import torch import triton import triton.language as tl from sglang.srt.utils import is_cpu, next_power_of_2 _is_cpu = is_cpu() if _is_cpu: from sgl_kernel import fill_accept_out_cache_loc_cpu, fill_bonus_tokens_cpu @triton.jit def fill_bonus_tokens( accept_tokens, accept_lens, bonus_tokens_ptr, accept_stride: tl.constexpr, ): # NOTE: we cannot fuse any in-place operations of `accept_lens` inside this kernel # because this kernel reads accept_lens pid = tl.program_id(axis=0) # `accept_lens` includes the bonus token; the last accepted slot is at -1. accept_len = tl.load(accept_lens + pid) # accept_stride = per-req width of accept_tokens (= accept_index.shape[1]). bonus_token_idx = accept_stride * pid + accept_len - 1 bonus_token = tl.load(accept_tokens + bonus_token_idx) tl.store(bonus_tokens_ptr + pid, bonus_token) def fill_bonus_tokens_func( accept_tokens: torch.Tensor, accept_lens: torch.Tensor, bonus_tokens: torch.Tensor, # mutable accept_stride: int, batch_size: int, ): if _is_cpu: fill_bonus_tokens_cpu( accept_tokens, accept_lens, bonus_tokens, accept_stride, ) return fill_bonus_tokens[(batch_size,)]( accept_tokens, accept_lens, bonus_tokens, accept_stride, ) @triton.jit def fill_accept_out_cache_loc( accept_index, out_cache_loc, accept_out_cache_loc, size_upper: tl.constexpr, ): pid = tl.program_id(axis=0) offset = tl.arange(0, size_upper) masks = (tl.load(accept_index + offset, offset < pid, other=-1) != -1).to(tl.int64) dst = tl.sum(masks) src = tl.load(accept_index + pid) if src > -1: value = tl.load(out_cache_loc + src) tl.store(accept_out_cache_loc + dst, value) def fill_accept_out_cache_loc_func( accept_index: torch.Tensor, out_cache_loc: torch.Tensor, accept_out_cache_loc: torch.Tensor, # mutable size: int, ): if _is_cpu: fill_accept_out_cache_loc_cpu( accept_index, out_cache_loc, accept_out_cache_loc, ) return fill_accept_out_cache_loc[(size,)]( accept_index, out_cache_loc, accept_out_cache_loc, next_power_of_2(size), )