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mindspore
提交 7185961e 编写于 作者: Y YuJianfeng
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Enable BatchNorm fusion pass

上级 4c32d7e6
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Showing with 292 addition and 152 deletion
mindspore/ccsrc/pre_activate/ascend/ascend_backend_optimization.cc
浏览文件 @ 7185961e
......@@ -19,6 +19,7 @@
#include "pre_activate/common/optimizer.h"
#include "pre_activate/ascend/ir_fission/bn_split.h"
#include "pre_activate/ascend/ir_fission/bn_grad_split.h"
#include "pre_activate/ascend/ir_fission/batch_norm_grad_split.h"
#include "pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.h"
#include "pre_activate/ascend/ir_fission/layer_norm_grad_split.h"
#include "pre_activate/pass/communication_op_fusion.h"
......@@ -87,7 +88,6 @@ void AddAscendBackendOptionalIRFusion(PassManager *ir_fusion_pm) {
ir_fusion_pm->AddPass(std::make_shared<ReshapeTransposeFusion>());
ir_fusion_pm->AddPass(std::make_shared<TransposeReshapeFusion>());
ir_fusion_pm->AddPass(std::make_shared<ClipByValueFusion>());
ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormFusion>());
ir_fusion_pm->AddPass(std::make_shared<TopKSplit>());
ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneWithDecayRule>());
ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneFusion>());
......@@ -193,8 +193,8 @@ void AscendBackendIRFusionOptimization(const std::shared_ptr<session::KernelGrap
}
auto optimizer = std::make_shared<GraphOptimizer>();
auto ir_fusion_pm = std::make_shared<PassManager>("ir_fusion_pm");
ir_fusion_pm->AddPass(std::make_shared<BnSplit>());
ir_fusion_pm->AddPass(std::make_shared<BnGradSplit>());
ir_fusion_pm->AddPass(std::make_shared<BatchNormGradSplit>());
ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormFusion>());
ir_fusion_pm->AddPass(std::make_shared<AddMemcpyAsync>());
if (context_ptr->ir_fusion_flag()) {
AddAscendBackendOptionalIRFusion(ir_fusion_pm.get());
......
mindspore/ccsrc/pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.cc
浏览文件 @ 7185961e
......@@ -23,6 +23,8 @@
namespace mindspore {
namespace opt {
namespace {
constexpr size_t kReplaceOutputIndex0 = 3;
constexpr size_t kReplaceOutputIndex1 = 4;
bool IsC(const BaseRef &n) {
if (utils::isa<AnfNodePtr>(n)) {
AnfNodePtr in = utils::cast<AnfNodePtr>(n);
......@@ -32,52 +34,6 @@ bool IsC(const BaseRef &n) {
return false;
}
AnfNodePtr GetBatchNormNode(const AnfNodePtr &node) {
MS_EXCEPTION_IF_NULL(node);
auto depend_cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(depend_cnode);
CheckCNodeInputSize(depend_cnode, kDependInputNum);
AnfNodePtr assign_sub = depend_cnode->input(2);
MS_EXCEPTION_IF_NULL(assign_sub);
auto assign_sub_cnode = assign_sub->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(assign_sub_cnode);
CheckCNodeInputSize(assign_sub_cnode, kAssignSubInputNum);
AnfNodePtr mul = assign_sub_cnode->input(2);
MS_EXCEPTION_IF_NULL(mul);
auto mul_cnode = mul->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(mul_cnode);
CheckCNodeInputSize(mul_cnode, kMulInputNum);
AnfNodePtr sub = mul_cnode->input(1);
MS_EXCEPTION_IF_NULL(sub);
auto sub_cnode = sub->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(sub_cnode);
CheckCNodeInputSize(sub_cnode, kSubInputNum);
AnfNodePtr tuple_getitem = sub_cnode->input(2);
MS_EXCEPTION_IF_NULL(tuple_getitem);
auto tuple_getitem_cnode = tuple_getitem->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(tuple_getitem_cnode);
CheckCNodeInputSize(tuple_getitem_cnode, kTupleGetitemInputNum);
return tuple_getitem_cnode->input(1);
}
bool CompareTupleGetitem(const AnfNodePtr &n1, const AnfNodePtr &n2) {
MS_EXCEPTION_IF_NULL(n1);
MS_EXCEPTION_IF_NULL(n2);
auto n1_cnode = n1->cast<CNodePtr>();
auto n2_cnode = n2->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(n1_cnode);
MS_EXCEPTION_IF_NULL(n2_cnode);
auto index_input1 = n1_cnode->input(kInputNodeOutputIndexInTupleGetItem);
MS_EXCEPTION_IF_NULL(index_input1);
auto value_node1 = index_input1->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node1);
auto index_input2 = n2_cnode->input(kInputNodeOutputIndexInTupleGetItem);
MS_EXCEPTION_IF_NULL(index_input2);
auto value_node2 = index_input2->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node2);
return GetValue<int>(value_node1->value()) < GetValue<int>(value_node2->value());
}
void GetBNOutput(const FuncGraphPtr &func_graph, const AnfNodePtr &bn, std::vector<AnfNodePtr> *bn_outputs) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(bn);
......@@ -92,54 +48,35 @@ void GetBNOutput(const FuncGraphPtr &func_graph, const AnfNodePtr &bn, std::vect
MS_EXCEPTION_IF_NULL(output);
bn_outputs->push_back(output);
}
sort(bn_outputs->begin(), bn_outputs->end(), CompareTupleGetitem);
}
} // namespace
const BaseRef FusedBatchNormFusion::DefinePattern() const {
const auto prim_batch_norm = std::make_shared<Primitive>(kBatchNormOpName);
std::shared_ptr<Var> Xs = std::make_shared<SeqVar>();
VarPtr index0 = std::make_shared<CondVar>(IsC);
VarPtr index1 = std::make_shared<CondVar>(IsC);
VarPtr index2 = std::make_shared<CondVar>(IsC);
VectorRef batch_norm = VectorRef({prim_batch_norm, data_input_var0_, data_input_var1_, data_input_var2_, Xs});
VectorRef batch_norm = VectorRef({batch_norm_var_, data_input0_var_, data_input1_var_, data_input2_var_, Xs});
VectorRef tuple_getitem0 = VectorRef({prim::kPrimTupleGetItem, batch_norm, index0});
VectorRef tuple_getitem1 = VectorRef({prim::kPrimTupleGetItem, batch_norm, index1});
VectorRef tuple_getitem2 = VectorRef({prim::kPrimTupleGetItem, batch_norm, index2});
VectorRef sub0 = VectorRef({prim::kPrimSub, variable_input_var0_, tuple_getitem1});
VectorRef sub1 = VectorRef({prim::kPrimSub, variable_input_var1_, tuple_getitem2});
VectorRef mul0 = VectorRef({prim::kPrimMul, sub0, constant_input_var0_});
VectorRef mul1 = VectorRef({prim::kPrimMul, sub1, constant_input_var1_});
VectorRef assign_sub0 = VectorRef({prim::kPrimAssignSub, variable_input_var0_, mul0});
VectorRef assign_sub1 = VectorRef({prim::kPrimAssignSub, variable_input_var1_, mul1});
VectorRef sub0 = VectorRef({prim::kPrimSub, variable_input0_var_, tuple_getitem1});
VectorRef sub1 = VectorRef({prim::kPrimSub, variable_input1_var_, tuple_getitem2});
VectorRef mul0 = VectorRef({prim::kPrimMul, sub0, constant_input0_var_});
VectorRef mul1 = VectorRef({prim::kPrimMul, sub1, constant_input1_var_});
VectorRef assign_sub0 = VectorRef({prim::kPrimAssignSub, variable_input0_var_, mul0});
VectorRef assign_sub1 = VectorRef({prim::kPrimAssignSub, variable_input1_var_, mul1});
VectorRef depend0 = VectorRef({prim::kPrimDepend, tuple_getitem0, assign_sub0});
return VectorRef({prim::kPrimDepend, depend0, assign_sub1});
}
abstract::AbstractTuplePtr FusedBatchNormFusion::CreateAbstractOfFusedBatchNorm(const EquivPtr &equiv,
const AnfNodePtr &bn) const {
MS_EXCEPTION_IF_NULL(equiv);
MS_EXCEPTION_IF_NULL(bn);
auto variable_input0 = utils::cast<AnfNodePtr>((*equiv)[variable_input_var0_]);
MS_EXCEPTION_IF_NULL(variable_input0);
auto variable_input1 = utils::cast<AnfNodePtr>((*equiv)[variable_input_var1_]);
MS_EXCEPTION_IF_NULL(variable_input1);
auto bn_abstract_tuple = dyn_cast<abstract::AbstractTuple>(bn->abstract());
MS_EXCEPTION_IF_NULL(bn_abstract_tuple);
if (bn_abstract_tuple->elements().size() != kBnOutputNum) {
MS_LOG(EXCEPTION) << "The abstract size of node bn must be " << kBnOutputNum << ", but it is "
<< bn_abstract_tuple->elements().size();
}
AbstractBasePtrList fused_bn_abstract_list{bn_abstract_tuple->elements()[0], variable_input0->abstract(),
variable_input1->abstract(), bn_abstract_tuple->elements()[3],
bn_abstract_tuple->elements()[4]};
auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(fused_bn_abstract_list);
return abstract_tuple;
}
ValuePtr FusedBatchNormFusion::GetFactor(const EquivPtr &equiv) const {
MS_EXCEPTION_IF_NULL(equiv);
auto constant_input = utils::cast<AnfNodePtr>((*equiv)[constant_input_var0_]);
auto iter_constant_input0 = (*equiv).find(constant_input0_var_);
if (iter_constant_input0 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the constant_input0 var after matched.";
}
auto constant_input = utils::cast<AnfNodePtr>(iter_constant_input0->second);
MS_EXCEPTION_IF_NULL(constant_input);
if (!constant_input->isa<ValueNode>()) {
return nullptr;
......@@ -158,53 +95,187 @@ ValuePtr FusedBatchNormFusion::GetFactor(const EquivPtr &equiv) const {
return MakeValue(tensor_data[0]);
}
const AnfNodePtr FusedBatchNormFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &equiv) const {
AnfNodePtr FusedBatchNormFusion::CreateBNTrainingReduce(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &equiv) const {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(node);
MS_EXCEPTION_IF_NULL(equiv);
// Set inputs
auto data_input0 = utils::cast<AnfNodePtr>((*equiv)[data_input_var0_]);
MS_EXCEPTION_IF_NULL(data_input0);
auto data_input1 = utils::cast<AnfNodePtr>((*equiv)[data_input_var1_]);
// Set input to create node
auto iter_data_input0 = (*equiv).find(data_input0_var_);
if (iter_data_input0 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the data_input0 var after matched.";
}
std::vector<AnfNodePtr> bn_training_reduce_inputs = {
NewValueNode(std::make_shared<Primitive>(kBNTrainingReduceOpName)),
utils::cast<AnfNodePtr>(iter_data_input0->second)};
auto bn_training_reduce = func_graph->NewCNode(bn_training_reduce_inputs);
MS_EXCEPTION_IF_NULL(bn_training_reduce);
bn_training_reduce->set_scope(node->scope());
// Set abstract
auto iter_data_input1 = (*equiv).find(data_input1_var_);
if (iter_data_input1 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the data_input1 var after matched.";
}
auto data_input1 = utils::cast<AnfNodePtr>(iter_data_input1->second);
MS_EXCEPTION_IF_NULL(data_input1);
auto data_input2 = utils::cast<AnfNodePtr>((*equiv)[data_input_var2_]);
auto iter_data_input2 = (*equiv).find(data_input2_var_);
if (iter_data_input2 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the data_input2 var after matched.";
}
auto data_input2 = utils::cast<AnfNodePtr>(iter_data_input2->second);
MS_EXCEPTION_IF_NULL(data_input2);
auto variable_input0 = utils::cast<AnfNodePtr>((*equiv)[variable_input_var0_]);
AbstractBasePtrList abstract_list{data_input1->abstract(), data_input2->abstract()};
auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(abstract_list);
bn_training_reduce->set_abstract(abstract_tuple);
return bn_training_reduce;
}
void FusedBatchNormFusion::GetBNTrainingUpdateInputs(const EquivPtr &equiv,
const std::vector<AnfNodePtr> &bn_training_reduce_outputs,
std::vector<AnfNodePtr> *bn_training_update_inputs) const {
MS_EXCEPTION_IF_NULL(equiv);
MS_EXCEPTION_IF_NULL(bn_training_update_inputs);
auto iter_data_input0 = (*equiv).find(data_input0_var_);
if (iter_data_input0 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the data_input0 var after matched.";
}
auto iter_data_input1 = (*equiv).find(data_input1_var_);
if (iter_data_input1 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the data_input1 var after matched.";
}
auto iter_data_input2 = (*equiv).find(data_input2_var_);
if (iter_data_input2 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the data_input2 var after matched.";
}
auto iter_variable_input0 = (*equiv).find(variable_input0_var_);
if (iter_variable_input0 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the variable_input0 var after matched.";
}
auto iter_variable_input1 = (*equiv).find(variable_input1_var_);
if (iter_variable_input1 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the variable_input1 var after matched.";
}
if (bn_training_reduce_outputs.size() != kBNTrainingReduceOutputNum) {
MS_LOG(EXCEPTION) << "The output size of node bn_training_reduce must be " << kBNTrainingReduceOutputNum
<< ", but it is " << bn_training_reduce_outputs.size();
}
*bn_training_update_inputs = {
NewValueNode(std::make_shared<Primitive>(kBNTrainingUpdateOpName)),
utils::cast<AnfNodePtr>(iter_data_input0->second),
bn_training_reduce_outputs[0],
bn_training_reduce_outputs[1],
utils::cast<AnfNodePtr>(iter_data_input1->second),
utils::cast<AnfNodePtr>(iter_data_input2->second),
utils::cast<AnfNodePtr>(iter_variable_input0->second),
utils::cast<AnfNodePtr>(iter_variable_input1->second),
};
}
void FusedBatchNormFusion::GetBNTrainingUpdateAbstractList(const EquivPtr &equiv, const AnfNodePtr &bn,
std::vector<AbstractBasePtr> *abstract_list) const {
MS_EXCEPTION_IF_NULL(equiv);
MS_EXCEPTION_IF_NULL(bn);
MS_EXCEPTION_IF_NULL(abstract_list);
auto bn_abstract_tuple = dyn_cast<abstract::AbstractTuple>(bn->abstract());
MS_EXCEPTION_IF_NULL(bn_abstract_tuple);
if (bn_abstract_tuple->elements().size() < kBnOutputNum) {
MS_LOG(EXCEPTION) << "The abstract size of node bn must not be less than " << kBnOutputNum << ", but it is "
<< bn_abstract_tuple->elements().size();
}
auto iter_variable_input0 = (*equiv).find(variable_input0_var_);
if (iter_variable_input0 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the variable_input0 var after matched.";
}
auto variable_input0 = utils::cast<AnfNodePtr>(iter_variable_input0->second);
MS_EXCEPTION_IF_NULL(variable_input0);
auto variable_input1 = utils::cast<AnfNodePtr>((*equiv)[variable_input_var1_]);
auto iter_variable_input1 = (*equiv).find(variable_input1_var_);
if (iter_variable_input1 == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the variable_input1 var after matched.";
}
auto variable_input1 = utils::cast<AnfNodePtr>(iter_variable_input1->second);
MS_EXCEPTION_IF_NULL(variable_input1);
std::vector<AnfNodePtr> fused_bn_inputs = {
NewValueNode(prim::kPrimFusedBatchNorm), data_input0, data_input1, data_input2, variable_input0, variable_input1};
auto fused_bn = func_graph->NewCNode(fused_bn_inputs);
fused_bn->set_scope(node->scope());
MS_EXCEPTION_IF_NULL(fused_bn);
*abstract_list = {bn_abstract_tuple->elements()[0], variable_input0->abstract(), variable_input1->abstract(),
bn_abstract_tuple->elements()[1], bn_abstract_tuple->elements()[2]};
}
AnfNodePtr FusedBatchNormFusion::CreateBNTrainingUpdate(
const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &equiv,
const std::vector<AnfNodePtr> &bn_training_reduce_outputs) const {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(node);
MS_EXCEPTION_IF_NULL(equiv);
// Set input
std::vector<AnfNodePtr> bn_training_update_inputs;
GetBNTrainingUpdateInputs(equiv, bn_training_reduce_outputs, &bn_training_update_inputs);
auto bn_training_update = func_graph->NewCNode(bn_training_update_inputs);
MS_EXCEPTION_IF_NULL(bn_training_update);
// Set abstract
AnfNodePtr bn = GetBatchNormNode(node);
fused_bn->set_abstract(CreateAbstractOfFusedBatchNorm(equiv, bn));
// Set attr
AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn, fused_bn);
auto iter_batch_norm = (*equiv).find(batch_norm_var_);
if (iter_batch_norm == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the batch_norm var after matched.";
}
AnfNodePtr bn = utils::cast<AnfNodePtr>(iter_batch_norm->second);
MS_EXCEPTION_IF_NULL(bn);
AbstractBasePtrList abstract_list;
GetBNTrainingUpdateAbstractList(equiv, bn, &abstract_list);
auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(abstract_list);
bn_training_update->set_abstract(abstract_tuple);
AnfAlgo::CopyNodeAttr(kAttrEpsilon, bn, bn_training_update);
ValuePtr factor = GetFactor(equiv);
if (factor == nullptr) {
return nullptr;
}
AnfAlgo::SetNodeAttr(kAttrMomentum, factor, fused_bn);
// Replace old nodes with outputs of fused_bn
std::vector<AnfNodePtr> fused_bn_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, fused_bn, kBnOutputNum, &fused_bn_outputs);
if (fused_bn_outputs.size() != kBnOutputNum) {
MS_LOG(EXCEPTION) << "The output size of node bn must be " << kBnOutputNum << ", but it is "
<< fused_bn_outputs.size();
AnfAlgo::SetNodeAttr(kAttrFactor, factor, bn_training_update);
AnfAlgo::SetNodeAttr(kAttrIsRef, MakeValue(true), bn_training_update);
bn_training_update->set_scope(node->scope());
return bn_training_update;
}
const AnfNodePtr FusedBatchNormFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &equiv) const {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(equiv);
MS_EXCEPTION_IF_NULL(node);
AnfNodePtr bn_training_reduce = CreateBNTrainingReduce(func_graph, node, equiv);
std::vector<AnfNodePtr> bn_training_reduce_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, bn_training_reduce, kBNTrainingReduceOutputNum,
&bn_training_reduce_outputs);
AnfNodePtr bn_training_update = CreateBNTrainingUpdate(func_graph, node, equiv, bn_training_reduce_outputs);
if (bn_training_update == nullptr) {
MS_LOG(DEBUG) << "Create BNTrainingUpdate failed for bn node " << node->DebugString();
return nullptr;
}
std::vector<AnfNodePtr> bn_training_update_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, bn_training_update, kBNTrainingUpdateOutputNum,
&bn_training_update_outputs);
if (bn_training_update_outputs.size() < kBNTrainingUpdateOutputNum) {
MS_LOG(EXCEPTION) << "The output size of node bn must be " << kBNTrainingUpdateOutputNum << ", but it is "
<< bn_training_update_outputs.size();
}
// Replace old bn outputs with new outputs
auto iter_batch_norm = (*equiv).find(batch_norm_var_);
if (iter_batch_norm == (*equiv).end()) {
MS_LOG(EXCEPTION) << "The equiv map is expected to contains the batch_norm var after matched.";
}
AnfNodePtr bn = utils::cast<AnfNodePtr>(iter_batch_norm->second);
std::vector<AnfNodePtr> bn_outputs;
GetBNOutput(func_graph, bn, &bn_outputs);
if (bn_outputs.size() != kBnOutputNum) {
MS_LOG(EXCEPTION) << "The output size of node bn must be " << kBnOutputNum << ", but it is " << bn_outputs.size();
}
auto manager = func_graph->manager();
MS_EXCEPTION_IF_NULL(manager);
(void)manager->Replace(bn_outputs[3], fused_bn_outputs[3]);
(void)manager->Replace(bn_outputs[4], fused_bn_outputs[4]);
return fused_bn_outputs[0];
for (const auto &output : bn_outputs) {
MS_EXCEPTION_IF_NULL(output);
auto tuple_getitem_cnode = output->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(tuple_getitem_cnode);
AnfNodePtr index_node = tuple_getitem_cnode->input(kInputNodeOutputIndexInTupleGetItem);
MS_EXCEPTION_IF_NULL(index_node);
auto value_node = index_node->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
int index = GetValue<int>(value_node->value());
if (index == kReplaceOutputIndex0 || index == kReplaceOutputIndex1) {
(void)manager->Replace(output, bn_training_update_outputs[index]);
}
}
return bn_training_update_outputs[0];
}
} // namespace opt
} // namespace mindspore
mindspore/ccsrc/pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.h
浏览文件 @ 7185961e
......@@ -19,6 +19,7 @@
#include <vector>
#include <memory>
#include "pre_activate/common/optimizer.h"
#include "utils/utils.h"
namespace mindspore {
namespace opt {
......@@ -26,29 +27,37 @@ class FusedBatchNormFusion : public PatternProcessPass {
public:
explicit FusedBatchNormFusion(bool multigraph = true)
: PatternProcessPass("fused_batch_norm_fusion", multigraph),
data_input_var0_(std::make_shared<Var>()),
data_input_var1_(std::make_shared<Var>()),
data_input_var2_(std::make_shared<Var>()),
variable_input_var0_(std::make_shared<Var>()),
variable_input_var1_(std::make_shared<Var>()),
constant_input_var0_(std::make_shared<Var>()),
constant_input_var1_(std::make_shared<Var>()) {}
data_input0_var_(std::make_shared<Var>()),
data_input1_var_(std::make_shared<Var>()),
data_input2_var_(std::make_shared<Var>()),
variable_input0_var_(std::make_shared<Var>()),
variable_input1_var_(std::make_shared<Var>()),
constant_input0_var_(std::make_shared<Var>()),
constant_input1_var_(std::make_shared<Var>()),
batch_norm_var_(std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimBatchNorm->name()))) {}
~FusedBatchNormFusion() override = default;
const BaseRef DefinePattern() const override;
const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
private:
abstract::AbstractTuplePtr CreateAbstractOfFusedBatchNorm(const EquivPtr &equiv, const AnfNodePtr &bn) const;
AnfNodePtr CreateBNTrainingReduce(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &equiv) const;
void GetBNTrainingUpdateInputs(const EquivPtr &equiv, const std::vector<AnfNodePtr> &bn_training_reduce_outputs,
std::vector<AnfNodePtr> *bn_training_update_inputs) const;
void GetBNTrainingUpdateAbstractList(const EquivPtr &equiv, const AnfNodePtr &bn,
std::vector<AbstractBasePtr> *abstract_list) const;
AnfNodePtr CreateBNTrainingUpdate(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &equiv,
const std::vector<AnfNodePtr> &bn_training_reduce_outputs) const;
ValuePtr GetFactor(const EquivPtr &equiv) const;
VarPtr data_input_var0_;
VarPtr data_input_var1_;
VarPtr data_input_var2_;
VarPtr variable_input_var0_;
VarPtr variable_input_var1_;
VarPtr constant_input_var0_;
VarPtr constant_input_var1_;
VarPtr data_input0_var_;
VarPtr data_input1_var_;
VarPtr data_input2_var_;
VarPtr variable_input0_var_;
VarPtr variable_input1_var_;
VarPtr constant_input0_var_;
VarPtr constant_input1_var_;
VarPtr batch_norm_var_;
};
} // namespace opt
} // namespace mindspore
......
mindspore/nn/layer/normalization.py
浏览文件 @ 7185961e
......@@ -62,6 +62,7 @@ class _BatchNorm(Cell):
self.beta = Parameter(initializer(
beta_init, num_features), name="beta", requires_grad=affine)
self.group = check_int_positive(device_num_each_group)
self.is_global = False
if self.group != 1:
self.rank_id = get_rank()
self.rank_size = get_group_size()
......@@ -80,15 +81,18 @@ class _BatchNorm(Cell):
self.cast = P.Cast()
self.dtype = P.DType()
self.reshape = P.Reshape()
self.is_ascend = context.get_context("device_target") == "Ascend"
if context.get_context("enable_ge"):
self.is_ge_backend = True
self.momentum = Tensor(1.0 - momentum, mstype.float32)
self.bn_train = P.BatchNorm(is_training=True,
epsilon=self.eps)
else:
self.is_ge_backend = False
self.momentum = 1.0 - momentum
if self.is_ge_backend or self.is_ascend:
self.bn_train = P.BatchNorm(is_training=True,
epsilon=self.eps)
else:
self.bn_train = P.FusedBatchNorm(mode=1,
epsilon=self.eps,
momentum=self.momentum)
......@@ -140,24 +144,23 @@ class _BatchNorm(Cell):
def construct(self, x):
if self.training and self.use_batch_statistics:
if self.is_ge_backend:
if self.is_global:
axes, re_shape = _shape_infer(F.shape(x), self.num_features)
y = self._global_sync(x, axes, re_shape)
else:
y, batch_mean, batch_var, _, _ = \
self.bn_train(x,
self.gamma,
self.beta,
None,
None)
mean_sub = self.sub_mean(self.moving_mean, batch_mean)
temp_mean = self.mul_mean(mean_sub, self.momentum)
mean_sub2 = self.sub_var(self.moving_variance, batch_var)
temp_variance = self.mul_var(mean_sub2, self.momentum)
y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
if self.is_ge_backend and self.is_global:
axes, re_shape = _shape_infer(F.shape(x), self.num_features)
y = self._global_sync(x, axes, re_shape)
elif self.is_ge_backend or self.is_ascend:
y, batch_mean, batch_var, _, _ = \
self.bn_train(x,
self.gamma,
self.beta,
None,
None)
mean_sub = self.sub_mean(self.moving_mean, batch_mean)
temp_mean = self.mul_mean(mean_sub, self.momentum)
mean_sub2 = self.sub_var(self.moving_variance, batch_var)
temp_variance = self.mul_var(mean_sub2, self.momentum)
y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
else:
y = self.bn_train(x,
self.gamma,
......
tests/ut/cpp/pre_activate/ascend/ir_fusion/fused_batch_norm_fusion_test.cc 0 → 100644
浏览文件 @ 7185961e
/**
* 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.
*/
#include "pre_activate/ascend/ir_fusion/fused_batch_norm_fusion.h"
#include "common/backend_common_test.h"
#include "common/py_func_graph_fetcher.h"
namespace mindspore {
namespace opt {
class TestHWFusedBatchNormFusion : public BackendCommon {
public:
TestHWFusedBatchNormFusion() : get_py_fun_("gtest_input.pre_activate.fused_batch_norm_fusion_test", true) {}
~TestHWFusedBatchNormFusion() override = default;
UT::PyFuncGraphFetcher get_py_fun_;
};
TEST_F(TestHWFusedBatchNormFusion, test_fused_batch_norm_fusion) {
FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_fused_batch_norm_fusion", "before");
EXPECT_NE(g, nullptr);
std::vector<int> shp_x{32, 64, 112, 112};
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
std::vector<int> shp_y{64};
auto y_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_y);
AbstractBasePtrList args_spec_list{x_abstract};
for (size_t i = 0; i < 6; ++i) {
args_spec_list.push_back(y_abstract);
}
auto kg = GetKernelGraph(g, args_spec_list);
auto optimizer = std::make_shared<opt::GraphOptimizer>();
auto pm = std::make_shared<opt::PassManager>();
pm->AddPass(std::make_shared<opt::FusedBatchNormFusion>());
optimizer->AddPassManager(pm);
FuncGraphPtr new_graph = optimizer->Optimize(kg);
FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_fused_batch_norm_fusion", "after");
EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
}
} // namespace opt
} // namespace mindspore
\ No newline at end of file
tests/ut/cpp/python_input/gtest_input/pre_activate/fused_batch_norm_fusion_test.py
浏览文件 @ 7185961e
......@@ -24,7 +24,8 @@ make_tuple = Primitive('make_tuple')
tuple_getitem = Primitive('tuple_getitem')
depend = Primitive('depend')
BatchNorm = P.BatchNorm()
FusedBatchNorm = P.FusedBatchNorm()
BNTrainingReduce = Primitive('BNTrainingReduce')
BNTrainingUpdate = Primitive('BNTrainingUpdate')
constant0 = Tensor(0.1, mstype.float32)
constant1 = Tensor(0.1, mstype.float32)
......@@ -40,7 +41,7 @@ class FnDict:
return self.fnDict[name]
def useless_test_fused_batch_norm_fusion(tag):
def test_fused_batch_norm_fusion(tag):
fns = FnDict()
@fns
......@@ -60,9 +61,11 @@ def useless_test_fused_batch_norm_fusion(tag):
@fns
def after(input0, input1, input2, input3, input4, var0, var1):
fused_batch_norm = FusedBatchNorm(input0, input1, input2, var0, var1)
outputs = make_tuple(tuple_getitem(fused_batch_norm, 0), tuple_getitem(fused_batch_norm, 3),
tuple_getitem(fused_batch_norm, 4))
bn_training_reduce = BNTrainingReduce(input0)
bn_training_update = BNTrainingUpdate(input0, tuple_getitem(bn_training_reduce, 0),
tuple_getitem(bn_training_reduce, 1), input1, input2, var0, var1)
outputs = make_tuple(tuple_getitem(bn_training_update, 0), tuple_getitem(bn_training_update, 3),
tuple_getitem(bn_training_update, 4))
output = tuple_getitem(outputs, 0)
return make_tuple(output)
......
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