未验证 提交 c6f173b0 编写于 作者: C Charles-hit 提交者: GitHub

support tile op backward refuse forward (#45942)

上级 1eefd66a
......@@ -2530,10 +2530,7 @@
forward : tile_grad (Tensor x, Tensor grad_out, IntArray repeat_times) -> Tensor(grad_x)
args : (Tensor grad_x_grad, IntArray repeat_times)
output : Tensor(grad_out_grad)
infer_meta :
func : TileInferMeta
kernel :
func : tile
invoke : tile(grad_x_grad, repeat_times)
- backward_api : tile_grad
forward : tile (Tensor x, IntArray repeat_times) -> Tensor(out)
......
......@@ -19,7 +19,10 @@ import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
from paddle.fluid import compiler, Program, program_guard, core
import gradient_checker
from decorator_helper import prog_scope
import paddle.fluid.layers as layers
#Situation 1: repeat_times is a list (without tensor)
......@@ -263,6 +266,80 @@ class TestTileAPI(unittest.TestCase):
assert np.array_equal(out_3.numpy(), np.tile(np_x, (2, 3)))
class TestTileDoubleGradCheck(unittest.TestCase):
def tile_wrapper(self, x):
return paddle.tile(x[0], [2, 1])
@prog_scope()
def func(self, place):
# the shape of input variable should be clearly specified, not inlcude -1.
eps = 0.005
dtype = np.float32
data = layers.data('data', [1, 2], False, dtype)
data.persistable = True
out = paddle.tile(data, [2, 1])
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
gradient_checker.double_grad_check([data],
out,
x_init=[data_arr],
place=place,
eps=eps)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
gradient_checker.double_grad_check_for_dygraph(self.tile_wrapper,
[data],
out,
x_init=[data_arr],
place=place)
def test_grad(self):
paddle.enable_static()
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestTileTripleGradCheck(unittest.TestCase):
def tile_wrapper(self, x):
return paddle.tile(x[0], [2, 1])
@prog_scope()
def func(self, place):
# the shape of input variable should be clearly specified, not inlcude -1.
eps = 0.005
dtype = np.float32
data = layers.data('data', [1, 2], False, dtype)
data.persistable = True
out = paddle.tile(data, [2, 1])
data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
gradient_checker.triple_grad_check([data],
out,
x_init=[data_arr],
place=place,
eps=eps)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
gradient_checker.triple_grad_check_for_dygraph(self.tile_wrapper,
[data],
out,
x_init=[data_arr],
place=place)
def test_grad(self):
paddle.enable_static()
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
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