Merge branch 'develop' into demo

python/paddle/fluid/__init__.py

@@ -46,7 +46,7 @@ from param_attr import ParamAttr, WeightNormParamAttr
 from data_feeder import DataFeeder
 from core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
 from transpiler import DistributeTranspiler, InferenceTranspiler, \
-    memory_optimize, release_memory
+    memory_optimize, release_memory, DistributeTranspilerConfig
 from concurrency import (Go, make_channel, channel_send, channel_recv,
                          channel_close, Select)
 from lod_tensor import create_lod_tensor, create_random_int_lodtensor

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

@@ -27,7 +27,6 @@ class TranspilerTest(unittest.TestCase):
         self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
         self.pserver1_ep = "127.0.0.1:6174"
         self.pserver2_ep = "127.0.0.1:6175"
-        self.slice_var_up = True
         self.sync_mode = True
         self.transpiler = None
 
@@ -52,27 +51,26 @@ class TranspilerTest(unittest.TestCase):
         self.origin_prog = main.clone()
         return main
 
-    def get_trainer(self):
-        t = self._transpiler_instance()
+    def get_trainer(self, config=None):
+        t = self._transpiler_instance(config)
         return t.get_trainer_program()
 
-    def get_pserver(self, ep):
-        t = self._transpiler_instance()
+    def get_pserver(self, ep, config=None):
+        t = self._transpiler_instance(config)
         pserver = t.get_pserver_program(ep)
         startup = t.get_startup_program(ep, pserver)
         return pserver, startup
 
-    def _transpiler_instance(self):
+    def _transpiler_instance(self, config=None):
         if not self.transpiler:
             main = self.get_main_program()
-            self.transpiler = fluid.DistributeTranspiler()
+            self.transpiler = fluid.DistributeTranspiler(config=config)
             self.transpiler.transpile(
                 self.trainer_id,
                 program=main,
                 pservers=self.pserver_eps,
-                trainers=self.trainers,
-                slice_var_up=self.slice_var_up,
-                sync_mode=self.sync_mode)
+                trainers=self.trainers)
+
         return self.transpiler
 
 
@@ -124,14 +122,67 @@ class TestBasicModel(TranspilerTest):
         self.assertEqual(set(pserver_params), set(trainer_params))
 
 
+class TestBasicModelWithLargeBlockSize(TranspilerTest):
+    def test_transpiler(self):
+        config = fluid.DistributeTranspilerConfig()
+        config.min_block_size = 1048576
+
+        pserver, startup = self.get_pserver(self.pserver1_ep, config)
+        pserver2, startup2 = self.get_pserver(self.pserver2_ep, config)
+
+        trainer = self.get_trainer(config)
+
+        self.assertEqual([op.type for op in trainer.global_block().ops], [
+            'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
+            'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
+            'elementwise_add_grad', 'send', 'mul_grad', 'send', 'send_barrier',
+            'recv', 'recv', 'fetch_barrier'
+        ])
+
+        self.assertEqual(len(pserver.blocks), 2)
+        # block0: listen_and_serv
+        self.assertEqual([op.type for op in pserver.blocks[0].ops],
+                         ["listen_and_serv"])
+        # block1~2: optimize pass
+        self.assertEqual([op.type for op in pserver.blocks[1].ops],
+                         ["sum", "scale", "sgd"])
+        # confirm startup program
+        self.assertEqual([op.type for op in startup.global_block().ops],
+                         ["fill_constant", "fill_constant", "fill_constant"])
+        # the variable #fc_w will be split into two blocks
+        fc_w_var = startup2.global_block().var("fc_w")
+        self.assertEqual(fc_w_var.shape, (1000L, 1000L))
+        # all parameters should be optimized on pserver
+
+        pserver_params = []
+        for prog in [pserver, pserver2]:
+            for blk in prog.blocks:
+                for op in blk.ops:
+                    if "Param" in op.input_names:
+                        param_name = op.input("Param")[0]
+                        is_block_idx = param_name.find(".block")
+                        if is_block_idx != -1:
+                            origin_param_name = param_name[:is_block_idx]
+                        else:
+                            origin_param_name = param_name
+                        pserver_params.append(origin_param_name)
+        trainer_params = []
+        for op in self.origin_prog.global_block().ops:
+            if "Param" in op.input_names:
+                trainer_params.append(op.input("Param")[0])
+        self.assertEqual(set(pserver_params), set(trainer_params))
+
+
 class TestNoSliceVar(TranspilerTest):
     def setUp(self):
         super(TestNoSliceVar, self).setUp()
-        self.slice_var_up = False
 
     def test_transpiler(self):
-        _, startup = self.get_pserver(self.pserver1_ep)
-        _, startup2 = self.get_pserver(self.pserver2_ep)
+        config = fluid.DistributeTranspilerConfig()
+        config.slice_var_up = False
+
+        _, startup = self.get_pserver(self.pserver1_ep, config)
+        _, startup2 = self.get_pserver(self.pserver2_ep, config)
 
         if startup.global_block().vars.has_key("fc_w"):
             fc_w_var = startup.global_block().vars["fc_w"]

python/paddle/fluid/transpiler/__init__.py

@@ -12,12 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from distribute_transpiler import DistributeTranspiler
+from distribute_transpiler import DistributeTranspiler, DistributeTranspilerConfig
 from inference_transpiler import InferenceTranspiler
 from memory_optimization_transpiler import memory_optimize, release_memory
 from ps_dispatcher import HashName, RoundRobin
 
 __all__ = [
     "DistributeTranspiler", "InferenceTranspiler", "memory_optimize",
-    "release_memory", "HashName", "RoundRobin"
+    "release_memory", "HashName", "RoundRobin", "DistributeTranspilerConfig"
 ]

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -64,7 +64,7 @@ def same_or_split_var(p_name, var_name):
     return p_name == var_name or p_name.startswith(var_name + ".block")
 
 
-def slice_variable(var_list, slice_count, min_block_size=8192):
+def slice_variable(var_list, slice_count, min_block_size):
     """
     We may need to split dense tensor to one or more blocks and put
     them equally onto parameter server. One block is a sub-tensor
@@ -110,6 +110,22 @@ def slice_variable(var_list, slice_count, min_block_size=8192):
     return blocks
 
 
+class DistributeTranspilerConfig(object):
+    """
+    slice_var_up (bool): Do Tensor slice for pservers, default is True.
+    split_method (PSDispatcher): RoundRobin or HashName can be used
+        try to choose the best method to balance loads for pservers.
+    min_block_size (int): Minimum splitted element number in block.
+        According:https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
+        We can use bandwidth effiently when data size is larger than 2MB.If you 
+        want to change it, please be sure you see the slice_variable function.
+    """
+
+    slice_var_up = True
+    split_method = None
+    min_block_size = 8192
+
+
 class DistributeTranspiler(object):
     """
     **DistributeTranspiler**
@@ -146,13 +162,23 @@ class DistributeTranspiler(object):
                 trainer_program = t.get_trainer_program()
     """
 
+    def __init__(self, config=None):
+        if config is not None:
+            self.config = config
+        else:
+            self.config = DistributeTranspilerConfig()
+
+        if self.config.split_method is None:
+            self.config.split_method = RoundRobin
+
+        assert (self.config.min_block_size >= 8192)
+        assert (self.config.split_method.__bases__[0] == PSDispatcher)
+
     def transpile(self,
                   trainer_id,
                   program=None,
                   pservers="127.0.0.1:6174",
                   trainers=1,
-                  slice_var_up=True,
-                  split_method=RoundRobin,
                   sync_mode=True):
         """
         Run the transpiler.
@@ -165,12 +191,8 @@ class DistributeTranspiler(object):
             pservers (str): comma separated ip:port string for the pserver
                 list.
             trainers (int): number of trainers in the distributed job.
-            slice_var_up (bool): Do Tensor slice for pservers, default is True.
-            split_method (PSDispatcher): RoundRobin or HashName can be used
-                try to choose the best method to balance loads for pservers.
             sync_mode (bool): Do sync training or not, default is True.
         """
-        assert (split_method.__bases__[0] == PSDispatcher)
         if program is None:
             program = default_main_program()
         self.origin_program = program
@@ -181,11 +203,11 @@ class DistributeTranspiler(object):
         self.pserver_endpoints = pserver_endpoints
         self.optimize_ops, self.params_grads = self._get_optimize_pass()
 
-        ps_dispatcher = split_method(self.pserver_endpoints)
+        ps_dispatcher = self.config.split_method(self.pserver_endpoints)
         self.has_distributed_lookup_table = self._has_distributed_lookup_table()
 
         # split and create vars, then put splited vars in dicts for later use.
-        self._init_splited_vars(slice_var_up)
+        self._init_splited_vars()
 
         # step 3.1: insert send op to send gradient vars to parameter servers
         ps_dispatcher.reset()
@@ -197,14 +219,14 @@ class DistributeTranspiler(object):
         #       fc_b@GRAD_trainer_0, fc_b@GRAD_trainer_1 --> pserver2
         # shuffle the map will avoid the uneven distribution above
         grad_var_mapping_items = self.grad_var_mapping.items()
-        if not slice_var_up:
+        if not self.config.slice_var_up:
             random.seed(self.trainer_num)
             random.shuffle(grad_var_mapping_items)
 
         for orig_varname, splited_vars in grad_var_mapping_items:
             eplist = ps_dispatcher.dispatch(splited_vars)
 
-            if not slice_var_up:
+            if not self.config.slice_var_up:
                 assert (len(splited_vars) == 1)
 
             if len(splited_vars) == 1:
@@ -627,7 +649,7 @@ class DistributeTranspiler(object):
                 ]
         return param_list, grad_list
 
-    def _init_splited_vars(self, slice_var_up):
+    def _init_splited_vars(self):
         # update these mappings for further transpile:
         # 1. param_var_mapping: param var name -> [splited params vars]
         # 2. grad_var_mapping: grad var name -> [splited grads vars]
@@ -651,17 +673,22 @@ class DistributeTranspiler(object):
         param_list, grad_list = self._update_dist_lookup_table_vars(
             param_list, grad_list, self.params_grads)
 
-        if slice_var_up:
+        if self.config.slice_var_up:
             # when we slice var up into blocks, we will slice the var according to
             # pserver services' count. A pserver may have two or more listening ports.
-            grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints))
+            grad_blocks = slice_variable(grad_list,
+                                         len(self.pserver_endpoints),
+                                         self.config.min_block_size)
             param_blocks = slice_variable(param_list,
-                                          len(self.pserver_endpoints))
+                                          len(self.pserver_endpoints),
+                                          self.config.min_block_size)
         else:
             # when we do NOT slice var up into blocks, we will always slice params
             # grads into one block.
-            grad_blocks = slice_variable(grad_list, 1)
-            param_blocks = slice_variable(param_list, 1)
+            grad_blocks = slice_variable(grad_list, 1,
+                                         self.config.min_block_size)
+            param_blocks = slice_variable(param_list, 1,
+                                          self.config.min_block_size)
         assert (len(grad_blocks) == len(param_blocks))
 
         # origin_varname -> [splited_var]
@@ -1001,6 +1028,7 @@ class DistributeTranspiler(object):
                     shape=splited_shape)  # flattend splited var
                 var_mapping[varname].append(var)
             program.global_block().sync_with_cpp()
+
         return var_mapping
 
     def create_splited_vars(self, source_var, block, tag):