parameter dispather. (#12666)

paddle/fluid/framework/threadpool.cc

@@ -20,6 +20,9 @@
 DEFINE_int32(io_threadpool_size, 100,
              "number of threads used for doing IO, default 100");
 
+DEFINE_int32(dist_threadpool_size, 0,
+             "number of threads used for distributed executed.");
+
 namespace paddle {
 namespace framework {
 
@@ -35,6 +38,10 @@ void ThreadPool::Init() {
   if (threadpool_.get() == nullptr) {
     // TODO(Yancey1989): specify the max threads number
     int num_threads = std::thread::hardware_concurrency();
+    if (FLAGS_dist_threadpool_size > 0) {
+      num_threads = FLAGS_dist_threadpool_size;
+      VLOG(1) << "set dist_threadpool_size to " << num_threads;
+    }
     PADDLE_ENFORCE_GT(num_threads, 0);
     threadpool_.reset(new ThreadPool(num_threads));
   }

paddle/fluid/operators/distributed/variable_response.cc

@@ -190,12 +190,15 @@ bool VariableResponse::ProcSerializedField(
 #endif
   }
 
+  VLOG(7) << "ProcSerializedField:" << meta_.varname()
+          << ", type:" << meta_.type() << std::endl;
   framework::DDim dims = GetDims(meta_.dims());
   if (meta_.type() == sendrecv::LOD_TENSOR) {
     PADDLE_ENFORCE(meta_.lod_size() >= 0, "lod info should be got first!");
     if (!CopyLodTensorData(input, *dev_ctx_, dims, num_bytes)) {
       return false;
     }
+
     return true;
   }
 
@@ -206,7 +209,9 @@ bool VariableResponse::ProcSerializedField(
     return true;
   }
 
-  return true;
+  PADDLE_ENFORCE("not supported var types:", meta_.varname(), meta_.type());
+
+  return false;
 }
 
 };  // namespace distributed

paddle/fluid/operators/listen_and_serv_op.cc

@@ -123,8 +123,11 @@ void ListenAndServOp::RunSyncLoop(
       optimize_prepared.begin(),
       std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
 
+  // Trainers will get all parameters from pserver in the
+  // startup program, so we will wait RequestGet first
+  rpc_service_->SetCond(distributed::kRequestGet);
+  rpc_service_->WaitBarrier(distributed::kRequestGet);
   rpc_service_->ResetBarrierCounter();
-
   while (true) {
     rpc_service_->Profiler().OneStep();
     // Get from multiple trainers, we don't care about the order in which

python/paddle/fluid/__init__.py

@@ -122,7 +122,7 @@ def __bootstrap__():
         'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
         'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb',
         'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
-        'cpu_deterministic'
+        "dist_threadpool_size", 'cpu_deterministic'
     ]
     if core.is_compiled_with_dist():
         read_env_flags.append('rpc_deadline')

python/paddle/fluid/initializer.py

@@ -15,7 +15,6 @@
 from . import framework
 import numpy as np
 import contextlib
-from .framework import convert_np_dtype_to_dtype_
 from .core import VarDesc
 
 __all__ = [

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -59,8 +59,8 @@ py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=$
 if(WITH_DISTRIBUTE)
     py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
     set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)
-    set_tests_properties(test_dist_mnist PROPERTIES TIMEOUT 180)
-    set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 180)
+    set_tests_properties(test_dist_mnist PROPERTIES TIMEOUT 200)
+    set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
 endif()
 py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)

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

@@ -26,6 +26,12 @@ from paddle.fluid.layers.io import ListenAndServ
 from paddle.fluid.layers.io import Recv
 from paddle.fluid.layers.io import Send
 
+from paddle.fluid import core
+
+RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
+)
+RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
+
 
 class TestSendOp(unittest.TestCase):
     def test_send(self):
@@ -89,18 +95,29 @@ class TestSendOp(unittest.TestCase):
     def init_client(self, place, port):
         main = fluid.Program()
         with fluid.program_guard(main):
+            main.global_block().append_op(
+                type="fetch_barrier",
+                inputs={},
+                outputs={},
+                attrs={
+                    "endpoints": ["127.0.0.1:{0}".format(port)],
+                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+                })
+
             x = layers.data(
                 shape=[32, 32],
                 dtype='float32',
                 name='X',
                 append_batch_size=False)
             fluid.initializer.Constant(value=2.3)(x, main.global_block())
+
             get_var = main.global_block().create_var(
                 name="scale_0.tmp_0",  # server side var
                 dtype="float32",
                 persistable=False,
                 shape=[32, 32])
             fluid.initializer.Constant(value=2.3)(get_var, main.global_block())
+
             Send("127.0.0.1:%d" % port, [x])
             o = Recv("127.0.0.1:%d" % port, [get_var])
 

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

@@ -18,6 +18,7 @@ import unittest
 import paddle.fluid as fluid
 from paddle.fluid.transpiler.distribute_transpiler import delete_ops
 import traceback
+import collections
 
 
 class TranspilerTest(unittest.TestCase):
@@ -53,9 +54,18 @@ class TranspilerTest(unittest.TestCase):
         self.origin_prog = main.clone()
         return main
 
-    def get_trainer(self, config=None, sync_mode=True):
-        t = self._transpiler_instance(config, sync_mode)
-        return t.get_trainer_program()
+    def get_trainer(self, config=None):
+        src = fluid.default_startup_program().clone()
+
+        t = self._transpiler_instance(config)
+
+        trainer_main = t.get_trainer_program()
+        trainer_startup = fluid.default_startup_program()
+
+        assert (src.num_blocks == 1)
+        assert (trainer_startup.num_blocks == src.num_blocks)
+
+        return trainer_main, trainer_startup
 
     def get_pserver(self, ep, config=None, sync_mode=True):
         t = self._transpiler_instance(config, sync_mode)
@@ -91,7 +101,21 @@ class TestBasicModel(TranspilerTest):
         pserver, startup = self.get_pserver(self.pserver1_ep)
         pserver2, startup2 = self.get_pserver(self.pserver2_ep)
 
-        trainer = self.get_trainer()
+        trainer, trainer_startup = self.get_trainer()
+
+        # splited var blocks should be in startup program
+        self.assertTrue("fc_w.block0" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_w.block1" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_w" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_b" in trainer_startup.global_block().vars)
+        self.assertTrue("fc_w@GRAD" not in trainer_startup.global_block().vars)
+        self.assertTrue("fc_b@GRAD" not in trainer_startup.global_block().vars)
+
+        src = [op.type for op in trainer_startup.global_block().ops]
+        dst = ['fill_constant', 'fill_constant', 'uniform_random', 'recv', 'recv', \
+               'fetch_barrier', 'concat']
+
+        self.assertEqual(src, dst)
 
         self.assertEqual([op.type for op in trainer.global_block().ops], [
             'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
@@ -142,7 +166,7 @@ class TestBasicModelWithLargeBlockSize(TranspilerTest):
         pserver, startup = self.get_pserver(self.pserver1_ep, config)
         pserver2, startup2 = self.get_pserver(self.pserver2_ep, config)
 
-        trainer = self.get_trainer(config)
+        trainer, _ = self.get_trainer(config)
 
         self.assertEqual([op.type for op in trainer.global_block().ops], [
             'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
@@ -226,7 +250,7 @@ class TestLRDecay(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         self.assertEqual(len(pserver.blocks), 4)
         lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
@@ -256,7 +280,7 @@ class TestLRDecayConditional(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         serv_op = pserver.blocks[0].ops[0]
         sub_blocks = []
@@ -305,7 +329,7 @@ class TestL2Decay(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         self.assertEqual(len(pserver.blocks), 3)
         self.assertEqual([op.type for op in pserver.blocks[1].ops],
@@ -340,7 +364,7 @@ class TestL2DecayWithPiecewise(TranspilerTest):
 
     def transpiler_test_impl(self):
         pserver, startup = self.get_pserver(self.pserver1_ep)
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
 
         self.assertEqual(len(pserver.blocks), 9)
         self.assertEqual([op.type for op in pserver.blocks[1].ops], [
@@ -415,7 +439,7 @@ class TestLocalLookupTable(TestDistLookupTableBase):
         self.assertEqual([op.type for op in pserver1.blocks[2].ops],
                          ["sum", "adam", "scale", "scale"])
 
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
@@ -453,7 +477,7 @@ class TestDistLookupTable(TestDistLookupTableBase):
         # 5 save table
         self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
 
-        trainer = self.get_trainer()
+        trainer, _ = self.get_trainer()
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',
@@ -486,7 +510,7 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
         self.assertEqual([op.type for op in pserver1.blocks[2].ops],
                          ["adam", "scale", "scale"])
 
-        trainer = self.get_trainer(config)
+        trainer, _ = self.get_trainer(config)
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
@@ -525,7 +549,7 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
         # 5 save table
         self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
 
-        trainer = self.get_trainer(config)
+        trainer, _ = self.get_trainer(config)
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
             'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -195,6 +195,9 @@ class DistributeTranspiler(object):
         if program is None:
             program = default_main_program()
         self.origin_program = program
+        self.origin_startup_program = default_startup_program().clone()
+
+        self.startup_program = default_startup_program()
         self.trainer_num = trainers
         self.sync_mode = sync_mode
         self.trainer_id = trainer_id
@@ -205,10 +208,10 @@ class DistributeTranspiler(object):
         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.
+        # step 1: split and create vars, then put splited vars in dicts for later use.
         self._init_splited_vars()
 
-        # step 3.1: insert send op to send gradient vars to parameter servers
+        # step 2: insert send op to send gradient vars to parameter servers
         ps_dispatcher.reset()
         send_vars = []
 
@@ -265,7 +268,7 @@ class DistributeTranspiler(object):
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
-        # step 3.2: insert recv op to receive parameters from parameter server
+        # step 3: insert recv op to receive parameters from parameter server
         recv_vars = []
         for _, var in enumerate(send_vars):
             recv_vars.append(self.grad_param_mapping[var])
@@ -312,6 +315,8 @@ class DistributeTranspiler(object):
                 outputs={"Out": [orig_param]},
                 attrs={"axis": 0})
 
+        self._get_trainer_startup_program(recv_vars=recv_vars, eplist=eplist)
+
         if self.has_distributed_lookup_table:
             self._replace_lookup_table_op_with_prefetch(program,
                                                         pserver_endpoints)
@@ -328,8 +333,78 @@ class DistributeTranspiler(object):
         # FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay?
         delete_ops(self.origin_program.global_block(), self.optimize_ops)
         self.origin_program.__str__()
+
         return self.origin_program
 
+    def _get_trainer_startup_program(self,
+                                     recv_vars,
+                                     eplist,
+                                     startup_program=None):
+        """
+        Get transpiled trainer side startup program.
+
+        Args:
+            startup_program(Program): Startup program.
+
+        Returns:
+            Program: trainer side startup program.
+        """
+        if startup_program is None:
+            startup_program = self.startup_program
+
+        # FIXME(gongwb): delete not need ops.
+        # note that: some parameter is not trainable and those ops can't be deleted.
+
+        for varname, splited_var in self.param_var_mapping.iteritems():
+            # Get the eplist of recv vars
+            eps = []
+            for var in splited_var:
+                index = [v.name for v in recv_vars].index(var.name)
+                eps.append(eplist[index])
+
+            for var in splited_var:
+                if startup_program.global_block().has_var(var.name):
+                    continue
+
+                startup_program.global_block().create_var(
+                    name=var.name,
+                    persistable=False,
+                    type=var.type,
+                    dtype=var.dtype,
+                    shape=var.shape,
+                    lod_level=var.lod_level)
+
+            op = startup_program.global_block().append_op(
+                type="recv",
+                inputs={},
+                outputs={"Out": splited_var},
+                attrs={
+                    "epmap": eps,
+                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+                })
+
+        startup_program.global_block().append_op(
+            type="fetch_barrier",
+            inputs={},
+            outputs={},
+            attrs={
+                "endpoints": self.pserver_endpoints,
+                RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+            })
+
+        for varname, splited_var in self.param_var_mapping.iteritems():
+            #add concat ops to merge splited parameters received from parameter servers.
+            if len(splited_var) <= 1:
+                continue
+            orig_param = startup_program.global_block().vars[varname]
+            startup_program.global_block().append_op(
+                type="concat",
+                inputs={"X": splited_var},
+                outputs={"Out": [orig_param]},
+                attrs={"axis": 0})
+
+        return startup_program
+
     def get_pserver_program(self, endpoint):
         """
         Get parameter server side program.
@@ -576,6 +651,8 @@ class DistributeTranspiler(object):
             new_outputs = dict()
             # do not append startup op if var is not on this pserver
             op_on_pserver = False
+            # TODO(gongwb): remove this line.
+            if op.type not in ["recv", "fetch_barrier", "concat"]:
                 for key in op.output_names:
                     newname, _ = _get_splited_name_and_shape(op.output(key)[0])
                     if newname:
@@ -1022,7 +1099,6 @@ class DistributeTranspiler(object):
                     var_mapping[varname] = \
                         [program.global_block().var(orig_var.name)]
                 continue
-
             var_mapping[varname] = []
             orig_shape = orig_var.shape
             orig_dim1_flatten = 1