fix: keep torch GPU reference independent from test_cpu

Author: cangtianhuang

engineV2.py

@@ -440,8 +440,6 @@ def validate_gpu_options(options) -> tuple:
             "expected -1 or a positive integer"
         )
     if getattr(options, "accuracy_stable_dual_gpu", False):
-        if getattr(options, "test_cpu", False):
-            raise ValueError("--accuracy_stable_dual_gpu=True does not support --test_cpu=True")
         if options.num_gpus < 2 or options.num_gpus % 2:
             raise ValueError("--accuracy_stable_dual_gpu=True requires an even --num_gpus")
         if options.num_workers_per_gpu != 1:
@@ -462,6 +460,29 @@ def normalize_accuracy_stable_dual_gpu_options(options):
     options.accuracy_stable = True
 
 
+def _mode_runs_torch_gpu_reference(options):
+    """只有执行 Torch reference 的模式才要求保留 GPU 运行时。"""
+    return any(
+        getattr(options, mode, False)
+        for mode in (
+            "accuracy",
+            "accuracy_stable",
+            "accuracy_stable_dual_gpu",
+            "torch_gpu_performance",
+            "paddle_torch_gpu_performance",
+        )
+    )
+
+
+def _requires_gpu_runtime(options):
+    """test_cpu 与 use_gpu_mode 正交地决定 GPU 运行时需求。"""
+    return bool(
+        not getattr(options, "test_cpu", False)
+        or getattr(options, "use_gpu_mode", False)
+        or _mode_runs_torch_gpu_reference(options)
+    )
+
+
 def _resolve_dump_options(parser, options):
     try:
         options.use_dump, options.dump_dir = resolve_dump_options(
@@ -496,9 +517,7 @@ def _apply_single_config_gpu_defaults(options):
 
 def _prepare_single_config_gpu(options):
     normalize_accuracy_stable_dual_gpu_options(options)
-    if getattr(options, "accuracy_stable_dual_gpu", False) and getattr(options, "test_cpu", False):
-        raise ValueError("--accuracy_stable_dual_gpu=True does not support --test_cpu=True")
-    if options.test_cpu:
+    if not _requires_gpu_runtime(options):
         options.gpu_workers_per_gpu_map = {}
         options.gpu_total_memory_map = {}
         options.runtime_config = TestRuntimeConfig.from_options(options)
@@ -1074,7 +1093,7 @@ def main():
         )
         return
     normalize_accuracy_stable_dual_gpu_options(options)
-    if options.api_config and not options.test_cpu:
+    if options.api_config and _requires_gpu_runtime(options):
         _apply_single_config_gpu_defaults(options)
 
     mode = [

engineV4.py

@@ -1135,6 +1135,29 @@ def normalize_accuracy_stable_dual_gpu_options(options):
     options.accuracy_stable = True
 
 
+def _mode_runs_torch_gpu_reference(options):
+    """只有执行 Torch reference 的模式才要求保留 GPU 运行时。"""
+    return any(
+        getattr(options, mode, False)
+        for mode in (
+            "accuracy",
+            "accuracy_stable",
+            "accuracy_stable_dual_gpu",
+            "torch_gpu_performance",
+            "paddle_torch_gpu_performance",
+        )
+    )
+
+
+def _requires_gpu_runtime(options):
+    """test_cpu 与 use_gpu_mode 正交地决定 GPU 运行时需求。"""
+    return bool(
+        not getattr(options, "test_cpu", False)
+        or getattr(options, "use_gpu_mode", False)
+        or _mode_runs_torch_gpu_reference(options)
+    )
+
+
 def validate_gpu_options(options) -> tuple:
     """Validate and normalize GPU-related options."""
     normalize_accuracy_stable_dual_gpu_options(options)
@@ -1162,8 +1185,6 @@ def validate_gpu_options(options) -> tuple:
             "expected -1 or a positive integer"
         )
     if getattr(options, "accuracy_stable_dual_gpu", False):
-        if getattr(options, "test_cpu", False):
-            raise ValueError("--accuracy_stable_dual_gpu=True does not support --test_cpu=True")
         if options.num_gpus < 2 or options.num_gpus % 2:
             raise ValueError("--accuracy_stable_dual_gpu=True requires an even --num_gpus")
         if options.num_workers_per_gpu != 1:
@@ -1205,9 +1226,7 @@ def _apply_single_config_gpu_defaults(options):
 
 def _prepare_single_config_gpu(options):
     normalize_accuracy_stable_dual_gpu_options(options)
-    if getattr(options, "accuracy_stable_dual_gpu", False) and getattr(options, "test_cpu", False):
-        raise ValueError("--accuracy_stable_dual_gpu=True does not support --test_cpu=True")
-    if options.test_cpu:
+    if not _requires_gpu_runtime(options):
         options.gpu_workers_per_gpu_map = {}
         options.gpu_total_memory_map = {}
         options.runtime_config = TestRuntimeConfig.from_options(options)
@@ -1745,7 +1764,7 @@ def main():
         )
         return
     normalize_accuracy_stable_dual_gpu_options(options)
-    if options.api_config and not options.test_cpu:
+    if options.api_config and _requires_gpu_runtime(options):
         _apply_single_config_gpu_defaults(options)
 
     mode = [

tester/api_config/config_analyzer.py

@@ -315,12 +315,31 @@ def get_cached_numpy(self, dtype, shape, generation_kind="input", scale=1.2):
         return get_cached_numpy_array(dtype, shape, generation_kind=generation_kind, scale=scale)
 
     def _use_gpu(self, api_config=None, dtype=None):
+        """判断是否启用 GPU tensor 生成；不代表 Paddle kernel 的 place。"""
         if not is_gpu_mode():
             return False
         if self.place is not None and "cpu" in str(self.place).lower():
             return False
         return "gpu" in paddle.device.get_device()
 
+    def _paddle_kernel_uses_gpu(self, api_config):
+        """test_cpu 只决定 Paddle kernel，不能被 use_gpu_mode 覆盖。"""
+        if self.place is not None and "cpu" in str(self.place).lower():
+            return False
+        if getattr(api_config, "test_cpu", False):
+            return False
+        return "gpu" in paddle.device.get_device()
+
+    def _torch_source_device_for_paddle(self, api_config):
+        """返回送入 Paddle 前的 Torch 源设备，遵守 Paddle kernel place。"""
+        if self.place is not None and "cpu" in str(self.place).lower():
+            return torch.device("cpu")
+        if getattr(api_config, "test_cpu", False):
+            return torch.device("cpu")
+        if self._paddle_kernel_uses_gpu(api_config):
+            return torch.device("cuda", torch.cuda.current_device())
+        return torch.device("cpu")
+
     def _supports_autograd(self, dtype=None):
         dtype = dtype or self.dtype
         return dtype in AUTOGRAD_DTYPES
@@ -3433,10 +3452,12 @@ def get_paddle_tensor(self, api_config):
                 and self._use_gpu(api_config)
             ):
                 self.paddle_tensor = self._make_gpu_paddle_tensor(api_config)
+                if getattr(api_config, "test_cpu", False):
+                    self.paddle_tensor = self.paddle_tensor._copy_to(paddle.CPUPlace(), False)
                 return self.paddle_tensor
             if self.cpu_tensor is not None:
                 torch_tensor = self.cpu_tensor.to(
-                    device=torch.device("cuda:0") if self._use_gpu(api_config) else "cpu",
+                    device=self._torch_source_device_for_paddle(api_config),
                     copy=True,
                 )
                 self.paddle_tensor = paddle.utils.dlpack.from_dlpack(
@@ -3445,7 +3466,10 @@ def get_paddle_tensor(self, api_config):
                 self.paddle_tensor.stop_gradient = not self._requires_autograd(api_config)
                 return self.paddle_tensor
             if self.numpy_tensor is None and self._use_gpu(api_config):
-                return self.get_gpu_paddle_tensor(api_config)
+                tensor = self.get_gpu_paddle_tensor(api_config)
+                if getattr(api_config, "test_cpu", False):
+                    self.paddle_tensor = tensor._copy_to(paddle.CPUPlace(), False)
+                return self.paddle_tensor
             if not self.is_contiguous and self.strides is not None:
                 self.paddle_tensor = self._create_strided_paddle_tensor(api_config)
                 print(
@@ -3462,10 +3486,11 @@ def get_paddle_tensor(self, api_config):
                     if self.dtype == "bfloat16"
                     else ("float16" if self.dtype in FLOAT8_DTYPES else self.dtype)
                 )
+                operator_place = paddle.CPUPlace() if getattr(api_config, "test_cpu", False) else self.place
                 self.paddle_tensor = paddle.to_tensor(
                     self.get_numpy_tensor(api_config),
                     dtype=intermediate_dtype,
-                    place=self.place,
+                    place=operator_place,
                 )
 
                 if self.dtype == "bfloat16":
@@ -3497,18 +3522,19 @@ def _create_strided_paddle_tensor(self, api_config):
         try:
             intermediate_dtype = "float16" if self.dtype in FLOAT8_DTYPES else self.dtype
             storage_size = self._strided_storage_size()
+            operator_place = paddle.CPUPlace() if getattr(api_config, "test_cpu", False) else self.place
             flat_tensor = paddle.zeros(
                 [storage_size],
                 dtype=intermediate_dtype,
-                device=self.place,
+                device=operator_place,
             )
             tensor = paddle.as_strided(flat_tensor, self.shape, self.strides)
             logical_tensor = self.get_numpy_tensor(api_config)
             if logical_tensor.size > 0:
                 tensor[...] = paddle.to_tensor(
                     logical_tensor,
                     dtype=intermediate_dtype,
-                    place=self.place,
+                    place=operator_place,
                 )
             if self.dtype in FLOAT8_DTYPES:
                 flat_tensor = paddle.cast(flat_tensor, dtype=self.dtype)

tester/base.py

@@ -493,8 +493,11 @@ class APITestBase:
     def __init__(self, api_config, use_torch=True, runtime_config=None):
         self.api_config = api_config
         self.api_config.use_torch = use_torch
+        self.use_torch = bool(use_torch)
         self.runtime_config = runtime_config or TestRuntimeConfig()
         self.gpu_mode_config = self.runtime_config.gpu_mode
+        # TensorConfig 需要知道 kernel place，不能从 GPU mode 反推。
+        self.api_config.test_cpu = self.runtime_config.test_cpu
         self.dump_context = (
             DumpContext(
                 os.environ.get("DUMP_DIR") or DEFAULT_DUMP_DIR, api_config=api_config.config
@@ -508,6 +511,14 @@ def __init__(self, api_config, use_torch=True, runtime_config=None):
             torch.set_num_threads(8)
             torch.set_printoptions(threshold=100, linewidth=120)
 
+    def torch_operator_device(self):
+        """返回当前 worker 的 Torch reference 设备。"""
+        return torch.device(f"{self.runtime_config.torch_operator_device_type}:0")
+
+    def requires_gpu_runtime(self):
+        """算子执行或 GPU mode 任一需要 GPU 时返回 True。"""
+        return self.use_torch or not self.runtime_config.test_cpu or self.gpu_mode_config.enabled
+
     def run_with_dump(self):
         """Execute the test with dump output capture and lifecycle reporting."""
         if self.dump_context is None:

tester/log_writer/log_report.py

@@ -66,16 +66,32 @@ def print_run_header(options, paddle_version):
             )
         )
 
+    torch_reference_gpu = any(
+        getattr(options, name, False)
+        for name in (
+            "accuracy",
+            "accuracy_stable",
+            "accuracy_stable_dual_gpu",
+            "torch_gpu_performance",
+            "paddle_torch_gpu_performance",
+        )
+    )
+    requires_gpu = not options.test_cpu or options.use_gpu_mode or torch_reference_gpu
+    compute = [
+        ("paddle_kernel_device", "CPU" if options.test_cpu else "GPU"),
+        ("torch_reference_device", "GPU" if torch_reference_gpu else "N/A"),
+        ("input_compare_device", "GPU" if options.use_gpu_mode else "CPU"),
+    ]
     if options.test_cpu:
-        compute = [("--test_cpu", True)]
-    else:
+        compute.append(("--test_cpu", True))
+    if requires_gpu:
         if not options.gpu_ids:
             gpu_ids_display = "all visible"
         elif options.gpu_ids == "-1":
             gpu_ids_display = "-1 (all visible)"
         else:
             gpu_ids_display = options.gpu_ids
-        compute = [("--gpu_ids", gpu_ids_display)]
+        compute.append(("--gpu_ids", gpu_ids_display))
         if options.use_gpu_mode:
             compute.append(("--use_gpu_mode", True))
             if getattr(options, "accuracy_stable_dual_gpu", False):

tester/paddle_cinn_vs_dygraph.py

@@ -9,7 +9,12 @@
 
 class APITestCINNVSDygraph(APITestBase):
     def __init__(self, api_config, **kwargs):
-        super().__init__(api_config)
+        # CINN 只执行 Paddle kernel，不应丢失 worker 的 test_cpu 设备协议。
+        super().__init__(
+            api_config,
+            use_torch=False,
+            runtime_config=kwargs.get("runtime_config"),
+        )
         self.test_amp = kwargs.get("test_amp", False)
         self.test_backward = kwargs.get("test_backward", False)
 

tester/runtime_config.py

@@ -23,8 +23,19 @@ class TestRuntimeConfig:
     random_seed: int = 0
     bitwise_alignment: bool = False
     exit_on_error: bool = False
+    # test_cpu 只控制 Paddle kernel；GPU mode 另行控制输入和比较策略。
+    test_cpu: bool = False
     gpu_mode: GpuModeConfig = field(default_factory=GpuModeConfig)
 
+    @property
+    def paddle_kernel_device_type(self):
+        return "cpu" if self.test_cpu else "cuda"
+
+    @property
+    def torch_operator_device_type(self):
+        # accuracy 模式始终执行 GPU Torch reference，即使 Paddle kernel 在 CPU。
+        return "cuda"
+
     @classmethod
     def from_options(cls, options):
         dual_gpu = bool(getattr(options, "accuracy_stable_dual_gpu", False))
@@ -37,6 +48,7 @@ def from_options(cls, options):
             random_seed=int(options.random_seed),
             bitwise_alignment=bool(options.bitwise_alignment),
             exit_on_error=bool(options.exit_on_error),
+            test_cpu=bool(getattr(options, "test_cpu", False)),
             gpu_mode=gpu_mode,
         )