# Motivation
As discussed in [#124479](https://github.com/pytorch/pytorch/pull/124479), `torch.amp.autocast` can NOT be completely equivalent to `torch.cuda.amp.autocast` and `torch.cpu.amp.autocast` since `torch.amp.autocast` has NOT the default `dtype` for CPU (`torch.bfloat16` by default) and CUDA (`torch.float16` by default) respectively. We would like `torch.amp.autocast` to be more generic to help the developer/customer write the device-agnostic code. Because there are not enough reasons to add device-specific autocast `torch.xxx.amp.autocast` for each device backend.
# Solution
When `None` is passed to `dtype`, we should use `torch.get_autocast_dtype` to get the related dtype for each backend. Meanwhile, `torch.get_autocast_dtype` is necessary to be supported in JIT path for BC.
# Additional Context
With this PR, `torch.amp.autocast(device_type='cuda')` is equivalent to `torch.cuda.amp.autocast`.
Add two new UTs to cover this change in eager and jit path respectively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125103
Approved by: https://github.com/albanD, https://github.com/jgong5, https://github.com/gujinghui
Removes the existing integration code & build of nvfuser in TorchScript.
Note that I intentionally left the part where we wipe out `third_party/nvfuser` repo. I'll do that in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111093
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74178
Autocasting + freezing should reduce model size in some scenarios, since half-precision constants should be smaller than full-precision constants. This also enables the jit autocast pass by default, so `torch._C._jit_set_autocast_mode(True)` doesn't need to be set in order to enable autocasting.
Test Plan: Imported from OSS
Reviewed By: zou3519, eellison
Differential Revision: D34914245
Pulled By: davidberard98
fbshipit-source-id: 301f3669431feabbd695ebbdfc9c17bd1be3b565
(cherry picked from commit 0530cd365ae1f148910100a5c2981e80d04e4883)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67925
Previously, the following would always fail, because autocasting would not be enabled in the called method:
```
torch.jit.script
def fn(x, y):
with autocast():
# CallMethod() to some method
fn(x, y)
```
This allows the above, if autocasting is globally enabled, e.g.
```
torch.jit.script
def fn(x, y):
with autocast():
# CallMethod() to some method
with autocast():
fn(x, y) # now
```
ghstack-source-id: 142667351
Test Plan: added test in test_jit_autocast.py
Reviewed By: navahgar
Differential Revision: D32214439
fbshipit-source-id: bb7db054e25e18f5e3d2fdb449c35b5942ab303e
Summary:
Adds mixed precision autocasting support between fp32/fp16 to torchscript/JIT. More in depth descriptoin can be found at [torch/csrc/jit/JIT-AUTOCAST.md](https://github.com/pytorch/pytorch/pull/63939/files#diff-1f1772aaa508841c5bb58b74ab98f49a1e577612cd9ea5c386c8714a75db830b)
This PR implemented an autocast optimization pass that inserts casting ops per AMP rule (torch/csrc/jit/passes/autocast.cpp), that mimics the behavior of eager autocast. The pass also takes into consideration the context of `torch.cuda.amp.autocast` and only inserts casting ops within the enabled context manager, giving feature parity as with eager amp autocast.
We currently provide JIT AMP autocast as a prototyping feature, so it is default off and could be turned on via `torch._C._jit_set_autocast_mode(True)`
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), restriction on the user facing python code is described in doc torch/csrc/jit/JIT-AUTOCAST.md
This is a prototype, there are also implementation limitation that's necessary to keep this PR small and get something functioning quickly on upstream, so we can iterate on designs.
Few limitation/challenge that is not properly resolved in this PR:
1. Autocast inserts cast operation, which would have impact on scalar type of output tensor feeding downstream operations. We are not currently propagating the updated scalar types, this would give issues/wrong results on operations in promotion rules.
2. Backward for autodiff in JIT misses the casting of dgrad to input scalar type, as what autograd does in eager. This forces us to explicitly mark the casting operation for certain operations (e.g. binary ops), otherwise, we might be feeding dgrad with mismatch scalar type to input. This could potentially break gradient function consuming dgrad. (e.g. gemm backwards, which assumes grad_output to be of same scalar type as input')
3. `torch.autocast` api has an optional argument `dtype` which is not currently supported in the JIT autocast and we require a static value.
Credit goes mostly to:
tlemo
kevinstephano
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63939
Reviewed By: navahgar
Differential Revision: D31093381
Pulled By: eellison
fbshipit-source-id: da6e26c668c38b01e296f304507048d6c1794314
