This commit adjusts the CUDA context management in the SerializeToCubin
pass. In particular, it uses the device 0 primary context instead of
creating a new CUDA context on each invocation of SerializeToCubin. This
yields very large improvements in compile time, especially if an
application (like a JIT compiler) is calling SerializeToCubin
repeatedly.
Differential Revision: https://reviews.llvm.org/D159487
Co-authored-by: Rohan Yadav <rohany@cs.stanford.edu>
This revision avoids the registration of dialect extensions in Pass::getDependentDialects.
Such registration of extensions can be dangerous because `DialectRegistry::isSubsetOf` is
always guaranteed to return false for extensions (i.e. there is no mechanism to track
whether a lambda is already in the list of already registered extensions).
When the context is already in a multi-threaded mode, this is guaranteed to assert.
Arguably a more structured registration mechanism for extensions with a unique ExtensionID
could be envisioned in the future.
In the process of cleaning this up, multiple usage inconsistencies surfaced around the
registration of translation extensions that this revision also cleans up.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D157703
This patch fixes the output of the error message that is printed when
the CUDA library cannot identity the error code. In that case, no error
message is provided by the library, and the previous implementation just
printed the content of a randomly initialized pointer. This patch
initializes the pointer to nullptr and only prints the content if that
has changed.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D156791
This reverts commit 2e0e00ed841951e358a85a871647be9b3a622f51
and reverts commit a6eb40692c795a9cc29266779ceca2e304141114
and reverts commit 585cbe3f639783bf0307b47504acbd205f135310.
15 tests are broken on the mlir-nvidia buildbot:
'cuModuleLoadData(&module, data)' failed with 'CUDA_ERROR_INVALID_SOURCE'
'cuModuleGetFunction(&function, module, name)' failed with 'CUDA_ERROR_INVALID_HANDLE'
'cuLaunchKernel(function, gridX, gridY, gridZ, blockX, blockY, blockZ, smem, stream, params, extra)' failed with 'CUDA_ERROR_INVALID_HANDLE'
'cuModuleUnload(module)' failed with 'CUDA_ERROR_INVALID_HANDLE'
Current SM version is 35 but it is deprecated long time ago. D155563 introduced ptxas compilations, using sm_35 causes failures in builtbot. This change increase default SM version to 50.
Differential Revision: https://reviews.llvm.org/D156098
Recent change introduces compilation with ptxas compiler. The change is important to be able to different versions of ptxas compiler without changing the compiler.
It causes some failures in builtbot. This change adds fallback mechanism to JIt compilation that is original path.
Differential Revision: https://reviews.llvm.org/D156096
This work improves how we compile the generated PTX code using the `ptxas` compiler. Currently, we rely on the driver's jit API to compile the PTX code. However, this approach has some limitations. It doesn't always produce the same binary output as the ptxas compiler, leading to potential inconsistencies in the generated Cubin files.
This work introduces a significant improvement by directly utilizing the ptxas compiler for PTX compilation. By doing so, we can achieve more consistent and reliable results in generating cubin files. Key Benefits:
- Using the Ptxas compiler directly ensures that the cubin files generated during the build process remain consistent with CUDA compilation using `nvcc` or `clang`.
- Another advantage of this work is that it allows developers to experiment with different ptxas compilers without the need to change the compiler. Performance among ptxas compiler versions are vary, therefore, one can easily try different ptxas compilers.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D155563
No matter how one constructs their SerializeTo* pass, we want to
ensure that the LLVM initialization code runs once and only once. This
commit adds a static once_flag to ensure that.
I've run into mysterious segfaults when calling MLIR GPU compiles from
multiple threads, and this commit is a potential fix for the issue.
Reviewed By: fmorac
Differential Revision: https://reviews.llvm.org/D155226
When targeting NVIDIA GPUs, seeing the generated PTX is important. Currently, we don't have simple way to do it.
This work adds dump-ptx to gpu-to-cubin pass. One can use it like `gpu-to-cubin{chip=sm_90 features=+ptx80 dump-ptx}`.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D155166
Before serializing, optimizations on llvm were only called on path to
hsaco, and not cubin. Define opt-level for `gpu-to-cubin` pass as well,
and move call to optimize llvm to a common place.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D151554
If MLIR_CUDA_RUNNER_ENABLED, register a 'gpu-to-cubin' conversion pass to mlir-opt.
The next step is to switch CUDA integration tests from mlir-cuda-runner to mlir-opt + mlir-cpu-runner and remove mlir-cuda-runner.
Depends On D98279
Reviewed By: herhut, rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D98203
