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update resnet network performence. 

less_bn pattern update.

fix clang-format

!19495 add allreduce operators by parameters for lessBN and add group parameters generator for lessBN, gc and grad freeze
Merge pull request !19495 from jinjiali-kali/less_bn

update resnet acc_mode scripts.
This commit is contained in:
linqingke 2021-06-22 15:26:56 +08:00
parent 0305441854
commit 9975c6a3a8
28 changed files with 380 additions and 82 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
mindspore/ccsrc/frontend/optimizer/irpass.cc
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@ -177,7 +177,8 @@ OptimizeIRPassLib::OptimizeIRPassLib() {
// Accelerated Algorithm
less_batch_normalization_ =
MakeSubstitution(std::make_shared<LessBatchNormalization>(), "less_batch_normalization", prim::kPrimAdd);
MakeSubstitution(std::make_shared<LessBatchNormalization>(), "less_batch_normalization",
{prim::kPrimAdd, prim::kPrimRelu6, prim::kPrimMatMul, prim::kPrimMakeTuple, prim::kPrimMaxPool});
// inline
inline_ = MakeSubstitution(std::make_shared<Inliner>(), "inline", IsCNodeGraph);

194
mindspore/ccsrc/frontend/optimizer/irpass/less_batch_normalization.cc
View File

@ -31,8 +31,8 @@ constexpr auto kFirstBranchPattern1 = 12;
constexpr auto kSecondBranchPattern1 = 3;
constexpr auto kFirstBranchStartIndexPattern1 = 4;
constexpr auto kFirstBranchEndIndexPattern1 = 11;
constexpr auto kSecondBranchStartIndexPattern1 = 12;
constexpr auto kSecondBranchEndIndexPattern1 = 14;
constexpr auto kSecondBranchStartIndexPattern1 = kFirstBranchPattern1;
constexpr auto kSecondBranchEndIndexPattern1 = 2 + kFirstBranchPattern1;
const std::vector<kStructureTuple> ResidualStructureBasePattern{
{kFirstBranchPattern1,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu},
@ -47,8 +47,8 @@ constexpr auto kFirstBranchPattern2 = 12;
constexpr auto kSecondBranchPattern2 = 1;
constexpr auto kFirstBranchStartIndexPattern2 = 4;
constexpr auto kFirstBranchEndIndexPattern2 = 11;
constexpr auto kSecondBranchStartIndexPattern2 = 12;
constexpr auto kSecondBranchEndIndexPattern2 = 13;
constexpr auto kSecondBranchStartIndexPattern2 = kFirstBranchPattern2;
constexpr auto kSecondBranchEndIndexPattern2 = 1 + kSecondBranchPattern2;
const std::vector<kStructureTuple> ResidualStructureShortCutPattern{
{kFirstBranchPattern2,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu},
@ -61,8 +61,8 @@ constexpr auto kFirstBranchPattern3 = 11;
constexpr auto kSecondBranchPattern3 = 3;
constexpr auto kFirstBranchStartIndexPattern3 = 4;
constexpr auto kFirstBranchEndIndexPattern3 = 10;
constexpr auto kSecondBranchStartIndexPattern3 = 11;
constexpr auto kSecondBranchEndIndexPattern3 = 13;
constexpr auto kSecondBranchStartIndexPattern3 = kFirstBranchPattern3;
constexpr auto kSecondBranchEndIndexPattern3 = 2 + kFirstBranchPattern3;
const std::vector<kStructureTuple> ResidualStructureFirstStepPattern{
{kFirstBranchPattern3,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem,
@ -73,15 +73,13 @@ const std::vector<kStructureTuple> ResidualStructureFirstStepPattern{
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D},
{kSecondBranchStartIndexPattern3, kSecondBranchEndIndexPattern3}}};
// Pattern 4
// Add -> BatchNorm -> Conv2D -> Relu ... -> End
// ↘ BatchNorm -> Conv2D -> -> -> -> ↗
constexpr auto kFirstBranchPattern4 = 8;
constexpr auto kSecondBranchPattern4 = 3;
constexpr auto kFirstBranchStartIndexPattern4 = 4;
constexpr auto kFirstBranchEndIndexPattern4 = 6;
constexpr auto kSecondBranchStartIndexPattern4 = 8;
constexpr auto kSecondBranchEndIndexPattern4 = 11;
const std::vector<kStructureTuple> BasicStructureBasePattern{
constexpr auto kSecondBranchStartIndexPattern4 = kFirstBranchPattern4;
constexpr auto kSecondBranchEndIndexPattern4 = 3 + kFirstBranchPattern4;
const std::vector<kStructureTuple> BasicStructBasePattern{
{kFirstBranchPattern4,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu},
{kFirstBranchStartIndexPattern4, kFirstBranchEndIndexPattern4}},
@ -89,37 +87,163 @@ const std::vector<kStructureTuple> BasicStructureBasePattern{
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D},
{kSecondBranchStartIndexPattern4, kSecondBranchEndIndexPattern4}}};
// Pattern 5
// Add -> BatchNorm -> Conv2D -> Relu ... -> End
// ↘ -> -> -> -> Relu -> -> -> -> ↗
constexpr auto kFirstBranchPattern5 = 8;
constexpr auto kFirstBranchPattern5 = 7;
constexpr auto kSecondBranchPattern5 = 1;
constexpr auto kFirstBranchStartIndexPattern5 = 4;
constexpr auto kFirstBranchEndIndexPattern5 = 6;
constexpr auto kSecondBranchStartIndexPattern5 = 8;
constexpr auto kSecondBranchEndIndexPattern5 = 11;
const std::vector<kStructureTuple> BasicStructureShortCutPattern{
constexpr auto kSecondBranchStartIndexPattern5 = kFirstBranchPattern5;
constexpr auto kSecondBranchEndIndexPattern5 = 3 + kFirstBranchPattern5;
const std::vector<kStructureTuple> BasicStructFirstStepPattern{
{kFirstBranchPattern5,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu},
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem,
prim::kPrimBatchNorm, prim::kPrimConv2D},
{kFirstBranchStartIndexPattern5, kFirstBranchEndIndexPattern5}},
{kSecondBranchPattern5, {prim::kPrimRelu}, {kSecondBranchStartIndexPattern5, kSecondBranchEndIndexPattern5}}};
{kSecondBranchPattern5, {prim::kPrimMaxPool}, {kSecondBranchStartIndexPattern5, kSecondBranchEndIndexPattern5}}};
// Pattern 6
// Add -> BatchNorm -> Conv2D -> Relu ... -> End
// ↘ -> -> -> -> MaxPool -> -> -> ↗
constexpr auto kFirstBranchPattern6 = 7;
constexpr auto kFirstBranchPattern6 = 8;
constexpr auto kSecondBranchPattern6 = 1;
constexpr auto kFirstBranchStartIndexPattern6 = 4;
constexpr auto kFirstBranchEndIndexPattern6 = 6;
constexpr auto kSecondBranchStartIndexPattern6 = 7;
constexpr auto kSecondBranchEndIndexPattern6 = 10;
const std::vector<kStructureTuple> BasicStructureFirstStepPattern{
constexpr auto kSecondBranchStartIndexPattern6 = kFirstBranchPattern6;
constexpr auto kSecondBranchEndIndexPattern6 = 3 + kFirstBranchPattern6;
const std::vector<kStructureTuple> BasicStructShortCutPattern{
{kFirstBranchPattern6,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem,
prim::kPrimBatchNorm, prim::kPrimConv2D},
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu},
{kFirstBranchStartIndexPattern6, kFirstBranchEndIndexPattern6}},
{kSecondBranchPattern6, {prim::kPrimMaxPool}, {kSecondBranchStartIndexPattern6, kSecondBranchEndIndexPattern6}}};
static const std::vector<std::vector<kStructureTuple>> kNeedMatchPattern = {
ResidualStructureBasePattern, ResidualStructureShortCutPattern, ResidualStructureFirstStepPattern,
BasicStructureBasePattern, BasicStructureShortCutPattern, BasicStructureFirstStepPattern};
{kSecondBranchPattern6, {prim::kPrimRelu}, {kSecondBranchStartIndexPattern6, kSecondBranchEndIndexPattern6}}};
// Pattern 7
constexpr auto kFirstBranchPattern7 = 1;
constexpr auto kSecondBranchPattern7 = 13;
constexpr auto kFirstBranchStartIndexPattern7 = SIZE_MAX;
constexpr auto kFirstBranchEndIndexPattern7 = SIZE_MAX;
constexpr auto kSecondBranchStartIndexPattern7 = 7;
constexpr auto kSecondBranchEndIndexPattern7 = 10;
const std::vector<kStructureTuple> InvertedResidualShortCutPattern{
{kFirstBranchPattern7,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm},
{kFirstBranchStartIndexPattern7, kFirstBranchEndIndexPattern7}},
{kSecondBranchPattern7,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu6, prim::kPrimTupleGetItem,
prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu6, prim::kPrimTupleGetItem, prim::kPrimBatchNorm,
prim::kPrimConv2D, prim::kPrimTupleGetItem, prim::kPrimBatchNorm},
{kSecondBranchStartIndexPattern7, kSecondBranchEndIndexPattern7}}};
// Pattern 8
constexpr auto kFirstBranchPattern8 = 4;
constexpr auto kFirstBranchStartIndexPattern8 = 0;
constexpr auto kFirstBranchEndIndexPattern8 = 3;
const std::vector<kStructureTuple> InvertedResidualPattern{
{kFirstBranchPattern8,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimAdd},
{kFirstBranchStartIndexPattern8, kFirstBranchEndIndexPattern8}}};
// Pattern 9
constexpr auto kFirstBranchPattern9 = 1;
constexpr auto kSecondBranchPattern9 = 12;
constexpr auto kFirstBranchStartIndexPattern9 = SIZE_MAX;
constexpr auto kFirstBranchEndIndexPattern9 = SIZE_MAX;
constexpr auto kSecondBranchStartIndexPattern9 = 7;
constexpr auto kSecondBranchEndIndexPattern9 = 10;
const std::vector<kStructureTuple> InvertedResidualShortCutPattern2{
{kFirstBranchPattern9, {prim::kPrimAdd}, {kFirstBranchStartIndexPattern9, kFirstBranchEndIndexPattern9}},
{kSecondBranchPattern9,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu6, prim::kPrimTupleGetItem,
prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu6, prim::kPrimTupleGetItem, prim::kPrimBatchNorm,
prim::kPrimConv2D, prim::kPrimAdd},
{kSecondBranchStartIndexPattern9, kSecondBranchEndIndexPattern9}}};
// Pattern 10
constexpr auto kFirstBranchPattern10 = 5;
constexpr auto kFirstBranchStartIndexPattern10 = 0;
constexpr auto kFirstBranchEndIndexPattern10 = 4;
const std::vector<kStructureTuple> InvertedResidualPattern2{
{kFirstBranchPattern10,
{prim::kPrimReduceMean, prim::kPrimRelu6, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D},
{kFirstBranchStartIndexPattern10, kFirstBranchEndIndexPattern10}}};
// Pattern 11
constexpr auto kFirstBranchPattern11 = 17;
constexpr auto kFirstBranchStartIndexPattern11 = 3;
constexpr auto kFirstBranchEndIndexPattern11 = 6;
const std::vector<kStructureTuple> InvertedResidualPattern3{
{kFirstBranchPattern11,
{prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu6, prim::kPrimTupleGetItem,
prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D,
prim::kPrimRelu6, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D, prim::kPrimRelu6,
prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D},
{kFirstBranchStartIndexPattern11, kFirstBranchEndIndexPattern11}}};
// Pattern 12
constexpr auto kFirstBranchPattern12 = 1;
constexpr auto kSecondBranchPattern12 = 9;
constexpr auto kFirstBranchStartIndexPattern12 = SIZE_MAX;
constexpr auto kFirstBranchEndIndexPattern12 = SIZE_MAX;
constexpr auto kSecondBranchStartIndexPattern12 = kFirstBranchPattern12 + 5;
constexpr auto kSecondBranchEndIndexPattern12 = kFirstBranchPattern12 + 8;
const std::vector<kStructureTuple> DenseBlockShortCutPattern{
{kFirstBranchPattern12, {prim::kPrimConcat}, {kFirstBranchStartIndexPattern12, kFirstBranchEndIndexPattern12}},
{kSecondBranchPattern12,
{prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D,
prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConcat},
{kSecondBranchStartIndexPattern12, kSecondBranchEndIndexPattern12}}};
// Pattern 13
constexpr auto kFirstBranchPattern13 = 5;
constexpr auto kFirstBranchStartIndexPattern13 = 0;
constexpr auto kFirstBranchEndIndexPattern13 = 4;
const std::vector<kStructureTuple> DenseBlockPattern{
{kFirstBranchPattern13,
{prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConcat},
{kFirstBranchStartIndexPattern13, kFirstBranchEndIndexPattern13}}};
// Pattern 14
constexpr auto kFirstBranchPattern14 = 9;
constexpr auto kSecondBranchPattern14 = 1;
constexpr auto kFirstBranchStartIndexPattern14 = 5;
constexpr auto kFirstBranchEndIndexPattern14 = 8;
constexpr auto kSecondBranchStartIndexPattern14 = SIZE_MAX;
constexpr auto kSecondBranchEndIndexPattern14 = SIZE_MAX;
const std::vector<kStructureTuple> DenseBlockShortCutPattern2{
{kFirstBranchPattern14,
{prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D,
prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConcat},
{kFirstBranchStartIndexPattern14, kFirstBranchEndIndexPattern14}},
{kSecondBranchPattern14, {prim::kPrimConcat}, {kSecondBranchStartIndexPattern14, kSecondBranchEndIndexPattern14}}};
// Pattern 15
constexpr auto kFirstBranchPattern15 = 9;
constexpr auto kSecondBranchPattern15 = 1;
constexpr auto kFirstBranchStartIndexPattern15 = 0;
constexpr auto kFirstBranchEndIndexPattern15 = 4;
constexpr auto kSecondBranchStartIndexPattern15 = SIZE_MAX;
constexpr auto kSecondBranchEndIndexPattern15 = SIZE_MAX;
const std::vector<kStructureTuple> DenseBlockPoolPattern{
{kFirstBranchPattern15,
{prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D,
prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimMaxPool},
{kFirstBranchStartIndexPattern15, kFirstBranchEndIndexPattern15}},
{kSecondBranchPattern15, {prim::kPrimConcat}, {kSecondBranchStartIndexPattern15, kSecondBranchEndIndexPattern15}}};
// Pattern 16
constexpr auto kFirstBranchPattern16 = 1;
constexpr auto kSecondBranchPattern16 = 9;
constexpr auto kFirstBranchStartIndexPattern16 = SIZE_MAX;
constexpr auto kFirstBranchEndIndexPattern16 = SIZE_MAX;
constexpr auto kSecondBranchStartIndexPattern16 = kFirstBranchPattern16;
constexpr auto kSecondBranchEndIndexPattern16 = kFirstBranchPattern16 + 4;
const std::vector<kStructureTuple> DenseBlockPoolPatter2{
{kFirstBranchPattern16, {prim::kPrimConcat}, {kFirstBranchStartIndexPattern16, kFirstBranchEndIndexPattern16}},
{kSecondBranchPattern16,
{prim::kPrimConv2D, prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimConv2D,
prim::kPrimRelu, prim::kPrimTupleGetItem, prim::kPrimBatchNorm, prim::kPrimMaxPool},
{kSecondBranchStartIndexPattern16, kSecondBranchEndIndexPattern16}}};
static const std::vector<std::vector<kStructureTuple>> kNeedMatchPattern = {ResidualStructureBasePattern,
ResidualStructureShortCutPattern,
ResidualStructureFirstStepPattern,
BasicStructBasePattern,
BasicStructFirstStepPattern,
BasicStructShortCutPattern,
InvertedResidualShortCutPattern,
InvertedResidualPattern,
InvertedResidualShortCutPattern2,
InvertedResidualPattern2,
InvertedResidualPattern3,
DenseBlockShortCutPattern,
DenseBlockPattern,
DenseBlockShortCutPattern2,
DenseBlockPoolPattern,
DenseBlockPoolPatter2};
const std::set<PrimitivePtr> kNeedRemoveNodeSet{
prim::kPrimLoad, prim::kPrimRefToEmbed, prim::kPrimApplyMomentum, prim::kPrimMomentum,
prim::kPrimApplyFtrl, prim::kPrimSGD, prim::kPrimApplyRMSProp, prim::kPrimAdam};
@ -286,7 +410,13 @@ AnfNodePtr LessBatchNormalization::operator()(const OptimizerPtr &optimizer, con
sum_match_node += std::get<0>(t);
total_match_node_.emplace_back(sum_match_node);
});
AnfVisitor::Match(prim::kPrimAdd, {IsCNode, IsCNode})(node);
auto cnode = node->cast<CNodePtr>();
if (cnode == nullptr || cnode->inputs().empty()) {
return nullptr;
}
auto prim = GetValueNode<PrimitivePtr>(cnode->input(0));
std::vector<PredicateFuncType> funcs(cnode->inputs().size() - 1, IsCNode);
AnfVisitor::Match(prim, funcs)(node);
if (is_match_) {
break;
}

10
mindspore/nn/acc/acc.py
View File

@ -40,6 +40,7 @@ class AutoAcc:
def __init__(self, level, kwargs):
if level not in _acc_config_level.keys():
level = 'O0'
self.level = level
acc_config = _acc_config_level[level]
self._acc_config = acc_config
self._fn_flag = True
@ -66,12 +67,11 @@ class AutoAcc:
optimizer_process = OptimizerProcess(optimizer)
group_params = self._param_processer.assign_parameter_group(network.trainable_params(),
self._gradient_groups)
optimizer_process.origin_params = self._param_processer.generate_group_params(group_params)
optimizer_process.origin_params = \
self._param_processer.generate_group_params(group_params, optimizer_process.origin_params)
if self._gc_flag:
parameters = optimizer_process.add_grad_centralization()
else:
parameters = optimizer_process.origin_params
optimizer = optimizer_process.generate_new_optimizer(parameters)
optimizer_process.add_grad_centralization()
optimizer = optimizer_process.generate_new_optimizer()
if self._acc_config["grad_freeze"]:
freeze_processer = GradientFreeze(self._param_groups, self._freeze_type,

89
mindspore/nn/acc/base.py
View File

@ -13,6 +13,7 @@
# limitations under the License.
# ============================================================================
"""base process"""
import copy
from mindspore.nn.cell import Cell
from mindspore.nn.optim import LARS
from mindspore import log as logger
@ -58,24 +59,37 @@ class OptimizerProcess:
if isinstance(parameters[0], Parameter):
logger.warning("Only group parameters support gradient centralization.")
return parameters
return
change_dict = parameters[0]
if 'order_params' in change_dict.keys():
logger.warning("Only support normal parameters for gradient centralization.")
return parameters
group_params = []
for group_param in parameters:
if 'order_params' in group_param.keys():
group_params.append(group_param)
continue
params_gc_value = []
params_value = []
for param in group_param['params']:
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
params_gc_value.append(param)
else:
params_value.append(param)
if params_gc_value:
new_group_param = copy.deepcopy(group_param)
new_group_param['params'] = params_gc_value
new_group_param['grad_centralization'] = True
group_params.append(new_group_param)
if params_value:
new_group_param = copy.deepcopy(group_param)
new_group_param['params'] = params_value
group_params.append(new_group_param)
self.origin_params = group_params
change_dict['grad_centralization'] = True
self.origin_params[0] = change_dict
return self.origin_params
def generate_new_optimizer(self, params):
def generate_new_optimizer(self):
"""Generate new optimizer."""
if not self.is_lars:
opt = self.opt_class(params=params, **self.opt_init_args)
opt = self.opt_class(params=self.origin_params, **self.opt_init_args)
else:
opt = LARS(self.opt_class(params=params, **self.opt_init_args), **self.lars_init_args)
opt = LARS(self.opt_class(params=self.origin_params, **self.opt_init_args), **self.lars_init_args)
return opt
@ -103,19 +117,46 @@ class ParameterProcess:
parameters[i].comm_fusion = self._parameter_indices
return parameters
def generate_group_params(self, parameters):
def generate_group_params(self, parameters, origin_params):
"""Generate group parameters."""
decayed_params = []
no_decayed_params = []
for param in parameters:
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
decayed_params.append(param)
else:
no_decayed_params.append(param)
group_params = [{'params': decayed_params, 'weight_decay': 0.0001},
{'params': no_decayed_params},
{'order_params': parameters}]
origin_params_copy = origin_params
if origin_params_copy is not None:
if not isinstance(origin_params_copy, list):
origin_params_copy = list(origin_params_copy)
if not origin_params_copy:
raise ValueError("Optimizer got an empty parameter list.")
if not isinstance(origin_params_copy[0], (dict, Parameter)):
raise TypeError("Only a list of Parameter or dict can be supported.")
if isinstance(origin_params_copy[0], Parameter):
group_params = [{"params": parameters}]
else:
group_params = []
params_name = [param.name for param in parameters]
new_params_count = copy.deepcopy(params_name)
for group_param in origin_params_copy:
if 'order_params' in group_param.keys():
new_group_param = copy.deepcopy(group_param)
new_group_param['order_params'] = parameters
group_params.append(new_group_param)
continue
params_value = []
for param in group_param['params']:
if param.name in params_name:
index = params_name.index(param.name)
params_value.append(parameters[index])
new_params_count.remove(param.name)
new_group_param = copy.deepcopy(group_param)
new_group_param['params'] = params_value
group_params.append(new_group_param)
if new_params_count:
params_value = []
for param in new_params_count:
index = params_name.index(param)
params_value.append(parameters[index])
group_params.append({"params": params_value})
return group_params
_gradient_accumulation_op = C.MultitypeFuncGraph("gradient_accumulation_op")

5
mindspore/nn/acc/grad_freeze.py
View File

@ -235,7 +235,8 @@ class GradientFreeze:
train_para_groups = self.split_parameters_groups(
network, self._param_groups)
for i in range(self._param_groups):
train_para_groups[i] = self._param_processer.generate_group_params(train_para_groups[i])
train_para_groups[i] = self._param_processer.generate_group_params(train_para_groups[i],
optimizer.init_params['params'])
train_strategy = self.generate_freeze_index_sequence(
self._param_groups, self._freeze_type, self._freeze_p, self._total_steps)
optimizer = FreezeOpt(optimizer, train_para_groups, train_strategy)
@ -248,7 +249,7 @@ def freeze_cell(reducer_flag, network, optimizer, sens, grad, use_grad_accumulat
if reducer_flag:
param_processer = ParameterProcess()
grad_reducers = (DistributedGradReducer(param_processer.assign_parameter_group(opt.parameters),
mean, degree, param_fusion=True) for opt in optimizer.opts)
mean, degree) for opt in optimizer.opts)
freeze_nets = tuple(_TrainFreezeCell(network, sens, grad, reducer,
use_grad_accumulation, opt, max_accumulation_step)
for reducer, opt in zip(grad_reducers, optimizer.opts))

2
mindspore/nn/optim/ada_grad.py
View File

@ -16,6 +16,7 @@
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
_ada_grad_opt = C.MultitypeFuncGraph("ada_grad_opt")
@ -144,6 +145,7 @@ class Adagrad(Optimizer):
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, accum=0.1, learning_rate=0.001,
update_slots=True, loss_scale=1.0, weight_decay=0.0):
super(Adagrad, self).__init__(learning_rate, params, weight_decay, loss_scale)

2
mindspore/nn/optim/adam.py
View File

@ -25,6 +25,7 @@ from mindspore.common.tensor import Tensor
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
_adam_opt = C.MultitypeFuncGraph("adam_opt")
_scaler_one = Tensor(1, mstype.int32)
@ -311,6 +312,7 @@ class Adam(Optimizer):
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
super(Adam, self).__init__(learning_rate, params, weight_decay, loss_scale)

3
mindspore/nn/optim/ftrl.py
View File

@ -19,6 +19,7 @@ import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer, _apply_decay, _grad_scale
from .optimizer import opt_init_args_register
_ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
@ -191,6 +192,8 @@ class FTRL(Optimizer):
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
use_locking=False, loss_scale=1.0, weight_decay=0.0):
super(FTRL, self).__init__(learning_rate, params, weight_decay, loss_scale=loss_scale)

2
mindspore/nn/optim/lamb.py
View File

@ -25,6 +25,7 @@ from mindspore.common.tensor import Tensor
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
from .. import layer
@ -266,6 +267,7 @@ class Lamb(Optimizer):
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, learning_rate, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0):
super(Lamb, self).__init__(learning_rate, params, weight_decay)
_check_param_value(beta1, beta2, eps, self.cls_name)

2
mindspore/nn/optim/lazyadam.py
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@ -23,6 +23,7 @@ from mindspore.common.tensor import Tensor
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
_lazy_adam_opt = C.MultitypeFuncGraph("lazy_adam_opt")
@ -231,6 +232,7 @@ class LazyAdam(Optimizer):
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
super(LazyAdam, self).__init__(learning_rate, params, weight_decay, loss_scale)

2
mindspore/nn/optim/proximal_ada_grad.py
View File

@ -18,6 +18,7 @@ from mindspore.common import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
_proximal_ada_grad_opt = C.MultitypeFuncGraph("proximal_ada_grad_opt")
@ -167,6 +168,7 @@ class ProximalAdagrad(Optimizer):
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, accum=0.1, learning_rate=0.001, l1=0.0, l2=0.0,
use_locking=False, loss_scale=1.0, weight_decay=0.0):
super(ProximalAdagrad, self).__init__(learning_rate, params, weight_decay, loss_scale)

3
mindspore/nn/optim/rmsprop.py
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@ -16,6 +16,7 @@
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
_centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
@ -175,6 +176,8 @@ class RMSProp(Optimizer):
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0):
super(RMSProp, self).__init__(learning_rate, params, weight_decay, loss_scale)

3
mindspore/nn/optim/sgd.py
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@ -19,6 +19,7 @@ from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
_sgd_opt = C.MultitypeFuncGraph("sgd_opt")
@ -133,6 +134,8 @@ class SGD(Optimizer):
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False,
loss_scale=1.0):

26
mindspore/nn/wrap/grad_reducer.py
View File

@ -49,13 +49,25 @@ def _init_allreduce_operators(length, split_indices):
def _init_allreduce_operators_by_parameters(parameters):
""" initialize allreduce communication operators by parameters"""
op_list = ()
param_fusion = False
last_comm_fusion = None
first_parameter_flag = True
for parameter in parameters:
comm_fusion = parameter.comm_fusion
if first_parameter_flag:
last_comm_fusion = comm_fusion
first_parameter_flag = False
elif not param_fusion:
if comm_fusion != last_comm_fusion:
param_fusion = True
last_comm_fusion = comm_fusion
op = AllReduce('sum', GlobalComm.WORLD_COMM_GROUP)
op.add_prim_attr('fusion', comm_fusion)
op.add_prim_attr('index', comm_fusion)
op_list = op_list + (op,)
return op_list
if not param_fusion:
op_list = ()
return op_list, param_fusion
@reduce_opt.register("Tensor", "Bool", "Function", "Function", "Bool", "Tensor")
@ -354,7 +366,7 @@ class DistributedGradReducer(Cell):
256.0
"""
def __init__(self, parameters, mean=True, degree=None, fusion_type=1, param_fusion=False):
def __init__(self, parameters, mean=True, degree=None, fusion_type=1):
super(DistributedGradReducer, self).__init__(auto_prefix=False)
self.map_ = C.Map()
if degree is None:
@ -371,12 +383,12 @@ class DistributedGradReducer(Cell):
if is_parallel_optimizer and split_indices:
self.split_fusion = True
self.op_list = _init_allreduce_operators(len(parameters), split_indices)
elif param_fusion:
self.split_fusion = True
self.op_list = _init_allreduce_operators_by_parameters(parameters)
else:
self.split_fusion = False
self.allreduce = AllReduce().add_prim_attr('fusion', fusion_type)
self.split_fusion = True
self.op_list, param_fusion = _init_allreduce_operators_by_parameters(parameters)
if not param_fusion:
self.split_fusion = False
self.allreduce = AllReduce().add_prim_attr('fusion', fusion_type)
self.allgather = AllGather(GlobalComm.WORLD_COMM_GROUP)
ps_filter = lambda x: x.is_param_ps
self.ps_parameters = tuple(ps_filter(x) for x in parameters)

12
mindspore/train/model.py
View File

@ -139,6 +139,7 @@ class Model:
self._device_number = _get_device_num()
self._global_rank = _get_global_rank()
self._parameter_broadcast = _get_parameter_broadcast()
self._metrics = metrics
self._check_amp_level_arg(optimizer, amp_level)
self._check_for_graph_cell(kwargs)
@ -175,7 +176,7 @@ class Model:
def _check_kwargs(self, kwargs):
for arg in kwargs:
if arg not in ['loss_scale_manager', 'keep_batchnorm_fp32', 'total_steps']:
if arg not in ['loss_scale_manager', 'keep_batchnorm_fp32']:
raise ValueError(f"Unsupported arg '{arg}'")
def _check_reuse_dataset(self, dataset):
@ -187,15 +188,18 @@ class Model:
def _build_acc_network(self, kwargs):
"""Build the acc network."""
processor = acc.AutoAcc(self._acc_level, kwargs)
if processor.level not in ["O1", "O2"]:
return
if self._optimizer is None:
logger.warning("In acc mode, the optimizer must be defined.")
return
if self._eval_network is None:
logger.warning("In acc mode, the eval_network must be defined.")
if self._eval_network is None and self._metrics is None:
logger.warning("In acc mode, the eval_network and metrics cannot be undefined at the same time.")
return
self._network, self._optimizer = processor.network_auto_process_train(self._network, self._optimizer)
self._eval_network = processor.network_auto_process_eval(self._eval_network)
if self._eval_network is not None:
self._eval_network = processor.network_auto_process_eval(self._eval_network)
def _build_train_network(self):
"""Build train network"""

1
model_zoo/official/cv/resnet/resnet101_imagenet2012_config.yaml
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@ -48,6 +48,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/resnet18_cifar10_config.yaml
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@ -48,6 +48,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/resnet18_imagenet2012_config.yaml
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@ -50,6 +50,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/resnet34_imagenet2012_config.yaml
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@ -50,6 +50,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/resnet50_cifar10_config.yaml
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@ -48,6 +48,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

78
model_zoo/official/cv/resnet/resnet50_imagenet2012_Acc_config.yaml Normal file
View File

@ -0,0 +1,78 @@
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
enable_modelarts: False
# Url for modelarts
data_url: ""
train_url: ""
checkpoint_url: ""
# Path for local
run_distribute: False
enable_profiling: False
data_path: "/cache/data"
output_path: "/cache/train"
load_path: "/cache/checkpoint_path/"
device_target: "Ascend"
checkpoint_path: "./checkpoint/"
checkpoint_file_path: ""
# ==============================================================================
# Training options
optimizer: "Momentum"
infer_label: ""
class_num: 1001
batch_size: 256
loss_scale: 1024
momentum: 0.9
weight_decay: 0.0001
epoch_size: 90
pretrain_epoch_size: 0
save_checkpoint: True
save_checkpoint_epochs: 5
keep_checkpoint_max: 10
warmup_epochs: 0
lr_decay_mode: "linear"
use_label_smooth: True
label_smooth_factor: 0.1
lr_init: 0
lr_max: 0.8
lr_end: 0.0
net_name: "resnet50"
dataset: "imagenet2012"
device_num: 1
pre_trained: ""
run_eval: False
eval_dataset_path: ""
parameter_server: False
filter_weight: False
save_best_ckpt: True
eval_start_epoch: 40
eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O1"
# Export options
device_id: 0
width: 224
height: 224
file_name: "resnet50"
file_format: "AIR"
ckpt_file: ""
network_dataset: "resnet50_imagenet2012"
---
# Help description for each configuration
enable_modelarts: "Whether training on modelarts, default: False"
data_url: "Dataset url for obs"
checkpoint_url: "The location of checkpoint for obs"
data_path: "Dataset path for local"
output_path: "Training output path for local"
load_path: "The location of checkpoint for obs"
device_target: "Target device type, available: [Ascend, GPU, CPU]"
enable_profiling: "Whether enable profiling while training, default: False"
num_classes: "Class for dataset"
batch_size: "Batch size for training and evaluation"
epoch_size: "Total training epochs."
checkpoint_path: "The location of the checkpoint file."
checkpoint_file_path: "The location of the checkpoint file."

1
model_zoo/official/cv/resnet/resnet50_imagenet2012_GPU_config.yaml
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@ -51,6 +51,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/resnet50_imagenet2012_Thor_config.yaml
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@ -51,6 +51,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/resnet50_imagenet2012_config.yaml
View File

@ -50,6 +50,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/resnet_benchmark_GPU.yaml
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@ -25,6 +25,7 @@ eval: False
save_ckpt: False
mode_name: "GRAPH"
dtype: "fp16"
acc_mode: "O0"
# Export options
device_id: 0

1
model_zoo/official/cv/resnet/se-resnet50_imagenet2012_config.yaml
View File

@ -51,6 +51,7 @@ eval_interval: 1
enable_cache: False
cache_session_id: ""
mode_name: "GRAPH"
acc_mode: "O0"
# Export options
device_id: 0

10
model_zoo/official/cv/resnet/src/dataset.py
View File

@ -124,9 +124,9 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
device_num = 1
if device_num == 1:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True)
else:
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True,
num_shards=device_num, shard_id=rank_id)
image_size = 224
@ -152,7 +152,7 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
type_cast_op = C2.TypeCast(mstype.int32)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=12)
# only enable cache for eval
if do_train:
enable_cache = False
@ -160,10 +160,10 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
if not cache_session_id:
raise ValueError("A cache session_id must be provided to use cache.")
eval_cache = ds.DatasetCache(session_id=int(cache_session_id), size=0)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8,
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12,
cache=eval_cache)
else:
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)

6
model_zoo/official/cv/resnet/train.py
View File

@ -105,7 +105,8 @@ def set_parameter():
gradients_mean=True)
set_algo_parameters(elementwise_op_strategy_follow=True)
if config.net_name == "resnet50" or config.net_name == "se-resnet50":
context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
if config.acc_mode not in ["O1", "O2"]:
context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
elif config.net_name == "resnet101":
context.set_auto_parallel_context(all_reduce_fusion_config=[80, 210, 313])
init()
@ -228,7 +229,8 @@ def train_net():
model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, eval_network=dist_eval_network)
else:
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics=metrics,
amp_level="O2", keep_batchnorm_fp32=False, eval_network=dist_eval_network)
amp_level="O2", acc_level=config.acc_mode, keep_batchnorm_fp32=False,
eval_network=dist_eval_network)
if config.optimizer == "Thor" and config.dataset == "imagenet2012":
from src.lr_generator import get_thor_damping