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: """ this functions are modified from https://github.com/ModelTC/lightllm """ # Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py @triton.jit def _context_flash_attention_kernel( Q, K, V, q_input_scale, k_input_scale, v_input_scale, pv_output_scale, sm_scale, B_Start_Loc, B_Seqlen, TMP, alibi_ptr, Out, stride_qbs, stride_qh, stride_qd, stride_kbs, stride_kh, stride_kd, stride_vbs, stride_vh, stride_vd, stride_obs, stride_oh, stride_od, stride_tmp_b, stride_tmp_h, stride_tmp_s, # suggtest set-up 64, 128, 256, 512 BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr, ): batch_id = tl.program_id(0) cur_head = tl.program_id(1) start_m = tl.program_id(2) # initialize offsets offs_n = tl.arange(0, BLOCK_N) offs_d = tl.arange(0, BLOCK_DMODEL) offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) # get batch info cur_batch_seq_len = tl.load(B_Seqlen + batch_id) cur_batch_start_index = tl.load(B_Start_Loc + batch_id) block_start_loc = BLOCK_M * start_m load_p_ptrs = ( Q + (cur_batch_start_index + offs_m[:, None]) * stride_qbs + cur_head * stride_qh + offs_d[None, :] * stride_qd ) q = tl.load(load_p_ptrs, mask=offs_m[:, None] < cur_batch_seq_len, other=0.0) q = q.to(tl.float16) * q_input_scale.to(tl.float16) k_ptrs = K + offs_n[None, :] * stride_kbs + cur_head * stride_kh + offs_d[:, None] * stride_kd v_ptrs = V + offs_n[:, None] * stride_vbs + cur_head * stride_vh + offs_d[None, :] * stride_vd t_ptrs = TMP + batch_id * stride_tmp_b + cur_head * stride_tmp_h + offs_m * stride_tmp_s m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") l_i = tl.zeros([BLOCK_M], dtype=tl.float32) acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) if alibi_ptr is not None: alibi_m = tl.load(alibi_ptr + cur_head) block_mask = tl.where(block_start_loc < cur_batch_seq_len, 1, 0) for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N): start_n = tl.multiple_of(start_n, BLOCK_N) k = tl.load( k_ptrs + (cur_batch_start_index + start_n) * stride_kbs, mask=(start_n + offs_n[None, :]) < cur_batch_seq_len, other=0.0, ) k = k.to(tl.float16) * k_input_scale.to(tl.float16) qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) qk += tl.dot(q, k) qk *= sm_scale if alibi_ptr is not None: alibi_loc = offs_m[:, None] - (start_n + offs_n[None, :]) qk -= alibi_loc * alibi_m qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf")) m_ij = tl.max(qk, 1) p = tl.exp(qk - m_ij[:, None]) l_ij = tl.sum(p, 1) # -- update m_i and l_i m_i_new = tl.maximum(m_i, m_ij) alpha = tl.exp(m_i - m_i_new) beta = tl.exp(m_ij - m_i_new) l_i_new = alpha * l_i + beta * l_ij # -- update output accumulator -- # scale p p_scale = beta / l_i_new p = p * p_scale[:, None] # scale acc acc_scale = l_i / l_i_new * alpha tl.store(t_ptrs, acc_scale) acc_scale = tl.load(t_ptrs) acc = acc * acc_scale[:, None] # update acc v = tl.load( v_ptrs + (cur_batch_start_index + start_n) * stride_vbs, mask=(start_n + offs_n[:, None]) < cur_batch_seq_len, other=0.0, ) v = v.to(tl.float16) * v_input_scale.to(tl.float16) p = p.to(v.dtype) acc += tl.dot(p, v) # update m_i and l_i l_i = l_i_new m_i = m_i_new acc = (acc / pv_output_scale.to(tl.float16)).to(tl.int8) off_o = ( (cur_batch_start_index + offs_m[:, None]) * stride_obs + cur_head * stride_oh + offs_d[None, :] * stride_od ) out_ptrs = Out + off_o tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len) return @torch.no_grad() def smooth_llama_context_attn_fwd( q, k, v, o, q_input_scale, k_input_scale, v_input_scale, pv_output_scale, b_start_loc, b_seq_len, max_input_len ): BLOCK = 128 # shape constraints Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1] assert Lq == Lk, "context process only supports equal query, key, value length" assert Lk == Lv, "context process only supports equal query, key, value length" assert Lk in {16, 32, 64, 128} sm_scale = 1.0 / math.sqrt(Lk) batch, head = b_seq_len.shape[0], q.shape[1] grid = (batch, head, triton.cdiv(max_input_len, BLOCK)) tmp = torch.empty((batch, head, max_input_len + 256), device=q.device, dtype=torch.float32) num_warps = 4 if Lk <= 64 else 8 _context_flash_attention_kernel[grid]( q, k, v, q_input_scale, k_input_scale, v_input_scale, pv_output_scale, sm_scale, b_start_loc, b_seq_len, tmp, None, o, q.stride(0), q.stride(1), q.stride(2), k.stride(0), k.stride(1), k.stride(2), v.stride(0), v.stride(1), v.stride(2), o.stride(0), o.stride(1), o.stride(2), tmp.stride(0), tmp.stride(1), tmp.stride(2), BLOCK_M=BLOCK, BLOCK_DMODEL=Lk, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1, ) return # Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/token_attention_nopad_att1.py @triton.jit def _token_attn_1_kernel( Q, K, q_input_scale, k_input_scale, 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) q = q.to(tl.float16) * q_input_scale.to(tl.float16) 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) k = k.to(tl.float16) * k_input_scale.to(tl.float16) 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 # Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/token_attention_nopad_att1.py @triton.jit def _token_attn_1_alibi_kernel( Q, K, q_input_scale, k_input_scale, 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) q = q.to(tl.float16) * q_input_scale.to(tl.float16) 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) k = k.to(tl.float16) * k_input_scale.to(tl.float16) 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, q_input_scale, k_input_scale, 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, q_input_scale, k_input_scale, 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, q_input_scale, k_input_scale, 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 # Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/token_attention_softmax_and_reducev.py @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 # Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/token_attention_nopad_att1.py @triton.jit def _token_attn_2_kernel( Prob, V, attn_out, v_input_scale, pv_output_scale, 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_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, ) v_value = v_value.to(tl.float16) * v_input_scale.to(tl.float16) acc += tl.sum(p_value[:, None] * v_value, 0) acc = (acc / pv_output_scale.to(tl.float16)).to(tl.int8) 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, v_input_scale, pv_output_scale, 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, v_input_scale, pv_output_scale, 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 smooth_token_attention_fwd( q, k, v, attn_out, q_input_scale, k_input_scale, v_input_scale, pv_output_scale, 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=torch.float32, device="cuda") token_attn_fwd_1( q.view(calcu_shape1), k, att_m_tensor, q_input_scale, k_input_scale, 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), v_input_scale, pv_output_scale, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_len_in_batch, ) prob = None return