refactor(mge/distribute): use is_root (and rank) in stead of rank and root at collective comm

GitOrigin-RevId: dccdb715533576db97bbc21fe61e640141c1e8b6

python_module/megengine/distributed/functional.py

@@ -26,25 +26,17 @@ def reduce_sum(
     tensor: Tensor,
     key: str,
     nr_ranks: Optional[int] = None,
-    rank: Optional[int] = None,
-    root: Optional[int] = 0,
+    is_root: Optional[bool] = None,
 ) -> Tensor:
     """Create reduce_sum operator for collective communication
 
     :param tensor: input tensor
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
-    :param root: rank of root node, use 0 as default
+    :param is_root: whether this is a root node
     """
     return _collective_comm(
-        tensor,
-        key,
-        CollParam.Mode.REDUCE_SUM,
-        nr_ranks,
-        rank,
-        root,
-        device=tensor.device,
+        tensor, key, CollParam.Mode.REDUCE_SUM, nr_ranks, is_root, device=tensor.device,
     )
 
 
@@ -52,24 +44,21 @@ def broadcast(
     tensor: Tensor,
     key: str,
     nr_ranks: Optional[int] = None,
-    rank: Optional[int] = None,
-    root: Optional[int] = 0,
+    is_root: Optional[bool] = None,
 ) -> Tensor:
     """Create broadcast operator for collective communication
 
     :param tensor: input tensor
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
-    :param root: rank of root node, use 0 as default
+    :param is_root: whether this is a root node
     """
     if key is None:
         key = tensor._symvar.name
+    if is_root is None:
+        is_root = get_rank() == 0
 
-    if rank is None:
-        rank = get_rank()
-
-    if rank == root:
+    if is_root:
         inp = tensor
     else:
         inp = tensor._symvar.owner_graph
@@ -79,8 +68,7 @@ def broadcast(
         key,
         CollParam.Mode.BROADCAST,
         nr_ranks,
-        rank,
-        root,
+        is_root,
         dtype=tensor.dtype,
         device=tensor.device,
     )
@@ -94,9 +82,9 @@ def all_gather(
     :param tensor: input tensor
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
+    :param rank: rank of this node
     """
-    return _collective_comm(tensor, key, CollParam.Mode.ALL_GATHER, nr_ranks, rank, 0)
+    return _collective_comm(tensor, key, CollParam.Mode.ALL_GATHER, nr_ranks, rank=rank)
 
 
 def reduce_scatter_sum(
@@ -107,69 +95,58 @@ def reduce_scatter_sum(
     :param tensor: input tensor
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
+    :param rank: rank of this node
     """
     return _collective_comm(
-        tensor, key, CollParam.Mode.REDUCE_SCATTER_SUM, nr_ranks, rank
+        tensor, key, CollParam.Mode.REDUCE_SCATTER_SUM, nr_ranks, rank=rank,
     )
 
 
-def all_reduce_sum(
-    tensor: Tensor, key: str, nr_ranks: Optional[int] = None, rank: Optional[int] = None
-) -> Tensor:
+def all_reduce_sum(tensor: Tensor, key: str, nr_ranks: Optional[int] = None) -> Tensor:
     """Create all_reduce_sum operator for collective communication
 
     :param tensor: input tensor
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
     """
-    return _collective_comm(tensor, key, CollParam.Mode.ALL_REDUCE_SUM, nr_ranks, rank)
+    return _collective_comm(tensor, key, CollParam.Mode.ALL_REDUCE_SUM, nr_ranks)
 
 
-def all_reduce_max(
-    tensor: Tensor, key: str, nr_ranks: Optional[int] = None, rank: Optional[int] = None
-) -> Tensor:
+def all_reduce_max(tensor: Tensor, key: str, nr_ranks: Optional[int] = None) -> Tensor:
     """Create all_reduce_max operator for collective communication
 
     :param tensor: input tensor
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
     """
-    return _collective_comm(tensor, key, CollParam.Mode.ALL_REDUCE_MAX, nr_ranks, rank)
+    return _collective_comm(tensor, key, CollParam.Mode.ALL_REDUCE_MAX, nr_ranks)
 
 
-def all_reduce_min(
-    tensor: Tensor, key: str, nr_ranks: Optional[int] = None, rank: Optional[int] = None
-) -> Tensor:
+def all_reduce_min(tensor: Tensor, key: str, nr_ranks: Optional[int] = None) -> Tensor:
     """Create all_reduce_min operator for collective communication
 
     :param tensor: input tensor
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
     """
-    return _collective_comm(tensor, key, CollParam.Mode.ALL_REDUCE_MIN, nr_ranks, rank)
+    return _collective_comm(tensor, key, CollParam.Mode.ALL_REDUCE_MIN, nr_ranks)
 
 
 def bcast_param(
     inp: Union[Buffer, Parameter],
     key: str,
     nr_ranks: Optional[int] = None,
-    rank: Optional[int] = None,
-    root: Optional[int] = 0,
+    is_root: Optional[bool] = None,
 ) -> None:
     """Broadcast parameters among devices
 
     :param inp: input Buffer or Parameter to be synchronized
     :param key: unique identifier for collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
-    :param root: rank of root node, use 0 as default
+    :param is_root: whether this is a root node
     """
     if not is_distributed():
         return
     assert isinstance(inp, (Buffer, Parameter))
-    bcast_res = broadcast(inp, key, nr_ranks, rank, root)
+    bcast_res = broadcast(inp, key, nr_ranks, is_root)
     add_update(inp, bcast_res, alpha=0)

python_module/megengine/distributed/helper.py

@@ -19,8 +19,8 @@ def collective_comm_symvar(
     key: str,
     op: CollParam.Mode,
     nr_ranks: Optional[int] = None,
+    is_root: Optional[bool] = None,
     rank: Optional[int] = None,
-    root: Optional[int] = 0,
     dtype: Optional[type] = None,
     device: Optional[mgb.CompNode] = None,
     comp_graph: Optional[mgb.CompGraph] = None,
@@ -31,8 +31,7 @@ def collective_comm_symvar(
     :param key: unique identifier for collective communication
     :param op: mode of collective communication
     :param nr_ranks: number of ranks, use util.get_world_size() as default
-    :param rank: rank of the current process, use util.get_rank() as default
-    :param root: rank of root node, use 0 as default
+    :param is_root: whether this node is root node
     :param dtype: output data type, use dtype of inp as default
     :param device: output comp node, use comp node of inp as default
     :param comp_graph: output comp graph, use comp graph of inp as default
@@ -41,8 +40,8 @@ def collective_comm_symvar(
         inp,
         key=str(key),
         nr_devices=nr_ranks if nr_ranks is not None else get_world_size(),
-        rank=rank if rank is not None else get_rank(),
-        root=root,
+        is_root=is_root if is_root is not None else (get_rank() == 0),
+        rank=rank if rank is not None else -1,
         server_addr=get_master_ip(),
         port=get_master_port(),
         param=CollParam(mode=op),

python_module/megengine/optimizer/optimizer.py

@@ -17,7 +17,13 @@ import numpy as np
 from .._internal.config import opr_priority_scope
 from ..core import Buffer, Parameter, Tensor, TensorDict
 from ..core.graph import get_default_graph
-from ..distributed import all_reduce_sum, bcast_param, get_world_size, is_distributed
+from ..distributed import (
+    all_reduce_sum,
+    bcast_param,
+    get_rank,
+    get_world_size,
+    is_distributed,
+)
 from ..distributed.util import get_group_id
 from ..functional import add_update
 from ..functional import grad as grad_func
@@ -222,7 +228,11 @@ class Optimizer(metaclass=ABCMeta):
     def bcast_param(self):
         for group in self.param_groups:
             for param in group["params"]:
-                bcast_param(param, "bcast_param_" + str(get_group_id()))
+                bcast_param(
+                    param,
+                    "bcast_param_" + str(get_group_id()),
+                    is_root=(get_rank() == 0),
+                )
 
     def state_dict(self) -> Dict:
         r"""Export the optimizer state.

python_module/src/cpp/opr_defs.cpp

@@ -93,10 +93,9 @@ SymbolVar _Opr::remote_recv(const std::string& server_addr, const int port,
 }
 
 SymbolVar _Opr::collective_comm_with_input(
-        SymbolVar inpvar, const std::string& key,
-        const size_t nr_devices, const uint32_t rank, const uint32_t root,
-        const std::string& server_addr, const int port,
-        PyObject* params, PyObject* dtype,
+        SymbolVar inpvar, const std::string& key, const size_t nr_devices,
+        const bool is_root, const int rank, const std::string& server_addr,
+        const int port, PyObject* params, PyObject* dtype,
         const std::string& backend, SharedND* output_buf,
         const OperatorNodeConfig& config, const SharedScalar& disable) {
     SymbolVarArray inputs(1, inpvar);
@@ -111,15 +110,15 @@ SymbolVar _Opr::collective_comm_with_input(
     if (dtype != Py_None) {
         _dtype = npy::dtype_np2mgb(dtype);
     }
-    return CollectiveComm::make(inputs, graph, key, nr_devices, rank, root, group_mgr,
-            dev_buffer_arr, param, _dtype, backend, config, disable.get_val())[0];
+    return CollectiveComm::make(inputs, graph, key, nr_devices, is_root, rank,
+                                group_mgr, dev_buffer_arr, param, _dtype,
+                                backend, config, disable.get_val())[0];
 }
 
 SymbolVar _Opr::collective_comm_without_input(
-        CompGraph& cg, const std::string& key,
-        const size_t nr_devices, const uint32_t rank, const uint32_t root,
-        const std::string& server_addr, const int port,
-        PyObject* params, PyObject* dtype,
+        CompGraph& cg, const std::string& key, const size_t nr_devices,
+        const bool is_root, const int rank, const std::string& server_addr,
+        const int port, PyObject* params, PyObject* dtype,
         const std::string& backend, SharedND* output_buf,
         const OperatorNodeConfig& config, const SharedScalar& disable) {
     SymbolVarArray inputs;
@@ -134,8 +133,9 @@ SymbolVar _Opr::collective_comm_without_input(
     if (dtype != Py_None) {
         _dtype = npy::dtype_np2mgb(dtype);
     }
-    return CollectiveComm::make(inputs, &graph, key, nr_devices, rank, root, group_mgr,
-            dev_buffer_arr, param, _dtype, backend, config, disable.get_val())[0];
+    return CollectiveComm::make(inputs, &graph, key, nr_devices, is_root, rank,
+                                group_mgr, dev_buffer_arr, param, _dtype,
+                                backend, config, disable.get_val())[0];
 }
 
 #else
@@ -172,7 +172,7 @@ SymbolVar _Opr::remote_recv(const std::string& server_addr, const int port,
 
 SymbolVar _Opr::collective_comm_with_input(
         SymbolVar inpvar, const std::string& key,
-        const size_t nr_devices, const uint32_t rank, const uint32_t root,
+        const size_t nr_devices, const bool is_root, const int rank,
         const std::string& server_addr, const int port, PyObject* params,
         PyObject* dtype, const std::string& backend, SharedND* output_buf,
         const OperatorNodeConfig& config, const SharedScalar& disable) {
@@ -181,7 +181,7 @@ SymbolVar _Opr::collective_comm_with_input(
 
 SymbolVar _Opr::collective_comm_without_input(
         CompGraph& cg, const std::string& key,
-        const size_t nr_devices, const uint32_t rank, const uint32_t root,
+        const size_t nr_devices, const bool is_root, const int rank,
         const std::string& server_addr, const int port, PyObject* params,
         PyObject* dtype, const std::string& backend, SharedND* output_buf,
         const OperatorNodeConfig& config, const SharedScalar& disable) {

python_module/src/cpp/opr_defs.h

@@ -94,17 +94,17 @@ static SymbolVar remote_recv(const std::string& server_addr, const int port,
 
 static SymbolVar collective_comm_with_input(
         SymbolVar inpvar, const std::string& key, const size_t nr_devices,
-        const uint32_t rank, const uint32_t root, const std::string& server_addr,
-        const int port, PyObject* params, PyObject* dtype,
-        const std::string& backend, SharedND* output_buf,
-        const OperatorNodeConfig& config, const SharedScalar& disable);
+        const bool is_root, const int rank, const std::string& server_addr, const int port,
+        PyObject* params, PyObject* dtype, const std::string& backend,
+        SharedND* output_buf, const OperatorNodeConfig& config,
+        const SharedScalar& disable);
 
 static SymbolVar collective_comm_without_input(
         CompGraph& graph, const std::string& key, const size_t nr_devices,
-        const uint32_t rank, const uint32_t root, const std::string& server_addr,
-        const int port, PyObject* params, PyObject* dtype,
-        const std::string& backend, SharedND* output_buf,
-        const OperatorNodeConfig& config, const SharedScalar& disable);
+        const bool is_root, const int rank, const std::string& server_addr, const int port,
+        PyObject* params, PyObject* dtype, const std::string& backend,
+        SharedND* output_buf, const OperatorNodeConfig& config,
+        const SharedScalar& disable);
 
 // misc
 static SymbolVarArray extern_c_opr_placeholder(

python_module/test/unit/distributed/test_functional.py

@@ -102,7 +102,7 @@ def test_all_gather():
             return
         _init_process_group_wrapper(world_size, rank, rank, backend, port_queue)
         inp = tensor(data)
-        output = dist.functional.all_gather(inp, "x")
+        output = dist.functional.all_gather(inp, "x", rank=rank)
         assert np.allclose(output.numpy(), expect)
 
     def check(shape, backend):
@@ -135,7 +135,7 @@ def test_reduce_scatter_sum():
             return
         _init_process_group_wrapper(world_size, rank, rank, backend, port_queue)
         inp = tensor(data)
-        output = dist.functional.reduce_scatter_sum(inp, "x")
+        output = dist.functional.reduce_scatter_sum(inp, "x", rank=rank)
         assert np.allclose(output.numpy(), expect)
 
     def check(shape, backend):

src/opr-mm/impl/collective_comm.cpp

@@ -368,8 +368,8 @@ CollectiveComm::ModeTrait& CollectiveComm::ModeTrait::from_mode(Mode mode) {
 
 CollectiveComm::CollectiveComm(
         VarNodeArray inputs, ComputingGraph* const graph,
-        const std::string& key, const size_t nr_devices, const uint32_t rank,
-        const uint32_t root, std::shared_ptr<GroupClient> group_client,
+        const std::string& key, const size_t nr_devices, const bool is_root,
+        const int rank, std::shared_ptr<GroupClient> group_client,
         const Param& param, const DType& dtype, const std::string& backend,
         const SmallVector<std::shared_ptr<DeviceTensorND>>& dev_buffer_arr,
         const OperatorNodeConfig& config,
@@ -380,9 +380,9 @@ CollectiveComm::CollectiveComm(
           m_backend(backend),
           m_group_client{std::move(group_client)},
           m_nr_devices(nr_devices),
+          m_is_root(is_root),
           m_rank(rank),
           m_key(key),
-          m_root(root),
           m_dev_buffers(dev_buffer_arr),
           m_disable{disable} {
     for (auto i : inputs) {
@@ -422,28 +422,28 @@ CollectiveComm::CollectiveComm(
 
 SymbolVarArray CollectiveComm::make(
         const SymbolVarArray& inputs, ComputingGraph* const graph,
-        const std::string& key, const size_t nr_devices, const uint32_t rank,
-               const uint32_t root, std::shared_ptr<GroupClient> group_client,
-               const Param& param, const DType& dtype, const std::string& backend,
-               const OperatorNodeConfig& config,
+        const std::string& key, const size_t nr_devices, const bool is_root,
+        const int rank, std::shared_ptr<GroupClient> group_client,
+        const Param& param, const DType& dtype, const std::string& backend,
+        const OperatorNodeConfig& config,
         const std::shared_ptr<DTypeScalar>& disable) {
     SmallVector<std::shared_ptr<DeviceTensorND>> dev_buffer_arr(nr_devices,
                                                                 nullptr);
-    return make(inputs, graph, key, nr_devices, rank, root, group_client,
+    return make(inputs, graph, key, nr_devices, is_root, rank, group_client,
                 dev_buffer_arr, param, dtype, backend, config);
 }
 
 SymbolVarArray CollectiveComm::make(
         const SymbolVarArray& inputs, ComputingGraph* const graph,
-        const std::string& key, const size_t nr_devices, const uint32_t rank,
-               const uint32_t root, std::shared_ptr<GroupClient> group_client,
+        const std::string& key, const size_t nr_devices, const bool is_root,
+        const int rank, std::shared_ptr<GroupClient> group_client,
         const SmallVector<std::shared_ptr<DeviceTensorND>>& dev_buffer_arr,
         const Param& param, const DType& dtype, const std::string& backend,
         const OperatorNodeConfig& config,
         const std::shared_ptr<DTypeScalar>& disable) {
     auto inpvars = cg::to_var_node_array(inputs);
     auto opr = graph->insert_opr(std::make_unique<CollectiveComm>(
-            inpvars, graph, key, nr_devices, rank, root, std::move(group_client),
+            inpvars, graph, key, nr_devices, is_root, rank, std::move(group_client),
             param, dtype, backend, dev_buffer_arr, config, disable));
     mgb_assert(!opr->output().empty());
     return cg::to_symbol_var_array(opr->output());
@@ -452,11 +452,14 @@ SymbolVarArray CollectiveComm::make(
 void CollectiveComm::opr_register() {
     if (m_init)
         return;
-    auto&& cuda_env = CompNodeEnv::from_comp_node(output(0)->comp_node())
-                                          .cuda_env();
+    auto&& comp_node = output(0)->comp_node();
+
+    auto reg_info = m_group_client->opr_register(
+            m_key, m_nr_devices, m_is_root, m_rank,
+            comp_node.get_uid());
 
-    auto hash = m_group_client->opr_register(m_key, m_nr_devices, m_rank,
-            reinterpret_cast<uintptr_t>(cuda_env.stream));
+    m_rank = reg_info.rank;
+    m_root = reg_info.root_rank;
 
     MegRayCommunicatorBuilder* builder;
 
@@ -468,7 +471,7 @@ void CollectiveComm::opr_register() {
     }
 
     m_megray_comm = builder->get_megray_comm(
-            hash, m_key, m_nr_devices, m_rank,
+            reg_info.hash, m_key, m_nr_devices, m_rank,
             get_megray_backend(m_backend), m_group_client);
 
     m_megray_ctx = MegRay::CudaContext::make(get_stream(output(0)));
@@ -606,8 +609,8 @@ VarNodeArray CollectiveComm::grad(const VarNodeArray& out_grads) const {
     }
 
     auto gvar = CollectiveComm::make(
-            og_syms, owner_graph(), m_key + ":grad", m_nr_devices, m_rank, m_root,
-            m_group_client, mode, m_dtype, m_backend,
+            og_syms, owner_graph(), m_key + ":grad", m_nr_devices, m_is_root,
+            m_rank, m_group_client, mode, m_dtype, m_backend,
             OperatorNodeConfig{}.comp_node_arr(cn_arr));
 
     if (m_param.mode == Param::Mode::ALL_REDUCE_MAX) {
@@ -733,11 +736,11 @@ cg::OperatorNodeBase* opr_shallow_copy_collective_mm(
         const cg::OperatorNodeBase& opr_, const VarNodeArray& inputs,
         const OperatorNodeConfig& config) {
     auto&& opr = opr_.cast_final_safe<opr::CollectiveComm>();
-    return opr::CollectiveComm::make(to_symbol_var_array(inputs),
-                                     ctx.owner_graph(opr_, inputs), opr.key(),
-                                     opr.nr_devices(), opr.rank(), opr.root(),
-                                     opr.group_client(), opr.dev_buffers(),
-                                     opr.param(), opr.dtype(), opr.backend(), config)[0]
+    return opr::CollectiveComm::make(
+                   to_symbol_var_array(inputs), ctx.owner_graph(opr_, inputs),
+                   opr.key(), opr.nr_devices(), opr.is_root(), opr.rank(),
+                   opr.group_client(), opr.dev_buffers(), opr.param(),
+                   opr.dtype(), opr.backend(), config)[0]
             .node()
             ->owner_opr();
 }

src/opr-mm/impl/collective_comm.oprdecl

@@ -6,8 +6,8 @@ decl_raw_opr(
             'to the same NCCL operation.', 'str'),
         Doc('nr_devices', 'Total number of devices involved in the NCCL '
             'operation to which this operator belongs.', 'int'),
-        Doc('rank', 'Rank of this operator', 'int'),
-        Doc('root', 'root rank of broadcast or reduce operation'),
+        Doc('is_root', 'whether this node is root node', 'bool'),
+        Doc('rank', 'rank of this node, if is -1, generate one', 'int'),
         Doc('server_addr', 'rpc server ip address'),
         Doc('port', 'server rpc listening port'),
         Doc('param', 'The only component of *param* is *mode*, which refers to '
@@ -28,12 +28,12 @@ decl_raw_opr(
     body = [
          'if isinstance(input, _mgb.SymbolVar):',
         ('    output = _mgb._Opr.collective_comm_with_input(input, key, '
-         'nr_devices, rank, root, server_addr, port, '
+         'nr_devices, is_root, rank, server_addr, port, '
          '[param.serialize()], dtype, backend, output_buffer, config, disable)'),
          'else:',
          '    assert isinstance(input, _mgb.CompGraph)',
         ('    output = _mgb._Opr.collective_comm_without_input(input, key, '
-         'nr_devices, rank, root, server_addr, port, '
+         'nr_devices, is_root, rank, server_addr, port, '
          '[param.serialize()], dtype, backend, output_buffer, config, disable)')
     ],
     desc = ('collective communication between multiple CompNodes on multiple '

src/opr-mm/impl/group_manager.cpp

@@ -16,16 +16,60 @@ using namespace opr;
 
 /* ================= GroupInfo ================= */
 
+void GroupInfo::sort_opr_infos() {
+    auto cmp = [](const GroupInfo::OprInfo& a, const GroupInfo::OprInfo& b) {
+        return a.comp_node_hash < b.comp_node_hash;
+    };
+    std::sort(m_opr_infos.begin(), m_opr_infos.end(), cmp);
+}
+
+void GroupInfo::gen_infos_from_opr_infos() {
+    // generate rank
+    bool rank_assgined = true;
+    for (auto& opr_info:m_opr_infos) {
+        if(opr_info.rank < 0) {
+            rank_assgined = false;
+            break;
+        }
+    }
+    if (!rank_assgined) {
+        for (size_t i = 0; i < m_opr_infos.size(); i++) {
+            m_opr_infos[i].rank = i;
+            m_rank_map.insert({m_opr_infos[i].comp_node_hash, i});
+        }
+    } else {
+        for (size_t i = 0; i < m_opr_infos.size(); i++) {
+            m_rank_map.insert(
+                    {m_opr_infos[i].comp_node_hash, m_opr_infos[i].rank});
+        }
+    }
+
+    // generate root rank
+    for (auto& opr_info:m_opr_infos) {
+        if (opr_info.is_root) {
+            m_root_rank = opr_info.rank;
+            break;
+        }
+    }
+
+    // generate group hash
+    auto xxhash = XXHash{};
+    for (auto&& opr_info : m_opr_infos) {
+        xxhash.update(&opr_info.comp_node_hash, sizeof(uint64_t))
+                .update(&opr_info.rank, sizeof(int));
+    }
+    m_hash = xxhash.digest();
+}
+
 void GroupInfo::add_opr(const std::string& key, size_t nr_expected_devices,
-        uint32_t rank, uintptr_t stream) {
+        bool is_root, int rank, uint64_t comp_node_hash) {
     std::unique_lock<std::mutex> lk{m_group_mtx};
     if (m_nr_expected_devs == 0) {
         m_nr_expected_devs = nr_expected_devices;
     } else {
         mgb_assert(m_nr_expected_devs == nr_expected_devices);
     }
-    OprInfo opr_info = {rank, stream};
-    m_opr_infos.push_back(std::move(opr_info));
+    m_opr_infos.push_back({comp_node_hash, is_root, rank});
     m_nr_registered_devs++;
     m_count++;
     if (m_nr_registered_devs > nr_expected_devices) {
@@ -38,6 +82,8 @@ void GroupInfo::add_opr(const std::string& key, size_t nr_expected_devices,
                 key.c_str(), nr_expected_devices, m_nr_registered_devs);
     }
     if (m_nr_expected_devs == m_nr_registered_devs) {
+        sort_opr_infos();
+        gen_infos_from_opr_infos();
         m_register_cv.notify_all();
     } else {
         m_register_cv.wait(lk,
@@ -66,6 +112,8 @@ void GroupInfo::clear() {
     m_count--;
     if (m_count == 0) {
         m_opr_infos.clear();
+        m_rank_map.clear();
+        m_root_rank = -1;
         m_nr_expected_devs = 0;
         m_nr_registered_devs = 0;
         m_output_shape.invalidate();
@@ -77,14 +125,18 @@ void GroupInfo::clear() {
 
 /* ================= GroupManager ================= */
 
-uint64_t GroupManager::opr_register(const std::string& key, size_t nr_devices,
-    uint32_t rank, uintptr_t stream) {
+GroupManager::RegisterInfo GroupManager::opr_register(const std::string& key,
+                                                      size_t nr_devices,
+                                                      bool is_root, int rank,
+                                                      uint64_t comp_node_hash) {
+    GroupManager::RegisterInfo ret{0, 0, 0};
     auto&& group = get_group(key);
-    group.add_opr(key, nr_devices, rank, stream);
-    auto&& opr_infos = group.opr_infos();
-    uint64_t hash = get_hash_key(opr_infos, rank);
+    group.add_opr(key, nr_devices, is_root, rank, comp_node_hash);
+    ret.rank = group.get_rank(comp_node_hash);
+    ret.root_rank = group.get_root_rank();
+    ret.hash = group.get_group_hash() + ret.rank;
     group.clear();
-    return hash;
+    return ret;
 }
 
 std::vector<std::string> GroupManager::gather_uid(const std::string& uid,
@@ -126,22 +178,6 @@ GroupInfo& GroupManager::get_group(const std::string& key) {
     return m_key2group_info[key];
 }
 
-uint64_t GroupManager::get_hash_key(const std::vector<GroupInfo::OprInfo>& _infos,
-        uint32_t rank) {
-    auto cmp = [](const GroupInfo::OprInfo& lhs, const GroupInfo::OprInfo& rhs) {
-        return lhs.rank < rhs.rank;
-    };
-    auto infos = _infos;
-    std::sort(infos.begin(), infos.end(), cmp);
-    auto xxhash = XXHash{};
-    for (auto&& opr_info : infos) {
-        xxhash.update(&opr_info.rank, sizeof(uint32_t))
-                .update(&opr_info.stream, sizeof(uintptr_t));
-    }
-    xxhash.update(&rank, sizeof(uint32_t));
-    return xxhash.digest();
-};
-
 uint32_t GroupManager::group_barrier(uint32_t size, uint32_t rank) {
     std::unique_lock<std::mutex> lk{m_barrier_mtx};
     if (m_barrier_set.empty()) {

src/opr-mm/impl/io_remote.cpp

@@ -48,12 +48,11 @@ SymbolVar RemoteSend::make(const PeerDesc& peer, SymbolVar var,
 
 void RemoteSend::scn_do_execute() {
     if (!m_init) {
-        auto&& cuda_env = CompNodeEnv::from_comp_node(output(0)->comp_node())
-                .cuda_env();
+        auto&& comp_node = output(0)->comp_node();
 
         // rank 0 for RemoteSend
-        auto hash = m_group_client->opr_register(m_peer.key, 2, 0,
-                reinterpret_cast<uintptr_t>(cuda_env.stream));
+        auto reg_info = m_group_client->opr_register(m_peer.key, 2, 0, false,
+                comp_node.get_uid());
 
         auto megray_comm_builder =
                 owner_graph()
@@ -62,7 +61,7 @@ void RemoteSend::scn_do_execute() {
                         .get_user_data_or_create<MegRayCommunicatorBuilder>();
 
         m_megray_comm = megray_comm_builder->get_megray_comm(
-                hash, m_peer.key, 2, 0, MegRay::MEGRAY_UCX, m_group_client);
+                reg_info.hash, m_peer.key, 2, 0, MegRay::MEGRAY_UCX, m_group_client);
         m_init = true;
     }
 
@@ -152,12 +151,12 @@ SymbolVar RemoteRecv::make(const PeerDesc& peer, cg::ComputingGraph& graph,
 
 void RemoteRecv::scn_do_execute() {
     if (!m_init) {
-        auto&& cuda_env = CompNodeEnv::from_comp_node(output(0)->comp_node())
-                .cuda_env();
+        auto&& comp_node = output(0)->comp_node();
 
         // rank 1 for RemoteRecv
-        auto hash = m_group_client->opr_register(m_peer.key, 2, 1,
-                reinterpret_cast<uintptr_t>(cuda_env.stream));
+        auto reg_info = m_group_client->opr_register(
+                m_peer.key, 2, false, 1,
+                comp_node.get_uid());
 
         auto megray_comm_builder =
                 owner_graph()
@@ -166,7 +165,7 @@ void RemoteRecv::scn_do_execute() {
                         .get_user_data_or_create<MegRayCommunicatorBuilder>();
 
         m_megray_comm = megray_comm_builder->get_megray_comm(
-                hash, m_peer.key, 2, 1, MegRay::MEGRAY_UCX, m_group_client);
+                reg_info.hash, m_peer.key, 2, 1, MegRay::MEGRAY_UCX, m_group_client);
         m_init = true;
     }
 

src/opr-mm/impl/mm_handler.cpp

@@ -68,9 +68,11 @@ private:
 void GroupServerProxy::opr_register(void* input_ptr, size_t input_len,
         std::string *output) {
     INFO_INIT(mm_handler, OprRegister);
-    uint64_t hash = m_mgr.opr_register(req.key(), req.nr_expected_devices(),
-        req.rank(), req.stream());
-    rsp.set_hash(hash);
+    auto ret = m_mgr.opr_register(req.key(), req.nr_expected_devices(),
+                                  req.is_root(), req.rank(), req.comp_node_hash());
+    rsp.set_hash(ret.hash);
+    rsp.set_rank(ret.rank);
+    rsp.set_root_rank(ret.root_rank);
     rsp.SerializeToString(output);
 }
 
@@ -122,11 +124,11 @@ void GroupServerProxy::group_barrier(void* input_ptr, size_t input_len,
 
 /* ======================== GroupClientProxy ========================== */
 
-#define INFO_INIT(space, f_name, name)                       \
+#define INFO_INIT(space, f_name, name)      \
     using Request = space::name##Request;   \
     using Response = space::name##Response; \
-    std::string func_name = #f_name;                  \
-    Request req;                                      \
+    std::string func_name = #f_name;        \
+    Request req;                            \
     Response rsp;
 
 #define SOLVE_REQUEST(name, req, rsp)                                \
@@ -145,15 +147,18 @@ GroupClientProxy::GroupClientProxy(const std::string& server_addr)
               m_stub{ZmqRpc::ZmqRpcClient::get_client("tcp://" + server_addr)} {
     }
 
-uint64_t GroupClientProxy::opr_register(const std::string& key, size_t nr_devices,
-    uint32_t rank, uintptr_t stream) {
+GroupManager::RegisterInfo GroupClientProxy::opr_register(
+        const std::string& key, size_t nr_devices, bool is_root, int rank,
+        uint64_t comp_node_hash) {
     INFO_INIT(mm_handler, opr_register, OprRegister)
     req.set_key(key);
+    req.set_is_root(is_root);
     req.set_rank(rank);
-    req.set_stream(stream);
+    req.set_comp_node_hash(comp_node_hash);
     req.set_nr_expected_devices(nr_devices);
     SOLVE_REQUEST(func_name, req, rsp);
-    return rsp.hash();
+    GroupManager::RegisterInfo ret{rsp.hash(), rsp.rank(), rsp.root_rank()};
+    return ret;
 }
 
 void GroupClientProxy::set_output_shape(const std::string& key,

src/opr-mm/include/megbrain/opr/collective_comm.h

@@ -26,18 +26,19 @@ public:
 
     using Param = megdnn::param::CollectiveComm;
 
-    CollectiveComm(VarNodeArray inputs, ComputingGraph* const graph,
-                     const std::string& key, const size_t nr_devices, const uint32_t rank,
-                     const uint32_t root, std::shared_ptr<GroupClient> group_client,
-                     const Param& param, const DType& dtype, const std::string& backend,
-                     const SmallVector<std::shared_ptr<DeviceTensorND>>& dev_buffer_arr,
-                     const OperatorNodeConfig& config,
-                     const std::shared_ptr<DTypeScalar>& disable);
+    CollectiveComm(
+            VarNodeArray inputs, ComputingGraph* const graph,
+            const std::string& key, const size_t nr_devices, const bool is_root,
+            const int rank, std::shared_ptr<GroupClient> group_client,
+            const Param& param, const DType& dtype, const std::string& backend,
+            const SmallVector<std::shared_ptr<DeviceTensorND>>& dev_buffer_arr,
+            const OperatorNodeConfig& config,
+            const std::shared_ptr<DTypeScalar>& disable);
 
     static SymbolVarArray make(
             const SymbolVarArray& inputs, ComputingGraph* const graph,
-            const std::string& key, const size_t nr_devices, const uint32_t rank,
-            const uint32_t root, std::shared_ptr<GroupClient> group_client,
+            const std::string& key, const size_t nr_devices, const bool is_root,
+            const int rank, std::shared_ptr<GroupClient> group_client,
             const SmallVector<std::shared_ptr<DeviceTensorND>>& dev_buffer_arr,
             const Param& param, const DType& dtype = {},
             const std::string& backend = "nccl",
@@ -45,15 +46,16 @@ public:
             const std::shared_ptr<DTypeScalar>& disable =
                     std::make_shared<DTypeScalar>(0));
 
-    static SymbolVarArray make(
-            const SymbolVarArray& inputs, ComputingGraph* const graph,
-            const std::string& key, const size_t nr_devices, const uint32_t rank,
-            const uint32_t root, std::shared_ptr<GroupClient> group_client,
-            const Param& param, const DType& dtype = {},
-            const std::string& backend = "nccl",
-            const OperatorNodeConfig& config = {},
-            const std::shared_ptr<DTypeScalar>& disable =
-                    std::make_shared<DTypeScalar>(0));
+    static SymbolVarArray make(const SymbolVarArray& inputs,
+                               ComputingGraph* const graph,
+                               const std::string& key, const size_t nr_devices,
+                               const bool is_root, const int rank,
+                               std::shared_ptr<GroupClient> group_client,
+                               const Param& param, const DType& dtype = {},
+                               const std::string& backend = "nccl",
+                               const OperatorNodeConfig& config = {},
+                               const std::shared_ptr<DTypeScalar>& disable =
+                                       std::make_shared<DTypeScalar>(0));
 
     const Param& param() const { return m_param; }
     const DType& dtype() const { return m_dtype; }
@@ -67,9 +69,9 @@ public:
         return m_dev_buffers;
     }
 
-    uint32_t rank() const { return m_rank; }
-    uint32_t root() const { return m_root; }
-    bool is_root() const { return m_rank == m_root; }
+    int rank() const { return m_rank; }
+    int root() const { return m_root; }
+    bool is_root() const { return m_is_root; }
 
     //! The key that identifies an NCCL clique.
     //! Operators with same keys belong to the same clique.
@@ -108,12 +110,13 @@ private:
 
     std::shared_ptr<GroupClient> m_group_client;
     size_t m_nr_devices = 0;
-    uint32_t m_rank;
+    bool m_is_root;
+    int m_rank;
     std::string m_key;
     //! XXHash generated from m_key
     size_t m_hash;
     //! root of BROADCAST and REDUCE operation
-    uint32_t m_root;
+    int m_root;
     //! rank of root of BROADCAST and REDUCE operation
     Maybe<TensorShape> m_broadcast_output_shape = None;
     // Whether shape infer is enabled. This is only used by BROADCAST operation,

src/opr-mm/include/megbrain/opr/group_manager.h

@@ -24,12 +24,13 @@ namespace opr {
 class GroupInfo {
     public:
         struct OprInfo {
-            uint32_t rank;
-            uintptr_t stream; 
+            uint64_t comp_node_hash;
+            bool is_root;
+            int rank;
         };
 
         void add_opr(const std::string& key, size_t nr_expected_devices,
-                uint32_t graph_id, uintptr_t stream);
+                bool is_root, int rank, uint64_t comp_node_hash);
 
         void set_output_shape(const std::string& key, const TensorShape& shape);
 
@@ -37,15 +38,25 @@ class GroupInfo {
 
         void clear();
 
-        const std::vector<OprInfo>& opr_infos() const {return m_opr_infos; }
+        const std::vector<OprInfo>& opr_infos() const { return m_opr_infos; }
+
+        int get_root_rank() const { return m_root_rank; }
+        int get_rank(uint64_t hash) const { return m_rank_map.at(hash); }
+        uint64_t get_group_hash() const { return m_hash; }
 
     private:
+        void sort_opr_infos();
+        void gen_infos_from_opr_infos();
+
         std::vector<OprInfo> m_opr_infos;
+        std::unordered_map<uint64_t, int> m_rank_map;
+        uint64_t m_hash;
         uint32_t m_nr_registered_devs;
         uint32_t m_nr_expected_devs;
         Maybe<TensorShape> m_output_shape;
 
         uint32_t m_count = 0;
+        int m_root_rank = -1;
         std::mutex m_group_mtx;
         std::condition_variable m_register_cv;
         std::condition_variable m_clear_cv;
@@ -61,10 +72,16 @@ class GroupManager {
     public:
         ~GroupManager() = default;
 
+        struct RegisterInfo
+        {
+            uint64_t hash;
+            int rank, root_rank;
+        };
+
         //! register oprs' info to server, return deduplicated hash
-        uint64_t opr_register(const std::string& key, size_t nr_devices, uint32_t rank,
-                uintptr_t stream);
-    
+        RegisterInfo opr_register(const std::string& key, size_t nr_devices,
+                                  bool is_root, int rank, uint64_t comp_node_hash);
+
         //! gather uids from all ranks
         std::vector<std::string> gather_uid(const std::string& uid,
                 const std::string& key, uint32_t size, uint32_t rank);
@@ -80,9 +97,6 @@ class GroupManager {
 
     private:
         GroupInfo& get_group(const std::string& key);
-
-        uint64_t get_hash_key(const std::vector<GroupInfo::OprInfo>& _infos,
-                uint32_t rank);
     
         //! key -> group info.
         std::unordered_map<std::string, GroupInfo> m_key2group_info;
@@ -112,9 +126,11 @@ class GroupClient {
         virtual ~GroupClient() = default;
     
     public:
-        virtual uint64_t opr_register(const std::string& key, size_t nr_devices,
-                uint32_t rank, uintptr_t stream) = 0;
-    
+        virtual GroupManager::RegisterInfo opr_register(const std::string& key,
+                                                        size_t nr_devices,
+                                                        bool is_root, int rank,
+                                                        uint64_t comp_node_hash) = 0;
+
         virtual std::vector<std::string> gather_uid(const std::string& uid,
                 const std::string& key, uint32_t size, uint32_t rank) = 0;
     

src/opr-mm/include/megbrain/opr/mm_handler.h

@@ -14,6 +14,7 @@
 #if MGB_ENABLE_OPR_MM
 
 #include "megbrain/opr/collective_comm.h"
+#include "megbrain/opr/group_manager.h"
 
 using namespace mgb;
 using namespace opr;
@@ -31,8 +32,10 @@ public:
     GroupClientProxy(const std::string& server_addr);
 
     //! graph registration, assign graph_id to worker.
-    uint64_t opr_register(const std::string& key, size_t nr_devices, uint32_t rank,
-        uintptr_t stream) override;
+    GroupManager::RegisterInfo opr_register(const std::string& key,
+                                            size_t nr_devices, bool is_root,
+                                            int rank,
+                                            uint64_t comp_node_hash) override;
 
     std::vector<std::string> gather_uid(const std::string& uid,
             const std::string& key, uint32_t size, uint32_t rank) override;

src/opr-mm/proto/mm_handler.proto

@@ -4,13 +4,16 @@ package mm_handler;
 
 message OprRegisterRequest {
     string key = 1;
-    uint32 rank = 2;
-    uint64 stream = 3;
-    uint32 nr_expected_devices = 4;
+    bool is_root = 2;
+    int32 rank = 3;
+    uint64 comp_node_hash = 4;
+    uint32 nr_expected_devices = 5;
 }
 
 message OprRegisterResponse {
-    uint64 hash = 1; 
+    uint64 hash = 1;
+    int32 rank = 2;
+    int32 root_rank = 3;
 }
 
 message GatherUidRequest {

src/opr-mm/test/collective_comm.cpp

@@ -45,9 +45,11 @@ class MockGroupClient final : public opr::GroupClient {
     public:
         ~MockGroupClient() override = default;
 
-        uint64_t opr_register(const std::string& key, size_t nr_devices, uint32_t rank,
-                uintptr_t stream) {
-            return m_mgr.opr_register(key, nr_devices, rank, stream);
+        opr::GroupManager::RegisterInfo opr_register(const std::string& key,
+                                                     size_t nr_devices,
+                                                     bool is_root, int rank,
+                                                     uintptr_t stream) {
+            return m_mgr.opr_register(key, nr_devices, is_root, rank, stream);
         }
 
         std::vector<std::string> gather_uid(const std::string& uid,
@@ -94,9 +96,9 @@ TEST(TestOprCollectiveComm, AllReduce) {
         auto x1c = opr::Copy::make(x1, cn1);
     
         auto y0 = opr::CollectiveComm::make({x0}, graph.get(), "all_reduce",
-                2, 0, 0, client, {mode}, dtype::Float32(), "nccl")[0];
+                2, false, 0, client, {mode}, dtype::Float32(), "nccl")[0];
         auto y1 = opr::CollectiveComm::make({x1c}, graph.get(), "all_reduce",
-                2, 1, 0, client, {mode}, dtype::Float32(), "nccl")[0];
+                2, false, 1, client, {mode}, dtype::Float32(), "nccl")[0];
         auto y_expect = make_all_reduce_output(mode, {x0, x1});
     
         auto func = graph->compile({make_callback_copy(y0, host_y0),
@@ -130,7 +132,7 @@ TEST(TestOprCollectiveComm, AllReduceMultiThread) {
             auto graph0 = ComputingGraph::make();
             auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0);
             auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "all_reduce",
-                    2, 0, 0, client, {mode}, dtype::Float32(), "nccl")[0];
+                    2, false, 0, client, {mode}, dtype::Float32(), "nccl")[0];
             auto func0 = graph0->compile({make_callback_copy(y0, host_y0)});
             func0->execute();
         };
@@ -139,7 +141,7 @@ TEST(TestOprCollectiveComm, AllReduceMultiThread) {
             auto graph1 = ComputingGraph::make();
             auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
             auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "all_reduce",
-                    2, 1, 0, client, {mode}, dtype::Float32(), "nccl")[0];
+                    2, false, 1, client, {mode}, dtype::Float32(), "nccl")[0];
             auto func1 = graph1->compile({make_callback_copy(y1, host_y1)});
             func1->execute();
         };
@@ -192,7 +194,7 @@ TEST(TestOprCollectiveComm, AllReduceWithGrad) {
         graph0->options().graph_opt_level = 0;
 
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
-        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "all_reduce", 2, 0, 0, client,
+        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "all_reduce", 2, false, 0, client,
                 {Mode::ALL_REDUCE_SUM}, dtype::Float32(), "nccl")[0];
         y0.node()->owner_opr()->node_prop().attribute().priority = -1;
 
@@ -211,7 +213,7 @@ TEST(TestOprCollectiveComm, AllReduceWithGrad) {
         graph1->options().graph_opt_level = 0;
 
         auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
-        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "all_reduce", 2, 1, 0, client,
+        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "all_reduce", 2, false, 1, client,
                 {Mode::ALL_REDUCE_SUM}, dtype::Float32(), "nccl")[0];
         y1.node()->owner_opr()->node_prop().attribute().priority = -1;
 
@@ -274,9 +276,9 @@ TEST(TestOprCollectiveComm, AllGather) {
     auto x1c = opr::Copy::make(x1, cn1);
 
     auto y0 = opr::CollectiveComm::make({x0}, graph.get(), "all_gather",
-            2, 0, 0, client, {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
+            2, false, 0, client, {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
     auto y1 = opr::CollectiveComm::make({x1c}, graph.get(), "all_gather",
-            2, 1, 0, client, {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
+            2, false, 1, client, {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
     auto y_expect = opr::Concat::make({x0, x1}, 0);
 
     auto func = graph->compile({make_callback_copy(y0, host_y0),
@@ -303,7 +305,7 @@ TEST(TestOprCollectiveComm, AllGatherMultiThread) {
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
-        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "all_gather", 2, 0, 0, client,
+        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "all_gather", 2, false, 0, client,
                 {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
         auto func0 = graph0->compile({make_callback_copy(y0, host_y0)});
         func0->execute();
@@ -312,7 +314,7 @@ TEST(TestOprCollectiveComm, AllGatherMultiThread) {
     auto run_1 = [&]() { // rank 1
         auto graph1 = ComputingGraph::make();
         auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
-        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "all_gather", 2, 1, 0, client,
+        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "all_gather", 2, false, 1, client,
                 {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
         auto func1 = graph1->compile({make_callback_copy(y1, host_y1)});
         func1->execute();
@@ -361,7 +363,7 @@ TEST(TestOprCollectiveComm, AllGatherWithGrad) {
         graph0->options().graph_opt_level = 0;
 
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
-        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "all_gather", 2, 0, 0, client,
+        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "all_gather", 2, false, 0, client,
                 {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
         y0.node()->owner_opr()->node_prop().attribute().priority = -1;
 
@@ -380,7 +382,7 @@ TEST(TestOprCollectiveComm, AllGatherWithGrad) {
         graph1->options().graph_opt_level = 0;
 
         auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
-        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "all_gather", 2, 1, 0, client,
+        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "all_gather", 2, false, 1, client,
                 {Mode::ALL_GATHER}, dtype::Float32(), "nccl")[0];
         y1.node()->owner_opr()->node_prop().attribute().priority = -1;
 
@@ -444,9 +446,9 @@ TEST(TestOprCollectiveComm, ReduceScatterSum) {
     auto x1c = opr::Copy::make(x1, cn1);
 
     auto y0 = opr::CollectiveComm::make({x0}, graph.get(), "reduce_scatter_sum",
-            2, 0, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
+            2, false, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
     auto y1 = opr::CollectiveComm::make({x1c}, graph.get(), "reduce_scatter_sum",
-            2, 1, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
+            2, false, 1, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
     auto y_expect = make_reduce_scatter_sum_output({x0, x1});
 
     auto func = graph->compile({make_callback_copy(y0, host_y0),
@@ -475,7 +477,7 @@ TEST(TestOprCollectiveComm, ReduceScatterSumMultiThread) {
         auto graph0 = ComputingGraph::make();
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
         auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "reduce_scatter_sum",
-                       2, 0, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
+                       2, false, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
         auto func0 = graph0->compile({make_callback_copy(y0, host_y0)});
         func0->execute();
     };
@@ -484,7 +486,7 @@ TEST(TestOprCollectiveComm, ReduceScatterSumMultiThread) {
         auto graph1 = ComputingGraph::make();
         auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
         auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "reduce_scatter_sum",
-                       2, 1, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
+                       2, false, 1, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
         auto func1 = graph1->compile({make_callback_copy(y1, host_y1)});
         func1->execute();
     };
@@ -534,7 +536,7 @@ TEST(TestOprCollectiveComm, ReduceScatterSumWithGrad) {
 
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
         auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "reduce_scatter_sum",
-                2, 0, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
+                2, false, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
         y0.node()->owner_opr()->node_prop().attribute().priority = -1;
 
         auto grad0 = opr::Host2DeviceCopy::make(*graph0, host_grad0, cn0);
@@ -553,7 +555,7 @@ TEST(TestOprCollectiveComm, ReduceScatterSumWithGrad) {
 
         auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
         auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "reduce_scatter_sum",
-                2, 1, 0, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
+                2, false, 1, client, {Mode::REDUCE_SCATTER_SUM}, dtype::Float32(), "nccl")[0];
         y1.node()->owner_opr()->node_prop().attribute().priority = -1;
 
         auto grad1 = opr::Host2DeviceCopy::make(*graph1, host_grad1, cn1);
@@ -616,9 +618,9 @@ TEST(TestOprCollectiveComm, ReduceSum) {
     auto x1c = opr::Copy::make(x1, cn1);
 
     auto y0 = opr::CollectiveComm::make({x0}, graph.get(), "reduce_sum",
-            2, 0, 0, client, {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
+            2, true, 0, client, {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
     auto y1 = opr::CollectiveComm::make({x1c}, graph.get(), "reduce_sum",
-            2, 1, 0, client, {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
+            2, false, 1, client, {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
     auto y_expect = x0 + x1;
 
     auto func = graph->compile({make_callback_copy(y0, host_y0),
@@ -644,7 +646,7 @@ TEST(TestOprCollectiveComm, ReduceSumMultiThread) {
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
-        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "reduce", 2, 0, 0, client,
+        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "reduce", 2, true, 0, client,
                 {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
         auto func0 = graph0->compile({make_callback_copy(y0, host_y0)});
         func0->execute();
@@ -653,7 +655,7 @@ TEST(TestOprCollectiveComm, ReduceSumMultiThread) {
     auto run_1 = [&]() { // rank 1
         auto graph1 = ComputingGraph::make();
         auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
-        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "reduce", 2, 1, 0, client,
+        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "reduce", 2, false, 1, client,
                 {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
         auto func1 = graph1->compile({{y1, nullptr}});
         func1->execute();
@@ -699,7 +701,7 @@ TEST(TestOprCollectiveComm, ReduceSumWithGrad) {
         graph0->options().graph_opt_level = 0;
 
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
-        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "reduce", 2, 0, 0, client,
+        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "reduce", 2, true, 0, client,
                 {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
         y0.node()->owner_opr()->node_prop().attribute().priority = -1;
 
@@ -718,7 +720,7 @@ TEST(TestOprCollectiveComm, ReduceSumWithGrad) {
         graph1->options().graph_opt_level = 0;
 
         auto x1 = opr::Host2DeviceCopy::make(*graph1, host_x1, cn1);
-        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "reduce", 2, 1, 0, client,
+        auto y1 = opr::CollectiveComm::make({x1}, graph1.get(), "reduce", 2, false, 1, client,
                 {Mode::REDUCE_SUM}, dtype::Float32(), "nccl")[0];
         y1.node()->owner_opr()->node_prop().attribute().priority = -1;
 
@@ -767,12 +769,12 @@ TEST(TestOprCollectiveComm, Broadcast) {
 
     auto x0 = opr::Host2DeviceCopy::make(*graph, host_x0, cn0);
     auto y0 = opr::CollectiveComm::make({x0}, graph.get(), "broadcast",
-            2, 0, 0, client, {Mode::BROADCAST}, dtype::Float32(), "nccl")[0];
+            2, true, 0, client, {Mode::BROADCAST}, dtype::Float32(), "nccl")[0];
     auto y_dev = std::make_shared<DeviceTensorND>(DeviceTensorND()
                                                   .comp_node(cn1)
                                                   .dtype(dtype::Float32())
                                                   .resize(host_x0->shape()));
-    auto y1 = opr::CollectiveComm::make({}, graph.get(), "broadcast", 2, 1, 0,
+    auto y1 = opr::CollectiveComm::make({}, graph.get(), "broadcast", 2, false, 1,
             client, {y_dev}, {Mode::BROADCAST}, dtype::Float32(), "nccl", {cn1})[0];
 
     auto func = graph->compile({make_callback_copy(y0, host_y0),
@@ -797,7 +799,7 @@ TEST(TestOprCollectiveComm, BroadcastMultiThread) {
     auto run_0 = [&]() { // rank 0
         auto graph0 = ComputingGraph::make();
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
-        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "broadcast", 2, 0, 0, client,
+        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "broadcast", 2, true, 0, client,
                 {Mode::BROADCAST}, dtype::Float32(), "nccl")[0];
         auto func0 = graph0->compile({make_callback_copy(y0, host_y0)});
         func0->execute();
@@ -809,7 +811,7 @@ TEST(TestOprCollectiveComm, BroadcastMultiThread) {
                                                       .comp_node(cn1)
                                                       .dtype(dtype::Float32())
                                                       .resize(host_x0->shape()));
-        auto y1 = opr::CollectiveComm::make({}, graph1.get(), "broadcast", 2, 1, 0, client,
+        auto y1 = opr::CollectiveComm::make({}, graph1.get(), "broadcast", 2, false, 1, client,
                 {y_dev}, {Mode::BROADCAST}, dtype::Float32(), "nccl", {cn1})[0];
         auto func1 = graph1->compile({make_callback_copy(y1, host_y1)});
         func1->execute();
@@ -845,7 +847,7 @@ TEST(TestOprCollectiveComm, BroadcastWithGrad) {
         graph0->options().graph_opt_level = 0;
 
         auto x0 = opr::Host2DeviceCopy::make(*graph0, host_x0, cn0);
-        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "broadcast", 2, 0, 0, client,
+        auto y0 = opr::CollectiveComm::make({x0}, graph0.get(), "broadcast", 2, true, 0, client,
                 {Mode::BROADCAST}, dtype::Float32(), "nccl")[0];
         y0.node()->owner_opr()->node_prop().attribute().priority = -1;
 
@@ -863,11 +865,11 @@ TEST(TestOprCollectiveComm, BroadcastWithGrad) {
         auto graph1 = ComputingGraph::make();
         graph1->options().graph_opt_level = 0;
 
-        auto y1 = opr::CollectiveComm::make({}, graph1.get(), "broadcast", 2, 1, 0, client,
+        auto y1 = opr::CollectiveComm::make({}, graph1.get(), "broadcast", 2, false, 1, client,
                 {Mode::BROADCAST}, dtype::Float32(), "nccl", {cn1})[0];
 
         auto grad1 = opr::Host2DeviceCopy::make(*graph1, host_grad1, cn1);
-        auto g = opr::CollectiveComm::make({grad1}, graph1.get(), "broadcast:grad", 2, 1, 0, client,
+        auto g = opr::CollectiveComm::make({grad1}, graph1.get(), "broadcast:grad", 2, false, 1, client,
                 Mode::REDUCE_SUM, dtype::Float32(), "nccl")[0];
         g.node()->owner_opr()->node_prop().attribute().priority = 1;
 

src/opr-mm/test/io_remote.cpp

@@ -26,11 +26,13 @@ class MockGroupClient final : public opr::GroupClient {
     public:
         ~MockGroupClient() override = default;
 
-        uint64_t opr_register(const std::string& key, size_t nr_devices, uint32_t rank,
-                uintptr_t stream) {
-            return m_mgr.opr_register(key, nr_devices, rank, stream);
+        opr::GroupManager::RegisterInfo opr_register(const std::string& key,
+                                                     size_t nr_devices,
+                                                     bool is_root, int rank,
+                                                     uint64_t comp_node_hash) {
+            return m_mgr.opr_register(key, nr_devices, is_root, rank, comp_node_hash);
         }
-        
+
         std::vector<std::string> gather_uid(const std::string& uid,
                 const std::string& key, uint32_t size, uint32_t rank) {
             return m_mgr.gather_uid(uid, key, size, rank);