treewide: refresh kmalloc_obj() conversionsgrafted

This is another run of the Coccinelle script for converting kmalloc()
family of allocations to kmalloc_obj() via the existing rules in
scripts/coccinelle/api/kmalloc_objs.cocci

This catches both the set of kmalloc() uses added since the first
kmalloc_obj() conversions in v7.0 and adds a large group missed in the
first pass due to Coccinelle not interacting well with the cleanup.h
scoped_...() family of macros[1]. I worked around this with spatch's
"--macro-file" argument to a file with all the scoped_...() macros mapped
to Coccinelle's YACFE_ITERATOR[2] as that was the closest viable control
flow indicator I could find.

Build tested allmodconfig on x86, arm64, arm, loongarch, mips, powerpc,
riscv, and s390 with no new warnings.

Link: https://lore.kernel.org/lkml/202609021314.8A9C0B8@keescook/ [1]
Link: https://github.com/coccinelle/coccinelle/blob/master/standard.h [2]
Signed-off-by: Kees Cook <kees+treewide@kernel.org>

1 files changed, 187 insertions, 0 deletions

diff --git a/Documentation/dev-tools/propeller.rst b/Documentation/dev-tools/propeller.rst
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+.. SPDX-License-Identifier: GPL-2.0
+
+=====================================
+Using Propeller with the Linux kernel
+=====================================
+
+This enables Propeller build support for the kernel when using Clang
+compiler. Propeller is a profile-guided optimization (PGO) method used
+to optimize binary executables. Like AutoFDO, it utilizes hardware
+sampling to gather information about the frequency of execution of
+different code paths within a binary. Unlike AutoFDO, this information
+is then used right before linking phase to optimize (among others)
+block layout within and across functions.
+
+A few important notes about adopting Propeller optimization:
+
+#. Although it can be used as a standalone optimization step, it is
+   strongly recommended to apply Propeller on top of AutoFDO,
+   AutoFDO+ThinLTO or Instrument FDO. The rest of this document
+   assumes this paradigm.
+
+#. Propeller uses another round of profiling on top of
+   AutoFDO/AutoFDO+ThinLTO/iFDO. The whole build process involves
+   "build-afdo - train-afdo - build-propeller - train-propeller -
+   build-optimized".
+
+#. Propeller requires LLVM 19 release or later for Clang/Clang++
+   and the linker(ld.lld).
+
+#. In addition to LLVM toolchain, Propeller requires a profiling
+   conversion tool: https://github.com/google/llvm-propeller.
+
+Current supported architectures include x86/X86_64 (via LBR),
+and arm64 (via SPE).
+
+The Propeller optimization process involves the following steps:
+
+#. Initial building: Build the AutoFDO or AutoFDO+ThinLTO binary as
+   you would normally do, but with a set of compile-time / link-time
+   flags, so that a special metadata section is created within the
+   kernel binary. The special section is only intend to be used by the
+   profiling tool, it is not part of the runtime image, nor does it
+   change kernel run time text sections.
+
+#. Profiling: The above kernel is then run with a representative
+   workload to gather execution frequency data. This data is collected
+   using hardware sampling, via perf. Propeller is most effective on
+   platforms supporting advanced PMU features like LBR on Intel
+   machines. This step is the same as profiling the kernel for AutoFDO
+   (the exact perf parameters can be different).
+
+#. Propeller profile generation: Perf output file is converted to a
+   pair of Propeller profiles via an offline tool.
+
+#. Optimized build: Build the AutoFDO or AutoFDO+ThinLTO optimized
+   binary as you would normally do, but with a compile-time /
+   link-time flag to pick up the Propeller compile time and link time
+   profiles. This build step uses 3 profiles - the AutoFDO profile,
+   the Propeller compile-time profile and the Propeller link-time
+   profile.
+
+#. Deployment: The optimized kernel binary is deployed and used
+   in production environments, providing improved performance
+   and reduced latency.
+
+Preparation
+===========
+
+Configure the kernel with::
+
+   CONFIG_AUTOFDO_CLANG=y
+   CONFIG_PROPELLER_CLANG=y
+
+Customization
+=============
+
+The default CONFIG_PROPELLER_CLANG setting covers kernel space objects
+for Propeller builds. One can, however, enable or disable Propeller build
+for individual files and directories by adding a line similar to the
+following to the respective kernel Makefile:
+
+- For enabling a single file (e.g. foo.o)::
+
+   PROPELLER_PROFILE_foo.o := y
+
+- For enabling all files in one directory::
+
+   PROPELLER_PROFILE := y
+
+- For disabling one file::
+
+   PROPELLER_PROFILE_foo.o := n
+
+- For disabling all files in one directory::
+
+   PROPELLER__PROFILE := n
+
+
+Workflow
+========
+
+Here is an example workflow for building an AutoFDO+Propeller kernel:
+
+1) Assuming an AutoFDO profile is already collected following
+   instructions in the AutoFDO document, build the kernel on the host
+   machine, with AutoFDO and Propeller build configs ::
+
+      CONFIG_AUTOFDO_CLANG=y
+      CONFIG_PROPELLER_CLANG=y
+
+   and ::
+
+      $ make LLVM=1 CLANG_AUTOFDO_PROFILE=<autofdo-profile-name>
+
+2) Install the kernel on the test machine.
+
+3) Run the load tests. The '-c' option in perf specifies the sample
+   event period. We suggest using a suitable prime number, like 500009,
+   for this purpose.
+
+   - For Intel platforms::
+
+      $ perf record -e BR_INST_RETIRED.NEAR_TAKEN:k -a -N -b -c <count> -o <perf_file> -- <loadtest>
+
+   - For AMD platforms::
+
+      $ perf record --pfm-event RETIRED_TAKEN_BRANCH_INSTRUCTIONS:k -a -N -b -c <count> -o <perf_file> -- <loadtest>
+
+   - For arm64 with SPE::
+     There are a few kernel features that must be enabled to collect SPE profiles on Arm.
+     Below is a list of the required features:
+
+      - CONFIG_ARM_SPE_PMU=y
+      - CONFIG_PID_IN_CONTEXTIDR=y
+      - kpti=off
+
+     Use the following command to generate SPE perf data file::
+
+      $ perf record -e 'arm_spe_0/branch_filter=1,load_filter=0,store_filter=0/' -a -N -c <count> --no-switch-events -o <perf_file> -- <loadtest>
+
+     Note you can repeat the above steps to collect multiple <perf_file>s.
+
+4) (Optional) Download the raw perf file(s) to the host machine.
+
+5) Use the generate_propeller_profiles tool (https://github.com/google/llvm-propeller) to
+   generate Propeller profile. ::
+
+      $ generate_propeller_profiles \
+             --binary=<vmlinux> --profile=<perf_file> \
+             --format=propeller --propeller_output_module_name \
+             --out=<propeller_profile_prefix>_cc_profile.txt \
+             --propeller_symorder=<propeller_profile_prefix>_ld_profile.txt
+
+   "<propeller_profile_prefix>" can be something like "/home/user/dir/any_string".
+
+   This command generates a pair of Propeller profiles:
+   "<propeller_profile_prefix>_cc_profile.txt" and
+   "<propeller_profile_prefix>_ld_profile.txt".
+
+   If there are more than 1 perf_file collected in the previous step,
+   you can create a temp list file "<perf_file_list>" with each line
+   containing one perf file name and run::
+
+      $ generate_propeller_profiles \
+             --binary=<vmlinux> --profile=@<perf_file_list> \
+             --format=propeller --propeller_output_module_name \
+             --out=<propeller_profile_prefix>_cc_profile.txt \
+             --propeller_symorder=<propeller_profile_prefix>_ld_profile.txt
+
+   For arm64 SPE, add the option '--profiler=perf_spe', like::
+
+      $ generate_propeller_profiles  \
+             --binary=<vmlinux> --profile=<perf_file> \
+             --profiler=perf_spe \
+             --format=propeller --propeller_output_module_name \
+             --out=<propeller_profile_prefix>_cc_profile.txt \
+             --propeller_symorder=<propeller_profile_prefix>_ld_profile.txt
+
+6) Rebuild the kernel using the AutoFDO and Propeller
+   profiles. ::
+
+      CONFIG_AUTOFDO_CLANG=y
+      CONFIG_PROPELLER_CLANG=y
+
+   and ::
+
+      $ make LLVM=1 CLANG_AUTOFDO_PROFILE=<profile_file> CLANG_PROPELLER_PROFILE_PREFIX=<propeller_profile_prefix>