I missed a few tests the first time around - this fixes out= op handling for `_return_and_correct_aliasing`, which failed a few tests in the python functionalization <> AOTAutograd PR above.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109662
Approved by: https://github.com/ezyang
ghstack dependencies: #108654
This issue is that `str(torch.ops.aten.conv2d.default._schema)` does not return the same schema that is in native_functions.yaml ([link](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml#L1654)).
Torchscript appears to change the default arg string `int[2] strides=1` to `int[2] strides=[1, 1]`. If you try to parse that with torchgen, torchgen is unhappy (it tries to split arguments on comma, but now we have a comma inside of the default argument).
Fixing the issue directly in torchgen was a bit more painful, so I opted just to undo the transformation that torchscript made: convert `=[1, 1]` back into `=1`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108897
Approved by: https://github.com/ezyang
ghstack dependencies: #106404, #107917
This is mostly a minor fix on top of @soulitzer's PR https://github.com/pytorch/pytorch/pull/107839.
(1) `strides` wasn't going through the new `set_tensor_attr_with_capsule` flow
(2) The dynamic shapes overload for `_make_wrapper_subclass` currently errors when you try to use custom sizes - I removed the error
(3) added a test
I need this later because I'm adding a `__torch_dispatch__` `FunctionalTensor` wrapper subclass, that needs to support dynamic shapes, and also plumb metadata calls to its inner tensor later.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107916
Approved by: https://github.com/ezyang, https://github.com/soulitzer
ghstack dependencies: #107915
This PR adds a `return_and_correct_aliasing()` utility, that wrapper subclasses can use to get correct aliasing. I updated `TwoTensor` to use it, and added some testing that the aliasing of my `TwoTensor` subclass now matches the aliasing behavior of normal tensors.
Right now my test just uses a few hand-picked opinfos (that have varying aliasing behavior). I thought all op infos might be overkill (does that take a while to run?), but I'm happy to add them all if people prefer.
One more general question about this PR: eventually, proper aliasing will be a **requirement** in order for AOTAutograd to handle aliasing/mutations on subclasses properly during compilation. How can we make sure that wrapper subclasses use this API? A few options (from talking to Richard):
(1) Yolo require subclasses to use the API and hope users do as well (what this PR does)
(2) Yolo require subclasses to use the API, but add a kwarg to `_make_wrapper_subclass`, e.g. `manual_aliasing=True`, that torch.compile checks for before allowing the subclass to be used in compilation
(3) Automatically run this API in our python fallback, for **every** tensor subclass that currently implements `__tensor_flatten__` (aka only the "traceable" subclasses)
(4) Automatically run this API in our python fallback, for **every** tensor subclass. This would be a bit higher blast radius, since it would change the existing aliasing behavior of wrapper subclasses. Maybe.. this is the right thing to do though?
Either way, my tentative plan is to do (1) to unblock, and revisit this later once we want to come up with public docs + a more general "tensor subclass in PT2 requirements" plan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107915
Approved by: https://github.com/ezyang
**Update:** Made refactor of the original PR. See the original description below, but here I'll describe the updates:
(1) TLS changes in `TorchDispatchModeTLS.h/cpp`.
I added a `TorchDispatchModeKey` enum, that (for now) just contains PROXY and FAKE. The ModeTLS used to just contain a `std::vector<std::shared_ptr<c10::SafePyObject>>` corresponding to the mode stack. It now **also** contains a separate array of "infra modes", indexed by mode key (PROXY and FAKE, with a new addition, FUNCTIONAL, coming later in the stack).
`TorchDispatchModeTLS::push_onto_stack` and `TorchDispatchModeTLS::pop_stack` are now a bit more complicated. Pushing accepts an optional mode_key, which if set, tells us to add the given mode directly to our "infra_modes" array. Popping will first check the "user mode" stack, before trying to pop anything from the infra mode stack. It also optionally returns the mode key of the mode we popped if there was one - that way if we push that same mode back onto the TLS later, we know where it goes.
`TorchDispatchModeTLS::dispatch_mode_enabled()` now accepts an optional `skip_infra_modes` param, so you can separately query if there are "any modes at all", or if there are "any user modes".
`TorchDispatchModeTLS::get/set/unset_mode()` all take in a mode key, and get/set/unset the mode at that particular mode key (meaning they are only meant to be used for infra modes).
There were also some mild codegen changes to support the new enum
(2) `fake_tensor.py/proxy_tensor.py/_python_dispatch.py`
The way I tell the infra that certain subclasses/modes are "infra" is through the enum: I gave `FakeTensor` and `FakeTensorMode` a `self._mode_key = torch._C.TorchDispatchModeKey.FAKE`. `TorchDispatchMode.__enter/exit__()` (in `_python_dispatch.py` now check if the current mode has a mode key, and if so they plumb it into any `push_onto_stack()` calls (which eventually instructs `TorchDispatchModeTLS` where to put the mode). Same thing for `ProxyTorchDispatchMode`.
I also had to change both of these mode's enter/exit, to handle the fact that there can no longer be multiple proxy/fake modes on the mode stack at once. I updated them both to have a `self.enter_stack: List[Optional[TorchDispatchMode]]` - whenever we push a given mode in `__enter__`, we remove the current ambient fake/proxy mode from the mode stack, and save it in `enter_stack`, so that on exit we can reset the state properly.
(2) dispatching logic in `python_arg_parser.cpp`
This is where the core dispatching logic changes are. I added two helpers, `dispatch_on_subclass()` and `dispatch_on_mode()`. The overall dispatching order is now:
```
(a) dispatch_on_mode() # try user modes first (where the mode stack automatically considers infra modes last)
(b) dispatch_on_subclass() # try user subclasses next (skipping infra subclasses)
(c) dispatch_on_subclass() # try infra subclasses next (skipping user subclasses)
```
Note that we still want "user subclasses" to run before "infra modes". As Ed helped me realize, this will work today: If proxy/fake modes in step 1, they'll return NotImplemented if they see a user subclass, allowing us to redispatch to the user subclass.
How do (b) and (c) distinguish between user and infra subclasses? Infra subclasses (FakeTensor, and later FunctionalTensor) are required to have a `_mode_key` hidden on the subclass - so we filter via arguments that do/don't have the _mode_key.
(3) I also changed `DoubleTensor` to `TwoTensor` to minimize confusion (@albanD pointed out that DoubleTensor would be easily confused with `torch.FloatTensor` and friends).
----- original description below -----
The main purpose of this PR is to fix the "ordering problem" between torch_dispatch modes, where we want to ensure that our Fake and Proxy dispatch modes always run **after** any dispatch modes created by the user, regardless of where they are in the stack. See this doc for more details: https://docs.google.com/document/d/1COQ291nOZvtFnzGTQMJqoYZ3sttEYFw_7HbfSyL8gcA/edit
Full set of changes below. I ended up including a few semi-related changes in this PR that I documented - but if folks would rather I separate them out, happy to try to do that.
**(1) Add dedicated TLS slots for FakeTensorMode and ProxyTensorMode**
This is the main component of this PR. There are two new slots, `TorchDispatchModeTLS.fake_mode_` and `TorchDispatchModeTLS.proxy_mode_`, which correspond to a single "global" fake and proxy mode. There is now an invariant that `torchDispatchModeState.stack_` can never contain either of these modes.
I also added a `TorchDispatchModeTLS::maybe_highest_mode()` helper that consults the `stack_` as well as both the proxy and fake slots, and returns the highest priority mode - this is because there are a few places in the codebase where we legitimately want to get the highest priority mode, *including* fake or proxy, if one is set.
This also made the implementations of the existing `disable_proxy_modes_tracing()` and `get_innermost_proxy_mode()` marginally simpler.
**(2) Updated the dispatching logic in handle_torch_function_no_python_arg_parser()**
This is the function that actually figures out which torch_dispatch implementation to call, given the current mode stack and tensor subclass inputs. This function got marginally more complicated as part of the refactor: First we inspect the mode stack and any non-fake subclass inputs. Then we check for the proxy mode slot. Then we check for the Fake mode slot, before finally checking for any fake subclass inputs.
**(3) new python `_get_fake_tensor_mode()` and `_get_proxy_tensor_mode()` API's**
Before, if you wanted to see if proxy or fake modes were active in python, you would have to consult the mode stack. Since these two modes are no longer part of the actual mode stack, I added two new API's to directly check if either proxy or fake modes are active.
**(4) Allow traceable tensor subclasses to access storages from python**
This is convenient later in the stack, where AOTAutograd needs to detect aliasing of inputs and outputs, where those inputs and outputs might be tensor subclasses. Previously, `x.untyped_storage()` would raise an error if `x` was a subclass. In this PR, I tried to relax this constraint as little as possible: `THPVariable_storage()` will only try to return a storage to python if the tensor subclass that you are passing in is "traceable"
**(5) Fixed subclass fakeification**
@wanchaol recently added support to be able to fakeify tensor subclasses. That fakeification logic works in most cases, but there is one case it doesn't handle: autograd metadata. In particular, since autograd sees our tensor subclasses and not their desugared tensors, we need to make sure that our fakeified subclass has the same autograd metadata as the original subclass. I updated `meta_utils.py` to make sure that the autograd metadata is correct.
**(6) make tensor subclasses resizeable**
Previously we didn't allow tensor subclasses to be resizeable. I ran into an issue where fakeifying a tensor subclass occasionally requires swapping out its storage, which can involve resizing the tensor. Mechanically, this required updating `at::for_blob()` to expose a way to request that the tensor that you create has resizeable storage, and then using this new API in `_make_wrapper_tensor()`.
**(7) Added a basic DoubleTensor subclass for testing**
I use this subclass more later in this stack in my AOTAutograd tests - but it serves as a simple subclass example to test the dispatch ordering in this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104482
Approved by: https://github.com/ezyang
ghstack dependencies: #107415
This was discussed in feedback from the original version of my "reorder proxy/fake" PR. This PR allows calls to `tensor.untyped_storage()` to **always** return a python storage object to the user. Previously, we would error loudly if we detected that the storage had a null dataptr.
Instead, I updated the python bindings for the python storage methods that I saw involve data access, to throw an error later, only if you try to access those methods (e.g. `storage.data_ptr()` will now raise an error if the data ptr is null).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107417
Approved by: https://github.com/albanD, https://github.com/ezyang, https://github.com/zou3519
This PR moves most custom op related tests from
test/test_python_dispatch.py to test/test_custom_ops.py. Motivation is
that I had a difficult time finding the custom op tests inside
test_python_dispatch.py.
This doesn't preserve blame, but it's OK - I'm the only person who has
really touched the moved tests so far :).
Test Plan:
- run tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106036
Approved by: https://github.com/bdhirsh, https://github.com/soulitzer
`register_functional_op`:
- constructs the functional variant of an op
- registers a functionalization kernel to the op
To get this to work:
- `register_functional_op` makes assumptions that it checks about the
op's schema. In particular, the op is not allowed to return anything it
mutates. We can relax these constraints in the future.
- We add a "boxed" python functionalization kernel that handles this
case.
I'm not actually sure (or convinced) this should be public API or how
it should work. If we want this to be public, then it should probably be
a torch.library API, but does that also mean we should give the same
lifetime guarantees? If so, then it would be up to the user to construct
a Library object to actually register the functional variant onto.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102293
Approved by: https://github.com/bdhirsh
We did this for TestCustomOp, now we are applying the same thing to
TestPythonRegistration.
This PR:
- changes TestPythonRegistration to register new ops under a single
namespace (self.test_ns)
- clean up the namespace by deleting it from torch.ops after each test
is done running.
This avoids a problem where if an op is re-defined, torch.ops.myns.op
crashes because we do some caching. The workaround in many of these
tests have been to just create an op with a different name, but this PR
makes it so that we don't need to do this.
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102292
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
When investigating failures in https://github.com/pytorch/pytorch/pull/100017 I realized that we were reentering FakeTensorMode even though there was already one on the stack. Although we have attempted assert for these cases in the past, e.g., as in https://github.com/pytorch/pytorch/pull/97186 it seems that the existing protections were insufficient.
In this particular case, the reapplication of FakeTensorMode was due to an interaction with NotImplemented multiple dispatch handling. If proxy tensor mode detects an unrecognized tensor type (this includes FakeTensor, if it is not tracked with a proxy), it will return NotImplemented to give this tensor a chance to unpack itself into proxyable operation. However, this is never the right thing for FakeTensor, where no unpacking is possible. However, today, FakeTensor attempts to reapply the FakeTensorMode, resulting in FakeTensorMode being twice on the stack.
This PR does a number of things:
* It adds an assert in `FakeTensorMode.__torch_dispatch__` that you must not already have this mode on the stack, this is ALWAYS an error
* It modifies `FakeTensor.__torch_dispatch__` to return `NotImplemented` if the mode is already active. This prevents us from readding the mode on the stack
* It adds a new logging artifact `not_implemented` which you can use to get debug logs about all of the times a `__torch_dispatch__` handler returned NotImplemented and why it did so. Your subclass has to manually opt into this logging, but I inserted the necessary logs for ProxyTensorMode and FakeTensor(Mode)
* `with fake_mode` now no-ops if the fake mode is already on the stack, which is what users want anyway
* I am BREAKING pre-autograd tracing, because it is currently doing something weird with the original C++ mode stack. Brian is going to follow up with a fix next week.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102091
Approved by: https://github.com/thiagocrepaldi, https://github.com/eellison, https://github.com/wanchaol, https://github.com/bdhirsh
This PR adds an explicit API for registering a backward formula for a
CustomOp. In the end state, we will likely have this explicit API and a
magic API (which is sugar on top of an explicit API), since different
parties of users prefer different ones.
Concretely, to define a backward formula for a CustomOp:
- a user must provide us a "save for backward" function that accepts
(inputs, output) and returns exactly what they want saved for backward
- a user must provide us a "backward" function that accepts
(ctx, saved, *grads) and returns us the grad_inputs. The grad_inputs
are returned as a dict mapping str to a gradient.
Please see the changes in custom_op_db.py for examples of the API.
There are a number of pieces to this PR and I'm happy to split it if it
helps. They are:
- The actual APIs for specifying the two functions
(impl_save_for_backward, impl_backward)
- The autograd kernel: we take the functions the user give us and
construct an autograd.Function object that we then register to
the Autograd dispatch key
- Indirection for the autograd kernel. We add a layer of indirection so
that one can swap out the autograd kernel. This is necessary because by
default, we register an "autograd not implemented" kernel as the
Autograd implementation but then swap it for the actual kernel when the
user provides it.
Test Plan:
- We apply this API to give backward formulas for things in
custom_op_db. We then hook up custom_op_db to the Autograd OpInfo tests.
- Various tests in test_python_dispatch.py to check error cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101824
Approved by: https://github.com/ezyang
torch/custom_op.py is getting long, and the autograd pieces are going to
make it even longer. I'm planning on just organizing the files under
a torch/_custom_op folder.
Note that the imports now look a bit crazy (from torch._custom_op.impl
import...) but they will look more OK when we figure out the plan to
make custom_op public (coming later).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101823
Approved by: https://github.com/ezyang, https://github.com/albanD, https://github.com/bdhirsh
`__del__` is a bit difficult to use, because when it is called, it is
not guaranteed that anything it uses has not been cleaned up.
Ed tells me he got the following exception one day, which is what
prompted this PR.
```
Exception ignored in: <function Library.__del__ at 0x7fa36d211e50>
Traceback (most recent call last):
File "/data/users/ezyang/a/pytorch/torch/library.py", line 139, in
__del__
AttributeError: 'NoneType' object has no attribute 'remove'
```
One solution is to use weakref.finalize, which lets one define a
function to be run when the object is deleted that can hold references
to specific things it needs.
Another solution is to just check if the object is None, but I like the
weakref solution better.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101829
Approved by: https://github.com/ezyang
The PyTorch Dispatcher's "no kernel found for DispatchKey" error message
is a bit long and winded. This PR adds a way to add a custom error
callback and changes the CustomOp API to use the custom error callback
to deliver better error messages.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101015
Approved by: https://github.com/ezyang
Previously, to specify e.g. int[], a user needed to do Tuple[int, ...].
This PR changes it to Sequence[int].
Bikeshedding: we could totally just use List[int] instead. The types
that the user gives us that we use to infer a schema is not entirely
faithful: for example, we convert `int` to SymInt.
I didn't feel strongly between Sequence[int] and List[int] so I went
with the more faithful one, plus Python recommends that you use Sequence
for input arguments (over list or tuple), though we don't subscribe to
that philosophy in general.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101190
Approved by: https://github.com/bdhirsh
This PR tells the custom op tests to destroy all custom ops with
specified namespace after each test.
The general problem is that if a test fails, the custom op isn't cleaned
up. We could fix this via try-finally, but using a tearDown method
seemed like a nice O(1) solution.
Test Plan:
- deleted some foo._destroy, verified that the test suite passes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100980
Approved by: https://github.com/soulitzer, https://github.com/bdhirsh
Previously the error message went through torch.library. This PR changes
it so that on each custom_op.impl_* call:
- we store a (function, location) tuple
- if a (function, location) tuple exists already, then we raise an
error.
This logic already existed for the abstract impl (the impl for meta and
fake tensors), so this PR just extends it to the others.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100979
Approved by: https://github.com/bdhirsh, https://github.com/soulitzer
Enables PyLint error codes implemented in ruff. These are un-opinionated static analysis checks on Python code that finds common bugs. After running all the PLE error codes that are implemented in ruff, I fixed the bugs, added a few ignores for malformed Python code that is part of our JIT test script, and finally added a few ignores for a false positive on PLE0605 and submitted an issue upstream to fix in ruff https://github.com/charliermarsh/ruff/issues/4345 .
Common bugs found here include analysis for malformed logging format calls, bad string format calls, invalid escape sequences, and more.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101079
Approved by: https://github.com/malfet
This adds a new operator debugprims::load_storage which does the unusual thing of loading a tensor from disk (via ContentStoreReader). This will be used in a later PR to implement delta debugging in the minifier, even when the repro is too big to fit into memory. The way it works is that you specify a name of the tensor you want to load, as well as enough metadata to reconstruct the tensor, if the store isn't available. If there is an active content store, we read and return the tensor from that store; otherwise we use `rand_strided` to create it.
I needed some infra improvements to do this:
* `custom_op` now supports factory functions. Factory functions have to be registered specially via `impl_factory`
* I modified `clone_input` to also support dtype conversion, which I use to change the dtype of a loaded tensor if necessary.
* ContentStore needs to work with a device argument, so we torch.load directly to the correct device. This is for fake tensor support.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100519
Approved by: https://github.com/zou3519, https://github.com/anijain2305
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
@custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
@custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100127
Approved by: https://github.com/ezyang
This PR makes a CustomOp live forever. The motivation for it living
forever is that:
1. It doesn't matter to a user if it lives forever or not
2. it is a higher-level abstraction over OpOverload, and OpOverload
assumes that OpOverload lives forever.
The only place where it matters that CustomOp lives forever is testing:
I don't want to generate random names for my CustomOp objects. To
resolve the testing problem, This PR adds a CustomOp._destroy() that
clears all the C++ state, including the OpOverloadPacket, that is
associated with the CustomOp object.
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100114
Approved by: https://github.com/ezyang
This PR:
- adds an abstract registration API for CustomOp (CustomOp.impl_abstract)
that is used for both FakeTensor and meta tensors
- deletes CustomOp.impl_meta
The user story behind this API is that it is the one-stop shop for
registering implementations for data-less Tensors, i.e. FakeTensor and
Meta tensor.
The abstract implementation provided by the user:
- gets registered as the FakeTensor implementation AND the meta formula
- can be written like a regular meta formula. If the user decides that
they need something more special (i.e. data-dependent output shape),
then they are able to query a current context object (FakeTensorImplCtx)
that has methods to construct new unbacked symints.
Caveats:
- we really need to make FakeTensor/FakeTensorMode public. Otherwise,
there isn't a way for the user to interactively test that their abstract
implementation is correct without running through large pieces of the
PT2 stack (make_fx or torch.compile).
- We do not memoize the symints produced by
ctx.create_unbacked_symint(). It is possible to do this in the
future, but it is difficult to do soundly and I am not convinced of
the utility outside of the nonzero() usecase mentioned in #95399
Public API:
- More docs will come when we actually expose this API to users by
putting it in a public namespace, unless you folks want it now.
- The APIs mentioned in `__all__` are the ones that are intended to be
public.
Test Plan:
- Updated existing custom_op_db operators
- Added new numpy_nonzero and numpy_nms operations that test operations
that have data-dependendent output shape.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99439
Approved by: https://github.com/ezyang
This PR introduces CustomOp, a wrapper around a dispatcher operator that allows
users to define custom operators. It adds the skeleton for CustomOp and
some very simple behavior: as of this PR:
- one can create a CustomOp for an operator that does not have inplace or aliasing
- give it CPU/CUDA and Meta implementations
- and trace it into a graph via make_fx.
The design follows
https://docs.google.com/document/d/19Uc5OUCA187q9BZggJb70RT2ZoSTDoG5QQkJkZwd25M/edit
Concretely, we implement the following things mentioned in the doc in this PR:
- Entrypoint 1 (CustomOp.define, creating a new custom operator)
- impl (to define device-specific code) and impl_meta (to define meta
formulas)
The goal for the short term is to get the code to a state where it can be trialed
by the export folks. On top of this PR, the blockers are:
- adding Entrypoint 3 (CustomOp.from_existing)
- adding a way to do data-dependent shape formulas
These will come in future PRs since this one is getting long.
Things that will come in the longer-near-term (before 2.1):
- adding the other entrypoints mentioned in the doc (2 & 3)
- more safety checks and better error messages
- support for views and mutation
- support for defining autograd formulas
- support for functionalization
- making this API public (it's private right now).
Test Plan:
- added a new test case, TestCustomOp. It mostly tests a bunch of error
cases.
- added OpInfos for custom operators and hooked these up to
test_proxy_tensor to test that they work with make_fx. These custom
operators were based off of the ones in the autograd_function_db.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98440
Approved by: https://github.com/ezyang
In C++ we have TORCH_LIBRARY_FRAGMENT. This PR adds the same
functionality to the Python torch.library API.
The motivation for this is: for the simple custom op API, we don't want
users to need to deal with Library objects. One way to hide this from
users is to create library fragments.
Test Plan:
- tests that you can create multiple fragments and def+impl operators on each.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98439
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
Running an operator registered in python returning a symint will result in the following error:
```
RuntimeError: Unable to cast Python instance of type <class 'torch.SymInt'> to C++ type 'long'
```
The interaction of 2 things make the issue being triggered:
- We use boxed kernel here. For boxed kernel, we need convert py::object to IValue in torch/csrc/autograd/python_variable.cpp pushPyOutToStack .
- In the schema parsing code in torch/csrc/jit/frontend/schema_type_parser.cpp SchemaTypeParser::parseFakeAndRealType , if a SymInt is found, we register a Int type instead (not sure why we do this), and register SymInt as the real type.
The result is we would convert an SymInt to int in pushPyOutToStack and cause the issue.
The fix is to use real type when we convert py::object to IValue.
BTW, registering the same op using C++ API does not trigger the issue.
```
TORCH_LIBRARY(clib, m) {
m.def("sqsum(SymInt a, SymInt b) -> SymInt", [](SymInt a, SymInt b) -> SymInt {
return a * a + b * b;
});
}
```
The reason is, the kernel registered in C++ is unboxed kernel and it does not trigger the code path above that converts an py::object to IValue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95240
Approved by: https://github.com/larryliu0820, https://github.com/ezyang
Applies some more harmless pyupgrades. This one gets rid of deprecated aliases in unit_tests and more upgrades yield for loops into yield from generators which are more performance and propagates more information / exceptions from original generator. This is the modern recommended way of forwarding generators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94309
Approved by: https://github.com/albanD
We would handle py::error_already_set correctly from pybind11 bindings,
but not from our regular TH bindings, which meant that anything from
an inner pybind11 function call was getting unconditionally transformed
into a RuntimeError. Not too many cases where we do this, but
PySymNodeImpl was one of them.
To test this, I need to raise a non-RuntimeError from a function which
is invoked from pybind11 and then propagated to a non-pybind11 call
site. I introduce GuardOnDataDependentSymNode for expressly this
purpose (this is how I discovered the bug anyway.)
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93238
Approved by: https://github.com/Skylion007, https://github.com/albanD
