Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into refine_code

Author: reyoung

doc/v2/howto/cluster/multi_cluster/index_en.rst

@@ -8,28 +8,28 @@ The user's cluster environment is not the same. To facilitate everyone's deploym
 ..  toctree::
   :maxdepth: 1
 
-  k8s_cn.md
-  k8s_distributed_cn.md
+  k8s_en.md
+  k8s_distributed_en.md
 
 `OpenMPI <https://www.open-mpi.org>`_ is a mature high-performance parallel computing framework, which is widely used in the field of HPC. The following guide describes how to use OpenMPI to build PaddlePaddle's cluster training task:
 
 ..  toctree::
   :maxdepth: 1
 
-  openmpi_cn.md
+  openmpi_en.md
 
 `Fabric <http://www.fabfile.org>`_ is a convenient tool for program deployment and management. We provide a way to deploy and manage with Fabric. If you want to know more about it, please read the following guidelines:
 
 ..  toctree::
   :maxdepth: 1
 
-  fabric_cn.md
+  fabric_en.md
 
 We also support the deployment of PaddlePaddle on AWS. Learn more about:
 
 ..  toctree::
   :maxdepth: 1
 
-  k8s_aws_cn.md
+  k8s_aws_en.md
 
-The examples can be found under `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ .
+The examples can be found under `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ .

paddle/fluid/platform/cuda_device_function.h

@@ -35,6 +35,16 @@ __forceinline__ __device__ T __shfl_sync(unsigned, T val, int src_line,
 #define FULL_WARP_MASK 0xFFFFFFFF
 #define CREATE_SHFL_MASK(mask, predicate) \
   mask = __ballot_sync(FULL_WARP_MASK, (predicate))
+template <typename T>
+__forceinline__ __device__ T __shfl_down_sync(unsigned mask, T val, int delta) {
+  return __shfl_down_sync(mask, val, delta);
+}
+
+template <typename T>
+__forceinline__ __device__ T __shfl_sync(unsigned mask, T val, int src_line,
+                                         int width) {
+  return __shfl_sync(mask, val, src_line, width);
+}
 #endif
 
 template <typename T>

python/paddle/fluid/__init__.py

@@ -21,14 +21,15 @@
 from executor import *
 
 import trainer
-from trainer import *
+from trainer import Trainer
+from trainer import BeginEpochEvent
+from trainer import EndEpochEvent
+from trainer import BeginStepEvent
+from trainer import EndStepEvent
 
 import inferencer
 from inferencer import Inferencer
 
-import params
-from params import Params
-
 import io
 import evaluator
 import initializer
@@ -57,7 +58,7 @@
 Tensor = LoDTensor
 
 __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ +\
-          trainer.__all__ + inferencer.__all__ + params.__all__ + [
+          trainer.__all__ + inferencer.__all__ + [
     'io',
     'initializer',
     'layers',

python/paddle/fluid/inferencer.py

@@ -12,18 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import core
+
 __all__ = ['Inferencer', ]
 
 
 class Inferencer(object):
-    def __init__(self, network_func, params, place=None):
+    def __init__(self, network_func, param_path=None, place=None):
         # 1. we need to generate a framework.Program by calling
         # network_func. Reference: fluid.program_guard in test_word2vec.py
 
         # 2. move the default_main_program to self.program.
 
         # 3. run the default_startup program.
-        self.params = params
+
+        # 4. load params from param_path into scope
+        self.scope = core.Scope()
         self.place = place
 
     def infer(self, inputs):

python/paddle/fluid/params.py

@@ -0,0 +1,140 @@
+# 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 paddle
+import paddle.fluid as fluid
+from functools import partial
+
+CLASS_DIM = 2
+EMB_DIM = 128
+HID_DIM = 512
+STACKED_NUM = 3
+
+
+def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
+    assert stacked_num % 2 == 1
+
+    emb = fluid.layers.embedding(
+        input=data, size=[input_dim, emb_dim], is_sparse=True)
+
+    fc1 = fluid.layers.fc(input=emb, size=hid_dim)
+    lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
+
+    inputs = [fc1, lstm1]
+
+    for i in range(2, stacked_num + 1):
+        fc = fluid.layers.fc(input=inputs, size=hid_dim)
+        lstm, cell = fluid.layers.dynamic_lstm(
+            input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
+        inputs = [fc, lstm]
+
+    fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
+    lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
+
+    prediction = fluid.layers.fc(input=[fc_last, lstm_last],
+                                 size=class_dim,
+                                 act='softmax')
+    return prediction
+
+
+def inference_network(word_dict):
+    data = fluid.layers.data(
+        name="words", shape=[1], dtype="int64", lod_level=1)
+
+    dict_dim = len(word_dict)
+    net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM,
+                           STACKED_NUM)
+    return net
+
+
+def train_network(word_dict):
+    prediction = inference_network(word_dict)
+    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(cost)
+    accuracy = fluid.layers.accuracy(input=prediction, label=label)
+    return avg_cost, accuracy
+
+
+def train(use_cuda, save_path):
+    BATCH_SIZE = 128
+    EPOCH_NUM = 5
+
+    word_dict = paddle.dataset.imdb.word_dict()
+
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.imdb.train(word_dict), buf_size=1000),
+        batch_size=BATCH_SIZE)
+
+    test_data = paddle.batch(
+        paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
+
+    def event_handler(event):
+        if isinstance(event, fluid.EndIteration):
+            if (event.batch_id % 10) == 0:
+                avg_cost, accuracy = trainer.test(reader=test_data)
+
+                print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
+                    event.batch_id + 1, avg_cost, accuracy))
+
+                if accuracy > 0.01:  # Low threshold for speeding up CI
+                    trainer.params.save(save_path)
+                    return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    trainer = fluid.Trainer(
+        partial(train_network, word_dict),
+        optimizer=fluid.optimizer.Adagrad(learning_rate=0.002),
+        place=place,
+        event_handler=event_handler)
+
+    trainer.train(train_data, EPOCH_NUM, event_handler=event_handler)
+
+
+def infer(use_cuda, save_path):
+    params = fluid.Params(save_path)
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    word_dict = paddle.dataset.imdb.word_dict()
+    inferencer = fluid.Inferencer(
+        partial(inference_network, word_dict), params, place=place)
+
+    def create_random_lodtensor(lod, place, low, high):
+        data = np.random.random_integers(low, high,
+                                         [lod[-1], 1]).astype("int64")
+        res = fluid.LoDTensor()
+        res.set(data, place)
+        res.set_lod([lod])
+        return res
+
+    lod = [0, 4, 10]
+    tensor_words = create_random_lodtensor(
+        lod, place, low=0, high=len(word_dict) - 1)
+    results = inferencer.infer({'words': tensor_words})
+    print("infer results: ", results)
+
+
+def main(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+    save_path = "understand_sentiment_stacked_lstm.inference.model"
+    train(use_cuda, save_path)
+    infer(use_cuda, save_path)
+
+
+if __name__ == '__main__':
+    for use_cuda in (False, True):
+        main(use_cuda=use_cuda)

python/paddle/fluid/tests/book/understand_sentiment/notest_understand_sentiment_stacked_lstm.py

@@ -39,7 +39,7 @@ def create_random_lodtensor(lod, place, low, high):
 dict_size = len(word_dict)
 
 
-def inference_network(is_sparse):
+def inference_program(is_sparse):
     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')
@@ -79,9 +79,9 @@ def inference_network(is_sparse):
     return predict_word
 
 
-def train_network(is_sparse):
+def train_program(is_sparse):
     next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
-    predict_word = inference_network(is_sparse)
+    predict_word = inference_program(is_sparse)
     cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
     avg_cost = fluid.layers.mean(cost)
     return avg_cost
@@ -100,23 +100,25 @@ def event_handler(event):
                 word_dict, N))
 
             if avg_cost < 5.0:
-                trainer.params.save(save_path)
+                trainer.save_params(save_path)
                 return
             if math.isnan(avg_cost):
                 sys.exit("got NaN loss, training failed.")
 
     trainer = fluid.Trainer(
-        partial(train_network, is_sparse),
+        partial(train_program, is_sparse),
         fluid.optimizer.SGD(learning_rate=0.001),
         place=place)
     trainer.train(
         reader=train_reader, num_epochs=100, event_handler=event_handler)
 
 
-def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
+def infer(use_cuda, is_sparse, save_path):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.Inferencer(inference_network, params, place=place)
+    inferencer = fluid.Inferencer(
+        partial(inference_program, is_sparse),
+        param_path=save_path,
+        place=place)
 
     lod = [0, 1]
     first_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
@@ -138,7 +140,7 @@ def main(use_cuda, is_sparse):
 
     save_path = "word2vec.inference.model"
     train(use_cuda, is_sparse, save_path)
-    infer(use_cuda, save_path)
+    infer(use_cuda, is_sparse, save_path)
 
 
 if __name__ == '__main__':