Add Hccl program group

paddle/fluid/operators/collective/CMakeLists.txt

@@ -22,6 +22,7 @@ endif()
 
 if(WITH_ASCEND)
     op_library(gen_nccl_id_op)
+    op_library(c_gen_nccl_id_op)
 endif()
 
 

paddle/fluid/operators/collective/c_gen_nccl_id_op.cc

@@ -23,11 +23,15 @@ limitations under the License. */
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/place.h"
 
+#ifdef PADDLE_WITH_NCCL
 #include "paddle/fluid/operators/collective/gen_nccl_id_op_helper.h"
+#endif
 
 namespace paddle {
 namespace operators {
 
+#ifdef PADDLE_WITH_NCCL
+
 class CGenNCCLIdOp : public framework::OperatorBase {
  public:
   CGenNCCLIdOp(const std::string& type,
@@ -57,6 +61,22 @@ class CGenNCCLIdOp : public framework::OperatorBase {
   }
 };
 
+#else
+class CGenNCCLIdOp : public framework::OperatorBase {
+ public:
+  CGenNCCLIdOp(const std::string& type,
+               const framework::VariableNameMap& inputs,
+               const framework::VariableNameMap& outputs,
+               const framework::AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  void RunImpl(const framework::Scope& scope,
+               const platform::Place& dev_place) const override {
+  }
+};
+
+#endif
+
 class CGenNCCLIdOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {

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

@@ -0,0 +1,104 @@
+# 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
+import time
+import paddle.fluid as fluid
+from paddle.fluid import unique_name
+import paddle.fluid.core as core
+import paddle
+from paddle.fluid.layer_helper import LayerHelper
+
+paddle.enable_static()
+
+OpRole = core.op_proto_and_checker_maker.OpRole
+OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
+OP_ROLE_VAR_KEY = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
+
+def init_communicator(startup_program, main_program, current_endpoint, endpoints, ring_id):
+    nranks = len(endpoints)
+    other_endpoints = endpoints[:]
+    other_endpoints.remove(current_endpoint)
+    group_rank=endpoints.index(current_endpoint)
+    assert group_rank >=0
+
+    block = startup_program.global_block()
+    nccl_id_var = block.create_var(
+        name=unique_name.generate('nccl_id'),
+        persistable=True,
+        type=core.VarDesc.VarType.RAW)
+    block.append_op(
+        type='c_gen_nccl_id',
+        inputs={},
+        outputs={'Out': nccl_id_var},
+        attrs={
+            'rank': group_rank,
+            'endpoint': current_endpoint,
+            'other_endpoints': other_endpoints,
+            OP_ROLE_KEY: OpRole.Forward,
+        })
+    block.append_op(
+        type='c_comm_init',
+        inputs={'X': nccl_id_var},
+        outputs={},
+        attrs={
+            'nranks': nranks,
+            'rank': group_rank,
+            'ring_id': ring_id,
+            OP_ROLE_KEY: OpRole.Forward,
+        })
+    block.create_var(
+        name="data",
+        persistable=True,
+        dtype='float32')
+
+    with fluid.program_guard(main_program):
+        op_type="c_allreduce_sum"
+        data=fluid.layers.fill_constant(shape=[1], dtype='float32', value=2.5)
+        helper = LayerHelper(op_type, **locals())
+        helper.append_op(
+            type=op_type,
+            inputs={'X': [data]},
+            outputs={'Out': [data]},
+            attrs={'ring_id': ring_id,
+                   'use_calc_stream': True})
+
+def train():
+    startup_programs=[]
+    main_programs=[]
+
+
+    trainer_endpoints=["127.0.0.1:6071","127.0.0.1:6072","127.0.0.1:6073","127.0.0.1:6074"]
+    groups=[[], [], []]
+    groups[0]=[trainer_endpoints[0], trainer_endpoints[1]]
+    groups[1]=[trainer_endpoints[2], trainer_endpoints[3]]
+    groups[2]=[trainer_endpoints[0], trainer_endpoints[2]]
+
+    for i in range(len(trainer_endpoints)):
+        startup_programs.append(fluid.Program())
+        main_programs.append(fluid.Program())
+
+    for idx, group in enumerate(groups):
+        for te in group:
+            te_idx = trainer_endpoints.index(te)
+            startup_program = startup_programs[te_idx]
+            main_program=main_programs[te_idx]
+            init_communicator(startup_program, main_program, te, group, idx)
+
+    print(len(startup_programs))
+    print(startup_programs[0])
+    print(main_programs[0])
+
+train()