!2499 HostAllGather and HostReduceScatter change to internal interface

Merge pull request !2499 from yihuaijie/master

mindspore/ccsrc/kernel/cpu/allgather_cpu_kernel.h

@@ -36,7 +36,7 @@ class AllGatherCPUKernel : public CPUKernel {
   std::vector<int> ranks_group_;
 };
 
-MS_REG_CPU_KERNEL(HostAllGather, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+MS_REG_CPU_KERNEL(_HostAllGather, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                   AllGatherCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/cpu/reduce_scatter_cpu_kernel.h

@@ -37,7 +37,7 @@ class ReduceScatterCPUKernel : public CPUKernel {
   std::vector<int> ranks_group_;
 };
 
-MS_REG_CPU_KERNEL(HostReduceScatter, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+MS_REG_CPU_KERNEL(_HostReduceScatter, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                   ReduceScatterCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/parallel/ops_info/ops_utils.h

@@ -145,7 +145,7 @@ constexpr char MIRROR_OPERATOR[] = "_MirrorOperator";
 constexpr char STRIDED_SLICE[] = "StridedSlice";
 constexpr char ALL_GATHER[] = "AllGather";
 constexpr char REDUCE_SCATTER[] = "ReduceScatter";
-constexpr char HOST_REDUCE_SCATTER[] = "HostReduceScatter";
+constexpr char HOST_REDUCE_SCATTER[] = "_HostReduceScatter";
 constexpr char EMBEDDING_LOOKUP[] = "EmbeddingLookup";
 constexpr char CONCAT[] = "Concat";
 constexpr char SOFTMAX_CROSS_ENTROPY_WITH_LOGITS[] = "SoftmaxCrossEntropyWithLogits";

mindspore/ccsrc/transform/convert.cc

@@ -55,9 +55,7 @@ const char kNameSimpleMeanGrad[] = "SimpleMeanGrad";
 const char kNameAllReduce[] = "AllReduce";
 const char kNameBroadcast[] = "Broadcast";
 const char kNameAllgather[] = "AllGather";
-const char kNameHostAllgather[] = "HostAllGather";
 const char kNameReduceScatter[] = "ReduceScatter";
-const char kNameHostReduceScatter[] = "HostReduceScatter";
 const char kNameReduceSum[] = "ReduceSum";
 const char kNameIsFinite[] = "isFinite";
 const char kNameReciprocal[] = "Reciprocal";

mindspore/ops/_grad/grad_comm_ops.py

@@ -18,9 +18,9 @@ import mindspore.common.dtype as mstype
 from mindspore.ops import functional as F
 from .. import operations as P
 from ..composite.multitype_ops.zeros_like_impl import zeros_like
-from ..operations.comm_ops import (AllGather, HostAllGather, AllReduce, _AlltoAll, Broadcast,
+from ..operations.comm_ops import (AllGather, _HostAllGather, AllReduce, _AlltoAll, Broadcast,
                                    _GetTensorSlice, _MirrorOperator, ReduceOp,
-                                   ReduceScatter, HostReduceScatter, _VirtualDiv)
+                                   ReduceScatter, _HostReduceScatter, _VirtualDiv)
 from .grad_base import bprop_getters
 
 
@@ -93,10 +93,10 @@ def get_bprop_all_gather(self):
     return bprop
 
 
-@bprop_getters.register(HostAllGather)
+@bprop_getters.register(_HostAllGather)
 def get_bprop_host_all_gather(self):
-    """Generate bprop for HostAllGather"""
-    host_all_gather_grad = HostReduceScatter(ReduceOp.SUM, self.group)
+    """Generate bprop for _HostAllGather"""
+    host_all_gather_grad = _HostReduceScatter(ReduceOp.SUM, self.group)
     if self.instance_name:
         instance_name = "grad" + self.instance_name
         host_all_gather_grad.set_prim_instance_name(instance_name)
@@ -126,10 +126,10 @@ def get_bprop_reduce_scatter(self):
     return bprop
 
 
-@bprop_getters.register(HostReduceScatter)
+@bprop_getters.register(_HostReduceScatter)
 def get_bprop_host_reduce_scatter(self):
-    """Generate bprop for HostReduceScatter"""
-    host_reduce_scatter_grad = HostAllGather(self.group)
+    """Generate bprop for _HostReduceScatter"""
+    host_reduce_scatter_grad = _HostAllGather(self.group)
     if self.instance_name:
         instance_name = "grad" + self.instance_name
         host_reduce_scatter_grad.set_prim_instance_name(instance_name)

mindspore/ops/operations/__init__.py

@@ -35,7 +35,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
 from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast,
                        _MirrorOperator, ReduceOp, _VirtualDataset,
                        _VirtualDiv, _GetTensorSlice,
-                       HostAllGather, HostReduceScatter)
+                       _HostAllGather, _HostReduceScatter)
 from .debug_ops import (ImageSummary, InsertGradientOf, HookBackward, ScalarSummary,
                         TensorSummary, HistogramSummary, Debug, Print)
 from .control_ops import ControlDepend, GeSwitch, Merge
@@ -244,10 +244,8 @@ __all__ = [
     'UnsortedSegmentSum',
     'UnsortedSegmentMin',
     "AllGather",
-    "HostAllGather",
     "AllReduce",
     "ReduceScatter",
-    "HostReduceScatter",
     "Broadcast",
     "ReduceOp",
     'ScalarCast',

mindspore/ops/operations/_grad_ops.py

@@ -1166,7 +1166,7 @@ class EmbeddingLookupCommGrad(PrimitiveWithInfer):
     Perform the gradient for the communication part of EmbeddingLookup operator.
 
     This works ONLY when 'reduce_scatter_flag' is True in 'EmbeddingLookup'. Roughly speaking,
-    this primitive is implemented by StridedSlice --> HostAllGather --> Concat. This primitive runs on host.
+    this primitive is implemented by StridedSlice --> _HostAllGather --> Concat. This primitive runs on host.
     """
     @prim_attr_register
     def __init__(self):
@@ -1177,8 +1177,8 @@ class EmbeddingLookupCommGrad(PrimitiveWithInfer):
         """
         This primitive is implemented by three steps:
             1) Split the 'dy' along dimension 0 into 'split_num' parts.
-            2) For each part, perform HostAllGather((0, 1, 2, 3, 4, 5, 6, 7)) on the host.
-            3) After HostAllGather, there are still 'split_num' parts in each process. Then, perform Concat on them
+            2) For each part, perform _HostAllGather((0, 1, 2, 3, 4, 5, 6, 7)) on the host.
+            3) After _HostAllGather, there are still 'split_num' parts in each process. Then, perform Concat on them
               along dimension 0.
 
         The output shape of this primitive: shape(output)[0] == shape(dy)[0] * 8

mindspore/ops/operations/comm_ops.py

@@ -176,13 +176,13 @@ class AllGather(PrimitiveWithInfer):
         raise NotImplementedError
 
 
-class HostAllGather(PrimitiveWithInfer):
+class _HostAllGather(PrimitiveWithInfer):
     """
     Gathers tensors from the specified communication group on host.
 
     Note:
         Tensor must have the same shape and format in all processes participating in the collective.
-        HostAllGather is a host-side operator, it depends on OpenMPI and must use build option -M on
+        _HostAllGather is a host-side operator, it depends on OpenMPI and must use build option -M on
         to enable it. Using mpirun command to run it:
         mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_host_all_gather.py
 
@@ -199,27 +199,6 @@ class HostAllGather(PrimitiveWithInfer):
     Outputs:
         Tensor. If the number of devices in the group is N,
         then the shape of output is :math:`(N, x_1, x_2, ..., x_R)`.
-
-    Examples:
-        >>> import mindspore.nn as nn
-        >>> import mindspore.context as context
-        >>> import mindspore.ops.operations as P
-        >>> from mindspore import Tensor
-        >>>
-        >>> context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
-        >>> context.set_mpi_config(enable_mpi=True)
-        >>>
-        >>> class Net(nn.Cell):
-        >>>     def __init__(self):
-        >>>         super(Net, self).__init__()
-        >>>         self.hostallgather = P.HostAllGather(group=(0, 1, 2, 3))
-        >>>
-        >>>     def construct(self, x):
-        >>>         return self.hostallgather(x)
-        >>>
-        >>> input_ = Tensor(np.ones([2, 8]).astype(np.float32))
-        >>> net = Net()
-        >>> output = net(input_)
     """
 
     @prim_attr_register
@@ -308,13 +287,13 @@ class ReduceScatter(PrimitiveWithInfer):
         raise NotImplementedError
 
 
-class HostReduceScatter(PrimitiveWithInfer):
+class _HostReduceScatter(PrimitiveWithInfer):
     """
     Reduces and scatters tensors from the specified communication group on host.
 
     Note:
         Tensor must have the same shape and format in all processes participating in the collective.
-        HostReduceScatter is a host-side operator, it depends on OpenMPI and must use build option
+        _HostReduceScatter is a host-side operator, it depends on OpenMPI and must use build option
         -M on to enable it. Using mpirun command to run it:
         mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_host_reduce_scatter.py
 
@@ -328,28 +307,6 @@ class HostReduceScatter(PrimitiveWithInfer):
                    or elements of group are not int.
         ValueError: If the first dimension of input can not be divided by group size,
                     or group is not set, or rank_id not in [0, 7].
-
-    Examples:
-        >>> import mindspore.nn as nn
-        >>> import mindspore.context as context
-        >>> import mindspore.ops.operations as P
-        >>> from mindspore import Tensor
-        >>> from mindspore.ops.operations.comm_ops import ReduceOp
-        >>>
-        >>> context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
-        >>> context.set_mpi_config(enable_mpi=True)
-        >>>
-        >>> class Net(nn.Cell):
-        >>>     def __init__(self):
-        >>>         super(Net, self).__init__()
-        >>>         self.hostreducescatter = P.HostReduceScatter(ReduceOp.SUM, group=[0, 1, 2, 3])
-        >>>
-        >>>     def construct(self, x):
-        >>>         return self.hostreducescatter(x)
-        >>>
-        >>> input_ = Tensor(np.ones([8, 8]).astype(np.float32))
-        >>> net = Net()
-        >>> output = net(input_)
     """
     @prim_attr_register
     def __init__(self, op=ReduceOp.SUM, group=None):

tests/st/ops/cpu/test_reduce_scatter.py

@@ -1,76 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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 numpy as np
-import pytest
-
-import mindspore.context as context
-import mindspore.nn as nn
-from mindspore import Tensor
-from mindspore.common import dtype as mstype
-from mindspore.ops import operations as P
-import mindspore._ms_mpi as mpi
-# run comand:
-# mpirun -output-filename log -merge-stderr-to-stdout -np 3 python test_reduce_scatter.py
-
-context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
-context.set_mpi_config(enable_mpi=True)
-
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.op = "sum"
-
-        self.reducescatter = P.HostReduceScatter(op=self.op, group=[0,1,2])
-
-    def construct(self, x):
-        return self.reducescatter(x)
-
-class AllGatherNet(nn.Cell):
-    def __init__(self):
-        super(AllGatherNet, self).__init__()
-        self.hostallgather = P.HostAllGather(group=(0, 1, 2))
-
-    def construct(self, x):
-        return self.hostallgather(x)  
-
-def test_net_reduce_scatter():
-    x = np.arange(12).astype(np.float32) * 0.1
-    
-    reducescatter = Net()
-    rankid = mpi.get_rank_id()
-    print("self rankid:", rankid)
-    output = reducescatter(Tensor(x, mstype.float32))
-    print("output:\n", output)
-    if rankid == 0:
-        expect_result = np.arange(4).astype(np.float32) * 0.3
-    if rankid == 1:
-        expect_result = np.arange(4, 8).astype(np.float32) * 0.3
-    if rankid == 2:
-        expect_result = np.arange(8, 12).astype(np.float32) * 0.3
-    diff = abs(output.asnumpy() - expect_result)
-    error = np.ones(shape=expect_result.shape) * 1.0e-6
-    assert np.all(diff < error)
-
-    allgather = AllGatherNet()
-    allgather_output = allgather(output)
-    print("allgather result:\n", allgather_output)
-    expect_allgather_result =  np.arange(12).astype(np.float32) * 0.3
-    diff = abs(allgather_output.asnumpy() - expect_allgather_result)
-    error = np.ones(shape=expect_allgather_result.shape) * 1.0e-6
-    assert np.all(diff < error)
-
-if __name__ == '__main__':
-    test_net_reduce_scatter()

tests/ut/python/communication/test_comm.py

@@ -26,7 +26,6 @@ from mindspore.nn import Momentum
 from mindspore.nn import ReLU
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.ops.operations.comm_ops import AllReduce, AllGather, _AlltoAll, ReduceOp, ReduceScatter
-from mindspore.ops.operations.comm_ops import HostAllGather, HostReduceScatter
 from mindspore.ops.operations.comm_ops import Broadcast
 
 # pylint: disable=W0212
@@ -87,21 +86,6 @@ class AllGatherNet(nn.Cell):
         return self.relu(x)
 
 
-class HostAllGatherNet(nn.Cell):
-    """HostAllGatherNet definition"""
-
-    def __init__(self, input_channel, output_channel):
-        super(HostAllGatherNet, self).__init__()
-        self.dense = Dense(input_channel, output_channel)
-        self.hostallgather = HostAllGather((0, 1))
-        self.relu = ReLU()
-
-    def construct(self, x):
-        x = self.dense(x)
-        x = self.hostallgather(x)
-        return self.relu(x)
-
-
 class ReduceScatterNet(nn.Cell):
     """ReduceScatterNet definition"""
 
@@ -117,21 +101,6 @@ class ReduceScatterNet(nn.Cell):
         return self.relu(x)
 
 
-class HostReduceScatterNet(nn.Cell):
-    """HostReduceScatterNet definition"""
-
-    def __init__(self, input_channel, out_channel, op):
-        super(HostReduceScatterNet, self).__init__()
-        self.dense = Dense(input_channel, out_channel)
-        self.hostreducescatter = HostReduceScatter(op, (0, 1))
-        self.relu = ReLU()
-
-    def construct(self, x):
-        x = self.dense(x)
-        x = self.hostreducescatter(x)
-        return self.relu(x)
-
-
 class AlltoAllNet(nn.Cell):
     """AlltoAllNet definition"""
 
@@ -185,21 +154,6 @@ def test_allgather():
     _executor.compile(network, input_tensor, label_tensor)
 
 
-def test_hostallgather():
-    """test_hostallgather"""
-    context.set_context(mode=context.GRAPH_MODE)
-    input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
-    label_tensor = Tensor(np.array([[1.2], [2.2], [3.2], [4.2]], dtype=np.float32))
-    network = HostAllGatherNet(2, 1)
-    loss_fn = nn.SoftmaxCrossEntropyWithLogits()
-    optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
-                         learning_rate=0.1,
-                         momentum=0.9)
-    network = WithLossCell(network, loss_fn)
-    network = TrainOneStepCell(network, optimizer)
-    _executor.compile(network, input_tensor, label_tensor)
-
-
 def run_reducescatter(op):
     """run_reducescatter"""
     context.set_context(mode=context.GRAPH_MODE)
@@ -221,21 +175,6 @@ def test_reducescatter():
     run_reducescatter(ReduceOp.SUM)
 
 
-def test_hostreducescatter():
-    """test_hostreducescatter"""
-    context.set_context(mode=context.GRAPH_MODE)
-    input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
-    label_tensor = Tensor(np.array([[1.2]], dtype=np.float32))
-    network = HostReduceScatterNet(2, 1, ReduceOp.SUM)
-    loss_fn = nn.SoftmaxCrossEntropyWithLogits()
-    optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
-                         learning_rate=0.1,
-                         momentum=0.9)
-    network = WithLossCell(network, loss_fn)
-    network = TrainOneStepCell(network, optimizer)
-    _executor.compile(network, input_tensor, label_tensor)
-
-
 def test_broadcast():
     """test_broadcast"""
     context.set_context(mode=context.GRAPH_MODE)