pytorch/torch/csrc/jit/JIT-AUTOCAST.md

# JIT scripting & Autocast

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- [Overview](#overview)
- [Usage](#usage)
- [Known limitations](#known-limitations)
    - [Diagnostics](#diagnostics)
    - [Autocast decorators](#autocast-decorators)
    - [Autocast argument must be a compile-time constant](#autocast-argument-must-be-a-compile-time-constant)
    - [Uncommon autocast usage patterns may not be supported](#uncommon-autocast-usage-patterns-may-not-be-supported)
    - [Limited support for promote autocast policy](#limited-support-for-promote-autocast-policy)
    - [Missing autocast policies](#missing-autocast-policies)
    - [Mixing eager mode and scripting autocast](#mixing-eager-mode-and-scripting-autocast)
    - [Mixing tracing and scripting autocast (script calling traced)](#mixing-tracing-and-scripting-autocast-script-calling-traced)
    - [Mixing tracing and scripting autocast (traced calling script)](#mixing-tracing-and-scripting-autocast-traced-calling-script)
    - [Disabling eager autocast with scripted autocast](#disabling-eager-autocast-with-scripted-autocast)
- [References](#references)

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## Overview

[Autocast][2] (aka Automatic Mixed Precision) is an optimization which helps
taking advantage of the storage and performance benefits of narrow types
(float16) while preserving the additional range and numerical precision of
float32.

The JIT support for autocast is subject to different constraints compared to the
eager mode implementation (mostly related to the fact that TorchScript is
statically typed) and this document attempts to list the known limitations.

## Usage

Explicit `with autocast()` scopes are supported inside scripted functions and
modules (subject to the limitations described below):

```python
import torch
from torch.cuda.amp import autocast

@torch.jit.script
def func(a, b):
    with autocast():
        return torch.mm(a, b)

a_float32 = torch.rand((8, 8), dtype=torch.float32, device="cuda")
b_float32 = torch.rand((8, 8), dtype=torch.float32, device="cuda")
result = func(a_float32, b_float32)
print(result.dtype) # expecting torch.float16
```

## Known limitations

This section documents the current set of known limitations. Ideally this list
will shrink as we advance with the design and implementation, although some of
the limitations are related to fundamental TorchScript aspects that are not easy
to change.

> One important goal is to avoid surprises (ex. autocast annotations
> silently ignored) and to report sensible diagnostics when something deviates
> from eager mode behavior.
>
> Please [report](https://github.com/csarofeen/pytorch/issues/new/choose) any
> issues not covered here.

#### Diagnostics

The current Autocast/JIT diagnostics should be improved:
- Some errors are not specific enough or not actionable
- Not all the errors point to the Python source location

#### Autocast decorators

Using `@autocast` is not currently supported in script mode (a diagnostic
will be emitted)

```python
import torch
from torch.cpu.amp import autocast

@autocast(enabled=True)
def helper(x):
    ...

@torch.jit.script
def foo(x):
    return helper(x) # not supported
```

Another example

```python
import torch
from torch.cpu.amp import autocast

@torch.jit.script
@autocast() # not supported
def foo(a, b, c, d):
    ...
```

#### Autocast argument must be a compile-time constant

```python
import torch
from torch.cpu.amp import autocast

@torch.jit.script
def fn(a, b, use_amp: bool):
    # runtime values for autocast enable argument are not supported
    with autocast(enabled=use_amp):
        return torch.mm(a, b)

```

#### Uncommon autocast usage patterns may not be supported

```python
import torch
from torch.cpu.amp import autocast

@torch.jit.script
def fn(a, b, c, d):
    with autocast(enabled=True) as autocast_instance: # not supported
        ...
        with autocast_instance:
            ...
```

#### Limited support for promote autocast policy

For some operations, autocast needs to [promote to the widest argument type][3].
When the concrete types are not available, the current implementation will
conservatively inject a promotion even when it may not be needed.

#### Missing autocast policies

Also related to the lack of concrete dtype availability, a few specialized
autocast policies are not yet supported with JIT scripting:
- [CastPolicy::fp32_append_dtype][5]

#### Mixing tracing and scripting autocast (script calling traced)

Calling a traced function from a scripted one mostly works, except for the case
where the traced part uses `autocast(False)`. After tracing, the `autocast` is
stripped from the TorchScript IR so it's effectively ignored:

> This is one known limitation where we don't have a way to emit a diagnostic!

```python
import torch
from torch.cpu.amp import autocast

def helper(a, b):
    with autocast(enabled=False):
        return torch.mm(a, b) * 2.0

traced = torch.jit.trace(helper, (x, y))

@torch.jit.script
def fn(a, b):
    with autocast(enabled=True):
        return traced(a, b)
```

#### Mixing tracing and scripting autocast (traced calling script)

Calling a scripted function from a trace is similar to calling the scripted
function from eager mode:

```python
import torch
from torch.cpu.amp import autocast

@torch.jit.script
def fn(a, b):
    return torch.mm(a, b)

def traced(a, b):
    with autocast(enabled=True):
        return fn(a, b)

# running TorchScript with Autocast enabled is not supported
torch.jit.trace(traced, (x, y))
```

#### Disabling eager autocast with scripted autocast

If eager-mode autocast is enabled and we try to disable autocasting from
within a scripted function, autocasting will still occur.

```python
import torch
from torch.cuda.amp import autocast

@torch.jit.script
def fn(a, b):
    with autocast(enabled=False):
        return torch.mm(a, b)

x = torch.rand((2, 2), device='cuda', dtype=torch.float)
y = torch.rand((2, 2), device='cuda', dtype=torch.float)

# this will print half-precision dtype
with autocast(enabled=True):
    print(fn(x, y).dtype)
```

## References

- [torch.cuda.amp Package][1]
- [Automatic Mixed Precision - Tutorial](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html)
- [Automatic Mixed Precision - Examples](https://pytorch.org/docs/stable/notes/amp_examples.html)

[1]: https://pytorch.org/docs/stable/amp.html
[2]: https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/
[3]: https://pytorch.org/docs/stable/amp.html#ops-that-promote-to-the-widest-input-type
[4]: https://github.com/csarofeen/pytorch/blob/4d8575604ad9fa5fdfc21037490a041d8d43bcae/aten/src/ATen/autocast_mode.cpp#L94
[5]: https://github.com/csarofeen/pytorch/blob/4d8575604ad9fa5fdfc21037490a041d8d43bcae/aten/src/ATen/autocast_mode.cpp#L99
[6]: https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html#adding-autocast