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
Fixes#157720
### What's in this PR?
This PR improves the error handling in `torch.compile` for `ndarray.astype('O')` (or `object`). It now explicitly raises a `torch._dynamo.exc.Unsupported` exception with a clear explanation, instead of failing with a less intuitive error during fake tensor propagation.
This is achieved by adding a check within `NumpyNdarrayVariable.call_method` for this specific `astype` pattern.
A new test, `test_ndarray_astype_object_graph_break`, is also added to `test/test_numpy_interop.py` to verify this new behavior.
### Background
Previously, attempting to `torch.compile` a function containing `ndarray.astype('O')` would result in a `TorchRuntimeError` wrapping a `TypeError: data type 'O' not understood`. This error message, originating deep within the tensor mechanism, was not very user-friendly and didn't clearly state *why* it was unsupported.
This change makes the failure more explicit and provides a better user experience by giving a direct, actionable error message.
**Old Behavior (Error Traceback):**
```
torch.dynamo.exc.TorchRuntimeError: Dynamo failed to run FX node with fake tensors: ... got TypeError("data type 'O' not understood")
```
**New Behavior (Error Message):**
```
torch.dynamo.exc.Unsupported: ndarray.astype(object)
Explanation: ndarray.astype('O') or ndarray.astype(object) is not supported by torch.compile, as there is no equivalent to object type in torch.
```
### Testing
A new test has been added to `test_numpy_interop.py` which decorates a function containing `ndarray.astype("O")` with `torch.compile`. The test asserts that a `torch._dynamo.exc.Unsupported` exception is raised, confirming the new error handling works as expected.
The test can be run with:
`pytest test/test_numpy_interop.py -k test_ndarray_astype_object_graph_break`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157810
Approved by: https://github.com/jansel
Two error messages in the codebase instruct the user to use `Tendor.dense()`. This method doesn't exist, but `Tensor.to_dense()` does, and this is what the user should be using instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152631
Approved by: https://github.com/jansel
This patch effectively ignores traceable_tensor_subclasses, allowing
Dynamo to always try tracing into the `__torch_function__` of tensor
subclass. This helps us with 2 things:
1. allowing users to directly benefit from better compilation of tensor
subclass, by just upgrading pytorch, without having to change legacy
library code (see earlier patches in the stack for examples).
2. potentially exposing more issues in compiling tensor subclass, so we
can get signals and improve them.
As a consequence, it exposed and fixes 2 subtle bugs:
1. In `build_torch_function_fn`, we could get
`torch._C._disabled_torch_function_impl` because we have a
`Parameter` subclass without `__torch_function__` override or if we
have a tensor subclass with `__torch_dispatch__` override. We graph
break on this for now, and plan to add support -- the logic for
simulating `torch._C._disabled_torch_function_impl` is already in
`SuperVariable`, we just need to reuse it.
2. Sometimes we create `SyntheticLocalSource` and need to remove all the
guards installed on it, but we only removed the ones whose source
_is_ the created synthetic source `s`, but forgot about chained
source like `s.foo`, this showed up as
`SYNTHETIC_LOCAL['tmp_0'].__torch_function__.__func__`.
Differential Revision: [D71906141](https://our.internmc.facebook.com/intern/diff/D71906141)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149792
Approved by: https://github.com/jansel, https://github.com/mlazos
ghstack dependencies: #149482, #149483, #149484
This fixes most of https://github.com/huggingface/diffusers/issues/10795,
except for `torch.Tensor._make_subclass`, which will be fixed in a
subsequent patch.
The relevant tensor subclass from the aforementioned issue is defined
here: fbf6b856cc/src/diffusers/quantizers/gguf/utils.py (L398-L435).
There are two things to note about the tensor subclass:
1. it calls `super().__torch_function__`, which is
`torch._C._disabled_torch_function_impl`, so this patch updates
`SuperVariable.call_method` to handle it (we can't do a simpler
polyfill due to some bug with `var_getattr` raising
`NotImplementedError`, which forgot to restore symbolic context).
2. it sets and reads attributes (`quant_type`), and
defines new methods (`as_data`), so this patch adds support for those.
3. it has a `__init__`, which Dynamo needs to trace through in
`TensorSubclassVariable.call_function`.
Differential Revision: [D71906140](https://our.internmc.facebook.com/intern/diff/D71906140)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149482
Approved by: https://github.com/jansel, https://github.com/mlazos
This patch effectively ignores traceable_tensor_subclasses, allowing
Dynamo to always try tracing into the `__torch_function__` of tensor
subclass. This helps us with 2 things:
1. allowing users to directly benefit from better compilation of tensor
subclass, by just upgrading pytorch, without having to change legacy
library code (see earlier patches in the stack for examples).
2. potentially exposing more issues in compiling tensor subclass, so we
can get signals and improve them.
As a consequence, it exposed and fixes 2 subtle bugs:
1. In `build_torch_function_fn`, we could get
`torch._C._disabled_torch_function_impl` because we have a
`Parameter` subclass without `__torch_function__` override or if we
have a tensor subclass with `__torch_dispatch__` override. We graph
break on this for now, and plan to add support -- the logic for
simulating `torch._C._disabled_torch_function_impl` is already in
`SuperVariable`, we just need to reuse it.
2. Sometimes we create `SyntheticLocalSource` and need to remove all the
guards installed on it, but we only removed the ones whose source
_is_ the created synthetic source `s`, but forgot about chained
source like `s.foo`, this showed up as
`SYNTHETIC_LOCAL['tmp_0'].__torch_function__.__func__`.
Differential Revision: [D71906141](https://our.internmc.facebook.com/intern/diff/D71906141)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149792
Approved by: https://github.com/jansel, https://github.com/mlazos
ghstack dependencies: #149482, #149483, #149484
This fixes most of https://github.com/huggingface/diffusers/issues/10795,
except for `torch.Tensor._make_subclass`, which will be fixed in a
subsequent patch.
The relevant tensor subclass from the aforementioned issue is defined
here: fbf6b856cc/src/diffusers/quantizers/gguf/utils.py (L398-L435).
There are two things to note about the tensor subclass:
1. it calls `super().__torch_function__`, which is
`torch._C._disabled_torch_function_impl`, so this patch updates
`SuperVariable.call_method` to handle it (we can't do a simpler
polyfill due to some bug with `var_getattr` raising
`NotImplementedError`, which forgot to restore symbolic context).
2. it sets and reads attributes (`quant_type`), and
defines new methods (`as_data`), so this patch adds support for those.
3. it has a `__init__`, which Dynamo needs to trace through in
`TensorSubclassVariable.call_function`.
Differential Revision: [D71906140](https://our.internmc.facebook.com/intern/diff/D71906140)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149482
Approved by: https://github.com/jansel, https://github.com/mlazos
This patch updates existing `test_return_..._subclass` tests in
`test/dynamo/test_subclasses.py`, so that they end up invoking the
`__torch_function__` method of the newly constructed tensor subclass
instnaces.
This exposes a bug in `TensorVariable.method_as_subclass`, where it
forgot to grab the `__func__` out of `__torch_function__`, which led to
the an error down the line.
This patch fixes `TensorVariable.method_as_subclass` by centralizing how
we extract and wrap torch function, in `build_torch_function_fn`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149481
Approved by: https://github.com/jansel
PR does following
* Turns `inference_mode` to False and `no_grad` for `convert_frame`, if the inference_mode is on globally.
* Turns off inference_mode for fake tensor prop. This ensures that converting from real inference tensor to a fake tensor removes the inference-ness.
* Graph breaks on is_inference and is_inference_mode_enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149321
Approved by: https://github.com/jansel, https://github.com/zou3519
This adds a strict mode `TORCHDYNAMO_UNBACKED_STRICT` to prevent graph breaking when we guard on data dependent. This is a better UX for those who are actively trying to make their model more dynamic, but aren't close enough to full graph to use that flag directly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147342
Approved by: https://github.com/laithsakka
BuiltinVariable.call_hasattr() overrides the base class - but actually behaves differently. The base is `obj.call_hasattr(tx, attr)` but BuiltinVariable's version is `<unused>.call_hasattr(tx, obj, attr)`.
The BuiltinVariable version is used as a pattern from `call_self_handler()` for `BuiltinVariable(hasattr)`. I think the other version is just used for internal `hasattr(obj, name)` so I renamed that one to `call_obj_hasattr`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145551
Approved by: https://github.com/anijain2305
This patch addresses the renaming part of #133027, specifically, it
renames the following and adds documentation for relevant classes.
1. `VariableTracker.mutable_local` to `mutation_type`
2. `MatableLocal `to `ValueMutationNew`
3. `MutableSideEffects `to `ValueMutationExisting`
4. `MutableLocalSource` to `SourceType`
5. `MutableLocalSource.Local` to `New`
Note that (2), (3) and (5) are mainly to bring consistency between them
and `AttributeMutationNew`, `AttributeMutationExisting`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139339
Approved by: https://github.com/jansel, https://github.com/mlazos, https://github.com/anijain2305
