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Zero
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from typing import *
from torch import Tensor
import torch
from torch import nn
import torch.nn.functional as tF
from torch.utils.checkpoint import checkpoint
from einops import rearrange
class RMSNorm(nn.Module):
def __init__(self,
dim: int,
eps: float = 1e-6,
):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x: Tensor):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x: Tensor):
output = self._norm(x.float()).type_as(x)
return output * self.weight
class Attention(nn.Module):
def __init__(self,
dim: int,
num_heads: int,
qk_norm: bool = True,
context_dim: Optional[int] = None,
):
super().__init__()
if context_dim is None:
context_dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.wq = nn.Linear(dim, num_heads * head_dim, bias=False)
self.wk = nn.Linear(context_dim, num_heads * head_dim, bias=False)
self.wv = nn.Linear(context_dim, num_heads * head_dim, bias=False)
self.wo = nn.Linear(num_heads * head_dim, dim, bias=False)
if qk_norm:
self.q_norm = nn.LayerNorm(num_heads * head_dim)
self.k_norm = nn.LayerNorm(num_heads * head_dim)
else:
self.q_norm = nn.Identity()
self.k_norm = nn.Identity()
# Initialize weights
nn.init.xavier_uniform_(self.wq.weight)
nn.init.xavier_uniform_(self.wk.weight)
nn.init.xavier_uniform_(self.wv.weight)
nn.init.xavier_uniform_(self.wo.weight)
def forward(self, x: Tensor, context: Optional[Tensor] = None):
if context is None:
context = x
q, k, v = self.wq(x), self.wk(context), self.wv(context)
q = self.q_norm(q)
k = self.k_norm(k)
q = rearrange(q, "b n (h d) -> b h n d", h=self.num_heads)
k = rearrange(k, "b n (h d) -> b h n d", h=self.num_heads)
v = rearrange(v, "b n (h d) -> b h n d", h=self.num_heads)
output = rearrange(tF.scaled_dot_product_attention(
q, k, v,
dropout_p=0., is_causal=False,
), "b h n d -> b n (h d)")
return self.wo(output)
class FeedForward(nn.Module):
def __init__(self,
dim: int,
hidden_dim: int,
multiple_of: int, # ensure `hidden_dim` is a multiple of this value
ffn_dim_multiplier: Optional[float] = None, # custom mulitplier for `hidden_dim`
):
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
# Custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
# Initialize weights
nn.init.xavier_uniform_(self.w1.weight)
nn.init.xavier_uniform_(self.w2.weight)
nn.init.xavier_uniform_(self.w3.weight)
def _forward_silu_gating(self, x1: Tensor, x3: Tensor):
return tF.silu(x1) * x3
def forward(self, x: Tensor):
return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
class LLaMaTransformerBlock(nn.Module):
def __init__(self,
dim: int,
num_heads: int,
use_cross_attention: bool = False,
context_dim: Optional[int] = None,
qk_norm: bool = True,
multiple_of: int = 256,
ffn_dim_multiplier: Optional[float] = None,
norm_eps: float = 1e-5,
):
super().__init__()
self.norm1 = RMSNorm(dim, norm_eps)
self.attn = Attention(dim, num_heads, qk_norm)
self.norm2 = RMSNorm(dim, norm_eps)
self.mlp = FeedForward(dim, dim * 4, multiple_of, ffn_dim_multiplier)
if use_cross_attention:
self.norm3 = RMSNorm(dim, norm_eps)
self.cross_attn = Attention(dim, num_heads, qk_norm, context_dim)
self.use_cross_attention = use_cross_attention
def forward(self, x: Tensor, context: Optional[Tensor] = None):
x = x + self.attn(self.norm1(x))
if context is not None:
x = x + self.cross_attn(self.norm3(x), context)
else:
assert not self.use_cross_attention
x = x + self.mlp(self.norm2(x))
return x
class TransformerBlock(nn.Module):
def __init__(self,
dim: int,
num_heads: int,
use_cross_attention: bool = False,
context_dim: Optional[int] = None,
**kwargs, # for compatibility with `LLaMaTransformerBlock`
):
super().__init__()
self.norm1 = nn.LayerNorm(dim)
self.attn = Attention(dim, num_heads, qk_norm=False)
self.norm2 = nn.LayerNorm(dim)
self.mlp = nn.Sequential(
nn.Linear(dim, dim * 4),
nn.GELU(),
nn.Linear(dim * 4, dim)
)
if use_cross_attention:
self.norm3 = nn.LayerNorm(dim)
self.cross_attn = Attention(dim, num_heads, qk_norm=False, context_dim=context_dim)
self.use_cross_attention = use_cross_attention
def forward(self, x: Tensor, context: Optional[Tensor] = None):
x = x + self.attn(self.norm1(x))
if context is not None:
x = x + self.cross_attn(self.norm3(x), context)
else:
assert not self.use_cross_attention
x = x + self.mlp(self.norm2(x))
return x
class Transformer(nn.Module):
def __init__(self,
num_blocks: int = 12,
dim: int = 512,
num_heads: int = 8,
llama_style: bool = True,
use_cross_attention: bool = False,
context_dim: Optional[int] = None,
):
super().__init__()
Block = LLaMaTransformerBlock if llama_style else TransformerBlock
self.blocks = nn.ModuleList([
Block(dim, num_heads, use_cross_attention, context_dim)
for _ in range(num_blocks)
])
self.grad_checkpointing = False
def set_grad_checkpointing(self, flag=True):
self.grad_checkpointing = flag
def forward(self, x: Tensor, context: Optional[Tensor] = None):
for block in self.blocks:
if self.grad_checkpointing:
x = checkpoint(block, x, context, use_reentrant=False)
else:
x = block(x, context)
return x
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