This is a follow-up of #117246.
I thought then it would be easy to edit a DictionaryAttr but it turns
out that these attributes are immutable and need to be passed during the
construction of the gpu.binary Op.
The first commit was using the NVVMTargetAttr to pass the information.
After feedback from @fabianmcg, this PR now passes the information
through a new option of the gpu-module-to-binary pass.
Please add reviewers, as you see fit.
GPU Dialect lowering to SYCL runtime is driven by spirv.target_env
attached to gpu.module. As a result of this, spirv.target_env remains as
an input to LLVMIR Translation.
A SPIRVToLLVMIRTranslation without any actual translation is added to
avoid an unregistered error in mlir-cpu-runner.
SelectObjectAttr.cpp is updated to
1) Pass binary size argument to getModuleLoadFn
2) Pass parameter count to getKernelLaunchFn
This change does not impact CUDA and ROCM usage since both
mlir_cuda_runtime and mlir_rocm_runtime are already updated to accept
and ignore the extra arguments.
NVIDIA Hopper architecture introduced the Cooperative Group Array (CGA).
It is a new level of parallelism, allowing clustering of Cooperative
Thread Arrays (CTA) to synchronize and communicate through shared memory
while running concurrently.
This PR enables support for CGA within the `gpu.launch_func` in the GPU
dialect. It extends `gpu.launch_func` to accommodate this functionality.
The GPU dialect remains architecture-agnostic, so we've added CGA
functionality as optional parameters. We want to leverage mechanisms
that we have in the GPU dialects such as outlining and kernel launching,
making it a practical and convenient choice.
An example of this implementation can be seen below:
```
gpu.launch_func @kernel_module::@kernel
clusters in (%1, %0, %0) // <-- Optional
blocks in (%0, %0, %0)
threads in (%0, %0, %0)
```
The PR also introduces index and dimensions Ops specific to clusters,
binding them to NVVM Ops:
```
%cidX = gpu.cluster_id x
%cidY = gpu.cluster_id y
%cidZ = gpu.cluster_id z
%cdimX = gpu.cluster_dim x
%cdimY = gpu.cluster_dim y
%cdimZ = gpu.cluster_dim z
```
We will introduce cluster support in `gpu.launch` Op in an upcoming PR.
See [the
documentation](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#cluster-of-cooperative-thread-arrays)
provided by NVIDIA for details.
This patch adds an NVPTX compilation path that enables JIT compilation
on NVIDIA targets. The following modifications were performed:
1. Adding a format field to the GPU object attribute, allowing the
translation attribute to use the correct runtime function to load the
module. Likewise, a dictionary attribute was added to add any possible
extra options.
2. Adding the `createObject` method to `GPUTargetAttrInterface`; this
method returns a GPU object from a binary string.
3. Adding the function `mgpuModuleLoadJIT`, which is only available for
NVIDIA GPUs, as there is no equivalent for AMD.
4. Adding the CMake flag `MLIR_GPU_COMPILATION_TEST_FORMAT` to specify
the format to use during testing.
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
**For an explanation of these patches see D154153.**
Commit message:
This patch adds the default offloading handler for GPU binary ops: `#gpu.select_object`,
it selects the object to embed based on an index or a target attribute, embedding
the object as a global string and launches the kernel using the scheme used in the
GPU to LLVM pass.
Depends on D154137
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D154147
