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# https://github.com/ml-jku/LaM-SLidE/blob/main/src/modules/torch_modules.py
import math
from functools import wraps
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from torch import Tensor
from torch.utils.checkpoint import checkpoint
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def cache_fn(f):
cache = None
@wraps(f)
def cached_fn(*args, _cache=True, **kwargs):
if not _cache:
return f(*args, **kwargs)
nonlocal cache
if cache is not None:
return cache
cache = f(*args, **kwargs)
return cache
return cached_fn
class GELU(nn.Module):
@staticmethod
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different
(and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def forward(self, x):
return self.gelu(x)
class Sin(nn.Module):
def forward(self, input: Tensor) -> Tensor:
return torch.sin(input)
def dropout_seq(seq, mask, dropout):
b, n, *_, device = *seq.shape, seq.device
logits = torch.randn(b, n, device=device)
if exists(mask):
logits = logits.masked_fill(~mask, -torch.finfo(logits.dtype).max)
keep_prob = 1.0 - dropout
num_keep = max(1, int(keep_prob * n))
keep_indices = logits.topk(num_keep, dim=1).indices
batch_indices = torch.arange(b, device=device)
batch_indices = rearrange(batch_indices, "b -> b 1")
seq = seq[batch_indices, keep_indices]
if exists(mask):
seq_counts = mask.sum(dim=-1)
seq_keep_counts = torch.ceil(seq_counts * keep_prob).int()
keep_mask = torch.arange(num_keep, device=device) < rearrange(seq_keep_counts, "b -> b 1")
mask = mask[batch_indices, keep_indices] & keep_mask
return seq, mask
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int):
super().__init__()
self.scale = nn.Parameter(torch.ones(dim))
def forward(self, x: Tensor):
x_dtype = x.dtype
x = x.float()
rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
return (x * rrms).to(dtype=x_dtype) * self.scale
class QKNorm(torch.nn.Module):
def __init__(self, dim: int):
super().__init__()
self.query_norm = RMSNorm(dim)
self.key_norm = RMSNorm(dim)
def forward(self, q: Tensor, k: Tensor, v: torch.Tensor) -> tuple[Tensor, Tensor]:
q = self.query_norm(q)
k = self.key_norm(k)
return q.to(v), k.to(v)
class PreNorm(nn.Module):
def __init__(self, dim, fn, context_dim=None):
super().__init__()
self.fn = fn
self.norm = nn.LayerNorm(dim)
self.norm_context = nn.LayerNorm(context_dim) if exists(context_dim) else None
def forward(self, x, context=None, mask=None):
x = self.norm(x)
if exists(self.norm_context):
context = self.norm_context(context)
return self.fn(x, context, mask)
return self.fn(x)
class FeedForward(nn.Module):
def __init__(
self,
dim: int,
depth: int = 1,
act: nn.Module = nn.GELU,
input_dim: int = None,
output_dim: int = None,
):
super().__init__()
input_dim = default(input_dim, dim)
output_dim = default(output_dim, dim)
layers = [nn.Sequential(nn.Linear(input_dim, dim), act())]
layers = layers + [nn.Sequential(nn.Linear(dim, dim), act()) for _ in range(1, depth)]
layers.append(nn.Linear(dim, output_dim))
self.net = nn.Sequential(*layers)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(
self, query_dim, context_dim=None, heads=8, dim_head=64, scale=None, qk_norm=False
):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = default(scale, dim_head**-0.5)
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_kv = nn.Linear(context_dim, inner_dim * 2, bias=False)
self.to_out = nn.Linear(inner_dim, query_dim)
self.norm = QKNorm(dim_head) if qk_norm else lambda q, k, v: (q, k)
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.to_q.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.to_kv.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.to_out.weight)
if self.to_out.bias is not None:
nn.init.constant_(self.to_out.bias, 0)
def forward(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k, v = self.to_kv(context).chunk(2, dim=-1)
q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
q, k = self.norm(q, k, v)
if mask is not None:
mask = repeat(mask, "b j -> (b h) () j", h=h)
out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, scale=self.scale)
out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
return self.to_out(out)
class CrossAttentionBlock(nn.Module):
def __init__(
self,
dim,
context_dim=None,
heads: int = 4,
dim_head: int = 64,
act=nn.GELU,
scale=None,
qk_norm=False,
):
super().__init__()
self.attn = PreNorm(
dim,
Attention(
query_dim=dim,
context_dim=context_dim,
heads=heads,
dim_head=dim_head,
scale=scale,
qk_norm=qk_norm,
),
context_dim=context_dim,
)
self.ff = PreNorm(dim, FeedForward(dim, act=act))
def forward(self, x, context=None, mask=None):
x = self.attn(x, context=context, mask=mask) + x
x = self.ff(x) + x
return x
class SelfAttention(nn.Module):
def __init__(self, dim, heads, dim_head, scale=None, qk_norm=False):
super().__init__()
inner_dim = dim_head * heads
self.scale = default(scale, dim_head**-0.5)
self.heads = heads
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
self.to_out = nn.Linear(inner_dim, dim)
self.norm = QKNorm(dim_head) if qk_norm else lambda q, k, _: (q, k)
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.to_qkv.weight, gain=1 / math.sqrt(2))
nn.init.xavier_uniform_(self.to_out.weight)
if self.to_out.bias is not None:
nn.init.constant_(self.to_out.bias, 0)
def forward(self, x, mask=None):
h = self.heads
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
q, k = self.norm(q, k, v)
if mask is not None:
mask = repeat(mask, "b j -> (b h) () j", h=h)
out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, scale=self.scale)
out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
return self.to_out(out)
class SelfAttentionBlock(nn.Module):
def __init__(
self,
dim: int,
heads: int,
dim_head: int = 64,
act=nn.GELU,
scale=None,
qk_norm=False,
):
super().__init__()
self.attn = PreNorm(dim, SelfAttention(dim, heads, dim_head, scale, qk_norm))
self.ff = PreNorm(dim, FeedForward(dim, act=act))
def forward(self, x, mask=None):
x = self.attn(x, mask=mask) + x
x = self.ff(x) + x
return x
class StackedRandomGenerator:
def __init__(self, device, seeds):
super().__init__()
self.generators = [
torch.Generator(device).manual_seed(int(seed) % (1 << 32)) for seed in seeds
]
def randn(self, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack(
[torch.randn(size[1:], generator=gen, **kwargs) for gen in self.generators]
)
def randn_like(self, input):
return self.randn(input.shape, dtype=input.dtype, layout=input.layout, device=input.device)
def randint(self, *args, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack(
[
torch.randint(*args, size=size[1:], generator=gen, **kwargs)
for gen in self.generators
]
)
def grad_checkpoint(func, args, checkpointing=False):
if checkpointing:
return checkpoint(func, *args, use_reentrant=False)
else:
return func(*args) |