wehub-resource-sync
192 lines
6.6 KiB
Python
192 lines
6.6 KiB
Python
# Copyright (c) 2025 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
|
|
|
|
import paddle
|
|
from paddle import base
|
|
|
|
|
|
def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
|
|
"""Reference forward implementation using np.matmul."""
|
|
# np.matmul does not support the transpose flags, so we manually
|
|
# transpose X and Y appropriately.
|
|
if transpose_X:
|
|
if X.ndim == 1:
|
|
X = X.reshape((X.size,))
|
|
elif X.ndim == 2:
|
|
X = X.T
|
|
else:
|
|
dim = list(range(len(X.shape)))
|
|
dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
|
|
X = np.transpose(X, tuple(dim))
|
|
if transpose_Y:
|
|
if Y.ndim == 1:
|
|
Y = Y.reshape((Y.size,))
|
|
else:
|
|
dim = list(range(len(Y.shape)))
|
|
dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
|
|
Y = np.transpose(Y, tuple(dim))
|
|
|
|
Out = np.matmul(X, Y)
|
|
return Out
|
|
|
|
|
|
class TestMatmulOutAndParamDecorator(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self.x_shape = [3, 4]
|
|
self.y_shape = [4, 3]
|
|
self.x_np = np.random.rand(*self.x_shape).astype(np.float32)
|
|
self.y_np = np.random.rand(*self.y_shape).astype(np.float32)
|
|
|
|
self.apis = [paddle.matmul, paddle.linalg.matmul]
|
|
self.test_types = [
|
|
# "decorator1",
|
|
# "decorator2",
|
|
"out",
|
|
# "out_decorator",
|
|
]
|
|
|
|
def do_test(self, api, test_type):
|
|
x = paddle.to_tensor(self.x_np, stop_gradient=False)
|
|
y = paddle.to_tensor(self.y_np, stop_gradient=False)
|
|
out = paddle.empty((3, 3), dtype='float32')
|
|
out.stop_gradient = False
|
|
|
|
if test_type == 'raw':
|
|
result = api(x, y)
|
|
result.mean().backward()
|
|
return result, x.grad, y.grad
|
|
elif test_type == 'decorator1':
|
|
result = api(x, y)
|
|
result.mean().backward()
|
|
return result, x.grad, y.grad
|
|
elif test_type == 'decorator2':
|
|
result = api(input=x, other=y)
|
|
result.mean().backward()
|
|
return result, x.grad, y.grad
|
|
elif test_type == 'out':
|
|
api(x, y, out=out)
|
|
out.mean().backward()
|
|
return out, x.grad, y.grad
|
|
elif test_type == 'out_decorator':
|
|
api(input=x, other=y, out=out)
|
|
out.mean().backward()
|
|
return out, x.grad, y.grad
|
|
else:
|
|
raise ValueError(f"Unknown test type: {test_type}")
|
|
|
|
def test_matmul_out(self):
|
|
out_std, grad_std, y_grad_std = self.do_test(paddle.matmul, 'raw')
|
|
for test_type in self.test_types:
|
|
out, grad, y_grad = self.do_test(paddle.matmul, test_type)
|
|
np.testing.assert_allclose(out.numpy(), out_std.numpy(), rtol=1e-20)
|
|
np.testing.assert_allclose(
|
|
grad.numpy(), grad_std.numpy(), rtol=1e-20
|
|
)
|
|
np.testing.assert_allclose(
|
|
y_grad.numpy(), y_grad_std.numpy(), rtol=1e-20
|
|
)
|
|
|
|
|
|
class TestMatMulAPI_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
paddle.enable_static()
|
|
self.x_shape = [5, 6]
|
|
self.y_shape = [6, 4]
|
|
self.dtype = 'float32'
|
|
self.init_data()
|
|
|
|
def init_data(self):
|
|
self.np_x_input = np.random.randint(0, 8, self.x_shape).astype(
|
|
self.dtype
|
|
)
|
|
self.np_y_input = np.random.randint(3, 9, self.y_shape).astype(
|
|
self.dtype
|
|
)
|
|
|
|
def test_dygraph_Compatibility(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.np_x_input)
|
|
y = paddle.to_tensor(self.np_y_input)
|
|
paddle_dygraph_out = []
|
|
# Position args (args)
|
|
out1 = paddle.matmul(x, y)
|
|
paddle_dygraph_out.append(out1)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.matmul(x=x, y=y)
|
|
paddle_dygraph_out.append(out2)
|
|
# Keywords args for torch
|
|
out3 = paddle.matmul(input=x, other=y)
|
|
paddle_dygraph_out.append(out3)
|
|
# Combined args and kwargs
|
|
out4 = paddle.matmul(x, other=y)
|
|
paddle_dygraph_out.append(out4)
|
|
# Tensor method args
|
|
out5 = x.matmul(y)
|
|
paddle_dygraph_out.append(out5)
|
|
# Tensor method kwargs
|
|
out6 = x.matmul(other=y)
|
|
paddle_dygraph_out.append(out6)
|
|
# Test out
|
|
out7 = paddle.empty([])
|
|
paddle.matmul(x, other=y, out=out7)
|
|
paddle_dygraph_out.append(out7)
|
|
# Numpy reference out
|
|
ref_out = reference_matmul(self.np_x_input, self.np_y_input)
|
|
# Check
|
|
for out in paddle_dygraph_out:
|
|
np.testing.assert_allclose(ref_out, out.numpy())
|
|
paddle.enable_static()
|
|
|
|
def test_static_Compatibility(self):
|
|
main = paddle.static.Program()
|
|
startup = paddle.static.Program()
|
|
with base.program_guard(main, startup):
|
|
x = paddle.static.data(
|
|
name="x", shape=self.x_shape, dtype=self.dtype
|
|
)
|
|
y = paddle.static.data(
|
|
name="y", shape=self.y_shape, dtype=self.dtype
|
|
)
|
|
# Position args (args)
|
|
out1 = paddle.matmul(x, y)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.matmul(x=x, y=y)
|
|
# Keywords args for torch
|
|
out3 = paddle.matmul(input=x, other=y)
|
|
# Combined args and kwargs
|
|
out4 = paddle.matmul(x, other=y)
|
|
# Tensor method args
|
|
out5 = x.matmul(y)
|
|
# Tensor method kwargs
|
|
out6 = x.matmul(other=y)
|
|
exe = base.Executor(paddle.CPUPlace())
|
|
fetches = exe.run(
|
|
main,
|
|
feed={"x": self.np_x_input, "y": self.np_y_input},
|
|
fetch_list=[out1, out2, out3, out4, out5, out6],
|
|
)
|
|
ref_out = reference_matmul(self.np_x_input, self.np_y_input)
|
|
for out in fetches:
|
|
np.testing.assert_allclose(out, ref_out)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|