mirror of https://github.com/hpcaitech/ColossalAI
653 lines
21 KiB
Python
653 lines
21 KiB
Python
import math
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import torch
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try:
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import triton
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import triton.language as tl
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HAS_TRITON = True
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except ImportError:
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HAS_TRITON = False
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print("please install triton from https://github.com/openai/triton")
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if HAS_TRITON:
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"""
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this functions are modified from https://github.com/ModelTC/lightllm
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"""
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# Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py
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@triton.jit
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def _context_flash_attention_kernel(
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Q,
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K,
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V,
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q_input_scale,
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k_input_scale,
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v_input_scale,
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pv_output_scale,
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sm_scale,
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B_Start_Loc,
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B_Seqlen,
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TMP,
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alibi_ptr,
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Out,
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stride_qbs,
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stride_qh,
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stride_qd,
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stride_kbs,
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stride_kh,
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stride_kd,
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stride_vbs,
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stride_vh,
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stride_vd,
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stride_obs,
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stride_oh,
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stride_od,
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stride_tmp_b,
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stride_tmp_h,
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stride_tmp_s,
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# suggtest set-up 64, 128, 256, 512
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BLOCK_M: tl.constexpr,
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BLOCK_DMODEL: tl.constexpr,
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BLOCK_N: tl.constexpr,
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):
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batch_id = tl.program_id(0)
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cur_head = tl.program_id(1)
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start_m = tl.program_id(2)
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# initialize offsets
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offs_n = tl.arange(0, BLOCK_N)
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offs_d = tl.arange(0, BLOCK_DMODEL)
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offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
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# get batch info
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cur_batch_seq_len = tl.load(B_Seqlen + batch_id)
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cur_batch_start_index = tl.load(B_Start_Loc + batch_id)
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block_start_loc = BLOCK_M * start_m
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load_p_ptrs = (
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Q
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+ (cur_batch_start_index + offs_m[:, None]) * stride_qbs
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+ cur_head * stride_qh
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+ offs_d[None, :] * stride_qd
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)
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q = tl.load(load_p_ptrs, mask=offs_m[:, None] < cur_batch_seq_len, other=0.0)
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q = q.to(tl.float16) * q_input_scale.to(tl.float16)
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k_ptrs = K + offs_n[None, :] * stride_kbs + cur_head * stride_kh + offs_d[:, None] * stride_kd
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v_ptrs = V + offs_n[:, None] * stride_vbs + cur_head * stride_vh + offs_d[None, :] * stride_vd
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t_ptrs = TMP + batch_id * stride_tmp_b + cur_head * stride_tmp_h + offs_m * stride_tmp_s
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m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
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l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
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acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
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if alibi_ptr is not None:
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alibi_m = tl.load(alibi_ptr + cur_head)
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block_mask = tl.where(block_start_loc < cur_batch_seq_len, 1, 0)
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for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):
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start_n = tl.multiple_of(start_n, BLOCK_N)
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k = tl.load(
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k_ptrs + (cur_batch_start_index + start_n) * stride_kbs,
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mask=(start_n + offs_n[None, :]) < cur_batch_seq_len,
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other=0.0,
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)
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k = k.to(tl.float16) * k_input_scale.to(tl.float16)
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qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
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qk += tl.dot(q, k)
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qk *= sm_scale
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if alibi_ptr is not None:
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alibi_loc = offs_m[:, None] - (start_n + offs_n[None, :])
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qk -= alibi_loc * alibi_m
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qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
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m_ij = tl.max(qk, 1)
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p = tl.exp(qk - m_ij[:, None])
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l_ij = tl.sum(p, 1)
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# -- update m_i and l_i
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m_i_new = tl.maximum(m_i, m_ij)
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alpha = tl.exp(m_i - m_i_new)
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beta = tl.exp(m_ij - m_i_new)
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l_i_new = alpha * l_i + beta * l_ij
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# -- update output accumulator --
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# scale p
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p_scale = beta / l_i_new
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p = p * p_scale[:, None]
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# scale acc
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acc_scale = l_i / l_i_new * alpha
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tl.store(t_ptrs, acc_scale)
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acc_scale = tl.load(t_ptrs)
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acc = acc * acc_scale[:, None]
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# update acc
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v = tl.load(
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v_ptrs + (cur_batch_start_index + start_n) * stride_vbs,
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mask=(start_n + offs_n[:, None]) < cur_batch_seq_len,
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other=0.0,
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)
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v = v.to(tl.float16) * v_input_scale.to(tl.float16)
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p = p.to(v.dtype)
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acc += tl.dot(p, v)
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# update m_i and l_i
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l_i = l_i_new
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m_i = m_i_new
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acc = (acc / pv_output_scale.to(tl.float16)).to(tl.int8)
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off_o = (
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(cur_batch_start_index + offs_m[:, None]) * stride_obs + cur_head * stride_oh + offs_d[None, :] * stride_od
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)
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out_ptrs = Out + off_o
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tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len)
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return
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@torch.no_grad()
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def smooth_llama_context_attn_fwd(
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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
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):
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BLOCK = 128
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# shape constraints
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Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
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assert Lq == Lk, "context process only supports equal query, key, value length"
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assert Lk == Lv, "context process only supports equal query, key, value length"
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assert Lk in {16, 32, 64, 128}
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sm_scale = 1.0 / math.sqrt(Lk)
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batch, head = b_seq_len.shape[0], q.shape[1]
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grid = (batch, head, triton.cdiv(max_input_len, BLOCK))
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tmp = torch.empty((batch, head, max_input_len + 256), device=q.device, dtype=torch.float32)
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num_warps = 4 if Lk <= 64 else 8
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_context_flash_attention_kernel[grid](
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q,
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k,
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v,
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q_input_scale,
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k_input_scale,
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v_input_scale,
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pv_output_scale,
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sm_scale,
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b_start_loc,
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b_seq_len,
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tmp,
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None,
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o,
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q.stride(0),
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q.stride(1),
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q.stride(2),
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k.stride(0),
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k.stride(1),
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k.stride(2),
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v.stride(0),
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v.stride(1),
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v.stride(2),
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o.stride(0),
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o.stride(1),
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o.stride(2),
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tmp.stride(0),
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tmp.stride(1),
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tmp.stride(2),
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BLOCK_M=BLOCK,
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BLOCK_DMODEL=Lk,
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BLOCK_N=BLOCK,
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num_warps=num_warps,
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num_stages=1,
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)
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return
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# Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/token_attention_nopad_att1.py
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@triton.jit
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def _token_attn_1_kernel(
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Q,
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K,
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q_input_scale,
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k_input_scale,
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sm_scale,
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kv_cache_loc,
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kv_cache_start_loc,
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kv_cache_seqlen,
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max_kv_cache_len,
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attn_out,
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kv_cache_loc_b_stride,
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kv_cache_loc_s_stride,
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q_batch_stride,
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q_head_stride,
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q_head_dim_stride,
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k_batch_stride,
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k_head_stride,
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k_head_dim_stride,
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attn_head_stride,
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attn_batch_stride,
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HEAD_DIM: tl.constexpr,
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BLOCK_N: tl.constexpr,
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):
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current_batch = tl.program_id(0)
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current_head = tl.program_id(1)
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start_n = tl.program_id(2)
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offs_d = tl.arange(0, HEAD_DIM)
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current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch)
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current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch)
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current_batch_start_index = max_kv_cache_len - current_batch_seq_len
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current_batch_end_index = max_kv_cache_len
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off_q = current_batch * q_batch_stride + current_head * q_head_stride + offs_d * q_head_dim_stride
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offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
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block_stard_index = start_n * BLOCK_N
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block_mask = tl.where(block_stard_index < current_batch_seq_len, 1, 0)
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for start_mark in range(0, block_mask, 1):
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q = tl.load(Q + off_q + start_mark)
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q = q.to(tl.float16) * q_input_scale.to(tl.float16)
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offs_n_new = current_batch_start_index + offs_n
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k_loc = tl.load(
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kv_cache_loc + kv_cache_loc_b_stride * current_batch + kv_cache_loc_s_stride * offs_n_new,
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mask=offs_n_new < current_batch_end_index,
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other=0,
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)
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off_k = k_loc[:, None] * k_batch_stride + current_head * k_head_stride + offs_d[None, :] * k_head_dim_stride
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k = tl.load(K + off_k, mask=offs_n_new[:, None] < current_batch_end_index, other=0.0)
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k = k.to(tl.float16) * k_input_scale.to(tl.float16)
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att_value = tl.sum(q[None, :] * k, 1)
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att_value *= sm_scale
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off_o = current_head * attn_head_stride + (current_batch_in_all_start_index + offs_n) * attn_batch_stride
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tl.store(attn_out + off_o, att_value, mask=offs_n_new < current_batch_end_index)
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return
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# Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/token_attention_nopad_att1.py
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@triton.jit
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def _token_attn_1_alibi_kernel(
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Q,
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K,
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q_input_scale,
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k_input_scale,
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sm_scale,
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alibi,
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kv_cache_loc,
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kv_cache_start_loc,
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kv_cache_seqlen,
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max_kv_cache_len,
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attn_out,
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kv_cache_loc_b_stride,
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kv_cache_loc_s_stride,
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q_batch_stride,
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q_head_stride,
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q_head_dim_stride,
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k_batch_stride,
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k_head_stride,
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k_head_dim_stride,
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attn_head_stride,
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attn_batch_stride,
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HEAD_DIM: tl.constexpr,
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BLOCK_N: tl.constexpr,
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):
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current_batch = tl.program_id(0)
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current_head = tl.program_id(1)
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start_n = tl.program_id(2)
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offs_d = tl.arange(0, HEAD_DIM)
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current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch)
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current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch)
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current_batch_start_index = max_kv_cache_len - current_batch_seq_len
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current_batch_end_index = max_kv_cache_len
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off_q = current_batch * q_batch_stride + current_head * q_head_stride + offs_d * q_head_dim_stride
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offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
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block_stard_index = start_n * BLOCK_N
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block_mask = tl.where(block_stard_index < current_batch_seq_len, 1, 0)
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for start_mark in range(0, block_mask, 1):
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alibi_m = tl.load(alibi + current_head)
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q = tl.load(Q + off_q + start_mark)
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q = q.to(tl.float16) * q_input_scale.to(tl.float16)
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offs_n_new = current_batch_start_index + offs_n
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k_loc = tl.load(
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kv_cache_loc + kv_cache_loc_b_stride * current_batch + kv_cache_loc_s_stride * offs_n_new,
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mask=offs_n_new < current_batch_end_index,
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other=0,
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)
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off_k = k_loc[:, None] * k_batch_stride + current_head * k_head_stride + offs_d[None, :] * k_head_dim_stride
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k = tl.load(K + off_k, mask=offs_n_new[:, None] < current_batch_end_index, other=0.0)
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k = k.to(tl.float16) * k_input_scale.to(tl.float16)
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att_value = tl.sum(q[None, :] * k, 1)
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att_value *= sm_scale
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att_value -= alibi_m * (current_batch_seq_len - 1 - offs_n)
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off_o = current_head * attn_head_stride + (current_batch_in_all_start_index + offs_n) * attn_batch_stride
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tl.store(attn_out + off_o, att_value, mask=offs_n_new < current_batch_end_index)
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return
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@torch.no_grad()
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def token_attn_fwd_1(
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q,
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k,
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attn_out,
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q_input_scale,
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k_input_scale,
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kv_cache_loc,
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kv_cache_start_loc,
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kv_cache_seqlen,
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max_kv_cache_len,
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alibi=None,
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):
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BLOCK = 32
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# shape constraints
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q_head_dim, k_head_dim = q.shape[-1], k.shape[-1]
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assert q_head_dim == k_head_dim
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assert k_head_dim in {16, 32, 64, 128}
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sm_scale = 1.0 / (k_head_dim**0.5)
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batch, head_num = kv_cache_loc.shape[0], q.shape[1]
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grid = (batch, head_num, triton.cdiv(max_kv_cache_len, BLOCK))
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num_warps = 4 if k_head_dim <= 64 else 8
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num_warps = 2
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if alibi is not None:
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_token_attn_1_alibi_kernel[grid](
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q,
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k,
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q_input_scale,
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k_input_scale,
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sm_scale,
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alibi,
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kv_cache_loc,
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kv_cache_start_loc,
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kv_cache_seqlen,
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max_kv_cache_len,
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attn_out,
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kv_cache_loc.stride(0),
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kv_cache_loc.stride(1),
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q.stride(0),
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q.stride(1),
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q.stride(2),
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k.stride(0),
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k.stride(1),
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k.stride(2),
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attn_out.stride(0),
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attn_out.stride(1),
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HEAD_DIM=k_head_dim,
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BLOCK_N=BLOCK,
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num_warps=num_warps,
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num_stages=1,
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)
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else:
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_token_attn_1_kernel[grid](
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q,
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k,
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q_input_scale,
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k_input_scale,
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sm_scale,
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kv_cache_loc,
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kv_cache_start_loc,
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kv_cache_seqlen,
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max_kv_cache_len,
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attn_out,
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kv_cache_loc.stride(0),
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kv_cache_loc.stride(1),
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q.stride(0),
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q.stride(1),
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q.stride(2),
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k.stride(0),
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k.stride(1),
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k.stride(2),
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attn_out.stride(0),
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attn_out.stride(1),
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HEAD_DIM=k_head_dim,
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BLOCK_N=BLOCK,
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num_warps=num_warps,
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num_stages=1,
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)
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return
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# Adapted from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/token_attention_softmax_and_reducev.py
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@triton.jit
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def _token_attn_softmax_fwd(
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softmax_logics,
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kv_cache_start_loc,
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kv_cache_seqlen,
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softmax_prob_out,
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logics_head_dim_stride,
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logics_batch_stride,
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prob_head_dim_stride,
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prob_batch_stride,
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BLOCK_SIZE: tl.constexpr,
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):
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current_batch = tl.program_id(0)
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current_head = tl.program_id(1)
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col_offsets = tl.arange(0, BLOCK_SIZE)
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current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch)
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current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch)
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row = tl.load(
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softmax_logics
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+ current_head * logics_head_dim_stride
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+ (current_batch_in_all_start_index + col_offsets) * logics_batch_stride,
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mask=col_offsets < current_batch_seq_len,
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other=-float("inf"),
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).to(tl.float32)
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row_minus_max = row - tl.max(row, axis=0)
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numerator = tl.exp(row_minus_max)
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denominator = tl.sum(numerator, axis=0)
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softmax_output = numerator / denominator
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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
|