fix assign used in while

mindspore/ccsrc/pipeline/pipeline.cc

@@ -314,7 +314,7 @@ std::map<std::string, std::pair<PrimitivePyPtr, std::string>> ExecutorPy::FetchI
     auto weight_name = weight_node->cast<ParameterPtr>()->name();
     // find the fakequant from input
     int count = 0;
-    int max_depth = 5;
+    const int max_depth = 5;
     while (!is_quant_cnode(x)) {
       if (count >= max_depth) {
         break;

mindspore/ccsrc/pipeline/static_analysis/abstract_value.cc

@@ -429,6 +429,11 @@ AbstractBasePtr AbstractTensor::Join(const AbstractBasePtr &other) {
   if (other_tensor == nullptr) {
     MS_LOG(EXCEPTION) << "Join failed as type mismatch, this: " << ToString() << ", other: " << other->ToString();
   }
+  if (*this == *other) {
+    if (sparse_grad() == other->sparse_grad()) {
+      return shared_from_base<AbstractBase>();
+    }
+  }
   auto element = element_->Join(other_tensor->element_);
   auto shape = ShapeJoin(this->shape(), other_tensor->shape());
   auto ret = std::make_shared<AbstractTensor>(element, shape);
@@ -812,6 +817,21 @@ bool AbstractRef::operator==(const AbstractBase &other) const {
   return false;
 }
 
+AbstractBasePtr AbstractRef::Join(const AbstractBasePtr &other) {
+  auto other_ref = other->cast<AbstractRefPtr>();
+  if (other_ref == nullptr) {
+    MS_LOG(EXCEPTION) << "Join failed as type mismatch, this: " << ToString() << ", other: " << other->ToString();
+  }
+  if (*this == *other) {
+    return shared_from_base<AbstractBase>();
+  }
+  auto ref_key = ref_key_->Join(other_ref->ref_key_);
+  auto ref = ref_->Join(other_ref->ref());
+  auto ref_origin = ref_origin_->Join(other_ref->ref_origin_);
+
+  return std::make_shared<AbstractRef>(ref_key, ref, ref_origin);
+}
+
 std::string AbstractRef::ToString() const {
   std::ostringstream buffer;
   buffer << type_name() << "("

mindspore/ccsrc/pipeline/static_analysis/abstract_value.h

@@ -564,6 +564,7 @@ class AbstractRef : public AbstractBase {
   AbstractBasePtr Broaden() const override {
     return std::make_shared<AbstractRef>(ref_key_->Broaden(), ref_->Broaden(), ref_origin_->Broaden());
   }
+  AbstractBasePtr Join(const AbstractBasePtr &other) override;
   std::size_t hash() const override {
     return ref_key_->hash() ^ ref_->hash() ^ ref_origin_->hash() ^ (std::hash<uint32_t>{}(this->tid()) << 1);
   }

mindspore/ccsrc/pipeline/static_analysis/evaluator.cc

@@ -166,6 +166,7 @@ AbstractBasePtrList FuncGraphEvaluator::BroadenUndeterminedArgs(const AbstractBa
         // If there is loop variant, all arguments need to be broaden to avoid wrong constant propagation.
         if (!(joined_args_spec_list == args_spec_list)) {
           func_graph_->set_flag(FUNC_GRAPH_FLAG_IGNORE_VALUES, true);
+          MS_LOG(DEBUG) << "Set " << func_graph_->ToString() << " with IGNORE_VALUES flag.";
         }
         return joined_args_spec_list;
       }
@@ -179,6 +180,7 @@ AbstractBasePtrList FuncGraphEvaluator::BroadenUndeterminedArgs(const AbstractBa
       if (!(joined_args_spec_list == args_spec_list)) {
         trace_.push_back(joined_args_spec_list);
         func_graph_->set_flag(FUNC_GRAPH_FLAG_IGNORE_VALUES, true);
+        MS_LOG(DEBUG) << "Set " << func_graph_->ToString() << " with IGNORE_VALUES flag.";
       }
       MS_LOG(DEBUG) << "Joined eval args: " << ::mindspore::ToString(joined_args_spec_list);
       return joined_args_spec_list;

tests/ut/python/pipeline/infer/test_net_infer.py

@@ -16,29 +16,54 @@
 import numpy as np
 
 import mindspore.nn as nn
-from mindspore import Tensor
+from mindspore import Tensor, context
+from mindspore.common.parameter import Parameter
+from mindspore.common.initializer import initializer
+import mindspore.ops.operations as op
 
+def test_net_infer():
+    """ test_net_infer """
+    class Net(nn.Cell):
+        """ Net definition """
 
-class Net(nn.Cell):
-    """ Net definition """
+        def __init__(self):
+            super(Net, self).__init__()
+            self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal')
+            self.bn = nn.BatchNorm2d(64)
+            self.fc = nn.Dense(64, 10)
+            self.relu = nn.ReLU()
+            self.flatten = nn.Flatten()
 
-    def __init__(self):
-        super(Net, self).__init__()
-        self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal')
-        self.bn = nn.BatchNorm2d(64)
-        self.fc = nn.Dense(64, 10)
-        self.relu = nn.ReLU()
-        self.flatten = nn.Flatten()
+        def construct(self, x):
+            x = self.conv(x)
+            x = self.relu(x)
+            x = self.flatten(x)
+            out = self.fc(x)
+            return out
+    Tensor(np.random.randint(0, 255, [1, 3, 224, 224]))
+    Net()
 
-    def construct(self, x):
-        x = self.conv(x)
-        x = self.relu(x)
-        x = self.flatten(x)
-        out = self.fc(x)
-        return out
 
+def test_assign_in_while():
+    context.set_context(mode=context.GRAPH_MODE)
+    class Net(nn.Cell):
+        def __init__(self, input_shape):
+            super().__init__()
+            self.assign = op.Assign()
+            self.inputdata = Parameter(initializer(1, input_shape), name="global_step")
 
-def test_net_infer():
-    """ test_net_infer """
-    Tensor(np.random.randint(0, 255, [1, 3, 224, 224]))
-    Net()
+        def construct(self, x, y, z):
+            out = z
+            while x < y:
+                inputdata = self.inputdata
+                x = x + 1
+                out = self.assign(inputdata, z)
+            return out
+
+    x = Tensor(np.array(1).astype(np.int32))
+    y = Tensor(np.array(3).astype(np.int32))
+    input_shape = (1024, 512)
+    z = Tensor(np.random.randn(*input_shape).astype(np.float32))
+    net = Net(input_shape)
+    ret = net(x, y, z)
+    assert ret == z