Summary: The FlexAttention path uses `_exchange_device`, so it will be needed eventually for MTIA as well.
Test Plan: `buck2 test fbcode//mtia/host_runtime/torch_mtia/tests:test_torch_mtia_api -- test_exchange_device`
Reviewed By: chaos5958
Differential Revision: D70072059
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149322
Approved by: https://github.com/chaos5958
Doing this removes the need of collecting `id` and therefore facilitates serialization. It also improves readability with recompilations. Earlier, recompile message will just show the `id`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149228
Approved by: https://github.com/jansel
This is a new version of https://github.com/pytorch/pytorch/pull/148561 fixing the ROCM test failure
Putting this up for a first pass review, though I will likely make a bunch of changes before landing to add more features, etc.
This diff implements a first version of a static CUDA kernel launcher in `torch._C`. The goal here is to take a cubin file and some metadata from a CompiledKernel from `triton`, and launch the cubin file directly.
Background doc: https://docs.google.com/document/d/1rjRcHl6MfauHG30nCoQX-9UKvKyIs4WWMy_GsGyqb9g/edit?tab=t.0#heading=h.ut5lf39lzq66
Normally, using triton's CompiledKernel.make_launcher(), we would pay the cost of codegenning C++ and running it at compile time. With this new approach, we can use one statically compiled library to launch the kernel.
The tradeoff here is that this new kernel launcher will not be able to use codegen to deal with different lengths/types of arguments. So we use templating to handle up to 10 arguments for now. We also allocate 8 bytes on the stack per argument no matter the argument type, which can take more memory than codegenning. On the other hand, we improve compile time on cold and warm start by not having to call the C++ compiler at all.
This diff does not add the launcher to torch, but introduces a basic test suite.
A list of TODOs that are not yet complete:
- Handle `nvTmaDesc` and `cuTensorMap`, which triton handles
- Embed the grid logic instead of passing in gridX,Y,Z
- Handle launch_enter and exit hooks? (Not sure if inductor has these)
- Benchmarking to see if there's runtime performance loss
- Probably lots of features of the triton C++ generated code that I haven't handled yet.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149238
Approved by: https://github.com/oulgen
we use dummy tensors in our initial trace, so we should never inline. the subclass dispatch might not support the dummy tensor, e.g. DTensor accumulate grad will check that both param and grad are DTensors
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149014
Approved by: https://github.com/jansel
ghstack dependencies: #149064
Putting this up for a first pass review, though I will likely make a bunch of changes before landing to add more features, etc.
This diff implements a first version of a static CUDA kernel launcher in `torch._C`. The goal here is to take a cubin file and some metadata from a CompiledKernel from `triton`, and launch the cubin file directly.
Background doc: https://docs.google.com/document/d/1rjRcHl6MfauHG30nCoQX-9UKvKyIs4WWMy_GsGyqb9g/edit?tab=t.0#heading=h.ut5lf39lzq66
Normally, using triton's CompiledKernel.make_launcher(), we would pay the cost of codegenning C++ and running it at compile time. With this new approach, we can use one statically compiled library to launch the kernel.
The tradeoff here is that this new kernel launcher will not be able to use codegen to deal with different lengths/types of arguments. So we use templating to handle up to 10 arguments for now. We also allocate 8 bytes on the stack per argument no matter the argument type, which can take more memory than codegenning. On the other hand, we improve compile time on cold and warm start by not having to call the C++ compiler at all.
This diff does not add the launcher to torch, but introduces a basic test suite.
A list of TODOs that are not yet complete, will do in separate diff:
- Handle `nvTmaDesc` and `cuTensorMap`, which triton handles
- Embed the grid logic instead of passing in gridX,Y,Z. With https://github.com/pytorch/pytorch/pull/147583, we should be able to handle all of the grid logic directly in _StaticCudaLauncher.launch_kernel, and get rid of the python evaluation.
- Handle launch_enter and exit hooks? (Not sure if inductor has these)
- Benchmarking to see if there's runtime performance loss
- Hooking it up with a config to inductor
- Testing harness to test against torch generated triton kernels
Differential Revision: [D69926783](https://our.internmc.facebook.com/intern/diff/D69926783/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148561
Approved by: https://github.com/aorenste, https://github.com/syed-ahmed
Summary: This DIFF https://www.internalfb.com/diff/D70471332 removed input "grid" when calling triton kernel. PyTorch execution trace need to make the appropriate change. It includes capturing ET and replay ET.
Test Plan:
buck2 run mode/opt caffe2/test:test_profiler_cuda -- profiler.test_execution_trace.TestExecutionTraceCUDA.test_execution_trace_with_pt2_cuda
buck2 run mode/opt param_bench/fb/integration_tests:test_et_replay
Differential Revision: D71152464
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149159
Approved by: https://github.com/sraikund16, https://github.com/jansel
Summary: no-except builds are terminating when this exception is thrown. We should proactively check if a backend is available before calling has_hooks, instead of trying and failing.
Test Plan: CI
Differential Revision: D71144456
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149152
Approved by: https://github.com/kwen2501
Summary:
Pytorch unitest hangs when jitting the Tensor kernel. The problem exists for LLVM version >= 18 due to this upstream change: 45bb45f2ae
`IRBuilderBase::CreateCall` will insert the instruction into the BasicBlock by default. And we don't need to explicitly insert the instruction when compiling the tensor kernel.
Test Plan:
## Test with the release toolchain
```
buck test 'mode/dev' //caffe2/test:jit -- --exact 'caffe2/test:jit - test_concat_invariant (test_jit_fuser_te.TestTEFuserDynamic)'
```
## Test with the Buckified toolchain
Apply this D71046097 to select the LLVM libraries.
```
# Build tests
buck build 'mode/dev-asan' //caffe2/test:jit --show-output
```
```
# Run test (Change HASH and paths accordingly)
HASH="b755f1c435832a1e"
ENABLE_FLATBUFFER=0 FB_OVERRIDE_PYBIND11_GIL_INCREF_DECREF_CHECK=1 MKL_NUM_THREADS=1 NO_MULTIPROCESSING_SPAWN=0 OMP_NUM_THREADS=1 PYTORCH_TEST=1 PYTORCH_TEST_FBCODE=1 PYTORCH_TEST_WITH_ASAN=1 PYTORCH_TEST_WITH_DEV_DBG_ASAN=1 PYTORCH_TEST_WITH_TSAN=0 PYTORCH_TEST_WITH_UBSAN=1 SKIP_TEST_BOTTLENECK=1 TENSORPIPE_TLS_DATACENTER=test_dc TEST_PILOT=True TPX_IS_TEST_EXECUTION=true TPX_TIMEOUT_SEC=6000 \
buck-out/v2/gen/$HASH/caffe2/test/__jit__/jit.par --test-filter test_jit_fuser_te.TestTEFuserDynamic.test_concat_invariant
```
Differential Revision: D71046799
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149058
Approved by: https://github.com/dcci, https://github.com/Skylion007
The default value for `run_single_threaded` was wrongly specified in the .cpp file instead of the header, breaking C++-side instantiation of `AOTIModelPackageLoader` with no arguments. This PR fixes this and adds a test for the use case of running with `AOTIModelPackageLoader` instead of `AOTIModelContainerRunner` on the C++ side.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149082
Approved by: https://github.com/desertfire
i'm changing CA initial trace to always trace as dynamic, fixes these errors:
```python
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.2139s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_autograd_python_custom_function_inplace - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_autograd_python_custom_function_inplace
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.0057s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_copy_slices_graph_task_updates - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_copy_slices_graph_task_updates
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.9662s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_inplace_on_view_weak_grad_fn - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_inplace_on_view_weak_grad_fn
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.0077s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_leaf_assignment - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_leaf_assignment
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [5.0485s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_setitem_mask - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_setitem_mask
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
FAILED [0.0102s] test/inductor/test_compiled_autograd.py::TestAutogradWithCompiledAutograd::test_tensor_hooks_inplace_over_view - RuntimeError: !has_symbolic_sizes_strides_ INTERNAL ASSERT FAILED at "/home/xmfan/core/a/pytorch/aten/src/ATen/TensorGeometry.h":63, please report a bug to PyTorch.
To execute this test, run the following from the base repo dir:
python test/test_autograd.py TestAutogradWithCompiledAutograd.test_tensor_hooks_inplace_over_view
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148799
Approved by: https://github.com/jansel, https://github.com/zou3519
Enables clang-tidy rule [`misc-use-internal-linkage`](https://clang.llvm.org/extra/clang-tidy/checks/misc/use-internal-linkage.html). This new check was introduced in Clang-Tidy 18 and is available due to recent update of Clang-Tidy 19.
The check marks functions and variables used only in the translation unit as static. Therefore undesired symbols are not leaked into other units, more link time optimisations are possible and the resulting binaries may be smaller.
The detected violations were mostly fixed by using static. In other cases, the symbols were indeed consumed by others files, then their declaring headers were included. Still some declarations were wrong and have been fixed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148948
Approved by: https://github.com/Skylion007
This PR adds two main parts:
- shim.h stable C APIs into torch::Library APIs
- a higher level API in torch/csrc/stable/library.h that calls into this shim.h + otherwise is self contained
Goal: custom kernel writers should be able to call the apis in the directories above in order to register their library in a way that allows their custom extension to run with a different libtorch version than it was built with.
Subplots resolved:
- Do we want a whole separate StableLibrary or do we want to freeze torch::Library and add `m.stable_impl(cstring, void (*fn)(void **, int64_t, int64_t)` into it
- Yes, we want a separate StableLibrary. We cannot freeze Library and it is NOT header only.
- Should I use unint64_t as the common denominator instead of void* to support 32bit architectures better?
- Yes, and done
- Should I add a stable `def` and `fragment` when those can be done in python?
- I think we do want these --- and now they're done
- Where should library_stable_impl.cpp live? -- no longer relevant
- I need some solid test cases to make sure everything's going ok. I've intentionally thrown in a bunch of random dtypes into the signature, but I still haven't tested returning multiple things, returning nothing, complex dtypes, etc.
- Have since tested all the torch library endpoints. the others can be tested in a followup to separate components that need to be in shim.h vs can be added later
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148124
Approved by: https://github.com/albanD, https://github.com/zou3519, https://github.com/atalman
This PR has multiple changes to `ProcessGroupNCCL` (which unfortunately are related):
1. When async_op=False, we directly launch the collective on "current" stream, instead of a trampoline stream and join back.
- Resolves#147729
- Resolves#146881
- Also saves two event syncs (which have overhead in case of HIP) and one pybind when we call `work.wait()` in distributed_c10d.py on behalf of user.
2. Entirely remove `record_stream` and use CPU-side stashing for managing tensor lifetime against recycling.
- Resolves#147168
3. Remove tensor life management when async_op=False; only use it when async_op=True.
4. To guard against user not calling `work.wait()`, we ask watchdog to unstash tensors after detecting completion of collectives, to prevent us from holding reference to tensors forever. This is a safety net, rather than a service guarantee, see discussion [here](https://github.com/pytorch/pytorch/issues/147168#issuecomment-2660142460).
5. Profile in async_op=False mode would look different -- collective kernels would show up in the same line and compute kernels.
Joint work with @cenzhaometa who wants to remove the event sync overhead.
Cc: @ngimel @awgu @Aidyn-A @skyw @wconstab @leonardo0lyj
Differential Revision: [D70937982](https://our.internmc.facebook.com/intern/diff/D70937982)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148590
Approved by: https://github.com/eqy, https://github.com/Aidyn-A, https://github.com/fduwjj
This has two fixes to improve IPC tensor release performance when using torchft's BabyProcessGroupNCCL.
1. release the IpcMutex when deleting the `ExpandableSegements` object to avoid synchronizing under the lock
2. release the GIL in WorkNCCL destructor since the shared tensor will be destructed there
Test plan:
Run with torchft + torchtitan
```
REPLICA_GROUP_ID=0 NGPU=2 CUDA_VISIBLE_DEVICES=0,1 CONFIG_FILE=./torchtitan/models/llama/train_configs/llama3_8b.toml ./run_train.sh --training.data_par
allel_shard_degree=2 --fault_tolerance.enable --fault_tolerance.group_size=2 --fault_tolerance.replica_id=0 --metrics.log_freq=1 --training.seq_len 4096
...
[rank0]:[titan] 2025-03-07 17:51:31,387 - root - INFO - step: 61 loss: 7.4825 memory: 79.73GiB(83.89%) tps: 317 tflops: 16.34 mfu: 1.65%
```
Check py-spy to verify no bottleneck on IPC lock when creating new shared tensors
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148805
Approved by: https://github.com/Skylion007, https://github.com/fegin, https://github.com/zdevito
as titled, previously the shard_dim_alltoall uses `all_to_all`, which essentially could incur lots of copies if the tensor become non-contiguous during splits, and alltoall itself also incur copies
This PR uses alltoall_single instead, so that we could minimize tensor copies.
tested on all the shard dim change tests and it works properly:
```
pytest test/distributed/tensor/test_redistribute.py -s -k shard_dim_alltoall
```
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148868
Approved by: https://github.com/tianyu-l
I noticed that this op was likely intended to be in the `extern "C"` portion of the file, but it was not added as such in https://github.com/pytorch/pytorch/pull/145250 which means this function is actually not stable/would get mangled by C++.
Following the thread there I am thinking there are two possible solutions:
(1) Since this op was never stable to begin with, and @Xia-Weiwen already landed the fallback, maybe this op is deletable + should get deleted before the 2.7 branch cut
(2) Or we could just move the op to the right portion of the code. While I like just deleting the op, I am hesitant to do in case there's something I haven't considered, so this PR does option 2.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148834
Approved by: https://github.com/desertfire
Latest LLVM introduced two changes related to the `Triple` usage that causes build failures when building pytorch.
## Failure in llvm_codegen.cpp:
Triple is stored in Modules instead of the string: 979c275097
## Failure in llvm_jit.cpp:
Triple argument is removed from LLJITBuilder::... : b18e5b6a36
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148833
Approved by: https://github.com/Skylion007
# Fix typo errors across PyTorch codebase
This PR fixes various spelling errors throughout the PyTorch codebase to improve documentation quality and code readability.
## Changes Made
### Documentation Fixes
- Changed "seperate" to "separate" in multiple files:
- `setup.py`: Build system documentation
- `torch/_library/triton.py`: AOT compilation comments
- `torch/csrc/dynamo/compiled_autograd.h`: Node compilation documentation
- `torch/export/_unlift.py`: Pass population comments
- `torch/export/exported_program.py`: Decomposition table notes
### Code Comments and Error Messages
- Changed "occured" to "occurred" in:
- `test/mobile/test_lite_script_module.py`: Exception handling comments
- `torch/export/_draft_export.py`: Error message text
- `aten/src/ATen/native/cuda/linalg/BatchLinearAlgebra.cpp`: MAGMA bug comment
- `torch/csrc/utils/python_numbers.h`: Overflow handling comment
- `torch/csrc/jit/OVERVIEW.md`: Graph compilation documentation
- `torch/_dynamo/symbolic_convert.py`: Error explanation
### API Documentation
- Changed "fullfill" to "fulfill" in `torch/distributed/checkpoint/state_dict_loader.py`
- Changed "accross" to "across" in:
- `torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp`
- `torch/distributed/distributed_c10d.py`
## Motivation
These changes improve code readability and maintain consistent spelling throughout the codebase. No functional changes were made; this is purely a documentation and comment improvement PR.
## Test Plan
No testing required as these changes only affect comments and documentation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148262
Approved by: https://github.com/janeyx99
Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>
This PR adds two main parts:
- shim.h stable C APIs into torch::Library APIs
- a higher level API in torch/csrc/stable/library.h that calls into this shim.h + otherwise is self contained
Goal: custom kernel writers should be able to call the apis in the directories above in order to register their library in a way that allows their custom extension to run with a different libtorch version than it was built with.
Subplots resolved:
- Do we want a whole separate StableLibrary or do we want to freeze torch::Library and add `m.stable_impl(cstring, void (*fn)(void **, int64_t, int64_t)` into it
- Yes, we want a separate StableLibrary. We cannot freeze Library and it is NOT header only.
- Should I use unint64_t as the common denominator instead of void* to support 32bit architectures better?
- Yes, and done
- Should I add a stable `def` and `fragment` when those can be done in python?
- I think we do want these --- and now they're done
- Where should library_stable_impl.cpp live? -- no longer relevant
- I need some solid test cases to make sure everything's going ok. I've intentionally thrown in a bunch of random dtypes into the signature, but I still haven't tested returning multiple things, returning nothing, complex dtypes, etc.
- Have since tested all the torch library endpoints. the others can be tested in a followup to separate components that need to be in shim.h vs can be added later
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148124
Approved by: https://github.com/albanD, https://github.com/zou3519
This PR has multiple changes to `ProcessGroupNCCL` (which unfortunately are related):
1. When async_op=False, we directly launch the collective on "current" stream, instead of a trampoline stream and join back.
- Resolves#147729
- Resolves#146881
- Also saves two event syncs (which have overhead in case of HIP) and one pybind when we call `work.wait()` in distributed_c10d.py on behalf of user.
2. Entirely remove `record_stream` and use CPU-side stashing for managing tensor lifetime against recycling.
- Resolves#147168
3. Remove tensor life management when async_op=False; only use it when async_op=True.
4. To guard against user not calling `work.wait()`, we ask watchdog to unstash tensors after detecting completion of collectives, to prevent us from holding reference to tensors forever. This is a safety net, rather than a service guarantee, see discussion [here](https://github.com/pytorch/pytorch/issues/147168#issuecomment-2660142460).
5. Profile in async_op=False mode would look different -- collective kernels would show up in the same line and compute kernels.
Joint work with @cenzhaometa who wants to remove the event sync overhead.
Cc: @ngimel @awgu @Aidyn-A @skyw @wconstab @leonardo0lyj
Differential Revision: [D70835197](https://our.internmc.facebook.com/intern/diff/D70835197)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148590
Approved by: https://github.com/eqy, https://github.com/Aidyn-A, https://github.com/fduwjj
This PR has multiple changes to `ProcessGroupNCCL` (which unfortunately are related):
1. When async_op=False, we directly launch the collective on "current" stream, instead of a trampoline stream and join back.
- Resolves#147729
- Resolves#146881
- Also saves two event syncs (which have overhead in case of HIP) and one pybind when we call `work.wait()` in distributed_c10d.py on behalf of user.
2. Entirely remove `record_stream` and use CPU-side stashing for managing tensor lifetime against recycling.
- Resolves#147168
3. Remove tensor life management when async_op=False; only use it when async_op=True.
4. To guard against user not calling `work.wait()`, we ask watchdog to unstash tensors after detecting completion of collectives, to prevent us from holding reference to tensors forever. This is a safety net, rather than a service guarantee, see discussion [here](https://github.com/pytorch/pytorch/issues/147168#issuecomment-2660142460).
5. Profile in async_op=False mode would look different -- collective kernels would show up in the same line and compute kernels.
Joint work with @cenzhaometa who wants to remove the event sync overhead.
Cc: @ngimel @awgu @Aidyn-A @skyw @wconstab @leonardo0lyj
Differential Revision: [D70835197](https://our.internmc.facebook.com/intern/diff/D70835197)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148590
Approved by: https://github.com/eqy, https://github.com/Aidyn-A, https://github.com/fduwjj
This adds `abort` and `shutdown` to `Backend` and `ProcessGroup` objects. This simplifies the logic in `distributed_c10d.py` by having a default noop implementation for all PGs.
This will be useful for torchft and upcoming versions of NCCL which will handle abort correctly. Currently `torchft` would have to call internal methods `_abort` on the PGNCCL object directly but with this change we can now just call `.abort()` and have it work for any PG implementation.
Test plan:
```
pytest distributed/test_backends.py distributed/test_c10d_common.py distributed/test_c10d_pypg.py
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148798
Approved by: https://github.com/kwen2501
This PR adds support for non-functional collectives under `FakeTensorMode` and `fake_pg`. It helps eliminate the patching of collectives for memory and runtime estimation.
It also modifies the `ModTracker` to enable the post-backward hook call for modules whose inputs don't require gradients but parameters do.
For the memory tracking, we now enable tracking DTensor dispatcher for custom dispatch functions like `entropy_loss`.
Dispatcher is only enabled for the memory tracking part and disabled as soon as it is done.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147566
Approved by: https://github.com/weifengpy
