A flexible framework of neural networks for deep learning
This is the release note of v6.6.0. See here for the complete list of solved issues and merged PRs.
max_pooling_2d (#8329)optimizer_hooks.GradientHardClipping for scalar array (#8372)F.negative_sampling in fp32 for fp16 inputs (#8309)optimizer_hooks.GradientHardClipping for ChainerX (#8377, thanks @kshitij12345!)/examples/seq2seq/README.md (#8404, thanks @tanaken0515!)type_check errors (#8456)LinkTestCase for L.GroupNormalization (#8355)CHAINER_CI in Travis CI (#8373)CHAINER_CI in ChainerX tests in Jenkins (#8375)CHAINER_CI in Chainer tests in FlexCI (#8381)FunctionTest modified input error (#8388)TestTriplet (#8396)fix_random in xfail backward tests (#8457)Convolution2D tests for older numpy versions (#8478)_modified_xlogx (#8486)This is the release note of v7.0.0rc1. See here for the complete list of solved issues and merged PRs.
This time, we will keep the current branches for active development (master for v7.x, v6 for v6.x) after the RC. We will maintain v6.x series until Python2 EOL, so we do not cut the new development version for now to avoid increasing the number of branches to maintain. New features will be included directly into v7 for a while, and maintenance changes will be backported to v6.
ONNX-Chainer which used to be a separate project has now been integrated to the Chainer repository and made more accessible to existing Chainer users (#8229). You can easily export Chainer model as ONNX format like this:
import onnx_chainer
onnx_chainer.export(chainer_model, pseudo_input, filename='model.onnx')
For a more detailed description on how to get started, please refer to the ONNX-Chainer section in the official documentation.
ChainerMN now works with ChainerX. In this release, the MNIST example has also been updated to demonstrate the usage. (#7844)
UpsamplingDeconvFilter and DownsamplingConvFilter initializer (#5290, thanks @knorth55!)chainerx.meshgrid (#6668, thanks @kshitij12345!)chainerx.hsplit (#7030, thanks @ishanrai05!)linalg.cholesky to ChainerX (#7329, thanks @IvanYashchuk!)linalg.eigh, linalg.eigvalsh to ChainerX (#7503, thanks @IvanYashchuk!)force_equal_length=False (#8071)RandomState instance (#8081, thanks @mr4msm!)chainerx.hinge (#8168)chainerx::SoftmaxCrossEntropy and chainerx.softmax_cross_entropy (#8250)chainermn.testing.to_device function (#8279)chainerx.copyto (#8314, thanks @kshitij12345!)TabularDataset.as_tuple/as_dict to TabularDataset.astuple/asdict (#7788)DeviceResident.to_gpu/to_cpu/to_intel64 (#8058)generate_matrix (#8167)chainerx.take (#8197)*GradState classes (#8224)gradient_check (#8236)F.batch_normalization (#8266)device argument from chainerx.diag and chainerx.diagflat (#8275)gradient_check (#8290)output_grad support on fake_as_funcnode (#8298)F.negative_sampling in fp32 for fp16 inputs (#8300)mode and align_corners arguments in F.resize_image keyword-only (#8009)weights and keepdims arguments in Variable.mean keyword-only (#8010)WeightStandardization keyword-only (#8011)call_before_training argument of Trainer.extend keyword-only (#8064)
ObservationAggregator and MultiNodeEarlyStoppingTrigger keyword-only (#8065)force_equal_length argument in scatter_dataset and scatter_index keyword-only (#8066)size argument of tabular.from_data keyword-only (#8067)chainerx::Take faster (#8295)F.batch_normalization with mixed dtype (#8149)__str__ of parameterized class (#8169)x and gamma/beta have different dtypes in F.batch_normalization (#8175)copy to __deepcopy__ in ChainerMN batch_normalization and replace to_gpu (#8185)Allocator (#8215)chainerx.ascontiguousarray (#8262)global_kernel_registry (#8265)gpu_id=0 in ChainerMN testing get_device (#8304)setup.cfg (#8180)AveragePoolPadMode enum (#8214)setup.py (#8218){Max,Average}PoolForwardBackward (#8223)readability-avoid-const-params-in-decls (#8225)gradient_check (#8238)F.softmax_cross_entropy (#8253)CreateSubgraph (#8310)resize_images documentation to reflect recent code changes (#8221, thanks @zu3st!)chainerx.ravel (#8233)chainerx.sigmoid_cross_entropy (#8249)libchainerx_base.a to link chainerx statically (#8247)generate.py in examples/wavenet (#8172, thanks @dhgrs!)F.scale test (#6969, thanks @ishanrai05!)test_n_step_rnn (#7483)TestAccuracy: Randomly reduce testing parameters (#7820)chx.linalg.solve (#7997)TestQR (#8114)pytest.skip() in combination with testing.repeat/retry (#8174)DummySerializer and DummyDeserializer from iterators_tests (#8176)BatchNormalization backward test tolerances (#8189)protobuf>=3.8 (#8190)CHAINER_TEST_PAIRWISE_PARAMETERIZATION and enable it only in Travis CI (#8211)attrs package version (#8219)HDF5Serializer test for h5py<2.9 (#8220)TestBatchNormalization (#8230)"jenkins" extras (#8241)clang-format-6.0 if possible and track the version of clang-format (#8242)DeprecationWarning filter from test_multi_node_chain_list (#8246)chainex_tests/unit_tests/routines_tests/test_linalg.py::Inverse (#8255)TestHuberLoss (#8271)ImportWarning just a warning in tests (#8291)gtest linkage (#8292, thanks @cloudhan!)test_average is slow in FlexCI (#8303)test_mnist in chainermn_tests (#8305)communicator_test for ChainerX+ChainerMN (#8313)ImportWarning ignore entry (#8186)WIN32_LEAN_AND_MEAN definition (#8205, thanks @cloudhan!)This is the release note of v6.5.0. See here for the complete list of solved issues and merged PRs.
print_runtime_info (#7860)__str__ of parameterized class (#8184)BatchNormalization backward test tolerances (#8196)L.BatchRenormalization and adjust tolerances (#8200)TestConvolution2DFunction::test_double_backward fp16 tolerance (#8201)attrs version (#8222)HDF5Serializer test for h5py<2.9 (#8256)This is the release note of v7.0.0b4. See here for the complete list of solved issues and merged PRs.
Many updates to ChainerX including new routines and support for loss scaling.
F.n_step_rnn and F.n_step_birnn (#5808)chainerx.vsplit to ChainerX (#7032, thanks @ishanrai05!)chainerx.linalg.qr to ChainerX (#7379, thanks @IvanYashchuk!)chainerx.accuracy (#7526, thanks @aksub99!)chainerx.{remainder/mod} (#7675, thanks @sky58!)F.zeta (#8059, thanks @UmashankarTriforce!)testing.generate_matrix to get matrices of given singular values (#8077)chainerx.fmod (#8110)chainerx.nonzero (#8124)chainerx::ArrayRepr for large inputs (#7708)FutureWarning on GPU-to-GPU transfer in StandardUpdater (#7952)typeid of kernels in libchainerx (#7970)variable.Parameter objects (#8022)ScanKernel (#8103)chainerx::Absolute device implementation (#7319)MultiprocessIterator and MultiprocessParallelUpdater (#7511)mixed16/float16 GroupNormalization (#7965)chx::Device object on ndarray pickling (#7988)chainerx::Dot edge cases with empty arrays (#8020)AddAt implementation for float16 arrays (#8055)fill_value in constant initializer (#8089)ArrayReprImpl (#7699)F.batch_normalization and ChainerMN backend implementations (#8039)-Wabsolute-value for clang (#8045)NativeCumsumKernel (#8053)-Wbraced-scalar-init for clang (#8076)arithmetic.{h,cc} (#8128)backend.copyto (#7832)chainerx.to_numpy (#7984)chainerx.take indices dtype (#7998)CHAINERX_ENABLE_{BLAS,LAPACK} (#8099)chainerx.minimum (#8146)chainerx.maximum doc (#8147)cblas.h and modified CMakeLists.txt (#8052, thanks @okdshin!)CHAINERX_ENABLE_LAPACK=0 causes error (#8086, thanks @cloudhan!)DeprecationWarning in test_maniplation.py (#7824)F.max_pooling_2d test (#7924)negative_sampling (#7975)F.lstm test parameterization (#7987)gradient_check test (#7989)TrueDiv tolerances (#8047)L.BatchRenormalization and adjust tolerances (#8080)h5py.File mode (#8090)np.empty (#8096)PseudoInverse test (#8102)test_normal.py (#8111)ignore::ImportWarning to setup.cfg (#8131)fix_random decorator to be used with OpTest (#8136)NStepRNN and NStepBiRNN (#8142)empty in F.cast test that can cause overflow warning (#8152)TestConvolution2DFunction::test_double_backward fp16 tolerance (#8163)setup.cfg (#8154)This is the release note of v6.4.0. See here for the complete list of solved issues and merged PRs.
GroupNormalization (#8113)MultiprocessIterator and MultiprocessParallelUpdater (#8126)deepcopy for chain parameters (#8150)backend.copyto (#8056)DecorrelatedBatchNormalizationTest and add stable input (#7940)F.batch_inv test (#7981)F.squared_error test (#8012)negative_sampling (#8019)gradient_check test (#8021)h5py.File mode (#8107)Contrastive.backward (#8108)test_normal.py (#8117)im2col test (#8135)This is the release note of v7.0.0b3. See here for the complete list of solved issues and merged PRs.
Due to the end-of-life (EOL) of Python 2 in January 2020, Python 2 support has been dropped in this release. Chainer v6.x continues to support Python 2. See the blog post for details.
F.max_pooling_2d refactoringImplementation of F.max_pooling_2d has been merged to F.max_pooling_nd. The behavior is unchanged, so ordinary users should not be affected by this change. However, the FunctionNode class recorded in the computational graph corresponding to F.max_pooling_2d has changed from MaxPooling2D to MaxPoolingND. The code explicitly depending on this class will need a fix.
chainerx.repeat (#7223, thanks @durswd!)TabularDataset.slice (#7251)chainer.dataset.tabular.DelegateDataset (#7276)ObservationAggregator extension to ChainerMN (#7302)scatter_dataset as well as scatter_index (#7327)chainer.dataset.tabular.from_data (#7361)linalg.svd, linalg.pinv to ChainerX (#7411, thanks @IvanYashchuk!)TabularDataset.convert/with_converter (#7428)linalg.solve, linalg.inv to ChainerX (#7474, thanks @IvanYashchuk!)Converter class (#7489)chainerx.sigmoid_cross_entropy (#7524, thanks @aksub99!)chainerx.cumsum (#7558, thanks @aksub99!)chainerx.nansum (#7719, thanks @aksub99!)chainerx.nanargmax and chainerx.nanargmin (#7755, thanks @aksub99!)tri* routines to ChainerX (#7791, thanks @IvanYashchuk!)CommunicatorBase class (#7814)numerical_grad_dtype to FunctionTestCase and LinkTestCase (#7817)tabular.from_data (#7847)chainerx.count_nonzero (#7852, thanks @aksub99!)chainerx.flatten (#7901, thanks @aksub99!)chainerx.ravel (#7904, thanks @aksub99!)roi_{average|max}_{pooling|align}_2d.py (#5636, thanks @knorth55!)Link.to_gpu unless compatible with to_device (#5762)F.dropout to use cuDNN by default (#7185, thanks @crcrpar!)F.average as accurate as backend (#7758)PureNcclCommunicator (#7793)type_check error message on evaluating bool expression (#7795)type_check (#7803)chx.leaky_relu/elu (#7816)None inputs to gradient check and generating None gradients in FunctionTestCase (#7831)print_runtime_info (#7833)F.clip for NumPy 1.17 (#7843)rtol * abs(b) in allclose output (#7848)TypeError in max_pooling_2d (#6835, thanks @ishanrai05!)PureNcclCommunicator (#7600)create_mnbn_model() bug (#7718)optimizer_hooks.GradientHardClipping for scalar array (#7760)backends.copyto from chainerx to non-chainerx (#7835)split_axis for intel64 when grad_ouputs contains None (#7836)CommunicatorBase (#7888)DeprecationWarning to initializer of BuildingBlock (#7909)Link.serialize and optimizers.Adam (#7918)F.max_pooling_2d (#7922)_fallback_workarounds in SpectralNormalization (#7539)links.rnn and functions.rnn (#7725)batched_copy to all Communicators (#7761)axis (#7799)linalg.svd python bindings layer in ChainerX (#7866, thanks @IvanYashchuk!)n_layer with n_layers for consistency (#7871)pooling_nd functions (#7938)F.max_pooling_2d into F.max_pooling_nd (#7939)static_graph docs code examples (#7875)scatter to doc (#7897)F.max_pooling_2d test (#6836, thanks @ishanrai05!)F.lstm test (#7808, thanks @dido1998!)F.slstm test (#7805, thanks @dido1998!)F.n_step_rnn test (#7804, thanks @dido1998!)F.n_step_lstm test (#7807, thanks @dido1998!)F.n_step_gru test (#7806, thanks @dido1998!)F.embed_id test (#7903, thanks @dido1998!)point_to_point communications (#7637)pseudo_connect (#7638)TestConv*TensorCore (#7710)chx.reshape (#7762)TestHuberLoss (#7837)F.average_pooling_2d test (#7841)F.clipped_relu test for NumPy 1.17 (#7842)test_accuracy.py to the list of slow test files (#7851)BatchNorm flaky of ChainerX (#7857)test_TrilTriu (#7865)chainerx.logsumexp test tolerance (#7867)F.tree_lstm test for ChainerX (#7881, thanks @dido1998!)ndarray.data access and fix wrong test (#7890)TrueDiv test (#7917)F.cast from negative floating-point to unsigned (#7920)L.CRF1d test (#7926)DecorrelatedBatchNormalizationTest and add stable input (#7932)chainerx.power test (#7950)TestContrastive (#7953)F.batch_inv test (#7971)This is the release note of v6.3.0. See here for the complete list of solved issues and merged PRs.
F.average as accurate as backend (#7782)type_check error message on evaluating bool expression (#7801)type_check (#7810)F.clip for NumPy 1.17 (#7855)Parameter.dtype for uninitialized parameter (#7749)UpdateRule.use_fp32_update for uninitialized parameter (#7751)PureNcclCommunicator (#7787)TypeError in max_pooling_2d (#7789, thanks @ishanrai05!)create_mnbn_model() bug (#7846)split_axis for intel64 when grad_ouputs contains None (#7931)F.max_pooling_2d (#7933)backends.copyto from/to chainerx (#7934)Link.serialize and optimizers.Adam (#7941)static_graph docs code examples (#7884)chx.reshape (#7792)test_communicator (#7822)F.clipped_relu test for NumPy 1.17 (#7854)TestHuberLoss (#7869)F.average_pooling_2d test (#7870)chainerx.logsumexp test tolerance (#7889)ndarray.data access and fix wrong test (#7913)F.cast from negative floating-point to unsigned (#7944)TestContrastive (#7959)TrueDiv test (#7972)L.CRF1d test (#7977)This is the release note of v7.0.0b2. See here for the complete list of solved issues and merged PRs.
ChainerX has several new backproppable ops such as ELU and softplus activation functions and loss functions including absolute error, squared error, Huber loss and Gaussian KL divergence. ChainerX is also supported in all OptimizerHooks when used through Chainer. TabularDataset has also been improved with new features.
Variable.grad getter now raises an error when it is called before calling cleargrad, zerograd, or setting the gradient directly. (#7146)BatchRenormalization (usage of epsilon) is fixed. It affects the inference behavior. (#7202)HierarchicalCommunicator, SingleNodeCommunicator and TwoDimensionalCommunicator and are no longer necessary as NCCL now supports inter-node communication. (#7697)WeightStandardization link hook (#6678, thanks @hitsgub!)chainerx.dsplit (#7031, thanks @ishanrai05!)chainerx.left_shift and chainerx.right_shift (#7339, thanks @sky58!)chainerx.elu (#7439, thanks @aksub99!)TabularDataset (#7493)TabluarDataset.__iter__ (#7601)Variable.mean (#7670)chainerx.softplus (#7679, thanks @aksub99!)top_data as -np.inf and argmax_data as -1 in F.roi_max_pooling_2d (#6237, thanks @knorth55!)cleargrad (#7146)chainerx.grad from chainer.grad (#7464)ImportError (#7518)device argument a keyword only argument. (#7537, thanks @kshitij12345!)Array::At and __getitem__ (#7561)chainerx.ndarray._is_chained (#7565)squared_difference and fix docs (#7582)allreduce_grad() and functions related with it (#7604)IndexError if the index __getitem__ takes is out of bounds (#7614)six.integer_types for axis check in F.concat (#7632, thanks @knorth55!)optimizer_hooks.GradientClipping for ChainerX (#7641)optimizer_hooks.GradientHardClipping for ChainerX (#7656, thanks @kshitij12345!)IntervalTrigger.__str__ (#7664, thanks @ktns!)GradientLARS optimizer hook working with ChainerX (#7669)absl::Span and related helpers instead of gsl::span (#7671)six.integer_types for axis checks (#7713)CHAINERX_BUILD_CUDA is set (#7752)None array in FunctionNode NaN check (#6283)CupyMemoryProfiler (#7003)running_var of F.batch_renormalization (#7202)MultiprocessIterator (#7486)initializers.Identity for ideep backend (#7548)chainermn.links.create_mnbn_model (#7603)PickleDataset crash when using multiprocessing (#7625, thanks @zaltoprofen!)AMSGrad with intel64 backend (#7661)chainer.grad for multiple devices (#7692)chainerx::Flip (#7727)Parameter.dtype for uninitialized parameter (#7735)UpdateRule.use_fp32_update for uninitialized parameter (#7736)backend.get_array_module not cuda.get_array_module (#7514, thanks @crcrpar!)squared_difference alias of squared_error (#7547)Optimizer and GradientMethod (#7585)chainerx.clipped_relu in F.clipped_relu (#7588)CMakeList.txt (#7647)Links (#6512)CHAINERX_CUDNN_USE_CUPY (#7574)ResNet prepare method (#7577)BackwardContext comment (#7595, thanks @crcrpar!)expand_dims.py (#7602)FunctionNode docs. (#7622)chainer/functions/math/average.py (#7653, thanks @ktns!)F.squeeze documentation (#7682)examples/vae/train_vae.py (#7578, thanks @m4saka!)F.polygamma test (#6970, thanks @ishanrai05!)F.cast test (#7034)y_shape not used in tests (#7610)optimizer_hooks.Lasso for ChainerX (#7657, thanks @kshitij12345!)GroupNormalization tests (#7684)optimizer_hooks.GradientNoise for ChainerX (#7709, thanks @kshitij12345!)protobuf (#7715)optimizer_hooks.WeightDecay for ChainerX (#7716, thanks @kshitij12345!)atol/rtol of chainerx.erf float16 test (#7721)TestHuberLoss (#7723)Contrastive.backward (#7745)TestContrastive (#7747)third-party.cmake (#7643)This is the release note of v6.2.0. See here for the complete list of solved issues and merged PRs.
six.integer_types for axis check in F.concat (#7712, thanks @knorth55!)six.integer_types for axis checks (#7770)chainermn.links.create_mnbn_model (#7618)CupyMemoryProfiler (#7639)None array in FunctionNode NaN check (#7642)AMSGrad with intel64 backend (#7689)PickleDataset crash when using multiprocessing (#7729, thanks @zaltoprofen!)MultiprocessIterator (#7742)chainer.grad for multiple devices (#7746)backend.get_array_module not cuda.get_array_module (#7619, thanks @crcrpar!)Optimizer and GradientMethod (#7644)chainer.get_device to doc (#6831)shape in generate_array (#7576)expand_dims.py (#7608)Links (#7628)BackwardContext comment (#7636, thanks @crcrpar!)FunctionNode docs. (#7659)F.squeeze documentation (#7688)examples/vae/train_vae.py (#7580, thanks @m4saka!)y_shape not used in tests (#7612)GroupNormalization tests (#7700)TestContrastive (#7765)This is the release note of v7.0.0b1. See here for the complete list of solved issues and merged PRs.
Power for ChainerX (#6496, thanks @dido1998!)chainerx.hstack, chainerx.vstack and chainerx.atleast_2d (#6886, thanks @kshitij12345!)TabularDataset (#7115)TabularDataset.concat/join (#7116)chainerx.expm1 and chainerx.exp2 (#7126, thanks @aksub99!)chainerx.log2 (#7139)TabularDataset.{transform/transform_batch} (#7150)chainerx.log1p (#7161, thanks @sky58!)chainerx::AsContiguous as a public C++ API (#7166)chainerx import in debug mode (#7178)chainer.as_array for consistency with chainer.as_variable (#7252, thanks @tkerola!)chainerx.moveaxis (#7265, thanks @kshitij12345!)chainerx.leaky_relu (#7351, thanks @aksub99!)chainerx.dstack and chainerx.atleast_3d (#7353, thanks @kshitij12345!)__abs__ with chainerx.ndarray (#7364)chainerx.erf (#7404, thanks @aksub99!)align_corners option to resize_images (#7429)resize_images (#7443)input_device to StandardUpdater (#7472)is_array_supported method on backend.Device (#7487)roi_max_align_2d and roi_average_align_2d (#6405, thanks @knorth55!)MPI_Status. (#6696, thanks @y1r!)F.copy (#6982)F.batch_renormalization, and related fixes (#7104)Variable.addgrad (#7132)cuda.DummyDevice inheritance (#7147)Device.name property (#7149)Link.serialize to support ChainerX (#7175)Variable.backward (#7196)require_grad() on ChainerX Variable.grad setter (#7198)FunctionNode.unchain and raise error in ChainerX fallback mode (#7216)Variable.copydata (#7226)MultiprocessParallelUpdater to support new devices (#7245)StackVector<int64_t, kMaxNdim> to Dims (#7258)chainerx::{Max,Min}imum (#7261)chx.backward not cause error even if backprop is not required (#7287)None arguments in chainerx.clip and chainerx.ndarray.clip (#7296)chainerx::Where (#7325)F.clip function with None parameter to min/max (#7333)Array::ToNative() (#7394)Variable (#7400)get_device error message when ChainerX is not available (#7401)get_device to raise a more correct error types (#7421)EXEPECT_ARRAY_* macros able to used outside ChainerX (#7434)F.convolution_2d (#7448)F.deconvolution_2d (#7449)F.copy between non-ChainerX and ChainerX devices only if backprop is not required (#7473)FunctionNode ChainerX fallback, reuse ChainerxDevice taken from inputs to create outputs (#7397)F.where (#6872)Bernoulli.log_prob (#7064, thanks @seiyab!)MultiNodeBatchNormalization (#7106)MultiNodeChainList should not assume float32 (#7165)L.Linear when called with n_batch_axes (#7167)L.BatchRenormalization (#7256)F.absolute_error for ChainerX (#7281, thanks @crcrpar!)_values_to_dicts so it works with unicode of python 2 too (#7316)chainerx.square (#7321)WeightDecay aware of loss scale (#7491)GradientMethod ChainerX fallback for uninitialized parameters (#7492)cuda.DummyDevice and cuda.get_device_from_array (#7148)math.cc (#7171)logic.cc (#7176)testing.backend.BackendConfig (#7212)math.cc (#7222)xp when possible (#7234)AMax and AMin to statistics routines (#7269)math.cc (#7270)_ for private classes under chainer.dataset.tabular (#7275)math.cc (#7298)math.cc (#7317)FindCuDNN.cmake (#7419)const& (#7453)cuda_fp16.h instead of cuda_fp16.hpp (#7480)math.h (#7501)AsTypeKernel (#7522, thanks @kshitij12345!)F.normalize documentation (#7062, thanks @crcrpar!)F.copy view behavior (#7135)backend.get_device_from_array (#7163)chainerx.md (#7179)optimizers.MSVAG to documentation (#7183)F.relu in doc (#7188)CommunicatorBase.allgather (#7192)chainer.utils.type_check (#7249, thanks @ktns!)observe_value and observe_lr trigger interval (#7266)robots.txt to allow indexing root (#7306)F.normalize documentation (#7371, thanks @crcrpar!)static_graph.rst (#7389)test_iter.epoch manually in the tutorial of training loop (#7405)shape in generate_array (#7450)tabular_dataset.py (#7495, thanks @nai62!)CUDNN_LIBNAME to be specified by environment variable (#7243)$MAKEFLAGS instead if set in Travis CI script (#7331)FindCuDNN.cmake, prioritize explicit variables over environment variables (#7441)typing == 3.6.6 (#7562)typing requirements (#7564)predict.py) (#7206)PlotReport.available() check in glance example (#7209)reset method in the PTB example (#7533)F.tensordot test (#6968, thanks @ishanrai05!)F.cumprod test (#6978, thanks @hikjik!)F.average test (#6995, thanks @hikjik!)test_cuda.py to backends_tests (#7144)chainerx.swapaxes test (#7184, thanks @kshitij12345!)Variable.grad and Variable.grad_var tests (#7191)Variable.zerograd test (#7199)chainerx.conv and chainerx.conv_transpose (#7203)TestTanh from test_math.py to test_trigonometric_hyperbolic.py (#7207)Variable.copydata test (#7224)CUDA_VISIBLE_DEVICES in ChainerX tests (#7290)chainer.as_array test (#7318)StandardUpdater tests with pytest style assertion (#7326)0 to 0.0 for python2 (#7373)dstack to invalid_shape test (#7457, thanks @kshitij12345!)pytest.mark.xfail instead of unittest.expectedFailure (#7488)