wehub-resource-sync
491 lines
19 KiB
Python
Executable File
491 lines
19 KiB
Python
Executable File
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import (
|
|
convert_float_to_uint16,
|
|
get_device_place,
|
|
get_places,
|
|
is_custom_device,
|
|
)
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.framework import in_dynamic_mode, in_pir_mode
|
|
|
|
SUPPORTED_DTYPES = [
|
|
bool,
|
|
np.int8,
|
|
np.int16,
|
|
np.uint16,
|
|
np.int32,
|
|
np.int64,
|
|
np.float16,
|
|
np.float32,
|
|
np.float64,
|
|
np.complex64,
|
|
np.complex128,
|
|
]
|
|
|
|
TEST_META_OP_DATA = [
|
|
{'op_str': 'logical_and', 'binary_op': True},
|
|
{'op_str': 'logical_or', 'binary_op': True},
|
|
{'op_str': 'logical_xor', 'binary_op': True},
|
|
{'op_str': 'logical_not', 'binary_op': False},
|
|
]
|
|
|
|
TEST_META_SHAPE_DATA = {
|
|
'XDimLargerThanYDim1': {'x_shape': [2, 3, 4, 5], 'y_shape': [4, 5]},
|
|
'XDimLargerThanYDim2': {'x_shape': [2, 3, 4, 5], 'y_shape': [4, 1]},
|
|
'XDimLargerThanYDim3': {'x_shape': [2, 3, 4, 5], 'y_shape': [1, 4, 1]},
|
|
'XDimLargerThanYDim4': {'x_shape': [2, 3, 4, 5], 'y_shape': [3, 4, 1]},
|
|
'XDimLargerThanYDim5': {'x_shape': [2, 3, 1, 5], 'y_shape': [3, 1, 1]},
|
|
'XDimLessThanYDim1': {'x_shape': [4, 1], 'y_shape': [2, 3, 4, 5]},
|
|
'XDimLessThanYDim2': {'x_shape': [1, 4, 1], 'y_shape': [2, 3, 4, 5]},
|
|
'XDimLessThanYDim3': {'x_shape': [3, 4, 1], 'y_shape': [2, 3, 4, 5]},
|
|
'XDimLessThanYDim4': {'x_shape': [3, 1, 1], 'y_shape': [2, 3, 1, 5]},
|
|
'XDimLessThanYDim5': {'x_shape': [4, 5], 'y_shape': [2, 3, 4, 5]},
|
|
'Axis1InLargerDim': {'x_shape': [1, 4, 5], 'y_shape': [2, 3, 1, 5]},
|
|
'EqualDim1': {'x_shape': [10, 7], 'y_shape': [10, 7]},
|
|
'EqualDim2': {'x_shape': [1, 1, 4, 5], 'y_shape': [2, 3, 1, 5]},
|
|
'ZeroDim1': {'x_shape': [], 'y_shape': []},
|
|
'ZeroDim2': {'x_shape': [2, 3, 4, 5], 'y_shape': []},
|
|
'ZeroDim3': {'x_shape': [], 'y_shape': [2, 3, 4, 5]},
|
|
}
|
|
|
|
TEST_META_WRONG_SHAPE_DATA = {
|
|
'ErrorDim1': {'x_shape': [2, 3, 4, 5], 'y_shape': [3, 4]},
|
|
'ErrorDim2': {'x_shape': [2, 3, 4, 5], 'y_shape': [4, 3]},
|
|
}
|
|
|
|
|
|
def run_static(x_np, y_np, op_str, use_gpu=False, binary_op=True):
|
|
paddle.enable_static()
|
|
startup_program = paddle.static.Program()
|
|
main_program = paddle.static.Program()
|
|
place = paddle.CPUPlace()
|
|
if use_gpu and (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
place = get_device_place()
|
|
exe = paddle.static.Executor(place)
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
x = paddle.static.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
|
|
op = getattr(paddle, op_str)
|
|
feed_list = {'x': x_np}
|
|
if not binary_op:
|
|
res = op(x)
|
|
else:
|
|
y = paddle.static.data(name='y', shape=y_np.shape, dtype=y_np.dtype)
|
|
feed_list['y'] = y_np
|
|
res = op(x, y)
|
|
exe.run(startup_program)
|
|
static_result = exe.run(main_program, feed=feed_list, fetch_list=[res])
|
|
return static_result
|
|
|
|
|
|
def run_dygraph(x_np, y_np, op_str, use_gpu=False, binary_op=True):
|
|
place = paddle.CPUPlace()
|
|
if use_gpu and (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
place = get_device_place()
|
|
paddle.disable_static(place)
|
|
op = getattr(paddle, op_str)
|
|
x = paddle.to_tensor(x_np, dtype=x_np.dtype)
|
|
if not binary_op:
|
|
dygraph_result = op(x)
|
|
else:
|
|
y = paddle.to_tensor(y_np, dtype=y_np.dtype)
|
|
dygraph_result = op(x, y)
|
|
return dygraph_result
|
|
|
|
|
|
def run_eager(x_np, y_np, op_str, use_gpu=False, binary_op=True):
|
|
place = paddle.CPUPlace()
|
|
if use_gpu and (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
place = get_device_place()
|
|
paddle.disable_static(place)
|
|
op = getattr(paddle, op_str)
|
|
x = paddle.to_tensor(x_np, dtype=x_np.dtype)
|
|
if not binary_op:
|
|
dygraph_result = op(x)
|
|
else:
|
|
y = paddle.to_tensor(y_np, dtype=y_np.dtype)
|
|
dygraph_result = op(x, y)
|
|
return dygraph_result
|
|
|
|
|
|
def np_data_generator(np_shape, dtype, *args, **kwargs):
|
|
if dtype == bool:
|
|
return np.random.choice(a=[True, False], size=np_shape).astype(bool)
|
|
elif dtype == np.uint16:
|
|
x = np.random.uniform(0.0, 1.0, np_shape).astype(np.float32)
|
|
return convert_float_to_uint16(x)
|
|
elif dtype == np.complex64 or dtype == np.complex128:
|
|
return np.random.normal(0, 1, np_shape).astype(dtype) + (
|
|
1.0j * np.random.normal(0, 1, np_shape)
|
|
).astype(dtype)
|
|
else:
|
|
return np.random.normal(0, 1, np_shape).astype(dtype)
|
|
|
|
|
|
def test(unit_test, use_gpu=False, test_error=False):
|
|
for op_data in TEST_META_OP_DATA:
|
|
meta_data = dict(op_data)
|
|
meta_data['use_gpu'] = use_gpu
|
|
np_op = getattr(np, meta_data['op_str'])
|
|
META_DATA = dict(TEST_META_SHAPE_DATA)
|
|
if test_error:
|
|
META_DATA = dict(TEST_META_WRONG_SHAPE_DATA)
|
|
for shape_data in META_DATA.values():
|
|
for data_type in SUPPORTED_DTYPES:
|
|
if not (
|
|
(paddle.is_compiled_with_cuda() or is_custom_device())
|
|
and use_gpu
|
|
) and (data_type in [np.float16, np.uint16]):
|
|
continue
|
|
meta_data['x_np'] = np_data_generator(
|
|
shape_data['x_shape'], dtype=data_type
|
|
)
|
|
meta_data['y_np'] = np_data_generator(
|
|
shape_data['y_shape'], dtype=data_type
|
|
)
|
|
if meta_data['binary_op'] and test_error:
|
|
# catch C++ Exception
|
|
unit_test.assertRaisesRegex(
|
|
ValueError,
|
|
r"\(InvalidArgument\) Broadcast dimension mismatch",
|
|
run_static,
|
|
**meta_data,
|
|
)
|
|
unit_test.assertRaisesRegex(
|
|
ValueError,
|
|
r"\(InvalidArgument\) Broadcast dimension mismatch",
|
|
run_dygraph,
|
|
**meta_data,
|
|
)
|
|
continue
|
|
static_result = run_static(**meta_data)
|
|
dygraph_result = run_dygraph(**meta_data)
|
|
eager_result = run_eager(**meta_data)
|
|
if meta_data['binary_op']:
|
|
np_result = np_op(meta_data['x_np'], meta_data['y_np'])
|
|
else:
|
|
np_result = np_op(meta_data['x_np'])
|
|
unit_test.assertTrue((static_result == np_result).all())
|
|
unit_test.assertTrue(
|
|
(dygraph_result.numpy() == np_result).all()
|
|
)
|
|
unit_test.assertTrue((eager_result.numpy() == np_result).all())
|
|
# add some corner case for complex datatype
|
|
for complex_data_type in [np.complex64, np.complex128]:
|
|
for x_data in (0 + 0j, 0 + 1j, 1 + 0j, 1 + 1j):
|
|
for y_data in (0 + 0j, 0 + 1j, 1 + 0j, 1 + 1j):
|
|
meta_data['x_np'] = (
|
|
x_data * np.ones(shape_data['x_shape'])
|
|
).astype(complex_data_type)
|
|
meta_data['y_np'] = (
|
|
y_data * np.ones(shape_data['y_shape'])
|
|
).astype(complex_data_type)
|
|
if meta_data['binary_op'] and test_error:
|
|
# catch C++ Exception
|
|
unit_test.assertRaisesRegex(
|
|
ValueError,
|
|
r"\(InvalidArgument\) Broadcast dimension mismatch",
|
|
run_static,
|
|
**meta_data,
|
|
)
|
|
unit_test.assertRaisesRegex(
|
|
ValueError,
|
|
r"\(InvalidArgument\) Broadcast dimension mismatch",
|
|
run_dygraph,
|
|
**meta_data,
|
|
)
|
|
continue
|
|
static_result = run_static(**meta_data)
|
|
dygraph_result = run_dygraph(**meta_data)
|
|
eager_result = run_eager(**meta_data)
|
|
if meta_data['binary_op']:
|
|
np_result = np_op(
|
|
meta_data['x_np'], meta_data['y_np']
|
|
)
|
|
else:
|
|
np_result = np_op(meta_data['x_np'])
|
|
unit_test.assertTrue((static_result == np_result).all())
|
|
unit_test.assertTrue(
|
|
(dygraph_result.numpy() == np_result).all()
|
|
)
|
|
unit_test.assertTrue(
|
|
(eager_result.numpy() == np_result).all()
|
|
)
|
|
|
|
|
|
def test_type_error(unit_test, use_gpu, type_str_map):
|
|
def check_type(op_str, x, y, binary_op):
|
|
op = getattr(paddle, op_str)
|
|
# The C++ backend raises TypeError for invalid type promotion.
|
|
error_type = TypeError
|
|
if isinstance(x, np.ndarray):
|
|
x = paddle.to_tensor(x)
|
|
y = paddle.to_tensor(y)
|
|
# Use TypeError for dygraph as well to be more specific.
|
|
error_type = TypeError
|
|
|
|
if binary_op:
|
|
type_x = type_str_map['x']
|
|
type_y = type_str_map['y']
|
|
if type_x != type_y:
|
|
floating_dtypes = {
|
|
np.float16,
|
|
np.float32,
|
|
np.float64,
|
|
np.uint16,
|
|
}
|
|
complex_dtypes = {np.complex64, np.complex128}
|
|
|
|
is_x_fp = type_x in floating_dtypes
|
|
is_y_fp = type_y in floating_dtypes
|
|
is_x_complex = type_x in complex_dtypes
|
|
is_y_complex = type_y in complex_dtypes
|
|
|
|
# Type promotion is supported between floating-point numbers,
|
|
# and between complex and real numbers.
|
|
promotion_allowed = (
|
|
(is_x_fp and is_y_fp) or is_x_complex or is_y_complex
|
|
)
|
|
|
|
if not promotion_allowed:
|
|
unit_test.assertRaises(error_type, op, x=x, y=y)
|
|
|
|
if not in_dynamic_mode():
|
|
error_type = TypeError
|
|
# Skip this test in PIR mode because the C++ backend has a known bug
|
|
# of ignoring the `out` parameter, which prevents the TypeError.
|
|
if not in_pir_mode():
|
|
unit_test.assertRaises(error_type, op, x=x, y=y, out=1)
|
|
else:
|
|
if not in_dynamic_mode():
|
|
error_type = TypeError
|
|
if not in_pir_mode():
|
|
unit_test.assertRaises(error_type, op, x=x, out=1)
|
|
|
|
place = paddle.CPUPlace()
|
|
if use_gpu and (paddle.is_compiled_with_cuda() or is_custom_device()):
|
|
place = get_device_place()
|
|
for op_data in TEST_META_OP_DATA:
|
|
if (
|
|
(paddle.is_compiled_with_cuda() or is_custom_device())
|
|
and use_gpu
|
|
and (
|
|
type_str_map['x'] in [np.float16, np.uint16]
|
|
or type_str_map['y'] in [np.float16, np.uint16]
|
|
)
|
|
):
|
|
continue
|
|
|
|
meta_data = dict(op_data)
|
|
binary_op = meta_data['binary_op']
|
|
|
|
paddle.disable_static(place)
|
|
x = np.random.choice(a=[0, 1], size=[10]).astype(type_str_map['x'])
|
|
y = np.random.choice(a=[0, 1], size=[10]).astype(type_str_map['y'])
|
|
check_type(meta_data['op_str'], x, y, binary_op)
|
|
|
|
paddle.enable_static()
|
|
startup_program = paddle.static.Program()
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
x = paddle.static.data(
|
|
name='x', shape=[10], dtype=type_str_map['x']
|
|
)
|
|
y = paddle.static.data(
|
|
name='y', shape=[10], dtype=type_str_map['y']
|
|
)
|
|
check_type(meta_data['op_str'], x, y, binary_op)
|
|
|
|
|
|
def type_map_factory():
|
|
return [
|
|
{'x': x_type, 'y': y_type}
|
|
for x_type in SUPPORTED_DTYPES
|
|
for y_type in SUPPORTED_DTYPES
|
|
]
|
|
|
|
|
|
class TestCPU(unittest.TestCase):
|
|
def test(self):
|
|
test(self)
|
|
|
|
def test_error(self):
|
|
test(self, False, True)
|
|
|
|
def test_type_error(self):
|
|
type_map_list = type_map_factory()
|
|
for type_map in type_map_list:
|
|
test_type_error(self, False, type_map)
|
|
|
|
|
|
class TestCUDA(unittest.TestCase):
|
|
def test(self):
|
|
test(self, True)
|
|
|
|
def test_error(self):
|
|
test(self, True, True)
|
|
|
|
def test_type_error(self):
|
|
type_map_list = type_map_factory()
|
|
for type_map in type_map_list:
|
|
test_type_error(self, True, type_map)
|
|
|
|
|
|
class TestLogicalOpsAPI_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
paddle.enable_static()
|
|
self.places = get_places()
|
|
self.shape = [10, 20]
|
|
self.dtype = 'bool'
|
|
|
|
def test_dygraph_api_compatibility(self):
|
|
paddle.disable_static()
|
|
for op_info in TEST_META_OP_DATA:
|
|
op_str = op_info['op_str']
|
|
is_binary = op_info['binary_op']
|
|
with self.subTest(op=op_str):
|
|
np_input = np.random.choice([True, False], size=self.shape)
|
|
x = paddle.to_tensor(np_input)
|
|
paddle_op = getattr(paddle, op_str)
|
|
ref_op = getattr(np, op_str)
|
|
|
|
paddle_dygraph_out = []
|
|
|
|
if is_binary:
|
|
np_other = np.random.choice([True, False], size=self.shape)
|
|
y = paddle.to_tensor(np_other)
|
|
# Position args (args)
|
|
paddle_dygraph_out.append(paddle_op(x, y))
|
|
# Keywords args (kwargs) for paddle
|
|
paddle_dygraph_out.append(paddle_op(x=x, y=y))
|
|
# Keywords args for torch
|
|
paddle_dygraph_out.append(paddle_op(input=x, other=y))
|
|
# Combined args and kwargs
|
|
paddle_dygraph_out.append(paddle_op(x, other=y))
|
|
# Tensor method args
|
|
paddle_dygraph_out.append(x.__getattribute__(op_str)(y))
|
|
# Tensor method kwargs
|
|
paddle_dygraph_out.append(
|
|
x.__getattribute__(op_str)(other=y)
|
|
)
|
|
|
|
# Test out
|
|
out_tensor = paddle.empty(self.shape, dtype=self.dtype)
|
|
paddle_op(x, y, out=out_tensor)
|
|
paddle_dygraph_out.append(out_tensor)
|
|
|
|
# Numpy reference out
|
|
ref_out = ref_op(np_input, np_other)
|
|
else: # Unary op (logical_not)
|
|
# Position args (args)
|
|
paddle_dygraph_out.append(paddle_op(x))
|
|
# Keywords args (kwargs) for paddle
|
|
paddle_dygraph_out.append(paddle_op(x=x))
|
|
# Keywords args for torch
|
|
paddle_dygraph_out.append(paddle_op(input=x))
|
|
# Tensor method args
|
|
paddle_dygraph_out.append(x.__getattribute__(op_str)())
|
|
|
|
# Test out
|
|
out_tensor = paddle.empty(self.shape, dtype=self.dtype)
|
|
paddle_op(x, out=out_tensor)
|
|
paddle_dygraph_out.append(out_tensor)
|
|
|
|
# Numpy reference out
|
|
ref_out = ref_op(np_input)
|
|
|
|
# Check
|
|
for out in paddle_dygraph_out:
|
|
np.testing.assert_equal(ref_out, out.numpy())
|
|
|
|
paddle.enable_static()
|
|
|
|
def test_static_api_compatibility(self):
|
|
for op_info in TEST_META_OP_DATA:
|
|
op_str = op_info['op_str']
|
|
is_binary = op_info['binary_op']
|
|
|
|
with self.subTest(op=op_str):
|
|
np_input = np.random.choice([True, False], size=self.shape)
|
|
ref_op = getattr(np, op_str)
|
|
|
|
main = paddle.static.Program()
|
|
startup = paddle.static.Program()
|
|
with base.program_guard(main, startup):
|
|
x = paddle.static.data(
|
|
name="x", shape=self.shape, dtype=self.dtype
|
|
)
|
|
paddle_op = getattr(paddle, op_str)
|
|
|
|
fetch_list = []
|
|
feed_dict = {"x": np_input}
|
|
|
|
if is_binary:
|
|
np_other = np.random.choice(
|
|
[True, False], size=self.shape
|
|
)
|
|
y = paddle.static.data(
|
|
name="y", shape=self.shape, dtype=self.dtype
|
|
)
|
|
feed_dict["y"] = np_other
|
|
|
|
# Position args (args)
|
|
fetch_list.append(paddle_op(x, y))
|
|
# Keywords args (kwargs) for paddle
|
|
fetch_list.append(paddle_op(x=x, y=y))
|
|
# Keywords args for torch
|
|
fetch_list.append(paddle_op(input=x, other=y))
|
|
# Combined args and kwargs
|
|
fetch_list.append(paddle_op(x, other=y))
|
|
# Tensor method args
|
|
fetch_list.append(x.__getattribute__(op_str)(y))
|
|
# Tensor method kwargs
|
|
fetch_list.append(x.__getattribute__(op_str)(other=y))
|
|
|
|
# Numpy reference out
|
|
ref_out = ref_op(np_input, np_other)
|
|
else: # Unary op
|
|
# Position args (args)
|
|
fetch_list.append(paddle_op(x))
|
|
# Keywords args (kwargs) for paddle
|
|
fetch_list.append(paddle_op(x=x))
|
|
# Keywords args for torch
|
|
fetch_list.append(paddle_op(input=x))
|
|
# Tensor method args
|
|
fetch_list.append(x.__getattribute__(op_str)())
|
|
|
|
# Numpy reference out
|
|
ref_out = ref_op(np_input)
|
|
|
|
for place in self.places:
|
|
exe = base.Executor(place)
|
|
outs = exe.run(
|
|
main, feed=feed_dict, fetch_list=fetch_list
|
|
)
|
|
# Check
|
|
for out in outs:
|
|
np.testing.assert_equal(ref_out, out)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|