5],"float32"), Tensor([10, 10, 5, 5],"float32"), Tensor([5],"float32"), )
paddle.lerp(Tensor([10, 5, 10, 1, 5],"float32"), Tensor([10, 5, 10, 5, 1],"float32"), Tensor([1],"float32"), )
paddle.lerp(Tensor([1],"float32"), Tensor([1],"float32"), Tensor([1],"float32"), )
paddle.lerp(Tensor([1],"float64"), Tensor([1],"float64"), Tensor([1],"float64"), )
paddle.lerp(Tensor([2, 1, 1, 1],"float32"), Tensor([2, 3, 8, 8],"float32"), 0.3, )
paddle.lerp(Tensor([2, 1, 8, 8],"float32"), Tensor([2, 3, 8, 8],"float32"), 1.1, )
paddle.lerp(Tensor([2, 1],"float32"), Tensor([2, 1],"float32"), Tensor([2, 1],"float32"), )
paddle.lerp(Tensor([2, 2, 5],"float32"), Tensor([3, 2, 1, 5],"float32"), 0.5, )
paddle.lerp(Tensor([2, 3, 8, 8],"float32"), Tensor([2, 3, 8, 8],"float32"), 2.1, )
paddle.lerp(Tensor([2, 5],"float32"), Tensor([2, 2, 5],"float32"), Tensor([2, 2, 2, 5],"float32"), )
paddle.lerp(Tensor([3, 28, 28],"float32"), Tensor([3, 28, 28],"float32"), 1.2, )
paddle.lerp(Tensor([3, 6, 3, 1, 2, 5],"float64"), Tensor([3, 6, 3, 1, 2, 5],"float64"), Tensor([3, 6, 3, 1, 2, 5],"float64"), )
paddle.lerp(Tensor([3, 6, 3, 4, 1, 5],"float64"), Tensor([3, 6, 3, 4, 1, 5],"float64"), Tensor([3, 6, 3, 4, 1, 5],"float64"), )
paddle.lerp(Tensor([3, 6, 3, 4, 2, 1],"float64"), Tensor([3, 6, 3, 4, 2, 1],"float64"), Tensor([3, 6, 3, 4, 2, 1],"float64"), )
paddle.lerp(Tensor([3, 8, 8],"float32"), Tensor([3, 8, 8],"float32"), 2.1, )
paddle.lerp(Tensor([300, 2048],"float32"), Tensor([300, 2048],"float32"), Tensor([1],"float32"), )
paddle.lerp(Tensor([4, 1, 6],"float64"), Tensor([4, 1, 6],"float64"), Tensor([4, 1, 6],"float64"), )
paddle.lerp(Tensor([4, 1],"float32"), Tensor([4, 1],"float32"), Tensor([4, 1],"float32"), )
paddle.lerp(Tensor([4, 1],"float64"), Tensor([4, 1],"float64"), Tensor([4, 1],"float64"), )
paddle.lerp(Tensor([4, 7, 1],"float64"), Tensor([4, 7, 1],"float64"), Tensor([4, 7, 1],"float64"), )
paddle.lerp(Tensor([4],"float32"), Tensor([4],"float32"), Tensor([4],"float32"), )
paddle.lerp(Tensor([5, 1, 4],"float64"), Tensor([5, 1, 4],"float64"), Tensor([5, 1, 4],"float64"), )
paddle.lerp(Tensor([5, 1],"float64"), Tensor([5, 1],"float64"), Tensor([5, 1],"float64"), )
paddle.lerp(Tensor([64, 64],"float32"), Tensor([],"float32"), Tensor([],"float32"), )
paddle.lerp(Tensor([7, 1],"float64"), Tensor([7, 1],"float64"), Tensor([7, 1],"float64"), )
paddle.lerp(Tensor([],"float32"), Tensor([64, 64],"float32"), Tensor([],"float32"), )
paddle.lerp(Tensor([],"float32"), Tensor([],"float32"), 0.5, )
paddle.lerp(Tensor([],"float32"), Tensor([],"float32"), Tensor([],"float32"), )
paddle.lerp(x=Tensor([4, 5, 4, 3],"float64"), y=Tensor([4, 5, 4, 3],"float64"), weight=0.0, )
paddle.lerp(x=Tensor([4, 5, 4, 3],"float64"), y=Tensor([4, 5, 4, 3],"float64"), weight=0.5, )
paddle.lerp(x=Tensor([4, 5, 4, 3],"float64"), y=Tensor([4, 5, 4, 3],"float64"), weight=1.0, )
paddle.lerp(x=Tensor([4, 5, 4],"float64"), y=Tensor([4, 5, 4],"float64"), weight=0.5, )
paddle.lerp(x=Tensor([4, 5],"float64"), y=Tensor([1],"float64"), weight=0.2, )
paddle.lerp(x=Tensor([4, 5],"float64"), y=Tensor([4, 5],"float64"), weight=0.5, )
paddle.lerp(x=Tensor([4],"float32"), y=Tensor([4],"float32"), weight=0.5, )
paddle.lerp(x=Tensor([4],"float64"), y=Tensor([4],"float64"), weight=0.5, )
Expand Down