add dist word2vec dist train

python/paddle/fluid/tests/unittests/test_dist_word2vec.py

@@ -0,0 +1,203 @@
+#   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
+import paddle.fluid.profiler as profiler
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import os
+import signal
+
+IS_SPARSE = True
+EMBED_SIZE = 32
+HIDDEN_SIZE = 256
+N = 5
+BATCH_SIZE = 32
+ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
+
+
+def get_model():
+    def __network__(words):
+        embed_first = fluid.layers.embedding(
+            input=words[0],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_second = fluid.layers.embedding(
+            input=words[1],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_third = fluid.layers.embedding(
+            input=words[2],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_forth = fluid.layers.embedding(
+            input=words[3],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+
+        concat_embed = fluid.layers.concat(
+            input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
+        hidden1 = fluid.layers.fc(input=concat_embed,
+                                  size=HIDDEN_SIZE,
+                                  act='sigmoid')
+        predict_word = fluid.layers.fc(input=hidden1,
+                                       size=dict_size,
+                                       act='softmax')
+        cost = fluid.layers.cross_entropy(input=predict_word, label=words[4])
+        avg_cost = fluid.layers.mean(cost)
+        return avg_cost, predict_word
+
+    word_dict = paddle.dataset.imikolov.build_dict()
+    dict_size = len(word_dict)
+
+    first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
+    second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
+    third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
+    forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
+    next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
+    avg_cost, predict_word = __network__(
+        [first_word, second_word, third_word, forth_word, next_word])
+
+    inference_program = paddle.fluid.default_main_program().clone()
+
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+    sgd_optimizer.minimize(avg_cost)
+
+    train_reader = paddle.batch(
+        paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
+    test_reader = paddle.batch(
+        paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE)
+
+    return inference_program, avg_cost, train_reader, test_reader, predict_word
+
+
+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
+
+
+def run_pserver(pserver_endpoints, trainers, current_endpoint):
+    get_model()
+    t = get_transpiler(0,
+                       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)
+
+
+class TestDistMnist(unittest.TestCase):
+    def setUp(self):
+        self._trainers = 1
+        self._pservers = 1
+        self._ps_endpoints = "127.0.0.1:9123"
+
+    def start_pserver(self, endpoint):
+        p = Process(
+            target=run_pserver,
+            args=(self._ps_endpoints, self._trainers, endpoint))
+        p.start()
+        return p.pid
+
+    def _wait_ps_ready(self, pid):
+        retry_times = 5
+        while True:
+            assert retry_times >= 0, "wait ps ready failed"
+            time.sleep(1)
+            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 stop_pserver(self, pid):
+        os.kill(pid, signal.SIGKILL)
+
+    def test_with_place(self):
+        p = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+
+        pserver_pid = self.start_pserver(self._ps_endpoints)
+        self._wait_ps_ready(pserver_pid)
+
+        self.run_trainer(p, 0)
+
+        self.stop_pserver(pserver_pid)
+
+    def run_trainer(self, place, trainer_id):
+        test_program, avg_cost, train_reader, test_reader, predict = get_model()
+        t = get_transpiler(trainer_id,
+                           fluid.default_main_program(), self._ps_endpoints,
+                           self._trainers)
+
+        trainer_prog = t.get_trainer_program()
+
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+
+        use_gpu = True if core.is_compiled_with_cuda() else False
+
+        exec_strategy = ExecutionStrategy()
+        exec_strategy.use_cuda = use_gpu
+        train_exe = fluid.ParallelExecutor(
+            use_cuda=use_gpu,
+            main_program=trainer_prog,
+            loss_name=avg_cost.name,
+            exec_strategy=exec_strategy)
+
+        feed_var_list = [
+            var for var in trainer_prog.global_block().vars.itervalues()
+            if var.is_data
+        ]
+
+        feeder = fluid.DataFeeder(feed_var_list, place)
+        for pass_id in xrange(10):
+            for batch_id, data in enumerate(train_reader()):
+                avg_loss_np = train_exe.run(feed=feeder.feed(data),
+                                            fetch_list=[avg_cost.name])
+                loss = np.array(avg_loss_np).mean()
+                if float(loss) < 5.0:
+                    return
+                if math.isnan(loss):
+                    assert ("Got Nan loss, training failed")
+
+
+if __name__ == "__main__":
+    unittest.main()