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Zero
File size: 6,443 Bytes
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from craftsman.utils.typing import *
from craftsman.utils.checkpoint import checkpoint
from .utils import init_linear, MLP
class MultiheadAttention(nn.Module):
def __init__(
self,
*,
n_ctx: int,
width: int,
heads: int,
init_scale: float,
qkv_bias: bool,
use_flash: bool = False
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.heads = heads
self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias)
self.c_proj = nn.Linear(width, width)
self.attention = QKVMultiheadAttention(heads=heads, n_ctx=n_ctx, use_flash=use_flash)
init_linear(self.c_qkv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
x = self.c_qkv(x)
x = checkpoint(self.attention, (x,), (), True)
x = self.c_proj(x)
return x
class QKVMultiheadAttention(nn.Module):
def __init__(self, *, heads: int, n_ctx: int, use_flash: bool = False):
super().__init__()
self.heads = heads
self.n_ctx = n_ctx
self.use_flash = use_flash
def forward(self, qkv):
bs, n_ctx, width = qkv.shape
attn_ch = width // self.heads // 3
scale = 1 / math.sqrt(math.sqrt(attn_ch))
qkv = qkv.view(bs, n_ctx, self.heads, -1)
q, k, v = torch.split(qkv, attn_ch, dim=-1)
if self.use_flash:
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
out = F.scaled_dot_product_attention(q, k, v).permute(0, 2, 1, 3).reshape(bs, n_ctx, -1)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
*,
n_ctx: int,
width: int,
heads: int,
init_scale: float = 1.0,
qkv_bias: bool = True,
use_flash: bool = False,
use_checkpoint: bool = False
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.attn = MultiheadAttention(
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_flash=use_flash
)
self.ln_1 = nn.LayerNorm(width)
self.mlp = MLP(width=width, init_scale=init_scale)
self.ln_2 = nn.LayerNorm(width)
def _forward(self, x: torch.Tensor):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
def forward(self, x: torch.Tensor):
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)
class MultiheadCrossAttention(nn.Module):
def __init__(
self,
*,
width: int,
heads: int,
init_scale: float,
qkv_bias: bool = True,
use_flash: bool = False,
n_data: Optional[int] = None,
data_width: Optional[int] = None,
):
super().__init__()
self.n_data = n_data
self.width = width
self.heads = heads
self.data_width = width if data_width is None else data_width
self.c_q = nn.Linear(width, width, bias=qkv_bias)
self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias)
self.c_proj = nn.Linear(width, width)
self.attention = QKVMultiheadCrossAttention(
heads=heads, n_data=n_data, use_flash=use_flash
)
init_linear(self.c_q, init_scale)
init_linear(self.c_kv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x, data):
x = self.c_q(x)
data = self.c_kv(data)
x = checkpoint(self.attention, (x, data), (), True)
x = self.c_proj(x)
return x
class QKVMultiheadCrossAttention(nn.Module):
def __init__(self, *, heads: int, use_flash: bool = False, n_data: Optional[int] = None):
super().__init__()
self.heads = heads
self.n_data = n_data
self.use_flash = use_flash
def forward(self, q, kv):
_, n_ctx, _ = q.shape
bs, n_data, width = kv.shape
attn_ch = width // self.heads // 2
scale = 1 / math.sqrt(math.sqrt(attn_ch))
q = q.view(bs, n_ctx, self.heads, -1)
kv = kv.view(bs, n_data, self.heads, -1)
k, v = torch.split(kv, attn_ch, dim=-1)
if self.use_flash:
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
out = F.scaled_dot_product_attention(q, k, v).permute(0, 2, 1, 3).reshape(bs, n_ctx, -1)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
class ResidualCrossAttentionBlock(nn.Module):
def __init__(
self,
*,
n_data: Optional[int] = None,
width: int,
heads: int,
data_width: Optional[int] = None,
init_scale: float = 0.25,
qkv_bias: bool = True,
use_flash: bool = False
):
super().__init__()
if data_width is None:
data_width = width
self.attn = MultiheadCrossAttention(
n_data=n_data,
width=width,
heads=heads,
data_width=data_width,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_flash=use_flash,
)
self.ln_1 = nn.LayerNorm(width)
self.ln_2 = nn.LayerNorm(data_width)
self.mlp = MLP(width=width, init_scale=init_scale)
self.ln_3 = nn.LayerNorm(width)
def forward(self, x: torch.Tensor, data: torch.Tensor):
x = x + self.attn(self.ln_1(x), self.ln_2(data))
x = x + self.mlp(self.ln_3(x))
return x |