add modelzoo network gnmtv2 testcase

requirements.txt

@@ -18,3 +18,6 @@ packaging >= 20.0
 pycocotools >= 2.0.0        # for st test
 tables >= 3.6.1             # for st test
 psutil >= 5.6.1
+subword-nmt>=0.3.7          # for st test
+sacrebleu>=1.4.14           # for st test
+sacremoses>=0.0.35          # for st test

tests/st/model_zoo_tests/gnmt_v2/test_gnmtv2_WMT_English_German.py

@@ -0,0 +1,64 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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 os
+import pytest
+
+from tests.st.model_zoo_tests import utils
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_single
+def test_gnmtv2_WMT_English_German():
+    cur_path = os.path.dirname(os.path.abspath(__file__))
+    model_path = "{}/../../../../model_zoo/official/nlp".format(cur_path)
+    model_name = "gnmt_v2"
+    utils.copy_files(model_path, cur_path, model_name)
+    cur_model_path = os.path.join(cur_path, model_name)
+
+    old_list = ['dataset_sink_mode=config.dataset_sink_mode']
+    new_list = ['dataset_sink_mode=config.dataset_sink_mode, sink_size=100']
+    utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
+    old_list = ['"epochs": 6,']
+    new_list = ['"epochs": 4,']
+    utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "config/config.json"))
+
+    mindrecord_file = "wmt16_de_en/train_tok_mindrecord/train.tok.clean.bpe.32000.en.mindrecord"
+    exec_network_shell = "cd {}/scripts; sh run_distributed_train_ascend.sh {} {}"\
+        .format(model_name, utils.rank_table_path, os.path.join(utils.data_root, mindrecord_file))
+    ret = os.system(exec_network_shell)
+    assert ret == 0
+
+    cmd = "ps -ef | grep python | grep train.py | grep train.tok.clean.bpe | grep -v grep"
+    ret = utils.process_check(120, cmd)
+    assert ret
+
+    log_file = os.path.join(cur_model_path, "scripts/device{}/log_gnmt_network{}.log")
+    for i in range(8):
+        per_step_time = utils.get_perf_data(log_file.format(i, i))
+        print("per_step_time is", per_step_time)
+        assert per_step_time < 270.0
+
+    log_file = os.path.join(cur_model_path, "scripts/device{}/loss.log")
+    loss_list = []
+    for i in range(8):
+        pattern1 = r"loss\: ([\d\.\+]+)\,"
+        loss = utils.parse_log_file(pattern1, log_file.format(i))
+        print("loss is", loss)
+        loss_list.append(loss[-1])
+    print("loss_list is", loss_list)
+    print(sum(loss_list) / len(loss_list))
+    assert sum(loss_list) / len(loss_list) < 120