Fixes the incorrect argument in the prefix-prefill test cases#3246
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simon-mo merged 1 commit intovllm-project:mainfrom Mar 16, 2024
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Signed-off-by: Tao He <sighingnow@gmail.com>
zhuohan123
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Mar 7, 2024
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| # Need this, otherwise when we capture the graph the process for GPU 1 would run on both | ||
| # GPU0 and GPU1 and things would hang | ||
| # | ||
| # see also similar issue: https://github.com/Dao-AILab/flash-attention/issues/523 | ||
| torch.cuda.set_device(device) |
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I am confused why do we need this? Can you give a more detailed example?
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There would be an error if we run the test case in environments with 2 GPU card, the test case test_contexted_kv_attention[cuda:0-dtype0-128-64-64] passed, but when run
test_contexted_kv_attention[cuda:1-dtype0-128-64-64] (note now it uses cuda:1), it failed and complains:
bin = self.cache[device][key]
if not warmup:
> bin.c_wrapper(
grid_0,
grid_1,
grid_2,
bin.num_warps,
bin.num_ctas,
bin.clusterDims[0],
bin.clusterDims[1],
bin.clusterDims[2],
bin.shared,
stream,
bin.cu_function,
CompiledKernel.launch_enter_hook,
CompiledKernel.launch_exit_hook,
bin,
*bin.assemble_tensormap_to_arg(non_constexpr_arg_values),
)
E ValueError: Pointer argument (at 0) cannot be accessed from Triton (cpu tensor?)
/usr/local/lib/python3.10/dist-packages/triton/runtime/jit.py:550: ValueErrorI'm not very clear about the root causes, but I found the same issue report in flash-attention and the fix from here: Dao-AILab/flash-attention#523 (comment), and confirmed it works.
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|
Hi @zhuohan123 any further comments on this patch? Thanks! |
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|
Hi @zhuohan123 @simon-mo, could you please take another look at this PR? Thanks! |
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See also comment in #3007