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32beb88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | # -*- coding: utf-8 -*-
"""
https://github.com/corl-team/rebased/blob/main/flash_linear_attention/fla/layers/rebased_fast.py
"""
from __future__ import annotations
from typing import Optional
import torch
import torch.nn as nn
from einops import rearrange
from fla.modules.feature_map import RebasedFeatureMap
from fla.ops.linear_attn import chunk_linear_attn, fused_chunk_linear_attn
from fla.ops.rebased import parallel_rebased
class ReBasedLinearAttention(nn.Module):
def __init__(
self,
hidden_size: int,
l_max: int = 2048,
feature_dim: int = 16,
num_key_value_heads: int = 16,
num_heads: int = 16,
use_gamma: Optional[bool] = True,
use_beta: Optional[bool] = True,
normalize: Optional[bool] = True,
causal: bool = True,
eps: float = 1e-5,
mode: str = "parallel",
layer_idx: Optional[int] = None,
**kwargs
) -> ReBasedLinearAttention:
super().__init__()
self.hidden_size = hidden_size
self.l_max = l_max
self.mode = mode
assert self.mode in ["fused_chunk", "parallel", 'chunk']
# linear attention
self.feature_dim = feature_dim
self.num_key_value_heads = num_key_value_heads
self.num_heads = num_heads
self.head_dim = self.hidden_size // self.num_key_value_heads
self.use_gamma = use_gamma
self.use_beta = use_beta
self.normalize = normalize
self.causal = causal
self.feature_map = RebasedFeatureMap(self.feature_dim, use_gamma, use_beta, normalize)
self.q_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.dropout = nn.Identity()
self.eps = eps
self.apply(self._initialize_weights)
def _initialize_weights(self, module: nn.Module):
if getattr(module, "_is_hf_initialized", False):
return
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5)
if module.bias is not None:
nn.init.zeros_(module.bias)
module._is_hf_initialized = True
def forward(self, hidden_states: torch.Tensor, **kwargs):
mode = self.mode
q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states)
q, k, v = map(lambda x: rearrange(x, "b l (h d) -> b h l d", h=self.num_heads), [q, k, v])
q, k = self.feature_map(q, flatten=(mode != 'parallel')), self.feature_map(k, flatten=(mode != 'parallel'))
if mode == "fused_chunk":
o = fused_chunk_linear_attn(q, k, v, normalize=True, scale=1)
elif mode == 'chunk':
o = chunk_linear_attn(q, k, v, normalize=True, scale=1)
elif mode == 'parallel':
assert q.shape[-1] <= 128
o = parallel_rebased(q, k, v, self.eps, True, True)
o = rearrange(o, "b h l d -> b l (h d)")
o = self.o_proj(o)
o = self.dropout(o)
return o
# https://github.com/HazyResearch/zoology/blob/main/zoology/mixers/based.py#L119
def forward_reference(self, hidden_states: torch.Tensor, filters: torch.Tensor = None, *args, **kwargs):
"""
x (torch.Tensor): tensor of shape (b, d, l)
y (torch.Tensor): tensor of shape (b, d, l)
"""
# hidden_states = hidden_states.transpose(1, 2)
b, l, _ = hidden_states.size()
q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states)
q = q.view(b, l, self.num_heads, self.feature_dim).transpose(1, 2)
k = k.view(b, l, self.num_key_value_heads, self.feature_dim).transpose(1, 2)
v = v.view(b, l, self.num_key_value_heads, self.head_dim).transpose(1, 2)
# Linear attention
q, k = self.feature_map(q), self.feature_map(k)
q, k, v = q.unsqueeze(-2), k.unsqueeze(-2), v.unsqueeze(-1)
# Compute attention
if self.causal:
y = ((q * (k * v).cumsum(2)).sum(-1) / ((q * k.cumsum(2)).sum(-1) + self.eps))
else:
y = ((q * (k * v).sum(2, True)).sum(-1) / ((q * k.sum(2, True)).sum(-1) + self.eps))
y = rearrange(y, 'b h l d -> b l (h d)')
y = self.o_proj(y.to(hidden_states.dtype))
y = self.dropout(y)
return y.to(hidden_states.dtype)
if __name__ == '__main__':
batch = 4
seq_len = 1024
hidden_size = 1024
dtype = torch.float32
x = torch.randn(batch, seq_len, hidden_size).to(dtype).cuda().requires_grad_(True)
dy = torch.randn(batch, seq_len, hidden_size).to(dtype).cuda()
model = ReBasedLinearAttention(hidden_size=hidden_size, mode='parallel').to(dtype).cuda()
y = model(x)
y.backward(dy, retain_graph=True)
x_grad, x.grad = x.grad, None
print(model.mode)
model.mode = 'fused_chunk'
y2 = model(x)
print(model.mode)
y2.backward(dy)
# assert y.allclose(y2, 0, 1e-4), breakpoint()
# assert x_grad.allclose(x.grad, 0, 1e-4), breakpoint()
print("Pass")
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