register fp16 for assign OP, test=release/1.7 (#22842)

paddle/fluid/operators/assign_op.cc

@@ -114,6 +114,7 @@ DECLARE_INPLACE_OP_INFERER(AssignOpInplaceInferer, {"X", "Out"});
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 REGISTER_OPERATOR(assign, ops::AssignOp,
                   ops::AssignGradMaker<paddle::framework::OpDesc>,
                   ops::AssignGradMaker<paddle::imperative::OpBase>,
@@ -122,11 +123,13 @@ REGISTER_OPERATOR(assign, ops::AssignOp,
 REGISTER_OP_CPU_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
                                ops::AssignKernel, int, ops::AssignKernel,
                                int64_t, ops::AssignKernel, bool,
+                               ops::AssignKernel, plat::float16,
                                ops::AssignKernel);
 
 #ifdef PADDLE_WITH_CUDA
 REGISTER_OP_CUDA_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
                                 ops::AssignKernel, int, ops::AssignKernel,
                                 int64_t, ops::AssignKernel, bool,
+                                ops::AssignKernel, plat::float16,
                                 ops::AssignKernel);
 #endif

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

@@ -37,6 +37,20 @@ class TestAssignOp(op_test.OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestAssignFP16Op(op_test.OpTest):
+    def setUp(self):
+        self.op_type = "assign"
+        x = np.random.random(size=(100, 10)).astype('float16')
+        self.inputs = {'X': x}
+        self.outputs = {'Out': x}
+
+    def test_forward(self):
+        self.check_output()
+
+    def test_backward(self):
+        self.check_grad(['X'], 'Out')
+
+
 class TestAssignOpError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
@@ -44,22 +58,18 @@ class TestAssignOpError(unittest.TestCase):
             x1 = fluid.create_lod_tensor(
                 np.array([[-1]]), [[1]], fluid.CPUPlace())
             self.assertRaises(TypeError, fluid.layers.assign, x1)
-            # When the type of input is Variable, the dtype of input must be float32, float64, int32, int64, bool.
-            x3 = fluid.layers.data(name='x3', shape=[4], dtype="float16")
+            # When the type of input is Variable, the dtype of input must be float16, float32, float64, int32, int64, bool.
+            x3 = fluid.layers.data(name='x3', shape=[4], dtype="uint8")
             self.assertRaises(TypeError, fluid.layers.assign, x3)
-            x4 = fluid.layers.data(name='x4', shape=[4], dtype="uint8")
-            self.assertRaises(TypeError, fluid.layers.assign, x4)
             # When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
-            x5 = np.array([[2.5, 2.5]], dtype='bool')
+            x4 = np.array([[2.5, 2.5]], dtype='bool')
+            self.assertRaises(TypeError, fluid.layers.assign, x4)
+            x5 = np.array([[2.5, 2.5]], dtype='float64')
             self.assertRaises(TypeError, fluid.layers.assign, x5)
-            x6 = np.array([[2.5, 2.5]], dtype='float16')
+            x6 = np.array([[2.5, 2.5]], dtype='int64')
             self.assertRaises(TypeError, fluid.layers.assign, x6)
-            x7 = np.array([[2.5, 2.5]], dtype='float64')
+            x7 = np.array([[2.5, 2.5]], dtype='uint8')
             self.assertRaises(TypeError, fluid.layers.assign, x7)
-            x8 = np.array([[2.5, 2.5]], dtype='int64')
-            self.assertRaises(TypeError, fluid.layers.assign, x8)
-            x9 = np.array([[2.5, 2.5]], dtype='uint8')
-            self.assertRaises(TypeError, fluid.layers.assign, x9)
 
 
 if __name__ == '__main__':