This is follow-up of #165037. It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165142
Approved by: https://github.com/albanD
Summary: This PR introduces shape guards to export. Previously only value ranges, equalities, and specializations would be tracked for symbolic expressions, and we had a forward hook to check them. Instead now we create a function to check shape guards and call it in the exported program.
Test Plan:
updated several tests
Rollback Plan:
Differential Revision: D80713603
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161178
Approved by: https://github.com/tugsbayasgalan
Summary:
A demo for creating AOTI delegate for NativeRT in OSS.
- It supports full graph lowering only.
- It leverages `executorch_call_delegate` HOP but doesn't rely on `executorch`.
- The delegate graph is obtained by tracing a `LoweredBackendModule` whose forward function calls `executorch_call_delegate`.
- The main difference between `executorch_call_delegate` and `aoti_call_delegate` is that the delegate graph from `executorch_call_delegate` doesn't have weights lifted as inputs.
- original_ep and delegate_ep are treated as flat EP dictionary and there is no nested structure.
- The naming contract is enforced by `model_name` and `backend_id`
Test Plan:
CI
Rollback Plan:
Differential Revision: D81641157
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162285
Approved by: https://github.com/dolpm
Summary:
Fixes#160749
For a model like
```
class M(torch.nn.Module):
def forward(self, x):
s = torch.sin(x)
z = 1j * s
return z
```
Its graph will be
```
graph():
%x : [num_users=1] = placeholder[target=x]
%sin : [num_users=1] = call_function[target=torch.ops.aten.sin.default](args = (%x,), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sin, 1j), kwargs = {})
return (mul,)
```
`1j` will appear as a constant complex argument in the `aten.mul`
Test Plan:
buck2 run mode/dev-nosan caffe2/test:test_export -- -r test_complex_constant
Rollback Plan:
Differential Revision: D80672323
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161517
Approved by: https://github.com/angelayi
Summary: this will only cause it in the event that we are serializing a triton hop. there are a few tests that do weird mocking stuff that this function doesn't like, so this will prevent it from being called there.
Test Plan:
att
Rollback Plan:
Differential Revision: D81261486
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161737
Approved by: https://github.com/angelayi
Summary:
We split the refactoring in two parts for forward compatibility concerns
First, we land the deserialization (loading part)
Then, we land the serialization (saving part)
Save weights and constants as individual files in PT2 archive. Each weight/constant will be saved as raw bytes, unless it is a custom object (TorchBind object) or a non-fake tensor subclass, for these two special cases we still save them using pickle.
The metadata of saved tensors along with the file name will be saved as `PayloadMeta`.
The mapping from FQN to `PayloadMeta` will be saved as `PayloadConfig` under `WEIGHTS_CONFIG_FORMAT` and `CONTANTS_CONFIG_FORMAT`
This changes the serialization in python side when calling `torch.export.save()`.
For deserialization in python `torch.export.load()`, we make it BC-safe by allowing loading legacy format weights/constants.
For deserialization in C++ `torch/nativert/ModelRunner.cpp`, we make this a BC breaking change as currently the OSS ModelRunner API is not being used.
The file structure
```
├── archive_format
├── archive_version
├── byteorder
├── .data
│ ├── serialization_id
│ └── version
├── data
│ ├── sample_inputs
│ │ └── model.pt
│ ├── constants
│ │ ├── tensor_0
│ │ ├── tensor_1
│ │ └── model_constants_config.json
│ └── weights
│ ├── weight_0
│ ├── weight_1
│ ├── weight_2
│ ├── weight_3
│ └── model_weights_config.json
└── models
└── model.json
```
Test Plan:
CI
Rollback Plan:
Differential Revision: D80035490
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160394
Approved by: https://github.com/SherlockNoMad
Summary:
Previously we will pass all serialized data to dataclass ctors.
Now we just loop over all the existing fields in dataclass and fetch only the field we need to run ctor.
This should help with the case when we deserializing a buffer with new field.
Test Plan:
CI
Rollback Plan:
Differential Revision: D80487716
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160918
Approved by: https://github.com/angelayi
Summary:
In HF model rwkv, we have parameter mutation under inference mode which should be safe. This PR does multiple things to make sure it works:
1. We execute global autograd mutation while tracing so that we can actually trace through parameter inplace mutation
2. Add support for parameter mutation under inference mode in AOTAutograd
3. Add support for parameter mutation under inference mode in export.
Test Plan:
test
Rollback Plan:
Differential Revision: D79460136
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159661
Approved by: https://github.com/ydwu4
We need to make function schema proxyable to trace a the auto_functionalized hop that takes function schema as inputs. The implementation basically follows how we support torchbind object:
1. upon seeing an untracked function schema arg, we creates a constant get_attr node
2. we track the function schema argument in export to support lift/unlift.
3. we need to support serde for functional schema. We'll add support for this in follow-up PRs.
However, compared with torchbind object:
1. we don't need a dynamo implementation, because the function schema is added when we auto_functionalize a hop to the argument of auto_functionalized. One potential use case is users re-traces an exported program with strict mode. Since non-strict is the default now, we don't see a use case yet.
2. we don't need an inductor implementation, because the function schema will go away after auto_functionalized re-inplacing pass.
edit: we greatly simplifies (and generalizes) the implementation following @zou3519 's suggestion of using pytree.register_constant
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152073
Approved by: https://github.com/zou3519
ghstack dependencies: #152072
In https://github.com/pytorch/pytorch/issues/151746, users ran into an error where a custom triton op cannot be resolved into an operator from string target. We improve the error message by reminding users to register the same custom operator at de-serialization time.
Now the error looks like this:
```python
torch._export.serde.serialize.SerializeError: We failed to resolve torch.ops.triton_kernel.add.default to an operator. If it's a custom op/custom triton op, this is usally because the custom op is not registered when deserializing. Please import the custom op to register it before deserializing. Otherwise, please file an issue on github. Unsupported target type for node Node(target='torch.ops.triton_kernel.add.default', inputs=[NamedArgument(name='x', arg=Argument(as_tensor=TensorArgument(name='linear')), kind=1), NamedArgument(name='y', arg=Argument(as_tensor=TensorArgument(name='mul')), kind=1)], outputs=[Argument(as_tensor=TensorArgument(name='add'))], metadata={'stack_trace': 'File "/data/users/yidi/pytorch/test.py", line 50, in forward\n output = triton_add(dense_output, bias)', 'nn_module_stack': 'L__self__,,__main__.SimpleModel', 'torch_fn': 'add.default_1;OpOverload.add.default'}, is_hop_single_tensor_return=None): <class 'str'>.```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152029
Approved by: https://github.com/jingsh
This has been pretty helpful for the size-oblivious rewrite. Wanted the variadic args version to avoid `sym_or(a, sym_or(b, sym_or(c, d)))` in favor of `sym_or(a, b, c, d)`. Happy to change this to ban the 1-arg version.
This is better than plain and/or because the whole symbolic expression gets preserved, and if we guard on it or defer as a runtime assert, we preserve all branches.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150456
Approved by: https://github.com/laithsakka
An internal model was serialized in 2023, and is now breaking while loading with the following error:
```
File "<eval_with_key>.1675", line 4
def forward(self, arg1163_1, arg1164_1, , arg1166_1, , arg1168_1, arg1169_1, arg1170_1, , arg1172_1, arg1173_1, arg1174_1, arg1175_1, arg1176_1, arg1177_1, arg1178_1, arg1179_1, arg1180_1, arg1181_1, arg1182_1, arg1183_1, arg1184_1, arg1185_1, arg1186_1, arg1187_1, arg1188_1, arg1189_1, arg1190_1, arg1191_1, arg1192_1, arg1193_1, arg1194_1, arg1195_1, arg1196_1, arg1197_1, arg1198_1, arg1199_1, arg1200_1, arg1201_1, arg1202_1, arg1203_1, arg1204_1, arg1205_1, arg1206_1, arg1207_1, arg1208_1, arg1209_1, arg1210_1, arg1211_1, arg1212_1, arg1213_1, arg1214_1, arg1215_1, arg1216_1, , arg1218_1, arg1219_1, arg1220_1, arg1221_1, arg1222_1, arg1223_1, arg1224_1, , arg1226_1, arg1227_1, arg1228_1, , arg1230_1, , , , , , , , , , , , , , , ):
^
SyntaxError: invalid syntax
```
The syntax errors are due to inputs that are `None` when exporting. Prior to changes in https://github.com/pytorch/pytorch/pull/123590 (landed 4/2024), input specs for none inputs look like `InputSpec(userInput=UserInputSpec(arg=Argument(asNone=True)))`, and during deserialization when creating a node, we would just use a dummy name `arg`. After to those changes, the input specs for none inputs look like `InputSpec(constantInput=InputToConstantInputSpec(name='y', value=ConstantValue(asNone=True)))`, and when creating a node we would use the name `y` as the name. However the PR didn't handle the case if it's loading an old package which doesn't have this name, so ended up putting empty names in the placeholder nodes.
This error was uncovered after https://github.com/pytorch/pytorch/pull/149717, where we now use the GraphModule's python codegen to run the UnflattenedModule instead of going through the interpreter path. The placeholder nodes having empty names caused the python codegen to fail.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150515
Approved by: https://github.com/yushangdi
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
Summary:
**Codegen**
- Skip some codegen parts for torchbind (such as arg decleration) because they are loaded in proxy executor, so we do not need to declare torchbind args in cpp code
- Added a helper method to get the schema of CallTorchBind HOP. The returned schema is only the schema of `obj.method()`.
**Serialization**
Add support for torchbind object in serialization
- For CallTorchBind HOP, we need to handle it specially because of it's schema. The output serialized args is in the format of `(obj, method, *args, **kwargs)`.
- it.TorchBindObject inputs are serialized to `as_custom_obj` Argument.
**Packaging**
Add torchbind objects file and `custom_objs_config.json` file to generated files output of `aot_compile`.
The json file is stored in the `data/aotinductor/<model_name>` folder in pt2 archive.
The torchbind objects are stored in data/constants/ folder in pt2 archive.
The format of torchbind objects are `f"{CUSTOM_OBJ_FILENAME_PREFIX}{custom_obj_idx}"`. e.g. `custom_obj_0`.
CustomClassHolder objects implement their own pickle methods.
Note that this `custom_objs_config.json` file is different from the `model_constants_config.json` file produced in package_sigmoid(). The keys in `custom_objs_config` directly correspond to the arg name in extern nodes json.
The key in `model_constants_config.json` produced by `package_sigmoid` is the attribute name in the user mode code.
This is required for both internal and OSS torchbind support.
For OSS torchbind support, we also need to package torchbind_constants into the .pt2 output.
**Work Left**
We still need to add torchbind support in ProxyExecutor for inductor.aoti_load_package to work. See other diffs in the stack.
Test Plan:
```
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:torchbind -- -r schema
buck run fbcode//mode/dev-nosan //caffe2/test/inductor:torchbind -- -r aot_compile
```
Differential Revision: D69490718
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148506
Approved by: https://github.com/angelayi
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
Plan: avoid the use of unbacked renamings, and introduce a pass run in `_produce_aten_artifact` that recomputes unbacked bindings. Decided to do this because in we don't serialize unbacked renamings (or any ShapeEnv state), so this used to compose poorly with de/serialization. This hopefully establishes the invariant that the unbacked binding keys are always in sync with the example values (i.e. same indices, and removed if the symbol is replaced / specialized).
For de/serialization, we don't stored unbacked bindings, and just rerun the pass.
Involved a refactor of compute_unbacked_bindings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147574
Approved by: https://github.com/avikchaudhuri
Summary: Currently inf is serialized as Infinity in JSON which is not standard compliant. Instead we will tweak all special floating points into strings and handle them at json layer.
Test Plan:
see D69060784
CI
Differential Revision: D69186425
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146490
Approved by: https://github.com/yiming0416
Summary: Implement an oss version of modelrunner with clean dependencies. The new oss model runner only removes thrift and only use json header to load the model.
Test Plan: Test will be added in the next diff separately. (D69060784)
Differential Revision: D68846877
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146440
Approved by: https://github.com/SherlockNoMad
