wehub-resource-sync
402 lines
11 KiB
Python
402 lines
11 KiB
Python
# 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 itertools
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import get_places
|
|
|
|
import paddle
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
def numpy_ref(_x, value, axes, starts, ends, strides):
|
|
x = np.copy(_x)
|
|
|
|
try:
|
|
value = np.broadcast_to(value, x.shape)
|
|
except:
|
|
pass
|
|
|
|
indices_x = []
|
|
indices_v = []
|
|
for ndim_idx in range(x.ndim):
|
|
if ndim_idx not in axes:
|
|
ind = list(range(x.shape[ndim_idx]))
|
|
indices_x.append(ind)
|
|
indices_v.append(ind)
|
|
else:
|
|
_idx = list(axes).index(ndim_idx)
|
|
ind_x = list(range(starts[_idx], ends[_idx], strides[_idx]))
|
|
ind_v = list(range(len(ind_x)))
|
|
indices_x.append(ind_x)
|
|
indices_v.append(ind_v)
|
|
|
|
for index_x, index_v in zip(
|
|
itertools.product(*indices_x), itertools.product(*indices_v)
|
|
):
|
|
x[index_x] = value[index_v]
|
|
|
|
return x
|
|
|
|
|
|
class TestSliceScatterApi(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2023)
|
|
self.init_shape()
|
|
self.place = get_places()
|
|
|
|
def init_np(self):
|
|
self.x_np = np.random.random(self.x_shape).astype(
|
|
'uint16' if self.dtype == 'bfloat16' else self.dtype
|
|
)
|
|
self.value_np = np.random.random(self.value_shape).astype(
|
|
'uint16' if self.dtype == 'bfloat16' else self.dtype
|
|
)
|
|
|
|
def init_dtype(self):
|
|
self.dtype = 'float64'
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [8, 6]
|
|
self.value_shape = [8, 2]
|
|
self.axes = [1]
|
|
self.starts = [2]
|
|
self.ends = [6]
|
|
self.strides = [2]
|
|
|
|
def test_api_static(self):
|
|
paddle.enable_static()
|
|
self.init_dtype()
|
|
self.init_np()
|
|
|
|
for place in self.place:
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data('x', self.x_shape, self.dtype)
|
|
value = paddle.static.data(
|
|
'value', self.value_shape, self.dtype
|
|
)
|
|
|
|
out = paddle.slice_scatter(
|
|
x,
|
|
value,
|
|
axes=self.axes,
|
|
starts=self.starts,
|
|
ends=self.ends,
|
|
strides=self.strides,
|
|
)
|
|
exe = paddle.static.Executor(place)
|
|
res = exe.run(
|
|
feed={
|
|
'x': self.x_np,
|
|
'value': self.value_np,
|
|
},
|
|
fetch_list=[out],
|
|
)[0]
|
|
|
|
out_ref = numpy_ref(
|
|
self.x_np,
|
|
self.value_np,
|
|
axes=self.axes,
|
|
starts=self.starts,
|
|
ends=self.ends,
|
|
strides=self.strides,
|
|
)
|
|
|
|
np.testing.assert_allclose(res, out_ref)
|
|
|
|
def test_api_dygraph(self):
|
|
self.init_dtype()
|
|
self.init_np()
|
|
for place in self.place:
|
|
paddle.disable_static(place)
|
|
x_tensor = paddle.to_tensor(self.x_np)
|
|
value_tensor = paddle.to_tensor(self.value_np)
|
|
out = paddle.slice_scatter(
|
|
x_tensor,
|
|
value_tensor,
|
|
axes=self.axes,
|
|
starts=self.starts,
|
|
ends=self.ends,
|
|
strides=self.strides,
|
|
)
|
|
out_ref = numpy_ref(
|
|
self.x_np,
|
|
self.value_np,
|
|
axes=self.axes,
|
|
starts=self.starts,
|
|
ends=self.ends,
|
|
strides=self.strides,
|
|
)
|
|
|
|
np.testing.assert_allclose(out.numpy(), out_ref)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestSliceScatterApiIntComplex128(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
self.dtype = 'complex128'
|
|
|
|
|
|
class TestSliceScatterApiIntComplex64(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
self.dtype = 'complex64'
|
|
|
|
|
|
class TestSliceScatterApiInt64(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
self.dtype = 'int64'
|
|
|
|
|
|
class TestSliceScatterApiInt32(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
self.dtype = 'int32'
|
|
|
|
|
|
class TestSliceScatterApiInt16(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
# old ir `set_value` not support this dtype
|
|
if paddle.framework.in_dynamic_or_pir_mode():
|
|
self.dtype = 'int16'
|
|
else:
|
|
self.dtype = 'float64'
|
|
|
|
|
|
class TestSliceScatterApiInt8(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
# old ir `set_value` not support this dtype
|
|
if paddle.framework.in_dynamic_or_pir_mode():
|
|
self.dtype = 'int8'
|
|
else:
|
|
self.dtype = 'float64'
|
|
|
|
|
|
class TestSliceScatterApiUint8(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
# old ir `set_value` not support this dtype
|
|
if paddle.framework.in_dynamic_or_pir_mode():
|
|
self.dtype = 'uint8'
|
|
else:
|
|
self.dtype = 'float64'
|
|
|
|
|
|
class TestSliceScatterApiBool(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
self.dtype = 'bool'
|
|
|
|
|
|
class TestSliceScatterApiBfloat16(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
# old ir `set_value` not support this dtype
|
|
if paddle.framework.in_dynamic_or_pir_mode():
|
|
self.dtype = 'bfloat16'
|
|
else:
|
|
self.dtype = 'float64'
|
|
|
|
|
|
class TestSliceScatterApiFloat16(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
self.dtype = 'float16'
|
|
|
|
|
|
class TestSliceScatterApiFloat32(TestSliceScatterApi):
|
|
def init_dtype(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestSliceScatterApi3D(TestSliceScatterApi):
|
|
def init_shape(self):
|
|
self.x_shape = [8, 6, 3]
|
|
self.value_shape = [8, 2, 3]
|
|
self.axes = [1]
|
|
self.starts = [2]
|
|
self.ends = [6]
|
|
self.strides = [2]
|
|
|
|
|
|
class TestSliceScatterApi3DFloat32(TestSliceScatterApi3D):
|
|
def init_dtype(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestSliceScatterApi4D(TestSliceScatterApi):
|
|
def init_shape(self):
|
|
self.x_shape = [8, 6, 3, 5]
|
|
self.value_shape = [8, 2, 3, 5]
|
|
self.axes = [1]
|
|
self.starts = [2]
|
|
self.ends = [6]
|
|
self.strides = [2]
|
|
|
|
|
|
class TestSliceScatterApi4DFloat32(TestSliceScatterApi4D):
|
|
def init_dtype(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestSliceScatterApi4DAxis3(TestSliceScatterApi):
|
|
def init_shape(self):
|
|
self.x_shape = [8, 6, 3, 9]
|
|
self.value_shape = [8, 6, 3, 2]
|
|
self.axes = [3]
|
|
self.starts = [2]
|
|
self.ends = [6]
|
|
self.strides = [2]
|
|
|
|
|
|
class TestSliceScatterApi4DAxis3Float32(TestSliceScatterApi4DAxis3):
|
|
def init_dtype(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestSliceScatterApiBroadcast2D(TestSliceScatterApi):
|
|
def init_shape(self):
|
|
self.x_shape = [8, 9]
|
|
self.value_shape = [8, 1]
|
|
self.axes = [1]
|
|
self.starts = [2]
|
|
self.ends = [6]
|
|
self.strides = [2]
|
|
|
|
|
|
class TestSliceScatterApiBroadcast2DFloat32(TestSliceScatterApiBroadcast2D):
|
|
def init_dtype(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestSliceScatterApiBroadcast3D(TestSliceScatterApi):
|
|
def init_shape(self):
|
|
self.x_shape = [8, 9, 6]
|
|
self.value_shape = [1, 9, 1]
|
|
self.axes = [0, 2]
|
|
self.starts = [2, 3]
|
|
self.ends = [7, 5]
|
|
self.strides = [3, 2]
|
|
|
|
|
|
class TestSliceScatterApiBroadcast3DFloat32(TestSliceScatterApiBroadcast3D):
|
|
def init_dtype(self):
|
|
self.dtype = 'float32'
|
|
|
|
|
|
class TestSliceScatterTensorApi(unittest.TestCase):
|
|
def test_tensor(self):
|
|
paddle.disable_static()
|
|
_x = np.random.rand(8, 6)
|
|
_value = np.random.rand(8, 3)
|
|
|
|
x = paddle.to_tensor(_x)
|
|
value = paddle.to_tensor(_value)
|
|
|
|
axes = [1]
|
|
starts = [0]
|
|
ends = [6]
|
|
strides = [2]
|
|
|
|
out = x.slice_scatter(value, axes, starts, ends, strides)
|
|
out_ref = numpy_ref(_x, _value, axes, starts, ends, strides)
|
|
|
|
np.testing.assert_allclose(out.numpy(), out_ref)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestSliceScatterApiError(unittest.TestCase):
|
|
def test_error_ndim(self):
|
|
paddle.disable_static()
|
|
with self.assertRaises(ValueError):
|
|
x = paddle.to_tensor(np.random.rand(8, 6, 3))
|
|
value = paddle.to_tensor(np.random.rand(8, 3))
|
|
_ = paddle.slice_scatter(
|
|
x, value, axes=[0], starts=[0], ends=[8], strides=[1]
|
|
)
|
|
|
|
def test_error_index(self):
|
|
paddle.disable_static()
|
|
with self.assertRaises(ValueError):
|
|
x = paddle.to_tensor(np.random.rand(8, 6))
|
|
value = paddle.to_tensor(np.random.rand(8, 3))
|
|
_ = paddle.slice_scatter(
|
|
x, value, axes=[1], starts=[0], ends=[6], strides=[1]
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
x = paddle.to_tensor(np.random.rand(8, 6))
|
|
value = paddle.to_tensor(np.random.rand(2, 6))
|
|
_ = paddle.slice_scatter(
|
|
x, value, axes=[0], starts=[0], ends=[8], strides=[1]
|
|
)
|
|
|
|
|
|
class TestSliceScatterApi_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2023)
|
|
self.init_shape()
|
|
self.place = get_places()
|
|
|
|
def init_np(self):
|
|
self.x_np = np.random.random(self.x_shape).astype(
|
|
'uint16' if self.dtype == 'bfloat16' else self.dtype
|
|
)
|
|
self.value_np = np.random.random(self.value_shape).astype(
|
|
'uint16' if self.dtype == 'bfloat16' else self.dtype
|
|
)
|
|
|
|
def init_dtype(self):
|
|
self.dtype = 'float64'
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [0, 6]
|
|
self.value_shape = [0, 2]
|
|
self.axes = [1]
|
|
self.starts = [2]
|
|
self.ends = [6]
|
|
self.strides = [2]
|
|
|
|
def test_api_dygraph(self):
|
|
self.init_dtype()
|
|
self.init_np()
|
|
for place in self.place:
|
|
paddle.disable_static(place)
|
|
x_tensor = paddle.to_tensor(self.x_np)
|
|
x_tensor.stop_gradient = False
|
|
value_tensor = paddle.to_tensor(self.value_np)
|
|
out = paddle.slice_scatter(
|
|
x_tensor,
|
|
value_tensor,
|
|
axes=self.axes,
|
|
starts=self.starts,
|
|
ends=self.ends,
|
|
strides=self.strides,
|
|
)
|
|
out_ref = numpy_ref(
|
|
self.x_np,
|
|
self.value_np,
|
|
axes=self.axes,
|
|
starts=self.starts,
|
|
ends=self.ends,
|
|
strides=self.strides,
|
|
)
|
|
np.testing.assert_allclose(out.numpy(), out_ref)
|
|
out.sum().backward()
|
|
np.testing.assert_allclose(x_tensor.grad.numpy(), x_tensor.numpy())
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|