wrap-torch2jax 0.7.1
Wrap your PyTorch for JAX! This package allows no-copy PyTorch calling from JAX under both eager execution and JIT.
Requires Python: >=3.7

### **NOTE: `wrap_torch2jax` is a pip alias for `torch2jax`**

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---

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# torch2jax

[Documentation](https://rdyro.github.io/torch2jax/)

<a href="https://rdyro.github.io/torch2jax/">
<p align="center">
<img src="images/torch2jax_logo2.png" style="max-width:800px;width:70%;display:block;margin-left:auto;margin-right:auto"/>
</p>
</a>
<br />


This package is designed to facilitate no-copy PyTorch calling from JAX under
both eager execution and JIT. It leverages the JAX C++ extension interface,
enabling operations on both CPU and GPU platforms. Moreover, it allows for
executing arbitrary PyTorch code from JAX under eager execution and JIT.

The intended application is efficiently running existing PyTorch code (like ML
models) in JAX applications with very low overhead.

This project was inspired by the jax2torch repository
[https://github.com/lucidrains/jax2torch](https://github.com/lucidrains/jax2torch)
and has been made possible due to an amazing tutorial on extending JAX
[https://github.com/dfm/extending-jax](https://github.com/dfm/extending-jax).
Comprehensive JAX documentation
[https://github.com/google/jax](https://github.com/google/jax) also
significantly contributed to this work.

Although I am unsure this functionality could be achieved without C++/CUDA, the
C++ compilation is efficiently done using PyTorch's portable CUDA & C++
compilation features, requiring minimal configuration.

# Install

```bash
$ pip install git+https://github.com/rdyro/torch2jax.git
```

`torch2jax` is now available on PyPI under the alias `wrap_torch2jax`:

```bash
$ pip install wrap-torch2jax
$ # then
$ python3
$ >>> from wrap_torch2jax import torch2jax, torch2jax_with_vjp
```

Tested on:
  - CPU: Python: `3.9 3.10 3.11 3.12` & JAX Versions `0.4.26 0.4.27 0.4.28 0.4.29 0.4.30 0.4.31`
  - CUDA: Python `3.9 3.10 3.11 3.12` & JAX Versions `0.4.30 0.4.31`

# Usage

With a single output

```python
import torch
import jax
from jax import numpy as jnp
from wrap_torch2jax import torch2jax  # this converts a Python function to JAX
from wrap_torch2jax import Size, dtype_t2j  # this is torch.Size, a tuple-like shape representation


def torch_fn(a, b):
    return a + b


shape = (10, 2)
a, b = torch.randn(shape), torch.randn(shape)
jax_fn = torch2jax(torch_fn, a, b)  # without output_shapes, torch_fn **will be evaluated once**
jax_fn = torch2jax(torch_fn, a, b, output_shapes=Size(a.shape))  # torch_fn will NOT be evaluated

# you can specify the whole input and output structure without instantiating the tensors
# torch_fn will NOT be evaluated
jax_fn = torch2jax(
    torch_fn,
    jax.ShapeDtypeStruct(a.shape, dtype_t2j(a.dtype)),
    jax.ShapeDtypeStruct(b.shape, dtype_t2j(b.dtype)),
    output_shapes=jax.ShapeDtypeStruct(a.shape, dtype_t2j(a.dtype)),
)

prngkey = jax.random.PRNGKey(0)
device = jax.devices("cuda")[0]  # both CPU and CUDA are supported
a = jax.device_put(jax.random.normal(prngkey, shape), device)
b = jax.device_put(jax.random.normal(prngkey, shape), device)

# call the no-copy torch function
out = jax_fn(a, b)

# call the no-copy torch function **under JIT**
out = jax.jit(jax_fn)(a, b)
```

With a multiple outputs

```python
def torch_fn(a, b):
    layer = torch.nn.Linear(2, 20).to(a)
    return a + b, torch.norm(a), layer(a * b)


shape = (10, 2)
a, b = torch.randn(shape), torch.randn(shape)
jax_fn = torch2jax(torch_fn, a, b)  # with example argumetns

prngkey = jax.random.PRNGKey(0)
device = jax.devices("cuda")[0]
a = jax.device_put(jax.random.normal(prngkey, shape), device)
b = jax.device_put(jax.random.normal(prngkey, shape), device)

# call the no-copy torch function
x, y, z = jax_fn(a, b)

# call the no-copy torch function **under JIT**
x, y, z = jax.jit(jax_fn)(a, b)

```

For a more advanced discussion on different ways of specifying input/output
specification of the wrapped function, take a look at:
[input_output_specification.ipynb](./examples/input_output_specification.ipynb)
notebook in the `examples` folder.

# Automatically defining gradients

Automatic reverse-mode gradient definitions are now supported for wrapped
pytorch functions with the method `torch2jax_with_vjp`

```python
import torch
import jax
from jax import numpy as jnp
import numpy as np

from wrap_torch2jax import torch2jax_with_vjp

def torch_fn(a, b):
  return torch.nn.MSELoss()(a, b)

shape = (6,)

xt, yt = torch.randn(shape), torch.randn(shape)

# `depth` determines how many times the function can be differentiated
jax_fn = torch2jax_with_vjp(torch_fn, xt, yt, depth=2) 


# we can now differentiate the function (derivatives are taken using PyTorch autodiff)
g_fn = jax.grad(jax_fn, argnums=(0, 1))
x, y = jnp.array(np.random.randn(*shape)), jnp.array(np.random.randn(*shape))

print(g_fn(x, y))

# JIT works too
print(jax.jit(g_fn)(x, y))
```

Caveats: 

- `jax.hessian(f)` will not work since `torch2jax` uses forward differentiation, but
  the same functionality can be achieved using `jax.jacobian(jax.jacobian(f))`
- input shapes are fixed for one wrapped function and cannot change, use
  `torch2jax_with_vjp/torch2jax` again if you need to alter the input shapes
- in line with JAX philosphy, PyTorch functions must be non-mutable,
  [torch.func](https://pytorch.org/docs/master/func.html) has a good description
  of how to convert e.g., PyTorch models, to non-mutable formulation

# NEW: Performant multi-gpu (experimental)

User feedback greatly appreciated, feel free to open an issue if you have any
questions or are running into issues: [https://github.com/rdyro/torch2jax/issues/new](https://github.com/rdyro/torch2jax/issues/new).

`torch2jax` should now support efficient multi-gpu calling. JAX, broadly,
provides 3 main ways of calling multi-device code:
  - `shard_map` - per-device view, the **recommended** way of using `torch2jax`
    - supports sharded multi-GPU arrays called in parallel
    - supports automatically defining gradients
  - `jax.jit` - the jit function supports automatic computation on sharded
  inputs; this is currently partially supported by providing the sharding spec 
  of the output
  - `jax.pmap` - (DOES NOT WORK) works somewhat like `shard_map`, but with
  `jnp.stack` instead of `jnp.concatenate` over devices, please use `shard_map`
  instead

```python
# Data-parallel model
import functools
import copy

import torch
import torch.nn as nn
import jax
from jax.experimental.shard_map import shard_map
from jax.sharding import PartitionSpec as P, NamedSharding
from wrap_torch2jax import torch2jax, torch2jax_with_vjp, tree_t2j


def _setattr(mod, key, delim: str = "."):
    if delim not in key:
        setattr(mod, key, None)
    else:
        key, key_remaining = key.split(delim, 1)
        _setattr(getattr(mod, key), key_remaining, delim=delim)


def _strip_model(model):
    for key in dict(model.named_parameters()).keys():
        _setattr(model, key, delim=".")


if __name__ == "__main__":
    model = nn.Sequential(nn.Linear(1024 * 1024, 1024), nn.SiLU(), nn.Linear(1024, 16)).to("cuda:0")
    params = dict(model.named_parameters())
    [p.requires_grad_(False) for p in params.values()]
    _strip_model(model)  # remove params from the model, leaving only a skeleton

    def call_model_torch(x, params):
        ys = []
        for _ in range(30):
            # functional_call uses the model in-place, we need a local copy
            local_model_skeleton = copy.deepcopy(model)
            ys.append(torch.func.functional_call(local_model_skeleton, params, x))
        return sum(ys)

    devices = jax.devices("cuda")
    mesh = jax.make_mesh((len(devices),), P("x"), devices=devices)
    params_sharding = NamedSharding(mesh, P())  # fully replicated
    batch_sharding = NamedSharding(mesh, P("x", None))  # sharded along batch

    x = jax.jit(
        lambda: jax.random.normal(jax.random.key(0), (128, 1024 * 1024)),
        out_shardings=batch_sharding,
    )()

    params = jax.tree.map(lambda p: jax.device_put(p, params_sharding), tree_t2j(params))
    params_spec = jax.tree.map(lambda _: params_sharding.spec, params)

    @jax.jit
    @functools.partial(
        shard_map,
        mesh=mesh,
        in_specs=(batch_sharding.spec, params_spec),
        out_specs=batch_sharding.spec,
        check_rep=False,
    )
    def fwd_fn(x, params):
        return torch2jax_with_vjp(call_model_torch, x, params, output_shapes=x[:, :16])(x, params)

    y = fwd_fn(x, params)

    # OR using JIT (but without gradients)
    fwd_fn = jax.jit(
        torch2jax(
            call_model_torch, x, params, output_shapes=x[:, :16], output_sharding_spec=P("x", None)
        )
    )

    y = fwd_fn(x, params)
```

<p align="center">
  <img src="images/data_parallel.png" style="width: 100%; max-width: 1000px; height: auto; max-height: 1000px;">
  <p align="center">Fig: Overlapping torch calls on multiple devices (RTX A4000 x 4)</p>
</p>


> Note: `jax.vmap`'s semantics might indicate that it can compute on sharded
arrays, it can work, but it is not recommend, and because of `torch2jax`'s
implementation will likely be executed sequentially (and likely be slow).

# Dealing with Changing Shapes

You can deal with changing input shapes by calling `torch2jax` (and
`torch2jax_with_vjp`) in the JAX function, both under JIT and eagerly!

```python
@jax.jit
def compute(a, b, c):
    d = torch2jax_with_vjp(
        torch_fn,
        jax.ShapeDtypeStruct(a.shape, dtype_t2j(a.dtype)),
        jax.ShapeDtypeStruct(b.shape, dtype_t2j(b.dtype)),
        output_shapes=jax.ShapeDtypeStruct(a.shape, dtype_t2j(a.dtype)),
    )(a, b)
    return d - c

print(compute(a, b, a))
```

# Timing Comparison vs `pure_callback`

This package achieves a much better performance when calling PyTorch code from
JAX because it does not copy its input arguments and does not move CUDA data off
the GPU.

<img src="images/time_difference.png">


# Current Limitations of `torch2jax`

- compilation happens on module import and can take 1-2 minutes (it will be cached afterwards)
- in the Pytorch function all arguments must be tensors, all outputs must be tensors
- all arguments must be on the same device and of the same datatype, either float32 or float64
- an input/output shape (e.g. `output_shapes=` kw argument) representations (for
  flexibility in input and output structure) must be wrapped in `torch.Size` or
  `jax.ShapeDtypeStruct`
- the current implementation does not support batching, that's on the roadmap
- the current implementation does not define the VJP rule, in current design, this has to be done in 
  Python

# Changelog

- version 0.6.1
  - added `vmap_method=` support for experimental pytorch-side batching support,
    see [https://github.com/rdyro/torch2jax/issues/28](https://github.com/rdyro/torch2jax/issues/28)

- version 0.6.0
  - proper multi-GPU support mostly with `shard_map` but also via `jax.jit` automatic sharding
  - `shard_map` and automatic `jax.jit` device parallelization should work, but `pmap` doesn't work
  - removed (deprecated)
    - torch2jax_flat - use the more flexible torch2jax
  - added input shapes validation - routines

- version 0.5.0
  - updating to the new JAX ffi interface

- version 0.4.11
  - compilation fixes and support for newer JAX versions

- version 0.4.10
  - support for multiple GPUs, currently, all arguments must and the output
    must be on the same GPU (but you can call the wrapped function with
    different GPUs in separate calls)
  - fixed the coming depreciation in JAX deprecating `.device()` for
    `.devices()`

- no version change
  - added helper script `install_package_aliased.py` to automatically install
    the package with a different name (to avoid a name conflict)

- version 0.4.7
  - support for newest JAX (0.4.17) with backwards compatibility maintained
  - compilation now delegated to python version subfolders for multi-python systems

- version 0.4.6
  - bug-fix: cuda stream is now synchronized before and after a torch call explicitly to
    avoid reading unwritten data

- version 0.4.5
  - `torch2jax_with_vjp` now automatically selects `use_torch_vjp=False` if the `True` fails
  - bug-fix: cuda stream is now synchronized after a torch call explicitly to
    avoid reading unwritten data

- version 0.4.4
  - introduced a `use_torch_vjp` (defaulting to True) flag in `torch2jax_with_vjp` which 
    can be set to False to use the old `torch.autograd.grad` for taking
    gradients, it is the slower method, but is more compatible

- version 0.4.3
  - added a note in README about specifying input/output structure without instantiating data

- version 0.4.2
  - added `examples/input_output_specification.ipynb` showing how input/output
  structure can be specified

- version 0.4.1
  - bug-fix: in `torch2jax_with_vjp`, nondiff arguments were erroneously memorized

- version 0.4.0
  - added batching (vmap support) using `torch.vmap`, this makes `jax.jacobian` work
  - robustified support for gradients
  - added mixed type arguments, including support for float16, float32, float64 and integer types
  - removed unnecessary torch function calls in defining gradients
  - added an example of wrapping a BERT model in JAX (with weights modified from JAX), `examples/bert_from_jax.ipynb`

- version 0.3.0
  - added a beta-version of a new wrapping method `torch2jax_with_vjp` which
  allows recursively defining reverse-mode gradients for the wrapped torch
  function that works in JAX both normally and under JIT

- version 0.2.0
  - arbitrary input and output structure is now allowed
  - removed the restriction on the number of arguments or their maximum dimension
  - old interface is available via `torch2jax.compat.torch2jax`

- version 0.1.2
  - full CPU only version support, selected via `torch.cuda.is_available()`
  - bug-fix: compilation should now cache properly

- version 0.1.1
  - bug-fix: functions do not get overwritten, manual fn id parameter replaced with automatic id generation
  - compilation caching is now better

- version 0.1.0
  - first working version of the package


# Roadmap

- [x] call PyTorch functions on JAX data without input data copy
- [x] call PyTorch functions on JAX data without input data copy under jit
- [x] support both GPU and CPU
- [x] (feature) support partial CPU building on systems without CUDA
- [x] (user-friendly) support functions with a single output (return a single output, not a tuple)
- [x] (user-friendly) support arbitrary argument input and output structure (use pytrees on the 
      Python side)
- [x] (feature) support batching (e.g., support for `jax.vmap`)
- [x] (feature) support integer input/output types
- [x] (feature) support mixed-precision arguments in inputs/outputs
- [x] (feature) support defining VJP for the wrapped function (import the experimental functionality 
      from [jit-JAXFriendlyInterface](https://github.com/rdyro/jfi-JAXFriendlyInterface))
- [x] (tests) test how well device mapping works on multiple GPUs
- [x] (tests) setup automatic tests for multiple versions of Python, PyTorch and JAX
- [ ] (feature) look into supporting in-place functions (support for output without copy)
- [ ] (feature) support TPU

# Related Work

Our Python package wraps PyTorch code as-is (so custom code and mutating code
will work!), but if you're looking for an automatic way to transcribe a
supported subset of PyTorch code to JAX, take a look at
[https://github.com/samuela/torch2jax/tree/main](https://github.com/samuela/torch2jax/tree/main).

We realize that two packages named the same is not ideal. As we work towards a
solution, here's a stop-gap solution. We offer a helper script to install the
package with an alias name, installing our package using pip under a different
name.

1. `$ git clone https://github.com/rdyro/torch2jax.git` - clone this repo
2. `$ python3 install_package_aliased.py new_name_torch2jax --install --test` - install and test this package under the name `new_name_torch2jax`
3. you can now use this package under the name `new_name_torch2jax`