# Adapted from ModelTC https://github.com/ModelTC/lightllm import math import torch try: import triton import triton.language as tl HAS_TRITON = True except ImportError: HAS_TRITON = False print("please install triton from https://github.com/openai/triton") if HAS_TRITON: @triton.jit def _token_attn_1_kernel(Q, K, sm_scale, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len, attn_out, kv_cache_loc_b_stride, kv_cache_loc_s_stride, q_batch_stride, q_head_stride, q_head_dim_stride, k_batch_stride, k_head_stride, k_head_dim_stride, attn_head_stride, attn_batch_stride, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr): current_batch = tl.program_id(0) current_head = tl.program_id(1) start_n = tl.program_id(2) offs_d = tl.arange(0, HEAD_DIM) current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch) current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch) current_batch_start_index = max_kv_cache_len - current_batch_seq_len current_batch_end_index = max_kv_cache_len off_q = current_batch * q_batch_stride + current_head * q_head_stride + offs_d * q_head_dim_stride offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N) block_stard_index = start_n * BLOCK_N block_mask = tl.where(block_stard_index < current_batch_seq_len, 1, 0) for start_mark in range(0, block_mask, 1): q = tl.load(Q + off_q + start_mark) offs_n_new = current_batch_start_index + offs_n k_loc = tl.load(kv_cache_loc + kv_cache_loc_b_stride * current_batch + kv_cache_loc_s_stride * offs_n_new, mask=offs_n_new < current_batch_end_index, other=0) off_k = k_loc[:, None] * k_batch_stride + current_head * k_head_stride + offs_d[None, :] * k_head_dim_stride k = tl.load(K + off_k, mask=offs_n_new[:, None] < current_batch_end_index, other=0.0) att_value = tl.sum(q[None, :] * k, 1) att_value *= sm_scale off_o = current_head * attn_head_stride + (current_batch_in_all_start_index + offs_n) * attn_batch_stride tl.store(attn_out + off_o, att_value, mask=offs_n_new < current_batch_end_index) return @triton.jit def _token_attn_1_alibi_kernel(Q, K, sm_scale, alibi, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len, attn_out, kv_cache_loc_b_stride, kv_cache_loc_s_stride, q_batch_stride, q_head_stride, q_head_dim_stride, k_batch_stride, k_head_stride, k_head_dim_stride, attn_head_stride, attn_batch_stride, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr): current_batch = tl.program_id(0) current_head = tl.program_id(1) start_n = tl.program_id(2) offs_d = tl.arange(0, HEAD_DIM) current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch) current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch) current_batch_start_index = max_kv_cache_len - current_batch_seq_len current_batch_end_index = max_kv_cache_len off_q = current_batch * q_batch_stride + current_head * q_head_stride + offs_d * q_head_dim_stride offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N) block_stard_index = start_n * BLOCK_N block_mask = tl.where(block_stard_index < current_batch_seq_len, 1, 0) for start_mark in range(0, block_mask, 1): alibi_m = tl.load(alibi + current_head) q = tl.load(Q + off_q + start_mark) offs_n_new = current_batch_start_index + offs_n k_loc = tl.load(kv_cache_loc + kv_cache_loc_b_stride * current_batch + kv_cache_loc_s_stride * offs_n_new, mask=offs_n_new < current_batch_end_index, other=0) off_k = k_loc[:, None] * k_batch_stride + current_head * k_head_stride + offs_d[None, :] * k_head_dim_stride k = tl.load(K + off_k, mask=offs_n_new[:, None] < current_batch_end_index, other=0.0) att_value = tl.sum(q[None, :] * k, 1) att_value *= sm_scale att_value -= alibi_m * (current_batch_seq_len - 1 - offs_n) off_o = current_head * attn_head_stride + (current_batch_in_all_start_index + offs_n) * attn_batch_stride tl.store(attn_out + off_o, att_value, mask=offs_n_new < current_batch_end_index) return @torch.no_grad() def token_attn_fwd_1(q, k, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len, alibi=None): BLOCK = 32 # shape constraints q_head_dim, k_head_dim = q.shape[-1], k.shape[-1] assert q_head_dim == k_head_dim assert k_head_dim in {16, 32, 64, 128} sm_scale = 1.0 / (k_head_dim**0.5) batch, head_num = kv_cache_loc.shape[0], q.shape[1] grid = (batch, head_num, triton.cdiv(max_kv_cache_len, BLOCK)) num_warps = 4 if k_head_dim <= 64 else 8 num_warps = 2 if alibi is not None: _token_attn_1_alibi_kernel[grid]( q, k, sm_scale, alibi, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len, attn_out, kv_cache_loc.stride(0), kv_cache_loc.stride(1), q.stride(0), q.stride(1), q.stride(2), k.stride(0), k.stride(1), k.stride(2), attn_out.stride(0), attn_out.stride(1), HEAD_DIM=k_head_dim, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1, ) else: _token_attn_1_kernel[grid]( q, k, sm_scale, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len, attn_out, kv_cache_loc.stride(0), kv_cache_loc.stride(1), q.stride(0), q.stride(1), q.stride(2), k.stride(0), k.stride(1), k.stride(2), attn_out.stride(0), attn_out.stride(1), HEAD_DIM=k_head_dim, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1, ) return @triton.jit def _token_attn_softmax_fwd(softmax_logics, kv_cache_start_loc, kv_cache_seqlen, softmax_prob_out, logics_head_dim_stride, logics_batch_stride, prob_head_dim_stride, prob_batch_stride, BLOCK_SIZE: tl.constexpr): current_batch = tl.program_id(0) current_head = tl.program_id(1) col_offsets = tl.arange(0, BLOCK_SIZE) current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch) current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch) row = tl.load(softmax_logics + current_head * logics_head_dim_stride + (current_batch_in_all_start_index + col_offsets) * logics_batch_stride, mask=col_offsets < current_batch_seq_len, other=-float('inf')).to(tl.float32) row_minus_max = row - tl.max(row, axis=0) numerator = tl.exp(row_minus_max) denominator = tl.sum(numerator, axis=0) softmax_output = numerator / denominator tl.store(softmax_prob_out + current_head * prob_head_dim_stride + (current_batch_in_all_start_index + col_offsets) * prob_batch_stride, softmax_output, mask=col_offsets < current_batch_seq_len) return @torch.no_grad() def token_attn_softmax_fwd(softmax_logics, kv_cache_start_loc, kv_cache_seqlen, softmax_prob_out, max_kv_cache_len): BLOCK_SIZE = triton.next_power_of_2(max_kv_cache_len) batch, head_num = kv_cache_start_loc.shape[0], softmax_logics.shape[0] num_warps = 4 if BLOCK_SIZE >= 2048: num_warps = 8 if BLOCK_SIZE >= 4096: num_warps = 16 _token_attn_softmax_fwd[(batch, head_num)]( softmax_logics, kv_cache_start_loc, kv_cache_seqlen, softmax_prob_out, softmax_logics.stride(0), softmax_logics.stride(1), softmax_prob_out.stride(0), softmax_prob_out.stride(1), num_warps=num_warps, BLOCK_SIZE=BLOCK_SIZE, ) return @triton.jit def _token_attn_2_kernel(Prob, V, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len, kv_cache_loc_b_stride, kv_cache_loc_s_stride, prob_head_dim_stride, prob_batch_stride, v_batch_stride, v_head_stride, v_head_dim_stride, attn_out_batch_stride, attn_out_head_stride, attn_out_head_dim_stride, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr): current_batch = tl.program_id(0) current_head = tl.program_id(1) offs_n = tl.arange(0, BLOCK_N) offs_d = tl.arange(0, HEAD_DIM) current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch) current_batch_start_index = max_kv_cache_len - current_batch_seq_len current_batch_end_index = current_batch_seq_len current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch) v_loc_off = current_batch * kv_cache_loc_b_stride + (current_batch_start_index + offs_n) * kv_cache_loc_s_stride p_offs = current_head * prob_head_dim_stride + (current_batch_in_all_start_index + offs_n) * prob_batch_stride v_offs = current_head * v_head_stride + offs_d[None, :] * v_head_dim_stride acc = tl.zeros([HEAD_DIM], dtype=tl.float32) for start_n in range(0, current_batch_seq_len, BLOCK_N): start_n = tl.multiple_of(start_n, BLOCK_N) p_value = tl.load(Prob + p_offs + start_n * kv_cache_loc_s_stride, mask=(start_n + offs_n) < current_batch_seq_len, other=0.0) v_loc = tl.load(kv_cache_loc + v_loc_off + start_n * kv_cache_loc_s_stride, mask=(start_n + offs_n) < current_batch_seq_len, other=0.0) v_value = tl.load(V + v_offs + v_loc[:, None] * v_batch_stride, mask=(start_n + offs_n[:, None]) < current_batch_seq_len, other=0.0) acc += tl.sum(p_value[:, None] * v_value, 0) acc = acc.to(tl.float16) off_o = current_batch * attn_out_batch_stride + current_head * attn_out_head_stride + offs_d * attn_out_head_dim_stride out_ptrs = attn_out + off_o tl.store(out_ptrs, acc) return @torch.no_grad() def token_attn_fwd_2(prob, v, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len): if triton.__version__ >= "2.1.0": BLOCK = 128 else: BLOCK = 64 batch, head = kv_cache_loc.shape[0], v.shape[1] grid = (batch, head) num_warps = 4 dim = v.shape[-1] _token_attn_2_kernel[grid]( prob, v, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len, kv_cache_loc.stride(0), kv_cache_loc.stride(1), prob.stride(0), prob.stride(1), v.stride(0), v.stride(1), v.stride(2), attn_out.stride(0), attn_out.stride(1), attn_out.stride(2), HEAD_DIM=dim, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1, ) return @torch.no_grad() def token_attention_fwd(q, k, v, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_len_in_batch, alibi=None): head_num = k.shape[1] batch_size = kv_cache_seq_len.shape[0] calcu_shape1 = (batch_size, head_num, k.shape[2]) total_token_num = k.shape[0] att_m_tensor = torch.empty((head_num, total_token_num), dtype=q.dtype, device="cuda") token_attn_fwd_1(q.view(calcu_shape1), k, att_m_tensor, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_len_in_batch, alibi=alibi) prob = torch.empty_like(att_m_tensor) token_attn_softmax_fwd(att_m_tensor, kv_cache_start_loc, kv_cache_seq_len, prob, max_len_in_batch) att_m_tensor = None token_attn_fwd_2(prob, v, attn_out.view(calcu_shape1), kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_len_in_batch) prob = None return