!21835 make alltoall and neighborexchange to be interface && revert pr 21395

Merge pull request !21835 from zhoufeng/revert-same-input-to-comm-op
This commit is contained in:
i-robot 2021-08-31 08:53:46 +00:00 committed by Gitee
commit 389f3a6b6c
47 changed files with 379 additions and 394 deletions

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@ -41,18 +41,15 @@ bool IsParameterOrValueNode(const AnfNodePtr &node) {
}
// NodeUsersMap, for node B input i use node A, it will be one item in map with key: A, and value: (B, i)
bool IsNodeOutPutUsedByOtherRealKernel(const AnfNodeIndexSet &node_users, const CNodePtr &known_user,
size_t known_index) {
bool IsNodeOutPutUsedByOtherRealKernel(const AnfNodeIndexSet &node_users) {
if (node_users.size() == 1) {
MS_LOG(INFO) << "This node only used once, no need to insert tensormove node.";
return false;
}
for (const auto &node_pair : node_users) {
auto &node = node_pair.first;
size_t idx = IntToSize(node_pair.second);
if (AnfAlgo::IsRealKernel(node) && !(known_user == node && known_index == idx)) {
MS_LOG(INFO) << "User " << node->DebugString() << " idx " << idx << " is real kernel and diff with known "
<< known_user->DebugString() << " idx " << known_index;
auto node = node_pair.first;
if (AnfAlgo::IsRealKernel(node) && !AnfAlgo::IsCommunicationOp(node)) {
MS_LOG(INFO) << "This node only used other real kernel: " << node->fullname_with_scope();
return true;
}
}
@ -61,13 +58,11 @@ bool IsNodeOutPutUsedByOtherRealKernel(const AnfNodeIndexSet &node_users, const
}
} // namespace
bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph, const CNodePtr &cur_node,
size_t input_idx) const {
bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph, const AnfNodePtr &input,
const CNodePtr &cur_node) const {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(cur_node);
auto input = cur_node->input(input_idx);
MS_EXCEPTION_IF_NULL(input);
MS_EXCEPTION_IF_NULL(cur_node);
if (IsPrimitiveCNode(cur_node, prim::kPrimReceive)) {
return false;
}
@ -99,7 +94,7 @@ bool InsertTensorMoveForHcclOp::NeedInsertTensorMove(const FuncGraphPtr &graph,
MS_LOG(EXCEPTION) << "node has no output in manager"
<< " trace: " << trace::DumpSourceLines(input);
}
if (IsNodeOutPutUsedByOtherRealKernel(iter->second, cur_node, input_idx)) {
if (IsNodeOutPutUsedByOtherRealKernel(iter->second)) {
return true;
}
}
@ -113,7 +108,7 @@ void InsertTensorMoveForHcclOp::InsertTensorMove(const FuncGraphPtr &graph, cons
std::vector<AnfNodePtr> new_inputs = {hccl_node->input(0)};
for (size_t i = 1; i < hccl_node->size(); ++i) {
auto input = hccl_node->input(i);
if (NeedInsertTensorMove(graph, hccl_node, i)) {
if (NeedInsertTensorMove(graph, input, hccl_node)) {
auto tensor_move = CreateTensorMoveOp(graph, input);
if (tensor_move == nullptr) {
MS_LOG(EXCEPTION) << "Create tensor_move op failed.";

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@ -32,7 +32,7 @@ class InsertTensorMoveForHcclOp : public PatternProcessPass {
private:
void InsertTensorMove(const FuncGraphPtr &graph, const CNodePtr &hccl_node) const;
bool NeedInsertTensorMove(const FuncGraphPtr &graph, const CNodePtr &cur_node, size_t input_idx) const;
bool NeedInsertTensorMove(const FuncGraphPtr &graph, const AnfNodePtr &input, const CNodePtr &cur_node) const;
KernelQueryPtr kernel_query_;
};
} // namespace opt

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@ -119,6 +119,7 @@ CNodePtr CreateAllToAllvNode(const FuncGraphPtr &graph, const CNodePtr &all_to_a
AnfAlgo::SetNodeAttr(kAttrSendRankIds, MakeValue<std::vector<int64_t>>(rank_ids), all_to_all_v);
AnfAlgo::SetNodeAttr(kAttrRecvRankIds, MakeValue<std::vector<int64_t>>(rank_ids), all_to_all_v);
AnfAlgo::SetNodeAttr(kAttrGroup, MakeValue<std::string>(group), all_to_all_v);
MS_LOG(INFO) << "Create AllToAllv success, split count " << split_count << ", rank size " << rank_size;
return all_to_all_v;
}

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@ -226,7 +226,7 @@ constexpr char DEVICE_NUM[] = "device_num";
constexpr char VIRTUAL_DIV[] = "_VirtualDiv";
constexpr char GET_TENSOR_SLICE[] = "_GetTensorSlice";
constexpr char SPLIT[] = "Split";
constexpr char ALL_TO_ALL[] = "_AlltoAll";
constexpr char ALL_TO_ALL[] = "AlltoAll";
constexpr char NEIGHBOREXCHANGE[] = "NeighborExchange";
constexpr char PERMUTE_BY_AXIS[] = "PermuteByAxis";
constexpr char CONCAT_BY_AXIS[] = "ConcatByAxis";

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@ -409,7 +409,7 @@ inline const PrimitivePtr kPrimSend = std::make_shared<Primitive>("Send");
inline const PrimitivePtr kPrimReceive = std::make_shared<Primitive>("Receive");
inline const PrimitivePtr kPrimAllReduce = std::make_shared<Primitive>("AllReduce");
inline const PrimitivePtr kPrimNeighborExchange = std::make_shared<Primitive>("NeighborExchange");
inline const PrimitivePtr kPrimAllToAll = std::make_shared<Primitive>("_AlltoAll");
inline const PrimitivePtr kPrimAllToAll = std::make_shared<Primitive>("AlltoAll");
inline const PrimitivePtr kPrimAllToAllv = std::make_shared<Primitive>("AllToAllv");
inline const PrimitivePtr kPrimAllSwap = std::make_shared<Primitive>("AllSwap");
inline const PrimitivePtr kPrimBroadcast = std::make_shared<Primitive>("Broadcast");

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@ -22,8 +22,8 @@ from mindspore.parallel._utils import _get_enable_parallel_optimizer
from .. import operations as P
from ...common.tensor import RowTensor
from ..composite.multitype_ops.zeros_like_impl import zeros_like
from ..operations.comm_ops import (AllGather, _MiniStepAllGather, _HostAllGather, AllReduce, _AlltoAll, Broadcast,
_GetTensorSlice, _MirrorOperator, _MirrorMiniStepOperator, ReduceOp,
from ..operations.comm_ops import (AllGather, _MiniStepAllGather, _HostAllGather, AllReduce, NeighborExchange, AlltoAll,
Broadcast, _GetTensorSlice, _MirrorOperator, _MirrorMiniStepOperator, ReduceOp,
ReduceScatter, _HostReduceScatter, _VirtualDiv, _VirtualAdd, AllSwap,
_VirtualAssignAdd, _VirtualAccuGrad, _MirrorMicroStepOperator, _MicroStepAllGather)
from .grad_base import bprop_getters
@ -357,10 +357,27 @@ def get_bprop_host_reduce_scatter(self):
return bprop
@bprop_getters.register(_AlltoAll)
@bprop_getters.register(NeighborExchange)
def get_bprop_neighborexchange(self):
"""Generate bprop for NeighborExchange."""
group = self.group
send_rank_ids = self.recv_rank_ids
recv_rank_ids = self.send_rank_ids
recv_shapes = self.send_shapes
send_shapes = self.recv_shapes
recv_type = self.recv_type
neighborexchange_grad = NeighborExchange(send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type, group)
def bprop(x, out, dout):
return (neighborexchange_grad(dout),)
return bprop
@bprop_getters.register(AlltoAll)
def get_bprop_all_to_all(self):
"""Generate bprop for AlltoAll."""
all_to_all_grad = _AlltoAll(self.split_count, self.concat_dim, self.split_dim, self.group)
all_to_all_grad = AlltoAll(self.split_count, self.concat_dim, self.split_dim, self.group)
if self.instance_name:
instance_name = "grad" + self.instance_name
all_to_all_grad.set_prim_instance_name(instance_name)

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@ -18,7 +18,6 @@ from .._grad.grad_base import get_bprop_fn
from . import grad_array_ops
from . import grad_inner_ops
from . import grad_nn_ops
from . import grad_comm_ops
from . import grad_math_ops
__all__ = ['get_bprop_fn']

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@ -1,35 +0,0 @@
# Copyright 2021 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.
# ============================================================================
"""Generate bprop for comm ops"""
from .._grad.grad_base import bprop_getters
from ..operations._inner_ops import NeighborExchange
@bprop_getters.register(NeighborExchange)
def get_bprop_neighborexchange(self):
"""Generate bprop for NeighborExchange."""
group = self.group
send_rank_ids = self.recv_rank_ids
recv_rank_ids = self.send_rank_ids
recv_shapes = self.send_shapes
send_shapes = self.recv_shapes
recv_type = self.recv_type
neighborexchange_grad = NeighborExchange(send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type, group)
def bprop(x, out, dout):
return (neighborexchange_grad(dout),)
return bprop

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@ -1,18 +1,19 @@
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@ -1,12 +1,12 @@
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0.1.0 MindSpore*1.4.1:×

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bprop.50:keep_probbprop.50:[CNode]52:2bprop.50:[CNode]52:2"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op36

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bprop.50:shape*
bprop.50:keep_prob*
bprop.50:out*
bprop.50:dout2
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bprop.50:dout2
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0.1.0 MindSpore*1.4.1:ø

0.1.0 MindSpore*1.4.1:Š
bprop.70:xbprop.70:[CNode]71bprop.70:[CNode]71"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op50


bprop.70:ybprop.70:[CNode]72bprop.70:[CNode]72"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op51
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bprop.70:xbprop.70:[CNode]71:1bprop.70:[CNode]71:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op50

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bprop.70:x*
bprop.70:y*
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bprop.70:dout2
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0.1.0 MindSpore*1.4.1:ø

0.1.0 MindSpore*1.4.1:Š
bprop.82:xbprop.82:[CNode]83bprop.82:[CNode]83"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op59


bprop.82:ybprop.82:[CNode]84bprop.82:[CNode]84"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op60
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bprop.82:xbprop.82:[CNode]83:1bprop.82:[CNode]83:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op59

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bprop.82:x*
bprop.82:y*
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bprop.82:dout2
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0.1.0 MindSpore*1.4.1:ø

0.1.0 MindSpore*1.4.1:Š
bprop.78:xbprop.78:[CNode]79bprop.78:[CNode]79"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op56


bprop.78:ybprop.78:[CNode]80bprop.78:[CNode]80"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op57
<EFBFBD>
bprop.78:[CNode]79
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bprop.78:[CNode]79:1
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bprop.78:x*
bprop.78:y*
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bprop.78:dout2
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0.1.0 MindSpore*1.4.1:ø

0.1.0 MindSpore*1.4.1:Š
bprop.63:xbprop.63:[CNode]64bprop.63:[CNode]64"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op45


bprop.63:ybprop.63:[CNode]65bprop.63:[CNode]65"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op46
<EFBFBD>
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bprop.63:[CNode]64:1
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bprop.63:x*
bprop.63:y*
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bprop.63:dout2
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0.1.0 MindSpore*1.4.1:°
b
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bprop.2:dout2
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0.1.0 MindSpore*1.4.1:Ë

0.1.0 MindSpore*1.4.1:×

bprop.29:xbprop.29:[CNode]30bprop.29:[CNode]30"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op20
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bprop.29:xbprop.29:[CNode]30:1bprop.29:[CNode]30:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op20
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bprop.29:x*
bprop.29:out*
bprop.29:dout2
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bprop.29:dout2
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0.1.0 MindSpore*1.4.1:ø

0.1.0 MindSpore*1.4.1:Š
bprop.90:xbprop.90:[CNode]91bprop.90:[CNode]91"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op65


bprop.90:ybprop.90:[CNode]92bprop.90:[CNode]92"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op66
<EFBFBD>
bprop.90:[CNode]91
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bprop.90:xbprop.90:[CNode]91:1bprop.90:[CNode]91:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op65

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bprop.90:[CNode]91:1
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bprop.90:x*
bprop.90:y*
bprop.90:out*
bprop.90:dout2
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bprop.90:dout2
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0.1.0 MindSpore*1.4.1:ø

0.1.0 MindSpore*1.4.1:Š
bprop.86:xbprop.86:[CNode]87bprop.86:[CNode]87"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op62


bprop.86:ybprop.86:[CNode]88bprop.86:[CNode]88"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op63
<EFBFBD>
bprop.86:[CNode]87
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bprop.86:xbprop.86:[CNode]87:1bprop.86:[CNode]87:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op62

bprop.86:ybprop.86:[CNode]88:2bprop.86:[CNode]88:2"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op63

bprop.86:[CNode]87:1
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bprop.86:x*
bprop.86:y*
bprop.86:out*
bprop.86:dout2
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bprop.86:dout2
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@ -1,18 +1,18 @@
0.1.0 MindSpore*1.4.1:

bprop.45:startbprop.45:[CNode]46bprop.45:[CNode]46"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op31
Š
bprop.45:stopbprop.45:[CNode]47bprop.45:[CNode]47"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op32

bprop.45:numbprop.45:[CNode]48bprop.45:[CNode]48"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op33

bprop.45:[CNode]46
bprop.45:[CNode]47
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0.1.0 MindSpore*1.4.1:Î
<EFBFBD>
bprop.45:startbprop.45:[CNode]46:1bprop.45:[CNode]46:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op31
Ž
bprop.45:stopbprop.45:[CNode]47:2bprop.45:[CNode]47:2"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op32
<EFBFBD>
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Ÿ
bprop.45:[CNode]46:1
bprop.45:[CNode]47:2
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bprop.45:start*
bprop.45:stop*
bprop.45:num*
bprop.45:out*
bprop.45:dout2
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bprop.45:dout2
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View File

@ -1,18 +1,19 @@
0.1.0 MindSpore*1.4.1:ø

0.1.0 MindSpore*1.4.1:Š
bprop.94:xbprop.94:[CNode]95bprop.94:[CNode]95"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op68


bprop.94:ybprop.94:[CNode]96bprop.94:[CNode]96"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op69
<EFBFBD>
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bprop.94:xbprop.94:[CNode]95:1bprop.94:[CNode]95:1"!S-Prim-hyper_map[zeros_like_leaf]:.Default/S-Prim-hyper_map[zeros_like_leaf]-op68

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bprop.94:[CNode]95:1
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bprop.94:x*
bprop.94:y*
bprop.94:out*
bprop.94:dout2
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bprop.94:dout2
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bprop.36:dout2
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@ -1,12 +1,12 @@
0.1.0 MindSpore*1.4.1:É

0.1.0 MindSpore*1.4.1:Õ
Š
bprop.10:xbprop.10:[CNode]11bprop.10:[CNode]11"!S-Prim-hyper_map[zeros_like_leaf]:-Default/S-Prim-hyper_map[zeros_like_leaf]-op7
l
bprop.10:[CNode]11bprop.10:[CNode]12bprop.10:[CNode]12"S-Prim-MakeTuple:Default/S-Prim-MakeTuple-op8bprop.10*
bprop.10:xbprop.10:[CNode]11:1bprop.10:[CNode]11:1"!S-Prim-hyper_map[zeros_like_leaf]:-Default/S-Prim-hyper_map[zeros_like_leaf]-op7
r
bprop.10:[CNode]11:1bprop.10:[CNode]12:2bprop.10:[CNode]12:2"S-Prim-MakeTuple:Default/S-Prim-MakeTuple-op8bprop.10*
bprop.10:x*
bprop.10:out*
bprop.10:dout2
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bprop.10:dout2
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@ -35,7 +35,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Stack, Unpack, Unsta
BatchToSpace, SpaceToBatchND, BatchToSpaceND, BroadcastTo, InplaceUpdate, ReverseSequence,
EmbeddingLookup, Unique, GatherD, Identity, Range, MaskedFill, MaskedSelect, SearchSorted,
TensorScatterMax, TensorScatterMin, TensorScatterSub)
from .comm_ops import (AllGather, AllReduce, _AlltoAll, AllSwap, ReduceScatter, Broadcast,
from .comm_ops import (AllGather, AllReduce, NeighborExchange, AlltoAll, AllSwap, ReduceScatter, Broadcast,
_MirrorOperator, _MirrorMiniStepOperator, _MiniStepAllGather, ReduceOp, _VirtualDataset,
_VirtualOutput, _VirtualDiv, _GetTensorSlice, _VirtualAdd, _VirtualAssignAdd, _VirtualAccuGrad,
_HostAllGather, _HostReduceScatter, _MirrorMicroStepOperator, _MicroStepAllGather)

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@ -492,36 +492,6 @@ class Receive(PrimitiveWithInfer):
return self.dtype
class NeighborExchange(Primitive):
"""
NeighborExchange is a collective operation.
NeighborExchange sends data from the local rank to ranks in the send_rank_ids,
as while receive data from recv_rank_ids.
Args:
send_rank_ids (list(int)): Ranks which the data is sent to.
recv_rank_ids (list(int)): Ranks which the data is received from.
recv_shapes (tuple(list(int))): Data shape which received from recv_rank_ids.
send_shapes (tuple(list(int))): Data shape which send to the send_rank_ids.
recv_type (type): Data type which received from recv_rank_ids
group (str):
"""
@prim_attr_register
def __init__(self, send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type,
group=GlobalComm.WORLD_COMM_GROUP):
self.init_prim_io_names(inputs=['x'], outputs=['output'])
self.send_rank_ids = send_rank_ids
self.recv_rank_ids = recv_rank_ids
self.recv_shapes = recv_shapes
self.send_shapes = send_shapes
self.recv_type = recv_type
def __call__(self, tensor):
raise NotImplementedError
class MatrixSetDiag(PrimitiveWithInfer):
r"""
Modifies the batched diagonal part of a batched tensor.

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@ -20,7 +20,7 @@ from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...communication.management import get_rank, get_group_size, GlobalComm, _get_group
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, PrimitiveWithCheck, prim_attr_register
from ..primitive import PrimitiveWithInfer, PrimitiveWithCheck, Primitive, prim_attr_register
from ...common.api import context
@ -613,7 +613,37 @@ class AllSwap(PrimitiveWithCheck):
return out
class _AlltoAll(PrimitiveWithInfer):
class NeighborExchange(Primitive):
"""
NeighborExchange is a collective operation.
NeighborExchange sends data from the local rank to ranks in the send_rank_ids,
as while receive data from recv_rank_ids.
Args:
send_rank_ids (list(int)): Ranks which the data is sent to.
recv_rank_ids (list(int)): Ranks which the data is received from.
recv_shapes (tuple(list(int))): Data shape which received from recv_rank_ids.
send_shapes (tuple(list(int))): Data shape which send to the send_rank_ids.
recv_type (type): Data type which received from recv_rank_ids
group (str):
"""
@prim_attr_register
def __init__(self, send_rank_ids, recv_rank_ids, recv_shapes, send_shapes, recv_type,
group=GlobalComm.WORLD_COMM_GROUP):
self.init_prim_io_names(inputs=['x'], outputs=['output'])
self.send_rank_ids = send_rank_ids
self.recv_rank_ids = recv_rank_ids
self.recv_shapes = recv_shapes
self.send_shapes = send_shapes
self.recv_type = recv_type
def __call__(self, tensor):
raise NotImplementedError
class AlltoAll(PrimitiveWithInfer):
"""
AlltoAll is a collective operation.

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@ -14,8 +14,7 @@
# ============================================================================
import mindspore as ms
from mindspore import context
from mindspore.ops.operations._inner_ops import NeighborExchange
from mindspore.ops.operations.comm_ops import _AlltoAll
from mindspore.ops.operations.comm_ops import AlltoAll, NeighborExchange
from mindspore.communication.management import GlobalComm, init
context.set_context(device_target="Ascend")
@ -44,9 +43,9 @@ def test_neighbor_exchange(tag):
return fns[tag]
def test_all_to_all(tag):
context.set_auto_parallel_context(device_num=8, global_rank=0)
context.set_auto_parallel_context(device_num=2, global_rank=0)
fns = FnDict()
altoall = _AlltoAll(split_count=8, split_dim=2, concat_dim=3)
altoall = AlltoAll(split_count=2, split_dim=2, concat_dim=3)
@fns
def before(x):
return altoall(x)

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@ -25,7 +25,7 @@ from mindspore.nn import Dense
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 AllReduce, AllGather, AlltoAll, ReduceOp, ReduceScatter
from mindspore.ops.operations.comm_ops import Broadcast, AllSwap
from mindspore.ops.operations.array_ops import Gather
import mindspore
@ -113,7 +113,7 @@ class AlltoAllNet(nn.Cell):
def __init__(self, input_channel, out_channel):
super(AlltoAllNet, self).__init__()
self.dense = Dense(input_channel, out_channel)
self.alltoall = _AlltoAll(1, 0, 1)
self.alltoall = AlltoAll(1, 0, 1)
self.relu = ReLU()
def construct(self, x):

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@ -25,7 +25,7 @@ from mindspore.common.parameter import Parameter
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim.momentum import Momentum
from mindspore.ops import operations as P
from mindspore.ops.operations.comm_ops import _AlltoAll
from mindspore.ops.operations.comm_ops import AlltoAll
from mindspore.parallel._utils import _reset_op_id
from mindspore.train import Model
from mindspore.context import ParallelMode
@ -130,7 +130,7 @@ def test_all_to_all_success():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=8, split_dim=2, concat_dim=3)
self.alltoallv = AlltoAll(split_count=8, split_dim=2, concat_dim=3)
def construct(self, x1):
out = self.alltoallv(x1)
@ -151,7 +151,7 @@ def test_all_to_all_invalid_split_count_value_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=7, split_dim=2, concat_dim=3)
self.alltoallv = AlltoAll(split_count=7, split_dim=2, concat_dim=3)
def construct(self, x1):
out = self.alltoallv(x1)
@ -173,7 +173,7 @@ def test_all_to_all_invalid_split_count_type_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=[8], split_dim=2, concat_dim=3)
self.alltoallv = AlltoAll(split_count=[8], split_dim=2, concat_dim=3)
def construct(self, x1):
out = self.alltoallv(x1)
@ -195,7 +195,7 @@ def test_all_to_all_invalid_split_dim_value_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=8, split_dim=4, concat_dim=3)
self.alltoallv = AlltoAll(split_count=8, split_dim=4, concat_dim=3)
def construct(self, x1):
out = self.alltoallv(x1)
@ -217,7 +217,7 @@ def test_all_to_all_invalid_split_dim_type_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=8, split_dim=(3,), concat_dim=3)
self.alltoallv = AlltoAll(split_count=8, split_dim=(3,), concat_dim=3)
def construct(self, x1):
out = self.alltoallv(x1)
@ -239,7 +239,7 @@ def test_all_to_all_invalid_concat_dim_value_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=8, split_dim=3, concat_dim=4)
self.alltoallv = AlltoAll(split_count=8, split_dim=3, concat_dim=4)
def construct(self, x1):
out = self.alltoallv(x1)
@ -261,7 +261,7 @@ def test_all_to_all_invalid_concat_dim_type_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=8, split_dim=3, concat_dim=([3],))
self.alltoallv = AlltoAll(split_count=8, split_dim=3, concat_dim=([3],))
def construct(self, x1):
out = self.alltoallv(x1)
@ -283,7 +283,7 @@ def test_all_to_all_invalid_split_count_cannot_be_divisible_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=3, split_dim=3, concat_dim=3)
self.alltoallv = AlltoAll(split_count=3, split_dim=3, concat_dim=3)
def construct(self, x1):
out = self.alltoallv(x1)
@ -305,7 +305,7 @@ def test_all_to_all_invalid_group_type_failed():
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.alltoallv = _AlltoAll(split_count=8, split_dim=3, concat_dim=3, group=3)
self.alltoallv = AlltoAll(split_count=8, split_dim=3, concat_dim=3, group=3)
def construct(self, x1):
out = self.alltoallv(x1)

View File

@ -21,7 +21,7 @@ import mindspore.nn as nn
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import TrainOneStepCell, Momentum
from mindspore.ops import operations as P
from mindspore.ops.operations._inner_ops import NeighborExchange
from mindspore.ops.operations.comm_ops import NeighborExchange
_w1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
_x1 = Tensor(np.ones([32, 16]), dtype=ms.float32)