Syncing nvfuser devel branch to upstream master. https://github.com/csarofeen/pytorch/
Bug fixes and minor refactor
Squashed commits to WAR github API
Commits that's actually in this PR from the devel branch:
```
4c60e7dff22a494632370e5df55c011007340d06 Add examples infrastructure for using nvFuser in a standalone program (#1725)
02a05d98334ffa580d73ccb28fdb8c577ad296fe Fix issue #1751 (#1753)
8a69aa320bd7629e1709fe5ceb7104d2c88ec84c Refactor NvFuser transpose API to match eager mode behavior (#1746)
ffdf6b7709048170d768217fcd7083fc8387f932 Remove BroadcastWithoutStride. (#1738)
02bab16035e70734450c02124f5cdaa95cf5749d Fix flipping of a boolean flag (#1745)
465d66890c8242e811224359cbdb1c2915490741 cleanup (#1744)
26d354e68720bc7dd2d3b1338ac01b707a230b6a fixing noncontig broadcast (#1742)
856b6b2f9073662dd98ca22ba6c3540e20eb1cdd Add IterDomainBuilder (#1736)
1fd974f912cd4c1e21cbd16e2abb23598d66a02f fixing warning for gcc7 (#1732)
de2740a43a869f8272c2648e091d7b8235097db9 disabling complex in python tests for #1730 (#1733)
fbbbe0a2e7c7a63e0e2719b8bfccb759b714221a fixing MSVC build (#1728)
b5feee5e2b28be688dbddc766f3c0220389c8175 Fix the fused reduction runtime kernel (#1729)
5247682dff5980bb66edf8d3aac25dea2ef2ced5 Re-entrant GroupedGridReduction (#1727)
```
RUN_TORCHBENCH: nvfuser
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79147
Approved by: https://github.com/davidberard98
Syncing nvfuser devel branch to upstream master. https://github.com/csarofeen/pytorch/
A few bigger updates:
1. Initial support of cp.async and cp.async.wait: https://github.com/csarofeen/pytorch/pull/1619
2. Emulate ampere's mma 16816 with Turing's mma 1688, for a unified interface: https://github.com/csarofeen/pytorch/pull/1643
3. Extending the infrastructure to support mma operators on turing and ampere arch: https://github.com/csarofeen/pytorch/pull/1440
Commits that's actually in this PR from the csarofeen branch
```
* dd2325294e236c5082c642819a1103bcfe4561a3 (csarofeen/devel) Fusion Segmenter: Unify single kernel and multi-kernel runtime path (#1710)
* b3d1c3f446355a2d276bac8272e7aa8b5bb6b1f0 Fix missing cooperative launch (#1726)
* dc670a226cbe52be46cecef47001f38bf9a09433 Async gmem copy support on sm80+ (#1619)
* 5e6a8dab5a71aefe0548bbfa15d1a93c556d23fe Add turing mma support and test (#1643)
* d6d6b7d3f10dd91dafa4cdbd5e460bbb38173af4 Fix rFactor when there are indirect root domain(s), and refactor (#1723)
* 7093e39150c6d80e0f9f767d56654714a2e8a927 Mma op integration on ampere (#1440)
* fade8da55e60a118c5595378896d34b862b2fcc3 patch python test for bfloat16 (#1724)
* 8fbd0b18743a72ac10478857c3d2351204375685 Fine-grained kernel profiling (#1720)
* 77c1b4fa633f9e631d267923f4537336fa328939 Adding dry run mode to skip arch dependent checks (#1702)
* 151d95b97bebefc94199bb4a53423ede32b55451 More precise concretization analysis (#1719)
* f4d3630ed54d7069dd377a64be1f91013b285b66 Enable complex python tests (#1667)
* 4ceeee509774cc2ce6c834a4dc1e313f71d94503 Minor bugfix in transform_rfactor.cpp (#1715)
* 3675c70faf218e86d2c78dbd3874b175a3b0a203 Separate root domain and rfactor domain in TransformPrinter (#1716)
* f68b830d5def65dadfe29d4edf52fc703369c84a Fix scheduling with polymorphic broadcast (#1714)
* 4ab5ef7ae2cfd8fffad1e1d882ae7c50631211dc updating_ci_machine (#1718)
* 56585c58b1ff338704cafb0cd6be2b3d536bed5a Merge pull request #1711 from csarofeen/upstream_master_bump_0517
* 174d453d3be0c11a5acb0fff3b3f36e19cfdaf81 Allow using nvFuser on CUDA extension (#1701)
* 18bee67495454b9a79625799776e746bd5e81c4c Validate LOOP concrete IDs have complete IterDomains (#1676)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78244
Approved by: https://github.com/csarofeen, https://github.com/malfet
Summary:
[Comment](https://github.com/pytorch/pytorch/pull/62445/files#r680132022) claims, it got added for consistency with top level CMakeLists.txt, but `-Wno-unused-variable` is not mentioned there.
Modify violations in 50+ files that were added in the interim by either removing unused variables, or decorating the code with `C10_UNUSED` if local variable is likely used to extend object lifetime until the end of the block.
Caused preventable revert in https://github.com/pytorch/pytorch/pull/72633#issuecomment-1092300787
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75538
Reviewed By: anjali411
Differential Revision: D35747333
Pulled By: malfet
fbshipit-source-id: 3fc5828e44a4c05ba0e89e92613e6ebbdb260626
(cherry picked from commit c179fba21cfa2a0093fad50ccad5a22dd7cff52c)
Summary:
Things changed in this PR that requires review:
test/forward_backward_compatibility/check_forward_backward_compatibility.py
Our previous function overload extension names were wrong and has been updated in this PR, hence the compatibility list updated.
nvfuser code updates with bug fixes towards failures we encountered in OpInfoTests as well as failures reported by AOTAutograd team.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73627
Reviewed By: Chillee
Differential Revision: D34765458
Pulled By: davidberard98
fbshipit-source-id: c81f3d6a1b723fb3a8ba419b7f82227f70440ca7
(cherry picked from commit b6a2c362c37051e44fac31687b2fe272f776551e)
Summary:
Things changed in this PR that requires review:
1. aten/src/ATen/core/interned_strings.h
2. torch/csrc/jit/ir/alias_analysis.h : exposing createValue to allow efficient mutation
3. torch/csrc/jit/runtime/symbolic_shape_registry.cpp : added gelu/tanh/erf in registry
4. torch/jit/_script.py : throws scripting model sees autocast as decorator since it's not supported
nvfuser code update:
1. codegen improvements and performance tuning
2. integration bug fixes for shape expression logic
3. kernel segmentation update to address perf regression from horizontal fusion
4. scalar cpu tensor promotion to support inter-device operation between cpu scalar tensor and cuda tensor
Things reverted from local changes:
aten::gelu with approximation (tracked in PR: https://github.com/pytorch/pytorch/pull/61439)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72127
Reviewed By: HamidShojanazeri
Differential Revision: D34113233
Pulled By: jbschlosser
fbshipit-source-id: b82cde32b71e324eca0ea57cb8c9f9647278ca74
(cherry picked from commit e009bc5c4e)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72013
Find and replace `assert(!"` with `assert(false && "`
Excludes headers and paths that contain "third-party" or "external"
Clang raises a `-Wstring-conversion` warning when treating a string as a boolean. This is not uncommon for asserts though (e.g. `assert(!"should never happen")`). Clang does permit `expr && "string"` though in order to support these assertion use cases.
Test Plan: ci pass
Differential Revision: D33823092
fbshipit-source-id: 9a1af012215bdc91f8b4162ddb2df28d51539773
(cherry picked from commit 0286910350)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69964
Things added in this PR that requires review:
1. cuLaunchCooperativeKernel driver API added
aten/src/ATen/cuda/detail/LazyNVRTC.cpp
aten/src/ATen/cuda/nvrtc_stub/ATenNVRTC.h
nvfuser code update:
1. perf turning on codegen scheduler that improves performance.
2. permutation support has been extended beyond contiguous/channels-last. (The improvements could be observed on PW benchmark)
Things reverted from local changes:
1. aten::gelu with approximation
2. local changes that is upstreamed in PR https://github.com/pytorch/pytorch/issues/68804
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69428
Reviewed By: ngimel
Differential Revision: D33073817
Pulled By: wconstab
fbshipit-source-id: e77d32e81d037d7370822b040456fd4c3bd68edb
Summary:
nvfuser code update:
1. Tuning heuristics on schedulers for reduction/normalization kernels;
2. bfloat16 on IO tensor support;
3. Refactored memory format support, now we can support dimension collapsing with non-coherent input tensors with different memory format. e.g. channels last tensor input to batch normalization. Note that we are currently limiting memory format to only Contiguous and Channels last;
4. Refactored nvfuser graph partitioning in `graph_fuser.cpp`, separated node merge and profile node API. Updated `profiling_record.cpp`.
Things that are reverted from our local branch:
1. changes on some entries in autodiff
2. aten::gelu with approximation
3. native_dropout(_backward)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67943
Reviewed By: ngimel
Differential Revision: D32288709
Pulled By: dzhulgakov
fbshipit-source-id: fc9491182ea7e0158bc112c66f096823c588eaf1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65610
- Replace HIP_PLATFORM_HCC with USE_ROCM
- Dont rely on CUDA_VERSION or HIP_VERSION and use USE_ROCM and ROCM_VERSION.
- In the next PR
- Will be removing the mapping from CUDA_VERSION to HIP_VERSION and CUDA to HIP in hipify.
- HIP_PLATFORM_HCC is deprecated, so will add HIP_PLATFORM_AMD to support HIP host code compilation on gcc.
cc jeffdaily sunway513 jithunnair-amd ROCmSupport amathews-amd
Reviewed By: jbschlosser
Differential Revision: D30909053
Pulled By: ezyang
fbshipit-source-id: 224a966ebf1aaec79beccbbd686fdf3d49267e06
Summary:
Syncing nvfuser code base from devel branch, Listing a few of our development since last sync:
- Extends support to normalization and reduction kernels.
- Multiple kernel launch for single `CudaFusionGroup`. Hierarchical caching system has been updated to cache graph segmentation.
- profile_ivalue is enabled to convert dynamic scalar into compile time constants, which are required by the codegen. (e.g. reduction axes).
To keep this PR simple and relatively review-free. We stripped most external changes and submitted them as separate PRs, so this gigantic PR is easier to handle.
internal updates are files located in:
1. updates in nvfuser codegen `torch/csrc/jit/coddgen/cuda`
2. added nvfuser specific benchmarks `benchmarks/cpp/nvfuser`
3. nvfuser jit cpp tests `test/cpp/jit/test_gpu.cpp` `test/cpp/jit/test_gpu_shift.cpp` `test/cpp/jit/test_gpu_validator.h`
updates affecting integration:
1. profile_ivalue enabled for nvfuser. related changes are in `torch/csrc/jit/runtime/*`,
2. exposed a few more symbols `aten/src/ATen/core/*` used by codegen
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63745
Reviewed By: saketh-are
Differential Revision: D30752939
Pulled By: malfet
fbshipit-source-id: ce122e80f01bcd3865f5bd3c4dfde660665fd84c
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
Summary:
In my last PR I've missed CUDA and distributed folders, fixing this now
This change is autogenerated by `python tool/clang_tidy.py -s`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57235
Reviewed By: janeyx99
Differential Revision: D28084444
Pulled By: malfet
fbshipit-source-id: bf222f69ee90c7872c3cb0931e8cdb84f0cb3cda
Summary:
1. Added CudaFusionGuard as the custom TypeCheck for nvfuser; enabled dynamic shape support with profiling executor;
2. dropped support for legacy fuser;
3. re-enabled nvfuser tests;
4. added registration for profiling record to allow profiling on user specified nodes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46452
Reviewed By: zou3519, anjali411
Differential Revision: D24364642
Pulled By: ngimel
fbshipit-source-id: daf53a9a6b6636e1ede420a3a6d0397d4a8b450b
Summary:
This enables the cuda fuser on ROCm and enables tests for them.
Part of this patch is based on work of Rohith Nallamaddi, thank you.
Errors are my own, of course.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45965
Reviewed By: seemethere
Differential Revision: D24170457
Pulled By: walterddr
fbshipit-source-id: 3dd25b3501a41d2f00acba3ce8642ce51c49c9a6
Summary:
A lot of changes are in this update, some highlights:
- Added Doxygen config file
- Split the fusion IR (higher level TE like IR) from kernel IR (lower level CUDA like IR)
- Improved latency with dynamic shape handling for the fusion logic
- Prevent recompilation for pointwise + reduction fusions when not needed
- Improvements to inner dimension reduction performance
- Added input -> kernel + kernel launch parameters cache, added eviction policy
- Added reduction fusions with multiple outputs (still single reduction stage)
- Fixed code generation bugs for symbolic tiled GEMM example
- Added thread predicates to prevent shared memory form being loaded multiple times
- Improved sync threads placements with shared memory and removed read before write race
- Fixes to FP16 reduction fusions where output would come back as FP32
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45218
Reviewed By: ezyang
Differential Revision: D23905183
Pulled By: soumith
fbshipit-source-id: 12f5ad4cbe03e9a25043bccb89e372f8579e2a79
