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### What this PR does / why we need it?
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The triton kernels in sample encounter some problems, scenarios are
shown below:
1. 【expand_kernel/ rejection_random_sample_kernel/
prepare_inputs_padded_kernel】, these three operations will use
‘tl.load(prt + offsets -1, mask)’ in their implementations, but triton
compiler reports that the masks in these scenarios are not static and
contiguous. As a result, compiler will first access this memory and
apply the mask. Therefore, I modified the code to ‘tl.load(prt
+tl.maximum(offsets - 1, 0), mask)’ to ensure no -1 reads.
2. 【sample_recovered_tokens_kernel/ rejection_random_sample_kernel】,
this kernel uses draft_token_id as an address offset for the load
operation. In the PD separation scenario, if the pad token is -1,
illegal memory reads and writes can occur. Therefore, i modified the
kernel and so they can do well with -1 token.
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Signed-off-by: ppppeng <zepengliu912@qq.com>
Co-authored-by: zepengliu912@qq.com <root@localhost.localdomain>
### What this PR does / why we need it?
Some parameters of Triton operators are unnecessarily modified with the
"constexpr" modifier. When these parameters change, recompilation is
triggered, which significantly affects the model performance. Therefore,
these parameters need to be rectified.
- vLLM version: v0.17.0
- vLLM main:
8b6325758c
Signed-off-by: HarpSealCC [844291270@qq.com](mailto:844291270@qq.com)
Signed-off-by: l30072083 <liuchengzhuo1@h-partners.com>
Co-authored-by: l30072083 <liuchengzhuo1@h-partners.com>
### What this PR does / why we need it?
Replace multiple PyTorch operations with a fused Triton kernel to
determine token indices for sampling during speculative decoding. This
reduces kernel launch overhead and memory traffic, improving overall
performance on Ascend hardware.
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
