[V1][TPU] Enable Top K

[NickLucche]

Enabling the topk optimization that was introduced in #15242.

Currently facing the very issue foreseen by @njhill here #15242 (comment).

ERROR 03-25 18:27:23 [core.py:343]     random_sampled = self.topk_topp_sampler(
ERROR 03-25 18:27:23 [core.py:343]                      ^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-25 18:27:23 [core.py:343]   File "/home/nick/vllm/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
ERROR 03-25 18:27:23 [core.py:343]     return self._call_impl(*args, **kwargs)
ERROR 03-25 18:27:23 [core.py:343]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-25 18:27:23 [core.py:343]   File "/home/nick/vllm/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
ERROR 03-25 18:27:23 [core.py:343]     return forward_call(*args, **kwargs)
ERROR 03-25 18:27:23 [core.py:343]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-25 18:27:23 [core.py:343]   File "/home/nick/vllm/vllm/v1/sample/ops/topk_topp_sampler.py", line 119, in forward_tpu
ERROR 03-25 18:27:23 [core.py:343]     topk_values, topk_indices = torch.topk(logits, k, dim=-1)
ERROR 03-25 18:27:23 [core.py:343]                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 03-25 18:27:23 [core.py:343] TypeError: topk(): argument 'k' (position 2) must be int, not Tensor

Dumping work for reference, will look into it asap.

Update:

• I've fixed topk only using @njhill work on [V1][Sampler] Faster top-k only implementation #15478 which should land soon on main
• Removed the env variable that would fallback to native impl (as dicussed in prev pr)
• Addressed some of the cruft leftover from [TPU][V1] Fix Sampler recompilation #15309

For completeness, I've run microbenchmarks and the new impl is slower (but of course correct):

//before
Running 32 elapsed time: 0.0018310546875
Running 32 elapsed time: 0.0017833709716796875
// after
 Running 32 elapsed time: 0.003275632858276367
Running 32 elapsed time: 0.003297090530395508

cc @hyeygit

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @NickLucche.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[NickLucche]

Let's hold until main is fixed to reduce entropy

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @NickLucche.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[hyeygit]

Thank you @NickLucche for this PR and thank you @njhill for fixing the batch case for top-k! In #15242 I only tested with the microbenchmark and test_sampler.py (scalar case only), not realizing that k can be batched. Thank you for the catch and sorry for the miss.

One thing to note is that on TPU torch.topk still involves a full vocab sort (see XLA lowering). The reason using torch.topk is so much faster on TPU is because of avoiding a full-vocab torch.scatter (as used in apply_top_k_top_p) which is extremely slow on TPU.