raise error if use multi-cards in fleet non_distributed mode;test=develop (#28093)

python/paddle/distributed/fleet/base/fleet_base.py

@@ -186,6 +186,15 @@ class Fleet(object):
         fleet.util._set_role_maker(self._role_maker)
 
         self.strategy_compiler = StrategyCompiler()
+
+        if self._role_maker._is_non_distributed() and self._is_collective:
+            if paddle.fluid.core.is_compiled_with_cuda():
+                gpus_num = paddle.fluid.core.get_cuda_device_count()
+                if gpus_num != 1:
+                    raise ValueError(
+                        "CUDA_VISIBLE_DEVICES shoule be set only 1 card if you use `python` to launch fleet program."
+                    )
+
         if paddle.fluid.framework.in_dygraph_mode():
             if self.worker_num() == 1:
                 return
@@ -569,8 +578,6 @@ class Fleet(object):
 
         """
         self.user_defined_optimizer = optimizer
-        if paddle.fluid.framework.in_dygraph_mode():
-            return self
 
         if strategy == None:
             strategy = DistributedStrategy()

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -129,6 +129,7 @@ if (NOT ${WITH_GPU})
     LIST(REMOVE_ITEM TEST_OPS test_parallel_dygraph_transformer)
     LIST(REMOVE_ITEM TEST_OPS test_parallel_dygraph_sync_batch_norm)
     LIST(REMOVE_ITEM TEST_OPS test_imperative_auto_mixed_precision)
+    LIST(REMOVE_ITEM TEST_OPS test_fleet_base_single)
 elseif(${CUDNN_VERSION} VERSION_LESS 7100)
     LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
 endif()

python/paddle/fluid/tests/unittests/test_fleet_base.py

@@ -171,45 +171,7 @@ class TestFleetDygraph(unittest.TestCase):
         final_strategy = fleet._final_strategy()
 
 
-class LinearNet(nn.Layer):
-    def __init__(self):
-        super(LinearNet, self).__init__()
-        self._linear1 = nn.Linear(10, 10)
-        self._linear2 = nn.Linear(10, 1)
-
-    def forward(self, x):
-        return self._linear2(self._linear1(x))
-
-
-class TestFleetDygraphSingle(unittest.TestCase):
-    def setUp(self):
-        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36213"
-        os.environ["PADDLE_CURRENT_ENDPOINTS"] = "127.0.0.1:36213"
-        os.environ["PADDLE_TRAINERS_NUM"] = "1"
-        os.environ["PADDLE_TRAINER_ID"] = "0"
-
-    def test_dygraph_single(self):
-        paddle.disable_static()
-        fleet.init(is_collective=True)
-
-        layer = LinearNet()
-        loss_fn = nn.MSELoss()
-        adam = paddle.optimizer.Adam(
-            learning_rate=0.001, parameters=layer.parameters())
-
-        adam = fleet.distributed_optimizer(adam)
-        dp_layer = fleet.distributed_model(layer)
-        for step in range(2):
-            inputs = paddle.randn([10, 10], 'float32')
-            outputs = dp_layer(inputs)
-            labels = paddle.randn([10, 1], 'float32')
-            loss = loss_fn(outputs, labels)
-            loss.backward()
-            adam.step()
-            adam.clear_grad()
-
-
-class TestFleetBaseSingleRunCollective(unittest.TestCase):
+class TestFleetBaseSingleError(unittest.TestCase):
     def setUp(self):
         os.environ.pop("PADDLE_TRAINER_ENDPOINTS")
 
@@ -221,71 +183,23 @@ class TestFleetBaseSingleRunCollective(unittest.TestCase):
         }
 
     def test_single_run_collective_minimize(self):
-        input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32')
+        def test_single_error():
+            input_x = paddle.static.data(
+                name="x", shape=[-1, 32], dtype='float32')
             input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
 
             fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
             prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
             cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
             avg_cost = paddle.mean(x=cost)
-
             fleet.init(is_collective=True)
-        optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-        optimizer = fleet.distributed_optimizer(optimizer)
-        optimizer.minimize(avg_cost)
-
-        place = fluid.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
-        ) else fluid.CPUPlace()
-
-        exe = fluid.Executor(place)
-        exe.run(paddle.static.default_startup_program())
 
-        for i in range(10):
-            cost_val = exe.run(feed=self.gen_data(), fetch_list=[avg_cost.name])
-            print("cost of step[{}] = {}".format(i, cost_val))
-
-
-class TestFleetBaseSingleRunPS(unittest.TestCase):
-    def setUp(self):
-        os.environ.pop("PADDLE_PSERVERS_IP_PORT_LIST")
-
-    def gen_data(self):
-        return {
-            "x": np.random.random(size=(128, 32)).astype('float32'),
-            "y": np.random.randint(
-                2, size=(128, 1)).astype('int64')
-        }
-
-    def test_single_run_ps_minimize(self):
-        input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32')
-        input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
-
-        fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
-        prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
-        cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
-        avg_cost = paddle.mean(x=cost)
-
-        fleet.init()
-        strategy = paddle.distributed.fleet.DistributedStrategy()
-        optimizer = fluid.optimizer.SGD(learning_rate=0.01)
-        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
-        optimizer.minimize(avg_cost)
-        if fleet.is_server():
-            fleet.init_server()
-            fleet.run_server()
-        elif fleet.is_worker():
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            exe.run(paddle.static.default_startup_program())
-            step = 100
-            for i in range(step):
-                cost_val = exe.run(program=fluid.default_main_program(),
-                                   feed=self.gen_data(),
-                                   fetch_list=[avg_cost.name])
-                print("worker_index: %d, step%d cost = %f" %
-                      (fleet.worker_index(), i, cost_val[0]))
-            fleet.save_persistables(exe, "fleet_single_model/")
-            print("save fleet models done.")
+        # in non_distributed mode(use `python` to launch), raise error if has multi cards
+        if fluid.core.is_compiled_with_cuda(
+        ) and fluid.core.get_cuda_device_count() > 1:
+            self.assertRaises(ValueError, test_single_error)
+        else:
+            test_single_error()
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/test_fleet_base_single.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import os
+cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
+if cuda_visible_devices is None or cuda_visible_devices == "":
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+else:
+    os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices.split(',')[0]
+import paddle
+import paddle.distributed.fleet as fleet
+import paddle.distributed.fleet.base.role_maker as role_maker
+import paddle.fluid as fluid
+import unittest
+import paddle.nn as nn
+
+
+class LinearNet(nn.Layer):
+    def __init__(self):
+        super(LinearNet, self).__init__()
+        self._linear1 = nn.Linear(10, 10)
+        self._linear2 = nn.Linear(10, 1)
+
+    def forward(self, x):
+        return self._linear2(self._linear1(x))
+
+
+class TestFleetDygraphSingle(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36213"
+        os.environ["PADDLE_CURRENT_ENDPOINTS"] = "127.0.0.1:36213"
+        os.environ["PADDLE_TRAINERS_NUM"] = "1"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+
+    def test_dygraph_single(self):
+        paddle.disable_static()
+        fleet.init(is_collective=True)
+
+        layer = LinearNet()
+        loss_fn = nn.MSELoss()
+        adam = paddle.optimizer.Adam(
+            learning_rate=0.001, parameters=layer.parameters())
+
+        adam = fleet.distributed_optimizer(adam)
+        dp_layer = fleet.distributed_model(layer)
+        for step in range(2):
+            inputs = paddle.randn([10, 10], 'float32')
+            outputs = dp_layer(inputs)
+            labels = paddle.randn([10, 1], 'float32')
+            loss = loss_fn(outputs, labels)
+            loss = dp_layer.scale_loss(loss)
+            loss.backward()
+            dp_layer.apply_collective_grads()
+            adam.step()
+            adam.clear_grad()
+
+
+class TestFleetBaseSingleRunCollective(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def gen_data(self):
+        return {
+            "x": np.random.random(size=(128, 32)).astype('float32'),
+            "y": np.random.randint(
+                2, size=(128, 1)).astype('int64')
+        }
+
+    def test_single_run_collective_minimize(self):
+        input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32')
+        input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
+
+        fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
+        prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
+        cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
+        avg_cost = paddle.mean(x=cost)
+
+        fleet.init(is_collective=True)
+        optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+        optimizer = fleet.distributed_optimizer(optimizer)
+        optimizer.minimize(avg_cost)
+
+        place = fluid.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+
+        exe = fluid.Executor(place)
+        exe.run(paddle.static.default_startup_program())
+
+        for i in range(10):
+            cost_val = exe.run(feed=self.gen_data(), fetch_list=[avg_cost.name])
+            print("cost of step[{}] = {}".format(i, cost_val))
+
+
+class TestFleetBaseSingleRunPS(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def gen_data(self):
+        return {
+            "x": np.random.random(size=(128, 32)).astype('float32'),
+            "y": np.random.randint(
+                2, size=(128, 1)).astype('int64')
+        }
+
+    def test_single_run_ps_minimize(self):
+        input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32')
+        input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
+
+        fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh')
+        prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
+        cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
+        avg_cost = paddle.mean(x=cost)
+
+        fleet.init()
+        strategy = paddle.distributed.fleet.DistributedStrategy()
+        optimizer = fluid.optimizer.SGD(learning_rate=0.01)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
+        optimizer.minimize(avg_cost)
+        if fleet.is_server():
+            fleet.init_server()
+            fleet.run_server()
+        elif fleet.is_worker():
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(paddle.static.default_startup_program())
+            step = 10
+            for i in range(step):
+                cost_val = exe.run(program=fluid.default_main_program(),
+                                   feed=self.gen_data(),
+                                   fetch_list=[avg_cost.name])
+                print("worker_index: %d, step%d cost = %f" %
+                      (fleet.worker_index(), i, cost_val[0]))
+
+
+if __name__ == "__main__":
+    unittest.main()