I still don't really understand the original purpose of that env var, but it appears that its usage is completely disconnected from MemPools and from `ncclMemAlloc`/`Free`. In fact, when that env var is set, we invoke `ncclCommRegister` for _all_ NCCL communicators for _all_ the memory segments managed by the allocator (both the global ones, allocated with `cudaMalloc`, and the ones in private MemPools), and we do that both for the segments that already exist when the PG is initialized and for all segments that will be allocated later.
I'm reworking the code a bit, by using a few helper functions, whose name should make this behavior clearer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150682
Approved by: https://github.com/kwen2501
ghstack dependencies: #150681
This consists mainly in two changes:
- ensure we can reliably obtain the device from a `NCCLComm` object (there was one constructor which didn't set the device)
- use a RAII pattern for acquiring the lock to the global dictionary of `NCCLComms` (which ensures the lock is released in case of exceptions)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150681
Approved by: https://github.com/kwen2501
Detail of the issue:
If PyTorch issues send/recv to each 2 rank comm, and these comms are managed by a single ProcessGroupNCCL instance, then comms need to abort either in sequence or in group.
I.e. the following sequential abort will cause hang in NCCL. recv(..., comm0, stream);
send(..., comm1, stream);
abort(comm1);
abort(comm0);
Fixes#119797
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150690
Approved by: https://github.com/kwen2501
When debugging FR missing dump and missing dump logs, I have couple initial findings:
1. On the same rank, if a second watchdog timeout triggers on a different PG(or subPG), that watchdog thread will immediately throw exception instead of sleeping. We want to fix that by still making the watchdog thread to wait for 1 min.
2. The FR dump takes about 900ms to 1200ms so, we are not checking the store frequently enough. But instead of changing the frequency from 1sec to 300ms, we finally decided to just let all ranks just sleep for 1 min universally rather than using a promise.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150652
Approved by: https://github.com/kwen2501
Summary:
Profiler side of memory snapshot.
1. Add API to actually do snapshot when client interface is called
2. Add ifdefs to builds so that kineto hooks snapshot correctly.
Design Philosophy: There is one interesting part of this implementation and it is during export. For export we are callign the python impl of the export rather than CPP even though we are already in CPP. This is because it is better to simply have one path of export rather than 2. Personally, I want there to be parity between auto-trace and on-demand so it if we can limit the side paths then we will have an easier time maintaining this relationship
Test Plan: {F1976563426}
Reviewed By: sanrise
Differential Revision: D70733247
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150559
Approved by: https://github.com/sanrise
Summary: To preserve global state guards we need to make the C++ type serialzable. Using json because it's easier to do and we don't have a lot of data in global state.
Test Plan: test_dynamo -k test_global_state_guard_serialization
Differential Revision: D72410611
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150636
Approved by: https://github.com/williamwen42
Enabled bf16 grouped gemm with an API similar to _scaled_group_gemm, except without scale and fast accum arguments. All transpose variants are enabled, unlike scaled gemm. Ideally we'd factor out a lot more code from scaled gemm, currently there's a lot of repetition between scaled and non-scaled versions. I factored out only a helper kernel that prepares arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150374
Approved by: https://github.com/drisspg
Summary:
We used RAIIAtenTensorHandle for ConstantMap, where RAIIAtenTensorHandle
is a unique_ptr, indicating that all memory handling is by the
AOTInductor internally.
In this PR, we introduce ConstantAtenTensorHandle which replaces
RAIIATenTensorHandle. This class holds a raw AtenTensorHandle, and also
owns a RAIIAtenTensorHandle if user decides to delegate memory
management to AOTInductor.
This is a prerequisite for user managed buffer, this PR, however only
introduces this class and make sure it works with existing AOTInductor
and has the default behavior identical as using RAIIAtenTensorHandle.
Test Plan:
Existing tests. No change should be introduced within this PR.
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150275
Approved by: https://github.com/chenyang78, https://github.com/desertfire
Summary:
Splitting the type definition of ConstantType into a separate header because it's needed by Sigmoid OSS but the entire model.h header include cause the following compilation error:
```
2025-04-01T18:12:42.0391272Z FAILED: caffe2/CMakeFiles/torch_cpu.dir/__/torch/csrc/nativert/kernels/AOTICallDelegateKernel.cpp.o
2025-04-01T18:12:42.0417705Z /opt/cache/bin/sccache /opt/cache/bin/clang++ -DAT_PER_OPERATOR_HEADERS -DBUILD_ONEDNN_GRAPH -DCAFFE2_BUILD_MAIN_LIB -DCPUINFO_SUPPORTED_PLATFORM=1 -DFMT_HEADER_ONLY=1 -DFXDIV_USE_INLINE_ASSEMBLY=0 -DHAVE_MALLOC_USABLE_SIZE=1 -DHAVE_MMAP=1 -DHAVE_SHM_OPEN=1 -DHAVE_SHM_UNLINK=1 -DIDEEP_USE_MKL -DMINIZ_DISABLE_ZIP_READER_CRC32_CHECKS -DNNP_CONVOLUTION_ONLY=0 -DNNP_INFERENCE_ONLY=0 -DONNXIFI_ENABLE_EXT=1 -DONNX_ML=1 -DONNX_NAMESPACE=onnx_torch -DTORCH_ENABLE_LLVM -DUSE_C10D_GLOO -DUSE_DISTRIBUTED -DUSE_EXTERNAL_MZCRC -DUSE_RPC -DUSE_TENSORPIPE -DXNN_LOG_LEVEL=0 -D_FILE_OFFSET_BITS=64 -Dtorch_cpu_EXPORTS -I/var/lib/jenkins/workspace/build/aten/src -I/var/lib/jenkins/workspace/aten/src -I/var/lib/jenkins/workspace/build -I/var/lib/jenkins/workspace -I/var/lib/jenkins/workspace/cmake/../third_party/benchmark/include -I/opt/llvm/include -I/var/lib/jenkins/workspace/third_party/onnx -I/var/lib/jenkins/workspace/build/third_party/onnx -I/var/lib/jenkins/workspace/nlohmann -I/var/lib/jenkins/workspace/torch/csrc/api -I/var/lib/jenkins/workspace/torch/csrc/api/include -I/var/lib/jenkins/workspace/caffe2/aten/src/TH -I/var/lib/jenkins/workspace/build/caffe2/aten/src/TH -I/var/lib/jenkins/workspace/build/caffe2/aten/src -I/var/lib/jenkins/workspace/build/caffe2/../aten/src -I/var/lib/jenkins/workspace/torch/csrc -I/var/lib/jenkins/workspace/third_party/miniz-3.0.2 -I/var/lib/jenkins/workspace/third_party/kineto/libkineto/include -I/var/lib/jenkins/workspace/third_party/kineto/libkineto/src -I/var/lib/jenkins/workspace/third_party/cpp-httplib -I/var/lib/jenkins/workspace/aten/src/ATen/.. -I/var/lib/jenkins/workspace/third_party/FXdiv/include -I/var/lib/jenkins/workspace/c10/.. -I/var/lib/jenkins/workspace/third_party/pthreadpool/include -I/var/lib/jenkins/workspace/third_party/cpuinfo/include -I/var/lib/jenkins/workspace/aten/src/ATen/native/quantized/cpu/qnnpack/include -I/var/lib/jenkins/workspace/aten/src/ATen/native/quantized/cpu/qnnpack/src -I/var/lib/jenkins/workspace/aten/src/ATen/native/quantized/cpu/qnnpack/deps/clog/include -I/var/lib/jenkins/workspace/third_party/NNPACK/include -I/var/lib/jenkins/workspace/third_party/fbgemm/include -I/
2025-04-01T18:12:42.0444143Z In file included from /var/lib/jenkins/workspace/torch/csrc/nativert/kernels/AOTICallDelegateKernel.cpp:5:
2025-04-01T18:12:42.0445081Z In file included from /var/lib/jenkins/workspace/torch/csrc/nativert/executor/AOTIDelegateExecutor.h:6:
2025-04-01T18:12:42.0446002Z In file included from /var/lib/jenkins/workspace/torch/csrc/nativert/executor/AOTInductorModelImpl.h:5:
2025-04-01T18:12:42.0447549Z /var/lib/jenkins/workspace/torch/csrc/inductor/aoti_runtime/model.h:78:13: error: function 'RAII_cpuMalloc' is not needed and will not be emitted [-Werror,-Wunneeded-internal-declaration]
2025-04-01T18:12:42.0448656Z RAIIDataPtr RAII_cpuMalloc(size_t num_bytes) {
```
model.h defines RAII_malloc functions directly into anonymous namespace which seems pretty sad. we should do something about it but may not in the current diff.
Test Plan: CI
Differential Revision: D72320413
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150545
Approved by: https://github.com/desertfire
Summary: Add an experimental feature to defer pytorch library initialization cost to post startup. As noted this feature is not thread safe, it requires the client to maintain thread safety at library load time.
Reviewed By: zou3519
Differential Revision: D71917841
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150537
Approved by: https://github.com/zou3519
This PR is the duplicated one for https://github.com/pytorch/pytorch/pull/139975.
This PR is to add torch._scaled_mm for CPU backend.
_scaled_mm_out_cpu and _scaled_mm_cpu are new added and included in torch._scaled_mm CPU dispatch. We also add _scaled_mm_out_cpu_emulated as a fallback function if the current platform cannot run FP8 matmul using oneDNN. And this PR also updates the various UTs related to FP8 to support CPU tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150410
Approved by: https://github.com/atalman
Summary:
My commandeer of https://github.com/pytorch/pytorch/pull/150102
Based on description of PR it seems that we need to add C calls for each starting python event with a callable such that when the tracing exits we will have a matching enter for any given exit. It adds some unnecessary events at worst but prevents segfaults/failures. My PR just cleans up some refcount impl and logging.
Contributors: @arjun-choudhry
Test Plan: Ran resnet test internally. Will check CI and ask reviewers to make sure it resolves their issues.
Differential Revision: D72207570
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150370
Approved by: https://github.com/aaronenyeshi
Add includes for torch.device, torch.dtype, torch.layout, and torch.memory_format to the cpp_wrapper common header, so that they get precompiled. Additionally, add move constructors and operator bool to RAIIPyObject.
Closes#142005.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149350
Approved by: https://github.com/desertfire
Summary:
My commandeer of https://github.com/pytorch/pytorch/pull/150102
Based on description of PR it seems that we need to add C calls for each starting python event with a callable such that when the tracing exits we will have a matching enter for any given exit. It adds some unnecessary events at worst but prevents segfaults/failures. My PR just cleans up some refcount impl and logging.
Test Plan: Ran resnet test internally. Will check CI and ask reviewers to make sure it resolves their issues.
Differential Revision: D72207570
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150370
Approved by: https://github.com/aaronenyeshi
Summary:
X-link: https://github.com/facebookincubator/gloo/pull/423
This modifies `connectFullMesh` to take in a shared_ptr<IStore> instead of a reference. This is an API breaking change but fairly easy to work around.
To have backwards compatibility in PyTorch during the commit phase we add a new ifdef `GLOO_SHARED_STORE` which can provide backwards compatibility until we update the pinned Gloo version in pytorch OSS repo.
This also adds a new `wait_get` method to `IStore` which will allow us to do a more efficient operation in PyTorch TCPStore. PyTorch's `Store::get` automatically waits so we want to make sure we can avoid waiting twice to reduce network traffic.
This change will land simultaneously in PyTorch and Gloo repos.
Test Plan:
```
buck2 test //gloo/... //caffe2/caffe2/contrib/gloo:
```
Differential Revision: D72084111
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150230
Approved by: https://github.com/fduwjj
Altering the flag to use the correct streamType in CUDAPluggableAllocator class for ROCm gpu. The flag TORCH_HIP_VERSION does not work for ROCm as intended. This flag is replaced with USE_ROCM. This is impacting Distributed Fused Adam in Rocm/APEX when using nccl_ub feature. This has been tested with rocm/apex.
See PR https://github.com/ROCm/apex/pull/184
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150010
Approved by: https://github.com/jeffdaily
Relanding #148590 due to merge conflict.
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.
Squashed contents:
* [ptd][nccl] use current-stream as nccl-stream under async=False mode (#147820)
PTD current workflow:
- PTD creates its own dedicated `ncclStream` for comm operation
- it will first add a dependency on current-stream (typically the compute stream) to ensure tensors are ready before invoking collective
such stream synchronization become expensive in Inference world (cpu overhead: 70us vs GPU kernel time: 160us).
This diff:
- async=False [default], will use current-stream as nccl-stream and avoid the stream-sync overhead
- async=True, will retain existing logic: create new nccl-stream, let it wait on current-stream to ensure tensors are ready
- pass down async from c10d down to NCCL-PG
this helps shave off 50% CPU overhead **(70us -> 35us)**, which reduce total CPU/GPU from **230us to 195us by 15%**
* [PGNCCL] Make avoid-record-stream default
* [c10d] Add asyncOp argument to Ops
* Change python side wait
* Pass asyncOp at ProcessGroup level
* Watchdog unstashing tensors as a safety net
* Stash tensors for reduce_scatter_v and all_gather_v
Pull Request approved: https://github.com/pytorch/pytorch/pull/149753
* [c10d] Move unstashing from watchdog to main thread
Pull Request approved: https://github.com/pytorch/pytorch/pull/150079
* [PGNCCL][BE] Merge mutex into TensorShelf for encapsulation
Pull Request approved: https://github.com/pytorch/pytorch/pull/150130
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150398
Approved by: https://github.com/atalman
Per title, this version uses symm mem input both as input source and as a work buffer, so input is modified after the end (similar to what fbgemm car reduction does). It is intended to be wrapped in an op that would first copy the real inputs to symm mem buffers that wouldn't be exposed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150153
Approved by: https://github.com/xw285cornell
The intent of the existing code is to
> // Assign system TIDs to start events based on the system TID of the next
// observed event with the same Python TID.
However, if there are start events that don't share the same Python TID as later observed events, then they are left with the default initialization of DeviceAndResource and assigned values of `0`. This is problematic because Kineto uses `device=0, resource=0` for the first GPU (or other backend) device.
This PR maintains the previous logic of using TIDs from later events if any are present, but defaults to the current process and system thread IDs if there aren't later events to reference.
This issue was discovered while working to implement a custom backend and some CPU start events were appearing on the same process and thread as the device in the trace.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149757
Approved by: https://github.com/sraikund16
Summary:
The original Diff D69500038 is reverted due to a false alarm on trunk health.
Implement torchbind support in OSSProxyExecutor.
Exactly the same as the implementation in FbProxyExecutor.
D69693697 - fbProxyExecutor
D69887230 - fbProxyExecutor but for torchbind method
D70746626 - Support None output type
Other changes:
- When generating the schema of the CallTrochBind HOP, the arg name of the torchbind object arg should be the same as the torchbind method's torchbind object arg (instead of `obj`).
- In `AOTIModelPackageLoader`, we extract everything in `data/constants` to `tmp_dir/data/aot_inductor/<model>/` folder, so the torchbind objs exist in the same folder as the rest of the files (e.g. cpp, so). This is to be consistent of how files are packaged internally (more details in internal Diff summary).
Note on using `filesystem`:
Seems like there'll be [issues](https://github.com/pytorch/pytorch/pull/137209) with using`filesystem` header in linux, so here I use string manipulation instead of `filesystem::path`.
Test Plan:
```
test/inductor:torchbind -- -r torchbind_aoti
test/inductor:torchbind -- -r aot_compile
```
Differential Revision: D72063691
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150196
Approved by: https://github.com/hl475, https://github.com/desertfire
Summary: Add extract_constant_map that allows users to inspect the constants being used by AOTInductor
Test Plan:
`python test/inductor/test_aot_inductor.py -k extract_constants_map`
`LD_LIBRARY_PATH=/data/users/$USER/pytorch/build/lib /data/users/$USER/pytorch/build/bin/test_aoti_inference`
Differential Revision: D72020400
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150163
Approved by: https://github.com/chenyang78
This supports `masterListenFd` which is required for full compatibility with the non-libuv TCPStore. The code was just missing a `uv_listen` call and now it works just fine.
This is required to migrate the last remaining uses of TCPStore off of the non-libuv backend.
Test plan:
```
pytest -v test/distributed/test_store.py -k test_take_over_listen_socket
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150215
Approved by: https://github.com/fduwjj
Per title, this version uses symm mem input both as input source and as a work buffer, so input is modified after the end (similar to what fbgemm car reduction does). It is intended to be wrapped in an op that would first copy the real inputs to symm mem buffers that wouldn't be exposed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150153
Approved by: https://github.com/xw285cornell
