!21168 Only replace the primal user with tuple_getitem once

Merge pull request !21168 from YuJianfeng/ad

mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc

@@ -909,9 +909,9 @@ CNodePtr GetPrimalUser(const CNodePtr &j_user, const std::map<FuncGraphPtr, std:
   return primal_user;
 }
 
-static std::vector<std::pair<CNodePtr, CNodePtr>> FindPrimalJPair(const FuncGraphManagerPtr &manager,
+static std::unordered_map<CNodePtr, std::vector<CNodePtr>> FindPrimalJPair(const FuncGraphManagerPtr &manager,
-                                                                  const FuncGraphPtr &primal_graph) {
+                                                                           const FuncGraphPtr &primal_graph) {
-  std::vector<std::pair<CNodePtr, CNodePtr>> primal_j_pair;
+  std::vector<CNodePtr> j_users;
   std::map<FuncGraphPtr, std::vector<CNodePtr>> primal_map;
   const auto &node_user_map = manager->node_users();
   // Search primal graph user cnodes.
@@ -930,20 +930,22 @@ static std::vector<std::pair<CNodePtr, CNodePtr>> FindPrimalJPair(const FuncGrap
       primal_map[fg] = {cnode};
     } else if (IsPrimitive(cnode->inputs().at(0), prim::kPrimJ)) {
       // To find J user.
-      auto j_user = GetJUser(node_user_map, cnode, index);
+      j_users.emplace_back(GetJUser(node_user_map, cnode, index));
-      (void)primal_j_pair.emplace_back(std::pair<CNodePtr, CNodePtr>(nullptr, j_user));
     }
   }
 
-  for (auto &[primal_user, j_user] : primal_j_pair) {
+  std::unordered_map<CNodePtr, std::vector<CNodePtr>> primal_user_to_j_users;
+  for (const auto &j_user : j_users) {
+    MS_EXCEPTION_IF_NULL(j_user);
     auto primal = GetPrimalUser(j_user, primal_map);
-    if (primal != nullptr) {
+    if (primal == nullptr) {
-      MS_LOG(DEBUG) << "Primal_J pair is found, where primal is: " << primal->DebugString()
+      continue;
-                    << " and J user is: " << j_user->DebugString();
-      primal_user = primal;
     }
+    MS_LOG(DEBUG) << "Primal_J pair is found, where primal is: " << primal->DebugString()
+                  << " and J user is: " << j_user->DebugString();
+    primal_user_to_j_users[primal].emplace_back(j_user);
   }
-  return primal_j_pair;
+  return primal_user_to_j_users;
 }
 
 static void RemovePrimalUpdateStates(const FuncGraphManagerPtr &manager, const CNodePtr &primal_call) {
@@ -1007,26 +1009,32 @@ void DFunctor::EliminatePrimalGraph() {
   // Find primal user and paired J user cnodes.
   auto manager = primal_graph_->manager();
   MS_EXCEPTION_IF_NULL(manager);
-  auto prim_j_pair = FindPrimalJPair(manager, primal_graph_);
+  auto primal_user_to_j_users = FindPrimalJPair(manager, primal_graph_);
-  for (auto &[primal_user, j_user] : prim_j_pair) {
+  for (const auto &iter : primal_user_to_j_users) {
-    if (primal_user == nullptr || j_user == nullptr) {
+    auto primal_user = iter.first;
-      // Skip if one of them not found.
+    auto &j_users = iter.second;
-      return;
+    MS_EXCEPTION_IF_NULL(primal_user);
+    if (j_users.size() == 1) {
+      // If both inputs are same except monads, we copy primal monad args to k graph
+      // so that they can be combined in CSE (common subexpression elimination) pass.
+      // Only do this when the size of j_users is 1 in order to keep the execution order.
+      const bool has_monad = CopyMonadArguments(primal_user, j_users[0]);
+      // Remove the UpdateState nodes after primal_user if need.
+      if (has_monad) {
+        RemovePrimalUpdateStates(manager, primal_user);
+      }
+    } else {
+      MS_LOG(INFO) << "There are multiple j users with the same primal user " << primal_user->DebugString();
     }
 
     // Replace primal graph with k graph.
     auto k_vnode = NewValueNode(k_graph_);
     primal_user->set_input(0, k_vnode);
-    primal_user->set_abstract(j_user->abstract());
+    if (j_users.empty()) {
-
+      MS_LOG(EXCEPTION) << "The J nodes for primal graph " << primal_graph_->ToString()
-    // If both inputs are same except monads, we copy primal monad args to k graph
+                        << " should be used by at least one other node.";
-    // so that they can be combined in CSE (common subexpression elimination) pass.
-    const bool has_monad = CopyMonadArguments(primal_user, j_user);
-    // Remove the UpdateState nodes after primal_user if need.
-    if (has_monad) {
-      RemovePrimalUpdateStates(manager, primal_user);
     }
-
+    primal_user->set_abstract(j_users[0]->abstract());
     // Insert tuple_getitem after primal user cnode.
     auto construct_wrapper = primal_user->func_graph();
     auto tuple_getitem = NewValueNode(prim::kPrimTupleGetItem);

tests/ut/python/optimizer/test_auto_grad.py

@@ -252,3 +252,112 @@ def test_limit_lift_fv_scope():
     grad_net = GradNet(net)
     grad_net.add_flags_recursive(defer_inline=True)
     grad_net(x, y)
+
+
+def test_same_primal_used_by_multi_j():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+            self.grad = ops.GradOperation()
+
+        def construct(self, x):
+            out = self.net(x)
+            gout = self.grad(self.net)(x)
+            gout1 = self.grad(self.net)(x)
+            return out, gout, gout1
+
+    x = Tensor(np.array([1.0], dtype=np.float32))
+    net = Net()
+    grad = GradNet(net)
+    grad(x)
+
+
+def test_same_primal_used_by_multi_j_with_monad1():
+    class AdamNet(nn.Cell):
+        def __init__(self, var, m, v):
+            super(AdamNet, self).__init__()
+            self.apply_adam = P.Adam()
+            self.var = Parameter(var, name="var")
+            self.m = Parameter(m, name="m")
+            self.v = Parameter(v, name="v")
+
+        def construct(self, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad):
+            self.apply_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+            return self.var
+
+    class AdamGradNet(nn.Cell):
+        def __init__(self, network):
+            super(AdamGradNet, self).__init__()
+            self.grad_fn = ops.GradOperation(sens_param=True)
+            self.sens = [Tensor(np.ones([3, 3, 3]).astype(np.float32)), Tensor(np.ones([3, 3, 3]).astype(np.float32))]
+            self.network = network
+
+        def construct(self, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad):
+            out = self.network(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+            gout1 = self.grad_fn(self.network)(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, self.sens[0])
+            gout2 = self.grad_fn(self.network)(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, self.sens[1])
+            return out, gout1, gout2
+
+    var = Tensor(np.ones([3, 3, 3]).astype(np.float32))
+    m = Tensor(np.ones([3, 3, 3]).astype(np.float32))
+    v = Tensor(np.ones([3, 3, 3]).astype(np.float32))
+    beta1_power = Tensor(np.array([0.9], dtype=np.float32))
+    beta2_power = Tensor(np.array([0.999], dtype=np.float32))
+    lr = Tensor(np.array([0.001], dtype=np.float32))
+    beta1 = Tensor(np.array([0.9], dtype=np.float32))
+    beta2 = Tensor(np.array([0.999], dtype=np.float32))
+    epsilon = Tensor(np.array([1e-8], dtype=np.float32))
+    grad = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
+    net = AdamNet(var, m, v)
+    grad_net = AdamGradNet(net)
+    grad_net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+
+
+def test_same_primal_used_by_multi_j_with_monad2():
+    class AdamNet(nn.Cell):
+        def __init__(self, var, m, v):
+            super(AdamNet, self).__init__()
+            self.apply_adam = P.Adam()
+            self.var = Parameter(var, name="var")
+            self.m = Parameter(m, name="m")
+            self.v = Parameter(v, name="v")
+
+        def construct(self, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad):
+            self.apply_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+            return self.var
+
+    class AdamGradNet(nn.Cell):
+        def __init__(self, network):
+            super(AdamGradNet, self).__init__()
+            self.grad = ops.GradOperation(sens_param=True)
+            self.sens = [Tensor(np.ones([3, 3, 3]).astype(np.float32)), Tensor(np.ones([3, 3, 3]).astype(np.float32))]
+            self.network = network
+
+        def construct(self, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad):
+            out = self.network(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)
+            grad_fn = self.grad(self.network)
+            gout1 = grad_fn(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, self.sens[0])
+            gout2 = grad_fn(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, self.sens[1])
+            return out, gout1, gout2
+
+    var = Tensor(np.ones([3, 3, 3]).astype(np.float32))
+    m = Tensor(np.ones([3, 3, 3]).astype(np.float32))
+    v = Tensor(np.ones([3, 3, 3]).astype(np.float32))
+    beta1_power = Tensor(np.array([0.9], dtype=np.float32))
+    beta2_power = Tensor(np.array([0.999], dtype=np.float32))
+    lr = Tensor(np.array([0.001], dtype=np.float32))
+    beta1 = Tensor(np.array([0.9], dtype=np.float32))
+    beta2 = Tensor(np.array([0.999], dtype=np.float32))
+    epsilon = Tensor(np.array([1e-8], dtype=np.float32))
+    grad = Tensor(np.random.rand(3, 3, 3).astype(np.float32))
+    net = AdamNet(var, m, v)
+    grad_net = AdamGradNet(net)
+    grad_net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad)