Change the invoking method of settiem from numpy to set_value op when value isn't tensor (#35701)

* Change the invoking method of settiem from numpy to set_value op when value is not tensor

* fix the check logic for inplace in setitem

* fix the unittest problem caused by setitem doesn't support fp16

* modify some code format in setitem

paddle/fluid/operators/slice_utils.h

@@ -36,17 +36,18 @@ inline void CheckAndUpdateSliceAttrs(const framework::DDim in_dims,
       if (infer_flags != nullptr && (*infer_flags)[i] == -1) {
         continue;
       }
-      T start = (*starts)[i] < 0 ? ((*starts)[i] + dim_value) : (*starts)[i];
-      start = std::max(start, static_cast<T>(0));
-
-      T end = (*ends)[i] < 0 ? ((*ends)[i] + dim_value) : (*ends)[i];
-      end = std::min(end, dim_value);
-
       T step = steps == nullptr ? 1 : (*steps)[i];
       PADDLE_ENFORCE_NE(
           step, 0, platform::errors::InvalidArgument(
                        "Step should not be 0, but received step = %d.", step));
 
+      T start = (*starts)[i] < 0 ? ((*starts)[i] + dim_value) : (*starts)[i];
+      start = std::max(start, static_cast<T>(0));
+
+      T end =
+          0 < step && (*ends)[i] < 0 ? ((*ends)[i] + dim_value) : (*ends)[i];
+      end = std::min(end, dim_value);
+
       if (step > 0) {
         start = std::min(start, dim_value);
         end = std::max(end, static_cast<T>(0));

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

@@ -40,12 +40,11 @@ class TensorFill_Test(unittest.TestCase):
             for dtype in typelist:
                 var = 1.
                 tensor = paddle.to_tensor(np_arr, place=p, dtype=dtype)
-                newtensor = tensor.clone()
-                newtensor[...] = var
+                target = tensor.numpy()
+                target[...] = var
 
                 tensor.fill_(var)  #var type is basic type in typelist
-                self.assertEqual((tensor.numpy() == newtensor.numpy()).all(),
-                                 True)
+                self.assertEqual((tensor.numpy() == target).all(), True)
 
     def test_tensor_fill_backward(self):
         typelist = ['float32']

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

@@ -35,12 +35,11 @@ class TensorFill_Test(unittest.TestCase):
                 np.array(six.moves.range(np.prod(self.shape))), self.shape)
             for dtype in typelist:
                 tensor = paddle.to_tensor(np_arr, place=p, dtype=dtype)
-                newtensor = tensor.clone()
-                newtensor[...] = 0
+                target = tensor.numpy()
+                target[...] = 0
 
                 tensor.zero_()
-                self.assertEqual(
-                    (tensor.numpy() == newtensor.numpy()).all().item(), True)
+                self.assertEqual((tensor.numpy() == target).all().item(), True)
 
 
 if __name__ == '__main__':

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

@@ -391,6 +391,11 @@ class TestVarBase(unittest.TestCase):
             self.assertTrue(cmp_float(x.grad.numpy(), [20.0]))
             self.assertTrue(cmp_float(detach_x.grad.numpy(), [60.0]))
 
+            with self.assertRaises(ValueError):
+                detach_x[:] = 5.0
+
+            detach_x.stop_gradient = True
+
             # Due to sharing of data with origin Tensor, There are some unsafe operations:
             with self.assertRaises(RuntimeError):
                 y = 2**x
@@ -438,10 +443,11 @@ class TestVarBase(unittest.TestCase):
             self.assertTrue(np.array_equal(y.numpy(), y_copy.numpy()))
 
             self.assertNotEqual(id(x), id(x_copy))
-            x_copy[:] = 5.
-            self.assertTrue(np.array_equal(x_copy.numpy(), [5.]))
             self.assertTrue(np.array_equal(x.numpy(), [2.]))
 
+            with self.assertRaises(ValueError):
+                x_copy[:] = 5.
+
             with self.assertRaises(RuntimeError):
                 copy.deepcopy(z)
 
@@ -805,8 +811,8 @@ class TestVarBase(unittest.TestCase):
         # case2:
         tensor_x = paddle.to_tensor(
             np.zeros(12).reshape(2, 6).astype(np.float32))
-        tensor_y1 = paddle.zeros([1]) + 2
-        tensor_y2 = paddle.zeros([1]) + 5
+        tensor_y1 = paddle.zeros([1], dtype='int32') + 2
+        tensor_y2 = paddle.zeros([1], dtype='int32') + 5
         tensor_x[:, tensor_y1:tensor_y2] = 42
         res = tensor_x.numpy()
         exp = np.array([[0., 0., 42., 42., 42., 0.],

python/paddle/nn/layer/common.py

@@ -1390,7 +1390,8 @@ class Embedding(Layer):
             is_bias=False)
 
         if in_dygraph_mode() and padding_idx != -1:
-            self.weight[padding_idx] = 0.0
+            with paddle.no_grad():
+                self.weight[padding_idx] = 0.0
 
     def forward(self, x):
         return F.embedding(