reshape support bool, test=develop (#27944)

paddle/fluid/operators/reshape_op.cc

@@ -621,15 +621,18 @@ REGISTER_OPERATOR(reshape2_grad_grad, ops::Reshape2DoubleGradOp,
 REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
                                ops::ReshapeKernel, int8_t, ops::ReshapeKernel,
                                uint8_t, ops::ReshapeKernel, int,
-                               ops::ReshapeKernel, int64_t, ops::ReshapeKernel);
+                               ops::ReshapeKernel, int64_t, ops::ReshapeKernel,
+                               bool, ops::ReshapeKernel);
 REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
                                double, ops::ReshapeGradKernel, int,
                                ops::ReshapeGradKernel, int64_t,
+                               ops::ReshapeGradKernel, bool,
                                ops::ReshapeGradKernel);
 REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad_grad, float,
                                ops::ReshapeDoubleGradKernel, double,
                                ops::ReshapeDoubleGradKernel, int,
                                ops::ReshapeDoubleGradKernel, int64_t,
+                               ops::ReshapeDoubleGradKernel, bool,
                                ops::ReshapeDoubleGradKernel);
 
 #ifdef PADDLE_WITH_CUDA
@@ -641,15 +644,17 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
                                 double, ops::ReshapeGradKernel, int,
                                 ops::ReshapeGradKernel, int64_t,
                                 ops::ReshapeGradKernel, plat::float16,
+
                                 ops::ReshapeGradKernel);
 REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
                                 ops::ReshapeKernel, int, ops::ReshapeKernel,
                                 int64_t, ops::ReshapeKernel, plat::float16,
-                                ops::ReshapeKernel);
+                                ops::ReshapeKernel, bool, ops::ReshapeKernel);
 REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
                                 double, ops::ReshapeGradKernel, int,
                                 ops::ReshapeGradKernel, int64_t,
                                 ops::ReshapeGradKernel, plat::float16,
+                                ops::ReshapeGradKernel, bool,
                                 ops::ReshapeGradKernel);
 
 REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad_grad, float,
@@ -657,6 +662,7 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad_grad, float,
                                 ops::ReshapeDoubleGradKernel, int,
                                 ops::ReshapeDoubleGradKernel, int64_t,
                                 ops::ReshapeDoubleGradKernel, plat::float16,
+                                ops::ReshapeDoubleGradKernel, bool,
                                 ops::ReshapeDoubleGradKernel);
 #endif
 
@@ -664,10 +670,11 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad_grad, float,
 REGISTER_OP_XPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
                                ops::ReshapeKernel, int, ops::ReshapeKernel,
                                int64_t, ops::ReshapeKernel, plat::float16,
-                               ops::ReshapeKernel);
+                               ops::ReshapeKernel, bool, ops::ReshapeKernel);
 REGISTER_OP_XPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
                                double, ops::ReshapeGradKernel, int,
                                ops::ReshapeGradKernel, int64_t,
                                ops::ReshapeGradKernel, plat::float16,
-                               ops::ReshapeGradKernel);
+                               ops::ReshapeGradKernel,
+                               bool ops::ReshapeGradKernel);
 #endif

python/paddle/fluid/layers/nn.py

@@ -6119,7 +6119,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
             return dygraph_utils._append_activation_in_dygraph(out, act)
 
     check_variable_and_dtype(
-        x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'reshape')
+        x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64',
+                 'bool'], 'reshape')
     check_type(shape, 'shape', (list, tuple, Variable), 'reshape')
     check_type(actual_shape, 'actual_shape', (Variable, type(None)), 'reshape')
 

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

@@ -226,6 +226,24 @@ class TestReshapeUint8Op(TestReshapeInt8Op):
         self.dtype = np.uint8
 
 
+class TestReshapeOpBool(TestReshapeOp):
+    def setUp(self):
+        self.init_data()
+        self.op_type = "reshape2"
+        self.inputs = {
+            "X": np.random.choice(
+                [True, False], size=self.ori_shape)
+        }
+        self.attrs = {"shape": self.new_shape}
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.infered_shape),
+            'XShape': np.random.random(self.ori_shape).astype("float32")
+        }
+
+    def test_check_grad(self):
+        pass
+
+
 # Test python API
 class TestReshapeAPI(unittest.TestCase):
     def _set_paddle_api(self):
@@ -324,7 +342,7 @@ class TestReshapeOpError(unittest.TestCase):
 
             # The x dtype of reshape_op must be float16, float32, float64, int32 or int64.
             def test_x_dtype():
-                x2 = self.data(name="x2", shape=[2, 25], dtype="bool")
+                x2 = self.data(name="x2", shape=[2, 25], dtype="int8")
                 self.reshape(x2, shape=[2, 5, 5])
 
             self.assertRaises(TypeError, test_x_dtype)

python/paddle/tensor/manipulation.py

@@ -1353,7 +1353,7 @@ def reshape(x, shape, name=None):
     the corresponding dimension of x.
 
     Args:
-        x(Tensor): An N-D Tensor. The data type is ``float32``, ``float64``, ``int32`` or ``int64``.
+        x(Tensor): An N-D Tensor. The data type is ``float32``, ``float64``, ``int32``, ``int64`` or ``bool``
         shape(list|tuple|Tensor): Define the target shape. At most one dimension of the target shape can be -1.
                         The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
                         If ``shape`` is an Tensor, it should be an 1-D Tensor .