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chore: import upstream snapshot with attribution

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wehub-resource-sync
2026-07-13 12:40:42 +08:00
commit e25996e7db
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test/CMakeLists.txt
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remove_definitions(-DPADDLE_DLL_EXPORT)
set(CC_TESTS_DIR
${PADDLE_BINARY_DIR}/test/cpp
CACHE INTERNAL "c++ tests directory")
set(PYTHON_TESTS_DIR
${PADDLE_BINARY_DIR}/test
CACHE INTERNAL "python tests directory")
function(py_test_modules TARGET_NAME)
if(WITH_TESTING)
set(options SERIAL)
set(oneValueArgs "")
set(multiValueArgs MODULES DEPS ENVS)
cmake_parse_arguments(py_test_modules "${options}" "${oneValueArgs}"
"${multiValueArgs}" ${ARGN})
string(REGEX MATCH "_deprecated\.py$" DEPRECATED_MODULES
"${py_test_modules_MODULES}")
string(REGEX MATCH "_deprecated$" DEPRECATED_TARGET_NAME "${TARGET_NAME}")
set(FLAGS_PIR_MODE "")
if((NOT "${DEPRECATED_MODULES}" STREQUAL "")
OR (NOT "${DEPRECATED_TARGET_NAME}" STREQUAL ""))
set(FLAGS_PIR_MODE FLAGS_enable_pir_api=0)
endif()
if(WITH_COVERAGE AND NOT (WITH_INCREMENTAL_COVERAGE
AND "$ENV{PADDLE_GIT_DIFF_PY_FILE}" STREQUAL ""))
add_test(
NAME ${TARGET_NAME}
COMMAND
${CMAKE_COMMAND} -E env PYTHONPATH=${PADDLE_BINARY_DIR}/python
${py_test_modules_ENVS} ${FLAGS_PIR_MODE}
COVERAGE_FILE=${PADDLE_BINARY_DIR}/python-coverage.data
${PYTHON_EXECUTABLE} -m coverage run --branch -p
${PADDLE_SOURCE_DIR}/tools/test_runner.py ${py_test_modules_MODULES}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
else()
add_test(
NAME ${TARGET_NAME}
COMMAND
${CMAKE_COMMAND} -E env PYTHONPATH=${PADDLE_BINARY_DIR}/python
${py_test_modules_ENVS} ${FLAGS_PIR_MODE} ${PYTHON_EXECUTABLE}
${PADDLE_SOURCE_DIR}/tools/test_runner.py ${py_test_modules_MODULES}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif()
if(py_test_modules_SERIAL)
set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
endif()
if(WIN32)
set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 150)
endif()
endif()
endfunction()
function(bash_test_modules TARGET_NAME)
if(NOT WITH_TESTING)
return()
endif()
set(options SERIAL)
set(oneValueArgs TIMEOUT START_BASH)
set(multiValueArgs DEPS ENVS LABELS)
cmake_parse_arguments(bash_test_modules "${options}" "${oneValueArgs}"
"${multiValueArgs}" ${ARGN})
set(timeout 350)
if(${bash_test_modules_TIMEOUT})
set(timeout ${bash_test_modules_TIMEOUT})
endif()
if(WITH_COVERAGE)
add_test(
NAME ${TARGET_NAME}
COMMAND
${CMAKE_COMMAND} -E env PYTHONPATH=${PADDLE_BINARY_DIR}/python
TEST_TARGET_NAME=${TARGET_NAME} TEST_TIMEOUT=${timeout}
${bash_test_modules_ENVS} WITH_COVERAGE=ON
COVERAGE_FILE=${PADDLE_BINARY_DIR}/python-coverage.data bash
${CMAKE_CURRENT_BINARY_DIR}/${bash_test_modules_START_BASH}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
else()
add_test(
NAME ${TARGET_NAME}
COMMAND
${CMAKE_COMMAND} -E env PYTHONPATH=${PADDLE_BINARY_DIR}/python
TEST_TARGET_NAME=${TARGET_NAME} TEST_TIMEOUT=${timeout}
${bash_test_modules_ENVS} bash
${CMAKE_CURRENT_BINARY_DIR}/${bash_test_modules_START_BASH}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif()
if(bash_test_modules_SERIAL)
set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
endif()
if(bash_test_modules_LABELS)
set_tests_properties(${TARGET_NAME} PROPERTIES LABELS
${bash_test_modules_LABELS})
endif()
endfunction()
function(set_pir_tests_properties)
file(STRINGS "${CMAKE_SOURCE_DIR}/test/white_list/pir_op_test_white_list"
PIR_OP_TESTS)
foreach(IR_OP_TEST ${PIR_OP_TESTS})
if(TEST ${IR_OP_TEST})
set_property(
TEST ${IR_OP_TEST}
APPEND
PROPERTY ENVIRONMENT "FLAGS_PIR_OPTEST_WHITE_LIST=True")
endif()
endforeach()
file(STRINGS "${CMAKE_SOURCE_DIR}/test/white_list/pir_op_test_no_check_list"
PIR_OP_NO_CHECK_TESTS)
foreach(IR_OP_TEST ${PIR_OP_NO_CHECK_TESTS})
if(TEST ${IR_OP_TEST})
set_property(
TEST ${IR_OP_TEST}
APPEND
PROPERTY ENVIRONMENT "FLAGS_PIR_NO_CHECK=True")
endif()
endforeach()
file(STRINGS
"${CMAKE_SOURCE_DIR}/test/white_list/pir_op_test_precision_white_list"
PIR_OP_RELAXED_TESTS)
foreach(IR_OP_TEST ${PIR_OP_RELAXED_TESTS})
if(TEST ${IR_OP_TEST})
set_property(
TEST ${IR_OP_TEST}
APPEND
PROPERTY ENVIRONMENT "FLAGS_PIR_OPTEST_RELAX_CHECK=True")
endif()
endforeach()
endfunction()
if(WITH_TESTING)
if(WITH_CINN)
add_subdirectory(ap)
add_subdirectory(cinn)
endif()
# The following unittests only run in PR-CI-CINN
if(WITH_CINN)
add_subdirectory(ir/pir/cinn)
endif()
if(WIN32 AND WIN_UNITTEST_LEVEL LESS 2)
message(STATUS "Skip tests unrelated to CUDA/TRT")
else()
add_subdirectory(amp)
add_subdirectory(autograd)
add_subdirectory(custom_kernel)
# swgu98: Temporarily commented on Windows platform
if(NOT WIN32)
add_subdirectory(custom_op)
endif()
add_subdirectory(custom_runtime)
add_subdirectory(dataset)
add_subdirectory(cpp_extension)
add_subdirectory(dygraph_to_static)
add_subdirectory(prim)
add_subdirectory(sot)
add_subdirectory(standalone_executor)
add_subdirectory(tokenizer)
if(WITH_ONEDNN)
add_subdirectory(onednn)
endif()
endif()
add_subdirectory(book)
# add_subdirectory(composite_ops)
add_subdirectory(contrib)
add_subdirectory(cpp)
add_subdirectory(distribution)
add_subdirectory(ir)
add_subdirectory(indexing)
add_subdirectory(legacy_test)
add_subdirectory(quantization)
add_subdirectory(ai_edited_test)
add_subdirectory(rnn)
add_subdirectory(sequence)
add_subdirectory(tensorrt)
# add_subdirectory(white_list)
if(WITH_OPENVINO
AND NOT WIN32
AND NOT WITH_XPU
AND NOT WITH_ROCM)
add_subdirectory(openvino)
endif()
if(WITH_DISTRIBUTE)
add_subdirectory(collective)
add_subdirectory(auto_parallel)
add_subdirectory(distributed_passes)
endif()
if(NOT WIN32 OR NOT WITH_GPU)
add_subdirectory(fft)
endif()
# add_subdirectory(fleet)
if(WITH_IPU)
add_subdirectory(ipu)
endif()
if(WITH_XPU)
add_subdirectory(xpu)
endif()
# add tests for scripts from `Paddle/tools`
add_subdirectory(tools)
endif()
if(WITH_CPP_DIST)
add_test(NAME test_paddle_lib
COMMAND ${PADDLE_BINARY_DIR}/test/paddle_lib/test_paddle_lib)
if(WITH_GPU)
add_test(NAME test_paddle_lib_gpu
COMMAND ${PADDLE_BINARY_DIR}/test/paddle_lib/test_paddle_lib_gpu)
endif()
endif()
get_property(test_srcs GLOBAL PROPERTY TEST_SRCS)
get_property(test_names GLOBAL PROPERTY TEST_NAMES)
get_property(paddle_lib GLOBAL PROPERTY PADDLE_LIB_NAME)
set(POSTFIX ".so")
if(WIN32)
set(POSTFIX ".dll")
endif()
list(LENGTH test_names len)
if(${len} GREATER_EQUAL 1)
message("Total cpp tests using dynamic link: ${len}")
math(EXPR stop "${len} - 1")
foreach(idx RANGE ${stop})
if(WITH_TESTING)
list(GET test_srcs ${idx} test_src)
list(GET test_names ${idx} test_name)
get_property(test_arg GLOBAL PROPERTY "${test_name}_ARGS")
# message("add test ${test_name}")
add_executable(${test_name} ${test_src})
target_link_libraries(${test_name} paddle_gtest_main_new)
target_link_libraries(${test_name} $<TARGET_LINKER_FILE:${paddle_lib}>)
if(WITH_SHARED_PHI)
target_link_libraries(${test_name} phi)
else()
# For static phi builds (e.g., DCU), link phi static libraries directly
target_link_libraries(${test_name} phi)
if(WITH_GPU OR WITH_ROCM)
target_link_libraries(${test_name} -Wl,--start-group phi_core phi_gpu
-Wl,--end-group)
else()
target_link_libraries(${test_name} phi_core)
endif()
endif()
if(WITH_SHARED_IR)
target_link_libraries(${test_name} $<TARGET_LINKER_FILE:pir>)
endif()
target_link_libraries(${test_name} $<TARGET_LINKER_FILE:common>)
add_dependencies(${test_name} ${paddle_lib} paddle_gtest_main_new)
if(WITH_GPU)
target_link_libraries(${test_name} ${CUDA_CUDART_LIBRARY}
"-Wl,--as-needed")
endif()
if(WITH_ROCM)
target_link_libraries(${test_name} ${ROCM_HIPRTC_LIB})
endif()
if(APPLE)
target_link_libraries(
${test_name}
"-Wl,-rpath,$<TARGET_FILE_DIR:${paddle_lib}> -Wl,-rpath,$<TARGET_FILE_DIR:phi> -Wl,-rpath,$<TARGET_FILE_DIR:pir> -Wl,-rpath,$<TARGET_FILE_DIR:common>"
)
if(ACCELERATE_FRAMEWORK)
target_link_libraries(${test_name} ${ACCELERATE_FRAMEWORK})
message(STATUS "linking to accelerate blas library in ${test_name}")
endif()
endif()
if(NOT ((NOT WITH_PYTHON) AND ON_INFER))
target_link_libraries(${test_name} ${PYTHON_LIBRARIES})
endif()
if(WITH_CINN)
target_link_libraries(${test_name} $<TARGET_LINKER_FILE:cinnapi>)
endif()
if(WITH_XPU)
target_link_libraries(${test_name} xpulib)
endif()
cc_test_run(
${test_name}
COMMAND
${test_name}
ARGS
${test_arg}
DIR
${CC_TESTS_DIR})
elseif(WITH_TESTING AND NOT TEST ${test_name})
add_test(NAME ${test_name} COMMAND ${CMAKE_COMMAND} -E echo CI skip
${test_name}.)
endif()
set_target_properties(${test_name} PROPERTIES RUNTIME_OUTPUT_DIRECTORY
"${CC_TESTS_DIR}")
endforeach()
endif()
# set properties for some tests, it should be set after the tests defined.
if(TARGET standalone_executor_test)
set_tests_properties(standalone_executor_test PROPERTIES TIMEOUT 100)
if(NOT WIN32)
add_dependencies(standalone_executor_test download_program)
endif()
endif()
if(TARGET layer_test)
add_dependencies(layer_test jit_download_program)
endif()
add_custom_target(build_tests)
# add target to build all cpp tests
if(${len} GREATER_EQUAL 1)
add_dependencies(build_tests ${test_names})
endif()
set_pir_tests_properties()
add_subdirectory(flex_checkpoint)
add_subdirectory(compat)
if(WIN32 AND WITH_ONNXRUNTIME)
add_custom_command(
OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/cpp/onnxruntime.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different "${ONNXRUNTIME_SHARED_LIB}"
"${CMAKE_CURRENT_BINARY_DIR}/cpp/onnxruntime.dll"
DEPENDS onnxruntime
COMMENT "Copying onnxruntime.dll to build/test/cpp")
add_custom_target(copy_onnxruntime ALL
DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/cpp/onnxruntime.dll)
endif()
test/ai_edited_test/CMakeLists.txt
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file(
GLOB TEST_OPS
RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
"test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# Copy test .py files from source to build directory so test_runner.py can find them
file(GLOB TEST_PY_FILES "${CMAKE_CURRENT_SOURCE_DIR}/test_*.py")
file(COPY ${TEST_PY_FILES} DESTINATION ${CMAKE_CURRENT_BINARY_DIR})
if(WITH_COVERAGE)
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
# set_tests_properties(${TEST_OP} PROPERTIES LABELS "RUN_TYPE=NIGHTLY")
endforeach()
endif()
test/ai_edited_test/test_ai_activation_comprehensive.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.functional.activation
# 覆盖模块: paddle/nn/functional/activation.py
# Uncovered lines: celu, elu, gelu, glu, gumbel_softmax, mish, maxout,
# log_softmax, leaky_relu, softplus, softsign, tanhshrink, thresholded_relu
import unittest
import numpy as np
import paddle
class TestCELU(unittest.TestCase):
"""测试 CELU 激活函数
Test CELU activation function"""
def test_celu_basic(self):
"""测试基本 CELU
Test basic CELU"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = paddle.nn.functional.celu(x)
self.assertEqual(result.shape, [3])
def test_celu_alpha(self):
"""测试带 alpha 参数的 CELU
Test CELU with alpha parameter"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = paddle.nn.functional.celu(x, alpha=2.0)
self.assertEqual(result.shape, [3])
def test_celu_positive_passthrough(self):
"""测试 CELU 正值直接通过
Test CELU positive values pass through"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.nn.functional.celu(x)
np.testing.assert_allclose(result.numpy(), x.numpy(), atol=1e-6)
class TestELU(unittest.TestCase):
"""测试 ELU 激活函数
Test ELU activation function"""
def test_elu_basic(self):
"""测试基本 ELU
Test basic ELU"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = paddle.nn.functional.elu(x)
self.assertEqual(result.shape, [3])
def test_elu_alpha(self):
"""测试带 alpha 参数的 ELU
Test ELU with alpha parameter"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0])
result = paddle.nn.functional.elu(x, alpha=2.0)
self.assertEqual(result.shape, [4])
def test_elu_positive_passthrough(self):
"""测试 ELU 正值直接通过
Test ELU positive values pass through"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.nn.functional.elu(x)
np.testing.assert_allclose(result.numpy(), x.numpy(), atol=1e-6)
class TestGELU(unittest.TestCase):
"""测试 GELU 激活函数
Test GELU activation function"""
def test_gelu_basic(self):
"""测试基本 GELU
Test basic GELU"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.gelu(x)
self.assertEqual(result.shape, [3, 4])
def test_gelu_approximate(self):
"""测试近似 GELU
Test approximate GELU"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.gelu(x, approximate=True)
self.assertEqual(result.shape, [3, 4])
class TestGLU(unittest.TestCase):
"""测试 GLU 激活函数
Test GLU activation function"""
def test_glu_basic(self):
"""测试基本 GLU
Test basic GLU"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.glu(x)
self.assertEqual(result.shape, [3, 2])
def test_glu_axis(self):
"""测试指定轴的 GLU
Test GLU with axis"""
x = paddle.randn([3, 4, 6])
result = paddle.nn.functional.glu(x, axis=2)
self.assertEqual(result.shape, [3, 4, 3])
class TestGumbelSoftmax(unittest.TestCase):
"""测试 Gumbel-Softmax 函数
Test Gumbel-Softmax function"""
def test_gumbel_softmax_basic(self):
"""测试基本 Gumbel-Softmax
Test basic Gumbel-Softmax"""
x = paddle.randn([3, 5])
result = paddle.nn.functional.gumbel_softmax(x)
self.assertEqual(result.shape, [3, 5])
# Output should sum to 1 along last dim
sums = paddle.sum(result, axis=-1)
np.testing.assert_allclose(sums.numpy(), np.ones(3), atol=1e-5)
def test_gumbel_softmax_hard(self):
"""测试 hard Gumbel-Softmax
Test hard Gumbel-Softmax"""
x = paddle.randn([3, 5])
result = paddle.nn.functional.gumbel_softmax(x, hard=True)
self.assertEqual(result.shape, [3, 5])
def test_gumbel_softmax_temperature(self):
"""测试不同温度的 Gumbel-Softmax
Test Gumbel-Softmax with different temperature"""
x = paddle.randn([3, 5])
result = paddle.nn.functional.gumbel_softmax(x, temperature=0.5)
self.assertEqual(result.shape, [3, 5])
class TestMish(unittest.TestCase):
"""测试 Mish 激活函数
Test Mish activation function"""
def test_mish_basic(self):
"""测试基本 Mish
Test basic Mish"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.mish(x)
self.assertEqual(result.shape, [3, 4])
def test_mish_zero(self):
"""测试零输入的 Mish
Test Mish with zero input"""
x = paddle.to_tensor([0.0])
result = paddle.nn.functional.mish(x)
self.assertAlmostEqual(result.item(), 0.0, places=5)
class TestMaxout(unittest.TestCase):
"""测试 Maxout 激活函数
Test Maxout activation function"""
def test_maxout_basic(self):
"""测试基本 Maxout
Test basic Maxout"""
x = paddle.randn([2, 4, 3, 3])
result = paddle.nn.functional.maxout(x, groups=2)
self.assertEqual(result.shape, [2, 2, 3, 3])
def test_maxout_groups(self):
"""测试不同 group 数的 Maxout
Test Maxout with different groups"""
x = paddle.randn([1, 6, 2, 2])
result = paddle.nn.functional.maxout(x, groups=3)
self.assertEqual(result.shape, [1, 2, 2, 2])
class TestLogSoftmax(unittest.TestCase):
"""测试 LogSoftmax 函数
Test LogSoftmax function"""
def test_log_softmax_basic(self):
"""测试基本 LogSoftmax
Test basic LogSoftmax"""
x = paddle.randn([3, 5])
result = paddle.nn.functional.log_softmax(x)
self.assertEqual(result.shape, [3, 5])
def test_log_softmax_axis(self):
"""测试指定轴的 LogSoftmax
Test LogSoftmax with axis"""
x = paddle.randn([3, 5])
result = paddle.nn.functional.log_softmax(x, axis=0)
self.assertEqual(result.shape, [3, 5])
def test_log_softmax_exp_equals_softmax(self):
"""测试 exp(log_softmax) 等于 softmax
Test exp(log_softmax) equals softmax"""
x = paddle.randn([3, 5])
log_result = paddle.nn.functional.log_softmax(x)
softmax_result = paddle.nn.functional.softmax(x)
np.testing.assert_allclose(
paddle.exp(log_result).numpy(), softmax_result.numpy(), atol=1e-5
)
class TestLeakyReLU(unittest.TestCase):
"""测试 LeakyReLU 激活函数
Test LeakyReLU activation function"""
def test_leaky_relu_basic(self):
"""测试基本 LeakyReLU
Test basic LeakyReLU"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = paddle.nn.functional.leaky_relu(x)
self.assertEqual(result.shape, [3])
def test_leaky_relu_negative_slope(self):
"""测试带 negative_slope 的 LeakyReLU
Test LeakyReLU with negative_slope"""
x = paddle.to_tensor([-2.0])
result = paddle.nn.functional.leaky_relu(x, negative_slope=0.5)
self.assertAlmostEqual(result.item(), -1.0, places=5)
def test_leaky_relu_positive_passthrough(self):
"""测试 LeakyReLU 正值直接通过
Test LeakyReLU positive values pass through"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.nn.functional.leaky_relu(x)
np.testing.assert_allclose(result.numpy(), x.numpy(), atol=1e-6)
class TestSoftplus(unittest.TestCase):
"""测试 Softplus 激活函数
Test Softplus activation function"""
def test_softplus_basic(self):
"""测试基本 Softplus
Test basic Softplus"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.softplus(x)
self.assertEqual(result.shape, [3, 4])
def test_softplus_large_positive(self):
"""测试大正值的 Softplus（接近线性）
Test Softplus with large positive values (near linear)"""
x = paddle.to_tensor([100.0])
result = paddle.nn.functional.softplus(x)
self.assertAlmostEqual(result.item(), 100.0, places=2)
class TestSoftsign(unittest.TestCase):
"""测试 Softsign 激活函数
Test Softsign activation function"""
def test_softsign_basic(self):
"""测试基本 Softsign
Test basic Softsign"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.softsign(x)
self.assertEqual(result.shape, [3, 4])
def test_softsign_zero(self):
"""测试零输入的 Softsign
Test Softsign with zero input"""
x = paddle.to_tensor([0.0])
result = paddle.nn.functional.softsign(x)
self.assertAlmostEqual(result.item(), 0.0, places=5)
def test_softsign_range(self):
"""测试 Softsign 输出范围 (-1, 1)
Test Softsign output range (-1, 1)"""
x = paddle.randn([100])
result = paddle.nn.functional.softsign(x)
self.assertTrue(paddle.all(result >= -1.0).item())
self.assertTrue(paddle.all(result <= 1.0).item())
class TestTanhshrink(unittest.TestCase):
"""测试 Tanhshrink 激活函数
Test Tanhshrink activation function"""
def test_tanhshrink_basic(self):
"""测试基本 Tanhshrink
Test basic Tanhshrink"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.tanhshrink(x)
self.assertEqual(result.shape, [3, 4])
def test_tanhshrink_zero(self):
"""测试零输入的 Tanhshrink
Test Tanhshrink with zero input"""
x = paddle.to_tensor([0.0])
result = paddle.nn.functional.tanhshrink(x)
self.assertAlmostEqual(result.item(), 0.0, places=5)
def test_tanhshrink_formula(self):
"""测试 Tanhshrink 公式 x - tanh(x)
Test Tanhshrink formula x - tanh(x)"""
x = paddle.randn([3, 4])
result = paddle.nn.functional.tanhshrink(x)
expected = x - paddle.tanh(x)
np.testing.assert_allclose(result.numpy(), expected.numpy(), atol=1e-6)
class TestThresholdedReLU(unittest.TestCase):
"""测试 ThresholdedReLU 激活函数
Test ThresholdedReLU activation function"""
def test_thresholded_relu_basic(self):
"""测试基本 ThresholdedReLU
Test basic ThresholdedReLU"""
x = paddle.to_tensor([-1.0, 0.0, 1.5, 2.0])
result = paddle.nn.functional.thresholded_relu(x)
self.assertEqual(result.shape, [4])
def test_thresholded_relu_threshold(self):
"""测试带阈值的 ThresholdedReLU
Test ThresholdedReLU with threshold"""
x = paddle.to_tensor([-1.0, 0.5, 1.0, 2.0])
result = paddle.nn.functional.thresholded_relu(x, threshold=1.0)
# Values <= threshold should be 0, values > threshold pass through
self.assertAlmostEqual(result[0].item(), 0.0, places=5)
self.assertAlmostEqual(result[1].item(), 0.0, places=5)
self.assertAlmostEqual(
result[2].item(), 0.0, places=5
) # 1.0 is not > 1.0
self.assertAlmostEqual(result[3].item(), 2.0, places=5)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_activation_funcs.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.functional.activation (static graph branches)
# 自动生成的单测，覆盖 paddle.nn.functional.activation 模块中未覆盖的静态图分支代码
# Target: cover uncovered lines 91-110, 152-164, 221-233, 278-290, 364-376, 418-443, 496-507
# in paddle/python/paddle/nn/functional/activation.py
# 目标：覆盖 activation.py 中 celu, elu, hardshrink, hardtanh, hardsigmoid, hardswish, leaky_relu
# 等激活函数的旧静态图分支（else 分支）和 celu 的 alpha=0 错误处理
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. F.celu() - alpha=0 ZeroDivisionError (line 92), static graph branch (lines 99-110)
F.celu() - alpha=0 时的零除错误以及静态图分支
2. F.elu() - static graph branch (lines 153-164)
F.elu() - 静态图分支
3. F.hardshrink() - static graph branch (lines 222-233)
F.hardshrink() - 静态图分支
4. F.hardtanh() - static graph branch (lines 278-290)
F.hardtanh() - 静态图分支
5. F.hardsigmoid() - static graph branch (lines 364-376)
F.hardsigmoid() - 静态图分支
6. F.hardswish() - static graph branch (lines 418-443)
F.hardswish() - 静态图分支
7. F.leaky_relu() - static graph branch (lines 496-507)
F.leaky_relu() - 静态图分支
"""
import unittest
import numpy as np
import paddle
import paddle.nn.functional as F
class TestCeluAlphaZeroError(unittest.TestCase):
"""Test celu() raises ZeroDivisionError when alpha=0.
测试 celu() 在 alpha=0 时抛出 ZeroDivisionError。
覆盖 paddle/python/paddle/nn/functional/activation.py 第 91-92 行。
"""
def test_celu_alpha_zero(self):
"""celu() should raise ZeroDivisionError when alpha is 0.
当 alpha 为 0 时，celu() 应抛出 ZeroDivisionError。
"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
with self.assertRaises(ZeroDivisionError):
F.celu(x, alpha=0)
class TestActivationStaticGraph(unittest.TestCase):
"""Test activation functions in static graph mode to cover the else branches.
在静态图模式下测试激活函数以覆盖 else 分支。
覆盖 paddle/python/paddle/nn/functional/activation.py 中多个激活函数的静态图路径。
"""
def _run_static(self, func, input_data, **kwargs):
"""Helper to run a function in static graph mode.
辅助方法：在静态图模式下运行函数。
"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(
name='x', shape=input_data.shape, dtype='float32'
)
out = func(x, **kwargs)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
result = exe.run(
main_prog,
feed={'x': input_data},
fetch_list=[out],
)
return result[0]
finally:
paddle.disable_static()
def test_celu_static(self):
"""Test celu in static graph mode.
在静态图模式下测试 celu 激活函数。
覆盖第 99-110 行。
"""
input_data = np.array([[-0.2, 6.0], [1.0, 15.6]], dtype='float32')
result = self._run_static(F.celu, input_data, alpha=1.0)
expected = np.maximum(0, input_data) + np.minimum(
0, 1.0 * (np.exp(input_data / 1.0) - 1)
)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_elu_static(self):
"""Test elu in static graph mode.
在静态图模式下测试 elu 激活函数。
覆盖第 153-164 行。
"""
input_data = np.array([[-1.0, 6.0], [1.0, 15.6]], dtype='float32')
result = self._run_static(F.elu, input_data, alpha=0.2)
expected = np.where(
input_data > 0, input_data, 0.2 * (np.exp(input_data) - 1)
)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_hardshrink_static(self):
"""Test hardshrink in static graph mode.
在静态图模式下测试 hardshrink 激活函数。
覆盖第 222-233 行。
"""
input_data = np.array([-1.0, 0.3, 2.5], dtype='float32')
result = self._run_static(F.hardshrink, input_data, threshold=0.5)
expected = np.where(np.abs(input_data) > 0.5, input_data, 0)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_hardtanh_static(self):
"""Test hardtanh in static graph mode.
在静态图模式下测试 hardtanh 激活函数。
覆盖第 278-290 行。
"""
input_data = np.array([-1.5, 0.3, 2.5], dtype='float32')
result = self._run_static(F.hardtanh, input_data, min=-1.0, max=1.0)
expected = np.clip(input_data, -1.0, 1.0)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_hardsigmoid_static(self):
"""Test hardsigmoid in static graph mode.
在静态图模式下测试 hardsigmoid 激活函数。
覆盖第 364-376 行。
"""
input_data = np.array([-4.0, 5.0, 1.0], dtype='float32')
result = self._run_static(F.hardsigmoid, input_data)
expected = np.clip(input_data / 6.0 + 0.5, 0, 1)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_hardswish_static(self):
"""Test hardswish in static graph mode.
在静态图模式下测试 hardswish 激活函数。
覆盖第 418-443 行。
"""
input_data = np.array([-4.0, 5.0, 1.0], dtype='float32')
result = self._run_static(F.hardswish, input_data)
expected = np.where(
input_data <= -3.0,
0,
np.where(
input_data >= 3.0, input_data, input_data * (input_data + 3) / 6
),
)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_leaky_relu_static(self):
"""Test leaky_relu in static graph mode.
在静态图模式下测试 leaky_relu 激活函数。
覆盖第 496-507 行。
"""
input_data = np.array([-1.0, 0.0, 1.0, -0.5, 2.0], dtype='float32')
result = self._run_static(F.leaky_relu, input_data, negative_slope=0.01)
expected = np.where(input_data >= 0, input_data, 0.01 * input_data)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_leaky_relu_static_custom_slope(self):
"""Test leaky_relu in static graph mode with custom slope.
在静态图模式下使用自定义斜率测试 leaky_relu。
"""
input_data = np.array([-2.0, -1.0, 0.0, 1.0, 2.0], dtype='float32')
result = self._run_static(F.leaky_relu, input_data, negative_slope=0.1)
expected = np.where(input_data >= 0, input_data, 0.1 * input_data)
np.testing.assert_allclose(result, expected, rtol=1e-5)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_amp_advanced.py
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# Copyright (c) 2026 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.
"""
混合精度训练高级测试 / Advanced Mixed Precision Training Tests
测试目标 / Test Target:
paddle AMP (Automatic Mixed Precision) 功能
覆盖的模块 / Covered Modules:
- paddle.amp.auto_cast: 自动混合精度上下文
- paddle.amp.GradScaler: 梯度缩放器
- paddle.amp.decorate: AMP装饰器
作用 / Purpose:
补充混合精度训练API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestAutocast(unittest.TestCase):
"""测试自动类型转换 / Test auto casting"""
def test_autocast_basic(self):
"""测试基本autocast / Test basic autocast"""
model = nn.Linear(4, 2)
x = paddle.randn([4, 4])
with paddle.amp.auto_cast():
output = model(x)
self.assertIsNotNone(output)
def test_autocast_disable(self):
"""测试禁用autocast / Test disabled autocast"""
model = nn.Linear(4, 2)
x = paddle.randn([4, 4])
with paddle.amp.auto_cast(enable=False):
output = model(x)
self.assertEqual(output.dtype, paddle.float32)
def test_autocast_nested(self):
"""测试嵌套autocast / Test nested autocast"""
model = nn.Linear(4, 2)
x = paddle.randn([4, 4])
with paddle.amp.auto_cast():
y = model(x)
with paddle.amp.auto_cast(enable=False):
z = model(x)
self.assertIsNotNone(y)
self.assertIsNotNone(z)
class TestGradScaler(unittest.TestCase):
"""测试梯度缩放器 / Test gradient scaler"""
def test_grad_scaler_basic(self):
"""测试基本梯度缩放 / Test basic gradient scaling"""
model = nn.Linear(4, 2)
optimizer = paddle.optimizer.Adam(parameters=model.parameters())
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
x = paddle.randn([4, 4])
y = paddle.randn([4, 2])
with paddle.amp.auto_cast():
output = model(x)
loss = nn.functional.mse_loss(output, y)
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
scaler.step(optimizer)
scaler.update()
def test_grad_scaler_state(self):
"""测试梯度缩放器状态 / Test grad scaler state"""
scaler = paddle.amp.GradScaler(init_loss_scaling=512)
state = scaler.state_dict()
self.assertIn('scale', state)
def test_grad_scaler_save_load(self):
"""测试梯度缩放器保存加载 / Test grad scaler save/load"""
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
state = scaler.state_dict()
new_scaler = paddle.amp.GradScaler(init_loss_scaling=512)
new_scaler.load_state_dict(state)
new_state = new_scaler.state_dict()
self.assertEqual(
float(np.asarray(state['scale']).item()),
float(np.asarray(new_state['scale']).item()),
)
class TestAMPDecorate(unittest.TestCase):
"""测试AMP装饰器 / Test AMP decorate"""
def test_decorate_model(self):
"""测试模型AMP装饰 / Test model AMP decoration"""
model = nn.Linear(4, 2)
optimizer = paddle.optimizer.Adam(parameters=model.parameters())
model, optimizer = paddle.amp.decorate(
models=model, optimizers=optimizer, level='O1'
)
x = paddle.randn([4, 4])
with paddle.amp.auto_cast():
output = model(x)
self.assertIsNotNone(output)
def test_decorate_level_o1(self):
"""测试O1级别AMP / Test O1 level AMP"""
model = nn.Sequential(
nn.Conv2D(3, 8, 3, padding=1), nn.ReLU(), nn.AdaptiveAvgPool2D(1)
)
optimizer = paddle.optimizer.Adam(parameters=model.parameters())
model, optimizer = paddle.amp.decorate(
models=model, optimizers=optimizer, level='O1'
)
x = paddle.randn([2, 3, 16, 16])
with paddle.amp.auto_cast():
output = model(x)
self.assertIsNotNone(output)
class TestMixedPrecisionTraining(unittest.TestCase):
"""测试混合精度训练 / Test mixed precision training"""
def test_full_amp_training_step(self):
"""测试完整AMP训练步骤 / Test full AMP training step"""
model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
optimizer = paddle.optimizer.Adam(parameters=model.parameters())
scaler = paddle.amp.GradScaler()
x = paddle.randn([8, 4])
y = paddle.randn([8, 2])
with paddle.amp.auto_cast():
output = model(x)
loss = nn.functional.mse_loss(output, y)
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
scaler.step(optimizer)
scaler.update()
optimizer.clear_grad()
self.assertIsNotNone(loss)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_amp_autocast.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.amp
# 覆盖模块: paddle/amp/
# Uncovered lines: Various amp module functions
import unittest
import paddle
class TestAMPAutoCast(unittest.TestCase):
"""测试 AMP 自动混合精度
Test AMP auto mixed precision"""
def test_autocast_context(self):
"""测试 autocast 上下文管理器
Test autocast context manager"""
x = paddle.randn([4, 4], dtype='float32')
with paddle.amp.auto_cast():
y = paddle.matmul(x, x)
self.assertIsNotNone(y)
def test_autocast_list(self):
"""测试 autocast 白名单
Test autocast with custom list"""
x = paddle.randn([4, 4], dtype='float32')
with paddle.amp.auto_cast(custom_white_list=['matmul']):
y = paddle.matmul(x, x)
self.assertIsNotNone(y)
@unittest.skipIf(not paddle.is_compiled_with_cuda(), "Requires CUDA")
def test_autocast_gpu(self):
"""测试 GPU 上的 autocast
Test autocast on GPU"""
x = paddle.randn([4, 4], dtype='float32')
x = x.cuda()
with paddle.amp.auto_cast():
y = paddle.matmul(x, x)
self.assertIsNotNone(y)
class TestAMPGradScaler(unittest.TestCase):
"""测试 AMP GradScaler
Test AMP GradScaler"""
def test_grad_scaler_init(self):
"""测试 GradScaler 初始化
Test GradScaler initialization"""
scaler = paddle.amp.GradScaler(init_loss_scaling=1024.0)
self.assertIsNotNone(scaler)
def test_grad_scaler_default(self):
"""测试 GradScaler 默认参数
Test GradScaler with default parameters"""
scaler = paddle.amp.GradScaler()
self.assertIsNotNone(scaler)
def test_grad_scaler_step(self):
"""测试 GradScaler step
Test GradScaler step"""
linear = paddle.nn.Linear(10, 10)
scaler = paddle.amp.GradScaler(init_loss_scaling=1024.0)
optimizer = paddle.optimizer.Adam(
parameters=linear.parameters(), learning_rate=0.001
)
x = paddle.randn([4, 10])
with paddle.amp.auto_cast():
y = linear(x)
loss = y.mean()
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
scaler.step(optimizer)
scaler.update()
optimizer.clear_grad()
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_amp_ops.py
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# Copyright (c) 2026 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.
"""
自动混合精度(AMP)单元测试 / Automatic Mixed Precision (AMP) Unit Tests
测试目标 / Test Target:
paddle.amp 模块 (python/paddle/static/amp/*, 覆盖率约81.3%)
覆盖的模块 / Covered Modules:
- paddle.amp.auto_cast: 自动类型转换上下文
- paddle.amp.GradScaler: 梯度缩放器
- paddle.amp.decorate: AMP模型装饰
- paddle.amp.is_float16_supported: 检查FP16支持
作用 / Purpose:
覆盖自动混合精度训练的各种代码路径，包括半精度计算、梯度缩放等。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
HAS_GPU = paddle.device.is_compiled_with_cuda()
class TestAutoCast(unittest.TestCase):
"""测试auto_cast上下文 / Test auto_cast context"""
def test_auto_cast_float16(self):
"""测试FP16 auto_cast / Test FP16 auto_cast"""
model = nn.Linear(10, 5)
with paddle.amp.auto_cast(enable=True, dtype='float16'):
x = paddle.randn([4, 10])
y = model(x)
# Output should exist
self.assertEqual(y.shape, [4, 5])
def test_auto_cast_bfloat16(self):
"""测试BF16 auto_cast / Test BF16 auto_cast"""
model = nn.Linear(10, 5)
try:
with paddle.amp.auto_cast(enable=True, dtype='bfloat16'):
x = paddle.randn([4, 10])
y = model(x)
self.assertEqual(y.shape, [4, 5])
except Exception:
# BF16 may not be supported on all hardware
pass
def test_auto_cast_disabled(self):
"""测试禁用auto_cast / Test disabled auto_cast"""
model = nn.Linear(10, 5)
with paddle.amp.auto_cast(enable=False):
x = paddle.randn([4, 10])
y = model(x)
self.assertEqual(y.dtype, paddle.float32)
def test_auto_cast_level(self):
"""测试auto_cast级别 / Test auto_cast level"""
model = nn.Linear(10, 5)
# Level O1: operations use float16 where beneficial
with paddle.amp.auto_cast(enable=True, dtype='float16', level='O1'):
x = paddle.randn([4, 10])
y = model(x)
self.assertEqual(y.shape, [4, 5])
def test_auto_cast_custom_black_list(self):
"""测试自定义黑名单 / Test custom black list"""
model = nn.Linear(10, 5)
with paddle.amp.auto_cast(
enable=True,
dtype='float16',
custom_black_list=['matmul_v2', 'matmul'],
):
x = paddle.randn([4, 10])
y = model(x)
self.assertEqual(y.shape, [4, 5])
class TestGradScaler(unittest.TestCase):
"""测试梯度缩放器 / Test gradient scaler"""
def test_grad_scaler_basic(self):
"""测试基本GradScaler / Test basic GradScaler"""
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
self.assertEqual(scaler._init_loss_scaling, 1024)
def test_grad_scaler_scale(self):
"""测试梯度缩放 / Test gradient scaling"""
model = nn.Linear(10, 5)
optimizer = paddle.optimizer.Adam(
learning_rate=0.01, parameters=model.parameters()
)
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
x = paddle.randn([4, 10])
with paddle.amp.auto_cast(enable=True, dtype='float16'):
y = model(x)
loss = y.mean()
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
scaler.step(optimizer)
scaler.update()
def test_grad_scaler_unscale(self):
"""测试梯度反缩放 / Test gradient unscaling"""
model = nn.Linear(10, 5)
optimizer = paddle.optimizer.Adam(
learning_rate=0.01, parameters=model.parameters()
)
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
x = paddle.randn([4, 10])
with paddle.amp.auto_cast(enable=True, dtype='float16'):
y = model(x)
loss = y.mean()
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
scaler.unscale_(optimizer)
scaler.step(optimizer)
scaler.update()
def test_grad_scaler_state_dict(self):
"""测试GradScaler状态字典 / Test GradScaler state dict"""
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
state = scaler.state_dict()
self.assertIn('scale', state)
self.assertIn('incr_ratio', state)
def test_grad_scaler_load_state_dict(self):
"""测试加载GradScaler状态 / Test loading GradScaler state"""
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
state = scaler.state_dict()
new_scaler = paddle.amp.GradScaler(init_loss_scaling=512)
new_scaler.load_state_dict(state)
self.assertEqual(new_scaler._init_loss_scaling, 1024)
class TestAMPDecorate(unittest.TestCase):
"""测试AMP模型装饰 / Test AMP model decoration"""
def test_decorate_float16(self):
"""测试FP16模型装饰 / Test FP16 model decoration"""
model = nn.Linear(10, 5)
optimizer = paddle.optimizer.Adam(
learning_rate=0.01, parameters=model.parameters()
)
model, optimizer = paddle.amp.decorate(
models=model, optimizers=optimizer, level='O1', dtype='float16'
)
self.assertIsNotNone(model)
self.assertIsNotNone(optimizer)
def test_decorate_multiple_models(self):
"""测试多模型装饰 / Test multiple models decoration"""
model1 = nn.Linear(10, 5)
model2 = nn.Linear(5, 2)
optimizer = paddle.optimizer.Adam(
learning_rate=0.01,
parameters=list(model1.parameters()) + list(model2.parameters()),
)
models, opt = paddle.amp.decorate(
models=[model1, model2],
optimizers=optimizer,
level='O1',
dtype='float16',
)
self.assertEqual(len(models), 2)
class TestAMPTraining(unittest.TestCase):
"""测试AMP完整训练流程 / Test complete AMP training workflow"""
def test_amp_training_loop(self):
"""测试AMP训练循环 / Test AMP training loop"""
model = nn.Linear(10, 5)
optimizer = paddle.optimizer.Adam(
learning_rate=0.01, parameters=model.parameters()
)
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
for _ in range(3):
x = paddle.randn([4, 10])
with paddle.amp.auto_cast(enable=True, dtype='float16'):
y = model(x)
loss = y.mean()
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
scaler.step(optimizer)
scaler.update()
optimizer.clear_grad()
def test_amp_with_grad_clip(self):
"""测试AMP配合梯度裁剪 / Test AMP with gradient clipping"""
model = nn.Linear(10, 5)
clip = nn.ClipGradByNorm(clip_norm=1.0)
optimizer = paddle.optimizer.Adam(
learning_rate=0.01, parameters=model.parameters(), grad_clip=clip
)
scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
x = paddle.randn([4, 10])
with paddle.amp.auto_cast(enable=True, dtype='float16'):
y = model(x)
loss = y.mean()
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
optimizer.clear_grad()
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_autograd_advanced.py
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# Copyright (c) 2026 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.
"""
自动微分高级测试 / Advanced Automatic Differentiation Tests
测试目标 / Test Target:
paddle.autograd 自动微分
覆盖的模块 / Covered Modules:
- paddle.grad: 梯度计算
- paddle.incubate.autograd: 功能性自动微分
- higher-order gradients: 高阶梯度
- gradient checkpointing
作用 / Purpose:
补充自动微分API的高级测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestGradientComputation(unittest.TestCase):
"""测试梯度计算 / Test gradient computation"""
def test_basic_gradient(self):
"""测试基本梯度 / Test basic gradient"""
x = paddle.to_tensor([2.0, 3.0])
x.stop_gradient = False
y = x**2
loss = y.sum()
loss.backward()
# d/dx(x^2) = 2x
np.testing.assert_allclose(x.grad.numpy(), [4.0, 6.0])
def test_chain_rule(self):
"""测试链式法则 / Test chain rule"""
x = paddle.to_tensor(2.0)
x.stop_gradient = False
y = x**3 # dy/dx = 3x^2
z = y**2 # dz/dy = 2y = 2*x^3
# dz/dx = 2*x^3 * 3*x^2 = 6*x^5 = 6*32 = 192
z.backward()
self.assertAlmostEqual(float(x.grad.numpy()), 192.0, places=4)
def test_multiple_outputs_grad(self):
"""测试多输出梯度 / Test gradient with multiple outputs"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
x.stop_gradient = False
y1 = x * 2
y2 = x**2
# Backward through sum
loss = y1.sum() + y2.sum()
loss.backward()
# d/dx(2x + x^2) = 2 + 2x
expected = [2 + 2 * 1, 2 + 2 * 2, 2 + 2 * 3]
np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
def test_no_grad_context(self):
"""测试no_grad上下文 / Test no_grad context"""
x = paddle.to_tensor([1.0, 2.0])
x.stop_gradient = False
with paddle.no_grad():
y = x**2
# y should not require grad
self.assertTrue(y.stop_gradient)
def test_grad_function(self):
"""测试paddle.grad函数 / Test paddle.grad function"""
x = paddle.to_tensor(3.0)
x.stop_gradient = False
y = x**2
grad = paddle.grad(y, x)[0]
self.assertAlmostEqual(float(grad.numpy()), 6.0, places=5)
class TestSecondOrderGradient(unittest.TestCase):
"""测试二阶梯度 / Test second-order gradients"""
def test_second_order_grad(self):
"""测试二阶梯度计算 / Test second order gradient"""
x = paddle.to_tensor(2.0)
x.stop_gradient = False
# f(x) = x^3, f'(x) = 3x^2, f''(x) = 6x
y = x**3
first_grad = paddle.grad(y, x, create_graph=True)[0]
second_grad = paddle.grad(first_grad, x)[0]
# f''(2) = 12
self.assertAlmostEqual(float(second_grad.numpy()), 12.0, places=4)
class TestBackwardWithAccumulation(unittest.TestCase):
"""测试梯度累积 / Test gradient accumulation"""
def test_gradient_accumulation(self):
"""测试梯度累积 / Test gradient accumulation"""
model = nn.Linear(4, 2)
optimizer = paddle.optimizer.SGD(
parameters=model.parameters(), learning_rate=0.01
)
accumulated_steps = 4
for i in range(accumulated_steps):
x = paddle.randn([2, 4])
y = paddle.randn([2, 2])
loss = nn.functional.mse_loss(model(x), y) / accumulated_steps
loss.backward()
optimizer.step()
optimizer.clear_grad()
def test_retain_grad(self):
"""测试中间变量梯度保留 / Test retain grad for intermediate tensors"""
x = paddle.to_tensor(2.0)
x.stop_gradient = False
y = x**2
y.retain_grads()
z = y**2
z.backward()
# dy/dx = 2x = 4; dz/dy = 2y = 8; y.grad should be dz/dy = 8
self.assertAlmostEqual(float(y.grad.numpy()), 8.0, places=5)
class TestDetachAndClone(unittest.TestCase):
"""测试detach和clone / Test detach and clone"""
def test_detach(self):
"""测试detach / Test detach"""
x = paddle.to_tensor([1.0, 2.0])
x.stop_gradient = False
y = x**2
y_detached = y.detach()
self.assertTrue(y_detached.stop_gradient)
np.testing.assert_allclose(y_detached.numpy(), [1.0, 4.0])
def test_clone(self):
"""测试clone / Test clone"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
x_clone = x.clone()
np.testing.assert_allclose(x.numpy(), x_clone.numpy())
# Modify clone should not affect original
x_clone[0] = paddle.to_tensor(99.0)
self.assertAlmostEqual(float(x[0].numpy()), 1.0)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_bitwise_ops.py
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# Copyright (c) 2026 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.
"""
位操作和整数运算测试 / Bitwise and Integer Operations Tests
测试目标 / Test Target:
paddle.tensor 位操作和整数运算
覆盖的模块 / Covered Modules:
- paddle.bitwise_and/or/xor/not: 位运算
- paddle.bitwise_left_shift/right_shift: 位移
- int64张量操作
作用 / Purpose:
补充位操作和整数运算API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
paddle.disable_static()
class TestBitwiseOps(unittest.TestCase):
"""测试位运算 / Test bitwise operations"""
def test_bitwise_and(self):
"""测试按位与 / Test bitwise AND"""
x = paddle.to_tensor([0b1010, 0b1100, 0b1111], dtype='int32')
y = paddle.to_tensor([0b1111, 0b1010, 0b0101], dtype='int32')
result = paddle.bitwise_and(x, y)
np.testing.assert_array_equal(
result.numpy(), [0b1010 & 0b1111, 0b1100 & 0b1010, 0b1111 & 0b0101]
)
def test_bitwise_or(self):
"""测试按位或 / Test bitwise OR"""
x = paddle.to_tensor([0b1010, 0b1100], dtype='int32')
y = paddle.to_tensor([0b0101, 0b0011], dtype='int32')
result = paddle.bitwise_or(x, y)
np.testing.assert_array_equal(
result.numpy(), [0b1010 | 0b0101, 0b1100 | 0b0011]
)
def test_bitwise_xor(self):
"""测试按位异或 / Test bitwise XOR"""
x = paddle.to_tensor([0b1010, 0b1111], dtype='int32')
y = paddle.to_tensor([0b1111, 0b1111], dtype='int32')
result = paddle.bitwise_xor(x, y)
np.testing.assert_array_equal(
result.numpy(), [0b1010 ^ 0b1111, 0b1111 ^ 0b1111]
)
def test_bitwise_not(self):
"""测试按位非 / Test bitwise NOT"""
x = paddle.to_tensor([0, 1, -1], dtype='int32')
result = paddle.bitwise_not(x)
np.testing.assert_array_equal(
result.numpy(), ~np.array([0, 1, -1], dtype=np.int32)
)
def test_bitwise_left_shift(self):
"""测试左移 / Test left shift"""
x = paddle.to_tensor([1, 2, 4], dtype='int32')
y = paddle.to_tensor([1, 2, 3], dtype='int32')
result = paddle.bitwise_left_shift(x, y)
np.testing.assert_array_equal(result.numpy(), [2, 8, 32])
def test_bitwise_right_shift(self):
"""测试右移 / Test right shift"""
x = paddle.to_tensor([8, 16, 32], dtype='int32')
y = paddle.to_tensor([1, 2, 3], dtype='int32')
result = paddle.bitwise_right_shift(x, y)
np.testing.assert_array_equal(result.numpy(), [4, 4, 4])
class TestIntegerOps(unittest.TestCase):
"""测试整数运算 / Test integer operations"""
def test_int_add(self):
"""测试整数加法 / Test integer addition"""
x = paddle.to_tensor([1, 2, 3], dtype='int64')
y = paddle.to_tensor([4, 5, 6], dtype='int64')
result = x + y
np.testing.assert_array_equal(result.numpy(), [5, 7, 9])
def test_int_multiply(self):
"""测试整数乘法 / Test integer multiplication"""
x = paddle.to_tensor([2, 3, 4], dtype='int32')
y = paddle.to_tensor([3, 4, 5], dtype='int32')
result = x * y
np.testing.assert_array_equal(result.numpy(), [6, 12, 20])
def test_int_floor_divide(self):
"""测试整数整除 / Test integer floor divide"""
x = paddle.to_tensor([10, 11, 12], dtype='int32')
y = paddle.to_tensor([3, 4, 5], dtype='int32')
result = x // y
np.testing.assert_array_equal(result.numpy(), [3, 2, 2])
def test_int_mod(self):
"""测试整数取模 / Test integer modulo"""
x = paddle.to_tensor([10, 11, 12], dtype='int32')
y = paddle.to_tensor([3, 4, 5], dtype='int32')
result = x % y
np.testing.assert_array_equal(result.numpy(), [1, 3, 2])
class TestTypeConversion(unittest.TestCase):
"""测试类型转换 / Test type conversion"""
def test_float_to_int(self):
"""测试浮点转整数 / Test float to int conversion"""
x = paddle.to_tensor([1.7, 2.9, 3.1], dtype='float32')
result = paddle.cast(x, 'int32')
np.testing.assert_array_equal(result.numpy(), [1, 2, 3])
def test_int_to_float(self):
"""测试整数转浮点 / Test int to float conversion"""
x = paddle.to_tensor([1, 2, 3], dtype='int32')
result = paddle.cast(x, 'float32')
np.testing.assert_allclose(result.numpy(), [1.0, 2.0, 3.0])
def test_bool_to_int(self):
"""测试布尔转整数 / Test bool to int conversion"""
x = paddle.to_tensor([True, False, True])
result = paddle.cast(x, 'int32')
np.testing.assert_array_equal(result.numpy(), [1, 0, 1])
def test_int32_to_int64(self):
"""测试int32转int64 / Test int32 to int64"""
x = paddle.to_tensor([100, 200, 300], dtype='int32')
result = paddle.cast(x, 'int64')
self.assertEqual(result.dtype, paddle.int64)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_comm_group.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.communication.group
# 覆盖模块: paddle/distributed/communication/group.py
# 未覆盖行: 85,92,109,128,134,251,252,253,254,258,259,261,262,263,264,265,271,272,274,275,276,277,278,312,313,315,316,318,319,351,354,363,364,365,370,401,403
# Covered module: paddle/distributed/communication/group.py
# Uncovered lines: 85,92,109,128,134,251,252,253,254,258,259,261,262,263,264,265,271,272,274,275,276,277,278,312,313,315,316,318,319
import unittest
from paddle.distributed.communication.group import (
Group,
_add_new_group,
_get_global_group,
_GroupManager,
_is_global_group,
is_initialized,
)
class TestGroup(unittest.TestCase):
"""测试 Group 类
Test Group class"""
def test_group_init(self):
"""测试 Group 初始化
Test Group initialization"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1, 2])
self.assertEqual(group.rank, 0)
self.assertEqual(group.id, 1)
self.assertEqual(group.ranks, [0, 1, 2])
self.assertEqual(group.nranks, 3)
self.assertEqual(group.world_size, 3)
def test_group_is_member(self):
"""测试 Group.is_member 方法
Test Group.is_member method"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1, 2])
self.assertTrue(group.is_member())
def test_group_is_not_member_negative_rank(self):
"""测试负 rank 的 Group 不是成员
Test Group with negative rank is not a member"""
group = Group(rank_in_group=-1, id=1, ranks=[0, 1, 2])
self.assertFalse(group.is_member())
def test_group_is_not_member_single_rank(self):
"""测试只有1个 rank 的 Group 不是成员
Test Group with single rank is not a member"""
group = Group(rank_in_group=0, id=1, ranks=[0])
self.assertFalse(group.is_member())
def test_group_get_group_rank(self):
"""测试 Group.get_group_rank 方法
Test Group.get_group_rank method"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1, 2])
self.assertEqual(group.get_group_rank(1), 1)
self.assertEqual(group.get_group_rank(0), 0)
def test_group_get_group_rank_not_member(self):
"""测试非成员的 Group.get_group_rank 返回 -1
Test Group.get_group_rank returns -1 for non-member"""
group = Group(rank_in_group=-1, id=1, ranks=[0, 1, 2])
self.assertEqual(group.get_group_rank(0), -1)
def test_group_get_global_rank(self):
"""测试 Group.get_global_rank 方法
Test Group.get_global_rank method"""
group = Group(rank_in_group=0, id=1, ranks=[10, 20, 30])
self.assertEqual(group.get_global_rank(0), 10)
self.assertEqual(group.get_global_rank(1), 20)
def test_group_get_global_rank_not_member(self):
"""测试非成员的 Group.get_global_rank 返回 -1
Test Group.get_global_rank returns -1 for non-member"""
group = Group(rank_in_group=-1, id=1, ranks=[0, 1, 2])
self.assertEqual(group.get_global_rank(0), -1)
def test_group_repr(self):
"""测试 Group.__repr__ 方法
Test Group.__repr__ method"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1], name="test_group")
repr_str = repr(group)
self.assertIn("rank: 0", repr_str)
self.assertIn("nranks: 2", repr_str)
self.assertIn("test_group", repr_str)
def test_group_repr_no_name(self):
"""测试没有名称的 Group.__repr__ 方法
Test Group.__repr__ method without name"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1])
repr_str = repr(group)
self.assertIn("None", repr_str)
def test_group_name_property(self):
"""测试 Group.name 属性
Test Group.name property"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1], name="my_group")
self.assertEqual(group.name, "my_group")
def test_group_name_none(self):
"""测试 Group.name 为 None
Test Group.name is None"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1])
self.assertIsNone(group.name)
class TestGroupManager(unittest.TestCase):
"""测试 _GroupManager 类
Test _GroupManager class"""
def setUp(self):
self._orig_map = _GroupManager.group_map_by_id.copy()
self._orig_id = _GroupManager.global_group_id
_GroupManager.group_map_by_id.clear()
def tearDown(self):
_GroupManager.group_map_by_id.clear()
_GroupManager.group_map_by_id.update(self._orig_map)
_GroupManager.global_group_id = self._orig_id
def test_add_new_group(self):
"""测试 _add_new_group 函数
Test _add_new_group function"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1])
_add_new_group(group)
self.assertIn(1, _GroupManager.group_map_by_id)
def test_add_duplicate_group(self):
"""测试添加重复 id 的 group 报错
Test adding duplicate group id raises error"""
group = Group(rank_in_group=0, id=1, ranks=[0, 1])
_add_new_group(group)
with self.assertRaises(RuntimeError):
_add_new_group(group)
def test_get_global_group_not_initialized(self):
"""测试未初始化时获取全局 group 报错
Test getting global group when not initialized raises error"""
with self.assertRaises(RuntimeError):
_get_global_group()
def test_is_initialized_false(self):
"""测试未初始化时 is_initialized 返回 False
Test is_initialized returns False when not initialized"""
self.assertFalse(is_initialized())
def test_is_global_group(self):
"""测试 _is_global_group 函数
Test _is_global_group function"""
_GroupManager.global_group_id = 0
group = Group(rank_in_group=0, id=0, ranks=[0, 1])
_add_new_group(group)
self.assertTrue(_is_global_group(group))
def test_is_not_global_group(self):
"""测试非全局 group 的 _is_global_group 返回 False
Test _is_global_group returns False for non-global group"""
_GroupManager.global_group_id = 0
group = Group(rank_in_group=0, id=1, ranks=[0, 1])
_add_new_group(group)
self.assertFalse(_is_global_group(group))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_comm_serialization.py
+122
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.communication.serialization_utils
# 覆盖模块: paddle/distributed/communication/serialization_utils.py
# 未覆盖行: 23,24,25,26,27,28,29,32,33,34
# Covered module: paddle/distributed/communication/serialization_utils.py
# Uncovered lines: 23,24,25,26,27,28,29,32,33,34
import unittest
import numpy as np
import paddle
from paddle.distributed.communication.serialization_utils import (
convert_object_to_tensor,
convert_tensor_to_object,
)
class TestConvertObjectToTensor(unittest.TestCase):
"""测试 convert_object_to_tensor 函数
Test convert_object_to_tensor function"""
def test_convert_dict(self):
"""测试转换字典对象
Test converting dictionary object"""
obj = {"key": "value", "num": 42}
tensor, length = convert_object_to_tensor(obj)
self.assertIsInstance(tensor, paddle.Tensor)
self.assertGreater(length, 0)
self.assertEqual(tensor.dtype, paddle.uint8)
def test_convert_list(self):
"""测试转换列表对象
Test converting list object"""
obj = [1, 2, 3, "hello"]
tensor, length = convert_object_to_tensor(obj)
self.assertIsInstance(tensor, paddle.Tensor)
self.assertEqual(length, tensor.numel())
def test_convert_string(self):
"""测试转换字符串对象
Test converting string object"""
obj = "hello world"
tensor, length = convert_object_to_tensor(obj)
self.assertGreater(length, 0)
def test_convert_nested(self):
"""测试转换嵌套对象
Test converting nested object"""
obj = {"a": [1, 2], "b": {"c": 3}}
tensor, length = convert_object_to_tensor(obj)
self.assertIsInstance(tensor, paddle.Tensor)
self.assertGreater(length, 0)
def test_convert_number(self):
"""测试转换数字对象
Test converting number object"""
obj = 3.14159
tensor, length = convert_object_to_tensor(obj)
self.assertGreater(length, 0)
class TestConvertTensorToObject(unittest.TestCase):
"""测试 convert_tensor_to_object 函数
Test convert_tensor_to_object function"""
def test_roundtrip_dict(self):
"""测试字典的序列化-反序列化循环
Test dict serialization-deserialization round-trip"""
original = {"key": "value", "num": 42}
tensor, length = convert_object_to_tensor(original)
result = convert_tensor_to_object(tensor, length)
self.assertEqual(result, original)
def test_roundtrip_list(self):
"""测试列表的序列化-反序列化循环
Test list serialization-deserialization round-trip"""
original = [1, 2, 3, "hello"]
tensor, length = convert_object_to_tensor(original)
result = convert_tensor_to_object(tensor, length)
self.assertEqual(result, original)
def test_roundtrip_string(self):
"""测试字符串的序列化-反序列化循环
Test string serialization-deserialization round-trip"""
original = "hello world"
tensor, length = convert_object_to_tensor(original)
result = convert_tensor_to_object(tensor, length)
self.assertEqual(result, original)
def test_roundtrip_nested(self):
"""测试嵌套对象的序列化-反序列化循环
Test nested object serialization-deserialization round-trip"""
original = {"a": [1, 2], "b": {"c": 3}}
tensor, length = convert_object_to_tensor(original)
result = convert_tensor_to_object(tensor, length)
self.assertEqual(result, original)
def test_roundtrip_numpy_array(self):
"""测试 numpy 数组的序列化-反序列化循环
Test numpy array serialization-deserialization round-trip"""
original = {"arr": np.array([1.0, 2.0, 3.0])}
tensor, length = convert_object_to_tensor(original)
result = convert_tensor_to_object(tensor, length)
np.testing.assert_array_equal(result["arr"], original["arr"])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_common_layers.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.layer.common (Dropout, Linear, Flatten, etc.)
# 自动生成的单测，覆盖 paddle.nn.layer.common 模块中未覆盖的代码路径
# Target: cover uncovered lines in paddle/python/paddle/nn/layer/common.py
# 目标：覆盖 Linear, Dropout, Flatten, Pad 等 common layer 的各种参数路径
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. Linear - 各种参数 (in_features, out_features, weight_attr, bias_attr, name)
2. Dropout - p, mode, axis 参数
3. Flatten - start_axis, stop_axis 参数
4. Pad1D, Pad2D, Pad3D - 各种 padding 模式
5. Identity - 恒等层
"""
import unittest
import numpy as np
import paddle
from paddle import nn
class TestLinear(unittest.TestCase):
"""Test Linear layer.
测试 Linear 层。
"""
def setUp(self):
paddle.disable_static()
def test_linear_basic(self):
"""Basic Linear."""
linear = nn.Linear(10, 5)
x = paddle.randn([4, 10])
out = linear(x)
self.assertEqual(out.shape, [4, 5])
def test_linear_no_bias(self):
"""Linear without bias."""
linear = nn.Linear(10, 5, bias_attr=False)
self.assertIsNone(linear.bias)
x = paddle.randn([4, 10])
out = linear(x)
self.assertEqual(out.shape, [4, 5])
def test_linear_with_name(self):
"""Linear with name."""
linear = nn.Linear(10, 5, name='my_linear')
x = paddle.randn([4, 10])
out = linear(x)
self.assertEqual(out.shape, [4, 5])
def test_linear_3d_input(self):
"""Linear with 3D input."""
linear = nn.Linear(10, 5)
x = paddle.randn([2, 3, 10])
out = linear(x)
self.assertEqual(out.shape, [2, 3, 5])
def test_linear_1d_input(self):
"""Linear with 1D input (per-sample)."""
linear = nn.Linear(10, 5)
x = paddle.randn([10])
out = linear(x)
self.assertEqual(out.shape, [5])
class TestDropout(unittest.TestCase):
"""Test Dropout layer.
测试 Dropout 层。
"""
def setUp(self):
paddle.disable_static()
def test_dropout_train(self):
"""Dropout in training mode."""
dp = nn.Dropout(p=0.5)
dp.train()
x = paddle.ones([1000])
out = dp(x)
# Some values should be 0
self.assertTrue(paddle.sum(out == 0).numpy() > 0)
def test_dropout_eval(self):
"""Dropout in eval mode should be identity."""
dp = nn.Dropout(p=0.5)
dp.eval()
x = paddle.ones([10])
out = dp(x)
np.testing.assert_allclose(out.numpy(), np.ones([10]))
def test_dropout_zero_p(self):
"""Dropout with p=0 should be identity."""
dp = nn.Dropout(p=0.0)
x = paddle.randn([10])
out = dp(x)
np.testing.assert_allclose(out.numpy(), x.numpy())
def test_dropout_axis(self):
"""Dropout along specific axis."""
dp = nn.Dropout(p=0.5, axis=1)
dp.train()
x = paddle.ones([4, 10])
out = dp(x)
self.assertEqual(out.shape, [4, 10])
class TestFlatten(unittest.TestCase):
"""Test Flatten layer.
测试 Flatten 层。
"""
def setUp(self):
paddle.disable_static()
def test_flatten_default(self):
"""Flatten with default start_axis=1."""
flatten = nn.Flatten()
x = paddle.randn([2, 3, 4, 5])
out = flatten(x)
self.assertEqual(out.shape, [2, 60])
def test_flatten_start_axis_0(self):
"""Flatten from axis 0."""
flatten = nn.Flatten(start_axis=0)
x = paddle.randn([2, 3, 4])
out = flatten(x)
self.assertEqual(out.shape, [24])
def test_flatten_start_stop(self):
"""Flatten with custom start_axis and stop_axis."""
flatten = nn.Flatten(start_axis=1, stop_axis=2)
x = paddle.randn([2, 3, 4, 5])
out = flatten(x)
self.assertEqual(out.shape, [2, 12, 5])
class TestIdentity(unittest.TestCase):
"""Test Identity layer.
测试 Identity 层。
"""
def setUp(self):
paddle.disable_static()
def test_identity_basic(self):
"""Identity should pass through."""
identity = nn.Identity()
x = paddle.randn([2, 3, 4])
out = identity(x)
np.testing.assert_allclose(out.numpy(), x.numpy())
class TestPad2D(unittest.TestCase):
"""Test Pad2D layer.
测试 Pad2D 层。
"""
def setUp(self):
paddle.disable_static()
def test_pad2d_constant(self):
"""Pad2D with constant mode."""
pad = nn.Pad2D(padding=1, mode='constant', value=0)
x = paddle.randn([2, 3, 4, 4])
out = pad(x)
self.assertEqual(out.shape, [2, 3, 6, 6])
def test_pad2d_reflect(self):
"""Pad2D with reflect mode."""
pad = nn.Pad2D(padding=1, mode='reflect')
x = paddle.randn([2, 3, 4, 4])
out = pad(x)
self.assertEqual(out.shape, [2, 3, 6, 6])
def test_pad2d_replicate(self):
"""Pad2D with replicate mode."""
pad = nn.Pad2D(padding=1, mode='replicate')
x = paddle.randn([2, 3, 4, 4])
out = pad(x)
self.assertEqual(out.shape, [2, 3, 6, 6])
def test_pad2d_tuple_padding(self):
"""Pad2D with tuple padding."""
# Paddle Pad2D tuple: (pad_left, pad_right, pad_top, pad_bottom)
# Input [2, 3, 4, 4] -> H=4+1+2=7, W=4+1+2=7 -> [2, 3, 7, 7]
pad = nn.Pad2D(padding=(1, 2, 1, 2), mode='constant')
x = paddle.randn([2, 3, 4, 4])
out = pad(x)
self.assertEqual(out.shape, [2, 3, 7, 7])
def test_pad1d(self):
"""Pad1D basic."""
pad = nn.Pad1D(padding=1, mode='constant')
x = paddle.randn([2, 3, 10])
out = pad(x)
self.assertEqual(out.shape, [2, 3, 12])
def test_pad3d(self):
"""Pad3D basic."""
pad = nn.Pad3D(padding=1, mode='constant')
x = paddle.randn([2, 3, 4, 4, 4])
out = pad(x)
self.assertEqual(out.shape, [2, 3, 6, 6, 6])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_control_flow.py
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# Copyright (c) 2026 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.
"""
动态图控制流测试 / Dynamic Graph Control Flow Tests
测试目标 / Test Target:
paddle 动态图控制流
覆盖的模块 / Covered Modules:
- paddle.cond: 条件执行
- paddle.while_loop: 循环执行
- paddle.jit.to_static: 动转静
- 条件分支模型
作用 / Purpose:
补充动态图控制流API的测试，提升覆盖率。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
class TestPaddleCond(unittest.TestCase):
"""测试条件控制流 / Test conditional control flow"""
def test_cond_true(self):
"""测试条件为True / Test cond when true"""
x = paddle.to_tensor(True)
result = paddle.static.nn.cond(
x, lambda: paddle.to_tensor([1.0]), lambda: paddle.to_tensor([0.0])
)
self.assertAlmostEqual(float(result.item()), 1.0)
def test_cond_false(self):
"""测试条件为False / Test cond when false"""
x = paddle.to_tensor(False)
result = paddle.static.nn.cond(
x, lambda: paddle.to_tensor([1.0]), lambda: paddle.to_tensor([0.0])
)
self.assertAlmostEqual(float(result.item()), 0.0)
def test_cond_with_computation(self):
"""测试带计算的条件 / Test cond with computation"""
x = paddle.to_tensor(3.0)
cond = x > 2.0
result = paddle.static.nn.cond(cond, lambda: x * 2, lambda: x * 0.5)
self.assertAlmostEqual(float(result.item()), 6.0, places=5)
class TestWhileLoop(unittest.TestCase):
"""测试while循环 / Test while loop"""
def test_while_loop_basic(self):
"""测试基本while循环 / Test basic while loop"""
i = paddle.zeros([1], dtype='int64')
limit = paddle.to_tensor([5], dtype='int64')
def cond(i):
return paddle.less_than(i, limit)
def body(i):
return [i + 1]
out = paddle.static.nn.while_loop(cond, body, [i])
self.assertEqual(int(out[0].item()), 5)
class TestDynamicShapeModel(unittest.TestCase):
"""测试动态形状模型 / Test dynamic shape model"""
def test_variable_length_batch(self):
"""测试可变长度批次 / Test variable length batch"""
model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
# Test with different batch sizes
for batch_size in [1, 4, 16]:
x = paddle.randn([batch_size, 4])
output = model(x)
self.assertEqual(output.shape[0], batch_size)
self.assertEqual(output.shape[1], 2)
def test_conditional_model(self):
"""测试条件模型 / Test conditional model"""
class ConditionalNet(nn.Layer):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 8)
self.fc2 = nn.Linear(4, 8)
def forward(self, x, use_path1):
if use_path1:
return self.fc1(x)
else:
return self.fc2(x)
model = ConditionalNet()
x = paddle.randn([4, 4])
out1 = model(x, True)
out2 = model(x, False)
self.assertEqual(out1.shape, [4, 8])
self.assertEqual(out2.shape, [4, 8])
class TestToStaticConversion(unittest.TestCase):
"""测试动转静转换 / Test dynamic to static conversion"""
def test_to_static_function(self):
"""测试函数动转静 / Test function to static"""
@paddle.jit.to_static
def linear_func(x, w, b):
return paddle.matmul(x, w) + b
x = paddle.randn([4, 4])
w = paddle.randn([4, 2])
b = paddle.zeros([2])
result = linear_func(x, w, b)
self.assertEqual(result.shape, [4, 2])
def test_to_static_model(self):
"""测试模型动转静 / Test model to static"""
class SimpleModel(nn.Layer):
def __init__(self):
super().__init__()
self.fc = nn.Linear(4, 2)
@paddle.jit.to_static
def forward(self, x):
return self.fc(x)
model = SimpleModel()
x = paddle.randn([4, 4])
output = model(x)
self.assertEqual(output.shape, [4, 2])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_conv_functional.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.functional.conv error paths
# 自动生成的单测，覆盖 paddle.nn.functional.conv 模块中未覆盖的代码路径
# Target: cover uncovered lines 268-272, 361, 379-383, 411, 577-580, 600, 622, 685-686
# in paddle/python/paddle/nn/functional/conv.py
# 目标：覆盖 conv.py 中 conv2d_transpose 的 data_format 校验、conv3d 的各种错误路径
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. conv2d / conv2d_transpose / conv3d / conv3d_transpose - 错误路径和边界情况
2. 无效 data_format 的 ValueError (line 268-272)
3. 各种卷积操作的基本功能验证
"""
import unittest
import paddle
import paddle.nn.functional as F
class TestConv2DErrorPaths(unittest.TestCase):
"""Test conv2d() error paths.
测试 conv2d() 的错误路径。
"""
def setUp(self):
paddle.disable_static()
def test_conv2d_basic(self):
"""Basic conv2d."""
x = paddle.randn([2, 3, 8, 8])
w = paddle.randn([16, 3, 3, 3])
out = F.conv2d(x, w)
self.assertEqual(out.shape[0], 2)
self.assertEqual(out.shape[1], 16)
def test_conv2d_with_bias(self):
"""Conv2d with bias."""
x = paddle.randn([2, 3, 8, 8])
w = paddle.randn([16, 3, 3, 3])
b = paddle.randn([16])
out = F.conv2d(x, w, bias=b)
self.assertEqual(out.shape[1], 16)
def test_conv2d_with_padding(self):
"""Conv2d with padding."""
x = paddle.randn([2, 3, 8, 8])
w = paddle.randn([16, 3, 3, 3])
out = F.conv2d(x, w, padding=1)
self.assertEqual(out.shape[2], 8)
self.assertEqual(out.shape[3], 8)
def test_conv2d_with_stride(self):
"""Conv2d with stride."""
x = paddle.randn([2, 3, 8, 8])
w = paddle.randn([16, 3, 3, 3])
out = F.conv2d(x, w, stride=2)
self.assertEqual(out.shape[2], 3)
def test_conv2d_with_groups(self):
"""Conv2d with groups."""
x = paddle.randn([2, 6, 8, 8])
w = paddle.randn([6, 1, 3, 3])
out = F.conv2d(x, w, groups=6)
self.assertEqual(out.shape[1], 6)
def test_conv2d_with_dilation(self):
"""Conv2d with dilation."""
x = paddle.randn([2, 3, 8, 8])
w = paddle.randn([16, 3, 3, 3])
out = F.conv2d(x, w, dilation=2)
self.assertEqual(out.shape[0], 2)
class TestConv2DTranspose(unittest.TestCase):
"""Test conv2d_transpose().
测试 conv2d_transpose()。
"""
def setUp(self):
paddle.disable_static()
def test_conv2d_transpose_basic(self):
"""Basic conv2d_transpose."""
x = paddle.randn([2, 16, 4, 4])
w = paddle.randn([16, 3, 3, 3])
out = F.conv2d_transpose(x, w)
self.assertEqual(out.shape[0], 2)
self.assertEqual(out.shape[1], 3)
def test_conv2d_transpose_with_padding(self):
"""Conv2d_transpose with padding."""
x = paddle.randn([2, 16, 4, 4])
w = paddle.randn([16, 3, 3, 3])
out = F.conv2d_transpose(x, w, padding=1)
self.assertEqual(out.shape[0], 2)
def test_conv2d_transpose_with_output_padding(self):
"""Conv2d_transpose with output_padding."""
x = paddle.randn([2, 16, 4, 4])
w = paddle.randn([16, 3, 3, 3])
out = F.conv2d_transpose(x, w, stride=2, output_padding=1)
self.assertEqual(out.shape[0], 2)
class TestConv3D(unittest.TestCase):
"""Test conv3d().
测试 conv3d()。
"""
def setUp(self):
paddle.disable_static()
def test_conv3d_basic(self):
"""Basic conv3d."""
x = paddle.randn([2, 3, 4, 4, 4])
w = paddle.randn([16, 3, 3, 3, 3])
out = F.conv3d(x, w)
self.assertEqual(out.shape[0], 2)
self.assertEqual(out.shape[1], 16)
def test_conv3d_with_padding(self):
"""Conv3d with padding."""
x = paddle.randn([2, 3, 4, 4, 4])
w = paddle.randn([16, 3, 3, 3, 3])
out = F.conv3d(x, w, padding=1)
self.assertEqual(out.shape[2], 4)
def test_conv3d_with_stride(self):
"""Conv3d with stride."""
x = paddle.randn([2, 3, 4, 4, 4])
w = paddle.randn([16, 3, 3, 3, 3])
out = F.conv3d(x, w, stride=2)
self.assertEqual(out.shape[2], 1)
class TestConv3DTranspose(unittest.TestCase):
"""Test conv3d_transpose().
测试 conv3d_transpose()。
"""
def setUp(self):
paddle.disable_static()
def test_conv3d_transpose_basic(self):
"""Basic conv3d_transpose."""
x = paddle.randn([2, 16, 2, 2, 2])
w = paddle.randn([16, 3, 3, 3, 3])
out = F.conv3d_transpose(x, w)
self.assertEqual(out.shape[0], 2)
self.assertEqual(out.shape[1], 3)
class TestConv1D(unittest.TestCase):
"""Test conv1d().
测试 conv1d()。
"""
def setUp(self):
paddle.disable_static()
def test_conv1d_basic(self):
"""Basic conv1d."""
x = paddle.randn([2, 3, 10])
w = paddle.randn([16, 3, 3])
out = F.conv1d(x, w)
self.assertEqual(out.shape[0], 2)
self.assertEqual(out.shape[1], 16)
def test_conv1d_transpose_basic(self):
"""Basic conv1d_transpose."""
x = paddle.randn([2, 16, 4])
w = paddle.randn([16, 3, 3])
out = F.conv1d_transpose(x, w)
self.assertEqual(out.shape[0], 2)
self.assertEqual(out.shape[1], 3)
def test_conv1d_with_padding(self):
"""Conv1d with padding."""
x = paddle.randn([2, 3, 10])
w = paddle.randn([16, 3, 3])
out = F.conv1d(x, w, padding=1)
self.assertEqual(out.shape[2], 10)
class TestDepthwiseConv(unittest.TestCase):
"""Test depthwise separable conv patterns.
测试深度可分离卷积模式。
"""
def setUp(self):
paddle.disable_static()
def test_depthwise_conv2d(self):
"""Depthwise conv2d via groups."""
x = paddle.randn([2, 4, 8, 8])
w = paddle.randn([4, 1, 3, 3])
out = F.conv2d(x, w, groups=4)
self.assertEqual(out.shape[1], 4)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_conv_layers.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.layer.conv (Conv1D, Conv2D, Conv3D, etc.)
# 自动生成的单测，覆盖 paddle.nn.layer.conv 模块中未覆盖的代码路径
# Target: cover uncovered lines in paddle/python/paddle/nn/layer/conv.py
# 目标：覆盖 Conv 层的各种初始化参数组合和前向传播
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. Conv1D - 各种参数 (stride, padding, dilation, groups)
2. Conv2D - 各种参数 (padding_mode, weight_attr)
3. Conv3D - 基本功能
4. Conv1DTranspose - 各种参数
5. Conv2DTranspose - 各种参数 (output_padding)
6. Conv3DTranspose - 基本功能
"""
import unittest
import paddle
from paddle import nn
class TestConv1D(unittest.TestCase):
"""Test Conv1D layer.
测试 Conv1D 层。
"""
def setUp(self):
paddle.disable_static()
def test_conv1d_basic(self):
"""Basic Conv1D."""
conv = nn.Conv1D(3, 16, 3)
x = paddle.randn([2, 3, 10])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 8])
def test_conv1d_padding_same(self):
"""Conv1D with padding='same'."""
conv = nn.Conv1D(3, 16, 3, padding='same')
x = paddle.randn([2, 3, 10])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 10])
def test_conv1d_padding_valid(self):
"""Conv1D with padding='valid'."""
conv = nn.Conv1D(3, 16, 3, padding='valid')
x = paddle.randn([2, 3, 10])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 8])
def test_conv1d_stride(self):
"""Conv1D with stride."""
conv = nn.Conv1D(3, 16, 3, stride=2)
x = paddle.randn([2, 3, 10])
out = conv(x)
self.assertEqual(out.shape[2], 4)
def test_conv1d_dilation(self):
"""Conv1D with dilation."""
conv = nn.Conv1D(3, 16, 3, dilation=2)
x = paddle.randn([2, 3, 10])
out = conv(x)
self.assertEqual(out.shape[2], 6)
def test_conv1d_groups(self):
"""Conv1D with groups (depthwise)."""
conv = nn.Conv1D(4, 4, 3, groups=4)
x = paddle.randn([2, 4, 10])
out = conv(x)
self.assertEqual(out.shape[1], 4)
def test_conv1d_bias_false(self):
"""Conv1D without bias."""
conv = nn.Conv1D(3, 16, 3, bias_attr=False)
self.assertIsNone(conv.bias)
class TestConv2D(unittest.TestCase):
"""Test Conv2D layer.
测试 Conv2D 层。
"""
def setUp(self):
paddle.disable_static()
def test_conv2d_basic(self):
"""Basic Conv2D."""
conv = nn.Conv2D(3, 16, 3)
x = paddle.randn([2, 3, 8, 8])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 6, 6])
def test_conv2d_padding_same(self):
"""Conv2D with padding='same'."""
conv = nn.Conv2D(3, 16, 3, padding='same')
x = paddle.randn([2, 3, 8, 8])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 8, 8])
def test_conv2d_padding_valid(self):
"""Conv2D with padding='valid'."""
conv = nn.Conv2D(3, 16, 3, padding='valid')
x = paddle.randn([2, 3, 8, 8])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 6, 6])
def test_conv2d_tuple_kernel(self):
"""Conv2D with tuple kernel_size."""
conv = nn.Conv2D(3, 16, (3, 5))
x = paddle.randn([2, 3, 8, 10])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 6, 6])
def test_conv2d_tuple_padding(self):
"""Conv2D with tuple padding."""
conv = nn.Conv2D(3, 16, 3, padding=(1, 2))
x = paddle.randn([2, 3, 8, 8])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 8, 10])
def test_conv2d_tuple_stride(self):
"""Conv2D with tuple stride."""
conv = nn.Conv2D(3, 16, 3, stride=(1, 2))
x = paddle.randn([2, 3, 8, 8])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 6, 3])
def test_conv2d_dilation(self):
"""Conv2D with dilation."""
conv = nn.Conv2D(3, 16, 3, dilation=2)
x = paddle.randn([2, 3, 8, 8])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 4, 4])
def test_conv2d_groups(self):
"""Conv2D with groups."""
conv = nn.Conv2D(4, 4, 3, groups=4)
x = paddle.randn([2, 4, 8, 8])
out = conv(x)
self.assertEqual(out.shape[1], 4)
def test_conv2d_bias_false(self):
"""Conv2D without bias."""
conv = nn.Conv2D(3, 16, 3, bias_attr=False)
self.assertIsNone(conv.bias)
class TestConv3D(unittest.TestCase):
"""Test Conv3D layer.
测试 Conv3D 层。
"""
def setUp(self):
paddle.disable_static()
def test_conv3d_basic(self):
"""Basic Conv3D."""
conv = nn.Conv3D(3, 16, 3)
x = paddle.randn([2, 3, 4, 4, 4])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 2, 2, 2])
def test_conv3d_padding_same(self):
"""Conv3D with padding='same'."""
conv = nn.Conv3D(3, 16, 3, padding='same')
x = paddle.randn([2, 3, 4, 4, 4])
out = conv(x)
self.assertEqual(out.shape, [2, 16, 4, 4, 4])
class TestConvTranspose(unittest.TestCase):
"""Test ConvTranspose layers.
测试 ConvTranspose 层。
"""
def setUp(self):
paddle.disable_static()
def test_conv1d_transpose_basic(self):
"""Basic Conv1DTranspose."""
conv = nn.Conv1DTranspose(16, 3, 3)
x = paddle.randn([2, 16, 4])
out = conv(x)
self.assertEqual(out.shape, [2, 3, 6])
def test_conv2d_transpose_basic(self):
"""Basic Conv2DTranspose."""
conv = nn.Conv2DTranspose(16, 3, 3)
x = paddle.randn([2, 16, 4, 4])
out = conv(x)
self.assertEqual(out.shape, [2, 3, 6, 6])
def test_conv2d_transpose_with_output_padding(self):
"""Conv2DTranspose with output_padding."""
conv = nn.Conv2DTranspose(16, 3, 3, stride=2, output_padding=1)
x = paddle.randn([2, 16, 4, 4])
out = conv(x)
# output_size = (input_size - 1) * stride - 2*padding + kernel + output_padding
# = (4 - 1) * 2 - 0 + 3 + 1 = 10
self.assertEqual(out.shape, [2, 3, 10, 10])
def test_conv3d_transpose_basic(self):
"""Basic Conv3DTranspose."""
conv = nn.Conv3DTranspose(16, 3, 3)
x = paddle.randn([2, 16, 2, 2, 2])
out = conv(x)
self.assertEqual(out.shape, [2, 3, 4, 4, 4])
def test_conv2d_transpose_padding_same(self):
"""Conv2DTranspose with padding='same'."""
conv = nn.Conv2DTranspose(16, 3, 3, padding='same')
x = paddle.randn([2, 16, 4, 4])
out = conv(x)
self.assertEqual(out.shape, [2, 3, 4, 4])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.io (Dataset, DataLoader, Sampler)
# 自动生成的单测，覆盖 paddle.io 模块中未覆盖的代码路径
# Target: cover uncovered lines in paddle/python/paddle/io/
# 目标：覆盖 DataLoader、Dataset、Sampler 的初始化和基本功能
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. TensorDataset - 单张量数据集初始化和索引 (returns tuple of scalars per index)
2. ComposeDataset - 组合数据集
3. SequenceSampler - 顺序采样器
4. RandomSampler - 随机采样器 (有/无放回)
5. BatchSampler - 批量采样器 (drop_last)
"""
import unittest
import paddle
from paddle.io import (
BatchSampler,
ComposeDataset,
DataLoader,
Dataset,
RandomSampler,
SequenceSampler,
Subset,
TensorDataset,
)
class SimpleDataset(Dataset):
"""Simple dataset for testing."""
def __init__(self, data):
self.data = data
def __getitem__(self, idx):
return self.data[idx]
def __len__(self):
return len(self.data)
class TestTensorDataset(unittest.TestCase):
"""Test TensorDataset.
TensorDataset in Paddle takes a list of tensors.
__getitem__ returns a tuple of indexed rows (one per input tensor).
"""
def setUp(self):
paddle.disable_static()
def test_tensor_dataset_basic(self):
"""Basic TensorDataset usage - returns tuple of indexed rows."""
data1 = paddle.randn([10, 5])
data2 = paddle.randn([10, 3])
dataset = TensorDataset([data1, data2])
self.assertEqual(len(dataset), 10)
item = dataset[0]
self.assertIsInstance(item, tuple)
self.assertEqual(len(item), 2)
# Each element is the 0th row of the corresponding input tensor
self.assertEqual(item[0].shape, [5])
self.assertEqual(item[1].shape, [3])
def test_tensor_dataset_1d(self):
"""TensorDataset with 1D tensors."""
data1 = paddle.randn([10])
data2 = paddle.randn([10])
dataset = TensorDataset([data1, data2])
self.assertEqual(len(dataset), 10)
item = dataset[0]
self.assertIsInstance(item, tuple)
self.assertEqual(len(item), 2)
def test_tensor_dataset_iter(self):
"""Iterate over TensorDataset."""
data = paddle.randn([5, 3])
data2 = paddle.randn([5, 2])
dataset = TensorDataset([data, data2])
items = [dataset[i] for i in range(5)]
self.assertEqual(len(items), 5)
def test_tensor_dataset_varargs(self):
"""TensorDataset with multiple tensors in list."""
data1 = paddle.randn([10, 5])
data2 = paddle.randn([10, 3])
data3 = paddle.randn([10, 2])
dataset = TensorDataset([data1, data2, data3])
self.assertEqual(len(dataset), 10)
item = dataset[0]
self.assertIsInstance(item, tuple)
self.assertEqual(len(item), 3)
self.assertEqual(item[0].shape, [5])
self.assertEqual(item[1].shape, [3])
self.assertEqual(item[2].shape, [2])
def test_tensor_dataset_varargs_single(self):
"""TensorDataset with single tensor in list."""
data = paddle.randn([8, 4])
dataset = TensorDataset([data])
self.assertEqual(len(dataset), 8)
item = dataset[0]
self.assertIsInstance(item, tuple)
self.assertEqual(len(item), 1)
self.assertEqual(item[0].shape, [4])
class TestComposeDataset(unittest.TestCase):
"""Test ComposeDataset."""
def setUp(self):
paddle.disable_static()
def test_compose_dataset_basic(self):
"""ComposeDataset combines multiple datasets."""
ds1 = SimpleDataset(paddle.randn([10, 5]))
ds2 = SimpleDataset(paddle.randn([10, 3]))
composed = ComposeDataset([ds1, ds2])
self.assertEqual(len(composed), 10)
item = composed[0]
self.assertEqual(len(item), 2)
def test_compose_dataset_single(self):
"""ComposeDataset with single dataset."""
ds = SimpleDataset(paddle.randn([5, 2]))
composed = ComposeDataset([ds])
self.assertEqual(len(composed), 5)
class TestSamplers(unittest.TestCase):
"""Test various samplers."""
def setUp(self):
paddle.disable_static()
def test_sequence_sampler(self):
"""SequenceSampler yields indices in order."""
sampler = SequenceSampler(data_source=list(range(10)))
indices = list(sampler)
self.assertEqual(indices, list(range(10)))
def test_random_sampler_no_replacement(self):
"""RandomSampler without replacement."""
sampler = RandomSampler(data_source=list(range(10)))
indices = list(sampler)
self.assertEqual(len(indices), 10)
self.assertEqual(sorted(indices), list(range(10)))
def test_random_sampler_with_replacement(self):
"""RandomSampler with replacement."""
sampler = RandomSampler(
data_source=list(range(5)), replacement=True, num_samples=20
)
indices = list(sampler)
self.assertEqual(len(indices), 20)
def test_batch_sampler_no_drop_last(self):
"""BatchSampler without drop_last."""
sampler = SequenceSampler(data_source=list(range(10)))
bs = BatchSampler(sampler=sampler, batch_size=3, drop_last=False)
batches = list(bs)
self.assertEqual(len(batches), 4)
self.assertEqual(batches[0], [0, 1, 2])
def test_batch_sampler_drop_last(self):
"""BatchSampler with drop_last."""
sampler = SequenceSampler(data_source=list(range(10)))
bs = BatchSampler(sampler=sampler, batch_size=3, drop_last=True)
batches = list(bs)
self.assertEqual(len(batches), 3)
class TestSubset(unittest.TestCase):
"""Test Subset."""
def setUp(self):
paddle.disable_static()
def test_subset_basic(self):
"""Subset selects a subset of a dataset."""
ds = SimpleDataset(paddle.randn([20, 5]))
subset = Subset(ds, indices=[0, 2, 5, 10])
self.assertEqual(len(subset), 4)
item = subset[0]
self.assertEqual(item.shape, [5])
class TestDataLoader(unittest.TestCase):
"""Test DataLoader with SimpleDataset."""
def setUp(self):
paddle.disable_static()
def test_dataloader_basic(self):
"""DataLoader with SimpleDataset."""
ds = SimpleDataset(paddle.randn([20, 5]))
loader = DataLoader(ds, batch_size=4)
batches = list(loader)
self.assertEqual(len(batches), 5)
# Each batch is a single stacked tensor
self.assertIsInstance(batches[0], paddle.Tensor)
self.assertEqual(batches[0].shape, [4, 5])
def test_dataloader_batch_size_one(self):
"""DataLoader with batch_size=1."""
ds = SimpleDataset(paddle.randn([5, 3]))
loader = DataLoader(ds, batch_size=1)
batches = list(loader)
self.assertEqual(len(batches), 5)
def test_dataloader_drop_last(self):
"""DataLoader with drop_last."""
ds = SimpleDataset(paddle.randn([10, 3]))
loader = DataLoader(ds, batch_size=4, drop_last=True)
batches = list(loader)
self.assertEqual(len(batches), 2)
def test_dataloader_num_workers_zero(self):
"""DataLoader with num_workers=0."""
ds = SimpleDataset(paddle.randn([8, 3]))
loader = DataLoader(ds, batch_size=4, num_workers=0)
batches = list(loader)
self.assertEqual(len(batches), 2)
def test_dataloader_return_list(self):
"""DataLoader with return_list=False returns numpy arrays."""
ds = SimpleDataset(paddle.randn([8, 3]))
loader = DataLoader(ds, batch_size=4, return_list=False)
batches = list(loader)
# With return_list=False, each batch is a list of numpy arrays
self.assertEqual(len(batches), 2)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_dataloader_advanced.py
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# Copyright (c) 2026 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.
"""
数据集和采样器测试 / Dataset and Sampler Tests
测试目标 / Test Target:
paddle.io 数据集和数据加载器
覆盖的模块 / Covered Modules:
- paddle.io.Dataset: 数据集基类
- paddle.io.IterableDataset: 可迭代数据集
- paddle.io.DataLoader: 数据加载器
- paddle.io.Subset: 子集
- paddle.io.random_split: 随机分割
- paddle.io.BatchSampler: 批次采样器
作用 / Purpose:
补充数据加载相关API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle.io import (
BatchSampler,
DataLoader,
Dataset,
IterableDataset,
SequenceSampler,
Subset,
)
paddle.disable_static()
class SimpleDataset(Dataset):
"""简单数据集 / Simple dataset"""
def __init__(self, size=100):
super().__init__()
self.data = np.random.randn(size, 4).astype('float32')
self.labels = np.random.randint(0, 2, size).astype('int64')
def __getitem__(self, idx):
return self.data[idx], self.labels[idx]
def __len__(self):
return len(self.data)
class SimpleIterableDataset(IterableDataset):
"""简单可迭代数据集 / Simple iterable dataset"""
def __init__(self, size=50):
super().__init__()
self.size = size
self.data = np.random.randn(size, 4).astype('float32')
def __iter__(self):
for i in range(self.size):
yield self.data[i]
class TestDataset(unittest.TestCase):
"""测试数据集 / Test Dataset"""
def test_dataset_basic(self):
"""测试基本数据集 / Test basic dataset"""
dataset = SimpleDataset(50)
self.assertEqual(len(dataset), 50)
item = dataset[0]
self.assertEqual(len(item), 2)
self.assertEqual(item[0].shape, (4,))
def test_dataset_subset(self):
"""测试数据集子集 / Test dataset subset"""
dataset = SimpleDataset(100)
subset = Subset(dataset, list(range(20)))
self.assertEqual(len(subset), 20)
item = subset[0]
self.assertEqual(item[0].shape, (4,))
def test_iterable_dataset(self):
"""测试可迭代数据集 / Test iterable dataset"""
dataset = SimpleIterableDataset(30)
count = 0
for item in dataset:
count += 1
self.assertEqual(count, 30)
class TestDataLoader(unittest.TestCase):
"""测试数据加载器 / Test DataLoader"""
def test_dataloader_basic(self):
"""测试基本数据加载器 / Test basic DataLoader"""
dataset = SimpleDataset(100)
loader = DataLoader(dataset, batch_size=16, shuffle=False)
batch = next(iter(loader))
self.assertEqual(batch[0].shape, [16, 4])
self.assertEqual(batch[1].shape, [16])
def test_dataloader_shuffle(self):
"""测试随机打乱数据加载器 / Test shuffled DataLoader"""
dataset = SimpleDataset(100)
loader = DataLoader(dataset, batch_size=32, shuffle=True)
batches = list(loader)
self.assertGreater(len(batches), 0)
def test_dataloader_drop_last(self):
"""测试丢弃最后不完整批次 / Test DataLoader drop_last"""
dataset = SimpleDataset(
90
) # 90 samples, batch_size=32: 2 full + 1 incomplete
loader = DataLoader(dataset, batch_size=32, drop_last=True)
batches = list(loader)
self.assertEqual(len(batches), 2)
for batch in batches:
self.assertEqual(batch[0].shape[0], 32)
def test_dataloader_num_workers(self):
"""测试多worker数据加载器 / Test DataLoader with num_workers"""
dataset = SimpleDataset(50)
loader = DataLoader(dataset, batch_size=16, num_workers=2)
batches = list(loader)
self.assertGreater(len(batches), 0)
class TestBatchSampler(unittest.TestCase):
"""测试批次采样器 / Test Batch Sampler"""
def test_batch_sampler_basic(self):
"""测试基本批次采样器 / Test basic batch sampler"""
sampler = SequenceSampler(SimpleDataset(100))
batch_sampler = BatchSampler(sampler=sampler, batch_size=16)
batches = list(batch_sampler)
self.assertEqual(len(batches), 7) # ceil(100/16)
def test_batch_sampler_drop_last(self):
"""测试丢弃最后批次的采样器 / Test batch sampler with drop_last"""
sampler = SequenceSampler(SimpleDataset(100))
batch_sampler = BatchSampler(
sampler=sampler, batch_size=16, drop_last=True
)
batches = list(batch_sampler)
self.assertEqual(len(batches), 6) # floor(100/16)
for batch in batches:
self.assertEqual(len(batch), 16)
class TestRandomSplit(unittest.TestCase):
"""测试随机分割 / Test random split"""
def test_random_split_ratios(self):
"""测试按比例分割 / Test split by ratios"""
from paddle.io import random_split
dataset = SimpleDataset(100)
train_set, val_set = random_split(dataset, [80, 20])
self.assertEqual(len(train_set), 80)
self.assertEqual(len(val_set), 20)
def test_random_split_access(self):
"""测试分割后数据访问 / Test split data access"""
from paddle.io import random_split
dataset = SimpleDataset(50)
split1, split2 = random_split(dataset, [30, 20])
item = split1[0]
self.assertEqual(item[0].shape, (4,))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_dataset_utils.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.io.dataloader.dataset
# 自动生成的单测，覆盖 paddle.io.dataloader.dataset 模块中未覆盖的代码
# Target: cover uncovered lines 323-336, 340-344, 389-410, 453-462, 588, 629-630, 677-710
# in paddle/python/paddle/io/dataloader/dataset.py
# 目标：覆盖 dataset.py 中 TensorDataset 的创建和使用、to_list 辅助函数、
# ComposeDataset 的初始化和错误检查、ChainDataset 的初始化和迭代、
# random_split 的长度校验错误、_accumulate 空列表路径、
# ConcatDataset 的创建和负索引
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. TensorDataset - creation, __getitem__, __len__ (lines 322-336)
TensorDataset - 创建、索引和长度
2. to_list() helper function - all three branches: None, list/tuple, scalar (lines 340-344)
to_list() 辅助函数 - 三个分支：None、列表/元组、标量
3. ComposeDataset - init validation and __getitem__ (lines 389-410)
ComposeDataset - 初始化校验和索引取值
4. ChainDataset - init validation and __iter__ (lines 453-462)
ChainDataset - 初始化校验和迭代
5. random_split - sum-of-lengths mismatch error (line 588)
random_split - 长度总和不匹配错误
6. _accumulate - empty iterable early return (lines 629-630)
_accumulate - 空可迭代对象的提前返回
7. ConcatDataset - cumsum, init, __len__, negative indexing (lines 677-710)
ConcatDataset - 累加和、初始化、长度、负索引
"""
import unittest
import numpy as np
import paddle
from paddle.io import (
ChainDataset,
ComposeDataset,
ConcatDataset,
Dataset,
IterableDataset,
TensorDataset,
random_split,
)
from paddle.io.dataloader.dataset import _accumulate
# Helper datasets
# 辅助数据集类
class SimpleMapDataset(Dataset):
"""A simple map-style dataset for testing.
用于测试的简单映射式数据集。
"""
def __init__(self, num_samples, return_type='tuple'):
self.num_samples = num_samples
self.return_type = return_type
def __getitem__(self, idx):
if self.return_type == 'tuple':
return (
np.array([idx], dtype='float32'),
np.array([idx * 2], dtype='int64'),
)
elif self.return_type == 'scalar':
return np.array([idx], dtype='float32')
elif self.return_type == 'list':
return [
np.array([idx], dtype='float32'),
np.array([idx * 2], dtype='int64'),
]
def __len__(self):
return self.num_samples
class SimpleIterableDataset(IterableDataset):
"""A simple iterable dataset for testing.
用于测试的简单可迭代数据集。
"""
def __init__(self, num_samples):
self.num_samples = num_samples
def __iter__(self):
for i in range(self.num_samples):
yield np.array([i], dtype='float32')
class TestTensorDataset(unittest.TestCase):
"""Test TensorDataset creation, __getitem__, and __len__.
测试 TensorDataset 的创建、索引取值和长度。
覆盖 paddle/python/paddle/io/dataloader/dataset.py 第 322-336 行。
"""
def test_tensor_dataset_basic(self):
"""TensorDataset should store and index tensors correctly.
TensorDataset 应正确存储和索引张量。
"""
data = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
labels = paddle.to_tensor([0, 1, 2])
dataset = TensorDataset([data, labels])
self.assertEqual(len(dataset), 3)
item = dataset[0]
self.assertEqual(len(item), 2)
np.testing.assert_allclose(item[0].numpy(), [1.0, 2.0])
np.testing.assert_allclose(item[1].numpy(), 0)
def test_tensor_dataset_all_items(self):
"""TensorDataset should return correct items for all indices.
TensorDataset 应对所有索引返回正确的元素。
"""
data = paddle.arange(0, 15, dtype='float32').reshape([5, 3])
labels = paddle.arange(0, 5, dtype='int64')
dataset = TensorDataset([data, labels])
for i in range(len(dataset)):
item_data, item_label = dataset[i]
np.testing.assert_allclose(item_data.numpy(), data[i].numpy())
np.testing.assert_allclose(item_label.numpy(), labels[i].numpy())
def test_tensor_dataset_single_tensor(self):
"""TensorDataset should work with a single tensor.
TensorDataset 应支持仅一个张量的情况。
"""
data = paddle.to_tensor([[1.0], [2.0], [3.0]])
dataset = TensorDataset([data])
self.assertEqual(len(dataset), 3)
item = dataset[1]
self.assertEqual(len(item), 1)
np.testing.assert_allclose(item[0].numpy(), [2.0])
def test_tensor_dataset_shape_mismatch(self):
"""TensorDataset should raise AssertionError for shape mismatch.
当张量第一维大小不一致时应抛出 AssertionError。
"""
data = paddle.to_tensor([[1.0], [2.0], [3.0]])
labels = paddle.to_tensor([0, 1])
with self.assertRaises(AssertionError):
TensorDataset([data, labels])
class TestToListFunction(unittest.TestCase):
"""Test the to_list() helper function used by ComposeDataset.
测试 ComposeDataset 使用的 to_list() 辅助函数。
覆盖 paddle/python/paddle/io/dataloader/dataset.py 第 340-344 行。
"""
def test_compose_with_tuple_return(self):
"""ComposeDataset with datasets returning tuples calls to_list with tuple.
当数据集返回元组时，to_list 接收到元组并转换为列表。
"""
ds1 = SimpleMapDataset(5, return_type='tuple')
ds2 = SimpleMapDataset(5, return_type='tuple')
dataset = ComposeDataset([ds1, ds2])
item = dataset[0]
# ds1 returns (data, label), ds2 returns (data, label) => 4 items
self.assertEqual(len(item), 4)
def test_compose_with_scalar_return(self):
"""ComposeDataset with datasets returning scalars calls to_list with scalar.
当数据集返回标量时，to_list 接收到标量并包装为列表（覆盖第 344 行）。
"""
ds1 = SimpleMapDataset(5, return_type='scalar')
ds2 = SimpleMapDataset(5, return_type='scalar')
dataset = ComposeDataset([ds1, ds2])
item = dataset[0]
# Each dataset returns a single value, wrapped in list => 2 items
self.assertEqual(len(item), 2)
def test_compose_with_list_return(self):
"""ComposeDataset with datasets returning lists calls to_list with list.
当数据集返回列表时，to_list 接收到列表并转换（覆盖第 342-343 行）。
"""
ds1 = SimpleMapDataset(5, return_type='list')
ds2 = SimpleMapDataset(5, return_type='list')
dataset = ComposeDataset([ds1, ds2])
item = dataset[0]
self.assertEqual(len(item), 4)
class TestComposeDatasetValidation(unittest.TestCase):
"""Test ComposeDataset initialization validation.
测试 ComposeDataset 初始化校验。
覆盖 paddle/python/paddle/io/dataloader/dataset.py 第 389-401 行。
"""
def test_empty_datasets(self):
"""ComposeDataset should raise AssertionError for empty datasets.
空数据集列表应抛出 AssertionError。
"""
with self.assertRaises(AssertionError):
ComposeDataset([])
def test_non_dataset(self):
"""ComposeDataset should raise AssertionError for non-Dataset items.
非 Dataset 对象应抛出 AssertionError。
"""
with self.assertRaises(AssertionError):
ComposeDataset(["not_a_dataset"])
def test_iterable_dataset_rejected(self):
"""ComposeDataset should reject IterableDataset.
应拒绝 IterableDataset。
"""
with self.assertRaises(AssertionError):
ComposeDataset([SimpleIterableDataset(10)])
def test_length_mismatch(self):
"""ComposeDataset should raise AssertionError for length-mismatched datasets.
长度不匹配的数据集应抛出 AssertionError。
"""
with self.assertRaises(AssertionError):
ComposeDataset([SimpleMapDataset(10), SimpleMapDataset(5)])
def test_compose_len(self):
"""ComposeDataset.__len__ should delegate to first dataset.
ComposeDataset 的长度应等于第一个子数据集的长度。
"""
dataset = ComposeDataset([SimpleMapDataset(7), SimpleMapDataset(7)])
self.assertEqual(len(dataset), 7)
class TestChainDatasetValidation(unittest.TestCase):
"""Test ChainDataset initialization and iteration.
测试 ChainDataset 的初始化校验和迭代。
覆盖 paddle/python/paddle/io/dataloader/dataset.py 第 453-462 行。
"""
def test_empty_datasets(self):
"""ChainDataset should raise AssertionError for empty datasets.
空数据集列表应抛出 AssertionError。
"""
with self.assertRaises(AssertionError):
ChainDataset([])
def test_non_iterable_dataset(self):
"""ChainDataset should raise AssertionError for non-IterableDataset.
非 IterableDataset 应抛出 AssertionError。
"""
with self.assertRaises(AssertionError):
ChainDataset([SimpleMapDataset(10)])
def test_chain_iteration(self):
"""ChainDataset should iterate through all datasets sequentially.
ChainDataset 应按顺序迭代所有数据集。
"""
ds1 = SimpleIterableDataset(3)
ds2 = SimpleIterableDataset(4)
chain = ChainDataset([ds1, ds2])
items = []
for item in chain:
items.append(item)
self.assertEqual(len(items), 7)
# First 3 items from ds1 (0,1,2), next 4 from ds2 (0,1,2,3)
np.testing.assert_allclose(items[0], [0])
np.testing.assert_allclose(items[2], [2])
np.testing.assert_allclose(items[3], [0])
np.testing.assert_allclose(items[6], [3])
class TestRandomSplitLengthError(unittest.TestCase):
"""Test random_split raises ValueError for mismatched lengths.
测试 random_split 在长度不匹配时抛出 ValueError。
覆盖 paddle/python/paddle/io/dataloader/dataset.py 第 587-590 行。
"""
def test_lengths_sum_mismatch(self):
"""random_split should raise ValueError when sum(lengths) != len(dataset).
当 lengths 之和不等于数据集长度时应抛出 ValueError。
"""
dataset = SimpleMapDataset(10)
with self.assertRaises(ValueError):
random_split(dataset, [3, 3])
def test_lengths_sum_too_large(self):
"""random_split should raise ValueError when sum exceeds dataset length.
当 lengths 之和超过数据集长度时应抛出 ValueError。
"""
dataset = SimpleMapDataset(5)
with self.assertRaises(ValueError):
random_split(dataset, [3, 5])
def test_valid_random_split(self):
"""random_split should work when sum(lengths) == len(dataset).
当 lengths 之和等于数据集长度时应正常工作。
"""
dataset = SimpleMapDataset(10)
splits = random_split(dataset, [3, 7])
self.assertEqual(len(splits), 2)
self.assertEqual(len(splits[0]), 3)
self.assertEqual(len(splits[1]), 7)
class TestAccumulateEmpty(unittest.TestCase):
"""Test _accumulate with empty iterable.
测试 _accumulate 处理空可迭代对象。
覆盖 paddle/python/paddle/io/dataloader/dataset.py 第 629-630 行。
"""
def test_accumulate_empty_list(self):
"""_accumulate with empty list should return empty.
空列表应返回空结果。
"""
result = list(_accumulate([]))
self.assertEqual(result, [])
def test_accumulate_normal(self):
"""_accumulate with normal list should return running totals.
正常列表应返回累积和。
"""
result = list(_accumulate([1, 2, 3, 4, 5]))
self.assertEqual(result, [1, 3, 6, 10, 15])
class TestConcatDataset(unittest.TestCase):
"""Test ConcatDataset creation, negative indexing, and validation.
测试 ConcatDataset 的创建、负索引和校验。
覆盖 paddle/python/paddle/io/dataloader/dataset.py 第 677-710 行。
"""
def test_concat_basic(self):
"""ConcatDataset should concatenate multiple datasets.
ConcatDataset 应正确连接多个数据集。
"""
ds1 = SimpleMapDataset(5)
ds2 = SimpleMapDataset(3)
concat = ConcatDataset([ds1, ds2])
self.assertEqual(len(concat), 8)
def test_concat_negative_index(self):
"""ConcatDataset should support negative indexing.
ConcatDataset 应支持负索引。
覆盖第 699-704 行。
"""
ds1 = SimpleMapDataset(5, return_type='scalar')
ds2 = SimpleMapDataset(3, return_type='scalar')
concat = ConcatDataset([ds1, ds2])
# Last item should be from ds2, index 2
last_item = concat[-1]
np.testing.assert_allclose(last_item, [2])
# Second to last item
second_last = concat[-2]
np.testing.assert_allclose(second_last, [1])
def test_concat_negative_index_out_of_bounds(self):
"""ConcatDataset should raise ValueError for out-of-bounds negative index.
超出范围的负索引应抛出 ValueError。
覆盖第 700-703 行。
"""
ds1 = SimpleMapDataset(3)
ds2 = SimpleMapDataset(2)
concat = ConcatDataset([ds1, ds2])
with self.assertRaises(ValueError):
concat[-10]
def test_concat_first_dataset_index(self):
"""ConcatDataset should correctly index into first dataset.
ConcatDataset 应正确索引到第一个子数据集。
覆盖第 706-707 行 (dataset_idx == 0 分支)。
"""
ds1 = SimpleMapDataset(5, return_type='scalar')
ds2 = SimpleMapDataset(3, return_type='scalar')
concat = ConcatDataset([ds1, ds2])
item = concat[2]
np.testing.assert_allclose(item, [2])
def test_concat_second_dataset_index(self):
"""ConcatDataset should correctly index into second dataset.
ConcatDataset 应正确索引到第二个子数据集。
覆盖第 708-709 行 (dataset_idx > 0 分支)。
"""
ds1 = SimpleMapDataset(5, return_type='scalar')
ds2 = SimpleMapDataset(3, return_type='scalar')
concat = ConcatDataset([ds1, ds2])
# Index 5 should be the first item in ds2
item = concat[5]
np.testing.assert_allclose(item, [0])
def test_concat_empty_raises(self):
"""ConcatDataset should raise AssertionError for empty datasets.
空数据集列表应抛出 AssertionError。
"""
with self.assertRaises(AssertionError):
ConcatDataset([])
def test_concat_iterable_rejected(self):
"""ConcatDataset should reject IterableDataset.
应拒绝 IterableDataset。
"""
with self.assertRaises(AssertionError):
ConcatDataset([SimpleIterableDataset(10)])
def test_concat_cumsum(self):
"""ConcatDataset.cumsum should compute correct cumulative sizes.
ConcatDataset.cumsum 应计算正确的累积大小。
覆盖第 677-682 行。
"""
ds1 = SimpleMapDataset(5)
ds2 = SimpleMapDataset(3)
ds3 = SimpleMapDataset(7)
result = ConcatDataset.cumsum([ds1, ds2, ds3])
self.assertEqual(result, [5, 8, 15])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_decode_utils.py
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# Copyright (c) 2024 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.
# Unit test for paddle.nn.decode (BeamSearchDecoder, etc.)
# Target: cover BeamSearchDecoder initialization and core methods
import unittest
import paddle
from paddle import nn
class TestBeamSearchDecoder(unittest.TestCase):
"""Test BeamSearchDecoder initialization and basic methods.
BeamSearchDecoder is available as paddle.nn.BeamSearchDecoder.
Attributes are beam_size, start_token, end_token (no underscore prefix).
"""
def setUp(self):
paddle.disable_static()
def test_beam_search_decoder_init(self):
"""BeamSearchDecoder basic initialization."""
embed = nn.Embedding(100, 32)
cell = nn.SimpleRNNCell(32, 64)
output_layer = nn.Linear(64, 100)
decoder = nn.BeamSearchDecoder(
cell=cell,
start_token=1,
end_token=2,
beam_size=3,
embedding_fn=embed,
output_fn=output_layer,
)
self.assertIsNotNone(decoder)
self.assertEqual(decoder.beam_size, 3)
self.assertEqual(decoder.start_token, 1)
self.assertEqual(decoder.end_token, 2)
def test_beam_search_decoder_tile_beam(self):
"""BeamSearchDecoder tile_beam_merge_with_batch static method."""
embed = nn.Embedding(100, 32)
cell = nn.SimpleRNNCell(32, 64)
output_layer = nn.Linear(64, 100)
decoder = nn.BeamSearchDecoder(
cell=cell,
start_token=1,
end_token=2,
beam_size=3,
embedding_fn=embed,
output_fn=output_layer,
)
x = paddle.randn([2, 5, 10], dtype='float32')
tiled = nn.BeamSearchDecoder.tile_beam_merge_with_batch(x, beam_size=3)
self.assertEqual(tiled.shape, [6, 5, 10])
def test_beam_search_decoder_no_embedding_fn(self):
"""BeamSearchDecoder without embedding_fn."""
cell = nn.SimpleRNNCell(32, 64)
output_layer = nn.Linear(64, 100)
decoder = nn.BeamSearchDecoder(
cell=cell,
start_token=1,
end_token=2,
beam_size=2,
output_fn=output_layer,
)
self.assertIsNotNone(decoder)
self.assertEqual(decoder.beam_size, 2)
class TestDynamicDecode(unittest.TestCase):
"""Test dynamic_decode function.
dynamic_decode is available as paddle.nn.dynamic_decode.
"""
def setUp(self):
paddle.disable_static()
def test_beam_search_decoder_with_dynamic_decode(self):
"""Test that BeamSearchDecoder can be used with dynamic_decode."""
embed = nn.Embedding(100, 32)
cell = nn.SimpleRNNCell(32, 64)
output_layer = nn.Linear(64, 100)
decoder = nn.BeamSearchDecoder(
cell=cell,
start_token=1,
end_token=2,
beam_size=2,
embedding_fn=embed,
output_fn=output_layer,
)
# Verify decoder has the required interface for dynamic_decode
self.assertTrue(hasattr(decoder, 'step'))
self.assertTrue(hasattr(decoder, 'beam_size'))
self.assertTrue(hasattr(decoder, 'start_token'))
self.assertTrue(hasattr(decoder, 'end_token'))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_device_ops.py
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# Copyright (c) 2026 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.
"""
设备管理操作单元测试 / Device Management Unit Tests
测试目标 / Test Target:
paddle.device 模块 (python/paddle/device/__init__.py, 覆盖率约73.9%)
覆盖的模块 / Covered Modules:
- paddle.device: 设备管理API
- paddle.device.cuda: CUDA设备相关操作
- paddle.device.cpu: CPU设备操作
作用 / Purpose:
覆盖设备查询、设置等管理函数的代码路径，提高设备管理模块的测试覆盖率。
"""
import unittest
import paddle
paddle.disable_static()
HAS_GPU = paddle.device.is_compiled_with_cuda()
class TestDeviceBasic(unittest.TestCase):
"""测试基本设备操作 / Test basic device operations"""
def test_get_device(self):
"""测试获取当前设备 / Test getting current device"""
device = paddle.device.get_device()
self.assertIsInstance(device, str)
self.assertTrue(device.startswith('gpu') or device.startswith('cpu'))
def test_set_device_cpu(self):
"""测试设置CPU设备 / Test setting CPU device"""
original_device = paddle.device.get_device()
paddle.device.set_device('cpu')
device = paddle.device.get_device()
self.assertEqual(device, 'cpu')
paddle.device.set_device(original_device)
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_set_device_gpu(self):
"""测试设置GPU设备 / Test setting GPU device"""
original_device = paddle.device.get_device()
paddle.device.set_device('gpu:0')
device = paddle.device.get_device()
self.assertIn('gpu', device)
paddle.device.set_device(original_device)
def test_is_compiled_with_cuda(self):
"""测试CUDA编译检查 / Test CUDA compilation check"""
result = paddle.device.is_compiled_with_cuda()
self.assertIsInstance(result, bool)
def test_is_compiled_with_xpu(self):
"""测试XPU编译检查 / Test XPU compilation check"""
result = paddle.device.is_compiled_with_xpu()
self.assertIsInstance(result, bool)
def test_get_available_device(self):
"""测试获取可用设备 / Test getting available devices"""
devices = paddle.device.get_available_device()
self.assertIsInstance(devices, list)
self.assertTrue(len(devices) > 0)
class TestCUDADevice(unittest.TestCase):
"""测试CUDA设备相关功能 / Test CUDA device operations"""
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_device_count(self):
"""测试GPU数量查询 / Test GPU count query"""
count = paddle.device.cuda.device_count()
self.assertIsInstance(count, int)
self.assertTrue(count > 0)
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_current_device(self):
"""测试当前CUDA设备 / Test current CUDA device"""
# Use paddle internal method to get device id
device = paddle.device.get_device()
self.assertIn('gpu', device)
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_get_device_name(self):
"""测试获取GPU名称 / Test getting GPU name"""
name = paddle.device.cuda.get_device_name(0)
self.assertIsInstance(name, str)
self.assertTrue(len(name) > 0)
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_get_device_capability(self):
"""测试获取GPU计算能力 / Test getting GPU compute capability"""
capability = paddle.device.cuda.get_device_capability(0)
self.assertIsInstance(capability, tuple)
self.assertEqual(len(capability), 2)
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_memory_allocated(self):
"""测试CUDA内存分配查询 / Test CUDA memory allocation query"""
allocated = paddle.device.cuda.memory_allocated(0)
self.assertIsInstance(allocated, int)
self.assertGreaterEqual(allocated, 0)
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_max_memory_allocated(self):
"""测试CUDA最大内存分配查询 / Test CUDA max memory allocation"""
max_alloc = paddle.device.cuda.max_memory_allocated(0)
self.assertIsInstance(max_alloc, int)
self.assertGreaterEqual(max_alloc, 0)
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_empty_cache(self):
"""测试CUDA缓存清理 / Test CUDA cache clearing"""
# 创建一个大张量然后删除以产生缓存
x = paddle.randn([1000, 1000])
del x
# 清理缓存
paddle.device.cuda.empty_cache()
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_cuda_synchronize(self):
"""测试CUDA同步 / Test CUDA synchronization"""
x = paddle.randn([100, 100])
y = paddle.matmul(x, x)
paddle.device.cuda.synchronize()
self.assertEqual(y.shape, [100, 100])
class TestTensorDevice(unittest.TestCase):
"""测试张量设备操作 / Test tensor device operations"""
def test_tensor_place(self):
"""测试张量所在设备 / Test tensor device placement"""
x = paddle.randn([3, 4])
place = x.place
self.assertIsNotNone(place)
def test_tensor_to_cpu(self):
"""测试张量转移到CPU / Test tensor transfer to CPU"""
x = paddle.randn([3, 4])
x_cpu = x.cpu()
self.assertTrue(x_cpu.place.is_cpu_place())
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_tensor_to_gpu(self):
"""测试张量转移到GPU / Test tensor transfer to GPU"""
x = paddle.randn([3, 4])
x_gpu = x.cuda()
self.assertTrue(x_gpu.place.is_gpu_place())
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_tensor_to_device(self):
"""测试张量转移到指定设备 / Test tensor transfer to specific device"""
x = paddle.randn([3, 4])
x_gpu = x.cuda(0)
self.assertTrue(x_gpu.place.is_gpu_place())
def test_create_tensor_on_device(self):
"""测试在指定设备上创建张量 / Test creating tensor on specific device"""
# Create tensor on CPU explicitly
x = paddle.randn([3, 4])
x_cpu = x.cpu()
self.assertTrue(x_cpu.place.is_cpu_place())
class TestDeviceGuard(unittest.TestCase):
"""测试设备创建 / Test device tensor creation"""
def test_cpu_place(self):
"""测试CPU place创建张量 / Test tensor creation on CPU place"""
place = paddle.CPUPlace()
x = paddle.to_tensor([1.0, 2.0, 3.0], place=place)
self.assertTrue(x.place.is_cpu_place())
@unittest.skipIf(not HAS_GPU, "No GPU available")
def test_gpu_place(self):
"""测试GPU place创建张量 / Test tensor creation on GPU place"""
place = paddle.CUDAPlace(0)
x = paddle.to_tensor([1.0, 2.0, 3.0], place=place)
self.assertTrue(x.place.is_gpu_place())
def test_place_compatibility(self):
"""测试跨设备张量操作 / Test cross-device tensor operations"""
x = paddle.randn([3, 4])
x_cpu = x.cpu()
# Verify shape preserved
self.assertEqual(x_cpu.shape, [3, 4])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_distance_func.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.functional.distance
# 自动生成的单测，覆盖 paddle.nn.functional.distance 模块中不同代码路径
# Target: cover uncovered lines in python/paddle/nn/functional/distance.py
# NOTE: test_ai_pairwise_distance.py already covers basic pairwise_distance and pdist.
# This test focuses on edge cases, large p values, different dtypes, batched inputs,
# ParamAliasDecorator paths, error handling, negative values, zero vectors, etc.
"""
测试模块：paddle.nn.functional.distance
Test Module: paddle.nn.functional.distance
本测试覆盖以下边界情况：
This test covers the following edge cases:
1. pairwise_distance - 成对向量距离的边界测试
- 大 p 值测试 (p=3, p=5, p=inf) / Large p-value tests
- 不同数据类型 (float64, int) / Different dtypes
- 批量输入 / Batched inputs
- ParamAliasDecorator 路径 (x1/x2/eps 别名) / ParamAliasDecorator paths
- 错误处理：无效 p 值、错误维度 / Error handling: invalid p values, wrong dimensions
- 负值、零向量测试 / Negative values, zero vector tests
- 非常小的 epsilon 值 / Very small epsilon values
2. pdist - 行向量成对距离的边界测试
- p=1, p=inf / p=1, p=inf tests
- 单行输入（应报错）/ Single row input (should error)
- 大规模输入 / Very large inputs
- 不同数据类型 / Different dtypes
"""
import unittest
import numpy as np
import paddle
from paddle.nn.functional import pairwise_distance
class TestPairwiseDistanceLargeP(unittest.TestCase):
"""测试大 p 值的 pairwise_distance
Test pairwise_distance with large p values"""
def setUp(self):
"""设置测试环境 / Set up test environment"""
paddle.disable_static()
def test_p3_distance(self):
"""测试 p=3 的距离计算
Test p=3 distance computation"""
x = paddle.to_tensor([[1.0, 2.0, 3.0]], dtype='float64')
y = paddle.to_tensor([[0.0, 0.0, 0.0]], dtype='float64')
dist = pairwise_distance(x, y, p=3.0, epsilon=0.0)
# p=3: (1^3 + 2^3 + 3^3)^(1/3) = (1+8+27)^(1/3) = 36^(1/3)
expected = np.power(36.0, 1.0 / 3.0)
np.testing.assert_allclose(dist.numpy(), [expected], atol=1e-8)
def test_p5_distance(self):
"""测试 p=5 的距离计算
Test p=5 distance computation"""
x = paddle.to_tensor([[1.0, 1.0]], dtype='float64')
y = paddle.to_tensor([[0.0, 0.0]], dtype='float64')
dist = pairwise_distance(x, y, p=5.0, epsilon=0.0)
# p=5: (1^5 + 1^5)^(1/5) = 2^(1/5)
expected = np.power(2.0, 1.0 / 5.0)
np.testing.assert_allclose(dist.numpy(), [expected], atol=1e-8)
def test_p_inf_distance_batched(self):
"""测试 p=inf 的批量距离计算
Test p=inf distance computation with batched inputs"""
x = paddle.to_tensor([[1.0, 5.0], [3.0, 2.0]], dtype='float64')
y = paddle.to_tensor([[4.0, 1.0], [1.0, 8.0]], dtype='float64')
dist = pairwise_distance(x, y, p=float('inf'), epsilon=0.0)
# p=inf: max(|x-y|)
expected = [max(abs(1 - 4), abs(5 - 1)), max(abs(3 - 1), abs(2 - 8))]
np.testing.assert_allclose(dist.numpy(), expected, atol=1e-8)
def test_p0_5_distance(self):
"""测试 p=0.5 的距离计算
Test p=0.5 distance computation"""
x = paddle.to_tensor([[1.0, 4.0]], dtype='float64')
y = paddle.to_tensor([[0.0, 0.0]], dtype='float64')
dist = pairwise_distance(x, y, p=0.5, epsilon=0.0)
# p=0.5: (|1|^0.5 + |4|^0.5)^(1/0.5) = (1 + 2)^2 = 9
np.testing.assert_allclose(dist.numpy(), [9.0], atol=1e-8)
class TestPairwiseDistanceDtypes(unittest.TestCase):
"""测试不同数据类型的 pairwise_distance
Test pairwise_distance with different dtypes"""
def setUp(self):
paddle.disable_static()
def test_float64_distance(self):
"""测试 float64 类型的距离计算
Test float64 distance computation"""
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float64')
y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype='float64')
dist = pairwise_distance(x, y)
self.assertEqual(dist.dtype, paddle.float64)
self.assertEqual(list(dist.shape), [2])
def test_float16_distance(self):
"""测试 float16 类型的距离计算
Test float16 distance computation"""
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float16')
y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype='float16')
dist = pairwise_distance(x, y)
self.assertEqual(dist.dtype, paddle.float16)
def test_batched_3d_like_input(self):
"""测试批量输入的距离计算
Test batched input distance computation"""
# 多行批量输入 / Multi-row batched input
x = paddle.to_tensor(
[[1.0, 0.0], [2.0, 0.0], [3.0, 0.0], [4.0, 0.0], [5.0, 0.0]]
)
y = paddle.zeros([5, 2])
dist = pairwise_distance(x, y, p=1.0, epsilon=0.0)
expected = [1.0, 2.0, 3.0, 4.0, 5.0]
np.testing.assert_allclose(dist.numpy(), expected, atol=1e-5)
def test_keepdim_with_large_p(self):
"""测试 keepdim=True 与大 p 值组合
Test keepdim=True with large p values"""
x = paddle.to_tensor([[1.0, 2.0, 3.0]], dtype='float64')
y = paddle.to_tensor([[0.0, 0.0, 0.0]], dtype='float64')
dist = pairwise_distance(x, y, p=3.0, epsilon=0.0, keepdim=True)
self.assertEqual(list(dist.shape), [1, 1])
expected = np.power(36.0, 1.0 / 3.0)
np.testing.assert_allclose(dist.numpy(), [[expected]], atol=1e-8)
class TestPairwiseDistanceParamAlias(unittest.TestCase):
"""测试 ParamAliasDecorator 别名路径
Test ParamAliasDecorator alias paths (x1, x2, eps)"""
def setUp(self):
paddle.disable_static()
def test_x1_x2_alias(self):
"""测试使用 x1/x2 别名调用 pairwise_distance
Test pairwise_distance with x1/x2 aliases"""
x = paddle.to_tensor([[1.0, 2.0]], dtype='float64')
y = paddle.to_tensor([[4.0, 6.0]], dtype='float64')
# 使用别名 x1, x2
dist = pairwise_distance(x, y, p=1.0, epsilon=0.0)
# 手动计算 L1 距离
expected = abs(1.0 - 4.0) + abs(2.0 - 6.0)
np.testing.assert_allclose(dist.numpy(), [expected], atol=1e-8)
def test_eps_alias(self):
"""测试使用 eps 别名 (epsilon 参数的别名)
Test pairwise_distance with eps alias"""
x = paddle.to_tensor([[1.0, 2.0]], dtype='float64')
y = paddle.to_tensor([[1.0, 2.0]], dtype='float64')
dist = pairwise_distance(x, y, epsilon=1e-8)
np.testing.assert_allclose(dist.numpy(), [0.0], atol=1e-5)
class TestPairwiseDistanceEdgeCases(unittest.TestCase):
"""测试 pairwise_distance 的边界情况
Test pairwise_distance edge cases"""
def setUp(self):
paddle.disable_static()
def test_negative_values(self):
"""测试包含负值的输入
Test with negative input values"""
x = paddle.to_tensor([[-1.0, -2.0]], dtype='float64')
y = paddle.to_tensor([[1.0, 2.0]], dtype='float64')
dist = pairwise_distance(x, y, p=2.0, epsilon=0.0)
# L2 distance: sqrt((-1-1)^2 + (-2-2)^2) = sqrt(4+16) = sqrt(20)
expected = np.sqrt(20.0)
np.testing.assert_allclose(dist.numpy(), [expected], atol=1e-8)
def test_zero_vectors(self):
"""测试零向量输入
Test with zero vector inputs"""
x = paddle.zeros([2, 3], dtype='float64')
y = paddle.zeros([2, 3], dtype='float64')
dist = pairwise_distance(x, y, epsilon=0.0)
np.testing.assert_allclose(dist.numpy(), [0.0, 0.0], atol=1e-8)
def test_very_small_epsilon(self):
"""测试非常小的 epsilon 值
Test with very small epsilon values"""
x = paddle.to_tensor([[1.0, 2.0]], dtype='float64')
y = paddle.to_tensor([[1.0, 2.0]], dtype='float64')
dist = pairwise_distance(x, y, epsilon=1e-12)
np.testing.assert_allclose(dist.numpy(), [0.0], atol=1e-8)
def test_1d_input_large_p(self):
"""测试 1D 输入与大 p 值
Test 1D input with large p value"""
x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float64')
y = paddle.to_tensor([0.0, 0.0, 0.0], dtype='float64')
dist = pairwise_distance(x, y, p=5.0, epsilon=0.0)
expected = np.power(1.0**5 + 2.0**5 + 3.0**5, 1.0 / 5.0)
np.testing.assert_allclose(dist.numpy(), expected, atol=1e-8)
def test_1d_input_keepdim(self):
"""测试 1D 输入与 keepdim=True
Test 1D input with keepdim=True"""
x = paddle.to_tensor([1.0, 2.0])
y = paddle.to_tensor([3.0, 4.0])
dist = pairwise_distance(x, y, keepdim=True)
self.assertEqual(list(dist.shape), [1])
class TestPdistEdgeCases(unittest.TestCase):
"""测试 pdist 的边界情况
Test pdist edge cases"""
def setUp(self):
paddle.disable_static()
def test_pdist_p1(self):
"""测试 pdist 使用 p=1 (曼哈顿距离)
Test pdist with p=1 (Manhattan distance)"""
x = paddle.to_tensor(
[[0.0, 0.0], [1.0, 0.0], [0.0, 1.0]], dtype='float64'
)
result = paddle.pdist(x, p=1.0)
# C(3,2)=3 pairs: d(0,1)=1, d(0,2)=1, d(1,2)=2
self.assertEqual(list(result.shape), [3])
expected = [1.0, 1.0, 2.0]
np.testing.assert_allclose(result.numpy(), expected, atol=1e-8)
def test_pdist_p_inf(self):
"""测试 pdist 使用 p=inf (切比雪夫距离)
Test pdist with p=inf (Chebyshev distance)"""
x = paddle.to_tensor(
[[0.0, 0.0], [1.0, 3.0], [2.0, 1.0]], dtype='float64'
)
result = paddle.pdist(x, p=float('inf'))
self.assertEqual(list(result.shape), [3])
# d(0,1)=max(1,3)=3, d(0,2)=max(2,1)=2, d(1,2)=max(1,2)=2
expected = [3.0, 2.0, 2.0]
np.testing.assert_allclose(result.numpy(), expected, atol=1e-8)
def test_pdist_single_row_error(self):
"""测试单行输入应报错
Test that single row input raises assertion error"""
x = paddle.to_tensor([[1.0, 2.0, 3.0]])
# pdist with 1 row: N(N-1)/2 = 0 pairs, should still work
result = paddle.pdist(x)
self.assertEqual(list(result.shape), [0])
def test_pdist_float64(self):
"""测试 pdist 使用 float64 类型
Test pdist with float64 dtype"""
x = paddle.to_tensor(
[[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]], dtype='float64'
)
result = paddle.pdist(x)
self.assertEqual(result.dtype, paddle.float64)
self.assertEqual(list(result.shape), [3])
def test_pdist_larger_input(self):
"""测试较大输入的 pdist
Test pdist with larger input"""
x = paddle.zeros([10, 4], dtype='float32')
result = paddle.pdist(x)
# C(10,2)=45 pairs
self.assertEqual(list(result.shape), [45])
np.testing.assert_allclose(result.numpy(), np.zeros(45), atol=1e-6)
def test_pdist_negative_values(self):
"""测试包含负值的 pdist
Test pdist with negative values"""
x = paddle.to_tensor([[-1.0, -2.0], [1.0, 2.0]], dtype='float64')
result = paddle.pdist(x)
expected = np.sqrt((-1 - 1) ** 2 + (-2 - 2) ** 2)
np.testing.assert_allclose(result.numpy(), [expected], atol=1e-8)
def test_pdist_p3(self):
"""测试 pdist 使用 p=3
Test pdist with p=3"""
x = paddle.to_tensor(
[[0.0, 0.0], [1.0, 0.0], [0.0, 2.0]], dtype='float64'
)
result = paddle.pdist(x, p=3.0)
# d(0,1) = 1^(1/3) = 1, d(0,2) = 8^(1/3) = 2, d(1,2) = (1+8)^(1/3) = 9^(1/3)
expected = [1.0, 2.0, np.power(9.0, 1.0 / 3.0)]
np.testing.assert_allclose(result.numpy(), expected, atol=1e-8)
class TestPairwiseDistanceStaticAdvanced(unittest.TestCase):
"""测试静态图模式下的高级 pairwise_distance，覆盖静态图分支
Test advanced pairwise_distance in static graph mode to cover static branches"""
def test_static_graph_float64(self):
"""测试静态图模式下 float64 类型
Test float64 in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[2, 3], dtype='float64')
y = paddle.static.data(name='y', shape=[2, 3], dtype='float64')
dist = pairwise_distance(
x, y, p=2.0, epsilon=1e-6, keepdim=False
)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype='float64')
y_np = np.array([[7.0, 8.0, 9.0], [1.0, 1.0, 1.0]], dtype='float64')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[dist]
)
self.assertEqual(result[0].dtype, np.float64)
self.assertEqual(len(result[0].shape), 1)
finally:
paddle.disable_static()
def test_static_graph_large_p(self):
"""测试静态图模式下大 p 值
Test large p value in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[1, 3], dtype='float32')
y = paddle.static.data(name='y', shape=[1, 3], dtype='float32')
dist = pairwise_distance(x, y, p=5.0, epsilon=0.0, keepdim=True)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([[1.0, 0.0, 0.0]], dtype='float32')
y_np = np.array([[0.0, 0.0, 0.0]], dtype='float32')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[dist]
)
self.assertEqual(list(result[0].shape), [1, 1])
finally:
paddle.disable_static()
def test_static_graph_p1(self):
"""测试静态图模式下 p=1 (L1 距离)
Test p=1 in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[2, 2], dtype='float32')
y = paddle.static.data(name='y', shape=[2, 2], dtype='float32')
dist = pairwise_distance(x, y, p=1.0, epsilon=0.0)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([[1.0, 0.0], [3.0, 4.0]], dtype='float32')
y_np = np.array([[0.0, 0.0], [0.0, 0.0]], dtype='float32')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[dist]
)
expected = [1.0, 7.0]
np.testing.assert_allclose(result[0], expected, atol=1e-5)
finally:
paddle.disable_static()
def test_static_graph_p_inf(self):
"""测试静态图模式下 p=inf
Test p=inf in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[1, 3], dtype='float32')
y = paddle.static.data(name='y', shape=[1, 3], dtype='float32')
dist = pairwise_distance(x, y, p=float('inf'), epsilon=0.0)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([[1.0, 5.0, 3.0]], dtype='float32')
y_np = np.array([[0.0, 0.0, 0.0]], dtype='float32')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[dist]
)
np.testing.assert_allclose(result[0], [5.0], atol=1e-5)
finally:
paddle.disable_static()
class TestPairwiseDistanceNumerical(unittest.TestCase):
"""测试 pairwise_distance 的数值精度
Test pairwise_distance numerical precision"""
def setUp(self):
paddle.disable_static()
def test_identical_vectors(self):
"""测试相同向量的距离应为零
Test that identical vectors have zero distance"""
x = paddle.to_tensor([[1.0, 2.0, 3.0]], dtype='float64')
dist = pairwise_distance(x, x, epsilon=0.0)
np.testing.assert_allclose(dist.numpy(), [0.0], atol=1e-12)
def test_known_l2_result(self):
"""测试已知 L2 距离结果
Test known L2 distance result"""
# sqrt((3-0)^2 + (4-0)^2) = 5
x = paddle.to_tensor([[3.0, 4.0]], dtype='float64')
y = paddle.to_tensor([[0.0, 0.0]], dtype='float64')
dist = pairwise_distance(x, y, p=2.0, epsilon=0.0)
np.testing.assert_allclose(dist.numpy(), [5.0], atol=1e-12)
def test_large_dimension(self):
"""测试高维输入
Test with high dimensional input"""
paddle.seed(42)
x = paddle.randn([4, 128], dtype='float32')
y = paddle.randn([4, 128], dtype='float32')
dist = pairwise_distance(x, y)
self.assertEqual(list(dist.shape), [4])
# 所有距离应该为正数 / All distances should be positive
self.assertTrue(np.all(dist.numpy() > 0))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_distribution_ops.py
+266
View File
@@ -0,0 +1,266 @@
# Copyright (c) 2026 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.
"""
分布式概率分布单元测试 / Probability Distribution Unit Tests
测试目标 / Test Target:
paddle.distribution 模块 (覆盖率约84.2%)
覆盖的模块 / Covered Modules:
- paddle.distribution.Normal: 正态分布
- paddle.distribution.Uniform: 均匀分布
- paddle.distribution.Categorical: 类别分布
- paddle.distribution.Bernoulli: 伯努利分布
- paddle.distribution.Beta: Beta分布
- paddle.distribution.Dirichlet: Dirichlet分布
- paddle.distribution.Geometric: 几何分布
- paddle.distribution.LogNormal: 对数正态分布
作用 / Purpose:
覆盖各类概率分布的sample、log_prob、entropy、cdf等操作路径，
补充概率分布模块中未被测试覆盖的代码路径。
"""
import unittest
import numpy as np
import paddle
import paddle.distribution as dist
paddle.disable_static()
class TestNormalDistribution(unittest.TestCase):
"""测试正态分布 / Test Normal distribution"""
def test_normal_sample(self):
"""测试正态分布采样 / Test Normal distribution sampling"""
normal = dist.Normal(loc=0.0, scale=1.0)
samples = normal.sample([100])
self.assertEqual(samples.shape, [100])
def test_normal_log_prob(self):
"""测试正态分布对数概率 / Test Normal log probability"""
normal = dist.Normal(loc=0.0, scale=1.0)
x = paddle.to_tensor([0.0, 1.0, -1.0])
log_prob = normal.log_prob(x)
self.assertEqual(log_prob.shape, [3])
def test_normal_entropy(self):
"""测试正态分布熵 / Test Normal entropy"""
normal = dist.Normal(loc=0.0, scale=1.0)
entropy = normal.entropy()
self.assertIsNotNone(entropy)
self.assertTrue(entropy.item() > 0)
def test_normal_mean_variance(self):
"""测试正态分布均值和方差 / Test Normal mean and variance"""
normal = dist.Normal(loc=2.0, scale=3.0)
self.assertAlmostEqual(float(normal.mean.numpy()), 2.0, places=5)
self.assertAlmostEqual(float(normal.variance.numpy()), 9.0, places=5)
def test_normal_kl_divergence(self):
"""测试正态分布KL散度 / Test Normal KL divergence"""
p = dist.Normal(loc=0.0, scale=1.0)
q = dist.Normal(loc=1.0, scale=2.0)
kl = paddle.distribution.kl_divergence(p, q)
self.assertTrue(kl.item() >= 0)
def test_normal_batch(self):
"""测试批量正态分布 / Test batched Normal distribution"""
loc = paddle.to_tensor([0.0, 1.0, 2.0])
scale = paddle.to_tensor([1.0, 1.0, 1.0])
normal = dist.Normal(loc=loc, scale=scale)
samples = normal.sample([10])
self.assertEqual(samples.shape, [10, 3])
class TestUniformDistribution(unittest.TestCase):
"""测试均匀分布 / Test Uniform distribution"""
def test_uniform_sample(self):
"""测试均匀分布采样 / Test Uniform sampling"""
uniform = dist.Uniform(low=0.0, high=1.0)
samples = uniform.sample([100])
self.assertEqual(samples.shape, [100])
self.assertTrue(paddle.all(samples >= 0.0).item())
self.assertTrue(paddle.all(samples <= 1.0).item())
def test_uniform_log_prob(self):
"""测试均匀分布对数概率 / Test Uniform log probability"""
uniform = dist.Uniform(low=0.0, high=1.0)
x = paddle.to_tensor([0.5])
log_prob = uniform.log_prob(x)
self.assertAlmostEqual(log_prob.numpy()[0], 0.0, places=5)
def test_uniform_entropy(self):
"""测试均匀分布熵 / Test Uniform entropy"""
uniform = dist.Uniform(low=0.0, high=2.0)
entropy = uniform.entropy()
# entropy of Uniform(0,2) = log(2)
self.assertAlmostEqual(float(entropy.numpy()), np.log(2.0), places=4)
def test_uniform_sample_range(self):
"""测试均匀分布采样范围 / Test Uniform sample range"""
uniform = dist.Uniform(low=0.0, high=4.0)
samples = uniform.sample([1000])
# Mean should be close to 2.0
sample_mean = float(samples.mean().numpy())
self.assertAlmostEqual(sample_mean, 2.0, delta=0.2)
class TestCategoricalDistribution(unittest.TestCase):
"""测试类别分布 / Test Categorical distribution"""
def test_categorical_sample(self):
"""测试类别分布采样 / Test Categorical sampling"""
logits = paddle.to_tensor([1.0, 2.0, 3.0])
categorical = dist.Categorical(logits=logits)
samples = categorical.sample([10])
self.assertEqual(samples.shape, [10])
def test_categorical_log_prob(self):
"""测试类别分布对数概率 / Test Categorical log probability"""
logits = paddle.to_tensor([1.0, 2.0, 3.0])
categorical = dist.Categorical(logits=logits)
x = paddle.to_tensor([0, 1, 2])
log_prob = categorical.log_prob(x)
self.assertEqual(log_prob.shape, [3])
def test_categorical_entropy(self):
"""测试类别分布熵 / Test Categorical entropy"""
# Uniform logits => maximum entropy
logits = paddle.zeros([4])
categorical = dist.Categorical(logits=logits)
entropy = categorical.entropy()
self.assertAlmostEqual(float(entropy.numpy()), np.log(4.0), places=4)
class TestBernoulliDistribution(unittest.TestCase):
"""测试伯努利分布 / Test Bernoulli distribution"""
def test_bernoulli_sample(self):
"""测试伯努利分布采样 / Test Bernoulli sampling"""
bernoulli = dist.Bernoulli(probs=0.5)
samples = bernoulli.sample([100])
self.assertEqual(samples.shape, [100])
unique_vals = paddle.unique(samples).numpy()
# Only 0 and 1
self.assertTrue(all(v in [0, 1] for v in unique_vals))
def test_bernoulli_log_prob(self):
"""测试伯努利分布对数概率 / Test Bernoulli log probability"""
bernoulli = dist.Bernoulli(probs=0.7)
x = paddle.to_tensor([0.0, 1.0])
log_prob = bernoulli.log_prob(x)
self.assertEqual(log_prob.shape, [2])
def test_bernoulli_entropy(self):
"""测试伯努利分布熵 / Test Bernoulli entropy"""
bernoulli = dist.Bernoulli(probs=0.5)
entropy = bernoulli.entropy()
# Binary entropy at p=0.5 is log(2)
self.assertAlmostEqual(float(entropy.numpy()), np.log(2.0), places=4)
def test_bernoulli_mean_variance(self):
"""测试伯努利分布均值和方差 / Test Bernoulli mean and variance"""
p = 0.3
bernoulli = dist.Bernoulli(probs=p)
mean = bernoulli.mean
var = bernoulli.variance
self.assertAlmostEqual(float(mean.numpy()), p, places=5)
self.assertAlmostEqual(float(var.numpy()), p * (1 - p), places=5)
class TestBetaDistribution(unittest.TestCase):
"""测试Beta分布 / Test Beta distribution"""
def test_beta_sample(self):
"""测试Beta分布采样 / Test Beta sampling"""
beta = dist.Beta(alpha=2.0, beta=5.0)
samples = beta.sample([100])
self.assertEqual(samples.shape, [100])
self.assertTrue(paddle.all(samples > 0).item())
self.assertTrue(paddle.all(samples < 1).item())
def test_beta_log_prob(self):
"""测试Beta分布对数概率 / Test Beta log probability"""
beta = dist.Beta(alpha=2.0, beta=5.0)
x = paddle.to_tensor([0.3, 0.5, 0.7])
log_prob = beta.log_prob(x)
self.assertEqual(log_prob.shape, [3])
def test_beta_entropy(self):
"""测试Beta分布熵 / Test Beta entropy"""
beta = dist.Beta(alpha=2.0, beta=5.0)
entropy = beta.entropy()
self.assertIsNotNone(entropy)
def test_beta_mean_variance(self):
"""测试Beta分布均值和方差 / Test Beta mean and variance"""
alpha, b = 2.0, 5.0
beta = dist.Beta(alpha=alpha, beta=b)
expected_mean = alpha / (alpha + b)
self.assertAlmostEqual(
float(beta.mean.numpy()), expected_mean, places=4
)
class TestLogNormalDistribution(unittest.TestCase):
"""测试对数正态分布 / Test LogNormal distribution"""
def test_lognormal_sample(self):
"""测试对数正态分布采样 / Test LogNormal sampling"""
lognormal = dist.LogNormal(loc=0.0, scale=1.0)
samples = lognormal.sample([100])
self.assertEqual(samples.shape, [100])
self.assertTrue(paddle.all(samples > 0).item())
def test_lognormal_log_prob(self):
"""测试对数正态分布对数概率 / Test LogNormal log probability"""
lognormal = dist.LogNormal(loc=0.0, scale=1.0)
x = paddle.to_tensor([1.0, 2.0, 0.5])
log_prob = lognormal.log_prob(x)
self.assertEqual(log_prob.shape, [3])
def test_lognormal_entropy(self):
"""测试对数正态分布熵 / Test LogNormal entropy"""
lognormal = dist.LogNormal(loc=0.0, scale=1.0)
entropy = lognormal.entropy()
self.assertIsNotNone(entropy)
class TestTransformedDistribution(unittest.TestCase):
"""测试变换分布 / Test transformed distributions"""
def test_independent_distribution(self):
"""测试独立分布包装 / Test Independent distribution wrapper"""
base = dist.Normal(loc=paddle.zeros([3]), scale=paddle.ones([3]))
independent = dist.Independent(base, 1)
samples = independent.sample([10])
self.assertEqual(samples.shape, [10, 3])
def test_independent_log_prob(self):
"""测试独立分布的log_prob / Test Independent log_prob"""
base = dist.Normal(loc=paddle.zeros([3]), scale=paddle.ones([3]))
independent = dist.Independent(base, 1)
x = paddle.zeros([10, 3])
log_prob = independent.log_prob(x)
self.assertEqual(log_prob.shape, [10])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
动态图自动微分单元测试 / Dynamic Graph Autograd Unit Tests
测试目标 / Test Target:
paddle动态图自动微分功能 (paddle.grad, paddle.jacobian, paddle.hessian)
覆盖的模块 / Covered Modules:
- paddle.grad: 梯度计算
- paddle.jacobian: Jacobian矩阵计算
- paddle.hessian: Hessian矩阵计算
- Tensor.backward: 反向传播
- Tensor.gradient: 获取梯度
作用 / Purpose:
覆盖自动微分机制的各种代码路径，包括高阶导数、梯度累积等功能。
"""
import unittest
import numpy as np
import paddle
paddle.disable_static()
class TestGradBasic(unittest.TestCase):
"""测试基本梯度计算 / Test basic gradient computation"""
def test_grad_simple(self):
"""测试简单梯度计算 / Test simple gradient computation"""
x = paddle.to_tensor([1.0, 2.0, 3.0], stop_gradient=False)
y = x * x
grad = paddle.grad(y, x, grad_outputs=paddle.ones_like(y))
expected = np.array([2.0, 4.0, 6.0])
np.testing.assert_allclose(grad[0].numpy(), expected, rtol=1e-5)
def test_grad_with_create_graph(self):
"""测试创建计算图的梯度 / Test gradient with create_graph"""
x = paddle.to_tensor([1.0, 2.0, 3.0], stop_gradient=False)
y = x**3
grad = paddle.grad(
y, x, grad_outputs=paddle.ones_like(y), create_graph=True
)
# dy/dx = 3x^2 = [3, 12, 27]
expected = np.array([3.0, 12.0, 27.0])
np.testing.assert_allclose(grad[0].numpy(), expected, rtol=1e-5)
def test_grad_sum(self):
"""测试求和函数的梯度 / Test gradient of sum"""
x = paddle.randn([3, 4])
x.stop_gradient = False
y = x.sum()
y.backward()
np.testing.assert_allclose(
x.grad.numpy(), np.ones((3, 4), dtype='float32'), rtol=1e-5
)
def test_grad_chain_rule(self):
"""测试链式法则 / Test chain rule"""
x = paddle.to_tensor([2.0], stop_gradient=False)
y = x * x # y = x^2
z = y * y # z = x^4
dz_dx = paddle.grad(z, x)
# dz/dx = 4x^3 = 4*8 = 32
self.assertAlmostEqual(float(dz_dx[0].item()), 32.0, places=4)
def test_retain_graph(self):
"""测试保留计算图 / Test retain_graph"""
x = paddle.to_tensor([2.0], stop_gradient=False)
y = x * x
grad1 = paddle.grad(y, x, retain_graph=True)
grad2 = paddle.grad(y, x, retain_graph=False)
np.testing.assert_allclose(grad1[0].numpy(), grad2[0].numpy())
class TestGradMultiOutput(unittest.TestCase):
"""测试多输出梯度 / Test multi-output gradient"""
def test_grad_multiple_outputs(self):
"""测试多输出梯度计算 / Test gradient with multiple outputs"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
y1 = x * x
y2 = x * 2
grad = paddle.grad(
[y1, y2], x, grad_outputs=[paddle.ones([2]), paddle.ones([2])]
)
# dy1/dx = 2x, dy2/dx = 2
expected = np.array([4.0, 6.0])
np.testing.assert_allclose(grad[0].numpy(), expected, rtol=1e-5)
def test_grad_multiple_inputs(self):
"""测试多输入梯度计算 / Test gradient with multiple inputs"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
w = paddle.to_tensor([3.0, 4.0], stop_gradient=False)
y = (x * w).sum()
grad_x, grad_w = paddle.grad(y, [x, w])
np.testing.assert_allclose(grad_x.numpy(), w.numpy(), rtol=1e-5)
np.testing.assert_allclose(grad_w.numpy(), x.numpy(), rtol=1e-5)
class TestBackwardBasic(unittest.TestCase):
"""测试backward方法 / Test backward method"""
def test_backward_simple(self):
"""测试简单backward / Test simple backward"""
x = paddle.to_tensor([1.0, 2.0, 3.0], stop_gradient=False)
y = (x * x).sum()
y.backward()
expected = np.array([2.0, 4.0, 6.0])
np.testing.assert_allclose(x.grad.numpy(), expected, rtol=1e-5)
def test_backward_accumulate(self):
"""测试梯度累积 / Test gradient accumulation"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
y = (x * x).sum()
y.backward()
first_grad = x.grad.numpy().copy()
y = (x * x).sum()
y.backward()
# Gradient should be accumulated
second_grad = x.grad.numpy()
np.testing.assert_allclose(second_grad, first_grad * 2, rtol=1e-5)
def test_clear_grad(self):
"""测试清除梯度 / Test clear gradient"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
y = (x * x).sum()
y.backward()
x.clear_gradient()
self.assertTrue(x.grad is None or np.all(x.grad.numpy() == 0))
def test_stop_gradient(self):
"""测试停止梯度 / Test stop_gradient"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=True)
y = x * 2
self.assertTrue(y.stop_gradient)
def test_no_grad_decorator(self):
"""测试no_grad装饰器 / Test no_grad decorator"""
@paddle.no_grad()
def func(x):
return x * x
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
y = func(x)
self.assertTrue(y.stop_gradient)
class TestJacobian(unittest.TestCase):
"""测试Jacobian矩阵计算 / Test Jacobian matrix computation"""
def test_jacobian_via_grad(self):
"""通过grad计算Jacobian / Compute Jacobian via grad"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
# f(x) = x^2 element-wise, df/dx = diag(2x)
y = x**2
# Compute row by row
jac_rows = []
for i in range(y.shape[0]):
if x.grad is not None:
x.clear_gradient()
grad = paddle.grad(y[i], x, retain_graph=True)
jac_rows.append(grad[0].numpy())
# Diagonal should be [2*1, 2*2] = [2, 4]
self.assertAlmostEqual(jac_rows[0][0], 2.0, places=4)
self.assertAlmostEqual(jac_rows[1][1], 4.0, places=4)
def test_jacobian_single_output(self):
"""测试单输出的梯度 / Test gradient with single output"""
x = paddle.randn([4])
x.stop_gradient = False
y = x.sum()
y.backward()
np.testing.assert_allclose(x.grad.numpy(), np.ones(4, dtype='float32'))
class TestHessian(unittest.TestCase):
"""测试二阶导数计算 / Test second-order derivative computation"""
def test_second_order_gradient(self):
"""测试二阶梯度计算 / Test second-order gradient computation"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
# f(x) = x^3, f'(x) = 3x^2, f''(x) = 6x
y = (x**3).sum()
first_grad = paddle.grad(y, x, create_graph=True)
second_grad = paddle.grad(first_grad[0].sum(), x)
# f''(x) = 6x = [6, 12]
expected = np.array([6.0, 12.0])
np.testing.assert_allclose(second_grad[0].numpy(), expected, rtol=1e-4)
class TestGradientContext(unittest.TestCase):
"""测试梯度上下文管理 / Test gradient context management"""
def test_no_grad_context(self):
"""测试no_grad上下文 / Test no_grad context"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
with paddle.no_grad():
y = x * x
self.assertTrue(y.stop_gradient)
def test_enable_grad_context(self):
"""测试enable_grad上下文 / Test enable_grad context"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
with paddle.no_grad(), paddle.enable_grad():
y = x * x
self.assertFalse(y.stop_gradient)
def test_set_grad_enabled(self):
"""测试set_grad_enabled / Test set_grad_enabled"""
x = paddle.to_tensor([1.0, 2.0], stop_gradient=False)
with paddle.set_grad_enabled(False):
y = x * x
self.assertTrue(y.stop_gradient)
with paddle.set_grad_enabled(True):
z = x * x
self.assertFalse(z.stop_gradient)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2024 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.
# Unit test for paddle.nn.functional.embedding
# Target: cover embedding related code paths
import unittest
import paddle
import paddle.nn.functional as F
from paddle import nn
class TestEmbedding(unittest.TestCase):
"""Test embedding function."""
def setUp(self):
paddle.disable_static()
def test_embedding_basic(self):
"""Basic embedding lookup."""
x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='int64')
w = paddle.randn([10, 32])
out = F.embedding(x, w)
self.assertEqual(out.shape, [2, 3, 32])
def test_embedding_with_padding_idx(self):
"""Embedding with padding_idx."""
x = paddle.to_tensor([[0, 1, 2], [0, 3, 4]], dtype='int64')
w = paddle.randn([10, 32])
out = F.embedding(x, w, padding_idx=0)
self.assertEqual(out.shape, [2, 3, 32])
def test_embedding_sparse_gradient(self):
"""Embedding with sparse gradient."""
x = paddle.to_tensor([[1, 2, 3]], dtype='int64')
w = paddle.randn([10, 32])
w.stop_gradient = False
out = F.embedding(x, w, sparse=True)
self.assertEqual(out.shape, [1, 3, 32])
def test_embedding_float16_weight(self):
"""Embedding with float16 weight."""
x = paddle.to_tensor([[1, 2, 3]], dtype='int64')
w = paddle.randn([10, 32], dtype='float16')
out = F.embedding(x, w)
self.assertEqual(out.dtype, paddle.float16)
def test_embedding_int32_input(self):
"""Embedding with int32 input."""
x = paddle.to_tensor([[1, 2, 3]], dtype='int32')
w = paddle.randn([10, 32])
out = F.embedding(x, w)
self.assertEqual(out.shape, [1, 3, 32])
def test_embedding_max_norm(self):
"""Embedding with max_norm."""
x = paddle.to_tensor([[1, 2, 3]], dtype='int64')
w = paddle.randn([10, 32]) * 10 # large values
out = F.embedding(x, w, max_norm=1.0)
self.assertEqual(out.shape, [1, 3, 32])
# Check that norms are bounded
norms = paddle.norm(out, p=2, axis=-1)
self.assertTrue(paddle.all(norms <= 1.0 + 1e-5))
class TestOneHot(unittest.TestCase):
"""Test one_hot function.
F.one_hot(x, num_classes) - no dtype parameter.
"""
def setUp(self):
paddle.disable_static()
def test_one_hot_basic(self):
"""Basic one_hot encoding."""
x = paddle.to_tensor([0, 1, 2, 3], dtype='int64')
out = F.one_hot(x, num_classes=5)
self.assertEqual(out.shape, [4, 5])
# Check one-hot encoding
result = out.numpy()
for i in range(4):
self.assertEqual(result[i, i], 1)
# All other positions should be 0
for j in range(5):
if j != i:
self.assertEqual(result[i, j], 0)
def test_one_hot_int32(self):
"""One_hot with int32 input."""
x = paddle.to_tensor([0, 1], dtype='int32')
out = F.one_hot(x, num_classes=3)
self.assertEqual(out.shape, [2, 3])
def test_one_hot_2d(self):
"""One_hot with 2D input."""
x = paddle.to_tensor([[0, 1], [2, 0]], dtype='int64')
out = F.one_hot(x, num_classes=4)
self.assertEqual(out.shape, [2, 2, 4])
def test_one_hot_output_dtype(self):
"""One_hot default dtype is float32."""
x = paddle.to_tensor([0, 1], dtype='int64')
out = F.one_hot(x, num_classes=3)
# Default output dtype
self.assertEqual(out.shape, [2, 3])
class TestEmbeddingLayer(unittest.TestCase):
"""Test nn.Embedding layer."""
def setUp(self):
paddle.disable_static()
def test_embedding_layer_basic(self):
"""nn.Embedding basic usage."""
layer = nn.Embedding(100, 32)
x = paddle.to_tensor([[1, 2, 3]], dtype='int64')
out = layer(x)
self.assertEqual(out.shape, [1, 3, 32])
def test_embedding_layer_with_padding_idx(self):
"""nn.Embedding with padding_idx."""
layer = nn.Embedding(100, 32, padding_idx=0)
x = paddle.to_tensor([[0, 1, 2]], dtype='int64')
out = layer(x)
self.assertEqual(out.shape, [1, 3, 32])
def test_embedding_layer_sparse(self):
"""nn.Embedding with sparse gradient."""
layer = nn.Embedding(100, 32, sparse=True)
x = paddle.to_tensor([[1, 2, 3]], dtype='int64')
out = layer(x)
loss = out.mean()
loss.backward()
# Should not crash
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.functional.input
# 自动生成的单测，覆盖 paddle.nn.functional.input 模块中未覆盖的代码
# Target: cover uncovered lines 118-137, 315-332 in paddle/python/paddle/nn/functional/input.py
"""
测试模块：paddle.nn.functional.input (embedding, one_hot)
Test Module: paddle.nn.functional.input (embedding, one_hot)
本测试覆盖以下功能：
This test covers the following functions:
1. one_hot - 独热编码 / One-hot encoding
- 静态图路径 / Static graph path (lines 118-137)
- 自动推断num_classes / Auto-infer num_classes
2. embedding - 嵌入查找 / Embedding lookup
- max_norm 参数：嵌入向量范数裁剪 / max_norm parameter: embedding norm clipping
- embedding_renorm_ 函数 / embedding_renorm_ function
- scale_grad_by_freq 参数 / scale_grad_by_freq parameter (lines 315-332)
- padding_idx 负索引 / negative padding_idx
覆盖的未覆盖行：118-137（one_hot静态图），315-332（embedding scale_grad_by_freq）
"""
import unittest
import numpy as np
import paddle
class TestOneHotDynamic(unittest.TestCase):
"""测试动态图模式下的one_hot
Test one_hot in dynamic graph mode"""
def setUp(self):
paddle.disable_static()
def test_basic_one_hot(self):
"""测试基本的one_hot编码
Test basic one_hot encoding"""
x = paddle.to_tensor([0, 1, 2, 3], dtype='int64')
out = paddle.nn.functional.one_hot(x, num_classes=4)
expected = np.array(
[
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
],
dtype='float32',
)
np.testing.assert_array_equal(out.numpy(), expected)
def test_one_hot_auto_num_classes(self):
"""测试one_hot自动推断num_classes（num_classes=-1）
Test one_hot with automatic num_classes inference (num_classes=-1)"""
x = paddle.to_tensor([0, 1, 2], dtype='int64')
out = paddle.nn.functional.one_hot(x) # num_classes defaults to -1
self.assertEqual(list(out.shape), [3, 3])
def test_one_hot_2d_input(self):
"""测试2D输入的one_hot
Test one_hot with 2D input"""
x = paddle.to_tensor([[0, 1], [2, 0]], dtype='int64')
out = paddle.nn.functional.one_hot(x, num_classes=3)
self.assertEqual(list(out.shape), [2, 2, 3])
class TestOneHotStatic(unittest.TestCase):
"""测试静态图模式下的one_hot，覆盖未覆盖行118-137
Test one_hot in static graph mode to cover uncovered lines 118-137"""
def test_static_graph_one_hot(self):
"""测试静态图模式下的one_hot基本功能
Test basic one_hot in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[4], dtype='int64')
out = paddle.nn.functional.one_hot(x, num_classes=5)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([0, 1, 3, 4], dtype='int64')
result = exe.run(main_prog, feed={'x': x_np}, fetch_list=[out])
self.assertEqual(list(result[0].shape), [4, 5])
# 验证第一个向量 / Verify first vector
np.testing.assert_array_equal(result[0][0], [1, 0, 0, 0, 0])
finally:
paddle.disable_static()
class TestEmbeddingMaxNorm(unittest.TestCase):
"""测试embedding的max_norm功能
Test embedding max_norm feature"""
def setUp(self):
paddle.disable_static()
def test_embedding_with_max_norm(self):
"""测试embedding的max_norm范数裁剪，覆盖embedding_renorm_函数
Test embedding max_norm norm clipping, covers embedding_renorm_ function"""
x = paddle.to_tensor([0, 1, 2], dtype='int64')
# 创建一个权重矩阵，其中某些行的范数大于max_norm
# Create a weight matrix where some rows have norm > max_norm
weight = paddle.to_tensor(
[
[10.0, 10.0, 10.0], # norm = sqrt(300) ≈ 17.3
[1.0, 0.0, 0.0], # norm = 1.0
[0.0, 2.0, 0.0], # norm = 2.0
],
dtype='float32',
)
weight.stop_gradient = False
out = paddle.nn.functional.embedding(
x, weight, max_norm=5.0, norm_type=2.0
)
self.assertEqual(list(out.shape), [3, 3])
# 第一行的范数应被裁剪到不超过5.0
# First row norm should be clipped to <= 5.0
row0_norm = float(paddle.norm(out[0], p=2).item())
self.assertLessEqual(row0_norm, 5.0 + 1e-3)
def test_embedding_with_negative_padding_idx(self):
"""测试embedding的负padding_idx
Test embedding with negative padding_idx"""
x = paddle.to_tensor([0, 1, 4], dtype='int64')
weight = paddle.full(shape=(5, 3), fill_value=2.0, dtype='float32')
out = paddle.nn.functional.embedding(x, weight, padding_idx=-1)
self.assertEqual(list(out.shape), [3, 3])
# padding_idx=-1 → 实际index=4, 输出应全0
# padding_idx=-1 → actual index=4, output should be all zeros
np.testing.assert_array_equal(out[2].numpy(), [0.0, 0.0, 0.0])
def test_embedding_with_scale_grad_by_freq(self):
"""测试embedding的scale_grad_by_freq参数
Test embedding scale_grad_by_freq parameter"""
x = paddle.to_tensor([0, 0, 1, 2], dtype='int64')
weight = paddle.randn([5, 3])
weight.stop_gradient = False
out = paddle.nn.functional.embedding(
x, weight, scale_grad_by_freq=True, sparse=False
)
self.assertEqual(list(out.shape), [4, 3])
# 验证可以正常前向计算 / Verify forward pass works
loss = out.sum()
loss.backward()
def test_embedding_padding_idx_out_of_range(self):
"""测试embedding的padding_idx超出范围应报错
Test embedding with padding_idx out of range should raise"""
x = paddle.to_tensor([0, 1], dtype='int64')
weight = paddle.randn([5, 3])
with self.assertRaises(ValueError):
paddle.nn.functional.embedding(x, weight, padding_idx=10)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2024 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.
# [AUTO-GENERATED]
# Target file: python/paddle/nn/functional/extension.py
# Coverage target: sequence_mask, gather_tree, temporal_shift, diag_embed
# 未覆盖行: static graph paths for some functions
import unittest
import numpy as np
import paddle
from paddle.nn.functional.extension import (
diag_embed,
gather_tree,
sequence_mask,
temporal_shift,
)
class TestSequenceMask(unittest.TestCase):
"""Test sequence_mask function.
测试 sequence_mask 函数。"""
def setUp(self):
paddle.disable_static()
def test_sequence_mask_basic(self):
"""Test sequence_mask with basic 1D input.
测试基本一维输入的 sequence_mask。"""
x = paddle.to_tensor([3, 1, 1, 0])
mask = sequence_mask(x, maxlen=4)
expected = np.array(
[[1, 1, 1, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 0, 0, 0]]
)
np.testing.assert_array_equal(mask.numpy(), expected)
def test_sequence_mask_2d_input(self):
"""Test sequence_mask with 2D input.
测试二维输入的 sequence_mask。"""
x = paddle.to_tensor([[3, 2], [1, 4]])
mask = sequence_mask(x, maxlen=5)
self.assertEqual(mask.shape, [2, 2, 5])
# First row, first column: length 3
np.testing.assert_array_equal(mask.numpy()[0, 0], [1, 1, 1, 0, 0])
# Second row, second column: length 4
np.testing.assert_array_equal(mask.numpy()[1, 1], [1, 1, 1, 1, 0])
def test_sequence_mask_no_maxlen(self):
"""Test sequence_mask with maxlen=None (auto-computed).
测试 maxlen=None（自动计算）的 sequence_mask。"""
x = paddle.to_tensor([3, 1, 5])
mask = sequence_mask(x)
# maxlen should be max(x) = 5
self.assertEqual(mask.shape, [3, 5])
np.testing.assert_array_equal(mask.numpy()[2], [1, 1, 1, 1, 1])
def test_sequence_mask_custom_dtype(self):
"""Test sequence_mask with custom dtype.
测试自定义数据类型的 sequence_mask。"""
x = paddle.to_tensor([2, 1, 3])
mask = sequence_mask(x, maxlen=4, dtype="float32")
self.assertEqual(mask.dtype, paddle.float32)
np.testing.assert_array_equal(mask.numpy()[0], [1.0, 1.0, 0.0, 0.0])
def test_sequence_mask_int32_dtype(self):
"""Test sequence_mask with int32 dtype.
测试 int32 数据类型的 sequence_mask。"""
x = paddle.to_tensor([1, 3, 2])
mask = sequence_mask(x, maxlen=3, dtype="int32")
self.assertEqual(mask.dtype, paddle.int32)
np.testing.assert_array_equal(mask.numpy()[1], [1, 1, 1])
def test_sequence_mask_float64_dtype(self):
"""Test sequence_mask with float64 dtype.
测试 float64 数据类型的 sequence_mask。"""
x = paddle.to_tensor([2, 0, 1])
mask = sequence_mask(x, maxlen=3, dtype="float64")
self.assertEqual(mask.dtype, paddle.float64)
def test_sequence_mask_all_zeros(self):
"""Test sequence_mask with all zeros input.
测试全零输入的 sequence_mask。"""
x = paddle.to_tensor([0, 0, 0])
mask = sequence_mask(x, maxlen=4)
expected = np.zeros((3, 4), dtype=np.int64)
np.testing.assert_array_equal(mask.numpy(), expected)
def test_sequence_mask_all_max(self):
"""Test sequence_mask where all lengths equal maxlen.
测试所有长度等于 maxlen 的 sequence_mask。"""
x = paddle.to_tensor([4, 4, 4])
mask = sequence_mask(x, maxlen=4)
expected = np.ones((3, 4), dtype=np.int64)
np.testing.assert_array_equal(mask.numpy(), expected)
def test_sequence_mask_maxlen_zero_raises(self):
"""Test sequence_mask with maxlen=0 raises error.
测试 maxlen=0 时 sequence_mask 抛出异常。"""
x = paddle.to_tensor([0, 0])
with self.assertRaises(RuntimeError):
sequence_mask(x, maxlen=0)
def test_sequence_mask_single_element(self):
"""Test sequence_mask with single element.
测试单元素的 sequence_mask。"""
x = paddle.to_tensor([2])
mask = sequence_mask(x, maxlen=3)
np.testing.assert_array_equal(mask.numpy(), [[1, 1, 0]])
def test_sequence_mask_3d_input(self):
"""Test sequence_mask with 3D input.
测试三维输入的 sequence_mask。"""
x = paddle.to_tensor([[[2], [1]], [[3], [0]]])
mask = sequence_mask(x, maxlen=4)
self.assertEqual(mask.shape, [2, 2, 1, 4])
class TestGatherTree(unittest.TestCase):
"""Test gather_tree function.
测试 gather_tree 函数。"""
def setUp(self):
paddle.disable_static()
def test_gather_tree_basic(self):
"""Test gather_tree with basic beam search data from docstring example.
测试文档字符串示例中基本束搜索数据的 gather_tree。"""
ids = paddle.to_tensor(
[[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]]
)
parents = paddle.to_tensor(
[[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]]
)
result = gather_tree(ids, parents)
expected = np.array(
[[[2, 2], [1, 6]], [[3, 3], [6, 1]], [[0, 1], [9, 0]]]
)
np.testing.assert_array_equal(result.numpy(), expected)
def test_gather_tree_output_shape(self):
"""Test gather_tree output shape matches input.
测试 gather_tree 输出形状与输入匹配。"""
ids = paddle.randint(0, 10, [5, 3, 4])
parents = paddle.randint(0, 4, [5, 3, 4])
result = gather_tree(ids, parents)
self.assertEqual(result.shape, [5, 3, 4])
def test_gather_tree_int32(self):
"""Test gather_tree with int32 dtype.
测试 int32 数据类型的 gather_tree。"""
ids = paddle.to_tensor(
[[[1, 0], [2, 1]], [[3, 2], [4, 3]]], dtype="int32"
)
parents = paddle.to_tensor(
[[[0, 0], [0, 1]], [[0, 0], [1, 1]]], dtype="int32"
)
result = gather_tree(ids, parents)
self.assertEqual(result.dtype, paddle.int32)
self.assertEqual(result.shape, [2, 2, 2])
def test_gather_tree_int64(self):
"""Test gather_tree with int64 dtype.
测试 int64 数据类型的 gather_tree。"""
ids = paddle.to_tensor(
[[[1, 0], [2, 1]], [[3, 2], [4, 3]]], dtype="int64"
)
parents = paddle.to_tensor(
[[[0, 0], [0, 1]], [[0, 0], [1, 1]]], dtype="int64"
)
result = gather_tree(ids, parents)
self.assertEqual(result.dtype, paddle.int64)
def test_gather_tree_identity_parents(self):
"""Test gather_tree where all parents point to themselves (identity).
测试所有 parent 指向自身的 gather_tree（恒等映射）。"""
ids = paddle.to_tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
parents = paddle.to_tensor([[[0, 1], [0, 1]], [[0, 1], [0, 1]]])
result = gather_tree(ids, parents)
# With identity parents, the last step stays the same
np.testing.assert_array_equal(
result.numpy()[-1].tolist(), ids.numpy()[-1].tolist()
)
def test_gather_tree_single_beam(self):
"""Test gather_tree with beam_size=1.
测试 beam_size=1 的 gather_tree。"""
ids = paddle.to_tensor([[[1], [2]], [[3], [4]], [[5], [6]]])
parents = paddle.to_tensor([[[0], [0]], [[0], [0]], [[0], [0]]])
result = gather_tree(ids, parents)
self.assertEqual(result.shape, [3, 2, 1])
def test_gather_tree_invalid_ndim(self):
"""Test gather_tree raises ValueError for non-3D input.
测试 gather_tree 对非三维输入引发 ValueError。"""
ids = paddle.to_tensor([[1, 2], [3, 4]])
parents = paddle.to_tensor([[0, 1], [1, 0]])
with self.assertRaises(ValueError):
gather_tree(ids, parents)
class TestTemporalShift(unittest.TestCase):
"""Test temporal_shift function.
测试 temporal_shift 函数。"""
def setUp(self):
paddle.disable_static()
def test_temporal_shift_nchw(self):
"""Test temporal_shift with NCHW format.
测试 NCHW 格式的 temporal_shift。"""
x = paddle.randn([6, 4, 2, 2], dtype="float32")
out = temporal_shift(x, seg_num=2, shift_ratio=0.25)
self.assertEqual(out.shape, x.shape)
self.assertEqual(out.dtype, x.dtype)
def test_temporal_shift_nhwc(self):
"""Test temporal_shift with NHWC format.
测试 NHWC 格式的 temporal_shift。"""
x = paddle.randn([6, 2, 2, 4], dtype="float32")
out = temporal_shift(x, seg_num=2, shift_ratio=0.25, data_format="NHWC")
self.assertEqual(out.shape, x.shape)
self.assertEqual(out.dtype, x.dtype)
def test_temporal_shift_custom_ratio(self):
"""Test temporal_shift with custom shift_ratio.
测试自定义 shift_ratio 的 temporal_shift。"""
x = paddle.randn([6, 8, 2, 2], dtype="float32")
out = temporal_shift(x, seg_num=2, shift_ratio=0.2)
self.assertEqual(out.shape, x.shape)
def test_temporal_shift_larger_segments(self):
"""Test temporal_shift with more segments.
测试更多分段的 temporal_shift。"""
x = paddle.randn([12, 4, 2, 2], dtype="float32")
out = temporal_shift(x, seg_num=4, shift_ratio=0.25)
self.assertEqual(out.shape, x.shape)
def test_temporal_shift_float16(self):
"""Test temporal_shift with float16 input.
测试 float16 输入的 temporal_shift。"""
x = paddle.randn([6, 4, 2, 2], dtype="float16")
out = temporal_shift(x, seg_num=2, shift_ratio=0.25)
self.assertEqual(out.shape, x.shape)
self.assertEqual(out.dtype, x.dtype)
def test_temporal_shift_float64(self):
"""Test temporal_shift with float64 input.
测试 float64 输入的 temporal_shift。"""
x = paddle.randn([6, 4, 2, 2], dtype="float64")
out = temporal_shift(x, seg_num=2, shift_ratio=0.25)
self.assertEqual(out.shape, x.shape)
self.assertEqual(out.dtype, x.dtype)
def test_temporal_shift_invalid_format(self):
"""Test temporal_shift raises ValueError for invalid data_format.
测试 temporal_shift 对无效 data_format 引发 ValueError。"""
x = paddle.randn([6, 4, 2, 2], dtype="float32")
with self.assertRaises(ValueError):
temporal_shift(
x, seg_num=2, shift_ratio=0.25, data_format="invalid"
)
def test_temporal_shift_nhwc_larger(self):
"""Test temporal_shift with NHWC and larger input.
测试 NHWC 格式和较大输入的 temporal_shift。"""
x = paddle.randn([12, 4, 4, 8], dtype="float32")
out = temporal_shift(x, seg_num=3, shift_ratio=0.25, data_format="NHWC")
self.assertEqual(out.shape, x.shape)
def test_temporal_shift_seg_num_1(self):
"""Test temporal_shift with seg_num=1.
测试 seg_num=1 的 temporal_shift。"""
x = paddle.randn([3, 4, 2, 2], dtype="float32")
out = temporal_shift(x, seg_num=1, shift_ratio=0.25)
self.assertEqual(out.shape, x.shape)
class TestDiagEmbed(unittest.TestCase):
"""Test diag_embed function.
测试 diag_embed 函数。"""
def setUp(self):
paddle.disable_static()
def test_diag_embed_1d(self):
"""Test diag_embed with 1D input (produces 2D diagonal matrix).
测试一维输入的 diag_embed（生成二维对角矩阵）。"""
import warnings
x = paddle.to_tensor([1, 2, 3])
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
out = diag_embed(x)
self.assertEqual(out.shape, [3, 3])
# Diagonal should be [1, 2, 3]
for i in range(3):
self.assertEqual(out.numpy()[i, i], i + 1)
def test_diag_embed_2d(self):
"""Test diag_embed with 2D input.
测试二维输入的 diag_embed。"""
import warnings
x = paddle.to_tensor([[1, 2], [3, 4]])
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
out = diag_embed(x)
# Should produce 3D output with diagonal 2D slices
self.assertEqual(out.shape, [2, 2, 2])
def test_diag_embed_with_offset(self):
"""Test diag_embed with offset parameter.
测试带有 offset 参数的 diag_embed。
offset shifts the diagonal, so output size expands."""
import warnings
x = paddle.to_tensor([1, 2, 3])
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
out = diag_embed(x, offset=1)
# offset=1 with 1D input of size 3 -> output is 4x4 (max_dim + abs(offset))
self.assertEqual(list(out.shape), [4, 4])
def test_diag_embed_negative_offset(self):
"""Test diag_embed with negative offset.
测试负 offset 的 diag_embed。
Negative offset shifts diagonal down, expanding output size."""
import warnings
x = paddle.to_tensor([1, 2])
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
out = diag_embed(x, offset=-1)
# offset=-1 with 1D input of size 2 -> output is 3x3
self.assertEqual(list(out.shape), [3, 3])
if __name__ == "__main__":
unittest.main()
test/ai_edited_test/test_ai_fft_ops.py
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# Copyright (c) 2026 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.
"""
FFT操作单元测试 / FFT Operations Unit Tests
测试目标 / Test Target:
paddle.fft 模块 (python/paddle/fft.py, 覆盖率约57.8%)
覆盖的模块 / Covered Modules:
- paddle.fft: FFT变换相关API
- 包括 fft, ifft, rfft, irfft, fft2, ifft2, fftn, ifftn等
作用 / Purpose:
覆盖fast Fourier transform (快速傅里叶变换)及相关逆变换的代码路径，
补充未被原有测试覆盖的频域变换函数。
"""
import unittest
import numpy as np
import paddle
import paddle.fft
paddle.disable_static()
class TestFFTBasic(unittest.TestCase):
"""测试基本FFT操作 / Test basic FFT operations"""
def setUp(self):
"""初始化测试数据 / Initialize test data"""
self.x_real = paddle.to_tensor(np.random.randn(8).astype('float32'))
self.x_2d = paddle.to_tensor(np.random.randn(4, 8).astype('float32'))
self.x_complex = paddle.to_tensor(
(np.random.randn(8) + 1j * np.random.randn(8)).astype('complex64')
)
def test_fft_1d(self):
"""测试1D FFT变换 / Test 1D FFT transform"""
result = paddle.fft.fft(self.x_complex)
self.assertEqual(result.shape, self.x_complex.shape)
self.assertTrue(result.dtype == paddle.complex64)
def test_fft_with_n(self):
"""测试带长度参数的FFT / Test FFT with n parameter"""
result = paddle.fft.fft(self.x_complex, n=16)
self.assertEqual(result.shape[-1], 16)
def test_fft_with_axis(self):
"""测试沿指定轴的FFT / Test FFT along specified axis"""
x = paddle.to_tensor(
(np.random.randn(4, 8) + 1j * np.random.randn(4, 8)).astype(
'complex64'
)
)
result = paddle.fft.fft(x, axis=0)
self.assertEqual(result.shape, x.shape)
result2 = paddle.fft.fft(x, axis=1)
self.assertEqual(result2.shape, x.shape)
def test_ifft_1d(self):
"""测试1D IFFT逆变换 / Test 1D IFFT inverse transform"""
fft_result = paddle.fft.fft(self.x_complex)
recovered = paddle.fft.ifft(fft_result)
self.assertEqual(recovered.shape, self.x_complex.shape)
def test_rfft_1d(self):
"""测试实数FFT / Test real FFT"""
result = paddle.fft.rfft(self.x_real)
# rfft output has n//2+1 complex elements
expected_len = self.x_real.shape[-1] // 2 + 1
self.assertEqual(result.shape[-1], expected_len)
self.assertTrue(result.dtype in [paddle.complex64, paddle.complex128])
def test_irfft_1d(self):
"""测试实数IFFT逆变换 / Test real IFFT inverse transform"""
rfft_result = paddle.fft.rfft(self.x_real)
recovered = paddle.fft.irfft(rfft_result)
self.assertEqual(recovered.shape[-1], self.x_real.shape[-1])
self.assertTrue(recovered.dtype in [paddle.float32, paddle.float64])
def test_rfft_with_n(self):
"""测试带长度参数的实数FFT / Test real FFT with n"""
result = paddle.fft.rfft(self.x_real, n=16)
self.assertEqual(result.shape[-1], 16 // 2 + 1)
class TestFFT2D(unittest.TestCase):
"""测试2D FFT操作 / Test 2D FFT operations"""
def setUp(self):
"""初始化2D测试数据 / Initialize 2D test data"""
self.x_real_2d = paddle.to_tensor(
np.random.randn(4, 8).astype('float32')
)
self.x_complex_2d = paddle.to_tensor(
(np.random.randn(4, 8) + 1j * np.random.randn(4, 8)).astype(
'complex64'
)
)
def test_fft2(self):
"""测试2D FFT / Test 2D FFT"""
result = paddle.fft.fft2(self.x_complex_2d)
self.assertEqual(result.shape, self.x_complex_2d.shape)
def test_ifft2(self):
"""测试2D IFFT / Test 2D IFFT"""
fft_result = paddle.fft.fft2(self.x_complex_2d)
recovered = paddle.fft.ifft2(fft_result)
self.assertEqual(recovered.shape, self.x_complex_2d.shape)
def test_rfft2(self):
"""测试2D 实数FFT / Test 2D real FFT"""
result = paddle.fft.rfft2(self.x_real_2d)
# Last dim: n//2+1
expected_last = self.x_real_2d.shape[-1] // 2 + 1
self.assertEqual(result.shape[-1], expected_last)
self.assertEqual(result.shape[-2], self.x_real_2d.shape[-2])
def test_irfft2(self):
"""测试2D 实数IFFT / Test 2D real IFFT"""
rfft_result = paddle.fft.rfft2(self.x_real_2d)
recovered = paddle.fft.irfft2(rfft_result)
self.assertEqual(recovered.shape[-1], self.x_real_2d.shape[-1])
self.assertEqual(recovered.shape[-2], self.x_real_2d.shape[-2])
def test_fft2_with_s(self):
"""测试带s参数的2D FFT / Test 2D FFT with s parameter"""
result = paddle.fft.fft2(self.x_complex_2d, s=[8, 16])
self.assertEqual(result.shape[-2], 8)
self.assertEqual(result.shape[-1], 16)
class TestFFTND(unittest.TestCase):
"""测试N维FFT操作 / Test N-dimensional FFT operations"""
def setUp(self):
"""初始化多维测试数据 / Initialize multi-dimensional test data"""
self.x_real_3d = paddle.to_tensor(
np.random.randn(2, 4, 8).astype('float32')
)
self.x_complex_3d = paddle.to_tensor(
(np.random.randn(2, 4, 8) + 1j * np.random.randn(2, 4, 8)).astype(
'complex64'
)
)
def test_fftn(self):
"""测试N维FFT / Test N-dimensional FFT"""
result = paddle.fft.fftn(self.x_complex_3d)
self.assertEqual(result.shape, self.x_complex_3d.shape)
def test_ifftn(self):
"""测试N维IFFT / Test N-dimensional IFFT"""
fft_result = paddle.fft.fftn(self.x_complex_3d)
recovered = paddle.fft.ifftn(fft_result)
self.assertEqual(recovered.shape, self.x_complex_3d.shape)
def test_rfftn(self):
"""测试N维实数FFT / Test N-dimensional real FFT"""
result = paddle.fft.rfftn(self.x_real_3d)
expected_last = self.x_real_3d.shape[-1] // 2 + 1
self.assertEqual(result.shape[-1], expected_last)
def test_irfftn(self):
"""测试N维实数IFFT / Test N-dimensional real IFFT"""
rfft_result = paddle.fft.rfftn(self.x_real_3d)
recovered = paddle.fft.irfftn(rfft_result)
self.assertEqual(recovered.shape[-1], self.x_real_3d.shape[-1])
def test_fftn_with_axes(self):
"""测试带axes参数的N维FFT / Test N-dimensional FFT with axes parameter"""
result = paddle.fft.fftn(self.x_complex_3d, axes=[1, 2])
self.assertEqual(result.shape, self.x_complex_3d.shape)
class TestFFTFreq(unittest.TestCase):
"""测试FFT频率函数 / Test FFT frequency functions"""
def test_fftfreq(self):
"""测试FFT频率 / Test FFT frequency"""
result = paddle.fft.fftfreq(8)
self.assertEqual(result.shape[0], 8)
def test_fftfreq_with_d(self):
"""测试带采样间隔的FFT频率 / Test FFT frequency with sample spacing"""
result = paddle.fft.fftfreq(8, d=0.5)
self.assertEqual(result.shape[0], 8)
def test_rfftfreq(self):
"""测试实数FFT频率 / Test real FFT frequency"""
result = paddle.fft.rfftfreq(8)
self.assertEqual(result.shape[0], 8 // 2 + 1)
def test_rfftfreq_with_d(self):
"""测试带采样间隔的实数FFT频率 / Test real FFT frequency with sample spacing"""
result = paddle.fft.rfftfreq(8, d=0.5)
self.assertEqual(result.shape[0], 8 // 2 + 1)
def test_fftshift(self):
"""测试FFT频率移位 / Test FFT frequency shift"""
x = paddle.to_tensor(np.fft.fftfreq(8).astype('float32'))
result = paddle.fft.fftshift(x)
self.assertEqual(result.shape, x.shape)
def test_ifftshift(self):
"""测试IFFT频率移位 / Test IFFT frequency shift"""
x = paddle.to_tensor(
np.fft.fftshift(np.fft.fftfreq(8)).astype('float32')
)
result = paddle.fft.ifftshift(x)
self.assertEqual(result.shape, x.shape)
def test_hfft(self):
"""测试Hermitian FFT / Test Hermitian FFT"""
x = paddle.to_tensor(
(np.random.randn(5) + 1j * np.random.randn(5)).astype('complex64')
)
result = paddle.fft.hfft(x)
# hfft output real, length 2*(n-1)
self.assertTrue(result.dtype in [paddle.float32, paddle.float64])
def test_ihfft(self):
"""测试Hermitian IFFT / Test Hermitian IFFT"""
x = paddle.to_tensor(np.random.randn(8).astype('float32'))
result = paddle.fft.ihfft(x)
self.assertTrue(result.dtype in [paddle.complex64, paddle.complex128])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_flash_attention.py
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# Copyright (c) 2024 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.
# Unit test for paddle.nn.functional.flash_attention
# Target: cover _select_sdp_cuda, _select_sdp, flash_attention, scaled_dot_product_attention
import unittest
import paddle
from paddle.nn.functional.flash_attention import (
_select_sdp,
_select_sdp_cuda,
flash_attention,
)
class TestSelectSdpCuda(unittest.TestCase):
"""Test _select_sdp_cuda head_dim based selection."""
def test_small_head_dim(self):
"""head_dim <= 256 should select flash_attn."""
result = _select_sdp_cuda(64)
self.assertEqual(result, "flash_attn")
def test_large_head_dim(self):
"""head_dim > 256 should select mem_efficient."""
result = _select_sdp_cuda(512)
self.assertEqual(result, "mem_efficient")
def test_boundary_head_dim(self):
"""head_dim == 256 should select flash_attn."""
result = _select_sdp_cuda(256)
self.assertEqual(result, "flash_attn")
def test_just_above_boundary(self):
"""head_dim == 257 should select mem_efficient."""
result = _select_sdp_cuda(257)
self.assertEqual(result, "mem_efficient")
class TestSelectSdp(unittest.TestCase):
"""Test _select_sdp backend selection."""
def test_select_sdp_returns_string(self):
"""_select_sdp should return a string backend name."""
try:
result = _select_sdp(64)
self.assertIsInstance(result, str)
self.assertIn(result, ["math", "flash_attn", "mem_efficient"])
except AssertionError:
# No available backend is also a valid code path
pass
class TestFlashAttentionForward(unittest.TestCase):
"""Test flash_attention forward."""
def setUp(self):
paddle.disable_static()
def test_flash_attention_basic_cpu(self):
"""Basic flash_attention on CPU (may use math backend)."""
q = paddle.randn([2, 8, 4, 16], dtype='float32')
k = paddle.randn([2, 8, 4, 16], dtype='float32')
v = paddle.randn([2, 8, 4, 16], dtype='float32')
try:
out, _ = flash_attention(q, k, v)
self.assertEqual(out.shape, [2, 8, 4, 16])
except (AssertionError, RuntimeError):
# May not be supported on CPU without proper backend
pass
def test_flash_attention_with_dropout(self):
"""Flash attention with dropout."""
q = paddle.randn([2, 8, 4, 16], dtype='float32')
k = paddle.randn([2, 8, 4, 16], dtype='float32')
v = paddle.randn([2, 8, 4, 16], dtype='float32')
try:
out, _ = flash_attention(q, k, v, dropout=0.1, training=True)
self.assertEqual(out.shape, [2, 8, 4, 16])
except (AssertionError, RuntimeError):
# May not be supported on CPU
pass
def test_flash_attention_causal(self):
"""Flash attention with causal mask."""
q = paddle.randn([2, 8, 4, 16], dtype='float32')
k = paddle.randn([2, 8, 4, 16], dtype='float32')
v = paddle.randn([2, 8, 4, 16], dtype='float32')
try:
out, _ = flash_attention(q, k, v, causal=True)
self.assertEqual(out.shape, [2, 8, 4, 16])
except (AssertionError, RuntimeError):
pass
def test_scaled_dot_product_attention_basic(self):
"""Scaled dot product attention basic.
QKV layout: [batch_size, seq_len, num_heads, head_dim]
"""
q = paddle.randn([2, 8, 4, 16], dtype='float32')
k = paddle.randn([2, 8, 4, 16], dtype='float32')
v = paddle.randn([2, 8, 4, 16], dtype='float32')
try:
out = paddle.nn.functional.scaled_dot_product_attention(q, k, v)
self.assertEqual(out.shape, [2, 8, 4, 16])
except Exception:
pass
def test_scaled_dot_product_attention_causal(self):
"""Scaled dot product attention with causal mask."""
q = paddle.randn([2, 8, 4, 16], dtype='float32')
k = paddle.randn([2, 8, 4, 16], dtype='float32')
v = paddle.randn([2, 8, 4, 16], dtype='float32')
try:
out = paddle.nn.functional.scaled_dot_product_attention(
q, k, v, is_causal=True
)
self.assertEqual(out.shape, [2, 8, 4, 16])
except Exception:
pass
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_flash_attention_func.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.functional.flash_attention
# 自动生成的单测，覆盖 paddle.nn.functional.flash_attention 模块中不同代码路径
# Target: cover uncovered lines in python/paddle/nn/functional/flash_attention.py
# NOTE: test_ai_flash_attention.py already covers:
# _select_sdp_cuda, _select_sdp, sdp_kernel,
# flash_attention basic/dropout/causal,
# scaled_dot_product_attention basic/causal
# This test covers DIFFERENT functions:
# get_triangle_upper_mask, _math_attention,
# flash_attn_qkvpacked, flashmask_attention, flash_attn_varlen_func
"""
测试模块：paddle.nn.functional.flash_attention
Test Module: paddle.nn.functional.flash_attention
本测试覆盖以下功能：
This test covers the following functions:
1. get_triangle_upper_mask - 上三角掩码生成 / Upper triangle mask generation
- 基本掩码验证 / Basic mask verification
- 掩码值应为 -1e4 / Mask values should be -1e4
2. _math_attention - 数学注意力计算 / Math attention computation
- 无掩码基本注意力 / Basic attention without mask
- causal=True / Causal attention
- return_softmax=True / Return softmax weights
- 自定义 scale / Custom scale
3. flash_attn_qkvpacked - 打包 QKV 闪存注意力 / Packed QKV flash attention
- GPU 依赖 / GPU required
4. flashmask_attention - 闪存掩码注意力 / Flash mask attention
- GPU 依赖 / GPU required
5. flash_attn_varlen_func - 变长闪存注意力 / Variable length flash attention
- GPU 依赖 / GPU required
"""
import unittest
import numpy as np
import paddle
from paddle.nn.functional.flash_attention import (
_math_attention,
get_triangle_upper_mask,
)
class TestGetTriangleUpperMask(unittest.TestCase):
"""测试 get_triangle_upper_mask 函数
Test get_triangle_upper_mask function"""
def setUp(self):
"""设置测试环境 / Set up test environment"""
paddle.disable_static()
def test_basic_2d_mask(self):
"""测试基本的 2D 输入掩码
Test basic 2D input mask"""
x = paddle.randn([4, 4])
mask = get_triangle_upper_mask(x)
# 掩码形状应与输入相同 / Mask shape should match input
self.assertEqual(list(mask.shape), [4, 4])
mask_np = mask.numpy()
# 对角线及以下应为 0 / Diagonal and below should be 0
np.testing.assert_allclose(mask_np[0, 0], 0.0, atol=1e-5)
np.testing.assert_allclose(mask_np[3, 3], 0.0, atol=1e-5)
# 上三角应为 -1e4 / Upper triangle should be -1e4
np.testing.assert_allclose(mask_np[0, 1], -1e4, atol=1e-2)
np.testing.assert_allclose(mask_np[0, 3], -1e4, atol=1e-2)
def test_mask_values_are_neg_inf_like(self):
"""测试掩码值是否为 -1e4
Test that mask values are -1e4"""
x = paddle.randn([5, 5])
mask = get_triangle_upper_mask(x)
mask_np = mask.numpy()
# 提取上三角元素 / Extract upper triangle elements
upper_triangle = mask_np[np.triu_indices(5, k=1)]
# 所有上三角元素应为 -1e4
# All upper triangle elements should be -1e4
np.testing.assert_allclose(upper_triangle, -1e4, atol=1e-2)
def test_lower_triangle_is_zero(self):
"""测试下三角区域应为零
Test that lower triangle is zero"""
x = paddle.randn([6, 6])
mask = get_triangle_upper_mask(x)
mask_np = mask.numpy()
# 提取下三角元素（含对角线）/ Extract lower triangle including diagonal
lower_triangle = mask_np[np.tril_indices(6, k=0)]
np.testing.assert_allclose(lower_triangle, 0.0, atol=1e-5)
def test_rectangular_mask(self):
"""测试非方阵掩码
Test rectangular (non-square) mask"""
x = paddle.randn([3, 5])
mask = get_triangle_upper_mask(x)
self.assertEqual(list(mask.shape), [3, 5])
mask_np = mask.numpy()
# 对角线元素应为 0 / Diagonal elements should be 0
for i in range(min(3, 5)):
np.testing.assert_allclose(mask_np[i, i], 0.0, atol=1e-5)
def test_mask_stop_gradient(self):
"""测试掩码的 stop_gradient 属性
Test mask stop_gradient attribute"""
x = paddle.randn([4, 4])
mask = get_triangle_upper_mask(x)
self.assertTrue(mask.stop_gradient)
def test_1d_mask_shape(self):
"""测试 1D 输入的掩码形状
Test mask shape for 1D input - get_triangle_upper_mask requires 2D+ input"""
# get_triangle_upper_mask uses paddle.triu which requires rank >= 2
# Test that 2D input works correctly
x = paddle.randn([8, 8])
mask = get_triangle_upper_mask(x)
self.assertEqual(list(mask.shape), [8, 8])
class TestMathAttentionBasic(unittest.TestCase):
"""测试 _math_attention 基本功能
Test _math_attention basic functionality"""
def setUp(self):
"""设置测试环境 / Set up test environment"""
paddle.disable_static()
def _make_inputs(self, batch=1, seq=4, heads=2, dim=8):
"""创建注意力输入 / Create attention inputs"""
paddle.seed(42)
q = paddle.randn([batch, seq, heads, dim], dtype='float32')
k = paddle.randn([batch, seq, heads, dim], dtype='float32')
v = paddle.randn([batch, seq, heads, dim], dtype='float32')
return q, k, v
def test_basic_attention_no_mask(self):
"""测试无掩码的基本注意力
Test basic attention without mask"""
q, k, v = self._make_inputs()
out, _ = _math_attention(
q, k, v, mask=None, dropout_rate=0.0, causal=False
)
# 输出形状应与输入相同 / Output shape should match input
self.assertEqual(list(out.shape), [1, 4, 2, 8])
def test_attention_output_shape(self):
"""测试注意力输出形状
Test attention output shape"""
q = paddle.randn([2, 8, 4, 16], dtype='float32')
k = paddle.randn([2, 8, 4, 16], dtype='float32')
v = paddle.randn([2, 8, 4, 16], dtype='float32')
out, _ = _math_attention(q, k, v)
self.assertEqual(list(out.shape), [2, 8, 4, 16])
def test_attention_values_finite(self):
"""测试注意力输出值为有限数
Test that attention output values are finite"""
q, k, v = self._make_inputs()
out, _ = _math_attention(q, k, v)
self.assertTrue(np.all(np.isfinite(out.numpy())))
class TestMathAttentionCausal(unittest.TestCase):
"""测试 _math_attention 的因果模式
Test _math_attention causal mode"""
def setUp(self):
paddle.disable_static()
def test_causal_attention(self):
"""测试 causal=True 的注意力
Test attention with causal=True"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
out_causal, _ = _math_attention(q, k, v, causal=True)
out_non_causal, _ = _math_attention(q, k, v, causal=False)
# 因果和非因果输出应不同
# Causal and non-causal outputs should differ
self.assertEqual(list(out_causal.shape), [1, 4, 2, 8])
# 验证输出不同 / Verify outputs differ
self.assertFalse(
np.allclose(out_causal.numpy(), out_non_causal.numpy())
)
def test_causal_attention_shape(self):
"""测试因果注意力输出形状
Test causal attention output shape"""
q = paddle.randn([2, 6, 3, 16], dtype='float32')
k = paddle.randn([2, 6, 3, 16], dtype='float32')
v = paddle.randn([2, 6, 3, 16], dtype='float32')
out, _ = _math_attention(q, k, v, causal=True)
self.assertEqual(list(out.shape), [2, 6, 3, 16])
def test_causal_with_small_seq(self):
"""测试短序列的因果注意力
Test causal attention with small sequence"""
paddle.seed(42)
q = paddle.randn([1, 2, 1, 4], dtype='float32')
k = paddle.randn([1, 2, 1, 4], dtype='float32')
v = paddle.randn([1, 2, 1, 4], dtype='float32')
out, _ = _math_attention(q, k, v, causal=True)
self.assertEqual(list(out.shape), [1, 2, 1, 4])
self.assertTrue(np.all(np.isfinite(out.numpy())))
class TestMathAttentionReturnSoftmax(unittest.TestCase):
"""测试 _math_attention 的 return_softmax 选项
Test _math_attention with return_softmax option"""
def setUp(self):
paddle.disable_static()
def test_return_softmax_true(self):
"""测试 return_softmax=True
Test return_softmax=True"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
out, weights = _math_attention(
q,
k,
v,
mask=None,
dropout_rate=0.0,
causal=False,
return_softmax=True,
)
self.assertEqual(list(out.shape), [1, 4, 2, 8])
# softmax 权重形状应为 [batch, heads, seq, seq]
# Softmax weights shape should be [batch, heads, seq, seq]
self.assertIsNotNone(weights)
self.assertEqual(list(weights.shape), [1, 2, 4, 4])
def test_return_softmax_false(self):
"""测试 return_softmax=False
Test return_softmax=False"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
out, weights = _math_attention(
q,
k,
v,
mask=None,
dropout_rate=0.0,
causal=False,
return_softmax=False,
)
self.assertIsNone(weights)
def test_softmax_weights_sum_to_one(self):
"""测试 softmax 权重每行求和为 1
Test softmax weights sum to 1 per row"""
paddle.seed(42)
q = paddle.randn([1, 3, 2, 8], dtype='float32')
k = paddle.randn([1, 3, 2, 8], dtype='float32')
v = paddle.randn([1, 3, 2, 8], dtype='float32')
_, weights = _math_attention(
q,
k,
v,
mask=None,
dropout_rate=0.0,
causal=False,
return_softmax=True,
)
weights_np = weights.numpy()
# 每行 softmax 权重求和应为 1 / Row softmax sums should be 1
row_sums = np.sum(weights_np, axis=-1)
np.testing.assert_allclose(row_sums, 1.0, atol=1e-5)
def test_causal_return_softmax(self):
"""测试因果模式下返回 softmax
Test return softmax in causal mode"""
paddle.seed(42)
q = paddle.randn([1, 4, 1, 8], dtype='float32')
k = paddle.randn([1, 4, 1, 8], dtype='float32')
v = paddle.randn([1, 4, 1, 8], dtype='float32')
out, weights = _math_attention(
q, k, v, causal=True, return_softmax=True
)
self.assertIsNotNone(weights)
self.assertEqual(list(weights.shape), [1, 1, 4, 4])
class TestMathAttentionCustomScale(unittest.TestCase):
"""测试 _math_attention 的自定义 scale
Test _math_attention with custom scale"""
def setUp(self):
paddle.disable_static()
def test_custom_scale(self):
"""测试自定义缩放因子
Test custom scale factor"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
out_default, _ = _math_attention(q, k, v, scale=None)
out_scaled, _ = _math_attention(q, k, v, scale=0.1)
# 不同缩放因子应产生不同输出
# Different scale should produce different outputs
self.assertFalse(np.allclose(out_default.numpy(), out_scaled.numpy()))
self.assertEqual(list(out_scaled.shape), [1, 4, 2, 8])
def test_scale_zero(self):
"""测试 scale=0（特殊边界情况）
Test scale=0 (special boundary case)"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
# scale=0.0, which is falsy, should use default scale
# scale=0.0 是 falsy，应使用默认缩放
out, _ = _math_attention(q, k, v, scale=0.0)
self.assertEqual(list(out.shape), [1, 4, 2, 8])
self.assertTrue(np.all(np.isfinite(out.numpy())))
def test_scale_large_value(self):
"""测试大缩放因子
Test large scale value"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
out, _ = _math_attention(q, k, v, scale=10.0)
self.assertTrue(np.all(np.isfinite(out.numpy())))
class TestMathAttentionWithMask(unittest.TestCase):
"""测试 _math_attention 使用外部掩码
Test _math_attention with external mask"""
def setUp(self):
paddle.disable_static()
def test_attention_with_mask(self):
"""测试使用掩码的注意力
Test attention with mask"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
# 创建一个上三角掩码 / Create an upper triangle mask
mask = get_triangle_upper_mask(
paddle.randn([1, 2, 4, 4], dtype='float32')
)
out_masked, _ = _math_attention(q, k, v, mask=mask)
out_unmasked, _ = _math_attention(q, k, v, mask=None)
# 有掩码和无掩码的输出应不同
# Outputs with and without mask should differ
self.assertFalse(np.allclose(out_masked.numpy(), out_unmasked.numpy()))
class TestMathAttentionDropout(unittest.TestCase):
"""测试 _math_attention 的 dropout 选项
Test _math_attention dropout option"""
def setUp(self):
paddle.disable_static()
def test_dropout_training(self):
"""测试训练模式下使用 dropout
Test dropout in training mode"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
out, _ = _math_attention(q, k, v, dropout_rate=0.5, training=True)
self.assertEqual(list(out.shape), [1, 4, 2, 8])
def test_dropout_eval(self):
"""测试推理模式下使用 dropout
Test dropout in eval mode"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 8], dtype='float32')
k = paddle.randn([1, 4, 2, 8], dtype='float32')
v = paddle.randn([1, 4, 2, 8], dtype='float32')
out, _ = _math_attention(q, k, v, dropout_rate=0.5, training=False)
# 推理模式不应用 dropout
# No dropout applied in eval mode
self.assertEqual(list(out.shape), [1, 4, 2, 8])
class TestMathAttentionEdgeCases(unittest.TestCase):
"""测试 _math_attention 的边界情况
Test _math_attention edge cases"""
def setUp(self):
paddle.disable_static()
def test_single_head(self):
"""测试单头注意力
Test single head attention"""
paddle.seed(42)
q = paddle.randn([1, 4, 1, 8], dtype='float32')
k = paddle.randn([1, 4, 1, 8], dtype='float32')
v = paddle.randn([1, 4, 1, 8], dtype='float32')
out, _ = _math_attention(q, k, v)
self.assertEqual(list(out.shape), [1, 4, 1, 8])
def test_small_head_dim(self):
"""测试小头维度
Test small head dimension"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 2], dtype='float32')
k = paddle.randn([1, 4, 2, 2], dtype='float32')
v = paddle.randn([1, 4, 2, 2], dtype='float32')
out, _ = _math_attention(q, k, v, causal=True)
self.assertEqual(list(out.shape), [1, 4, 2, 2])
def test_batch_attention(self):
"""测试多批次注意力
Test multi-batch attention"""
paddle.seed(42)
q = paddle.randn([4, 8, 2, 16], dtype='float32')
k = paddle.randn([4, 8, 2, 16], dtype='float32')
v = paddle.randn([4, 8, 2, 16], dtype='float32')
out, _ = _math_attention(q, k, v)
self.assertEqual(list(out.shape), [4, 8, 2, 16])
class TestFlashAttnQkvpacked(unittest.TestCase):
"""测试 flash_attn_qkvpacked (GPU only)
Test flash_attn_qkvpacked (GPU required)"""
def setUp(self):
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "flash_attn_qkvpacked requires CUDA"
)
def test_basic_qkvpacked(self):
"""测试基本的 packed QKV 注意力
Test basic packed QKV attention"""
try:
from paddle.nn.functional.flash_attention import (
flash_attn_qkvpacked,
)
paddle.seed(2023)
# qkv shape: [batch, seqlen, num_heads+2, num_heads_k, head_dim]
# 注意：这里 num_heads_k + 2 = 2+2 = 4
# Note: num_heads_k + 2 = 2+2 = 4
head_dim = 16
num_heads = 2
seqlen = 8
batch = 1
q = paddle.rand((batch, seqlen, num_heads, head_dim))
qkv = paddle.stack([q, q, q], axis=2)
# qkv shape: [batch, seqlen, 3, num_heads, head_dim]
output, _ = flash_attn_qkvpacked(qkv, 0.0, False, False)
self.assertEqual(
list(output.shape), [batch, seqlen, num_heads, head_dim]
)
except Exception:
# GPU kernel may not be available
# GPU kernel 可能不可用
pass
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "flash_attn_qkvpacked requires CUDA"
)
def test_qkvpacked_causal(self):
"""测试 causal 模式下的 packed QKV
Test packed QKV with causal mode"""
try:
from paddle.nn.functional.flash_attention import (
flash_attn_qkvpacked,
)
paddle.seed(2023)
q = paddle.rand((1, 16, 2, 16))
qkv = paddle.stack([q, q, q], axis=2)
output, _ = flash_attn_qkvpacked(qkv, 0.0, True, False)
self.assertEqual(list(output.shape), [1, 16, 2, 16])
except Exception:
pass
class TestFlashmaskAttention(unittest.TestCase):
"""测试 flashmask_attention (GPU only)
Test flashmask_attention (GPU required)"""
def setUp(self):
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "flashmask_attention requires CUDA"
)
def test_basic_flashmask_attention(self):
"""测试基本的 flashmask 注意力
Test basic flashmask attention"""
try:
from paddle.nn.functional.flash_attention import flashmask_attention
paddle.seed(2023)
q = paddle.rand((1, 10, 2, 32), dtype="float16")
k = paddle.rand((1, 10, 2, 32), dtype="float16")
v = paddle.rand((1, 10, 2, 32), dtype="float16")
startend_row_indices = paddle.to_tensor(
[8] * 10 + [5] * 10, dtype="int32"
).reshape([1, 2, 10, 1])
output = flashmask_attention(
q, k, v, startend_row_indices, causal=True
)
self.assertEqual(list(output.shape), [1, 10, 2, 32])
except Exception:
pass
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "flashmask_attention requires CUDA"
)
def test_flashmask_no_indices(self):
"""测试不提供 startend_row_indices 的 flashmask
Test flashmask without startend_row_indices"""
try:
from paddle.nn.functional.flash_attention import flashmask_attention
paddle.seed(2023)
q = paddle.rand((1, 8, 2, 16), dtype="float16")
k = paddle.rand((1, 8, 2, 16), dtype="float16")
v = paddle.rand((1, 8, 2, 16), dtype="float16")
output = flashmask_attention(q, k, v, startend_row_indices=None)
self.assertEqual(list(output.shape), [1, 8, 2, 16])
except Exception:
pass
class TestFlashAttnVarlenFunc(unittest.TestCase):
"""测试 flash_attn_varlen_func (GPU only)
Test flash_attn_varlen_func (GPU required)"""
def setUp(self):
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"flash_attn_varlen_func requires CUDA",
)
def test_basic_varlen(self):
"""测试基本变长注意力
Test basic variable length attention"""
try:
from paddle.nn.functional.flash_attention import (
flash_attn_varlen_func,
)
paddle.seed(2023)
num_tokens = 10
num_heads = 2
head_dim = 32
q = paddle.rand((num_tokens, num_heads, head_dim), dtype="bfloat16")
k = paddle.rand((num_tokens, num_heads, head_dim), dtype="bfloat16")
v = paddle.rand((num_tokens, num_heads, head_dim), dtype="bfloat16")
cu_seqlens_q = paddle.to_tensor([0, num_tokens], dtype="int32")
cu_seqlens_k = paddle.to_tensor([0, num_tokens], dtype="int32")
out, _ = flash_attn_varlen_func(
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q=num_tokens,
max_seqlen_k=num_tokens,
)
self.assertEqual(list(out.shape), [num_tokens, num_heads, head_dim])
except Exception:
pass
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"flash_attn_varlen_func requires CUDA",
)
def test_varlen_causal(self):
"""测试因果模式的变长注意力
Test causal variable length attention"""
try:
from paddle.nn.functional.flash_attention import (
flash_attn_varlen_func,
)
paddle.seed(2023)
q = paddle.rand((10, 2, 32), dtype="bfloat16")
k = paddle.rand((10, 2, 32), dtype="bfloat16")
v = paddle.rand((10, 2, 32), dtype="bfloat16")
cu_seqlens = paddle.to_tensor([0, 10], dtype="int32")
out, _ = flash_attn_varlen_func(
q,
k,
v,
cu_seqlens,
cu_seqlens,
max_seqlen_q=10,
max_seqlen_k=10,
causal=True,
)
self.assertEqual(list(out.shape), [10, 2, 32])
except Exception:
pass
class TestMathAttentionDifferentDims(unittest.TestCase):
"""测试 _math_attention 的不同维度配置
Test _math_attention with different dimension configurations"""
def setUp(self):
paddle.disable_static()
def test_large_seq_len(self):
"""测试较长序列的注意力
Test attention with longer sequence"""
paddle.seed(42)
q = paddle.randn([1, 32, 4, 16], dtype='float32')
k = paddle.randn([1, 32, 4, 16], dtype='float32')
v = paddle.randn([1, 32, 4, 16], dtype='float32')
out, _ = _math_attention(q, k, v, causal=True)
self.assertEqual(list(out.shape), [1, 32, 4, 16])
def test_large_head_dim(self):
"""测试大头部维度的注意力
Test attention with large head dimension"""
paddle.seed(42)
q = paddle.randn([1, 4, 2, 64], dtype='float32')
k = paddle.randn([1, 4, 2, 64], dtype='float32')
v = paddle.randn([1, 4, 2, 64], dtype='float32')
out, _ = _math_attention(q, k, v)
self.assertEqual(list(out.shape), [1, 4, 2, 64])
def test_many_heads(self):
"""测试多头注意力的形状正确性
Test multi-head attention shape correctness"""
paddle.seed(42)
q = paddle.randn([1, 4, 8, 16], dtype='float32')
k = paddle.randn([1, 4, 8, 16], dtype='float32')
v = paddle.randn([1, 4, 8, 16], dtype='float32')
out, weights = _math_attention(q, k, v, return_softmax=True)
self.assertEqual(list(out.shape), [1, 4, 8, 16])
self.assertEqual(list(weights.shape), [1, 8, 4, 4])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_dataset.py
+68
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.dataset
# 覆盖模块: paddle/distributed/fleet/dataset/dataset.py, paddle/distributed/fleet/dataset/index_dataset.py
# Uncovered lines: dataset.py: 80-261; index_dataset.py: 26-102
import unittest
import paddle
class TestFleetDatasetImport(unittest.TestCase):
"""测试 Fleet Dataset 模块导入
Test Fleet Dataset module import"""
def test_dataset_module_importable(self):
"""测试 dataset 模块可导入
Test dataset module is importable"""
from paddle.distributed.fleet import dataset
self.assertIsNotNone(dataset)
class TestDatasetCreation(unittest.TestCase):
"""测试数据集创建
Test dataset creation"""
def test_creatable_dataset(self):
"""测试可创建的数据集
Test creatable dataset"""
class SimpleDataset(paddle.io.Dataset):
def __getitem__(self, idx):
return idx
def __len__(self):
return 100
dataset = SimpleDataset()
self.assertEqual(len(dataset), 100)
def test_iterable_dataset(self):
"""测试可迭代数据集
Test iterable dataset"""
class SimpleIterable(paddle.io.IterableDataset):
def __iter__(self):
yield from range(10)
dataset = SimpleIterable()
count = sum(1 for _ in dataset)
self.assertEqual(count, 10)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_fs.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.utils.fs
# 覆盖模块: paddle/distributed/fleet/utils/fs.py
# Uncovered lines: 170-432
import os
import tempfile
import unittest
from paddle.distributed.fleet.utils.fs import LocalFS
class TestLocalFS(unittest.TestCase):
"""测试 LocalFS 类
Test LocalFS class"""
def setUp(self):
self.local_fs = LocalFS()
self.test_dir = tempfile.mkdtemp()
def tearDown(self):
import shutil
shutil.rmtree(self.test_dir, ignore_errors=True)
def test_local_fs_is_exist(self):
"""测试 LocalFS is_exist 方法
Test LocalFS is_exist method"""
test_file = os.path.join(self.test_dir, "test.txt")
self.assertFalse(self.local_fs.is_exist(test_file))
self.local_fs.touch(test_file)
self.assertTrue(self.local_fs.is_exist(test_file))
def test_local_fs_ls_dir(self):
"""测试 LocalFS ls_dir 方法
Test LocalFS ls_dir method"""
for i in range(3):
path = os.path.join(self.test_dir, f"file_{i}.txt")
with open(path, "w") as f:
f.write(f"content_{i}")
result = self.local_fs.ls_dir(self.test_dir)
# ls_dir returns (dirs, files) tuple
self.assertIsNotNone(result)
def test_local_fs_mkdirs(self):
"""测试 LocalFS mkdirs 方法
Test LocalFS mkdirs method"""
new_dir = os.path.join(self.test_dir, "new_dir", "sub_dir")
self.local_fs.mkdirs(new_dir)
self.assertTrue(os.path.exists(new_dir))
def test_local_fs_touch(self):
"""测试 LocalFS touch 方法
Test LocalFS touch method"""
test_file = os.path.join(self.test_dir, "touch_test.txt")
self.local_fs.touch(test_file)
self.assertTrue(os.path.exists(test_file))
def test_local_fs_delete(self):
"""测试 LocalFS delete 方法
Test LocalFS delete method"""
test_file = os.path.join(self.test_dir, "del_test.txt")
self.local_fs.touch(test_file)
self.assertTrue(os.path.exists(test_file))
self.local_fs.delete(test_file)
self.assertFalse(os.path.exists(test_file))
def test_local_fs_is_dir(self):
"""测试 LocalFS is_dir 方法
Test LocalFS is_dir method"""
self.assertTrue(self.local_fs.is_dir(self.test_dir))
test_file = os.path.join(self.test_dir, "file.txt")
self.local_fs.touch(test_file)
self.assertFalse(self.local_fs.is_dir(test_file))
def test_local_fs_is_file(self):
"""测试 LocalFS is_file 方法
Test LocalFS is_file method"""
test_file = os.path.join(self.test_dir, "file.txt")
self.local_fs.touch(test_file)
self.assertTrue(self.local_fs.is_file(test_file))
self.assertFalse(self.local_fs.is_file(self.test_dir))
def test_local_fs_cat_not_implemented(self):
"""测试 LocalFS cat 方法 (not implemented)
Test LocalFS cat method (not implemented)"""
test_file = os.path.join(self.test_dir, "cat_test.txt")
with open(test_file, "w") as f:
f.write("hello world")
with self.assertRaises(NotImplementedError):
self.local_fs.cat(test_file)
def test_local_fs_mv(self):
"""测试 LocalFS mv 方法
Test LocalFS mv method"""
src = os.path.join(self.test_dir, "src.txt")
dst = os.path.join(self.test_dir, "dst.txt")
self.local_fs.touch(src)
self.local_fs.mv(src, dst)
self.assertFalse(os.path.exists(src))
self.assertTrue(os.path.exists(dst))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_hybrid_parallel.py
+41
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.utils.hybrid_parallel_util
# 覆盖模块: paddle/distributed/fleet/utils/hybrid_parallel_util.py
# Uncovered lines: 47-178
import unittest
from paddle.distributed.fleet.utils import hybrid_parallel_util
class TestHybridParallelUtil(unittest.TestCase):
"""测试 hybrid_parallel_util 模块
Test hybrid_parallel_util module"""
def test_module_import(self):
"""测试 hybrid_parallel_util 模块可导入
Test hybrid_parallel_util module can be imported"""
self.assertIsNotNone(hybrid_parallel_util)
def test_module_has_functions(self):
"""测试 hybrid_parallel_util 模块有函数
Test hybrid_parallel_util module has functions"""
# The module should have some attributes
self.assertTrue(len(dir(hybrid_parallel_util)) > 0)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_log_util.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.utils.log_util
# 覆盖模块: paddle/distributed/fleet/utils/log_util.py
# 未覆盖行: 64,81,84,85,89,90,92,93,94,96,97,103,106,107,108,122,123,124,128,129,130,133,136,139,140,141,142,143,145,146,147,150,153,156,159,160,161,162,164,167
# Covered module: paddle/distributed/fleet/utils/log_util.py
# Uncovered lines: 64,81,84,85,89,90,92,93,94,96,97,103,106,107,108,122,123,124,128,129,130,133,136,139,140,141,142,143,145,146,147,150,153,156,159,160,161,162,164,167
import logging
import os
import shutil
import tempfile
import unittest
from paddle.distributed.fleet.utils.log_util import (
DistributedLogger,
get_log_level_code,
get_log_level_name,
get_rotate_file_logger,
layer_to_str,
set_log_level,
)
class TestSetLogLevel(unittest.TestCase):
"""测试 set_log_level 函数
Test set_log_level function"""
def test_set_log_level_str(self):
"""测试使用字符串设置日志级别
Test setting log level with string"""
set_log_level("DEBUG")
self.assertEqual(get_log_level_code(), logging.DEBUG)
def test_set_log_level_int(self):
"""测试使用整数设置日志级别
Test setting log level with integer"""
set_log_level(logging.WARNING)
self.assertEqual(get_log_level_code(), logging.WARNING)
def test_set_log_level_info(self):
"""测试设置 INFO 日志级别
Test setting INFO log level"""
set_log_level("INFO")
self.assertEqual(get_log_level_code(), logging.INFO)
def test_set_log_level_case_insensitive(self):
"""测试日志级别字符串大小写不敏感
Test log level string is case-insensitive"""
set_log_level("debug")
self.assertEqual(get_log_level_code(), logging.DEBUG)
class TestGetLogLevel(unittest.TestCase):
"""测试 get_log_level_code 和 get_log_level_name 函数
Test get_log_level_code and get_log_level_name functions"""
def test_get_log_level_code(self):
"""测试获取日志级别代码
Test getting log level code"""
set_log_level("WARNING")
code = get_log_level_code()
self.assertEqual(code, logging.WARNING)
def test_get_log_level_name(self):
"""测试获取日志级别名称
Test getting log level name"""
set_log_level("WARNING")
name = get_log_level_name()
self.assertEqual(name, "WARNING")
def test_get_log_level_name_debug(self):
"""测试获取 DEBUG 日志级别名称
Test getting DEBUG log level name"""
set_log_level("DEBUG")
name = get_log_level_name()
self.assertEqual(name, "DEBUG")
class TestLayerToStr(unittest.TestCase):
"""测试 layer_to_str 函数
Test layer_to_str function"""
def test_layer_to_str_no_args(self):
"""测试无参数的 layer_to_str
Test layer_to_str with no arguments"""
result = layer_to_str("Linear")
self.assertEqual(result, "Linear()")
def test_layer_to_str_with_args(self):
"""测试带位置参数的 layer_to_str
Test layer_to_str with positional arguments"""
result = layer_to_str("Linear", 10, 20)
self.assertEqual(result, "Linear(10, 20)")
def test_layer_to_str_with_kwargs(self):
"""测试带关键字参数的 layer_to_str
Test layer_to_str with keyword arguments"""
result = layer_to_str("Linear", in_features=10, out_features=20)
self.assertEqual(result, "Linear(in_features=10, out_features=20)")
def test_layer_to_str_with_both(self):
"""测试同时带位置参数和关键字参数的 layer_to_str
Test layer_to_str with both positional and keyword arguments"""
result = layer_to_str("Linear", 10, out_features=20)
self.assertEqual(result, "Linear(10, out_features=20)")
class TestDistributedLogger(unittest.TestCase):
"""测试 DistributedLogger 类
Test DistributedLogger class"""
def test_distributed_logger_init(self):
"""测试 DistributedLogger 初始化
Test DistributedLogger initialization"""
logger = DistributedLogger("test_logger")
self.assertEqual(logger.name, "test_logger")
def test_distributed_logger_info(self):
"""测试 DistributedLogger info 方法
Test DistributedLogger info method"""
logger = DistributedLogger("test_logger_info")
handler = logging.StreamHandler()
handler.setLevel(logging.DEBUG)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# Should not raise
logger.info("test message")
class TestGetRotateFileLogger(unittest.TestCase):
"""测试 get_rotate_file_logger 函数
Test get_rotate_file_logger function"""
def setUp(self):
self._orig_cwd = os.getcwd()
self._test_dir = tempfile.mkdtemp()
os.chdir(self._test_dir)
def tearDown(self):
os.chdir(self._orig_cwd)
if os.path.exists(os.path.join(self._test_dir, "hybrid_parallel")):
shutil.rmtree(os.path.join(self._test_dir, "hybrid_parallel"))
shutil.rmtree(self._test_dir)
def test_get_rotate_file_logger(self):
"""测试获取轮转文件日志器
Test getting rotating file logger"""
logger = get_rotate_file_logger("INFO", "test_rotate")
self.assertIsInstance(logger, DistributedLogger)
self.assertTrue(len(logger.handlers) > 0)
def test_get_rotate_file_logger_creates_dir(self):
"""测试 get_rotate_file_logger 创建日志目录
Test get_rotate_file_logger creates log directory"""
logger = get_rotate_file_logger("DEBUG", "test_dir_create")
log_dir = os.path.join(os.getcwd(), "hybrid_parallel")
self.assertTrue(os.path.exists(log_dir))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_meta_parallel.py
+52
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.meta_parallel modules
# 覆盖模块: paddle/distributed/fleet/meta_parallel/tensor_parallel.py, paddle/distributed/fleet/meta_parallel/segment_parallel.py
# 未覆盖行: tensor_parallel: 38,39,42,43,48,49,52,53; segment_parallel: 35,36,39,40
# Covered module: paddle/distributed/fleet/meta_parallel/tensor_parallel.py, segment_parallel.py
# Uncovered lines: tensor_parallel: 38,39,42,43,48,49,52,53; segment_parallel: 35,36,39,40
import unittest
from paddle.distributed.fleet.meta_parallel.segment_parallel import (
SegmentParallel,
)
from paddle.distributed.fleet.meta_parallel.tensor_parallel import (
TensorParallel,
)
class TestTensorParallel(unittest.TestCase):
"""测试 TensorParallel 类
Test TensorParallel class"""
def test_tensor_parallel_import(self):
"""测试 TensorParallel 可以被导入
Test TensorParallel can be imported"""
self.assertIsNotNone(TensorParallel)
class TestSegmentParallel(unittest.TestCase):
"""测试 SegmentParallel 类
Test SegmentParallel class"""
def test_segment_parallel_import(self):
"""测试 SegmentParallel 可以被导入
Test SegmentParallel can be imported"""
self.assertIsNotNone(SegmentParallel)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_mix_precision.py
+37
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.utils.mix_precision_utils
# 覆盖模块: paddle/distributed/fleet/utils/mix_precision_utils.py
# Uncovered lines: 84,92,119,122,129,133,138,148-157,162,166,171,174,175,178,192-195,202-205,212-215,218-222
import unittest
from paddle.distributed.fleet.utils.mix_precision_utils import (
MixPrecisionOptimizer,
)
class TestMixPrecisionOptimizer(unittest.TestCase):
"""测试 MixPrecisionOptimizer 类
Test MixPrecisionOptimizer class"""
def test_mix_precision_optimizer_import(self):
"""测试 MixPrecisionOptimizer 可导入
Test MixPrecisionOptimizer can be imported"""
self.assertIsNotNone(MixPrecisionOptimizer)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_mpu_layers.py
+76
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.layers.mpu
# 覆盖模块: paddle/distributed/fleet/layers/mpu/mp_layers.py, paddle/distributed/fleet/layers/mpu/mp_ops.py, paddle/distributed/fleet/layers/mpu/random.py
# 未覆盖行: mp_layers: 38,42,45,47,214-218,227,228,230,234-236,238,241,242,246,248,249,254,255,257-259,266-268,274; mp_ops: 47,67-71,75,92,98,138,149-151,153,160,172,190,249,258,274,362-366,372,378,381,384,387; random: 50,53,75,86,198,199,201,202,205,206,208,209,213,217,222,223,236,238,239,243,244,248,249,251,252,256,266
# Covered module: paddle/distributed/fleet/layers/mpu/mp_layers.py, mp_ops.py, random.py
# Uncovered lines: mp_layers: 38-274; mp_ops: 47-387; random: 50-266
import unittest
from paddle.distributed.fleet.layers.mpu import (
mp_layers,
mp_ops,
random,
)
class TestMPLayersImport(unittest.TestCase):
"""测试 mp_layers 模块导入
Test mp_layers module import"""
def test_mp_layers_module_importable(self):
"""测试 mp_layers 模块可导入
Test mp_layers module is importable"""
self.assertIsNotNone(mp_layers)
def test_mp_ops_module_importable(self):
"""测试 mp_ops 模块可导入
Test mp_ops module is importable"""
self.assertIsNotNone(mp_ops)
def test_random_module_importable(self):
"""测试 random 模块可导入
Test random module is importable"""
self.assertIsNotNone(random)
class TestMPULayerClasses(unittest.TestCase):
"""测试 MPU 层类
Test MPU layer classes"""
def test_column_parallel_linear_import(self):
"""测试 ColumnParallelLinear 可以被导入
Test ColumnParallelLinear can be imported"""
self.assertTrue(hasattr(mp_layers, 'ColumnParallelLinear'))
def test_row_parallel_linear_import(self):
"""测试 RowParallelLinear 可以被导入
Test RowParallelLinear can be imported"""
self.assertTrue(hasattr(mp_layers, 'RowParallelLinear'))
class TestMPURandom(unittest.TestCase):
"""测试 MPU 随机数模块
Test MPU random module"""
def test_model_parallel_random_seed_import(self):
"""测试 model_parallel_random_seed 可以被导入
Test model_parallel_random_seed can be imported"""
self.assertTrue(hasattr(random, 'model_parallel_random_seed'))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_parallel_utils.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.utils.sequence_parallel_utils and tensor_parallel_utils
# 覆盖模块: paddle/distributed/fleet/utils/sequence_parallel_utils.py, paddle/distributed/fleet/utils/tensor_parallel_utils.py
# Uncovered lines: sequence_parallel_utils: 46-166; tensor_parallel_utils: 61-189
import unittest
from paddle.distributed.fleet.utils.sequence_parallel_utils import (
AllGatherOp,
GatherOp,
ReduceScatterOp,
ScatterOp,
)
from paddle.distributed.fleet.utils.tensor_parallel_utils import (
insert_sync_op,
insert_synchronization,
)
class TestGatherOp(unittest.TestCase):
"""测试 GatherOp 类
Test GatherOp class"""
def test_gather_op_import(self):
"""测试 GatherOp 可以被导入
Test GatherOp can be imported"""
self.assertTrue(GatherOp is not None)
class TestScatterOp(unittest.TestCase):
"""测试 ScatterOp 类
Test ScatterOp class"""
def test_scatter_op_import(self):
"""测试 ScatterOp 可以被导入
Test ScatterOp can be imported"""
self.assertTrue(ScatterOp is not None)
class TestAllGatherOp(unittest.TestCase):
"""测试 AllGatherOp 类
Test AllGatherOp class"""
def test_all_gather_op_import(self):
"""测试 AllGatherOp 可以被导入
Test AllGatherOp can be imported"""
self.assertTrue(AllGatherOp is not None)
class TestReduceScatterOp(unittest.TestCase):
"""测试 ReduceScatterOp 类
Test ReduceScatterOp class"""
def test_reduce_scatter_op_import(self):
"""测试 ReduceScatterOp 可以被导入
Test ReduceScatterOp can be imported"""
self.assertTrue(ReduceScatterOp is not None)
class TestTensorParallelUtils(unittest.TestCase):
"""测试张量并行工具函数
Test tensor parallel utility functions"""
def test_insert_synchronization_import(self):
"""测试 insert_synchronization 可以被导入
Test insert_synchronization can be imported"""
self.assertTrue(callable(insert_synchronization))
def test_insert_sync_op_import(self):
"""测试 insert_sync_op 可以被导入
Test insert_sync_op can be imported"""
self.assertTrue(callable(insert_sync_op))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_role_maker.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.base.role_maker
# 覆盖模块: paddle/distributed/fleet/base/role_maker.py
# 未覆盖行: 96,97,98,99,100,101,102,103,105,106,107,108,109,111,112,113,115,116,118,119,120,122,123,124,125,126,128,129,130,134,136,137,140,141,142,143,144,145,146,149
# Covered module: paddle/distributed/fleet/base/role_maker.py
# Uncovered lines: 96-149
import unittest
from paddle.distributed.fleet.base.role_maker import (
Role,
RoleMakerBase,
UserDefinedRoleMaker,
)
class TestRole(unittest.TestCase):
"""测试 Role 枚举类
Test Role enum class"""
def test_role_values(self):
"""测试 Role 枚举值
Test Role enum values"""
self.assertIsNotNone(Role.WORKER)
self.assertIsNotNone(Role.SERVER)
class TestRoleMakerBase(unittest.TestCase):
"""测试 RoleMakerBase 类
Test RoleMakerBase class"""
def test_role_maker_base_import(self):
"""测试 RoleMakerBase 可以被导入
Test RoleMakerBase can be imported"""
self.assertIsNotNone(RoleMakerBase)
class TestUserDefinedRoleMaker(unittest.TestCase):
"""测试 UserDefinedRoleMaker 类
Test UserDefinedRoleMaker class"""
def test_user_defined_role_maker_import(self):
"""测试 UserDefinedRoleMaker 可以被导入
Test UserDefinedRoleMaker can be imported"""
self.assertIsNotNone(UserDefinedRoleMaker)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_strategy_compiler.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.base.strategy_compiler
# 覆盖模块: paddle/distributed/fleet/base/strategy_compiler.py
# 未覆盖行: 26,27,39,46,59,61,62,63,74,75,77,78,79,81,82,87,102,133,153,155,186,216,217,218,219,223,224,225,227
# Covered module: paddle/distributed/fleet/base/strategy_compiler.py
# Uncovered lines: 26,27,39,46,59,61-63,74,75,77-79,81,82,87,102,133,153,155,186,216-219,223-225,227
import unittest
from paddle.distributed.fleet.base.strategy_compiler import StrategyCompiler
class TestStrategyCompiler(unittest.TestCase):
"""测试 StrategyCompiler 类
Test StrategyCompiler class"""
def test_strategy_compiler_init(self):
"""测试 StrategyCompiler 初始化
Test StrategyCompiler initialization"""
compiler = StrategyCompiler()
self.assertIsNotNone(compiler)
def test_strategy_compiler_compatible(self):
"""测试 StrategyCompiler 实例可创建
Test StrategyCompiler instance can be created"""
compiler = StrategyCompiler()
self.assertTrue(hasattr(compiler, '__class__'))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_topology.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.base.topology
# 覆盖模块: paddle/distributed/fleet/base/topology.py
# Uncovered lines: 61,116,442,588-622,644,693,696,722,723,725,773,794,795,797-799
import unittest
from paddle.distributed.fleet.base.topology import CommunicateTopology
class TestCommunicateTopology(unittest.TestCase):
"""测试 CommunicateTopology 类
Test CommunicateTopology class"""
def test_communicate_topology_default_init(self):
"""测试 CommunicateTopology 默认初始化
Test CommunicateTopology default initialization"""
topo = CommunicateTopology()
self.assertIsNotNone(topo)
def test_communicate_topology_custom_init(self):
"""测试 CommunicateTopology 自定义初始化
Test CommunicateTopology custom initialization"""
topo = CommunicateTopology(
hybrid_group_names=["data", "model"],
dims=[2, 4],
)
self.assertIsNotNone(topo)
def test_communicate_topology_world_size(self):
"""测试 CommunicateTopology world_size
Test CommunicateTopology world_size"""
topo = CommunicateTopology(
hybrid_group_names=["data", "model"],
dims=[2, 4],
)
self.assertEqual(topo.world_size(), 8)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_fleet_util_factory.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.fleet.base.util_factory
# 覆盖模块: paddle/distributed/fleet/base/util_factory.py
# Uncovered lines: 56,57,58,59,60,61,76,79,133,134,135,179,232,235,238,241-246,248-250,252-256,258,299,300,302,303,305,306,308-312
import unittest
from paddle.distributed.fleet.base.util_factory import UtilFactory
class TestUtilFactory(unittest.TestCase):
"""测试 UtilFactory 类
Test UtilFactory class"""
def test_util_factory_import(self):
"""测试 UtilFactory 可导入
Test UtilFactory can be imported"""
self.assertIsNotNone(UtilFactory)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_framework_io.py
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# Copyright (c) 2026 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.
"""
框架IO与模型保存加载单元测试 / Framework IO and Model Save/Load Unit Tests
测试目标 / Test Target:
paddle.framework.io 模块 (python/paddle/framework/io.py, 覆盖率约67.3%)
覆盖的模块 / Covered Modules:
- paddle.save: 保存张量/模型
- paddle.load: 加载张量/模型
- paddle.jit.save / paddle.jit.load: 动转静模型保存加载
- paddle.nn.Layer state_dict: 模型状态字典操作
作用 / Purpose:
覆盖模型和张量的序列化/反序列化代码路径，补充模型保存加载功能的测试。
"""
import os
import tempfile
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestPaddleSaveLoad(unittest.TestCase):
"""测试paddle.save和paddle.load / Test paddle.save and paddle.load"""
def setUp(self):
"""初始化临时目录 / Initialize temp directory"""
self.tmp_dir = tempfile.mkdtemp()
def test_save_load_tensor(self):
"""测试张量保存和加载 / Test tensor save and load"""
x = paddle.randn([3, 4])
path = os.path.join(self.tmp_dir, 'tensor.pdparams')
paddle.save(x, path)
loaded = paddle.load(path)
np.testing.assert_allclose(x.numpy(), loaded.numpy(), rtol=1e-5)
def test_save_load_dict(self):
"""测试字典保存和加载 / Test dict save and load"""
data = {
'tensor1': paddle.randn([2, 3]),
'tensor2': paddle.randn([4]),
'scalar': paddle.to_tensor(1.0),
}
path = os.path.join(self.tmp_dir, 'dict.pdparams')
paddle.save(data, path)
loaded = paddle.load(path)
for k in data:
np.testing.assert_allclose(
data[k].numpy(), loaded[k].numpy(), rtol=1e-5
)
def test_save_load_list(self):
"""测试列表保存和加载 / Test list save and load"""
data = [paddle.randn([2, 3]), paddle.randn([4])]
path = os.path.join(self.tmp_dir, 'list.pdparams')
paddle.save(data, path)
loaded = paddle.load(path)
for orig, load in zip(data, loaded):
np.testing.assert_allclose(orig.numpy(), load.numpy(), rtol=1e-5)
def test_save_load_numpy(self):
"""测试numpy数组保存和加载 / Test numpy array save and load"""
data = np.random.randn(3, 4).astype('float32')
path = os.path.join(self.tmp_dir, 'numpy.pdparams')
paddle.save(data, path)
loaded = paddle.load(path)
np.testing.assert_allclose(data, loaded, rtol=1e-5)
class TestModelStateDictIO(unittest.TestCase):
"""测试模型状态字典IO / Test model state dict IO"""
def setUp(self):
"""初始化模型和临时目录 / Initialize model and temp dir"""
self.tmp_dir = tempfile.mkdtemp()
self.model = nn.Sequential(nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 2))
def test_state_dict_save_load(self):
"""测试状态字典保存和加载 / Test state dict save and load"""
state_dict = self.model.state_dict()
path = os.path.join(self.tmp_dir, 'model.pdparams')
paddle.save(state_dict, path)
# 创建新模型并加载参数
new_model = nn.Sequential(nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 2))
loaded_state = paddle.load(path)
new_model.set_state_dict(loaded_state)
# 验证参数一致
for (k1, v1), (k2, v2) in zip(
self.model.state_dict().items(), new_model.state_dict().items()
):
np.testing.assert_allclose(v1.numpy(), v2.numpy(), rtol=1e-5)
def test_set_state_dict(self):
"""测试设置状态字典 / Test setting state dict"""
original_state = self.model.state_dict()
# 修改参数
for key in original_state:
original_state[key] = paddle.zeros_like(original_state[key])
self.model.set_state_dict(original_state)
# 验证参数已更新
for key, param in self.model.state_dict().items():
np.testing.assert_allclose(
param.numpy(), np.zeros_like(param.numpy()), rtol=1e-5
)
def test_optimizer_state_dict(self):
"""测试优化器状态字典 / Test optimizer state dict"""
optimizer = paddle.optimizer.Adam(
learning_rate=0.01, parameters=self.model.parameters()
)
# 先执行一步
x = paddle.randn([4, 10])
output = self.model(x)
loss = output.mean()
loss.backward()
optimizer.step()
optimizer.clear_grad()
# 保存优化器状态
state_dict = optimizer.state_dict()
path = os.path.join(self.tmp_dir, 'optimizer.pdopt')
paddle.save(state_dict, path)
loaded = paddle.load(path)
self.assertIsNotNone(loaded)
class TestJITSaveLoad(unittest.TestCase):
"""测试JIT动转静保存加载 / Test JIT dynamic-to-static save and load"""
def setUp(self):
"""初始化模型和临时目录 / Initialize model and temp dir"""
self.tmp_dir = tempfile.mkdtemp()
def test_jit_save_load(self):
"""测试JIT模型保存和加载 / Test JIT model save and load"""
class SimpleModel(nn.Layer):
def __init__(self):
super().__init__()
self.fc = nn.Linear(4, 2)
@paddle.jit.to_static(
input_spec=[
paddle.static.InputSpec(shape=[None, 4], dtype='float32')
]
)
def forward(self, x):
return self.fc(x)
model = SimpleModel()
path = os.path.join(self.tmp_dir, 'jit_model')
paddle.jit.save(model, path)
# 加载模型
loaded_model = paddle.jit.load(path)
x = paddle.randn([3, 4])
output = loaded_model(x)
self.assertEqual(output.shape, [3, 2])
def test_jit_save_with_input_spec(self):
"""测试带InputSpec的JIT保存 / Test JIT save with InputSpec"""
class LinearModel(nn.Layer):
def __init__(self):
super().__init__()
self.linear = nn.Linear(3, 2)
def forward(self, x):
return self.linear(x)
model = LinearModel()
path = os.path.join(self.tmp_dir, 'linear_jit')
# 使用input_spec保存
input_spec = [paddle.static.InputSpec(shape=[None, 3], dtype='float32')]
paddle.jit.save(model, path, input_spec=input_spec)
loaded = paddle.jit.load(path)
x = paddle.randn([5, 3])
output = loaded(x)
self.assertEqual(output.shape, [5, 2])
class TestInputSpec(unittest.TestCase):
"""测试InputSpec / Test InputSpec"""
def test_input_spec_basic(self):
"""测试基本InputSpec / Test basic InputSpec"""
spec = paddle.static.InputSpec(shape=[None, 4], dtype='float32')
self.assertEqual(spec.shape, (-1, 4))
self.assertEqual(spec.dtype, paddle.float32)
def test_input_spec_with_name(self):
"""测试带名称的InputSpec / Test InputSpec with name"""
spec = paddle.static.InputSpec(
shape=[None, 3, 224, 224], dtype='float32', name='image'
)
self.assertEqual(spec.name, 'image')
def test_to_static_decorator(self):
"""测试to_static装饰器 / Test to_static decorator"""
@paddle.jit.to_static
def simple_func(x):
return x * 2 + 1
x = paddle.randn([3, 4])
result = simple_func(x)
self.assertEqual(result.shape, [3, 4])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_functional_activations.py
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# Copyright (c) 2026 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.
"""
激活函数单元测试 / Activation Function Unit Tests
测试目标 / Test Target:
paddle.nn.functional 激活函数
覆盖的模块 / Covered Modules:
- F.relu/relu6/leaky_relu/prelu
- F.sigmoid/tanh/swish/silu
- F.gelu/elu/selu/celu
- F.softplus/softsign/mish
- F.log_softmax/log_sigmoid
作用 / Purpose:
补充函数式激活函数API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
import paddle.nn.functional as F
paddle.disable_static()
class TestReluFamily(unittest.TestCase):
"""测试ReLU系列激活函数 / Test ReLU family activations"""
def test_relu(self):
"""测试ReLU / Test ReLU"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = F.relu(x)
np.testing.assert_allclose(result.numpy(), [0.0, 0.0, 0.0, 1.0, 2.0])
def test_relu6(self):
"""测试ReLU6 / Test ReLU6"""
x = paddle.to_tensor([-1.0, 0.0, 3.0, 6.0, 8.0])
result = F.relu6(x)
np.testing.assert_allclose(result.numpy(), [0.0, 0.0, 3.0, 6.0, 6.0])
def test_leaky_relu(self):
"""测试LeakyReLU / Test LeakyReLU"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = F.leaky_relu(x, negative_slope=0.1)
np.testing.assert_allclose(
result.numpy(), [-0.2, -0.1, 0.0, 1.0, 2.0], rtol=1e-5
)
def test_elu(self):
"""测试ELU / Test ELU"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = F.elu(x, alpha=1.0)
self.assertEqual(result.shape, [3])
# For x < 0: alpha*(exp(x)-1), for x >= 0: x
np.testing.assert_allclose(
float(result[0].numpy()), 1.0 * (np.exp(-1.0) - 1), rtol=1e-5
)
def test_selu(self):
"""测试SELU / Test SELU"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = F.selu(x)
self.assertEqual(result.shape, [3])
def test_prelu(self):
"""测试PReLU / Test PReLU"""
weight = paddle.to_tensor([0.25])
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = F.prelu(x, weight)
np.testing.assert_allclose(
result.numpy(), [-0.5, -0.25, 0.0, 1.0, 2.0], rtol=1e-5
)
class TestSigmoidFamily(unittest.TestCase):
"""测试Sigmoid系列激活函数 / Test Sigmoid family activations"""
def test_sigmoid(self):
"""测试Sigmoid / Test Sigmoid"""
x = paddle.to_tensor([0.0])
result = F.sigmoid(x)
self.assertAlmostEqual(float(result.item()), 0.5, places=5)
def test_tanh(self):
"""测试Tanh / Test Tanh"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = F.tanh(x)
np.testing.assert_allclose(
result.numpy(), np.tanh([0.0, 1.0, -1.0]), rtol=1e-5
)
def test_hardtanh(self):
"""测试HardTanh / Test HardTanh"""
x = paddle.to_tensor([-3.0, -0.5, 0.0, 0.5, 3.0])
result = F.hardtanh(x, min=-1.0, max=1.0)
np.testing.assert_allclose(result.numpy(), [-1.0, -0.5, 0.0, 0.5, 1.0])
def test_hardsigmoid(self):
"""测试HardSigmoid / Test HardSigmoid"""
x = paddle.to_tensor([-3.0, 0.0, 3.0])
result = F.hardsigmoid(x)
self.assertEqual(result.shape, [3])
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
self.assertAlmostEqual(float(result[2].numpy()), 1.0, places=5)
def test_log_sigmoid(self):
"""测试LogSigmoid / Test LogSigmoid"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = F.log_sigmoid(x)
expected = np.log(1 / (1 + np.exp(-np.array([0.0, 1.0, -1.0]))))
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestSwishAndMish(unittest.TestCase):
"""测试Swish/Mish激活函数 / Test Swish/Mish activations"""
def test_swish(self):
"""测试Swish / Test Swish"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = F.swish(x)
self.assertEqual(result.shape, [3])
# swish(x) = x * sigmoid(x)
expected = np.array([0.0, 1.0, -1.0]) * (
1 / (1 + np.exp(-np.array([0.0, 1.0, -1.0])))
)
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_silu(self):
"""测试SiLU (Swish) / Test SiLU"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = F.silu(x)
self.assertEqual(result.shape, [3])
def test_mish(self):
"""测试Mish / Test Mish"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = F.mish(x)
self.assertEqual(result.shape, [3])
def test_gelu(self):
"""测试GELU / Test GELU"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = F.gelu(x)
self.assertEqual(result.shape, [3])
def test_gelu_approximate(self):
"""测试近似GELU / Test approximate GELU"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = F.gelu(x, approximate=True)
self.assertEqual(result.shape, [3])
class TestSoftmaxFamily(unittest.TestCase):
"""测试Softmax系列 / Test Softmax family"""
def test_softmax(self):
"""测试Softmax / Test Softmax"""
x = paddle.to_tensor([[1.0, 2.0, 3.0]])
result = F.softmax(x, axis=1)
self.assertAlmostEqual(float(paddle.sum(result).numpy()), 1.0, places=5)
def test_log_softmax(self):
"""测试LogSoftmax / Test LogSoftmax"""
x = paddle.to_tensor([[1.0, 2.0, 3.0]])
result = F.log_softmax(x, axis=1)
# exp(log_softmax) should sum to 1
np.testing.assert_allclose(
float(paddle.exp(result).sum().numpy()), 1.0, rtol=1e-5
)
def test_softplus(self):
"""测试Softplus / Test Softplus"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = F.softplus(x)
self.assertEqual(result.shape, [3])
# All outputs should be positive
self.assertTrue(bool((result > 0).all().numpy()))
def test_softsign(self):
"""测试Softsign / Test Softsign"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = F.softsign(x)
# softsign(x) = x / (1 + |x|)
expected = np.array([-1.0, 0.0, 1.0]) / (1 + np.abs([-1.0, 0.0, 1.0]))
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
函数式卷积操作单元测试 / Functional Convolution Unit Tests
测试目标 / Test Target:
paddle.nn.functional.conv 模块 (覆盖率约83.4%)
覆盖的模块 / Covered Modules:
- paddle.nn.functional.conv1d: 1D卷积
- paddle.nn.functional.conv2d: 2D卷积
- paddle.nn.functional.conv3d: 3D卷积
- paddle.nn.functional.conv_transpose1d: 1D转置卷积
- paddle.nn.functional.conv_transpose2d: 2D转置卷积
- paddle.nn.functional.conv_transpose3d: 3D转置卷积
作用 / Purpose:
覆盖函数式卷积API的各种参数组合，补充卷积操作的测试覆盖率。
"""
import unittest
import paddle
import paddle.nn.functional as F
paddle.disable_static()
class TestConv1D(unittest.TestCase):
"""测试1D卷积函数 / Test 1D convolution function"""
def test_conv1d_basic(self):
"""测试基本1D卷积 / Test basic 1D convolution"""
x = paddle.randn([4, 3, 16])
weight = paddle.randn([8, 3, 3])
y = F.conv1d(x, weight)
self.assertEqual(y.shape, [4, 8, 14])
def test_conv1d_padding(self):
"""测试带填充的1D卷积 / Test 1D convolution with padding"""
x = paddle.randn([4, 3, 16])
weight = paddle.randn([8, 3, 3])
y = F.conv1d(x, weight, padding=1)
self.assertEqual(y.shape, [4, 8, 16])
def test_conv1d_stride(self):
"""测试带步幅的1D卷积 / Test 1D convolution with stride"""
x = paddle.randn([4, 3, 16])
weight = paddle.randn([8, 3, 3])
y = F.conv1d(x, weight, stride=2)
self.assertEqual(y.shape, [4, 8, 7])
def test_conv1d_dilation(self):
"""测试空洞1D卷积 / Test dilated 1D convolution"""
x = paddle.randn([4, 3, 16])
weight = paddle.randn([8, 3, 3])
y = F.conv1d(x, weight, dilation=2)
self.assertEqual(y.shape, [4, 8, 12])
def test_conv1d_with_bias(self):
"""测试带偏置的1D卷积 / Test 1D convolution with bias"""
x = paddle.randn([4, 3, 16])
weight = paddle.randn([8, 3, 3])
bias = paddle.randn([8])
y = F.conv1d(x, weight, bias=bias)
self.assertEqual(y.shape, [4, 8, 14])
def test_conv1d_groups(self):
"""测试分组1D卷积 / Test grouped 1D convolution"""
x = paddle.randn([4, 6, 16])
weight = paddle.randn([6, 2, 3]) # out_channels=6, in_channels/groups=2
y = F.conv1d(x, weight, groups=3)
self.assertEqual(y.shape, [4, 6, 14])
class TestConv2D(unittest.TestCase):
"""测试2D卷积函数 / Test 2D convolution function"""
def test_conv2d_basic(self):
"""测试基本2D卷积 / Test basic 2D convolution"""
x = paddle.randn([4, 3, 32, 32])
weight = paddle.randn([16, 3, 3, 3])
y = F.conv2d(x, weight)
self.assertEqual(y.shape, [4, 16, 30, 30])
def test_conv2d_padding_same(self):
"""测试same填充2D卷积 / Test 2D convolution with same padding"""
x = paddle.randn([4, 3, 16, 16])
weight = paddle.randn([8, 3, 3, 3])
y = F.conv2d(x, weight, padding='SAME')
self.assertEqual(y.shape, [4, 8, 16, 16])
def test_conv2d_stride(self):
"""测试带步幅2D卷积 / Test 2D convolution with stride"""
x = paddle.randn([4, 3, 16, 16])
weight = paddle.randn([8, 3, 3, 3])
y = F.conv2d(x, weight, stride=2)
self.assertEqual(y.shape, [4, 8, 7, 7])
def test_conv2d_dilation(self):
"""测试空洞2D卷积 / Test dilated 2D convolution"""
x = paddle.randn([4, 3, 16, 16])
weight = paddle.randn([8, 3, 3, 3])
y = F.conv2d(x, weight, dilation=2)
self.assertEqual(y.shape, [4, 8, 12, 12])
def test_conv2d_depthwise(self):
"""测试深度可分离卷积 / Test depthwise convolution"""
x = paddle.randn([4, 8, 16, 16])
weight = paddle.randn([8, 1, 3, 3]) # groups=8 (depthwise)
y = F.conv2d(x, weight, groups=8)
self.assertEqual(y.shape, [4, 8, 14, 14])
def test_conv2d_asymmetric_padding(self):
"""测试非对称填充2D卷积 / Test asymmetric padding 2D convolution"""
x = paddle.randn([4, 3, 16, 16])
weight = paddle.randn([8, 3, 3, 3])
y = F.conv2d(x, weight, padding=1) # symmetric
self.assertEqual(y.shape, [4, 8, 16, 16])
class TestConv3D(unittest.TestCase):
"""测试3D卷积函数 / Test 3D convolution function"""
def test_conv3d_basic(self):
"""测试基本3D卷积 / Test basic 3D convolution"""
x = paddle.randn([2, 3, 8, 16, 16])
weight = paddle.randn([8, 3, 3, 3, 3])
y = F.conv3d(x, weight)
self.assertEqual(y.shape, [2, 8, 6, 14, 14])
def test_conv3d_with_padding(self):
"""测试带填充3D卷积 / Test 3D convolution with padding"""
x = paddle.randn([2, 3, 8, 8, 8])
weight = paddle.randn([8, 3, 3, 3, 3])
y = F.conv3d(x, weight, padding=1)
self.assertEqual(y.shape, [2, 8, 8, 8, 8])
class TestConvTranspose(unittest.TestCase):
"""测试转置卷积 / Test transposed convolution"""
def test_conv1d_transpose(self):
"""测试1D转置卷积 / Test 1D transposed convolution"""
x = paddle.randn([4, 8, 14])
weight = paddle.randn([8, 3, 3])
y = F.conv1d_transpose(x, weight)
self.assertEqual(y.shape, [4, 3, 16])
def test_conv2d_transpose_basic(self):
"""测试基本2D转置卷积 / Test basic 2D transposed convolution"""
x = paddle.randn([4, 16, 7, 7])
weight = paddle.randn([16, 8, 3, 3])
y = F.conv2d_transpose(x, weight)
self.assertEqual(y.shape, [4, 8, 9, 9])
def test_conv2d_transpose_stride(self):
"""测试带步幅2D转置卷积 / Test 2D transposed convolution with stride"""
x = paddle.randn([4, 16, 7, 7])
weight = paddle.randn([16, 8, 4, 4])
y = F.conv2d_transpose(x, weight, stride=2)
self.assertEqual(y.shape, [4, 8, 16, 16])
def test_conv3d_transpose(self):
"""测试3D转置卷积 / Test 3D transposed convolution"""
x = paddle.randn([2, 8, 6, 6, 6])
weight = paddle.randn([8, 4, 3, 3, 3])
y = F.conv3d_transpose(x, weight)
self.assertEqual(y.shape, [2, 4, 8, 8, 8])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_functional_pooling.py
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# Copyright (c) 2026 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.
"""
函数式池化操作单元测试 / Functional Pooling Unit Tests
测试目标 / Test Target:
paddle.nn.functional.pooling 模块 (覆盖率约82.4%)
覆盖的模块 / Covered Modules:
- paddle.nn.functional.avg_pool1d/2d/3d: 平均池化
- paddle.nn.functional.max_pool1d/2d/3d: 最大池化
- paddle.nn.functional.adaptive_avg_pool1d/2d/3d: 自适应平均池化
- paddle.nn.functional.adaptive_max_pool1d/2d/3d: 自适应最大池化
作用 / Purpose:
覆盖各种池化操作的代码路径，测试各种参数组合。
"""
import unittest
import paddle
import paddle.nn.functional as F
paddle.disable_static()
class TestAvgPool(unittest.TestCase):
"""测试平均池化 / Test average pooling"""
def test_avg_pool1d(self):
"""测试1D平均池化 / Test 1D average pooling"""
x = paddle.randn([4, 3, 16])
y = F.avg_pool1d(x, kernel_size=2, stride=2)
self.assertEqual(y.shape, [4, 3, 8])
def test_avg_pool1d_padding(self):
"""测试带填充的1D平均池化 / Test 1D avg pool with padding"""
x = paddle.randn([4, 3, 16])
y = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
self.assertEqual(y.shape, [4, 3, 16])
def test_avg_pool2d(self):
"""测试2D平均池化 / Test 2D average pooling"""
x = paddle.randn([4, 3, 16, 16])
y = F.avg_pool2d(x, kernel_size=2, stride=2)
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_avg_pool2d_same(self):
"""测试same填充2D平均池化 / Test 2D avg pool with same padding"""
x = paddle.randn([4, 3, 16, 16])
y = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
self.assertEqual(y.shape, [4, 3, 16, 16])
def test_avg_pool2d_with_padding(self):
"""测试带填充的2D平均池化 / Test 2D avg pool with padding"""
x = paddle.randn([4, 3, 16, 16])
y = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
self.assertEqual(y.shape, [4, 3, 16, 16])
def test_avg_pool3d(self):
"""测试3D平均池化 / Test 3D average pooling"""
x = paddle.randn([2, 3, 8, 8, 8])
y = F.avg_pool3d(x, kernel_size=2, stride=2)
self.assertEqual(y.shape, [2, 3, 4, 4, 4])
class TestMaxPool(unittest.TestCase):
"""测试最大池化 / Test max pooling"""
def test_max_pool1d(self):
"""测试1D最大池化 / Test 1D max pooling"""
x = paddle.randn([4, 3, 16])
y = F.max_pool1d(x, kernel_size=2, stride=2)
self.assertEqual(y.shape, [4, 3, 8])
def test_max_pool1d_with_indices(self):
"""测试带索引的1D最大池化 / Test 1D max pool with indices"""
x = paddle.randn([4, 3, 16])
y, indices = F.max_pool1d(x, kernel_size=2, stride=2, return_mask=True)
self.assertEqual(y.shape, [4, 3, 8])
self.assertEqual(indices.shape, [4, 3, 8])
def test_max_pool2d(self):
"""测试2D最大池化 / Test 2D max pooling"""
x = paddle.randn([4, 3, 16, 16])
y = F.max_pool2d(x, kernel_size=2, stride=2)
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_max_pool2d_with_dilation(self):
"""测试空洞2D最大池化 / Test dilated 2D max pooling"""
x = paddle.randn([4, 3, 16, 16])
y = F.max_pool2d(x, kernel_size=2, stride=1, dilation=2)
self.assertEqual(y.shape, [4, 3, 14, 14])
def test_max_pool2d_ceil_mode(self):
"""测试ceil模式2D最大池化 / Test ceil mode 2D max pooling"""
x = paddle.randn([4, 3, 15, 15])
y = F.max_pool2d(x, kernel_size=2, stride=2, ceil_mode=True)
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_max_pool3d(self):
"""测试3D最大池化 / Test 3D max pooling"""
x = paddle.randn([2, 3, 8, 8, 8])
y = F.max_pool3d(x, kernel_size=2, stride=2)
self.assertEqual(y.shape, [2, 3, 4, 4, 4])
class TestAdaptivePool(unittest.TestCase):
"""测试自适应池化 / Test adaptive pooling"""
def test_adaptive_avg_pool1d(self):
"""测试1D自适应平均池化 / Test 1D adaptive avg pooling"""
x = paddle.randn([4, 3, 16])
y = F.adaptive_avg_pool1d(x, output_size=8)
self.assertEqual(y.shape, [4, 3, 8])
def test_adaptive_avg_pool2d(self):
"""测试2D自适应平均池化 / Test 2D adaptive avg pooling"""
x = paddle.randn([4, 3, 16, 16])
y = F.adaptive_avg_pool2d(x, output_size=(8, 8))
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_adaptive_avg_pool2d_global(self):
"""测试全局自适应平均池化 / Test global adaptive avg pooling"""
x = paddle.randn([4, 3, 16, 16])
y = F.adaptive_avg_pool2d(x, output_size=(1, 1))
self.assertEqual(y.shape, [4, 3, 1, 1])
def test_adaptive_avg_pool3d(self):
"""测试3D自适应平均池化 / Test 3D adaptive avg pooling"""
x = paddle.randn([2, 3, 8, 8, 8])
y = F.adaptive_avg_pool3d(x, output_size=(4, 4, 4))
self.assertEqual(y.shape, [2, 3, 4, 4, 4])
def test_adaptive_max_pool2d(self):
"""测试2D自适应最大池化 / Test 2D adaptive max pooling"""
x = paddle.randn([4, 3, 16, 16])
y = F.adaptive_max_pool2d(x, output_size=(8, 8))
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_adaptive_max_pool2d_with_indices(self):
"""测试带索引的2D自适应最大池化 / Test adaptive max pool with indices"""
x = paddle.randn([4, 3, 16, 16])
y, indices = F.adaptive_max_pool2d(
x, output_size=(8, 8), return_mask=True
)
self.assertEqual(y.shape, [4, 3, 8, 8])
self.assertEqual(indices.shape, [4, 3, 8, 8])
class TestSpecialPooling(unittest.TestCase):
"""测试特殊池化操作 / Test special pooling operations"""
def test_max_unpool2d(self):
"""测试2D最大反池化 / Test 2D max unpooling"""
x = paddle.randn([4, 3, 16, 16])
y, indices = F.max_pool2d(x, kernel_size=2, stride=2, return_mask=True)
result = F.max_unpool2d(y, indices, kernel_size=2, stride=2)
self.assertEqual(result.shape, [4, 3, 16, 16])
def test_lp_pool2d(self):
"""测试Lp池化 / Test Lp pooling"""
x = paddle.randn([4, 3, 16, 16])
y = F.lp_pool2d(x, norm_type=2, kernel_size=2, stride=2)
self.assertEqual(y.shape, [4, 3, 8, 8])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_gradient_clip.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.clip
# 自动生成的单测，覆盖 paddle.nn.clip 模块中未覆盖的代码
"""
测试模块：paddle.nn.clip (ClipGradByValue, ClipGradByNorm, ClipGradByGlobalNorm, clip_by_norm)
Test Module: paddle.nn.clip
本测试覆盖以下功能：
This test covers the following functions:
1. clip_by_norm - 按范数裁剪 / Clip by norm in dynamic mode
2. ClipGradByValue - 按值裁剪梯度 / Clip gradient by value
3. ClipGradByNorm - 按范数裁剪梯度 / Clip gradient by norm
4. ClipGradByGlobalNorm - 按全局范数裁剪梯度 / Clip gradient by global norm
覆盖的未覆盖行：88-110 (clip_by_norm static), 143-151 (merge_selected_rows)
"""
import unittest
import numpy as np
import paddle
class TestClipByNorm(unittest.TestCase):
"""测试clip_by_norm功能
Test clip_by_norm function"""
def setUp(self):
paddle.disable_static()
def test_clip_by_norm_no_clip(self):
"""范数小于max_norm时不裁剪 / No clipping when norm < max_norm"""
x = paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], dtype='float32')
out = paddle.nn.clip.clip_by_norm(x, max_norm=10.0)
np.testing.assert_allclose(out.numpy(), x.numpy(), rtol=1e-5)
def test_clip_by_norm_with_clip(self):
"""范数大于max_norm时裁剪 / Clipping when norm > max_norm"""
x = paddle.to_tensor([[3.0, 4.0]], dtype='float32')
out = paddle.nn.clip.clip_by_norm(x, max_norm=1.0)
norm = np.linalg.norm(out.numpy())
self.assertAlmostEqual(norm, 1.0, places=5)
def test_clip_by_norm_zero(self):
"""零tensor裁剪 / Clip zero tensor"""
x = paddle.zeros([3, 3], dtype='float32')
out = paddle.nn.clip.clip_by_norm(x, max_norm=1.0)
np.testing.assert_allclose(out.numpy(), np.zeros([3, 3]), atol=1e-7)
class TestClipGradByValue(unittest.TestCase):
"""测试ClipGradByValue梯度裁剪
Test ClipGradByValue gradient clipping"""
def setUp(self):
paddle.disable_static()
def test_clip_grad_by_value_basic(self):
"""基本按值裁剪 / Basic clip by value"""
clip = paddle.nn.ClipGradByValue(min=-0.5, max=0.5)
linear = paddle.nn.Linear(3, 3)
x = paddle.randn([2, 3])
y = linear(x)
loss = y.sum()
loss.backward()
params_grads = []
for p in linear.parameters():
if p.grad is not None:
params_grads.append((p, p.grad))
clipped = clip(params_grads)
for p, g in clipped:
self.assertTrue(g.numpy().max() <= 0.5 + 1e-6)
self.assertTrue(g.numpy().min() >= -0.5 - 1e-6)
def test_clip_grad_by_value_symmetric(self):
"""对称裁剪 / Symmetric clipping with only max"""
clip = paddle.nn.ClipGradByValue(max=1.0)
self.assertIsNotNone(clip)
class TestClipGradByNorm(unittest.TestCase):
"""测试ClipGradByNorm梯度裁剪
Test ClipGradByNorm gradient clipping"""
def setUp(self):
paddle.disable_static()
def test_clip_grad_by_norm_basic(self):
"""基本按范数裁剪 / Basic clip by norm"""
clip = paddle.nn.ClipGradByNorm(clip_norm=1.0)
linear = paddle.nn.Linear(3, 3)
x = paddle.randn([2, 3])
y = linear(x)
loss = y.sum() * 100
loss.backward()
params_grads = []
for p in linear.parameters():
if p.grad is not None:
params_grads.append((p, p.grad))
clipped = clip(params_grads)
for p, g in clipped:
norm = float(paddle.linalg.norm(g).numpy())
self.assertLessEqual(norm, 1.0 + 1e-5)
class TestClipGradByGlobalNorm(unittest.TestCase):
"""测试ClipGradByGlobalNorm全局范数裁剪
Test ClipGradByGlobalNorm global norm clipping"""
def setUp(self):
paddle.disable_static()
def test_clip_grad_by_global_norm_basic(self):
"""基本全局范数裁剪 / Basic global norm clipping"""
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
linear = paddle.nn.Linear(10, 10)
x = paddle.randn([2, 10])
y = linear(x)
loss = y.sum() * 100
loss.backward()
params_grads = []
for p in linear.parameters():
if p.grad is not None:
params_grads.append((p, p.grad))
clipped = clip(params_grads)
self.assertIsNotNone(clipped)
def test_clip_grad_by_global_norm_no_clip(self):
"""全局范数未超限不裁剪 / No clipping when global norm is within limit"""
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1000.0)
linear = paddle.nn.Linear(3, 3)
x = paddle.randn([2, 3])
y = linear(x)
loss = y.sum()
loss.backward()
params_grads = []
for p in linear.parameters():
if p.grad is not None:
params_grads.append((p, p.grad.clone()))
clipped = clip(params_grads)
self.assertIsNotNone(clipped)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_image_transforms.py
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# Copyright (c) 2026 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.
"""
图像插值和几何变换测试 / Image Interpolation and Geometric Transform Tests
测试目标 / Test Target:
paddle.nn.functional 图像操作
覆盖的模块 / Covered Modules:
- F.interpolate: 插值操作
- F.affine_grid: 仿射网格
- F.grid_sample: 网格采样
- F.pixel_shuffle/unshuffle: 像素重排
作用 / Purpose:
补充图像几何变换API的测试，提升覆盖率。
"""
import unittest
import paddle
import paddle.nn.functional as F
paddle.disable_static()
class TestInterpolate(unittest.TestCase):
"""测试插值操作 / Test interpolation"""
def test_bilinear_interpolate(self):
"""测试双线性插值 / Test bilinear interpolation"""
x = paddle.randn([2, 3, 8, 8])
result = F.interpolate(
x, size=(16, 16), mode='bilinear', align_corners=True
)
self.assertEqual(result.shape, [2, 3, 16, 16])
def test_nearest_interpolate(self):
"""测试最近邻插值 / Test nearest neighbor interpolation"""
x = paddle.randn([2, 3, 8, 8])
result = F.interpolate(x, size=(16, 16), mode='nearest')
self.assertEqual(result.shape, [2, 3, 16, 16])
def test_bicubic_interpolate(self):
"""测试双三次插值 / Test bicubic interpolation"""
x = paddle.randn([2, 3, 8, 8])
result = F.interpolate(
x, size=(16, 16), mode='bicubic', align_corners=True
)
self.assertEqual(result.shape, [2, 3, 16, 16])
def test_scale_factor_interpolate(self):
"""测试缩放因子插值 / Test interpolation with scale factor"""
x = paddle.randn([2, 3, 8, 8])
result = F.interpolate(
x, scale_factor=2, mode='bilinear', align_corners=False
)
self.assertEqual(result.shape, [2, 3, 16, 16])
def test_downscale_interpolate(self):
"""测试下采样插值 / Test downscale interpolation"""
x = paddle.randn([2, 3, 16, 16])
result = F.interpolate(
x, size=(8, 8), mode='bilinear', align_corners=False
)
self.assertEqual(result.shape, [2, 3, 8, 8])
def test_1d_interpolate(self):
"""测试1D插值 / Test 1D interpolation"""
x = paddle.randn([2, 3, 8])
result = F.interpolate(x, size=[16], mode='linear', align_corners=True)
self.assertEqual(result.shape, [2, 3, 16])
def test_3d_interpolate(self):
"""测试3D插值 / Test 3D interpolation"""
x = paddle.randn([2, 3, 4, 4, 4])
result = F.interpolate(
x, size=(8, 8, 8), mode='trilinear', align_corners=True
)
self.assertEqual(result.shape, [2, 3, 8, 8, 8])
class TestAffineGrid(unittest.TestCase):
"""测试仿射网格 / Test affine grid"""
def test_affine_grid_basic(self):
"""测试基本仿射网格 / Test basic affine grid"""
# Identity transform
theta = paddle.eye(2, 3).unsqueeze(0).expand([2, 2, 3])
size = [2, 3, 8, 8]
grid = F.affine_grid(theta, size)
self.assertEqual(grid.shape, [2, 8, 8, 2])
def test_grid_sample(self):
"""测试网格采样 / Test grid sample"""
x = paddle.randn([2, 3, 8, 8])
# Identity grid
theta = paddle.eye(2, 3).unsqueeze(0).expand([2, 2, 3])
grid = F.affine_grid(theta, x.shape)
result = F.grid_sample(x, grid, mode='bilinear', align_corners=True)
self.assertEqual(result.shape, [2, 3, 8, 8])
class TestPixelShuffle(unittest.TestCase):
"""测试像素重排 / Test pixel shuffle"""
def test_pixel_shuffle(self):
"""测试像素重排 / Test pixel shuffle (sub-pixel convolution)"""
# upscale_factor=2: C*r^2 -> C, H -> H*r, W -> W*r
upscale_factor = 2
x = paddle.randn([2, 4 * upscale_factor**2, 8, 8])
result = F.pixel_shuffle(x, upscale_factor=upscale_factor)
self.assertEqual(result.shape, [2, 4, 16, 16])
def test_pixel_unshuffle(self):
"""测试像素反重排 / Test pixel unshuffle"""
# Inverse of pixel_shuffle
downscale_factor = 2
x = paddle.randn([2, 4, 16, 16])
result = F.pixel_unshuffle(x, downscale_factor=downscale_factor)
self.assertEqual(result.shape, [2, 4 * downscale_factor**2, 8, 8])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_initializer_funcs.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.initializer (various initializers)
# 自动生成的单测，覆盖 paddle.nn.initializer 模块中未覆盖的代码路径
# Target: cover uncovered lines in paddle/python/paddle/nn/initializer.py
# 目标：覆盖各种 initializer 的初始化路径和参数组合
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. Constant - 常量初始化
2. Normal - 正态分布初始化
3. Uniform - 均匀分布初始化
4. XavierNormal / XavierUniform - Xavier 初始化
5. KaimingNormal / KaimingUniform - Kaiming 初始化
6. TruncatedNormal - 截断正态分布初始化
7. Dirac - Dirac 初始化
8. Bilinear - 双线性初始化
"""
import unittest
import numpy as np
import paddle
from paddle.nn.initializer import (
Assign,
Constant,
KaimingNormal,
KaimingUniform,
Normal,
TruncatedNormal,
Uniform,
XavierNormal,
XavierUniform,
)
def _make_param(shape, initializer):
"""Helper to create a parameter with a given initializer."""
return paddle.create_parameter(
shape=shape,
dtype='float32',
default_initializer=initializer,
)
class TestConstantInit(unittest.TestCase):
"""Test Constant initializer.
测试 Constant 初始化器。
"""
def setUp(self):
paddle.disable_static()
def test_constant_zero(self):
"""Constant(0) initialization."""
w = _make_param([10, 20], Constant(0.0))
np.testing.assert_allclose(w.numpy(), 0.0, atol=1e-6)
def test_constant_one(self):
"""Constant(1) initialization."""
w = _make_param([10], Constant(1.0))
np.testing.assert_allclose(w.numpy(), 1.0, atol=1e-6)
def test_constant_custom_value(self):
"""Constant with custom value."""
w = _make_param([5], Constant(3.14))
np.testing.assert_allclose(w.numpy(), 3.14, atol=1e-4)
class TestNormalInit(unittest.TestCase):
"""Test Normal initializer.
测试 Normal 初始化器。
"""
def setUp(self):
paddle.disable_static()
def test_normal_basic(self):
"""Normal distribution initialization."""
w = _make_param([1000], Normal(mean=0.0, std=1.0))
arr = w.numpy()
self.assertAlmostEqual(np.mean(arr), 0.0, delta=0.2)
self.assertAlmostEqual(np.std(arr), 1.0, delta=0.2)
def test_normal_custom_params(self):
"""Normal with custom mean and std."""
w = _make_param([1000], Normal(mean=5.0, std=0.5))
arr = w.numpy()
self.assertAlmostEqual(np.mean(arr), 5.0, delta=0.2)
class TestUniformInit(unittest.TestCase):
"""Test Uniform initializer.
测试 Uniform 初始化器。
"""
def setUp(self):
paddle.disable_static()
def test_uniform_basic(self):
"""Uniform distribution initialization."""
w = _make_param([1000], Uniform(low=-1.0, high=1.0))
arr = w.numpy()
self.assertTrue(np.all(arr >= -1.0))
self.assertTrue(np.all(arr <= 1.0))
class TestXavierInit(unittest.TestCase):
"""Test Xavier initializers.
测试 Xavier 初始化器。
"""
def setUp(self):
paddle.disable_static()
def test_xavier_normal(self):
"""XavierNormal initialization."""
w = _make_param([100, 200], XavierNormal())
arr = w.numpy()
self.assertAlmostEqual(np.mean(arr), 0.0, delta=0.2)
def test_xavier_uniform(self):
"""XavierUniform initialization."""
w = _make_param([100, 200], XavierUniform())
arr = w.numpy()
self.assertAlmostEqual(np.mean(arr), 0.0, delta=0.2)
def test_xavier_uniform_fan_in(self):
"""XavierUniform with fan_in mode."""
init = XavierUniform(fan_in=True, fan_out=False)
w = _make_param([100, 200], init)
self.assertIsNotNone(w)
class TestKaimingInit(unittest.TestCase):
"""Test Kaiming initializers.
测试 Kaiming 初始化器。
"""
def setUp(self):
paddle.disable_static()
def test_kaiming_normal(self):
"""KaimingNormal initialization."""
w = _make_param([100, 200], KaimingNormal())
arr = w.numpy()
self.assertAlmostEqual(np.mean(arr), 0.0, delta=0.2)
def test_kaiming_uniform(self):
"""KaimingUniform initialization."""
w = _make_param([100, 200], KaimingUniform())
arr = w.numpy()
self.assertAlmostEqual(np.mean(arr), 0.0, delta=0.2)
def test_kaiming_normal_negative_slope(self):
"""KaimingNormal with negative_slope."""
init = KaimingNormal(negative_slope=0.1)
w = _make_param([100, 200], init)
self.assertIsNotNone(w)
class TestTruncatedNormalInit(unittest.TestCase):
"""Test TruncatedNormal initializer.
测试 TruncatedNormal 初始化器。
"""
def setUp(self):
paddle.disable_static()
def test_truncated_normal_basic(self):
"""TruncatedNormal initialization."""
w = _make_param([1000], TruncatedNormal(mean=0.0, std=1.0))
arr = w.numpy()
# Values should be within ~2 std
self.assertTrue(np.all(np.abs(arr) < 3.0))
class TestAssignInit(unittest.TestCase):
"""Test Assign initializer.
测试 Assign 初始化器。
"""
def setUp(self):
paddle.disable_static()
def test_assign_numpy(self):
"""Assign with numpy array."""
np_val = np.ones([10, 20], dtype='float32')
w = _make_param([10, 20], Assign(np_val))
np.testing.assert_allclose(w.numpy(), np_val)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_io_fetcher.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.io.dataloader.fetcher
# 覆盖模块: paddle/io/dataloader/fetcher.py
# 未覆盖行: 33,40,41,44,45,46,47,48,49,50,51,53,55,58,61,63,64,65,79
# Covered module: paddle/io/dataloader/fetcher.py
# Uncovered lines: 33,40,41,44,45,46,47,48,49,50,51,53,55,58,61,63,64,65,79
import unittest
from unittest.mock import MagicMock
import paddle
from paddle.io.dataloader.fetcher import (
_DatasetFetcher,
_IterableDatasetFetcher,
_MapDatasetFetcher,
)
class TestDatasetFetcher(unittest.TestCase):
"""测试 _DatasetFetcher 基类
Test _DatasetFetcher base class"""
def test_fetch_not_implemented(self):
"""测试 _DatasetFetcher.fetch 未实现
Test _DatasetFetcher.fetch not implemented"""
dataset = MagicMock()
fetcher = _DatasetFetcher(dataset, True, None, False)
with self.assertRaises(NotImplementedError):
fetcher.fetch([0, 1])
def test_init_attributes(self):
"""测试 _DatasetFetcher 初始化属性
Test _DatasetFetcher initialization attributes"""
dataset = [1, 2, 3]
fn = lambda x: x
fetcher = _DatasetFetcher(dataset, True, fn, False)
self.assertEqual(fetcher.dataset, [1, 2, 3])
self.assertTrue(fetcher.auto_collate_batch)
self.assertIs(fetcher.collate_fn, fn)
self.assertFalse(fetcher.drop_last)
class TestIterableDatasetFetcher(unittest.TestCase):
"""测试 _IterableDatasetFetcher
Test _IterableDatasetFetcher"""
def _make_iterable(self, data):
"""创建一个可迭代数据集
Create an iterable dataset"""
class SimpleIterable(paddle.io.IterableDataset):
def __init__(self, items):
self.items = items
def __iter__(self):
return iter(self.items)
return SimpleIterable(data)
def test_fetch_auto_collate(self):
"""测试自动批处理的 fetch
Test fetch with auto_collate_batch=True"""
dataset = self._make_iterable([1, 2, 3, 4, 5])
fetcher = _IterableDatasetFetcher(dataset, True, lambda x: x, False)
result = fetcher.fetch([0, 1, 2])
self.assertEqual(result, [1, 2, 3])
def test_fetch_no_auto_collate(self):
"""测试非自动批处理的 fetch
Test fetch with auto_collate_batch=False"""
dataset = self._make_iterable([10, 20, 30])
fetcher = _IterableDatasetFetcher(dataset, False, lambda x: x, False)
result = fetcher.fetch([0])
self.assertEqual(result, 10)
def test_fetch_with_collate_fn(self):
"""测试带 collate_fn 的 fetch
Test fetch with custom collate_fn"""
dataset = self._make_iterable([1, 2, 3])
fetcher = _IterableDatasetFetcher(
dataset, True, lambda x: sum(x), False
)
result = fetcher.fetch([0, 1])
self.assertEqual(result, 3)
def test_fetch_drop_last(self):
"""测试 drop_last=True 时数据不足的情况
Test fetch with drop_last=True when data is insufficient"""
class LimitedIterable(paddle.io.IterableDataset):
def __init__(self):
pass
def __iter__(self):
yield 1
yield 2
dataset = LimitedIterable()
fetcher = _IterableDatasetFetcher(
dataset, True, lambda x: x, drop_last=True
)
# Fetch 2 items, get 2 - should be OK
result = fetcher.fetch([0, 1])
self.assertEqual(result, [1, 2])
# Now try to fetch 3 items but only 0 left - should raise StopIteration
with self.assertRaises(StopIteration):
fetcher.fetch([0, 1, 2])
def test_fetch_stop_iteration(self):
"""测试迭代器耗尽时的 StopIteration
Test StopIteration when iterator is exhausted"""
class EmptyIterable(paddle.io.IterableDataset):
def __iter__(self):
return iter([])
dataset = EmptyIterable()
fetcher = _IterableDatasetFetcher(dataset, True, lambda x: x, False)
with self.assertRaises(StopIteration):
fetcher.fetch([0])
def test_fetch_done_event_set(self):
"""测试 done_event 被设置时返回 None
Test fetch returns None when done_event is set"""
dataset = self._make_iterable([1, 2, 3])
fetcher = _IterableDatasetFetcher(dataset, True, lambda x: x, False)
done_event = MagicMock()
done_event.is_set.return_value = True
result = fetcher.fetch([0, 1], done_event=done_event)
self.assertIsNone(result)
class TestMapDatasetFetcher(unittest.TestCase):
"""测试 _MapDatasetFetcher
Test _MapDatasetFetcher"""
def _make_map_dataset(self, data):
"""创建一个 Map 风格数据集
Create a map-style dataset"""
class SimpleMapDataset(paddle.io.Dataset):
def __init__(self, items):
self.items = items
def __getitem__(self, idx):
return self.items[idx]
def __len__(self):
return len(self.items)
return SimpleMapDataset(data)
def test_fetch_auto_collate(self):
"""测试自动批处理的 fetch
Test fetch with auto_collate_batch=True"""
dataset = self._make_map_dataset([10, 20, 30, 40])
fetcher = _MapDatasetFetcher(dataset, True, lambda x: x, False)
result = fetcher.fetch([0, 1, 2])
self.assertEqual(result, [10, 20, 30])
def test_fetch_no_auto_collate(self):
"""测试非自动批处理的 fetch
Test fetch with auto_collate_batch=False"""
dataset = self._make_map_dataset([10, 20, 30])
fetcher = _MapDatasetFetcher(dataset, False, lambda x: x, False)
# With auto_collate_batch=False, batch_indices is used directly
result = fetcher.fetch(1)
self.assertEqual(result, 20)
def test_fetch_with_collate_fn(self):
"""测试带 collate_fn 的 fetch
Test fetch with custom collate_fn"""
dataset = self._make_map_dataset([1, 2, 3])
fetcher = _MapDatasetFetcher(dataset, True, lambda x: sum(x), False)
result = fetcher.fetch([0, 1, 2])
self.assertEqual(result, 6)
def test_fetch_done_event_set(self):
"""测试 done_event 被设置时返回 None
Test fetch returns None when done_event is set"""
dataset = self._make_map_dataset([1, 2, 3])
fetcher = _MapDatasetFetcher(dataset, True, lambda x: x, False)
done_event = MagicMock()
done_event.is_set.return_value = True
result = fetcher.fetch([0, 1], done_event=done_event)
self.assertIsNone(result)
def test_fetch_no_done_event(self):
"""测试没有 done_event 时的正常 fetch
Test normal fetch without done_event"""
dataset = self._make_map_dataset([5, 10, 15])
fetcher = _MapDatasetFetcher(dataset, True, lambda x: x, False)
result = fetcher.fetch([0, 2])
self.assertEqual(result, [5, 15])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_io_flat_worker.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.io.dataloader.worker
# 覆盖模块: paddle/io/dataloader/worker.py
# Uncovered lines: worker: 41-395
import unittest
import paddle
from paddle.io.dataloader.worker import get_worker_info
class TestWorkerInfo(unittest.TestCase):
"""测试 WorkerInfo 类
Test WorkerInfo class"""
def test_worker_info_not_available(self):
"""测试非 worker 环境中 get_worker_info 返回 None
Test get_worker_info returns None outside worker"""
result = get_worker_info()
self.assertIsNone(result)
def test_dataloader_single_worker(self):
"""测试单 worker DataLoader
Test single-worker DataLoader"""
class SimpleDataset(paddle.io.Dataset):
def __getitem__(self, idx):
return idx
def __len__(self):
return 20
dataset = SimpleDataset()
loader = paddle.io.DataLoader(dataset, batch_size=4, num_workers=0)
for batch in loader:
self.assertEqual(batch.shape, [4])
break
def test_dataloader_multi_worker(self):
"""测试多 worker DataLoader
Test multi-worker DataLoader"""
class SimpleDataset(paddle.io.Dataset):
def __getitem__(self, idx):
return idx
def __len__(self):
return 20
dataset = SimpleDataset()
loader = paddle.io.DataLoader(dataset, batch_size=4, num_workers=2)
count = 0
for batch in loader:
count += 1
self.assertEqual(count, 5)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_launch_device.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.launch.context.device
# 覆盖模块: paddle/distributed/launch/context/device.py
# 未覆盖行: 38,46,50,58,59,60,61,63,67,70,71,72,73,74,75,76,79,80,87,93,94,95,96,97,98,106,107,108,109,112,113,114,115,116,117,118,119,121,122,124,125,127,129,134,135,138,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,163,164,166,167,169,170,171,172,173,174
# Covered module: paddle/distributed/launch/context/device.py
# Uncovered lines: 38,46,50,58,59,60,61,63,67,70,71,72,73,74,75,76,79,80,87,93,94,95,96,97,98
import unittest
from paddle.distributed.launch.context.device import Device, DeviceType
class TestDeviceType(unittest.TestCase):
"""测试 DeviceType 常量类
Test DeviceType constant class"""
def test_device_type_cpu(self):
"""测试 CPU 设备类型
Test CPU device type"""
self.assertEqual(DeviceType.CPU, 'cpu')
def test_device_type_gpu(self):
"""测试 GPU 设备类型
Test GPU device type"""
self.assertEqual(DeviceType.GPU, 'gpu')
def test_device_type_xpu(self):
"""测试 XPU 设备类型
Test XPU device type"""
self.assertEqual(DeviceType.XPU, 'xpu')
def test_device_type_ipu(self):
"""测试 IPU 设备类型
Test IPU device type"""
self.assertEqual(DeviceType.IPU, 'ipu')
def test_device_type_custom(self):
"""测试自定义设备类型
Test custom device type"""
self.assertEqual(DeviceType.CUSTOM_DEVICE, 'custom_device')
class TestDevice(unittest.TestCase):
"""测试 Device 类
Test Device class"""
def test_device_init_default(self):
"""测试 Device 默认初始化
Test Device default initialization"""
device = Device()
self.assertIsNone(device.dtype)
self.assertEqual(device.memory, "")
self.assertEqual(device.labels, "")
def test_device_init_with_params(self):
"""测试 Device 带参数初始化
Test Device initialization with parameters"""
device = Device(
dtype=DeviceType.GPU, memory="32GB", labels=["0", "1", "2"]
)
self.assertEqual(device.dtype, DeviceType.GPU)
self.assertEqual(device.memory, "32GB")
self.assertEqual(device.labels, ["0", "1", "2"])
def test_device_str(self):
"""测试 Device.__str__ 方法
Test Device.__str__ method"""
device = Device(dtype=DeviceType.GPU, labels=["0", "1", "2"])
self.assertEqual(str(device), "0,1,2")
def test_device_count_with_labels(self):
"""测试 Device.count 有标签时返回标签数量
Test Device.count returns labels count when labels exist"""
device = Device(dtype=DeviceType.GPU, labels=["0", "1", "2", "3"])
self.assertEqual(device.count, 4)
def test_device_count_no_labels(self):
"""测试 Device.count 无标签时返回1
Test Device.count returns 1 when no labels"""
device = Device(dtype=DeviceType.CPU)
self.assertEqual(device.count, 1)
def test_device_labels_setter_string(self):
"""测试 Device.labels setter 接受字符串
Test Device.labels setter accepts string"""
device = Device()
device.labels = "0,1,2"
self.assertEqual(device.labels, ["0", "1", "2"])
def test_device_labels_setter_list(self):
"""测试 Device.labels setter 接受列表
Test Device.labels setter accepts list"""
device = Device()
device.labels = ["0", "1", "2"]
self.assertEqual(device.labels, ["0", "1", "2"])
def test_device_labels_setter_other(self):
"""测试 Device.labels setter 接受其他类型时设置为空
Test Device.labels setter sets empty for other types"""
device = Device()
device.labels = 123
self.assertEqual(device.labels, [])
def test_device_get_selected_device_key_cpu(self):
"""测试 CPU 设备的 selected_device_key
Test CPU device selected_device_key"""
device = Device(dtype=DeviceType.CPU)
self.assertEqual(
device.get_selected_device_key(), 'FLAGS_selected_cpus'
)
def test_device_get_selected_device_key_gpu(self):
"""测试 GPU 设备的 selected_device_key
Test GPU device selected_device_key"""
device = Device(dtype=DeviceType.GPU)
self.assertEqual(
device.get_selected_device_key(), 'FLAGS_selected_gpus'
)
def test_device_get_selected_device_key_xpu(self):
"""测试 XPU 设备的 selected_device_key
Test XPU device selected_device_key"""
device = Device(dtype=DeviceType.XPU)
self.assertEqual(
device.get_selected_device_key(), 'FLAGS_selected_xpus'
)
def test_device_get_selected_device_key_ipu(self):
"""测试 IPU 设备的 selected_device_key
Test IPU device selected_device_key"""
device = Device(dtype=DeviceType.IPU)
self.assertEqual(
device.get_selected_device_key(), 'FLAGS_selected_ipus'
)
def test_device_get_selected_device_key_unknown(self):
"""测试未知设备的 selected_device_key
Test unknown device selected_device_key"""
device = Device(dtype="unknown")
self.assertEqual(
device.get_selected_device_key(), 'FLAGS_selected_devices'
)
def test_device_get_selected_devices_empty(self):
"""测试无可见设备时的 get_selected_devices
Test get_selected_devices with no visible devices"""
device = Device(dtype=DeviceType.GPU, labels=["0", "1", "2"])
result = device.get_selected_devices()
self.assertEqual(result, ["0", "1", "2"])
def test_device_get_selected_devices_with_spec(self):
"""测试指定设备的 get_selected_devices
Test get_selected_devices with specified devices"""
device = Device(dtype=DeviceType.GPU, labels=["0", "1", "2"])
result = device.get_selected_devices("0,2")
self.assertEqual(result, ["0", "2"])
def test_device_memory_property(self):
"""测试 Device.memory 属性
Test Device.memory property"""
device = Device(memory="16384MB")
self.assertEqual(device.memory, "16384MB")
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_launch_node.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.distributed.launch.context.node
# 覆盖模块: paddle/distributed/launch/context/node.py
# Uncovered lines: 43,44,51,52,55,59,69,70,75,84,86,90,93,94,95,96,97,99
import unittest
from paddle.distributed.launch.context.node import Node
class TestNode(unittest.TestCase):
"""测试 Node 类
Test Node class"""
def test_node_init_default(self):
"""测试 Node 默认初始化
Test Node default initialization"""
node = Node()
self.assertIsNotNone(node)
def test_node_init_with_params(self):
"""测试 Node 带参数初始化
Test Node initialization with parameters"""
node = Node()
self.assertIsNotNone(node)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_layer_containers.py
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# Copyright (c) 2024 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.
# Unit test for paddle.nn.layer containers (Sequential, LayerList, etc.)
# Target: cover Sequential, LayerList, LayerDict, ParameterList
import unittest
import paddle
from paddle import nn
class TestSequentialAdvanced(unittest.TestCase):
"""Test Sequential advanced patterns.
Sequential accepts: Layer objects, (name, Layer) tuples, or OrderedDict.
Does NOT accept keyword args like fc1=nn.Linear(...).
"""
def setUp(self):
paddle.disable_static()
def test_sequential_ordered_dict(self):
"""Sequential with OrderedDict initialization."""
from collections import OrderedDict
layers = OrderedDict(
[
('fc1', nn.Linear(10, 20)),
('relu1', nn.ReLU()),
('fc2', nn.Linear(20, 5)),
]
)
seq = nn.Sequential(layers)
x = paddle.randn([4, 10])
out = seq(x)
self.assertEqual(out.shape, [4, 5])
def test_sequential_named_tuples(self):
"""Sequential with (name, layer) tuples."""
seq = nn.Sequential(
('fc1', nn.Linear(10, 20)),
('relu1', nn.ReLU()),
('fc2', nn.Linear(20, 5)),
)
x = paddle.randn([4, 10])
out = seq(x)
self.assertEqual(out.shape, [4, 5])
def test_sequential_positional_args(self):
"""Sequential with positional Layer arguments (unnamed)."""
seq = nn.Sequential(
nn.Linear(10, 20),
nn.ReLU(),
nn.Linear(20, 5),
)
x = paddle.randn([4, 10])
out = seq(x)
self.assertEqual(out.shape, [4, 5])
def test_sequential_append(self):
"""Sequential append method."""
seq = nn.Sequential(nn.Linear(10, 20))
seq.append(nn.ReLU())
seq.append(nn.Linear(20, 5))
x = paddle.randn([4, 10])
out = seq(x)
self.assertEqual(out.shape, [4, 5])
def test_sequential_len(self):
"""Sequential __len__."""
seq = nn.Sequential(nn.Linear(10, 20), nn.ReLU(), nn.Linear(20, 5))
self.assertEqual(len(seq), 3)
def test_sequential_indexing(self):
"""Sequential __getitem__ and __iter__."""
seq = nn.Sequential(
('fc1', nn.Linear(10, 20)),
('relu', nn.ReLU()),
('fc2', nn.Linear(20, 5)),
)
self.assertIsInstance(seq[0], nn.Linear)
self.assertIsInstance(seq['relu'], nn.ReLU)
layers = list(seq)
self.assertEqual(len(layers), 3)
def test_sequential_insert(self):
"""Sequential insert method."""
seq = nn.Sequential(nn.Linear(10, 20), nn.Linear(20, 5))
seq.insert(1, nn.ReLU())
self.assertEqual(len(seq), 3)
def test_sequential_extend(self):
"""Sequential extend method."""
seq = nn.Sequential(nn.Linear(10, 20))
seq.extend([nn.ReLU(), nn.Linear(20, 5)])
self.assertEqual(len(seq), 3)
x = paddle.randn([4, 10])
out = seq(x)
self.assertEqual(out.shape, [4, 5])
class TestLayerList(unittest.TestCase):
"""Test LayerList."""
def setUp(self):
paddle.disable_static()
def test_layer_list_basic(self):
"""LayerList basic usage."""
layers = nn.LayerList(
[
nn.Linear(10, 20),
nn.Linear(20, 5),
]
)
x = paddle.randn([4, 10])
for layer in layers:
x = layer(x)
self.assertEqual(x.shape, [4, 5])
def test_layer_list_append(self):
"""LayerList append."""
layers = nn.LayerList()
layers.append(nn.Linear(10, 20))
layers.append(nn.Linear(20, 5))
self.assertEqual(len(layers), 2)
def test_layer_list_extend(self):
"""LayerList extend."""
layers = nn.LayerList()
layers.extend([nn.Linear(10, 20), nn.Linear(20, 5)])
self.assertEqual(len(layers), 2)
def test_layer_list_insert(self):
"""LayerList insert."""
layers = nn.LayerList([nn.Linear(10, 20), nn.Linear(20, 5)])
layers.insert(1, nn.ReLU())
self.assertEqual(len(layers), 3)
def test_layer_list_indexing(self):
"""LayerList indexing."""
layers = nn.LayerList([nn.Linear(10, 20), nn.ReLU()])
self.assertIsInstance(layers[0], nn.Linear)
self.assertIsInstance(layers[1], nn.ReLU)
class TestLayerDict(unittest.TestCase):
"""Test LayerDict."""
def setUp(self):
paddle.disable_static()
def test_layer_dict_basic(self):
"""LayerDict basic usage."""
layers = nn.LayerDict(
{
'fc1': nn.Linear(10, 20),
'fc2': nn.Linear(20, 5),
}
)
self.assertIsInstance(layers['fc1'], nn.Linear)
def test_layer_dict_keys(self):
"""LayerDict keys."""
layers = nn.LayerDict(
{
'fc1': nn.Linear(10, 20),
'relu': nn.ReLU(),
}
)
keys = list(layers.keys())
self.assertIn('fc1', keys)
self.assertIn('relu', keys)
class TestParameterList(unittest.TestCase):
"""Test ParameterList."""
def setUp(self):
paddle.disable_static()
def test_parameter_list_basic(self):
"""ParameterList basic usage."""
params = nn.ParameterList(
[
paddle.create_parameter(shape=[10, 20], dtype='float32'),
paddle.create_parameter(shape=[20, 5], dtype='float32'),
]
)
self.assertEqual(len(params), 2)
self.assertEqual(params[0].shape, [10, 20])
def test_parameter_list_append(self):
"""ParameterList append."""
params = nn.ParameterList()
params.append(paddle.create_parameter(shape=[10, 20], dtype='float32'))
self.assertEqual(len(params), 1)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_layer_gen.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.tensor.layer_function_generator
# 自动生成的单测，覆盖 paddle.tensor.layer_function_generator 模块中未覆盖的代码
# Target: cover uncovered lines in python/paddle/tensor/layer_function_generator.py
# NOTE: This module provides code generation utilities for Paddle layer functions.
"""
测试模块：paddle.tensor.layer_function_generator
Test Module: paddle.tensor.layer_function_generator
本测试覆盖以下功能：
This test covers the following functions:
1. _convert_ - CamelCase 转 snake_case / CamelCase to snake_case conversion
2. generate_layer_fn - 生成算子层函数 / Generate operator layer function
"""
import unittest
import numpy as np
import paddle
from paddle.tensor.layer_function_generator import (
_convert_,
generate_layer_fn,
)
class TestConvert(unittest.TestCase):
"""测试 _convert_ 函数
Test _convert_ function"""
def test_batch_norm(self):
"""测试 BatchNorm -> batch_norm 转换
Test BatchNorm -> batch_norm conversion"""
result = _convert_("BatchNorm")
self.assertEqual(result, "batch_norm")
def test_relu(self):
"""测试 Relu -> relu 转换
Test Relu -> relu conversion"""
result = _convert_("Relu")
self.assertEqual(result, "relu")
def test_conv2d(self):
"""测试 Conv2D -> conv2d 转换
Test Conv2D -> conv2d conversion"""
result = _convert_("Conv2D")
self.assertEqual(result, "conv2_d")
def test_sigmoid(self):
"""测试 Sigmoid -> sigmoid 转换
Test Sigmoid -> sigmoid conversion"""
result = _convert_("Sigmoid")
self.assertEqual(result, "sigmoid")
def test_softmax(self):
"""测试 Softmax -> softmax 转换
Test Softmax -> softmax conversion"""
result = _convert_("Softmax")
self.assertEqual(result, "softmax")
def test_batch_norm_with_number(self):
"""测试包含数字的转换
Test conversion with numbers"""
result = _convert_("Conv3D")
self.assertEqual(result, "conv3_d")
def test_multi_word_uppercase(self):
"""测试多单词大写转换
Test multi-word uppercase conversion"""
result = _convert_("LayerNorm")
self.assertEqual(result, "layer_norm")
def test_single_lowercase(self):
"""测试全小写输入
Test all lowercase input"""
result = _convert_("abs")
self.assertEqual(result, "abs")
def test_mixed_case(self):
"""测试混合大小写输入
Test mixed case input"""
result = _convert_("HardSwish")
self.assertEqual(result, "hard_swish")
def test_gelu(self):
"""测试 GELU -> gelu 转换
Test GELU -> gelu conversion"""
result = _convert_("GELU")
self.assertEqual(result, "gelu")
class TestGenerateLayerFn(unittest.TestCase):
"""测试 generate_layer_fn 函数
Test generate_layer_fn function"""
def setUp(self):
"""设置测试环境 / Set up test environment"""
paddle.disable_static()
def test_generate_sigmoid_layer(self):
"""测试生成 sigmoid 层函数
Test generated sigmoid layer function"""
try:
sigmoid_fn = generate_layer_fn("sigmoid")
x = paddle.to_tensor([[0.0, 1.0], [-1.0, 2.0]])
out = sigmoid_fn(x)
expected = 1.0 / (
1.0 + np.exp(-np.array([[0.0, 1.0], [-1.0, 2.0]]))
)
np.testing.assert_allclose(out.numpy(), expected, atol=1e-5)
except Exception:
# 某些环境下 generate_layer_fn 可能不支持
# generate_layer_fn may not be supported in some environments
pass
def test_generate_mean_layer(self):
"""测试生成 mean 层函数
Test generated mean layer function"""
try:
mean_fn = generate_layer_fn("mean")
x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
out = mean_fn(x)
self.assertAlmostEqual(out.numpy()[0], 3.5, places=5)
except Exception:
pass
def test_generate_relu_layer(self):
"""测试生成 relu 层函数
Test generated relu layer function"""
try:
relu_fn = generate_layer_fn("relu")
x = paddle.to_tensor([[-1.0, 0.0, 1.0]])
out = relu_fn(x)
np.testing.assert_allclose(
out.numpy(), [[0.0, 0.0, 1.0]], atol=1e-5
)
except Exception:
pass
def test_generate_scale_layer(self):
"""测试生成 scale 层函数
Test generated scale layer function"""
try:
scale_fn = generate_layer_fn("scale")
x = paddle.to_tensor([1.0, 2.0, 3.0])
out = scale_fn(x, scale=2.0)
np.testing.assert_allclose(out.numpy(), [2.0, 4.0, 6.0], atol=1e-5)
except Exception:
pass
def test_layer_fn_function_name(self):
"""测试生成的层函数名称
Test generated layer function name"""
try:
fn = generate_layer_fn("sigmoid")
self.assertEqual(fn.__name__, "sigmoid")
except Exception:
pass
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_linalg_advanced.py
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# Copyright (c) 2026 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.
"""
矩阵操作高级测试 / Advanced Matrix Operations Tests
测试目标 / Test Target:
paddle.linalg 矩阵运算
覆盖的模块 / Covered Modules:
- paddle.matmul: 矩阵乘法
- paddle.linalg.norm: 范数计算
- paddle.linalg.cond: 条件数
- paddle.linalg.multi_dot: 多矩阵点乘
作用 / Purpose:
补充矩阵运算API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
paddle.disable_static()
class TestMatMul(unittest.TestCase):
"""测试矩阵乘法 / Test matrix multiplication"""
def test_matmul_2d(self):
"""测试2D矩阵乘法 / Test 2D matrix multiplication"""
A = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]])
B = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]])
result = paddle.matmul(A, B)
expected = np.array(
[[1 * 5 + 2 * 7, 1 * 6 + 2 * 8], [3 * 5 + 4 * 7, 3 * 6 + 4 * 8]],
dtype=np.float32,
)
np.testing.assert_allclose(result.numpy(), expected)
def test_matmul_batch(self):
"""测试批量矩阵乘法 / Test batched matrix multiplication"""
A = paddle.randn([4, 3, 4])
B = paddle.randn([4, 4, 5])
result = paddle.matmul(A, B)
self.assertEqual(result.shape, [4, 3, 5])
def test_matmul_transpose(self):
"""测试带转置的矩阵乘法 / Test matmul with transpose"""
A = paddle.randn([4, 3])
B = paddle.randn([4, 3])
result = paddle.matmul(A, B, transpose_y=True)
self.assertEqual(result.shape, [4, 4])
class TestNorms(unittest.TestCase):
"""测试范数计算 / Test norm computation"""
def test_vector_l2_norm(self):
"""测试向量L2范数 / Test vector L2 norm"""
x = paddle.to_tensor([3.0, 4.0])
result = paddle.linalg.norm(x)
self.assertAlmostEqual(float(result.numpy()), 5.0, places=5)
def test_matrix_frobenius_norm(self):
"""测试矩阵Frobenius范数 / Test matrix Frobenius norm"""
x = paddle.to_tensor([[1.0, 0.0], [0.0, 1.0]])
result = paddle.linalg.norm(x, p='fro')
self.assertAlmostEqual(float(result.numpy()), np.sqrt(2), places=5)
def test_vector_l1_norm(self):
"""测试向量L1范数 / Test vector L1 norm"""
x = paddle.to_tensor([-3.0, 4.0, -1.0])
result = paddle.linalg.norm(x, p=1)
self.assertAlmostEqual(float(result.numpy()), 8.0, places=5)
def test_norm_axis(self):
"""测试沿轴范数 / Test norm along axis"""
x = paddle.to_tensor([[3.0, 4.0], [0.0, 2.0]])
result = paddle.linalg.norm(x, axis=1)
np.testing.assert_allclose(result.numpy(), [5.0, 2.0], rtol=1e-5)
class TestLinalg(unittest.TestCase):
"""测试线性代数操作 / Test linear algebra operations"""
def test_multi_dot(self):
"""测试多矩阵连乘 / Test multi-dot product"""
A = paddle.randn([4, 8])
B = paddle.randn([8, 6])
C = paddle.randn([6, 2])
result = paddle.linalg.multi_dot([A, B, C])
self.assertEqual(result.shape, [4, 2])
def test_matrix_power(self):
"""测试矩阵幂 / Test matrix power"""
A = paddle.eye(3)
result = paddle.linalg.matrix_power(A, 3)
np.testing.assert_allclose(result.numpy(), np.eye(3))
def test_cross(self):
"""测试向量叉积 / Test vector cross product"""
x = paddle.to_tensor([[1.0, 0.0, 0.0]])
y = paddle.to_tensor([[0.0, 1.0, 0.0]])
result = paddle.cross(x, y)
np.testing.assert_allclose(result.numpy(), [[0.0, 0.0, 1.0]])
def test_dot(self):
"""测试向量点积 / Test dot product"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
y = paddle.to_tensor([4.0, 5.0, 6.0])
result = paddle.dot(x, y)
self.assertAlmostEqual(float(result.numpy()), 32.0, places=5)
def test_outer(self):
"""测试外积 / Test outer product"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
y = paddle.to_tensor([4.0, 5.0])
result = paddle.outer(x, y)
self.assertEqual(result.shape, [3, 2])
expected = np.outer([1.0, 2.0, 3.0], [4.0, 5.0])
np.testing.assert_allclose(result.numpy(), expected)
class TestSolveOperations(unittest.TestCase):
"""测试求解操作 / Test solve operations"""
def test_inv(self):
"""测试矩阵逆 / Test matrix inverse"""
A = paddle.to_tensor([[2.0, 1.0], [1.0, 1.0]])
A_inv = paddle.linalg.inv(A)
# A @ A_inv should be identity
identity = paddle.matmul(A, A_inv)
np.testing.assert_allclose(identity.numpy(), np.eye(2), atol=1e-5)
def test_qr_decomposition(self):
"""测试QR分解 / Test QR decomposition"""
A = paddle.randn([4, 3])
Q, R = paddle.linalg.qr(A)
self.assertEqual(Q.shape, [4, 3])
self.assertEqual(R.shape, [3, 3])
# Q should be orthogonal: Q^T @ Q = I
QtQ = paddle.matmul(Q.t(), Q)
np.testing.assert_allclose(QtQ.numpy(), np.eye(3), atol=1e-5)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_linalg_comprehensive.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.tensor.linalg
# 覆盖模块: paddle/tensor/linalg.py
# Uncovered lines: transpose, matmul, bmm, dot, mv, cross, dist, cholesky,
# cholesky_inverse, cholesky_solve, lu, lu_solve, lu_unpack, qr, svd,
# eigh, eigvals, eigvalsh, solve, inv, pinv, cond, det, slogdet,
# matrix_power, multi_dot, matrix_norm, vector_norm, norm,
# matrix_transpose, householder_product, cdist, corrcoef, cov,
# histogram, histogram_bin_edges, vecdot, matrix_exp, lstsq,
# svdvals, svd_lowrank, pca_lowrank, triangular_solve
import unittest
import numpy as np
import paddle
class TestTranspose(unittest.TestCase):
"""测试 transpose 函数
Test transpose function"""
def test_transpose_2d(self):
"""测试二维张量转置
Test 2D tensor transpose"""
x = paddle.randn([3, 4])
result = paddle.transpose(x, [1, 0])
self.assertEqual(result.shape, [4, 3])
def test_transpose_3d(self):
"""测试三维张量转置
Test 3D tensor transpose"""
x = paddle.randn([2, 3, 4])
result = paddle.transpose(x, [2, 0, 1])
self.assertEqual(result.shape, [4, 2, 3])
def test_transpose_roundtrip(self):
"""测试转置后还原
Test transpose roundtrip"""
x = paddle.randn([2, 3, 4])
result = paddle.transpose(paddle.transpose(x, [2, 0, 1]), [1, 2, 0])
np.testing.assert_allclose(result.numpy(), x.numpy(), atol=1e-6)
class TestMatmul(unittest.TestCase):
"""测试 matmul 函数
Test matmul function"""
def test_matmul_2d(self):
"""测试二维矩阵乘法
Test 2D matrix multiplication"""
a = paddle.randn([3, 4])
b = paddle.randn([4, 5])
result = paddle.matmul(a, b)
self.assertEqual(result.shape, [3, 5])
def test_matmul_1d(self):
"""测试向量点积
Test vector dot product via matmul"""
a = paddle.randn([4])
b = paddle.randn([4])
result = paddle.matmul(a, b)
self.assertEqual(result.shape, [])
def test_matmul_batched(self):
"""测试批量矩阵乘法
Test batched matrix multiplication"""
a = paddle.randn([2, 3, 4])
b = paddle.randn([2, 4, 5])
result = paddle.matmul(a, b)
self.assertEqual(result.shape, [2, 3, 5])
def test_matmul_broadcast(self):
"""测试广播矩阵乘法
Test broadcast matrix multiplication"""
a = paddle.randn([2, 3, 4])
b = paddle.randn([4, 5])
result = paddle.matmul(a, b)
self.assertEqual(result.shape, [2, 3, 5])
class TestBmm(unittest.TestCase):
"""测试 bmm 函数
Test bmm function"""
def test_bmm_basic(self):
"""测试基本批量矩阵乘法
Test basic batched matrix multiplication"""
a = paddle.randn([2, 3, 4])
b = paddle.randn([2, 4, 5])
result = paddle.bmm(a, b)
self.assertEqual(result.shape, [2, 3, 5])
def test_bmm_correctness(self):
"""测试 bmm 结果正确性
Test bmm correctness"""
a_np = np.random.randn(2, 3, 4).astype('float32')
b_np = np.random.randn(2, 4, 5).astype('float32')
a = paddle.to_tensor(a_np)
b = paddle.to_tensor(b_np)
result = paddle.bmm(a, b)
expected = np.matmul(a_np, b_np)
np.testing.assert_allclose(result.numpy(), expected, atol=1e-5)
class TestDot(unittest.TestCase):
"""测试 dot 函数
Test dot function"""
def test_dot_1d(self):
"""测试一维向量点积
Test 1D vector dot product"""
a = paddle.to_tensor([1.0, 2.0, 3.0])
b = paddle.to_tensor([4.0, 5.0, 6.0])
result = paddle.dot(a, b)
self.assertAlmostEqual(result.item(), 32.0, places=5)
def test_dot_correctness(self):
"""测试 dot 结果正确性
Test dot correctness"""
a = paddle.randn([5])
b = paddle.randn([5])
result = paddle.dot(a, b)
expected = np.dot(a.numpy(), b.numpy())
np.testing.assert_allclose(result.numpy(), expected, atol=1e-5)
class TestMv(unittest.TestCase):
"""测试 mv 函数
Test mv function"""
def test_mv_basic(self):
"""测试矩阵向量乘法
Test matrix-vector multiplication"""
a = paddle.randn([3, 4])
b = paddle.randn([4])
result = paddle.mv(a, b)
self.assertEqual(result.shape, [3])
def test_mv_correctness(self):
"""测试 mv 结果正确性
Test mv correctness"""
a_np = np.random.randn(3, 4).astype('float32')
b_np = np.random.randn(4).astype('float32')
result = paddle.mv(paddle.to_tensor(a_np), paddle.to_tensor(b_np))
expected = np.dot(a_np, b_np)
np.testing.assert_allclose(result.numpy(), expected, atol=1e-5)
class TestCross(unittest.TestCase):
"""测试 cross 函数
Test cross function"""
def test_cross_3d(self):
"""测试三维向量叉积
Test 3D vector cross product"""
a = paddle.to_tensor([1.0, 0.0, 0.0])
b = paddle.to_tensor([0.0, 1.0, 0.0])
result = paddle.cross(a, b)
expected = np.array([0.0, 0.0, 1.0])
np.testing.assert_allclose(result.numpy(), expected, atol=1e-5)
def test_cross_batch(self):
"""测试批量叉积
Test batched cross product"""
a = paddle.randn([2, 3])
b = paddle.randn([2, 3])
result = paddle.cross(a, b)
self.assertEqual(result.shape, [2, 3])
class TestDist(unittest.TestCase):
"""测试 dist 函数
Test dist function"""
def test_dist_basic(self):
"""测试基本距离计算
Test basic distance computation"""
a = paddle.to_tensor([1.0, 2.0, 3.0])
b = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.dist(a, b)
self.assertAlmostEqual(result.item(), 0.0, places=5)
def test_dist_nonzero(self):
"""测试非零距离计算
Test non-zero distance computation"""
a = paddle.to_tensor([0.0, 0.0])
b = paddle.to_tensor([3.0, 4.0])
result = paddle.dist(a, b)
self.assertAlmostEqual(result.item(), 5.0, places=4)
class TestCholesky(unittest.TestCase):
"""测试 cholesky 分解
Test cholesky decomposition"""
def test_cholesky_basic(self):
"""测试基本 cholesky 分解
Test basic cholesky decomposition"""
a = paddle.randn([4, 4])
a = paddle.matmul(a, a.T) + paddle.eye(4) * 0.5
L = paddle.linalg.cholesky(a)
self.assertEqual(L.shape, [4, 4])
# Verify L * L^T ≈ a
reconstructed = paddle.matmul(L, L.T)
np.testing.assert_allclose(reconstructed.numpy(), a.numpy(), atol=1e-4)
def test_cholesky_batched(self):
"""测试批量 cholesky 分解
Test batched cholesky decomposition"""
a = paddle.randn([2, 4, 4])
a = paddle.matmul(a, a.transpose([0, 2, 1])) + paddle.eye(4) * 0.5
L = paddle.linalg.cholesky(a)
self.assertEqual(L.shape, [2, 4, 4])
def test_cholesky_upper(self):
"""测试上三角 cholesky 分解
Test upper triangular cholesky"""
a = paddle.randn([3, 3])
a = paddle.matmul(a, a.T) + paddle.eye(3) * 0.5
U = paddle.linalg.cholesky(a, upper=True)
self.assertEqual(U.shape, [3, 3])
class TestCholeskyInverse(unittest.TestCase):
"""测试 cholesky_inverse 函数
Test cholesky_inverse function"""
def test_cholesky_inverse_basic(self):
"""测试基本 cholesky 逆
Test basic cholesky inverse"""
a = paddle.randn([3, 3])
a = paddle.matmul(a, a.T) + paddle.eye(3) * 0.5
L = paddle.linalg.cholesky(a)
inv = paddle.linalg.cholesky_inverse(L)
self.assertEqual(inv.shape, [3, 3])
# Verify A * inv(A) ≈ I
result = paddle.matmul(a, inv)
np.testing.assert_allclose(result.numpy(), np.eye(3), atol=1e-4)
class TestCholeskySolve(unittest.TestCase):
"""测试 cholesky_solve 函数
Test cholesky_solve function"""
def test_cholesky_solve_basic(self):
"""测试基本 cholesky 求解
Test basic cholesky solve"""
a = paddle.randn([3, 3])
a = paddle.matmul(a, a.T) + paddle.eye(3) * 0.5
L = paddle.linalg.cholesky(a)
b = paddle.randn([3, 1])
# cholesky_solve(x, y): y is the Cholesky factor, x is the RHS
x = paddle.linalg.cholesky_solve(b, L)
self.assertEqual(x.shape, [3, 1])
class TestLU(unittest.TestCase):
"""测试 LU 分解
Test LU decomposition"""
def test_lu_basic(self):
"""测试基本 LU 分解
Test basic LU decomposition"""
a = paddle.randn([3, 3])
lu, p = paddle.linalg.lu(a)
self.assertEqual(lu.shape, [3, 3])
self.assertEqual(p.shape, [3])
def test_lu_with_infos(self):
"""测试带 info 的 LU 分解
Test LU decomposition with info"""
a = paddle.randn([3, 3])
lu, p, info = paddle.linalg.lu(a, get_infos=True)
self.assertEqual(lu.shape, [3, 3])
self.assertEqual(p.shape, [3])
self.assertEqual(info.shape, [])
def test_lu_unpack(self):
"""测试 LU 解包
Test LU unpack"""
a = paddle.randn([3, 3])
lu, p = paddle.linalg.lu(a)
P, L, U = paddle.linalg.lu_unpack(lu, p)
self.assertEqual(P.shape, [3, 3])
self.assertEqual(L.shape, [3, 3])
self.assertEqual(U.shape, [3, 3])
def test_lu_solve(self):
"""测试 LU 求解
Test LU solve"""
a = paddle.randn([3, 3])
b = paddle.randn([3, 1])
lu, p = paddle.linalg.lu(a)
x = paddle.linalg.lu_solve(b, lu, p)
self.assertEqual(x.shape, [3, 1])
class TestQR(unittest.TestCase):
"""测试 QR 分解
Test QR decomposition"""
def test_qr_basic(self):
"""测试基本 QR 分解
Test basic QR decomposition"""
a = paddle.randn([4, 3])
q, r = paddle.linalg.qr(a)
self.assertEqual(q.shape, [4, 3])
self.assertEqual(r.shape, [3, 3])
def test_qr_reduced(self):
"""测试缩减 QR 分解
Test reduced QR decomposition"""
a = paddle.randn([5, 3])
q, r = paddle.linalg.qr(a, mode='reduced')
self.assertEqual(q.shape, [5, 3])
self.assertEqual(r.shape, [3, 3])
def test_qr_complete(self):
"""测试完全 QR 分解
Test complete QR decomposition"""
a = paddle.randn([5, 3])
q, r = paddle.linalg.qr(a, mode='complete')
self.assertEqual(q.shape, [5, 5])
self.assertEqual(r.shape, [5, 3])
class TestSVD(unittest.TestCase):
"""测试 SVD 分解
Test SVD decomposition"""
def test_svd_basic(self):
"""测试基本 SVD 分解
Test basic SVD decomposition"""
a = paddle.randn([4, 3])
u, s, vh = paddle.linalg.svd(a)
self.assertEqual(u.shape, [4, 3])
self.assertEqual(s.shape, [3])
self.assertEqual(vh.shape, [3, 3])
def test_svd_reconstruction(self):
"""测试 SVD 重构
Test SVD reconstruction"""
a = paddle.randn([4, 3])
u, s, vh = paddle.linalg.svd(a)
# Reconstruct: U @ diag(s) @ Vh
reconstructed = paddle.matmul(u[:, :3] * s.unsqueeze(0), vh)
np.testing.assert_allclose(reconstructed.numpy(), a.numpy(), atol=1e-4)
class TestEigh(unittest.TestCase):
"""测试 eigh 特征值分解
Test eigh eigenvalue decomposition"""
def test_eigh_basic(self):
"""测试基本对称特征值分解
Test basic symmetric eigenvalue decomposition"""
a = paddle.randn([3, 3])
a = (a + a.T) / 2
eigenvalues, eigenvectors = paddle.linalg.eigh(a)
self.assertEqual(eigenvalues.shape, [3])
self.assertEqual(eigenvectors.shape, [3, 3])
def test_eigh_ascending(self):
"""测试升序特征值
Test ascending eigenvalues"""
a = paddle.randn([3, 3])
a = (a + a.T) / 2
eigenvalues, _ = paddle.linalg.eigh(a)
# Default is ascending
diffs = eigenvalues[1:] - eigenvalues[:-1]
self.assertTrue(paddle.all(diffs >= -1e-5).item())
class TestEigvalsh(unittest.TestCase):
"""测试 eigvalsh 函数
Test eigvalsh function"""
def test_eigvalsh_basic(self):
"""测试基本特征值计算
Test basic eigenvalue computation"""
a = paddle.randn([3, 3])
a = (a + a.T) / 2
eigenvalues = paddle.linalg.eigvalsh(a)
self.assertEqual(eigenvalues.shape, [3])
class TestSolve(unittest.TestCase):
"""测试 solve 函数
Test solve function"""
def test_solve_basic(self):
"""测试基本线性方程组求解
Test basic linear system solve"""
a = paddle.randn([3, 3])
b = paddle.randn([3, 1])
x = paddle.linalg.solve(a, b)
self.assertEqual(x.shape, [3, 1])
def test_solve_correctness(self):
"""测试求解正确性
Test solve correctness"""
a_np = np.random.randn(3, 3).astype('float32')
b_np = np.random.randn(3, 1).astype('float32')
x = paddle.linalg.solve(paddle.to_tensor(a_np), paddle.to_tensor(b_np))
expected = np.linalg.solve(a_np, b_np)
np.testing.assert_allclose(x.numpy(), expected, atol=1e-4)
class TestInv(unittest.TestCase):
"""测试 inv 函数
Test inv function"""
def test_inv_basic(self):
"""测试基本矩阵求逆
Test basic matrix inversion"""
a = paddle.randn([3, 3])
a = a + paddle.eye(3) * 5 # Make well-conditioned
inv_a = paddle.linalg.inv(a)
self.assertEqual(inv_a.shape, [3, 3])
def test_inv_correctness(self):
"""测试逆矩阵正确性
Test inverse matrix correctness"""
a_np = np.random.randn(3, 3).astype('float32')
a_np = a_np + np.eye(3) * 5
a = paddle.to_tensor(a_np)
inv_a = paddle.linalg.inv(a)
result = paddle.matmul(a, inv_a)
np.testing.assert_allclose(result.numpy(), np.eye(3), atol=1e-4)
class TestPinv(unittest.TestCase):
"""测试 pinv 函数
Test pinv function"""
def test_pinv_basic(self):
"""测试基本伪逆计算
Test basic pseudo-inverse computation"""
a = paddle.randn([3, 4])
pinv_a = paddle.linalg.pinv(a)
self.assertEqual(pinv_a.shape, [4, 3])
def test_pinv_square(self):
"""测试方阵伪逆
Test square matrix pseudo-inverse"""
a = paddle.randn([3, 3])
a = a + paddle.eye(3) * 5
pinv_a = paddle.linalg.pinv(a)
self.assertEqual(pinv_a.shape, [3, 3])
class TestCond(unittest.TestCase):
"""测试 cond 函数
Test cond function"""
def test_cond_basic(self):
"""测试基本条件数计算
Test basic condition number computation"""
a = paddle.randn([3, 3])
result = paddle.linalg.cond(a)
self.assertEqual(result.shape, [])
self.assertGreater(result.item(), 0)
def test_cond_identity(self):
"""测试单位矩阵条件数为1
Test identity matrix condition number is 1"""
a = paddle.eye(3)
result = paddle.linalg.cond(a)
self.assertAlmostEqual(result.item(), 1.0, places=4)
class TestDet(unittest.TestCase):
"""测试 det 函数
Test det function"""
def test_det_basic(self):
"""测试基本行列式计算
Test basic determinant computation"""
a = paddle.randn([3, 3])
result = paddle.linalg.det(a)
self.assertEqual(result.shape, [])
def test_det_identity(self):
"""测试单位矩阵行列式为1
Test identity matrix determinant is 1"""
a = paddle.eye(3)
result = paddle.linalg.det(a)
self.assertAlmostEqual(result.item(), 1.0, places=5)
class TestSlogdet(unittest.TestCase):
"""测试 slogdet 函数
Test slogdet function"""
def test_slogdet_basic(self):
"""测试基本符号对数行列式
Test basic sign-log-determinant"""
a = paddle.randn([3, 3])
sign, logabsdet = paddle.linalg.slogdet(a)
self.assertEqual(sign.shape, [])
self.assertEqual(logabsdet.shape, [])
def test_slogdet_positive(self):
"""测试正定矩阵的 slogdet
Test slogdet of positive definite matrix"""
a = paddle.randn([3, 3])
a = paddle.matmul(a, a.T) + paddle.eye(3)
sign, logabsdet = paddle.linalg.slogdet(a)
self.assertAlmostEqual(sign.item(), 1.0, places=5)
class TestMatrixPower(unittest.TestCase):
"""测试 matrix_power 函数
Test matrix_power function"""
def test_matrix_power_square(self):
"""测试矩阵平方
Test matrix square"""
a = paddle.randn([3, 3])
result = paddle.linalg.matrix_power(a, 2)
self.assertEqual(result.shape, [3, 3])
def test_matrix_power_identity(self):
"""测试矩阵零次幂为单位矩阵
Test matrix power 0 is identity"""
a = paddle.randn([3, 3])
result = paddle.linalg.matrix_power(a, 0)
np.testing.assert_allclose(result.numpy(), np.eye(3), atol=1e-5)
def test_matrix_power_neg(self):
"""测试矩阵负幂为逆
Test negative matrix power"""
a = paddle.randn([3, 3])
a = a + paddle.eye(3) * 5
result = paddle.linalg.matrix_power(a, -1)
expected = paddle.linalg.inv(a)
np.testing.assert_allclose(result.numpy(), expected.numpy(), atol=1e-4)
class TestMultiDot(unittest.TestCase):
"""测试 multi_dot 函数
Test multi_dot function"""
def test_multi_dot_3(self):
"""测试三个矩阵连乘
Test 3-matrix chain multiplication"""
a = paddle.randn([2, 3])
b = paddle.randn([3, 4])
c = paddle.randn([4, 2])
result = paddle.linalg.multi_dot([a, b, c])
self.assertEqual(result.shape, [2, 2])
def test_multi_dot_2(self):
"""测试两个矩阵连乘
Test 2-matrix chain multiplication"""
a = paddle.randn([3, 4])
b = paddle.randn([4, 5])
result = paddle.linalg.multi_dot([a, b])
self.assertEqual(result.shape, [3, 5])
class TestNorm(unittest.TestCase):
"""测试 norm 函数
Test norm function"""
def test_norm_frobenius(self):
"""测试 Frobenius 范数
Test Frobenius norm"""
a = paddle.randn([3, 4])
result = paddle.linalg.norm(a)
self.assertEqual(result.shape, [])
def test_matrix_norm(self):
"""测试矩阵范数
Test matrix norm"""
a = paddle.randn([3, 4])
result = paddle.linalg.matrix_norm(a)
self.assertEqual(result.shape, [])
def test_vector_norm(self):
"""测试向量范数
Test vector norm"""
a = paddle.randn([3, 4])
result = paddle.linalg.vector_norm(a)
self.assertEqual(result.shape, [])
def test_vector_norm_l1(self):
"""测试 L1 向量范数
Test L1 vector norm"""
a = paddle.to_tensor([1.0, -2.0, 3.0])
result = paddle.linalg.vector_norm(a, p=1)
self.assertAlmostEqual(result.item(), 6.0, places=5)
def test_vector_norm_inf(self):
"""测试无穷向量范数
Test infinity vector norm"""
a = paddle.to_tensor([1.0, -3.0, 2.0])
result = paddle.linalg.vector_norm(a, p=float('inf'))
self.assertAlmostEqual(result.item(), 3.0, places=5)
class TestMatrixTranspose(unittest.TestCase):
"""测试 matrix_transpose 函数
Test matrix_transpose function"""
def test_matrix_transpose_basic(self):
"""测试基本矩阵转置
Test basic matrix transpose"""
a = paddle.randn([2, 3, 4])
result = paddle.linalg.matrix_transpose(a)
self.assertEqual(result.shape, [2, 4, 3])
def test_matrix_transpose_2d(self):
"""测试二维矩阵转置
Test 2D matrix transpose"""
a = paddle.randn([3, 4])
result = paddle.linalg.matrix_transpose(a)
self.assertEqual(result.shape, [4, 3])
class TestHouseholderProduct(unittest.TestCase):
"""测试 householder_product 函数
Test householder_product function"""
def test_householder_product_basic(self):
"""测试基本 Householder 乘积
Test basic Householder product"""
a = paddle.randn([4, 3])
tau = paddle.ones([3])
q = paddle.linalg.householder_product(a, tau)
self.assertEqual(q.shape, [4, 3])
class TestCdist(unittest.TestCase):
"""测试 cdist 函数
Test cdist function"""
def test_cdist_basic(self):
"""测试基本成对距离
Test basic pairwise distance"""
a = paddle.randn([3, 2])
b = paddle.randn([4, 2])
result = paddle.cdist(a, b)
self.assertEqual(result.shape, [3, 4])
def test_cdist_self(self):
"""测试自身成对距离
Test self pairwise distance"""
a = paddle.randn([3, 2])
result = paddle.cdist(a, a)
self.assertEqual(result.shape, [3, 3])
# Diagonal should be 0
diag = paddle.diag(result)
np.testing.assert_allclose(diag.numpy(), np.zeros(3), atol=1e-5)
class TestCorrcoef(unittest.TestCase):
"""测试 corrcoef 函数
Test corrcoef function"""
def test_corrcoef_basic(self):
"""测试基本相关系数矩阵
Test basic correlation coefficient matrix"""
a = paddle.randn([3, 5])
result = paddle.linalg.corrcoef(a)
self.assertEqual(result.shape, [3, 3])
def test_corrcoef_diag(self):
"""测试相关系数矩阵对角线为1
Test correlation matrix diagonal is 1"""
a = paddle.randn([3, 100])
result = paddle.linalg.corrcoef(a)
diag = paddle.diag(result)
np.testing.assert_allclose(diag.numpy(), np.ones(3), atol=1e-4)
class TestCov(unittest.TestCase):
"""测试 cov 函数
Test cov function"""
def test_cov_basic(self):
"""测试基本协方差矩阵
Test basic covariance matrix"""
a = paddle.randn([3, 5])
result = paddle.linalg.cov(a)
self.assertEqual(result.shape, [3, 3])
def test_cov_symmetric(self):
"""测试协方差矩阵对称性
Test covariance matrix symmetry"""
a = paddle.randn([3, 50])
result = paddle.linalg.cov(a)
np.testing.assert_allclose(result.numpy(), result.numpy().T, atol=1e-5)
class TestHistogram(unittest.TestCase):
"""测试 histogram 函数
Test histogram function"""
def test_histogram_basic(self):
"""测试基本直方图
Test basic histogram"""
a = paddle.randn([100])
result = paddle.histogram(a, bins=10, min=-3.0, max=3.0)
self.assertEqual(result.shape, [10])
def test_histogram_bin_edges(self):
"""测试直方图 bin 边界
Test histogram bin edges"""
a = paddle.randn([100])
edges = paddle.histogram_bin_edges(a, bins=10)
self.assertEqual(edges.shape, [11])
class TestVecdot(unittest.TestCase):
"""测试 vecdot 函数
Test vecdot function"""
def test_vecdot_basic(self):
"""测试基本向量点积
Test basic vector dot product"""
a = paddle.to_tensor([1.0, 2.0, 3.0])
b = paddle.to_tensor([4.0, 5.0, 6.0])
result = paddle.linalg.vecdot(a, b)
self.assertAlmostEqual(result.item(), 32.0, places=5)
class TestMatrixExp(unittest.TestCase):
"""测试 matrix_exp 函数
Test matrix_exp function"""
def test_matrix_exp_zero(self):
"""测试零矩阵的指数为单位矩阵
Test matrix exponential of zero matrix is identity"""
a = paddle.zeros([3, 3])
result = paddle.linalg.matrix_exp(a)
np.testing.assert_allclose(result.numpy(), np.eye(3), atol=1e-5)
def test_matrix_exp_shape(self):
"""测试 matrix_exp 输出形状
Test matrix_exp output shape"""
a = paddle.randn([3, 3])
result = paddle.linalg.matrix_exp(a)
self.assertEqual(result.shape, [3, 3])
class TestLstsq(unittest.TestCase):
"""测试 lstsq 函数
Test lstsq function"""
def test_lstsq_basic(self):
"""测试基本最小二乘求解
Test basic least squares solve"""
a = paddle.randn([5, 3])
b = paddle.randn([5, 1])
result, residuals, rank, singular_values = paddle.linalg.lstsq(a, b)
self.assertEqual(result.shape, [3, 1])
class TestSvdvals(unittest.TestCase):
"""测试 svdvals 函数
Test svdvals function"""
def test_svdvals_basic(self):
"""测试基本奇异值计算
Test basic singular value computation"""
a = paddle.randn([4, 3])
result = paddle.linalg.svdvals(a)
self.assertEqual(result.shape, [3])
def test_svdvals_descending(self):
"""测试奇异值降序排列
Test singular values in descending order"""
a = paddle.randn([4, 3])
result = paddle.linalg.svdvals(a)
# Values should be non-negative and non-increasing
diffs = result[:-1] - result[1:]
self.assertTrue(paddle.all(diffs >= -1e-5).item())
class TestSvdLowrank(unittest.TestCase):
"""测试 svd_lowrank 函数
Test svd_lowrank function"""
def test_svd_lowrank_basic(self):
"""测试基本低秩 SVD
Test basic low-rank SVD"""
a = paddle.randn([5, 4])
U, S, V = paddle.linalg.svd_lowrank(a, q=3)
self.assertEqual(U.shape, [5, 3])
self.assertEqual(S.shape, [3])
self.assertEqual(V.shape, [4, 3])
class TestPcaLowrank(unittest.TestCase):
"""测试 pca_lowrank 函数
Test pca_lowrank function"""
def test_pca_lowrank_basic(self):
"""测试基本低秩 PCA
Test basic low-rank PCA"""
a = paddle.randn([5, 4])
U, S, V = paddle.linalg.pca_lowrank(a, q=3)
self.assertEqual(U.shape, [5, 3])
self.assertEqual(S.shape, [3])
self.assertEqual(V.shape, [4, 3])
class TestTriangularSolve(unittest.TestCase):
"""测试 triangular_solve 函数
Test triangular_solve function"""
def test_triangular_solve_lower(self):
"""测试下三角方程组求解
Test lower triangular solve"""
a = paddle.randn([3, 3])
a = paddle.tril(a) + paddle.eye(3) * 5 # Make lower triangular
b = paddle.randn([3, 1])
x = paddle.linalg.triangular_solve(a, b)
self.assertEqual(x.shape, [3, 1])
def test_triangular_solve_upper(self):
"""测试上三角方程组求解
Test upper triangular solve"""
a = paddle.randn([3, 3])
a = paddle.triu(a) + paddle.eye(3) * 5 # Make upper triangular
b = paddle.randn([3, 1])
x = paddle.linalg.triangular_solve(a, b, upper=True)
self.assertEqual(x.shape, [3, 1])
class TestEigvals(unittest.TestCase):
"""测试 eigvals 函数
Test eigvals function"""
def test_eigvals_basic(self):
"""测试基本特征值计算
Test basic eigenvalue computation"""
a = paddle.randn([3, 3])
result = paddle.linalg.eigvals(a)
self.assertEqual(result.shape, [3])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_linalg_ops.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.linalg (vector_norm, matrix_norm, cond)
# 自动生成的单测，覆盖 paddle.tensor.linalg 模块中未覆盖的代码
"""
测试模块：paddle.linalg (vector_norm, matrix_norm, cond, cross, diagonal)
Test Module: paddle.linalg
本测试覆盖以下功能：
This test covers the following functions:
1. vector_norm - 向量范数 / Vector norm with various p values
2. matrix_norm - 矩阵范数 / Matrix norm
3. cond - 条件数 / Condition number
4. cross - 叉积 / Cross product
5. diagonal - 对角线 / Diagonal extraction
覆盖的未覆盖行：vector_norm各分支, cond的p值分支
"""
import unittest
import numpy as np
import paddle
class TestVectorNorm(unittest.TestCase):
"""测试vector_norm向量范数
Test vector_norm with various p values"""
def setUp(self):
paddle.disable_static()
def test_vector_norm_p2(self):
"""L2范数（默认） / L2 norm (default)"""
x = paddle.to_tensor([3.0, 4.0], dtype='float32')
out = paddle.linalg.vector_norm(x, p=2)
np.testing.assert_allclose(float(out.numpy()), 5.0, rtol=1e-5)
def test_vector_norm_p1(self):
"""L1范数 / L1 norm"""
x = paddle.to_tensor([-3.0, 4.0], dtype='float32')
out = paddle.linalg.vector_norm(x, p=1)
np.testing.assert_allclose(float(out.numpy()), 7.0, rtol=1e-5)
def test_vector_norm_p0(self):
"""L0范数（非零元素个数） / L0 norm (count nonzero)"""
x = paddle.to_tensor([0.0, 1.0, 0.0, 2.0, 3.0], dtype='float32')
out = paddle.linalg.vector_norm(x, p=0)
np.testing.assert_allclose(float(out.numpy()), 3.0, rtol=1e-5)
def test_vector_norm_inf(self):
"""无穷范数 / Infinity norm"""
x = paddle.to_tensor([-5.0, 3.0, 4.0], dtype='float32')
out = paddle.linalg.vector_norm(x, p=float('inf'))
np.testing.assert_allclose(float(out.numpy()), 5.0, rtol=1e-5)
def test_vector_norm_neg_inf(self):
"""负无穷范数 / Negative infinity norm"""
x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
out = paddle.linalg.vector_norm(x, p=float('-inf'))
np.testing.assert_allclose(float(out.numpy()), 1.0, rtol=1e-5)
def test_vector_norm_with_axis(self):
"""指定axis的范数 / Norm along specific axis"""
x = paddle.arange(24, dtype='float32').reshape([2, 3, 4]) - 12
out = paddle.linalg.vector_norm(x, p=2, axis=[1, 2])
self.assertEqual(list(out.shape), [2])
def test_vector_norm_keepdim(self):
"""保持维度 / Keepdim"""
x = paddle.ones([3, 4], dtype='float32')
out = paddle.linalg.vector_norm(x, p=2, axis=1, keepdim=True)
self.assertEqual(list(out.shape), [3, 1])
class TestCrossProduct(unittest.TestCase):
"""测试叉积运算
Test cross product"""
def setUp(self):
paddle.disable_static()
def test_cross_3d(self):
"""3D向量叉积 / 3D vector cross product"""
x = paddle.to_tensor([1.0, 0.0, 0.0], dtype='float32')
y = paddle.to_tensor([0.0, 1.0, 0.0], dtype='float32')
out = paddle.cross(x, y)
np.testing.assert_allclose(out.numpy(), [0.0, 0.0, 1.0], rtol=1e-5)
def test_cross_batched(self):
"""批量叉积 / Batched cross product"""
x = paddle.to_tensor(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype='float32'
)
y = paddle.to_tensor(
[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], dtype='float32'
)
out = paddle.cross(x, y, axis=-1)
self.assertEqual(list(out.shape), [2, 3])
class TestDiagonal(unittest.TestCase):
"""测试对角线提取
Test diagonal extraction"""
def setUp(self):
paddle.disable_static()
def test_diagonal_main(self):
"""主对角线 / Main diagonal"""
x = paddle.arange(9, dtype='float32').reshape([3, 3])
out = paddle.diagonal(x)
np.testing.assert_allclose(out.numpy(), [0.0, 4.0, 8.0], rtol=1e-5)
def test_diagonal_offset(self):
"""偏移对角线 / Offset diagonal"""
x = paddle.arange(12, dtype='float32').reshape([3, 4])
out = paddle.diagonal(x, offset=1)
np.testing.assert_allclose(out.numpy(), [1.0, 6.0, 11.0], rtol=1e-5)
def test_diagonal_negative_offset(self):
"""负偏移对角线 / Negative offset diagonal"""
x = paddle.arange(12, dtype='float32').reshape([4, 3])
out = paddle.diagonal(x, offset=-1)
np.testing.assert_allclose(out.numpy(), [3.0, 7.0, 11.0], rtol=1e-5)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_logic_ops.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.tensor.logic
# 自动生成的单测，覆盖 paddle.tensor.logic 模块中未覆盖的代码
# Target: cover uncovered lines in paddle/python/paddle/tensor/logic.py
# including static graph branches for equal, less_than, less_equal, not_equal, greater_equal
# and inplace logic operations
"""
测试模块：paddle.tensor.logic
Test Module: paddle.tensor.logic
本测试覆盖以下功能：
This test covers the following functions:
1. is_empty - 检测Tensor是否为空 / Test if a Tensor is empty (lines 152-158)
2. equal_all - 检测两个Tensor是否完全相等 / Test if two Tensors are entirely equal (lines 197-204)
3. equal 静态图路径 / equal static graph path (lines 259-305)
4. greater_equal 静态图路径 / greater_equal static graph path (lines 397-405)
5. less_equal 静态图路径 / less_equal static graph path (lines 513-521)
6. less_than 静态图路径 / less_than static graph path (lines 614-623)
7. not_equal 静态图路径 / not_equal static graph path (lines 727-736)
8. 动态图inplace操作 / dynamic inplace operations
9. equal with scalar y / equal与标量y的比较
覆盖的未覆盖行：152-155, 197-204, 259, 278, 297-305, 359, 378, 397-405,
475, 494, 513-521, 576, 595, 614-623, 689, 708, 727-736, 789, 797
"""
import unittest
import numpy as np
import paddle
class TestIsEmpty(unittest.TestCase):
"""测试is_empty函数
Test is_empty function"""
def setUp(self):
paddle.disable_static()
def test_non_empty_tensor(self):
"""测试非空Tensor应返回False
Test that non-empty Tensor returns False"""
x = paddle.randn([3, 4])
result = paddle.is_empty(x)
self.assertFalse(result.item())
def test_empty_tensor(self):
"""测试空Tensor应返回True
Test that empty Tensor returns True"""
x = paddle.empty([0, 3])
result = paddle.is_empty(x)
self.assertTrue(result.item())
class TestEqualAll(unittest.TestCase):
"""测试equal_all函数
Test equal_all function"""
def setUp(self):
paddle.disable_static()
def test_equal_tensors(self):
"""测试两个相同Tensor应返回True
Test two identical Tensors should return True"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 2, 3])
result = paddle.equal_all(x, y)
self.assertTrue(result.item())
def test_not_equal_tensors(self):
"""测试两个不同Tensor应返回False
Test two different Tensors should return False"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 4, 3])
result = paddle.equal_all(x, y)
self.assertFalse(result.item())
class TestEqualWithScalar(unittest.TestCase):
"""测试equal与标量比较
Test equal with scalar comparison"""
def setUp(self):
paddle.disable_static()
def test_equal_with_int(self):
"""测试Tensor与int比较
Test Tensor compared with int"""
x = paddle.to_tensor([1, 2, 3])
result = paddle.equal(x, 2)
expected = np.array([False, True, False])
np.testing.assert_array_equal(result.numpy(), expected)
def test_equal_with_float(self):
"""测试Tensor与float比较
Test Tensor compared with float"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.equal(x, 2.0)
expected = np.array([False, True, False])
np.testing.assert_array_equal(result.numpy(), expected)
def test_equal_with_bool(self):
"""测试Tensor与bool比较
Test Tensor compared with bool"""
x = paddle.to_tensor([True, False, True])
result = paddle.equal(x, True)
expected = np.array([True, False, True])
np.testing.assert_array_equal(result.numpy(), expected)
def test_equal_invalid_type_raises(self):
"""测试equal传入不支持的类型应报错
Test equal with unsupported type should raise TypeError"""
x = paddle.to_tensor([1, 2, 3])
with self.assertRaises(TypeError):
paddle.equal(x, "string")
class TestInplaceLogicOps(unittest.TestCase):
"""测试逻辑运算的inplace版本
Test inplace versions of logic operations"""
def setUp(self):
paddle.disable_static()
def test_logical_and_inplace(self):
"""测试logical_and_的inplace操作
Test logical_and_ inplace operation"""
x = paddle.to_tensor([True, False, True, False])
y = paddle.to_tensor([True, True, False, False])
paddle.logical_and_(x, y)
expected = np.array([True, False, False, False])
np.testing.assert_array_equal(x.numpy(), expected)
def test_logical_or_inplace(self):
"""测试logical_or_的inplace操作
Test logical_or_ inplace operation"""
x = paddle.to_tensor([True, False, True, False])
y = paddle.to_tensor([True, True, False, False])
paddle.logical_or_(x, y)
expected = np.array([True, True, True, False])
np.testing.assert_array_equal(x.numpy(), expected)
def test_logical_xor_inplace(self):
"""测试logical_xor_的inplace操作
Test logical_xor_ inplace operation"""
x = paddle.to_tensor([True, False, True, False])
y = paddle.to_tensor([True, True, False, False])
paddle.logical_xor_(x, y)
expected = np.array([False, True, True, False])
np.testing.assert_array_equal(x.numpy(), expected)
def test_logical_not_inplace(self):
"""测试logical_not_的inplace操作
Test logical_not_ inplace operation"""
x = paddle.to_tensor([True, False, True])
paddle.logical_not_(x)
expected = np.array([False, True, False])
np.testing.assert_array_equal(x.numpy(), expected)
def test_equal_inplace(self):
"""测试equal_的inplace操作
Test equal_ inplace operation"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 3])
result = paddle.equal_(x, y)
expected = np.array([True, False, True])
np.testing.assert_array_equal(result.numpy(), expected)
def test_not_equal_inplace(self):
"""测试not_equal_的inplace操作
Test not_equal_ inplace operation"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([1, 3, 3])
result = paddle.not_equal_(x, y)
expected = np.array([False, True, False])
np.testing.assert_array_equal(result.numpy(), expected)
def test_greater_equal_inplace(self):
"""测试greater_equal_的inplace操作
Test greater_equal_ inplace operation"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([2, 2, 1])
result = paddle.greater_equal_(x, y)
expected = np.array([False, True, True])
np.testing.assert_array_equal(result.numpy(), expected)
def test_greater_than_inplace(self):
"""测试greater_than_的inplace操作
Test greater_than_ inplace operation"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([2, 2, 1])
result = paddle.greater_than_(x, y)
expected = np.array([False, False, True])
np.testing.assert_array_equal(result.numpy(), expected)
def test_less_equal_inplace(self):
"""测试less_equal_的inplace操作
Test less_equal_ inplace operation"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([2, 2, 1])
result = paddle.less_equal_(x, y)
expected = np.array([True, True, False])
np.testing.assert_array_equal(result.numpy(), expected)
def test_less_than_inplace(self):
"""测试less_than_的inplace操作
Test less_than_ inplace operation"""
x = paddle.to_tensor([1, 2, 3])
y = paddle.to_tensor([2, 2, 1])
result = paddle.less_than_(x, y)
expected = np.array([True, False, False])
np.testing.assert_array_equal(result.numpy(), expected)
class TestStaticGraphLogicOps(unittest.TestCase):
"""测试静态图模式下的逻辑比较运算，覆盖未覆盖的静态图代码路径
Test static graph logic comparison ops to cover uncovered static graph paths"""
def test_equal_static(self):
"""测试静态图模式下的equal操作
Test equal in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[3], dtype='int32')
y = paddle.static.data(name='y', shape=[3], dtype='int32')
out = paddle.equal(x, y)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([1, 2, 3], dtype='int32')
y_np = np.array([1, 3, 3], dtype='int32')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[out]
)
np.testing.assert_array_equal(result[0], [True, False, True])
finally:
paddle.disable_static()
def test_greater_equal_static(self):
"""测试静态图模式下的greater_equal操作
Test greater_equal in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[3], dtype='float32')
y = paddle.static.data(name='y', shape=[3], dtype='float32')
out = paddle.greater_equal(x, y)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([1.0, 2.0, 3.0], dtype='float32')
y_np = np.array([2.0, 2.0, 1.0], dtype='float32')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[out]
)
np.testing.assert_array_equal(result[0], [False, True, True])
finally:
paddle.disable_static()
def test_less_equal_static(self):
"""测试静态图模式下的less_equal操作
Test less_equal in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[3], dtype='float32')
y = paddle.static.data(name='y', shape=[3], dtype='float32')
out = paddle.less_equal(x, y)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([1.0, 2.0, 3.0], dtype='float32')
y_np = np.array([2.0, 2.0, 1.0], dtype='float32')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[out]
)
np.testing.assert_array_equal(result[0], [True, True, False])
finally:
paddle.disable_static()
def test_less_than_static(self):
"""测试静态图模式下的less_than操作
Test less_than in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[3], dtype='float64')
y = paddle.static.data(name='y', shape=[3], dtype='float64')
out = paddle.less_than(x, y)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([1.0, 2.0, 3.0], dtype='float64')
y_np = np.array([2.0, 2.0, 1.0], dtype='float64')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[out]
)
np.testing.assert_array_equal(result[0], [True, False, False])
finally:
paddle.disable_static()
def test_not_equal_static(self):
"""测试静态图模式下的not_equal操作
Test not_equal in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[3], dtype='int64')
y = paddle.static.data(name='y', shape=[3], dtype='int64')
out = paddle.not_equal(x, y)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([1, 2, 3], dtype='int64')
y_np = np.array([1, 3, 3], dtype='int64')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[out]
)
np.testing.assert_array_equal(result[0], [False, True, False])
finally:
paddle.disable_static()
def test_equal_all_static(self):
"""测试静态图模式下的equal_all操作
Test equal_all in static graph mode"""
paddle.enable_static()
try:
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, startup_prog):
x = paddle.static.data(name='x', shape=[3], dtype='int32')
y = paddle.static.data(name='y', shape=[3], dtype='int32')
out = paddle.equal_all(x, y)
exe = paddle.static.Executor(paddle.CPUPlace())
exe.run(startup_prog)
x_np = np.array([1, 2, 3], dtype='int32')
y_np = np.array([1, 2, 3], dtype='int32')
result = exe.run(
main_prog, feed={'x': x_np, 'y': y_np}, fetch_list=[out]
)
self.assertTrue(result[0].item())
finally:
paddle.disable_static()
class TestBitwiseOps(unittest.TestCase):
"""测试bitwise相关操作
Test bitwise operations"""
def setUp(self):
paddle.disable_static()
def test_bitwise_invert(self):
"""测试bitwise_invert操作
Test bitwise_invert operation"""
x = paddle.to_tensor([-5, -1, 1])
result = paddle.bitwise_invert(x)
expected = np.array([4, 0, -2])
np.testing.assert_array_equal(result.numpy(), expected)
def test_bitwise_invert_inplace(self):
"""测试bitwise_invert_的inplace操作
Test bitwise_invert_ inplace operation"""
x = paddle.to_tensor([-5, -1, 1])
paddle.bitwise_invert_(x)
expected = np.array([4, 0, -2])
np.testing.assert_array_equal(x.numpy(), expected)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_loss_functions.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.functional.loss
# 自动生成的单测，覆盖 paddle.nn.functional.loss 模块中未覆盖的代码
"""
测试模块：paddle.nn.functional.loss (log_loss, margin_ranking_loss, soft_margin_loss, multi_label_soft_margin_loss)
Test Module: paddle.nn.functional.loss
本测试覆盖以下功能：
This test covers the following functions:
1. log_loss - 对数损失 / Log loss function
2. margin_ranking_loss - 排序损失 / Margin ranking loss
3. soft_margin_loss - 软间隔损失 / Soft margin loss
4. multi_label_soft_margin_loss - 多标签软间隔损失 / Multi-label soft margin loss
覆盖的未覆盖行：166-181 (log_loss), margin_ranking_loss分支
"""
import unittest
import paddle
import paddle.nn.functional as F
class TestLogLoss(unittest.TestCase):
"""测试log_loss对数损失函数
Test log_loss function"""
def setUp(self):
paddle.disable_static()
def test_log_loss_basic(self):
"""基本log_loss / Basic log_loss"""
input_data = paddle.to_tensor([[0.8], [0.3], [0.6]], dtype='float32')
label_data = paddle.to_tensor([[1.0], [0.0], [1.0]], dtype='float32')
loss = F.log_loss(input=input_data, label=label_data)
self.assertEqual(list(loss.shape), [3, 1])
self.assertTrue(float(loss.sum().numpy()) > 0)
def test_log_loss_with_epsilon(self):
"""带epsilon的log_loss / Log loss with custom epsilon"""
input_data = paddle.to_tensor([[0.99], [0.01]], dtype='float32')
label_data = paddle.to_tensor([[1.0], [0.0]], dtype='float32')
loss = F.log_loss(input=input_data, label=label_data, epsilon=1e-6)
self.assertTrue(float(loss.sum().numpy()) > 0)
class TestMarginRankingLoss(unittest.TestCase):
"""测试margin_ranking_loss排序损失
Test margin_ranking_loss"""
def setUp(self):
paddle.disable_static()
def test_margin_ranking_loss_basic(self):
"""基本排序损失 / Basic margin ranking loss"""
input1 = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
input2 = paddle.to_tensor([2.0, 1.0, 1.0], dtype='float32')
label = paddle.to_tensor([1.0, 1.0, -1.0], dtype='float32')
loss = F.margin_ranking_loss(input1, input2, label, margin=0.0)
self.assertIsNotNone(loss)
def test_margin_ranking_loss_with_margin(self):
"""带margin的排序损失 / Margin ranking loss with margin"""
input1 = paddle.to_tensor([3.0, 2.0], dtype='float32')
input2 = paddle.to_tensor([1.0, 4.0], dtype='float32')
label = paddle.to_tensor([1.0, -1.0], dtype='float32')
loss = F.margin_ranking_loss(input1, input2, label, margin=0.5)
self.assertIsNotNone(loss)
def test_margin_ranking_loss_reduction_none(self):
"""reduction=none / Margin ranking loss with no reduction"""
input1 = paddle.randn([5], dtype='float32')
input2 = paddle.randn([5], dtype='float32')
label = paddle.sign(paddle.randn([5]))
loss = F.margin_ranking_loss(input1, input2, label, reduction='none')
self.assertEqual(list(loss.shape), [5])
class TestSoftMarginLoss(unittest.TestCase):
"""测试soft_margin_loss软间隔损失
Test soft_margin_loss"""
def setUp(self):
paddle.disable_static()
def test_soft_margin_loss_basic(self):
"""基本软间隔损失 / Basic soft margin loss"""
input_data = paddle.to_tensor([0.5, -0.5, 1.0], dtype='float32')
label = paddle.to_tensor([1.0, -1.0, 1.0], dtype='float32')
loss = F.soft_margin_loss(input_data, label)
self.assertIsNotNone(loss)
self.assertTrue(float(loss.numpy()) > 0)
def test_soft_margin_loss_reduction_none(self):
"""reduction=none / Soft margin loss with no reduction"""
input_data = paddle.randn([3, 4], dtype='float32')
label = paddle.sign(paddle.randn([3, 4]))
loss = F.soft_margin_loss(input_data, label, reduction='none')
self.assertEqual(list(loss.shape), [3, 4])
def test_soft_margin_loss_reduction_sum(self):
"""reduction=sum / Soft margin loss with sum reduction"""
input_data = paddle.randn([3, 4], dtype='float32')
label = paddle.sign(paddle.randn([3, 4]))
loss = F.soft_margin_loss(input_data, label, reduction='sum')
self.assertEqual(list(loss.shape), [])
class TestMultiLabelSoftMarginLoss(unittest.TestCase):
"""测试多标签软间隔损失
Test multi_label_soft_margin_loss"""
def setUp(self):
paddle.disable_static()
def test_multi_label_basic(self):
"""基本多标签损失 / Basic multi-label loss"""
input_data = paddle.to_tensor(
[[0.5, -0.3, 0.8], [0.2, 0.1, -0.4]], dtype='float32'
)
label = paddle.to_tensor(
[[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]], dtype='float32'
)
loss = F.multi_label_soft_margin_loss(input_data, label)
self.assertIsNotNone(loss)
def test_multi_label_with_weight(self):
"""带权重的多标签损失 / Multi-label loss with weight"""
input_data = paddle.randn([2, 4], dtype='float32')
label = paddle.to_tensor([[1, 0, 1, 0], [0, 1, 0, 1]], dtype='float32')
weight = paddle.to_tensor([1.0, 2.0, 1.0, 2.0], dtype='float32')
loss = F.multi_label_soft_margin_loss(input_data, label, weight=weight)
self.assertIsNotNone(loss)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_lr_scheduler.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.optimizer.lr
# 自动生成的单测，覆盖 paddle.optimizer.lr 模块中未覆盖的代码
# Target: cover uncovered lines 158, 245-251, 965, 1329, 1333, 1438, 1789, 1793
# in paddle/python/paddle/optimizer/lr.py
# 目标：覆盖 lr.py 中 LRScheduler 基类参数校验、state_dict Tensor 处理、
# StepDecay 参数校验、LinearWarmup 参数校验、CosineAnnealingDecay 参数校验、
# LambdaDecay 参数校验等未覆盖行
"""
This test covers the following modules and code paths:
这个测试覆盖以下模块和代码路径：
1. LRScheduler base class - negative learning_rate ValueError (line 158)
LRScheduler 基类 - 负学习率 ValueError
2. LRScheduler.state_dict() - Tensor value handling in state dict (lines 245-251)
LRScheduler.state_dict() - 状态字典中 Tensor 值的处理
3. LinearWarmup - invalid learning_rate type (line 965)
LinearWarmup - 无效学习率类型的 TypeError
4. StepDecay - invalid step_size type (line 1329), gamma >= 1.0 (line 1333)
StepDecay - step_size 类型校验和 gamma 值校验
5. LambdaDecay - invalid lr_lambda type (line 1438)
LambdaDecay - lr_lambda 非可调用对象的 TypeError
6. CosineAnnealingDecay - invalid T_max type (line 1789), invalid eta_min type (line 1793)
CosineAnnealingDecay - T_max 和 eta_min 的类型校验
"""
import unittest
import numpy as np
import paddle
from paddle.optimizer import lr
class TestLRSchedulerNegativeLR(unittest.TestCase):
"""Test LRScheduler raises ValueError for negative learning rate.
测试 LRScheduler 在负学习率时抛出 ValueError。
覆盖 paddle/python/paddle/optimizer/lr.py 第 158 行。
"""
def test_negative_learning_rate(self):
"""LRScheduler should raise ValueError for negative learning_rate.
负学习率应抛出 ValueError。
"""
with self.assertRaises(ValueError):
lr.StepDecay(learning_rate=-0.1, step_size=10)
def test_zero_learning_rate(self):
"""LRScheduler should accept zero learning rate.
零学习率应被接受（不抛异常）。
"""
scheduler = lr.StepDecay(learning_rate=0.0, step_size=10)
self.assertEqual(scheduler(), 0.0)
class TestLRSchedulerStateDictTensor(unittest.TestCase):
"""Test LRScheduler.state_dict() properly handles Tensor values.
测试 LRScheduler.state_dict() 正确处理 Tensor 类型的值。
覆盖 paddle/python/paddle/optimizer/lr.py 第 245-251 行。
"""
def test_state_dict_with_tensor_value(self):
"""state_dict should convert Tensor values to float.
state_dict 中的 Tensor 值应转换为 float。
"""
scheduler = lr.StepDecay(learning_rate=0.1, step_size=10)
# Manually inject a Tensor into internal state to test conversion
scheduler.last_lr = paddle.to_tensor([0.05])
state = scheduler.state_dict()
self.assertIsInstance(state['last_lr'], float)
np.testing.assert_allclose(state['last_lr'], 0.05, rtol=1e-5)
def test_state_dict_normal(self):
"""Normal state_dict should contain last_epoch and last_lr.
正常的 state_dict 应包含 last_epoch 和 last_lr。
"""
scheduler = lr.StepDecay(learning_rate=0.1, step_size=10)
scheduler.step()
state = scheduler.state_dict()
self.assertIn('last_epoch', state)
self.assertIn('last_lr', state)
def test_state_dict_set_and_load(self):
"""state_dict and set_state_dict should be symmetric.
state_dict 和 set_state_dict 应具有对称性。
"""
scheduler1 = lr.StepDecay(learning_rate=0.1, step_size=5)
for _ in range(7):
scheduler1.step()
state = scheduler1.state_dict()
scheduler2 = lr.StepDecay(learning_rate=0.1, step_size=5)
scheduler2.set_state_dict(state)
self.assertEqual(scheduler1(), scheduler2())
class TestLinearWarmupTypeError(unittest.TestCase):
"""Test LinearWarmup raises TypeError for invalid learning_rate type.
测试 LinearWarmup 在 learning_rate 类型无效时抛出 TypeError。
覆盖 paddle/python/paddle/optimizer/lr.py 第 965 行。
"""
def test_invalid_lr_type_string(self):
"""LinearWarmup should raise TypeError for string learning_rate.
字符串类型的 learning_rate 应抛出 TypeError。
"""
with self.assertRaises(TypeError):
lr.LinearWarmup(
learning_rate="0.1",
warmup_steps=100,
start_lr=0.0,
end_lr=0.1,
)
def test_valid_lr_float(self):
"""LinearWarmup should accept float learning_rate.
浮点型 learning_rate 应被接受。
"""
scheduler = lr.LinearWarmup(
learning_rate=0.1,
warmup_steps=100,
start_lr=0.0,
end_lr=0.1,
)
self.assertIsNotNone(scheduler)
def test_valid_lr_scheduler(self):
"""LinearWarmup should accept LRScheduler as learning_rate.
LRScheduler 类型的 learning_rate 应被接受。
"""
base_scheduler = lr.StepDecay(learning_rate=0.1, step_size=10)
scheduler = lr.LinearWarmup(
learning_rate=base_scheduler,
warmup_steps=50,
start_lr=0.0,
end_lr=0.1,
)
self.assertIsNotNone(scheduler)
class TestStepDecayValidation(unittest.TestCase):
"""Test StepDecay input validation.
测试 StepDecay 的输入参数校验。
覆盖 paddle/python/paddle/optimizer/lr.py 第 1329、1333 行。
"""
def test_step_size_not_int(self):
"""StepDecay should raise TypeError when step_size is not int.
当 step_size 不是 int 时应抛出 TypeError。
"""
with self.assertRaises(TypeError):
lr.StepDecay(learning_rate=0.1, step_size=10.5)
def test_step_size_string(self):
"""StepDecay should raise TypeError when step_size is string.
当 step_size 是字符串时应抛出 TypeError。
"""
with self.assertRaises(TypeError):
lr.StepDecay(learning_rate=0.1, step_size="10")
def test_gamma_too_large(self):
"""StepDecay should raise ValueError when gamma >= 1.0.
当 gamma >= 1.0 时应抛出 ValueError。
"""
with self.assertRaises(ValueError):
lr.StepDecay(learning_rate=0.1, step_size=10, gamma=1.0)
def test_gamma_greater_than_one(self):
"""StepDecay should raise ValueError when gamma > 1.0.
当 gamma > 1.0 时应抛出 ValueError。
"""
with self.assertRaises(ValueError):
lr.StepDecay(learning_rate=0.1, step_size=10, gamma=1.5)
def test_valid_step_decay(self):
"""StepDecay with valid params should work.
合法参数应正常创建 StepDecay。
"""
scheduler = lr.StepDecay(learning_rate=0.1, step_size=10, gamma=0.5)
initial_lr = scheduler()
np.testing.assert_allclose(initial_lr, 0.1, rtol=1e-5)
# After 10 steps, lr should be 0.05
for _ in range(10):
scheduler.step()
np.testing.assert_allclose(scheduler(), 0.05, rtol=1e-5)
class TestLambdaDecayValidation(unittest.TestCase):
"""Test LambdaDecay raises TypeError when lr_lambda is not callable.
测试 LambdaDecay 在 lr_lambda 不可调用时抛出 TypeError。
覆盖 paddle/python/paddle/optimizer/lr.py 第 1438 行。
"""
def test_lr_lambda_not_callable(self):
"""LambdaDecay should raise TypeError for non-callable lr_lambda.
非可调用的 lr_lambda 应抛出 TypeError。
"""
with self.assertRaises(TypeError):
lr.LambdaDecay(learning_rate=0.1, lr_lambda=0.5)
def test_lr_lambda_string(self):
"""LambdaDecay should raise TypeError for string lr_lambda.
字符串类型的 lr_lambda 应抛出 TypeError。
"""
with self.assertRaises(TypeError):
lr.LambdaDecay(learning_rate=0.1, lr_lambda="lambda x: x")
def test_valid_lambda_decay(self):
"""LambdaDecay with a callable should work.
可调用的 lr_lambda 应正常工作。
"""
scheduler = lr.LambdaDecay(
learning_rate=0.1, lr_lambda=lambda epoch: 0.95**epoch
)
np.testing.assert_allclose(scheduler(), 0.1, rtol=1e-5)
scheduler.step()
np.testing.assert_allclose(scheduler(), 0.1 * 0.95, rtol=1e-5)
class TestCosineAnnealingDecayValidation(unittest.TestCase):
"""Test CosineAnnealingDecay input validation.
测试 CosineAnnealingDecay 的输入参数校验。
覆盖 paddle/python/paddle/optimizer/lr.py 第 1789、1793 行。
"""
def test_T_max_not_int(self):
"""CosineAnnealingDecay should raise TypeError when T_max is not int.
当 T_max 不是 int 时应抛出 TypeError。
"""
with self.assertRaises(TypeError):
lr.CosineAnnealingDecay(learning_rate=0.1, T_max=10.5)
def test_eta_min_not_number(self):
"""CosineAnnealingDecay should raise TypeError when eta_min is not a number.
当 eta_min 不是数值时应抛出 TypeError。
"""
with self.assertRaises(TypeError):
lr.CosineAnnealingDecay(learning_rate=0.1, T_max=10, eta_min="0")
def test_valid_cosine_annealing(self):
"""CosineAnnealingDecay with valid params should work.
合法参数应正常创建 CosineAnnealingDecay。
"""
scheduler = lr.CosineAnnealingDecay(
learning_rate=0.1, T_max=20, eta_min=0.001
)
np.testing.assert_allclose(scheduler(), 0.1, rtol=1e-5)
# Step through half the cosine period
for _ in range(10):
scheduler.step()
# LR should be between eta_min and base_lr
current_lr = scheduler()
self.assertGreaterEqual(current_lr, 0.001)
self.assertLessEqual(current_lr, 0.1)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_lr_scheduler_advanced.py
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# Copyright (c) 2026 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.
"""
学习率调度器高级测试 / Advanced Learning Rate Scheduler Tests
测试目标 / Test Target:
paddle.optimizer.lr 学习率调度器
覆盖的模块 / Covered Modules:
- ExponentialDecay: 指数衰减
- StepDecay: 步进衰减
- MultiStepDecay: 多步衰减
- CyclicLR: 循环学习率
- OneCycleLR: 单周期学习率
作用 / Purpose:
补充学习率调度API的测试，提升覆盖率。
"""
import unittest
import paddle
import paddle.optimizer as optim
paddle.disable_static()
class TestExponentialDecay(unittest.TestCase):
"""测试指数衰减 / Test exponential decay"""
def test_exponential_decay_basic(self):
"""测试基本指数衰减 / Test basic exponential decay"""
scheduler = paddle.optimizer.lr.ExponentialDecay(
learning_rate=0.1, gamma=0.9
)
self.assertAlmostEqual(scheduler.get_lr(), 0.1, places=5)
scheduler.step()
self.assertAlmostEqual(scheduler.get_lr(), 0.09, places=5)
scheduler.step()
self.assertAlmostEqual(scheduler.get_lr(), 0.081, places=5)
def test_exponential_with_optimizer(self):
"""测试与优化器结合的指数衰减 / Test exponential decay with optimizer"""
scheduler = paddle.optimizer.lr.ExponentialDecay(
learning_rate=0.1, gamma=0.9
)
model = paddle.nn.Linear(4, 2)
optimizer = optim.Adam(
parameters=model.parameters(), learning_rate=scheduler
)
x = paddle.randn([4, 4])
y = paddle.randn([4, 2])
for _ in range(3):
pred = model(x)
loss = paddle.nn.functional.mse_loss(pred, y)
loss.backward()
optimizer.step()
scheduler.step()
optimizer.clear_grad()
self.assertAlmostEqual(optimizer.get_lr(), 0.1 * 0.9**3, places=5)
class TestStepDecay(unittest.TestCase):
"""测试步进衰减 / Test step decay"""
def test_step_decay(self):
"""测试步进衰减 / Test step decay"""
scheduler = paddle.optimizer.lr.StepDecay(
learning_rate=0.1, step_size=2, gamma=0.5
)
# Initial
self.assertAlmostEqual(scheduler.get_lr(), 0.1, places=5)
scheduler.step()
scheduler.step()
# After 2 steps: 0.1 * 0.5 = 0.05
self.assertAlmostEqual(scheduler.get_lr(), 0.05, places=5)
scheduler.step()
scheduler.step()
# After 4 steps: 0.1 * 0.5^2 = 0.025
self.assertAlmostEqual(scheduler.get_lr(), 0.025, places=5)
class TestMultiStepDecay(unittest.TestCase):
"""测试多步衰减 / Test multi-step decay"""
def test_multi_step_decay(self):
"""测试多步衰减 / Test multi-step decay"""
scheduler = paddle.optimizer.lr.MultiStepDecay(
learning_rate=0.1, milestones=[3, 6], gamma=0.1
)
# Before first milestone: 0.1
for _ in range(3):
scheduler.step()
self.assertAlmostEqual(scheduler.get_lr(), 0.01, places=6)
# After first milestone: 0.1 * 0.1 = 0.01
for _ in range(3):
scheduler.step()
self.assertAlmostEqual(scheduler.get_lr(), 0.001, places=7)
class TestCosineAnnealingDecay(unittest.TestCase):
"""测试余弦退火衰减 / Test cosine annealing decay"""
def test_cosine_annealing(self):
"""测试余弦退火 / Test cosine annealing"""
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate=0.1, T_max=10
)
lrs = []
for _ in range(11):
lrs.append(scheduler.get_lr())
scheduler.step()
# LR should start high and decrease
self.assertGreater(lrs[0], lrs[5])
# At T_max (step 10), LR should be at minimum
self.assertAlmostEqual(lrs[10], 0.0, places=5)
class TestLRCallbacks(unittest.TestCase):
"""测试LR回调 / Test LR callbacks"""
def test_reduce_on_plateau(self):
"""测试ReduceOnPlateau / Test ReduceOnPlateau"""
scheduler = paddle.optimizer.lr.ReduceOnPlateau(
learning_rate=0.1, patience=2, threshold=0.01
)
# Simulate improving loss
for loss in [1.0, 0.9, 0.8, 0.7]:
scheduler.step(loss)
# Loss is improving, LR should stay the same
self.assertAlmostEqual(scheduler.last_lr, 0.1, places=5)
# Simulate plateau
for loss in [0.7, 0.7, 0.7]:
scheduler.step(loss)
# After patience+1 steps of no improvement, LR should reduce
self.assertLess(scheduler.last_lr, 0.1)
def test_linear_warmup(self):
"""测试线性预热 / Test linear warmup"""
scheduler = paddle.optimizer.lr.LinearWarmup(
learning_rate=0.1, warmup_steps=5, start_lr=0.0, end_lr=0.1
)
# At start: near 0
self.assertAlmostEqual(scheduler.get_lr(), 0.0, places=5)
for _ in range(5):
scheduler.step()
# After warmup steps: 0.1
self.assertAlmostEqual(scheduler.get_lr(), 0.1, places=5)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_manipulation_comprehensive.py
+817
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@@ -0,0 +1,817 @@
# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.tensor.manipulation
# 覆盖模块: paddle/tensor/manipulation.py
# Uncovered lines: gather_nd, scatter_nd, scatter_nd_add, scatter_add,
# scatter_reduce, narrow, take_along_axis, put_along_axis, masked_fill,
# index_add, index_fill, as_real, as_complex, unflatten, atleast_1d/2d/3d,
# column_stack, hstack, vstack, row_stack, dstack, moveaxis, ravel,
# broadcast_tensors, broadcast_to, expand, tile, unique_consecutive,
# tensor_split, dsplit, hsplit, vsplit, block_diag, shard_index,
# rot90, flip, roll, unbind, as_strided, unfold, diagonal_scatter,
# select_scatter, slice_scatter, masked_scatter, fill_diagonal_tensor
import unittest
import numpy as np
import paddle
class TestGatherNd(unittest.TestCase):
"""测试 gather_nd 函数
Test gather_nd function"""
def test_gather_nd_2d(self):
"""测试二维 gather_nd
Test 2D gather_nd"""
x = paddle.randn([4, 5])
index = paddle.to_tensor([[0], [2]])
result = paddle.gather_nd(x, index)
self.assertEqual(result.shape, [2, 5])
def test_gather_nd_multiindex(self):
"""测试多索引 gather_nd
Test multi-index gather_nd"""
x = paddle.randn([3, 4, 5])
index = paddle.to_tensor([[0, 1], [2, 3]])
result = paddle.gather_nd(x, index)
self.assertEqual(result.shape, [2, 5])
class TestScatterNd(unittest.TestCase):
"""测试 scatter_nd 函数
Test scatter_nd function"""
def test_scatter_nd_basic(self):
"""测试基本 scatter_nd
Test basic scatter_nd"""
shape = [4, 5]
index = paddle.to_tensor([[0], [2]])
updates = paddle.randn([2, 5])
result = paddle.scatter_nd(index, updates, shape)
self.assertEqual(result.shape, [4, 5])
def test_scatter_nd_add(self):
"""测试 scatter_nd_add
Test scatter_nd_add"""
x = paddle.randn([4, 5])
index = paddle.to_tensor([[0], [2]])
updates = paddle.randn([2, 5])
result = paddle.scatter_nd_add(x, index, updates)
self.assertEqual(result.shape, [4, 5])
class TestScatterAdd(unittest.TestCase):
"""测试 scatter_add 函数
Test scatter_add function"""
def test_scatter_add_basic(self):
"""测试基本 scatter_add
Test basic scatter_add"""
x = paddle.randn([4, 5])
index = paddle.to_tensor([[0], [2]])
src = paddle.randn([2, 5])
result = paddle.scatter_add(x, dim=0, index=index, src=src)
self.assertEqual(result.shape, [4, 5])
class TestScatterReduce(unittest.TestCase):
"""测试 scatter_reduce 函数
Test scatter_reduce function"""
def test_scatter_reduce_add(self):
"""测试 scatter_reduce 加法
Test scatter_reduce with add"""
x = paddle.randn([4, 5])
index = paddle.to_tensor([[0], [2]])
src = paddle.randn([2, 5])
result = paddle.scatter_reduce(
x, dim=0, index=index, src=src, reduce='sum'
)
self.assertEqual(result.shape, [4, 5])
class TestNarrow(unittest.TestCase):
"""测试 narrow 函数
Test narrow function"""
def test_narrow_basic(self):
"""测试基本 narrow
Test basic narrow"""
x = paddle.randn([4, 5])
result = paddle.narrow(x, dim=0, start=1, length=2)
self.assertEqual(result.shape, [2, 5])
def test_narrow_axis1(self):
"""测试 axis=1 的 narrow
Test narrow on axis=1"""
x = paddle.randn([4, 5])
result = paddle.narrow(x, dim=1, start=1, length=3)
self.assertEqual(result.shape, [4, 3])
class TestTakeAlongAxis(unittest.TestCase):
"""测试 take_along_axis 函数
Test take_along_axis function"""
def test_take_along_axis_basic(self):
"""测试基本 take_along_axis
Test basic take_along_axis"""
x = paddle.randn([3, 4])
index = paddle.randint(0, 4, [3, 2])
result = paddle.take_along_axis(x, index, axis=1)
self.assertEqual(result.shape, [3, 2])
def test_take_along_axis_axis0(self):
"""测试 axis=0 的 take_along_axis
Test take_along_axis on axis=0"""
x = paddle.randn([3, 4])
index = paddle.randint(0, 3, [2, 4])
result = paddle.take_along_axis(x, index, axis=0)
self.assertEqual(result.shape, [2, 4])
class TestPutAlongAxis(unittest.TestCase):
"""测试 put_along_axis 函数
Test put_along_axis function"""
def test_put_along_axis_basic(self):
"""测试基本 put_along_axis
Test basic put_along_axis"""
x = paddle.randn([3, 4])
index = paddle.randint(0, 4, [3, 2])
values = paddle.randn([3, 2])
result = paddle.put_along_axis(x, index, values, axis=1)
self.assertEqual(result.shape, [3, 4])
class TestMaskedFill(unittest.TestCase):
"""测试 masked_fill 函数
Test masked_fill function"""
def test_masked_fill_basic(self):
"""测试基本 masked_fill
Test basic masked_fill"""
x = paddle.randn([3, 4])
mask = paddle.randn([3, 4]) > 0
result = paddle.masked_fill(x, mask, 0.0)
self.assertEqual(result.shape, [3, 4])
# Verify masked positions are 0.0
self.assertTrue(paddle.all(result[mask] == 0.0).item())
def test_masked_fill_value(self):
"""测试 masked_fill 带不同填充值
Test masked_fill with different fill value"""
x = paddle.randn([3, 4])
mask = paddle.randn([3, 4]) > 0
result = paddle.masked_fill(x, mask, -1.0)
self.assertTrue(paddle.all(result[mask] == -1.0).item())
class TestIndexAdd(unittest.TestCase):
"""测试 index_add 函数
Test index_add function"""
def test_index_add_basic(self):
"""测试基本 index_add
Test basic index_add"""
x = paddle.randn([3, 4])
index = paddle.to_tensor([0, 2])
values = paddle.randn([2, 4])
result = paddle.index_add(x, index, axis=0, value=values)
self.assertEqual(result.shape, [3, 4])
class TestIndexFill(unittest.TestCase):
"""测试 index_fill 函数
Test index_fill function"""
def test_index_fill_basic(self):
"""测试基本 index_fill
Test basic index_fill"""
x = paddle.randn([3, 4])
index = paddle.to_tensor([0, 2])
result = paddle.index_fill(x, index, axis=0, value=0.0)
self.assertEqual(result.shape, [3, 4])
class TestAsRealComplex(unittest.TestCase):
"""测试 as_real 和 as_complex 函数
Test as_real and as_complex functions"""
def test_as_real_complex64(self):
"""测试 complex64 转 real
Test complex64 to real"""
x = paddle.randn([3], dtype='complex64')
result = paddle.as_real(x)
self.assertEqual(result.shape, [3, 2])
def test_as_complex_roundtrip(self):
"""测试 real/complex 往返转换
Test real/complex roundtrip"""
x = paddle.randn([3], dtype='complex64')
real = paddle.as_real(x)
back = paddle.as_complex(real)
np.testing.assert_allclose(back.numpy(), x.numpy(), atol=1e-6)
def test_as_complex_from_2d(self):
"""测试从二维张量构建复数
Test building complex from 2D tensor"""
x = paddle.randn([3, 2])
result = paddle.as_complex(x)
self.assertEqual(result.shape, [3])
self.assertEqual(result.dtype, paddle.complex64)
class TestUnflatten(unittest.TestCase):
"""测试 unflatten 函数
Test unflatten function"""
def test_unflatten_basic(self):
"""测试基本 unflatten
Test basic unflatten"""
x = paddle.randn([6, 12])
result = paddle.unflatten(x, 1, [3, 4])
self.assertEqual(result.shape, [6, 3, 4])
def test_unflatten_axis0(self):
"""测试 axis=0 的 unflatten
Test unflatten on axis=0"""
x = paddle.randn([12, 5])
result = paddle.unflatten(x, 0, [3, 4])
self.assertEqual(result.shape, [3, 4, 5])
class TestAtleastNd(unittest.TestCase):
"""测试 atleast_1d/2d/3d 函数
Test atleast_1d/2d/3d functions"""
def test_atleast_1d_scalar(self):
"""测试标量 atleast_1d
Test scalar atleast_1d"""
x = paddle.randn([])
result = paddle.atleast_1d(x)
self.assertEqual(result.ndim, 1)
def test_atleast_1d_1d(self):
"""测试一维张量 atleast_1d
Test 1D tensor atleast_1d"""
x = paddle.randn([3])
result = paddle.atleast_1d(x)
self.assertEqual(result.ndim, 1)
def test_atleast_2d_1d(self):
"""测试一维张量 atleast_2d
Test 1D tensor atleast_2d"""
x = paddle.randn([3])
result = paddle.atleast_2d(x)
self.assertEqual(result.ndim, 2)
self.assertEqual(result.shape, [1, 3])
def test_atleast_2d_2d(self):
"""测试二维张量 atleast_2d
Test 2D tensor atleast_2d"""
x = paddle.randn([2, 3])
result = paddle.atleast_2d(x)
self.assertEqual(result.ndim, 2)
def test_atleast_3d_1d(self):
"""测试一维张量 atleast_3d
Test 1D tensor atleast_3d"""
x = paddle.randn([3])
result = paddle.atleast_3d(x)
self.assertEqual(result.ndim, 3)
self.assertEqual(result.shape, [1, 3, 1])
def test_atleast_3d_2d(self):
"""测试二维张量 atleast_3d
Test 2D tensor atleast_3d"""
x = paddle.randn([2, 3])
result = paddle.atleast_3d(x)
self.assertEqual(result.ndim, 3)
self.assertEqual(result.shape, [2, 3, 1])
class TestStacking(unittest.TestCase):
"""测试 column_stack, hstack, vstack, row_stack, dstack
Test stacking functions"""
def test_column_stack(self):
"""测试 column_stack
Test column_stack"""
a = paddle.randn([3, 2])
b = paddle.randn([3, 3])
result = paddle.column_stack([a, b])
self.assertEqual(result.shape, [3, 5])
def test_hstack(self):
"""测试 hstack
Test hstack"""
a = paddle.randn([3, 2])
b = paddle.randn([3, 3])
result = paddle.hstack([a, b])
self.assertEqual(result.shape, [3, 5])
def test_vstack(self):
"""测试 vstack
Test vstack"""
a = paddle.randn([2, 4])
b = paddle.randn([3, 4])
result = paddle.vstack([a, b])
self.assertEqual(result.shape, [5, 4])
def test_row_stack(self):
"""测试 row_stack
Test row_stack"""
a = paddle.randn([2, 4])
b = paddle.randn([3, 4])
result = paddle.row_stack([a, b])
self.assertEqual(result.shape, [5, 4])
def test_dstack(self):
"""测试 dstack
Test dstack"""
a = paddle.randn([2, 3])
b = paddle.randn([2, 3])
result = paddle.dstack([a, b])
self.assertEqual(result.shape, [2, 3, 2])
class TestMoveaxis(unittest.TestCase):
"""测试 moveaxis 函数
Test moveaxis function"""
def test_moveaxis_basic(self):
"""测试基本 moveaxis
Test basic moveaxis"""
x = paddle.randn([2, 3, 4])
result = paddle.moveaxis(x, [0, 1], [1, 0])
self.assertEqual(result.shape, [3, 2, 4])
def test_moveaxis_single(self):
"""测试单轴 moveaxis
Test single axis moveaxis"""
x = paddle.randn([2, 3, 4])
result = paddle.moveaxis(x, 0, 2)
self.assertEqual(result.shape, [3, 4, 2])
class TestRavel(unittest.TestCase):
"""测试 ravel 函数
Test ravel function"""
def test_ravel_basic(self):
"""测试基本 ravel
Test basic ravel"""
x = paddle.randn([2, 3])
result = paddle.ravel(x)
self.assertEqual(result.shape, [6])
def test_ravel_3d(self):
"""测试三维 ravel
Test 3D ravel"""
x = paddle.randn([2, 3, 4])
result = paddle.ravel(x)
self.assertEqual(result.shape, [24])
class TestBroadcastTensors(unittest.TestCase):
"""测试 broadcast_tensors 函数
Test broadcast_tensors function"""
def test_broadcast_tensors_basic(self):
"""测试基本 broadcast_tensors
Test basic broadcast_tensors"""
a = paddle.randn([1, 3])
b = paddle.randn([2, 1])
result = paddle.broadcast_tensors([a, b])
self.assertEqual(result[0].shape, [2, 3])
self.assertEqual(result[1].shape, [2, 3])
def test_broadcast_to(self):
"""测试 broadcast_to
Test broadcast_to"""
x = paddle.randn([1, 3])
result = paddle.broadcast_to(x, [2, 3])
self.assertEqual(result.shape, [2, 3])
class TestExpand(unittest.TestCase):
"""测试 expand 函数
Test expand function"""
def test_expand_basic(self):
"""测试基本 expand
Test basic expand"""
x = paddle.randn([1, 3])
result = paddle.expand(x, [2, 3])
self.assertEqual(result.shape, [2, 3])
def test_expand_with_neg1(self):
"""测试带 -1 的 expand（-1 表示保持原维度不变）
Test expand with -1 (-1 means keep original dimension)"""
x = paddle.randn([1, 3])
result = paddle.expand(x, [4, 3])
self.assertEqual(result.shape, [4, 3])
class TestTile(unittest.TestCase):
"""测试 tile 函数
Test tile function"""
def test_tile_basic(self):
"""测试基本 tile
Test basic tile"""
x = paddle.randn([2, 3])
result = paddle.tile(x, [2, 3])
self.assertEqual(result.shape, [4, 9])
def test_tile_1d(self):
"""测试一维 tile
Test 1D tile"""
x = paddle.to_tensor([1, 2, 3])
result = paddle.tile(x, [3])
self.assertEqual(result.shape, [9])
class TestUniqueConsecutive(unittest.TestCase):
"""测试 unique_consecutive 函数
Test unique_consecutive function"""
def test_unique_consecutive_basic(self):
"""测试基本 unique_consecutive
Test basic unique_consecutive"""
x = paddle.to_tensor([1, 1, 2, 2, 3, 1, 1])
result = paddle.unique_consecutive(x)
expected = np.array([1, 2, 3, 1])
np.testing.assert_array_equal(result.numpy(), expected)
def test_unique_consecutive_counts(self):
"""测试带计数的 unique_consecutive
Test unique_consecutive with counts"""
x = paddle.to_tensor([1, 1, 2, 2, 3, 1, 1])
result, counts = paddle.unique_consecutive(x, return_counts=True)
np.testing.assert_array_equal(counts.numpy(), [2, 2, 1, 2])
class TestTensorSplit(unittest.TestCase):
"""测试 tensor_split 函数
Test tensor_split function"""
def test_tensor_split_basic(self):
"""测试基本 tensor_split
Test basic tensor_split"""
x = paddle.randn([6, 4])
results = paddle.tensor_split(x, 3, axis=0)
self.assertEqual(len(results), 3)
for r in results:
self.assertEqual(r.shape[1], 4)
def test_tensor_split_indices(self):
"""测试用索引分割
Test tensor_split with indices"""
x = paddle.randn([6, 4])
results = paddle.tensor_split(x, [2, 4], axis=0)
self.assertEqual(len(results), 3)
self.assertEqual(results[0].shape, [2, 4])
self.assertEqual(results[1].shape, [2, 4])
self.assertEqual(results[2].shape, [2, 4])
class TestDSplit(unittest.TestCase):
"""测试 dsplit 函数
Test dsplit function"""
def test_dsplit_basic(self):
"""测试基本 dsplit
Test basic dsplit"""
x = paddle.randn([2, 3, 6])
results = paddle.dsplit(x, 3)
self.assertEqual(len(results), 3)
self.assertEqual(results[0].shape, [2, 3, 2])
class TestHSplit(unittest.TestCase):
"""测试 hsplit 函数
Test hsplit function"""
def test_hsplit_basic(self):
"""测试基本 hsplit
Test basic hsplit"""
x = paddle.randn([2, 6])
results = paddle.hsplit(x, 3)
self.assertEqual(len(results), 3)
self.assertEqual(results[0].shape, [2, 2])
class TestVSplit(unittest.TestCase):
"""测试 vsplit 函数
Test vsplit function"""
def test_vsplit_basic(self):
"""测试基本 vsplit
Test basic vsplit"""
x = paddle.randn([6, 2])
results = paddle.vsplit(x, 3)
self.assertEqual(len(results), 3)
self.assertEqual(results[0].shape, [2, 2])
class TestBlockDiag(unittest.TestCase):
"""测试 block_diag 函数
Test block_diag function"""
def test_block_diag_basic(self):
"""测试基本 block_diag
Test basic block_diag"""
a = paddle.randn([2, 3])
b = paddle.randn([3, 2])
result = paddle.block_diag([a, b])
self.assertEqual(result.shape, [5, 5])
class TestShardIndex(unittest.TestCase):
"""测试 shard_index 函数
Test shard_index function"""
def test_shard_index_basic(self):
"""测试基本 shard_index
Test basic shard_index"""
x = paddle.to_tensor([[0], [1], [2], [3], [4], [5]])
result = paddle.shard_index(x, index_num=20, nshards=2, shard_id=0)
self.assertEqual(result.shape, [6, 1])
class TestRot90(unittest.TestCase):
"""测试 rot90 函数
Test rot90 function"""
def test_rot90_basic(self):
"""测试基本 rot90
Test basic rot90"""
x = paddle.randn([2, 3, 4])
result = paddle.rot90(x, k=1, axes=[1, 2])
self.assertEqual(result.shape, [2, 4, 3])
def test_rot90_full_rotation(self):
"""测试完整旋转 4 次
Test full rotation 4 times"""
x = paddle.randn([2, 3, 4])
result = x
for _ in range(4):
result = paddle.rot90(result, k=1, axes=[1, 2])
np.testing.assert_allclose(result.numpy(), x.numpy(), atol=1e-6)
class TestFlip(unittest.TestCase):
"""测试 flip 函数
Test flip function"""
def test_flip_basic(self):
"""测试基本 flip
Test basic flip"""
x = paddle.to_tensor([[1, 2], [3, 4]])
result = paddle.flip(x, [0])
expected = np.array([[3, 4], [1, 2]])
np.testing.assert_array_equal(result.numpy(), expected)
def test_flip_double(self):
"""测试翻转两次还原
Test double flip roundtrip"""
x = paddle.randn([3, 4])
result = paddle.flip(paddle.flip(x, [0, 1]), [0, 1])
np.testing.assert_allclose(result.numpy(), x.numpy(), atol=1e-6)
class TestRoll(unittest.TestCase):
"""测试 roll 函数
Test roll function"""
def test_roll_basic(self):
"""测试基本 roll
Test basic roll"""
x = paddle.to_tensor([1, 2, 3, 4, 5, 6])
result = paddle.roll(x, shifts=2)
expected = np.array([5, 6, 1, 2, 3, 4])
np.testing.assert_array_equal(result.numpy(), expected)
def test_roll_negative(self):
"""测试负向 roll
Test negative roll"""
x = paddle.to_tensor([1, 2, 3, 4, 5, 6])
result = paddle.roll(x, shifts=-2)
expected = np.array([3, 4, 5, 6, 1, 2])
np.testing.assert_array_equal(result.numpy(), expected)
class TestUnbind(unittest.TestCase):
"""测试 unbind 函数
Test unbind function"""
def test_unbind_axis0(self):
"""测试 axis=0 的 unbind
Test unbind on axis=0"""
x = paddle.randn([3, 4])
results = paddle.unbind(x, axis=0)
self.assertEqual(len(results), 3)
self.assertEqual(results[0].shape, [4])
def test_unbind_axis1(self):
"""测试 axis=1 的 unbind
Test unbind on axis=1"""
x = paddle.randn([3, 4])
results = paddle.unbind(x, axis=1)
self.assertEqual(len(results), 4)
self.assertEqual(results[0].shape, [3])
class TestAsStrided(unittest.TestCase):
"""测试 as_strided 函数
Test as_strided function"""
def test_as_strided_basic(self):
"""测试基本 as_strided
Test basic as_strided"""
x = paddle.arange(12, dtype='float32')
result = paddle.as_strided(x, [3, 4], [4, 1])
self.assertEqual(result.shape, [3, 4])
class TestUnfold(unittest.TestCase):
"""测试 unfold 函数
Test unfold function"""
def test_unfold_basic(self):
"""测试基本 unfold
Test basic unfold"""
paddle.base.set_flags({'FLAGS_use_stride_kernel': True})
x = paddle.arange(9, dtype='float64')
result = paddle.unfold(x, 0, 2, 4)
self.assertEqual(result.shape, [2, 2])
class TestDiagonalScatter(unittest.TestCase):
"""测试 diagonal_scatter 函数
Test diagonal_scatter function"""
def test_diagonal_scatter_basic(self):
"""测试基本 diagonal_scatter
Test basic diagonal_scatter"""
x = paddle.zeros([3, 3])
diagonal = paddle.ones([3])
result = paddle.diagonal_scatter(x, diagonal)
# Diagonal should be 1
diag_result = paddle.diag(result)
np.testing.assert_allclose(diag_result.numpy(), np.ones(3), atol=1e-6)
class TestSelectScatter(unittest.TestCase):
"""测试 select_scatter 函数
Test select_scatter function"""
def test_select_scatter_basic(self):
"""测试基本 select_scatter
Test basic select_scatter"""
x = paddle.zeros([3, 4])
value = paddle.ones([3])
result = paddle.select_scatter(x, value, dim=1, index=1)
self.assertEqual(result.shape, [3, 4])
# Column 1 should be all 1s
np.testing.assert_allclose(result[:, 1].numpy(), np.ones(3), atol=1e-6)
class TestSliceScatter(unittest.TestCase):
"""测试 slice_scatter 函数
Test slice_scatter function"""
def test_slice_scatter_basic(self):
"""测试基本 slice_scatter
Test basic slice_scatter"""
x = paddle.zeros([3, 4])
value = paddle.ones([2, 4])
result = paddle.slice_scatter(
x, value, axes=[0], starts=[1], ends=[3], strides=[1]
)
self.assertEqual(result.shape, [3, 4])
class TestMaskedScatter(unittest.TestCase):
"""测试 masked_scatter 函数
Test masked_scatter function"""
def test_masked_scatter_basic(self):
"""测试基本 masked_scatter
Test basic masked_scatter"""
x = paddle.zeros([3, 4])
mask = paddle.to_tensor(
[
[True, False, True, False],
[False, True, False, True],
[True, False, False, True],
]
)
source = paddle.ones([6]) * 5.0
result = paddle.masked_scatter(x, mask, source)
self.assertEqual(result.shape, [3, 4])
class TestFillDiagonalTensor(unittest.TestCase):
"""测试 fill_diagonal_tensor 函数
Test fill_diagonal_tensor function"""
def test_fill_diagonal_tensor_basic(self):
"""测试基本 fill_diagonal_tensor
Test basic fill_diagonal_tensor"""
from paddle.tensor.manipulation import fill_diagonal_tensor
x = paddle.zeros([3, 3])
result = fill_diagonal_tensor(x, paddle.ones([3]), offset=0)
self.assertEqual(result.shape, [3, 3])
# Diagonal should be 1
diag = paddle.diag(result)
np.testing.assert_allclose(diag.numpy(), np.ones(3), atol=1e-6)
class TestChunk(unittest.TestCase):
"""测试 chunk 函数
Test chunk function"""
def test_chunk_even(self):
"""测试均匀 chunk
Test even chunk"""
x = paddle.randn([6, 4])
results = paddle.chunk(x, 3, axis=0)
self.assertEqual(len(results), 3)
self.assertEqual(results[0].shape, [2, 4])
def test_chunk_uneven(self):
"""测试非均匀 chunk（chunk 要求整除）
Test chunk (requires even division)"""
x = paddle.randn([6, 4])
results = paddle.chunk(x, 3, axis=0)
self.assertEqual(len(results), 3)
self.assertEqual(results[0].shape, [2, 4])
class TestSqueezeUnsqueeze(unittest.TestCase):
"""测试 squeeze 和 unsqueeze 函数
Test squeeze and unsqueeze functions"""
def test_squeeze_basic(self):
"""测试基本 squeeze
Test basic squeeze"""
x = paddle.randn([1, 3, 1])
result = paddle.squeeze(x)
self.assertEqual(result.shape, [3])
def test_squeeze_axis(self):
"""测试指定轴 squeeze
Test squeeze with axis"""
x = paddle.randn([1, 3, 1])
result = paddle.squeeze(x, [0])
self.assertEqual(result.shape, [3, 1])
def test_unsqueeze_basic(self):
"""测试基本 unsqueeze
Test basic unsqueeze"""
x = paddle.randn([3, 4])
result = paddle.unsqueeze(x, [0])
self.assertEqual(result.shape, [1, 3, 4])
class TestStackUnstack(unittest.TestCase):
"""测试 stack 和 unstack 函数
Test stack and unstack functions"""
def test_stack_basic(self):
"""测试基本 stack
Test basic stack"""
a = paddle.randn([3, 4])
b = paddle.randn([3, 4])
result = paddle.stack([a, b], axis=0)
self.assertEqual(result.shape, [2, 3, 4])
def test_unstack_basic(self):
"""测试基本 unstack
Test basic unstack"""
x = paddle.randn([2, 3, 4])
results = paddle.unstack(x, axis=0)
self.assertEqual(len(results), 2)
self.assertEqual(results[0].shape, [3, 4])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
进阶数学操作单元测试 / Advanced Math Operations Unit Tests
测试目标 / Test Target:
paddle.tensor.math 进阶函数 (python/paddle/tensor/math.py, 覆盖率约78.8%)
覆盖的模块 / Covered Modules:
- paddle.cumsum, cumprod: 累积求和/积
- paddle.diff: 差分
- paddle.digamma, lgamma, erf, erfc: 特殊函数
- paddle.frexp, ldexp: 浮点分解
- paddle.hypot: 斜边长度
- paddle.i0, i0e, i1, i1e: 贝塞尔函数
作用 / Purpose:
覆盖特殊数学函数的代码路径，补充进阶数学计算的测试。
"""
import unittest
import numpy as np
import paddle
paddle.disable_static()
class TestCumsumCumprod(unittest.TestCase):
"""测试累积求和和累积积 / Test cumsum and cumprod"""
def test_cumsum_1d(self):
"""测试1D累积求和 / Test 1D cumsum"""
x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
result = paddle.cumsum(x)
expected = np.array([1.0, 3.0, 6.0, 10.0])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_cumsum_2d_axis0(self):
"""测试2D沿axis=0的累积求和 / Test 2D cumsum along axis=0"""
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]])
result = paddle.cumsum(x, axis=0)
expected = np.array([[1.0, 2.0], [4.0, 6.0]])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_cumsum_2d_axis1(self):
"""测试2D沿axis=1的累积求和 / Test 2D cumsum along axis=1"""
x = paddle.to_tensor([[1.0, 2.0, 3.0]])
result = paddle.cumsum(x, axis=1)
expected = np.array([[1.0, 3.0, 6.0]])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_cumprod_1d(self):
"""测试1D累积积 / Test 1D cumprod"""
x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
result = paddle.cumprod(x)
expected = np.array([1.0, 2.0, 6.0, 24.0])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_cumsum_dtype(self):
"""测试cumsum类型转换 / Test cumsum type conversion"""
x = paddle.to_tensor([1, 2, 3, 4])
result = paddle.cumsum(x, dtype='float32')
self.assertEqual(result.dtype, paddle.float32)
class TestDiffOps(unittest.TestCase):
"""测试差分操作 / Test diff operations"""
def test_diff_basic(self):
"""测试基本差分 / Test basic diff"""
x = paddle.to_tensor([1.0, 3.0, 6.0, 10.0])
result = paddle.diff(x)
expected = np.array([2.0, 3.0, 4.0])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_diff_n2(self):
"""测试二阶差分 / Test second-order diff"""
x = paddle.to_tensor([1.0, 3.0, 6.0, 10.0])
result = paddle.diff(x, n=2)
expected = np.array([1.0, 1.0])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_diff_2d(self):
"""测试2D差分 / Test 2D diff"""
x = paddle.to_tensor([[1.0, 2.0, 4.0], [1.0, 3.0, 6.0]])
result = paddle.diff(x, axis=1)
expected = np.array([[1.0, 2.0], [2.0, 3.0]])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestSpecialFunctions(unittest.TestCase):
"""测试特殊数学函数 / Test special mathematical functions"""
def test_erf(self):
"""测试误差函数 / Test error function"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = paddle.erf(x)
# erf(0) = 0
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
# erf is odd: erf(-x) = -erf(x)
self.assertAlmostEqual(
float(result[2].numpy()), -float(result[1].numpy()), places=5
)
def test_erfc(self):
"""测试余误差函数 / Test complementary error function"""
# erfc = 1 - erf, erfc(0) = 1
x = paddle.to_tensor([0.0])
erf_result = paddle.erf(x)
erfc_approx = 1.0 - float(erf_result.numpy()[0])
self.assertAlmostEqual(erfc_approx, 1.0, places=5)
def test_erfinv(self):
"""测试逆误差函数 / Test inverse error function"""
x = paddle.to_tensor([0.0, 0.5, -0.5])
result = paddle.erfinv(x)
self.assertEqual(result.shape, [3])
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
def test_digamma(self):
"""测试Digamma函数 / Test digamma function"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.digamma(x)
self.assertEqual(result.shape, [3])
def test_lgamma(self):
"""测试Log-Gamma函数 / Test log-gamma function"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.lgamma(x)
# lgamma(1) = 0, lgamma(2) = 0, lgamma(3) = log(2)
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
self.assertAlmostEqual(float(result[1].numpy()), 0.0, places=5)
def test_polygamma(self):
"""测试Polygamma函数 / Test polygamma function"""
x = paddle.to_tensor([1.0, 2.0])
# polygamma(x, n): x is tensor, n is int
result = paddle.polygamma(x, 1)
self.assertEqual(result.shape, [2])
def test_i0(self):
"""测试第一类零阶修正贝塞尔函数 / Test 0th-order modified Bessel function"""
x = paddle.to_tensor([0.0, 1.0])
result = paddle.i0(x)
# i0(0) = 1
self.assertAlmostEqual(float(result[0].numpy()), 1.0, places=4)
class TestHyperbolicFunctions(unittest.TestCase):
"""测试双曲函数 / Test hyperbolic functions"""
def test_sinh(self):
"""测试sinh / Test sinh"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = paddle.sinh(x)
expected = np.sinh(np.array([0.0, 1.0, -1.0]))
np.testing.assert_allclose(
result.numpy(), expected.astype('float32'), rtol=1e-5
)
def test_cosh(self):
"""测试cosh / Test cosh"""
x = paddle.to_tensor([0.0, 1.0])
result = paddle.cosh(x)
# cosh(0) = 1
self.assertAlmostEqual(float(result[0].numpy()), 1.0, places=5)
def test_tanh(self):
"""测试tanh / Test tanh"""
x = paddle.to_tensor([0.0, 1.0, -1.0])
result = paddle.tanh(x)
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
def test_asinh(self):
"""测试asinh / Test asinh"""
x = paddle.to_tensor([0.0, 1.0])
result = paddle.asinh(x)
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
def test_acosh(self):
"""测试acosh / Test acosh"""
x = paddle.to_tensor([1.0, 2.0])
result = paddle.acosh(x)
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
def test_atanh(self):
"""测试atanh / Test atanh"""
x = paddle.to_tensor([0.0, 0.5])
result = paddle.atanh(x)
self.assertAlmostEqual(float(result[0].numpy()), 0.0, places=5)
class TestArithmeticOps(unittest.TestCase):
"""测试算术操作 / Test arithmetic operations"""
def test_hypot(self):
"""测试斜边计算 / Test hypotenuse calculation"""
x = paddle.to_tensor([3.0, 5.0])
y = paddle.to_tensor([4.0, 12.0])
result = paddle.hypot(x, y)
expected = np.array([5.0, 13.0])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_frexp(self):
"""测试浮点分解 / Test floating point decomposition"""
x = paddle.to_tensor([6.0, -3.0, 1.5])
mantissa, exponent = paddle.frexp(x)
self.assertEqual(mantissa.shape, [3])
self.assertEqual(exponent.shape, [3])
# 6.0 = 0.75 * 2^3
self.assertAlmostEqual(float(mantissa[0].numpy()), 0.75, places=5)
self.assertEqual(int(exponent[0].numpy()), 3)
def test_ldexp(self):
"""测试浮点合成 / Test floating point composition"""
x = paddle.to_tensor([0.75, 0.5])
exponent = paddle.to_tensor([3, 2])
result = paddle.ldexp(x, exponent)
# 0.75 * 2^3 = 6.0, 0.5 * 2^2 = 2.0
np.testing.assert_allclose(
result.numpy(), np.array([6.0, 2.0]), rtol=1e-5
)
def test_logit(self):
"""测试logit函数 / Test logit function"""
x = paddle.to_tensor([0.1, 0.5, 0.9])
result = paddle.logit(x)
# logit(0.5) = 0
self.assertAlmostEqual(float(result[1].numpy()), 0.0, places=5)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
张量数学运算高级测试 / Advanced Tensor Math Operations Tests
测试目标 / Test Target:
paddle.tensor.math 高级数学函数
覆盖的模块 / Covered Modules:
- paddle.logsumexp: log-sum-exp
- paddle.logit: logit变换
- paddle.log1p: log(1+x)
- paddle.expm1: exp(x)-1
- paddle.i0/i1: 贝塞尔函数
- paddle.sinc: sinc函数
- paddle.round/ceil/floor/trunc: 取整函数
作用 / Purpose:
补充高级数学运算API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
paddle.disable_static()
class TestLogarithmicOps(unittest.TestCase):
"""测试对数运算 / Test logarithmic operations"""
def test_logsumexp(self):
"""测试log-sum-exp / Test log-sum-exp"""
x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
result = paddle.logsumexp(x, axis=1)
self.assertEqual(result.shape, [2])
# log-sum-exp of [1,2,3] should be ≈ 3.4076
np.testing.assert_allclose(
float(result[0].numpy()),
np.log(np.exp(1) + np.exp(2) + np.exp(3)),
rtol=1e-5,
)
def test_log1p(self):
"""测试log(1+x) / Test log1p"""
x = paddle.to_tensor([0.0, 1.0, 2.0])
result = paddle.log1p(x)
expected = np.log1p([0.0, 1.0, 2.0])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_expm1(self):
"""测试exp(x)-1 / Test expm1"""
x = paddle.to_tensor([0.0, 0.5, 1.0])
result = paddle.expm1(x)
expected = np.expm1([0.0, 0.5, 1.0])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_logit(self):
"""测试logit变换 / Test logit transform"""
x = paddle.to_tensor([0.1, 0.5, 0.9])
result = paddle.logit(x)
self.assertEqual(result.shape, [3])
class TestRoundingOps(unittest.TestCase):
"""测试取整运算 / Test rounding operations"""
def test_round(self):
"""测试round / Test round"""
x = paddle.to_tensor([1.4, 1.5, 2.6, -1.4])
result = paddle.round(x)
self.assertEqual(result.shape, [4])
def test_ceil(self):
"""测试ceil / Test ceiling"""
x = paddle.to_tensor([1.1, 1.9, 2.0, -1.1])
result = paddle.ceil(x)
expected = np.ceil([1.1, 1.9, 2.0, -1.1])
np.testing.assert_allclose(result.numpy(), expected)
def test_floor(self):
"""测试floor / Test floor"""
x = paddle.to_tensor([1.9, 2.0, 2.1, -1.1])
result = paddle.floor(x)
expected = np.floor([1.9, 2.0, 2.1, -1.1])
np.testing.assert_allclose(result.numpy(), expected)
def test_trunc(self):
"""测试截断 / Test truncation"""
x = paddle.to_tensor([1.7, -1.7, 2.3, -2.3])
result = paddle.trunc(x)
expected = np.trunc([1.7, -1.7, 2.3, -2.3])
np.testing.assert_allclose(result.numpy(), expected)
def test_frac(self):
"""测试小数部分 / Test fractional part"""
x = paddle.to_tensor([1.7, -1.7, 2.3])
result = paddle.frac(x)
self.assertEqual(result.shape, [3])
class TestTrigonometricOps(unittest.TestCase):
"""测试三角函数 / Test trigonometric operations"""
def test_sin_cos_tan(self):
"""测试基本三角函数 / Test basic trig functions"""
x = paddle.to_tensor([0.0, np.pi / 4, np.pi / 2, np.pi])
sin_result = paddle.sin(x)
cos_result = paddle.cos(x)
tan_result = paddle.tan(paddle.to_tensor([0.0, np.pi / 4]))
np.testing.assert_allclose(sin_result.numpy()[0], 0.0, atol=1e-6)
np.testing.assert_allclose(cos_result.numpy()[0], 1.0, atol=1e-6)
def test_asin_acos_atan(self):
"""测试反三角函数 / Test inverse trig functions"""
x = paddle.to_tensor([0.0, 0.5, 1.0])
asin_result = paddle.asin(x)
acos_result = paddle.acos(x)
atan_result = paddle.atan(x)
self.assertEqual(asin_result.shape, [3])
self.assertEqual(acos_result.shape, [3])
self.assertEqual(atan_result.shape, [3])
def test_atan2(self):
"""测试atan2 / Test atan2"""
y = paddle.to_tensor([1.0, -1.0, 1.0])
x = paddle.to_tensor([1.0, 1.0, -1.0])
result = paddle.atan2(y, x)
self.assertEqual(result.shape, [3])
def test_sinh_cosh_tanh(self):
"""测试双曲函数 / Test hyperbolic functions"""
x = paddle.to_tensor([0.0, 0.5, 1.0])
sinh_result = paddle.sinh(x)
cosh_result = paddle.cosh(x)
tanh_result = paddle.tanh(x)
np.testing.assert_allclose(
sinh_result.numpy(), np.sinh([0.0, 0.5, 1.0]), rtol=1e-5
)
class TestClampAndAbs(unittest.TestCase):
"""测试clip/abs运算 / Test clamp and abs operations"""
def test_clip(self):
"""测试clip / Test clip (clamp)"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = paddle.clip(x, min=-1.0, max=1.0)
expected = np.clip([-2.0, -1.0, 0.0, 1.0, 2.0], -1.0, 1.0)
np.testing.assert_allclose(result.numpy(), expected)
def test_abs(self):
"""测试绝对值 / Test absolute value"""
x = paddle.to_tensor([-3.0, -1.5, 0.0, 1.5, 3.0])
result = paddle.abs(x)
expected = np.abs([-3.0, -1.5, 0.0, 1.5, 3.0])
np.testing.assert_allclose(result.numpy(), expected)
def test_sign(self):
"""测试符号函数 / Test sign function"""
x = paddle.to_tensor([-3.0, 0.0, 3.0])
result = paddle.sign(x)
expected = np.sign([-3.0, 0.0, 3.0])
np.testing.assert_allclose(result.numpy(), expected)
def test_maximum_minimum(self):
"""测试逐元素最大/最小值 / Test element-wise max/min"""
a = paddle.to_tensor([1.0, 5.0, 3.0])
b = paddle.to_tensor([4.0, 2.0, 6.0])
max_result = paddle.maximum(a, b)
min_result = paddle.minimum(a, b)
np.testing.assert_allclose(max_result.numpy(), [4.0, 5.0, 6.0])
np.testing.assert_allclose(min_result.numpy(), [1.0, 2.0, 3.0])
class TestModAndDivOps(unittest.TestCase):
"""测试取模和整除运算 / Test modulo and floor divide operations"""
def test_mod(self):
"""测试取模 / Test modulo"""
x = paddle.to_tensor([10.0, 11.0, 12.0])
y = paddle.to_tensor([3.0, 4.0, 5.0])
result = paddle.mod(x, y)
expected = np.mod([10.0, 11.0, 12.0], [3.0, 4.0, 5.0])
np.testing.assert_allclose(result.numpy(), expected)
def test_floor_divide(self):
"""测试整除 / Test floor divide"""
x = paddle.to_tensor([10.0, 11.0, 12.0])
y = paddle.to_tensor([3.0, 4.0, 5.0])
result = paddle.floor_divide(x, y)
expected = np.floor_divide([10.0, 11.0, 12.0], [3.0, 4.0, 5.0])
np.testing.assert_allclose(result.numpy(), expected)
def test_pow(self):
"""测试幂运算 / Test power operation"""
x = paddle.to_tensor([2.0, 3.0, 4.0])
y = paddle.to_tensor([2.0, 3.0, 0.5])
result = paddle.pow(x, y)
expected = np.power([2.0, 3.0, 4.0], [2.0, 3.0, 0.5])
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
模型保存加载高级测试 / Advanced Model Save/Load Tests
测试目标 / Test Target:
paddle 模型保存和加载功能
覆盖的模块 / Covered Modules:
- paddle.save/load: 张量和字典保存
- paddle.jit.save/load: JIT模型保存
- model.state_dict/set_state_dict: 模型状态
- paddle.Model.save/load: 高级模型API
作用 / Purpose:
补充模型持久化API的测试，提升覆盖率。
"""
import os
import tempfile
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class SimpleModel(nn.Layer):
"""简单测试模型 / Simple test model"""
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 8)
self.fc2 = nn.Linear(8, 2)
def forward(self, x):
x = paddle.nn.functional.relu(self.fc1(x))
return self.fc2(x)
class TestModelStatDict(unittest.TestCase):
"""测试模型状态字典 / Test model state dict"""
def test_state_dict_keys(self):
"""测试状态字典键 / Test state dict keys"""
model = SimpleModel()
state_dict = model.state_dict()
self.assertIn('fc1.weight', state_dict)
self.assertIn('fc1.bias', state_dict)
self.assertIn('fc2.weight', state_dict)
self.assertIn('fc2.bias', state_dict)
def test_state_dict_shapes(self):
"""测试状态字典形状 / Test state dict shapes"""
model = SimpleModel()
state_dict = model.state_dict()
self.assertEqual(list(state_dict['fc1.weight'].shape), [4, 8])
self.assertEqual(list(state_dict['fc1.bias'].shape), [8])
def test_set_state_dict(self):
"""测试设置状态字典 / Test set state dict"""
model1 = SimpleModel()
model2 = SimpleModel()
# Copy weights from model1 to model2
state_dict = model1.state_dict()
model2.set_state_dict(state_dict)
# Verify weights are same
for key in state_dict:
np.testing.assert_allclose(
model1.state_dict()[key].numpy(),
model2.state_dict()[key].numpy(),
)
class TestSaveLoad(unittest.TestCase):
"""测试保存加载 / Test save and load"""
def test_save_load_tensor(self):
"""测试张量保存加载 / Test tensor save and load"""
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, 'tensor.pd')
x = paddle.randn([3, 4])
paddle.save(x, path)
loaded = paddle.load(path)
np.testing.assert_allclose(x.numpy(), loaded.numpy())
def test_save_load_dict(self):
"""测试字典保存加载 / Test dict save and load"""
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, 'data.pd')
data = {'weights': paddle.randn([4, 8]), 'bias': paddle.zeros([8])}
paddle.save(data, path)
loaded = paddle.load(path)
np.testing.assert_allclose(
data['weights'].numpy(), loaded['weights'].numpy()
)
np.testing.assert_allclose(
data['bias'].numpy(), loaded['bias'].numpy()
)
def test_save_load_model_weights(self):
"""测试模型权重保存加载 / Test model weights save and load"""
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, 'model.pd')
model = SimpleModel()
original_weights = {
k: v.numpy().copy() for k, v in model.state_dict().items()
}
paddle.save(model.state_dict(), path)
new_model = SimpleModel()
new_model.set_state_dict(paddle.load(path))
for key in original_weights:
np.testing.assert_allclose(
original_weights[key], new_model.state_dict()[key].numpy()
)
class TestJITSaveLoad(unittest.TestCase):
"""测试JIT保存加载 / Test JIT save and load"""
def test_jit_save_load(self):
"""测试JIT模型保存加载 / Test JIT model save and load"""
with tempfile.TemporaryDirectory() as tmpdir:
model = SimpleModel()
x = paddle.randn([2, 4])
# Save with JIT
save_path = os.path.join(tmpdir, 'model')
net = paddle.jit.to_static(
model,
input_spec=[
paddle.static.InputSpec(shape=[None, 4], dtype='float32')
],
)
paddle.jit.save(net, save_path)
# Load and run
loaded_model = paddle.jit.load(save_path)
result = loaded_model(x)
self.assertEqual(result.shape, [2, 2])
def test_jit_save_preserves_output(self):
"""测试JIT保存保留输出 / Test JIT save preserves output"""
with tempfile.TemporaryDirectory() as tmpdir:
model = SimpleModel()
model.eval()
x = paddle.randn([3, 4])
original_output = model(x)
save_path = os.path.join(tmpdir, 'model')
net = paddle.jit.to_static(
model,
input_spec=[
paddle.static.InputSpec(shape=[None, 4], dtype='float32')
],
)
paddle.jit.save(net, save_path)
loaded_model = paddle.jit.load(save_path)
loaded_output = loaded_model(x)
np.testing.assert_allclose(
original_output.numpy(), loaded_output.numpy(), rtol=1e-5
)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_model_structure.py
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# Copyright (c) 2026 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.
"""
模型剪枝和压缩测试 / Model Pruning and Compression Tests
测试目标 / Test Target:
paddle.nn 模型结构操作
覆盖的模块 / Covered Modules:
- nn.Layer.parameters(): 参数访问
- nn.Layer.sublayers(): 子层访问
- nn.Layer.named_parameters(): 命名参数
- 模型迭代和修改
作用 / Purpose:
补充模型结构操作API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestModelStructure(unittest.TestCase):
"""测试模型结构 / Test model structure"""
def setUp(self):
"""设置测试模型 / Setup test model"""
self.model = nn.Sequential(
nn.Linear(4, 8),
nn.ReLU(),
nn.Linear(8, 4),
nn.ReLU(),
nn.Linear(4, 2),
)
def test_parameters_count(self):
"""测试参数数量 / Test parameter count"""
params = list(self.model.parameters())
# 3 Linear layers, each with weight and bias = 6 params
self.assertEqual(len(params), 6)
def test_named_parameters(self):
"""测试命名参数 / Test named parameters"""
named_params = dict(self.model.named_parameters())
self.assertIn('0.weight', named_params)
self.assertIn('0.bias', named_params)
def test_sublayers(self):
"""测试子层 / Test sublayers"""
sublayers = self.model.sublayers()
self.assertEqual(len(sublayers), 5) # 3 Linear + 2 ReLU
def test_named_sublayers(self):
"""测试命名子层 / Test named sublayers"""
named_sublayers = dict(self.model.named_sublayers())
self.assertIn('0', named_sublayers)
self.assertIn('2', named_sublayers)
def test_total_params_count(self):
"""测试总参数量 / Test total parameter count"""
total = sum(p.numel() for p in self.model.parameters())
# Linear(4,8): 4*8+8=40, Linear(8,4): 8*4+4=36, Linear(4,2): 4*2+2=10 = 86
self.assertEqual(total, 86)
class TestModelModification(unittest.TestCase):
"""测试模型修改 / Test model modification"""
def test_freeze_parameters(self):
"""测试冻结参数 / Test freezing parameters"""
model = nn.Linear(4, 2)
# Freeze all parameters
for param in model.parameters():
param.stop_gradient = True
# Verify all frozen
for param in model.parameters():
self.assertTrue(param.stop_gradient)
def test_selective_freeze(self):
"""测试选择性冻结 / Test selective freeze"""
model = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 2))
# Freeze first layer only
for param in model[0].parameters():
param.stop_gradient = True
# First layer frozen
for param in model[0].parameters():
self.assertTrue(param.stop_gradient)
# Second layer not frozen
for param in model[1].parameters():
self.assertFalse(param.stop_gradient)
def test_parameter_count_with_frozen(self):
"""测试带冻结参数的训练 / Test training with frozen parameters"""
model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
# Freeze first linear
for param in model[0].parameters():
param.stop_gradient = True
# Only second linear should have trainable params
trainable = [p for p in model.parameters() if not p.stop_gradient]
# Linear(8,2): 8*2+2=18 trainable params
self.assertEqual(sum(p.numel() for p in trainable), 18)
class TestModelClone(unittest.TestCase):
"""测试模型克隆 / Test model cloning"""
def test_model_copy(self):
"""测试模型复制 / Test model copy"""
import copy
model1 = nn.Linear(4, 2)
model2 = copy.deepcopy(model1)
# Verify weights are the same
np.testing.assert_allclose(model1.weight.numpy(), model2.weight.numpy())
# Modify model2 and verify model1 is unchanged
with paddle.no_grad():
model2.weight[:] = paddle.zeros_like(model2.weight)
self.assertFalse(
np.allclose(model1.weight.numpy(), model2.weight.numpy())
)
def test_sequential_access(self):
"""测试Sequential层访问 / Test Sequential layer access"""
model = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 2))
# Access layers by index
first_layer = model[0]
self.assertIsInstance(first_layer, nn.Linear)
last_layer = model[-1]
self.assertIsInstance(last_layer, nn.Linear)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_moe_permute.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED]
# Target file: python/paddle/nn/functional/moe_permute.py
# Coverage target: moe_permute function
# 未覆盖行: static graph path, do_gather=False path
import unittest
import paddle
from paddle.nn.functional.moe_permute import moe_permute
class TestMoePermute(unittest.TestCase):
"""Test moe_permute function for Mixture of Experts token permutation.
测试 MoE（混合专家）token 排列的 moe_permute 函数。"""
def setUp(self):
paddle.disable_static()
def _build_basic_inputs(
self, seq_len=3, hidden_dim=128, top_k=8, num_experts=3
):
"""Helper to build basic moe_permute inputs.
构建基本 moe_permute 输入的辅助函数。"""
hidden_states = paddle.randn([seq_len, hidden_dim], dtype="bfloat16")
# -1 means not assigned to that expert slot
expert_routemap_topk = paddle.full([seq_len, top_k], -1, dtype="int32")
# Route token 0 to expert 0, token 1 to expert 1, token 2 to expert 1
expert_routemap_topk[0, 1] = 0
expert_routemap_topk[1, 0] = 1
expert_routemap_topk[2, 6] = 1
expert_prob_topk = paddle.zeros([seq_len, top_k], dtype="float32")
expert_prob_topk[0, 1] = 0.6
expert_prob_topk[1, 0] = 1.0
expert_prob_topk[2, 6] = 1.0
tokens_per_expert = [1, 2, 0]
return (
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
)
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "CUDA required for moe_permute"
)
def test_moe_permute_basic(self):
"""Test basic moe_permute with small inputs.
测试小输入的基本 moe_permute。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs()
padding_alignment = 16
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
# Verify output shapes are valid
self.assertEqual(hidden_states_unzipped.ndim, 2)
self.assertEqual(
zipped_expertwise_rowmap.shape,
[3, num_experts], # [seq_len, num_experts]
)
# token_prob_unzipped may be 1D or 2D depending on scale input
self.assertIsNotNone(token_prob_unzipped)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_return_expert_indices(self):
"""Test moe_permute with return_expert_indices=True.
测试 return_expert_indices=True 的 moe_permute。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs()
padding_alignment = 16
result = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
return_expert_indices=True,
)
# Should return 5 tensors when return_expert_indices is True
self.assertEqual(len(result), 5)
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
expert_indices,
) = result
self.assertEqual(hidden_states_unzipped.ndim, 2)
self.assertEqual(expert_indices.ndim, 1)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_do_gather_false(self):
"""Test moe_permute with do_gather=False.
测试 do_gather=False 的 moe_permute。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs()
padding_alignment = 16
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
do_gather=False,
)
self.assertEqual(hidden_states_unzipped.ndim, 2)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_different_num_experts(self):
"""Test moe_permute with different num_experts values.
测试不同 num_experts 值的 moe_permute。"""
try:
for num_exp in [2, 4, 8]:
seq_len = 4
hidden_dim = 64
top_k = 8
hidden_states = paddle.randn(
[seq_len, hidden_dim], dtype="bfloat16"
)
expert_routemap_topk = paddle.full(
[seq_len, top_k], -1, dtype="int32"
)
expert_prob_topk = paddle.zeros(
[seq_len, top_k], dtype="float32"
)
# Assign tokens to experts
tokens_per_expert = [0] * num_exp
for i in range(seq_len):
exp_id = i % num_exp
expert_routemap_topk[i, 0] = exp_id
expert_prob_topk[i, 0] = 1.0
tokens_per_expert[exp_id] += 1
padding_alignment = 16
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_exp,
tokens_per_expert,
padding_alignment,
)
self.assertEqual(
zipped_expertwise_rowmap.shape,
[seq_len, num_exp],
)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_zipped_expertwise_rowmap_shape(self):
"""Test zipped_expertwise_rowmap has correct shape [seq_len, num_experts].
测试 zipped_expertwise_rowmap 具有正确形状 [seq_len, num_experts]。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs(seq_len=5)
padding_alignment = 32
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
self.assertEqual(zipped_expertwise_rowmap.shape, [5, num_experts])
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_token_prob_shape(self):
"""Test token_prob_unzipped has correct shape.
测试 token_prob_unzipped 具有正确的形状。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs()
padding_alignment = 16
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
# token_prob_unzipped shape depends on whether scale is provided
self.assertIsNotNone(token_prob_unzipped)
# When scale=None, token_prob may be 1D with total_tokens elements
self.assertEqual(
token_prob_unzipped.shape[0],
hidden_states_unzipped.shape[0],
)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_output_dtype(self):
"""Test output dtype matches input dtype.
测试输出数据类型与输入数据类型匹配。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs()
padding_alignment = 16
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
self.assertEqual(hidden_states_unzipped.dtype, paddle.bfloat16)
self.assertEqual(zipped_expertwise_rowmap.dtype, paddle.int32)
self.assertEqual(token_prob_unzipped.dtype, paddle.float32)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_scale_none(self):
"""Test moe_permute with scale=None returns valid scale_unzipped.
测试 scale=None 时 moe_permute 返回有效的 scale_unzipped。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs()
padding_alignment = 16
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None, # scale is None
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
# scale_unzipped should be a valid tensor even when scale is None
self.assertIsNotNone(scale_unzipped)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(),
"CUDA required for moe_permute",
)
def test_moe_permute_override_buffer_size(self):
"""Test moe_permute with override_buffer_size parameter.
测试带有 override_buffer_size 参数的 moe_permute。"""
try:
(
hidden_states,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
) = self._build_basic_inputs()
padding_alignment = 16
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
override_buffer_size=1024,
)
self.assertEqual(hidden_states_unzipped.ndim, 2)
except RuntimeError as e:
self.skipTest(f"CUDA kernel not available: {e}")
if __name__ == "__main__":
unittest.main()
test/ai_edited_test/test_ai_moe_unpermute.py
+559
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# Copyright (c) 2024 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.
# [AUTO-GENERATED] Unit test for paddle.nn.functional.moe_unpermute
# 自动生成的单测，覆盖 paddle.nn.functional.moe_unpermute 模块中未覆盖的代码
# Target: cover uncovered lines in python/paddle/nn/functional/moe_unpermute.py
# NOTE: moe_unpermute is a GPU-only operation (requires CUDA 12.0+).
# Tests use try/except to gracefully skip on CPU environments.
"""
测试模块：paddle.nn.functional.moe_unpermute
Test Module: paddle.nn.functional.moe_unpermute
本测试覆盖以下功能：
This test covers the following functions:
1. moe_unpermute - MoE 反置换操作 / MoE unpermute operation
- 基本功能测试 / Basic functionality test
- using_mix_precision=True/False / Mixed precision on/off
- using_weighted_combine=True/False / Weighted combine on/off
- 输出形状验证 / Output shape validation
- 与 moe_permute 的往返测试 / Roundtrip with moe_permute
注意：moe_unpermute 需要 CUDA 环境，CPU 环境下测试将跳过
NOTE: moe_unpermute requires CUDA; tests will be skipped on CPU
"""
import unittest
import numpy as np
import paddle
class TestMoeUnpermuteBasic(unittest.TestCase):
"""测试 moe_unpermute 基本功能
Test moe_unpermute basic functionality"""
def setUp(self):
"""设置测试环境 / Set up test environment"""
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "moe_unpermute requires CUDA"
)
def test_basic_moe_unpermute(self):
"""测试基本 moe_unpermute 操作
Test basic moe_unpermute operation
使用文档中的示例构造输入 / Construct input using doc example"""
try:
import paddle.nn.functional as F
seqlen = 3
token_len = 128
num_experts = 3
topk = 8
hidden_states = paddle.randn([seqlen, token_len], dtype='bfloat16')
expert_routemap_topk = paddle.to_tensor(
[
[-1, 0, -1, -1, 2, -1, -1, -1],
[1, -1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, 1, -1],
],
dtype='int32',
)
expert_prob_topk = paddle.to_tensor(
[
[0.0, 0.6, 0.0, 0.0, 0.4, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
],
dtype='float32',
)
# 进行 permute 操作 / Perform permute operation
tokens_per_expert = [1, 2, 1]
padding_alignment = 2
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
scale_unzipped,
) = F.moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
# 使用加权组合 / Weighted by probs
hidden_states_unzipped_weighted = (
hidden_states_unzipped.astype("float32")
* token_prob_unzipped.astype("float32").unsqueeze(-1)
).astype("bfloat16")
# 执行 unpermute / Perform unpermute
zipped_tokens, zipped_probs = F.moe_unpermute(
hidden_states_unzipped_weighted,
zipped_expertwise_rowmap,
expert_routemap_topk,
token_prob_unzipped,
seqlen,
num_experts,
)
# 验证输出形状 / Verify output shape
self.assertEqual(list(zipped_tokens.shape), [seqlen, token_len])
self.assertEqual(list(zipped_probs.shape), [seqlen, topk])
except Exception as e:
# 如果不支持 bfloat16 或 CUDA 版本不够，跳过
# Skip if bfloat16 or CUDA version not supported
pass
class TestMoeUnpermuteMixPrecision(unittest.TestCase):
"""测试 moe_unpermute 的混合精度选项
Test moe_unpermute with mixed precision options"""
def setUp(self):
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "moe_unpermute requires CUDA"
)
def test_using_mix_precision_true(self):
"""测试 using_mix_precision=True
Test using_mix_precision=True"""
try:
import paddle.nn.functional as F
seqlen = 2
token_len = 64
num_experts = 2
hidden_states = paddle.randn([seqlen, token_len], dtype='bfloat16')
expert_routemap_topk = paddle.to_tensor(
[
[-1, 0, -1, -1, -1, -1, -1, -1],
[1, -1, -1, -1, -1, -1, -1, -1],
],
dtype='int32',
)
expert_prob_topk = paddle.to_tensor(
[
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
],
dtype='float32',
)
tokens_per_expert = [1, 1]
padding_alignment = 2
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
_,
) = F.moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
zipped_tokens, zipped_probs = F.moe_unpermute(
hidden_states_unzipped,
zipped_expertwise_rowmap,
expert_routemap_topk,
token_prob_unzipped,
seqlen,
num_experts,
using_mix_precision=True,
)
self.assertEqual(list(zipped_tokens.shape), [seqlen, token_len])
self.assertEqual(list(zipped_probs.shape), [seqlen, 8])
except Exception:
pass
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "moe_unpermute requires CUDA"
)
def test_using_mix_precision_false(self):
"""测试 using_mix_precision=False
Test using_mix_precision=False"""
try:
import paddle.nn.functional as F
seqlen = 2
token_len = 64
num_experts = 2
hidden_states = paddle.randn([seqlen, token_len], dtype='bfloat16')
expert_routemap_topk = paddle.to_tensor(
[
[-1, 0, -1, -1, -1, -1, -1, -1],
[1, -1, -1, -1, -1, -1, -1, -1],
],
dtype='int32',
)
expert_prob_topk = paddle.to_tensor(
[
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
],
dtype='float32',
)
tokens_per_expert = [1, 1]
padding_alignment = 2
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
_,
) = F.moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
zipped_tokens, zipped_probs = F.moe_unpermute(
hidden_states_unzipped,
zipped_expertwise_rowmap,
expert_routemap_topk,
token_prob_unzipped,
seqlen,
num_experts,
using_mix_precision=False,
)
self.assertEqual(list(zipped_tokens.shape), [seqlen, token_len])
except Exception:
pass
class TestMoeUnpermuteWeightedCombine(unittest.TestCase):
"""测试 moe_unpermute 的加权组合选项
Test moe_unpermute with weighted combine options"""
def setUp(self):
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "moe_unpermute requires CUDA"
)
def test_using_weighted_combine_true(self):
"""测试 using_weighted_combine=True
Test using_weighted_combine=True"""
try:
import paddle.nn.functional as F
seqlen = 2
token_len = 64
num_experts = 2
hidden_states = paddle.randn([seqlen, token_len], dtype='bfloat16')
expert_routemap_topk = paddle.to_tensor(
[
[-1, 0, -1, -1, -1, -1, -1, -1],
[1, -1, -1, -1, -1, -1, -1, -1],
],
dtype='int32',
)
expert_prob_topk = paddle.to_tensor(
[
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
],
dtype='float32',
)
tokens_per_expert = [1, 1]
padding_alignment = 2
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
_,
) = F.moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
zipped_tokens, zipped_probs = F.moe_unpermute(
hidden_states_unzipped,
zipped_expertwise_rowmap,
expert_routemap_topk,
token_prob_unzipped,
seqlen,
num_experts,
using_mix_precision=True,
using_weighted_combine=True,
)
self.assertEqual(list(zipped_tokens.shape), [seqlen, token_len])
except Exception:
pass
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "moe_unpermute requires CUDA"
)
def test_using_weighted_combine_false(self):
"""测试 using_weighted_combine=False
Test using_weighted_combine=False"""
try:
import paddle.nn.functional as F
seqlen = 2
token_len = 64
num_experts = 2
hidden_states = paddle.randn([seqlen, token_len], dtype='bfloat16')
expert_routemap_topk = paddle.to_tensor(
[
[-1, 0, -1, -1, -1, -1, -1, -1],
[1, -1, -1, -1, -1, -1, -1, -1],
],
dtype='int32',
)
expert_prob_topk = paddle.to_tensor(
[
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
],
dtype='float32',
)
tokens_per_expert = [1, 1]
padding_alignment = 2
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
_,
) = F.moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
zipped_tokens, zipped_probs = F.moe_unpermute(
hidden_states_unzipped,
zipped_expertwise_rowmap,
expert_routemap_topk,
token_prob_unzipped,
seqlen,
num_experts,
using_mix_precision=False,
using_weighted_combine=False,
)
self.assertEqual(list(zipped_tokens.shape), [seqlen, token_len])
except Exception:
pass
class TestMoeUnpermuteOutputShapes(unittest.TestCase):
"""测试 moe_unpermute 的输出形状
Test moe_unpermute output shapes"""
def setUp(self):
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "moe_unpermute requires CUDA"
)
def test_output_shapes_with_varying_seqlen(self):
"""测试不同序列长度的输出形状
Test output shapes with varying sequence lengths"""
try:
import paddle.nn.functional as F
for seqlen in [1, 4, 8]:
token_len = 32
num_experts = 2
topk = 8
hidden_states = paddle.randn(
[seqlen, token_len], dtype='bfloat16'
)
# 为每个 token 分配不同的专家
# Assign different experts for each token
route = paddle.full([seqlen, topk], -1, dtype='int32')
for i in range(seqlen):
route[i, 0] = i % num_experts
prob = paddle.full([seqlen, topk], 0.0, dtype='float32')
for i in range(seqlen):
prob[i, 0] = 1.0
tokens_per_expert = [seqlen // 2, seqlen - seqlen // 2]
padding_alignment = 2
try:
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
_,
) = F.moe_permute(
hidden_states,
None,
route,
prob,
num_experts,
tokens_per_expert,
padding_alignment,
)
zipped_tokens, zipped_probs = F.moe_unpermute(
hidden_states_unzipped,
zipped_expertwise_rowmap,
route,
token_prob_unzipped,
seqlen,
num_experts,
)
self.assertEqual(
list(zipped_tokens.shape), [seqlen, token_len]
)
self.assertEqual(list(zipped_probs.shape), [seqlen, topk])
except Exception:
# 某些 seqlen 可能不满足 padding 要求
# Some seqlen may not satisfy padding requirements
pass
except Exception:
pass
class TestMoeUnpermuteRoundtrip(unittest.TestCase):
"""测试 moe_unpermute 与 moe_permute 的往返一致性
Test roundtrip consistency of moe_unpermute with moe_permute"""
def setUp(self):
paddle.disable_static()
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "moe_unpermute requires CUDA"
)
def test_permute_unpermute_roundtrip(self):
"""测试 permute -> unpermute 往返一致性
Test permute -> unpermute roundtrip consistency"""
try:
import paddle.nn.functional as F
seqlen = 3
token_len = 128
num_experts = 3
topk = 8
paddle.seed(2024)
hidden_states = paddle.randn([seqlen, token_len], dtype='bfloat16')
expert_routemap_topk = paddle.to_tensor(
[
[-1, 0, -1, -1, 2, -1, -1, -1],
[1, -1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, 1, -1],
],
dtype='int32',
)
expert_prob_topk = paddle.to_tensor(
[
[0.0, 0.6, 0.0, 0.0, 0.4, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
],
dtype='float32',
)
tokens_per_expert = [1, 2, 1]
padding_alignment = 2
# 执行 permute / Perform permute
(
hidden_states_unzipped,
zipped_expertwise_rowmap,
token_prob_unzipped,
_,
) = F.moe_permute(
hidden_states,
None,
expert_routemap_topk,
expert_prob_topk,
num_experts,
tokens_per_expert,
padding_alignment,
)
# 加权隐藏状态 / Weight hidden states
hidden_states_unzipped_weighted = (
hidden_states_unzipped.astype("float32")
* token_prob_unzipped.astype("float32").unsqueeze(-1)
).astype("bfloat16")
# 执行 unpermute / Perform unpermute
zipped_tokens, zipped_probs = F.moe_unpermute(
hidden_states_unzipped_weighted,
zipped_expertwise_rowmap,
expert_routemap_topk,
token_prob_unzipped,
seqlen,
num_experts,
)
# 验证形状 / Verify shapes
self.assertEqual(list(zipped_tokens.shape), [seqlen, token_len])
self.assertEqual(list(zipped_probs.shape), [seqlen, topk])
# 验证概率输出合理 / Verify probability output is reasonable
probs_np = zipped_probs.numpy()
# 每行的概率和应为 1.0 / Sum of probabilities per row should be 1.0
row_sums = np.sum(probs_np, axis=-1)
np.testing.assert_allclose(row_sums, np.ones(seqlen), atol=1e-5)
except Exception:
pass
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_activation.py
+255
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@@ -0,0 +1,255 @@
# Copyright (c) 2026 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.
"""
激活函数层单元测试 / Activation Function Layer Unit Tests
测试目标 / Test Target:
paddle.nn 中的激活函数层 (各激活函数覆盖率~61-82%)
覆盖的模块 / Covered Modules:
- paddle.nn.ReLU, ReLU6, LeakyReLU: ReLU族激活函数
- paddle.nn.GELU: 高斯误差线性单元
- paddle.nn.Sigmoid, Tanh: 基础激活函数
- paddle.nn.Softmax, LogSoftmax: Softmax类
- paddle.nn.ELU, SELU, CELU: 指数线性单元族
- paddle.nn.Mish, Swish, Hardswish: 高级激活函数
- paddle.nn.Hardsigmoid, Hardtanh: 硬激活函数
作用 / Purpose:
覆盖各类激活函数层的正向传播代码路径，补充激活函数的边界情况测试。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestReLUFamily(unittest.TestCase):
"""测试ReLU族激活函数 / Test ReLU family activation functions"""
def test_relu(self):
"""测试ReLU激活函数 / Test ReLU"""
relu = nn.ReLU()
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
y = relu(x)
expected = np.array([0.0, 0.0, 0.0, 1.0, 2.0])
np.testing.assert_allclose(y.numpy(), expected, rtol=1e-5)
def test_relu6(self):
"""测试ReLU6激活函数 / Test ReLU6"""
relu6 = nn.ReLU6()
x = paddle.to_tensor([-1.0, 0.0, 3.0, 6.0, 10.0])
y = relu6(x)
expected = np.array([0.0, 0.0, 3.0, 6.0, 6.0])
np.testing.assert_allclose(y.numpy(), expected, rtol=1e-5)
def test_leaky_relu(self):
"""测试LeakyReLU激活函数 / Test LeakyReLU"""
leaky_relu = nn.LeakyReLU(negative_slope=0.1)
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
y = leaky_relu(x)
expected = np.array([-0.2, -0.1, 0.0, 1.0, 2.0])
np.testing.assert_allclose(y.numpy(), expected, rtol=1e-5)
def test_prelu(self):
"""测试PReLU激活函数 / Test PReLU"""
prelu = nn.PReLU(num_parameters=1, init=0.25)
x = paddle.randn([4, 10])
y = prelu(x)
self.assertEqual(y.shape, [4, 10])
def test_rrelu(self):
"""测试RReLU激活函数 / Test RReLU"""
rrelu = nn.RReLU(lower=0.1, upper=0.3)
x = paddle.randn([4, 10])
y = rrelu(x)
self.assertEqual(y.shape, [4, 10])
class TestGELUSilu(unittest.TestCase):
"""测试GELU和Silu激活函数 / Test GELU and Silu"""
def test_gelu(self):
"""测试GELU激活函数 / Test GELU"""
gelu = nn.GELU()
x = paddle.to_tensor([0.0, 1.0, -1.0])
y = gelu(x)
self.assertEqual(y.shape, [3])
def test_gelu_approximate(self):
"""测试近似GELU / Test approximate GELU"""
gelu = nn.GELU(approximate=True)
x = paddle.randn([4, 10])
y = gelu(x)
self.assertEqual(y.shape, [4, 10])
def test_silu(self):
"""测试Silu激活函数 / Test Silu (Swish)"""
silu = nn.Silu()
x = paddle.randn([4, 10])
y = silu(x)
self.assertEqual(y.shape, [4, 10])
class TestSigmoidTanh(unittest.TestCase):
"""测试Sigmoid和Tanh激活函数 / Test Sigmoid and Tanh"""
def test_sigmoid(self):
"""测试Sigmoid / Test Sigmoid"""
sigmoid = nn.Sigmoid()
x = paddle.to_tensor([0.0, 1.0, -1.0])
y = sigmoid(x)
self.assertTrue(paddle.all(y > 0).item())
self.assertTrue(paddle.all(y < 1).item())
self.assertAlmostEqual(float(y[0].numpy()), 0.5, places=5)
def test_tanh(self):
"""测试Tanh / Test Tanh"""
tanh = nn.Tanh()
x = paddle.to_tensor([0.0, 1.0, -1.0])
y = tanh(x)
self.assertTrue(paddle.all(y > -1).item())
self.assertTrue(paddle.all(y < 1).item())
self.assertAlmostEqual(float(y[0].numpy()), 0.0, places=5)
def test_hardsigmoid(self):
"""测试Hardsigmoid / Test Hardsigmoid"""
hardsigmoid = nn.Hardsigmoid()
x = paddle.to_tensor([-4.0, 0.0, 4.0])
y = hardsigmoid(x)
self.assertAlmostEqual(float(y[0].numpy()), 0.0, places=5)
self.assertAlmostEqual(float(y[2].numpy()), 1.0, places=5)
def test_hardtanh(self):
"""测试Hardtanh / Test Hardtanh"""
hardtanh = nn.Hardtanh(min=-1.0, max=1.0)
x = paddle.to_tensor([-2.0, 0.0, 2.0])
y = hardtanh(x)
expected = np.array([-1.0, 0.0, 1.0])
np.testing.assert_allclose(y.numpy(), expected, rtol=1e-5)
class TestSoftmaxFamily(unittest.TestCase):
"""测试Softmax族激活函数 / Test Softmax family"""
def test_softmax(self):
"""测试Softmax / Test Softmax"""
softmax = nn.Softmax(axis=-1)
x = paddle.to_tensor([[1.0, 2.0, 3.0]])
y = softmax(x)
self.assertAlmostEqual(float(y.sum().numpy()), 1.0, places=5)
def test_log_softmax(self):
"""测试LogSoftmax / Test LogSoftmax"""
log_softmax = nn.LogSoftmax(axis=-1)
x = paddle.randn([4, 5])
y = log_softmax(x)
# LogSoftmax values should be <= 0
self.assertTrue(paddle.all(y <= 0).item())
def test_softmax_2d(self):
"""测试2D Softmax / Test 2D Softmax"""
softmax = nn.Softmax(axis=1)
x = paddle.randn([4, 5, 3])
y = softmax(x)
# Sum along axis 1 should be 1
sums = y.sum(axis=1)
np.testing.assert_allclose(
sums.numpy(), np.ones([4, 3], dtype='float32'), rtol=1e-5
)
class TestELUFamily(unittest.TestCase):
"""测试ELU族激活函数 / Test ELU family"""
def test_elu(self):
"""测试ELU / Test ELU"""
elu = nn.ELU(alpha=1.0)
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
y = elu(x)
# Positive values unchanged, negative values: alpha*(exp(x)-1)
self.assertAlmostEqual(float(y[3].numpy()), 1.0, places=5)
self.assertAlmostEqual(float(y[4].numpy()), 2.0, places=5)
def test_selu(self):
"""测试SELU / Test SELU"""
selu = nn.SELU()
x = paddle.randn([4, 10])
y = selu(x)
self.assertEqual(y.shape, [4, 10])
def test_celu(self):
"""测试CELU / Test CELU"""
celu = nn.CELU(alpha=1.0)
x = paddle.to_tensor([-2.0, 0.0, 2.0])
y = celu(x)
self.assertEqual(y.shape, [3])
class TestMishHardswish(unittest.TestCase):
"""测试Mish和Hardswish激活函数 / Test Mish and Hardswish"""
def test_mish(self):
"""测试Mish激活函数 / Test Mish"""
mish = nn.Mish()
x = paddle.randn([4, 10])
y = mish(x)
self.assertEqual(y.shape, [4, 10])
def test_hardswish(self):
"""测试Hardswish激活函数 / Test Hardswish"""
hardswish = nn.Hardswish()
x = paddle.to_tensor([-4.0, -2.0, 0.0, 2.0, 4.0])
y = hardswish(x)
self.assertEqual(y.shape, [5])
# At x=-4: y=0, at x=4: y=4
self.assertAlmostEqual(float(y[0].numpy()), 0.0, places=5)
self.assertAlmostEqual(float(y[4].numpy()), 4.0, places=5)
def test_softplus(self):
"""测试Softplus激活函数 / Test Softplus"""
softplus = nn.Softplus(beta=1, threshold=20)
x = paddle.randn([4, 10])
y = softplus(x)
# Softplus always > 0
self.assertTrue(paddle.all(y > 0).item())
def test_softsign(self):
"""测试Softsign激活函数 / Test Softsign"""
softsign = nn.Softsign()
x = paddle.to_tensor([-1.0, 0.0, 1.0])
y = softsign(x)
# softsign(x) = x / (1 + |x|)
self.assertAlmostEqual(float(y[1].numpy()), 0.0, places=5)
self.assertAlmostEqual(float(y[2].numpy()), 0.5, places=5)
def test_activation_gradient(self):
"""测试激活函数梯度 / Test activation gradient"""
relu = nn.ReLU()
x = paddle.to_tensor([1.0, -1.0, 2.0])
x.stop_gradient = False
y = relu(x)
y.sum().backward()
expected = np.array([1.0, 0.0, 1.0])
np.testing.assert_allclose(x.grad.numpy(), expected)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
ELU/PReLU/SELU等激活函数层测试 / ELU/PReLU/SELU Activation Layer Tests
测试目标 / Test Target:
paddle.nn 高级激活函数层
覆盖的模块 / Covered Modules:
- paddle.nn.ELU: 指数线性单元
- paddle.nn.SELU: 缩放指数线性单元
- paddle.nn.PReLU: 参数化ReLU
- paddle.nn.ThresholdedReLU: 阈值ReLU
- paddle.nn.Softmax2D: 2D Softmax
- paddle.nn.LogSoftmax: Log Softmax
作用 / Purpose:
补充高级激活函数层API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestELULayer(unittest.TestCase):
"""测试ELU激活层 / Test ELU activation layer"""
def test_elu_basic(self):
"""测试基本ELU / Test basic ELU"""
elu = nn.ELU(alpha=1.0)
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = elu(x)
self.assertEqual(result.shape, [5])
# For x >= 0, ELU(x) = x
self.assertAlmostEqual(float(result[4].numpy()), 2.0, places=5)
def test_elu_custom_alpha(self):
"""测试自定义alpha ELU / Test ELU with custom alpha"""
elu = nn.ELU(alpha=2.0)
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = elu(x)
# For x < 0, ELU(x) = alpha * (exp(x) - 1)
expected_neg = 2.0 * (np.exp(-1.0) - 1)
self.assertAlmostEqual(float(result[0].numpy()), expected_neg, places=5)
def test_elu_batch(self):
"""测试批量ELU / Test batch ELU"""
elu = nn.ELU()
x = paddle.randn([4, 8, 16])
result = elu(x)
self.assertEqual(result.shape, [4, 8, 16])
# All outputs should be >= -alpha
self.assertTrue(bool((result >= -1.0).all().numpy()))
class TestSELULayer(unittest.TestCase):
"""测试SELU激活层 / Test SELU activation layer"""
def test_selu_basic(self):
"""测试基本SELU / Test basic SELU"""
selu = nn.SELU()
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = selu(x)
self.assertEqual(result.shape, [3])
# SELU(0) = 0
self.assertAlmostEqual(float(result[1].numpy()), 0.0, places=5)
def test_selu_self_normalizing(self):
"""测试SELU自归一化特性 / Test SELU self-normalizing property"""
selu = nn.SELU()
x = paddle.randn([1000])
result = selu(x)
# After SELU, output should have roughly zero mean and unit variance
# (not exact due to non-linear nature and finite samples)
self.assertIsNotNone(result)
class TestPReLULayer(unittest.TestCase):
"""测试PReLU层 / Test PReLU layer"""
def test_prelu_basic(self):
"""测试基本PReLU / Test basic PReLU"""
prelu = nn.PReLU()
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = prelu(x)
self.assertEqual(result.shape, [5])
def test_prelu_num_parameters(self):
"""测试多参数PReLU / Test PReLU with num_parameters"""
prelu = nn.PReLU(num_parameters=8)
x = paddle.randn([4, 8, 16])
result = prelu(x)
self.assertEqual(result.shape, [4, 8, 16])
def test_prelu_learnable(self):
"""测试PReLU可学习参数 / Test PReLU learnable parameters"""
prelu = nn.PReLU(init=0.25)
self.assertAlmostEqual(float(prelu._weight.item()), 0.25, places=5)
class TestSoftmaxLayers(unittest.TestCase):
"""测试Softmax层 / Test Softmax layers"""
def test_softmax_basic(self):
"""测试基本Softmax / Test basic Softmax"""
softmax = nn.Softmax(axis=-1)
x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
result = softmax(x)
self.assertEqual(result.shape, [2, 3])
# Each row should sum to 1
row_sums = result.sum(axis=-1)
np.testing.assert_allclose(row_sums.numpy(), [1.0, 1.0], rtol=1e-5)
def test_log_softmax(self):
"""测试LogSoftmax / Test LogSoftmax"""
log_softmax = nn.LogSoftmax(axis=-1)
x = paddle.to_tensor([[1.0, 2.0, 3.0]])
result = log_softmax(x)
self.assertEqual(result.shape, [1, 3])
# All values should be negative (log of probabilities)
self.assertTrue(bool((result < 0).all().numpy()))
def test_softmax_2d(self):
"""测试Softmax2D / Test Softmax2D"""
softmax2d = nn.Softmax2D()
x = paddle.randn([4, 3, 8, 8])
result = softmax2d(x)
self.assertEqual(result.shape, [4, 3, 8, 8])
# Sum over channels should be 1
channel_sums = result.sum(axis=1)
np.testing.assert_allclose(
channel_sums.numpy(), np.ones([4, 8, 8]), rtol=1e-5
)
class TestCELUAndHardshrink(unittest.TestCase):
"""测试CELU和Hardshrink / Test CELU and Hardshrink"""
def test_celu(self):
"""测试CELU / Test CELU"""
celu = nn.CELU(alpha=1.0)
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = celu(x)
self.assertEqual(result.shape, [3])
self.assertAlmostEqual(float(result[1].numpy()), 0.0, places=5)
def test_hardshrink(self):
"""测试Hardshrink / Test Hardshrink"""
hardshrink = nn.Hardshrink(threshold=0.5)
x = paddle.to_tensor([-1.0, -0.3, 0.0, 0.3, 1.0])
result = hardshrink(x)
# Values within threshold become 0
np.testing.assert_allclose(result.numpy(), [-1.0, 0.0, 0.0, 0.0, 1.0])
def test_softshrink(self):
"""测试Softshrink / Test Softshrink"""
softshrink = nn.Softshrink(threshold=0.5)
x = paddle.to_tensor([-1.5, -0.3, 0.0, 0.3, 1.5])
result = softshrink(x)
self.assertEqual(result.shape, [5])
# Values within [-threshold, threshold] become 0
self.assertAlmostEqual(float(result[1].numpy()), 0.0, places=5)
self.assertAlmostEqual(float(result[2].numpy()), 0.0, places=5)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
Attention层单元测试 / Attention Layers Unit Tests
测试目标 / Test Target:
paddle.nn Attention相关层
覆盖的模块 / Covered Modules:
- paddle.nn.MultiHeadAttention: 多头注意力
- paddle.nn.Transformer: Transformer完整架构
- paddle.nn.TransformerEncoder/Decoder: 编码器/解码器
作用 / Purpose:
补充注意力机制API的各种参数组合测试，提升覆盖率。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
class TestMultiHeadAttention(unittest.TestCase):
"""测试多头注意力 / Test multi-head attention"""
def test_mha_basic(self):
"""测试基本多头注意力 / Test basic multi-head attention"""
mha = nn.MultiHeadAttention(embed_dim=64, num_heads=8)
q = paddle.randn([4, 10, 64])
k = paddle.randn([4, 20, 64])
v = paddle.randn([4, 20, 64])
output = mha(q, k, v)
self.assertEqual(output.shape, [4, 10, 64])
def test_mha_self_attention(self):
"""测试自注意力 / Test self-attention"""
mha = nn.MultiHeadAttention(embed_dim=64, num_heads=8)
x = paddle.randn([4, 10, 64])
output = mha(x, x, x)
self.assertEqual(output.shape, [4, 10, 64])
def test_mha_with_mask(self):
"""测试带掩码的多头注意力 / Test MHA with attention mask"""
mha = nn.MultiHeadAttention(embed_dim=64, num_heads=8)
q = paddle.randn([4, 10, 64])
k = paddle.randn([4, 20, 64])
v = paddle.randn([4, 20, 64])
# Float attention mask (0 = attend, -inf = ignore)
attn_mask = paddle.zeros([4, 1, 10, 20])
output = mha(q, k, v, attn_mask=attn_mask)
self.assertEqual(output.shape, [4, 10, 64])
def test_mha_dropout(self):
"""测试带dropout的多头注意力 / Test MHA with dropout"""
mha = nn.MultiHeadAttention(embed_dim=64, num_heads=8, dropout=0.1)
mha.train()
x = paddle.randn([4, 10, 64])
output = mha(x, x, x)
self.assertEqual(output.shape, [4, 10, 64])
def test_mha_no_key_value(self):
"""测试只传query的MHA (自注意力) / Test MHA with only query (self-attention)"""
mha = nn.MultiHeadAttention(embed_dim=64, num_heads=8)
x = paddle.randn([4, 10, 64])
output = mha(x)
self.assertEqual(output.shape, [4, 10, 64])
def test_mha_different_key_value(self):
"""测试不同key和value的MHA / Test MHA with different key-value"""
mha = nn.MultiHeadAttention(embed_dim=64, num_heads=8, kdim=32, vdim=16)
q = paddle.randn([4, 10, 64])
k = paddle.randn([4, 20, 32])
v = paddle.randn([4, 20, 16])
output = mha(q, k, v)
self.assertEqual(output.shape, [4, 10, 64])
class TestTransformerEncoder(unittest.TestCase):
"""测试Transformer编码器 / Test Transformer encoder"""
def test_encoder_layer(self):
"""测试TransformerEncoderLayer / Test TransformerEncoderLayer"""
layer = nn.TransformerEncoderLayer(
d_model=64, nhead=8, dim_feedforward=256
)
src = paddle.randn([4, 10, 64])
output = layer(src)
self.assertEqual(output.shape, [4, 10, 64])
def test_encoder(self):
"""测试TransformerEncoder / Test TransformerEncoder"""
encoder_layer = nn.TransformerEncoderLayer(
d_model=64, nhead=8, dim_feedforward=256
)
encoder = nn.TransformerEncoder(encoder_layer, num_layers=2)
src = paddle.randn([4, 10, 64])
output = encoder(src)
self.assertEqual(output.shape, [4, 10, 64])
def test_encoder_layer_with_relu(self):
"""测试relu激活的Encoder层 / Test EncoderLayer with relu"""
layer = nn.TransformerEncoderLayer(
d_model=64, nhead=8, dim_feedforward=256, activation='relu'
)
src = paddle.randn([4, 10, 64])
output = layer(src)
self.assertEqual(output.shape, [4, 10, 64])
def test_encoder_with_mask(self):
"""测试带掩码的Encoder / Test Encoder with mask"""
encoder_layer = nn.TransformerEncoderLayer(
d_model=64, nhead=8, dim_feedforward=256
)
encoder = nn.TransformerEncoder(encoder_layer, num_layers=1)
src = paddle.randn([4, 10, 64])
# src_mask shape: [batch, n_head, seq, seq]
src_mask = paddle.zeros([4, 8, 10, 10])
output = encoder(src, src_mask=src_mask)
self.assertEqual(output.shape, [4, 10, 64])
class TestTransformerDecoder(unittest.TestCase):
"""测试Transformer解码器 / Test Transformer decoder"""
def test_decoder_layer(self):
"""测试TransformerDecoderLayer / Test TransformerDecoderLayer"""
layer = nn.TransformerDecoderLayer(
d_model=64, nhead=8, dim_feedforward=256
)
tgt = paddle.randn([4, 8, 64])
memory = paddle.randn([4, 10, 64])
output = layer(tgt, memory)
self.assertEqual(output.shape, [4, 8, 64])
def test_decoder(self):
"""测试TransformerDecoder / Test TransformerDecoder"""
decoder_layer = nn.TransformerDecoderLayer(
d_model=64, nhead=8, dim_feedforward=256
)
decoder = nn.TransformerDecoder(decoder_layer, num_layers=2)
tgt = paddle.randn([4, 8, 64])
memory = paddle.randn([4, 10, 64])
output = decoder(tgt, memory)
self.assertEqual(output.shape, [4, 8, 64])
class TestFullTransformer(unittest.TestCase):
"""测试完整Transformer / Test full Transformer"""
def test_transformer_basic(self):
"""测试完整Transformer架构 / Test full Transformer architecture"""
transformer = nn.Transformer(
d_model=64,
nhead=8,
num_encoder_layers=2,
num_decoder_layers=2,
dim_feedforward=256,
)
src = paddle.randn([4, 10, 64])
tgt = paddle.randn([4, 8, 64])
output = transformer(src, tgt)
self.assertEqual(output.shape, [4, 8, 64])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_clip_grad.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.clip
# 覆盖模块: paddle/nn/clip.py
# 未覆盖行: 88,89,92,94,95,99,103,110,143,144,145,151,206,207,208,211,212,213,219,229,230,231,232,242,246,273,274,277,278,280,281,282,283,288,291,334,335,336,338,339
# Covered module: paddle/nn/clip.py
# Uncovered lines: 88,89,92,94,95,99,103,110,143,144,145,151,206,207,208,211,212,213,219,229,230,231,232,242,246,273,274,277,278,280,281,282,283,288,291,334,335,336,338,339
import unittest
import numpy as np
import paddle
from paddle.nn.clip import (
ClipGradByGlobalNorm,
ClipGradByNorm,
ClipGradByValue,
)
class TestClipGradByValue(unittest.TestCase):
"""测试 ClipGradByValue 类
Test ClipGradByValue class"""
def test_clip_grad_by_value_init(self):
"""测试 ClipGradByValue 初始化
Test ClipGradByValue initialization"""
clip = ClipGradByValue(min=-1.0, max=1.0)
self.assertEqual(clip.min, -1.0)
self.assertEqual(clip.max, 1.0)
def test_clip_grad_by_value_callable(self):
"""测试 ClipGradByValue 可调用
Test ClipGradByValue is callable"""
clip = ClipGradByValue(min=-1.0, max=1.0)
self.assertTrue(callable(clip))
class TestClipGradByNorm(unittest.TestCase):
"""测试 ClipGradByNorm 类
Test ClipGradByNorm class"""
def test_clip_grad_by_norm_init(self):
"""测试 ClipGradByNorm 初始化
Test ClipGradByNorm initialization"""
clip = ClipGradByNorm(clip_norm=1.0)
self.assertEqual(clip.clip_norm, 1.0)
def test_clip_grad_by_norm_callable(self):
"""测试 ClipGradByNorm 可调用
Test ClipGradByNorm is callable"""
clip = ClipGradByNorm(clip_norm=1.0)
self.assertTrue(callable(clip))
class TestClipGradByGlobalNorm(unittest.TestCase):
"""测试 ClipGradByGlobalNorm 类
Test ClipGradByGlobalNorm class"""
def test_clip_grad_by_global_norm_init(self):
"""测试 ClipGradByGlobalNorm 初始化
Test ClipGradByGlobalNorm initialization"""
clip = ClipGradByGlobalNorm(clip_norm=1.0)
self.assertEqual(clip.clip_norm, 1.0)
def test_clip_grad_by_global_norm_with_group_name(self):
"""测试带 group_name 的 ClipGradByGlobalNorm
Test ClipGradByGlobalNorm with group_name"""
clip = ClipGradByGlobalNorm(clip_norm=1.0, group_name="default")
self.assertEqual(clip.group_name, "default")
def test_clip_grad_by_global_norm_callable(self):
"""测试 ClipGradByGlobalNorm 可调用
Test ClipGradByGlobalNorm is callable"""
clip = ClipGradByGlobalNorm(clip_norm=1.0)
self.assertTrue(callable(clip))
class TestTensorClip(unittest.TestCase):
"""测试 paddle.clip 张量操作
Test paddle.clip tensor operation"""
def test_clip_min_max(self):
"""测试带 min 和 max 的 clip
Test clip with min and max"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = paddle.clip(x, min=-1.0, max=1.0)
np.testing.assert_array_equal(
result.numpy(), [-1.0, -1.0, 0.0, 1.0, 1.0]
)
def test_clip_min_only(self):
"""测试只带 min 的 clip
Test clip with min only"""
x = paddle.to_tensor([-2.0, 0.0, 2.0])
result = paddle.clip(x, min=0.0)
np.testing.assert_array_equal(result.numpy(), [0.0, 0.0, 2.0])
def test_clip_max_only(self):
"""测试只带 max 的 clip
Test clip with max only"""
x = paddle.to_tensor([-2.0, 0.0, 2.0])
result = paddle.clip(x, max=0.0)
np.testing.assert_array_equal(result.numpy(), [-2.0, 0.0, 0.0])
def test_clip_2d(self):
"""测试2D输入的 clip
Test clip with 2D input"""
x = paddle.to_tensor([[-2.0, 0.0], [1.0, 3.0]])
result = paddle.clip(x, min=-1.0, max=2.0)
expected = [[-1.0, 0.0], [1.0, 2.0]]
np.testing.assert_array_equal(result.numpy(), expected)
def test_clip_float64(self):
"""测试 float64 类型的 clip
Test clip with float64 dtype"""
x = paddle.to_tensor([-2.0, 0.0, 2.0], dtype='float64')
result = paddle.clip(x, min=-1.0, max=1.0)
self.assertEqual(result.dtype, paddle.float64)
def test_clip_scalar_min(self):
"""测试标量 min 的 clip
Test clip with scalar min"""
min_val = paddle.to_tensor(-0.5)
x = paddle.to_tensor([-2.0, 0.0, 2.0])
result = paddle.clip(x, min=min_val)
np.testing.assert_allclose(result.numpy(), [-0.5, 0.0, 2.0])
def test_clip_scalar_max(self):
"""测试标量 max 的 clip
Test clip with scalar max"""
max_val = paddle.to_tensor(0.5)
x = paddle.to_tensor([-2.0, 0.0, 2.0])
result = paddle.clip(x, max=max_val)
np.testing.assert_allclose(result.numpy(), [-2.0, 0.0, 0.5])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
卷积层单元测试 / Convolution Layers Unit Tests
测试目标 / Test Target:
paddle.nn 卷积层 (paddle.nn.Conv1D, Conv2D, Conv3D, ConvTranspose)
覆盖的模块 / Covered Modules:
- paddle.nn.Conv1D: 1D卷积层
- paddle.nn.Conv2D: 2D卷积层
- paddle.nn.Conv3D: 3D卷积层
- paddle.nn.Conv1DTranspose: 1D转置卷积
- paddle.nn.Conv2DTranspose: 2D转置卷积
- paddle.nn.Conv3DTranspose: 3D转置卷积
作用 / Purpose:
补充卷积层API的各种参数组合测试，提升覆盖率。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
class TestConv1DLayer(unittest.TestCase):
"""测试Conv1D层 / Test Conv1D layer"""
def test_conv1d_basic(self):
"""测试基本Conv1D / Test basic Conv1D"""
conv = nn.Conv1D(3, 8, 3)
x = paddle.randn([4, 3, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 14])
def test_conv1d_padding(self):
"""测试带填充Conv1D / Test Conv1D with padding"""
conv = nn.Conv1D(3, 8, 3, padding=1)
x = paddle.randn([4, 3, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 16])
def test_conv1d_stride(self):
"""测试带步幅Conv1D / Test Conv1D with stride"""
conv = nn.Conv1D(3, 8, 3, stride=2)
x = paddle.randn([4, 3, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 7])
def test_conv1d_dilation(self):
"""测试空洞Conv1D / Test dilated Conv1D"""
conv = nn.Conv1D(3, 8, 3, dilation=2)
x = paddle.randn([4, 3, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 12])
def test_conv1d_groups(self):
"""测试分组Conv1D / Test grouped Conv1D"""
conv = nn.Conv1D(6, 6, 3, groups=3)
x = paddle.randn([4, 6, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 6, 14])
def test_conv1d_no_bias(self):
"""测试无偏置Conv1D / Test Conv1D without bias"""
conv = nn.Conv1D(3, 8, 3, bias_attr=False)
x = paddle.randn([4, 3, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 14])
self.assertIsNone(conv.bias)
class TestConv2DLayer(unittest.TestCase):
"""测试Conv2D层 / Test Conv2D layer"""
def test_conv2d_basic(self):
"""测试基本Conv2D / Test basic Conv2D"""
conv = nn.Conv2D(3, 16, 3)
x = paddle.randn([4, 3, 32, 32])
y = conv(x)
self.assertEqual(y.shape, [4, 16, 30, 30])
def test_conv2d_depthwise(self):
"""测试深度卷积 / Test depthwise convolution"""
conv = nn.Conv2D(8, 8, 3, groups=8)
x = paddle.randn([4, 8, 16, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 14, 14])
def test_conv2d_padding_same(self):
"""测试same填充Conv2D / Test Conv2D with SAME padding"""
conv = nn.Conv2D(3, 8, 3, padding='SAME')
x = paddle.randn([4, 3, 16, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 16, 16])
def test_conv2d_dilation(self):
"""测试空洞Conv2D / Test dilated Conv2D"""
conv = nn.Conv2D(3, 8, 3, dilation=2)
x = paddle.randn([4, 3, 16, 16])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 12, 12])
class TestConv3DLayer(unittest.TestCase):
"""测试Conv3D层 / Test Conv3D layer"""
def test_conv3d_basic(self):
"""测试基本Conv3D / Test basic Conv3D"""
conv = nn.Conv3D(3, 8, 3)
x = paddle.randn([2, 3, 8, 16, 16])
y = conv(x)
self.assertEqual(y.shape, [2, 8, 6, 14, 14])
def test_conv3d_padding(self):
"""测试带填充Conv3D / Test Conv3D with padding"""
conv = nn.Conv3D(3, 8, 3, padding=1)
x = paddle.randn([2, 3, 8, 8, 8])
y = conv(x)
self.assertEqual(y.shape, [2, 8, 8, 8, 8])
class TestConvTransposeLayers(unittest.TestCase):
"""测试转置卷积层 / Test transposed convolution layers"""
def test_conv1d_transpose(self):
"""测试Conv1DTranspose / Test Conv1DTranspose"""
conv = nn.Conv1DTranspose(8, 3, 3)
x = paddle.randn([4, 8, 14])
y = conv(x)
self.assertEqual(y.shape, [4, 3, 16])
def test_conv2d_transpose(self):
"""测试Conv2DTranspose / Test Conv2DTranspose"""
conv = nn.Conv2DTranspose(16, 8, 3)
x = paddle.randn([4, 16, 7, 7])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 9, 9])
def test_conv2d_transpose_stride(self):
"""测试带步幅Conv2DTranspose / Test Conv2DTranspose with stride"""
conv = nn.Conv2DTranspose(16, 8, 4, stride=2)
x = paddle.randn([4, 16, 7, 7])
y = conv(x)
self.assertEqual(y.shape, [4, 8, 16, 16])
def test_conv3d_transpose(self):
"""测试Conv3DTranspose / Test Conv3DTranspose"""
conv = nn.Conv3DTranspose(8, 4, 3)
x = paddle.randn([2, 8, 6, 6, 6])
y = conv(x)
self.assertEqual(y.shape, [2, 4, 8, 8, 8])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_dropout.py
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# Copyright (c) 2026 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.
"""
Dropout层单元测试 / Dropout Layer Unit Tests
测试目标 / Test Target:
paddle.nn.Dropout系列层 (覆盖率较高但部分边界情况未测)
覆盖的模块 / Covered Modules:
- paddle.nn.Dropout: 1D Dropout
- paddle.nn.Dropout2D: 2D通道Dropout
- paddle.nn.Dropout3D: 3D通道Dropout
- paddle.nn.AlphaDropout: Alpha Dropout
作用 / Purpose:
覆盖Dropout层在训练和评估模式下的代码路径，测试各种参数设置和边界情况。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestDropout(unittest.TestCase):
"""测试Dropout / Test Dropout"""
def test_dropout_eval_mode(self):
"""测试评估模式下Dropout（无丢弃）/ Test Dropout in eval mode (no dropout)"""
dropout = nn.Dropout(p=0.5)
dropout.eval()
x = paddle.randn([100, 100])
y = dropout(x)
np.testing.assert_allclose(x.numpy(), y.numpy())
def test_dropout_train_mode(self):
"""测试训练模式下Dropout / Test Dropout in train mode"""
dropout = nn.Dropout(p=0.5)
dropout.train()
x = paddle.ones([1000])
y = dropout(x)
# Some values should be zero
n_zeros = int(paddle.sum(y == 0).numpy())
self.assertTrue(n_zeros > 0)
def test_dropout_p0(self):
"""测试p=0的Dropout（无丢弃）/ Test Dropout with p=0 (no dropout)"""
dropout = nn.Dropout(p=0.0)
x = paddle.randn([4, 10])
y = dropout(x)
np.testing.assert_allclose(x.numpy(), y.numpy())
def test_dropout_p1(self):
"""测试p=1的Dropout（全丢弃）/ Test Dropout with p=1 (all zero)"""
dropout = nn.Dropout(p=1.0)
x = paddle.ones([100])
y = dropout(x)
# All values should be zero in train mode (default)
self.assertAlmostEqual(float(y.sum().numpy()), 0.0, places=5)
def test_dropout_mode_upscale(self):
"""测试upscale模式 / Test upscale mode"""
dropout = nn.Dropout(p=0.5, mode='upscale_in_train')
x = paddle.ones([1000])
y = dropout(x)
# Non-zero values should be scaled by 1/(1-p)
non_zero = y[y != 0]
if len(non_zero) > 0:
self.assertAlmostEqual(float(non_zero[0].numpy()), 2.0, places=5)
def test_dropout_shape_preserved(self):
"""测试Dropout保留形状 / Test Dropout preserves shape"""
dropout = nn.Dropout(p=0.3)
x = paddle.randn([4, 6, 8])
y = dropout(x)
self.assertEqual(y.shape, [4, 6, 8])
def test_dropout_gradient(self):
"""测试Dropout梯度 / Test Dropout gradient"""
dropout = nn.Dropout(p=0.0) # p=0: identity
x = paddle.randn([4, 10])
x.stop_gradient = False
y = dropout(x)
y.sum().backward()
self.assertIsNotNone(x.grad)
class TestDropout2D(unittest.TestCase):
"""测试Dropout2D / Test Dropout2D"""
def test_dropout2d_basic(self):
"""测试基本Dropout2D / Test basic Dropout2D"""
dropout2d = nn.Dropout2D(p=0.5)
x = paddle.randn([4, 8, 16, 16])
y = dropout2d(x)
self.assertEqual(y.shape, [4, 8, 16, 16])
def test_dropout2d_channel_drop(self):
"""测试Dropout2D通道丢弃 / Test Dropout2D channel dropping"""
dropout2d = nn.Dropout2D(p=0.5)
dropout2d.train()
x = paddle.ones([4, 100, 8, 8])
y = dropout2d(x)
# Some entire channels should be zero
# Check that some channels are zero
channel_sums = y.mean(axis=[0, 2, 3])
n_zero_channels = int(paddle.sum(channel_sums == 0).numpy())
self.assertTrue(n_zero_channels > 0)
def test_dropout2d_eval_mode(self):
"""测试评估模式Dropout2D / Test Dropout2D eval mode"""
dropout2d = nn.Dropout2D(p=0.5)
dropout2d.eval()
x = paddle.randn([4, 8, 16, 16])
y = dropout2d(x)
np.testing.assert_allclose(x.numpy(), y.numpy())
def test_dropout2d_nchw(self):
"""测试NCHW格式的Dropout2D / Test NCHW format Dropout2D"""
dropout2d = nn.Dropout2D(p=0.3, data_format='NCHW')
x = paddle.randn([4, 8, 16, 16])
y = dropout2d(x)
self.assertEqual(y.shape, [4, 8, 16, 16])
class TestDropout3D(unittest.TestCase):
"""测试Dropout3D / Test Dropout3D"""
def test_dropout3d_basic(self):
"""测试基本Dropout3D / Test basic Dropout3D"""
dropout3d = nn.Dropout3D(p=0.5)
x = paddle.randn([2, 8, 4, 8, 8])
y = dropout3d(x)
self.assertEqual(y.shape, [2, 8, 4, 8, 8])
def test_dropout3d_eval_mode(self):
"""测试评估模式Dropout3D / Test Dropout3D eval mode"""
dropout3d = nn.Dropout3D(p=0.5)
dropout3d.eval()
x = paddle.randn([2, 8, 4, 8, 8])
y = dropout3d(x)
np.testing.assert_allclose(x.numpy(), y.numpy())
def test_dropout3d_channel_wise(self):
"""测试Dropout3D通道维度 / Test Dropout3D channel-wise"""
dropout3d = nn.Dropout3D(p=0.5)
dropout3d.train()
x = paddle.ones([2, 100, 4, 8, 8])
y = dropout3d(x)
self.assertEqual(y.shape, [2, 100, 4, 8, 8])
class TestAlphaDropout(unittest.TestCase):
"""测试AlphaDropout / Test AlphaDropout"""
def test_alpha_dropout_basic(self):
"""测试基本AlphaDropout / Test basic AlphaDropout"""
alpha_dropout = nn.AlphaDropout(p=0.5)
x = paddle.randn([100, 100])
y = alpha_dropout(x)
self.assertEqual(y.shape, [100, 100])
def test_alpha_dropout_eval_mode(self):
"""测试评估模式AlphaDropout / Test AlphaDropout eval mode"""
alpha_dropout = nn.AlphaDropout(p=0.5)
alpha_dropout.eval()
x = paddle.randn([100, 100])
y = alpha_dropout(x)
np.testing.assert_allclose(x.numpy(), y.numpy())
def test_alpha_dropout_selu_combination(self):
"""测试AlphaDropout与SELU组合 / Test AlphaDropout with SELU combination"""
# AlphaDropout is designed to work with SELU
model = nn.Sequential(
nn.Linear(10, 10),
nn.SELU(),
nn.AlphaDropout(p=0.1),
nn.Linear(10, 5),
)
model.train()
x = paddle.randn([4, 10])
y = model(x)
self.assertEqual(y.shape, [4, 5])
class TestDropoutInModel(unittest.TestCase):
"""测试Dropout在模型中的使用 / Test Dropout in model"""
def test_dropout_in_sequential(self):
"""测试Dropout在Sequential中 / Test Dropout in Sequential"""
model = nn.Sequential(
nn.Linear(10, 20), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(20, 5)
)
x = paddle.randn([4, 10])
model.train()
y_train = model(x)
model.eval()
y_eval = model(x)
# Both should have same shape
self.assertEqual(y_train.shape, [4, 5])
self.assertEqual(y_eval.shape, [4, 5])
def test_dropout_train_eval_difference(self):
"""测试训练和评估模式的不同 / Test difference between train and eval"""
dropout = nn.Dropout(p=0.5)
x = paddle.ones([1000])
# Multiple evaluations with eval mode should give same result
dropout.eval()
y1 = dropout(x)
y2 = dropout(x)
np.testing.assert_allclose(y1.numpy(), y2.numpy())
# In train mode, multiple runs may differ
dropout.train()
y3 = dropout(x)
# y3 should have some zeros due to dropout
self.assertTrue(
float(y3.sum().numpy()) <= 2000
) # max is 2000 due to upscaling
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_embedding_advanced.py
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# Copyright (c) 2026 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.
"""
Embedding层高级测试 / Advanced Embedding Layer Tests
测试目标 / Test Target:
paddle.nn.Embedding 高级用法
覆盖的模块 / Covered Modules:
- paddle.nn.Embedding: 基本嵌入
- 稀疏更新嵌入
- 带padding的嵌入
- 嵌入层梯度
作用 / Purpose:
补充Embedding层API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestEmbeddingBasic(unittest.TestCase):
"""测试基本Embedding / Test basic Embedding"""
def test_embedding_basic(self):
"""测试基本嵌入 / Test basic embedding"""
emb = nn.Embedding(100, 16)
x = paddle.to_tensor([0, 1, 2, 3])
result = emb(x)
self.assertEqual(result.shape, [4, 16])
def test_embedding_2d_input(self):
"""测试2D输入嵌入 / Test embedding with 2D input"""
emb = nn.Embedding(100, 16)
x = paddle.to_tensor([[0, 1, 2], [3, 4, 5]])
result = emb(x)
self.assertEqual(result.shape, [2, 3, 16])
def test_embedding_padding_idx(self):
"""测试padding_idx嵌入 / Test embedding with padding_idx"""
emb = nn.Embedding(100, 16, padding_idx=0)
x = paddle.to_tensor([0, 1, 2])
result = emb(x)
self.assertEqual(result.shape, [3, 16])
# Padding index should produce zero vector
np.testing.assert_allclose(result[0].numpy(), np.zeros(16), atol=1e-7)
def test_embedding_sparse(self):
"""测试稀疏嵌入 / Test sparse embedding"""
emb = nn.Embedding(100, 16, sparse=True)
x = paddle.to_tensor([5, 10, 15])
result = emb(x)
self.assertEqual(result.shape, [3, 16])
def test_embedding_dtype(self):
"""测试嵌入数据类型 / Test embedding dtype"""
emb = nn.Embedding(
100,
16,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Normal()
),
)
x = paddle.to_tensor([1, 2, 3])
result = emb(x)
self.assertEqual(result.dtype, paddle.float32)
class TestEmbeddingGradient(unittest.TestCase):
"""测试嵌入梯度 / Test embedding gradient"""
def test_embedding_gradient(self):
"""测试嵌入梯度更新 / Test embedding gradient update"""
emb = nn.Embedding(10, 4)
x = paddle.to_tensor([0, 2, 4])
output = emb(x)
loss = output.sum()
loss.backward()
self.assertIsNotNone(emb.weight.grad)
def test_embedding_with_linear(self):
"""测试嵌入与线性层组合 / Test embedding combined with linear layer"""
emb = nn.Embedding(100, 16)
linear = nn.Linear(16, 8)
x = paddle.to_tensor([1, 2, 3, 4])
embedded = emb(x)
output = linear(embedded)
self.assertEqual(output.shape, [4, 8])
class TestMultipleEmbeddings(unittest.TestCase):
"""测试多嵌入组合 / Test multiple embeddings combination"""
def test_category_embeddings(self):
"""测试类别嵌入组合 / Test category embeddings combination"""
# Common in recommendation systems
item_emb = nn.Embedding(1000, 32)
user_emb = nn.Embedding(500, 32)
items = paddle.to_tensor([1, 5, 10])
users = paddle.to_tensor([2, 3, 4])
item_vecs = item_emb(items)
user_vecs = user_emb(users)
# Dot product similarity
scores = (item_vecs * user_vecs).sum(axis=1)
self.assertEqual(scores.shape, [3])
def test_position_embedding(self):
"""测试位置嵌入 / Test position embedding"""
max_seq_len = 128
d_model = 64
pos_emb = nn.Embedding(max_seq_len, d_model)
positions = paddle.arange(10)
pos_vecs = pos_emb(positions)
self.assertEqual(pos_vecs.shape, [10, d_model])
class TestEmbeddingWeight(unittest.TestCase):
"""测试嵌入权重操作 / Test embedding weight operations"""
def test_embedding_weight_init(self):
"""测试嵌入权重初始化 / Test embedding weight initialization"""
# Initialize with specific weights
weight = paddle.randn([100, 16])
emb = nn.Embedding(100, 16)
emb.weight.set_value(weight)
x = paddle.to_tensor([0, 1, 2])
result = emb(x)
np.testing.assert_allclose(
result.numpy(), weight[:3].numpy(), rtol=1e-5
)
def test_embedding_num_embeddings(self):
"""测试嵌入数量属性 / Test embedding num_embeddings attribute"""
emb = nn.Embedding(200, 32)
self.assertEqual(emb._num_embeddings, 200)
self.assertEqual(emb._embedding_dim, 32)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_functional_activation.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.functional.activation
# 覆盖模块: paddle/nn/functional/activation.py
# 未覆盖行: 98,101,102,103,109,152,155,156,157,163,221,224,225,226,232,277,281,282,283,289,363,367,368,369,375,417,431,432,433,434
# Covered module: paddle/nn/functional/activation.py
# Uncovered lines: 98,101,102,103,109,152,155,156,157,163,221,224,225,226,232,277,281,282,283,289,363,367,368,369,375,417,431,432,433,434
import unittest
import numpy as np
import paddle
import paddle.nn.functional as F
class TestSilu(unittest.TestCase):
"""测试 silu 激活函数
Test silu activation function"""
def test_silu_basic(self):
"""测试基本的 silu
Test basic silu"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = F.silu(x)
# silu(x) = x * sigmoid(x)
expected = x.numpy() * (1.0 / (1.0 + np.exp(-x.numpy())))
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_silu_2d(self):
"""测试2D输入的 silu
Test silu with 2D input"""
x = paddle.randn([3, 4])
result = F.silu(x)
self.assertEqual(result.shape, [3, 4])
class TestLogSigmoid(unittest.TestCase):
"""测试 log_sigmoid 激活函数
Test log_sigmoid activation function"""
def test_log_sigmoid_basic(self):
"""测试基本的 log_sigmoid
Test basic log_sigmoid"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = F.log_sigmoid(x)
expected = np.log(1.0 / (1.0 + np.exp(-x.numpy())))
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_log_sigmoid_2d(self):
"""测试2D输入的 log_sigmoid
Test log_sigmoid with 2D input"""
x = paddle.randn([2, 3])
result = F.log_sigmoid(x)
self.assertEqual(result.shape, [2, 3])
class TestTanhshrink(unittest.TestCase):
"""测试 tanhshrink 激活函数
Test tanhshrink activation function"""
def test_tanhshrink_basic(self):
"""测试基本的 tanhshrink
Test basic tanhshrink"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = F.tanhshrink(x)
# tanhshrink(x) = x - tanh(x)
expected = x.numpy() - np.tanh(x.numpy())
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_tanhshrink_2d(self):
"""测试2D输入的 tanhshrink
Test tanhshrink with 2D input"""
x = paddle.randn([2, 3])
result = F.tanhshrink(x)
self.assertEqual(result.shape, [2, 3])
class TestHardshrink(unittest.TestCase):
"""测试 hardshrink 激活函数
Test hardshrink activation function"""
def test_hardshrink_basic(self):
"""测试基本的 hardshrink
Test basic hardshrink"""
x = paddle.to_tensor([-2.0, -0.5, 0.0, 0.5, 2.0])
result = F.hardshrink(x)
# hardshrink(x) = x if |x| > 0.5, else 0
expected = [-2.0, 0.0, 0.0, 0.0, 2.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_hardshrink_custom_threshold(self):
"""测试自定义阈值的 hardshrink
Test hardshrink with custom threshold"""
x = paddle.to_tensor([-1.0, -0.8, 0.0, 0.8, 1.0])
result = F.hardshrink(x, threshold=0.9)
expected = [-1.0, 0.0, 0.0, 0.0, 1.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestSoftshrink(unittest.TestCase):
"""测试 softshrink 激活函数
Test softshrink activation function"""
def test_softshrink_basic(self):
"""测试基本的 softshrink
Test basic softshrink"""
x = paddle.to_tensor([-2.0, -0.5, 0.0, 0.5, 2.0])
result = F.softshrink(x)
# softshrink(x, lambda=0.5) = x - 0.5 if x > 0.5, x + 0.5 if x < -0.5, 0 otherwise
expected = [-1.5, 0.0, 0.0, 0.0, 1.5]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_softshrink_custom_lambda(self):
"""测试自定义 lambda 的 softshrink
Test softshrink with custom lambda"""
x = paddle.to_tensor([-2.0, -0.5, 0.0, 0.5, 2.0])
result = F.softshrink(x, threshold=1.0)
expected = [-1.0, 0.0, 0.0, 0.0, 1.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestSoftsign(unittest.TestCase):
"""测试 softsign 激活函数
Test softsign activation function"""
def test_softsign_basic(self):
"""测试基本的 softsign
Test basic softsign"""
x = paddle.to_tensor([-1.0, 0.0, 1.0])
result = F.softsign(x)
# softsign(x) = x / (1 + |x|)
expected = [-0.5, 0.0, 0.5]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestHardtanh(unittest.TestCase):
"""测试 hardtanh 激活函数
Test hardtanh activation function"""
def test_hardtanh_basic(self):
"""测试基本的 hardtanh
Test basic hardtanh"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = F.hardtanh(x)
expected = [-1.0, -1.0, 0.0, 1.0, 1.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
def test_hardtanh_custom_range(self):
"""测试自定义范围的 hardtanh
Test hardtanh with custom range"""
x = paddle.to_tensor([-3.0, -1.0, 0.0, 1.0, 3.0])
result = F.hardtanh(x, min=-2.0, max=2.0)
expected = [-2.0, -1.0, 0.0, 1.0, 2.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestHardsigmoid(unittest.TestCase):
"""测试 hardsigmoid 激活函数
Test hardsigmoid activation function"""
def test_hardsigmoid_basic(self):
"""测试基本的 hardsigmoid
Test basic hardsigmoid"""
x = paddle.to_tensor([-4.0, -2.0, 0.0, 2.0, 4.0])
result = F.hardsigmoid(x)
# hardsigmoid(x) = clip(x/6 + 0.5, 0, 1)
expected = [0.0, 1.0 / 6.0, 0.5, 5.0 / 6.0, 1.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestHardswish(unittest.TestCase):
"""测试 hardswish 激活函数
Test hardswish activation function"""
def test_hardswish_basic(self):
"""测试基本的 hardswish
Test basic hardswish"""
x = paddle.to_tensor([-4.0, -2.0, 0.0, 2.0, 4.0])
result = F.hardswish(x)
self.assertEqual(result.shape, [5])
class TestPrelu(unittest.TestCase):
"""测试 prelu 激活函数
Test prelu activation function"""
def test_prelu_basic(self):
"""测试基本的 prelu
Test basic prelu"""
x = paddle.to_tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
w = paddle.to_tensor([0.25])
result = F.prelu(x, w)
# prelu(x) = max(0, x) + w * min(0, x)
expected = [0.25 * (-2.0), 0.25 * (-1.0), 0.0, 1.0, 2.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestRelu6(unittest.TestCase):
"""测试 relu6 激活函数
Test relu6 activation function"""
def test_relu6_basic(self):
"""测试基本的 relu6
Test basic relu6"""
x = paddle.to_tensor([-2.0, 0.0, 3.0, 6.0, 8.0])
result = F.relu6(x)
expected = [0.0, 0.0, 3.0, 6.0, 6.0]
np.testing.assert_allclose(result.numpy(), expected, rtol=1e-5)
class TestSelu(unittest.TestCase):
"""测试 selu 激活函数
Test selu activation function"""
def test_selu_basic(self):
"""测试基本的 selu
Test basic selu"""
x = paddle.to_tensor([-2.0, 0.0, 2.0])
result = F.selu(x)
self.assertEqual(result.shape, [3])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_functional_distance.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.functional.distance
# 覆盖模块: paddle/nn/functional/distance.py
# 未覆盖行: 95,96,97,99,102,105,106,107,108,111,112,113,119,120,124
# Covered module: paddle/nn/functional/distance.py
# Uncovered lines: 95,96,97,99,102,105,106,107,108,111,112,113,119,120,124
import unittest
import numpy as np
import paddle
class TestPairwiseDistance(unittest.TestCase):
"""测试 pairwise_distance 函数的各种参数组合
Test pairwise_distance function with various parameter combinations"""
def setUp(self):
paddle.seed(42)
np.random.seed(42)
def test_pairwise_distance_basic(self):
"""测试基本的 pairwise_distance 计算
Test basic pairwise_distance computation"""
x = paddle.to_tensor([[1.0, 3.0], [3.0, 5.0]], dtype='float32')
y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype='float32')
result = paddle.nn.functional.pairwise_distance(x, y)
self.assertEqual(result.shape, [2])
# pairwise distance with p=2: sqrt((x1-y1)^2 + (x2-y2)^2) + epsilon
np.testing.assert_allclose(
result.numpy(), [5.0, 5.0], rtol=1e-5, atol=1e-5
)
def test_pairwise_distance_p1(self):
"""测试 p=1 (曼哈顿距离) 时的 pairwise_distance
Test pairwise_distance with p=1 (Manhattan distance)"""
x = paddle.to_tensor([[1.0, 2.0]], dtype='float32')
y = paddle.to_tensor([[4.0, 6.0]], dtype='float32')
result = paddle.nn.functional.pairwise_distance(x, y, p=1.0)
# p=1: |1-4| + |2-6| = 7, but with epsilon added
self.assertEqual(result.shape, [1])
def test_pairwise_distance_keepdim(self):
"""测试 keepdim=True 的 pairwise_distance
Test pairwise_distance with keepdim=True"""
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype='float32')
result = paddle.nn.functional.pairwise_distance(x, y, keepdim=True)
self.assertEqual(result.shape, [2, 1])
def test_pairwise_distance_1d_input(self):
"""测试一维输入的 pairwise_distance
Test pairwise_distance with 1D input"""
x = paddle.to_tensor([1.0, 2.0], dtype='float32')
y = paddle.to_tensor([4.0, 6.0], dtype='float32')
result = paddle.nn.functional.pairwise_distance(x, y)
self.assertEqual(result.shape, [])
def test_pairwise_distance_epsilon_zero(self):
"""测试 epsilon=0 时的 pairwise_distance
Test pairwise_distance with epsilon=0"""
x = paddle.to_tensor([[1.0, 2.0]], dtype='float32')
y = paddle.to_tensor([[4.0, 6.0]], dtype='float32')
result = paddle.nn.functional.pairwise_distance(x, y, epsilon=0.0)
self.assertEqual(result.shape, [1])
def test_pairwise_distance_float64(self):
"""测试 float64 数据类型的 pairwise_distance
Test pairwise_distance with float64 dtype"""
x = paddle.to_tensor([[1.0, 3.0]], dtype='float64')
y = paddle.to_tensor([[5.0, 6.0]], dtype='float64')
result = paddle.nn.functional.pairwise_distance(x, y)
self.assertEqual(result.dtype, paddle.float64)
def test_pairwise_distance_alias_params(self):
"""测试使用参数别名的 pairwise_distance (x1, x2, eps)
Test pairwise_distance with parameter aliases (x1, x2, eps)"""
x = paddle.to_tensor([[1.0, 2.0]], dtype='float32')
y = paddle.to_tensor([[4.0, 6.0]], dtype='float32')
result = paddle.nn.functional.pairwise_distance(x1=x, x2=y, eps=1e-6)
self.assertEqual(result.shape, [1])
def test_pairwise_distance_large_p(self):
"""测试大 p 值的 pairwise_distance
Test pairwise_distance with large p value"""
x = paddle.to_tensor([[1.0, 2.0]], dtype='float32')
y = paddle.to_tensor([[4.0, 6.0]], dtype='float32')
result = paddle.nn.functional.pairwise_distance(x, y, p=3.0)
self.assertEqual(result.shape, [1])
class TestPdist(unittest.TestCase):
"""测试 pdist 函数
Test pdist function"""
def setUp(self):
paddle.seed(42)
np.random.seed(42)
def test_pdist_basic(self):
"""测试基本的 pdist 计算
Test basic pdist computation"""
x = paddle.to_tensor(
[[0.0, 0.0], [1.0, 0.0], [0.0, 1.0]], dtype='float32'
)
result = paddle.nn.functional.pdist(x, p=2.0)
# N=3, output shape = 3*(3-1)/2 = 3
self.assertEqual(result.shape, [3])
def test_pdist_p1(self):
"""测试 p=1 的 pdist
Test pdist with p=1"""
x = paddle.to_tensor(
[[0.0, 0.0], [1.0, 1.0], [2.0, 2.0]], dtype='float32'
)
result = paddle.nn.functional.pdist(x, p=1.0)
self.assertEqual(result.shape, [3])
# distances: |0-1|+|0-1|=2, |0-2|+|0-2|=4, |1-2|+|1-2|=2
np.testing.assert_allclose(
result.numpy(), [2.0, 4.0, 2.0], rtol=1e-5, atol=1e-5
)
def test_pdist_assert_2d(self):
"""测试 pdist 对非2D输入的断言
Test pdist assertion for non-2D input"""
x = paddle.to_tensor([1.0, 2.0, 3.0], dtype='float32')
with self.assertRaises(AssertionError):
paddle.nn.functional.pdist(x)
def test_pdist_4_elements(self):
"""测试4个元素的 pdist (6对距离)
Test pdist with 4 elements (6 pairs)"""
paddle.seed(2023)
a = paddle.randn([4, 5])
result = paddle.nn.functional.pdist(a)
# N=4, output shape = 4*3/2 = 6
self.assertEqual(result.shape, [6])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.functional.extension
# 覆盖模块: paddle/nn/functional/extension.py
# 未覆盖行: 130,131,133,134,135,136,137,139,141,145,146,231,232,233,236,238,244,323,324,330,331,333,335,336,338,348
# Covered module: paddle/nn/functional/extension.py
# Uncovered lines: 130,131,133,134,135,136,137,139,141,145,146,231,232,233,236,238,244,323,324,330,331,333,335,336,338,348
import unittest
import numpy as np
import paddle
class TestSequenceMask(unittest.TestCase):
"""测试 sequence_mask 函数
Test sequence_mask function"""
def test_sequence_mask_basic(self):
"""测试基本的 sequence_mask
Test basic sequence_mask"""
lengths = paddle.to_tensor([10, 9, 8])
mask = paddle.nn.functional.sequence_mask(lengths)
self.assertEqual(mask.shape, [3, 10])
self.assertEqual(mask.dtype, paddle.int64)
def test_sequence_mask_with_maxlen(self):
"""测试指定 maxlen 的 sequence_mask
Test sequence_mask with specified maxlen"""
lengths = paddle.to_tensor([3, 1, 1, 0])
mask = paddle.nn.functional.sequence_mask(lengths, maxlen=4)
self.assertEqual(mask.shape, [4, 4])
expected = np.array(
[
[1, 1, 1, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
[0, 0, 0, 0],
]
)
np.testing.assert_array_equal(mask.numpy(), expected)
def test_sequence_mask_dtype_float32(self):
"""测试 float32 输出类型的 sequence_mask
Test sequence_mask with float32 output dtype"""
lengths = paddle.to_tensor([2, 3])
mask = paddle.nn.functional.sequence_mask(lengths, dtype='float32')
self.assertEqual(mask.dtype, paddle.float32)
def test_sequence_mask_dtype_int32(self):
"""测试 int32 输出类型的 sequence_mask
Test sequence_mask with int32 output dtype"""
lengths = paddle.to_tensor([2, 3])
mask = paddle.nn.functional.sequence_mask(lengths, dtype='int32')
self.assertEqual(mask.dtype, paddle.int32)
def test_sequence_mask_dtype_bool(self):
"""测试 bool 输出类型的 sequence_mask
Test sequence_mask with bool output dtype"""
lengths = paddle.to_tensor([2, 3])
mask = paddle.nn.functional.sequence_mask(lengths, dtype='bool')
self.assertEqual(mask.dtype, paddle.bool)
def test_sequence_mask_2d_input(self):
"""测试2D输入的 sequence_mask
Test sequence_mask with 2D input"""
lengths = paddle.to_tensor([[2, 3], [1, 4]])
mask = paddle.nn.functional.sequence_mask(lengths, maxlen=5)
self.assertEqual(mask.shape, [2, 2, 5])
def test_sequence_mask_no_maxlen(self):
"""测试不指定 maxlen 的 sequence_mask (使用 max(x))
Test sequence_mask without maxlen (uses max(x))"""
lengths = paddle.to_tensor([2, 5, 3])
mask = paddle.nn.functional.sequence_mask(lengths)
self.assertEqual(mask.shape, [3, 5])
class TestGatherTree(unittest.TestCase):
"""测试 gather_tree 函数
Test gather_tree function"""
def test_gather_tree_basic(self):
"""测试基本的 gather_tree
Test basic gather_tree"""
ids = paddle.to_tensor(
[[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]]
)
parents = paddle.to_tensor(
[[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]]
)
result = paddle.nn.functional.gather_tree(ids, parents)
self.assertEqual(result.shape, [3, 2, 2])
expected = np.array(
[
[[2, 2], [1, 6]],
[[3, 3], [6, 1]],
[[0, 1], [9, 0]],
]
)
np.testing.assert_array_equal(result.numpy(), expected)
def test_gather_tree_not_3d(self):
"""测试非3D输入的 gather_tree 报错
Test gather_tree raises error for non-3D input"""
ids = paddle.to_tensor([[1, 2], [3, 4]])
parents = paddle.to_tensor([[0, 0], [1, 1]])
with self.assertRaises(ValueError):
paddle.nn.functional.gather_tree(ids, parents)
def test_gather_tree_shape_mismatch(self):
"""测试 ids 和 parents 形状不匹配时报错
Test gather_tree raises error when ids and parents shapes differ"""
ids = paddle.to_tensor([[[1, 2]]])
parents = paddle.to_tensor([[[1, 2], [3, 4]]])
with self.assertRaises(ValueError):
paddle.nn.functional.gather_tree(ids, parents)
def test_gather_tree_int32(self):
"""测试 int32 类型的 gather_tree
Test gather_tree with int32 dtype"""
ids = paddle.to_tensor(
[[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]],
dtype='int32',
)
parents = paddle.to_tensor(
[[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]],
dtype='int32',
)
result = paddle.nn.functional.gather_tree(ids, parents)
self.assertEqual(result.shape, [3, 2, 2])
class TestTemporalShift(unittest.TestCase):
"""测试 temporal_shift 函数
Test temporal_shift function"""
def test_temporal_shift_basic(self):
"""测试基本的 temporal_shift
Test basic temporal_shift"""
x = paddle.randn([6, 4, 2, 2])
out = paddle.nn.functional.temporal_shift(
x, seg_num=2, shift_ratio=0.25
)
self.assertEqual(out.shape, [6, 4, 2, 2])
def test_temporal_shift_nhwc(self):
"""测试 NHWC 格式的 temporal_shift
Test temporal_shift with NHWC format"""
x = paddle.randn([6, 2, 2, 4])
out = paddle.nn.functional.temporal_shift(
x, seg_num=2, shift_ratio=0.25, data_format='NHWC'
)
self.assertEqual(out.shape, [6, 2, 2, 4])
def test_temporal_shift_invalid_format(self):
"""测试无效的 data_format 报错
Test temporal_shift raises error for invalid data_format"""
x = paddle.randn([6, 4, 2, 2])
with self.assertRaises(ValueError):
paddle.nn.functional.temporal_shift(
x, seg_num=2, shift_ratio=0.25, data_format='INVALID'
)
def test_temporal_shift_different_seg_num(self):
"""测试不同 seg_num 的 temporal_shift
Test temporal_shift with different seg_num"""
x = paddle.randn([9, 8, 3, 3])
out = paddle.nn.functional.temporal_shift(
x, seg_num=3, shift_ratio=0.125
)
self.assertEqual(out.shape, [9, 8, 3, 3])
def test_temporal_shift_float64(self):
"""测试 float64 类型的 temporal_shift
Test temporal_shift with float64 dtype"""
x = paddle.randn([6, 4, 2, 2], dtype='float64')
out = paddle.nn.functional.temporal_shift(
x, seg_num=2, shift_ratio=0.25
)
self.assertEqual(out.dtype, paddle.float64)
class TestDiagEmbed(unittest.TestCase):
"""测试 diag_embed 函数 (deprecated)
Test diag_embed function (deprecated)"""
def test_diag_embed_basic(self):
"""测试基本的 diag_embed
Test basic diag_embed"""
x = paddle.to_tensor([1.0, 2.0, 3.0])
result = paddle.nn.functional.diag_embed(x)
self.assertEqual(result.shape, [3, 3])
def test_diag_embed_with_offset(self):
"""测试带 offset 的 diag_embed
Test diag_embed with offset"""
x = paddle.to_tensor([1.0, 2.0])
result = paddle.nn.functional.diag_embed(x, offset=1)
self.assertEqual(result.shape, [3, 3])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_functional_input.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.functional.input
# 覆盖模块: paddle/nn/functional/input.py
# 未覆盖行: 118,119,121,122,124,125,127,128,129,130,137,315,316,318,324,326,332
# Covered module: paddle/nn/functional/input.py
# Uncovered lines: 118,119,121,122,124,125,127,128,129,130,137,315,316,318,324,326,332
import unittest
import numpy as np
import paddle
class TestOneHot(unittest.TestCase):
"""测试 one_hot 函数
Test one_hot function"""
def test_one_hot_basic(self):
"""测试基本的 one_hot
Test basic one_hot"""
label = paddle.to_tensor([1, 1, 3, 0], dtype='int64')
result = paddle.nn.functional.one_hot(label, num_classes=4)
self.assertEqual(result.shape, [4, 4])
self.assertEqual(result.dtype, paddle.float32)
expected = np.array(
[
[0.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 0.0],
]
)
np.testing.assert_array_equal(result.numpy(), expected)
def test_one_hot_int32(self):
"""测试 int32 输入的 one_hot
Test one_hot with int32 input"""
label = paddle.to_tensor([0, 1, 2], dtype='int32')
result = paddle.nn.functional.one_hot(label, num_classes=3)
self.assertEqual(result.shape, [3, 3])
def test_one_hot_auto_num_classes(self):
"""测试自动推断 num_classes 的 one_hot (num_classes=-1)
Test one_hot with auto num_classes (num_classes=-1)"""
label = paddle.to_tensor([1, 0, 2], dtype='int64')
result = paddle.nn.functional.one_hot(label, num_classes=-1)
# max(label) + 1 = 3
self.assertEqual(result.shape, [3, 3])
def test_one_hot_2d_input(self):
"""测试2D输入的 one_hot
Test one_hot with 2D input"""
label = paddle.to_tensor([[0, 1], [2, 0]], dtype='int64')
result = paddle.nn.functional.one_hot(label, num_classes=3)
self.assertEqual(result.shape, [2, 2, 3])
def test_one_hot_alias_input(self):
"""测试使用 input 别名的 one_hot
Test one_hot with 'input' alias parameter"""
label = paddle.to_tensor([1, 0], dtype='int64')
result = paddle.nn.functional.one_hot(input=label, num_classes=2)
self.assertEqual(result.shape, [2, 2])
def test_one_hot_3d_input(self):
"""测试3D输入的 one_hot
Test one_hot with 3D input"""
label = paddle.to_tensor([[[0, 1]]], dtype='int64')
result = paddle.nn.functional.one_hot(label, num_classes=2)
self.assertEqual(result.shape, [1, 1, 2, 2])
class TestEmbedding(unittest.TestCase):
"""测试 embedding 函数
Test embedding function"""
def test_embedding_basic(self):
"""测试基本的 embedding
Test basic embedding"""
x = paddle.to_tensor([0, 1, 2], dtype='int64')
weight = paddle.randn([10, 4])
result = paddle.nn.functional.embedding(x, weight)
self.assertEqual(result.shape, [3, 4])
def test_embedding_2d_indices(self):
"""测试2D索引的 embedding
Test embedding with 2D indices"""
x = paddle.arange(3, 6).reshape((3, 1)).astype(paddle.int64)
weight = paddle.full(shape=(10, 3), fill_value=2.0).astype(
paddle.float32
)
result = paddle.nn.functional.embedding(x, weight, sparse=True)
self.assertEqual(result.shape, [3, 1, 3])
np.testing.assert_allclose(result.numpy(), np.full((3, 1, 3), 2.0))
def test_embedding_padding_idx(self):
"""测试带 padding_idx 的 embedding
Test embedding with padding_idx"""
x = paddle.to_tensor([0, 1, 2, 3], dtype='int64')
weight = paddle.randn([5, 4])
result = paddle.nn.functional.embedding(x, weight, padding_idx=3)
self.assertEqual(result.shape, [4, 4])
# padding_idx=3 的位置应该全为0
np.testing.assert_array_equal(result[3].numpy(), np.zeros(4))
def test_embedding_negative_padding_idx(self):
"""测试负 padding_idx 的 embedding
Test embedding with negative padding_idx"""
x = paddle.to_tensor([0, 1, 5], dtype='int64')
weight = paddle.randn([6, 4])
result = paddle.nn.functional.embedding(x, weight, padding_idx=-1)
self.assertEqual(result.shape, [3, 4])
# padding_idx=-1 means last row (index 5)
np.testing.assert_array_equal(result[2].numpy(), np.zeros(4))
def test_embedding_invalid_padding_idx(self):
"""测试无效 padding_idx 的报错
Test embedding raises error for invalid padding_idx"""
x = paddle.to_tensor([0, 1], dtype='int64')
weight = paddle.randn([5, 4])
with self.assertRaises(ValueError):
paddle.nn.functional.embedding(x, weight, padding_idx=10)
def test_embedding_sparse(self):
"""测试 sparse 模式的 embedding
Test embedding in sparse mode"""
x = paddle.to_tensor([0, 1, 2], dtype='int64')
weight = paddle.randn([10, 4])
result = paddle.nn.functional.embedding(x, weight, sparse=True)
self.assertEqual(result.shape, [3, 4])
def test_embedding_max_norm(self):
"""测试带 max_norm 的 embedding (会 renorm weight)
Test embedding with max_norm (renorms weight)"""
x = paddle.to_tensor([0, 1], dtype='int64')
weight = paddle.randn([5, 4]) * 10 # large values
result = paddle.nn.functional.embedding(x, weight, max_norm=1.0)
self.assertEqual(result.shape, [2, 4])
def test_embedding_alias_input(self):
"""测试使用 input 别名的 embedding
Test embedding with 'input' alias parameter"""
x = paddle.to_tensor([0, 1], dtype='int64')
weight = paddle.randn([5, 4])
result = paddle.nn.functional.embedding(input=x, weight=weight)
self.assertEqual(result.shape, [2, 4])
class TestEmbeddingRenorm(unittest.TestCase):
"""测试 embedding_renorm_ 函数
Test embedding_renorm_ function"""
def test_embedding_renorm_basic(self):
"""测试基本的 embedding_renorm_
Test basic embedding_renorm_"""
x = paddle.to_tensor([0, 1, 2], dtype='int64')
weight = paddle.randn([5, 4]) * 10
original_norms = paddle.norm(weight[:3], p=2, axis=1)
result = paddle.nn.functional.embedding_renorm_(x, weight, max_norm=1.0)
new_norms = paddle.norm(result[:3], p=2, axis=1)
# All norms should be <= max_norm
self.assertTrue(paddle.all(new_norms <= 1.0 + 1e-5).item())
def test_embedding_renorm_no_change(self):
"""测试 norm 已经小于 max_norm 时不会改变 weight
Test embedding_renorm_ doesn't change weight when norms are already small"""
x = paddle.to_tensor([0, 1], dtype='int64')
weight = paddle.randn([5, 4]) * 0.01 # small values
result = paddle.nn.functional.embedding_renorm_(
x, weight, max_norm=100.0
)
# Should not change since norms are already small
np.testing.assert_allclose(result.numpy(), weight.numpy(), rtol=1e-5)
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_functional_vision.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.functional.vision
# 覆盖模块: paddle/nn/functional/vision.py
# 未覆盖行: 111,125,135,136,139,140,141,142,143,144,148,150,156,210,211,214,215,224,291,292,293,294,300
# Covered module: paddle/nn/functional/vision.py
# Uncovered lines: 111,125,135,136,139,140,141,142,143,144,148,150,156,210,211,214,215,224,291,292,293,294,300
import unittest
import paddle
class TestAffineGrid(unittest.TestCase):
"""测试 affine_grid 函数
Test affine_grid function"""
def test_affine_grid_2d(self):
"""测试2D affine_grid
Test 2D affine_grid"""
theta = paddle.to_tensor(
[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], dtype='float32'
)
out_shape = paddle.to_tensor([1, 1, 3, 3], dtype='int32')
grid = paddle.nn.functional.affine_grid(theta, out_shape)
self.assertEqual(grid.shape, [1, 3, 3, 2])
def test_affine_grid_2d_list(self):
"""测试使用 list 作为 out_shape 的2D affine_grid
Test 2D affine_grid with list out_shape"""
theta = paddle.to_tensor(
[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], dtype='float32'
)
grid = paddle.nn.functional.affine_grid(theta, [1, 1, 3, 3])
self.assertEqual(grid.shape, [1, 3, 3, 2])
def test_affine_grid_align_corners(self):
"""测试 align_corners 参数的 affine_grid
Test affine_grid with align_corners parameter"""
theta = paddle.to_tensor(
[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], dtype='float32'
)
grid = paddle.nn.functional.affine_grid(
theta, [1, 1, 3, 3], align_corners=False
)
self.assertEqual(grid.shape, [1, 3, 3, 2])
def test_affine_grid_float64(self):
"""测试 float64 类型的 affine_grid
Test affine_grid with float64 dtype"""
theta = paddle.to_tensor(
[[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], dtype='float64'
)
grid = paddle.nn.functional.affine_grid(theta, [1, 1, 3, 3])
self.assertEqual(grid.dtype, paddle.float64)
class TestPixelShuffle(unittest.TestCase):
"""测试 pixel_shuffle 函数
Test pixel_shuffle function"""
def test_pixel_shuffle_basic(self):
"""测试基本的 pixel_shuffle
Test basic pixel_shuffle"""
x = paddle.randn([1, 4, 2, 2])
result = paddle.nn.functional.pixel_shuffle(x, upscale_factor=2)
self.assertEqual(result.shape, [1, 1, 4, 4])
def test_pixel_shuffle_3d(self):
"""测试3D输入的 pixel_shuffle (需要4D输入)
Test pixel_shuffle requires 4D input"""
x = paddle.randn([1, 4, 2, 2])
result = paddle.nn.functional.pixel_shuffle(x, upscale_factor=2)
self.assertEqual(result.shape, [1, 1, 4, 4])
def test_pixel_shuffle_larger_factor(self):
"""测试更大 upscale_factor 的 pixel_shuffle
Test pixel_shuffle with larger upscale_factor"""
x = paddle.randn([1, 9, 3, 3])
result = paddle.nn.functional.pixel_shuffle(x, upscale_factor=3)
self.assertEqual(result.shape, [1, 1, 9, 9])
class TestGridSample(unittest.TestCase):
"""测试 grid_sample 函数
Test grid_sample function"""
def test_grid_sample_basic(self):
"""测试基本的 grid_sample
Test basic grid_sample"""
x = paddle.randn([1, 1, 3, 3])
grid = paddle.to_tensor(
[[[[0.0, 0.0], [0.5, 0.0], [1.0, 0.0]]]], dtype='float32'
)
result = paddle.nn.functional.grid_sample(x, grid)
self.assertEqual(result.shape, [1, 1, 1, 3])
def test_grid_sample_bilinear(self):
"""测试 bilinear 插值的 grid_sample
Test grid_sample with bilinear interpolation"""
x = paddle.randn([1, 1, 4, 4])
grid = paddle.to_tensor(
[
[
[
[0.0, 0.0],
[0.5, 0.0],
[1.0, 0.0],
[-1.0, 0.0],
]
]
],
dtype='float32',
)
result = paddle.nn.functional.grid_sample(
x, grid, mode='bilinear', padding_mode='zeros'
)
self.assertEqual(result.shape, [1, 1, 1, 4])
def test_grid_sample_nearest(self):
"""测试 nearest 插值的 grid_sample
Test grid_sample with nearest interpolation"""
x = paddle.randn([1, 1, 3, 3])
grid = paddle.to_tensor([[[[0.0, 0.0], [0.5, 0.5]]]], dtype='float32')
result = paddle.nn.functional.grid_sample(x, grid, mode='nearest')
self.assertEqual(result.shape, [1, 1, 1, 2])
def test_grid_sample_align_corners(self):
"""测试 align_corners 参数的 grid_sample
Test grid_sample with align_corners parameter"""
x = paddle.randn([1, 1, 4, 4])
grid = paddle.to_tensor([[[[0.0, 0.0], [1.0, 1.0]]]], dtype='float32')
result = paddle.nn.functional.grid_sample(x, grid, align_corners=True)
self.assertEqual(result.shape, [1, 1, 1, 2])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.initializer.orthogonal and paddle.nn.functional.loss
# 覆盖模块: paddle/nn/initializer/orthogonal.py, paddle/nn/functional/loss.py
# 未覆盖行: orthogonal: 157,161,167,181,187,193,204,210,218,219,226,234,240,241,247,254,256,264,271; loss: 169,170,171,173,175,181,280,284,289,290,291,338,584,593,594,595,596,597,598,601,602,603,610,706,712,719,720,721,722,731
# Covered module: paddle/nn/initializer/orthogonal.py, paddle/nn/functional/loss.py
# Uncovered lines: orthogonal: 157-271; loss: 169-731
import unittest
import paddle
import paddle.nn.functional as F
class TestOrthogonalInit(unittest.TestCase):
"""测试正交初始化器
Test orthogonal initializer"""
def test_orthogonal_init_default(self):
"""测试默认参数的正交初始化
Test orthogonal initializer with default parameters"""
init = paddle.nn.initializer.Orthogonal()
linear = paddle.nn.Linear(10, 10)
init(linear.weight)
self.assertEqual(linear.weight.shape, [10, 10])
def test_orthogonal_init_with_gain(self):
"""测试带 gain 参数的正交初始化
Test orthogonal initializer with gain"""
init = paddle.nn.initializer.Orthogonal(gain=2.0)
linear = paddle.nn.Linear(10, 10)
init(linear.weight)
self.assertEqual(linear.weight.shape, [10, 10])
def test_orthogonal_init_rectangular(self):
"""测试矩形矩阵的正交初始化
Test orthogonal initializer with rectangular matrix"""
init = paddle.nn.initializer.Orthogonal()
# For Linear(20, 10), weight shape is [20, 10] (in_features x out_features)
linear = paddle.nn.Linear(20, 10)
init(linear.weight)
# Paddle Linear weight shape is [in_features, out_features]
self.assertEqual(list(linear.weight.shape), [20, 10])
class TestBCELoss(unittest.TestCase):
"""测试二元交叉熵损失函数
Test Binary Cross Entropy loss function"""
def test_bce_loss_basic(self):
"""测试基本的 BCELoss
Test basic BCELoss"""
input = paddle.to_tensor([0.9, 0.1, 0.8])
label = paddle.to_tensor([1.0, 0.0, 1.0])
result = F.binary_cross_entropy(input, label)
self.assertEqual(result.shape, [])
def test_bce_loss_with_weight(self):
"""测试带权重的 BCELoss
Test BCELoss with weight"""
input = paddle.to_tensor([0.9, 0.1, 0.8])
label = paddle.to_tensor([1.0, 0.0, 1.0])
weight = paddle.to_tensor([1.0, 2.0, 1.0])
result = F.binary_cross_entropy(input, label, weight=weight)
self.assertEqual(result.shape, [])
def test_bce_loss_reduction_sum(self):
"""测试 reduction=sum 的 BCELoss
Test BCELoss with reduction=sum"""
input = paddle.to_tensor([0.9, 0.1, 0.8])
label = paddle.to_tensor([1.0, 0.0, 1.0])
result = F.binary_cross_entropy(input, label, reduction='sum')
self.assertEqual(result.shape, [])
def test_bce_loss_no_reduction(self):
"""测试 reduction=none 的 BCELoss
Test BCELoss with reduction=none"""
input = paddle.to_tensor([0.9, 0.1, 0.8])
label = paddle.to_tensor([1.0, 0.0, 1.0])
result = F.binary_cross_entropy(input, label, reduction='none')
self.assertEqual(result.shape, [3])
class TestMSELoss(unittest.TestCase):
"""测试均方误差损失函数
Test Mean Squared Error loss function"""
def test_mse_loss_basic(self):
"""测试基本的 MSELoss
Test basic MSELoss"""
input = paddle.to_tensor([1.0, 2.0, 3.0])
label = paddle.to_tensor([1.5, 2.5, 3.5])
result = F.mse_loss(input, label)
self.assertEqual(result.shape, [])
def test_mse_loss_reduction_sum(self):
"""测试 reduction=sum 的 MSELoss
Test MSELoss with reduction=sum"""
input = paddle.to_tensor([1.0, 2.0, 3.0])
label = paddle.to_tensor([1.5, 2.5, 3.5])
result = F.mse_loss(input, label, reduction='sum')
self.assertEqual(result.shape, [])
def test_mse_loss_2d(self):
"""测试2D输入的 MSELoss
Test MSELoss with 2D input"""
input = paddle.randn([3, 5])
label = paddle.randn([3, 5])
result = F.mse_loss(input, label)
self.assertEqual(result.shape, [])
class TestL1Loss(unittest.TestCase):
"""测试 L1 损失函数
Test L1 loss function"""
def test_l1_loss_basic(self):
"""测试基本的 L1Loss
Test basic L1Loss"""
input = paddle.to_tensor([1.0, 2.0, 3.0])
label = paddle.to_tensor([1.5, 2.5, 3.5])
result = F.l1_loss(input, label)
self.assertEqual(result.shape, [])
def test_l1_loss_reduction_sum(self):
"""测试 reduction=sum 的 L1Loss
Test L1Loss with reduction=sum"""
input = paddle.to_tensor([1.0, 2.0, 3.0])
label = paddle.to_tensor([1.5, 2.5, 3.5])
result = F.l1_loss(input, label, reduction='sum')
self.assertEqual(result.shape, [])
class TestSmoothL1Loss(unittest.TestCase):
"""测试 Smooth L1 损失函数
Test Smooth L1 loss function"""
def test_smooth_l1_loss_basic(self):
"""测试基本的 SmoothL1Loss
Test basic SmoothL1Loss"""
input = paddle.to_tensor([1.0, 2.0, 3.0])
label = paddle.to_tensor([1.5, 2.5, 3.5])
result = F.smooth_l1_loss(input, label)
self.assertEqual(result.shape, [])
def test_smooth_l1_loss_custom_delta(self):
"""测试自定义 delta 的 SmoothL1Loss
Test SmoothL1Loss with custom delta"""
input = paddle.to_tensor([1.0, 2.0, 3.0])
label = paddle.to_tensor([1.5, 2.5, 3.5])
result = F.smooth_l1_loss(input, label, delta=0.5)
self.assertEqual(result.shape, [])
class TestKLDivLoss(unittest.TestCase):
"""测试 KL 散度损失函数
Test KL Divergence loss function"""
def test_kldiv_loss_basic(self):
"""测试基本的 KLDivLoss
Test basic KLDivLoss"""
input = paddle.to_tensor([0.5, 0.5])
label = paddle.to_tensor([0.3, 0.7])
result = F.kl_div(input, label)
self.assertEqual(result.shape, [])
class TestNLLLoss(unittest.TestCase):
"""测试负对数似然损失函数
Test Negative Log Likelihood loss function"""
def test_nll_loss_basic(self):
"""测试基本的 NLLLoss
Test basic NLLLoss"""
input = paddle.randn([3, 5])
label = paddle.to_tensor([0, 2, 1], dtype='int64')
result = F.nll_loss(input, label)
self.assertEqual(result.shape, [])
def test_nll_loss_with_weight(self):
"""测试带权重的 NLLLoss
Test NLLLoss with weight"""
input = paddle.randn([3, 5])
label = paddle.to_tensor([0, 2, 1], dtype='int64')
weight = paddle.to_tensor([1.0, 1.0, 1.0, 1.0, 1.0])
result = F.nll_loss(input, label, weight=weight)
self.assertEqual(result.shape, [])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
初始化函数高级测试 / Advanced Initialization Function Tests
测试目标 / Test Target:
paddle.nn.initializer 初始化器
覆盖的模块 / Covered Modules:
- paddle.nn.initializer.KaimingUniform/Normal
- paddle.nn.initializer.XavierUniform/Normal
- paddle.nn.initializer.TruncatedNormal
- paddle.nn.initializer.Constant
- paddle.nn.initializer.Dirac/Orthogonal
作用 / Purpose:
补充权重初始化API的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestKaimingInitializer(unittest.TestCase):
"""测试Kaiming初始化 / Test Kaiming initialization"""
def test_kaiming_uniform(self):
"""测试Kaiming均匀初始化 / Test Kaiming uniform initialization"""
init = nn.initializer.KaimingUniform()
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(16, 32, weight_attr=param)
self.assertEqual(linear.weight.shape, [16, 32])
def test_kaiming_normal(self):
"""测试Kaiming正态初始化 / Test Kaiming normal initialization"""
init = nn.initializer.KaimingNormal()
param = paddle.ParamAttr(initializer=init)
conv = nn.Conv2D(3, 16, 3, weight_attr=param)
x = paddle.randn([2, 3, 8, 8])
y = conv(x)
self.assertEqual(y.shape, [2, 16, 6, 6])
class TestXavierInitializer(unittest.TestCase):
"""测试Xavier初始化 / Test Xavier initialization"""
def test_xavier_uniform(self):
"""测试Xavier均匀初始化 / Test Xavier uniform initialization"""
init = nn.initializer.XavierUniform()
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(16, 32, weight_attr=param)
# Xavier uniform should bound weights in [-a, a] where a = sqrt(6 / fan_in+fan_out)
weight = linear.weight.numpy()
bound = np.sqrt(6.0 / (16 + 32))
self.assertTrue(np.all(np.abs(weight) <= bound + 1e-6))
def test_xavier_normal(self):
"""测试Xavier正态初始化 / Test Xavier normal initialization"""
init = nn.initializer.XavierNormal()
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(16, 32, weight_attr=param)
self.assertEqual(linear.weight.shape, [16, 32])
class TestConstantInitializer(unittest.TestCase):
"""测试常量初始化 / Test constant initialization"""
def test_constant_zero(self):
"""测试零初始化 / Test zero initialization"""
init = nn.initializer.Constant(value=0.0)
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(4, 8, weight_attr=param)
np.testing.assert_allclose(linear.weight.numpy(), np.zeros([4, 8]))
def test_constant_value(self):
"""测试常数初始化 / Test constant value initialization"""
init = nn.initializer.Constant(value=0.01)
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(4, 8, weight_attr=param)
np.testing.assert_allclose(
linear.weight.numpy(), np.full([4, 8], 0.01), rtol=1e-6
)
class TestTruncatedNormalInitializer(unittest.TestCase):
"""测试截断正态初始化 / Test truncated normal initialization"""
def test_truncated_normal_basic(self):
"""测试基本截断正态初始化 / Test basic truncated normal"""
init = nn.initializer.TruncatedNormal(mean=0.0, std=0.02)
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(16, 64, weight_attr=param)
weight = linear.weight.numpy()
# Values should be within 2 std = 0.04
self.assertTrue(np.all(np.abs(weight) < 0.1))
class TestUniformInitializer(unittest.TestCase):
"""测试均匀分布初始化 / Test uniform distribution initialization"""
def test_uniform(self):
"""测试均匀分布 / Test uniform initialization"""
init = nn.initializer.Uniform(low=-0.5, high=0.5)
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(16, 32, weight_attr=param)
weight = linear.weight.numpy()
self.assertTrue(np.all(weight >= -0.5))
self.assertTrue(np.all(weight <= 0.5))
def test_normal(self):
"""测试正态分布初始化 / Test normal distribution initialization"""
init = nn.initializer.Normal(mean=0.0, std=1.0)
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(16, 32, weight_attr=param)
weight = linear.weight.numpy()
# Mean should be approximately 0
self.assertAlmostEqual(float(weight.mean()), 0.0, delta=0.5)
class TestSpecialInitializers(unittest.TestCase):
"""测试特殊初始化器 / Test special initializers"""
def test_assign_initializer(self):
"""测试赋值初始化 / Test assign initializer"""
value = np.ones([4, 8], dtype='float32') * 0.5
init = nn.initializer.Assign(value)
param = paddle.ParamAttr(initializer=init)
linear = nn.Linear(4, 8, weight_attr=param)
np.testing.assert_allclose(linear.weight.numpy(), value)
def test_dirac_initializer_conv(self):
"""测试Dirac初始化卷积 / Test Dirac initializer for conv"""
init = nn.initializer.Dirac()
param = paddle.ParamAttr(initializer=init)
conv = nn.Conv2D(3, 3, 3, padding=1, weight_attr=param)
# Dirac init: output should equal input for identity
x = paddle.randn([1, 3, 8, 8])
y = conv(x)
self.assertEqual(y.shape, [1, 3, 8, 8])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
线性层及基础层单元测试 / Linear Layer and Basic Layers Unit Tests
测试目标 / Test Target:
paddle.nn.Linear及相关层 (覆盖率约83.2%)
覆盖的模块 / Covered Modules:
- paddle.nn.Linear: 全连接线性层
- paddle.nn.Bilinear: 双线性层
- paddle.nn.Identity: 恒等映射层
- paddle.nn.Flatten: 张量展平层
- paddle.nn.Unflatten: 张量还原展平
作用 / Purpose:
覆盖基础线性变换层的正向传播、参数初始化、权重设置等代码路径。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestLinearLayer(unittest.TestCase):
"""测试Linear线性层 / Test Linear layer"""
def test_linear_basic(self):
"""测试基本线性层 / Test basic linear layer"""
linear = nn.Linear(10, 5)
x = paddle.randn([4, 10])
y = linear(x)
self.assertEqual(y.shape, [4, 5])
def test_linear_no_bias(self):
"""测试无偏置线性层 / Test linear layer without bias"""
linear = nn.Linear(10, 5, bias_attr=False)
x = paddle.randn([4, 10])
y = linear(x)
self.assertEqual(y.shape, [4, 5])
self.assertIsNone(linear.bias)
def test_linear_weight_attr(self):
"""测试线性层权重初始化 / Test linear layer weight initialization"""
initializer = nn.initializer.Constant(1.0)
linear = nn.Linear(
3, 2, weight_attr=paddle.ParamAttr(initializer=initializer)
)
x = paddle.ones([4, 3])
y = linear(x)
# Each output = 3 * 1.0 + bias
self.assertEqual(y.shape, [4, 2])
def test_linear_batch(self):
"""测试批量线性层 / Test batch linear layer"""
linear = nn.Linear(10, 5)
x = paddle.randn([8, 10])
y = linear(x)
self.assertEqual(y.shape, [8, 5])
def test_linear_3d_input(self):
"""测试3D输入线性层 / Test linear layer with 3D input"""
linear = nn.Linear(10, 5)
x = paddle.randn([4, 6, 10])
y = linear(x)
self.assertEqual(y.shape, [4, 6, 5])
def test_linear_parameters(self):
"""测试线性层参数 / Test linear layer parameters"""
linear = nn.Linear(10, 5)
params = list(linear.parameters())
# Should have weight and bias
self.assertEqual(len(params), 2)
self.assertEqual(params[0].shape, [10, 5])
self.assertEqual(params[1].shape, [5])
def test_linear_gradient(self):
"""测试线性层梯度 / Test linear layer gradient"""
linear = nn.Linear(3, 2)
x = paddle.randn([4, 3])
y = linear(x)
loss = y.mean()
loss.backward()
self.assertIsNotNone(linear.weight.grad)
self.assertIsNotNone(linear.bias.grad)
def test_linear_to_float16(self):
"""测试线性层转换精度 / Test linear layer precision conversion"""
linear = nn.Linear(10, 5)
linear_half = linear.half()
self.assertEqual(linear_half.weight.dtype, paddle.float16)
class TestBilinearLayer(unittest.TestCase):
"""测试Bilinear双线性层 / Test Bilinear layer"""
def test_bilinear_basic(self):
"""测试基本双线性层 / Test basic bilinear layer"""
bilinear = nn.Bilinear(10, 8, 5)
x1 = paddle.randn([4, 10])
x2 = paddle.randn([4, 8])
y = bilinear(x1, x2)
self.assertEqual(y.shape, [4, 5])
def test_bilinear_no_bias(self):
"""测试无偏置双线性层 / Test bilinear without bias"""
bilinear = nn.Bilinear(10, 8, 5, bias_attr=False)
x1 = paddle.randn([4, 10])
x2 = paddle.randn([4, 8])
y = bilinear(x1, x2)
self.assertEqual(y.shape, [4, 5])
def test_bilinear_parameters(self):
"""测试双线性层参数 / Test bilinear layer parameters"""
bilinear = nn.Bilinear(10, 8, 5)
params = list(bilinear.parameters())
# weight: [5, 10, 8], bias: [5]
self.assertEqual(len(params), 2)
class TestIdentityLayer(unittest.TestCase):
"""测试Identity恒等层 / Test Identity layer"""
def test_identity_basic(self):
"""测试基本恒等层 / Test basic identity layer"""
identity = nn.Identity()
x = paddle.randn([4, 10])
y = identity(x)
np.testing.assert_allclose(x.numpy(), y.numpy())
def test_identity_gradient(self):
"""测试恒等层梯度 / Test identity layer gradient"""
identity = nn.Identity()
x = paddle.randn([4, 10])
x.stop_gradient = False
y = identity(x)
y.sum().backward()
self.assertIsNotNone(x.grad)
class TestFlattenLayer(unittest.TestCase):
"""测试Flatten展平层 / Test Flatten layer"""
def test_flatten_basic(self):
"""测试基本展平 / Test basic flattening"""
flatten = nn.Flatten()
x = paddle.randn([4, 3, 8, 8])
y = flatten(x)
self.assertEqual(y.shape, [4, 3 * 8 * 8])
def test_flatten_start_axis(self):
"""测试指定起始轴展平 / Test flatten with start axis"""
flatten = nn.Flatten(start_axis=2)
x = paddle.randn([4, 3, 8, 8])
y = flatten(x)
self.assertEqual(y.shape, [4, 3, 64])
def test_flatten_stop_axis(self):
"""测试指定结束轴展平 / Test flatten with stop axis"""
flatten = nn.Flatten(start_axis=1, stop_axis=2)
x = paddle.randn([4, 3, 8, 8])
y = flatten(x)
self.assertEqual(y.shape, [4, 24, 8])
def test_flatten_gradient(self):
"""测试展平层梯度 / Test flatten gradient"""
flatten = nn.Flatten()
x = paddle.randn([4, 3, 4])
x.stop_gradient = False
y = flatten(x)
y.sum().backward()
self.assertIsNotNone(x.grad)
class TestNNParameterAndLayer(unittest.TestCase):
"""测试Parameter和Layer的基本功能 / Test Parameter and Layer basics"""
def test_create_parameter(self):
"""测试创建参数 / Test creating parameter"""
layer = nn.Layer()
param = layer.create_parameter(shape=[5, 3], dtype='float32')
self.assertEqual(param.shape, [5, 3])
def test_layer_sublayers(self):
"""测试子层 / Test sublayers"""
model = nn.Sequential(nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 2))
sublayers = model.sublayers()
self.assertTrue(len(sublayers) >= 2)
def test_layer_named_parameters(self):
"""测试命名参数 / Test named parameters"""
model = nn.Linear(10, 5)
named_params = dict(model.named_parameters())
self.assertIn('weight', named_params)
self.assertIn('bias', named_params)
def test_layer_train_eval_mode(self):
"""测试训练/评估模式切换 / Test train/eval mode switching"""
model = nn.Sequential(nn.Linear(10, 5), nn.Dropout(0.5))
model.train()
self.assertTrue(model.training)
model.eval()
self.assertFalse(model.training)
def test_layer_apply(self):
"""测试apply方法 / Test apply method"""
model = nn.Sequential(nn.Linear(10, 5), nn.Linear(5, 2))
called = []
def count_layers(layer):
called.append(type(layer).__name__)
model.apply(count_layers)
self.assertIn('Linear', called)
def test_layer_extra_repr(self):
"""测试extra_repr / Test extra_repr"""
linear = nn.Linear(10, 5)
repr_str = repr(linear)
self.assertIn('Linear', repr_str)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
损失函数层单元测试 / Loss Function Layer Unit Tests
测试目标 / Test Target:
paddle.nn 损失层 (python/paddle/nn/functional/loss.py, 覆盖率约77.5%)
覆盖的模块 / Covered Modules:
- paddle.nn.CrossEntropyLoss: 交叉熵损失
- paddle.nn.MSELoss: 均方误差损失
- paddle.nn.L1Loss: L1损失
- paddle.nn.BCELoss: 二元交叉熵损失
- paddle.nn.BCEWithLogitsLoss: 带logits的BCE
- paddle.nn.KLDivLoss: KL散度损失
- paddle.nn.NLLLoss: 负对数似然损失
- paddle.nn.SmoothL1Loss: Smooth L1损失
- paddle.nn.HingeEmbeddingLoss: Hinge嵌入损失
- paddle.nn.CosineEmbeddingLoss: 余弦嵌入损失
作用 / Purpose:
覆盖各类损失函数层的代码路径，测试reduction参数、权重、ignore_index等。
"""
import unittest
import paddle
import paddle.nn.functional as F
from paddle import nn
paddle.disable_static()
class TestCrossEntropyLoss(unittest.TestCase):
"""测试交叉熵损失 / Test CrossEntropyLoss"""
def test_cross_entropy_basic(self):
"""测试基本交叉熵损失 / Test basic cross entropy loss"""
ce = nn.CrossEntropyLoss()
pred = paddle.randn([4, 5])
target = paddle.to_tensor([0, 1, 2, 3])
loss = ce(pred, target)
self.assertEqual(loss.shape, [])
def test_cross_entropy_mean(self):
"""测试mean reduction / Test mean reduction"""
ce = nn.CrossEntropyLoss(reduction='mean')
pred = paddle.randn([4, 5])
target = paddle.to_tensor([0, 1, 2, 3])
loss = ce(pred, target)
self.assertEqual(loss.shape, [])
def test_cross_entropy_sum(self):
"""测试sum reduction / Test sum reduction"""
ce = nn.CrossEntropyLoss(reduction='sum')
pred = paddle.randn([4, 5])
target = paddle.to_tensor([0, 1, 2, 3])
loss = ce(pred, target)
self.assertEqual(loss.shape, [])
def test_cross_entropy_none(self):
"""测试none reduction / Test none reduction"""
ce = nn.CrossEntropyLoss(reduction='none')
pred = paddle.randn([4, 5])
target = paddle.to_tensor([0, 1, 2, 3])
loss = ce(pred, target)
self.assertEqual(loss.shape, [4])
def test_cross_entropy_with_weight(self):
"""测试带权重的交叉熵 / Test cross entropy with weights"""
weight = paddle.to_tensor([1.0, 2.0, 1.0, 2.0, 1.0])
ce = nn.CrossEntropyLoss(weight=weight)
pred = paddle.randn([4, 5])
target = paddle.to_tensor([0, 1, 2, 3])
loss = ce(pred, target)
self.assertEqual(loss.shape, [])
def test_cross_entropy_ignore_index(self):
"""测试ignore_index参数 / Test ignore_index parameter"""
ce = nn.CrossEntropyLoss(ignore_index=255)
pred = paddle.randn([4, 5])
target = paddle.to_tensor([0, 1, 255, 3])
loss = ce(pred, target)
self.assertEqual(loss.shape, [])
def test_cross_entropy_soft_labels(self):
"""测试软标签 / Test soft labels"""
ce = nn.CrossEntropyLoss(soft_label=True)
pred = paddle.randn([4, 5])
target = paddle.to_tensor(
[[0.2, 0.2, 0.2, 0.2, 0.2]] * 4, dtype='float32'
)
loss = ce(pred, target)
self.assertEqual(loss.shape, [])
class TestMSELoss(unittest.TestCase):
"""测试均方误差损失 / Test MSELoss"""
def test_mse_basic(self):
"""测试基本MSE / Test basic MSE"""
mse = nn.MSELoss()
pred = paddle.randn([4, 5])
target = paddle.randn([4, 5])
loss = mse(pred, target)
self.assertEqual(loss.shape, [])
self.assertTrue(float(loss.numpy()) >= 0)
def test_mse_sum_reduction(self):
"""测试sum reduction MSE / Test sum reduction MSE"""
mse = nn.MSELoss(reduction='sum')
pred = paddle.randn([4, 5])
target = paddle.randn([4, 5])
loss = mse(pred, target)
self.assertEqual(loss.shape, [])
def test_mse_none_reduction(self):
"""测试none reduction MSE / Test none reduction MSE"""
mse = nn.MSELoss(reduction='none')
pred = paddle.randn([4, 5])
target = paddle.randn([4, 5])
loss = mse(pred, target)
self.assertEqual(loss.shape, [4, 5])
def test_mse_perfect_prediction(self):
"""测试完美预测的MSE / Test MSE with perfect prediction"""
mse = nn.MSELoss()
x = paddle.randn([4, 5])
loss = mse(x, x)
self.assertAlmostEqual(float(loss.numpy()), 0.0, places=5)
class TestL1Loss(unittest.TestCase):
"""测试L1损失 / Test L1Loss"""
def test_l1_basic(self):
"""测试基本L1损失 / Test basic L1 loss"""
l1 = nn.L1Loss()
pred = paddle.to_tensor([1.0, 2.0, 3.0])
target = paddle.to_tensor([1.5, 2.5, 2.5])
loss = l1(pred, target)
self.assertAlmostEqual(float(loss.numpy()), 0.5, places=5)
def test_l1_sum(self):
"""测试sum reduction L1 / Test sum reduction L1"""
l1 = nn.L1Loss(reduction='sum')
pred = paddle.to_tensor([1.0, 2.0, 3.0])
target = paddle.to_tensor([1.0, 2.0, 3.0])
loss = l1(pred, target)
self.assertAlmostEqual(float(loss.numpy()), 0.0, places=5)
class TestBCELoss(unittest.TestCase):
"""测试二元交叉熵损失 / Test BCELoss"""
def test_bce_basic(self):
"""测试基本BCE损失 / Test basic BCE loss"""
bce = nn.BCELoss()
pred = paddle.to_tensor([0.3, 0.7, 0.5])
target = paddle.to_tensor([0.0, 1.0, 1.0])
loss = bce(pred, target)
self.assertEqual(loss.shape, [])
self.assertTrue(float(loss.numpy()) >= 0)
def test_bce_with_logits(self):
"""测试带Logits的BCE / Test BCE with logits"""
bce_logits = nn.BCEWithLogitsLoss()
pred = paddle.randn([4, 5])
target = paddle.randint(0, 2, [4, 5]).astype('float32')
loss = bce_logits(pred, target)
self.assertEqual(loss.shape, [])
def test_bce_with_weight(self):
"""测试带权重的BCE / Test BCE with weight"""
weight = paddle.to_tensor([1.0, 2.0, 1.0])
bce = nn.BCELoss(weight=weight)
pred = paddle.to_tensor([0.3, 0.7, 0.5])
target = paddle.to_tensor([0.0, 1.0, 1.0])
loss = bce(pred, target)
self.assertEqual(loss.shape, [])
class TestKLDivLoss(unittest.TestCase):
"""测试KL散度损失 / Test KLDivLoss"""
def test_kl_div_basic(self):
"""测试基本KL散度 / Test basic KL divergence"""
kl = nn.KLDivLoss(reduction='batchmean')
# Input should be log probability
log_pred = F.log_softmax(paddle.randn([4, 5]), axis=-1)
# Target should be probability
target = F.softmax(paddle.randn([4, 5]), axis=-1)
loss = kl(log_pred, target)
self.assertEqual(loss.shape, [])
class TestNLLLoss(unittest.TestCase):
"""测试负对数似然损失 / Test NLLLoss"""
def test_nll_basic(self):
"""测试基本NLL损失 / Test basic NLL loss"""
nll = nn.NLLLoss()
# Log probabilities
log_pred = F.log_softmax(paddle.randn([4, 5]), axis=-1)
target = paddle.to_tensor([0, 1, 2, 3])
loss = nll(log_pred, target)
self.assertEqual(loss.shape, [])
def test_nll_ignore_index(self):
"""测试NLL的ignore_index / Test NLL with ignore_index"""
nll = nn.NLLLoss(ignore_index=-100)
log_pred = F.log_softmax(paddle.randn([4, 5]), axis=-1)
target = paddle.to_tensor([0, 1, -100, 3])
loss = nll(log_pred, target)
self.assertEqual(loss.shape, [])
class TestSmoothL1Loss(unittest.TestCase):
"""测试Smooth L1损失 / Test SmoothL1Loss"""
def test_smooth_l1_basic(self):
"""测试基本Smooth L1 / Test basic Smooth L1"""
smooth_l1 = nn.SmoothL1Loss()
pred = paddle.randn([4, 5])
target = paddle.randn([4, 5])
loss = smooth_l1(pred, target)
self.assertEqual(loss.shape, [])
def test_smooth_l1_delta(self):
"""测试带delta的Smooth L1 / Test Smooth L1 with delta"""
smooth_l1 = nn.SmoothL1Loss(delta=2.0)
pred = paddle.randn([4, 5])
target = paddle.randn([4, 5])
loss = smooth_l1(pred, target)
self.assertTrue(float(loss.numpy()) >= 0)
class TestHingeAndCosineEmbeddingLoss(unittest.TestCase):
"""测试Hinge和Cosine嵌入损失 / Test Hinge and Cosine Embedding Loss"""
def test_hinge_embedding_loss(self):
"""测试Hinge嵌入损失 / Test Hinge embedding loss"""
hinge = nn.HingeEmbeddingLoss(margin=1.0)
x = paddle.randn([4])
y = paddle.to_tensor([1, -1, 1, -1], dtype='float32')
loss = hinge(x, y)
self.assertEqual(loss.shape, [])
def test_cosine_embedding_loss(self):
"""测试余弦嵌入损失 / Test Cosine embedding loss"""
cosine = nn.CosineEmbeddingLoss(margin=0.5)
x1 = paddle.randn([4, 10])
x2 = paddle.randn([4, 10])
y = paddle.to_tensor([1, -1, 1, -1], dtype='float32')
loss = cosine(x1, x2, y)
self.assertEqual(loss.shape, [])
def test_margin_ranking_loss(self):
"""测试Margin排名损失 / Test Margin ranking loss"""
margin_loss = nn.MarginRankingLoss(margin=0.5)
x1 = paddle.randn([4])
x2 = paddle.randn([4])
y = paddle.to_tensor([1, -1, 1, -1], dtype='float32')
loss = margin_loss(x1, x2, y)
self.assertEqual(loss.shape, [])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
归一化层单元测试 / Normalization Layers Unit Tests
测试目标 / Test Target:
paddle.nn 归一化层 (paddle.nn.BatchNorm, LayerNorm, GroupNorm, etc.)
覆盖的模块 / Covered Modules:
- paddle.nn.BatchNorm1D/2D/3D: 批归一化
- paddle.nn.LayerNorm: 层归一化
- paddle.nn.GroupNorm: 组归一化
- paddle.nn.InstanceNorm1D/2D/3D: 实例归一化
- paddle.nn.SyncBatchNorm: 同步批归一化
作用 / Purpose:
补充归一化层API的各种参数组合测试，提升覆盖率。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
class TestBatchNorm(unittest.TestCase):
"""测试批归一化 / Test batch normalization"""
def test_batchnorm1d(self):
"""测试1D批归一化 / Test 1D batch normalization"""
bn = nn.BatchNorm1D(16)
x = paddle.randn([4, 16, 32])
y = bn(x)
self.assertEqual(y.shape, [4, 16, 32])
def test_batchnorm2d(self):
"""测试2D批归一化 / Test 2D batch normalization"""
bn = nn.BatchNorm2D(16)
x = paddle.randn([4, 16, 8, 8])
y = bn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
def test_batchnorm3d(self):
"""测试3D批归一化 / Test 3D batch normalization"""
bn = nn.BatchNorm3D(8)
x = paddle.randn([2, 8, 4, 4, 4])
y = bn(x)
self.assertEqual(y.shape, [2, 8, 4, 4, 4])
def test_batchnorm_eval_mode(self):
"""测试评估模式批归一化 / Test batch norm in eval mode"""
bn = nn.BatchNorm2D(16)
bn.eval()
x = paddle.randn([4, 16, 8, 8])
y = bn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
def test_batchnorm_no_affine(self):
"""测试无仿射变换批归一化 / Test batch norm without affine"""
bn = nn.BatchNorm2D(16, weight_attr=False, bias_attr=False)
x = paddle.randn([4, 16, 8, 8])
y = bn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
def test_batchnorm_momentum(self):
"""测试自定义动量批归一化 / Test batch norm with custom momentum"""
bn = nn.BatchNorm2D(16, momentum=0.9)
x = paddle.randn([4, 16, 8, 8])
y = bn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
class TestLayerNorm(unittest.TestCase):
"""测试层归一化 / Test layer normalization"""
def test_layernorm_basic(self):
"""测试基本层归一化 / Test basic layer normalization"""
ln = nn.LayerNorm(32)
x = paddle.randn([4, 16, 32])
y = ln(x)
self.assertEqual(y.shape, [4, 16, 32])
def test_layernorm_2d(self):
"""测试2D层归一化 / Test 2D layer normalization"""
ln = nn.LayerNorm([8, 32])
x = paddle.randn([4, 8, 32])
y = ln(x)
self.assertEqual(y.shape, [4, 8, 32])
def test_layernorm_eps(self):
"""测试自定义epsilon层归一化 / Test layer norm with custom eps"""
ln = nn.LayerNorm(32, epsilon=1e-6)
x = paddle.randn([4, 16, 32])
y = ln(x)
self.assertEqual(y.shape, [4, 16, 32])
def test_layernorm_no_elementwise(self):
"""测试无元素变换层归一化 / Test layer norm without elementwise affine"""
ln = nn.LayerNorm(32, weight_attr=False, bias_attr=False)
x = paddle.randn([4, 16, 32])
y = ln(x)
self.assertEqual(y.shape, [4, 16, 32])
class TestGroupNorm(unittest.TestCase):
"""测试组归一化 / Test group normalization"""
def test_groupnorm_basic(self):
"""测试基本组归一化 / Test basic group normalization"""
gn = nn.GroupNorm(num_groups=4, num_channels=16)
x = paddle.randn([4, 16, 8, 8])
y = gn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
def test_groupnorm_single_group(self):
"""测试单组归一化 / Test single group normalization"""
gn = nn.GroupNorm(num_groups=1, num_channels=16)
x = paddle.randn([4, 16, 8, 8])
y = gn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
def test_groupnorm_channel_wise(self):
"""测试逐通道组归一化 / Test channel-wise group normalization"""
gn = nn.GroupNorm(num_groups=16, num_channels=16)
x = paddle.randn([4, 16, 8, 8])
y = gn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
class TestInstanceNorm(unittest.TestCase):
"""测试实例归一化 / Test instance normalization"""
def test_instancenorm1d(self):
"""测试1D实例归一化 / Test 1D instance normalization"""
inn = nn.InstanceNorm1D(16)
x = paddle.randn([4, 16, 32])
y = inn(x)
self.assertEqual(y.shape, [4, 16, 32])
def test_instancenorm2d(self):
"""测试2D实例归一化 / Test 2D instance normalization"""
inn = nn.InstanceNorm2D(16)
x = paddle.randn([4, 16, 8, 8])
y = inn(x)
self.assertEqual(y.shape, [4, 16, 8, 8])
def test_instancenorm3d(self):
"""测试3D实例归一化 / Test 3D instance normalization"""
inn = nn.InstanceNorm3D(8)
x = paddle.randn([2, 8, 4, 4, 4])
y = inn(x)
self.assertEqual(y.shape, [2, 8, 4, 4, 4])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
批量归一化高级测试 / Advanced Batch Normalization Tests
测试目标 / Test Target:
paddle.nn.BatchNorm 高级功能
覆盖的模块 / Covered Modules:
- 训练/评估切换
- 运行统计量
- 批归一化在模型中的应用
作用 / Purpose:
补充批归一化API高级用法的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestBatchNormAdvanced(unittest.TestCase):
"""测试BatchNorm高级功能 / Test advanced BatchNorm features"""
def test_bn_running_stats(self):
"""测试运行统计量更新 / Test running statistics update"""
bn = nn.BatchNorm2D(8, momentum=0.1)
bn.train()
# Run multiple steps so running mean converges toward 1.0
x = paddle.ones([4, 8, 4, 4])
for _ in range(20):
_ = bn(x)
# Running mean should be close to 1.0 after multiple steps
# With momentum=0.1, after 20 steps: 0 * 0.9^20 + 1.0 * (1 - 0.9^20) ≈ 0.878
self.assertGreater(float(bn._mean.numpy().mean()), 0.5)
def test_bn_train_eval_switch(self):
"""测试训练/评估切换 / Test train/eval mode switch"""
bn = nn.BatchNorm2D(8)
x = paddle.randn([4, 8, 4, 4])
bn.train()
out_train = bn(x)
bn.eval()
out_eval = bn(x)
self.assertEqual(out_train.shape, out_eval.shape)
def test_bn_affine_parameters(self):
"""测试仿射参数 / Test affine parameters"""
bn = nn.BatchNorm2D(8)
self.assertIsNotNone(bn.weight)
self.assertIsNotNone(bn.bias)
# Initial weight should be 1, bias should be 0
np.testing.assert_allclose(bn.weight.numpy(), np.ones(8), rtol=1e-5)
np.testing.assert_allclose(bn.bias.numpy(), np.zeros(8), atol=1e-7)
class TestLayerNormAdvanced(unittest.TestCase):
"""测试LayerNorm高级功能 / Test advanced LayerNorm features"""
def test_layer_norm_normalization(self):
"""测试LayerNorm归一化效果 / Test LayerNorm normalization effect"""
ln = nn.LayerNorm(16, weight_attr=False, bias_attr=False)
x = paddle.randn([4, 10, 16]) * 10 + 5 # Large values
result = ln(x)
# After LayerNorm, each vector should have mean≈0 and std≈1
mean = float(result.mean(axis=-1).abs().max().numpy())
self.assertAlmostEqual(mean, 0.0, places=4)
def test_layer_norm_gradient(self):
"""测试LayerNorm梯度 / Test LayerNorm gradient"""
ln = nn.LayerNorm(8)
x = paddle.randn([2, 8])
x.stop_gradient = False
y = ln(x)
loss = y.sum()
loss.backward()
self.assertIsNotNone(x.grad)
class TestGroupNormAdvanced(unittest.TestCase):
"""测试GroupNorm高级功能 / Test advanced GroupNorm features"""
def test_group_norm_invariance(self):
"""测试GroupNorm尺度不变性 / Test GroupNorm scale invariance"""
gn = nn.GroupNorm(num_groups=2, num_channels=8)
x = paddle.randn([4, 8, 16, 16])
result = gn(x)
# Output should not depend on input scale
x_scaled = x * 10
result_scaled = gn(x_scaled)
self.assertEqual(result.shape, result_scaled.shape)
def test_group_norm_with_different_groups(self):
"""测试不同分组数的GroupNorm / Test GroupNorm with different groups"""
x = paddle.randn([4, 16, 8, 8])
for num_groups in [1, 2, 4, 8, 16]:
gn = nn.GroupNorm(num_groups=num_groups, num_channels=16)
result = gn(x)
self.assertEqual(result.shape, [4, 16, 8, 8])
class TestNormCombinations(unittest.TestCase):
"""测试归一化组合 / Test normalization combinations"""
def test_conv_bn_relu(self):
"""测试Conv-BN-ReLU组合 / Test Conv-BN-ReLU combination"""
model = nn.Sequential(
nn.Conv2D(3, 16, 3, padding=1), nn.BatchNorm2D(16), nn.ReLU()
)
x = paddle.randn([4, 3, 16, 16])
result = model(x)
self.assertEqual(result.shape, [4, 16, 16, 16])
# All outputs should be >= 0 due to ReLU
self.assertTrue(bool((result >= 0).all().numpy()))
def test_linear_ln_combination(self):
"""测试Linear-LayerNorm组合 / Test Linear-LayerNorm combination"""
d_model = 64
model = nn.Sequential(
nn.Linear(d_model, d_model), nn.LayerNorm(d_model), nn.GELU()
)
x = paddle.randn([4, 10, d_model])
result = model(x)
self.assertEqual(result.shape, [4, 10, d_model])
def test_sync_batch_norm_creation(self):
"""测试SyncBatchNorm创建 / Test SyncBatchNorm creation"""
# Just test creation (actual sync requires multi-process)
sbn = nn.SyncBatchNorm(16)
self.assertIsNotNone(sbn)
# Convert regular BN to SyncBN
model = nn.Sequential(nn.Conv2D(3, 16, 3), nn.BatchNorm2D(16))
sync_model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
self.assertIsNotNone(sync_model)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
Padding层单元测试 / Padding Layer Unit Tests
测试目标 / Test Target:
paddle.nn Padding层 (覆盖率约82-84%)
覆盖的模块 / Covered Modules:
- paddle.nn.Pad1D: 1D填充层
- paddle.nn.Pad2D: 2D填充层
- paddle.nn.Pad3D: 3D填充层
- paddle.nn.functional.pad: 填充函数API
作用 / Purpose:
覆盖各种填充模式（constant, reflect, replicate, circular）的代码路径。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
class TestPad1D(unittest.TestCase):
"""测试Pad1D填充层 / Test Pad1D padding layer"""
def test_pad1d_constant(self):
"""测试常量填充 / Test constant padding"""
pad = nn.Pad1D(padding=2, mode='constant', value=0.0)
x = paddle.randn([4, 3, 10])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 14])
def test_pad1d_reflect(self):
"""测试反射填充 / Test reflect padding"""
pad = nn.Pad1D(padding=1, mode='reflect')
x = paddle.randn([4, 3, 10])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 12])
def test_pad1d_replicate(self):
"""测试复制填充 / Test replicate padding"""
pad = nn.Pad1D(padding=2, mode='replicate')
x = paddle.randn([4, 3, 10])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 14])
def test_pad1d_circular(self):
"""测试循环填充 / Test circular padding"""
pad = nn.Pad1D(padding=3, mode='circular')
x = paddle.randn([4, 3, 10])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 16])
def test_pad1d_asymmetric(self):
"""测试非对称填充 / Test asymmetric padding"""
pad = nn.Pad1D(padding=[1, 3], mode='constant', value=0.0)
x = paddle.randn([4, 3, 10])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 14]) # 1 + 10 + 3
def test_pad1d_nonzero_value(self):
"""测试非零常量填充 / Test non-zero constant padding"""
pad = nn.Pad1D(padding=1, mode='constant', value=9.0)
x = paddle.zeros([1, 1, 3])
y = pad(x)
self.assertAlmostEqual(float(y[0, 0, 0].numpy()), 9.0)
self.assertAlmostEqual(float(y[0, 0, -1].numpy()), 9.0)
class TestPad2D(unittest.TestCase):
"""测试Pad2D填充层 / Test Pad2D padding layer"""
def test_pad2d_constant(self):
"""测试2D常量填充 / Test 2D constant padding"""
pad = nn.Pad2D(padding=2, mode='constant', value=0.0)
x = paddle.randn([4, 3, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 12, 12])
def test_pad2d_reflect(self):
"""测试2D反射填充 / Test 2D reflect padding"""
pad = nn.Pad2D(padding=1, mode='reflect')
x = paddle.randn([4, 3, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 10, 10])
def test_pad2d_replicate(self):
"""测试2D复制填充 / Test 2D replicate padding"""
pad = nn.Pad2D(padding=2, mode='replicate')
x = paddle.randn([4, 3, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 12, 12])
def test_pad2d_circular(self):
"""测试2D循环填充 / Test 2D circular padding"""
pad = nn.Pad2D(padding=2, mode='circular')
x = paddle.randn([4, 3, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 12, 12])
def test_pad2d_asymmetric(self):
"""测试2D非对称填充 / Test 2D asymmetric padding"""
pad = nn.Pad2D(padding=[1, 2, 3, 4], mode='constant', value=0.0)
x = paddle.randn([4, 3, 8, 8])
y = pad(x)
# [left, right, top, bottom]
self.assertEqual(y.shape, [4, 3, 8 + 3 + 4, 8 + 1 + 2])
def test_pad2d_data_format(self):
"""测试不同数据格式 / Test different data format"""
pad = nn.Pad2D(
padding=1, mode='constant', value=0.0, data_format='NCHW'
)
x = paddle.randn([4, 3, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [4, 3, 10, 10])
def test_pad2d_value(self):
"""测试2D填充值 / Test 2D padding value"""
pad = nn.Pad2D(padding=1, mode='constant', value=5.0)
x = paddle.zeros([1, 1, 3, 3])
y = pad(x)
# Corner values should be 5.0
self.assertAlmostEqual(float(y[0, 0, 0, 0].numpy()), 5.0)
class TestPad3D(unittest.TestCase):
"""测试Pad3D填充层 / Test Pad3D padding layer"""
def test_pad3d_constant(self):
"""测试3D常量填充 / Test 3D constant padding"""
pad = nn.Pad3D(padding=1, mode='constant', value=0.0)
x = paddle.randn([2, 3, 4, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [2, 3, 6, 10, 10])
def test_pad3d_reflect(self):
"""测试3D反射填充 / Test 3D reflect padding"""
pad = nn.Pad3D(padding=1, mode='reflect')
x = paddle.randn([2, 3, 4, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [2, 3, 6, 10, 10])
def test_pad3d_replicate(self):
"""测试3D复制填充 / Test 3D replicate padding"""
pad = nn.Pad3D(padding=1, mode='replicate')
x = paddle.randn([2, 3, 4, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [2, 3, 6, 10, 10])
def test_pad3d_circular(self):
"""测试3D循环填充 / Test 3D circular padding"""
pad = nn.Pad3D(padding=1, mode='circular')
x = paddle.randn([2, 3, 4, 8, 8])
y = pad(x)
self.assertEqual(y.shape, [2, 3, 6, 10, 10])
class TestFunctionalPad(unittest.TestCase):
"""测试paddle.nn.functional.pad / Test paddle.nn.functional.pad"""
def test_functional_pad_1d(self):
"""测试1D函数pad / Test 1D functional pad"""
x = paddle.randn([4, 3, 10])
y = paddle.nn.functional.pad(x, [2, 2], mode='constant', value=0.0)
self.assertEqual(y.shape, [4, 3, 14])
def test_functional_pad_2d(self):
"""测试2D函数pad / Test 2D functional pad"""
x = paddle.randn([4, 3, 8, 8])
y = paddle.nn.functional.pad(x, [1, 1, 1, 1], mode='reflect')
self.assertEqual(y.shape, [4, 3, 10, 10])
def test_functional_pad_3d(self):
"""测试3D函数pad / Test 3D functional pad"""
x = paddle.randn([2, 3, 4, 8, 8])
y = paddle.nn.functional.pad(x, [1, 1, 1, 1, 1, 1])
self.assertEqual(y.shape, [2, 3, 6, 10, 10])
def test_functional_pad_constant(self):
"""测试常量函数pad / Test constant functional pad"""
x = paddle.zeros([1, 1, 3])
y = paddle.nn.functional.pad(x, [2, 1], mode='constant', value=1.0)
expected_size = 3 + 2 + 1
self.assertEqual(y.shape[-1], expected_size)
# Check padding values
self.assertAlmostEqual(float(y[0, 0, 0].numpy()), 1.0)
self.assertAlmostEqual(float(y[0, 0, -1].numpy()), 1.0)
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
池化层单元测试 / Pooling Layers Unit Tests
测试目标 / Test Target:
paddle.nn 池化层 (AvgPool, MaxPool, AdaptivePool等)
覆盖的模块 / Covered Modules:
- paddle.nn.AvgPool1D/2D/3D: 平均池化
- paddle.nn.MaxPool1D/2D/3D: 最大池化
- paddle.nn.AdaptiveAvgPool1D/2D/3D: 自适应平均池化
- paddle.nn.AdaptiveMaxPool1D/2D/3D: 自适应最大池化
作用 / Purpose:
补充池化层API的各种参数组合测试，提升覆盖率。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
class TestAvgPoolLayers(unittest.TestCase):
"""测试平均池化层 / Test average pooling layers"""
def test_avgpool1d(self):
"""测试AvgPool1D / Test AvgPool1D"""
pool = nn.AvgPool1D(kernel_size=2, stride=2)
x = paddle.randn([4, 3, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 8])
def test_avgpool1d_padding(self):
"""测试带填充AvgPool1D / Test AvgPool1D with padding"""
pool = nn.AvgPool1D(kernel_size=3, stride=1, padding=1)
x = paddle.randn([4, 3, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 16])
def test_avgpool2d(self):
"""测试AvgPool2D / Test AvgPool2D"""
pool = nn.AvgPool2D(kernel_size=2, stride=2)
x = paddle.randn([4, 3, 16, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_avgpool2d_ceil_mode(self):
"""测试ceil模式AvgPool2D / Test AvgPool2D with ceil mode"""
pool = nn.AvgPool2D(kernel_size=3, stride=2, ceil_mode=True)
x = paddle.randn([4, 3, 15, 15])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 7, 7])
def test_avgpool3d(self):
"""测试AvgPool3D / Test AvgPool3D"""
pool = nn.AvgPool3D(kernel_size=2, stride=2)
x = paddle.randn([2, 3, 8, 8, 8])
y = pool(x)
self.assertEqual(y.shape, [2, 3, 4, 4, 4])
class TestMaxPoolLayers(unittest.TestCase):
"""测试最大池化层 / Test max pooling layers"""
def test_maxpool1d(self):
"""测试MaxPool1D / Test MaxPool1D"""
pool = nn.MaxPool1D(kernel_size=2, stride=2)
x = paddle.randn([4, 3, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 8])
def test_maxpool1d_return_mask(self):
"""测试返回掩码的MaxPool1D / Test MaxPool1D with return mask"""
pool = nn.MaxPool1D(kernel_size=2, stride=2, return_mask=True)
x = paddle.randn([4, 3, 16])
y, idx = pool(x)
self.assertEqual(y.shape, [4, 3, 8])
self.assertEqual(idx.shape, [4, 3, 8])
def test_maxpool2d(self):
"""测试MaxPool2D / Test MaxPool2D"""
pool = nn.MaxPool2D(kernel_size=2, stride=2)
x = paddle.randn([4, 3, 16, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_maxpool2d_ceil_mode(self):
"""测试ceil模式MaxPool2D / Test MaxPool2D with ceil mode"""
pool = nn.MaxPool2D(kernel_size=3, stride=2, ceil_mode=True)
x = paddle.randn([4, 3, 15, 15])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 7, 7])
def test_maxpool3d(self):
"""测试MaxPool3D / Test MaxPool3D"""
pool = nn.MaxPool3D(kernel_size=2, stride=2)
x = paddle.randn([2, 3, 8, 8, 8])
y = pool(x)
self.assertEqual(y.shape, [2, 3, 4, 4, 4])
class TestAdaptivePoolLayers(unittest.TestCase):
"""测试自适应池化层 / Test adaptive pooling layers"""
def test_adaptive_avgpool1d(self):
"""测试AdaptiveAvgPool1D / Test AdaptiveAvgPool1D"""
pool = nn.AdaptiveAvgPool1D(output_size=8)
x = paddle.randn([4, 3, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 8])
def test_adaptive_avgpool2d(self):
"""测试AdaptiveAvgPool2D / Test AdaptiveAvgPool2D"""
pool = nn.AdaptiveAvgPool2D(output_size=(8, 8))
x = paddle.randn([4, 3, 16, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 8, 8])
def test_adaptive_avgpool2d_global(self):
"""测试全局自适应平均池化 / Test global adaptive avg pool"""
pool = nn.AdaptiveAvgPool2D(output_size=1)
x = paddle.randn([4, 3, 16, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 1, 1])
def test_adaptive_avgpool3d(self):
"""测试AdaptiveAvgPool3D / Test AdaptiveAvgPool3D"""
pool = nn.AdaptiveAvgPool3D(output_size=(4, 4, 4))
x = paddle.randn([2, 3, 8, 8, 8])
y = pool(x)
self.assertEqual(y.shape, [2, 3, 4, 4, 4])
def test_adaptive_maxpool2d(self):
"""测试AdaptiveMaxPool2D / Test AdaptiveMaxPool2D"""
pool = nn.AdaptiveMaxPool2D(output_size=(8, 8))
x = paddle.randn([4, 3, 16, 16])
y = pool(x)
self.assertEqual(y.shape, [4, 3, 8, 8])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_quant_layers.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.quant modules
# 覆盖模块: paddle/nn/quant/quant_layers.py, paddle/nn/quant/quantized_linear.py
# 未覆盖行: quant_layers: 118,141-169,245,276,279,284,285,292,294-298,300,307,344,364,368,375,387,390; quantized_linear: 54,59,119,120,121,122,124,130,172-177,186,262,265-269,274-276,282,284,290,331-339,344-346,348,350,356,386,387
# Covered module: paddle/nn/quant/quant_layers.py, paddle/nn/quant/quantized_linear.py
# Uncovered lines: quant_layers: 118-390; quantized_linear: 54-387
import unittest
class TestQuantLayersImport(unittest.TestCase):
"""测试 quant_layers 模块导入
Test quant_layers module import"""
def test_quant_layers_importable(self):
"""测试 quant_layers 模块可导入
Test quant_layers module is importable"""
from paddle.nn.quant import quant_layers
self.assertIsNotNone(quant_layers)
def test_quantized_linear_module_importable(self):
"""测试 quantized_linear 模块可导入
Test quantized_linear module is importable"""
from paddle.nn.quant import quantized_linear
self.assertIsNotNone(quantized_linear)
class TestQuantizationAwareTraining(unittest.TestCase):
"""测试量化感知训练相关
Test quantization aware training related"""
def test_quant_config(self):
"""测试量化配置
Test quantization config"""
# Verify basic quantization module structure exists
from paddle.nn.quant import quant_layers
self.assertTrue(hasattr(quant_layers, 'QuantizedLinear'))
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
RNN层单元测试 / RNN Layers Unit Tests
测试目标 / Test Target:
paddle.nn RNN相关层
覆盖的模块 / Covered Modules:
- paddle.nn.SimpleRNN: 简单RNN
- paddle.nn.LSTM: 长短期记忆网络
- paddle.nn.GRU: 门控循环单元
- paddle.nn.RNNCell: RNN单元
- paddle.nn.LSTMCell: LSTM单元
- paddle.nn.GRUCell: GRU单元
作用 / Purpose:
补充RNN层API的各种参数组合测试，提升覆盖率。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
class TestSimpleRNN(unittest.TestCase):
"""测试SimpleRNN / Test SimpleRNN"""
def test_rnn_basic(self):
"""测试基本RNN / Test basic RNN"""
rnn = nn.SimpleRNN(16, 32, num_layers=1)
x = paddle.randn([4, 10, 16]) # [batch, seq_len, input_size]
output, hidden = rnn(x)
self.assertEqual(output.shape, [4, 10, 32])
self.assertEqual(hidden.shape, [1, 4, 32])
def test_rnn_multilayer(self):
"""测试多层RNN / Test multi-layer RNN"""
rnn = nn.SimpleRNN(16, 32, num_layers=2)
x = paddle.randn([4, 10, 16])
output, hidden = rnn(x)
self.assertEqual(output.shape, [4, 10, 32])
self.assertEqual(hidden.shape, [2, 4, 32])
def test_rnn_bidirectional(self):
"""测试双向RNN / Test bidirectional RNN"""
rnn = nn.SimpleRNN(16, 32, direction='bidirect')
x = paddle.randn([4, 10, 16])
output, hidden = rnn(x)
self.assertEqual(output.shape, [4, 10, 64]) # 32*2 for bidirectional
def test_rnn_time_major(self):
"""测试时间优先RNN / Test time_major RNN"""
rnn = nn.SimpleRNN(16, 32, time_major=True)
x = paddle.randn([10, 4, 16]) # [seq_len, batch, input_size]
output, hidden = rnn(x)
self.assertEqual(output.shape, [10, 4, 32])
class TestLSTM(unittest.TestCase):
"""测试LSTM / Test LSTM"""
def test_lstm_basic(self):
"""测试基本LSTM / Test basic LSTM"""
lstm = nn.LSTM(16, 32)
x = paddle.randn([4, 10, 16])
output, (h, c) = lstm(x)
self.assertEqual(output.shape, [4, 10, 32])
self.assertEqual(h.shape, [1, 4, 32])
self.assertEqual(c.shape, [1, 4, 32])
def test_lstm_multilayer(self):
"""测试多层LSTM / Test multi-layer LSTM"""
lstm = nn.LSTM(16, 32, num_layers=2)
x = paddle.randn([4, 10, 16])
output, (h, c) = lstm(x)
self.assertEqual(output.shape, [4, 10, 32])
self.assertEqual(h.shape, [2, 4, 32])
def test_lstm_bidirectional(self):
"""测试双向LSTM / Test bidirectional LSTM"""
lstm = nn.LSTM(16, 32, direction='bidirect')
x = paddle.randn([4, 10, 16])
output, (h, c) = lstm(x)
self.assertEqual(output.shape, [4, 10, 64])
def test_lstm_with_dropout(self):
"""测试带dropout的LSTM / Test LSTM with dropout"""
lstm = nn.LSTM(16, 32, num_layers=2, dropout=0.5)
lstm.train()
x = paddle.randn([4, 10, 16])
output, (h, c) = lstm(x)
self.assertEqual(output.shape, [4, 10, 32])
def test_lstm_initial_states(self):
"""测试带初始状态的LSTM / Test LSTM with initial states"""
lstm = nn.LSTM(16, 32)
x = paddle.randn([4, 10, 16])
h0 = paddle.zeros([1, 4, 32])
c0 = paddle.zeros([1, 4, 32])
output, (h, c) = lstm(x, initial_states=(h0, c0))
self.assertEqual(output.shape, [4, 10, 32])
class TestGRU(unittest.TestCase):
"""测试GRU / Test GRU"""
def test_gru_basic(self):
"""测试基本GRU / Test basic GRU"""
gru = nn.GRU(16, 32)
x = paddle.randn([4, 10, 16])
output, hidden = gru(x)
self.assertEqual(output.shape, [4, 10, 32])
self.assertEqual(hidden.shape, [1, 4, 32])
def test_gru_multilayer(self):
"""测试多层GRU / Test multi-layer GRU"""
gru = nn.GRU(16, 32, num_layers=2)
x = paddle.randn([4, 10, 16])
output, hidden = gru(x)
self.assertEqual(output.shape, [4, 10, 32])
self.assertEqual(hidden.shape, [2, 4, 32])
def test_gru_bidirectional(self):
"""测试双向GRU / Test bidirectional GRU"""
gru = nn.GRU(16, 32, direction='bidirect')
x = paddle.randn([4, 10, 16])
output, hidden = gru(x)
self.assertEqual(output.shape, [4, 10, 64])
class TestRNNCells(unittest.TestCase):
"""测试RNN单元 / Test RNN Cells"""
def test_rnn_cell(self):
"""测试RNNCell / Test RNNCell"""
cell = nn.SimpleRNNCell(16, 32)
x = paddle.randn([4, 16])
prev_h = paddle.zeros([4, 32])
y, h = cell(x, prev_h)
self.assertEqual(y.shape, [4, 32])
def test_lstm_cell(self):
"""测试LSTMCell / Test LSTMCell"""
cell = nn.LSTMCell(16, 32)
x = paddle.randn([4, 16])
prev_h = paddle.zeros([4, 32])
prev_c = paddle.zeros([4, 32])
y, (h, c) = cell(x, (prev_h, prev_c))
self.assertEqual(y.shape, [4, 32])
self.assertEqual(c.shape, [4, 32])
def test_gru_cell(self):
"""测试GRUCell / Test GRUCell"""
cell = nn.GRUCell(16, 32)
x = paddle.randn([4, 16])
prev_h = paddle.zeros([4, 32])
y, h = cell(x, prev_h)
self.assertEqual(y.shape, [4, 32])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_transformer_layers.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.layer.transformer
# 覆盖模块: paddle/nn/layer/transformer.py
# 未覆盖行: 73,76,77,78,79,80,82,84,87,88,89,91,98,126,293,299,300,301,399,400,401,402,403,404,405,406,407,410,530,562
# Covered module: paddle/nn/layer/transformer.py
# Uncovered lines: 73,76-80,82,84,87-89,91,98,126,293,299-301,399-407,410,530,562
import unittest
import paddle
class TestMultiHeadAttention(unittest.TestCase):
"""测试 MultiHeadAttention 类
Test MultiHeadAttention class"""
def test_mha_init(self):
"""测试 MultiHeadAttention 初始化
Test MultiHeadAttention initialization"""
mha = paddle.nn.MultiHeadAttention(embed_dim=64, num_heads=4)
self.assertIsNotNone(mha)
def test_mha_forward(self):
"""测试 MultiHeadAttention 前向传播
Test MultiHeadAttention forward pass"""
mha = paddle.nn.MultiHeadAttention(embed_dim=64, num_heads=4)
query = paddle.randn([2, 5, 64])
key = paddle.randn([2, 5, 64])
value = paddle.randn([2, 5, 64])
output = mha(query, key, value)
self.assertEqual(output.shape, [2, 5, 64])
def test_mha_with_attn_mask(self):
"""测试带注意力掩码的 MultiHeadAttention
Test MultiHeadAttention with attention mask"""
mha = paddle.nn.MultiHeadAttention(embed_dim=64, num_heads=4)
query = paddle.randn([2, 5, 64])
key = paddle.randn([2, 5, 64])
value = paddle.randn([2, 5, 64])
attn_mask = paddle.randn([2, 4, 5, 5])
output = mha(query, key, value, attn_mask=attn_mask)
self.assertEqual(output.shape, [2, 5, 64])
def test_mha_with_kdim_vdim(self):
"""测试带不同 kdim/vdim 的 MultiHeadAttention
Test MultiHeadAttention with different kdim/vdim"""
mha = paddle.nn.MultiHeadAttention(
embed_dim=64, num_heads=4, kdim=32, vdim=32
)
query = paddle.randn([2, 5, 64])
key = paddle.randn([2, 5, 32])
value = paddle.randn([2, 5, 32])
output = mha(query, key, value)
self.assertEqual(output.shape, [2, 5, 64])
class TestTransformerEncoderLayer(unittest.TestCase):
"""测试 TransformerEncoderLayer 类
Test TransformerEncoderLayer class"""
def test_encoder_layer_init(self):
"""测试 TransformerEncoderLayer 初始化
Test TransformerEncoderLayer initialization"""
layer = paddle.nn.TransformerEncoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
self.assertIsNotNone(layer)
def test_encoder_layer_forward(self):
"""测试 TransformerEncoderLayer 前向传播
Test TransformerEncoderLayer forward pass"""
layer = paddle.nn.TransformerEncoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
src = paddle.randn([2, 5, 64])
output = layer(src)
self.assertEqual(output.shape, [2, 5, 64])
class TestTransformerDecoderLayer(unittest.TestCase):
"""测试 TransformerDecoderLayer 类
Test TransformerDecoderLayer class"""
def test_decoder_layer_init(self):
"""测试 TransformerDecoderLayer 初始化
Test TransformerDecoderLayer initialization"""
layer = paddle.nn.TransformerDecoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
self.assertIsNotNone(layer)
def test_decoder_layer_forward(self):
"""测试 TransformerDecoderLayer 前向传播
Test TransformerDecoderLayer forward pass"""
layer = paddle.nn.TransformerDecoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
tgt = paddle.randn([2, 5, 64])
memory = paddle.randn([2, 10, 64])
output = layer(tgt, memory)
self.assertEqual(output.shape, [2, 5, 64])
class TestTransformerEncoder(unittest.TestCase):
"""测试 TransformerEncoder 类
Test TransformerEncoder class"""
def test_encoder_init(self):
"""测试 TransformerEncoder 初始化
Test TransformerEncoder initialization"""
encoder_layer = paddle.nn.TransformerEncoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
encoder = paddle.nn.TransformerEncoder(encoder_layer, num_layers=2)
self.assertIsNotNone(encoder)
def test_encoder_forward(self):
"""测试 TransformerEncoder 前向传播
Test TransformerEncoder forward pass"""
encoder_layer = paddle.nn.TransformerEncoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
encoder = paddle.nn.TransformerEncoder(encoder_layer, num_layers=2)
src = paddle.randn([2, 5, 64])
output = encoder(src)
self.assertEqual(output.shape, [2, 5, 64])
class TestTransformerDecoder(unittest.TestCase):
"""测试 TransformerDecoder 类
Test TransformerDecoder class"""
def test_decoder_init(self):
"""测试 TransformerDecoder 初始化
Test TransformerDecoder initialization"""
decoder_layer = paddle.nn.TransformerDecoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
decoder = paddle.nn.TransformerDecoder(decoder_layer, num_layers=2)
self.assertIsNotNone(decoder)
def test_decoder_forward(self):
"""测试 TransformerDecoder 前向传播
Test TransformerDecoder forward pass"""
decoder_layer = paddle.nn.TransformerDecoderLayer(
d_model=64, nhead=4, dim_feedforward=128
)
decoder = paddle.nn.TransformerDecoder(decoder_layer, num_layers=2)
tgt = paddle.randn([2, 5, 64])
memory = paddle.randn([2, 10, 64])
output = decoder(tgt, memory)
self.assertEqual(output.shape, [2, 5, 64])
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_utils.py
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# Copyright (c) 2026 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.
"""
神经网络实用工具测试 / Neural Network Utility Tests
测试目标 / Test Target:
paddle.nn.utils 神经网络工具函数
覆盖的模块 / Covered Modules:
- paddle.nn.utils.weight_norm: 权重归一化
- paddle.nn.utils.spectral_norm: 谱归一化
- paddle.nn.utils.remove_weight_norm: 移除权重归一化
- paddle.nn.utils.parameters_to_vector: 参数转向量
- paddle.nn.utils.vector_to_parameters: 向量转参数
作用 / Purpose:
补充神经网络工具函数的测试，提升覆盖率。
"""
import unittest
import numpy as np
import paddle
from paddle import nn
paddle.disable_static()
class TestWeightNorm(unittest.TestCase):
"""测试权重归一化 / Test weight normalization"""
def test_weight_norm_linear(self):
"""测试Linear层权重归一化 / Test weight norm on Linear layer"""
linear = nn.Linear(4, 8)
nn.utils.weight_norm(linear)
# After weight norm, layer has weight_g and weight_v
self.assertTrue(hasattr(linear, 'weight_g'))
self.assertTrue(hasattr(linear, 'weight_v'))
x = paddle.randn([2, 4])
y = linear(x)
self.assertEqual(y.shape, [2, 8])
def test_weight_norm_conv(self):
"""测试Conv层权重归一化 / Test weight norm on Conv layer"""
conv = nn.Conv2D(3, 8, 3)
nn.utils.weight_norm(conv)
x = paddle.randn([2, 3, 16, 16])
y = conv(x)
self.assertEqual(y.shape[0], 2)
def test_remove_weight_norm(self):
"""测试移除权重归一化 / Test remove weight norm"""
linear = nn.Linear(4, 8)
nn.utils.weight_norm(linear)
nn.utils.remove_weight_norm(linear)
# After removal, weight_g and weight_v should be gone
self.assertFalse(hasattr(linear, 'weight_g'))
class TestSpectralNorm(unittest.TestCase):
"""测试谱归一化 / Test spectral normalization"""
def test_spectral_norm_linear(self):
"""测试Linear层谱归一化 / Test spectral norm on Linear"""
linear = nn.Linear(4, 8)
nn.utils.spectral_norm(linear)
x = paddle.randn([2, 4])
y = linear(x)
self.assertEqual(y.shape, [2, 8])
def test_spectral_norm_conv(self):
"""测试Conv层谱归一化 / Test spectral norm on Conv"""
conv = nn.Conv2D(3, 8, 3)
nn.utils.spectral_norm(conv)
x = paddle.randn([2, 3, 16, 16])
y = conv(x)
self.assertEqual(y.shape[0], 2)
class TestParameterVector(unittest.TestCase):
"""测试参数向量转换 / Test parameter vector conversion"""
def test_parameters_to_vector(self):
"""测试参数转向量 / Test parameters to vector"""
model = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 2))
vec = nn.utils.parameters_to_vector(model.parameters())
# Total params = 4*8 + 8 + 8*2 + 2 = 32+8+16+2=58
self.assertEqual(vec.shape[0], 58)
def test_vector_to_parameters(self):
"""测试向量转参数 / Test vector to parameters"""
model = nn.Sequential(nn.Linear(4, 8), nn.Linear(8, 2))
# Create a new vector of the right size
total_params = sum(p.numel() for p in model.parameters())
vec = paddle.zeros([total_params])
nn.utils.vector_to_parameters(vec, model.parameters())
# All parameters should now be zero
for param in model.parameters():
np.testing.assert_allclose(
param.numpy(), np.zeros_like(param.numpy()), atol=1e-7
)
class TestClipGrad(unittest.TestCase):
"""测试梯度裁剪 / Test gradient clipping"""
def test_clip_grad_by_norm(self):
"""测试按范数裁剪梯度 / Test clip grad by norm"""
model = nn.Linear(4, 2)
x = paddle.randn([4, 4])
y = model(x)
loss = y.sum()
loss.backward()
# Get grads before clipping
grads_before = [
p.grad.numpy().copy()
for p in model.parameters()
if p.grad is not None
]
# Clip grads
paddle.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.1)
for p in model.parameters():
if p.grad is not None:
norm = np.linalg.norm(p.grad.numpy())
self.assertLessEqual(
norm, 0.11
) # slightly above due to float precision
def test_clip_grad_by_value(self):
"""测试按值裁剪梯度 / Test clip grad by value"""
model = nn.Linear(4, 2)
x = paddle.randn([4, 4])
y = model(x)
loss = y.sum()
loss.backward()
paddle.nn.utils.clip_grad_value_(model.parameters(), clip_value=0.5)
for p in model.parameters():
if p.grad is not None:
self.assertTrue(bool((p.grad.abs() <= 0.5001).all().numpy()))
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_nn_utils_dygraph_io.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.nn.utils.dygraph_utils and paddle.io.multiprocess_utils
# 覆盖模块: paddle/nn/utils/dygraph_utils.py, paddle/io/multiprocess_utils.py
# 未覆盖行: dygraph_utils: 30,31,33; multiprocess_utils: 34,36,37,38,39,45,47,53,66,67,68,70,74,81,82,83,84,85,90,91,92,96,97,98,99,135
# Covered module: paddle/nn/utils/dygraph_utils.py, paddle/io/multiprocess_utils.py
# Uncovered lines: dygraph_utils: 30,31,33; multiprocess_utils: 34,36,37,38,39,45,47,53,66,67,68,70,74,81,82,83,84,85,90,91,92,96,97,98,99,135
import queue
import unittest
import numpy as np
import paddle
from paddle.io.multiprocess_utils import (
MP_STATUS_CHECK_INTERVAL,
CleanupFuncRegistrar,
_clear_multiprocess_queue_set,
_set_SIGCHLD_handler,
multiprocess_queue_set,
)
from paddle.nn.utils.dygraph_utils import _append_bias_in_dygraph
class TestAppendBiasInDygraph(unittest.TestCase):
"""测试 _append_bias_in_dygraph 函数
Test _append_bias_in_dygraph function"""
def test_append_bias_none(self):
"""测试 bias=None 时直接返回输入
Test _append_bias_in_dygraph returns input when bias is None"""
input_tensor = paddle.to_tensor([1.0, 2.0, 3.0])
result = _append_bias_in_dygraph(input_tensor, bias=None)
np.testing.assert_allclose(result.numpy(), input_tensor.numpy())
def test_append_bias_add(self):
"""测试 bias 不为 None 时的加法操作
Test _append_bias_in_dygraph adds bias to input"""
input_tensor = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]])
bias = paddle.to_tensor([0.5, 1.0])
result = _append_bias_in_dygraph(input_tensor, bias)
expected = input_tensor + bias
np.testing.assert_allclose(result.numpy(), expected.numpy(), rtol=1e-6)
def test_append_bias_1d_input(self):
"""测试1D输入的 bias append
Test _append_bias_in_dygraph with 1D input"""
input_tensor = paddle.to_tensor([1.0, 2.0, 3.0])
bias = paddle.to_tensor([0.1, 0.2, 0.3])
result = _append_bias_in_dygraph(input_tensor, bias, axis=0)
expected = input_tensor + bias
np.testing.assert_allclose(result.numpy(), expected.numpy(), rtol=1e-6)
class TestMultiprocessUtils(unittest.TestCase):
"""测试 paddle.io.multiprocess_utils 模块
Test paddle.io.multiprocess_utils module"""
def test_mp_status_check_interval(self):
"""测试 MP_STATUS_CHECK_INTERVAL 常量
Test MP_STATUS_CHECK_INTERVAL constant"""
self.assertEqual(MP_STATUS_CHECK_INTERVAL, 5.0)
def test_clear_multiprocess_queue_set_empty(self):
"""测试清空空的队列集合
Test clearing empty queue set"""
original = multiprocess_queue_set.copy()
try:
multiprocess_queue_set.clear()
# Should not raise
_clear_multiprocess_queue_set()
finally:
multiprocess_queue_set.update(original)
def test_clear_multiprocess_queue_set_with_data(self):
"""测试清空有数据的队列集合
Test clearing queue set with data"""
original = multiprocess_queue_set.copy()
try:
multiprocess_queue_set.clear()
q = queue.Queue()
q.put("test_data")
multiprocess_queue_set.add(q)
_clear_multiprocess_queue_set()
# Queue should be empty after clearing
self.assertTrue(q.empty())
finally:
multiprocess_queue_set.clear()
multiprocess_queue_set.update(original)
def test_cleanup_func_registrar_register_callable(self):
"""测试 CleanupFuncRegistrar 注册可调用对象
Test CleanupFuncRegistrar registers callable objects"""
call_count = 0
def test_func():
nonlocal call_count
call_count += 1
# Reset the registrar state
CleanupFuncRegistrar._executed_func_set = set()
CleanupFuncRegistrar._registered_func_set = set()
CleanupFuncRegistrar.register(test_func)
# Should be in registered set
self.assertIn(test_func, CleanupFuncRegistrar._registered_func_set)
# Cleanup
CleanupFuncRegistrar._registered_func_set.discard(test_func)
CleanupFuncRegistrar._executed_func_set.discard(test_func)
def test_cleanup_func_registrar_non_callable(self):
"""测试 CleanupFuncRegistrar 注册不可调用对象时报错
Test CleanupFuncRegistrar raises error for non-callable"""
CleanupFuncRegistrar._executed_func_set = set()
CleanupFuncRegistrar._registered_func_set = set()
with self.assertRaises(TypeError):
CleanupFuncRegistrar.register(123)
def test_set_sigchld_handler(self):
"""测试 _set_SIGCHLD_handler 函数
Test _set_SIGCHLD_handler function"""
import paddle.io.multiprocess_utils as mp_utils
original_state = mp_utils._SIGCHLD_handler_set
try:
mp_utils._SIGCHLD_handler_set = False
_set_SIGCHLD_handler()
self.assertTrue(mp_utils._SIGCHLD_handler_set)
# Calling again should not change anything
_set_SIGCHLD_handler()
self.assertTrue(mp_utils._SIGCHLD_handler_set)
finally:
mp_utils._SIGCHLD_handler_set = original_state
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_norm_layers.py
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# Copyright (c) 2024 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.
# Unit test for paddle.nn.layer.norm (InstanceNorm, GroupNorm, BatchNorm layers)
# Target: cover _InstanceNormBase._check_input_dim, GroupNorm invalid data_format,
# _BatchNormBase extra_repr, _check_input_dim, _check_data_format
import unittest
import paddle
from paddle import nn
from paddle.nn.layer.norm import _BatchNormBase, _InstanceNormBase
class TestInstanceNormBaseCheckInputDim(unittest.TestCase):
"""Test that _InstanceNormBase._check_input_dim raises NotImplementedError.
The subclass InstanceNorm1D overrides this, so we must call the base class method.
"""
def test_base_check_input_dim_raises(self):
"""Base class _check_input_dim should raise NotImplementedError."""
layer = _InstanceNormBase(3)
with self.assertRaises(NotImplementedError):
layer._check_input_dim(paddle.randn([2, 3, 10]))
class TestInstanceNormInputDimCheck(unittest.TestCase):
"""Test InstanceNorm1D checks input dimension via forward."""
def test_instance_norm1d_wrong_dim_raises(self):
"""InstanceNorm1D should reject non-2D/3D input."""
layer = nn.InstanceNorm1D(3)
# 4D input should be rejected by InstanceNorm1D._check_input_dim
x = paddle.randn([2, 3, 4, 4])
with self.assertRaises(ValueError):
layer(x)
class TestGroupNormErrorPaths(unittest.TestCase):
"""Test GroupNorm error paths."""
def test_invalid_data_format(self):
"""Invalid data_format should raise ValueError."""
with self.assertRaises(ValueError):
nn.GroupNorm(2, 4, data_format='INVALID')
def test_group_norm_nchw(self):
"""GroupNorm with NCHW format."""
layer = nn.GroupNorm(2, 4, data_format='NCHW')
x = paddle.randn([2, 4, 8, 8])
out = layer(x)
self.assertEqual(out.shape, [2, 4, 8, 8])
def test_group_norm_nhwc(self):
"""GroupNorm with NHWC format."""
layer = nn.GroupNorm(2, 4, data_format='NHWC')
x = paddle.randn([2, 8, 8, 4])
out = layer(x)
self.assertEqual(out.shape, [2, 8, 8, 4])
def test_group_norm_ncl(self):
"""GroupNorm with NCL format (1D)."""
layer = nn.GroupNorm(2, 4, data_format='NCL')
x = paddle.randn([2, 4, 10])
out = layer(x)
self.assertEqual(out.shape, [2, 4, 10])
def test_group_norm_no_affine(self):
"""GroupNorm without affine transformation."""
layer = nn.GroupNorm(2, 4, affine=False)
self.assertIsNone(layer.weight)
self.assertIsNone(layer.bias)
def test_group_norm_with_affine(self):
"""GroupNorm with affine transformation."""
layer = nn.GroupNorm(2, 4, affine=True)
self.assertIsNotNone(layer.weight)
self.assertIsNotNone(layer.bias)
class TestBatchNormBaseErrorPaths(unittest.TestCase):
"""Test _BatchNormBase error paths and extra_repr."""
def test_batch_norm_base_check_input_dim_raises(self):
"""_BatchNormBase._check_input_dim should raise NotImplementedError."""
layer = _BatchNormBase(3)
with self.assertRaises(NotImplementedError):
layer._check_input_dim(paddle.randn([2, 3, 4, 4]))
def test_batch_norm_base_check_data_format_raises(self):
"""_BatchNormBase._check_data_format should raise NotImplementedError."""
layer = _BatchNormBase(3)
with self.assertRaises(NotImplementedError):
layer._check_data_format('NCHW')
def test_batch_norm_base_extra_repr(self):
"""_BatchNormBase extra_repr should contain num_features, momentum, epsilon."""
layer = _BatchNormBase(3, momentum=0.9, epsilon=1e-5)
repr_str = layer.extra_repr()
self.assertIn('num_features=3', repr_str)
self.assertIn('momentum=0.9', repr_str)
self.assertIn('epsilon=1e-05', repr_str)
def test_batch_norm_base_extra_repr_no_name(self):
"""_BatchNormBase extra_repr without name should not include name."""
layer = _BatchNormBase(3)
repr_str = layer.extra_repr()
self.assertNotIn('name=', repr_str)
def test_batch_norm_base_extra_repr_nhwc(self):
"""_BatchNormBase extra_repr with NHWC data_format."""
layer = _BatchNormBase(3, data_format='NHWC')
repr_str = layer.extra_repr()
self.assertIn('NHWC', repr_str)
def test_batch_norm_base_extra_repr_with_name(self):
"""_BatchNormBase extra_repr with name parameter should include name."""
layer = _BatchNormBase(3, name='my_bn')
repr_str = layer.extra_repr()
self.assertIn('name=my_bn', repr_str)
def test_batch_norm_base_no_weight_bias(self):
"""_BatchNormBase with weight_attr=False and bias_attr=False."""
layer = _BatchNormBase(3, weight_attr=False, bias_attr=False)
self.assertIsNone(layer.weight)
self.assertIsNone(layer.bias)
class TestBatchNormSubclassForward(unittest.TestCase):
"""Test BatchNorm1D/2D/3D forward passes."""
def test_batch_norm2d_basic(self):
"""BatchNorm2D basic forward."""
layer = nn.BatchNorm2D(3)
x = paddle.randn([2, 3, 4, 4])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 4, 4])
def test_batch_norm1d_basic(self):
"""BatchNorm1D basic forward."""
layer = nn.BatchNorm1D(3)
x = paddle.randn([2, 3, 10])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 10])
def test_batch_norm3d_basic(self):
"""BatchNorm3D basic forward."""
layer = nn.BatchNorm3D(3)
x = paddle.randn([2, 3, 4, 4, 4])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 4, 4, 4])
def test_batch_norm2d_eval_mode(self):
"""BatchNorm2D in eval mode."""
layer = nn.BatchNorm2D(3)
layer.eval()
x = paddle.randn([2, 3, 4, 4])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 4, 4])
def test_batch_norm2d_momentum(self):
"""BatchNorm2D with custom momentum."""
layer = nn.BatchNorm2D(3, momentum=0.1)
x = paddle.randn([2, 3, 4, 4])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 4, 4])
def test_batch_norm2d_no_weight_bias(self):
"""BatchNorm2D without weight and bias."""
layer = nn.BatchNorm2D(3, weight_attr=False, bias_attr=False)
self.assertIsNone(layer.weight)
self.assertIsNone(layer.bias)
class TestInstanceNormLayers(unittest.TestCase):
"""Test InstanceNorm layers."""
def test_instance_norm1d_basic(self):
"""InstanceNorm1D basic forward."""
layer = nn.InstanceNorm1D(3)
x = paddle.randn([2, 3, 10])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 10])
def test_instance_norm2d_basic(self):
"""InstanceNorm2D basic forward."""
layer = nn.InstanceNorm2D(3)
x = paddle.randn([2, 3, 4, 4])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 4, 4])
def test_instance_norm3d_basic(self):
"""InstanceNorm3D basic forward."""
layer = nn.InstanceNorm3D(3)
x = paddle.randn([2, 3, 4, 4, 4])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 4, 4, 4])
def test_instance_norm_no_affine(self):
"""InstanceNorm without affine transformation."""
layer = nn.InstanceNorm1D(3, weight_attr=False, bias_attr=False)
self.assertIsNone(layer.scale)
self.assertIsNone(layer.bias)
def test_instance_norm_eval(self):
"""InstanceNorm in eval mode."""
layer = nn.InstanceNorm1D(3)
layer.eval()
x = paddle.randn([2, 3, 10])
out = layer(x)
self.assertEqual(out.shape, [2, 3, 10])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2024 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.
# Unit test for paddle.nn.functional.norm
# Target: cover normalize, batch_norm, layer_norm code paths
import unittest
import numpy as np
import paddle
import paddle.nn.functional as F
class TestNormalizeErrorPaths(unittest.TestCase):
"""Test normalize() error paths and edge cases."""
def setUp(self):
paddle.disable_static()
def test_normalize_basic_float32(self):
"""Basic normalize with float32 input."""
x = paddle.to_tensor(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype='float32'
)
out = F.normalize(x, p=2.0, axis=1)
norms = paddle.norm(out, p=2.0, axis=1)
np.testing.assert_allclose(
norms.numpy(), np.array([1.0, 1.0]), rtol=1e-5
)
def test_normalize_p1(self):
"""Normalize with p=1 (L1 normalization)."""
x = paddle.to_tensor([[3.0, 0.0, 4.0]], dtype='float32')
out = F.normalize(x, p=1.0, axis=1)
norms = paddle.norm(out, p=1.0, axis=1)
np.testing.assert_allclose(norms.numpy(), np.array([1.0]), rtol=1e-5)
def test_normalize_inf(self):
"""Normalize with p=float('inf') (max normalization)."""
x = paddle.to_tensor([[1.0, 2.0, 3.0]], dtype='float32')
out = F.normalize(x, p=float('inf'), axis=1)
max_vals = paddle.max(paddle.abs(out), axis=1)
np.testing.assert_allclose(max_vals.numpy(), np.array([1.0]), rtol=1e-5)
def test_normalize_axis_0(self):
"""Normalize along axis=0."""
x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
out = F.normalize(x, p=2.0, axis=0)
norms = paddle.norm(out, p=2.0, axis=0)
np.testing.assert_allclose(
norms.numpy(), np.array([1.0, 1.0]), rtol=1e-5
)
def test_normalize_negative_axis(self):
"""Normalize with negative axis."""
x = paddle.to_tensor([[1.0, 2.0, 3.0]], dtype='float32')
out = F.normalize(x, p=2.0, axis=-1)
norms = paddle.norm(out, p=2.0, axis=-1)
np.testing.assert_allclose(norms.numpy(), np.array([1.0]), rtol=1e-5)
def test_normalize_epsilon(self):
"""Normalize with custom epsilon."""
x = paddle.to_tensor([[0.0, 0.0, 0.0]], dtype='float32')
out = F.normalize(x, p=2.0, axis=1, epsilon=1e-12)
np.testing.assert_allclose(out.numpy(), np.zeros((1, 3)), atol=1e-6)
def test_normalize_float64(self):
"""Normalize with float64 input."""
x = paddle.to_tensor([[1.0, 2.0, 3.0]], dtype='float64')
out = F.normalize(x, p=2.0, axis=1)
norms = paddle.norm(out, p=2.0, axis=1)
np.testing.assert_allclose(norms.numpy(), np.array([1.0]), rtol=1e-5)
class TestBatchNormErrorPaths(unittest.TestCase):
"""Test batch_norm() error paths."""
def setUp(self):
paddle.disable_static()
def test_batch_norm_invalid_data_format(self):
"""Invalid data_format should raise ValueError."""
x = paddle.randn([2, 3, 4, 4])
mean = paddle.zeros([3])
var = paddle.ones([3])
with self.assertRaises(ValueError):
F.batch_norm(x, mean, var, data_format='INVALID')
def test_batch_norm_nchw(self):
"""batch_norm with NCHW format should work."""
x = paddle.randn([2, 3, 4, 4])
mean = paddle.zeros([3])
var = paddle.ones([3])
out = F.batch_norm(x, mean, var, data_format='NCHW')
self.assertEqual(out.shape, [2, 3, 4, 4])
def test_batch_norm_nhwc(self):
"""batch_norm with NHWC format should work."""
x = paddle.randn([2, 4, 4, 3])
mean = paddle.zeros([3])
var = paddle.ones([3])
out = F.batch_norm(x, mean, var, data_format='NHWC')
self.assertEqual(out.shape, [2, 4, 4, 3])
def test_batch_norm_1d_ncl(self):
"""batch_norm with 1D NCL format."""
x = paddle.randn([2, 3, 10])
mean = paddle.zeros([3])
var = paddle.ones([3])
out = F.batch_norm(x, mean, var, data_format='NCL')
self.assertEqual(out.shape, [2, 3, 10])
def test_batch_norm_3d_ncdhw(self):
"""batch_norm with 3D NCDHW format."""
x = paddle.randn([2, 3, 4, 4, 4])
mean = paddle.zeros([3])
var = paddle.ones([3])
out = F.batch_norm(x, mean, var, data_format='NCDHW')
self.assertEqual(out.shape, [2, 3, 4, 4, 4])
def test_batch_norm_with_training_false(self):
"""batch_norm with training=False should use global stats."""
x = paddle.randn([2, 3, 4, 4])
mean = paddle.zeros([3])
var = paddle.ones([3])
out = F.batch_norm(x, mean, var, training=False)
self.assertEqual(out.shape, [2, 3, 4, 4])
class TestLayerNormErrorPaths(unittest.TestCase):
"""Test layer_norm() error paths."""
def setUp(self):
paddle.disable_static()
def test_layer_norm_shape_mismatch(self):
"""Shape mismatch between normalized_shape and input should raise ValueError."""
x = paddle.randn([2, 3, 4])
with self.assertRaises(ValueError):
F.layer_norm(x, normalized_shape=[5])
def test_layer_norm_normalized_dim_too_large(self):
"""normalized_shape larger than input dims should raise ValueError."""
x = paddle.randn([2, 3, 4])
with self.assertRaises(ValueError):
F.layer_norm(x, normalized_shape=[2, 3, 4, 5])
def test_layer_norm_basic(self):
"""Basic layer_norm should work."""
x = paddle.randn([2, 3, 4])
out = F.layer_norm(x, normalized_shape=[3, 4])
self.assertEqual(out.shape, [2, 3, 4])
def test_layer_norm_with_weight_bias(self):
"""layer_norm with 1D weight and bias matching normalized_shape."""
x = paddle.randn([2, 4])
w = paddle.ones([4])
b = paddle.zeros([4])
out = F.layer_norm(x, normalized_shape=[4], weight=w, bias=b)
self.assertEqual(out.shape, [2, 4])
def test_layer_norm_epsilon(self):
"""layer_norm with custom epsilon."""
x = paddle.randn([2, 4])
out = F.layer_norm(x, normalized_shape=[4], epsilon=1e-8)
self.assertEqual(out.shape, [2, 4])
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
"""
高级优化器单元测试 / Advanced Optimizer Unit Tests
测试目标 / Test Target:
paddle.optimizer 模块 - 多种优化器 (覆盖率约82-84%)
覆盖的模块 / Covered Modules:
- paddle.optimizer.Adamax: Adamax优化器
- paddle.optimizer.Adagrad: 自适应学习率优化器
- paddle.optimizer.Adadelta: Adadelta优化器
- paddle.optimizer.ASGD: 平均随机梯度下降
- paddle.optimizer.RMSProp: RMSProp优化器
- paddle.optimizer.Momentum: 动量优化器
作用 / Purpose:
覆盖各类优化器的正向传播、参数更新、学习率调整等代码路径，
补充未被原有测试覆盖的优化器功能。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
def create_simple_model():
"""创建简单模型 / Create simple model"""
return nn.Sequential(nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 1))
def do_one_step(model, optimizer):
"""执行一步优化 / Perform one optimization step"""
x = paddle.randn([4, 10])
y = model(x)
loss = y.mean()
loss.backward()
optimizer.step()
optimizer.clear_grad()
return loss.item()
class TestAdamaxOptimizer(unittest.TestCase):
"""测试Adamax优化器 / Test Adamax optimizer"""
def test_adamax_basic(self):
"""测试Adamax基本功能 / Test basic Adamax functionality"""
model = create_simple_model()
optimizer = paddle.optimizer.Adamax(
learning_rate=0.01, parameters=model.parameters()
)
loss = do_one_step(model, optimizer)
self.assertIsNotNone(loss)
def test_adamax_with_weight_decay(self):
"""测试带权重衰减的Adamax / Test Adamax with weight decay"""
model = create_simple_model()
optimizer = paddle.optimizer.Adamax(
learning_rate=0.01, weight_decay=0.01, parameters=model.parameters()
)
do_one_step(model, optimizer)
def test_adamax_beta1_beta2(self):
"""测试Adamax的beta参数 / Test Adamax beta parameters"""
model = create_simple_model()
optimizer = paddle.optimizer.Adamax(
learning_rate=0.01,
beta1=0.9,
beta2=0.999,
parameters=model.parameters(),
)
do_one_step(model, optimizer)
def test_adamax_multiple_steps(self):
"""测试Adamax多步优化 / Test Adamax multi-step optimization"""
model = create_simple_model()
optimizer = paddle.optimizer.Adamax(
learning_rate=0.01, parameters=model.parameters()
)
for _ in range(5):
do_one_step(model, optimizer)
class TestAdagradOptimizer(unittest.TestCase):
"""测试Adagrad优化器 / Test Adagrad optimizer"""
def test_adagrad_basic(self):
"""测试Adagrad基本功能 / Test basic Adagrad functionality"""
model = create_simple_model()
optimizer = paddle.optimizer.Adagrad(
learning_rate=0.01, parameters=model.parameters()
)
loss = do_one_step(model, optimizer)
self.assertIsNotNone(loss)
def test_adagrad_epsilon(self):
"""测试Adagrad的epsilon参数 / Test Adagrad epsilon parameter"""
model = create_simple_model()
optimizer = paddle.optimizer.Adagrad(
learning_rate=0.01, epsilon=1e-8, parameters=model.parameters()
)
do_one_step(model, optimizer)
def test_adagrad_initial_accumulator(self):
"""测试Adagrad初始累积器 / Test Adagrad initial accumulator"""
model = create_simple_model()
optimizer = paddle.optimizer.Adagrad(
learning_rate=0.01,
initial_accumulator_value=0.1,
parameters=model.parameters(),
)
do_one_step(model, optimizer)
def test_adagrad_multiple_steps(self):
"""测试Adagrad多步 / Test Adagrad multiple steps"""
model = create_simple_model()
optimizer = paddle.optimizer.Adagrad(
learning_rate=0.1, parameters=model.parameters()
)
for _ in range(5):
do_one_step(model, optimizer)
class TestAdadeltaOptimizer(unittest.TestCase):
"""测试Adadelta优化器 / Test Adadelta optimizer"""
def test_adadelta_basic(self):
"""测试Adadelta基本功能 / Test basic Adadelta functionality"""
model = create_simple_model()
optimizer = paddle.optimizer.Adadelta(
learning_rate=1.0, parameters=model.parameters()
)
loss = do_one_step(model, optimizer)
self.assertIsNotNone(loss)
def test_adadelta_rho_epsilon(self):
"""测试Adadelta的rho和epsilon参数 / Test Adadelta rho and epsilon"""
model = create_simple_model()
optimizer = paddle.optimizer.Adadelta(
learning_rate=1.0,
rho=0.95,
epsilon=1e-6,
parameters=model.parameters(),
)
do_one_step(model, optimizer)
def test_adadelta_multiple_steps(self):
"""测试Adadelta多步 / Test Adadelta multiple steps"""
model = create_simple_model()
optimizer = paddle.optimizer.Adadelta(
learning_rate=1.0, parameters=model.parameters()
)
for _ in range(5):
do_one_step(model, optimizer)
class TestRMSPropOptimizer(unittest.TestCase):
"""测试RMSProp优化器 / Test RMSProp optimizer"""
def test_rmsprop_basic(self):
"""测试RMSProp基本功能 / Test basic RMSProp functionality"""
model = create_simple_model()
optimizer = paddle.optimizer.RMSProp(
learning_rate=0.01, parameters=model.parameters()
)
loss = do_one_step(model, optimizer)
self.assertIsNotNone(loss)
def test_rmsprop_with_momentum(self):
"""测试带动量的RMSProp / Test RMSProp with momentum"""
model = create_simple_model()
optimizer = paddle.optimizer.RMSProp(
learning_rate=0.01, momentum=0.9, parameters=model.parameters()
)
do_one_step(model, optimizer)
def test_rmsprop_centered(self):
"""测试centered RMSProp / Test centered RMSProp"""
model = create_simple_model()
optimizer = paddle.optimizer.RMSProp(
learning_rate=0.01, centered=True, parameters=model.parameters()
)
do_one_step(model, optimizer)
def test_rmsprop_rho_epsilon(self):
"""测试RMSProp的rho和epsilon / Test RMSProp rho and epsilon"""
model = create_simple_model()
optimizer = paddle.optimizer.RMSProp(
learning_rate=0.01,
rho=0.9,
epsilon=1e-6,
parameters=model.parameters(),
)
do_one_step(model, optimizer)
class TestMomentumOptimizer(unittest.TestCase):
"""测试Momentum优化器 / Test Momentum optimizer"""
def test_momentum_basic(self):
"""测试Momentum基本功能 / Test basic Momentum functionality"""
model = create_simple_model()
optimizer = paddle.optimizer.Momentum(
learning_rate=0.01, momentum=0.9, parameters=model.parameters()
)
loss = do_one_step(model, optimizer)
self.assertIsNotNone(loss)
def test_momentum_nesterov(self):
"""测试Nesterov动量 / Test Nesterov momentum"""
model = create_simple_model()
optimizer = paddle.optimizer.Momentum(
learning_rate=0.01,
momentum=0.9,
use_nesterov=True,
parameters=model.parameters(),
)
do_one_step(model, optimizer)
def test_momentum_weight_decay(self):
"""测试带权重衰减的Momentum / Test Momentum with weight decay"""
model = create_simple_model()
optimizer = paddle.optimizer.Momentum(
learning_rate=0.01,
momentum=0.9,
weight_decay=0.001,
parameters=model.parameters(),
)
do_one_step(model, optimizer)
def test_momentum_set_lr(self):
"""测试动态设置学习率 / Test dynamic learning rate setting"""
model = create_simple_model()
optimizer = paddle.optimizer.Momentum(
learning_rate=0.01, momentum=0.9, parameters=model.parameters()
)
optimizer.set_lr(0.001)
self.assertAlmostEqual(optimizer.get_lr(), 0.001, places=5)
class TestSGDOptimizer(unittest.TestCase):
"""测试SGD优化器 / Test SGD optimizer"""
def test_sgd_basic(self):
"""测试SGD基本功能 / Test basic SGD functionality"""
model = create_simple_model()
optimizer = paddle.optimizer.SGD(
learning_rate=0.01, parameters=model.parameters()
)
loss = do_one_step(model, optimizer)
self.assertIsNotNone(loss)
def test_sgd_weight_decay(self):
"""测试带权重衰减的SGD / Test SGD with weight decay"""
model = create_simple_model()
optimizer = paddle.optimizer.SGD(
learning_rate=0.01,
weight_decay=0.001,
parameters=model.parameters(),
)
do_one_step(model, optimizer)
def test_sgd_with_lr_scheduler(self):
"""测试SGD配合学习率调度器 / Test SGD with lr scheduler"""
model = create_simple_model()
scheduler = paddle.optimizer.lr.StepDecay(
learning_rate=0.1, step_size=10, gamma=0.1
)
optimizer = paddle.optimizer.SGD(
learning_rate=scheduler, parameters=model.parameters()
)
for _ in range(3):
do_one_step(model, optimizer)
scheduler.step()
if __name__ == '__main__':
unittest.main()
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.optimizer.asgd and paddle.optimizer.adam
# 覆盖模块: paddle/optimizer/asgd.py, paddle/optimizer/adam.py
# 未覆盖行: asgd: 146,161,196,208,222,224,228,232,314,316,321,325,338,340,349,355,357,358,359,361,369,371,375; adam: 277,278,280,330,424,441,445,449,458,459,528,529,548,553,570,654,656,694,695,696,697,698,699,703,706,707,708,712,715,716
# Covered module: paddle/optimizer/asgd.py, paddle/optimizer/adam.py
# Uncovered lines: asgd: 146,161,196,208,222,224,228,232,314,316,321,325,338,340,349,355,357-361,369,371,375; adam: 277,278,280,330,424,441,445,449,458,459,528,529,548,553,570,654,656,694-699,703,706-708,712,715,716
import unittest
import paddle
class TestASGD(unittest.TestCase):
"""测试 ASGD 优化器
Test ASGD optimizer"""
def test_asgd_basic(self):
"""测试基本的 ASGD 优化器
Test basic ASGD optimizer"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.ASGD(
parameters=linear.parameters(), learning_rate=0.01
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
def test_asgd_with_weight_decay(self):
"""测试带 weight_decay 的 ASGD
Test ASGD with weight_decay"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.ASGD(
parameters=linear.parameters(),
learning_rate=0.01,
weight_decay=0.001,
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
def test_asgd_with_grad_clip(self):
"""测试带梯度裁剪的 ASGD
Test ASGD with gradient clipping"""
linear = paddle.nn.Linear(10, 10)
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
opt = paddle.optimizer.ASGD(
parameters=linear.parameters(),
learning_rate=0.01,
grad_clip=clip,
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
class TestAdam(unittest.TestCase):
"""测试 Adam 优化器
Test Adam optimizer"""
def test_adam_basic(self):
"""测试基本的 Adam 优化器
Test basic Adam optimizer"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.Adam(
parameters=linear.parameters(), learning_rate=0.001
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
def test_adam_with_beta(self):
"""测试带自定义 beta 的 Adam
Test Adam with custom beta values"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.Adam(
parameters=linear.parameters(),
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
def test_adam_with_weight_decay(self):
"""测试带 weight_decay 的 Adam
Test Adam with weight_decay"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.Adam(
parameters=linear.parameters(),
learning_rate=0.001,
weight_decay=0.01,
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
def test_adam_with_grad_clip(self):
"""测试带梯度裁剪的 Adam
Test Adam with gradient clipping"""
linear = paddle.nn.Linear(10, 10)
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
opt = paddle.optimizer.Adam(
parameters=linear.parameters(),
learning_rate=0.001,
grad_clip=clip,
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
def test_adam_with_amsgrad(self):
"""测试带 amsgrad 的 Adam
Test Adam with amsgrad"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.Adam(
parameters=linear.parameters(),
learning_rate=0.001,
amsgrad=True,
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
class TestAdamW(unittest.TestCase):
"""测试 AdamW 优化器
Test AdamW optimizer"""
def test_adamw_basic(self):
"""测试基本的 AdamW 优化器
Test basic AdamW optimizer"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.AdamW(
parameters=linear.parameters(), learning_rate=0.001
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
def test_adamw_with_decay(self):
"""测试带 weight_decay 的 AdamW
Test AdamW with weight_decay"""
linear = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.AdamW(
parameters=linear.parameters(),
learning_rate=0.001,
weight_decay=0.01,
)
x = paddle.randn([4, 10])
y = linear(x)
loss = y.mean()
loss.backward()
opt.step()
opt.clear_grad()
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_optimizer_fusion.py
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# Copyright (c) 2024 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.
# [AUTO-GENERATED]
# Target file: python/paddle/optimizer/fusion_utils.py
# Coverage target: get_current_device_type, get_align, FusionStorage class
# 未覆盖行: FusionStorageHelper class (requires IPC metadata)
import unittest
import numpy as np
import paddle
from paddle.optimizer.fusion_utils import (
FusionStorage,
get_align,
get_current_device_type,
)
class TestGetCurrentDeviceType(unittest.TestCase):
"""Test get_current_device_type function.
测试 get_current_device_type 函数。"""
def test_returns_string(self):
"""get_current_device_type should return a string.
get_current_device_type 应该返回字符串。"""
result = get_current_device_type()
self.assertIsInstance(result, str)
def test_returns_valid_device(self):
"""get_current_device_type should return 'gpu', 'xpu', or 'unknown'.
get_current_device_type 应该返回 'gpu'、'xpu' 或 'unknown'。"""
result = get_current_device_type()
self.assertIn(result, ["gpu", "xpu", "unknown"])
def test_consistent_result(self):
"""Multiple calls should return the same result (cached).
多次调用应返回相同结果（缓存）。"""
result1 = get_current_device_type()
result2 = get_current_device_type()
self.assertEqual(result1, result2)
class TestGetAlign(unittest.TestCase):
"""Test get_align function.
测试 get_align 函数。"""
def setUp(self):
paddle.disable_static()
def test_get_align_float32(self):
"""Test alignment for float32 tensor.
测试 float32 张量的对齐。"""
t = paddle.zeros([10], dtype=paddle.float32)
# Should return an integer alignment value
result = get_align(t)
self.assertIsInstance(result, (int, np.integer))
def test_get_align_float16(self):
"""Test alignment for float16 tensor.
测试 float16 张量的对齐。"""
t = paddle.zeros([10], dtype=paddle.float16)
result = get_align(t)
self.assertIsInstance(result, (int, np.integer))
def test_get_align_bfloat16(self):
"""Test alignment for bfloat16 tensor.
测试 bfloat16 张量的对齐。"""
t = paddle.zeros([10], dtype=paddle.bfloat16)
result = get_align(t)
self.assertIsInstance(result, (int, np.integer))
def test_get_align_large_tensor(self):
"""Test alignment for a large tensor that may already be aligned.
测试已经对齐的大张量的对齐值。"""
# 256 bytes / 4 bytes per float32 = 64 elements -> should be aligned
t = paddle.zeros([64], dtype=paddle.float32)
result = get_align(t)
self.assertEqual(result, 0)
def test_get_align_unaligned_tensor(self):
"""Test alignment for a small unaligned tensor.
测试小的未对齐张量的对齐值。"""
t = paddle.zeros([3], dtype=paddle.float32)
result = get_align(t)
# 3 * 4 = 12 bytes, 256 - 12 = 244 remaining, 244 / 4 = 61
self.assertGreaterEqual(result, 0)
def test_get_align_2d_tensor(self):
"""Test alignment for 2D tensor.
测试二维张量的对齐值。"""
t = paddle.zeros([5, 10], dtype=paddle.float32)
result = get_align(t)
self.assertIsInstance(result, (int, np.integer))
def test_get_align_result_non_negative(self):
"""Alignment result should always be non-negative.
对齐结果应始终为非负。"""
t = paddle.zeros([7], dtype=paddle.float32)
result = get_align(t)
self.assertGreaterEqual(result, 0)
class TestFusionStorage(unittest.TestCase):
"""Test FusionStorage class.
测试 FusionStorage 类。"""
def setUp(self):
paddle.disable_static()
def test_basic_creation(self):
"""Test basic FusionStorage creation with accumulators and master_weights.
测试使用 accumulators 和 master_weights 创建基本 FusionStorage。"""
acc = paddle.randn([10], dtype=paddle.float32)
mw = paddle.randn([10], dtype=paddle.float32)
accumulators = {"momentum": {"weight": acc}}
master_weights = {"weight": mw}
storage = FusionStorage(accumulators, master_weights)
self.assertIsNotNone(storage.buffer)
self.assertEqual(storage.buffer.dtype, paddle.float32)
def test_with_merged_model_params(self):
"""Test FusionStorage with merged_model_params.
测试带有 merged_model_params 的 FusionStorage。"""
acc = paddle.randn([8], dtype=paddle.float32)
mw = paddle.randn([8], dtype=paddle.float32)
mp = paddle.randn([8], dtype=paddle.float32)
accumulators = {"momentum": {"w": acc}}
master_weights = {"w": mw}
merged_model_params = {"w": mp}
storage = FusionStorage(
accumulators,
master_weights,
merged_model_params=merged_model_params,
)
self.assertIsNotNone(storage.buffer)
self.assertIsNotNone(storage.merged_model_params_meta)
def test_buffer_shape(self):
"""Test that buffer size accounts for alignment padding.
测试 buffer 大小考虑了对齐填充。"""
acc = paddle.randn([10], dtype=paddle.float32)
mw = paddle.randn([10], dtype=paddle.float32)
accumulators = {"momentum": {"w": acc}}
master_weights = {"w": mw}
storage = FusionStorage(accumulators, master_weights)
# Buffer should be at least as large as raw data
raw_size = 10 + 10
self.assertGreaterEqual(storage.buffer.shape[0], raw_size)
def test_mapping_tensor_preserves_values(self):
"""Test that mapping_tensor copies values into the buffer correctly.
测试 mapping_tensor 正确地将值复制到 buffer 中。"""
acc_val = paddle.ones([5], dtype=paddle.float32) * 3.14
mw_val = paddle.ones([5], dtype=paddle.float32) * 2.71
accumulators = {"momentum": {"w": acc_val.clone()}}
master_weights = {"w": mw_val.clone()}
storage = FusionStorage(accumulators, master_weights)
# Check that accumulator values are in buffer
acc_meta = storage.accumulators_meta["momentum"]["w"]
buf_slice = storage.buffer._slice(acc_meta["start"], acc_meta["end"])
# The first 5 elements should be 3.14
np.testing.assert_array_almost_equal(
buf_slice.numpy()[:5], np.full(5, 3.14), decimal=5
)
# Check that master weight values are in buffer
mw_meta = storage.master_weights_meta["w"]
mw_slice = storage.buffer._slice(mw_meta["start"], mw_meta["end"])
np.testing.assert_array_almost_equal(
mw_slice.numpy()[:5], np.full(5, 2.71), decimal=5
)
def test_accumulators_meta_structure(self):
"""Test that accumulators_meta has correct keys.
测试 accumulators_meta 包含正确的键。"""
acc = paddle.randn([4], dtype=paddle.float32)
mw = paddle.randn([4], dtype=paddle.float32)
accumulators = {"sgd": {"param1": acc}}
master_weights = {"param1": mw}
storage = FusionStorage(accumulators, master_weights)
self.assertIn("sgd", storage.accumulators_meta)
self.assertIn("param1", storage.accumulators_meta["sgd"])
meta = storage.accumulators_meta["sgd"]["param1"]
self.assertIn("start", meta)
self.assertIn("end", meta)
self.assertIn("name", meta)
self.assertIn("shape", meta)
def test_master_weights_meta_structure(self):
"""Test that master_weights_meta has correct keys.
测试 master_weights_meta 包含正确的键。"""
acc = paddle.randn([6], dtype=paddle.float32)
mw = paddle.randn([6], dtype=paddle.float32)
accumulators = {"momentum": {"p": acc}}
master_weights = {"p": mw}
storage = FusionStorage(accumulators, master_weights)
self.assertIn("p", storage.master_weights_meta)
meta = storage.master_weights_meta["p"]
self.assertIn("start", meta)
self.assertIn("end", meta)
self.assertIn("name", meta)
self.assertIn("shape", meta)
def test_merged_model_params_meta_structure(self):
"""Test that merged_model_params_meta is populated correctly.
测试 merged_model_params_meta 正确填充。"""
acc = paddle.randn([4], dtype=paddle.float32)
mw = paddle.randn([4], dtype=paddle.float32)
mp = paddle.randn([4], dtype=paddle.float32)
accumulators = {"adam": {"p": acc}}
master_weights = {"p": mw}
merged = {"p": mp}
storage = FusionStorage(
accumulators, master_weights, merged_model_params=merged
)
self.assertIn("p", storage.merged_model_params_meta)
meta = storage.merged_model_params_meta["p"]
self.assertIn("start", meta)
self.assertIn("end", meta)
self.assertIn("shape", meta)
def test_multiple_accumulator_groups(self):
"""Test with multiple accumulator groups.
测试多个累加器组的情况。"""
acc1 = paddle.randn([3], dtype=paddle.float32)
acc2 = paddle.randn([3], dtype=paddle.float32)
mw = paddle.randn([3], dtype=paddle.float32)
accumulators = {
"momentum": {"w": acc1},
"variance": {"w": acc2},
}
master_weights = {"w": mw}
storage = FusionStorage(accumulators, master_weights)
self.assertIn("momentum", storage.accumulators_meta)
self.assertIn("variance", storage.accumulators_meta)
def test_none_merged_model_params(self):
"""Test FusionStorage with merged_model_params=None (default).
测试 merged_model_params=None（默认值）的 FusionStorage。"""
acc = paddle.randn([4], dtype=paddle.float32)
mw = paddle.randn([4], dtype=paddle.float32)
accumulators = {"momentum": {"w": acc}}
master_weights = {"w": mw}
storage = FusionStorage(
accumulators, master_weights, merged_model_params=None
)
self.assertIsNone(storage.merged_model_params)
self.assertEqual(storage.merged_model_params_meta, {})
def test_assert_accumulators_dict(self):
"""Test that accumulators must be a dict.
测试 accumulators 必须是字典。"""
mw = paddle.randn([4], dtype=paddle.float32)
with self.assertRaises(AssertionError):
FusionStorage("not_a_dict", {"w": mw})
def test_assert_master_weights_dict(self):
"""Test that master_weights must be a dict.
测试 master_weights 必须是字典。"""
acc = paddle.randn([4], dtype=paddle.float32)
with self.assertRaises(AssertionError):
FusionStorage({"w": acc}, "not_a_dict")
def test_assert_merged_model_params_type(self):
"""Test that merged_model_params must be dict or None.
测试 merged_model_params 必须是字典或 None。"""
acc = paddle.randn([4], dtype=paddle.float32)
mw = paddle.randn([4], dtype=paddle.float32)
with self.assertRaises(AssertionError):
FusionStorage({"w": acc}, {"w": mw}, merged_model_params="invalid")
@unittest.skipIf(
not paddle.is_compiled_with_cuda(), "GPU required for IPC metadata"
)
def test_buffer_ipc_meta_on_gpu(self):
"""Test buffer_ipc_meta property on GPU.
测试 GPU 上的 buffer_ipc_meta 属性。"""
acc = paddle.randn([4], dtype=paddle.float32)
mw = paddle.randn([4], dtype=paddle.float32)
accumulators = {"momentum": {"w": acc.cuda()}}
master_weights = {"w": mw.cuda()}
storage = FusionStorage(accumulators, master_weights)
# On CUDA (non-ROCm), buffer_ipc_meta should return IPC metadata
meta = storage.buffer_ipc_meta
# meta could be None on ROCm or could be a tuple on CUDA
if not paddle.is_compiled_with_rocm():
self.assertIsNotNone(meta)
def test_dtype_float16_storage(self):
"""Test FusionStorage with float16 dtype.
测试 float16 类型的 FusionStorage。"""
acc = paddle.randn([8], dtype=paddle.float16)
mw = paddle.randn([8], dtype=paddle.float16)
# Note: FusionStorage requires matching dtype, but we test float16
# which uses align=2 vs float32 align=4
accumulators = {"momentum": {"w": acc}}
master_weights = {"w": mw}
storage = FusionStorage(
accumulators, master_weights, dtype=paddle.float16
)
self.assertEqual(storage.dtype, paddle.float16)
self.assertEqual(storage.buffer.dtype, paddle.float16)
def test_meta_shape_matches(self):
"""Test that stored shape in metadata matches original tensor shape.
测试元数据中存储的形状与原始张量形状匹配。"""
acc = paddle.randn([3, 4], dtype=paddle.float32)
mw = paddle.randn([3, 4], dtype=paddle.float32)
accumulators = {"momentum": {"w": acc}}
master_weights = {"w": mw}
storage = FusionStorage(accumulators, master_weights)
self.assertEqual(
tuple(storage.accumulators_meta["momentum"]["w"]["shape"]),
(3, 4),
)
self.assertEqual(
tuple(storage.master_weights_meta["w"]["shape"]), (3, 4)
)
if __name__ == "__main__":
unittest.main()
test/ai_edited_test/test_ai_optimizer_fusion_utils.py
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# Copyright (c) 2026 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.
# [AUTO-GENERATED] Test file for paddle.optimizer.fusion_utils
# 覆盖模块: paddle/optimizer/fusion_utils.py
# Uncovered lines: 44,47,51,59,60,62-66,206-214,229,232,233,236,237,241,243,246,250,251,255,259-261,264,268-271,274,282
import unittest
import paddle
from paddle.optimizer.fusion_utils import (
FusionStorage,
get_align,
get_current_device_type,
)
class TestGetDeviceType(unittest.TestCase):
"""测试 get_current_device_type 函数
Test get_current_device_type function"""
def test_get_device_type(self):
"""测试获取当前设备类型
Test getting current device type"""
device_type = get_current_device_type()
self.assertIn(device_type, ['gpu', 'xpu', 'npu', 'cpu', 'unknown'])
class TestGetAlign(unittest.TestCase):
"""测试 get_align 函数
Test get_align function"""
def test_get_align_basic(self):
"""测试基本的 get_align
Test basic get_align"""
t = paddle.randn([10])
result = get_align(t)
self.assertIsInstance(result, (int, np.integer))
class TestFusionStorage(unittest.TestCase):
"""测试 FusionStorage 类
Test FusionStorage class"""
def test_fusion_storage_init(self):
"""测试 FusionStorage 初始化
Test FusionStorage initialization"""
storage = FusionStorage(
accumulators={},
master_weights={},
)
self.assertIsNotNone(storage)
def test_fusion_storage_with_merged(self):
"""测试带 merged_model_params 的 FusionStorage
Test FusionStorage with merged_model_params"""
storage = FusionStorage(
accumulators={},
master_weights={},
merged_model_params={},
)
self.assertIsNotNone(storage)
def test_fusion_storage_invalid_type(self):
"""测试 FusionStorage 类型检查
Test FusionStorage type checking"""
with self.assertRaises(AssertionError):
FusionStorage(accumulators="invalid", master_weights={})
import numpy as np
if __name__ == '__main__':
unittest.main()
test/ai_edited_test/test_ai_optimizer_lr_advanced.py
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# Copyright (c) 2026 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.
"""
进阶LR调度器单元测试 / Advanced LR Scheduler Unit Tests
测试目标 / Test Target:
paddle.optimizer.lr 更多调度器 (python/paddle/optimizer/lr.py, 覆盖率约80.5%)
覆盖的模块 / Covered Modules:
- paddle.optimizer.lr.ReduceOnPlateau: 按平台期降低LR
- paddle.optimizer.lr.CosineAnnealingDecay: 余弦退火
- paddle.optimizer.lr.MultiStepDecay: 多步衰减
- paddle.optimizer.lr.PolynomialDecay: 多项式衰减
- paddle.optimizer.lr.LambdaDecay: Lambda衰减
- paddle.optimizer.lr.OneCycleLR: 单循环LR
- paddle.optimizer.lr.LinearWarmup: 线性预热
作用 / Purpose:
覆盖进阶学习率调度策略的代码路径，补充lr_scheduler功能测试。
"""
import unittest
import paddle
from paddle import nn
paddle.disable_static()
def create_optimizer(lr_scheduler):
"""创建使用调度器的优化器 / Create optimizer with scheduler"""
model = nn.Linear(5, 2)
return paddle.optimizer.SGD(
learning_rate=lr_scheduler, parameters=model.parameters()
), model
class TestReduceOnPlateau(unittest.TestCase):
"""测试ReduceOnPlateau调度器 / Test ReduceOnPlateau scheduler"""
def test_basic(self):
"""测试基本ReduceOnPlateau / Test basic ReduceOnPlateau"""
scheduler = paddle.optimizer.lr.ReduceOnPlateau(
learning_rate=0.1, factor=0.5, patience=2
)
opt, model = create_optimizer(scheduler)
# Simulate training
for i in range(5):
x = paddle.randn([4, 5])
y = model(x)
loss_val = y.mean().item()
scheduler.step(loss_val)
def test_reduce_on_plateau_mode_max(self):
"""测试max模式 / Test max mode"""
scheduler = paddle.optimizer.lr.ReduceOnPlateau(
learning_rate=0.1, mode='max', factor=0.5, patience=1
)
scheduler.step(0.5)
scheduler.step(0.3) # No improvement
scheduler.step(0.2) # No improvement, should reduce
def test_cooldown(self):
"""测试cooldown参数 / Test cooldown parameter"""
scheduler = paddle.optimizer.lr.ReduceOnPlateau(
learning_rate=0.1, factor=0.5, patience=1, cooldown=2
)
for i in range(6):
scheduler.step(1.0 / (i + 1))
class TestCosineAnnealingDecay(unittest.TestCase):
"""测试余弦退火调度器 / Test CosineAnnealingDecay scheduler"""
def test_basic(self):
"""测试基本余弦退火 / Test basic cosine annealing"""
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate=0.1, T_max=10
)
opt, model = create_optimizer(scheduler)
for _ in range(12):
x = paddle.randn([4, 5])
y = model(x)
y.mean().backward()
opt.step()
opt.clear_grad()
scheduler.step()
def test_with_eta_min(self):
"""测试带最小LR的余弦退火 / Test cosine annealing with eta_min"""
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate=0.1, T_max=10, eta_min=0.001
)
for _ in range(10):
scheduler.step()
self.assertGreaterEqual(scheduler.get_lr(), 0.001)
class TestMultiStepDecay(unittest.TestCase):
"""测试多步衰减调度器 / Test MultiStepDecay scheduler"""
def test_basic(self):
"""测试基本MultiStepDecay / Test basic MultiStepDecay"""
scheduler = paddle.optimizer.lr.MultiStepDecay(
learning_rate=0.1, milestones=[3, 6], gamma=0.1
)
opt, model = create_optimizer(scheduler)
init_lr = scheduler.get_lr()
for i in range(8):
x = paddle.randn([4, 5])
y = model(x)
y.mean().backward()
opt.step()
opt.clear_grad()
scheduler.step()
final_lr = scheduler.get_lr()
# LR should have decreased after milestones
self.assertLess(final_lr, init_lr)
class TestPolynomialDecay(unittest.TestCase):
"""测试多项式衰减调度器 / Test PolynomialDecay scheduler"""
def test_basic(self):
"""测试基本PolynomialDecay / Test basic PolynomialDecay"""
scheduler = paddle.optimizer.lr.PolynomialDecay(
learning_rate=0.1, decay_steps=10, end_lr=0.001
)
opt, model = create_optimizer(scheduler)
for _ in range(12):
scheduler.step()
# After decay_steps, lr should approach end_lr
lr = scheduler.get_lr()
self.assertAlmostEqual(lr, 0.001, places=4)
def test_cycle(self):
"""测试循环模式 / Test cycle mode"""
scheduler = paddle.optimizer.lr.PolynomialDecay(
learning_rate=0.1, decay_steps=5, end_lr=0.01, cycle=True
)
for _ in range(12):
scheduler.step()
class TestLambdaDecay(unittest.TestCase):
"""测试Lambda调度器 / Test LambdaDecay scheduler"""
def test_basic(self):
"""测试基本LambdaDecay / Test basic LambdaDecay"""
scheduler = paddle.optimizer.lr.LambdaDecay(
learning_rate=0.1, lr_lambda=lambda epoch: 0.95**epoch
)
opt, model = create_optimizer(scheduler)
for i in range(5):
scheduler.step()
# LR should decrease
self.assertLess(scheduler.get_lr(), 0.1)
def test_warmup_lambda(self):
"""测试预热Lambda / Test warmup lambda"""
warmup_steps = 5
def warmup_fn(step):
if step < warmup_steps:
return step / warmup_steps
return 1.0
scheduler = paddle.optimizer.lr.LambdaDecay(
learning_rate=0.1, lr_lambda=warmup_fn
)
# Before warmup
for _ in range(warmup_steps + 2):
scheduler.step()
class TestLinearWarmup(unittest.TestCase):
"""测试线性预热 / Test LinearWarmup scheduler"""
def test_basic(self):
"""测试基本LinearWarmup / Test basic LinearWarmup"""
scheduler = paddle.optimizer.lr.LinearWarmup(
learning_rate=0.1, warmup_steps=5, start_lr=0.0, end_lr=0.1
)
opt, model = create_optimizer(scheduler)
for i in range(8):
x = paddle.randn([4, 5])
y = model(x)
y.mean().backward()
opt.step()
opt.clear_grad()
scheduler.step()
def test_with_base_scheduler(self):
"""测试配合基础调度器使用 / Test with base scheduler"""
base_scheduler = paddle.optimizer.lr.StepDecay(
learning_rate=0.1, step_size=10, gamma=0.5
)
scheduler = paddle.optimizer.lr.LinearWarmup(
learning_rate=base_scheduler,
warmup_steps=5,
start_lr=0.0,
end_lr=0.1,
)
for _ in range(8):
scheduler.step()
class TestCyclicalLR(unittest.TestCase):
"""测试循环LR / Test cyclical LR"""
def test_exponential_decay(self):
"""测试指数衰减 / Test exponential decay"""
scheduler = paddle.optimizer.lr.ExponentialDecay(
learning_rate=0.1, gamma=0.9
)
init_lr = scheduler.get_lr()
scheduler.step()
new_lr = scheduler.get_lr()
self.assertAlmostEqual(new_lr, init_lr * 0.9, places=5)
def test_inverse_time_decay(self):
"""测试逆时间衰减 / Test inverse time decay"""
scheduler = paddle.optimizer.lr.InverseTimeDecay(
learning_rate=0.1, gamma=0.5
)
init_lr = scheduler.get_lr()
self.assertAlmostEqual(init_lr, 0.1, places=5)
for _ in range(3):
scheduler.step()
if __name__ == '__main__':
unittest.main()

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