[kernel] MiniMax-Text-01 decode lightning_attn with triton (#2920)
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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@triton.jit
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def _decode_kernel(
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Q,
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K,
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V,
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KV,
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Out,
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S,
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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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):
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off_bh = tl.program_id(0)
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off_h = off_bh % h
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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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kv_offset = off_bh * d * e
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s = tl.load(S + off_h)
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ratio = tl.exp(-s)
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d_idx = tl.arange(0, d)
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e_idx = tl.arange(0, e)
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q = tl.load(Q + qk_offset + d_idx)
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k = tl.load(K + qk_offset + d_idx)
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v = tl.load(V + v_offset + e_idx)
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kv = tl.load(KV + kv_offset + d_idx[:, None] * e + e_idx[None, :])
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k_v_prod = k[:, None] * v[None, :]
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kv = ratio * kv + k_v_prod
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tl.store(
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KV + kv_offset + d_idx[:, None] * e + e_idx[None, :], kv.to(KV.dtype.element_ty)
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)
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o = tl.sum(q[:, None] * kv, axis=0)
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tl.store(Out + o_offset + e_idx, o.to(Out.dtype.element_ty))
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def lightning_attn_decode(q, k, v, kv, s):
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"""Triton implementation of Lightning Attention decode operation"""
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b, h, n, d = q.shape
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e = v.shape[-1]
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assert n == 1, "Sequence length must be 1 in decode mode"
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# Pad dimensions to power of 2
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d_padded = next_power_of_2(d)
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e_padded = next_power_of_2(e)
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# Pad inputs
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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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v_padded = F.pad(v, (0, e_padded - e))
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kv_padded = F.pad(kv, (0, e_padded - e, 0, d_padded - d))
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# Ensure inputs are contiguous
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q_padded = q_padded.contiguous()
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k_padded = k_padded.contiguous()
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v_padded = v_padded.contiguous()
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kv_padded = kv_padded.contiguous().to(torch.float32)
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s = s.contiguous()
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# Create output tensor (padded)
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o_padded = torch.empty(b, h, n, e_padded, dtype=v.dtype, device=v.device)
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# Launch kernel
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grid = (b * h, 1)
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_decode_kernel[grid](
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q_padded,
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k_padded,
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v_padded,
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kv_padded,
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o_padded,
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s,
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b=b,
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h=h,
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n=n,
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d=d_padded,
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e=e_padded,
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)
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# Remove padding
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o = o_padded[..., :e]
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kv_out = kv_padded[..., :d, :e]
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return o, kv_out
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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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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) # [b, n, h, d] -> [b, h, n, d]
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k = k.transpose(1, 2) # [b, n, h, d] -> [b, h, n, d]
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v = v.transpose(1, 2) # [b, n, h, d] -> [b, h, n, e]
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self.offset += 1
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ratio = torch.exp(-slope_rate) # [h, 1, 1]
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# decode mode
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kv = past_key_value # [b, h, d, e]
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output = []
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for i in range(n):
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# kv: [b, h, d, e]
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# ratio: [h, 1, 1]
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# k: [b, h, n, d]
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# v: [b, h, n, e]
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# k[:, :, i : i + 1]: [b, h, 1, d]
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# v[:, :, i : i + 1]: [b, h, 1, e]
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# ratio * kv: [b, h, d, e]
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# 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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# [b, h, d, e] + [b, h, d, e] -> [b, h, d, e]
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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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# q[:, :, i : i + 1]: [b, h, 1, d]
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# kv.to(q.dtype): [b, h, d, e]
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# 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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# [b, h, 1, d] * [b, h, d, e] -> [b, h, 1, e]
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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 get_activation_fn(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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return lambda x: x
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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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def test_lightning_attention_implementations(model_params):
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torch.manual_seed(42)
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batch_size = 64
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seq_len = 1
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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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slope_rate = _build_slope_tensor(model_params["num_attention_heads"]).to(device)
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model_attn = MiniMaxText01LightningAttention(**model_params).to(dtype).to(device)
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model_attn.eval()
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d = model_params["head_dim"]
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past_kv = torch.randn(
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batch_size,
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model_params["num_attention_heads"],
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d,
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d,
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dtype=dtype,
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device=device,
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)
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with torch.no_grad():
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model_output, _, new_kv = model_attn.inference(
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hidden_states,
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attn_mask=attention_mask,
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slope_rate=slope_rate,
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past_key_value=past_kv,
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)
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qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
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new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
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qkv = qkv.view(*new_shape)
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q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
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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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triton_output, triton_new_kv = lightning_attn_decode(q, k, v, past_kv, slope_rate)
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triton_output = triton_output.transpose(1, 2).contiguous()
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triton_output = triton_output.view(batch_size, seq_len, -1)
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triton_output = model_attn.norm(triton_output)
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triton_output = torch.sigmoid(model_attn.output_gate(hidden_states)) * triton_output
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triton_output = model_attn.out_proj(triton_output)
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torch.testing.assert_close(
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model_output,
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triton_output,
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rtol=1e-3,
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atol=1e-2,
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msg="Lightning attention implementations produce different output results",
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)
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torch.testing.assert_close(
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new_kv,
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triton_new_kv,
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rtol=1e-3,
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atol=1e-2,
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msg="Lightning attention implementations produce different kv results",
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)
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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(n)
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else:
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closest_power_of_2 = 2 ** math.floor(math.log2(n))
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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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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 get_benchmark():
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batch_size_range = [2**i for i in range(0, 12)] # max 2048
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seq_length_range = [1] # decode mode sequence length is fixed to 1
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configs = list(itertools.product(batch_size_range, seq_length_range))
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=["batch_size", "seq_len"],
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x_vals=[list(_) for _ in configs],
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line_arg="provider",
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line_vals=["Original", "Triton"],
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line_names=[
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"Original PyTorch Implementation",
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"Triton Implementation",
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],
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styles=[("blue", "-"), ("green", "-")],
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ylabel="us",
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plot_name="lightning-attention-decode-performance",
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args={},
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)
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)
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def benchmark(batch_size, seq_len, provider):
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dtype = torch.bfloat16
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device = torch.device("cuda")
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params = {
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"hidden_size": 6144,
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"num_attention_heads": 64,
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"head_dim": 96,
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"hidden_act": "gelu",
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}
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hidden_states = torch.randn(
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batch_size, seq_len, 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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slope_rate = _build_slope_tensor(params["num_attention_heads"]).to(device)
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model_attn = MiniMaxText01LightningAttention(**params).to(dtype).to(device)
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model_attn.eval()
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d = params["head_dim"]
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past_kv = torch.randn(
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batch_size,
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params["num_attention_heads"],
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d,
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d,
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dtype=dtype,
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device=device,
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)
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quantiles = [0.5, 0.2, 0.8]
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if provider == "Original":
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ms, min_ms, max_ms = triton.testing.do_bench(
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lambda: model_attn.inference(
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hidden_states,
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attn_mask=attention_mask,
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slope_rate=slope_rate,
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past_key_value=past_kv,
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),
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quantiles=quantiles,
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)
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else:
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def run_triton():
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qkv = model_attn.act(model_attn.qkv_proj(hidden_states))
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new_shape = qkv.size()[:-1] + (model_attn.num_heads, -1)
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qkv = qkv.view(*new_shape)
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q, k, v = torch.split(qkv, [model_attn.head_dim] * 3, dim=-1)
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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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output, new_kv = lightning_attn_decode(q, k, v, past_kv, slope_rate)
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output = output.transpose(1, 2).contiguous()
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output = output.view(batch_size, seq_len, -1)
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output = model_attn.norm(output)
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output = torch.sigmoid(model_attn.output_gate(hidden_states)) * output
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return model_attn.out_proj(output)
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ms, min_ms, max_ms = triton.testing.do_bench(
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run_triton,
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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_decode/",
|
||||
help="Path to save lightning attention decode benchmark results",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
params = {
|
||||
"hidden_size": 6144,
|
||||
"num_attention_heads": 64,
|
||||
"head_dim": 96,
|
||||
"hidden_act": "silu",
|
||||
}
|
||||
# Run correctness test first
|
||||
# Adapted from https://huggingface.co/MiniMaxAI/MiniMax-Text-01/blob/main/config.json
|
||||
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