def init_dtype(self):
if core.is_compiled_with_cuda():
self.dtype = "float64"
self.ids_dtype = "int64"
elif core.is_compiled_with_npu():
self.dtype = "float32"
self.ids_dtype = "int32"
def setUp(self):
self.init_dtype()
self.initcase()
if core.is_compiled_with_npu():
self.__class__.use_npu = True
elif core.is_compiled_with_cuda():
self.__class__.exist_fp64_check_grad = True
def setUp(self):
np.random.seed(0)
self.shape = [3, 3]
self.index_shape = [1, 3]
self.index_np = np.array([[0, 1, 2]]).astype('int64')
self.x_np = np.random.random(self.shape).astype(np.float32)
self.place = [paddle.CPUPlace()]
self.axis = 0
if core.is_compiled_with_cuda():
self.place.append(paddle.CUDAPlace(0))
def setUp(self):
np.random.seed(0)
self.shape = [1, 3]
self.index_shape = [1, 1]
self.index_np = np.array([[0]]).astype('int64')
self.x_np = np.random.random(self.shape).astype(np.float32)
self.place = [paddle.CPUPlace()]
self.axis = 0
self.value_np = 99.0
self.value_shape = [1]
self.x_feed = copy.deepcopy(self.x_np)
if core.is_compiled_with_cuda():
self.place.append(paddle.CUDAPlace(0))
def initcase(self):
self.op_type = "c_embedding"
table = np.random.random((17, 64)).astype(self.dtype)
ids = np.random.randint(low=0, high=17 * 2,
size=(2, 4)).astype(self.ids_dtype)
self.start_index = 10
ids[0][1] = 12
ids[0][2] = 12
ids[1][2] = 12
ids[1][3] = 12
self.end_index = self.start_index + 17
self.inputs = {'W': table, 'Ids': ids}
np_out = get_c_embedding(self.start_index, self.end_index, table, ids)
self.outputs = {'Out': np_out.reshape((2, 4, 64))}
self.attrs = {'start_index': self.start_index}
if core.is_compiled_with_npu():
self.__class__.use_npu = True
elif core.is_compiled_with_cuda():
self.__class__.exist_fp64_check_grad = True
param_out, moment1_out, \
moment2_out = adamw_step(self.inputs, self.attrs)
self.outputs = {
'Moment1Out': moment1_out,
'Moment2Out': moment2_out,
'ParamOut': param_out,
'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
}
def test_check_output(self):
self.check_output()
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestAdamW2(OpTest):
def setUp(self):
'''Test AdamW Op with supplied attributes
'''
self.op_type = "adamw"
param = np.random.uniform(-1, 1, (2, 2)).astype("float32")
grad = np.random.uniform(-1, 1, (2, 2)).astype("float32")
moment1 = np.random.uniform(-1, 1, (2, 2)).astype("float32")
# The second moment is positive
moment2 = np.random.random((2, 2)).astype("float32")
learning_rate = 0.004
beta1 = 0.78
beta2 = 0.836
def test_check_grad(self):
if core.is_compiled_with_cuda():
self.check_grad_with_place(core.CUDAPlace(0), ['W'], 'Out')
elif core.is_compiled_with_npu():
self.check_grad_with_place(core.NPUPlace(0), ['W'], 'Out')
def test_check_output(self):
if core.is_compiled_with_cuda():
self.check_output_with_place(core.CUDAPlace(0))
elif core.is_compiled_with_npu():
self.check_output_with_place(core.NPUPlace(0))
