Turn on rnn_use_batch of Paddle for accelartion.

Improve xmap_reader_mp by adding a flush thread.

deep_speech_2/data_utils/utility.py

@@ -9,6 +9,7 @@ import os
 import tarfile
 import time
 from Queue import Queue
+from threading import Thread
 from multiprocessing import Process, Manager
 from paddle.v2.dataset.common import md5file
 
@@ -100,7 +101,8 @@ def xmap_readers_mp(mapper, reader, process_num, buffer_size, order=False):
             in_queue.put((order_id, sample))
         in_queue.put(end_flag)
 
-    # define a worker to handle samples from in_queue by mapper and put results to out_queue
+    # define a worker to handle samples from in_queue by mapper and put results
+    # to out_queue
     def handle_worker(in_queue, out_queue, mapper):
         sample = in_queue.get()
         while not isinstance(sample, XmapEndSignal):
@@ -109,7 +111,8 @@ def xmap_readers_mp(mapper, reader, process_num, buffer_size, order=False):
         in_queue.put(end_flag)
         out_queue.put(end_flag)
 
-    # define a worker to handle samples from in_queue by mapper and put results to out_queue with order
+    # define a worker to handle samples from in_queue by mapper and put results
+    # to out_queue with order
     def order_handle_worker(in_queue, out_queue, mapper, out_order):
         ins = in_queue.get()
         while not isinstance(ins, XmapEndSignal):
@@ -123,6 +126,18 @@ def xmap_readers_mp(mapper, reader, process_num, buffer_size, order=False):
         in_queue.put(end_flag)
         out_queue.put(end_flag)
 
+    # define a thread worker to flush samples from Manager.Queue to Queue
+    # for acceleration
+    def flush_worker(in_queue, out_queue):
+        finish = 0
+        while finish < process_num:
+            sample = in_queue.get()
+            if isinstance(sample, XmapEndSignal):
+                finish += 1
+            else:
+                out_queue.put(sample)
+        out_queue.put(end_flag)
+
     def xreader():
         # prepare shared memory
         manager = Manager()
@@ -147,13 +162,16 @@ def xmap_readers_mp(mapper, reader, process_num, buffer_size, order=False):
             w.daemon = True
             w.start()
 
+        # start a thread to read data from slow Manager.Queue
+        flush_queue = Queue(buffer_size)
+        t = Thread(target=flush_worker, args=(out_queue, flush_queue))
+        t.daemon = True
+        t.start()
+
         # get results
-        finish = 0
-        while finish < process_num:
-            sample = out_queue.get()
-            if isinstance(sample, XmapEndSignal):
-                finish += 1
-            else:
-                yield sample
+        sample = flush_queue.get()
+        while not isinstance(sample, XmapEndSignal):
+            yield sample
+            sample = flush_queue.get()
 
     return xreader

deep_speech_2/infer.py

@@ -116,7 +116,9 @@ def infer():
 
 def main():
     print_arguments(args)
-    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
+    paddle.init(use_gpu=args.use_gpu,
+                rnn_use_batch=True,
+                trainer_count=args.trainer_count)
     infer()
 
 

deep_speech_2/test.py

@@ -119,7 +119,9 @@ def evaluate():
 
 def main():
     print_arguments(args)
-    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
+    paddle.init(use_gpu=args.use_gpu,
+                rnn_use_batch=True,
+                trainer_count=args.trainer_count)
     evaluate()
 
 

deep_speech_2/tools/tune.py

@@ -217,7 +217,9 @@ def tune():
 
 def main():
     print_arguments(args)
-    paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
+    paddle.init(use_gpu=args.use_gpu,
+                rnn_use_batch=True,
+                trainer_count=args.trainer_count)
     tune()
 
 

deep_speech_2/train.py

@@ -119,6 +119,7 @@ def train():
 def main():
     print_arguments(args)
     paddle.init(use_gpu=args.use_gpu,
+                rnn_use_batch=True,
                 trainer_count=args.trainer_count,
                 log_clipping=True)
     train()