调整paddle_backend

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

@@ -11,11 +11,12 @@ from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
 
 if _NEED_IMPORT_PADDLE:
     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)]
-    paddle.distributed.all_gather(gathered_result, result, group)
+    dist.all_gather(gathered_result, result, group)
     return gathered_result
 
 class PaddleBackend(Backend):
@@ -36,13 +37,13 @@ class PaddleBackend(Backend):
                     tensor = paddle.stack(tensor)
                 # 第一步, aggregate结果
                 if method == 'sum':
-                    tensor = paddle.sum(tensor, dim=0)
+                    tensor = paddle.sum(tensor, axis=0)
                 elif method == 'mean':
-                    tensor = paddle.mean(tensor, dim=0)
+                    tensor = paddle.mean(tensor, axis=0)
                 elif method == 'max':
-                    tensor, _ = paddle.max(tensor, dim=0)
+                    tensor, _ = paddle.max(tensor, axis=0)
                 elif method == 'min':
-                    tensor, _ = paddle.min(tensor, dim=0)
+                    tensor, _ = paddle.min(tensor, axis=0)
                 else:
                     raise AggregateMethodError(should_have_aggregate_method=False)
 
@@ -80,11 +81,12 @@ class PaddleBackend(Backend):
         聚合 group 中所有的 result；由于不同 group 中 result 大小不同，因此在适当的时候需要进行 padding
         """
         # TODO check 正确性
-        if group is None:
-            group = paddle.distributed.get_group(0)
+        # 有 paddle 那边的 bug，2.3 版本的时候修复了，到时候改一下
+        # if group is None:
+        #     group = dist.get_group(0)
 
-        world_size = group.nranks
-        paddle.distributed.barrier(group=group)
+        world_size = group.nranks if group is not None else dist.get_world_size()
+        dist.barrier(group=group)
 
         # 张量为 标量的情况，简单地gather就好
         if result.ndim == 0:
@@ -93,10 +95,10 @@ class PaddleBackend(Backend):
         # 获得 result 的 shape
         local_size = paddle.to_tensor(result.shape)
         # 将 group 中所有 result 的大小聚合在一起
-        local_sizes = [paddle.zeros_like(local_size) for _ in range(world_size)]
-        paddle.distributed.all_gather(local_sizes, local_size, group=group)
+        local_sizes = []
+        dist.all_gather(local_sizes, local_size, group=group)
         # 堆叠后，计算出 shape 每一维度的最大值
-        max_size = paddle.stack(local_sizes).max(axis=0).values
+        max_size = paddle.stack(local_sizes).max(axis=0)
         all_sizes_equal = all(all(ls == max_size) for ls in local_sizes)
 
         # 如果所有的结果大小相同，那么可以直接聚合
@@ -111,10 +113,10 @@ class PaddleBackend(Backend):
             pad_dims.append(val.item())
         result_padded = paddle.nn.functional.pad(result, pad_dims)
         # 重新进行聚合
-        gathered_result = [paddle.zeros_like(result_padded) for _ in range(world_size)]
-        paddle.distributed.all_gather(gathered_result, result_padded, group)
+        gathered_result = []
+        dist.all_gather(gathered_result, result_padded, group)
         for idx, item_size in enumerate(local_sizes):
-            slice_param = [slice(dim_size) for dim_size in item_size]
+            slice_param = [slice(dim_size) for dim_size in item_size.tolist()]
             gathered_result[idx] = gathered_result[idx][slice_param]
         return gathered_result
 

tests/core/controllers/test_trainer_paddle.py

@@ -0,0 +1,151 @@
+import pytest
+import os
+from typing import Any
+from dataclasses import dataclass
+
+from paddle.optimizer import Adam
+from paddle.io import DataLoader
+
+from fastNLP.core.controllers.trainer import Trainer
+from fastNLP.core.metrics.accuracy import Accuracy
+from fastNLP.core.callbacks.progress_callback import RichCallback
+from fastNLP.envs import FASTNLP_DISTRIBUTED_CHECK
+
+
+from tests.helpers.models.paddle_model import PaddleNormalModel_Classification
+from tests.helpers.datasets.paddle_data import PaddleDataset_MNIST
+from tests.helpers.callbacks.helper_callbacks import RecordLossCallback, RecordMetricCallback
+from tests.helpers.utils import magic_argv_env_context
+
+@dataclass
+class MNISTTrainPaddleConfig:
+    num_labels: int = 10
+    feature_dimension: int = 784
+
+    batch_size: int = 32
+    shuffle: bool = True
+    validate_every = -5
+
+    driver: str = "paddle"
+    device = "gpu"
+
+@dataclass
+class MNISTTrainFleetConfig:
+    num_labels: int = 10
+    feature_dimension: int = 784
+
+    batch_size: int = 32
+    shuffle: bool = True
+    validate_every = -5
+
+@dataclass
+class TrainerParameters:
+    model: Any = None
+    optimizers: Any = None
+    train_dataloader: Any = None
+    validate_dataloaders: Any = None
+    input_mapping: Any = None
+    output_mapping: Any = None
+    metrics: Any = None
+
+# @pytest.fixture(params=[0], autouse=True)
+# def model_and_optimizers(request):
+#     """
+#     初始化单卡模式的模型和优化器
+#     """
+#     trainer_params = TrainerParameters()
+#     print(paddle.device.get_device())
+
+#     if request.param == 0:
+#         trainer_params.model = PaddleNormalModel_Classification(
+#             num_labels=MNISTTrainPaddleConfig.num_labels,
+#             feature_dimension=MNISTTrainPaddleConfig.feature_dimension
+#         )
+#         trainer_params.optimizers = Adam(parameters=trainer_params.model.parameters(), learning_rate=0.0001)
+#         train_dataloader = DataLoader(
+#             dataset=PaddleDataset_MNIST("train"),
+#             batch_size=MNISTTrainPaddleConfig.batch_size,
+#             shuffle=True
+#         )
+#         val_dataloader = DataLoader(
+#             dataset=PaddleDataset_MNIST(mode="test"),
+#             batch_size=MNISTTrainPaddleConfig.batch_size,
+#             shuffle=True
+#         )
+#         trainer_params.train_dataloader = train_dataloader
+#         trainer_params.validate_dataloaders = val_dataloader
+#         trainer_params.validate_every = MNISTTrainPaddleConfig.validate_every
+#         trainer_params.metrics = {"acc": Accuracy()}
+
+#     return trainer_params
+
+
+@pytest.mark.parametrize("driver,device", [("paddle", "cpu"), ("paddle", 1)])
+# @pytest.mark.parametrize("driver,device", [("fleet", [0, 1])])
+@pytest.mark.parametrize("callbacks", [[RecordMetricCallback(monitor="acc#acc", metric_threshold=0.7, larger_better=True), 
+                                        RichCallback(5), RecordLossCallback(loss_threshold=0.3)]])
+@magic_argv_env_context
+def test_trainer_paddle(
+        # model_and_optimizers: TrainerParameters,
+        driver,
+        device,
+        callbacks,
+        n_epochs=15,
+):
+    trainer_params = TrainerParameters()
+
+    trainer_params.model = PaddleNormalModel_Classification(
+        num_labels=MNISTTrainPaddleConfig.num_labels,
+        feature_dimension=MNISTTrainPaddleConfig.feature_dimension
+    )
+    trainer_params.optimizers = Adam(parameters=trainer_params.model.parameters(), learning_rate=0.0001)
+    train_dataloader = DataLoader(
+        dataset=PaddleDataset_MNIST("train"),
+        batch_size=MNISTTrainPaddleConfig.batch_size,
+        shuffle=True
+    )
+    val_dataloader = DataLoader(
+        dataset=PaddleDataset_MNIST(mode="test"),
+        batch_size=MNISTTrainPaddleConfig.batch_size,
+        shuffle=True
+    )
+    trainer_params.train_dataloader = train_dataloader
+    trainer_params.validate_dataloaders = val_dataloader
+    trainer_params.validate_every = MNISTTrainPaddleConfig.validate_every
+    trainer_params.metrics = {"acc": Accuracy(backend="paddle")}
+    if not isinstance(device, (int, str)) and len(device) > 1 and FASTNLP_DISTRIBUTED_CHECK not in os.environ:
+        with pytest.raises(SystemExit) as exc:
+            trainer = Trainer(
+                model=trainer_params.model,
+                driver=driver,
+                device=device,
+                optimizers=trainer_params.optimizers,
+                train_dataloader=trainer_params.train_dataloader,
+                validate_dataloaders=trainer_params.validate_dataloaders,
+                validate_every=trainer_params.validate_every,
+                input_mapping=trainer_params.input_mapping,
+                output_mapping=trainer_params.output_mapping,
+                metrics=trainer_params.metrics,
+
+                n_epochs=n_epochs,
+                callbacks=callbacks,
+            )
+        assert exc.value.code == 0
+        return
+    else:
+        trainer = Trainer(
+            model=trainer_params.model,
+            driver=driver,
+            device=device,
+            optimizers=trainer_params.optimizers,
+            train_dataloader=trainer_params.train_dataloader,
+            validate_dataloaders=trainer_params.validate_dataloaders,
+            validate_every=trainer_params.validate_every,
+            input_mapping=trainer_params.input_mapping,
+            output_mapping=trainer_params.output_mapping,
+            metrics=trainer_params.metrics,
+
+            n_epochs=n_epochs,
+            callbacks=callbacks,
+        )
+        trainer.run()

tests/core/drivers/paddle_driver/test_paddle_driver.py

@@ -1,17 +1,11 @@
 import unittest
 
 import torch
-from fastNLP.envs.set_env import set_env
-from fastNLP.envs.set_env_on_import import set_env_on_import_paddle
 
-set_env_on_import_paddle()
-set_env("paddle")
+from fastNLP.core.drivers.paddle_driver.paddle_driver import PaddleDriver
 import paddle
 from paddle.io import Dataset, DataLoader
 
-from fastNLP.core.drivers.paddle_driver.paddle_driver import PaddleDriver
-
-
 class Net(paddle.nn.Layer):
     def __init__(self):
         super(Net, self).__init__()