!26825 [Fallback] supports the use of attr/method on the interpreted nodes

Merge pull request !26825 from huangbingjian/support_numpy_method

mindspore/ccsrc/pipeline/jit/parse/parse.cc

@@ -844,7 +844,7 @@ AnfNodePtr Parser::ParseAttribute(const FunctionBlockPtr &block, const py::objec
 
   // Create the apply node
   auto attr_cnode = block->func_graph()->NewCNodeInOrder({op_node, value_node, attr_node});
-  if (value_node->interpret()) {
+  if (value_node->interpret() || IsPrimitiveCNode(value_node, prim::kPrimPyInterpret)) {
     attr_cnode->set_interpret(true);
   }
   return attr_cnode;

tests/st/fallback/test_graph_fallback.py

@@ -81,3 +81,56 @@ def test_np_tensor_list():
     tensor_list = np_tensor_list()
     print("tensor_list:", tensor_list)
     assert len(tensor_list) == 3
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_list_count():
+    """
+    Feature: Fallback feature
+    Description: support attr/method of builtin type.
+    Expectation: No exception.
+    """
+    @ms_function
+    def list_count():
+        x = list([1, 2, 3])
+        res = x.count(1)
+        return res
+    assert list_count() == 1
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_list_append():
+    """
+    Feature: Fallback feature
+    Description: support attr/method of builtin type.
+    Expectation: No exception.
+    """
+    @ms_function
+    def list_append():
+        x = list([1, 2, 3])
+        x.append(4)
+        return Tensor(x)
+    assert np.all(list_append().asnumpy() == np.array([1, 2, 3, 4]))
+
+
+@pytest.mark.skip(reason='Not support graph fallback feature yet')
+def test_list_insert():
+    """
+    Feature: Fallback feature
+    Description: support attr/method of builtin type.
+    Expectation: No exception.
+    """
+    @ms_function
+    def list_insert():
+        x = list([1, 3, 4])
+        x.insert(1, 2)
+        return Tensor(x)
+    assert np.all(list_insert().asnumpy() == np.array([1, 2, 3, 4]))

tests/st/fallback/test_graph_fallback_numpy.py

@@ -20,7 +20,11 @@ from mindspore import ms_function, context, Tensor
 context.set_context(mode=context.GRAPH_MODE)
 
 
-@pytest.mark.skip(reason='Not support graph fallback feature yet')
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_np_linspace():
     """
     Feature: JIT Fallback
@@ -33,16 +37,14 @@ def test_np_linspace():
         b = Tensor(np.linspace(1, 1, 10))
         c = Tensor(np.linspace(10, 20, 5, endpoint=False))
         d = Tensor(np.linspace(10, 20, 5, endpoint=True))
-        e = Tensor(np.linspace(1, 10, 10, retstep=True))
-        f = Tensor(np.linspace(1, 10, 10).reshape([10, 1]))
-        return a, b, c, d, e, f
-    a, b, c, d, e, f = np_linspace()
+        e = Tensor(np.linspace(1, 10, 10).reshape([10, 1]))
+        return a, b, c, d, e
+    a, b, c, d, e = np_linspace()
     print("a:", a)
     print("b:", b)
     print("c:", c)
     print("d:", d)
     print("e:", e)
-    print("f:", f)
 
 
 @pytest.mark.level0
@@ -130,6 +132,7 @@ def test_np_array_advanced_index_1():
     assert np.all(e.asnumpy() == np.array([[1, 2], [4, 5], [7, 8], [10, 11]]))
 
 
+# Not support <class 'complex'> yet.
 @pytest.mark.skip(reason='Not support graph fallback feature yet')
 def test_np_array_advanced_index_2():
     """
@@ -178,7 +181,11 @@ def test_np_array_advanced_index_3():
     print("c:", c)
 
 
-@pytest.mark.skip(reason='Not support graph fallback feature yet')
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_np_reshape():
     """
     Feature: JIT Fallback
@@ -193,24 +200,11 @@ def test_np_reshape():
     assert np.all(np_reshape().asnumpy() == np.array([[0, 1, 2, 3], [4, 5, 6, 7]]))
 
 
-@pytest.mark.skip(reason='Not support graph fallback feature yet')
-def test_np_ndarray_flat():
-    """
-    Feature: JIT Fallback
-    Description: Test numpy.flat() method in graph mode.
-    Expectation: No exception.
-    """
-    @ms_function
-    def np_ndarray_flat():
-        x = np.arange(9).reshape(3, 3)
-        out = 0
-        for element in x.flat:
-            out += element
-        return out
-    assert np_ndarray_flat() == 36
-
-
-@pytest.mark.skip(reason='Not support graph fallback feature yet')
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_np_ndarray_flatten():
     """
     Feature: JIT Fallback
@@ -225,7 +219,11 @@ def test_np_ndarray_flatten():
     assert np.all(np_ndarray_flatten().asnumpy() == np.array([0, 1, 2, 3, 4, 5, 6, 7]))
 
 
-@pytest.mark.skip(reason='Not support graph fallback feature yet')
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_np_ravel():
     """
     Feature: JIT Fallback
@@ -259,7 +257,11 @@ def test_np_transpose():
     assert np.all(np_transpose().asnumpy() == np.array([0, 1, 2, 3]))
 
 
-@pytest.mark.skip(reason='Not support graph fallback feature yet')
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_np_rollaxis():
     """
     Feature: JIT Fallback
@@ -269,13 +271,19 @@ def test_np_rollaxis():
     @ms_function
     def np_rollaxis():
         x = np.arange(8).reshape(2, 2, 2)
+        tensor_x = Tensor(x)
         y = np.rollaxis(x, 2, 0)
-        return x[1, 1, 0], y[1, 1, 0]
+        tensor_y = Tensor(y)
+        return tensor_x[1, 1, 0], tensor_y[1, 1, 0]
     x, y = np_rollaxis()
     assert x == 6 and y == 5
 
 
-@pytest.mark.skip(reason='Not support graph fallback feature yet')
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_np_swapaxes():
     """
     Feature: JIT Fallback
@@ -285,8 +293,10 @@ def test_np_swapaxes():
     @ms_function
     def np_swapaxes():
         x = np.arange(8).reshape(2, 2, 2)
+        tensor_x = Tensor(x)
         y = np.swapaxes(x, 2, 0)
-        return x[1, 1, 0], y[1, 1, 0]
+        tensor_y = Tensor(y)
+        return tensor_x[1, 1, 0], tensor_y[1, 1, 0]
     x, y = np_swapaxes()
     assert x == 6 and y == 3