# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # SPDX-FileCopyrightText: Songlin Yang, Yu Zhang # # This file contains code copied from the flash-linear-attention project. # The original source code was licensed under the MIT license and included # the following copyright notice: # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang # ruff: noqa: E501 from typing import Optional import torch from vllm.triton_utils import tl, triton from .index import prepare_chunk_indices, prepare_chunk_offsets from .op import exp from .utils import is_nvidia_hopper, use_cuda_graph NUM_WARPS = [2, 4] if is_nvidia_hopper else [2, 4, 8, 16] @triton.heuristics( { "USE_G": lambda args: args["g"] is not None, "USE_INITIAL_STATE": lambda args: args["h0"] is not None, "STORE_FINAL_STATE": lambda args: args["ht"] is not None, "SAVE_NEW_VALUE": lambda args: args["v_new"] is not None, "IS_VARLEN": lambda args: args["cu_seqlens"] is not None, } ) @triton.jit(do_not_specialize=["T"]) def chunk_gated_delta_rule_fwd_kernel_h_blockdim64( k, v, w, v_new, g, h, h0, ht, cu_seqlens, chunk_offsets, T, H: tl.constexpr, Hg: tl.constexpr, K: tl.constexpr, V: tl.constexpr, BT: tl.constexpr, BV: tl.constexpr, USE_G: tl.constexpr, USE_INITIAL_STATE: tl.constexpr, STORE_FINAL_STATE: tl.constexpr, SAVE_NEW_VALUE: tl.constexpr, IS_VARLEN: tl.constexpr, ): i_v, i_nh = tl.program_id(0), tl.program_id(1) i_n, i_h = i_nh // H, i_nh % H if IS_VARLEN: bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32) T = eos - bos NT = tl.cdiv(T, BT) boh = tl.load(chunk_offsets + i_n).to(tl.int32) else: bos, eos = i_n * T, i_n * T + T NT = tl.cdiv(T, BT) boh = i_n * NT # [BK, BV] b_h1 = tl.zeros([64, BV], dtype=tl.float32) if K > 64: b_h2 = tl.zeros([64, BV], dtype=tl.float32) if K > 128: b_h3 = tl.zeros([64, BV], dtype=tl.float32) if K > 192: b_h4 = tl.zeros([64, BV], dtype=tl.float32) # calculate offset h += (boh * H + i_h) * K * V v += (bos * H + i_h) * V k += (bos * Hg + i_h // (H // Hg)) * K w += (bos * H + i_h) * K if SAVE_NEW_VALUE: v_new += (bos * H + i_h) * V stride_v = H * V stride_h = H * K * V stride_k = Hg * K stride_w = H * K if USE_INITIAL_STATE: h0 = h0 + i_nh * K * V if STORE_FINAL_STATE: ht = ht + i_nh * K * V # load initial state if USE_INITIAL_STATE: p_h0_1 = tl.make_block_ptr(h0, (K, V), (V, 1), (0, i_v * BV), (64, BV), (1, 0)) b_h1 += tl.load(p_h0_1, boundary_check=(0, 1)).to(tl.float32) if K > 64: p_h0_2 = tl.make_block_ptr(h0, (K, V), (V, 1), (64, i_v * BV), (64, BV), (1, 0)) b_h2 += tl.load(p_h0_2, boundary_check=(0, 1)).to(tl.float32) if K > 128: p_h0_3 = tl.make_block_ptr(h0, (K, V), (V, 1), (128, i_v * BV), (64, BV), (1, 0)) b_h3 += tl.load(p_h0_3, boundary_check=(0, 1)).to(tl.float32) if K > 192: p_h0_4 = tl.make_block_ptr(h0, (K, V), (V, 1), (192, i_v * BV), (64, BV), (1, 0)) b_h4 += tl.load(p_h0_4, boundary_check=(0, 1)).to(tl.float32) # main recurrence for i_t in range(NT): p_h1 = tl.make_block_ptr(h + i_t * stride_h, (K, V), (V, 1), (0, i_v * BV), (64, BV), (1, 0)) tl.store(p_h1, b_h1.to(p_h1.dtype.element_ty), boundary_check=(0, 1)) if K > 64: p_h2 = tl.make_block_ptr(h + i_t * stride_h, (K, V), (V, 1), (64, i_v * BV), (64, BV), (1, 0)) tl.store(p_h2, b_h2.to(p_h2.dtype.element_ty), boundary_check=(0, 1)) if K > 128: p_h3 = tl.make_block_ptr(h + i_t * stride_h, (K, V), (V, 1), (128, i_v * BV), (64, BV), (1, 0)) tl.store(p_h3, b_h3.to(p_h3.dtype.element_ty), boundary_check=(0, 1)) if K > 192: p_h4 = tl.make_block_ptr(h + i_t * stride_h, (K, V), (V, 1), (192, i_v * BV), (64, BV), (1, 0)) tl.store(p_h4, b_h4.to(p_h4.dtype.element_ty), boundary_check=(0, 1)) p_v = tl.make_block_ptr(v, (T, V), (stride_v, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) p_v_new = ( tl.make_block_ptr(v_new, (T, V), (stride_v, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) if SAVE_NEW_VALUE else None ) b_v_new = tl.zeros([BT, BV], dtype=tl.float32) p_w = tl.make_block_ptr(w, (T, K), (stride_w, 1), (i_t * BT, 0), (BT, 64), (1, 0)) b_w = tl.load(p_w, boundary_check=(0, 1)) b_v_new += tl.dot(b_w, b_h1.to(b_w.dtype)) if K > 64: p_w = tl.make_block_ptr(w, (T, K), (stride_w, 1), (i_t * BT, 64), (BT, 64), (1, 0)) b_w = tl.load(p_w, boundary_check=(0, 1)) b_v_new += tl.dot(b_w, b_h2.to(b_w.dtype)) if K > 128: p_w = tl.make_block_ptr(w, (T, K), (stride_w, 1), (i_t * BT, 128), (BT, 64), (1, 0)) b_w = tl.load(p_w, boundary_check=(0, 1)) b_v_new += tl.dot(b_w, b_h3.to(b_w.dtype)) if K > 192: p_w = tl.make_block_ptr(w, (T, K), (stride_w, 1), (i_t * BT, 192), (BT, 64), (1, 0)) b_w = tl.load(p_w, boundary_check=(0, 1)) b_v_new += tl.dot(b_w, b_h4.to(b_w.dtype)) b_v_new = -b_v_new + tl.load(p_v, boundary_check=(0, 1)) if SAVE_NEW_VALUE: p_v_new = tl.make_block_ptr(v_new, (T, V), (stride_v, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) tl.store(p_v_new, b_v_new.to(p_v_new.dtype.element_ty), boundary_check=(0, 1)) if USE_G: m_t = (i_t * BT + tl.arange(0, BT)) < T last_idx = min((i_t + 1) * BT, T) - 1 b_g_last = tl.load(g + bos * H + last_idx * H + i_h) p_g = tl.make_block_ptr(g + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)) b_g = tl.load(p_g, boundary_check=(0,)) b_v_new = b_v_new * tl.where(m_t, tl.exp(b_g_last - b_g), 0)[:, None] b_g_last = tl.exp(b_g_last) b_h1 = b_h1 * b_g_last if K > 64: b_h2 = b_h2 * b_g_last if K > 128: b_h3 = b_h3 * b_g_last if K > 192: b_h4 = b_h4 * b_g_last b_v_new = b_v_new.to(k.dtype.element_ty) p_k = tl.make_block_ptr(k, (K, T), (1, stride_k), (0, i_t * BT), (64, BT), (0, 1)) b_k = tl.load(p_k, boundary_check=(0, 1)) b_h1 += tl.dot(b_k, b_v_new) if K > 64: p_k = tl.make_block_ptr(k, (K, T), (1, stride_k), (64, i_t * BT), (64, BT), (0, 1)) b_k = tl.load(p_k, boundary_check=(0, 1)) b_h2 += tl.dot(b_k, b_v_new) if K > 128: p_k = tl.make_block_ptr(k, (K, T), (1, stride_k), (128, i_t * BT), (64, BT), (0, 1)) b_k = tl.load(p_k, boundary_check=(0, 1)) b_h3 += tl.dot(b_k, b_v_new) if K > 192: p_k = tl.make_block_ptr(k, (K, T), (1, stride_k), (192, i_t * BT), (64, BT), (0, 1)) b_k = tl.load(p_k, boundary_check=(0, 1)) b_h4 += tl.dot(b_k, b_v_new) # epilogue if STORE_FINAL_STATE: p_ht = tl.make_block_ptr(ht, (K, V), (V, 1), (0, i_v * BV), (64, BV), (1, 0)) tl.store(p_ht, b_h1.to(p_ht.dtype.element_ty), boundary_check=(0, 1)) if K > 64: p_ht = tl.make_block_ptr(ht, (K, V), (V, 1), (64, i_v * BV), (64, BV), (1, 0)) tl.store(p_ht, b_h2.to(p_ht.dtype.element_ty), boundary_check=(0, 1)) if K > 128: p_ht = tl.make_block_ptr(ht, (K, V), (V, 1), (128, i_v * BV), (64, BV), (1, 0)) tl.store(p_ht, b_h3.to(p_ht.dtype.element_ty), boundary_check=(0, 1)) if K > 192: p_ht = tl.make_block_ptr(ht, (K, V), (V, 1), (192, i_v * BV), (64, BV), (1, 0)) tl.store(p_ht, b_h4.to(p_ht.dtype.element_ty), boundary_check=(0, 1)) def chunk_gated_delta_rule_fwd_h( k: torch.Tensor, w: torch.Tensor, u: torch.Tensor, g: Optional[torch.Tensor] = None, initial_state: Optional[torch.Tensor] = None, output_final_state: bool = False, chunk_size: int = 64, # SY: remove this argument and force chunk size 64? save_new_value: bool = True, cu_seqlens: Optional[torch.LongTensor] = None, ) -> tuple[torch.Tensor, torch.Tensor]: B, T, Hg, K, V = *k.shape, u.shape[-1] H = u.shape[-2] BT = chunk_size chunk_indices = prepare_chunk_indices( cu_seqlens, chunk_size) if cu_seqlens is not None else None # N: the actual number of sequences in the batch with either equal or variable lengths if cu_seqlens is None: N, NT, chunk_offsets = B, triton.cdiv(T, BT), None else: N, NT, chunk_offsets = len(cu_seqlens) - 1, len( chunk_indices), prepare_chunk_offsets(cu_seqlens, BT) assert K <= 256, "current kernel does not support head dimension larger than 256." h = k.new_empty(B, NT, H, K, V) final_state = k.new_empty( N, H, K, V, dtype=torch.float32) if output_final_state else None v_new = torch.empty_like(u) if save_new_value else None def grid(meta): return (triton.cdiv(V, meta['BV']), N * H) chunk_gated_delta_rule_fwd_kernel_h_blockdim64[grid]( k=k, v=u, w=w, v_new=v_new, g=g, h=h, h0=initial_state, ht=final_state, cu_seqlens=cu_seqlens, chunk_offsets=chunk_offsets, T=T, H=H, Hg=Hg, K=K, V=V, BT=BT, BV=64, ) return h, v_new, final_state