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!5729 [AutoParallel]Add FuseBatchNormEx op 

Merge pull request !5729 from lichen/add_batchnormex_op
This commit is contained in:
mindspore-ci-bot 2020-09-04 11:17:22 +08:00 committed by Gitee
parent 680b6ded1e d22f506431
commit c064c01b6b
7 changed files with 86 additions and 28 deletions
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14
mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h
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@ -32,12 +32,13 @@ namespace parallel {
class GatherV2PInfo : public OperatorInfo {
public:
GatherV2PInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
const PrimitiveAttrs &attrs)
const PrimitiveAttrs &attrs, const std::string &replace_op_name = GATHERV2)
: OperatorInfo(name, inputs_shape, outputs_shape, attrs, std::make_shared<GatherV2PCost>()),
axis_(0),
bias_(0),
index_offset_(0),
slice_size_(0) {}
slice_size_(0),
replace_op_name_(replace_op_name) {}
~GatherV2PInfo() override = default;
Status Init(const StrategyPtr &strategy) override;
Status InitForCostModel(const StrategyPtr &strategy) override;
@ -69,10 +70,10 @@ class GatherV2PInfo : public OperatorInfo {
int32_t axis_;
std::string target_ = DEVICE;
std::string replace_op_name_ = GATHERV2;
int64_t bias_;
int64_t index_offset_;
int64_t slice_size_;
std::string replace_op_name_ = GATHERV2;
Shape out_dev_matrix_shape_;
Group group_;
bool manual_split_ = false;
@ -83,12 +84,9 @@ class GatherV2PInfo : public OperatorInfo {
class SparseGatherV2Info : public GatherV2PInfo {
public:
SparseGatherV2Info(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
const PrimitiveAttrs &attrs)
: GatherV2PInfo(name, inputs_shape, outputs_shape, attrs) {}
const PrimitiveAttrs &attrs, const std::string &replace_op_name = SPARSE_GATHERV2)
: GatherV2PInfo(name, inputs_shape, outputs_shape, attrs, replace_op_name) {}
~SparseGatherV2Info() override = default;
private:
std::string replace_op_name_ = SPARSE_GATHERV2;
};
class EmbeddingLookupInfo : public GatherV2PInfo {

1
mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h
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@ -197,6 +197,7 @@ constexpr char ARGMAXWITHVALUE[] = "ArgMaxWithValue";
constexpr char ARGMINWITHVALUE[] = "ArgMinWithValue";
constexpr char CONV2D[] = "Conv2D";
constexpr char FUSE_BATCH_NORM[] = "FusedBatchNorm";
constexpr char FUSE_BATCH_NORM_EX[] = "FusedBatchNormEx";
constexpr char BATCH_NORM[] = "BatchNorm";
constexpr char LAYER_NORM[] = "LayerNorm";
constexpr char POOLING[] = "Pooling";

2
mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc
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@ -263,7 +263,7 @@ bool IsSplittableOperator(const std::string &op_name) {
LOG, REDUCE_MEAN, REAL_DIV, SIGMOID, POW, MAXIMUM, MINIMUM, EQUAL, NOT_EQUAL, LOGICALNOT, GATHERV2, SQRT, CONCAT,
STRIDEDSLICE, GET_NEXT, CAST, NEG, SQUARE, BATCH_MATMUL, EXPAND_DIMS, SQUEEZE, SPARSE_GATHERV2, TILE, DROPOUT,
SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, SIGMOID_CROSS_ENTROPY_WITH_LOGITS, SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS,
EMBEDDING_LOOKUP};
EMBEDDING_LOOKUP, FUSE_BATCH_NORM_EX};
// clang-format on
auto iter = splittable_op.find(op_name);

3
mindspore/ccsrc/frontend/parallel/step_parallel.cc
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@ -570,8 +570,7 @@ std::vector<AnfNodePtr> ReplaceOpInput(const Operator &replace_op, const std::st
MS_LOG(EXCEPTION) << "Failure: " << node->ToString() << " size is smaller than 2";
}
std::vector<AnfNodePtr> replace_input = {NewValueNode(pyop_instance), node->input(1)};
auto prim = GetValueNode<PrimitivePtr>(node->input(0));
if (prim->name() == EMBEDDING_LOOKUP) {
if (replace_op.first == EMBEDDING_LOOKUP) {
replace_input = {NewValueNode(pyop_instance), node->input(1), node->input(2)};
}
if (!params.empty()) {

2
mindspore/ccsrc/utils/comm_manager.cc
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@ -40,7 +40,7 @@ CommManager &CommManager::GetInstance() noexcept {
#define HCCL_RUN_CHECK(op_name, group, op) \
do { \
auto hccl_result = (op); \
if (hccl_result != tagHcclResult::HCCL_SUCCESS) { \
if (hccl_result != 0) { \
MS_LOG(ERROR) << op_name << " failed: #" << group << "#"; \
return false; \
} \

76
tests/ut/python/parallel/test_batchnorm_ex_batch_parallel.py Normal file
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@ -0,0 +1,76 @@
# 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 mindspore as ms
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.parameter import Parameter
from mindspore.ops import composite as C
from mindspore.ops import operations as P
import mindspore.nn as nn
from tests.ut.python.ops.test_math_ops import VirtualLoss
grad_all = C.GradOperation(get_all=True)
class NetWithLoss(nn.Cell):
def __init__(self, network):
super(NetWithLoss, self).__init__()
self.loss = VirtualLoss()
self.network = network
def construct(self, x, y, b):
predict = self.network(x, y, b)
return self.loss(predict)
class GradWrap(nn.Cell):
def __init__(self, network):
super(GradWrap, self).__init__()
self.network = network
def construct(self, x, y, b):
return grad_all(self.network)(x, y, b)
# model_parallel test
def test_two_matmul_batchnorm_ex():
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul1 = P.MatMul().set_strategy(strategy1)
self.norm = P.FusedBatchNormEx()
self.gamma = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="gamma")
self.beta = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="beta")
self.mean = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="mean")
self.var = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="var")
self.matmul2 = P.MatMul().set_strategy(strategy2)
def construct(self, x, y, b):
out = self.matmul1(x, y)
out = self.norm(out, self.gamma, self.beta, self.mean, self.var)[0]
out = self.matmul2(out, b)
return out
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8)
strategy1 = ((4, 2), (2, 1))
strategy2 = ((1, 8), (8, 1))
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
net.set_auto_parallel()
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
b = Tensor(np.ones([64, 64]), dtype=ms.float32)
_executor.compile(net, x, y, b)

16
tests/ut/python/parallel/test_sparse_gather_v2.py
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@ -13,7 +13,6 @@
# limitations under the License.
# ============================================================================
import numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
@ -158,18 +157,6 @@ def test_gatherv2_semi_auto7():
_executor.compile(net, x, y)
def test_gatherv2_semi_auto8():
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
strategy1 = ((8,), (1, 1))
strategy2 = ((4, 2), (4, 2))
net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
net.set_auto_parallel()
x = Tensor(np.ones([64]), dtype=ms.float32)
y = Tensor(np.ones([64, 64]), dtype=ms.float32)
_executor.compile(net, x, y)
def test_gatherv2_auto0():
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel")
net = GradWrap(NetWithLoss(Net(0)))
@ -188,7 +175,6 @@ def test_gatherv2_auto1():
_executor.compile(net, x, y)
@pytest.mark.skip(reason="The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen")
def test_gatherv2_cpu0():
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
strategy1 = ((8, 1), (1, 1))
@ -201,7 +187,6 @@ def test_gatherv2_cpu0():
_executor.compile(net, x, y)
@pytest.mark.skip(reason="The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen")
def test_gatherv2_cpu1():
context.set_auto_parallel_context(device_num=16, global_rank=0, parallel_mode="semi_auto_parallel")
strategy1 = ((16, 1), (1, 1))
@ -214,7 +199,6 @@ def test_gatherv2_cpu1():
_executor.compile(net, x, y)
@pytest.mark.skip(reason="The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen")
def test_gatherv2_cpu2():
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
strategy1 = ((1, 8), (1, 1))