!20715 [ME][Auto-Monad]Add ref user to UpdateState to ensure the order

Merge pull request !20715 from Margaret_wangrui/auto_monad_ref_users_master

mindspore/ccsrc/backend/optimizer/pass/optimize_updatestate.cc

@@ -20,7 +20,6 @@
 #include <string>
 #include "base/core_ops.h"
 #include "utils/utils.h"
-#include "backend/session/kernel_graph.h"
 
 namespace mindspore {
 namespace opt {

mindspore/ccsrc/backend/session/gpu_session.cc

@@ -53,6 +53,7 @@
 #include "backend/optimizer/pass/communication_op_fusion.h"
 #include "backend/optimizer/gpu/concat_outputs_for_all_gather.h"
 #include "backend/optimizer/pass/getitem_tuple.h"
+#include "backend/optimizer/pass/optimize_updatestate.h"
 #include "common/trans.h"
 #include "debug/anf_ir_dump.h"
 #include "debug/data_dump/e2e_dump.h"
@@ -184,6 +185,8 @@ void GPUSession::HardwareOptimize(const std::shared_ptr<KernelGraph> &kernel_gra
   pm->AddPass(std::make_shared<opt::InsertFormatTransformOp>());
   pm->AddPass(std::make_shared<opt::RemoveFormatTransformPair>());
   pm->AddPass(std::make_shared<opt::RemoveRedundantFormatTransform>());
+  // Remove node only used by UpdateState, in order to ensure the correct execution sequence in CudnnInplaceAggregate.
+  pm->AddPass(std::make_shared<opt::OptimizeUpdateState>());
   pm->AddPass(std::make_shared<opt::CudnnInplaceAggregate>());
   pm->AddPass(std::make_shared<opt::ReluV2Pass>());
   pm->AddPass(std::make_shared<opt::AddReluV2Fusion>());

mindspore/ccsrc/backend/session/session_basic.cc

@@ -628,7 +628,6 @@ void SessionBasic::GetNewCNodeInputs(const CNodePtr &cnode, KernelGraph *graph,
   MS_EXCEPTION_IF_NULL(cnode_inputs);
   auto origin_inputs = cnode->inputs();
   const bool is_depend = IsPrimitiveCNode(cnode, prim::kPrimDepend);
-  const bool is_updatestate = IsPrimitiveCNode(cnode, prim::kPrimUpdateState);
   // if has multiple depends,only select first depend as parameter
   for (size_t input_idx = 1; input_idx < origin_inputs.size(); input_idx++) {
     auto anf = origin_inputs[input_idx];
@@ -637,8 +636,7 @@ void SessionBasic::GetNewCNodeInputs(const CNodePtr &cnode, KernelGraph *graph,
     if (graph->GetBackendAnfByFrontAnf(anf) != nullptr) {
       (void)cnode_inputs->emplace_back(graph->GetBackendAnfByFrontAnf(anf));
       continue;
-    } else if ((is_depend && input_idx > kRealInputIndexInDepend) ||
-               (is_updatestate && input_idx > kUpdateStateRealInput)) {
+    } else if ((is_depend && input_idx > kRealInputIndexInDepend)) {
       cnode_inputs->push_back(NewValueNode(MakeValue(SizeToInt(input_idx))));
       continue;
     } else if (other_graph_cnode->find(anf) != other_graph_cnode->end()) {

mindspore/ccsrc/pipeline/jit/static_analysis/order_enforce.cc

@@ -79,6 +79,31 @@ class OrderEnforcer {
     return abs != nullptr && abs->isa<abstract::AbstractRef>();
   }
 
+  // Find Load or parameter users as the candidate nodes to enforce order of execution.
+  std::unordered_set<AnfNodePtr> GetSpecialOperatorRealUsers(const AnfNodePtr &node) {
+    auto &node_users = manager_->node_users();
+    auto iter = node_users.find(node);
+    if (iter == node_users.end()) {
+      return {};
+    }
+    std::unordered_set<AnfNodePtr> real_users;
+    auto &users = iter->second;
+    for (auto &user : users) {
+      auto &user_node = user.first;
+      real_users.insert(user_node);
+    }
+    return real_users;
+  }
+
+  bool IsOneOfPrimitive(const AnfNodePtr &node, const std::set<PrimitivePtr> &special_node_types) {
+    for (const auto &type : special_node_types) {
+      if (IsPrimitiveCNode(node, type)) {
+        return true;
+      }
+    }
+    return false;
+  }
+
   void EnforceOrderForOtherCNode(const CNodePtr &cnode) {
     // Find refs from the cnode inputs.
     auto &inputs = cnode->inputs();
@@ -87,6 +112,7 @@ class OrderEnforcer {
     if (!IsPrimitiveCNode(last_input, prim::kPrimUpdateState)) {
       return;
     }
+    const std::set<PrimitivePtr> special_operators = {prim::kPrimExpandDims};
     for (size_t i = 1; i < inputs.size(); ++i) {
       auto &input = inputs.at(i);
       if (!IsRef(input)) {
@@ -96,7 +122,17 @@ class OrderEnforcer {
       auto loads = FindLoadUsers(input);
       for (auto load : loads) {
         std::unordered_set<AnfNodePtr> load_users = FindUsers(load);
-        AddInputEdges(last_input->cast<CNodePtr>(), load_users);
+        std::unordered_set<AnfNodePtr> real_users;
+        for (auto load_user : load_users) {
+          // check the special operator, only one level of user is considered for now
+          if (IsOneOfPrimitive(load_user, special_operators)) {
+            std::unordered_set<AnfNodePtr> special_real_users = GetSpecialOperatorRealUsers(load_user);
+            real_users.insert(special_real_users.begin(), special_real_users.end());
+          } else {
+            real_users.insert(load_user);
+          }
+        }
+        AddInputEdges(last_input->cast<CNodePtr>(), real_users);
       }
     }
   }
@@ -126,7 +162,10 @@ class OrderEnforcer {
   void AddInputEdges(const CNodePtr &update_state, const std::unordered_set<AnfNodePtr> &load_users) {
     auto sorted_load_users = SortLoadUsers(load_users);
     for (auto &load_user : sorted_load_users) {
-      if (!IsDependOn(load_user, update_state) && !IsPrimitiveCNode(load_user, prim::kPrimUpdateState)) {
+      if (IsPrimitiveCNode(load_user, prim::kPrimMakeTuple) || IsPrimitiveCNode(load_user, prim::kPrimUpdateState)) {
+        continue;
+      }
+      if (!IsDependOn(load_user, update_state)) {
         processed_nodes_.insert(load_user);
         if (!IsInUpdateState(load_user, update_state)) {
           manager_->AddEdge(update_state, load_user);
@@ -225,7 +264,6 @@ class OrderEnforcer {
     return loads;
   }
 
- private:
   const FuncGraphPtr &func_graph_;
   FuncGraphManagerPtr manager_;
   std::unordered_map<AnfNodePtr, size_t> topo_sort_map_;

mindspore/ccsrc/runtime/hardware/gpu/gpu_device_context.cc

@@ -38,6 +38,7 @@
 #include "debug/rdr/running_data_recorder.h"
 #include "utils/comm_manager.h"
 #include "debug/debugger/debugger.h"
+#include "backend/optimizer/pass/optimize_updatestate.h"
 
 namespace mindspore {
 namespace device {
@@ -251,6 +252,8 @@ void GPUDeviceContext::OptimizeGraphWithDeviceInfo(const KernelGraphPtr &graph)
   pm->AddPass(std::make_shared<opt::RemoveFormatTransformPair>());
   pm->AddPass(std::make_shared<opt::RemoveRedundantFormatTransform>());
   if (ms_context->get_param<int>(MS_CTX_EXECUTION_MODE) == kGraphMode) {
+    // Remove node only used by UpdateState, in order to ensure the correct execution sequence in CudnnInplaceAggregate.
+    pm->AddPass(std::make_shared<opt::OptimizeUpdateState>());
     pm->AddPass(std::make_shared<opt::CudnnInplaceAggregate>());
   }
   pm->AddPass(std::make_shared<opt::ReluV2Pass>());

mindspore/ccsrc/vm/segment_runner.cc

@@ -59,11 +59,11 @@ AnfNodePtrList GetOutput(const AnfNodePtrList &nodes, const NodeUsersMap &users,
       continue;
     }
     auto &node_users = iter->second;
-    const bool has_outer_user = std::any_of(
-      std::begin(node_users), std::end(node_users), [&seen](const std::pair<AnfNodePtr, int64_t> &u) -> bool {
-        const bool is_outer_user = (seen.find(u.first) == seen.end());
-        return is_outer_user && !(IsPrimitiveCNode(u.first, prim::kPrimUpdateState) && u.second > 2);
-      });
+    const bool has_outer_user = std::any_of(std::begin(node_users), std::end(node_users),
+                                            [&seen](const std::pair<AnfNodePtr, int64_t> &u) -> bool {
+                                              const bool is_outer_user = (seen.find(u.first) == seen.end());
+                                              return is_outer_user;
+                                            });
     if (has_outer_user) {
       output.emplace_back(node);
     }
@@ -127,16 +127,6 @@ std::tuple<FuncGraphPtr, AnfNodePtrList, AnfNodePtrList> TransformSegmentToAnfGr
       for (size_t i = value_start_index; i < inps.size(); ++i) {
         args.emplace_back(NewValueNode(MakeValue(0)));
       }
-    } else if (IsPrimitive(fn, prim::kPrimUpdateState)) {
-      args.emplace_back(RefSubGraphNode(fg, inps[1], &inputs, &eqv));
-      args.emplace_back(RefSubGraphNode(fg, inps[kUpdateStateRealInput], &inputs, &eqv));
-      const size_t additional_input_index = 3;
-      for (size_t i = additional_input_index; i < inps.size(); ++i) {
-        auto &input = inps[i];
-        if (eqv.find(input) != eqv.end()) {
-          args.emplace_back(RefSubGraphNode(fg, input, &inputs, &eqv));
-        }
-      }
     } else {
       (void)std::transform(std::begin(inps) + 1, std::end(inps), std::back_inserter(args),
                            [&fg, &inputs, &eqv](const AnfNodePtr &a) { return RefSubGraphNode(fg, a, &inputs, &eqv); });

tests/st/auto_monad/test_auto_monad_addn_adam.py

@@ -0,0 +1,83 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import pytest
+from mindspore.nn import Cell
+from mindspore import context, Tensor, Parameter
+import mindspore.ops.operations as P
+import mindspore as ms
+import numpy as np
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class AutoMonadAddnAdamNet(Cell):
+    def __init__(self, var, m, v):
+        super().__init__()
+        self.apply_adam = P.Adam()
+        self.var = Parameter(var, name="var")
+        self.m = Parameter(m, name="m")
+        self.v = Parameter(v, name="v")
+        self.addn = P.AddN()
+        self.mul = P.Mul()
+
+    def construct(self, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad):
+        out = self.addn((self.var, self.m, self.v))
+        self.apply_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+        return out, self.var, self.m, self.v
+
+
+def _count_unequal_element(data_expected, data_me, rtol, atol):
+    assert data_expected.shape == data_me.shape
+    total_count = len(data_expected.flatten())
+    error = np.abs(data_expected - data_me)
+    greater = np.greater(error, atol + np.abs(data_me) * rtol)
+    loss_count = np.count_nonzero(greater)
+    assert (loss_count / total_count) < rtol, \
+        "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
+            format(data_expected[greater], data_me[greater], error[greater])
+
+
+def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
+    if np.any(np.isnan(data_expected)):
+        assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
+    elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
+        _count_unequal_element(data_expected, data_me, rtol, atol)
+    else:
+        assert True
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_auto_monad_addn_adam():
+    var = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
+    m = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
+    v = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
+    net = AutoMonadAddnAdamNet(var, m, v)
+    beta1_power = Tensor(0.9, ms.float32)
+    beta2_power = Tensor(0.999, ms.float32)
+    lr = Tensor(0.1, ms.float32)
+    beta1 = Tensor(0.9, ms.float32)
+    beta2 = Tensor(0.999, ms.float32)
+    epsilon = Tensor(1e-8, ms.float32)
+    grad = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
+    out, new_var, new_m, new_v = net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+    net = AutoMonadAddnAdamNet(var, m, v)
+    context.set_context(mode=context.PYNATIVE_MODE)
+    out_pyn, new_var_pyn, new_m_pyn, new_v_pyn = net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+    allclose_nparray(out_pyn.asnumpy(), out.asnumpy(), 0.001, 0.001)
+    allclose_nparray(new_var_pyn.asnumpy(), new_var.asnumpy(), 0.001, 0.001)
+    allclose_nparray(new_m_pyn.asnumpy(), new_m.asnumpy(), 0.001, 0.001)
+    allclose_nparray(new_v_pyn.asnumpy(), new_v.asnumpy(), 0.001, 0.001)