iio: proximity: vl53l0x-i2c: claim direct mode for raw readsgrafted

vl53l0x_read_raw() starts a single-shot ranging measurement and reads
back the result. Once the triggered buffer is enabled the sensor runs in
continuous mode and its data-ready interrupt is routed to the trigger,
so a concurrent in_distance_raw read disturbs the streaming setup and
never gets its completion, returning -ETIMEDOUT.

The original submission claimed direct mode here, but it was dropped
during review because the driver had no buffer support at the time [1].
Continuous (buffered) mode was later added without restoring the
claim [2], reintroducing the conflict.

Reject direct reads while buffered capture is active by claiming direct
mode around the measurement, as the vl53l1x sibling already does.

Fixes: 762186c6e7b1 ("iio: proximity: vl53l0x-i2c: Added continuous mode support")
Link: https://lore.kernel.org/linux-iio/20180911160300.GA9212@himanshu-Vostro-3559/ [1]
Link: https://lore.kernel.org/linux-iio/20240909101508.263085-3-abhashkumarjha123@gmail.com/ [2]
Signed-off-by: Donggeun Yoo <donggeunyoo.kernel@gmail.com>
Cc: stable@vger.kernel.org
Signed-off-by: Jonathan Cameron <jonathan.cameron@oss.qualcomm.com>

1 files changed, 110 insertions, 0 deletions

diff --git a/Documentation/accel/introduction.rst b/Documentation/accel/introduction.rst
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+.. SPDX-License-Identifier: GPL-2.0
+
+============
+Introduction
+============
+
+The Linux compute accelerators subsystem is designed to expose compute
+accelerators in a common way to user-space and provide a common set of
+functionality.
+
+These devices can be either stand-alone ASICs or IP blocks inside an SoC/GPU.
+Although these devices are typically designed to accelerate
+Machine-Learning (ML) and/or Deep-Learning (DL) computations, the accel layer
+is not limited to handling these types of accelerators.
+
+Typically, a compute accelerator will belong to one of the following
+categories:
+
+- Edge AI - doing inference at an edge device. It can be an embedded ASIC/FPGA,
+  or an IP inside a SoC (e.g. laptop web camera). These devices
+  are typically configured using registers and can work with or without DMA.
+
+- Inference data-center - single/multi user devices in a large server. This
+  type of device can be stand-alone or an IP inside a SoC or a GPU. It will
+  have on-board DRAM (to hold the DL topology), DMA engines and
+  command submission queues (either kernel or user-space queues).
+  It might also have an MMU to manage multiple users and might also enable
+  virtualization (SR-IOV) to support multiple VMs on the same device. In
+  addition, these devices will usually have some tools, such as profiler and
+  debugger.
+
+- Training data-center - Similar to Inference data-center cards, but typically
+  have more computational power and memory b/w (e.g. HBM) and will likely have
+  a method of scaling-up/out, i.e. connecting to other training cards inside
+  the server or in other servers, respectively.
+
+All these devices typically have different runtime user-space software stacks,
+that are tailored-made to their h/w. In addition, they will also probably
+include a compiler to generate programs to their custom-made computational
+engines. Typically, the common layer in user-space will be the DL frameworks,
+such as PyTorch and TensorFlow.
+
+Sharing code with DRM
+=====================
+
+Because this type of devices can be an IP inside GPUs or have similar
+characteristics as those of GPUs, the accel subsystem will use the
+DRM subsystem's code and functionality. i.e. the accel core code will
+be part of the DRM subsystem and an accel device will be a new type of DRM
+device.
+
+This will allow us to leverage the extensive DRM code-base and
+collaborate with DRM developers that have experience with this type of
+devices. In addition, new features that will be added for the accelerator
+drivers can be of use to GPU drivers as well.
+
+Differentiation from GPUs
+=========================
+
+Because we want to prevent the extensive user-space graphic software stack
+from trying to use an accelerator as a GPU, the compute accelerators will be
+differentiated from GPUs by using a new major number and new device char files.
+
+Furthermore, the drivers will be located in a separate place in the kernel
+tree - drivers/accel/.
+
+The accelerator devices will be exposed to the user space with the dedicated
+261 major number and will have the following convention:
+
+- device char files - /dev/accel/accel\*
+- sysfs             - /sys/class/accel/accel\*/
+- debugfs           - /sys/kernel/debug/accel/\*/
+
+Getting Started
+===============
+
+First, read the DRM documentation at Documentation/gpu/index.rst.
+Not only it will explain how to write a new DRM driver but it will also
+contain all the information on how to contribute, the Code Of Conduct and
+what is the coding style/documentation. All of that is the same for the
+accel subsystem.
+
+Second, make sure the kernel is configured with CONFIG_DRM_ACCEL.
+
+To expose your device as an accelerator, two changes are needed to
+be done in your driver (as opposed to a standard DRM driver):
+
+- Add the DRIVER_COMPUTE_ACCEL feature flag in your drm_driver's
+  driver_features field. It is important to note that this driver feature is
+  mutually exclusive with DRIVER_RENDER and DRIVER_MODESET. Devices that want
+  to expose both graphics and compute device char files should be handled by
+  two drivers that are connected using the auxiliary bus framework.
+
+- Change the open callback in your driver fops structure to accel_open().
+  Alternatively, your driver can use DEFINE_DRM_ACCEL_FOPS macro to easily
+  set the correct function operations pointers structure.
+
+External References
+===================
+
+email threads
+-------------
+
+* `Initial discussion on the New subsystem for acceleration devices <https://lore.kernel.org/lkml/CAFCwf11=9qpNAepL7NL+YAV_QO=Wv6pnWPhKHKAepK3fNn+2Dg@mail.gmail.com/>`_ - Oded Gabbay (2022)
+* `patch-set to add the new subsystem <https://lore.kernel.org/lkml/20221022214622.18042-1-ogabbay@kernel.org/>`_ - Oded Gabbay (2022)
+
+Conference talks
+----------------
+
+* `LPC 2022 Accelerators BOF outcomes summary <https://airlied.blogspot.com/2022/09/accelerators-bof-outcomes-summary.html>`_ - Dave Airlie (2022)