Monday, 4 May 2009

Progress on Native Client

Back in January I wrote that I was porting glibc to Native Client. I have made some good progress since then.

The port is at the stage where it can run simple statically-linked and dynamically-linked executables, from the command line and from the web browser.

In particular, Python works. I have put together a simple interactive top-level that demonstrates running Python from the web browser:

Upstream NaCl doesn't support any filename-based calls such as open(), but we do. In this setup, open() does not of course access the real filesystem on the host system. open() in NaCl-glibc sends a message to the Javascript code on the web page. The Javascript code can fetch the requested file, create a file descriptor for the file using the plugin's API, and send the file descriptor to the NaCl subprocess in reply.

This has not involved modifying Python at all, although I have added an extension module to wrap a couple of NaCl-specific system calls (imc_sendmsg() and imc_recvmsg()). The Python build runs to completion, including the parts where it runs the executable it builds. A simple instruction-rewriting trick means that dynamically-linked NaCl executables can run outside of NaCl, directly under Linux, linked against the regular Linux glibc. This means we can avoid some of the problems associated with cross-compilation when building for NaCl.

This work has involved extending Native Client:

  • Adding support for dynamic loading of code. Initially I have focused on just making it work. Now I'll focus on ensuring this is secure.
  • Writing a custom browser plugin for NaCl which allows Javascript code to handle asynchronous callbacks from NaCl subprocesses.
  • Making various changes to the NaCl versions of gcc and binutils to support dynamically linked code.
Hopefully these changes can get merged upstream eventually. Some of the toolchain changes have already gone in.

See the web page for instructions on how to get the code and try it out.

Saturday, 18 April 2009

Python variable binding semantics, part 2

Time for me to follow up my previous blog post and explain the code snippets.

Snippet 1

funcs = []
for x in (1, 2, 3):
    funcs.append(lambda: x)
for f in funcs:
    print f()
Although you might expect this Python code snippet to print "1 2 3", it actually prints "3 3 3". The reason for this is that the same mutable variable binding for x is used across all iterations of the loop. The for loop does not create new variable bindings.

The function closures that are created inside the loop do not capture the value of x; they capture the cell object that x is bound to under the hood (or the globals dictionary if this code snippet is run at the top level), and each iteration mutates the same cell object.

Interestingly, Python does not restrict the target of the for loop to be a variable or tuple of variables. It can be any lvalue expression that can appear on the left hand side of an assignment, including indexing expressions. For example:

x = [10, 20]
for x[0] in (1,2,3):
    pass
print x # Prints [3, 20]

Snippet 2

This problem is Python's variable binding semantics is not specific to lambdas and also occurs if you use def.
funcs = []
for x in (1, 2, 3):
    def myfunc():
        return x
    funcs.append(myfunc)
for f in funcs:
    print f()
This also prints "3 3 3".

Snippet 3

The remaining snippets are examples of ways to work around this problem. They print the desired "1 2 3".

One way is to use default arguments:

funcs = []
for x in (1, 2, 3):
    funcs.append(lambda x=x: x)
for f in funcs:
    print f()
This is actually the trick that was used to get the effect of lexical scoping before lexical scoping was added to Python.

The default argument captures the value of x at the point where the function closure is created, and this value gets rebound to x when the function is called.

Although it is concise, I'm not too keen on this workaround. It is an abuse of default arguments. There is a risk that you accidentally pass too many arguments to the function and thereby override the captured value of x.

Snippet 4

funcs = []
for x in (1, 2, 3):
    def g(x):
        funcs.append(lambda: x)
    g(x)
for f in funcs:
    print f()
This is quite an ugly workaround, but it's perhaps the most general one. The loop body is wrapped in a function. The x inside function g and the x outside are statically different variable bindings. A new mutable binding of x is created on every call to g, but this binding is never modified.

Snippets 5 and 6

The remaining two snippets are attempts to make the code less ugly, by giving names to the intermediate functions that are introduced to rebind x and thereby give the desired behaviour.
funcs = []
def append_a_func(x):
    funcs.append(lambda: x)
for x in (1, 2, 3):
    append_a_func(x)
for f in funcs:
    print f()

def make_func(x):
    return lambda: x
funcs = []
for x in (1, 2, 3):
    funcs.append(make_func(x))
for f in funcs:
    print f()
I suppose there is a downside to de-uglifying the code. If the code looks too normal, there's a risk that someone will refactor it to the incorrect version without testing that it works properly when looping over lists containing multiple items.

Can the language be fixed?

The root cause is that Python does away with explicit variable binding declarations. If a variable is assigned within a function -- including being assigned as the target of a for loop -- it becomes local to the function, but variables are never local to smaller statement blocks such as a loop body. Python doesn't have block scoping. There are a couple of naive ways in which you might try to fix the language so that the code does what you would expect, both of which have problems. We could change the semantics of function closure creation so that closures take a snapshot of variables' values. But this would break mutually recursive function definitions, and cases where functions are referred to before they are defined, such as:
def f(): # This "def" would raise an UnboundLocalError or a NameError
    g()
def g():
    pass
We could change the semantics of for loops so that the target variable is given a variable binding that is local to the loop body. But this wouldn't change the status of variable bindings created by assignment, so this code would still have the unwanted behaviour:
for x in (1, 2, 3):
    y = x
    funcs.append(lambda: y)

Linting

It should be possible to create a lint tool to detect this hazard. It could look for closures that capture variables that are assigned by or in loops. I wonder how many false positives it would give on a typical codebase.

Javascript

Javascript suffers from the same problem:
funcs = [];
for(var x = 0; x < 3; x++) {
    funcs.push(function() { return x; })
}
for(var i = 0; i < funcs.length; i++) {
    print(funcs[i]());
}
This code prints "3 3 3" when run using rhino.

In Javascript's case the affliction is entirely gratuitous. It happens because var declarations are implicitly hoisted up to the top of the function, for no apparent reason. It is gratuitous because this is not the result of a trade-off.

The good news, though, is that the problem is recognised and a fix to Javascript is planned. I think the plan is for Ecmascript 3.1 to introduce a let declaration as an alternative to var without the hoisting behaviour.

List comprehensions

Back to Python. The same problem also applies to list comprehensions and generator expressions.
funcs = [(lambda: x) for x in (1, 2, 3)]
for f in funcs:
    print f()

funcs = list((lambda: x) for x in (1, 2, 3))
for f in funcs:
    print f()

These both print "3 3 3".

(Note that I added brackets around the lambdas for clarity but the syntax does not require them.)

This is forgivable for list comprehensions, at least in Python 2.x, because the assignment to x escapes into the surrounding function. (See my earlier post.)

But for generators (and for list comprehensions in Python 3.0), the scope of x is limited to the comprehension. Semantically, it would be easy to limit the scope of x to within a loop iteration, so that each iteration introduces a new variable binding.

Tuesday, 31 March 2009

Python variable binding semantics

What do the six following chunks of code do, and what would you like them to do? Which do you prefer?
funcs = []
for x in (1, 2, 3):
    funcs.append(lambda: x)
for f in funcs:
    print f()

funcs = []
for x in (1, 2, 3):
    def myfunc():
        return x
    funcs.append(myfunc)
for f in funcs:
    print f()

funcs = []
for x in (1, 2, 3):
    funcs.append(lambda x=x: x)
for f in funcs:
    print f()

funcs = []
for x in (1, 2, 3):
    def g(x):
        funcs.append(lambda: x)
    g(x)
for f in funcs:
    print f()

funcs = []
def append_a_func(x):
    funcs.append(lambda: x)
for x in (1, 2, 3):
    append_a_func(x)
for f in funcs:
    print f()

def make_func(x):
    return lambda: x
funcs = []
for x in (1, 2, 3):
    funcs.append(make_func(x))
for f in funcs:
    print f()

Sunday, 22 February 2009

OpenStreetMap

I tried out OpenStreetMap the other day after reading the recent article about it on LWN. Amazingly it looks pretty complete for the parts of the UK and US that I looked at. I don't know how much comes from people gathering data by travelling around or by entering data from satellite photos or out-of-copyright maps, but whichever of these it is, it's very impressive.

It is also better than Google Maps in some ways:

  • It has more details: it shows footpaths, rivers and streams, wooded areas, and paths across parks. It has outlines of interesting buildings where people have entered data for them.
  • I think the default renderer (Mapnik) looks better than the Google Maps equivalent, especially when zoomed out to a level where streets are one pixel thick but still distinguishable. Google Maps gives too much prominence to the major roads -- it renders them thickly, in bright colours, and with large labels, which tends to drown out the details. Mapnik is more subtle. The map just looks more interesting.
  • It uses more of the browser window and doesn't waste as much space on sidebars. It's almost a trivial point, but it makes a difference.

Friday, 16 January 2009

Testing using golden files in Python

This is the third post in a series about automated testing in Python (earlier posts: 1, 2). This post is about testing using golden files.

A golden test is a fancy way of doing assertEquals() on a string or a directory tree, where the expected output is kept in a separate file or files -- the golden files. If the actual output does not match the expected output, the test runner can optionally run an interactive file comparison tool such as Meld to display the differences and allow you to selectively merge the differences into the golden file.

This is useful when

  • the data being checked is large - too large to embed into the Python source; or
  • the data contains relatively inconsequential details, such as boilerplate text or formatting, which might be changed frequently.
As an example, suppose you have code to format some data as HTML. You can have a test which creates some example data, formats this as HTML, and compares the result to a golden file. Something like this:
class ExampleTest(golden_test.GoldenTestCase):

    def test_formatting_html(self):
        obj = make_some_example_object()
        temp_dir = self.make_temp_dir()
        format_as_html(obj, temp_dir)
        self.assert_golden(temp_dir, os.path.join(os.path.dirname(__file__),
                                                  "golden-files"))

if __name__ == "__main__":
    golden_test.main()
By default, the test runs non-interactively, which is what you want on a continuous integration machine, and it will print a diff if it fails. To switch on the semi-interactive mode which runs Meld, you run the test with the option --meld.

Here is a simple version of the test helper (taken from here):

import os
import subprocess
import sys
import unittest

class GoldenTestCase(unittest.TestCase):

    run_meld = False

    def assert_golden(self, dir_got, dir_expect):
        assert os.path.exists(dir_expect), dir_expect
        proc = subprocess.Popen(["diff", "--recursive", "-u", "-N",
                                 "--exclude=.*", dir_expect, dir_got],
                                stdout=subprocess.PIPE)
        stdout, stderr = proc.communicate()
        if len(stdout) > 0:
            if self.run_meld:
                # Put expected output on the right because that is the
                # side we usually edit.
                subprocess.call(["meld", dir_got, dir_expect])
            raise AssertionError(
                "Differences from golden files found.\n"
                "Try running with --meld to update golden files.\n"
                "%s" % stdout)
        self.assertEquals(proc.wait(), 0)

def main():
    if sys.argv[1:2] == ["--meld"]:
        GoldenTestCase.run_meld = True
        sys.argv.pop(1)
    unittest.main()
(It's a bit cheesy to modify global state on startup to enable melding, but because unittest doesn't make it easy to pass parameters into tests this is the simplest way of doing it.)

Golden tests have the same sort of advantages that are associated with test-driven development in general.

  • Golden files are checked into version control and help to make changesets self-documenting. A changeset that affects the program's output will include patches that demonstrate how the output is affected. You can see the history of the program's output in version control. (This assumes that everyone runs the tests before committing!)
  • Sometimes you can point people at the golden files if they want to see example output. For HTML, sometimes you can contrive the CSS links to work so that the HTML looks right when viewed in a browser.
  • And of course, this can catch cases where you didn't intend to change the program's output.
My typical workflow is to add a test with some example input that I want the program to handle, and run the test with --meld until the output I want comes up on the left-hand side in Meld. I mark the output as OK by copying it over to the right-hand side. This is not quite test-first, because I am letting the test suggest what its expected output should be. But it is possible to do this in an even more test-first manner by typing the output you expect into the right-hand side.

Other times, one change will affect many locations in the golden files, and adding a new test is not necessary. It's usually not too difficult to quickly eyeball the differences with Meld.

Here are some of the things that I have used golden files to test:

  • formatting of automatically-generated e-mails
  • automatically generated configuration files
  • HTML formatting logs (build_log_test.py and its golden files)
  • pretty-printed output of X Windows messages in xjack-xcb (golden_test.py and golden_test_data). This ended up testing several components in one go:
    • the XCB protocol definitions for X11
    • the encoders and decoders that work off of the XCB protocol definitions
    • the pretty printer for the decoder
On the other hand, sometimes the overhead of having a separate file isn't worth it, and if the expected output is small (maybe 5 lines or so) I might instead paste it into the Python source as a multiline string.

It can be tempting to overuse golden tests. As with any test suite, avoid creating one big example that tries to cover all cases. (This is particularly tempting if the test is slow to run.) Try to create smaller, separate examples. The test helper above is not so good at this, because if you are not careful it can end up running Meld several times. In the past I have put several examples into (from unittest's point of view) one test case, so that it runs Meld only once.

Golden tests might not work so well if components can be changed independently. For example, if your XML library changes its whitespace pretty-printing, the tests' output could change. This is less of a problem if your code is deployed with tightly-controlled versions of libraries, because you can just update the golden files when you upgrade libraries.

A note on terminology: I think I got the term "golden file" from my previous workplace, where other people were using them, and the term seems to enjoy some limited use judging from Google. "Golden test", however, may have been a term that I have made up and that no-one else outside my workplace is using for this meaning.

Sunday, 11 January 2009

On ABI and API compatibility

If you are going to create a new execution environment, such as a new OS, it can make things simpler if it presents the same interfaces as an existing system. It makes porting easier. So,
  • If you can, keep the ABI the same.
  • If you can't keep the ABI the same, at least keep the API the same.

Don't be tempted to say "we're changing X; we may as well take this opportunity to change Y, which has always bugged me". Only change things if there is a good reason.

For an example, let's look at the case of GNU Hurd.

  • In principle, the Hurd's glibc could present the same ABI as Linux's glibc (they share the same codebase, after all), but partly because of a different in threading libraries, they were made incompatible. Unifying the ABIs was planned, but it appears that 10 years later it has not happened (Hurd has a libc0.3 package instead of libc6).

    Using the same ABI would have meant that the same executables would work on Linux and the Hurd. Debian would not have needed to rebuild all its packages for a separate "hurd-i386" architecture. It would have saved a lot of effort.

    I suspect that if glibc were ported to the Hurd today, it would not be hard to make the ABIs the same. The threading code has changed a lot in the intervening time. I think it is cleaner now.

  • The Hurd's glibc also changed the API: they decided not to define PATH_MAX. The idea was that if there was a program that used fixed-length buffers for storing filenames, you'd be forced to fix it. Well, that wasn't a good idea. It just created unnecessary work. Hurd developers and users had enough on their plates without having to fix unrelated implementation quality issues in programs they wanted to use.
Similarly, it would help if glibc for NaCl (Native Client) could have the same ABI as glibc for Linux. Programs have to be recompiled for NaCl with nacl-gcc to meet the requirements of NaCl's validator, but could the resulting code still run directly on Linux, linked with the regular i386 Linux glibc and other libraries? The problem here is that code compiled for NaCl will expect all code addresses to be 32-byte-aligned. If the NaCl code is given a function address or a return address which is not 32-byte-aligned, things will go badly wrong when it tries to jump to it. Some background: In normal x86 code, to return from a function you write this:
        ret
This pops an address off the stack and jumps to it. In NaCl, this becomes:
        popl %ecx
        and $0xffffffe0, %ecx
        jmp *%ecx
This pops an address off the stack, rounds it down to the nearest 32 byte boundary and jumps to it. If the calling function's call instruction was not placed at the end of a 32 byte block (which NaCl's assembler will arrange), the return address will not be aligned and this code will jump to the wrong location.

However, there is a way around this. We can get the NaCl assembler and linker to keep a list of all the places where a forcible alignment instruction (the and $0xffffffe0, %ecx above) was inserted, and put this list into the executable or library in a special section or segment. Then when we want to run the executable or library directly on Linux, we can rewrite all these locations so that the sequence above becomes

        popl %ecx
        nop
        nop
        jmp *%ecx
or maybe even just
        ret
        nop
        nop
        nop
        nop
        nop
We can reuse the relocations mechanism to store these rewrites. The crafty old linker already does something similar for thread-local variable accesses. When it knows that a thread-local variable is being accessed from the library where it is defined, it can rewrite the general-purpose-but-slow instruction sequence for TLS variable access into a faster instruction sequence. The general purpose instruction sequence even contains nops to allow for rewriting to the slightly-longer fast sequence.

This arrangement for running NaCl-compiled code could significantly simplify the process of building and testing code when porting it to NaCl. It can help us avoid the difficulties associated with cross-compiling.

Sunday, 4 January 2009

What does NaCl mean for Plash?

Google's Native Client (NaCl), announced last month, is an ingenious hack to get around the problem that existing OSes don't provide adequate security mechanisms for sandboxing native code.

You can look at NaCl as an interesting combination of OS-based and language-based security mechanisms:

  • NaCl uses a code verifier to prevent use of unsafe instructions such as those that perform system calls. This is not a million miles away from programming language subsets like Cajita and Joe-E, except that it operates at the level of x86 instructions rather than source code.

    Since x86 instructions are variable-length and unaligned, NaCl has to stop you from jumping into an unsafe instruction hidden in the middle of a safe instruction. It does that by requiring that all indirect jumps are jumps to the start of 32-byte-aligned blocks; instructions are not allowed to straddle these blocks.

  • It uses the x86 architecture's little-used segmentation feature to limit memory accesses to a range of address space. So the processor is doing bounds checking for free.

    Actually, segmentation has been used before - in L4 and EROS's "small spaces" facility for switching between processes with small address spaces without flushing the TLB. NaCl gets the same benefit: switching between trusted and untrusted code should be fast; faster than trapping system calls with ptrace(), for example.

Plash is also a hack to get sandboxing, specifically on Linux, but it has some limitations:
  1. it doesn't block network access;
  2. it doesn't limit CPU and memory resource usage, so sandboxed programs can still cause denial of service;
  3. it requires a custom glibc, which can be a pain to build;
  4. it changes the API/ABI that sandboxed programs see in some small but significant ways:
    • some syscalls are effectively disabled; programs must go through libc for these calls, which stops statically linked programs from working;
    • /proc/self doesn't work, and Plash's architecture makes it hard to emulate /proc.
Interface changes mean some programs require patching, e.g. to not depend on /proc. If there were more people behind Plash, these interface changes wouldn't be a big problem. These problems can be addressed, with work. But Plash hasn't really caught on, so the manpower isn't there.

NaCl also breaks the ABI - it breaks it totally. Code must be recompiled. However, NaCl provides bigger benefits in return. It allows programs to be deployed in new contexts: on Windows; in a web browser. It is more secure than Plash, because it can block network access and limit the amount of memory a process can allocate. Also, because NaCl mediates access more completely, it would be easier to emulate interfaces like /proc.

NaCl isn't only useful as a browser plugin. We could use it as a general purpose OS security mechanism. We could have GNU/Linux programs running on Windows (without the Linux bit).

Currently NaCl does not support all the features you'd need in a modern OS. In particular, dynamic linking. NaCl doesn't yet support loading code beyond an initial statically linked ELF executable. But we can add this. I am making a start at porting glibc, along with its dynamic linker. After all, I have ported glibc once before!