- Extend `test_torch_dispatch_meta_outplace` to test torch ops that do not have an out parameter but have aten op overloads that have out parameters. Additionally, Python decompositions may register `OpOverloadPacket`'s so decompositions need to be tested to ensure all `OpOverloads` still function for the `Meta` key (e.g. if a python decomposition is registered for an aten op `aten.foo` with overloads `[default, out]`, the python function needs to support receiving out arguments)
- Add out parameter wrappers to python decomps for aten ops that have out overloads
CC. @ezyang @albanD @lezcano
Fixes#107713
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107707
Approved by: https://github.com/lezcano
Python decompositions wrapped by `out_wrapper` need to be unwrapped before compiling with TorchScript since:
- `out_wrapper` extends the decompositions signature with an out parameter, however this `out` parameter is not present in the source code of the original decomposition so the resulting `ScriptFunction` will not have an `out` parameter
- `out_wrapper` is in the `torch._prims_common.wrappers` module so its `globals()` are different to the globals of the decomposition to be wrapped. This may cause symbol resolution to fail with the TorchScript compiler since it is compiling the unwrapped decomps source code rather than the wrapper
The python decomposition for `aten.trace` is wrapped as an example, other decompositions are to be fixed in https://github.com/pytorch/pytorch/pull/107707
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109367
Approved by: https://github.com/lezcano
- Update cross-ref FakeMode test to use ShapeEnv. Dynamic ops can now
return an unbacked SymInt. We always accept this as equal to whatever
the real value was.
- Relax test so it works on all classes, not just unittest.TestCase
- Properly wrap the original method, so things like
pytree.mark.parametrize are carried over
- Support dynamic shapes by default for make_fx `tracing_mode="fake"` without symbolifying everything else
Fixes https://github.com/pytorch/pytorch/issues/108927
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108929
Approved by: https://github.com/zou3519
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR adds a `py_context_manager_DEPRECATED` that converts a C++ RAII
guard to an object that may be either used as Python context manager or
as a "Python RAII guard".
We don't convert all of them to Python context manager only due to BC
reasons; people in OSS and internally actually rely on these APIs and I
don't want to break them. We are justified in breaking BC if we wanted
to, but it seemed like too much work for not a lot of gain.
The API is postfixed with "DEPRECATED" to indicate that people should
really use `py_context_manager` (converts C++ RAII guard to Python
context manager) instead.
Test Plan:
- this PR converts all PyTorch usages of _AutoDispatchBelowAutograd to
context manager. I can do the rest in follow-ups.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102579
Approved by: https://github.com/bdhirsh, https://github.com/albanD
Currently, we track 'origins' on IR nodes so that we have some idea about what FX IR nodes contributed to any given fused kernel. However, the origins are dumped into an undifferentiated set, so if you have, e.g., multiple outputs, you cannot easily tell which output corresponds to which FX node.
This PR introduce a more precise notion of tracking "origin_node" which says that the contents of this Buffer/Loop node corresponds EXACTLY to the output of a particular FX node; e.g., if you serialized each intermediate when running the generated inductor code, you could compare them with the corresponding intermediates from the original FX graph.
Tracking origin_node in all cases requires quite a bit of effort, so this PR introduces the tracking on a strictly best effort basis. The logic in torch/_inductor/graph.py sets up the associations, but only when it is "obvious" which IR node should get the assignment, and there is work in torch/_inductor/ir.py for propagating this information around as necessary. Like origins, origin_node is not a true dataclass field (as this would break all existing positional arg call sites), instead, it is added post facto via `__post_init__`. At the moment, it is only valid for Buffer/Loop to have an origin_node, but we could imagine relaxing this in the future.
The payoff is in torch/_inductor/codegen/wrapper.py and torch/_inductor/codegen/triton.py where we currently just print the FX node name and the tensor (but a more useful integration will be coming later.)
I also introduce a debugging tool `debug_ir_traceback` which tracks tracebacks of where IRNodes were allocated, to help you understand why a node doesn't have an `origin_node`.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100110
Approved by: https://github.com/voznesenskym
This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit#
It is essentially https://github.com/pytorch/pytorch/pull/95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1.
What's in the PR:
* nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question.
* The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise.
* PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`)
* Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`)
* I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful.
* Added `constrain_unify` to let you specify two unbacked SymInts must have the same value
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95387
Approved by: https://github.com/voznesenskym
This patch started with only the change in `torch/_prims_common/__init__.py`. Unfortunately, this change by itself fails tests. The reason it fails tests is sym_max produces sympy.Max expression, which impedes our ability to actually reason symbolically about the resulting expressions. We much prefer to insert a guard on `l > 1` and get a Sympy expression without Max in it, if we can. In the upcoming unbacked SymInts PR, we can't necessarily do this, but without unbacked SymInts, we always can.
To do this, we introduce `alternate_impl_if_hinted_methods`. The idea is that if all of the arguments into max/min have hints, we will just go ahead and introduce a guard and then return one argument or the other, depending on the result. This is done by rewrapping the SymNode into SymInt/SymFloat and then running builtins.min/max, but we also could have just manually done the guarding (see also https://github.com/pytorch/pytorch/pull/94365 )
However, a very subtle problem emerges when you do this. When we do builtins min/max, we return the argument SymNode directly, without actually allocating a fresh SymNode. Suppose we do a min-max with a constant (as is the case in `sym_max(l, 1)`. This means that we can return a constant SymNode as the result of the computation. Constant SymNodes get transformed into regular integers, which then subsequently trigger the assert at https://github.com/pytorch/pytorch/pull/94400/files#diff-03557db7303b8540f095b4f0d9cd2280e1f42f534f67d8695f756ec6c02d3ec7L620
After thinking about this a bit, I think the assert is wrong. It should be OK for SymNode methods to return constants. The reason the assert was originally added was that ProxyTensorMode cannot trace a constant return. But this is fine: if you return a constant, no tracing is necessary; you know you have enough guards that it is guaranteed to be a constant no matter what the input arguments are, so you can burn it in. You might also be wondering why a change to SymNode method affects the assert from the dispatch mode dispatch: the call stack typically looks like SymNode.binary_magic_impl -> SymProxyTensorMode -> SymNode.binary_magic_impl again; so you hit the binary_magic_impl twice!
No new tests, the use of sym_max breaks preexisting tests and then the rest of the PR makes the tests pass again.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94400
Approved by: https://github.com/Chillee
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
Optimize unnecessary collection cast calls, unnecessary calls to list, tuple, and dict, and simplify calls to the sorted builtin. This should strictly improve speed and improve readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94323
Approved by: https://github.com/albanD
Fixes#92676
`arange` infers the output dtype from the argument types, but in order to reduce
falling back to ATen, inductor preferred to cast whole number float arguments to
int which gave the wrong output dtype. Instead, this decomposes floating point
arange into the prim equivalent for integers.
This also changes the signature of `prims.arange` to
```python
prims.iota(length, *, start, step, **factory_kwargs)
```
which only supports integers arguments. This is done because calculating the
output size from `start, end, step` is surprisingly complex and liable to off by
one errors so should not be duplicated in each backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93353
Approved by: https://github.com/ngimel, https://github.com/lezcano
This PR is the first step towards refactors the build for nvfuser in order to have the coegen being a standalone library.
Contents inside this PR:
1. nvfuser code base has been moved to `./nvfuser`, from `./torch/csrc/jit/codegen/cuda/`, except for registration code for integration (interface.h/interface.cpp)
2. splits the build system so nvfuser is generating its own `.so` files. Currently there are:
- `libnvfuser_codegen.so`, which contains the integration, codegen and runtime system of nvfuser
- `nvfuser.so`, which is nvfuser's python API via pybind. Python frontend is now exposed via `nvfuser._C.XXX` instead of `torch._C._nvfuser`
3. nvfuser cpp tests is currently being compiled into `nvfuser_tests`
4. cmake is refactored so that:
- nvfuser now has its own `CMakeLists.txt`, which is under `torch/csrc/jit/codegen/cuda/`.
- nvfuser backend code is not compiled inside `libtorch_cuda_xxx` any more
- nvfuser is added as a subdirectory under `./CMakeLists.txt` at the very end after torch is built.
- since nvfuser has dependency on torch, the registration of nvfuser at runtime is done via dlopen (`at::DynamicLibrary`). This avoids circular dependency in cmake, which will be a nightmare to handle. For details, look at `torch/csrc/jit/codegen/cuda/interface.cpp::LoadingNvfuserLibrary`
Future work that's scoped in following PR:
- Currently since nvfuser codegen has dependency on torch, we need to refactor that out so we can move nvfuser into a submodule and not rely on dlopen to load the library. @malfet
- Since we moved nvfuser into a cmake build, we effectively disabled bazel build for nvfuser. This could impact internal workload at Meta, so we need to put support back. cc'ing @vors
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89621
Approved by: https://github.com/davidberard98
This PR moves the definitions for:
* `sym_int`
* `sym_ceil` (used only for `sym_int`)
* `sym_floor` (used only for `sym_int`)
* `sym_float`
from `torch/fx/experimental/symbolic_shapes.py` to `torch/__init__.py`, where `SymInt` and `SymFloat` are already defined.
This removes the need for several in-line imports, and enables proper JIT script gating for #91318. I'm very open to doing this in a better way!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91317
Approved by: https://github.com/ezyang, https://github.com/anijain2305
It turns out that we *do* need to update *_scatter ops to return the exact same strides as their inputs. I added a test to `test/test_functionalization.py`, which now trips thanks to Ed's functionalization stride debugging check. It only actually ends up tripping silent correctness if you try to .backward() on that function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91029
Approved by: https://github.com/ezyang
Use Prims to implement group_norm, group_norm_backward and mean_var
Use `torch._ops.ops` instead of `torch.ops` in numerous subpackages in
order to be able to make them importable from `torch/backend/mps/__init__.py` as this alias is defined in
15af4b1cee/torch/__init__.py (L1095)
is executed last during init process.
Add `__all__` to `torch/backends/mps/__init__.py` as well as alias all imports as private
Add `TestNNMPS.test_group_norm_backward` that validates no NaNs are generated during the backward pass
Fixes https://github.com/pytorch/pytorch/issues/88331
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91190
Approved by: https://github.com/albanD
