Merge pull request #12426 from panyx0718/better_dist_test

Add transformer dist test and factor out some common utils.

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

@@ -49,6 +49,7 @@ list(REMOVE_ITEM TEST_OPS test_dist_train)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
 list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
+list(REMOVE_ITEM TEST_OPS test_dist_transformer)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
@@ -61,4 +62,5 @@ if(WITH_DISTRIBUTE)
 endif()
 py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
+py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
 py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)

python/paddle/fluid/tests/unittests/dist_transformer.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.
+
+import numpy as np
+import argparse
+import time
+import math
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+import os
+import sys
+import transformer_model
+import paddle.dataset.wmt16 as wmt16
+
+# Fix seed for test
+fluid.default_startup_program().random_seed = 1
+fluid.default_main_program().random_seed = 1
+
+WMT16_RECORDIO_FILE = "/tmp/wmt16.recordio"
+
+
+class ModelHyperParams(object):
+    # Dictionary size for source and target language. This model directly uses
+    # paddle.dataset.wmt16 in which <bos>, <eos> and <unk> token has
+    # alreay been added, but the <pad> token is not added. Transformer requires
+    # sequences in a mini-batch are padded to have the same length. A <pad> token is
+    # added into the original dictionary in paddle.dateset.wmt16.
+
+    # size of source word dictionary.
+    src_vocab_size = 10000
+    # index for <pad> token in source language.
+    src_pad_idx = src_vocab_size
+
+    # size of target word dictionay
+    trg_vocab_size = 10000
+    # index for <pad> token in target language.
+    trg_pad_idx = trg_vocab_size
+
+    # position value corresponding to the <pad> token.
+    pos_pad_idx = 0
+
+    # max length of sequences. It should plus 1 to include position
+    # padding token for position encoding.
+    max_length = 50
+
+    # the dimension for word embeddings, which is also the last dimension of
+    # the input and output of multi-head attention, position-wise feed-forward
+    # networks, encoder and decoder.
+
+    d_model = 512
+    # size of the hidden layer in position-wise feed-forward networks.
+    d_inner_hid = 1024
+    # the dimension that keys are projected to for dot-product attention.
+    d_key = 64
+    # the dimension that values are projected to for dot-product attention.
+    d_value = 64
+    # number of head used in multi-head attention.
+    n_head = 8
+    # number of sub-layers to be stacked in the encoder and decoder.
+    n_layer = 6
+    # dropout rate used by all dropout layers.
+    dropout = 0.1
+
+
+def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
+    """
+    Pad the instances to the max sequence length in batch, and generate the
+    corresponding position data and attention bias. Then, convert the numpy
+    data to tensors and return a dict mapping names to tensors.
+    """
+
+    def __pad_batch_data(insts,
+                         pad_idx,
+                         is_target=False,
+                         return_pos=True,
+                         return_attn_bias=True,
+                         return_max_len=True):
+        """
+        Pad the instances to the max sequence length in batch, and generate the
+        corresponding position data and attention bias.
+        """
+        return_list = []
+        max_len = max(len(inst) for inst in insts)
+        inst_data = np.array(
+            [inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
+        return_list += [inst_data.astype("int64").reshape([-1, 1])]
+        if return_pos:
+            inst_pos = np.array([[
+                pos_i + 1 if w_i != pad_idx else 0
+                for pos_i, w_i in enumerate(inst)
+            ] for inst in inst_data])
+
+            return_list += [inst_pos.astype("int64").reshape([-1, 1])]
+        if return_attn_bias:
+            if is_target:
+                # This is used to avoid attention on paddings and subsequent
+                # words.
+                slf_attn_bias_data = np.ones((inst_data.shape[0], max_len,
+                                              max_len))
+                slf_attn_bias_data = np.triu(slf_attn_bias_data, 1).reshape(
+                    [-1, 1, max_len, max_len])
+                slf_attn_bias_data = np.tile(slf_attn_bias_data,
+                                             [1, n_head, 1, 1]) * [-1e9]
+            else:
+                # This is used to avoid attention on paddings.
+                slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
+                                               (max_len - len(inst))
+                                               for inst in insts])
+                slf_attn_bias_data = np.tile(
+                    slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
+                    [1, n_head, max_len, 1])
+            return_list += [slf_attn_bias_data.astype("float32")]
+        if return_max_len:
+            return_list += [max_len]
+        return return_list if len(return_list) > 1 else return_list[0]
+
+    src_word, src_pos, src_slf_attn_bias, src_max_len = __pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, is_target=False)
+    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = __pad_batch_data(
+        [inst[1] for inst in insts], trg_pad_idx, is_target=True)
+    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
+                                [1, 1, trg_max_len, 1]).astype("float32")
+    lbl_word = __pad_batch_data([inst[2] for inst in insts], trg_pad_idx, False,
+                                False, False, False)
+    lbl_weight = (lbl_word != trg_pad_idx).astype("float32").reshape([-1, 1])
+
+    return [
+        src_word, src_pos, trg_word, trg_pos, src_slf_attn_bias,
+        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+    ]
+
+
+def transformer(use_feed):
+    assert not use_feed, "transfomer doesn't support feed yet"
+    return transformer_model.transformer(
+        ModelHyperParams.src_vocab_size + 1,
+        ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,
+        ModelHyperParams.n_layer, ModelHyperParams.n_head,
+        ModelHyperParams.d_key, ModelHyperParams.d_value,
+        ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
+        ModelHyperParams.dropout, ModelHyperParams.src_pad_idx,
+        ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx)
+
+
+def get_model():
+    avg_cost = transformer(use_feed=False)
+    optimizer = fluid.optimizer.Adam()
+    optimizer.minimize(avg_cost)
+    return avg_cost
+
+
+def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers):
+    t = fluid.DistributeTranspiler()
+    t.transpile(
+        trainer_id=trainer_id,
+        program=main_program,
+        pservers=pserver_endpoints,
+        trainers=trainers)
+    return t
+
+
+class DistTransformer2x2(object):
+    def run_pserver(self, pserver_endpoints, trainers, current_endpoint,
+                    trainer_id):
+        get_model()
+        t = get_transpiler(trainer_id,
+                           fluid.default_main_program(), pserver_endpoints,
+                           trainers)
+        pserver_prog = t.get_pserver_program(current_endpoint)
+        startup_prog = t.get_startup_program(current_endpoint, pserver_prog)
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+        exe.run(pserver_prog)
+
+    def _wait_ps_ready(self, pid):
+        retry_times = 20
+        while True:
+            assert retry_times >= 0, "wait ps ready failed"
+            time.sleep(3)
+            print("waiting ps ready: ", pid)
+            try:
+                # the listen_and_serv_op would touch a file which contains the listen port
+                # on the /tmp directory until it was ready to process all the RPC call.
+                os.stat("/tmp/paddle.%d.port" % pid)
+                return
+            except os.error:
+                retry_times -= 1
+
+    def run_trainer(self, place, endpoints, trainer_id, trainers, is_dist=True):
+        avg_cost = get_model()
+        if is_dist:
+            t = get_transpiler(trainer_id,
+                               fluid.default_main_program(), endpoints,
+                               trainers)
+            trainer_prog = t.get_trainer_program()
+        else:
+            trainer_prog = fluid.default_main_program()
+
+        startup_exe = fluid.Executor(place)
+        startup_exe.run(fluid.default_startup_program())
+
+        strategy = fluid.ExecutionStrategy()
+        strategy.num_threads = 1
+        strategy.allow_op_delay = False
+        exe = fluid.ParallelExecutor(
+            True, loss_name=avg_cost.name, exec_strategy=strategy)
+
+        first_loss, = exe.run(fetch_list=[avg_cost.name])
+        print(first_loss)
+        for i in xrange(5):
+            _ = exe.run(fetch_list=[avg_cost.name])
+        last_loss, = exe.run(fetch_list=[avg_cost.name])
+        print(last_loss)
+
+
+def main(role="pserver",
+         endpoints="127.0.0.1:9123",
+         trainer_id=0,
+         current_endpoint="127.0.0.1:9123",
+         trainers=1,
+         is_dist=True):
+
+    reader = paddle.batch(
+        wmt16.train(ModelHyperParams.src_vocab_size,
+                    ModelHyperParams.trg_vocab_size),
+        batch_size=transformer_model.batch_size)
+
+    with fluid.recordio_writer.create_recordio_writer(
+            WMT16_RECORDIO_FILE) as writer:
+        for batch in reader():
+            for tensor in prepare_batch_input(
+                    batch, ModelHyperParams.src_pad_idx,
+                    ModelHyperParams.trg_pad_idx, ModelHyperParams.n_head):
+                t = fluid.LoDTensor()
+                t.set(tensor, fluid.CPUPlace())
+                writer.append_tensor(t)
+            writer.complete_append_tensor()
+
+    model = DistTransformer2x2()
+    if role == "pserver":
+        model.run_pserver(endpoints, trainers, current_endpoint, trainer_id)
+    else:
+        p = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        model.run_trainer(p, endpoints, trainer_id, trainers, is_dist)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 7:
+        print(
+            "Usage: python dist_transformer.py [pserver/trainer] [endpoints] [trainer_id] [current_endpoint] [trainers] [is_dist]"
+        )
+    role = sys.argv[1]
+    endpoints = sys.argv[2]
+    trainer_id = int(sys.argv[3])
+    current_endpoint = sys.argv[4]
+    trainers = int(sys.argv[5])
+    is_dist = True if sys.argv[6] == "TRUE" else False
+    main(
+        role=role,
+        endpoints=endpoints,
+        trainer_id=trainer_id,
+        current_endpoint=current_endpoint,
+        trainers=trainers,
+        is_dist=is_dist)

python/paddle/fluid/tests/unittests/test_dist_base.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.
+import time
+
+import unittest
+import os
+import sys
+import signal
+import subprocess
+
+
+class TestDistBase(unittest.TestCase):
+    def setUp(self):
+        self._trainers = 2
+        self._pservers = 2
+        self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
+        self._python_interp = "python"
+
+    def start_pserver(self, model_file):
+        ps0_ep, ps1_ep = self._ps_endpoints.split(",")
+        ps0_cmd = "%s %s pserver %s 0 %s %d TRUE" % \
+            (self._python_interp, model_file, self._ps_endpoints, ps0_ep,
+             self._trainers)
+        ps1_cmd = "%s %s pserver %s 0 %s %d TRUE" % \
+            (self._python_interp, model_file, self._ps_endpoints, ps1_ep,
+             self._trainers)
+
+        ps0_proc = subprocess.Popen(
+            ps0_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        ps1_proc = subprocess.Popen(
+            ps1_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        return ps0_proc, ps1_proc
+
+    def _wait_ps_ready(self, pid):
+        retry_times = 50
+        while True:
+            assert retry_times >= 0, "wait ps ready failed"
+            time.sleep(3)
+            try:
+                # the listen_and_serv_op would touch a file which contains the listen port
+                # on the /tmp directory until it was ready to process all the RPC call.
+                os.stat("/tmp/paddle.%d.port" % pid)
+                return
+            except os.error as e:
+                sys.stderr.write('waiting for pserver: %s, left retry %d\n' %
+                                 (e, retry_times))
+                retry_times -= 1
+
+    def check_with_place(self, model_file, delta=1e-3):
+        # *ATTENTION* THIS TEST NEEDS AT LEAST 2GPUS TO RUN
+        required_envs = {
+            "PATH": os.getenv("PATH"),
+            "PYTHONPATH": os.getenv("PYTHONPATH"),
+            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH"),
+            "FLAGS_fraction_of_gpu_memory_to_use": "0.15"
+        }
+        # Run local to get a base line
+        env_local = {"CUDA_VISIBLE_DEVICES": "0"}
+        env_local.update(required_envs)
+        local_cmd = "%s %s trainer %s 0 %s %d FLASE" % \
+            (self._python_interp, model_file,
+             "127.0.0.1:1234", "127.0.0.1:1234", 1)
+        local_proc = subprocess.Popen(
+            local_cmd.split(" "),
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env_local)
+        local_proc.wait()
+        out, err = local_proc.communicate()
+        local_ret = out
+        sys.stderr.write('local_loss: %s\n' % local_ret)
+        sys.stderr.write('local_stderr: %s\n' % err)
+
+        # Run dist train to compare with local results
+        ps0, ps1 = self.start_pserver(model_file)
+        self._wait_ps_ready(ps0.pid)
+        self._wait_ps_ready(ps1.pid)
+
+        ps0_ep, ps1_ep = self._ps_endpoints.split(",")
+        tr0_cmd = "%s %s trainer %s 0 %s %d TRUE" % \
+            (self._python_interp, model_file, self._ps_endpoints, ps0_ep,
+             self._trainers)
+        tr1_cmd = "%s %s trainer %s 1 %s %d TRUE" % \
+            (self._python_interp, model_file, self._ps_endpoints, ps1_ep,
+             self._trainers)
+
+        env0 = {"CUDA_VISIBLE_DEVICES": "0"}
+        env1 = {"CUDA_VISIBLE_DEVICES": "1"}
+        env0.update(required_envs)
+        env1.update(required_envs)
+        FNULL = open(os.devnull, 'w')
+
+        tr0_proc = subprocess.Popen(
+            tr0_cmd.split(" "),
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env0)
+        tr1_proc = subprocess.Popen(
+            tr1_cmd.split(" "),
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env1)
+
+        tr0_proc.wait()
+        tr1_proc.wait()
+        out, err = tr0_proc.communicate()
+        sys.stderr.write('dist_stderr: %s\n' % err)
+        loss_data0 = out
+        sys.stderr.write('dist_loss: %s\n' % loss_data0)
+        lines = loss_data0.split("\n")
+        dist_first_loss = eval(lines[0].replace(" ", ","))[0]
+        dist_last_loss = eval(lines[1].replace(" ", ","))[0]
+
+        local_lines = local_ret.split("\n")
+        local_first_loss = eval(local_lines[0])[0]
+        local_last_loss = eval(local_lines[1])[0]
+
+        self.assertAlmostEqual(local_first_loss, dist_first_loss, delta=delta)
+        self.assertAlmostEqual(local_last_loss, dist_last_loss, delta=delta)
+
+        # check tr0_out
+        # FIXME: ensure the server process is killed
+        # replace with ps0.terminate()
+        os.kill(ps0.pid, signal.SIGKILL)
+        os.kill(ps1.pid, signal.SIGKILL)
+        FNULL.close()

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

@@ -11,127 +11,14 @@
 # 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 argparse
-import time
-import math
-
 import unittest
-import os
-import sys
-import signal
-import subprocess
-
-
-class TestDistSeResneXt2x2(unittest.TestCase):
-    def setUp(self):
-        self._trainers = 2
-        self._pservers = 2
-        self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
-        self._python_interp = "python"
-
-    def start_pserver(self):
-        ps0_ep, ps1_ep = self._ps_endpoints.split(",")
-        ps0_cmd = "%s dist_se_resnext.py pserver %s 0 %s %d TRUE" % \
-            (self._python_interp, self._ps_endpoints, ps0_ep, self._trainers)
-        ps1_cmd = "%s dist_se_resnext.py pserver %s 0 %s %d TRUE" % \
-            (self._python_interp, self._ps_endpoints, ps1_ep, self._trainers)
-
-        ps0_proc = subprocess.Popen(
-            ps0_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        ps1_proc = subprocess.Popen(
-            ps1_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        return ps0_proc, ps1_proc
-
-    def _wait_ps_ready(self, pid):
-        retry_times = 20
-        while True:
-            assert retry_times >= 0, "wait ps ready failed"
-            time.sleep(3)
-            try:
-                # the listen_and_serv_op would touch a file which contains the listen port
-                # on the /tmp directory until it was ready to process all the RPC call.
-                os.stat("/tmp/paddle.%d.port" % pid)
-                return
-            except os.error:
-                retry_times -= 1
-
-    def test_with_place(self):
-        # *ATTENTION* THIS TEST NEEDS AT LEAST 2GPUS TO RUN
-        required_envs = {
-            "PATH": os.getenv("PATH"),
-            "PYTHONPATH": os.getenv("PYTHONPATH"),
-            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH"),
-            "FLAGS_fraction_of_gpu_memory_to_use": "0.15"
-        }
-        # Run local to get a base line
-        env_local = {"CUDA_VISIBLE_DEVICES": "0"}
-        env_local.update(required_envs)
-        local_cmd = "%s dist_se_resnext.py trainer %s 0 %s %d FLASE" % \
-            (self._python_interp, "127.0.0.1:1234", "127.0.0.1:1234", 1)
-        local_proc = subprocess.Popen(
-            local_cmd.split(" "),
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
-            env=env_local)
-        local_proc.wait()
-        out, err = local_proc.communicate()
-        local_ret = out
-        sys.stderr.write('local_loss: %s\n' % local_ret)
-        sys.stderr.write('local_stderr: %s\n' % err)
-
-        # Run dist train to compare with local results
-        ps0, ps1 = self.start_pserver()
-        self._wait_ps_ready(ps0.pid)
-        self._wait_ps_ready(ps1.pid)
-
-        ps0_ep, ps1_ep = self._ps_endpoints.split(",")
-        tr0_cmd = "%s dist_se_resnext.py trainer %s 0 %s %d TRUE" % \
-            (self._python_interp, self._ps_endpoints, ps0_ep, self._trainers)
-        tr1_cmd = "%s dist_se_resnext.py trainer %s 1 %s %d TRUE" % \
-            (self._python_interp, self._ps_endpoints, ps1_ep, self._trainers)
-
-        env0 = {"CUDA_VISIBLE_DEVICES": "0"}
-        env1 = {"CUDA_VISIBLE_DEVICES": "1"}
-        env0.update(required_envs)
-        env1.update(required_envs)
-        FNULL = open(os.devnull, 'w')
-
-        tr0_proc = subprocess.Popen(
-            tr0_cmd.split(" "),
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
-            env=env0)
-        tr1_proc = subprocess.Popen(
-            tr1_cmd.split(" "),
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
-            env=env1)
-
-        tr0_proc.wait()
-        tr1_proc.wait()
-        out, err = tr0_proc.communicate()
-        sys.stderr.write('dist_stderr: %s\n' % err)
-        loss_data0 = out
-        sys.stderr.write('dist_loss: %s\n' % loss_data0)
-        lines = loss_data0.split("\n")
-        dist_first_loss = eval(lines[0].replace(" ", ","))[0]
-        dist_last_loss = eval(lines[1].replace(" ", ","))[0]
-
-        local_lines = local_ret.split("\n")
-        local_first_loss = eval(local_lines[0])[0]
-        local_last_loss = eval(local_lines[1])[0]
+from test_dist_base import TestDistBase
 
-        self.assertAlmostEqual(local_first_loss, dist_first_loss)
-        self.assertAlmostEqual(local_last_loss, dist_last_loss)
 
-        # check tr0_out
-        # FIXME: ensure the server process is killed
-        # replace with ps0.terminate()
-        os.kill(ps0.pid, signal.SIGKILL)
-        os.kill(ps1.pid, signal.SIGKILL)
-        FNULL.close()
+class TestDistSeResneXt2x2(TestDistBase):
+    def test_se_resnext(self):
+        # TODO(paddle-dev): Is the delta too large?
+        self.check_with_place("dist_se_resnext.py", delta=0.2)
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/test_dist_transformer.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.
+
+import unittest
+from test_dist_base import TestDistBase
+
+
+class TestDistTransformer2x2(TestDistBase):
+    def test_transformer(self):
+        # TODO(paddle-dev): check if the delta is OK.
+        # Usually start around ~8000 and converge to ~5000
+        self.check_with_place("dist_transformer.py", delta=400)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -21,7 +21,7 @@ import paddle
 import paddle.dataset.wmt16 as wmt16
 import os
 
-WMT16_RECORDIO_FILE = "./wmt16_test_pe.recordio"
+WMT16_RECORDIO_FILE = "/tmp/wmt16.recordio"
 
 
 class ModelHyperParams(object):

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

@@ -403,7 +403,7 @@ def transformer(
         trg_pad_idx,
         pos_pad_idx, ):
     file_obj = fluid.layers.open_recordio_file(
-        filename='./wmt16.recordio',
+        filename='/tmp/wmt16.recordio',
         shapes=[
             [batch_size * max_length, 1],
             [batch_size * max_length, 1],