By Christian Stigen Larsen

Posted 08 Nov 2017 — updated 11 Nov 2017

In this post I'll show how to write a rudimentary, native x86-64 just-in-time compiler (JIT) in CPython, using only the built-in modules.

Update: This post made the front page of HN, and I've incorporated some of the discussion feedback. I've also written a follow-up post that JITs Python bytecode to x86-64.

The code here specifically targets the UNIX systems macOS and Linux, but should be easily translated to other systems such as Windows. The complete code is available on github.com/cslarsen/minijit.

The goal is to generate new versions of the below assembly code at runtime and execute it.

48 b8 ed ef be ad de movabs $0xdeadbeefed, %rax 00 00 00 48 0f af c7 imul %rdi,%rax c3 retq

We will mainly deal with the left hand side — the byte sequence 48 b8 ed ... and so on. Those fifteen machine code bytes comprise an x86-64 function that multiplies its argument with the constant 0xdeadbeefed . The JIT step will create functions with different such constants. While being a contrived form of specialization, it illuminates the basic mechanics of just-in-time compilation.

Our general strategy is to rely on the built-in ctypes Python module to load the C standard library. From there, we can access system functions to interface with the virtual memory manager. We'll use mmap to fetch a page-aligned block of memory. It needs to be aligned for it to become executable. That's the reason why we can't simply use the usual C function malloc , because it may return memory that spans page boundaries.

The function mprotect will be used to mark the memory block as read-only and executable. After that, we should be able to call into our freshly compiled block of code through ctypes.

The boiler-plate part

Before we can do anything, we need to load the standard C library.

import ctypes import sys if sys.platform.startswith("darwin"): libc = ctypes.cdll.LoadLibrary("libc.dylib") # ... elif sys.platform.startswith("linux"): libc = ctypes.cdll.LoadLibrary("libc.so.6") # ... else: raise RuntimeError("Unsupported platform")

There are other ways to achieve this, for example

>>> import ctypes >>> import ctypes.util >>> libc = ctypes.CDLL(ctypes.util.find_library("c")) >>> libc <CDLL '/usr/lib/libc.dylib', handle 110d466f0 at 103725ad0>

To find the page size, we'll call sysconf(_SC_PAGESIZE) . The _SC_PAGESIZE constant is 29 on macOS but 30 on Linux. We'll just hard-code those in our program. You can find them by digging into system header files or writing a simple C program that print them out. A more robust and elegant solution would be to use the cffi module instead of ctypes, because it can automatically parse header files. However, since I wanted to stick to the default CPython distribution, we'll continue using ctypes.

We need a few additional constants for mmap and friends. They're just written out below. You may have to look them up for other UNIX variants.

import ctypes import sys if sys.platform.startswith("darwin"): libc = ctypes.cdll.LoadLibrary("libc.dylib") _SC_PAGESIZE = 29 MAP_ANONYMOUS = 0x1000 MAP_PRIVATE = 0x0002 PROT_EXEC = 0x04 PROT_NONE = 0x00 PROT_READ = 0x01 PROT_WRITE = 0x02 MAP_FAILED = -1 # voidptr actually elif sys.platform.startswith("linux"): libc = ctypes.cdll.LoadLibrary("libc.so.6") _SC_PAGESIZE = 30 MAP_ANONYMOUS = 0x20 MAP_PRIVATE = 0x0002 PROT_EXEC = 0x04 PROT_NONE = 0x00 PROT_READ = 0x01 PROT_WRITE = 0x02 MAP_FAILED = -1 # voidptr actually else: raise RuntimeError("Unsupported platform")

Although not strictly required, it is very useful to tell ctypes the signature of the functions we'll use. That way, we'll get exceptions if we mix invalid types. For example

# Set up sysconf sysconf = libc.sysconf sysconf.argtypes = [ctypes.c_int] sysconf.restype = ctypes.c_long

tells ctypes that sysconf is a function that takes a single integer and produces a long integer. After this, we can get the current page size with

pagesize = sysconf(_SC_PAGESIZE)

The machine code we are going to generate will be interpreted as unsigned 8-bit bytes, so we need to declare a new pointer type:

# 8-bit unsigned pointer type c_uint8_p = ctypes.POINTER(ctypes.c_uint8)

Below we just dish out the remaining signatures for the functions that we'll use. For error reporting, it's good to have the strerror function available. We'll use munmap to destroy the machine code block after we're done with it. It lets the operating system reclaim that memory.

strerror = libc.strerror strerror.argtypes = [ctypes.c_int] strerror.restype = ctypes.c_char_p mmap = libc.mmap mmap.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_int, ctypes.c_int, ctypes.c_int, # Below is actually off_t, which is 64-bit on macOS ctypes.c_int64] mmap.restype = c_uint8_p munmap = libc.munmap munmap.argtypes = [ctypes.c_void_p, ctypes.c_size_t] munmap.restype = ctypes.c_int mprotect = libc.mprotect mprotect.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_int] mprotect.restype = ctypes.c_int

At this point, it's hard to justify using Python rather than C. With C, we don't need any of the above boiler-plate code. But down the line, Python will allow us to experiment much more easily.

Now we're ready to write the mmap wrapper.

def create_block(size): ptr = mmap(0, size, PROT_WRITE | PROT_READ, MAP_PRIVATE | MAP_ANONYMOUS, 0, 0) if ptr == MAP_FAILED: raise RuntimeError(strerror(ctypes.get_errno())) return ptr

This function uses mmap to allocate page-aligned memory for us. We mark the memory region as readable and writable with the PROT flags, and we also mark it as private and anonymous. The latter means the memory will not be visible from other processes and that it will not be file-backed. The Linux mmap manual page covers the details (but be sure to view the man page for your system). If the mmap call fails, we raise it as a Python error.

To mark memory as executable,

def make_executable(block, size): if mprotect(block, size, PROT_READ | PROT_EXEC) != 0: raise RuntimeError(strerror(ctypes.get_errno()))

With this mprotect call, we mark the region as readable and executable. If we wanted to, we could have made it writable as well, but some systems will refuse to execute writable memory. This is sometimes called the W^X security feature.

To destroy the memory block, we'll use

def destroy_block(block, size): if munmap(block, size) == -1: raise RuntimeError(strerror(ctypes.get_errno()))

I edited out a badly placed del in that function after the HN submission.

The fun part

Now we're finally ready to create an insanely simple piece of JIT code!

Recall the assembly listing at the top: It's a small function — without a local stack frame — that multiplies an input number with a constant. In Python, we'd write that as

def create_multiplication_function(constant): return lambda n: n * constant

This is indeed a contrived example, but qualifies as JIT. After all, we do create native code at runtime and execute it. It's easy to imagine more advanced examples such as JIT-compiling Brainfuck to x86-64 machine code. Or using AVX instructions for blazing fast, vectorized math ops.

The disassembly at the top of this post was actually generated by compiling and disassembling the following C code:

#include <stdint.h> uint64_t multiply(uint64_t n) { return n*0xdeadbeefedULL; }

If you want to compile it yourself, use something like

$ gcc -Os -fPIC -shared -fomit-frame-pointer \ -march=native multiply.c -olibmultiply.so

Here I optimized for space ( -Os ) to generate as little machine code as possible, with position-independent code ( -fPIC ) to prevent using absolute jumps, without any frame pointers ( -fomit-frame-pointer ) to remove superfluous stack setup code (but it may be required for more advanced functions) and using the current CPU's native instruction set ( -march=native ).

We could have passed -S to produce a disassembly listing, but we're actually interested in the machine code, so we'll rather use a tool like objdump :

$ objdump -d libmultiply.so ... 0000000000000f71 <_multiply>: f71: 48 b8 ed ef be ad de movabs $0xdeadbeefed,%rax f78: 00 00 00 f7b: 48 0f af c7 imul %rdi,%rax f7f: c3 retq

In case you are not familiar with assembly, I'll let you know how this function works. First, the movabs function just puts an immediate number in the RAX register. Immediate is assembly-jargon for encoding something right in the machine code. In other words, it's an embedded argument for the movabs instruction. So RAX now holds the constant 0xdeadbeefed .

Also — by AMD64 convention — the first integer argument will be in RDI, and the return value in RAX. So RDI will hold the number to multiply with. That's what imul does. It multiplies RAX and RDI and puts the result in RAX. Finally, we pop a 64-bit return address off the stack and jump to it with RETQ. At this level, it's easy to imagine how one could implement continuation-passing style.

Note that the constant 0xdeadbeefed is in little-endian format. We need to remember to do the same when we patch the code. (By the way, a good mnemonic for remembering the word order is that little endian means "little-end first").

We are now ready to put everything in a Python function.

def make_multiplier(block, multiplier): # Encoding of: movabs <multiplier>, rax block[0] = 0x48 block[1] = 0xb8 # Little-endian encoding of multiplication constant block[2] = (multiplier & 0x00000000000000ff) >> 0 block[3] = (multiplier & 0x000000000000ff00) >> 8 block[4] = (multiplier & 0x0000000000ff0000) >> 16 block[5] = (multiplier & 0x00000000ff000000) >> 24 block[6] = (multiplier & 0x000000ff00000000) >> 32 block[7] = (multiplier & 0x0000ff0000000000) >> 40 block[8] = (multiplier & 0x00ff000000000000) >> 48 block[9] = (multiplier & 0xff00000000000000) >> 56 # Encoding of: imul rdi, rax block[10] = 0x48 block[11] = 0x0f block[12] = 0xaf block[13] = 0xc7 # Encoding of: retq block[14] = 0xc3 # Return a ctypes function with the right prototype function = ctypes.CFUNCTYPE(ctypes.c_uint64) function.restype = ctypes.c_uint64 return function

At the bottom, we return the ctypes function signature to be used with this code. It's somewhat arbitrarily placed, but I thought it was good to keep the signature close to the machine code.

The final part

Now that we have the basic parts we can weave everything together. The first part is to allocate one page of memory:

pagesize = sysconf(_SC_PAGESIZE) block = create_block(pagesize)

Next, we generate the machine code. Let's pick the number 101 to use as a multiplier.

mul101_signature = make_multiplier(block, 101)

We now mark the memory region as executable and read-only:

make_executable(block, pagesize)

Take the address of the first byte in the memory block and cast it to a callable ctypes function with proper signature:

address = ctypes.cast(block, ctypes.c_void_p).value mul101 = mul101_signature(address)

To get the memory address of the block, we use ctypes to cast it to a void pointer and extract its value. Finally, we instantiate an actual function from this address using the mul101_signature constructor.

Voila! We now have a piece of native code that we can call from Python. If you're in a REPL, you can try it directly:

>>> print(mul101(8)) 808

Note that this small multiplication function will run slower than a native Python calculation. That's mainly because ctypes, being a foreign-function library, has a lot of overhead: It needs to inspect what dynamic types you pass the function every time you call it, then unbox them, convert them and then do the same with the return value. So the trick is to either use assembly because you have to access some new Intel instruction, or because you compile something like Brainfuck to native code.

Finally, if you want to, you can let the system reclaim the memory holding the function. Beware that after this, you will probably crash the process if you try calling the code again. So probably best to delete all references in Python as well:

destroy_block(block, pagesize) del block del mul101

If you run the code in its complete form from the GitHub repository, you can put the multiplication constant on the command line:

$ python mj.py 101 Pagesize: 4096 Allocating one page of memory JIT-compiling a native mul-function w/arg 101 Making function block executable Testing function OK mul(0) = 0 OK mul(1) = 101 OK mul(2) = 202 OK mul(3) = 303 OK mul(4) = 404 OK mul(5) = 505 OK mul(6) = 606 OK mul(7) = 707 OK mul(8) = 808 OK mul(9) = 909 Deallocating function

Debugging JIT-code

If you want to continue learning with this simple program, you'll quickly want to disassemble the machine code you generate. One option is to simply use gdb or lldb, but you need to know where to break. One trick is to just print the hex value of the block address and then wait for a keystroke:

print("address: 0x%x" % address) print("Press ENTER to continue") raw_input()

Then you just run the program in the debugger, break into the debugger while the program is pausing, and disassemble the memory location. Of course you can also step-debug through the assembly code if you want to see what's going on. Here's an example lldb session:

$ lldb python ... (lldb) run mj.py 101 ... (lldb) c Process 19329 resuming ... address 0x1002fd000 Press ENTER to continue

At this point, hit CTRL+C to break back into the debugger, then disassemble from the memory location:

(lldb) x/3i 0x1002fd000 0x1002fd000: 48 b8 65 00 00 00 00 00 00 00 movabsq $0x65, %rax 0x1002fd00a: 48 0f af c7 imulq %rdi, %rax 0x1002fd00e: c3 retq

Notice that 65 hex is 101 in decimal, which was the command line argument we passed above.

If you only want a disassembler inside Python, I recommend the Capstone module.

What's next?

A good exercise would be to JIT-compile Brainfuck programs to native code. If you want to jump right in, I've made a GitHub repository at github.com/cslarsen/brainfuck-jit. I even have a Speaker Deck presentation to go with it. It performs JIT-ing and optimizations, but uses GNU Lightning to compile native code instead of this approach. It should be extremely simple to boot out GNU Lightning in favor or some code generation of your own. An interesting note on the Brainfuck project is that if you just JIT-compile each Brainfuck instruction one-by-one, you won't get much of a speed boost, even if you run native code. The entire speed boost is done in the code optimization stage, where you can bulk up integer operations into one or a few x86 instructions. Another candidate for such compilation would be the Forth language.

Also, before you get serious about expanding this JIT-compiler, take a look at the PeachPy project. It goes way beyond this and includes a disassembler and supports seemingly the entire x86-64 instruction set right up to AVX.

As mentioned, there is a good deal of overhad when using ctypes to call into functions. You can use the cffi module to overcome some of this, but the fact remains that if you want to call very small JIT-ed functions a large number of times, it's usually faster to just use pure Python.

What other cool uses are there? I've seen some math libraries in Python that switch to vector operations for higher performance. But I can imagine other fun things as well. For example, tools to compress and decompress native code, access virtualization primitives, sign code and so on. I do know that some BPF tools and regex modules JIT-compile queries for faster processing.

What I think is fun about this exercise is to get into deeper territory than pure assembly. One thing that comes to mind is how different instructions are disassembled to the same mnemonic. For example, the RETQ instruction has a different opcode than an ordinary RET, because it operates on 64-bit values. This is something that may not be important when doing assembly programming, because it's a detail that may not always matter, but it's worth being aware of the difference. I saw that gcc, lldb and objdump gave slightly different disassembly listings of the same code for RETQ and MOVABSQ.

There's another takeaway. I've mentioned that the native Brainfuck compiler I made didn't initially produce very fast code. I had to optimize to get it fast. So things won't go fast just because you use AVX, Cuda or whatever. The cold truth is that gcc contains a vast database of optimizations that you cannot possibly replicate by hand. Felix von Letiner has a classic talk about source code optimization that I recommend for more on this.

What about actual compilation?

A few people commented that they had expected to see more about the actual compilation step. Fair point. As it stands, this is indeed a very restricted form of compilation, where we barely do anything with the code at runtime — we just patch in a constant. I may write a follow-up post that focuses solely on the compilation stage. Stay tuned!