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Wasmer Python embedding 1.0

Announcing the immediate availability of the Wasmer Python embedding 1.0 version!

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Syrus Akbary

Founder & CEO

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January 28, 2021

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After the release of Wasmer 1.0, we are thrilled to announce the immediate availability of the Wasmer Python embedding 1.0 version.

Time flies! It’s been a little bit less than 2 years since we first released wasmer, the Wasmer embedding for Python. Thanks to a vibrant and awesome community, this project has been greatly welcomed! After 1.8 million installations in the wild, it’s our great pleasure to introduce the 1.0 version with a stable API, lightning performances, cross-compilation, three compilers, two engines, and many more advanced features. Let’s dig in!

Light by default

By default, the wasmer package comes as light as possible, the rest are opt-in features. wasmer provides by default:

  • the entire Wasmer runtime,
  • 2 headless engines.

What’s an engine? An engine is responsible to drive the compilation from a WebAssembly module to executable code, and the execution of this executable code. Note that the engine is also responsible to store the executable code somewhere, along with serializing and deserializing it when caching the compilation result. The Wasmer Python embedding comes with the JIT engine (wasmer.engine.JIT, the default) and the Native engine (wasmer.engine.Native).

In a nutshell, the JIT engine stores the executable code in memory, whilst the Native engine stores the executable code in a native shared library object (.so, .dylib or .dll files depending on the Operating System where it runs).

Because the wasmer package comes with zero compilers, the engines are said headless. In other words, the engines aren’t able to compile, they can only execute an already-compiled WebAssembly module.

Why headless engines are the default? Because it’s super light, and it’s the common path. The wasmer wheel is around 1.5Mb without the compilers, or around 15Mb with all of them. It obviously impacts the startup time, and the required space in memory. It also makes it possible to fit wasmer in very small boards (thinking of the Internet of Things). Or more basically, it speeds up installation and setup. See a graph below comparing all the wheel sizes.

Pick your best compiler on the fly

As we said in the previous section, the wasmer package comes with headless engines. What if we want to compile a WebAssembly module to executable code? Then we need to install a compiler. We provide 3 of them as standalone packages:

  1. wasmer-compiler-singlepass, the Singlepass compiler provides a super fast compilation time, and a slower execution time. It is not prone to JIT-bombs. It’s ideal for blockchains.
  2. wasmer-compiler-cranelift, the Cranelift compiler provides a fast compilation time, and a fast execution time. It’s ideal for development.
  3. wasmer-compiler-llvm, the LLVM compiler provides a slow compilation time, but a very fast execution time, close to native. It’s ideal for production.

An example is worth a thousand words.

# Import types from the `wasmer` package.
# Also import one compiler, here Cranelift.
from wasmer import engine, Store, Module, Instance
from wasmer_compiler_cranelift import Compiler

# Let's define the store, that holds the engine, that holds the compiler.
store = Store(engine.JIT(Compiler))

# Let's compile and instantiate the module.
module = Module(store, open('my_program.wasm', 'rb').read())
instance = Instance(module)

# Call the exported `sum` function naturally.
result = instance.exports.sum(5, 37)

assert result == 42

Notice how everything fits together nicely. Now let’s imagine we have serialized the compiled module into a file as such:

serialized_module_file.write(module.serialize())

One can simply execute the WebAssembly compiled-module with no compiler, only with a headless engine as follows:

# Import types from the `wasmer` package.
from wasmer import engine, Store, Instance, Module

store = Store(engine.JIT()) # we don't pass a compiler!

# Deserialize the compiled WebAssembly module, and instantiate it.
module = Module.deserialize(store, serialized_module_file.read())
instance = Instance(module)

result = instance.exports.sum(1, 2)

assert result == 3

Et voilà ! It works with no compiler this time.

We believe that this flexibility is a game changer since it can address everyone’s needs while keeping the Wasmer runtime light and fast.

LLVM infrastructure just for you, pypi

A (funny?) side note we wanted to share. We did write, from scratch, an entire build system to produce custom LLVM builds specifically designed for pypi. Indeed, the default LLVM pre-builts provided by the LLVM project themselves aren’t compatible with pypi binary wheels policy. The most geeky of you might want to take a look at it: it’s really simple but so useful. Feel free to fork the project.

Wheel size

A wheel is a Python package. It’s what a user downloads and installs when running pip install wasmer for example.

We thought it would be interesting to see how light the wheels are now. To do that, we have fetch the size of the:

  • wasmer wheel alone (with headless engines),
  • wasmer + wasmer-compiler-singlepass wheels,
  • wasmer + wasmer-compiler-cranelift wheels,
  • wasmer + wasmer-compiler-llvm wheels.

We have computed those sizes for 3 platforms (macOS, Linux, and Windows) and 2 architectures (amd64 and aarch64). (On Linux aarch64, the wasmer-compiler-singlepass wheel is missing because it does not support (yet) aarch64. On Windows amd64, the wasmer-compiler-llvm wheel is missing due to a temporary issue, it should be fixed quickly.)

The results are presented in the following graph:

Size of wheels (in Mb); lower is better.

On average, the wasmer wheel has a size of 1.4Mb; the wasmer + wasmer-compiler-singlepass wheels have a size of 2Mb; the wasmer + wasmer-compiler-cranelift wheels have a size of 2.8Mb; the wasmer + wasmer-compiler-llvm wheels have a size of 13Mb.

Improved and simplified API

Between the 0.4 and the 1.0 version, we have rewritten the entire project with a new, improved API. The new API is as close as possible to the Wasmer runtime API (the original one, written in Rust), so that it reduces the cognitive effort for new users to switch from one project to another.

All the WebAssembly externals are now supported, which includes Function, Global, Memory, and Table. All of them can be used as imports or as exports. Well, this is now straighforward.

To simplify the declaration of imports, we provide an ImportObject API. It works as follows:

from wasmer import engine, wat2wasm, Store, Module, ImportObject, Function, FunctionType, Type, Instance
from wasmer_compiler_cranelift import Compiler

# Let's declare the Wasm module with the text representation.
# If this module was written in Rust, it would have been:
#
# ```rs
# extern "C" {
#     fn sum(x: i32, y: i32) -> i32;
# }
#
# #[no_mangle]
# pub extern "C" fn add_one(x: i32) -> i32 {
#     unsafe { sum(x, 1) }
# }
# ```
wasm_bytes = wat2wasm(
    """
    (module
      (import "env" "sum" (func $sum (param i32 i32) (result i32)))
      (func (export "add_one") (param $x i32) (result i32)
        local.get $x
        i32.const 1
        call $sum))
    """
)

# Create a store.
store = Store(engine.JIT(Compiler))

# Let's compile the WebAssembly module.
module = Module(store, wasm_bytes)

# When creating an `Instance`, we can pass an `ImportObject`. All
# entities that must be imported are registered inside the
# `ImportObject`.
import_object = ImportObject()

# Let's write the Python function that is going to be imported,
# i.e. called by the WebAssembly module.
def sum(x: int, y: int) -> int:
    return x + y

# The type of `sum` is infered from the Python declaration.
# It can be specified as the third argument of `Function`
# otherwise (see the documentation).
sum_host_function = Function(store, sum)

# Now let's register the `sum` import inside the `env` namespace.
import_object.register(
    "env",
    {
        "sum": sum_host_function,
    }
)

# Let's instantiate the module!
instance = Instance(module, import_object)

# And finally, call the `add_one` exported function!
assert instance.exports.add_one(41) == 42

Again, this is straighforward and brings no surprise. This is one new feature amongst many others.

Faster memory operations

The Memory class (which represents an imported or an exported memory) provided views, which were classes like Uint8View, Int8View, etc. up to Int32View, through the memory.uint8_view() method and siblings. These view classes were useful to read from and write into a WebAssembly memory. Even if those operations weren’t slow, they could be faster. Let’s introduce Buffer! A buffer is generated by the memory.buffer getter. It implements the Python buffer protocol, so it is possible to read and write bytes with the bytes, bytearray, or memoryview standard functions.

Here is quick benchmark to show how the Buffer improves performance when reading 1 and 255 bytes from memory (the lower the better):

Reading 1 and 255 bytes in memory (in ns).

When reading 1 byte, views are 2 times slower than Buffer with memoryview or bytearray. When reading 255 bytes, views are 16 times slower than Buffer. We consider this result as a non-negligible improvement.

Cross-compilation

The compilers come with a great feature: They support cross-compilation. It means that from an amd64 (x86_64), we can compile for an aarch64 (ARM64) machine, or from Linux to macOS etc. For example:

from wasmer import engine, target, Store, Module

# Let's define the target “triple”. Historically, such things had
# three fields, though additional fields have been added over time.
triple = target.Triple('x86_64-linux-musl')

# Let's define the CPU features.
cpu_features = target.CpuFeatures()
cpu_features.add('sse2')

# Let's build the target!
target = target.Target(triple, cpu_features)

# And now, let's define the store with its engine.
store = Store(engine.JIT(Compiler, target))

# Finally, compile the WebAssembly module for this target.
module = Module(store, wasm_byte)

assert isinstance(module, Module)

What’s next? Serialize and send your WebAssembly compiled module on its host mate, where a potentially Wasmer with headless engines is waiting despairingly.

We believe that cross-compilation will help users to pre-compile their WebAssembly modules on a robust dedicated machine, and then deploy them in various environments like a breath.

WASI

The wasmer package now features WASI (The WebAssembly System Interface), with all the snapshot previews; understand, all the versions.

Basically, the wasi sub-package helps to configure an ImportObject object that is passed to Instance. Let’s see how it works with an example. We want to execute this Rust program, that prints its arguments, its environment variables, and that lists the content of its current working directory.

from wasmer import engine, wasi, Store, Module, ImportObject, Instance
from wasmer_compiler_cranelift import Compiler

# As usual, let's fetch some bytes and compile the module.
wasm_bytes = open('wasi.wasm', 'rb').read()
store = Store(engine.JIT(Compiler))
module = Module(store, wasm_bytes)

# First, let's extract the WASI version from the module. Why? Because
# WASI exists in multiple versions, and it doesn't work the
# same way. So, to ensure compatibility, we need to know the version.
wasi_version = wasi.get_version(module, strict=True)

# Second, create a `wasi.Environment`. It contains everything related
# to WASI. To build such an environment, we must use the
# `wasi.StateBuilder`.
#
# In this case, we specify the program name: `wasi_test_program`. We
# also specify the program is invoked with the `--test` argument, in
# addition to two environment variables: `COLOR` and
# `APP_SHOULD_LOG`. Finally, we map the `the_host_current_dir` to the
# current directory. There it is:
wasi_env =
    wasi.StateBuilder('wasi_test_program'). \
        argument('--test'). \
        environment('COLOR', 'true'). \
        environment('APP_SHOULD_LOG', 'false'). \
        map_directory('the_host_current_dir', '.'). \
        finalize()

# From the WASI environment, we generate a custom import object. Why?
# Because WASI is, from the user perspective, a bunch of
# imports. Consequently `generate_import_object`… generates a
# pre-configured import object.
#
# Do you remember when we said WASI has multiple versions? Well, we
# need the WASI version here!
import_object = wasi_env.generate_import_object(store, wasi_version)

# Now we can instantiate the module.
instance = Instance(module, import_object)

# The entry point for a WASI WebAssembly module is a function named
# `_start`. Let's call it and see what happens!
instance.exports._start()

This program aboves prints the following data on stdout:

Found program name: `wasi_test_program`
Found 1 arguments: --test
Found 2 environment variables: COLOR=true, APP_SHOULD_LOG=false
Found 1 preopened directories: DirEntry("/the_host_current_dir")

3 instructions and WASI is configured, neat!

Ready to use on major platforms and architectures

One of the promises of WebAssembly is its universality. By design, it aims at being run anywhere.

To fulfill this promise, we provide more than 57 wheels for various platforms and architectures, including Linux, Darwin (macOS) and Windows, and amd64 (Intel) along with aarch64 (ARM). All our packages work with Python 3.6 up to 3.9.

Documentation and examples

We take pride in making our projects really polished. As we did with the previous releases, we spent a lot of time polishing the user experience.

We have written extensive documentation for our users. It contains more than 50 examples, all of them illustrating how to use specific functions or classes. We focused on providing comfortable navigation in documentation for beginners, with hints and useful links. All the examples can be copy-pasted into a Python shell, and they will all work as is.

In addition to this documentation, we provide a set of more detailed examples. A document lists and explains all of them. One can search by keywords to find appropriate examples faster. Every example is extensively documented to explain every detail to the user.

We believe this kind of documentation and examples will help users to get onboard and be familiar with WebAssembly more quickly.

Discovering compiler capabilities with a benchmark

Designing benchmarks is hard, so we designed a benchmark that only shows why we provide multiple compilers and engines. The benchmark illustrates how the compilers address different needs when the balance between compilation-time or execution-time varies.

Because benchmarks are not our best way to party, we tried to make them fun. The idea is to execute JavaScript inside Python, via wasmer and QuickJS compiled as a WebAssembly module (qjs.wasm). We don’t execute something fancy, just console.log('hello'). Because too much fun is not always good.

Benchmarks are run inside an Ubuntu virtual machine, with 4 CPU cores and 4Gb of memory; the host has an Intel Xeon CPU (3.10Ghz). It’s not really important for what we are going to illustrate.

So first, let’s compile the 2.4Mb qjs.wasm WebAssembly module with our Singlepass, Cranelift and LLVM compilers. Times are given in microseconds (μs).

Create a Module with the JIT engine + the Cranelift, LLVM and Singlepass compilers (in μs).

The results are the following. wasmer with the Singlepass compiler is the fastest to compile qjs.wasm in 102ms. wasmer with the Cranelift compiler is the second in 295ms. Finally wasmer with the LLVM compiler in 5252ms (5sec).

We can’t conclude anything based on that, except that if a compiler takes time, it’s likely because it optimises the generated code heavily, which could mean that the execution time will be improved.

Because it’s expensive to compile a module into executable code, we want to cache it. And compilation should ideally be always done once. So let’s measure how long it takes to get a Module from a pre-compiled WebAssembly module. We want to see if there is a difference between a module that has been compiled with the Singlepass, Cranelift or LLVM compiler. We are going to use a headless engine here, so the wasmer package alone with no compiler.

Create a Module with the JIT headless engine (module is pre-compiled) (in μs).

The results are the following. wasmer with the LLVM compiler is the fastest to rebuild a Module from a pre-compiled module in 9.8ms, following by  — head-to-head — wasmer with the Cranelift and the Singlepass compilers in 11.5ms.

That result is interesting. LLVM is the slower compiler to compile a WebAssembly module, but once compiled it’s faster to load the module! That’s a good news.

Take a breath. Up to now, we have successfully compiled qjs.wasm to executable code. So let’s use it! As we said, we are going to execute console.log('hello'). It requires WASI, so it’s a nice real-world test. Let’s see how they perform.

Executing console.log("hello") with qjs.wasm, compiled with the Cranelift, LLVM and Singlepass compilers (in μs).

First, wasmer is successfully able to print hello on the standard output. This string was printed by JavaScript, through WebAssembly, inside Python. How fun is that :-)?

Second, we see interesting results. wasmer with the LLVM compiler is by far the faster to execute qjs.wasm. It was compile-time well spent! It prints hello in JavaScript in 0.219ms. wasmer with the Cranelift compiler prints hello in 0.485ms. Finally, wasmer with the Singlepass compiler prints hello in 0.560ms. It’s very likely that a more computation intensive test would uncover the difference between Cranelift and Singlepass deeper.

This benchmark confirms what we said earlier:

  • The Singlepass compiler provides a fast compilation but a slower execution, which can be perfect for small/simple WebAssembly modules,
  • The Cranelift compiler provides a good balance between compilation and execution time,
  • The LLVM compiler provides a slow but optimised compilation, and a very fast execution, which is perfect for complex WebAssembly modules.

The fact that wasmer comes with headless engines (with no compiler) is helpful, as rebuilding a Module from a pre-compiled module is really useful.

In the case of wasmer with an already pre-compiled module with the LLVM compiler, it takes 9.8ms for the full startup + 0.2ms for the execution, for a total of 10ms to print hello.

Conclusion

The 1.0 version testifies of an API and features stability and maturity. We believe that the new design with the two headless engines, and the compilers as standalone packages, is a great improvement and provides more flexibility. It allows installing wasmer in more devices.

With the help of the new cross-compilation API, we believe that it’s easier than ever to execute WebAssembly anywhere.

Performance has been improved. WASI is now supported, up to the latest version (up to wasi_snapshot_preview1 at the time of writing).

Documentation and examples have been meticulously written to help users new to WebAssembly, or to help advanced users. We believe it will facilitate the usage of WebAssembly in the Python ecosystem.

What’s next?

Nonetheless, the wasmer Python package does not cover all the latest WebAssembly features (for example, the multi-value proposal is implemented, but the reference type proposal is still under implementation). Be reassured though, it’s very likely that your usual compiler (like rustc) does not support those in-progress proposals yet, so it’s not a blocker. But we still have work!

Test it!

The packages are available on pypi:

Wheels for aarch64 aren’t published on pypi (because it doesn’t fulfill the manylinux2010 policy regarding binary wheels, see the PEP 571). They are freely downloadable from the Github release.

Join a community of more than 1000 Python and WebAssembly passionate developers!

About the Author

Syrus Akbary is an enterpreneur and programmer. Specifically known for his contributions to the field of WebAssembly. He is the Founder and CEO of Wasmer, an innovative company that focuses on creating developer tools and infrastructure for running WebAss

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Syrus Akbary

Founder & CEO

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