Summary:
This stores information on where fx graphs come from, which makes it
significantly easier to debug.
One outstanding question
1) I only stored the kernel stack traces, do we also want the node mappings?
Test Plan:
I wrote a explicit logging test which makes a module, fx traces it, compiles it, and makes sure the logging infomration shows up.
```
clr@devvm17763 ~/fbsource/fbcode/caffe2/test/dynamo
% buck2 test @//mode/opt fbcode//caffe2/test/dynamo:test_dynamo -- test_utils
File changed: fbsource//xplat/caffe2/test/dynamo/test_utils.py
File changed: fbcode//caffe2/test/dynamo/test_utils.py
Buck UI: https://www.internalfb.com/buck2/528dea32-2416-4a62-a1ec-39f3c0efdd2e
Test UI: https://www.internalfb.com/intern/testinfra/testrun/13229324015574003
Network: Up: 0B Down: 0B
Executing actions. Remaining 0/2
Command: test.
Time elapsed: 17.3s
Tests finished: Pass 16. Fail 0. Fatal 0. Skip 0. Build failure 0
```
Rollback Plan:
Differential Revision: D82037582
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162669
Approved by: https://github.com/yushangdi
Summary:
The implementation adds the ability to:
Set custom metadata strings that will be attached to all subsequent allocations
Clear or change the metadata at any point
View the metadata in memory snapshots via _dump_snapshot()
Test Plan: Added test in test_cuda.py and check manually in snapshot to see that metadata was added.
Differential Revision: D84654933
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165490
Approved by: https://github.com/yushangdi
1. Run distributed job with B200 runner, periodically.
2. discovered generic distributed test issue that certain unit test hard-coded ranks, calling for require_exact_world_size(world_size) API instead of require_world_size(world_size).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159323
Approved by: https://github.com/eqy
Co-authored-by: Aidyn-A <aidyn.b.aitzhan@gmail.com>
when `with_export=True`, `aot_export_joint_with_descriptors` should take the graph produced by `_dynamo_graph_capture_for_export`
```
python test/functorch/test_aot_joint_with_descriptors.py -k test_preserve_annotate_simple
python test/functorch/test_aot_joint_with_descriptors.py -k test_preserve_annotate_flex_attention
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165660
Approved by: https://github.com/yushangdi
Improve FakeTensor cache to handle SymNode and tracing properly.
For now, when we're proxy tracing just don't bother caching operations that contain SymNodes in the output. The problem is that the proxy tracer relies on SymNode identity and our cache doesn't preserve that. It can be fixed (and I left some notes in _validate_symbolic_output_for_caching() how) but it's not worth it for now.
If we aren't proxy tracing then caching is fine.
Thus these changes:
1. Our cache key needs to include whether we were actively tracing or not - this way if we create a cache entry when we weren't tracing and then we try to use it when we ARE tracing it gets rerun.
2. If there's a SymNode in the output then bypass tracing.
3. Some general cleanup of the output validation - we were unnecessarily doing it as a two-step process when it could just be a single step (it's still two parts internally but only a single outer try/except).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164718
Approved by: https://github.com/bobrenjc93
ghstack dependencies: #165266, #164717
In a training library we hit a weird conflict between dtensor, dynamic shapes, and proxy tensor.
The problem is occuring because in sharding_prop we use FakeTensors to compute an operation size (so we don't have to use the full "real" data). We turn off proxy tracing while we're doing that because we don't want the FakeTensor ops to end up in the graph. We then use that size when doing later operations.
Normally this is no problem - but when those sizes are dynamic shapes then we have a problem - the proxy tracer wants to track the provenance of all shape operations (`s1*s2`) but since tracing is disabled it doesn't see the operation and when we then use the result shape later on the proxy tracer gets all confused (because the SymNode appeared out of nowhere).
At first we were thinking to never disable shape tracing - but that caused a slew of other downstream problems (lots of code that actually needs the shape tracing to be disabled) so instead we enable having a "sym tracing override" and surgically when we disable proxy tracing we leave shape tracing enabled.
After this change the dtensor embedding is "fixed" but then runs afoul of a FakeTensor cache bug - which is fixed in the next PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164717
Approved by: https://github.com/bobrenjc93, https://github.com/ezyang
ghstack dependencies: #165266
Moving some code around in proxy_tensor in preparation for the next PR. There we
no actual changes (other than simple relabeling such as `self.tracer` ->
`tracer`):
- Move _compute_proxy() out of ProxyTorchDispatchMode.
- Give `sympy_expr_tracker` a structured type instead of `object`.
- Split SymNode registration out of ProxyTorchDispatchMode.__sym_dispatch__() so
it can be reused.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165266
Approved by: https://github.com/ezyang, https://github.com/mlazos
While enabling this test discovered lack of support for sub meshes. Added limited support
for sub meshes by properly computing rank coordinates for a given sub mesh. The implementation
follows similar approach to collectives. We infer all sub meshes for the given dimensions and
compute each rank's coordinates with respect to is sub mesh.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165596
Approved by: https://github.com/ezyang
Summary:
Currently `get_c2_fbandroid_xplat_compiler_flags()` is reading the `caffe2.strip_glog` buckconfig which we want to get rid of.
This diff removes the `fbandroid_compiler_flags` arg and merges it with compiler_flags with a nested select and the select version of the method
The goal is to get rid of all the usages of `get_c2_fbandroid_xplat_compiler_flags()` so that we can get rid of the `caffe2.strip_glog` buckconfig
Test Plan: CI
bifferential Revision: D84626885
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165558
Approved by: https://github.com/malfet
Summary: Refactor `scaled_mm` Inductor template to support template choice based on scaling mode. This modification sets up the infrastructure for adding new templates based on new scaling modes, such as deepseek-style scaling (a follow-up diff), as new scaling modes (deepseek, block, group) scale before the accumulation (as opposed to per-tensor and per-row scaling, which apply scaling after accumulation). This modification also further enables Inductor to infer a scaling type based on the shape of the scaling tensors, which makes existing infrastructure more extensible to new scaling modes.
Test Plan:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling_rowwise --atol=20 --rtol=2 2>&1 | tee ~/personal/random.log
```
bifferential Revision: D83591083
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164318
Approved by: https://github.com/drisspg, https://github.com/slayton58
Adding ag+mm support for the case, when gather_dim is last dim of matmul (reduction dim).
When we decompose matmul by reduction dimension we result in partials that needs additional reduction,
we allocate memory for accumulator.
Decomposition should not produce small (thin) mms that can not efficiently load the GPU. Limiting for minimal size of the shard 1024 (found empirically by testing in torchtitan).
scaled_mm is not supported yet for this case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163068
Approved by: https://github.com/ngimel
AOTriton uses prebuilt runtime binaries if the user's ROCm version matches the ones used to generate the prebuilt runtime. However, since there's no prebuilt runtime available for Windows, this check needs to be bypassed for Windows. This PR enables it by changing condition to always build AOTriton runtime from source on Windows.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165538
Approved by: https://github.com/xinyazhang, https://github.com/jeffdaily
Fixes#158232
The autocast caching heuristic in `aten/src/ATen/autocast_mode.cpp:139` did not account for gradient mode state when deciding whether to cache. FSDP2 is not directly related.
~~This PR adds `GradMode::is_enabled()` check to caching condition. Caching is now disabled in `no_grad()` contexts to prevent storing tensors with incorrect gradient state. Ensures correctness at the cost of using cache.~~
This PR proposes separate caches for gradient-enabled and gradient-disabled modes.
Adds tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165068
Approved by: https://github.com/ngimel, https://github.com/janeyx99
Add mx fp4 support in Blas.cpp.
Updated the scale_kernel_dispatch array and ScaledGemmImplementation enum to include MXFP4 support.
Modify the tests under test_scaled_matmul_cuda accordingly.
PYTORCH_TEST_WITH_ROCM=1 python test/test_scaled_matmul_cuda.py -v -k test_blockwise
115 test passed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165528
Approved by: https://github.com/jeffdaily
By adding a few small helpers (e.g., a `splice` method to `_MeshLayout`, and making `_init_process_groups` static and thus stateless) we can substantially shorten the definition of the unflatten method, and help readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165556
Approved by: https://github.com/fduwjj
ghstack dependencies: #165554, #165555
The refactoring of DeviceMesh is heavily constrained by the signature of its constructor, which is a public API which contains some "legacy" concepts which we'd love to get rid of, such as an explicit/materialized `mesh` Tensor.
In other languages the solution to this would be to add a private overload of the constructor. Python doesn't natively allow this, but in this PR I managed to build something that approximates it.
This new private constructor basically only takes `_layout`, `_global_rank_permutation`, and `mesh_dim_names`.
With such a constructor we can effectively simplify a lot of callsites and get rid of the `_create_mesh_from_ranks` helper method. That's a good thing because it was instantiating many DeviceMeshes in a for loop, which always felt unnecessary.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165555
Approved by: https://github.com/fduwjj, https://github.com/fegin
ghstack dependencies: #165554
Bucketing of multiple dtypes to be processed in one bucketed collective.
First target is to bucket bf16 and f32, but already can be used with other dtypes.
For now multidtype bucketing is only supported with "custom_ops" mode.
Non custom_ops needs additional work on inductor side.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162470
Approved by: https://github.com/eellison
Adding bf16 for the backward pass of `torch._fake_quantize_learnable_per_tensor_affine()`.
Note that for testing, we modified the seed to avoid increasing tolerance due to cases where difference in Python vs CPP downcasting causes tensor mismatches. (e.g. 27.87704 vs 27.8408 before downcasting, 27.7500 vs 27.8750 after downcasting for Python vs CPP op)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165362
Approved by: https://github.com/andrewor14
Use linux.c7i.2xlarge as the default runner for the _linux-build.yml workflow. In testing we found that switching from c5 - c7i grants a 15-20% faster build times despite c7i costing 5% more. This should reduce costs of jobs using _linux-build.yml.
Relates to pytorch/test-infra#7175.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164747
Approved by: https://github.com/atalman
The goal of this PR is to avoid storing the explicit `mesh` Tensor inside each DeviceMesh, and instead compute it on-the-fly when the end user needs it, and try to replace all of its internal usages with `_layout` and the newly-introduced `_global_rank_permutation` Tensor. The name of this attribute is up for debate. The advantage of the `_global_rank_permutation` Tensor is that it is _the same_ Tensor for the root mesh and all its children, so it doesn't need to be copied/reallocated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165554
Approved by: https://github.com/fduwjj
I'm cleaning this PR up as a proper way of disabling functionalization via config in AOTDispatcher. I removed the non-functionalization related changes from the original version:
(1) preventing proxy mode (and functionalization) from incorrectly decomposing CIA ops (Ed has a PR for it here: https://github.com/pytorch/pytorch/pull/164939)
(2) preventing python-dispatcher-based decomps above autograd from running. I'm not doing this for now, will likely do it in a followup
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164577
Approved by: https://github.com/ezyang
ghstack dependencies: #165372
