This is a list of changes introduced in each release that users should be aware of when migrating from older versions. Most changes are carefully designed not to break existing code; however changes that may possibly break them are highlighted with a box.
Introduction of Backend Namespace¶
chainer.backends subpackage for future support of various backend libraries other than NumPy and CuPy.
By this change,
chainer.cuda module is now moved to
This does not break the existing code; you can safely continue to use
from chainer import cuda) but it is now encouraged to use
from chainer.backends import cuda instead.
Namespace Changes for Updaters¶
chainer.training.ParallelUpdater are now moved to
chainer.training.updaters.ParallelUpdater respectively, to align with the namespace convention of other subpackages.
See the discussion in #2982 for more details.
This change does not break the existing code; you can safely continue to use updater classes directly under
chainer.training but it is now encouraged to use
Prohibition of Mixed Use of Arrays on Different Devices in Function Arguments¶
Argument validation of functions is now strictened to check device consistency of argument variables to provide better error messages to users. Suppose the following code:
v1 = chainer.Variable(np.arange(10, dtype=np.float32)) # CPU v2 = chainer.Variable(cupy.arange(10, dtype=cupy.float32)) # GPU # The line below raises an exception, because arguments are on different device. F.maximum(v1, v2)
Prior to v4, the above code raises an exception like
ValueError: object __array__ method not producing an array, which was difficult to understand.
In v4, the error message would become
TypeError: incompatible array types are mixed in the forward input (Maximum).
This kind of error usually occurs by mistake (for example, not performing
to_gpu for some variables).
As the argument validation is strictened, call of functions intentionally mixing NumPy/CuPy arrays in arguments will not work in Chainer v4. Please transfer all arrays to the same device before calling functions.
References to Function Nodes Not Retained in TimerHook and CupyMemoryProfilerHook¶
To reduce memory consumption, references to the function nodes will no longer be retained in the
See the discussion in #4300 for more details.
The existing code using function nodes retained in
call_history attribute of these hooks will not work.
The first element of
call_history became the name of the function, instead of the function node instance itself.
You can define your own function hook if you need to access the function node instances.
Update of Docker Images¶
Chainer official Docker images (see Installation for details) are now updated to use CUDA 8.0 and cuDNN 6.0. This change was introduced because CUDA 7.5 does not support NVIDIA Pascal GPUs.
To use these images, you may need to upgrade the NVIDIA driver on your host. See Requirements of nvidia-docker for details.
Introduction of New-style Functions¶
Many of Functions are already migrated to new-style, although some of functions are still old-style (classes inheriting from
We are going to migrate more old-style functions to new-style in upcoming minor releases.
This does not break the existing code.
Old-style functions (classes inheriting from
Function) are still supported in v3 and future versions of Chainer.
If you are going to write new functions, it is encouraged to use
FunctionNode to support double backward.
Users relying on undocumented function APIs (directly instantiating old-style classes) may experience an error like
TypeError: 'SomeFunction' object is not callable after upgrading to v3.
Please use the function APIs documented in Functions.
Changed Behavior of matmul Function¶
The existing code using
chainer.functions.matmul() may require modification to work with Chainer v3.
Removed use_cudnn Argument in spatial_transformer_grid and spatial_transformer_sampler Functions¶
The existing code using
use_cudnn argument of
chainer.functions.spatial_transformer_sampler() require modification to work with Chainer v3.
Please use the configuration context (e.g.,
with chainer.using_config('use_cudnn', 'auto'):) to enable or disable use of cuDNN.
See Configuring Chainer for details.