python-bpf/docs/user-guide/compilation.md

Compilation

PythonBPF provides several functions and classes for compiling Python code into BPF bytecode and loading it into the kernel.

Overview

The compilation process transforms Python code into executable BPF programs:

1. Python AST → LLVM IR generation (using llvmlite)
2. LLVM IR → BPF bytecode (using llc)
3. BPF Object → Kernel loading (using libbpf)

Compilation Functions

compile_to_ir()

Compile Python source to LLVM Intermediate Representation.

Signature

def compile_to_ir(filename: str, output: str, loglevel=logging.WARNING)

Parameters

• filename - Path to the Python source file to compile
• output - Path where the LLVM IR file (.ll) should be written
• loglevel - Logging level (default: logging.WARNING)

Usage

from pythonbpf import compile_to_ir
import logging

# Compile to LLVM IR
compile_to_ir(
    filename="my_bpf_program.py",
    output="my_bpf_program.ll",
    loglevel=logging.DEBUG
)

Output

This function generates an .ll file containing LLVM IR, which is human-readable assembly-like code. This is useful for:

• Debugging compilation issues
• Understanding code generation

compile()

Compile Python source to BPF object file.

Signature

def compile(filename: str = None, output: str = None, loglevel=logging.WARNING)

Parameters

• filename - Path to the Python source file (default: calling file)
• output - Path for the output object file (default: same name with .o extension)
• loglevel - Logging level (default: logging.WARNING)

Usage

from pythonbpf import compile
import logging

# Compile current file
compile()

# Compile specific file
compile(filename="my_program.py", output="my_program.o")

# Compile with debug logging
compile(loglevel=logging.DEBUG)

Output

This function generates a .o file containing BPF bytecode that can be:

• Loaded into the kernel
• Inspected with bpftool
• Verified with the BPF verifier
• Distributed as a compiled binary

BPF Class

The BPF class provides a high-level interface to compile, load, and attach BPF programs.

Signature

class BPF:
    def __init__(self, filename: str = None, loglevel=logging.WARNING)
    def load(self)
    def attach_all(self)
    def load_and_attach(self)

Parameters

• filename - Path to Python source file (default: calling file)
• loglevel - Logging level (default: logging.WARNING)

Methods

init()

Create a BPF object and compile the source.

from pythonbpf import BPF

# Compile current file
b = BPF()

# Compile specific file
b = BPF(filename="my_program.py")

load()

Load the compiled BPF program into the kernel.

b = BPF()
b.load()

This method:

• Loads the BPF object file into the kernel
• Creates maps
• Verifies the BPF program
• Returns a BpfObject instance

attach_all()

Attach all BPF programs to their specified hooks.

b = BPF()
b.load()
b.attach_all()

This method:

• Attaches tracepoints
• Attaches kprobes/kretprobes
• Attaches XDP programs
• Enables all hooks

load_and_attach()

Convenience method that loads and attaches in one call.

b = BPF()
b.load_and_attach()

Equivalent to:

b = BPF()
b.load()
b.attach_all()

Complete Example

Here's a complete example showing the compilation workflow:

from pythonbpf import bpf, section, bpfglobal, BPF, trace_pipe
from ctypes import c_void_p, c_int64

@bpf
@section("tracepoint/syscalls/sys_enter_execve")
def trace_exec(ctx: c_void_p) -> c_int64:
    print("Process started")
    return 0

@bpf
@bpfglobal
def LICENSE() -> str:
    return "GPL"

if __name__ == "__main__":
    # Method 1: Simple compilation and loading
    b = BPF()
    b.load_and_attach()
    trace_pipe()

    # Method 2: Step-by-step
    # b = BPF()
    # b.load()
    # b.attach_all()
    # trace_pipe()

    # Method 3: Manual compilation
    # from pythonbpf import compile
    # compile(filename="my_program.py", output="my_program.o")
    # # Then load with pylibbpf directly

Compilation Pipeline Details

AST Parsing

The Python ast module parses your source code:

import ast
tree = ast.parse(source_code, filename)

The AST is then walked to find:

• Functions decorated with @bpf
• Classes decorated with @struct
• Map definitions with @map
• Global variables with @bpfglobal

IR Generation

PythonBPF uses llvmlite to generate LLVM IR:

from llvmlite import ir

# Create module
module = ir.Module(name='bpf_module')
module.triple = 'bpf'

# Generate IR for each BPF function
# ...

Key aspects of IR generation:

• Type conversion (Python types → LLVM types)
• Function definitions
• Map declarations
• Global variable initialization
• Debug information

BPF Compilation

The LLVM IR is compiled to BPF bytecode using llc:

llc -march=bpf -filetype=obj input.ll -o output.o

Kernel Loading

The compiled object is loaded using pylibbpf:

from pylibbpf import BpfObject

obj = BpfObject(path="program.o")
obj.load()

Debugging Compilation

Logging

Enable debug logging to see compilation details:

import logging
from pythonbpf import BPF

b = BPF(loglevel=logging.DEBUG)

This will show:

• AST parsing details
• IR generation steps
• Compilation commands
• Loading status

Inspecting LLVM IR

Generate and inspect the IR file:

from pythonbpf import compile_to_ir

compile_to_ir("program.py", "program.ll")

Then examine program.ll to understand the generated code.

Using bpftool

Inspect compiled objects with bpftool:

# Show program info
bpftool prog show

# Dump program instructions
bpftool prog dump xlated id <ID>

# Dump program JIT code
bpftool prog dump jited id <ID>

# Show maps
bpftool map show

# Dump map contents
bpftool map dump id <ID>

Verifier Errors

If the kernel verifier rejects your program:

• Check dmesg for detailed error messages:

  sudo dmesg | tail -50
  

Compilation Options

Optimization Levels

While PythonBPF doesn't expose optimization flags directly, you can:

1. Manually compile IR with specific flags:

   llc -march=bpf -O2 -filetype=obj program.ll -o program.o
   

2. Modify the compilation pipeline in your code

Debug Information

PythonBPF automatically generates debug information (DWARF) for:

• Function names
• Variable names
• Type information

This helps with:

• Stack traces
• Debugging with bpftool
• Source-level debugging

Working with Compiled Objects

Loading Pre-compiled Objects

You can load previously compiled objects:

from pylibbpf import BpfObject

# Load object file
obj = BpfObject(path="my_program.o")
obj.load()

# Attach programs
# (specific attachment depends on program type)

Distribution

Distribute compiled BPF objects:

1. Compile once:

   from pythonbpf import compile
   compile(filename="program.py", output="program.o")
   

2. Ship program.o file

3. Load on target systems:

   from pylibbpf import BpfObject
   obj = BpfObject(path="program.o")
   obj.load()
   

Version Compatibility

BPF objects are generally compatible across kernel versions, but:

• Some features require specific kernel versions
• Helper functions may not be available on older kernels
• BTF (BPF Type Format) requirements vary

Troubleshooting

Compilation Fails

If compilation fails:

• Check Python syntax
• Verify all decorators are correct
• Ensure type hints are present
• Check for unsupported Python features

Loading Fails

If loading fails:

• Check dmesg for verifier errors
• Verify LICENSE is set correctly
• Ensure helper functions are valid
• Check map definitions

Programs Don't Attach

If attachment fails:

• Verify section names are correct
• Check that hooks exist on your kernel
• Ensure you have sufficient permissions
• Verify kernel version supports the feature

Next Steps

• Learn about {doc}helpers for available BPF helper functions
• Explore {doc}maps for data storage
• See {doc}decorators for compilation markers