[execution] support for higher dim memory allocation in the execution engine
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- Dominant language
- Python
- Stars
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- Avg merge
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Description
I have kernel (fused attention) that deals with 4d tensors. and it fails with.
```
File "/home/jovyan/lighthouse/examples/xegpu/fused_attention.py", line 362, in
times = runner.benchmark(
^^^^^^^^^^^^^^^^^
File "/home/jovyan/lighthouse/lighthouse/execution/runner.py", line 186, in benchmark
return self._execute_kernel(
^^^^^^^^^^^^^^^^^^^^^
File "/home/jovyan/lighthouse/lighthouse/execution/runner.py", line 154, in _execute_kernel
with allocator() as inputs:
File "/home/jovyan/miniforge3/envs/imex-dev-torch-mlir/lib/python3.11/contextlib.py", line 137, in __enter__
return next(self.gen)
^^^^^^^^^^^^^^
File "/home/jovyan/lighthouse/lighthouse/execution/memory_manager.py", line 139, in clone_host_buffers
buf = self.alloc(
^^^^^^^^^^^
File "/home/jovyan/lighthouse/lighthouse/execution/memory_manager.py", line 88, in alloc
assert rank in (1, 2), "Only 1d or 2d arrays are supported."
^^^^^^^^^^^^^^
AssertionError: Only 1d or 2d arrays are supported.
```
any plans for supporting this? workarounds also welcome.
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Research direction
Reproduce the failure from examples/xegpu/fused_attention.py around line 362, then inspect lighthouse/execution/memory_manager.py, especially alloc and clone_host_buffers, and the call path in lighthouse/execution/runner.py. Done means the fused-attention example can allocate and execute its 4D tensors without triggering the current rank assertion.
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Assessment
- Tech stack
- python
- Domain
- backend
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100