This enables Gloo CUDA when used with a backend that supports GPUDirect which currently is only the IBVERBS backend.
This requires some changes to Gloo which are in https://github.com/pytorch/gloo/pull/441
Since we're now depending on gloo_cuda we need to split ProcessGroupGloo into two pieces, one with the CPU bits (libtorch_cpu) and one with CUDA kernels in libtorch_cuda. This unfortunately requires some major refactoring as some CPU code is shared across both.
The gloo submodule is updated to depend on the new Gloo changes
Test plan:
```py
import os
import time
transport = "TCP"
#transport = "IBVERBS"
os.environ["GLOO_DEVICE_TRANSPORT"] = transport
rank = int(os.environ["RANK"])
os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
ibv = "mlx5_0:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_9:1,mlx5_10:1,mlx5_11:1".split(",")[rank]
ibv_name, ibv_port = ibv.split(":")
os.environ["TORCH_GLOO_IBV_NAME"] = ibv_name
os.environ["TORCH_GLOO_IBV_PORT"] = ibv_port
os.environ["TORCH_GLOO_IBV_INDEX"] = "3"
import torch
import torch.distributed as dist
dist.init_process_group("gloo")
rank = dist.get_rank()
# initial sanity check
#device = "cpu"
#t = torch.zeros(10, device=device)
#dist.all_reduce(t)
#print("sanity complete")
device = "cpu"
iters = 10
warmup_iters = 2
for nelem in [10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000]:
t = torch.zeros(nelem, device=device)
torch.cuda.current_stream().synchronize()
for i in range(warmup_iters):
dist.all_reduce(t)
torch.cuda.current_stream().synchronize()
start = time.perf_counter()
for i in range(iters):
dist.all_reduce(t)
torch.cuda.current_stream().synchronize()
dur = (time.perf_counter() - start)
qps = iters/dur
bandwidth_gb = t.nbytes * iters / dur / 1e9
gb = t.nbytes / 1e9
if rank == 0:
print(f"{transport=} {device=} {iters=} {nelem=} {qps=} {gb=} {bandwidth_gb=}\n", end="")
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153406
Approved by: https://github.com/fduwjj
Summary:
- Replace `C10_CUDA_KERNEL_LAUNCH_CHECK()` in the `KernelLauncher`, as the
latter does not print __FILE__ and __LINE__
The existing `C10_CUDA_KERNEL_LAUNCH_CHECK()` implementation does not print the source file and line number when a CUDA kernel launch throws an error, leaving users confused with a context-less message like `CUDA error: invalid arguments`. This new check is a slimmed re-implementation of the macro with extra context information added to the error (beyond just file and line number) so that we can at least locate the FBGEMM source file or template where the error first surfaces.
Test Plan:
```
buck2 run 'fbcode//mode/opt' fbcode//deeplearning/fbgemm/fbgemm_gpu/test/utils:kernel_launcher
buck2 run 'fbcode//mode/opt-amd-gpu' fbcode//deeplearning/fbgemm/fbgemm_gpu/test/utils:kernel_launcher
```
Reviewed By: sryap
Differential Revision: D74364031
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153178
Approved by: https://github.com/atalman, https://github.com/huydhn
Redundant exception types in `except (PermissionError, OSError):`. Write `except OSError:`, which catches exactly the same exceptions.
https://github.com/pytorch/pytorch/actions/runs/13935844871/job/39141062991
When hipify files, or writing cprofile files, PermissionError is not enough when the file is located in a place that is not writable at all, or other OS errors happened when writing files.
This fix makes the code more robust.
Example error log:
```log
File "deepspeed/ops/adam/fused_adam.py", line 94, in __init__
fused_adam_cuda = FusedAdamBuilder().load()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "deepspeed/ops/op_builder/builder.py", line 540, in load
return self.jit_load(verbose)
^^^^^^^^^^^^^^^^^^^^^^
File "deepspeed/ops/op_builder/builder.py", line 587, in jit_load
op_module = load(name=self.name,
^^^^^^^^^^^^^^^^^^^^
File "torch/utils/cpp_extension.py", line 1597, in load
return _jit_compile(
^^^^^^^^^^^^^
File "torch/utils/cpp_extension.py", line 2031, in _jit_compile
hipify_result = hipify_python.hipify(
^^^^^^^^^^^^^^^^^^^^^
File "torch/utils/hipify/hipify_python.py", line 1167, in hipify
preprocess_file_and_save_result(output_directory, filepath, all_files, header_include_dirs,
File "torch/utils/hipify/hipify_python.py", line 213, in preprocess_file_and_save_result
result = preprocessor(output_directory, filepath, all_files, header_include_dirs, stats,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "torch/utils/hipify/hipify_python.py", line 940, in preprocessor
output_source = RE_QUOTE_HEADER.sub(mk_repl('#include "{0}"', True), output_source)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "torch/utils/hipify/hipify_python.py", line 919, in repl
preprocess_file_and_save_result(output_directory,
File "torch/utils/hipify/hipify_python.py", line 213, in preprocess_file_and_save_result
result = preprocessor(output_directory, filepath, all_files, header_include_dirs, stats,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "torch/utils/hipify/hipify_python.py", line 986, in preprocessor
with clean_ctx.open(fout_path, 'w', encoding='utf-8') as fout:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "torch/utils/hipify/hipify_python.py", line 123, in open
return open(fn, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
OSError: [Errno 30] Read-only file system: 'deepspeed/ops/csrc/adam/multi_tensor_apply_hip.cuh'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149464
Approved by: https://github.com/janeyx99
- Updated HIP flags for Windows (removed non Windows flags on Windows case, added runtime library)
- Set hipcc call for Windows case
- Removed CUDA flags (not used in ROCm) on Windows
- Updated Windows compiler (added case when using ROCm on Windows)
- Fixed path issue in hipify_python
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147382
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
TLDR: Follow up/ Build on top of https://github.com/pytorch/pytorch/pull/144476. add OCP FP8 support for gfx950
refer to https://github.com/pytorch/ao/pull/1677
This pull request includes several changes to improve compatibility and support for new GPU architectures and data types, particularly for ROCm. The key updates involve adding support for new ROCm versions and GPU architectures, updating data type handling, and removing outdated checks.
### Improvements to GPU Architecture and ROCm Version Support:
* [`aten/src/ATen/Context.cpp`](diffhunk://#diff-33de472d304acbe57d693c8567370c638068bedc1aa0ce8e9dc115dad05a7810L323-R326): Added support for new GPU architectures `gfx1200`, `gfx1201`, and `gfx950` based on ROCm version checks.
* [`aten/src/ATen/native/cuda/Blas.cpp`](diffhunk://#diff-e8a569efee1e650172f120a0fdcda024fe3e4703a4ee3336425c8f685af6b3abL196-R199): Updated architecture support in multiple functions to include `gfx1200`, `gfx1201`, and `gfx950` based on ROCm version checks. [[1]](diffhunk://#diff-e8a569efee1e650172f120a0fdcda024fe3e4703a4ee3336425c8f685af6b3abL196-R199) [[2]](diffhunk://#diff-e8a569efee1e650172f120a0fdcda024fe3e4703a4ee3336425c8f685af6b3abL865-R876)
### Updates to Data Type Handling:
* [`aten/src/ATen/cuda/CUDADataType.h`](diffhunk://#diff-9188bb13b1a49f459141f5f9b875593d1c5ce2beb5ad711fdbaf5bc7089ec015L81-L98): Enhanced data type conversion to include new float8 types for both CUDA and ROCm environments.
* [`aten/src/ATen/cuda/tunable/GemmHipblaslt.h`](diffhunk://#diff-bfa1a3b5d4bef1892bf50338775f3b0fd8cd31fc1868148f3968b98aefb68e3fL29-R80): Updated `HipDataTypeFor` template to handle new float8 types and added hard-coded enum values for ROCm versions prior to 6.3.
### Removal of Outdated Checks:
* [`cmake/public/LoadHIP.cmake`](diffhunk://#diff-b98e27b9a5f196a6965a99ee5a7bb15b3fc633d6375b767635b1b04ccb2fd3d5L169-L197): Removed the check for `HIP_NEW_TYPE_ENUMS` as it is no longer necessary with the updated ROCm versions. [[1]](diffhunk://#diff-b98e27b9a5f196a6965a99ee5a7bb15b3fc633d6375b767635b1b04ccb2fd3d5L169-L197) [[2]](diffhunk://#diff-b98e27b9a5f196a6965a99ee5a7bb15b3fc633d6375b767635b1b04ccb2fd3d5L211-R182)
These changes ensure better compatibility and performance on newer hardware and software environments, particularly for users leveraging ROCm and CUDA for deep learning and scientific computing tasks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146632
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
TLDR: Follow up/ Build on top of https://github.com/pytorch/pytorch/pull/144476. add OCP FP8 support for gfx950
refer to https://github.com/pytorch/ao/pull/1677
This pull request includes several changes to improve compatibility and support for new GPU architectures and data types, particularly for ROCm. The key updates involve adding support for new ROCm versions and GPU architectures, updating data type handling, and removing outdated checks.
### Improvements to GPU Architecture and ROCm Version Support:
* [`aten/src/ATen/Context.cpp`](diffhunk://#diff-33de472d304acbe57d693c8567370c638068bedc1aa0ce8e9dc115dad05a7810L323-R326): Added support for new GPU architectures `gfx1200`, `gfx1201`, and `gfx950` based on ROCm version checks.
* [`aten/src/ATen/native/cuda/Blas.cpp`](diffhunk://#diff-e8a569efee1e650172f120a0fdcda024fe3e4703a4ee3336425c8f685af6b3abL196-R199): Updated architecture support in multiple functions to include `gfx1200`, `gfx1201`, and `gfx950` based on ROCm version checks. [[1]](diffhunk://#diff-e8a569efee1e650172f120a0fdcda024fe3e4703a4ee3336425c8f685af6b3abL196-R199) [[2]](diffhunk://#diff-e8a569efee1e650172f120a0fdcda024fe3e4703a4ee3336425c8f685af6b3abL865-R876)
### Updates to Data Type Handling:
* [`aten/src/ATen/cuda/CUDADataType.h`](diffhunk://#diff-9188bb13b1a49f459141f5f9b875593d1c5ce2beb5ad711fdbaf5bc7089ec015L81-L98): Enhanced data type conversion to include new float8 types for both CUDA and ROCm environments.
* [`aten/src/ATen/cuda/tunable/GemmHipblaslt.h`](diffhunk://#diff-bfa1a3b5d4bef1892bf50338775f3b0fd8cd31fc1868148f3968b98aefb68e3fL29-R80): Updated `HipDataTypeFor` template to handle new float8 types and added hard-coded enum values for ROCm versions prior to 6.3.
### Removal of Outdated Checks:
* [`cmake/public/LoadHIP.cmake`](diffhunk://#diff-b98e27b9a5f196a6965a99ee5a7bb15b3fc633d6375b767635b1b04ccb2fd3d5L169-L197): Removed the check for `HIP_NEW_TYPE_ENUMS` as it is no longer necessary with the updated ROCm versions. [[1]](diffhunk://#diff-b98e27b9a5f196a6965a99ee5a7bb15b3fc633d6375b767635b1b04ccb2fd3d5L169-L197) [[2]](diffhunk://#diff-b98e27b9a5f196a6965a99ee5a7bb15b3fc633d6375b767635b1b04ccb2fd3d5L211-R182)
These changes ensure better compatibility and performance on newer hardware and software environments, particularly for users leveraging ROCm and CUDA for deep learning and scientific computing tasks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146632
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
For correct import and export of functions when the dynamic linkage is used for HIP libraries on windows, the appropriate export/import macros need to be put in place. This Pull Request utilizes existing CUDA import/export macros by converting them to corresponding HIP macros during the hipification process.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144098
Approved by: https://github.com/jeffdaily
hipGraphExecDestroy doesn't immediately free memory since rocm6.2.
They wait for next sync point in order to free the memory, this is to ensure that all hipGraphLaunch are finished before we release any memory.
We need to ensure all async opreations finish before deleting the object.
capture_dev_ variable is used to save the device number when capture_begin() method is called
But CUDAGraph can be created and destroyed without calling capture_begin() method. `capture_dev_ = UNDEFINED_DEVICE;` allows to detect such a case and skip sync
Tests impacted:
test_cuda.py::TestCuda::test_graph_make_graphed_callables_*
distributed/test_c10d_nccl.py::ProcessGroupNCCLTest::test_allreduce_in_cudagraph
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138722
Approved by: https://github.com/malfet, https://github.com/eqy, https://github.com/jeffdaily
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
Summary:
AMD lowering duration is 1.55x longer than H100. Profiling shows hipification related functions took 22% of overall lowering time.
This diff cuts that time by safely memoize the trie to regex logic. The trick is to incrementally build a state of the trie during the trie construction. The state is the hash of all the words added to the trie.
Differential Revision: D65659445
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140156
Approved by: https://github.com/ColinPeppler
Co-authored-by: Kefei Lu <kefeilu@meta.com>
Summary: We hipify NCCLUtils.h from nccl.h to rccl/rccl.h. This follows the format of the rocm rpm suite (the header is in include/rccl/rccl.h), however the source code is just src/rccl.h. Using the rccl/rccl.h will make us find the rpm's header but not the src code's header.
Test Plan:
buck run mode/opt-amd-gpu -c hpc_comms.use_rccl=develop -c fbcode.split-dwarf=True --config rccl.build_rdma_core=true --config rccl.adhoc_brcm=true //aps_models/ads/icvr:icvr_launcher -- mode=local_ctr_cvr_cmf_rep_1000x_v1_no_atom data_loader.dataset.table_ds=[2024-09-04] data_loader.dataset.batch_size=512 max_ind_range=10
w/o this diff, it'll show 2.18 nccl version
Differential Revision: D62371434
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135472
Approved by: https://github.com/jeffdaily, https://github.com/cenzhaometa
This change modifies the `hipify_python.py` script to properly detect all directories, `include` and `ignore` paths during hipification process on Windows, by changing the path syntax convention to a UNIX-like one.
Since in many places the script assumes a UNIX-like convention by using paths with forward slashes `/`, I decided to accommodate for it by converting Windows paths to UNIX-like ones. By doing it so, the number of changes to the file is limited. Moreover this early-on unification allows for the rest of the code to have a battle-tested linux-like behaviour.
Another option would be to use `Path` object from `pathlib` to represent all paths in the script, however, it would impact a broader share of a code and would hence require a more meticulous evaluation in terms of non-altered logic and edge cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135360
Approved by: https://github.com/jeffdaily, https://github.com/jithunnair-amd
Another attempt to update NVTX to NVTX3. We now avoid changing NVTX header inclusion of existing code. The advantage of NVTX3 over NVTX is that it is a header-only library so that linking with NVTX3 can greatly simplify our CMake and other building scripts for finding libraries in user environments. In addition, NVTX are indeed still present in the latest CUDA versions, but they're no longer a compiled library: It's now a header-only library. That's why there isn't a .lib file anymore.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109843
Approved by: https://github.com/peterbell10, https://github.com/eqy
Co-authored-by: Ivan Zaitsev <108101595+izaitsevfb@users.noreply.github.com>
- 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
