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未验证 提交 7f93e2b0 编写于 作者: Z zhangyikun02 提交者： GitHub
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update unittests for tile op and silce op on XPU, test=kunlun (#40227)

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python/paddle/fluid/tests/unittests/xpu/test_slice_op_xpu.py
浏览文件 @ 7f93e2b0
...@@ -18,16 +18,23 @@ import sys ...@@ -18,16 +18,23 @@ import sys
import unittest import unittest
sys.path.append("..") sys.path.append("..")
from op_test import OpTest from op_test import OpTest
from op_test_xpu import XPUOpTest
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
paddle.enable_static() paddle.enable_static()
# Situation 1: starts(list, no tensor), ends(list, no tensor) # Situation 1: starts(list, no tensor), ends(list, no tensor)
# 1.1 without attr(decrease) # 1.1 without attr(decrease)
@unittest.skipIf(not paddle.is_compiled_with_xpu(), class XPUTestSliceOp(XPUOpTestWrapper):
"core is not compiled with XPU") def __init__(self):
class TestSliceOp(OpTest): self.op_name = 'slice'
self.use_dynamic_create_class = False
class TestSliceOp(XPUOpTest):
def setUp(self): def setUp(self):
self.dtype = self.in_type
self.place = paddle.XPUPlace(0)
self.op_type = "slice" self.op_type = "slice"
self.config() self.config()
self.inputs = {'Input': self.input} self.inputs = {'Input': self.input}
...@@ -41,39 +48,36 @@ class TestSliceOp(OpTest): ...@@ -41,39 +48,36 @@ class TestSliceOp(OpTest):
} }
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [1, 0, 2] self.starts = [1, 0, 2]
self.ends = [3, 3, 4] self.ends = [3, 3, 4]
self.axes = [0, 1, 2] self.axes = [0, 1, 2]
self.infer_flags = [1, 1, 1] self.infer_flags = [1, 1, 1]
self.out = self.input[1:3, 0:3, 2:4, :] self.out = self.input[1:3, 0:3, 2:4, :]
def test_check_output(self):
place = paddle.XPUPlace(0)
self.check_output_with_place(place)
def test_check_grad_normal(self): def test_check_grad_normal(self):
place = paddle.XPUPlace(0) if self.dtype == np.float16:
self.check_grad_with_place(place, ['Input'], 'Out') self.check_grad_with_place(self.place, ['Input'], 'Out')
else:
user_defined_grad_outputs = np.random.random(
@unittest.skipIf(not paddle.is_compiled_with_xpu(), self.out.shape).astype(self.dtype)
"core is not compiled with XPU") self.check_grad_with_place(
class TestCase1(TestSliceOp): self.place, ['Input'],
'Out',
user_defined_grad_outputs=user_defined_grad_outputs)
class TestCase1(TestSliceOp):
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [-3, 0, 2] self.starts = [-3, 0, 2]
self.ends = [3, 100, -1] self.ends = [3, 100, -1]
self.axes = [0, 1, 2] self.axes = [0, 1, 2]
self.infer_flags = [1, 1, 1] self.infer_flags = [1, 1, 1]
self.out = self.input[-3:3, 0:100, 2:-1, :] self.out = self.input[-3:3, 0:100, 2:-1, :]
class TestCase2(TestSliceOp):
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestCase2(TestSliceOp):
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [-3, 0, 2] self.starts = [-3, 0, 2]
self.ends = [3, 100, -1] self.ends = [3, 100, -1]
self.axes = [0, 1, 3] self.axes = [0, 1, 3]
...@@ -82,10 +86,15 @@ class TestCase2(TestSliceOp): ...@@ -82,10 +86,15 @@ class TestCase2(TestSliceOp):
# 1.2 with attr(decrease) # 1.2 with attr(decrease)
@unittest.skipIf(not paddle.is_compiled_with_xpu(), class XPUTestSliceOp_decs_dim(XPUOpTestWrapper):
"core is not compiled with XPU") def __init__(self):
class TestSliceOp_decs_dim(OpTest): self.op_name = 'slice'
self.use_dynamic_create_class = False
class TestSliceOp_decs_dim(XPUOpTest):
def setUp(self): def setUp(self):
self.dtype = self.in_type
self.place = paddle.XPUPlace(0)
self.op_type = "slice" self.op_type = "slice"
self.config() self.config()
self.inputs = {'Input': self.input} self.inputs = {'Input': self.input}
...@@ -100,7 +109,7 @@ class TestSliceOp_decs_dim(OpTest): ...@@ -100,7 +109,7 @@ class TestSliceOp_decs_dim(OpTest):
} }
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [1, 0, 2] self.starts = [1, 0, 2]
self.ends = [2, 3, 4] self.ends = [2, 3, 4]
self.axes = [0, 1, 2] self.axes = [0, 1, 2]
...@@ -109,19 +118,22 @@ class TestSliceOp_decs_dim(OpTest): ...@@ -109,19 +118,22 @@ class TestSliceOp_decs_dim(OpTest):
self.out = self.input[1, 0:3, 2:4, :] self.out = self.input[1, 0:3, 2:4, :]
def test_check_output(self): def test_check_output(self):
place = paddle.XPUPlace(0) self.check_output_with_place(self.place)
self.check_output_with_place(place)
def test_check_grad_normal(self): def test_check_grad_normal(self):
place = paddle.XPUPlace(0) if self.dtype == np.float16:
self.check_grad_with_place(place, ['Input'], 'Out') self.check_grad_with_place(self.place, ['Input'], 'Out')
else:
user_defined_grad_outputs = np.random.random(
@unittest.skipIf(not paddle.is_compiled_with_xpu(), self.out.shape).astype(self.dtype)
"core is not compiled with XPU") self.check_grad_with_place(
class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim): self.place, ['Input'],
'Out',
user_defined_grad_outputs=user_defined_grad_outputs)
class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim):
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [1, 0, 2] self.starts = [1, 0, 2]
self.ends = [2, 1, 4] self.ends = [2, 1, 4]
self.axes = [0, 1, 2] self.axes = [0, 1, 2]
...@@ -129,12 +141,9 @@ class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim): ...@@ -129,12 +141,9 @@ class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim):
self.infer_flags = [1, 1, 1] self.infer_flags = [1, 1, 1]
self.out = self.input[1, 0, 2:4, :] self.out = self.input[1, 0, 2:4, :]
class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [-1, 0, 2] self.starts = [-1, 0, 2]
self.ends = [1000000, 1, 4] self.ends = [1000000, 1, 4]
self.axes = [0, 1, 2] self.axes = [0, 1, 2]
...@@ -142,12 +151,9 @@ class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim): ...@@ -142,12 +151,9 @@ class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):
self.infer_flags = [1, 1, 1] self.infer_flags = [1, 1, 1]
self.out = self.input[-1, 0, 2:4, :] self.out = self.input[-1, 0, 2:4, :]
class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 7]).astype("float32") self.input = np.random.random([3, 4, 5, 7]).astype(self.dtype)
self.starts = [0, 1, 2, 3] self.starts = [0, 1, 2, 3]
self.ends = [1, 2, 3, 4] self.ends = [1, 2, 3, 4]
self.axes = [0, 1, 2, 3] self.axes = [0, 1, 2, 3]
...@@ -155,12 +161,9 @@ class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim): ...@@ -155,12 +161,9 @@ class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):
self.infer_flags = [1, 1, 1] self.infer_flags = [1, 1, 1]
self.out = self.input[0, 1, 2, 3:4] self.out = self.input[0, 1, 2, 3:4]
class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [-1] self.starts = [-1]
self.ends = [1000000] self.ends = [1000000]
self.axes = [3] self.axes = [3]
...@@ -168,12 +171,9 @@ class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim): ...@@ -168,12 +171,9 @@ class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):
self.infer_flags = [1, 1, 1] self.infer_flags = [1, 1, 1]
self.out = self.input[:, :, :, -1] self.out = self.input[:, :, :, -1]
class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):
@unittest.skipIf(not paddle.is_compiled_with_xpu(),
"core is not compiled with XPU")
class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):
def config(self): def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float32") self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
self.starts = [0, 1, 2, 3] self.starts = [0, 1, 2, 3]
self.ends = [1, 2, 3, 4] self.ends = [1, 2, 3, 4]
self.axes = [0, 1, 2, 3] self.axes = [0, 1, 2, 3]
...@@ -182,5 +182,10 @@ class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim): ...@@ -182,5 +182,10 @@ class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):
self.out = self.input[0, 1, 2, 3:4] self.out = self.input[0, 1, 2, 3:4]
support_types = get_xpu_op_support_types('slice')
for stype in support_types:
create_test_class(globals(), XPUTestSliceOp, stype)
create_test_class(globals(), XPUTestSliceOp_decs_dim, stype)
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
python/paddle/fluid/tests/unittests/xpu/test_tile_op_xpu.py
浏览文件 @ 7f93e2b0
...@@ -24,27 +24,32 @@ import paddle ...@@ -24,27 +24,32 @@ import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard from paddle.fluid import compiler, Program, program_guard
from paddle.fluid import core from paddle.fluid import core
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
paddle.enable_static() paddle.enable_static()
np.random.seed(10) np.random.seed(10)
#Situation 1: repeat_times is a list (without tensor) #Situation 1: repeat_times is a list (without tensor)
class TestTileOpRank1(XPUOpTest): class XPUTestTileOpRank1(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'tile'
self.use_dynamic_create_class = False
class TestTileOpRank1(XPUOpTest):
def setUp(self): def setUp(self):
self.set_xpu() self.dtype = self.in_type
self.__class__.no_need_check_grad = True
self.place = paddle.XPUPlace(0) self.place = paddle.XPUPlace(0)
self.op_type = "tile" self.op_type = "tile"
self.init_data() self.init_data()
self.inputs = {
self.inputs = {'X': np.random.random(self.ori_shape).astype("float32")} 'X': np.random.random(self.ori_shape).astype(self.dtype)
}
self.attrs = {'repeat_times': self.repeat_times} self.attrs = {'repeat_times': self.repeat_times}
output = np.tile(self.inputs['X'], self.repeat_times) output = np.tile(self.inputs['X'], self.repeat_times)
self.outputs = {'Out': output} self.outputs = {'Out': output}
def set_xpu(self):
self.__class__.use_xpu = True
def init_data(self): def init_data(self):
self.ori_shape = [100] self.ori_shape = [100]
self.repeat_times = [2] self.repeat_times = [2]
...@@ -52,51 +57,48 @@ class TestTileOpRank1(XPUOpTest): ...@@ -52,51 +57,48 @@ class TestTileOpRank1(XPUOpTest):
def test_check_output(self): def test_check_output(self):
self.check_output_with_place(self.place) self.check_output_with_place(self.place)
def test_check_grad(self): #with dimension expanding
pass class TestTileOpRank2Expanding(TestTileOpRank1):
#with dimension expanding
class TestTileOpRank2Expanding(TestTileOpRank1):
def init_data(self): def init_data(self):
self.ori_shape = [120] self.ori_shape = [120]
self.repeat_times = [2, 2] self.repeat_times = [2, 2]
class TestTileOpRank2(TestTileOpRank1):
class TestTileOpRank2(TestTileOpRank1):
def init_data(self): def init_data(self):
self.ori_shape = [12, 14] self.ori_shape = [12, 14]
self.repeat_times = [2, 3] self.repeat_times = [2, 3]
class TestTileOpRank3_Corner(TestTileOpRank1):
class TestTileOpRank3_Corner(TestTileOpRank1):
def init_data(self): def init_data(self):
self.ori_shape = (2, 10, 5) self.ori_shape = (2, 10, 5)
self.repeat_times = (1, 1, 1) self.repeat_times = (1, 1, 1)
class TestTileOpRank3_Corner2(TestTileOpRank1):
class TestTileOpRank3_Corner2(TestTileOpRank1):
def init_data(self): def init_data(self):
self.ori_shape = (2, 10, 5) self.ori_shape = (2, 10, 5)
self.repeat_times = (2, 2) self.repeat_times = (2, 2)
class TestTileOpRank3(TestTileOpRank1):
class TestTileOpRank3(TestTileOpRank1):
def init_data(self): def init_data(self):
self.ori_shape = (2, 4, 15) self.ori_shape = (2, 4, 15)
self.repeat_times = (2, 1, 4) self.repeat_times = (2, 1, 4)
class TestTileOpRank4(TestTileOpRank1):
class TestTileOpRank4(TestTileOpRank1):
def init_data(self): def init_data(self):
self.ori_shape = (2, 4, 5, 7) self.ori_shape = (2, 4, 5, 7)
self.repeat_times = (3, 2, 1, 2) self.repeat_times = (3, 2, 1, 2)
# Situation 2: repeat_times is a list (with tensor) # Situation 2: repeat_times is a list (with tensor)
class TestTileOpRank1_tensor_attr(XPUOpTest): class XPUTestTileOpRank1_tensor_attr(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'tile'
self.use_dynamic_create_class = False
class TestTileOpRank1_tensor_attr(XPUOpTest):
def setUp(self): def setUp(self):
self.set_xpu() self.dtype = self.in_type
self.__class__.no_need_check_grad = True
self.place = paddle.XPUPlace(0) self.place = paddle.XPUPlace(0)
self.op_type = "tile" self.op_type = "tile"
self.init_data() self.init_data()
...@@ -106,16 +108,13 @@ class TestTileOpRank1_tensor_attr(XPUOpTest): ...@@ -106,16 +108,13 @@ class TestTileOpRank1_tensor_attr(XPUOpTest):
(1)).astype('int32') * ele)) (1)).astype('int32') * ele))
self.inputs = { self.inputs = {
'X': np.random.random(self.ori_shape).astype("float32"), 'X': np.random.random(self.ori_shape).astype(self.dtype),
'repeat_times_tensor': repeat_times_tensor, 'repeat_times_tensor': repeat_times_tensor,
} }
self.attrs = {"repeat_times": self.infer_repeat_times} self.attrs = {"repeat_times": self.infer_repeat_times}
output = np.tile(self.inputs['X'], self.repeat_times) output = np.tile(self.inputs['X'], self.repeat_times)
self.outputs = {'Out': output} self.outputs = {'Out': output}
def set_xpu(self):
self.__class__.use_xpu = True
def init_data(self): def init_data(self):
self.ori_shape = [100] self.ori_shape = [100]
self.repeat_times = [2] self.repeat_times = [2]
...@@ -124,18 +123,13 @@ class TestTileOpRank1_tensor_attr(XPUOpTest): ...@@ -124,18 +123,13 @@ class TestTileOpRank1_tensor_attr(XPUOpTest):
def test_check_output(self): def test_check_output(self):
self.check_output_with_place(self.place) self.check_output_with_place(self.place)
def test_check_grad(self): class TestTileOpRank2_Corner_tensor_attr(TestTileOpRank1_tensor_attr):
pass
class TestTileOpRank2_Corner_tensor_attr(TestTileOpRank1_tensor_attr):
def init_data(self): def init_data(self):
self.ori_shape = [12, 14] self.ori_shape = [12, 14]
self.repeat_times = [1, 1] self.repeat_times = [1, 1]
self.infer_repeat_times = [1, -1] self.infer_repeat_times = [1, -1]
class TestTileOpRank2_attr_tensor(TestTileOpRank1_tensor_attr):
class TestTileOpRank2_attr_tensor(TestTileOpRank1_tensor_attr):
def init_data(self): def init_data(self):
self.ori_shape = [12, 14] self.ori_shape = [12, 14]
self.repeat_times = [2, 3] self.repeat_times = [2, 3]
...@@ -143,24 +137,27 @@ class TestTileOpRank2_attr_tensor(TestTileOpRank1_tensor_attr): ...@@ -143,24 +137,27 @@ class TestTileOpRank2_attr_tensor(TestTileOpRank1_tensor_attr):
# Situation 3: repeat_times is a tensor # Situation 3: repeat_times is a tensor
class TestTileOpRank1_tensor(XPUOpTest): class XPUTestTileOpRank1_tensor(XPUOpTestWrapper):
def __init__(self):
self.op_name = 'tile'
self.use_dynamic_create_class = False
class TestTileOpRank1_tensor(XPUOpTest):
def setUp(self): def setUp(self):
self.set_xpu() self.dtype = self.in_type
self.__class__.no_need_check_grad = True
self.place = paddle.XPUPlace(0) self.place = paddle.XPUPlace(0)
self.op_type = "tile" self.op_type = "tile"
self.init_data() self.init_data()
self.inputs = { self.inputs = {
'X': np.random.random(self.ori_shape).astype("float32"), 'X': np.random.random(self.ori_shape).astype(self.dtype),
'RepeatTimes': np.array(self.repeat_times).astype("int32"), 'RepeatTimes': np.array(self.repeat_times).astype("int32"),
} }
self.attrs = {} self.attrs = {}
output = np.tile(self.inputs['X'], self.repeat_times) output = np.tile(self.inputs['X'], self.repeat_times)
self.outputs = {'Out': output} self.outputs = {'Out': output}
def set_xpu(self):
self.__class__.use_xpu = True
def init_data(self): def init_data(self):
self.ori_shape = [100] self.ori_shape = [100]
self.repeat_times = [2] self.repeat_times = [2]
...@@ -168,77 +165,17 @@ class TestTileOpRank1_tensor(XPUOpTest): ...@@ -168,77 +165,17 @@ class TestTileOpRank1_tensor(XPUOpTest):
def test_check_output(self): def test_check_output(self):
self.check_output_with_place(self.place) self.check_output_with_place(self.place)
def test_check_grad(self): class TestTileOpRank2_tensor(TestTileOpRank1_tensor):
pass
class TestTileOpRank2_tensor(TestTileOpRank1_tensor):
def init_data(self): def init_data(self):
self.ori_shape = [12, 14] self.ori_shape = [12, 14]
self.repeat_times = [2, 3] self.repeat_times = [2, 3]
# Situation 4: input x is Integer support_types = get_xpu_op_support_types('tile')
class TestTileOpInteger(XPUOpTest): for stype in support_types:
def setUp(self): create_test_class(globals(), XPUTestTileOpRank1, stype)
self.set_xpu() create_test_class(globals(), XPUTestTileOpRank1_tensor_attr, stype)
self.place = paddle.XPUPlace(0) create_test_class(globals(), XPUTestTileOpRank1_tensor, stype)
self.op_type = "tile"
self.inputs = {
'X': np.random.randint(
10, size=(4, 4, 5)).astype("int32")
}
self.attrs = {'repeat_times': [2, 1, 4]}
output = np.tile(self.inputs['X'], (2, 1, 4))
self.outputs = {'Out': output}
def set_xpu(self):
self.__class__.use_xpu = True
def test_check_output(self):
self.check_output_with_place(self.place)
# Situation 5: input x is Integer
class TestTileOpInt64_t(XPUOpTest):
def setUp(self):
self.set_xpu()
self.place = paddle.XPUPlace(0)
self.op_type = "tile"
self.inputs = {
'X': np.random.randint(
10, size=(2, 4, 5)).astype("int64")
}
self.attrs = {'repeat_times': [2, 1, 4]}
output = np.tile(self.inputs['X'], (2, 1, 4))
self.outputs = {'Out': output}
def set_xpu(self):
self.__class__.use_xpu = True
def test_check_output(self):
self.check_output_with_place(self.place)
# Situation 6: input x is Bool
class TestTileOpBool(XPUOpTest):
def setUp(self):
self.set_xpu()
self.place = paddle.XPUPlace(0)
self.op_type = "tile"
self.inputs = {
'X': np.random.randint(
10, size=(2, 4, 5)).astype("bool")
}
self.attrs = {'repeat_times': [2, 1, 4]}
output = np.tile(self.inputs['X'], (2, 1, 4))
self.outputs = {'Out': output}
def set_xpu(self):
self.__class__.use_xpu = True
def test_check_output(self):
self.check_output_with_place(self.place)
# Test python API # Test python API
......
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