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!13834 [GraphKernel] Remove two graph kernel frontend pass 

From: @tronzhang
Reviewed-by: @gaoxiong1,@gaoxiong1,@dylangeng
Signed-off-by: @dylangeng
pull/13834/MERGE
mindspore-ci-bot Gitee 4 years ago
parent
203106c133 87bf1ec80f
commit
a06234ed13
12 changed files with 34 additions and 330 deletions
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  1. +1
    -1
      akg
  2. +3
    -5
      mindspore/_extends/graph_kernel/expanders/clip_by_norm_no_div_sum.py
  3. +19
    -15
      mindspore/_extends/graph_kernel/model/graph_split.py
  4. +0
    -4
      mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_generator.cc
  5. +0
    -1
      mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_generator.h
  6. +11
    -4
      mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc
  7. +0
    -152
      mindspore/ccsrc/frontend/optimizer/graph_kernel_reuse.cc
  8. +0
    -50
      mindspore/ccsrc/frontend/optimizer/graph_kernel_reuse.h
  9. +0
    -5
      mindspore/ccsrc/frontend/optimizer/irpass.cc
  10. +0
    -3
      mindspore/ccsrc/frontend/optimizer/irpass.h
  11. +0
    -84
      mindspore/ccsrc/frontend/optimizer/irpass/mark_interface_fusion.h
  12. +0
    -6
      mindspore/ccsrc/pipeline/jit/pass.cc

+ 1
- 1
akg

@@ -1 +1 @@
Subproject commit 78799c123d966ce78ae7c9f6860264628262a16e
Subproject commit 1b0aacec7c125083dbe9fd54174495f92f6bb191

+ 3
- 5
mindspore/_extends/graph_kernel/expanders/clip_by_norm_no_div_sum.py View File

@@ -24,12 +24,10 @@ class ClipByNormNoDivSum(Expander):
input_x0, input_x1, input_x2, input_x3 = self.inputs

# cal result
greater_res = graph_builder.emit('Greater', [input_x0, input_x1], attrs={'fusion': 'SelectGT_000'})
select_res0 = graph_builder.emit('Select', [greater_res, input_x0, input_x2],
attrs={'fusion': 'SelectGT_000_end'})
greater_res = graph_builder.emit('Greater', [input_x0, input_x1])
select_res0 = graph_builder.emit('Select', [greater_res, input_x0, input_x2])
sqrt_res = graph_builder.emit('Sqrt', [select_res0])
select_res1 = graph_builder.emit('Select', [greater_res, sqrt_res, input_x0],
attrs={'fusion': 'SelectGT_000_end'})
select_res1 = graph_builder.emit('Select', [greater_res, sqrt_res, input_x0])
result = graph_builder.emit('Maximum', [select_res1, input_x3])

return result

+ 19
- 15
mindspore/_extends/graph_kernel/model/graph_split.py View File

@@ -123,7 +123,7 @@ class GraphSplitByPattern:
self.output_excluded.update(area.output_excluded)
self.update_stitch_info(area.stitch_info)

def check_circle(self, to):
def check_acyclic(self, to):
"""Check circle. It returns false if circle exists"""
def _reached(area, to):
for out, _ in area.out_relations.items():
@@ -215,6 +215,10 @@ class GraphSplitByPattern:
with open(filename, 'w') as f:
f.write(subgraphs_str)

def do_split(self):
"""Split graph by pattern"""
raise Exception("do_split() is not implemented in {}".format(self.__class__.__name__))

def split(self):
"""Split graph by pattern"""
self.do_split()
@@ -270,11 +274,11 @@ class GraphSplitGpu(GraphSplitByPattern):
return None
min_area, forward_fuse = None, False
for a, _ in dom.out_relations.items():
if a.pattern <= PrimLib.BROADCAST and dom.check_circle(a) and \
if a.pattern <= PrimLib.BROADCAST and dom.check_acyclic(a) and \
(min_area is None or a.pattern < min_area.pattern):
min_area = a
for a, _ in dom.in_relations.items():
if a.pattern <= PrimLib.BROADCAST and a.check_circle(dom) and \
if a.pattern <= PrimLib.BROADCAST and a.check_acyclic(dom) and \
len(dom.ops[0].inputs[0].to_ops) == 1 and not a.is_output and \
(min_area is None or a.pattern < min_area.pattern):
min_area, forward_fuse = a, True
@@ -294,7 +298,7 @@ class GraphSplitGpu(GraphSplitByPattern):
return None
fused = []
for a, r in dom.in_relations.items():
if a.pattern <= PrimLib.BROADCAST and r == PrimLib.ELEMWISE and a.check_circle(dom) and \
if a.pattern <= PrimLib.BROADCAST and r == PrimLib.ELEMWISE and a.check_acyclic(dom) and \
a.dom_op().output.shape == dom.dom_op().output.shape:
fused.append(a)
return fused, True
@@ -319,7 +323,7 @@ class GraphSplitGpu(GraphSplitByPattern):
return None
fused = []
for a, r in dom.out_relations.items():
if _broadcast_pat_exclude(dom, a, r) or not dom.check_circle(a) or \
if _broadcast_pat_exclude(dom, a, r) or not dom.check_acyclic(a) or \
(fused and fused[0].dom_op().output.shape != a.dom_op().output.shape):
return None
fused.append(a)
@@ -341,7 +345,7 @@ class GraphSplitGpu(GraphSplitByPattern):
return True
return False

def _reduce_pat_exclude(dom, a, r):
def _reduce_pat_exclude(_, a, r):
if len(a.ops) > self.REDUCE_FUSE_DEPTH:
return True
if use_poly_reduce:
@@ -373,7 +377,7 @@ class GraphSplitGpu(GraphSplitByPattern):
_is_atomic_add_available(dom):
# to evade the precision problem.
continue
if not _reduce_pat_exclude(dom, a, r) and a.check_circle(dom):
if not _reduce_pat_exclude(dom, a, r) and a.check_acyclic(dom):
fused.append(a)
return fused, True

@@ -415,7 +419,7 @@ class GraphSplitGpu(GraphSplitByPattern):
fused = []
for a, r in dom.out_relations.items():
if a.pattern <= PrimLib.BROADCAST and r <= PrimLib.BROADCAST and \
dom.check_circle(a) and not dom.reduce_out_exclude(a):
dom.check_acyclic(a) and not dom.reduce_out_exclude(a):
fused.append(a)
return fused, False

@@ -429,7 +433,7 @@ class GraphSplitGpu(GraphSplitByPattern):

fused = []
for a, r in dom.out_relations.items():
if a.pattern <= PrimLib.REDUCE and r <= PrimLib.BROADCAST and dom.check_circle(a):
if a.pattern <= PrimLib.REDUCE and r <= PrimLib.BROADCAST and dom.check_acyclic(a):
if _reduce_nums(a.ops) < 2:
# softmax
if len(a.ops) > 4 and len(a.ops[0].inputs[0].shape) == 4:
@@ -442,7 +446,7 @@ class GraphSplitGpu(GraphSplitByPattern):
return None
fused = []
for a, _ in dom.in_relations.items():
if a.pattern <= PrimLib.BROADCAST and a.check_circle(dom):
if a.pattern <= PrimLib.BROADCAST and a.check_acyclic(dom):
fused.append(a)
return fused, True

@@ -490,11 +494,11 @@ class GraphSplitAscend(GraphSplitByPattern):
return None
min_area, forward_fuse = None, False
for a, _ in dom.out_relations.items():
if a.pattern <= PrimLib.BROADCAST and dom.check_circle(a) and \
if a.pattern <= PrimLib.BROADCAST and dom.check_acyclic(a) and \
(min_area is None or a.pattern < min_area.pattern):
min_area = a
for a, _ in dom.in_relations.items():
if a.pattern <= PrimLib.BROADCAST and a.check_circle(dom) and \
if a.pattern <= PrimLib.BROADCAST and a.check_acyclic(dom) and \
len(dom.ops[0].inputs[0].to_ops) == 1 and not a.is_output and \
(min_area is None or a.pattern < min_area.pattern):
min_area, forward_fuse = a, True
@@ -514,7 +518,7 @@ class GraphSplitAscend(GraphSplitByPattern):
return None
fused = []
for a, r in dom.in_relations.items():
if a.pattern <= PrimLib.BROADCAST and r == PrimLib.ELEMWISE and a.check_circle(dom) and \
if a.pattern <= PrimLib.BROADCAST and r == PrimLib.ELEMWISE and a.check_acyclic(dom) and \
a.dom_op().output.shape == dom.dom_op().output.shape:
fused.append(a)
return fused, True
@@ -537,7 +541,7 @@ class GraphSplitAscend(GraphSplitByPattern):
return None
fused = []
for a, r in dom.out_relations.items():
if _broadcast_pat_exclude(dom, a, r) or not dom.check_circle(a) or \
if _broadcast_pat_exclude(dom, a, r) or not dom.check_acyclic(a) or \
(fused and fused[0].dom_op().output.shape != a.dom_op().output.shape):
return None
fused.append(a)
@@ -564,7 +568,7 @@ class GraphSplitAscend(GraphSplitByPattern):
return None
fused = []
for a, r in dom.in_relations.items():
if not _reduce_pat_exclude(dom, a, r) and a.check_circle(dom):
if not _reduce_pat_exclude(dom, a, r) and a.check_acyclic(dom):
fused.append(a)
return fused, True



+ 0
- 4
mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_generator.cc View File

@@ -665,10 +665,6 @@ bool AkgKernelJsonGenerator::GenSingleJsons(const std::vector<AnfNodePtr> &anf_n
auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
MS_EXCEPTION_IF_NULL(primitive);

if (primitive->GetAttr("fusion") != nullptr) {
node_json["fusion"] = primitive->GetAttr("fusion")->ToString();
}

(*node_json_map)[anf_node] = node_json;
}
return true;


+ 0
- 1
mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_generator.h View File

@@ -49,7 +49,6 @@ constexpr auto kJsonKeyPtrAddress = "ptr_address";
constexpr auto kJsonKeyCompositeGraph = "composite_graph";
constexpr auto kJsonKeyPlatform = "platform";
constexpr auto kJsonKeyOpFullName = "op_full_name";
constexpr auto kJsonKeyFusion = "fusion";
constexpr auto kJsonKeyParallelFusion = "parallel_fusion";
constexpr auto kJsonKeyFusionType = "fusion_type";
constexpr auto kJsonKeySubGraph = "sub_graph";


+ 11
- 4
mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc View File

@@ -207,7 +207,7 @@ bool ConvertNonscalarTensorToParameter(const FuncGraphPtr &fg, AnfNodePtrList *i
MS_EXCEPTION_IF_NULL(inputs_ptr);
auto nodes = TopoSort(fg->get_return());

OrderedMap<ValuePtr, AnfNodePtrList> vmap;
std::vector<std::pair<tensor::TensorPtr, AnfNodePtrList>> v_replace;
std::vector<AnfNodePtr> scalar_tensors;
for (const auto &node : nodes) {
if (!node->isa<CNode>()) {
@@ -223,14 +223,21 @@ bool ConvertNonscalarTensorToParameter(const FuncGraphPtr &fg, AnfNodePtrList *i
if (TensorElementAllTheSame(tensor)) {
scalar_tensors.emplace_back(tnode);
} else {
vmap[GetValueNode(tnode)].push_back(tnode);
auto tensor_iter = std::find_if(
v_replace.begin(), v_replace.end(),
[&tensor](const std::pair<tensor::TensorPtr, AnfNodePtrList> &vl) { return vl.first->ValueEqual(*tensor); });
if (tensor_iter == v_replace.end()) {
v_replace.emplace_back(tensor, AnfNodePtrList{tnode});
} else {
tensor_iter->second.push_back(tnode);
}
}
}
}

ReplaceTensorWithScalar(fg, scalar_tensors);

if (vmap.empty()) {
if (v_replace.empty()) {
return false;
}

@@ -241,7 +248,7 @@ bool ConvertNonscalarTensorToParameter(const FuncGraphPtr &fg, AnfNodePtrList *i
}

auto &inputs = *inputs_ptr;
for (auto iter : vmap) {
for (auto iter : v_replace) {
auto value_nodes = iter.second;
if (value_nodes.empty()) {
MS_LOG(EXCEPTION) << "Invalid value in map!";


+ 0
- 152
mindspore/ccsrc/frontend/optimizer/graph_kernel_reuse.cc View File

@@ -1,152 +0,0 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#include "frontend/optimizer/graph_kernel_reuse.h"
#include <vector>
#include <algorithm>
#include <string>
#include "ir/graph_utils.h"

namespace mindspore {
/* namespace to support opt */
namespace opt {
bool GraphKernelReuse::CompareNode(const AnfNodePtr a, const AnfNodePtr b) {
if (a->abstract() && b->abstract()) {
auto a_type = a->abstract()->GetTypeTrack();
auto b_type = b->abstract()->GetTypeTrack();
if (a_type != b_type) {
return false;
}

auto a_shape = a->abstract()->GetShapeTrack();
auto b_shape = b->abstract()->GetShapeTrack();
if (a_shape != nullptr && a_shape == b_shape) {
return true;
}

if (a_shape != nullptr && b_shape != nullptr && a_shape->isa<abstract::Shape>() &&
b_shape->isa<abstract::Shape>()) {
return a_shape->cast<abstract::ShapePtr>()->shape() == b_shape->cast<abstract::ShapePtr>()->shape();
}
}
return false;
}

bool GraphKernelReuse::DoReplace(const FuncGraphManagerPtr manager) {
bool changed = false;
auto fgs = manager->func_graphs();
for (FuncGraphPtr &fg : fgs) {
if (!fg->has_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL)) {
continue;
}
std::string key = GetValue<std::string>(fg->get_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL));
if (graph_kernel_ops.find(key) != graph_kernel_ops.end()) {
if (find(graph_kernel_ops[key].begin(), graph_kernel_ops[key].end(), fg) == graph_kernel_ops[key].end()) {
FuncGraphPtr new_fg = nullptr;
for (auto &cfg : graph_kernel_ops[key]) {
// If two graphs have different size then continue
auto fg_topos = TopoSort(fg->get_return());
auto cfg_topos = TopoSort(cfg->get_return());
if (fg_topos.size() != cfg_topos.size()) {
continue;
}

// Compare const tensor
bool has_same = true;
for (size_t i = 0; i < fg_topos.size(); ++i) {
if (IsValueNode<tensor::Tensor>(fg_topos[i])) {
if (!IsValueNode<tensor::Tensor>(cfg_topos[i])) {
has_same = false;
break;
}

auto tensor1 = GetValueNode<tensor::TensorPtr>(fg_topos[i]);
auto tensor2 = GetValueNode<tensor::TensorPtr>(cfg_topos[i]);
if (!tensor1->ValueEqual(*tensor2)) {
has_same = false;
break;
}
}
}

if (!has_same) {
continue;
}

auto fg_input = fg->parameters();
auto cfg_input = cfg->parameters();
if (fg_input.size() != cfg_input.size()) {
continue;
}
// Compare input
for (size_t i = 0; i < fg_input.size(); ++i) {
if (!CompareNode(fg_input[i], cfg_input[i])) {
has_same = false;
break;
}
}
if (!has_same) {
continue;
}

// Compare output
if (!CompareNode(fg->output(), cfg->output())) {
continue;
}

// Find reusable fg
new_fg = cfg;
break;
}

if (new_fg != nullptr) {
// Replace current fg with existing fg
auto users = fg->func_graph_cnodes_index();
for (auto &iter : users) {
auto cnode = iter.first->first->cast<CNodePtr>();
auto new_input = cnode->inputs();
auto main_graph = cnode->func_graph();
MS_EXCEPTION_IF_NULL(main_graph);
if (IsPrimitiveCNode(cnode, prim::kPrimPartial)) {
new_input[1] = NewValueNode(new_fg);
} else {
new_input[0] = NewValueNode(new_fg);
}
auto new_cnode = main_graph->NewCNode(new_input);
manager->Replace(iter.first->first, new_cnode);
changed = true;
}
} else {
// Add current fg to map
graph_kernel_ops[key].push_back(fg);
}
}
} else {
graph_kernel_ops[key] = {fg};
}
}

return changed;
}

bool GraphKernelReuse::ReuseGraphKernel(const FuncGraphPtr root, const FuncGraphManagerPtr manager) {
MS_EXCEPTION_IF_NULL(manager);
manager->AddFuncGraph(root);

return DoReplace(manager);
}
} // namespace opt
} // namespace mindspore

+ 0
- 50
mindspore/ccsrc/frontend/optimizer/graph_kernel_reuse.h View File

@@ -1,50 +0,0 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#ifndef MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_GRAPH_KERNEL_OP_REUSE_H
#define MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_GRAPH_KERNEL_OP_REUSE_H

#include <unordered_map>
#include <string>
#include <vector>
#include "mindspore/ccsrc/backend/session/anf_runtime_algorithm.h"
#include "frontend/optimizer/optimizer.h"

namespace mindspore {
namespace opt {
// Common subexpression elimination.
class GraphKernelReuse {
public:
GraphKernelReuse() : count(0) {}
virtual ~GraphKernelReuse() = default;

bool operator()(const FuncGraphPtr &root, const OptimizerPtr &optimizer) {
bool chg = ReuseGraphKernel(root, optimizer->resource()->manager());
return chg;
}

bool CompareNode(const AnfNodePtr a, const AnfNodePtr other);
bool DoReplace(const FuncGraphManagerPtr manager);

bool ReuseGraphKernel(const FuncGraphPtr root, const FuncGraphManagerPtr manager);

private:
std::unordered_map<std::string, std::vector<FuncGraphPtr>> graph_kernel_ops;
int64_t count;
};
} // namespace opt
} // namespace mindspore
#endif // MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_GRAPH_KERNEL_OP_REUSE_H

+ 0
- 5
mindspore/ccsrc/frontend/optimizer/irpass.cc View File

@@ -29,7 +29,6 @@
#include "frontend/optimizer/irpass/incorporate_call.h"
#include "frontend/optimizer/irpass/incorporate_getitem.h"
#include "frontend/optimizer/irpass/item_tuple_or_list_eliminate.h"
#include "frontend/optimizer/irpass/mark_interface_fusion.h"
#include "frontend/optimizer/irpass/merge_addn.h"
#include "frontend/optimizer/irpass/accumulaten_eliminate.h"
#include "frontend/optimizer/irpass/minmax_grad.h"
@@ -198,10 +197,6 @@ OptimizeIRPassLib::OptimizeIRPassLib() {
// AddN eliminate
addn_eliminate_ = MakeSubstitution(std::make_shared<AddNEliminater>(), "addn_eliminate", IsCNodeGraphKernel);

// Mark interface fusion
mark_interface_fusion_ =
MakeSubstitution(std::make_shared<MarkInterfaceFusion>(), "mark_interface_fusion", prim::kPrimSelect);

// RowTensor Eliminate
row_tensor_eliminate_ = MakeSubstitution(
std::make_shared<RowTensorEliminater>(), "row_tensor_eliminate",


+ 0
- 3
mindspore/ccsrc/frontend/optimizer/irpass.h View File

@@ -124,9 +124,6 @@ class OptimizeIRPassLib {
// AddN eliminate
SubstitutionPtr addn_eliminate_;

// Fusion
SubstitutionPtr mark_interface_fusion_;

// RowTensor Eliminate
SubstitutionPtr row_tensor_eliminate_;



+ 0
- 84
mindspore/ccsrc/frontend/optimizer/irpass/mark_interface_fusion.h View File

@@ -1,84 +0,0 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#ifndef MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_MARK_INTERFACE_FUSION_H
#define MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_MARK_INTERFACE_FUSION_H

#include <string>
#include <sstream>
#include <unordered_map>

#include "backend/session/anf_runtime_algorithm.h"
#include "frontend/optimizer/optimizer.h"
#include "frontend/optimizer/irpass.h"
#include "frontend/optimizer/anf_visitor.h"
#include "frontend/operator/ops.h"
#include "ir/graph_utils.h"
#include "frontend/operator/composite/composite.h"

namespace mindspore {
namespace opt {
namespace irpass {
static int64_t count = 0;

std::string GetFusionNumber() {
std::stringstream ss;
ss << std::setw(4) << std::setfill('0') << count;
std::string num = ss.str();
++count;

return "_" + num;
}

// Mark CNodes which can be merged in kernel build
class MarkInterfaceFusion : public AnfVisitor {
public:
AnfNodePtr operator()(const OptimizerPtr &, const AnfNodePtr &node) override {
if (node->func_graph()->has_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL) && IsPrimitiveCNode(node, prim::kPrimSelect)) {
auto cnode = node->cast<CNodePtr>();
auto condition = cnode->input(1);
std::string cmp;
std::unordered_map<std::string, std::string> cmp_list = {{"GreaterEqual", "GE"}, {"Greater", "GT"},
{"LessEqual", "LE"}, {"Less", "LT"},
{"Equal", "EQ"}, {"NotEqual", "NE"}};
if (IsPrimitiveCNode(condition)) {
auto prim_name = GetCNodeFuncName(condition->cast<CNodePtr>());
if (cmp_list.count(prim_name) != 0) {
// Mark Select and compare node
cmp = cmp_list[prim_name];
auto cnt = GetFusionNumber();
AnfAlgo::SetNodeAttr("fusion", MakeValue("Select" + cmp + cnt), condition);
AnfAlgo::SetNodeAttr("fusion", MakeValue("Select" + cmp + cnt + "_end"), node);
for (size_t i = 1; i < cnode->inputs().size(); ++i) {
if (IsPrimitiveCNode(cnode->input(i), prim::kPrimZerosLike)) {
AnfAlgo::SetNodeAttr("fusion", MakeValue("Select" + cmp + cnt), cnode->input(i));
}
}
}
}
}
return nullptr;
}

void Visit(const AnfNodePtr &) override {}

private:
AnfNodePtr y_{nullptr};
};
} // namespace irpass
} // namespace opt
} // namespace mindspore
#endif // MINDSPORE_CCSRC_FRONTEND_OPTIMIZER_IRPASS_MARK_INTERFACE_FUSION_H

+ 0
- 6
mindspore/ccsrc/pipeline/jit/pass.cc View File

@@ -29,7 +29,6 @@
#include "pipeline/jit/remove_value_node_dup.h"
#include "frontend/optimizer/optimizer.h"
#include "frontend/optimizer/cse_pass.h"
#include "frontend/optimizer/graph_kernel_reuse.h"
#include "frontend/optimizer/clean.h"
#include "frontend/optimizer/irpass.h"
#include "frontend/optimizer/graph_transform.h"
@@ -261,12 +260,7 @@ OptPassGroupMap GetOptPassesPynativeElim(const opt::irpass::OptimizeIRPassLib &i
}

OptPassGroupMap GetOptPassesGraphKernelA(const opt::irpass::OptimizeIRPassLib &irpass) {
opt::OptPassConfig interface_fusion = opt::OptPassConfig({
irpass.mark_interface_fusion_,
});
OptPassGroupMap map({
{"graph_kernel_reuse", opt::OptPassConfig(opt::GraphKernelReuse())},
{"interface_fusion", interface_fusion},
{"renormalize", opt::OptPassConfig::Renormalize()},
{"cse", opt::OptPassConfig(opt::CSEPass(false))},
});


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