add data_generator into paddle.fluid.incubate.data_generator, add op run log...

add data_generator into paddle.fluid.incubate.data_generator, add op run log in hogwild_device_worker and downpour_device_worker
test=develop

paddle/fluid/framework/downpour_worker.cc

@@ -236,7 +236,9 @@ void DownpourWorker::TrainFilesWithProfiler() {
       }
       if (!need_skip) {
         timeline.Start();
+        VLOG(3) << "Going to run op " << op_name[run_op_idx];
         op->Run(*thread_scope_, place_);
+        VLOG(3) << "Op " << op_name[run_op_idx] << " Finished";
         timeline.Pause();
         op_total_time[run_op_idx++] += timeline.ElapsedSec();
         total_time += timeline.ElapsedSec();

paddle/fluid/framework/hogwild_worker.cc

@@ -97,7 +97,9 @@ void HogwildWorker::TrainFilesWithProfiler() {
     total_time += timeline.ElapsedSec();
     for (size_t i = 0; i < ops_.size(); ++i) {
       timeline.Start();
+      VLOG(3) << "Going to run op " << op_name[i];
       ops_[i]->Run(*thread_scope_, place_);
+      VLOG(3) << "Op " << op_name[i] << " Finished";
       timeline.Pause();
       op_total_time[i] += timeline.ElapsedSec();
       total_time += timeline.ElapsedSec();

python/paddle/fluid/incubate/data_generator/__init__.py

+# Copyright (c) 2019 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 os
+import sys
+
+__all__ = ['MultiSlotDataGenerator']
+
+
+class DataGenerator(object):
+    def __init__(self):
+        self._proto_info = None
+        self.batch_size_ = 32
+
+    def _set_line_limit(self, line_limit):
+        if not isinstance(line_limit, int):
+            raise ValueError("line_limit%s must be in int type" %
+                             type(line_limit))
+        if line_limit < 1:
+            raise ValueError("line_limit can not less than 1")
+        self._line_limit = line_limit
+
+    def set_batch(self, batch_size):
+        self.batch_size_ = batch_size
+
+    def run_from_memory(self):
+        '''
+        This function generator data from memory, it is usually used for
+        debug and benchmarking
+        '''
+        batch_samples = []
+        line_iter = self.generate_sample(None)
+        for user_parsed_line in line_iter():
+            if user_parsed_line == None:
+                continue
+            batch_samples.append(user_parsed_line)
+            if len(batch_samples) == self.batch_size_:
+                batch_iter = self.generate_batch(batch_samples)
+                for sample in batch_iter():
+                    sys.stdout.write(self._gen_str(sample))
+                batch_samples = []
+        if len(batch_samples) > 0:
+            batch_iter = self.generate_batch(batch_samples)
+            for sample in batch_iter():
+                sys.stdout.write(self._gen_str(sample))
+
+    def run_from_stdin(self):
+        '''
+        This function reads the data row from stdin, parses it with the
+        process function, and further parses the return value of the 
+        process function with the _gen_str function. The parsed data will
+        be wrote to stdout and the corresponding protofile will be
+        generated.
+
+        '''
+        batch_samples = []
+        for line in sys.stdin:
+            line_iter = self.generate_sample(line)
+            for user_parsed_line in line_iter():
+                if user_parsed_line == None:
+                    continue
+                batch_samples.append(user_parsed_line)
+                if len(batch_samples) == self.batch_size_:
+                    batch_iter = self.generate_batch(batch_samples)
+                    for sample in batch_iter():
+                        sys.stdout.write(self._gen_str(sample))
+                    batch_samples = []
+        if len(batch_samples) > 0:
+            batch_iter = self.generate_batch(batch_samples)
+            for sample in batch_iter():
+                sys.stdout.write(self._gen_str(sample))
+
+    def _gen_str(self, line):
+        '''
+        Further processing the output of the process() function rewritten by
+        user, outputting data that can be directly read by the datafeed,and
+        updating proto_info infomation.
+
+        Args:
+            line(str): the output of the process() function rewritten by user.
+
+        Returns:
+            Return a string data that can be read directly by the datafeed.
+        '''
+        raise NotImplementedError(
+            "pls use MultiSlotDataGenerator or PairWiseDataGenerator")
+
+    def generate_sample(self, line):
+        '''
+        This function needs to be overridden by the user to process the 
+        original data row into a list or tuple.
+
+        Args:
+            line(str): the original data row
+
+        Returns:
+            Returns the data processed by the user.
+              The data format is list or tuple: 
+            [(name, [feasign, ...]), ...] 
+              or ((name, [feasign, ...]), ...)
+             
+            For example:
+            [("words", [1926, 08, 17]), ("label", [1])]
+              or (("words", [1926, 08, 17]), ("label", [1]))
+
+        Note:
+            The type of feasigns must be in int or float. Once the float
+            element appears in the feasign, the type of that slot will be
+            processed into a float.
+        '''
+        raise NotImplementedError(
+            "Please rewrite this function to return a list or tuple: " +
+            "[(name, [feasign, ...]), ...] or ((name, [feasign, ...]), ...)")
+
+    def generate_batch(self, samples):
+        def local_iter():
+            for sample in samples:
+                yield sample
+
+        return local_iter
+
+
+class MultiSlotDataGenerator(DataGenerator):
+    def _gen_str(self, line):
+        '''
+        Further processing the output of the process() function rewritten by
+        user, outputting data that can be directly read by the MultiSlotDataFeed,
+        and updating proto_info infomation.
+
+        The input line will be in this format:
+            >>> [(name, [feasign, ...]), ...] 
+            >>> or ((name, [feasign, ...]), ...)
+        The output will be in this format:
+            >>> [ids_num id1 id2 ...] ...
+        The proto_info will be in this format:
+            >>> [(name, type), ...]
+        
+        For example, if the input is like this:
+            >>> [("words", [1926, 08, 17]), ("label", [1])]
+            >>> or (("words", [1926, 08, 17]), ("label", [1]))
+        the output will be:
+            >>> 3 1234 2345 3456 1 1
+        the proto_info will be:
+            >>> [("words", "uint64"), ("label", "uint64")]
+
+        Args:
+            line(str): the output of the process() function rewritten by user.
+
+        Returns:
+            Return a string data that can be read directly by the MultiSlotDataFeed.
+        '''
+        if not isinstance(line, list) and not isinstance(line, tuple):
+            raise ValueError(
+                "the output of process() must be in list or tuple type")
+        output = ""
+
+        if self._proto_info is None:
+            self._proto_info = []
+            for item in line:
+                name, elements = item
+                if not isinstance(name, str):
+                    raise ValueError("name%s must be in str type" % type(name))
+                if not isinstance(elements, list):
+                    raise ValueError("elements%s must be in list type" %
+                                     type(elements))
+                if not elements:
+                    raise ValueError(
+                        "the elements of each field can not be empty, you need padding it in process()."
+                    )
+                self._proto_info.append((name, "uint64"))
+                if output:
+                    output += " "
+                output += str(len(elements))
+                for elem in elements:
+                    if isinstance(elem, float):
+                        self._proto_info[-1] = (name, "float")
+                    elif not isinstance(elem, int) and not isinstance(elem,
+                                                                      long):
+                        raise ValueError(
+                            "the type of element%s must be in int or float" %
+                            type(elem))
+                    output += " " + str(elem)
+        else:
+            if len(line) != len(self._proto_info):
+                raise ValueError(
+                    "the complete field set of two given line are inconsistent.")
+            for index, item in enumerate(line):
+                name, elements = item
+                if not isinstance(name, str):
+                    raise ValueError("name%s must be in str type" % type(name))
+                if not isinstance(elements, list):
+                    raise ValueError("elements%s must be in list type" %
+                                     type(elements))
+                if not elements:
+                    raise ValueError(
+                        "the elements of each field can not be empty, you need padding it in process()."
+                    )
+                if name != self._proto_info[index][0]:
+                    raise ValueError(
+                        "the field name of two given line are not match: require<%s>, get<%d>."
+                        % (self._proto_info[index][0], name))
+                if output:
+                    output += " "
+                output += str(len(elements))
+                for elem in elements:
+                    if self._proto_info[index][1] != "float":
+                        if isinstance(elem, float):
+                            self._proto_info[index] = (name, "float")
+                        elif not isinstance(elem, int) and not isinstance(elem,
+                                                                          long):
+                            raise ValueError(
+                                "the type of element%s must be in int or float"
+                                % type(elem))
+                    output += " " + str(elem)
+        return output + "\n"

python/paddle/fluid/incubate/data_generator/test_data_generator.py

+#   Copyright (c) 2019 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
+from __init__ import *
+
+
+class SyntheticData(MultiSlotDataGenerator):
+    def generate_sample(self, line):
+        def data_iter():
+            for i in range(10000):
+                yield ("words", [1, 2, 3, 4]), ("label", [0])
+
+        return data_iter
+
+
+sd = SyntheticData()
+sd.run_from_memory()