[MLU] add fleet init api and collective api pytest for mlu (#40010)

* [MLU] add fleet init api and collective api pytest for mlu

* fix no value for argument 'data_type' in method call

python/paddle/distributed/collective.py

@@ -267,6 +267,10 @@ def new_group(ranks=None, backend=None):
                 place = core.NPUPlace(genv.device_id)
                 core.HCCLParallelContext(strategy,
                                          place).init_with_ring_id(ring_id)
+            elif core.is_compiled_with_mlu():
+                place = core.MLUPlace(genv.device_id)
+                core.CNCLParallelContext(strategy,
+                                         place).init_with_ring_id(ring_id)
             else:
                 assert False, ("no cuda device found")
         else:

python/paddle/distributed/parallel.py

@@ -58,9 +58,9 @@ def _start_kv_server(port, http_server_d, size):
 
 def _is_cpuonly(backend):
     check_backend(backend)
-    if backend in ['auto', 'nccl', 'bkcl', 'hccl', 'heter'] and (
+    if backend in ['auto', 'nccl', 'bkcl', 'hccl', 'heter', 'cncl'] and (
             core.is_compiled_with_cuda() or core.is_compiled_with_xpu() or
-            core.is_compiled_with_npu()):
+            core.is_compiled_with_npu() or core.is_compiled_with_mlu()):
 
         # passes 'auto' and can use cuda or xpu, use the default logics. so return False
         return False
@@ -152,7 +152,8 @@ def init_parallel_env():
     is_cpu_only = _is_cpuonly(backend)
     # 1. gpu xpu check, must be gpu or xpu, 
     if not (is_cpu_only or core.is_compiled_with_cuda() or
-            core.is_compiled_with_xpu() or core.is_compiled_with_npu()):
+            core.is_compiled_with_xpu() or core.is_compiled_with_npu() or
+            core.is_compiled_with_mlu()):
         raise NotImplementedError(
             "If you want to use CPU-only version, please use 'gloo' as backend")
 
@@ -162,6 +163,8 @@ def init_parallel_env():
         _check_var_exists('FLAGS_selected_xpus')
     elif not is_cpu_only and core.is_compiled_with_npu():
         _check_var_exists('FLAGS_selected_npus')
+    elif not is_cpu_only and core.is_compiled_with_mlu():
+        _check_var_exists('FLAGS_selected_mlus')
 
     _check_var_exists("PADDLE_TRAINER_ID")
     _check_var_exists("PADDLE_CURRENT_ENDPOINT")
@@ -213,6 +216,8 @@ def init_parallel_env():
         place = core.XPUPlace(parallel_env.device_id)
     elif core.is_compiled_with_npu():
         place = core.NPUPlace(parallel_env.device_id)
+    elif core.is_compiled_with_mlu():
+        place = core.MLUPlace(parallel_env.device_id)
 
     _set_expected_place(place)
     # init nccl or hccl or bkcl or heter context
@@ -231,6 +236,9 @@ def init_parallel_env():
     elif core.is_compiled_with_npu():
         parallel_helper._set_parallel_ctx(
             core.HCCLParallelContext(strategy, place))
+    elif core.is_compiled_with_mlu():
+        parallel_helper._set_parallel_ctx(
+            core.CNCLParallelContext(strategy, place))
 
     if backend != "heter":
         other_endpoints = strategy.trainer_endpoints[:]

python/paddle/fluid/dygraph/parallel.py

@@ -128,6 +128,9 @@ class ParallelEnv(object):
         elif core.is_compiled_with_npu():
             selected_npus = os.getenv("FLAGS_selected_npus", "0").split(",")
             self._device_id = int(selected_npus[0])
+        elif core.is_compiled_with_mlu():
+            selected_mlus = os.getenv("FLAGS_selected_mlus", "0").split(",")
+            self._device_id = int(selected_mlus[0])
 
         self._trainer_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS",
                                             "").split(",")

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

@@ -13,13 +13,17 @@ if (WITH_MLU)
     endforeach(TEST_OP)
 
     if(WITH_CNCL)
-	foreach(TEST_OP ${TEST_DIST_OPS})
+        foreach(TEST_OP ${TEST_DIST_OPS})
             py_test_modules(${TEST_OP} MODULES ${TEST_OP})
         endforeach(TEST_OP)
         bash_test_modules(test_launch_async_mlu START_BASH test_launch_async_mlu.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
-	bash_test_modules(test_launch_cloud_mlu START_BASH test_launch_cloud_mlu.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
-	bash_test_modules(test_launch_nproc_mlu START_BASH test_launch_nproc_mlu.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
+        bash_test_modules(test_launch_cloud_mlu START_BASH test_launch_cloud_mlu.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
+        bash_test_modules(test_launch_nproc_mlu START_BASH test_launch_nproc_mlu.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
+        bash_test_modules(test_c_comm_init_op_mlu START_BASH test_c_comm_init_op_mlu.sh ENVS PADDLE_BINARY_DIR=${PADDLE_BINARY_DIR})
         set_tests_properties(test_collective_broadcast PROPERTIES TIMEOUT 120)
-	set_tests_properties(test_collective_allreduce PROPERTIES TIMEOUT 120)
+        set_tests_properties(test_collective_allreduce PROPERTIES TIMEOUT 120)
+        set_tests_properties(test_collective_broadcast_api_mlu PROPERTIES TIMEOUT 120)
+        set_tests_properties(test_collective_allreduce_api_mlu PROPERTIES TIMEOUT 120)
+        set_tests_properties(test_c_comm_init_op_mlu PROPERTIES TIMEOUT 120)
     endif(WITH_CNCL)
 endif()

python/paddle/fluid/tests/unittests/mlu/c_comm_init_op_mlu.py

+# Copyright (c) 2022 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.
+
+from __future__ import print_function
+
+import unittest
+import os
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.distributed.fleet.base.private_helper_function import wait_server_ready
+import paddle
+
+paddle.enable_static()
+
+
+class TestCCommInitOp(unittest.TestCase):
+    def setUp(self):
+        self.endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS").split(',')
+        self.current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
+        self.nranks = len(self.endpoints)
+        self.rank = self.endpoints.index(self.current_endpoint)
+        self.mlu_id = int(os.getenv("FLAGS_selected_mlus"))
+        self.place = fluid.MLUPlace(self.mlu_id)
+        self.exe = fluid.Executor(self.place)
+        self.endpoints.remove(self.current_endpoint)
+        self.other_endpoints = self.endpoints
+        if self.rank == 0:
+            wait_server_ready(self.other_endpoints)
+
+    def test_specifying_devices(self):
+        program = fluid.Program()
+        block = program.global_block()
+        cncl_id_var = block.create_var(
+            name=fluid.unique_name.generate('cncl_id'),
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.RAW)
+        block.append_op(
+            type='c_gen_cncl_id',
+            inputs={},
+            outputs={'Out': cncl_id_var},
+            attrs={
+                'rank': self.rank,
+                'endpoint': self.current_endpoint,
+                'other_endpoints': self.other_endpoints
+            })
+        block.append_op(
+            type='c_comm_init',
+            inputs={'X': cncl_id_var},
+            outputs={},
+            attrs={
+                'nranks': self.nranks,
+                'rank': self.rank,
+                'ring_id': 0,
+                'device_id': self.mlu_id
+            })
+        self.exe.run(program)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/mlu/collective_allreduce_api.py

+# Copyright (c) 2022 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.
+
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import os
+import sys
+import signal
+import time
+import socket
+from contextlib import closing
+from six import string_types
+import math
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+import paddle.fluid.unique_name as nameGen
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import paddle.fluid.layers as layers
+from functools import reduce
+from test_collective_api_base_mlu import TestCollectiveAPIRunnerBase, runtime_main
+
+paddle.enable_static()
+
+
+class TestCollectiveAllreduceAPI(TestCollectiveAPIRunnerBase):
+    def __init__(self):
+        self.global_ring_id = 0
+
+    def get_model(self, main_prog, startup_program, rank):
+        with fluid.program_guard(main_prog, startup_program):
+            tindata = layers.data(
+                name="tindata", shape=[10, 1000], dtype='float32')
+            paddle.distributed.all_reduce(tindata)
+            return [tindata]
+
+
+if __name__ == "__main__":
+    runtime_main(TestCollectiveAllreduceAPI, "allreduce")

python/paddle/fluid/tests/unittests/mlu/collective_broadcast_api.py

+# Copyright (c) 2022 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.
+
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import os
+import sys
+import signal
+import time
+import socket
+from contextlib import closing
+from six import string_types
+import math
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+import paddle.fluid.unique_name as nameGen
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import paddle.fluid.layers as layers
+from functools import reduce
+from test_collective_api_base_mlu import TestCollectiveAPIRunnerBase, runtime_main
+
+paddle.enable_static()
+
+
+class TestCollectiveBroadcastAPI(TestCollectiveAPIRunnerBase):
+    def __init__(self):
+        self.global_ring_id = 0
+
+    def get_model(self, main_prog, startup_program, rank):
+        with fluid.program_guard(main_prog, startup_program):
+            tindata = layers.data(
+                name="tindata", shape=[10, 1000], dtype="float32")
+            paddle.distributed.broadcast(tindata, src=1)
+            return [tindata]
+
+
+if __name__ == "__main__":
+    runtime_main(TestCollectiveBroadcastAPI, "broadcast")

python/paddle/fluid/tests/unittests/mlu/test_c_comm_init_op_mlu.sh

+#!/bin/bash
+
+# Copyright (c) 2022 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.
+
+set -e
+# use default values
+# FIXME: random fails on Unknown command lines -c (or -m).
+launch_py=${PADDLE_BINARY_DIR}/python/paddle/distributed/launch.py
+MLU_VISIBLE_DEVICES=0,1 python ${launch_py} c_comm_init_op_mlu.py

python/paddle/fluid/tests/unittests/mlu/test_collective_allreduce_api_mlu.py

+# Copyright (c) 2022 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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+import paddle
+
+from test_collective_api_base_mlu import TestDistBase
+
+paddle.enable_static()
+
+
+class TestCollectiveAllreduceAPI(TestDistBase):
+    def _setup_config(self):
+        pass
+
+    def test_allreduce_cncl_fp16(self):
+        self.check_with_place("collective_allreduce_api.py", "allreduce",
+                              "float16")
+
+    def test_allreduce_cncl_fp32(self):
+        self.check_with_place("collective_allreduce_api.py", "allreduce",
+                              "float32")
+
+    def test_allreduce_cncl_int32(self):
+        self.check_with_place("collective_allreduce_api.py", "allreduce",
+                              "int32")
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/mlu/test_collective_api_base_mlu.py

+# Copyright (c) 2022 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.
+
+from __future__ import print_function
+import numpy as np
+import unittest
+import os
+import sys
+import subprocess
+import pickle
+from contextlib import closing
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+
+
+def DataTypeCast(date_type):
+    np_data_type = None
+
+    if date_type == "float16":
+        np_data_type = np.float16
+    elif date_type == "float32":
+        np_data_type = np.float32
+    elif date_type == "int32":
+        np_data_type = np.int32
+    else:
+        raise ValueError("This data type is not support!")
+
+    return np_data_type
+
+
+class TestCollectiveAPIRunnerBase(object):
+    def get_model(self, train_prog, startup_prog, rank, indata=None):
+        raise NotImplementedError(
+            "get model should be implemented by child class.")
+
+    def run_trainer(self, args):
+        train_prog = fluid.Program()
+        startup_prog = fluid.Program()
+        endpoints = args["endpoints"].split(",")
+        rank = args["trainerid"]
+        current_endpoint = args["currentendpoint"]
+        nranks = 2
+        paddle.distributed.init_parallel_env()
+        device_id = int(os.getenv("FLAGS_selected_mlus", "0"))
+        place = fluid.MLUPlace(device_id)
+        np.random.seed(os.getpid())
+        np_data_type = DataTypeCast(args["data_type"])
+        indata = np.random.random((10, 1000)).astype(np_data_type)
+        if args['static_mode']:
+            result = self.get_model(train_prog, startup_prog, rank)
+            exe = fluid.Executor(place)
+            exe.run(startup_prog)
+            fetch_list = []
+            for elem in result:
+                fetch_list.append(elem.name)
+            out = exe.run(train_prog,
+                          feed={'tindata': indata},
+                          fetch_list=fetch_list)
+        else:
+            out = self.get_model(train_prog, startup_prog, rank, indata)
+            #print(out, sys.stderr)
+        sys.stdout.buffer.write(pickle.dumps(out))
+
+
+def runtime_main(test_class, col_type):
+    args = {}
+    model = test_class()
+    args["trainerid"] = int(os.getenv("PADDLE_TRAINER_ID"))
+    args["trainernum"] = int(os.getenv("PADDLE_TRAINERS_NUM"))
+    args["endpoints"] = os.getenv('PADDLE_TRAINER_ENDPOINTS')
+    args["currentendpoint"] = os.getenv("PADDLE_CURRENT_ENDPOINT")
+    args["col_type"] = col_type
+    args["backend"] = os.getenv("BACKEND")
+    args["path_id"] = int(os.getenv("PATH_ID"))
+    args["static_mode"] = int(os.getenv("STATIC_MODE"))
+    args["data_type"] = os.getenv("DATA_TYPE")
+    model.run_trainer(args)
+
+
+import paddle.compat as cpt
+import socket
+from contextlib import closing
+
+
+class TestDistBase(unittest.TestCase):
+    def setUp(self):
+        self._port_set = set()
+        self._trainers = 2
+        self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
+            self._find_free_port(), self._find_free_port())
+        self._python_interp = sys.executable
+
+    def _find_free_port(self):
+        def __free_port():
+            with closing(socket.socket(socket.AF_INET,
+                                       socket.SOCK_STREAM)) as s:
+                s.bind(('', 0))
+                return s.getsockname()[1]
+
+        while True:
+            port = __free_port()
+            if port not in self._port_set:
+                self._port_set.add(port)
+                return port
+
+    def _run_cluster(self, model_file, envs):
+        worker_endpoints = self._ps_endpoints.split(",")
+        w0_ep, w1_ep = worker_endpoints
+        #print("w0_ep:",w0_ep," w1_ep:",w1_ep)
+        env0 = {
+            "FLAGS_selected_mlus": "0",
+            "PADDLE_TRAINER_ID": "0",
+            "PADDLE_TRAINERS_NUM": "2",
+            "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints,
+            "PADDLE_CURRENT_ENDPOINT": w0_ep
+        }
+
+        env1 = {
+            "FLAGS_selected_mlus": "1",
+            "PADDLE_TRAINER_ID": "1",
+            "PADDLE_TRAINERS_NUM": "2",
+            "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints,
+            "PADDLE_CURRENT_ENDPOINT": w1_ep
+        }
+        #update environment
+        env0.update(envs)
+        env1.update(envs)
+        if os.getenv('WITH_COVERAGE', 'OFF') == 'ON':
+            tr_cmd = "%s -m coverage run --branch -p %s"
+        else:
+            tr_cmd = "%s %s"
+        tr0_cmd = tr_cmd % (self._python_interp, model_file)
+        tr1_cmd = tr_cmd % (self._python_interp, model_file)
+        tr0_pipe = open("/tmp/tr0_err_%d.log" % os.getpid(), "w")
+        tr1_pipe = open("/tmp/tr1_err_%d.log" % os.getpid(), "w")
+        #print(tr0_cmd) 
+        tr0_proc = subprocess.Popen(
+            tr0_cmd.strip().split(),
+            stdout=subprocess.PIPE,
+            stderr=tr0_pipe,
+            env=env0)
+
+        tr1_proc = subprocess.Popen(
+            tr0_cmd.strip().split(),
+            stdout=subprocess.PIPE,
+            stderr=tr1_pipe,
+            env=env1)
+
+        tr0_out, tr0_err = tr0_proc.communicate()
+        tr1_out, tr1_err = tr1_proc.communicate()
+        sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err)
+        sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
+        # close trainer file
+        tr0_pipe.close()
+        tr1_pipe.close()
+        with open("/tmp/tr0_err_%d.log" % os.getpid(), "r") as f:
+            sys.stderr.write('trainer 0 stderr file: %s\n' % f.read())
+        with open("/tmp/tr1_err_%d.log" % os.getpid(), "r") as f:
+            sys.stderr.write('trainer 1 stderr file: %s\n' % f.read())
+        return pickle.loads(tr0_out), pickle.loads(
+            tr1_out), tr0_proc.pid, tr1_proc.pid
+
+    def check_with_place(self,
+                         model_file,
+                         col_type,
+                         data_type,
+                         path_id="0",
+                         static_mode="1",
+                         check_error_log=False,
+                         need_envs={}):
+        required_envs = {
+            "FLAGS_fraction_of_gpu_memory_to_use": "0.15",
+            "FLAGS_eager_delete_tensor_gb": "0.0",
+            "PATH": os.getenv("PATH"),
+            "PYTHONPATH": os.getenv("PYTHONPATH", ""),
+            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
+            "LD_PRELOAD": os.getenv("LD_PRELOAD", ""),
+            "FLAGS_call_stack_level": "2",
+            "GLOG_v": "3",
+            "STATIC_MODE": static_mode,
+            "PADDLE_WITH_GLOO": '0',
+            "BACKEND": "cncl",
+            "PATH_ID": path_id,
+            "DATA_TYPE": data_type
+        }
+        required_envs.update(need_envs)
+        if check_error_log:
+            required_envs["GLOG_v"] = "3"
+            required_envs["GLOG_logtostderr"] = "1"
+            required_envs["GLOO_LOG_LEVEL"] = "TRACE"
+        tr0_out, tr1_out, pid0, pid1 = self._run_cluster(model_file,
+                                                         required_envs)
+        np_data_type = DataTypeCast(data_type)
+        np.random.seed(pid0)
+        input1 = np.random.random((10, 1000)).astype(np_data_type)
+        np.random.seed(pid1)
+        input2 = np.random.random((10, 1000)).astype(np_data_type)
+        if col_type == "broadcast":
+            need_result = input2
+            self.assertTrue(np.allclose(tr0_out, need_result))
+            self.assertTrue(np.allclose(tr1_out, need_result))
+        elif col_type == "allreduce":
+            need_result = input1 + input2
+            self.assertTrue(
+                np.allclose(
+                    tr0_out, need_result, rtol=1e-05, atol=1e-05))
+            self.assertTrue(
+                np.allclose(
+                    tr1_out, need_result, rtol=1e-05, atol=1e-05))
+        else:
+            pass

python/paddle/fluid/tests/unittests/mlu/test_collective_broadcast_api_mlu.py

+# Copyright (c) 2022 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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+import paddle
+
+from test_collective_api_base_mlu import TestDistBase
+
+paddle.enable_static()
+
+
+class TestCollectiveBroadcastAPI(TestDistBase):
+    def _setup_config(self):
+        pass
+
+    def test_broadcast_cncl_fp16(self):
+        self.check_with_place("collective_broadcast_api.py", "broadcast",
+                              "float16")
+
+    def test_broadcast_cncl_fp32(self):
+        self.check_with_place("collective_broadcast_api.py", "broadcast",
+                              "float32")
+
+    def test_broadcast_cncl_int32(self):
+        self.check_with_place("collective_broadcast_api.py", "broadcast",
+                              "int32")
+
+
+if __name__ == '__main__':
+    unittest.main()