Topo service

bluefog/torch/__init__.py

@@ -14,10 +14,6 @@
 # limitations under the License.
 # ==============================================================================
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import collections
 import os
 import torch
@@ -30,10 +26,10 @@
     DistributedWinPutOptimizer,
     DistributedAllreduceOptimizer,
     DistributedNeighborAllreduceOptimizer,
-    DistributedHierarchicalNeighborAllreduceOptimizer
+    DistributedHierarchicalNeighborAllreduceOptimizer,
 )
 
-check_extension('bluefog.torch', __file__, 'mpi_lib')
+check_extension("bluefog.torch", __file__, "mpi_lib")
 
 from bluefog.torch.mpi_ops import init, shutdown
 from bluefog.torch.mpi_ops import size, local_size, rank, local_rank
@@ -74,4 +70,38 @@
 
 from bluefog.torch.mpi_ops import timeline_start_activity, timeline_end_activity
 from bluefog.torch.mpi_ops import timeline_context
-from bluefog.torch.utility import broadcast_optimizer_state, broadcast_parameters, allreduce_parameters
+
+from bluefog.torch.utility import (
+    broadcast_optimizer_state,
+    broadcast_parameters,
+    allreduce_parameters,
+)
+
+from bluefog.common.topology_util import (
+    GetRecvWeights,
+    GetSendWeights,
+    IsRegularGraph,
+    IsTopologyEquivalent,
+)
+
+from bluefog.common.topology_util import (
+    ExponentialTwoGraph,
+    ExponentialGraph,
+    FullyConnectedGraph,
+    MeshGrid2DGraph,
+    RingGraph,
+    StarGraph,
+    SymmetricExponentialGraph,
+)
+
+from bluefog.common.topology_util import (
+    GetDynamicOnePeerSendRecvRanks,
+    GetExp2DynamicSendRecvMachineRanks,
+    GetInnerOuterRingDynamicSendRecvRanks,
+    GetInnerOuterExpo2DynamicSendRecvRanks,
+)
+
+from bluefog.torch.topology_util import (
+    InferSourceFromDestinationRanks,
+    InferDestinationFromSourceRanks,
+)

bluefog/torch/topology_util.py

@@ -0,0 +1,108 @@
+from typing import Any, List, Optional, Tuple, Union
+import collections
+
+import numpy as np
+import torch
+import bluefog.torch as bf
+
+
+def _check_ranks(rank_list: List[Any], self_rank: int, size: int) -> [bool, str]:
+    for rank in rank_list:
+        if not isinstance(rank, int):
+            return False, "contain element that is not integer."
+        if (rank < 0) or (rank >= size):
+            return False, "contain element that is not between 0 and size-1."
+    if len(set(rank_list)) != len(rank_list):
+        return False, "contain duplicated elements."
+    if self_rank in rank_list:
+        return False, "contain self rank."
+    return True, ""
+
+
+def InferSourceFromDestinationRanks(
+    dst_ranks: List[int], construct_adjacency_matrix: bool = False,
+) -> Union[List[int], Tuple[List[int], np.array]]:
+    """Infer the source ranks from destination ranks. This is collective communication call.
+
+    Args:
+        dst_ranks: A list of destination ranks.
+        construct_adjacency_matrix: If true, adjacency matrix will be return instead.
+            Element w_{ij} represents the weights sending from node i to node j.
+            We use column normalized style, i.e. the sum of receiving weight is 1.
+
+    Raises:
+        ValueError: If dst_ranks or src_ranks does not contain integer from 0 to size-1.
+
+    Returns:
+        If construct_adjacency_matrix is false, returns the source ranks list.
+        If construct_adjacency_matrix is true, returns the the source ranks list
+        and a 2-D numpy array.
+    """
+    is_valid, error_msg = _check_ranks(dst_ranks, bf.rank(), bf.size())
+    assert is_valid, f"The format of dst_ranks is wrong: {error_msg}"
+    return _infer_topo(
+        dst_ranks,
+        transpose=False,
+        construct_adjacency_matrix=construct_adjacency_matrix,
+    )
+
+
+def InferDestinationFromSourceRanks(
+    src_ranks: List[int], construct_adjacency_matrix: bool = False,
+) -> Union[List[int], np.array]:
+    """Infer the destination ranks from source ranks. This is collective communication call.
+
+    Args:
+        src_ranks: A list of destination ranks.
+        construct_adjacency_matrix: If true, adjacency matrix will be return instead.
+            Element w_{ij} represents the weights sending from node i to node j.
+            We use column normalized style, i.e. the sum of receiving weight is 1.
+
+    Raises:
+        ValueError: If dst_ranks or src_ranks does not contain integer from 0 to size-1.
+
+    Returns:
+        If construct_adjacency_matrix is false, returns the destination ranks list.
+        If construct_adjacency_matrix is true, returns the the sodestinationrce ranks
+        list and a 2-D numpy array.
+    """
+    is_valid, error_msg = _check_ranks(src_ranks, bf.rank(), bf.size())
+    assert is_valid, f"The format of src_ranks is wrong: {error_msg}"
+    return _infer_topo(
+        src_ranks,
+        transpose=True,
+        construct_adjacency_matrix=construct_adjacency_matrix,
+    )
+
+
+def _infer_topo(
+    rank_list: List[int], transpose: bool, construct_adjacency_matrix: bool
+):
+    degree = len(rank_list)
+    all_degree_list = bf.allgather(torch.tensor([degree], dtype=torch.int32)).numpy()
+    all_rank_list = bf.allgather(torch.tensor(rank_list, dtype=torch.int32)).numpy()
+    adjacency_dict = dict()
+    displacement = 0
+    for i, degree in enumerate(all_degree_list):
+        adjacency_dict[i] = sorted(all_rank_list[displacement : displacement + degree])
+        displacement += degree
+
+    inv_adjacency_dict = collections.defaultdict(list)
+    for k, adj in adjacency_dict.items():
+        for v in adj:
+            inv_adjacency_dict[v].append(k)
+    return_list = inv_adjacency_dict.get(bf.rank())
+    if return_list is None:
+        return_list = []
+
+    if not construct_adjacency_matrix:
+        return return_list
+
+    # construct_adjacency_matrix
+    W = np.eye(bf.size())
+    for k, adj in adjacency_dict.items():
+        W[k, adj] = 1
+    if transpose:
+        W = W.T
+
+    return return_list, W / W.sum(axis=1)

bluefog/torch/utility.py

@@ -14,8 +14,10 @@
 # limitations under the License.
 # ==============================================================================
 
+from typing import Any, List, Optional
 import collections
 
+import numpy as np
 import torch
 import bluefog.torch as bf
 

test/torch_basics_test.py

@@ -28,8 +28,18 @@
 
 from common import mpi_env_rank_and_size
 import bluefog.torch as bf
-from bluefog.common.topology_util import ExponentialGraph, RingGraph, RingGraph
-from bluefog.common.topology_util import IsTopologyEquivalent
+from bluefog.torch import (
+    ExponentialGraph,
+    RingGraph,
+    StarGraph,
+    MeshGrid2DGraph,
+    FullyConnectedGraph,
+)
+from bluefog.torch import (
+    IsTopologyEquivalent,
+    InferDestinationFromSourceRanks,
+    InferSourceFromDestinationRanks,
+)
 
 warnings.filterwarnings("ignore", message="numpy.dtype size changed")
 warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
@@ -75,10 +85,12 @@ def test_set_topology_fail_with_win_create(self):
 
         if size == 1:
             expected_topology = nx.from_numpy_array(
-                np.array([[0.5]]), create_using=nx.DiGraph)
+                np.array([[0.5]]), create_using=nx.DiGraph
+            )
         elif size == 2:
             expected_topology = nx.from_numpy_array(
-                np.array([[0, 0.2], [0.2, 0]]), create_using=nx.DiGraph)
+                np.array([[0, 0.2], [0.2, 0]]), create_using=nx.DiGraph
+            )
         else:
             expected_topology = RingGraph(size)
 
@@ -96,10 +108,16 @@ def test_set_and_load_topology(self):
         bf.init()
         size = bf.size()
         if size == 4:
-            expected_topology = nx.DiGraph(np.array(
-                [[1/3., 1/3., 1/3., 0.], [0., 1/3., 1/3., 1/3.],
-                 [1/3., 0., 1/3., 1/3.], [1/3., 1/3., 0., 1/3.]]
-            ))
+            expected_topology = nx.DiGraph(
+                np.array(
+                    [
+                        [1 / 3.0, 1 / 3.0, 1 / 3.0, 0.0],
+                        [0.0, 1 / 3.0, 1 / 3.0, 1 / 3.0],
+                        [1 / 3.0, 0.0, 1 / 3.0, 1 / 3.0],
+                        [1 / 3.0, 1 / 3.0, 0.0, 1 / 3.0],
+                    ]
+                )
+            )
         elif size == 1:
             expected_topology = nx.DiGraph(np.array([[1.0]]))
         else:
@@ -113,37 +131,81 @@ def test_in_out_neighbors_expo2(self):
         rank = bf.rank()
         size = bf.size()
         assert bf.set_topology(ExponentialGraph(size))
-        in_neighobrs = bf.in_neighbor_ranks()
+        in_neighbors = bf.in_neighbor_ranks()
         out_neighbors = bf.out_neighbor_ranks()
 
         degree = int(np.ceil(np.log2(size)))
-        expected_in_neighbors = sorted([(rank - 2**i) %
-                                        size for i in range(degree)])
-        expected_out_neighbors = sorted([(rank + 2**i) %
-                                         size for i in range(degree)])
-        assert sorted(in_neighobrs) == expected_in_neighbors
+        expected_in_neighbors = sorted([(rank - 2 ** i) % size for i in range(degree)])
+        expected_out_neighbors = sorted([(rank + 2 ** i) % size for i in range(degree)])
+        assert sorted(in_neighbors) == expected_in_neighbors
         assert sorted(out_neighbors) == expected_out_neighbors
 
     def test_in_out_neighbors_biring(self):
         bf.init()
         rank = bf.rank()
         size = bf.size()
         assert bf.set_topology(RingGraph(size))
-        in_neighobrs = bf.in_neighbor_ranks()
+        in_neighbors = bf.in_neighbor_ranks()
         out_neighbors = bf.out_neighbor_ranks()
 
-        expected_in_neighbors = list(set(
-            map(lambda x: x % size, [rank - 1, rank + 1])))
-        expected_out_neighbors = list(set(
-            map(lambda x: x % size, [rank - 1, rank + 1])))
+        expected_in_neighbors = list(set(map(lambda x: x % size, [rank - 1, rank + 1])))
+        expected_out_neighbors = list(
+            set(map(lambda x: x % size, [rank - 1, rank + 1]))
+        )
 
         if size <= 1:
             expected_in_neighbors = []
             expected_out_neighbors = []
 
-        assert sorted(in_neighobrs) == expected_in_neighbors
+        assert sorted(in_neighbors) == expected_in_neighbors
         assert sorted(out_neighbors) == expected_out_neighbors
 
 
+@pytest.mark.parametrize(
+    "topo_func",
+    [ExponentialGraph, RingGraph, StarGraph, MeshGrid2DGraph, FullyConnectedGraph],
+)
+def test_infer_destination_from_source_ranks(topo_func):
+    bf.init()
+    size = bf.size()
+    bf.set_topology(topo_func(size))
+    topo = bf.load_topology()
+    in_neighbors = bf.in_neighbor_ranks()
+    out_neighbors = bf.out_neighbor_ranks()
+
+    # Make the W into average rule.
+    expected_W = (nx.to_numpy_array(topo) > 0).astype(float)
+    expected_W /= expected_W.sum(axis=0)
+
+    src_ranks, W = InferDestinationFromSourceRanks(
+        src_ranks=in_neighbors, construct_adjacency_matrix=True
+    )
+    assert sorted(src_ranks) == out_neighbors
+    np.testing.assert_allclose(W, expected_W)
+
+
+@pytest.mark.parametrize(
+    "topo_func",
+    [ExponentialGraph, RingGraph, StarGraph, MeshGrid2DGraph, FullyConnectedGraph],
+)
+def test_infer_source_from_destination_ranks(topo_func):
+    bf.init()
+    size = bf.size()
+    bf.set_topology(topo_func(size))
+    topo = bf.load_topology()
+    in_neighbors = bf.in_neighbor_ranks()
+    out_neighbors = bf.out_neighbor_ranks()
+
+    # Make the W into average rule.
+    expected_W = (nx.to_numpy_array(topo) > 0).astype(float)
+    expected_W /= expected_W.sum(axis=0)
+
+    dst_ranks, W = InferSourceFromDestinationRanks(
+        dst_ranks=out_neighbors, construct_adjacency_matrix=True
+    )
+    assert sorted(dst_ranks) == in_neighbors
+    np.testing.assert_allclose(W, expected_W)
+
+
 if __name__ == "__main__":
     unittest.main()