增加paddle单卡的accuracy测试用例

fastNLP/core/metrics/backend/paddle_backend/backend.py

@@ -14,11 +14,13 @@ if _NEED_IMPORT_PADDLE:
     import paddle.distributed as dist
     from paddle.fluid.dygraph import parallel_helper
 
+
 def _simple_gather_all_tensors(result, group: Any, world_size: int) -> List:
     gathered_result = [paddle.zeros_like(result) for _ in range(world_size)]
     dist.all_gather(gathered_result, result, group)
     return gathered_result
 
+
 class PaddleBackend(Backend):
     def __init__(self):
         super().__init__()
@@ -124,4 +126,3 @@ class PaddleBackend(Backend):
         # TODO 如果在这里处理的话，会不会在别的地方引起bug？
         device = get_device_from_visible(device)
         return paddle_to(tensor, device)
-

fastNLP/core/metrics/backend/torch_backend/backend.py

@@ -11,7 +11,6 @@ from fastNLP.core.drivers.torch_driver.dist_utils import fastnlp_torch_all_gathe
 if _NEED_IMPORT_TORCH:
     import torch
     import torch.distributed as dist
-    import torch.nn.functional as F
 
 
 def _simple_gather_all_tensors(result, group: Any, world_size: int) -> List:
@@ -33,7 +32,7 @@ class TorchBackend(Backend):
             if dist.is_initialized():
                 if method is None:
                     raise AggregateMethodError(should_have_aggregate_method=True)
-                tensor = fastnlp_torch_all_gather(tensor)
+                tensor = self.all_gather_object(tensor)
                 if isinstance(tensor[0], torch.Tensor):
                     tensor = torch.stack(tensor)
                 # 第一步, aggregate结果

tests/core/metrics/test_accutacy_paddle.py

@@ -0,0 +1,59 @@
+import os
+
+import pytest
+import paddle
+import paddle.distributed
+import paddle.distributed.fleet.base.role_maker as role_maker
+import paddle.distributed.fleet as fleet
+from fastNLP.core.metrics import Accuracy
+from fastNLP.core.drivers.paddle_driver.fleet_launcher import FleetLauncher
+
+############################################################################
+#
+# 测试 单机单卡情况下的Accuracy
+#
+############################################################################
+def test_accuracy_single():
+    pred = paddle.to_tensor([[1.19812393, -0.82041764, -0.53517765, -0.73061031, -1.45006669,
+                              0.46514302],
+                             [-0.85775983, -2.18273783, -1.07505429, -1.45561373, 0.40011844,
+                              1.02202022],
+                             [-0.39487389, 0.65682763, -0.62424040, 0.53692561, -0.28390560,
+                              -0.02559055],
+                             [-0.22586937, -0.07676325, -0.95977223, 0.36395910, -0.91758579,
+                              -0.83857095],
+                             [0.25136873, 2.49652624, 1.06251311, 1.60194016, 1.01451588,
+                              0.08403367],
+                             [0.10844281, 1.19017303, -0.11378096, 1.12686944, -0.08654942,
+                              0.48605862],
+                             [1.27320433, -1.13902378, 1.47072780, -0.98665696, -0.42589864,
+                              0.64618838],
+                             [0.83809763, -0.05356205, 0.03042423, -0.28371972, 0.81611472,
+                              -0.45802942],
+                             [0.38535264, 0.09721313, 2.27187467, 0.32045507, -0.20711982,
+                              -0.13550705],
+                             [-0.75228405, -1.34161997, 1.08697927, 0.33218071, -1.19470012,
+                              2.58735061]])
+    tg = paddle.to_tensor([1, 2, 1, 3, 5, 4, 4, 2, 1, 5])
+    acc_metric = Accuracy()
+    acc_metric.update(pred, tg)
+    result = acc_metric.get_metric()
+    true_result = {'acc': 0.3}
+    assert true_result == result
+
+
+############################################################################
+#
+# 测试 单机多卡情况下的Accuracy
+#
+############################################################################
+def test_accuracy_ddp():
+    launcher = FleetLauncher(devices=[0, 1])
+    launcher.launch()
+    role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+    fleet.init(role)
+    if fleet.is_server():
+        pass
+    elif fleet.is_worker():
+        print(os.getenv("PADDLE_TRAINER_ID"))
+