【Cherry-Pick】add distributed_infer (#30300) (#30427)

* test=develop, add distributed_infer (#30300)

* test=develop, add distributed_infer

* test=develop, fix unittest cmakefile conflict

* test=develop, fix test_dist_fleet_base

paddle/fluid/distributed/service/brpc_ps_server.cc

@@ -103,6 +103,7 @@ int32_t BrpcPsService::initialize() {
   _service_handler_map[PS_BARRIER] = &BrpcPsService::barrier;
   _service_handler_map[PS_START_PROFILER] = &BrpcPsService::start_profiler;
   _service_handler_map[PS_STOP_PROFILER] = &BrpcPsService::stop_profiler;
+  _service_handler_map[PS_PUSH_GLOBAL_STEP] = &BrpcPsService::push_global_step;
 
   // shard初始化,server启动后才可从env获取到server_list的shard信息
   initialize_shard_info();

paddle/fluid/distributed/table/common_sparse_table.cc

@@ -94,23 +94,28 @@ struct Meta {
 
 void ProcessALine(const std::vector<std::string>& columns, const Meta& meta,
                   std::vector<std::vector<float>>* values) {
-  PADDLE_ENFORCE_EQ(columns.size(), meta.names.size() + 1,
+  PADDLE_ENFORCE_EQ(columns.size(), 2,
                     paddle::platform::errors::InvalidArgument(
-                        "record in txt do not match meta."));
+                        "The data format does not meet the requirements. It "
+                        "should look like feasign_id \t params."));
 
-  values->reserve(columns.size() - 1);
-
-  for (int x = 1; x < columns.size(); ++x) {
-    auto& column = columns[x];
-    auto val_ = paddle::string::split_string<std::string>(column, ",");
+  auto load_values = paddle::string::split_string<std::string>(columns[1], ",");
+  values->reserve(meta.names.size());
 
+  int offset = 0;
+  for (int x = 0; x < meta.names.size(); ++x) {
     std::vector<float> val;
-    std::transform(val_.begin(), val_.end(), std::back_inserter(val),
-                   [](std::string va) { return std::stof(va); });
-    PADDLE_ENFORCE_EQ(val.size(), meta.dims[x - 1],
+    auto start = load_values.begin() + offset;
+    auto end = load_values.begin() + offset + meta.dims[x];
+    PADDLE_ENFORCE_LE(offset + meta.dims[x], load_values.size(),
                       paddle::platform::errors::InvalidArgument(
-                          "record in txt do not match meta."));
+                          "The data format in txt does not meet the field "
+                          "requirements defined in meta"));
+
+    std::transform(start, end, std::back_inserter(val),
+                   [](std::string va) { return std::stof(va); });
     values->push_back(val);
+    offset += meta.dims[x];
   }
 }
 

python/paddle/distributed/fleet/__init__.py

@@ -19,7 +19,7 @@ from .base.fleet_base import Fleet
 from .base.util_factory import UtilBase
 from .dataset import *
 from .data_generator import MultiSlotDataGenerator, MultiSlotStringDataGenerator
-#from . import metrics
+from . import metrics
 
 __all__ = [
     "DistributedStrategy",

python/paddle/distributed/fleet/metrics/metric.py

@@ -13,11 +13,10 @@
 # limitations under the License.
 """Fleet Metrics"""
 
-import paddle.fluid as fluid
 import math
 import numpy as np
-from paddle.fluid.framework import Variable
-import paddle.distributed.fleet as fleet
+from paddle.static import Variable
+import paddle
 
 
 def sum(input, scope=None, util=None):
@@ -46,9 +45,9 @@ def sum(input, scope=None, util=None):
           print("sum array: ", paddle.distributed.fleet.sum(res))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
     if isinstance(input, Variable):
         input = np.array(scope.find_var(input.name).get_tensor())
     elif isinstance(input, str):
@@ -86,9 +85,9 @@ def max(input, scope=None, util=None):
           print("max array: ", paddle.distributed.fleet.max(res))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
     if isinstance(input, Variable):
         input = np.array(scope.find_var(input.name).get_tensor())
     elif isinstance(input, str):
@@ -126,9 +125,9 @@ def min(input, scope=None, util=None):
           print("min array: ", paddle.distributed.fleet.min(res))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
     if isinstance(input, Variable):
         input = np.array(scope.find_var(input.name).get_tensor())
     elif isinstance(input, str):
@@ -168,9 +167,9 @@ def auc(stat_pos, stat_neg, scope=None, util=None):
           print("auc: ", paddle.distributed.fleet.auc(pos, neg))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
 
     if isinstance(stat_pos, Variable):
         stat_pos = np.array(scope.find_var(stat_pos.name).get_tensor())
@@ -246,9 +245,9 @@ def mae(abserr, total_ins_num, scope=None, util=None):
           print("mae: ", paddle.distributed.fleet.mae(res, total_ins_num))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
 
     if isinstance(abserr, Variable):
         abserr = np.array(scope.find_var(abserr.name).get_tensor())
@@ -289,9 +288,9 @@ def rmse(sqrerr, total_ins_num, scope=None, util=None):
           print("rmse: ", paddle.distributed.fleet.rmse(res, total_ins_num))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
 
     if isinstance(sqrerr, Variable):
         sqrerr = np.array(scope.find_var(sqrerr.name).get_tensor())
@@ -331,9 +330,9 @@ def mse(sqrerr, total_ins_num, scope=None, util=None):
           print("mse: ", paddle.distributed.fleet.mse(metric, total_ins_num))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
 
     if isinstance(sqrerr, Variable):
         sqrerr = np.array(scope.find_var(sqrerr.name).get_tensor())
@@ -384,9 +383,9 @@ def acc(correct, total, scope=None, util=None):
           print("accuracy: ", paddle.distributed.fleet.acc(correct_num, total_num))
     """
     if scope is None:
-        scope = fluid.global_scope()
+        scope = paddle.static.global_scope()
     if util is None:
-        util = fleet.util
+        util = paddle.distributed.fleet.util
 
     if isinstance(correct, Variable):
         correct = np.array(scope.find_var(correct.name).get_tensor())

python/paddle/distributed/fleet/runtime/the_one_ps.py

@@ -30,6 +30,9 @@ def conv_indent(indent):
     return "".join([" "] * indent)
 
 
+PSERVER_SAVE_SUFFIX = "_txt"
+
+
 class Accessor:
     def __init__(self):
         self.accessor_class = ""
@@ -789,9 +792,9 @@ class TheOnePSRuntime(RuntimeBase):
         begin = time.time()
         for var_name in load_varnames:
             table_id = sparse_table_maps[var_name]
-            path = os.path.join(dirname, var_name,
+            path = os.path.join(dirname, var_name + PSERVER_SAVE_SUFFIX,
                                 "{}.block{}.txt".format(var_name, pserver_id))
-            meta = os.path.join(dirname, var_name,
+            meta = os.path.join(dirname, var_name + PSERVER_SAVE_SUFFIX,
                                 "{}.block{}.meta".format(var_name, pserver_id))
             self._server.load_sparse(path, meta, table_id)
         end = time.time()

python/paddle/distributed/fleet/utils/__init__.py

@@ -13,4 +13,4 @@
 # limitations under the License.
 
 from .fs import LocalFS, HDFSClient
-from .ps_util import Distributed
+from .ps_util import DistributedInfer

python/paddle/distributed/fleet/utils/ps_util.py

@@ -14,11 +14,104 @@
 """Parameter Server utils"""
 
 import numpy as np
+import os
+import paddle
 
 
-class Distributed:
-    @staticmethod
-    def estimate(main_program, varname2tables):
+class DistributedInfer:
+    """
+    Utility class for distributed infer of PaddlePaddle.
+    """
+
+    def __init__(self, main_program=None, startup_program=None):
+        if main_program:
+            self.origin_main_program = main_program.clone()
+        else:
+            self.origin_main_program = paddle.static.default_main_program(
+            ).clone()
+
+        if startup_program:
+            self.origin_startup_program = startup_program
+        else:
+            self.origin_startup_program = paddle.static.default_startup_program(
+            )
+        self.sparse_table_maps = None
+
+    def init_distributed_infer_env(self,
+                                   exe,
+                                   loss,
+                                   role_maker=None,
+                                   dirname=None):
+        import paddle.distributed.fleet as fleet
+
+        if fleet.fleet._runtime_handle is None:
+            fleet.init(role_maker=role_maker)
+
+            fake_optimizer = paddle.optimizer.SGD()
+            strategy = fleet.DistributedStrategy()
+            strategy.a_sync = True
+            optimizer = fleet.distributed_optimizer(
+                fake_optimizer, strategy=strategy)
+            optimizer.minimize(
+                loss, startup_program=self.origin_startup_program)
+
+            if fleet.is_server():
+                fleet.init_server(dirname=dirname)
+                fleet.run_server()
+            else:
+                exe.run(paddle.static.default_startup_program())
+                fleet.init_worker()
+                self._init_dense_params(exe, dirname)
+            global_startup_program = paddle.static.default_startup_program()
+            global_startup_program = self.origin_startup_program
+            global_main_program = paddle.static.default_main_program()
+            global_main_program = self.origin_main_program
+
+    def _get_sparse_table_map(self):
+        import paddle.distributed.fleet as fleet
+
+        if self.sparse_table_maps is None:
+            self.sparse_table_maps = {}
+            send_ctx = fleet.fleet._runtime_handle._communicator.send_ctx_
+            for gradname, ctx in send_ctx.items():
+                if ctx.is_sparse:
+                    param = gradname.strip("@GRAD")
+                    self.sparse_table_maps[param] = ctx.table_id()
+                else:
+                    continue
+        return self.sparse_table_maps
+
+    def _init_dense_params(self, exe=None, dirname=None):
+        import paddle.distributed.fleet as fleet
+
+        sparse_table_maps = self._get_sparse_table_map()
+
+        if dirname is not None and exe is not None:
+            all_persist_vars = [
+                v for v in self.origin_main_program.list_vars()
+                if paddle.static.io.is_persistable(v)
+            ]
+            dense_persist_vars = [(v.name, v) for v in all_persist_vars
+                                  if v.name not in sparse_table_maps]
+            need_load_vars = [
+                v[1] for v in dense_persist_vars
+                if os.path.isfile(os.path.join(dirname, v[0]))
+            ]
+            paddle.static.load_vars(
+                exe,
+                dirname,
+                main_program=self.origin_main_program,
+                vars=need_load_vars)
+
+    def get_dist_infer_program(self):
+        import paddle.distributed.fleet as fleet
+
+        varname2tables = self._get_sparse_table_map()
+        convert_program = self._convert_program(self.origin_main_program,
+                                                varname2tables)
+        return convert_program
+
+    def _convert_program(self, main_program, varname2tables):
         def distributed_ops_pass(program):
             SPARSE_OP_TYPE_DICT = {"lookup_table": "W", "lookup_table_v2": "W"}
 

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

@@ -661,6 +661,7 @@ endif()
 
 if (WITH_DISTRIBUTE)
     set_tests_properties(test_communicator_half_async PROPERTIES TIMEOUT 120)
+    set_tests_properties(test_dist_fleet_infer PROPERTIES TIMEOUT 200)
 endif()
 
 if (WITH_DISTRIBUTE AND NOT APPLE)

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

@@ -28,7 +28,7 @@ import numpy as np
 
 import ctr_dataset_reader
 from test_dist_fleet_base import runtime_main, FleetDistRunnerBase
-from paddle.distributed.fleet.utils.ps_util import Distributed
+from paddle.distributed.fleet.utils.ps_util import DistributedInfer
 import paddle.distributed.fleet as fleet
 
 paddle.enable_static()
@@ -165,17 +165,11 @@ class TestDistCTR2x2(FleetDistRunnerBase):
         with open(os.path.join(dirname, "__model__.proto"), "w") as wn:
             wn.write(str(program))
 
-    def do_distributed_testing(self, args, test_main_program,
-                               test_startup_program):
+    def do_distributed_testing(self, fleet):
         """
         do distributed
         """
-        device_env = os.getenv("DEVICE", 'cpu')
-        if device_env == 'cpu':
-            device = fluid.CPUPlace()
-        elif device_env == 'gpu':
-            device = fluid.CUDAPlace(0)
-        exe = fluid.Executor(device)
+        exe = self.get_executor()
 
         batch_size = 4
         test_reader = paddle.batch(fake_ctr_reader(), batch_size=batch_size)
@@ -188,7 +182,7 @@ class TestDistCTR2x2(FleetDistRunnerBase):
         try:
             while True:
                 batch_idx += 1
-                loss_val = exe.run(program=test_main_program,
+                loss_val = exe.run(program=paddle.static.default_main_program(),
                                    fetch_list=[self.avg_cost.name])
                 loss_val = np.mean(loss_val)
                 message = "TEST ---> batch_idx: {} loss: {}\n".format(batch_idx,
@@ -207,12 +201,7 @@ class TestDistCTR2x2(FleetDistRunnerBase):
         Args:
             fleet(Fleet api): the fleet object of Parameter Server, define distribute training role
         """
-        device_env = os.getenv("DEVICE", 'cpu')
-        if device_env == 'cpu':
-            device = fluid.CPUPlace()
-        elif device_env == 'gpu':
-            device = fluid.CUDAPlace(0)
-        exe = fluid.Executor(device)
+        exe = self.get_executor()
         exe.run(fluid.default_startup_program())
         fleet.init_worker()
 
@@ -250,13 +239,7 @@ class TestDistCTR2x2(FleetDistRunnerBase):
     def do_dataset_training(self, fleet):
         train_file_list = ctr_dataset_reader.prepare_fake_data()
 
-        device_env = os.getenv("DEVICE", 'cpu')
-        if device_env == 'cpu':
-            device = fluid.CPUPlace()
-        elif device_env == 'gpu':
-            device = fluid.CUDAPlace(0)
-        exe = fluid.Executor(device)
-
+        exe = self.get_executor()
         exe.run(fluid.default_startup_program())
         fleet.init_worker()
 

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

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-from paddle.distributed.fleet.utils.ps_util import Distributed
+from paddle.distributed.fleet.utils.ps_util import DistributedInfer
 from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
 import paddle.distributed.fleet as fleet
 import paddle.distributed.fleet.base.role_maker as role_maker
@@ -37,11 +37,6 @@ import tempfile
 import unittest
 
 import paddle
-import paddle.fluid as fluid
-import paddle.distributed.fleet.base.role_maker as role_maker
-import paddle.distributed.fleet as fleet
-from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
-from paddle.distributed.fleet.utils.ps_util import Distributed
 paddle.enable_static()
 
 __all__ = ['FleetDistRunnerBase', 'TestFleetBase', 'runtime_main']
@@ -58,6 +53,9 @@ class FleetDistRunnerBase(object):
         do training : exe run program
     """
 
+    def __init__(self):
+        self._exe = None
+
     def build_role(self, args):
 
         if args.role.upper() == "PSERVER":
@@ -159,6 +157,16 @@ class FleetDistRunnerBase(object):
         raise NotImplementedError(
             "get_model should be implemented by child classes.")
 
+    def get_executor(self):
+        if self._exe is None:
+            device_env = os.getenv("DEVICE", 'cpu')
+            if device_env == 'cpu':
+                device = fluid.CPUPlace()
+            elif device_env == 'gpu':
+                device = fluid.CUDAPlace(0)
+            self._exe = fluid.Executor(device)
+        return self._exe
+
     def do_dataset_training(self, fleet):
         raise NotImplementedError(
             "do_dataset_training should be implemented by child classes.")
@@ -193,6 +201,7 @@ class TestFleetBase(unittest.TestCase):
         self._trainers = 2
         self._pservers = 2
         self._need_test = 0
+        self._model_dir = ""
         self._port_set = set()
 
         global DIST_UT_PORT
@@ -290,6 +299,10 @@ class TestFleetBase(unittest.TestCase):
             self._trainers, self._mode, self._geo_sgd_need_push_nums,
             self._reader, gloo_path, self._need_test)
 
+        if self._model_dir:
+            tr_cmd += " --model_dir {}".format(self._model_dir)
+            ps_cmd += " --model_dir {}".format(self._model_dir)
+
         # Run dist train to compare with local results
         ps0, ps1, ps0_pipe, ps1_pipe = self._start_pserver(ps_cmd, env)
         tr0, tr1, tr0_pipe, tr1_pipe = self._start_trainer(tr_cmd, env)
@@ -381,14 +394,32 @@ def runtime_main(test_class):
         '--geo_sgd_need_push_nums', type=int, required=False, default=2)
     parser.add_argument('--reader', type=str, required=False, default='dataset')
     parser.add_argument('--test', type=int, required=False, default=0)
+    parser.add_argument('--model_dir', type=str, required=False, default="")
     args = parser.parse_args()
 
     model = test_class()
     role = model.build_role(args)
+
+    if args.test and args.model_dir != "":
+        avg_cost = model.net(args, is_train=False)
+        dist_infer = DistributedInfer()
+        dist_infer.init_distributed_infer_env(
+            exe=model.get_executor(),
+            loss=model.avg_cost,
+            role_maker=role,
+            dirname=args.model_dir)
+        if fleet.is_worker():
+            with paddle.static.program_guard(
+                    main_program=dist_infer.get_dist_infer_program()):
+                model.do_distributed_testing(fleet)
+                fleet.stop_worker()
+                return
+
     fleet.init(role)
     strategy = model.build_strategy(args)
     avg_cost = model.net(args)
     model.build_optimizer(avg_cost, strategy)
+
     if args.role == "pserver":
         model.run_pserver(args)
     else:
@@ -398,26 +429,17 @@ def runtime_main(test_class):
             model.run_pyreader_trainer(args)
 
         if args.test:
-            test_origin_program = fluid.Program()
-            test_startup_program = fluid.Program()
-            with fluid.program_guard(
+            test_origin_program = paddle.static.Program()
+            test_startup_program = paddle.static.Program()
+            with paddle.static.program_guard(
                     main_program=test_origin_program,
                     startup_program=test_startup_program):
-                with fluid.unique_name.guard():
+                with paddle.utils.unique_name.guard():
                     avg_cost = model.net(args, is_train=False)
-            send_ctx = fleet.fleet._runtime_handle._communicator.send_ctx_
-            varname2tables = {}
-            for gradname, ctx in send_ctx.items():
-                if ctx.is_sparse:
-                    param = gradname.strip("@GRAD")
-                    varname2tables[param] = ctx.table_id()
-                else:
-                    continue
-            ps_util = Distributed()
-            test_main_program = ps_util.estimate(test_origin_program,
-                                                 varname2tables)
-            print(str(test_main_program))
-            print(str(test_startup_program))
-            model.do_distributed_testing(args, test_main_program,
-                                         test_startup_program)
+            dist_infer = DistributedInfer(
+                main_program=test_origin_program,
+                startup_program=test_startup_program)
+            with paddle.static.program_guard(
+                    main_program=dist_infer.get_dist_infer_program()):
+                model.do_distributed_testing(fleet)
         fleet.stop_worker()

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

+# Copyright (c) 2018 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 os
+import shutil
+import unittest
+import tempfile
+import tarfile
+from test_dist_fleet_base import TestFleetBase
+from paddle.dataset.common import download, DATA_HOME
+
+
+class TestDistCtrInfer(TestFleetBase):
+    def _setup_config(self):
+        self._mode = "async"
+        self._reader = "pyreader"
+        self._need_test = 1
+
+        data_url = "https://fleet.bj.bcebos.com/unittest/ctr_saved_params.tar.gz"
+        data_md5 = "aa7e8286ced566ea8a67410be7482438"
+        module_name = "ctr_saved_params"
+        path = download(data_url, module_name, data_md5)
+        print('ctr_params is downloaded at ' + path)
+        tar = tarfile.open(path)
+        unzip_folder = tempfile.mkdtemp()
+        tar.extractall(unzip_folder)
+        self._model_dir = unzip_folder
+
+    def check_with_place(self,
+                         model_file,
+                         delta=1e-3,
+                         check_error_log=False,
+                         need_envs={}):
+        required_envs = {
+            "PATH": os.getenv("PATH", ""),
+            "PYTHONPATH": os.getenv("PYTHONPATH", ""),
+            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
+            "FLAGS_rpc_deadline": "30000",  # 5sec to fail fast
+            "http_proxy": "",
+            "FLAGS_communicator_send_queue_size": "2",
+            "FLAGS_communicator_max_merge_var_num": "2",
+            "CPU_NUM": "2",
+        }
+
+        required_envs.update(need_envs)
+
+        if check_error_log:
+            required_envs["GLOG_v"] = "3"
+            required_envs["GLOG_logtostderr"] = "1"
+
+        tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
+
+    def test_dist_infer(self):
+        self.check_with_place(
+            "dist_fleet_ctr.py", delta=1e-5, check_error_log=False)
+        shutil.rmtree(self._model_dir)
+
+
+class TestDistCtrTrainInfer(TestFleetBase):
+    def _setup_config(self):
+        self._mode = "async"
+        self._reader = "pyreader"
+        self._need_test = 1
+
+    def check_with_place(self,
+                         model_file,
+                         delta=1e-3,
+                         check_error_log=False,
+                         need_envs={}):
+        required_envs = {
+            "PATH": os.getenv("PATH", ""),
+            "PYTHONPATH": os.getenv("PYTHONPATH", ""),
+            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
+            "FLAGS_rpc_deadline": "30000",  # 5sec to fail fast
+            "http_proxy": "",
+            "FLAGS_communicator_send_queue_size": "2",
+            "FLAGS_communicator_max_merge_var_num": "2",
+            "CPU_NUM": "2",
+        }
+
+        required_envs.update(need_envs)
+
+        if check_error_log:
+            required_envs["GLOG_v"] = "3"
+            required_envs["GLOG_logtostderr"] = "1"
+
+        tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
+
+    def test_dist_train_infer(self):
+        self.check_with_place(
+            "dist_fleet_ctr.py", delta=1e-5, check_error_log=False)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -73,6 +73,7 @@ class TestFleetMetric(unittest.TestCase):
                 pass
 
         self.util = FakeUtil(FakeFleet())
+        fleet.util = self.util
 
     def test_metric_1(self):
         """Test cases for metrics."""
@@ -104,14 +105,14 @@ class TestFleetMetric(unittest.TestCase):
             metric.rmse(t1, 3, scope, self.util)
             metric.mse(t1, 3, scope, self.util)
             metric.acc(t, t1, scope, self.util)
-            metric.sum(str(t.name), scope, self.util)
-            metric.max(str(t.name), scope, self.util)
-            metric.min(str(t.name), scope, self.util)
-            metric.auc(str(t1.name), str(t.name), scope, self.util)
-            metric.mae(str(t1.name), 3, scope, self.util)
-            metric.rmse(str(t1.name), 3, scope, self.util)
-            metric.mse(str(t1.name), 3, scope, self.util)
-            metric.acc(str(t.name), str(t1.name), scope, self.util)
+            metric.sum(str(t.name))
+            metric.max(str(t.name))
+            metric.min(str(t.name))
+            metric.auc(str(t1.name), str(t.name))
+            metric.mae(str(t1.name), 3)
+            metric.rmse(str(t1.name), 3)
+            metric.mse(str(t1.name), 3)
+            metric.acc(str(t.name), str(t1.name))
         arr = np.array([1, 2, 3, 4])
         metric.sum(arr, util=self.util)
         metric.max(arr, util=self.util)