[TRTLLM-12669][refactor] Remove allow_advanced_sampling and capture dual CUDA graphs#14745
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…tected dual-graph dispatch Remove the static `allow_advanced_sampling` config flag and replace it with a per-step auto-detected `is_all_greedy_sample` boolean on SpecMetadata. The flag is computed in `populate_sampling_params_for_one_model` from the actual temperature/top_k/top_p of every request in the batch. `is_all_greedy_sample` is included in the CUDA graph key so we lazily capture two graph variants (argmax fast-path vs advanced sampling kernel) and dispatch by replaying the right one based on the current batch composition. Both variants stay CUDA-graph-compatible because the dispatch is a host-side decision outside the captured region. Additional optimizations for the all-greedy batch (the common default): - Populate skips per-token list building and 6 H->D copies entirely. - Rejection sampling is bypassed (argmax is equivalent for all-greedy) in both linear and dynamic-tree paths. - _compute_and_store_draft_probs is skipped, saving a softmax pass and draft-probs copy. Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
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Replace static config flag with auto-detected per-step uses_advanced_sampling based on actual sampling params. Include this in CUDA graph key so we lazily capture two graph variants (argmax fast-path vs advanced sampling kernel) and dispatch by replaying the right one.
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Description
allow_advanced_samplingconfig flag fromDecodingBaseConfig.is_all_greedy_samplebased onactual
temperature/top_k/top_pof requests in the batch.captured (argmax fast-path vs advanced sampling kernel) and dispatched
at replay time based on batch composition.
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