kbuild: rust: keep Rust objects out of Clang LTO with inline helpersgrafted

Under `CONFIG_LTO_CLANG` + `CONFIG_RUST_INLINE_HELPERS`, one may hit
`objtool` errors such as:

    vmlinux.o: error: objtool: _R..._3Gsp4boot+0xd6a:
    can't find jump dest instruction at .text._R..._3Gsp4boot+0x1dfd

The reason is that in such builds, the Clang invocation that compiles
the combined Rust plus helpers bitcode emits LLVM bitcode (again) --
the final code generation happens in the linker's LTO step, which the
`-mllvm` trap options passed to Clang do not reach.

This, in turn, means that unreachable traps are missing, and the
impossible paths do not merely fallthrough to the next symbol, but past
the end of their own section, since LTO builds place each function in
its own section.

Thus filter `CC_FLAGS_LTO` out of the Clang invocation, so that it always
emits machine code directly, with the traps in place.

Assisted-by: LLM
Cc: Gary Guo <gary@garyguo.net>
Cc: Boqun Feng <boqun@kernel.org>
Cc: Alice Ryhl <aliceryhl@google.com>
Cc: Matthew Maurer <mmaurer@google.com>
Cc: Josh Poimboeuf <jpoimboe@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: stable@vger.kernel.org
Fixes: 3a2486cc1da5 ("kbuild: rust: provide an option to inline C helpers into Rust")
Acked-by: Gary Guo <gary@garyguo.net>
Link: https://patch.msgid.link/20260816133233.197500-2-ojeda@kernel.org
Signed-off-by: Miguel Ojeda <ojeda@kernel.org>

1 files changed, 34 insertions, 0 deletions

diff --git a/drivers/accel/Kconfig b/drivers/accel/Kconfig
new file mode 100644
index 000000000..bdf48ccaf
--- /dev/null
+++ b/drivers/accel/Kconfig
@@ -0,0 +1,34 @@
+# SPDX-License-Identifier: GPL-2.0-only
+#
+# Compute Acceleration device configuration
+#
+# This framework provides support for compute acceleration devices, such
+# as, but not limited to, Machine-Learning and Deep-Learning acceleration
+# devices
+#
+if DRM
+
+menuconfig DRM_ACCEL
+	bool "Compute Acceleration Framework"
+	help
+	  Framework for device drivers of compute acceleration devices, such
+	  as, but not limited to, Machine-Learning and Deep-Learning
+	  acceleration devices.
+	  If you say Y here, you need to select the module that's right for
+	  your acceleration device from the list below.
+	  This framework is integrated with the DRM subsystem as compute
+	  accelerators and GPUs share a lot in common and can use almost the
+	  same infrastructure code.
+	  Having said that, acceleration devices will have a different
+	  major number than GPUs, and will be exposed to user-space using
+	  different device files, called accel/accel* (in /dev, sysfs
+	  and debugfs).
+
+source "drivers/accel/amdxdna/Kconfig"
+source "drivers/accel/ethosu/Kconfig"
+source "drivers/accel/habanalabs/Kconfig"
+source "drivers/accel/ivpu/Kconfig"
+source "drivers/accel/qaic/Kconfig"
+source "drivers/accel/rocket/Kconfig"
+
+endif