wehub-resource-sync
361 lines
14 KiB
Python
361 lines
14 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 (
|
|
convert_float_to_uint16,
|
|
convert_uint16_to_float,
|
|
get_device_place,
|
|
)
|
|
|
|
import paddle
|
|
|
|
|
|
class TestMultiplyApi(unittest.TestCase):
|
|
def _run_static_graph_case(self, x_data, y_data):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
paddle.enable_static()
|
|
x = paddle.static.data(
|
|
name='x', shape=x_data.shape, dtype=x_data.dtype
|
|
)
|
|
y = paddle.static.data(
|
|
name='y', shape=y_data.shape, dtype=y_data.dtype
|
|
)
|
|
res = paddle.outer(x, y)
|
|
|
|
place = get_device_place()
|
|
exe = paddle.static.Executor(place)
|
|
outs = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': x_data, 'y': y_data},
|
|
fetch_list=[res],
|
|
)
|
|
res = outs[0]
|
|
return res
|
|
|
|
def _run_dynamic_graph_case(self, x_data, y_data):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(x_data)
|
|
y = paddle.to_tensor(y_data)
|
|
res = paddle.outer(x, y)
|
|
return res.numpy()
|
|
|
|
def test_multiply_static(self):
|
|
np.random.seed(7)
|
|
|
|
# test static computation graph: 3-d array
|
|
x_data = np.random.rand(2, 10, 10).astype(np.float64)
|
|
y_data = np.random.rand(2, 5, 10).astype(np.float64)
|
|
res = self._run_static_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test static computation graph: 2-d array
|
|
x_data = np.random.rand(200, 5).astype(np.float64)
|
|
y_data = np.random.rand(50, 5).astype(np.float64)
|
|
res = self._run_static_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test static computation graph: 1-d array
|
|
x_data = np.random.rand(50).astype(np.float64)
|
|
y_data = np.random.rand(50).astype(np.float64)
|
|
res = self._run_static_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test static computation graph: 1-d int32 array
|
|
x_data = np.random.rand(50).astype(np.int32)
|
|
y_data = np.random.rand(50).astype(np.int32)
|
|
res = self._run_static_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test static computation graph: 1-d int64 array
|
|
x_data = np.random.rand(50).astype(np.int64)
|
|
y_data = np.random.rand(50).astype(np.int64)
|
|
res = self._run_static_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test static computation graph: 3-d int32 big array
|
|
x_data = np.random.randint(-80000, 80000, [5, 10, 10]).astype(np.int32)
|
|
y_data = np.random.randint(-80000, 80000, [2, 10]).astype(np.int32)
|
|
res = self._run_static_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test static computation graph: 3-d int64 big array
|
|
x_data = np.random.randint(-80000, 80000, [5, 10, 10]).astype(np.int64)
|
|
y_data = np.random.randint(-80000, 80000, [2, 10]).astype(np.int64)
|
|
res = self._run_static_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
def test_multiply_dynamic(self):
|
|
# test dynamic computation graph: 3-d array
|
|
x_data = np.random.rand(5, 10, 10).astype(np.float64)
|
|
y_data = np.random.rand(2, 10).astype(np.float64)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test dynamic computation graph: 2-d array
|
|
x_data = np.random.rand(20, 50).astype(np.float64)
|
|
y_data = np.random.rand(50).astype(np.float64)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test dynamic computation graph: Scalar
|
|
x_data = np.random.rand(20, 10).astype(np.float32)
|
|
y_data = np.random.rand(1).astype(np.float32).item()
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=10000.0)
|
|
|
|
# test dynamic computation graph: 2-d array Complex
|
|
x_data = np.random.rand(20, 50).astype(
|
|
np.float64
|
|
) + 1j * np.random.rand(20, 50).astype(np.float64)
|
|
y_data = np.random.rand(50).astype(np.float64) + 1j * np.random.rand(
|
|
50
|
|
).astype(np.float64)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test dynamic computation graph: 3-d array Complex
|
|
x_data = np.random.rand(5, 10, 10).astype(
|
|
np.float64
|
|
) + 1j * np.random.rand(5, 10, 10).astype(np.float64)
|
|
y_data = np.random.rand(2, 10).astype(np.float64) + 1j * np.random.rand(
|
|
2, 10
|
|
).astype(np.float64)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test dynamic computation graph: 3-d int32 array
|
|
x_data = np.random.rand(5, 10, 10).astype(np.int32)
|
|
y_data = np.random.rand(2, 10).astype(np.int32)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test dynamic computation graph: 3-d int64 array
|
|
x_data = np.random.rand(5, 10, 10).astype(np.int64)
|
|
y_data = np.random.rand(2, 10).astype(np.int64)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test dynamic computation graph: 3-d int32 big array
|
|
x_data = np.random.randint(-80000, 80000, [5, 10, 10]).astype(np.int32)
|
|
y_data = np.random.randint(-80000, 80000, [2, 10]).astype(np.int32)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
# test dynamic computation graph: 3-d int64 big array
|
|
x_data = np.random.randint(-80000, 80000, [5, 10, 10]).astype(np.int64)
|
|
y_data = np.random.randint(-80000, 80000, [2, 10]).astype(np.int64)
|
|
res = self._run_dynamic_graph_case(x_data, y_data)
|
|
np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
|
|
|
|
|
|
class TestMultiplyError(unittest.TestCase):
|
|
def test_errors_dynamic(self):
|
|
np.random.seed(7)
|
|
|
|
# test dynamic computation graph: dtype must be Tensor type
|
|
x_data = np.random.randn(200).astype(np.float64)
|
|
y_data = np.random.randn(200).astype(np.float64)
|
|
y = paddle.to_tensor(y_data)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"multiply\(\): argument 'x' \(position 0\) must be Tensor, but got numpy.ndarray ",
|
|
paddle.outer,
|
|
x_data,
|
|
y,
|
|
)
|
|
|
|
# test dynamic computation graph: dtype must be Tensor type
|
|
x_data = np.random.randn(200).astype(np.float32)
|
|
y_data = np.random.randn(200).astype(np.float32)
|
|
x = paddle.to_tensor(x_data)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"multiply\(\): argument 'y' \(position 1\) must be Tensor, but got numpy.ndarray ",
|
|
paddle.outer,
|
|
x,
|
|
y_data,
|
|
)
|
|
|
|
# test dynamic computation graph: dtype must be Tensor type
|
|
x_data = np.random.randn(200).astype(np.float32)
|
|
y_data = np.random.randn(200).astype(np.float32)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"multiply\(\): argument 'x' \(position 0\) must be Tensor, but got numpy.ndarray",
|
|
paddle.outer,
|
|
x_data,
|
|
y_data,
|
|
)
|
|
|
|
|
|
class TestMultiplyApi_ZeroSize(unittest.TestCase):
|
|
def test_multiply_dynamic(self):
|
|
x_data = np.random.rand(5, 10, 0).astype(np.float64)
|
|
y_data = np.random.rand(0, 10).astype(np.float64)
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(x_data)
|
|
y = paddle.to_tensor(y_data)
|
|
x.stop_gradient = False
|
|
y.stop_gradient = False
|
|
res = paddle.outer(x, y)
|
|
np.testing.assert_allclose(
|
|
res.numpy(), np.outer(x_data, y_data), rtol=1e-05
|
|
)
|
|
loss = paddle.sum(res)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
|
|
def test_multiply_dynamic1(self):
|
|
x_data = np.random.rand(0).astype(np.float32)
|
|
y_data = np.random.rand(1).astype(np.float32)
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(x_data)
|
|
y = paddle.to_tensor(y_data)
|
|
res = paddle.outer(x, y)
|
|
np_res = np.outer(x_data, y_data)
|
|
np.testing.assert_allclose(res.numpy(), np_res, rtol=1e-05)
|
|
|
|
|
|
class TestOuterOutAndParamDecorator(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self.shape = [3]
|
|
self.out_shape = [self.shape[0], self.shape[0]]
|
|
self.x_np = np.random.rand(*self.shape).astype("float32")
|
|
self.y_np = np.random.rand(*self.shape).astype("float32")
|
|
|
|
self.apis = [paddle.outer, paddle.ger]
|
|
|
|
self.test_types = ["decorator1", "decorator2", "out", "out_decorator"]
|
|
|
|
def do_test(self, api, test_type):
|
|
x = paddle.to_tensor(self.x_np)
|
|
y = paddle.to_tensor(self.y_np)
|
|
x.stop_gradient = y.stop_gradient = False
|
|
out = paddle.zeros(self.out_shape, dtype="float32")
|
|
out.stop_gradient = False
|
|
|
|
if test_type == "raw":
|
|
out = api(x, y)
|
|
loss = out.mean()
|
|
loss.backward()
|
|
x_grad, y_grad = x.grad, y.grad
|
|
return out, x_grad, y_grad
|
|
elif test_type == "decorator1":
|
|
res = api(x, vec2=y)
|
|
loss = res.mean()
|
|
loss.backward()
|
|
x_grad, y_grad = x.grad, y.grad
|
|
return res, x_grad, y_grad
|
|
elif test_type == "decorator2":
|
|
out = api(vec2=y, input=x)
|
|
loss = out.mean()
|
|
loss.backward()
|
|
x_grad, y_grad = x.grad, y.grad
|
|
return out, x_grad, y_grad
|
|
elif test_type == "out":
|
|
res = api(x, y, out=out)
|
|
loss = out.mean()
|
|
loss.backward()
|
|
x_grad, y_grad = x.grad, y.grad
|
|
return out, x_grad, y_grad
|
|
elif test_type == "out_decorator":
|
|
res = api(out=out, vec2=y, input=x)
|
|
loss = out.mean()
|
|
loss.backward()
|
|
x_grad, y_grad = x.grad, y.grad
|
|
return out, x_grad, y_grad
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Test type {test_type} is not implemented."
|
|
)
|
|
|
|
def test_outer_out_decorator(self):
|
|
out_std, x_grad_std, y_grad_std = self.do_test(paddle.outer, "raw")
|
|
for api in self.apis:
|
|
for test_type in self.test_types:
|
|
out, x_grad, y_grad = self.do_test(api, test_type)
|
|
np.testing.assert_allclose(
|
|
out.numpy(), out_std.numpy(), rtol=1e-20
|
|
)
|
|
np.testing.assert_allclose(
|
|
x_grad.numpy(), x_grad_std.numpy(), rtol=1e-20
|
|
)
|
|
np.testing.assert_allclose(
|
|
y_grad.numpy(), y_grad_std.numpy(), rtol=1e-20
|
|
)
|
|
|
|
|
|
class TestOuterAlias(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
def test_outer_alias(self):
|
|
"""
|
|
Test the alias of outer function.
|
|
``outer(input=x, vec2=y)`` is equivalent to ``outer(x=x, y=y)``
|
|
"""
|
|
shape_cases = [
|
|
[2],
|
|
[2, 4],
|
|
[2, 4, 8],
|
|
]
|
|
dtype_cases = [
|
|
"float32",
|
|
"float64",
|
|
"int32",
|
|
"int64",
|
|
]
|
|
if paddle.is_compiled_with_cuda():
|
|
dtype_cases.extend(["float16", "bfloat16"])
|
|
|
|
for shape in shape_cases:
|
|
for dtype in dtype_cases:
|
|
x = paddle.rand(shape).astype(dtype)
|
|
y = paddle.rand(shape).astype(dtype)
|
|
|
|
# Test all alias combinations
|
|
combinations = [
|
|
{"x": x, "y": y},
|
|
{"input": x, "y": y},
|
|
{"x": x, "vec2": y},
|
|
{"input": x, "vec2": y},
|
|
]
|
|
|
|
x_numpy = x.numpy()
|
|
y_numpy = y.numpy()
|
|
|
|
# Get baseline result
|
|
if dtype == "bfloat16":
|
|
x_numpy = convert_uint16_to_float(x_numpy)
|
|
y_numpy = convert_uint16_to_float(y_numpy)
|
|
expected = np.outer(x_numpy, y_numpy)
|
|
if dtype == "bfloat16":
|
|
expected = convert_float_to_uint16(expected)
|
|
|
|
rtol = 1e-5 if dtype != "bfloat16" else 1e-4
|
|
|
|
for params in combinations:
|
|
out = paddle.outer(**params)
|
|
np.testing.assert_allclose(out.numpy(), expected, rtol=rtol)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|