Cherry-pick PR38014, fix dygraph_grad_maker to support set_value (#38014) (#38521)

Cherry-pick PR #38014

paddle/fluid/imperative/basic_engine.cc

@@ -173,27 +173,11 @@ void BasicEngine::PrepareGradAccumulators(
     for (const auto& var : pair.second) {
       if (!var) continue;
 
-      if (!var->HasGradNode()) {
-        auto& accumulator = accumulators_[var.get()];
-        if (!accumulator) {
-          if (FLAGS_sort_sum_gradient) {
-            accumulator.reset(new SortedGradientAccumulator(var.get()));
-          } else {
-            accumulator.reset(new EagerGradientAccumulator(var.get()));
-          }
-        }
-
-        accumulator->IncreaseRefCnt();
-
-        VLOG(3) << "Prepare to acccumulate variable grad " << var->Name() << "("
-                << var.get()
-                << ") that don't have grad node  with reference count "
-                << accumulator->RefCnt();
-      } else {
+      bool find_grad_node_of_var = false;
+      if (var->HasGradNode()) {
         // Because Inplace op overwrites the grad_node of the input grad_var. So
         // only the information of grad_pending_node can be used to find the
         // grad_node of grad_var.
-        bool find_grad_node_of_var = false;
         for (auto& grad_pending_node : grad_pending_nodes) {
           PADDLE_ENFORCE_NOT_NULL(
               grad_pending_node,
@@ -245,11 +229,33 @@ void BasicEngine::PrepareGradAccumulators(
             break;
           }
         }
-        PADDLE_ENFORCE_EQ(
-            find_grad_node_of_var, true,
-            platform::errors::NotFound(
-                "No grad node corresponding to grad Tensor (%s) was found.",
-                var->Name()));
+        if (!find_grad_node_of_var) {
+          // Special case: `set_value` is inplace op, and it can change
+          // the var with `stop_gradient=True` to the var with
+          // `stop_gradient=False `.
+          // This inplace var has grad_node (the inplace op), but it
+          // isn't the input of grad_pending_op.
+          VLOG(6) << "No grad node corresponding to grad Tensor ("
+                  << var->Name() << ") was found.";
+        }
+      }
+
+      if (!var->HasGradNode() || !find_grad_node_of_var) {
+        auto& accumulator = accumulators_[var.get()];
+        if (!accumulator) {
+          if (FLAGS_sort_sum_gradient) {
+            accumulator.reset(new SortedGradientAccumulator(var.get()));
+          } else {
+            accumulator.reset(new EagerGradientAccumulator(var.get()));
+          }
+        }
+
+        accumulator->IncreaseRefCnt();
+
+        VLOG(3) << "Prepare to acccumulate variable grad " << var->Name() << "("
+                << var.get()
+                << ") that don't have grad node  with reference count "
+                << accumulator->RefCnt();
       }
     }
   }
@@ -435,16 +441,8 @@ void BasicEngine::Execute() {
           std::unordered_map<VariableWrapper*,
                              std::unique_ptr<GradientAccumulator>>::iterator
               iter;
-          if (!var->HasGradNode()) {
-            VLOG(10) << "Find gradient of var (" << var->Name()
-                     << ") with no grad_node.";
-            iter = accumulators_.find(var.get());
-            PADDLE_ENFORCE_EQ(
-                iter != accumulators_.end(), true,
-                platform::errors::NotFound(
-                    "Cannot find gradient of variable %s", var->Name()));
-          } else {
           bool flag_find_grad = false;
+          if (var->HasGradNode()) {
             VLOG(10) << "Find gradient of var (" << var->Name()
                      << ") with grad_node.";
             for (auto& grad_pending_node :
@@ -459,8 +457,16 @@ void BasicEngine::Execute() {
                 }
               }
             }
+            if (!flag_find_grad) {
+              VLOG(6) << "Cannot find gradient of variable " << var->Name();
+            }
+          }
+          if (!var->HasGradNode() || !flag_find_grad) {
+            VLOG(10) << "Find gradient of var (" << var->Name()
+                     << ") with no grad_node.";
+            iter = accumulators_.find(var.get());
             PADDLE_ENFORCE_EQ(
-                flag_find_grad, true,
+                iter != accumulators_.end(), true,
                 platform::errors::NotFound(
                     "Cannot find gradient of variable %s", var->Name()));
           }

paddle/fluid/imperative/dygraph_grad_maker.h

@@ -269,8 +269,14 @@ class TracedGradOp {
         for (auto& var : vars) {
           if (var && !var->OverridedStopGradient() && var->GradNode()) {
             if (map_dirty_grad_node_.find(var) != map_dirty_grad_node_.end()) {
+              // Because inplace var isn't a leaf var, it should have
+              // dirty_grad_node.
               node_->InsertGradPendingNode(map_dirty_grad_node_[var]);
-            } else {
+            } else if (node_ != var->GradNode()) {
+              // For non-inplace var.
+              // Special case: `set_value` is inplace op, and it can change
+              // the var with `stop_gradient=True` to the var with
+              // `stop_gradient=False`.
               node_->InsertGradPendingNode(var->GradNode());
             }
           }

python/paddle/fluid/tests/unittests/test_set_value_op.py

@@ -1322,5 +1322,41 @@ class TestGradientTruncated(unittest.TestCase):
             array = array[0]
 
 
+class TestSetValueInplaceLeafVar(unittest.TestCase):
+    def test_inplace_var_become_leaf_var(self):
+        paddle.disable_static()
+
+        a_grad_1, b_grad_1, a_grad_2, b_grad_2 = 0, 1, 2, 3
+        with paddle.fluid.dygraph.guard():
+            paddle.seed(100)
+            a = paddle.rand(shape=[1, 4])
+            b = paddle.rand(shape=[1, 4])
+            a.stop_gradient = False
+            b.stop_gradient = False
+            c = a / b
+            c.sum().backward()
+            a_grad_1 = a.grad.numpy()
+            b_grad_1 = b.grad.numpy()
+
+        with paddle.fluid.dygraph.guard():
+            paddle.seed(100)
+            a = paddle.rand(shape=[1, 4])
+            b = paddle.rand(shape=[1, 4])
+            a.stop_gradient = False
+            b.stop_gradient = False
+            c = a / b
+            d = paddle.zeros((4, 4))
+            self.assertTrue(d.stop_gradient)
+            d[0, :] = c
+            self.assertFalse(d.stop_gradient)
+            d[0, :].sum().backward()
+            a_grad_2 = a.grad.numpy()
+            b_grad_2 = b.grad.numpy()
+
+        self.assertTrue(np.array_equal(a_grad_1, a_grad_2))
+        self.assertTrue(np.array_equal(b_grad_1, b_grad_2))
+        paddle.enable_static()
+
+
 if __name__ == '__main__':
     unittest.main()