- Add AMD support for int4 kernel
- Only supports CDNA2 and CDNA3 gpus for now
- Uses `mfma_f32_16x16x16bf16` instruction for matrix multiply
- Uses `v_and_or_b32` instruction and `__hfma2` instrinsic for unpacking bf16 values
- Enable hipify for `__nv_bfloat16` and `__nv_bfloat162` data types
- Enable int4 unit tests for CDNA2 and CDNA3 AMD gpus
- Fix torchscript issues due to hipify for `__nv_bfloat16` type
- TorchScript has its own implementation for bfloat16 type
- Implemented in `__nv_bloat16` structure at [resource_strings.h](https://github.com/pytorch/pytorch/blob/main/torch/csrc/jit/codegen/fuser/cuda/resource_strings.h)
- So, we shouldn't hipify any reference of `__nv_bfloat16` in the torchscript implementation
- Hence moved the `__nv_bfloat16` direct references in `codegen.cpp` and `cuda_codegen.cpp` to `resource_strings.h` which is already exempted from hipify
Fixes#124699
Fixes pytorch-labs/gpt-fast/issues/154
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129710
Approved by: https://github.com/malfet
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
This patch implements `with sdpa_kernel(SDPBackend.EFFICIENT_ATTENTION):` by reusing AOTriton's accelerated SDPA implementation
Known limitations:
- Only supports MI200/MI300X GPUs
- Does not support varlen
- Does not support `CausalVariant`
- Optional arguments `causal_diagonal` and `seqlen_k` in `_efficient_attention_forward/backward` must be null
- Does not work well with inductor's SDPA rewriter. The rewriter has been updated to only use math and flash attention on ROCM.
This PR also uses a different approach of installing AOTriton binary instead of building it from source in the base docker image. More details on motivation: https://github.com/pytorch/pytorch/pull/124885#issuecomment-2153229129
`PYTORCH_TEST_WITH_ROCM=1 PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda" python test/test_transformers.py` yields "55028 passed, 20784 skipped" results with this change. [Previous result](https://hud.pytorch.org/pr/127528) of `test_transformers.py` was 0 error, 0 failure, 55229 skipped out of 75517 tests in total (the XML report does not contain total number of passed tests).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124885
Approved by: https://github.com/malfet
This patch addresses the major limitations in our previous [PR #115981](https://github.com/pytorch/pytorch/pull/115981) through the new dedicated repository [AOTriton](https://github.com/ROCm/aotriton)
- [x] Only supports MI200 series GPU (i.e., `gcnArchName == gfx90a:sramecc+:xnack-`).
* MI300X is supported. More architectures will be added once Triton support them.
- [x] Only supports power of two sequence lengths.
* Now it support arbitrary sequence length
- [ ] No support for varlen APIs.
* varlen API will be supported in future release of AOTriton
- [x] Only support head dimension 16,32,64,128.
* Now it support arbitrary head dimension <= 256
- [x] Performance is still being optimized.
* Kernel is selected according to autotune information from Triton.
Other improvements from AOTriton include
* Allow more flexible Tensor storage layout
* More flexible API
This is a more extensive fix to #112997
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121561
Approved by: https://github.com/huydhn
This patch addresses the major limitations in our previous [PR #115981](https://github.com/pytorch/pytorch/pull/115981) through the new dedicated repository [AOTriton](https://github.com/ROCm/aotriton)
- [x] Only supports MI200 series GPU (i.e., `gcnArchName == gfx90a:sramecc+:xnack-`).
* MI300X is supported. More architectures will be added once Triton support them.
- [x] Only supports power of two sequence lengths.
* Now it support arbitrary sequence length
- [ ] No support for varlen APIs.
* varlen API will be supported in the next release of AOTriton
- [x] Only support head dimension 16,32,64,128.
* Now it support arbitrary head dimension <= 256
- [x] Performance is still being optimized.
* Kernel is selected according to autotune information from Triton.
Other improvements from AOTriton include
* Allow more flexible Tensor storage layout
* More flexible API
This is a more extensive fix to #112997
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121561
Approved by: https://github.com/malfet, https://github.com/atalman
This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
Note about the Updates:
This PR:
1. skips more flash attention related UTs on MI200
2. Fix additional ATen compiling errors after hipification
3. Fix the author "root" of a specific commit
4. Includes the patch from Nikita in favor of block level static initialization.
CAVEAT: This revised PR has a commit that modifies the CI to force its running on MI200 nodes. That specific commit must be reverted before merge.
Original PR (https://github.com/pytorch/pytorch/pull/114309) Note:
This pull requests add initial Flash Attention support for AMD/ROCM platform. It added a specialized Triton repository/branch as a compile-time dependency for Flash Attention math library on AMD/ROCM. This triton submodule is not used at runtime and will not be shipped to the final pytorch package. We have the plan to release this specialized Triton as a separate project.
Know limitations:
- Only supports MI200 series GPU (i.e., `gcnArchName == gfx90a:sramecc+:xnack-`.
- Only supports power of two sequence lengths.
- No support for varlen APIs.
- Only support head dimension 16,32,64,128.
- Performance is still being optimized.
Fixes#112997
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115981
Approved by: https://github.com/malfet
This pull requests add initial Flash Attention support for AMD/ROCM platform. It added a specialized Triton repository/branch as a compile-time dependency for Flash Attention math library on AMD/ROCM. This triton submodule is not used at runtime and will not be shipped to the final pytorch package. We have the plan to release this specialized Triton as a separate project.
Know limitations:
- [ ] Only supports MI200 series GPU (i.e., `gcnArchName == gfx90a:sramecc+:xnack-`.
- [ ] Only supports power of two sequence lengths.
- [ ] No support for varlen APIs.
- [ ] Only support head dimension 16,32,64,128.
- [ ] Performance is still being optimized.
Fixes https://github.com/pytorch/pytorch/issues/112997
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114309
Approved by: https://github.com/jeffdaily, https://github.com/malfet
---------
Co-authored-by: Joseph Groenenboom <joseph.groenenboom@amd.com>
# Summary
## PR Dependencies
I don't use ghstack :( this is a PR where it would have been helpful. That beings said I am going to peel off some PRs to make reviewing this easier:
- [x] Separate build flags for Flash and MemEff: #107985
### Description
This pull request updates the version of _scaled_dot_product_flash_attention from version 1 to version 2. The changes are based on the flash attention code originally authored by @tridao
### Changes Made
The majority of the changes in this pull request involve:
- Copying over the flash_attention sources.
- Updating header files.
- Removing padding and slicing code from within the flash_attention kernel and relocating it to the composite implicit region of the SDPA. This was need to make the kernel functional and appease autograd.
- Introducing a simple kernel generator to generate different instantiations of the forward and backward flash templates.
- Adding conditional compilation (ifdef) to prevent building when nvcc is invoked with gencode < sm80.
- Introducing a separate dependent option for mem_eff_attention, as flash_attention v2 lacks support for Windows and cannot be built for sm50 generation codes.
- Modifying build.sh to reduce parallelization on sm86 runners and to lower the maximum parallelization on the manywheel builds. This adjustment was made to address out-of-memory issues during the compilation of FlashAttentionV2 sources.
- Adding/Updating tests.
### Notes for Reviewers
This is not a fun review, and I apologize in advance.
Most of the files-changed are in the flash_attn/ folder. The only files of interest here IMO:
- aten/src/ATen/native/transformers/cuda/flash_attn/flash_api.cpp
- aten/src/ATen/native/transformers/cuda/flash_attn/kernels/generate_kernels.py ( this has been incorporated upstream to flash-attention github)
There are a number of files all related to avoiding OOMs in CI/CD. These are typically shell scripts.
### Follow up items
- Include the updates from e07aa036db and 9e5e8bc91e | https://github.com/pytorch/pytorch/issues/108108
### Work Items
- [x] I don't think Windows will be supported for 3.1.0 - Need to update cmakee
- [x] Let multi_query/attention pass through and test | UPDATE: I have the fast path implemented here: https://github.com/pytorch/pytorch/pull/106730 but since this will require changes to semantics of math to call repeat_interleave, I think this should be done as a followup.
- [x] Had to drop cutlass back to 3.0.0 to get it to compile. Need to figure out how to upgrade to 3.1.0 and later. Spoke with Tri and he is going to be taking a look. Note: compiling with clang currently errors for the cute headers.
- [x] Update test exercise above codepath
- [x] Still need to disable on seq_len % 128 != 0 for backward( Tri beat me to it a4f148b6ab)
- [x] Add determinism warning to BWD, Tri got to this one as well: 1c41d2b
- [x] Update dispatcher to universally prefer FlashV2
- [x] Update tests to exercise new head_dims
- [x] Move the head_dim padding from kernel to top level composite implicit function in order to make it purely functional
- [x] Create template generator script
- [x] Initial cmake support for building kernels/ folder
- [x] Replay CudaGraph changes
### Results
#### Forward only
The TFlops are reported here are on a100 that is underclocked.
#### Forward+Backward
Ran a sweep and for large compute bound sizes we do see a ~2x performance increase for forw+back.
<img width="1684" alt="Screenshot 2023-07-20 at 3 47 47 PM" src="https://github.com/pytorch/pytorch/assets/32754868/fdd26e07-0077-4878-a417-f3a418b6fb3b">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105602
Approved by: https://github.com/huydhn, https://github.com/cpuhrsch
Mostly refactor, that moves all the tests from `test_cuda` that benefit from multiGPU environment into its own file.
- Add `TestCudaMallocAsync` class for Async tests ( to separate them from `TestCudaComm`)
- Move individual tests from `TestCuda` to `TestCudaMultiGPU`
- Move `_create_scaling_models_optimizers` and `_create_scaling_case` to `torch.testing._internal.common_cuda`
- Add newly created `test_cuda_multigpu` to the multigpu periodic test
<!--
copilot:summary
-->
### <samp>🤖 Generated by Copilot at f4d46fa</samp>
This pull request fixes a flaky test and improves the testing of gradient scaling on multiple GPUs. It adds verbose output for two CUDA tests, and refactors some common code into helper functions in `torch/testing/_internal/common_cuda.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104059
Approved by: https://github.com/huydhn
Implements a simple content-addressable store for storages (with tensors implemented as cheap references on top), enabling incremental serialization of tensors to disk, which I intend to use in the accuracy repro extractor. Check the comment at the top of torch/utils/_content_store.py for more details on the intended use case.
One major piece of this PR is implementing the content hash for tensors. For our prospective use case, we may need to repeatedly hash up to 80 GB of tensor data every time we snapshot (and we may snapshot multiple times). Using a conventional cryptographic hash and hashing each snapshot would likely take on order of minutes, which seemed too slow to me. So instead, I implemented a crappy hash function that can be run on GPU. It is at least somewhat theoretically grounded: using random parameters generated by Philox, we use the standard shift-multiply and xor sum universal hash family. The hash function is a bit dorky though; instead of properly doing 160-bit math, it just runs 32-bit hash five times and cats them together. By the way, this sets the first precedent for kernel in PyTorch library which MUST be torch.compile'd to be run (in fact, this kernel does not run in eager mode because of the use of xor_sum, which doesn't actually exist in ATen.)
I had to add a few more primitives to inductor, namely randint (over the entire int range) and xor_sum. Fortunately, these primitives are natively supported by Triton/C++, and so they were very easy to plumb through. xor_sum is exposed as a prim, while randint special cases on when low/high span the entire 32-bit signed integer range.
Thanks to Jeff Johnson for letting me bounce ideas of him on a Saturday morning lol.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99809
Approved by: https://github.com/voznesenskym
# Summary
This PR creates _flash_attention_backward and _scaled_dot_product_flash_attention_backward native functions and registers them to the respective derivatives.yaml.
The goal is to replicate the torch.autograd.Function defined in the FlashAttention repo [here](33e0860c9c/flash_attn/flash_attn_interface.py (L126)) natively in PyTorch. One thing that we don't have access to is ctx.save_for_backward in native PyTorch so in order to save these variables I extended the returned objects from the forward functions.
### MetaFunctions
I also updated the FlashAttention meta functions to mirror the real outputs now. As well I added a meta registration for backwards. I have an XLMR training script and while eager training now works with FlashAttention compiling this module fails with the inductor error down below.
### Questions?
Performance issues vs mem efficient when using torch.nn.mha_forward
TorchCompile -> See purposed solution below.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92917
Approved by: https://github.com/cpuhrsch
Follow-up of #89582 to drop flags like `CUDA11OrLater` in tests. Note that in some places it appears that `TEST_WITH_ROCM` is _implicitly_ guarded against via the `CUDA11OrLater` version check, based on my best-guess of how `torch.version.cuda` would behave in ROCM builds, so I've added `not TEST_WITH_ROCM` in cases where ROCM wasn't previously explicitly allowed.
CC @ptrblck @malfet @ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92605
Approved by: https://github.com/ngimel
Enables:
test_bmm_cuda_float64
test_bmm_deterministic_cuda_float64
test_csr_matvec_cuda_complex128
test_csr_matvec_cuda_complex64
test_csr_matvec_cuda_float32
test_csr_matvec_cuda_float64
To enable the above tests had to add some more hip mappings for the hipification process.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78939
Approved by: https://github.com/pruthvistony, https://github.com/malfet
