add suport for parameter of const value pass as mixed precision args

fix pylint

mindspore/ccsrc/pipeline/parse/parse.cc

@@ -68,9 +68,7 @@ AnfNodePtr GetMixedPrecisionCastHelp(const FuncGraphPtr &func_graph, const AnfNo
     return param;
   }
   auto cast_helper = prim::GetPythonOps("_mp_cast_helper", "mindspore.ops.composite.base");
-  auto partial =
-    func_graph->NewCNode({NewValueNode(prim::kPrimPartial), NewValueNode(cast_helper), NewValueNode(dst_type)});
-  auto cast = func_graph->NewCNode({NewValueNode(prim::kCompositeHyperMap), partial, param});
+  auto cast = func_graph->NewCNode({NewValueNode(cast_helper), NewValueNode(dst_type), param});
   return cast;
 }
 

mindspore/ops/composite/base.py

@@ -307,3 +307,12 @@ def _mixed_precision_cast_helper_2(type_, x):
     if F.issubclass_(F.dtype(x), mstype.float_):
         return P.Cast()(x, type_)
     return x
+
+@_mp_cast_helper.register("TypeType", "Tuple")
+@core
+def _mixed_precision_cast_helper_3(type_, x):
+    """if x is a tuple"""
+    t = ()
+    for item in x:
+        t = t + (_mp_cast_helper(type_, item),)
+    return t

tests/ut/python/parameter_feature/test_parameter.py

@@ -19,7 +19,7 @@ from mindspore.nn import Cell
 from mindspore.ops import operations as P
 import mindspore.ops.composite as C
 
-context.set_context(mode=context.GRAPH_MODE)
+context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
 
 def test_parser_three_default_mixed_args_subnet():
 
@@ -227,3 +227,43 @@ def test_net_vargs_expand():
 
     net.set_train()
     net(x, y, sens)
+    
+
+def test_mixed_precision_const_parameter():
+    class NetLoss(Cell):
+        def __init__(self):
+            super(NetLoss, self).__init__()
+            self.shape = P.Shape()
+            self.up_sample1 = P.ResizeBilinear((14, 14))
+            self.up_sample2 = P.ResizeBilinear((28, 28))
+            self.up_sample3 = P.ResizeBilinear((36, 36))
+        def construct(self, x, y, z, *args):
+            ret = 0
+            if args[0] == self.shape(z)[2]:
+                if args[0] == 14:
+                    ret = self.up_sample1(y) + x
+                elif args[0] == 28:
+                    ret = self.up_sample2(y) - x
+                else:
+                    ret = x / y
+            else:
+                ret = x * y
+            ret = ret * z
+            return ret
+    class NetMain(Cell):
+        def __init__(self, loss_fn):
+            super(NetMain, self).__init__()
+            self.loss_fn = loss_fn
+            self.shape = P.Shape()
+        def construct(self, x, y, z):
+            size_x = self.shape(x)[2]
+            size_y = self.shape(y)[2]
+            ret = self.loss_fn(x, y, z, size_x, size_y)
+            return ret
+    loss_fn = NetLoss()
+    net = NetMain(loss_fn)
+    net.add_flags_recursive(fp32=True)
+    x = Tensor(np.ones((1, 3, 28, 28), np.float32))
+    y = Tensor(np.ones((1, 3, 14, 14), np.float32))
+    z = Tensor(np.ones((1, 3, 28, 28), np.float32))
+    out = net(x, y, z)
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