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from functools import lru_cache
from typing import Optional
import torch
import torch.nn as nn
from flash_attn import flash_attn_func
from torch.nn import RMSNorm
from torch.nn import functional as F
def drop_path(
x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (
x.ndim - 1
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(torch.nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def extra_repr(self):
return f"drop_prob={round(self.drop_prob,3):0.3f}"
def find_multiple(n: int, k: int):
if n % k == 0:
return n
return n + k - (n % k)
@lru_cache(maxsize=16)
def get_causal_mask(seq_q, seq_k, device):
offset = seq_k - seq_q
i = torch.arange(seq_q, device=device).unsqueeze(1)
j = torch.arange(seq_k, device=device).unsqueeze(0)
causal_mask = (j > (offset + i)).bool()
causal_mask = causal_mask.unsqueeze(0).unsqueeze(0)
return causal_mask
class Attention(nn.Module):
def __init__(
self,
dim,
n_head,
attn_dropout_p,
resid_dropout_p,
causal: bool = True,
):
super().__init__()
assert dim % n_head == 0
self.dim = dim
self.head_dim = dim // n_head
self.scale = self.head_dim**-0.5
self.n_head = n_head
total_kv_dim = (self.n_head * 3) * self.head_dim
self.wqkv = nn.Linear(dim, total_kv_dim, bias=False)
self.wo = nn.Linear(dim, dim, bias=False)
self.attn_dropout_p = attn_dropout_p
self.resid_dropout = nn.Dropout(resid_dropout_p)
self.causal = causal
self.k_cache = None
self.v_cache = None
self.kv_cache_size = None
def enable_kv_cache(self, bsz, max_seq_len):
if self.kv_cache_size != (bsz, max_seq_len):
device = self.wo.weight.device
dtype = self.wo.weight.dtype
self.k_cache = torch.zeros(
(bsz, self.n_head, max_seq_len, self.head_dim),
device=device,
dtype=dtype,
)
self.v_cache = torch.zeros(
(bsz, self.n_head, max_seq_len, self.head_dim),
device=device,
dtype=dtype,
)
self.kv_cache_size = (bsz, max_seq_len)
def update_kv_cache(
self, start_pos, end_pos, keys: torch.Tensor, values: torch.Tensor
):
self.k_cache[:, :, start_pos:end_pos, :] = keys
self.v_cache[:, :, start_pos:end_pos, :] = values
return (
self.k_cache[:, :, :end_pos, :],
self.v_cache[:, :, :end_pos, :],
)
def naive_attention(self, xq, keys, values, is_causal):
xq = xq * self.scale
# q: [B, H, 1, D], k: [B, H, D, L] -> attn [B, H, 1, L]
attn = xq @ keys.transpose(-1, -2)
seq_q, seq_k = attn.shape[-2], attn.shape[-1]
if is_causal and seq_q > 1:
causal_mask = get_causal_mask(seq_q, seq_k, attn.device)
attn.masked_fill_(causal_mask, float("-inf"))
attn = torch.softmax(attn, dim=-1)
if self.attn_dropout_p > 0 and self.training:
attn = F.dropout(attn, p=self.attn_dropout_p, training=self.training)
# [B, H, 1, L] @ [B, H, L, D] -> [B, H, 1, D]
return attn @ values
def forward(
self,
x: torch.Tensor,
freqs_cis: torch.Tensor = None,
start_pos: Optional[int] = None,
end_pos: Optional[int] = None,
):
bsz, seqlen, _ = x.shape
xq, xk, xv = self.wqkv(x).chunk(3, dim=-1)
xq = xq.view(bsz, seqlen, self.n_head, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_head, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_head, self.head_dim)
if freqs_cis is not None:
xq = apply_rotary_emb(xq, freqs_cis)
xk = apply_rotary_emb(xk, freqs_cis)
is_causal = self.causal
if self.k_cache is not None and start_pos is not None:
xq, xk, xv = map(lambda x: x.transpose(1, 2), (xq, xk, xv))
keys, values = self.update_kv_cache(start_pos, end_pos, xk, xv)
output = self.naive_attention(xq, keys, values, is_causal=is_causal)
output = output.transpose(1, 2).contiguous()
else:
output = flash_attn_func(
xq,
xk,
xv,
causal=is_causal,
dropout_p=self.attn_dropout_p if self.training else 0,
)
output = output.view(bsz, seqlen, self.dim)
output = self.resid_dropout(self.wo(output))
return output
class FeedForward(nn.Module):
def __init__(self, dim, dropout_p=0.1, mlp_ratio=4.0):
super().__init__()
hidden_dim = mlp_ratio * dim
hidden_dim = int(2 * hidden_dim / 3)
hidden_dim = find_multiple(hidden_dim, 256)
self.w1 = nn.Linear(dim, hidden_dim * 2, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.ffn_dropout = nn.Dropout(dropout_p)
def forward(self, x):
h1, h2 = self.w1(x).chunk(2, dim=-1)
return self.ffn_dropout(self.w2(F.silu(h1) * h2))
class TransformerBlock(nn.Module):
def __init__(
self,
dim,
n_head,
attn_dropout_p: float = 0.0,
resid_dropout_p: float = 0.0,
drop_path: float = 0.0,
causal: bool = True,
):
super().__init__()
self.attention = Attention(
dim=dim,
n_head=n_head,
attn_dropout_p=attn_dropout_p,
resid_dropout_p=resid_dropout_p,
causal=causal,
)
self.feed_forward = FeedForward(
dim=dim,
dropout_p=resid_dropout_p,
)
self.attention_norm = RMSNorm(dim, eps=1e-6)
self.ffn_norm = RMSNorm(dim, eps=1e-6)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(
self,
x: torch.Tensor,
freqs_cis: torch.Tensor,
):
h = x + self.drop_path(self.attention(self.attention_norm(x), freqs_cis))
out = h + self.drop_path(self.feed_forward(self.ffn_norm(h)))
return out
def forward_onestep(
self,
x: torch.Tensor,
freqs_cis: torch.Tensor,
start_pos: int,
end_pos: int,
):
h = x + self.drop_path(
self.attention(self.attention_norm(x), freqs_cis, start_pos, end_pos)
)
out = h + self.drop_path(self.feed_forward(self.ffn_norm(h)))
return out
def get_2d_pos(resolution, patch_size, num_scales=1):
max_pos = resolution // patch_size
coords_list = []
for i in range(num_scales):
scale = 2 ** (num_scales - i - 1)
P = max(resolution // scale // patch_size, 1)
edge = float(max_pos) / P
centers = (torch.arange(P, dtype=torch.float32) + 0.5) * edge
grid_y, grid_x = torch.meshgrid(centers, centers, indexing="ij")
coords = torch.stack([grid_x.reshape(-1), grid_y.reshape(-1)], dim=1)
coords_list.append(coords)
return torch.cat(coords_list, dim=0)
def precompute_freqs_cis_2d(
pos_2d, n_elem: int, base: float = 10000, cls_token_num=120
):
# split the dimension into half, one for x and one for y
half_dim = n_elem // 2
freqs = 1.0 / (
base ** (torch.arange(0, half_dim, 2)[: (half_dim // 2)].float() / half_dim)
)
t = pos_2d + 1.0
if cls_token_num > 0:
t = torch.cat(
[torch.zeros((cls_token_num, 2), device=freqs.device), t],
dim=0,
)
freqs = torch.outer(t.flatten(), freqs).view(*t.shape[:-1], -1)
return torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1)
def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor):
# x: (bs, seq_len, n_head, head_dim)
# freqs_cis (seq_len, head_dim // 2, 2)
xshaped = x.float().reshape(
*x.shape[:-1], -1, 2
) # (bs, seq_len, n_head, head_dim//2, 2)
freqs_cis = freqs_cis.view(
1, xshaped.size(1), 1, xshaped.size(3), 2
) # (1, seq_len, 1, head_dim//2, 2)
x_out2 = torch.stack(
[
xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
],
dim=-1,
)
x_out2 = x_out2.flatten(3)
return x_out2.type_as(x)
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