Fixes#163330
I tried to reproduce the bug with my 4-GPU setup (the original issue used 8 GPUs). I created several different test scenarios, trying to trigger the bug by:
- creating two different device meshes
- slicing them in various ways
- checking if get_root_mesh() would get confused
but the bug didn't show up! Everything worked correctly in `2.10`. I found that there was a massive refactoring of the `DeviceMesh` code (PR #163213) that landed on October 2nd. That PR completely rewrote how `DeviceMesh` tracks relationships between parent meshes and submeshes using. It seems like this refactoring fixed the bug! But I added a regression test to make sure it doesn't come back. The test (`test_get_root_mesh_multiple_independent_meshes`) does exactly what the bug report described:
- creates two independent meshes
- slices them both
- verifies that each submesh correctly points back to its real parent
- makes sure submeshes from mesh1 don't incorrectly claim mesh2 as their parent
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164731
Approved by: https://github.com/fduwjj
Python 3.13 added PyObject_GetOptionalAttrString. I'm not 100% certain that it is strictly better than the old approach in all cases, but based on documentation/comments it seems to be meant for this type of use, and it's faster when I profile torchtitan training (which gets to the "check for the `__torch_function__` attr on some object" part of maybe_has_torch_function frequently enough to notice, but wastes a bunch of time generating exceptions that we then suppressed here).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164624
Approved by: https://github.com/Skylion007
Summary:
Previously, weight deduplication was done by simply grouping tensors with their untyped storage and saving the first tensor in the group.
A more rigorous approach would be to find a complete tensor that covers the storage and store that tensor. This is particularly important for GPU weights because when saving to raw bytes, we move the weight to CPU first, and if the weight being saved is not a complete one, it will lose the storage information during the copy to CPU.
In this diff, we reuse code in `_package_weights.py` for better weights and constants deduplication in `torch.export.save`.
Test Plan: buck2 run mode/dev-nosan caffe2/test:test_export -- -r test_weight_sharing_gpu
Differential Revision: D83523690
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164196
Approved by: https://github.com/angelayi
Summary: Relax absolute tolerance from 1e-2 to 1e-1 for `test_non_contiguous_input_mm_plus_mm` in `test_max_autotune.py`.
Test Plan: `test_max_autotune.py`
Differential Revision: D83391942
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164022
Approved by: https://github.com/eellison
Summary:
Fix decompose_k test failure (`test_max_autotune_decompose_k `) in `test_max_autotune.py` on B200s by setting `torch._inductor.config` patches for variables `comprehensive_padding` and `shape_padding`. Initial failure was `AssertionError: False is not true : Could not find a split in {3, 9, 2187, 81, 243, 729, 27} in # AOT ID: ['6_forward']`.
Refactor decompose_k test to follow patch semantics when setting all environment variables within a test.
Test Plan:
`test_max_autotune.py`:
```
buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/inductor:max_autotune -c fbcode.nvcc_arch=b200a -c fbcode.enable_gpu_sections=true -c fbcode.platform010_cuda_version=12.8 -c fbcode.re_gpu_tests=False -- test_max_autotune_decompose_k
```
Differential Revision: D83390563
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164021
Approved by: https://github.com/njriasan, https://github.com/mlazos, https://github.com/eellison
Adds [control_deps](https://en.wikipedia.org/wiki/Control_dependency) higher-order operator to enforce explicit scheduling dependencies in FX graphs. This prevents unwanted operation reordering/fusion by giving nodes additional dependencies, which we also respect in inductor by adding weakdeps on the additional dependencies.
This can be generally useful (such as for ordering collectives) but in this case I am using it so that fusions do not interfere with aten planned comm-compute overlap.
There's definitely some similarity with the `with_effects` hop. Talked with @angelayi - when @zou3519 is back we will figure out how we want to consolidate.
The implementation needs to be a subgraph (as opposed to `with_effects`) because inductor relies on `V.graph.current_node`. Changing the signature of the node with `with_effects` breaks this, and additionally, also breaks striding constraints on the wrapped node - see this [TODO](aed66248a0/torch/fx/experimental/proxy_tensor.py (L1246-L1249)). By maintaining the node with its original calling structure in subgraph this all works.
Example transformation:
Before:
```
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1), kwargs = {})
%mm : [num_users=1] = call_function[target=torch.ops.aten.mm.default](args = (%arg1_1, %arg1_1), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 2), kwargs = {})
```
After:
```
add: "f32[256, 256]" = torch.ops.aten.add.Tensor(arg0_1, 1)
mm: "f32[256, 256]" = torch.ops.higher_order.control_deps((add,), subgraph_mm, arg1_1, arg1_1)
mul: "f32[256, 256]" = torch.ops.higher_order.control_deps((mm,), subgraph_mul, add)
```
The mm operation now explicitly depends on add completing first, and mul depends on mm, with original operations preserved in subgraphs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164568
Approved by: https://github.com/ezyang, https://github.com/IvanKobzarev
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
```bash
DEBUG /data/vllm-community-homes/vllm-user-6/pytorch/aten/src/ATen/cuda/CUDAGraph.h(59): warning #68-D: integer conversion resulted in a change of sign
DEBUG CaptureId_t capture_id_ = -1;
DEBUG ^
DEBUG
DEBUG Remark: The warnings can be suppressed with "-diag-suppress <warning-number>"
DEBUG
DEBUG /data/vllm-community-homes/vllm-user-6/pytorch/aten/src/ATen/cuda/CUDAGraph.h(59): warning #68-D: integer conversion resulted in a change of sign
DEBUG CaptureId_t capture_id_ = -1;
DEBUG ^
DEBUG
DEBUG Remark: The warnings can be suppressed with "-diag-suppress <warning-number>"
DEBUG
DEBUG /data/vllm-community-homes/vllm-user-6/pytorch/aten/src/ATen/cuda/CUDAGraph.h(59): warning #68-D: integer conversion resulted in a change of sign
DEBUG CaptureId_t capture_id_ = -1;
DEBUG ^
```
Cuda won't use 0 as a capture id, so it is safe to initialize with 0, which also matches the initialization in `pytorch/aten/src/ATen/native/cudnn/RNN.cpp:2362`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163898
Approved by: https://github.com/houseroad
Fixes#89034
Updated tensor_to_numpy() function in tensor_numpy.cpp to handle ZeroTensors by throwing an error if force=False and returning an array full of zeros if force=True.
@ngimel, I just saw that you mentioned PyTorch is not too concerned with this issue but I had already worked on it so I figured I would push it anyways and see what you thought. Feel free to close the PR if you think it is not worth merging.
@albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164487
Approved by: https://github.com/ngimel, https://github.com/albanD
Fixes#162812
Adds support for either passing a device directly into get_rng_state, or passing in a string or int (which is then wrapped into a device inside, as in torch.cuda.get_rng_state).
I wasn't exactly sure where tests for this should go, please let me know. I used this script for testing:
```python
import torch
# note: when running with CUDA GPU, first three tests will give the same result,
# as will the last two
# test with no device specified
print(torch.get_rng_state())
# test with CPU
cpu_device = torch.device("cpu")
print(torch.get_rng_state(cpu_device))
# test with direct name
print(torch.get_rng_state("cpu"))
# test with CUDA
cuda_device = torch.device("cuda:0")
print(torch.get_rng_state(cuda_device))
# test with integer
print(torch.get_rng_state(0))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163034
Approved by: https://github.com/ezyang, https://github.com/cyyever
# Propagate custom meta data to backward
Support propagating the user annotation tags to backward graph, by extending the `copy_fwd_metadata_to_bw_nodes` utils (recommended by @xmfan , thanks!).
Example annotation API (added in https://github.com/pytorch/pytorch/pull/163673):
```
class M(torch.nn.Module):
def forward(self, x):
with fx_traceback.annotate({"pp_stage": 0}):
with fx_traceback.annotate({"fdsp_bucket": 0}):
x = x + 1
x = x - 2
with fx_traceback.annotate({"cuda_stream": 2, "fsdp_bucket": 1}):
x = x * 2
x = x / 3
return x
```
Assumptions (some inherited from https://github.com/pytorch/pytorch/pull/126573):
- I am trusting the seq_nr mapping introduced to aot_autograd nodes in https://github.com/pytorch/pytorch/pull/103129
- I am also trusting that the forward is single threaded, since seq_nr is thread local. If this isn't always true, we'll need to also plumb thread_id through the same machinery which is populating seq_nr.
- **(This is changed in this PR!) I assume all backward graph nodes has "is_backward" for 'partitioner_tag', and all other nodes are forward graph nodes**. If we don't run export before `aot_export_join_with_descriptors`, then none of the nodes has "nn_module_stack" in node meta. If we do run export first, then we don't need this change.
- I copy "custom" node meta from forward to backward graph nodes.
Question:
- Is it a good idea to copy all "custom" node meta? Or should we create a dedicated key in custom node meta to be copied? @SherlockNoMad
- Do we expect people to run export before using `aot_export_join_with_descriptors`?
- Can we assume the following for graph produced by `aot_export_join_with_descriptors`? "all backward graph nodes has "is_backward" for 'partitioner_tag', and all other nodes are forward graph nodes". Maybe this is a question for @ezyang
```
python test/functorch/test_aot_joint_with_descriptors.py -k test_preserve_
python test/export/test_export.py -k preserve_anno
python test/distributed/tensor/test_dtensor_export.py
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164174
Approved by: https://github.com/xmfan, https://github.com/SherlockNoMad
Even though `offset_intragraph_` only tracks RNG consumption within a single graph replay, we have observed that the 32bit storage for these offsets is easy to overshoot, especially for cases with big CUDA graph captures including kernels that are generating a large amount of random numbers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164515
Approved by: https://github.com/eee4017, https://github.com/eqy
In reality we found the current mismatch order does not match the actual error distribution, so we reorder it a bit as following:
1. We do collective type check first
2. Then size check (excluding all2all)
3. dtype check
4. state check
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164606
Approved by: https://github.com/VieEeEw
Add a deterministic mode to skip the on device benchmarking that we know should affect numeric. This include
- pad-mm
- dynamic rblock scaling
- template autotuning
- coordinate descent tuning for reduction
- reduction config autotuning in CachingAutotuner. For reduction both RBLOCK, num_warps should affect numeric. XBLOCK does not. We can still autotune XBLOCK for reductions.
- benchmarking for computation communication reordering pass
The mode definitely has perf hit.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163589
Approved by: https://github.com/v0i0
Summary: Minor refactor where we push some args in the aot joint with descriptors workflow that are not used in export stage to the compile stage where they are actually used.
Test Plan: existing tests should pass
Differential Revision: D83850316
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164584
Approved by: https://github.com/tugsbayasgalan
1\ DTensor abstraction on its own, does not support arbitrary length shards in its distributed tensors representation. It supports a single uneven shard, bit it has to be the last shard in the sharding dimension.
2\ However, DCP supports an API called checkpointable. This API allows you to define your custom shardable tensor structure. I have given a UT example ( look for CheckpointableDistTensor). Therefore, one option is to use CheckpointableDistTensor to save/load uneven shards.
3\ While exploring this path, I also noticed that torch.rec module also encountered a similar problem while working with DTensor. They workaround it by implementing Checkpointable API in DTensor and introducing an auxillary structure called LocalShardsWrapper. This is the second option we can use to unblock data loader resharding work.
In summary;
Use LocalShardWrapper + DTensor as the first option to unblock.
Second preference is to use new implementation of Checkpointable API. ( similar to CheckpointbaleDistTensor I have introduced in this example).
Differential Revision: D80182564
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160533
Approved by: https://github.com/saumishr
This PR is to enable the following tests rocm.
test/test_jit.py::TestBackends::test_save_load
test/test_jit.py::TestBackends::test_execution
test/test_jit.py::TestBackends::test_errors
test/test_jit.py::TestCUDA::test_current_stream
Verified that the tests pass on AMD gfx90a and gfx942 arch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164582
Approved by: https://github.com/jeffdaily
Increase the tolerance for the following UTs as there was a slight mismatch seen on MI200.
- test_data_parallel.py:test_strided_grad_layout
- test_c10d_nccl.py:test_grad_layout_1devicemodule_1replicaperprocess
Skip for MI200:
- test_fully_shard_training.py:test_2d_mlp_with_nd_mesh
- test_2d_composability.py:test_train_parity_2d_mlp
- test_fully_shard_overlap.py:test_fully_shard_training_overlap
Fixes#159489Fixes#159488Fixes#152700Fixes#125555Fixes#134139
Working as is on both MI200 and MI300:
Fixes#125991Fixes#125918
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164390
Approved by: https://github.com/jeffdaily
Summary: Added a set of fixes triggered by fm training job. Overall the theme here is that we should get rid of saved objects as much as possible when they are not used in guard reconstruction. Sometimes for objects that cannot be saved (like local functions) we still try our best to save their closures.
Test Plan:
test_guard_serialization.py
test_lazy_awatiable.py
Differential Revision: D83766926
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164490
Approved by: https://github.com/jamesjwu
Summary:
This diff adds the feature of allocating a large pinned memory segment upfront based on the provided config. This large segment is then used to serve all the small pinned memory requests to avoid expensive device level APIs (slow paths).
Example:
PYTORCH_CUDA_ALLOC_CONF=pinned_reserve_segment_size_mb:2048
This reserves a 2GB pinned memory segment for the process and then all incoming small requests are just served from this segment and no cudaHostAlloc/cudaHostRegister apis are being called.
Differential Revision: D83779074
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164501
Approved by: https://github.com/yangw-dev
So this fixes at least two issues:
1) When we are invoking inductor backend, we apply pre-grad passes which try to find correct fake mode to use. In the nested case, we will run into clash when there is closure variable in the inductor region because non-strict would have fakified this variable before hand and inner torch.compile would have created a new fresh fake mode. This is not a problem in regular torch.compile because inner torch.compile gets ignored. I don't know if we are supposed to inherit fake mode from parent context in this case. But we can avoid this problem if we just default to eager backend which is fine in this case because the point of export is to capture aten operators. Going to inductor would mean we will lose inner torch.compile ops.
2) There is custom torch function modes in export that track number of torch fns executed and inner compile itself doesn't work because of guard failure as this mode state gets changed. I noticed torch.cond fixes this problem by carefully stashing the torch function mode and defer it in the backend. So the correct thing to do here is just re-use torch.cond implementation unconditionally.
So the things i did for fixing above were:
1) Always default to eager backend when compile is invoked inside export. I needed to make how torch.cond sets up the fresh tracing env into an util that can be shared.
2) The previous eager backend for torch.cond was wrong because the context managers didn't actually persist until the backend is invoked.
3) torch.cond used only disable TorchFunctionMetadata tf mode and stash it for later, but in fact, we should do both TorchFunctionMetadata and PreDispatchTorchFunctionMode.
With above fixes, we are able to export flex attention in export.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164171
Approved by: https://github.com/ydwu4
This PR moves the call to copy the generated code from `/tmp/...` so that it is still called if attempting to compile the generated code fails. In both cases now, the generated code will be copied across to `torch_compile_debug/run_.../torchinductor/output_code.py` which makes debugging bad generated code easier.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161615
Approved by: https://github.com/eellison
This pull request adds support for running operator microbenchmarks on ROCm (AMD GPU) environments in the CI workflow. The main changes involve introducing new build and test jobs for ROCm in the `.github/workflows/operator_microbenchmark.yml` file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164173
Approved by: https://github.com/huydhn
We want to refactor the internal bookkeeping of DeviceMesh so that:
Simply the bookkeeping logics and make it generic enough so that it is easy to support new transformations like flatten noncontiguous dim, reshape and unflatten. (We leveraged the CuTe layout). This new layout also let us handle non-contiguous slicing, flatten, transpose possible.
Concretely, in this PR, we do the following:
1. Use the `_MeshLayout` to handle all index operations rather use a map to record mesh dims.
2. Removed `flatten_name_to_root_dims`, because now we can directly get layout from a flattened device mesh.
3. Replaced `_get_slice_mesh_dims` with `_get_slice_mesh_layout`.
4. Use the newly added function `check_overlap` to check layout overlap.
5. Use a new function `to_remapping_tensor` to use layout ranks as indices when the mesh tensor is not representable as CuTe. The reason is that layout acts as a backend of mesh tensor bookkeeping (indexing indices), it needs to be used as indices for remap back to the mesh tensor for new DeviceMesh generation and backend init. For example, in the case of 2K to 4K, the underlying layout is (2K, 1) but the actual value of the mesh tensor is [2K, 2K+1, ....,]. While flattening, slicing, we need to remap the layout back to the new mesh tensor so it maps the actual device allocation. For example, in the 2K to 4K case, if the shape is (1K, 1K) with dim_names ("dp", "tp"). Then when slicing "tp", the mesh tensor should be (2K, 2K+1, ..., 3K-1) or (3K, 3K+1, ... 4K-1). not the global ranks generated from the layout. (1K, 1).
Verified that loss curve is very close for DeepSeekV3 on torchtitan, note that exact same match is challenging because even if we run the baseline twice, the loss curve does not exactly match.
<img width="1113" height="490" alt="image" src="https://github.com/user-attachments/assets/7877b5a4-337e-4ad8-b878-2378f4f0f38d" />
The PR looks big indeed but we don't change any existing behavior of DeviceMesh, so it is a pure refactor.
With this refactoring we also enabled the slicing and flatten of non-contiguous dims of a device mesh which is hard to implement without cute layout.
This is a continue of https://github.com/pytorch/pytorch/pull/161106 (original one got messed with EasyCLA)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163213
Approved by: https://github.com/lw, https://github.com/fegin
Modified `multimem_one_shot_all_reduce_out` function to accept a `root` argument, making it a `multimem_reduce` op.
The original `multimem_one_shot_all_reduce` op becomes a caller of the `multimem_reduce`, with each rank providing its own rank id as root.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164517
Approved by: https://github.com/ngimel
`grad_dtype` is a new attribute on Tensor to control gradient dtype:
- Access/setting is leaf-only.
- grad_dtype is respected when (1) when assigning to .grad, and (2) in the engine after the previous node produces incoming gradients for AccumulateGrad. (See table below for details)
- Not setting grad_dtype preserves the current behavior. Accessing it returns `t.dtype`
- `grad_dtype` cannot be set when there is already a `.grad` present and the dtypes conflict.
| `grad_dtype` setting | Setting `.grad` manually | Incoming gradient from autograd engine |
|-----------------------|--------------------------|-----------------------------------------|
| **Default (tensor’s dtype)** | `.grad` must match tensor’s dtype | Engine casts incoming grad to tensor’s dtype |
| **Set to specific dtype** | `.grad` must match that dtype | Engine casts incoming grad to the specified dtype |
| **Set to `None`** | `.grad` may be any dtype | Engine does not cast; accepts incoming grad dtype as-is |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162815
Approved by: https://github.com/albanD
Fixes#162129. Added validation in _rank_not_in_group() to check if ```FakeProcessGroup``` is properly initialized before use, raising a clear error message if ```torch.distributed.init_process_group(backend='fake')``` hasn't been called first.
This prevents silent failures and ensures proper dispatch system integration for all distributed operations.
Added test case test_fake_process_group_direct_usage_error() that validates the error is raised for ```all_reduce``` and ```all_to_all_single``` operations.
Please let me know if additional distributed operators should be tested or if any other updates are needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163665
Approved by: https://github.com/ezyang
**Summary**
Raise error when the `local_tensor` argument passed to `DTensor.from_local` is
a DTensor, this prevents users from accidentally calling `from_local` over a DTensor
object.
The error message is organized in this way:
```
the local_tensor argument only accepts torch.Tensor but got <class 'torch.distributed.tensor.DTensor'> value.
```
**Test**
`pytest test/distributed/tensor/test_dtensor.py -k test_from_local`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164496
Approved by: https://github.com/ezyang
it was previously quite misleading since it looks like the inputs to the
dynamo graph are plain tensors when in reality they are tensor subclasses
before
```
class GraphModule(torch.nn.Module):
def forward(self, L_input_batch_inputs_: "i64[2, 512][512, 1]cuda:0", L_self_parameters_weight_: "f32[202048, 256][256, 1]cuda:0"):
```
after
```
class GraphModule(torch.nn.Module):
def forward(self, L_input_batch_inputs_: "DTensor(i64[2, 512][512, 1]cuda:0)", L_self_parameters_weight_: "DTensor(f32[202048, 256][256, 1]cuda:0)"):
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164403
Approved by: https://github.com/ezyang
Shared memory is allocated by creating a file in /dev/shm (by default) that can run out of space. Pytorch reserves the file size by calling ftruncate() that creates a sparse file, so it succeeds even if sufficient disk space is not available.
This could lead to a situation when a shared memory region is successfully created but a subsequent access to a shared memory page results in SIGBUS due to the disk being full.
Using posix_fallocate() instead of ftruncate() eliminates this problem because the former syscall always allocates space and it returns an error if the disk is full.
Related to https://github.com/pytorch/pytorch/issues/5040
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161910
Approved by: https://github.com/mikaylagawarecki
Changelog:
1. When we run into an operation we didn't proxy, we end up emitting fake constants. We error under a config and we disable the config for some internal users. The reason we want to error is this signals a coverage problem we need to address but at the same time, we don't wnat to be disruptive to already working flows.
2. Previous attribute mutation detection logic in non-strict didn't account for nested module structure. This fixes silent incorrectness issue of exporting esm and qwen in non-strict and some torchbench models like levit_128 and demucs.
3. Previous logic didn't work on the cases where we mutate a container attribute as the previous approach used to pytree over old and new attributes resulting in length mismatch. We gracefully handle this now.
Differential Revision: [D83673054](https://our.internmc.facebook.com/intern/diff/D83673054)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164372
Approved by: https://github.com/avikchaudhuri
Add `struct AOTInductorConstantMapEntry` to represent the constant map in AOTI Model. We cannot use `std::unordered_map` for cross-compilation, because it is not ABI stable.
it will be tested when we test `update_user_managed_constant_buffer` for windows cross-compilation
Example usage:
```
// Load constants. Create random constants here.
auto* fc1_w = new slim::SlimTensor(slim::empty({16, 10}, c10::kFloat, c10::Device(c10::kCUDA, 0)));
fc1_w->fill_(1.0);
.....
// Build pairs
std::vector<AOTInductorConstantPair> constants{
{"fc1_weight", fc1_w},
{"fc1_bias", fc1_b},
{"fc2_weight", fc2_w},
{"fc2_bias", fc2_b},
};
// Call runtime (pass raw pointer + size)
update_user_managed_constant_buffer_abi(
container_handle,
constants.data(),
constants.size(),
/*use_inactive=*/false,
/*validate_full_update=*/true);
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163819
Approved by: https://github.com/desertfire
This reverts commit b1033789fea2bc82901eafed498a5252985b80e9.
Reverted https://github.com/pytorch/pytorch/pull/164256 on behalf of https://github.com/yangw-dev due to failed internal test: (pytorch.tritonbench.test.test_gpu.main.TestTritonbenchGpu) Error Details: torch._inductor.exc.InductorError: LoweringException: NoValidChoicesError: No choices to select. Provided reason: All choices failed to compile for backend. please consider adding ATEN into max_autotune_gemm_backends config (defined in torch/_inductor/config.py) to allow at least one choice. ([comment](https://github.com/pytorch/pytorch/pull/164256#issuecomment-3362359624))
Summary:
as title
- Some IR nodes are created during `finalize_multi_template_buffers()` in Scheduler. This PR adds provenance (`origin_node` and `origins`) for those nodes.
- Extract `assign_origin_node` function
Differential Revision: D82871244
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164255
Approved by: https://github.com/mlazos
We should surface the CUDA architecture matrix to make things more transparent. I believe this can later become its own page where we will publish supported matrix for each release.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164471
Approved by: https://github.com/Camyll
We want to refactor the internal bookkeeping of DeviceMesh so that:
Simply the bookkeeping logics and make it generic enough so that it is easy to support new transformations like flatten noncontiguous dim, reshape and unflatten. (We leveraged the CuTe layout). This new layout also let us handle non-contiguous slicing, flatten, transpose possible.
Concretely, in this PR, we do the following:
1. Use the `_MeshLayout` to handle all index operations rather use a map to record mesh dims.
2. Removed `flatten_name_to_root_dims`, because now we can directly get layout from a flattened device mesh.
3. Replaced `_get_slice_mesh_dims` with `_get_slice_mesh_layout`.
4. Use the newly added function `check_overlap` to check layout overlap.
5. Use a new function `to_remapping_tensor` to use layout ranks as indices when the mesh tensor is not representable as CuTe. The reason is that layout acts as a backend of mesh tensor bookkeeping (indexing indices), it needs to be used as indices for remap back to the mesh tensor for new DeviceMesh generation and backend init. For example, in the case of 2K to 4K, the underlying layout is (2K, 1) but the actual value of the mesh tensor is [2K, 2K+1, ....,]. While flattening, slicing, we need to remap the layout back to the new mesh tensor so it maps the actual device allocation. For example, in the 2K to 4K case, if the shape is (1K, 1K) with dim_names ("dp", "tp"). Then when slicing "tp", the mesh tensor should be (2K, 2K+1, ..., 3K-1) or (3K, 3K+1, ... 4K-1). not the global ranks generated from the layout. (1K, 1).
Verified that loss curve is very close for DeepSeekV3 on torchtitan, note that exact same match is challenging because even if we run the baseline twice, the loss curve does not exactly match.
<img width="1113" height="490" alt="image" src="https://github.com/user-attachments/assets/7877b5a4-337e-4ad8-b878-2378f4f0f38d" />
The PR looks big indeed but we don't change any existing behavior of DeviceMesh, so it is a pure refactor.
With this refactoring we also enabled the slicing and flatten of non-contiguous dims of a device mesh which is hard to implement without cute layout.
This is a continue of https://github.com/pytorch/pytorch/pull/161106 (original one got messed with EasyCLA)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163213
Approved by: https://github.com/lw, https://github.com/fegin
# Summary
This happends when flex_attention is not tagged with the ` CheckpointPolicy.MUST_SAVE` policy. This causes the lse to be unrealized. I think in general this probably not the best policy but we shoudn't error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164421
Approved by: https://github.com/Skylion007
Actually we would like to not graph break even in the case of Dynamo. But there is a weird-unsolved bug with Kineto + Dynamo when there are distributed jobs that lead to NCCL timeouts. This bug is a rare edege case, but we have not been able to root cause it yet.
But for export, we do not anticipate JIT tracing in distributed job training and therefore this PR is safe for export.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164418
Approved by: https://github.com/StrongerXi, https://github.com/williamwen42
We do some fixes in pinned memory allocation stats collection and better differentiate between active vs allocated bytes.
Reviewed By: bbus, sayitmemory
Differential Revision: D83162346
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164412
Approved by: https://github.com/mradmila
A couple minor things to clean up the structure of `compile_fx` before we hit pre grad passes:
1. After patching config and recursively calling `compile_fx`, we don't need the patches any more. We make the subsequent logic call a `_maybe_wrap_and_compile_fx_main` (both when cpp wrapper exists and doesn't).
2. There's some recursive wrapping that happens on inputs and outputs before hitting pre grad passes, which are now also separated out before calling a `_compile_fx_main`, where actual work finally happens.
These also happen to fix a couple of TODOs in the old code.
Differential Revision: D83500704
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164169
Approved by: https://github.com/zhxchen17
Fixes#156052 and #156444.
This PR setup the privateuseone key in Python to be used as a python backend for pytorch.
Meaning that, after calling `setup_privateuseone_for_python_backend('npy')`, one can use a subclass to with that device to hold arbitrary python data as "device data" and use `torch.library` to register ops that takes that Tensor.
Changes done in this PR:
1. Register an vanilla Device Guard: I extended NoOpDeviceGuard to have allow device index of 0 and to not raise errors when event related functions are accessed. If I don't do those, when calling backward I would get errors. (CPU backend uses NoOpDeviceGuard just fine, although there seems to be special treatment of CPU in the autograd engine.
2. Tensor subclass allows not having `__torch_dispatch__` if the device is not CUDA or CPU. The comment of the check suggests it was to avoid segfault when calling into ops that expects a storage. Here we have a different device so will not call into those ops.
3. python function that invokes the other incantations to setup the privateusekey backend.
This took inspiration of https://github.com/bdhirsh/pytorch_open_registration_example and https://github.com/tinygrad/tinygrad/blob/master/extra/torch_backend/wrapped_tensor.cpp; great thanks to @bdhirsh and @geohot.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157859
Approved by: https://github.com/albanD
**Summary:** In order to test numerics for replicate + pp, stage.py needs to be able to call replicate's backward manually as pipeline parallelism doesn't have this feature.
**Test Case**
1. pytest test/distributed/_composable/test_composability/test_pp_composability.py -k test_replicate_pp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164031
Approved by: https://github.com/weifengpy, https://github.com/H-Huang
ghstack dependencies: #163897
```Py
@triton.jit
def foo(dest, src):
nvshmem.get_nbi(dest, src, 100, 0)
# Some independent computation which overlaps with the get operation
...
# Wait for completion of the get operation
nvshmem.quiet()
```
Allows us to overlap comm and compute in the same kernel, instead of two kernels + signals.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163540
Approved by: https://github.com/ngimel, https://github.com/fegin
The current behavior is to do "nothing", which means you will corrupt
data. If you're doing something similar to LocalTensor, where you're
overriding the behavior of collectives to do something numerically,
this can be unwelcome behavior. If you can error when this happens
it can help prevent silent numerical incorrectness.
Authored with claude code.
Signed-off-by: Edward Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162841
Approved by: https://github.com/dcci
----
This PR will be part of a series of PR's that aims to remove `.ci/aarch64_linux` folder entirely, such that Aarch64 manylinux build happens as part of `.ci/manywheel/build.sh`, the same as other platforms.
In this PR:
- We prebuild + install Arm Compute Library in the manylinux docker image ( at /acl ), instead of a build time for every pytorch build. Also updated jammy install path to be /acl too.
- We can therefore remove build_ArmComputeLibrary functions from the ci build scripts.
- There is also some refactoring of install_openblas.sh and install_acl.sh to align them together ( similar formatting, similar variable names, same place for version number update )
- We had 2 places to define openblas version, this has been reduced to 1 now ( install_openblas.sh ).
- ACL_VERSION and OPENBLAS_VERSION are now able to be overriden at build.sh level for developers, but there is only 1 version of each hardcoded for ci.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159737
Approved by: https://github.com/seemethere, https://github.com/aditew01
In #163455 , the `reshape` was not a pure view op.
The `permute` before it created an non-contiguous tensor, which would trigger a data copy during the reshape.
This PR improved the implementation by remove the `urtensor` intermediate tensor completely.
By simply expanding the `xtensor` would achieve the `repeat` effect.
Before this PR, there were two data copies (in `urtensor.copy_` and `urtensor.reshape`).
Now, there is only one data copy in the `.copy_()`.
Reshape would not copy data because it is on a contiguous tensor.
One more note is that we do want at one copy because we want to duplicate the elements for the repeats.
User can inplace modify single elements without afffecting others.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163842
Approved by: https://github.com/Skylion007
Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
Both Linux, Windows and MacOS CI workflows should use `.ci/docker/requirements-ci.txt`
TODOS:
- Investigate why `choco install cmake` is needed to successfully detect MKL
- Move `psutil` installation from specific scripts into requirements-ci.txt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163396
Approved by: https://github.com/Skylion007
Summary: `nn.Parameter()` by default has `requires_grad=True` and would cause issues when there are non-float parameters.
Test Plan: buck2 run mode/dev-nosan caffe2/test:test_export -- -r test_non_float_weight
Differential Revision: D83598796
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164290
Approved by: https://github.com/angelayi
Summary:
New test sizes for `test_scaled_mm_vs_emulated_block_wise` all fail with
```
RuntimeError: Invalid scaling configuration
```
Disable these new tests for now (the remaining test is a parametrized
version of the original test case)
Test Plan:
`pytest test/test_scaled_matmul_cuda.py`
Reviewers:
Subscribers:
Tasks:
Tags:
Signed-off-by: Simon Layton <simonlayton@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164259
Approved by: https://github.com/jananisriram
ghstack dependencies: #164266
To run this, you need to install `mingw64-gcc-c++` and download windows cuda library toolkit.
See design doc and demo instructions in https://docs.google.com/document/d/1iDaChqA5nNKkBFTzsdkmoomvQlXHbnlb1Z4yEp7xaJA/edit?tab=t.0
If cross_platform_target is windows, we do the following:
- do not link to `sleef`. This can be improved in the future if we need it. Currently I avoid it because that requires extra setup on the linux side
- Use `mingw64-gcc-c++` to compile
- Use `WINDOWS_CUDA_HOME` instead of `CUDA_HOME` when linking to cuda
```
python test/inductor/test_aot_inductor_windows.py -k so
```
Other changes:
- de-couples compile_standalone config and dynamic link flag
- create a new aot_inductor_mode config module, which is used to control configs in aot_inductor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163188
Approved by: https://github.com/desertfire
Summary: This diffs add an API to query expandable segment size for each stream so that we can use this info to warmup the segment in advance, so we dont incur any performance penalty during steady state inference for new CUDA memory allocations.
Differential Revision: D76447308
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163771
Approved by: https://github.com/bbus
Previous work #158352 delivered CUDAGraph memory footprint reduction with no replay-time impact, but capture time regressed (up to 20× slower) due to repeated full-graph traversals. See previous benchmark results [here](https://github.com/pytorch/pytorch/pull/158352#issuecomment-3215947565)
This PR removes capture/reply overhead while preserving the memory savings:
1. **Terminals as free markers**
We stop inserting empty nodes and instead record the current stream terminals as free markers. This avoids mutating the user’s graph and keeps semantics unchanged.
2. **Incremental, cached reachability**
We add a **per-graph reuse context** that caches reverse-traversal state:
* `graph_reuse_context[graph].visited[stream]` tracks nodes already seen from that stream’s terminal frontier.
* On each allocation during capture, we resume traversal from the latest terminals and only visit unseen nodes.
* A block is freed when all its recorded markers are in the visited set of its allocation stream—i.e., all markers are proven predecessors of future work.
See [the performance results here](https://docs.google.com/spreadsheets/d/e/2PACX-1vRPvdd9Xa8W87ixbiA0da_qvOhrUAjUpFz0G-_j-MsDnoeRyhEa4_ut_W3rqcg1VVZVFJ-gucwov-3b/pubhtml?gid=1468302443&single=true), we sweep synthetic multi-stream CUDA Graphs built by `capture_benchmark.py` (same as before, we generate random interleaving of alloc/free/join with given probabilities, see [gist here](https://gist.github.com/eee4017/e2092d215b1d4bd46534148939af39e3)), and we compare median capture/replay times and memory. On an NVIDIA H100 PCIe across 24 configs, the optimization preserves reserved memory reduction at ~24–98%, leaves allocated memory unchanged, and brings capture time back to baseline (range 0.96–1.04× vs. baseline) with replay time unchanged (range 0.97–1.11×).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162186
Approved by: https://github.com/eqy, https://github.com/ngimel
2025-09-30 22:28:46 +00:00
869 changed files with 16523 additions and 6114 deletions
cuda-bindings>=12.0,<13.0 ; platform_machine != "s390x" and platform_system != "Darwin"
#Description: required for testing CUDAGraph::raw_cuda_graph(). See https://nvidia.github.io/cuda-python/cuda-bindings/latest/support.html for how this version was chosen. Note "Any fix in the latest bindings would be backported to the prior major version" means that only the newest version of cuda-bindings will get fixes. Depending on the latest version of 12.x is okay because all 12.y versions will be supported via "CUDA minor version compatibility". Pytorch builds against 13.z versions of cuda toolkit work with 12.x versions of cuda-bindings as well because newer drivers work with old toolkits.
# Copied from https://github.com/pytorch/test-infra/blob/be01a40157c36cd5a48391fdf44a7bc3ebd4c7e3/aws/ami/windows/scripts/Installers/Install-Pip-Dependencies.ps1#L16 with some adjustments
# pytest-rerunfailures==10.3 as 10.2 fails with INTERNALERROR> pluggy._manager.PluginValidationError: unknown hook 'pytest_configure_node'
Following is the release cadence. All future dates below are tentative. For latest updates on the release schedule, please follow [dev discuss](https://dev-discuss.pytorch.org/c/release-announcements/27). Please note: Patch Releases are optional.
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