Fix accuracy bitwise_alignment bug (#600)

Author: DanielSun11

engineV2-README.md

@@ -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
 

engineV2.py

@@ -53,6 +53,8 @@
     "random_seed",
     "bos_conf_path",
     "bcecmd_path",
+    "bitwise_alignment",
+    "exit_on_error",
 }
 
 DEVICE_TYPE = None
@@ -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
@@ -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)
@@ -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()

tester/api_config/monitor_config/accuracy/GPU/monitoring_configs_4.txt

@@ -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, )