add ReduceMin fission pass

mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc

@@ -16,7 +16,6 @@
 #include "backend/optimizer/ascend/ascend_backend_optimization.h"
 #include <memory>
 #include <string>
-#include <set>
 #include "backend/optimizer/common/optimizer.h"
 #include "backend/optimizer/ascend/ir_fission/bn_split.h"
 #include "backend/optimizer/ascend/ir_fission/bn_grad_split.h"
@@ -24,6 +23,7 @@
 #include "backend/optimizer/ascend/ir_fission/batch_norm_bert_fission.h"
 #include "backend/optimizer/ascend/ir_fission/single_batch_norm_fission.h"
 #include "backend/optimizer/ascend/ir_fission/tensor_scatter_update_fission.h"
+#include "backend/optimizer/ascend/ir_fission/reduce_min_fission.h"
 #include "backend/optimizer/ascend/ir_fusion/fused_batch_norm_fusion.h"
 #include "backend/optimizer/ascend/ir_fission/layer_norm_grad_split.h"
 #include "backend/optimizer/pass/communication_op_fusion.h"
@@ -111,18 +111,9 @@
 namespace mindspore {
 namespace opt {
 namespace {
-void AddAscendBackendOptionalIRFusion(PassManager *ir_fusion_pm) {
+void AddAscendIRFusionRulesPass(PassManager *ir_fusion_pm) {
   MS_EXCEPTION_IF_NULL(ir_fusion_pm);
-  ir_fusion_pm->AddPass(std::make_shared<BatchNormBertFission>());
-  ir_fusion_pm->AddPass(std::make_shared<SingleBatchNormFission>());
-  ir_fusion_pm->AddPass(std::make_shared<SquareSumFusion>());
-  ir_fusion_pm->AddPass(std::make_shared<ClipByNormNoDivSquareSumFusion>());
   ir_fusion_pm->AddPass(std::make_shared<LambUpdateWithLRRuleFusion>());
-  ir_fusion_pm->AddPass(std::make_shared<SoftmaxGradExtFusion>());
-  ir_fusion_pm->AddPass(std::make_shared<SoftmaxGradExtFusionV2>());
-  ir_fusion_pm->AddPass(std::make_shared<SoftmaxGradExtFusionV3>());
-  ir_fusion_pm->AddPass(std::make_shared<ConfusionMulGradFusion>());
-  ir_fusion_pm->AddPass(std::make_shared<ConfusionSoftmaxGradRule>());
   ir_fusion_pm->AddPass(std::make_shared<LambNextMVWithDecayRuleCond1>());
   ir_fusion_pm->AddPass(std::make_shared<LambNextMVWithDecayRuleCond2>());
   ir_fusion_pm->AddPass(std::make_shared<LambNextMVWithDecayRuleCond3>());
@@ -133,10 +124,6 @@ void AddAscendBackendOptionalIRFusion(PassManager *ir_fusion_pm) {
   ir_fusion_pm->AddPass(std::make_shared<LambNextMVRuleCond4>());
   ir_fusion_pm->AddPass(std::make_shared<LambNextRightRule>());
   ir_fusion_pm->AddPass(std::make_shared<LambUpdateWithLrV2>());
-  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<TopKSplit>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneCond1Fusion>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneCond2Fusion>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneCond3Fusion>());
@@ -146,6 +133,27 @@ void AddAscendBackendOptionalIRFusion(PassManager *ir_fusion_pm) {
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneWithDecayRuleCond3>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneWithDecayRuleCond4>());
   ir_fusion_pm->AddPass(std::make_shared<AdamApplyOneWithDecayRuleCond5>());
+  ir_fusion_pm->AddPass(std::make_shared<ClipByNormNoDivSquareSumFusion>());
+  ir_fusion_pm->AddPass(std::make_shared<SquareSumFusion>());
+  ir_fusion_pm->AddPass(std::make_shared<ClipByValueFusion>());
+}
+
+void AddAscendIRFusionPass(PassManager *ir_fusion_pm) {
+  MS_EXCEPTION_IF_NULL(ir_fusion_pm);
+  ir_fusion_pm->AddPass(std::make_shared<BatchNormBertFission>());
+  ir_fusion_pm->AddPass(std::make_shared<SingleBatchNormFission>());
+  ir_fusion_pm->AddPass(std::make_shared<BatchNorm2BNInfer>());
+  ir_fusion_pm->AddPass(std::make_shared<BatchNormGrad2BNInferGrad>());
+  ir_fusion_pm->AddPass(std::make_shared<BatchNormGradInferFission>());
+  ir_fusion_pm->AddPass(std::make_shared<GetitemTuple>());
+  ir_fusion_pm->AddPass(std::make_shared<SoftmaxGradExtFusion>());
+  ir_fusion_pm->AddPass(std::make_shared<SoftmaxGradExtFusionV2>());
+  ir_fusion_pm->AddPass(std::make_shared<SoftmaxGradExtFusionV3>());
+  ir_fusion_pm->AddPass(std::make_shared<ConfusionMulGradFusion>());
+  ir_fusion_pm->AddPass(std::make_shared<ConfusionSoftmaxGradRule>());
+  ir_fusion_pm->AddPass(std::make_shared<ReshapeTransposeFusion>());
+  ir_fusion_pm->AddPass(std::make_shared<TransposeReshapeFusion>());
+  ir_fusion_pm->AddPass(std::make_shared<TopKSplit>());
   ir_fusion_pm->AddPass(std::make_shared<MomentumLossscaleFusion>());
   ir_fusion_pm->AddPass(std::make_shared<MulAddFusion>());
   ir_fusion_pm->AddPass(std::make_shared<MulAddNFusion>());
@@ -153,15 +161,12 @@ void AddAscendBackendOptionalIRFusion(PassManager *ir_fusion_pm) {
   ir_fusion_pm->AddPass(std::make_shared<AddnFission>());
   ir_fusion_pm->AddPass(std::make_shared<DereluFusion>());
   ir_fusion_pm->AddPass(std::make_shared<TransposeTransDataFusion>());
-  ir_fusion_pm->AddPass(std::make_shared<GetitemTuple>());
-  ir_fusion_pm->AddPass(std::make_shared<BatchNorm2BNInfer>());
-  ir_fusion_pm->AddPass(std::make_shared<BatchNormGrad2BNInferGrad>());
-  ir_fusion_pm->AddPass(std::make_shared<BatchNormGradInferFission>());
   ir_fusion_pm->AddPass(std::make_shared<SplitFission>());
   ir_fusion_pm->AddPass(std::make_shared<TensorScatterUpdateFission>());
   ir_fusion_pm->AddPass(std::make_shared<GetitemTuple>());
   ir_fusion_pm->AddPass(std::make_shared<PackFission>());
   ir_fusion_pm->AddPass(std::make_shared<ConcatFission>());
+  ir_fusion_pm->AddPass(std::make_shared<ReduceMinFission>());
 }
 }  // namespace
 
@@ -265,9 +270,8 @@ void AscendBackendIRFusionOptimization(const std::shared_ptr<session::KernelGrap
     ir_fusion_pm->AddPass(std::make_shared<FusedBatchNormMixPrecisionFusion1>());
   }
   ir_fusion_pm->AddPass(std::make_shared<InsertPadForNMSWithMask>());
-  if (context_ptr->ir_fusion_flag()) {
-    AddAscendBackendOptionalIRFusion(ir_fusion_pm.get());
-  }
+  AddAscendIRFusionRulesPass(ir_fusion_pm.get());
+  AddAscendIRFusionPass(ir_fusion_pm.get());
 
   if (context_ptr->enable_task_sink() && context_ptr->loop_sink_flag() && ConfigManager::GetInstance().iter_num() > 1) {
     ir_fusion_pm->AddPass(std::make_shared<InsertMemcpyAsyncForGetNext>());

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/reduce_min_fission.cc

@@ -0,0 +1,144 @@
+/**
+ * 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 "backend/optimizer/ascend/ir_fission/reduce_min_fission.h"
+#include <memory>
+#include <vector>
+#include "backend/session/anf_runtime_algorithm.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+CNodePtr CreateReduceMin(const FuncGraphPtr &graph, const AnfNodePtr &input, const CNodePtr &old_node) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(input);
+  MS_EXCEPTION_IF_NULL(old_node);
+  std::vector<AnfNodePtr> inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimReduceMin->name())), input};
+  CNodePtr reduce_min = graph->NewCNode(inputs);
+  reduce_min->set_scope(old_node->scope());
+  AnfAlgo::CopyNodeAttr(kAttrKeepDims, old_node, reduce_min);
+  return reduce_min;
+}
+
+bool NeedOptmize(const TypeId &dtype, const std::vector<size_t> &shape, const std::vector<int> &axis) {
+  if (dtype != kNumberTypeFloat32) {
+    MS_LOG(INFO) << "ReduceMin's input Dtype is not float32, no need optimize!";
+    return false;
+  }
+  if (shape.size() == 0 || shape.size() == 1) {
+    MS_LOG(INFO) << "ReduceMin's input shape size is " << shape.size() << ", no need optimize!";
+    return false;
+  }
+  if (axis.size() == 1) {
+    MS_LOG(INFO) << "ReduceMin axis size is 1, no need optimize!";
+    return false;
+  }
+  int last_dim = SizeToInt(shape.size() - 1);
+  if (std::find(axis.begin(), axis.end(), -1) == axis.end() &&
+      std::find(axis.begin(), axis.end(), last_dim) == axis.end()) {
+    MS_LOG(INFO) << "Attribute of axis does not contain the last axis, not match!";
+    return false;
+  }
+  return true;
+}
+
+std::vector<int> CalFirstAxis(const std::vector<size_t> &shape, const std::vector<int> &axis) {
+  std::vector<int> axis_fisrt;
+  int last_dim = SizeToInt(shape.size() - 1);
+  std::copy_if(axis.begin(), axis.end(), std::back_inserter(axis_fisrt),
+               [&last_dim](int v) { return v != -1 && v != last_dim; });
+
+  int dim_size = SizeToInt(shape.size());
+  if (axis_fisrt.empty()) {
+    for (int i = 0; i < dim_size - 1; ++i) {
+      axis_fisrt.push_back(i);
+    }
+  }
+
+  for (size_t i = 0; i < axis_fisrt.size(); ++i) {
+    if (axis_fisrt[i] < -dim_size || axis_fisrt[i] > dim_size - 1) {
+      MS_LOG(EXCEPTION) << "The axis of ReduceMin verify failed, quit optimizing";
+    }
+    if (axis_fisrt[i] < 0) {
+      axis_fisrt[i] = dim_size + axis_fisrt[i];
+    }
+  }
+  return axis_fisrt;
+}
+
+std::vector<size_t> GetInferShape(const std::vector<size_t> &shape, const std::vector<int> &axis_first,
+                                  bool keep_dims) {
+  std::vector<size_t> shape_first;
+  for (size_t item = 0; item < shape.size(); ++item) {
+    if (axis_first.end() != std::find(axis_first.begin(), axis_first.end(), item)) {
+      if (keep_dims) {
+        // If keep_dims is true, curretn dimesion set to 1
+        shape_first.push_back(1);
+      }
+    } else {
+      // item is not in ConstValueAxis
+      shape_first.push_back(shape[item]);
+    }
+  }
+  return shape_first;
+}
+}  // namespace
+
+const BaseRef ReduceMinFission::DefinePattern() const {
+  VarPtr X = std::make_shared<Var>();
+  return VectorRef({prim::kPrimReduceMin, X});
+}
+
+const AnfNodePtr ReduceMinFission::Process(const FuncGraphPtr &graph, const AnfNodePtr &node, const EquivPtr &) const {
+  if (graph == nullptr || node == nullptr) {
+    return nullptr;
+  }
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  CheckCNodeInputSize(cnode, 2);
+  auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0);
+  auto dtype = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, 0);
+  if (!AnfAlgo::HasNodeAttr(kAttrAxis, cnode)) {
+    MS_LOG(INFO) << "ReduceMin has no axis, no need optimize!";
+    return nullptr;
+  }
+  auto axis = AnfAlgo::GetNodeAttr<std::vector<int>>(cnode, kAttrAxis);
+  if (!AnfAlgo::HasNodeAttr(kAttrKeepDims, cnode)) {
+    MS_LOG(INFO) << "ReduceMin has no keep_dims, no need optimize!";
+    return nullptr;
+  }
+  auto keep_dims = AnfAlgo::GetNodeAttr<bool>(cnode, kAttrKeepDims);
+
+  if (!NeedOptmize(dtype, shape, axis)) {
+    MS_LOG(INFO) << "No need optimize for this ReduceMin. " << cnode->DebugString();
+    return nullptr;
+  }
+
+  // Create reduce_min1
+  CNodePtr reduce_min1 = CreateReduceMin(graph, cnode->input(1), cnode);
+  std::vector<int> axis_fisrt = CalFirstAxis(shape, axis);
+  std::vector<size_t> shape_first = GetInferShape(shape, axis_fisrt, keep_dims);
+  AnfAlgo::SetOutputInferTypeAndShape({dtype}, {shape_first}, reduce_min1.get());
+  AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_fisrt), reduce_min1);
+
+  // Create reduce_min2
+  CNodePtr reduce_min2 = CreateReduceMin(graph, reduce_min1, cnode);
+  reduce_min2->set_abstract(cnode->abstract());
+  std::vector<int> axis_last = {-1};
+  AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis_last), reduce_min2);
+  return reduce_min2;
+}
+}  // namespace opt
+}  // namespace mindspore

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/reduce_min_fission.h

@@ -0,0 +1,33 @@
+/**
+ * Copyright 2019 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_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_REDUCE_MIN_FISSION_H_
+#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_REDUCE_MIN_FISSION_H_
+
+#include "backend/optimizer/common/optimizer.h"
+#include "backend/optimizer/common/helper.h"
+
+namespace mindspore {
+namespace opt {
+class ReduceMinFission : public PatternProcessPass {
+ public:
+  explicit ReduceMinFission(bool multigraph = true) : PatternProcessPass("reduce_min_fission", multigraph) {}
+  ~ReduceMinFission() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_ASCEND_IR_FISSION_REDUCE_MIN_FISSION_H_

tests/ut/cpp/pre_activate/ascend/ir_fission/reduce_min_fission_test.cc

@@ -0,0 +1,56 @@
+/**
+ * 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 "common/backend_common_test.h"
+#include "common/py_func_graph_fetcher.h"
+#include "debug/anf_ir_dump.h"
+#define private public
+#define protected public
+#include "backend/optimizer/ascend/ir_fission/reduce_min_fission.h"
+#undef private
+#undef protected
+
+namespace mindspore {
+namespace opt {
+class TestHWOptReduceMinFission : public BackendCommon {
+ public:
+  TestHWOptReduceMinFission() : get_py_fun_("gtest_input.pre_activate.reduce_min_fission_test", true) {}
+  ~TestHWOptReduceMinFission() override = default;
+
+  UT::PyFuncGraphFetcher get_py_fun_;
+};
+
+TEST_F(TestHWOptReduceMinFission, test_fission) {
+  FuncGraphPtr g = get_py_fun_.CallAndParseRet("test_reduce_min_fission", "before");
+  EXPECT_NE(g, nullptr);
+  std::vector<int> shp{32, 32, 32, 32};
+  auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+  AbstractBasePtrList args_spec_list;
+  args_spec_list.push_back(x_abstract);
+  auto kg = GetKernelGraph(g, args_spec_list);
+
+  auto optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pm = std::make_shared<opt::PassManager>();
+  auto split_fission = std::make_shared<opt::ReduceMinFission>();
+  pm->AddPass(split_fission);
+  optimizer->AddPassManager(pm);
+  FuncGraphPtr new_graph = optimizer->Optimize(kg);
+
+  FuncGraphPtr g_after = get_py_fun_.CallAndParseRet("test_reduce_min_fission", "after");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_graph));
+}
+}  // namespace opt
+}  // namespace mindspore

tests/ut/cpp/python_input/gtest_input/pre_activate/reduce_min_fission_test.py

@@ -0,0 +1,51 @@
+# 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.
+# ============================================================================
+
+from mindspore.ops import Primitive
+from mindspore.ops import operations as P
+
+make_tuple = Primitive('make_tuple')
+tuple_getitem = Primitive('tuple_getitem')
+reduce_min = P.ReduceMin(keep_dims=False)
+reduce_min1 = Primitive('ReduceMin')
+reduce_min2 = Primitive('ReduceMin')
+
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+
+def test_reduce_min_fission(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x):
+        res = reduce_min(x, (2, 3))
+        return res
+
+    @fns
+    def after(x):
+        res = reduce_min1(x)
+        res = reduce_min2(res)
+        return make_tuple(res)
+
+    return fns[tag]