Simplifies and optimizes dict construction using the `fromkeys` classmethod ctor. This also makes it really obvious when all the keys will have the same static value, which could be a bug if unintentional. It is also significantly faster than using a dict comprehension. The rule is in preview, but I am adding a forward fix for when it becomes stable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118637
Approved by: https://github.com/albanD
This diff introduces an env var `_USE_NATIVE_C10D_FUNCTIONAL` that tells `_functional_collective` to use native `c10d_functional` ops. The Python version and the native version will co-exist until we completely switch to the native version after more testing and verification.
NOTE: `DeviceMesh` support for native `c10d_functional` will be added in a subsequent PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113057
Approved by: https://github.com/LucasLLC, https://github.com/wconstab, https://github.com/wanchaol
The original motivation for MYPYINDUCTOR was a faster type checking configuration that only checked a subset of files. With the removal of `follow_imports = ignore`, we are now able to use dmypy to do fast incremental typechecking, eliminating the need for this.
Perhaps erroneously, when I tee'ed up this PR I elected to delete the `follow_imports = skip` designations in the mypy-inductor.ini. This lead to a number of extra type error suppressions that I manually edited. You will need to review.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118432
Approved by: https://github.com/Skylion007
ghstack dependencies: #118414, #118418
After this refactor:
* ```TorchVariable``` definition and all references are removed.
* All ```is_allowed``` references except one are removed.
- The only left one is in ```torch/_dynamo/decorators:_disallow_in_graph_helper```. It was called when users put ```disallow_in_graph``` decorator on a function. Since we use the lists in ```trace_rules``` to decide the function's trace rule, so the decorator would only be used as customer function rather than torch functions. I'll defer this to a separate decorator refactor PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116312
Approved by: https://github.com/jansel
When exporting a model with a convolution kernel on cpu, if mkldnn is disabled and nnpack is enabled, export will go down the nnpack optimized convolution kernel for certain shapes ((code pointer)[cd449e260c/aten/src/ATen/native/Convolution.cpp (L542-L552)]). This means that we will automatically create a guard on that certain shape. If users want to export without any restrictions, one option is to disable nnpack. However, no config function exists for this, so this PR is adding a config function, similar to the `set_mkldnn_enabled` function.
Original context is in https://fb.workplace.com/groups/1075192433118967/posts/1349589822345892/?comment_id=1349597102345164&reply_comment_id=1349677642337110.
To test the flag, the following script runs successfully:
```
import os
import torch
from torchvision.models import ResNet18_Weights, resnet18
torch.set_float32_matmul_precision("high")
model = resnet18(weights=ResNet18_Weights.DEFAULT)
model.eval()
with torch.no_grad():
# device = "cuda" if torch.cuda.is_available() else "cpu"
torch.backends.mkldnn.set_flags(False)
torch.backends.nnpack.set_flags(False) # <--- Added config
device = "cpu"
model = model.to(device=device)
example_inputs = (torch.randn(2, 3, 224, 224, device=device),)
batch_dim = torch.export.Dim("batch", min=2, max=32)
so_path = torch._export.aot_compile(
model,
example_inputs,
# Specify the first dimension of the input x as dynamic
dynamic_shapes={"x": {0: batch_dim}},
# Specify the generated shared library path
options={
"aot_inductor.output_path": os.path.join(os.getcwd(), "resnet18_pt2.so"),
"max_autotune": True,
},
)
```
I'm not sure who to add as reviewer, so please feel free to add whoever is relevant!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116152
Approved by: https://github.com/malfet
After this refactor:
* ```TorchVariable``` definition and all references are removed.
* All ```is_allowed``` references except one are removed.
- The only left one is in ```torch/_dynamo/decorators:_disallow_in_graph_helper```. It was called when users put ```disallow_in_graph``` decorator on a function. Since we use the lists in ```trace_rules``` to decide the function's trace rule, so the decorator would only be used as customer function rather than torch functions. I'll defer this to a separate decorator refactor PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116312
Approved by: https://github.com/jansel
Major change in this PR is to make torch context manager class a separate ```TorchCtxManagerClassVariable```, since we have dynamo implementation for these ctx managers.
I was thinking to wrap them as ```UserDefinedClassVariable``` and do dispatch at ```USCVariable.call_function```, but it seems almost the same amount of work and this way is more clear.
This is on the way of moving ```TorchVariable``` to ```TorchFunctionVariable``` which will only handle the functions who would be allowed in graph (e.g, ```torch.sin```) and constant folded (e.g, ```torch.is_floating_point```). All other torch functions would be go through skip/inline rules, and would be wrapped as ```UserFunctionVariable``` (for inlined) and ```SkipFilesVariable``` (for skipped).
The next steps:
* Wrap torch modules, classes, objects as regular ```PythonModuleVariable```, ```UserDefinedClassVariable``` and ```UserDefinedObjectVariable```.
* Generate the allow in graph torch functions list and wrap them as ```TorchFunctionVariable```.
* Finally merge ```skipfiles.check``` and ```is_allowed``` into one function ```allow_skip.check(fn)``` which would return a Enum of allow, skip and inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111622
Approved by: https://github.com/jansel
