overlap sendop and backward ops

paddle/fluid/operators/recv_op.cc

@@ -36,19 +36,22 @@ class RecvOp : public framework::OperatorBase {
                const platform::Place& place) const override {
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
+    auto client_var_name = Output("RPCClient");
+    PADDLE_ENFORCE_NOT_NULL(scope.FindVar(client_var_name),
+                            "Can not find variable '%s' in the scope.",
+                            client_var_name);
+    auto* client_var = scope.FindVar(client_var_name);
+    detail::RPCClient* rpc_client = client_var->GetMutable<detail::RPCClient>();
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
 
     for (size_t i = 0; i < outs.size(); i++) {
-      VLOG(3) << "getting " << outs[i];
-      client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
+      VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
+      rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
-    PADDLE_ENFORCE(client_.Wait());
+    PADDLE_ENFORCE(rpc_client->Wait());
   }
-
- private:
-  mutable detail::RPCClient client_;
 };
 
 class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -56,6 +59,9 @@ class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
   RecvOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddOutput("Out", "(Tensor) Variables to get from server.").AsDuplicable();
+    AddOutput("RPCClient",
+              "(RPCClient) The RPC client object which is"
+              "initialized at most once.");
     AddComment(R"DOC(
 Recv operator
 

python/paddle/fluid/transpiler/__init__.py

@@ -15,8 +15,9 @@ from distribute_transpiler import DistributeTranspiler
 from inference_transpiler import InferenceTranspiler
 from memory_optimization_transpiler import memory_optimize, release_memory
 from distribute_transpiler_simple import SimpleDistributeTranspiler
+from ps_dispatcher import HashName, RoundRobin
 
 __all__ = [
     "DistributeTranspiler", "InferenceTranspiler", "SimpleDistributeTranspiler",
-    "memory_optimize", "release_memory"
+    "memory_optimize", "release_memory", "HashName", "RoundRobin"
 ]

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -17,7 +17,8 @@ from __future__ import print_function
 import math
 
 import distributed_splitter as splitter
-from .. import core
+from ps_dispatcher import RoundRobin, HashName, PSDispatcher
+from .. import core, framework
 from ..framework import Program, default_main_program, Variable, Parameter
 
 LOOKUP_TABLE_TYPE = "lookup_table"
@@ -144,13 +145,27 @@ def delete_ops(block, ops):
     block.program.sync_with_cpp()
 
 
+def find_op_by_input_arg(block, arg_name):
+    for index, op in enumerate(block.ops):
+        if arg_name in op.input_arg_names:
+            return index
+    return -1
+
+
+def find_op_by_output_arg(block, arg_name):
+    for index, op in enumerate(block.ops):
+        if arg_name in op.output_arg_names:
+            return index
+    return -1
+
+
 class DistributeTranspiler:
     def transpile(self,
                   trainer_id,
                   program=None,
                   pservers="127.0.0.1:6174",
                   trainers=1,
-                  split_method=splitter.round_robin,
+                  split_method=RoundRobin,
                   sync_mode=True):
         """
             Transpile the program to distributed data-parallelism programs.
@@ -184,14 +199,14 @@ class DistributeTranspiler:
             :type pservers: string
             :param trainers: total number of workers/trainers in the job
             :type trainers: int
-            :param split_method: A function to determin how to split variables
-                to different servers equally.
-            :type split_method: function
+            :param split_method: A instance to determin how to dispatch variable
+                blocks to different servers equally.
+            :type split_method: A instance based on PSDispatcher class.  
             :type sync_mode: boolean default True
             :param sync_mode: if sync_mode is set True, it means that dist transpiler
             will transpile the program into sync_mode pserver and trainer program.
         """
-        assert (callable(split_method))
+        assert (split_method.__bases__[0] == PSDispatcher)
         if program is None:
             program = default_main_program()
         self.origin_program = program
@@ -204,6 +219,7 @@ class DistributeTranspiler:
         pserver_endpoints = pservers.split(",")
         self.pserver_endpoints = pserver_endpoints
         self.optimize_ops, params_grads = self._get_optimize_pass()
+        ps_dispatcher = split_method(pserver_endpoints)
 
         # process lookup_table_op
         # 1. check all lookup_table_op is distributed
@@ -268,56 +284,67 @@ class DistributeTranspiler:
         grad_var_mapping = self._append_split_op(program, grad_blocks)
         param_var_mapping = self._create_vars_from_blocklist(program,
                                                              param_blocks)
-        # step3: Add gradients as send op inputs and parameters as send
-        # op outputs.
-        send_inputs = []
-        send_outputs = []
-        for b in grad_blocks:  # append by order
-            varname, block_id, _ = b.split(":")
-            send_inputs.append(grad_var_mapping[varname][int(block_id)])
-        for b in param_blocks:
-            varname, block_id, _ = b.split(":")
-            send_outputs.append(param_var_mapping[varname][int(block_id)])
-        # let send_op know which endpoint to send which var to, eplist has the same
-        # order as send_inputs.
-        eplist = split_method(send_inputs, pserver_endpoints)
-        # create mapping of endpoint -> split var to create pserver side program
-        self.param_grad_ep_mapping = dict()
-        for i, ep in enumerate(eplist):
-            param = send_outputs[i]
-            grad = send_inputs[i]
-            if not self.param_grad_ep_mapping.has_key(ep):
-                self.param_grad_ep_mapping[ep] = {"params": [], "grads": []}
-            self.param_grad_ep_mapping[ep]["params"].append(param)
-            self.param_grad_ep_mapping[ep]["grads"].append(grad)
-
         rpc_client_var = program.global_block().create_var(
             name=RPC_CLIENT_VAR_NAME,
             persistable=True,
             type=core.VarDesc.VarType.RAW)
 
-        # create send_op
+        # step 3: transpile trainer side program, insert recv op and send op.
+
+        # create mapping of endpoint -> split var to create pserver side program
+        self.param_grad_ep_mapping = dict()
+        [
+            self.param_grad_ep_mapping.update({
+                ep: {
+                    "params": [],
+                    "grads": []
+                }
+            }) for ep in self.pserver_endpoints
+        ]
+
+        # step 3.1: insert send op to send gradient vars to parameter servers
+        ps_dispatcher.reset()
+        for varname, send_vars in grad_var_mapping.items():
+            index = find_op_by_output_arg(program.global_block(), varname)
+            eplist = ps_dispatcher.dispatch(send_vars)
+            program.global_block().insert_op(
+                index=index,
+                type="send_vars",
+                inputs={"X": send_vars},
+                outputs={"RPCClient": rpc_client_var},
+                attrs={"epmap": eplist})
+
+        if self.sync_mode:
+            program.global_block().append_op(
+                type="send_barrier",
+                inputs={},
+                outputs={"RPCClient": rpc_client_var},
+                attrs={"endpoints": pserver_endpoints})
+
+        # step 3.2: insert recv op to receive parameters from parameter server
+        ps_dispatcher.reset()
+        recv_vars = []
+        for b in param_blocks:
+            varname, block_id, _ = b.split(":")
+            recv_vars.append(param_var_mapping[varname][int(block_id)])
+        for b in grad_blocks:
+            varname, block_id, _ = b.split(":")
+            send_vars.append(grad_var_mapping[varname][int(block_id)])
+
+        eplist = ps_dispatcher.dispatch(recv_vars)
+
+        for i, ep in enumerate(eplist):
+            self.param_grad_ep_mapping[ep]["params"].append(recv_vars[i])
+            self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
+
         program.global_block().append_op(
-            type="send",
-            inputs={"X": send_inputs},
-            outputs={"Out": send_outputs,
+            type="recv",
+            inputs={},
+            outputs={"Out": recv_vars,
                      "RPCClient": rpc_client_var},
-            attrs={
-                "endpoints": pserver_endpoints,
-                "epmap": eplist,
-                "sync_mode": self.sync_mode
-            })
-        # step4: Concat the parameters splits together after recv.
-        for varname, splited_var in param_var_mapping.iteritems():
-            if len(splited_var) <= 1:
-                continue
-            orig_param = program.global_block().vars[varname]
-            program.global_block().append_op(
-                type="concat",
-                inputs={"X": splited_var},
-                outputs={"Out": [orig_param]},
-                attrs={"axis": 0})
+            attrs={"epmap": eplist})
 
+        # TODO(Yancey1989): check dist lookup table
         if self.has_distributed_lookup_table:
             self._replace_lookup_table_op_with_prefetch(program, rpc_client_var,
                                                         eplist)

python/paddle/fluid/transpiler/distributed_splitter.py

@@ -1,57 +0,0 @@
-#   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.
-
-
-def hash_name(varlist, pserver_endpoints):
-    """
-    hash variable names to several endpoints.
-
-    Args:
-        varlist(list): a list of Variables
-
-    Returns(dict): a map of pserver endpoint -> varname
-    """
-
-    def _hash_block(block_str, total):
-        return hash(block_str) % total
-
-    eplist = []
-    for var in varlist:
-        server_id = _hash_block(var.name(), len(pserver_endpoints))
-        server_for_param = pserver_endpoints[server_id]
-        eplist.append(server_for_param)
-    return eplist
-
-
-def round_robin(varlist, pserver_endpoints):
-    """
-    Distribute variables to several endpoints.
-    Args:
-        varlist(list): a list of variables
-        pserver_endpoints(list): a list of pserver endpoints
-
-    Returns(list[int]): the endpoint for each variable
-    """
-    assert (len(varlist) >= len(pserver_endpoints))
-
-    eplist = []
-    pserver_idx = 0
-    for var in varlist:
-        server_for_param = pserver_endpoints[pserver_idx]
-        eplist.append(server_for_param)
-
-        pserver_idx += 1
-        if pserver_idx >= len(pserver_endpoints):
-            pserver_idx = 0
-    return eplist

python/paddle/fluid/transpiler/ps_dispatcher.py

@@ -0,0 +1,78 @@
+#   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.
+
+
+class PSDispatcher(object):
+    """
+    DistributedSpliter is the base class for dispatching vars
+    into different pserver instance.
+    You need to implement the `dispatch` inferface.
+    """
+
+    def __init__(self, pserver_endpoints):
+        self._eps = pserver_endpoints
+        self._step = 0
+
+    @property
+    def eps(self):
+        return self._eps
+
+    def reset(self):
+        self._step = 0
+
+    def dispatch(self, varlist):
+        """
+        :param varlist: a list of Variables
+        :return: a map of pserver endpoint -> varname 
+        """
+        AssertionError("Interface has not been implemented.")
+
+
+class HashName(PSDispatcher):
+    """
+      Hash variable names to servral endpoints
+    """
+
+    def __init__(self, pserver_endpoints):
+        super(self.__class__, self).__init__(pserver_endpoints)
+
+    def _hash_block(self, block_str, total):
+        return hash(block_str) % total
+
+    def dispatch(self, varlist):
+        eplist = []
+        for var in varlist:
+            server_id = self._hash_block(var.name(), len(self._eps))
+            server_for_param = self._eps[server_id]
+            eplist.append(server_for_param)
+        return eplist
+
+
+class RoundRobin(PSDispatcher):
+    """
+    Distribute variables to serveral endpoints.
+    """
+
+    def __init__(self, pserver_endpoints):
+        super(self.__class__, self).__init__(pserver_endpoints)
+
+    def dispatch(self, varlist):
+        eplist = []
+        for var in varlist:
+            server_for_param = self._eps[self._step]
+            eplist.append(server_for_param)
+            self._step += 1
+            if self._step >= len(self._eps):
+                self._step = 0
+        return eplist