wehub-resource-sync
315 lines
8.5 KiB
Python
315 lines
8.5 KiB
Python
# Copyright (c) 2020 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 OpTest, get_device_place
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base import core
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
def dist(x, y, p):
|
|
if p == 0.0:
|
|
out = np.count_nonzero(x - y)
|
|
elif p == float("inf"):
|
|
out = np.max(np.abs(x - y))
|
|
elif p == float("-inf"):
|
|
out = np.min(np.abs(x - y))
|
|
else:
|
|
out = np.power(np.sum(np.power(np.abs(x - y), p)), 1.0 / p)
|
|
return np.array(out).astype(x.dtype)
|
|
|
|
|
|
class TestDistOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = 'dist'
|
|
self.python_api = paddle.dist
|
|
self.attrs = {}
|
|
self.init_case()
|
|
self.init_data_type()
|
|
self.inputs = {
|
|
"X": np.random.random(self.x_shape).astype(self.data_type),
|
|
"Y": np.random.random(self.y_shape).astype(self.data_type),
|
|
}
|
|
|
|
self.attrs["p"] = self.p
|
|
self.outputs = {
|
|
"Out": dist(self.inputs["X"], self.inputs["Y"], self.attrs["p"])
|
|
}
|
|
self.gradient = self.calc_gradient()
|
|
|
|
def init_case(self):
|
|
self.x_shape = 120
|
|
self.y_shape = 120
|
|
self.p = 0.0
|
|
|
|
def init_data_type(self):
|
|
self.data_type = (
|
|
np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
)
|
|
|
|
def calc_gradient(self):
|
|
x = self.inputs["X"]
|
|
y = self.inputs["Y"]
|
|
p = self.attrs["p"]
|
|
if p == 0:
|
|
grad = np.zeros(x.shape)
|
|
elif p in [float("inf"), float("-inf")]:
|
|
norm = dist(x, y, p)
|
|
x_minux_y_abs = np.abs(x - y)
|
|
grad = np.sign(x - y)
|
|
grad[x_minux_y_abs != norm] = 0
|
|
else:
|
|
norm = dist(x, y, p)
|
|
grad = (
|
|
np.power(norm, 1 - p)
|
|
* np.power(np.abs(x - y), p - 1)
|
|
* np.sign(x - y)
|
|
)
|
|
|
|
def get_reduce_dims(x, y):
|
|
x_reduce_dims = []
|
|
y_reduce_dims = []
|
|
|
|
if x.ndim >= y.ndim:
|
|
y_reshape = tuple([1] * (x.ndim - y.ndim) + list(y.shape))
|
|
y = y.reshape(y_reshape)
|
|
else:
|
|
x_reshape = tuple([1] * (y.ndim - x.ndim) + list(x.shape))
|
|
x = x.reshape(x_reshape)
|
|
for i in range(x.ndim):
|
|
if x.shape[i] > y.shape[i]:
|
|
y_reduce_dims.append(i)
|
|
elif x.shape[i] < y.shape[i]:
|
|
x_reduce_dims.append(i)
|
|
return x_reduce_dims, y_reduce_dims
|
|
|
|
x_reduce_dims, y_reduce_dims = get_reduce_dims(x, y)
|
|
if len(x_reduce_dims) != 0:
|
|
x_grad = np.sum(grad, tuple(x_reduce_dims)).reshape(x.shape)
|
|
else:
|
|
x_grad = grad
|
|
if len(y_reduce_dims) != 0:
|
|
y_grad = -np.sum(grad, tuple(y_reduce_dims)).reshape(y.shape)
|
|
else:
|
|
y_grad = -grad
|
|
|
|
return x_grad, y_grad
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(
|
|
["X", "Y"], "Out", user_defined_grads=self.gradient, check_pir=True
|
|
)
|
|
|
|
|
|
class TestDistOpCase1(TestDistOp):
|
|
def init_case(self):
|
|
self.x_shape = (3, 5, 5, 6)
|
|
self.y_shape = (5, 5, 6)
|
|
self.p = 1.0
|
|
|
|
|
|
class TestDistOpCase2(TestDistOp):
|
|
def init_case(self):
|
|
self.x_shape = (10, 10)
|
|
self.y_shape = (4, 10, 10)
|
|
self.p = 2.0
|
|
|
|
|
|
class TestDistOpCase3(TestDistOp):
|
|
def init_case(self):
|
|
self.x_shape = (15, 10)
|
|
self.y_shape = (15, 10)
|
|
self.p = float("inf")
|
|
|
|
|
|
class TestDistOpCase4(TestDistOp):
|
|
def init_case(self):
|
|
self.x_shape = (2, 3, 4, 5, 8)
|
|
self.y_shape = (3, 1, 5, 8)
|
|
self.p = float("-inf")
|
|
|
|
|
|
class TestDistOpCase5(TestDistOp):
|
|
def init_case(self):
|
|
self.x_shape = (4, 1, 4, 8)
|
|
self.y_shape = (2, 2, 1, 4, 4, 8)
|
|
self.p = 1.5
|
|
|
|
|
|
class TestDistBF16Op(OpTest):
|
|
def init_data_type(self):
|
|
self.data_type = 'bfloat16'
|
|
|
|
|
|
class TestDistBF16OpCase1(TestDistBF16Op):
|
|
def init_case(self):
|
|
self.x_shape = (3, 5, 5, 6)
|
|
self.y_shape = (5, 5, 6)
|
|
self.p = 1.0
|
|
|
|
|
|
class TestDistBF16OpCase2(TestDistBF16Op):
|
|
def init_case(self):
|
|
self.x_shape = (10, 10)
|
|
self.y_shape = (4, 10, 10)
|
|
self.p = 2.0
|
|
|
|
|
|
class TestDistBF16OpCase3(TestDistBF16Op):
|
|
def init_case(self):
|
|
self.x_shape = (15, 10)
|
|
self.y_shape = (15, 10)
|
|
self.p = float("inf")
|
|
|
|
|
|
class TestDistBF16OpCase4(TestDistBF16Op):
|
|
def init_case(self):
|
|
self.x_shape = (2, 3, 4, 5, 8)
|
|
self.y_shape = (3, 1, 5, 8)
|
|
self.p = float("-inf")
|
|
|
|
|
|
class TestDistBF16OpCase5(TestDistBF16Op):
|
|
def init_case(self):
|
|
self.x_shape = (4, 1, 4, 8)
|
|
self.y_shape = (2, 2, 1, 4, 4, 8)
|
|
self.p = 1.5
|
|
|
|
|
|
class TestDistFP16Op(OpTest):
|
|
def init_data_type(self):
|
|
self.data_type = 'float16'
|
|
|
|
|
|
class TestDistFP16OpCase1(TestDistFP16Op):
|
|
def init_case(self):
|
|
self.x_shape = (3, 5, 5, 6)
|
|
self.y_shape = (5, 5, 6)
|
|
self.p = 1.0
|
|
|
|
|
|
class TestDistFP16OpCase2(TestDistFP16Op):
|
|
def init_case(self):
|
|
self.x_shape = (10, 10)
|
|
self.y_shape = (4, 10, 10)
|
|
self.p = 2.0
|
|
|
|
|
|
class TestDistFP16OpCase3(TestDistFP16Op):
|
|
def init_case(self):
|
|
self.x_shape = (15, 10)
|
|
self.y_shape = (15, 10)
|
|
self.p = float("inf")
|
|
|
|
|
|
class TestDistFP16OpCase4(TestDistFP16Op):
|
|
def init_case(self):
|
|
self.x_shape = (2, 3, 4, 5, 8)
|
|
self.y_shape = (3, 1, 5, 8)
|
|
self.p = float("-inf")
|
|
|
|
|
|
class TestDistFP16OpCase5(TestDistFP16Op):
|
|
def init_case(self):
|
|
self.x_shape = (4, 1, 4, 8)
|
|
self.y_shape = (2, 2, 1, 4, 4, 8)
|
|
self.p = 1.5
|
|
|
|
|
|
class TestDistOp_ZeroSize1(TestDistOp):
|
|
def setUp(self):
|
|
self.op_type = 'dist'
|
|
self.python_api = paddle.dist
|
|
self.attrs = {}
|
|
self.init_case()
|
|
self.init_data_type()
|
|
self.inputs = {
|
|
"X": np.random.random(self.x_shape).astype(self.data_type),
|
|
"Y": np.random.random(self.y_shape).astype(self.data_type),
|
|
}
|
|
|
|
self.attrs["p"] = self.p
|
|
self.outputs = {
|
|
"Out": dist(self.inputs["X"], self.inputs["Y"], self.attrs["p"])
|
|
}
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(["X", "Y"], "Out", check_pir=True)
|
|
|
|
def init_case(self):
|
|
self.x_shape = (0, 1, 5, 6)
|
|
self.y_shape = (0, 5, 6)
|
|
self.p = 1.0
|
|
|
|
|
|
class TestDistOp_ZeroSize2(TestDistOp_ZeroSize1):
|
|
def init_case(self):
|
|
self.x_shape = (0, 1, 5, 6)
|
|
self.y_shape = (1, 5, 6)
|
|
self.p = 1.0
|
|
|
|
|
|
class TestDistAPI(unittest.TestCase):
|
|
def init_data_type(self):
|
|
self.data_type = (
|
|
'float32' if core.is_compiled_with_rocm() else 'float64'
|
|
)
|
|
|
|
def test_api(self):
|
|
self.init_data_type()
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with base.program_guard(main_program, startup_program):
|
|
x = paddle.static.data(
|
|
name='x', shape=[2, 3, 4, 5], dtype=self.data_type
|
|
)
|
|
y = paddle.static.data(
|
|
name='y', shape=[3, 1, 5], dtype=self.data_type
|
|
)
|
|
p = 2
|
|
x_i = np.random.random((2, 3, 4, 5)).astype(self.data_type)
|
|
y_i = np.random.random((3, 1, 5)).astype(self.data_type)
|
|
result = paddle.dist(x, y, p)
|
|
place = get_device_place()
|
|
exe = base.Executor(place)
|
|
out = exe.run(
|
|
main_program,
|
|
feed={'x': x_i, 'y': y_i},
|
|
fetch_list=[result],
|
|
)
|
|
np.testing.assert_allclose(dist(x_i, y_i, p), out[0], rtol=1e-05)
|
|
|
|
def test_grad_x(self):
|
|
paddle.disable_static()
|
|
a = paddle.rand([2, 2, 3, 2])
|
|
b = paddle.rand([1, 1, 3, 1])
|
|
a.stop_gradient = False
|
|
c = paddle.dist(a, b, 2)
|
|
c.backward()
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|