parallel envs variable check

mindspore/communication/_comm_helper.py

@@ -84,6 +84,7 @@ class GlobalComm:
     BACKEND = DEFAULT_BACKEND
     WORLD_COMM_GROUP = HCCL_WORLD_COMM_GROUP
     INITED = False
+    CHECK_ENVS = True
 
 def is_hccl_available():
     """

mindspore/communication/_hccl_management.py

@@ -24,7 +24,7 @@ HCCL_LIB_CTYPES = ""
 
 def check_group(group):
     """
-    A function that check if a collection communication group is leagal.
+    A function that check if a collection communication group is legal.
 
     Returns:
         None
@@ -39,7 +39,7 @@ def check_group(group):
 
 def check_rank_num(rank_num):
     """
-    A function that check if a collection communication rank number is leagal.If not raise error.
+    A function that check if a collection communication rank number is legal.If not raise error.
 
     Returns:
         None
@@ -53,7 +53,7 @@ def check_rank_num(rank_num):
 
 def check_rank_id(rank_id):
     """
-    A function that check if a collection communication rank id is leagal.If not raise error.
+    A function that check if a collection communication rank id is legal.If not raise error.
 
     Returns:
         None
@@ -112,7 +112,7 @@ def create_group(group, rank_num, rank_ids):
         c_group = c_str(group)
         ret = HCCL_LIB_CTYPES.HcomCreateGroup(c_group, c_rank_num, c_array_rank_ids)
         if ret != 0:
-            raise RuntimeError('Create group error.')
+            raise RuntimeError('Create group error, the error code is ', ret)
     else:
         raise TypeError('Rank ids must be a python list.')
 

mindspore/communication/management.py

@@ -36,6 +36,28 @@ def _get_group(group):
     return group
 
 
+def _check_parallel_envs():
+    """
+    Check whether parallel environment variables have been exported or not.
+
+    Raises:
+        RuntimeError: If parallel environment variables have not been exported or have been exported to wrong values.
+    """
+    if not GlobalComm.CHECK_ENVS:
+        return
+    import os
+    rank_id_str = os.getenv("RANK_ID")
+    if not rank_id_str:
+        raise RuntimeError("Environment variables RANK_ID has not been exported")
+    try:
+        int(rank_id_str)
+    except ValueError:
+        print("RANK_ID should be number")
+    rank_table_file_str = os.getenv("MINDSPORE_HCCL_CONFIG_PATH")
+    rank_table_file_str_old = os.getenv("RANK_TABLE_FILE")
+    if not rank_table_file_str and not rank_table_file_str_old:
+        raise RuntimeError("Get hccl rank_table_file failed, "
+                           "please export MINDSPORE_HCCL_CONFIG_PATH or RANK_TABLE_FILE.")
 
 def init(backend_name=None):
     """
@@ -68,6 +90,7 @@ def init(backend_name=None):
     if backend_name == "hccl":
         if device_target != "Ascend":
             raise RuntimeError("Device target should be 'Ascend' to init hccl, but got {}".format(device_target))
+        _check_parallel_envs()
         init_hccl()
         GlobalComm.BACKEND = Backend("hccl")
         GlobalComm.WORLD_COMM_GROUP = HCCL_WORLD_COMM_GROUP

tests/ut/python/communication/test_comm.py

@@ -30,13 +30,16 @@ from mindspore.ops.operations.comm_ops import Broadcast, AllSwap
 from mindspore.ops.operations.array_ops import Gather
 import mindspore
 
+
 # pylint: disable=W0212
 # W0212: protected-access
 
 tag = 0
 
 context.set_context(device_target="Ascend")
+GlobalComm.CHECK_ENVS = False
 init("hccl")
+GlobalComm.CHECK_ENVS = True
 
 
 class AllReduceNet(nn.Cell):

tests/ut/python/parallel/test_auto_parallel_resnet.py

@@ -31,11 +31,13 @@ from mindspore.parallel import set_algo_parameters
 from mindspore.parallel._utils import _reset_op_id as resset_op_id
 from mindspore.train.model import Model
 from mindspore.context import ParallelMode
+from mindspore.communication._comm_helper import GlobalComm
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_id=0)
+GlobalComm.CHECK_ENVS = False
 init()
-
+GlobalComm.CHECK_ENVS = True
 
 def weight_variable():
     return TruncatedNormal(0.02)

tests/ut/python/parallel/test_auto_parallel_resnet_predict.py

@@ -18,11 +18,15 @@ from mindspore.communication.management import init
 from mindspore.parallel import set_algo_parameters
 from mindspore.train.model import Model
 from mindspore.context import ParallelMode
+from mindspore.communication._comm_helper import GlobalComm
 from .test_auto_parallel_resnet import resnet50
 
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_id=0)
+GlobalComm.CHECK_ENVS = False
 init()
+GlobalComm.CHECK_ENVS = True
 
 def test_train_32k_8p(batch_size=32, num_classes=32768):
     dev_num = 8

tests/ut/python/parallel/test_broadcast_dict.py

@@ -19,7 +19,7 @@ import mindspore.nn as nn
 from mindspore import Tensor, Parameter
 from mindspore.communication.management import init
 from mindspore.ops import operations as P
-
+from mindspore.communication._comm_helper import GlobalComm
 
 class DataParallelNet(nn.Cell):
     def __init__(self):
@@ -49,7 +49,9 @@ def test_param_broadcast():
     context.set_context(mode=context.GRAPH_MODE)
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode="data_parallel", parameter_broadcast=True)
+    GlobalComm.CHECK_ENVS = False
     init()
+    GlobalComm.CHECK_ENVS = True
     network = DataParallelNet()
     network.set_train()
 
@@ -62,7 +64,9 @@ def test_param_not_broadcast():
     context.set_context(mode=context.GRAPH_MODE)
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode="data_parallel", parameter_broadcast=False)
+    GlobalComm.CHECK_ENVS = False
     init()
+    GlobalComm.CHECK_ENVS = True
     network = ModelParallelNet()
     network.set_train()
 

tests/ut/python/parallel/test_gather_v2_primitive.py

@@ -29,6 +29,7 @@ from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 from mindspore.train import Model
 from mindspore.context import ParallelMode
+from mindspore.communication._comm_helper import GlobalComm
 
 context.set_context(mode=context.GRAPH_MODE)
 device_number = 32
@@ -124,7 +125,9 @@ class TrainOneStepCell(Cell):
 
 
 def net_trains(criterion, rank):
+    GlobalComm.CHECK_ENVS = False
     init()
+    GlobalComm.CHECK_ENVS = True
     lr = 0.1
     momentum = 0.9
     max_epoch = 20

tests/ut/python/parallel/test_optimizer.py

@@ -24,7 +24,7 @@ from mindspore.nn import Momentum
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.ops import operations as P
 from mindspore.context import ParallelMode
-
+from mindspore.communication._comm_helper import GlobalComm
 
 class Net(nn.Cell):
     def __init__(self, input_channel, out_channel):
@@ -47,7 +47,9 @@ def test_dense_gen_graph():
     context.set_context(mode=context.GRAPH_MODE)
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode=ParallelMode.HYBRID_PARALLEL, gradients_mean=True, device_num=8)
+    GlobalComm.CHECK_ENVS = False
     init()
+    GlobalComm.CHECK_ENVS = True
     network = Net(512, 128)
 
     loss_fn = nn.SoftmaxCrossEntropyWithLogits()

tests/ut/python/train/test_amp.py

@@ -21,6 +21,7 @@ from mindspore import Tensor
 from mindspore import amp
 from mindspore import nn
 from mindspore.communication.management import init
+from mindspore.communication._comm_helper import GlobalComm
 from mindspore.context import ParallelMode
 from mindspore.train import Model
 from ....dataset_mock import MindData
@@ -156,8 +157,8 @@ def test_compile_model_train_O2_parallel():
     net = NetNoLoss(16, 16)
     loss = nn.MSELoss()
     optimizer = nn.Momentum(net.trainable_params(), 0.1, 0.9, 0.00004, 1024.0)
-
+    GlobalComm.CHECK_ENVS = False
     init()
-
+    GlobalComm.CHECK_ENVS = True
     model = Model(net, loss_fn=loss, optimizer=optimizer, metrics={"acc"}, amp_level="O2")
     model.train(2, dataset, dataset_sink_mode=False)

tests/ut/python/train/test_dataset_helper.py

@@ -19,9 +19,9 @@ import numpy as np
 import mindspore.context as context
 from mindspore.communication.management import init
 from mindspore.train.dataset_helper import DatasetHelper
+from mindspore.communication._comm_helper import GlobalComm
 from ....dataset_mock import MindData
 
-
 def get_dataset(batch_size=1):
     dataset_types = (np.int32, np.int32, np.int32, np.int32, np.int32, np.int32, np.int32)
     dataset_shapes = ((batch_size, 128), (batch_size, 128), (batch_size, 128), (batch_size, 1),
@@ -75,7 +75,9 @@ def test_dataset_iter_normal():
 
 @pytest.mark.skipif('not context.get_context("enable_ge")')
 def test_dataset_iter_ge():
+    GlobalComm.CHECK_ENVS = False
     init("hccl")
+    GlobalComm.CHECK_ENVS = True
     dataset = get_dataset(32)
     dataset_helper = DatasetHelper(dataset, dataset_sink_mode=True, sink_size=10)
     count = 0
@@ -87,7 +89,9 @@ def test_dataset_iter_ge():
 
 @pytest.mark.skipif('context.get_context("enable_ge")')
 def test_dataset_iter_ms_loop_sink():
+    GlobalComm.CHECK_ENVS = False
     init("hccl")
+    GlobalComm.CHECK_ENVS = True
     context.set_context(enable_loop_sink=True)
     dataset = get_dataset(32)
     dataset_helper = DatasetHelper(dataset, dataset_sink_mode=True, sink_size=10)
@@ -101,7 +105,9 @@ def test_dataset_iter_ms_loop_sink():
 
 @pytest.mark.skipif('context.get_context("enable_ge")')
 def test_dataset_iter_ms():
+    GlobalComm.CHECK_ENVS = False
     init("hccl")
+    GlobalComm.CHECK_ENVS = True
     context.set_context(enable_loop_sink=False)
     dataset = get_dataset(32)
     DatasetHelper(dataset, dataset_sink_mode=True, sink_size=10)