Recently we updated the `export` API to take an experimental `dynamic_shapes` argument that was meant to subsume the existing `constraints` argument.
This PR deprecates `constraints` (with a warning on its use, but without actually removing it). Simultaneously it replaces all uses of `constraints` in docs, examples, and tests with corresponding uses of `dynamic_shapes` (preserving behavior). This exercise fortunately revealed some minor bugs in the implementation which have also been fixed in this PR.
Some uses of `constraints` still remain, e.g., when `torch._dynamo.export` is called directly. (Meta-internal uses will be updated in a separate diff.)
Differential Revision: D49676049
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110143
Approved by: https://github.com/tugsbayasgalan
Our experience using `constraints` / `dynamic_dim` with the existing export API has found it to be (subjectively) clunky and (objectively) verbose in common cases.
This PR implements a new design for the export API that replaces the use of `constraints` / `dynamic_dim` with a new way of specifying dynamic shapes, involving the following concepts:
* a constructor `Dim` for first-class named dynamic dimensions with ranges (similar to `functorch.dim`, and analogous to internal symbolic sizes)
* a mechanism that uses the above in `export` calls to associate inputs to their dynamic shape specifications (`dynamic_shapes`)
Design doc: https://docs.google.com/presentation/d/168U7XK72C_WSsZpGESP6Cho9udh193fi0gfjxCNcJ4E/edit#slide=id.p (Meta-only). Note that we only implement Option 1 in that doc. An older version of this PR also implemented Option 3, which is an alternative way of specifying dynamic shapes using tensor type annotations on the exported callable; but we have moved that to future work for now.
See docs for these new features in `torch.export`. The existing `torch.export.export` is modified to use the new API, `torch._export.export__RC__`, whenever `constraints=None`. We have not deprecated the existing API yet, but will do in a follow-up.
Constraint violation errors arising through use of the new API will now contain suggested fixes using the new API. No longer do we need to report all specializations for static dimensions and suggest all constraints over dynamic dimensions to fix such errors. Instead, due to the redesign, the suggested fixes are much more concise, only involving modifying the definitions of relevant `Dim`s.
Differential Revision: [D48919204](https://our.internmc.facebook.com/intern/diff/D48919204/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108448
Approved by: https://github.com/suo, https://github.com/gmagogsfm
The order of LOC can change and so it should not be used in creating a link. Also, a specific LOC is not needed here given the function name as used in general in overall documentaton.
Previously, a fix was provided by updating the line number for the mentioned issue in this PR but the LOC was eventually changed resulting a broken link.
Fixes#102183
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108957
Approved by: https://github.com/ezyang
**This PR is a 99% copy paste of Sam Gross** (@colesbury) work at https://github.com/pytorch/pytorch/pull/100642. Copied from there
--------
The NN_MODULE guard now subsumes guards on Module attributes. The check_fn will fail if the module attributes are changed (such as Module.training), parameters, submodules, and buffers are added or removed, and if fields are changed on the type itself.
This gives up specificity in the guard check -- if any field is changed the check_fn fails -- for faster overall checks.
-----
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108528
Approved by: https://github.com/ezyang
Summary:
This diff demonstrates a simplified E2E workflow for PT2 Inference stack:
1. Model author with `torch.export()`
2. Model processing with `aot_inductor.compile()`
3. Model served with a new Inference Runtime API, named `ModelRunner`
`torch.export()` and `aot_inductor.compile()` produces a zip file using `PyTorchStreamWriter`.
Runtime reads the zip file with `PyTorchStreamReader`.
The zip file contains
{F1080328179}
More discussion on packaging can be found in https://docs.google.com/document/d/1C-4DP5yu7ZhX1aB1p9JcVZ5TultDKObM10AqEtmZ-nU/edit?usp=sharing
Runtime can now switch between two Execution modes:
1. Graph Interpreter mode, implemented based on Sigmoid's Executor
2. AOTInductor mode, implemented based on FBAOTInductorModel
Test Plan:
buck2 run mode/dev-nosan mode/inplace -c fbcode.enable_gpu_sections=True //sigmoid/inference/test:e2e_test
Export and Lower with AOTInductor
buck2 run mode/dev-sand mode/inplace -c fbcode.enable_gpu_sections=True sigmoid/inference:export_package
Run with GraphInterpreter and AOTInducotr
buck2 run mode/dev-nosan //sigmoid/inference:main
Reviewed By: suo
Differential Revision: D47781098
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108482
Approved by: https://github.com/zhxchen17
We have a plethora of error types for various errors raised from c10d. These include `RuntimeError`, `TimeoutError`, `SocketError`, `DistBackendError` etc.
This results in messy code during error handling somewhat like this:
```
if "NCCL" in exception_str:
...
if "Timed out initializing process group in store based barrier on rank" in exception_str:
...
if "The client socket has timed out after" in exception_str:
...
if "Broken pipe" in exception_str:
...
if "Connection reset by peer" in exception_str:
...
```
To address this issue, in this PR I've ensured added these error types:
1. **DistError** - the base type of all distributed errors
2. **DistBackendError** - this already existed and referred to PG backend errors
3. **DistStoreError** - for errors originating from the store
4. **DistNetworkError** - for general network errors coming from the socket library
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108191
Approved by: https://github.com/H-Huang
We have a plethora of error types for various errors raised from c10d. These include `RuntimeError`, `TimeoutError`, `SocketError`, `DistBackendError` etc.
This results in messy code during error handling somewhat like this:
```
if "NCCL" in exception_str:
...
if "Timed out initializing process group in store based barrier on rank" in exception_str:
...
if "The client socket has timed out after" in exception_str:
...
if "Broken pipe" in exception_str:
...
if "Connection reset by peer" in exception_str:
...
```
To address this issue, in this PR I've ensured added these error types:
1. **DistError** - the base type of all distributed errors
2. **DistBackendError** - this already existed and referred to PG backend errors
3. **DistStoreError** - for errors originating from the store
4. **DistNetworkError** - for general network errors coming from the socket library
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107651
Approved by: https://github.com/H-Huang
This reworks the DORT backend factory function to support the options kwarg of torch.compile, and defines a concrete OrtBackendOptions type that can be used to influence the backend.
Caching is also implemented in order to reuse backends with equal options.
Wrapping the backend in auto_autograd also becomes an option, which allows `OrtBackend` to always be returned as the callable for torch.compile; wrapping happens internally if opted into (True by default).
Lastly, expose options for configuring preferred execution providers (will be attempted first), whether or not to attempt to infer an ORT EP from a torch found device in the graph or inputs, and finally the default/fallback EPs.
### Demo
The following demo runs `Gelu` through `torch.compile(backend="onnxrt")` using various backend options through a dictionary form and a strongly typed form. It additionally exports the model through both the ONNX TorchScript exporter and the new TorchDynamo exporter.
```python
import math
import onnx.inliner
import onnxruntime
import torch
import torch.onnx
torch.manual_seed(0)
class Gelu(torch.nn.Module):
def forward(self, x):
return x * (0.5 * torch.erf(math.sqrt(0.5) * x) + 1.0)
@torch.compile(
backend="onnxrt",
options={
"preferred_execution_providers": [
"NotARealEP",
"CPUExecutionProvider",
],
"export_options": torch.onnx.ExportOptions(dynamic_shapes=True),
},
)
def dort_gelu(x):
return Gelu()(x)
ort_session_options = onnxruntime.SessionOptions()
ort_session_options.log_severity_level = 0
dort_gelu2 = torch.compile(
Gelu(),
backend="onnxrt",
options=torch.onnx._OrtBackendOptions(
preferred_execution_providers=[
"NotARealEP",
"CPUExecutionProvider",
],
export_options=torch.onnx.ExportOptions(dynamic_shapes=True),
ort_session_options=ort_session_options,
),
)
x = torch.randn(10)
torch.onnx.export(Gelu(), (x,), "gelu_ts.onnx")
export_output = torch.onnx.dynamo_export(Gelu(), x)
export_output.save("gelu_dynamo.onnx")
inlined_model = onnx.inliner.inline_local_functions(export_output.model_proto)
onnx.save_model(inlined_model, "gelu_dynamo_inlined.onnx")
print("Torch Eager:")
print(Gelu()(x))
print("DORT:")
print(dort_gelu(x))
print(dort_gelu2(x))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107973
Approved by: https://github.com/BowenBao
Compared to #104848, this PR makes a step further: when the enable_sparse_support decorator is applied to `torch.autograd.gradcheck`, the resulting callable is equivalent to `torch.autograd.gradcheck` with an extra feature of supporting functions that can have input sparse tensors or/and can return sparse tensors.
At the same time, the underlying call to `torch.autograd.gradcheck` will operate on strided tensors only. This basically means that torch/autograd/gradcheck.py can be cleaned up by removing the code that deals with sparse tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107150
Approved by: https://github.com/albanD, https://github.com/amjames, https://github.com/cpuhrsch
ghstack dependencies: #107638, #107777
Summary:
This is a stride based attribute for a tensor available in Python.
This can help inspect tensors generated using `torch.empty_permuted(.., physical_layout, ...)`, where physical_layout should match the dim_order returned here. `empty_permuted` will be renamed to use dim_order as the param name in the future. And also help Executorch export pipeline with implementing dim_order based tensors.
Differential Revision: D48134476
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106835
Approved by: https://github.com/ezyang
