Fix vmap problem

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

@@ -707,9 +707,9 @@ AbstractBasePtr GetLogicalViewAbs(const AbstractBasePtr &physical_view_abs, cons
         return GetLogicalViewAbs(sub_abs, sub_in_axes, axis_size);
       });
     if (physical_view_abs->isa<AbstractList>()) {
-      return std::make_shared<AbstractList>(logical_view_abs_list);
+      return std::make_shared<AbstractList>(logical_view_abs_list, physical_view_abs_sequence->sequence_nodes());
     }
-    return std::make_shared<AbstractTuple>(logical_view_abs_list);
+    return std::make_shared<AbstractTuple>(logical_view_abs_list, physical_view_abs_sequence->sequence_nodes());
   }
   ValuePtr in_axis = in_axes;
   if (in_axis->isa<Int64Imm>()) {

tests/st/vmap/test_vmap_graph.py

@@ -24,6 +24,7 @@ import mindspore.ops.functional as F
 from mindspore import dtype as mstype
 from mindspore.common import Tensor
 from mindspore.ops.functional import vmap
+from mindspore.common.api import ms_function
 from mindspore.common.parameter import Parameter
 
 context.set_context(mode=context.GRAPH_MODE)
@@ -351,3 +352,40 @@ def test_vmap_nested_axes():
     assert res3 == expect_res3
     assert np.allclose(res4.asnumpy(), expect_res4.asnumpy())
     assert np.allclose(res5.asnumpy(), expect_res5.asnumpy())
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_vmap_with_tuple_input():
+    """
+    Feature: vmap
+    Description: When vmap use tuple inputs in graph, it must ensure the inputs is not eliminated.
+    Expectation: success
+    """
+    def real_fn(x, y):
+        return x * y
+
+    def foo(fn):
+        @ms_function
+        def wrapped(*args):
+            def fn2(x, y):
+                return F.jvp(fn, x, y)
+            res = F.vmap(fn2)(args, args)
+            return res
+        return wrapped
+
+    shape = (2, 3)
+    a = F.ones(shape, mstype.int32)
+    b = F.ones(shape, mstype.int32) * 2
+    res = foo(real_fn)(a, b)
+
+    assert isinstance(res, tuple)
+    assert len(res) == 2
+    assert isinstance(res[0], Tensor)
+    assert isinstance(res[1], Tensor)
+    assert np.allclose(res[0].asnumpy(), np.array([[2, 2, 2], [2, 2, 2]]))
+    assert np.allclose(res[1].asnumpy(), np.array([[4, 4, 4], [4, 4, 4]]))