[kernel] MiniMax-Text-01 prefill lightning_attn with triton (#2911)
This commit is contained in:
Xiaoyu Zhang
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import itertools
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import math
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import os
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import triton
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import triton.language as tl
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from einops import rearrange
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# Adapted from https://github.com/OpenNLPLab/lightning-attention/blob/main/lightning_attn/ops/triton/lightning_attn2.py
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@triton.jit
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def _fwd_kernel(
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Q,
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K,
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V,
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Out,
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S, # log lambda
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b: tl.constexpr,
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h: tl.constexpr,
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n: tl.constexpr,
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d: tl.constexpr,
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e: tl.constexpr,
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BLOCK: tl.constexpr,
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NUM_BLOCK: tl.constexpr,
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BLOCK_MODEL: tl.constexpr,
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):
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##### get offset
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off_bh = tl.program_id(0)
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off_h = off_bh % h
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off_e = tl.program_id(1)
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qk_offset = off_bh * n * d
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v_offset = off_bh * n * e
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o_offset = off_bh * n * e
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# channel offset
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e_offset = off_e * BLOCK_MODEL
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##### get block ptr
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Q_block_ptr = Q + qk_offset + tl.arange(0, d)[None, :]
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K_trans_block_ptr = K + qk_offset + tl.arange(0, d)[:, None]
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V_block_ptr = V + v_offset + e_offset + tl.arange(0, BLOCK_MODEL)[None, :]
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O_block_ptr = Out + o_offset + e_offset + tl.arange(0, BLOCK_MODEL)[None, :]
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S_block_ptr = S + off_h
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##### init diag decay(Lambda); q, k decay; kv
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s = tl.load(S_block_ptr)
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# q, k decay
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off_block = tl.arange(
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0, BLOCK
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) # Not bug, this is a bit different from algorithm 1, but is mathematically equivalent
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q_decay = tl.exp(-s.to(tl.float32) * off_block[:, None])
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k_trans_decay = tl.exp(-s.to(tl.float32) * (BLOCK - off_block[None, :]))
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block_decay = tl.exp(-s.to(tl.float32) * BLOCK)
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# diag decay
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index = off_block[:, None] - off_block[None, :]
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s_index = s * index
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s_index = tl.where(index >= 0, -s_index, float("-inf"))
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diag_decay = tl.exp(s_index)
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kv = tl.zeros([d, BLOCK_MODEL], dtype=tl.float32)
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##### compute
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for i in range(NUM_BLOCK):
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# load
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q = tl.load(
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Q_block_ptr + off_block[:, None] * d, mask=off_block[:, None] < n, other=0.0
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).to(tl.float32)
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k_trans = tl.load(
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K_trans_block_ptr + off_block[None, :] * d,
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mask=off_block[None, :] < n,
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other=0.0,
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).to(tl.float32)
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v = tl.load(
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V_block_ptr + off_block[:, None] * e, mask=off_block[:, None] < n, other=0.0
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).to(tl.float32)
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# compute
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qk = tl.dot(q, k_trans) * diag_decay
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o_intra = tl.dot(qk, v)
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o_inter = tl.dot(q, kv) * q_decay
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o = o_intra + o_inter
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# save and update
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tl.store(
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O_block_ptr + off_block[:, None] * e,
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o.to(O_block_ptr.dtype.element_ty),
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mask=off_block[:, None] < n,
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)
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kv = block_decay * kv + tl.dot(k_trans * k_trans_decay, v)
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off_block += BLOCK
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def lightning_attn2(q, k, v, s):
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q = q.contiguous()
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k = k.contiguous()
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v = v.contiguous()
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s = s.contiguous()
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b, h, n, d = q.shape
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e = v.shape[-1]
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# Pad d to next power of 2
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d_padded = next_power_of_2(d)
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if d_padded != d:
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q_padded = F.pad(q, (0, d_padded - d))
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k_padded = F.pad(k, (0, d_padded - d))
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else:
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q_padded = q
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k_padded = k
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# Pad e to next power of 2
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e_padded = next_power_of_2(e)
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if e_padded != e:
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v_padded = F.pad(v, (0, e_padded - e))
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else:
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v_padded = v
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o_padded = torch.empty((b, h, n, e_padded), dtype=q.dtype, device=q.device)
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BLOCK = 64
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NUM_BLOCK = triton.cdiv(q.shape[2], BLOCK)
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# parallel over channel
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BLOCK_MODEL = min(triton.next_power_of_2(e_padded), 32)
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grid = (b * h, triton.cdiv(e_padded, BLOCK_MODEL))
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_fwd_kernel[grid](
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q_padded,
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k_padded,
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v_padded,
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o_padded,
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s,
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b,
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h,
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n,
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d_padded,
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e_padded,
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BLOCK=BLOCK,
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NUM_BLOCK=NUM_BLOCK,
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BLOCK_MODEL=BLOCK_MODEL,
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)
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# Remove padding from output
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if e_padded != e:
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o = o_padded[..., :e]
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else:
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o = o_padded
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return o
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def is_support(dim):
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return 16 % dim
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def next_power_of_2(n):
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return 2 ** (int(math.ceil(math.log(n, 2))))
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def lightning_attn_func(q, k, v, s):
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b, h, n, d = q.shape
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e = v.shape[-1]
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assert is_support(d) and is_support(e)
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# pad v's feature dim to power of 2
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e_pad = next_power_of_2(e)
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need_pad = e_pad != e
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if need_pad:
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v = F.pad(v, (0, e_pad - e))
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if d > 128:
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# split over head
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if 64 % d:
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m = 64
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elif 32 % d:
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m = 32
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elif 16 % d:
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m = 16
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arr = [m * i for i in range(d // m + 1)]
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if arr[-1] != d:
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arr.append(d)
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n = len(arr)
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o = 0
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for i in range(n - 1):
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start = arr[i]
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end = arr[i + 1]
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q1 = q[..., start:end]
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k1 = k[..., start:end]
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o += lightning_attn2(q1, k1, v, s)
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else:
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o = lightning_attn2(q, k, v, s)
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if need_pad:
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o = o[:, :, :, :e]
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return o
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debug = eval(os.environ.get("debug", default="False"))
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BLOCK = 256
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# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MiniMaxText01
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class MiniMaxText01RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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MiniMaxText01RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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# Copied from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/modeling_minimax_text_01.py
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def get_activation_fn(activation):
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if debug:
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logger.info(f"activation: {activation}")
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if activation == "gelu":
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return F.gelu
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elif activation == "relu":
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return F.relu
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elif activation == "elu":
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return F.elu
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elif activation == "sigmoid":
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return F.sigmoid
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elif activation == "exp":
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def f(x):
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with torch.no_grad():
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x_max = torch.max(x, dim=-1, keepdims=True).values
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y = torch.exp(x - x_max)
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return y
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return f
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elif activation == "leak":
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return F.leaky_relu
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elif activation == "1+elu":
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def f(x):
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return 1 + F.elu(x)
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return f
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elif activation == "2+elu":
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def f(x):
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return 2 + F.elu(x)
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return f
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elif activation == "silu" or activation == "swish":
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return F.silu
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elif activation == "sine":
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return torch.sin
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else:
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logger.info(f"activation: does not support {activation}, use Identity!!!")
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return lambda x: x
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# Copied from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/modeling_minimax_text_01.py
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class MiniMaxText01LightningAttention(nn.Module):
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def __init__(self, config=None, layer_idx: Optional[int] = None, **kwargs):
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super().__init__()
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if config is None:
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config = type("Config", (), kwargs)
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bias = False
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
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self.out_proj = nn.Linear(
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self.head_dim * self.num_heads, self.hidden_size, bias=bias
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)
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self.act = get_activation_fn(config.hidden_act)
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self.norm = MiniMaxText01RMSNorm(self.head_dim * self.num_heads)
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self.qkv_proj = nn.Linear(
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self.hidden_size, 3 * self.head_dim * self.num_heads, bias=bias
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)
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self.output_gate = nn.Linear(
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self.hidden_size, self.head_dim * self.num_heads, bias=bias
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)
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# for inference only
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self.offset = 0
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self.layer_idx = layer_idx
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def forward(
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self,
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hidden_states,
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attn_mask: Optional[torch.Tensor] = None, # (b, h, n, m)
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output_attentions: bool = False,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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use_cache: bool = False,
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slope_rate: Optional[torch.Tensor] = None,
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**kwargs,
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):
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if (not self.training) and (not do_eval):
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return self.inference(
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hidden_states,
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attn_mask,
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output_attentions,
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past_key_value,
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use_cache,
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slope_rate,
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)
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def inference(
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self,
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x,
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attn_mask: Optional[torch.Tensor] = None, # (b, n)
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output_attentions: bool = False,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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use_cache: bool = False,
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slope_rate: Optional[torch.Tensor] = None, # (h, 1, 1)
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):
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# x: b n d
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b, n, d = x.shape
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# linear map
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qkv = self.act(self.qkv_proj(x))
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new_shape = qkv.size()[:-1] + (self.num_heads, -1)
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qkv = qkv.view(*new_shape)
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q, k, v = torch.split(qkv, [self.head_dim] * 3, dim=3)
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q = q.transpose(1, 2)
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k = k.transpose(1, 2)
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v = v.transpose(1, 2)
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if past_key_value is None:
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self.offset = q.shape[-2]
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else:
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self.offset += 1
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# for align with metaseq
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ratio = torch.exp(-slope_rate)
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# only use for the first time
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if past_key_value is None:
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slope_rate = slope_rate.to(torch.float32)
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if attn_mask is not None:
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v = v.masked_fill(
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(1 - attn_mask).unsqueeze(1).unsqueeze(-1).to(torch.bool), 0
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)
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NUM_BLOCK = (n + BLOCK - 1) // BLOCK
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b, h, n, d = q.shape
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e = v.shape[-1]
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# other
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array = torch.arange(BLOCK).to(q) + 1
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q_decay = torch.exp(-slope_rate * array.reshape(-1, 1))
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k_decay = torch.exp(-slope_rate * (BLOCK - array.reshape(-1, 1)))
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index = array[:, None] - array[None, :]
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s_index = (
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slope_rate
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* index[
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None,
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None,
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]
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)
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s_index = torch.where(index >= 0, -s_index, float("-inf"))
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diag_decay = torch.exp(s_index)
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kv = torch.zeros(b, h, d, e).to(torch.float32).to(q.device)
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output = torch.empty((b, h, n, e), dtype=q.dtype, device=q.device)
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for i in range(NUM_BLOCK):
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si = i * BLOCK
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ei = min(si + BLOCK, n)
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m = ei - si
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qi = q[:, :, si:ei].contiguous()
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ki = k[:, :, si:ei].contiguous()
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vi = v[:, :, si:ei].contiguous()
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qkv_none_diag = torch.matmul(qi * q_decay[:, :m], kv).to(torch.float32)
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# diag
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qk = (
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torch.matmul(qi, ki.transpose(-1, -2)).to(torch.float32)
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* diag_decay[:, :, :m, :m]
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)
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qkv_diag = torch.matmul(qk, vi.to(torch.float32))
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block_decay = torch.exp(-slope_rate * m)
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output[:, :, si:ei] = qkv_none_diag + qkv_diag
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kv = block_decay * kv + torch.matmul(
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(ki * k_decay[:, -m:]).transpose(-1, -2).to(vi.dtype), vi
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)
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else:
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kv = past_key_value
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output = []
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for i in range(n):
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kv = ratio * kv + torch.einsum(
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"... n d, ... n e -> ... d e",
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k[:, :, i : i + 1],
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v[:, :, i : i + 1],
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)
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qkv = torch.einsum(
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"... n e, ... e d -> ... n d", q[:, :, i : i + 1], kv.to(q.dtype)
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)
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output.append(qkv)
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output = torch.concat(output, dim=-2)
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# reshape
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output = rearrange(output, "b h n d -> b n (h d)")
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# normalize
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output = self.norm(output)
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# gate
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output = F.sigmoid(self.output_gate(x)) * output
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# outproj
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output = self.out_proj(output)
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attn_weights = None
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return output, attn_weights, kv
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def _build_slope_tensor(n_attention_heads: int):
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def get_slopes(n):
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def get_slopes_power_of_2(n):
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start = 2 ** (-(2 ** -(math.log2(n) - 3)))
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ratio = start
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return [start * ratio**i for i in range(n)]
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if math.log2(n).is_integer():
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return get_slopes_power_of_2(
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n
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) # In the paper, we only train models that have 2^a heads for some a. This function has
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else: # some good properties that only occur when the input is a power of 2. To maintain that even
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closest_power_of_2 = 2 ** math.floor(
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math.log2(n)
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) # when the number of heads is not a power of 2, we use this workaround.
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return (
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get_slopes_power_of_2(closest_power_of_2)
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+ get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
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)
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# h, 1, 1
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slopes = torch.tensor(get_slopes(n_attention_heads)).reshape(
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n_attention_heads, 1, 1
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)
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return slopes
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def test_lightning_attention_implementations(model_params):
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torch.manual_seed(42)
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batch_size = 2
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seq_len = 1024
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dtype = torch.bfloat16
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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hidden_states = torch.randn(
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batch_size, seq_len, model_params["hidden_size"], dtype=dtype, device=device
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)
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attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
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|
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slope_rate = _build_slope_tensor(model_params["num_attention_heads"]).to(device)
|
||||
|
||||
model_attn = MiniMaxText01LightningAttention(**model_params).to(dtype).to(device)
|
||||
model_attn.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
model_output, _, _ = model_attn.inference(
|
||||
hidden_states, attn_mask=attention_mask, slope_rate=slope_rate
|
||||
)
|
||||
|
||||
qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
|
||||
new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
|
||||
qkv = qkv.view(*new_shape)
|
||||
q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
|
||||
q = q.transpose(1, 2)
|
||||
k = k.transpose(1, 2)
|
||||
v = v.transpose(1, 2)
|
||||
|
||||
lib_output = lightning_attn_func(q, k, v, slope_rate)
|
||||
lib_output = lib_output.transpose(1, 2).contiguous()
|
||||
lib_output = lib_output.view(batch_size, seq_len, -1)
|
||||
lib_output = model_attn.norm(lib_output)
|
||||
lib_output = torch.sigmoid(model_attn.output_gate(hidden_states)) * lib_output
|
||||
lib_output = model_attn.out_proj(lib_output)
|
||||
|
||||
torch.testing.assert_close(
|
||||
model_output,
|
||||
lib_output,
|
||||
rtol=1e-3,
|
||||
atol=1e-2,
|
||||
msg="Lightning attention implementations produce different results",
|
||||
)
|
||||
|
||||
|
||||
def get_benchmark():
|
||||
batch_size_range = [2**i for i in range(0, 7)] # max 64
|
||||
seq_length_range = [256, 512, 1024, 2048, 4096] # max 4096
|
||||
configs = list(itertools.product(batch_size_range, seq_length_range))
|
||||
|
||||
@triton.testing.perf_report(
|
||||
triton.testing.Benchmark(
|
||||
x_names=["batch_size", "seq_len"],
|
||||
x_vals=[list(_) for _ in configs],
|
||||
line_arg="provider",
|
||||
line_vals=["MiniMax-Text-01", "OpenNLPLab"],
|
||||
line_names=[
|
||||
"MiniMax-Text-01 Model Implementation",
|
||||
"OpenNLPLab Library Implementation",
|
||||
],
|
||||
styles=[("blue", "-"), ("green", "-")],
|
||||
ylabel="us",
|
||||
plot_name="lightning-attention-prefill-performance",
|
||||
args={},
|
||||
)
|
||||
)
|
||||
def benchmark(batch_size, seq_len, provider):
|
||||
dtype = torch.bfloat16
|
||||
device = torch.device("cuda")
|
||||
|
||||
params = {
|
||||
"hidden_size": 6144,
|
||||
"num_attention_heads": 64,
|
||||
"head_dim": 96,
|
||||
"hidden_act": "gelu",
|
||||
}
|
||||
|
||||
hidden_states = torch.randn(
|
||||
batch_size, seq_len, params["hidden_size"], dtype=dtype, device=device
|
||||
)
|
||||
|
||||
attention_mask = torch.ones(batch_size, seq_len, dtype=dtype, device=device)
|
||||
|
||||
slope_rate = _build_slope_tensor(params["num_attention_heads"]).to(device)
|
||||
model_attn = MiniMaxText01LightningAttention(**params).to(dtype).to(device)
|
||||
model_attn.eval()
|
||||
|
||||
quantiles = [0.5, 0.2, 0.8]
|
||||
if provider == "MiniMax-Text-01":
|
||||
ms, min_ms, max_ms = triton.testing.do_bench(
|
||||
lambda: model_attn.inference(
|
||||
hidden_states, attn_mask=attention_mask, slope_rate=slope_rate
|
||||
),
|
||||
quantiles=quantiles,
|
||||
)
|
||||
else:
|
||||
|
||||
def run_lib():
|
||||
qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
|
||||
new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
|
||||
qkv = qkv.view(*new_shape)
|
||||
q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
|
||||
q = q.transpose(1, 2)
|
||||
k = k.transpose(1, 2)
|
||||
v = v.transpose(1, 2)
|
||||
|
||||
lib_output = lightning_attn_func(q, k, v, slope_rate)
|
||||
lib_output = lib_output.transpose(1, 2).contiguous()
|
||||
lib_output = lib_output.view(batch_size, seq_len, -1)
|
||||
lib_output = model_attn.norm(lib_output)
|
||||
lib_output = (
|
||||
torch.sigmoid(model_attn.output_gate(hidden_states)) * lib_output
|
||||
)
|
||||
return model_attn.out_proj(lib_output)
|
||||
|
||||
ms, min_ms, max_ms = triton.testing.do_bench(
|
||||
run_lib,
|
||||
quantiles=quantiles,
|
||||
)
|
||||
|
||||
return 1000 * ms, 1000 * max_ms, 1000 * min_ms
|
||||
|
||||
return benchmark
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--save_path",
|
||||
type=str,
|
||||
default="./configs/benchmark_ops/lightning_attention_prefill/",
|
||||
help="Path to save lightning attention prefill benchmark results",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
# Run correctness test first
|
||||
# Adapted from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/config.json
|
||||
params = {
|
||||
"hidden_size": 6144,
|
||||
"num_attention_heads": 64,
|
||||
"head_dim": 96,
|
||||
"hidden_act": "silu",
|
||||
}
|
||||
test_lightning_attention_implementations(params)
|
||||
|
||||
# Run performance benchmark
|
||||
benchmark = get_benchmark()
|
||||
benchmark.run(print_data=True, save_path=args.save_path)
|
||||
Reference in New Issue
Block a user