# Copyright (c) 2026 LightSeek Foundation # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """Speculative decoding chain sampling ops: verify_chain_greedy, chain_speculative_sampling_target_only.""" import functools from pathlib import Path import torch @functools.cache def _load_sampling_chain_module(): import tvm_ffi objs_dir = Path(__file__).parent / "objs" / "sampling_chain" so_path = objs_dir / "sampling_chain.so" if not so_path.exists(): raise RuntimeError( f"tokenspeed_kernel sampling_chain library not found at {so_path}. " "Run: pip install -e tokenspeed_kernel/python/" ) return tvm_ffi.load_module(str(so_path)) def verify_chain_greedy( predicts: torch.Tensor, accept_index: torch.Tensor, accept_token_num: torch.Tensor, candidates: torch.Tensor, target_predict: torch.Tensor, batch_size: int, num_draft_tokens: int, enable_pdl: bool = False, ) -> None: _load_sampling_chain_module().verify_chain_greedy( predicts, accept_index, accept_token_num, candidates, target_predict, int(batch_size), int(num_draft_tokens), bool(enable_pdl), ) def chain_speculative_sampling_target_only( predicts: torch.Tensor, accept_index: torch.Tensor, accept_token_num: torch.Tensor, candidates: torch.Tensor, uniform_samples: torch.Tensor, uniform_samples_for_final_sampling: torch.Tensor, target_probs: torch.Tensor, draft_probs: torch.Tensor | None = None, threshold_single: float = 1.0, threshold_acc: float = 1.0, deterministic: bool = True, enable_pdl: bool = False, ) -> None: """Target-only chain speculative sampling. When ``draft_probs`` is ``None``, the kernel treats draft probabilities as all zeros and avoids the corresponding GMEM traffic. """ _load_sampling_chain_module().chain_speculative_sampling_target_only( predicts, accept_index, accept_token_num, candidates, uniform_samples, uniform_samples_for_final_sampling, target_probs, draft_probs, float(threshold_single), float(threshold_acc), bool(deterministic), bool(enable_pdl), )