mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
317 lines
10 KiB
Python
317 lines
10 KiB
Python
import math
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import torch
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from torch import einsum, nn
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from imaginairy.modules.diffusion.util import checkpoint
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from imaginairy.utils import get_device
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out * 2)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * F.gelu(gate)
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = dim_out if dim_out is not None else dim
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project_in = (
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nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
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if not glu
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else GEGLU(dim, inner_dim)
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)
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self.net = nn.Sequential(
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project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
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)
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def forward(self, x):
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return self.net(x)
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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def Normalize(in_channels):
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return torch.nn.GroupNorm(
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num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
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)
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class LinearAttention(nn.Module):
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def __init__(self, dim, heads=4, dim_head=32):
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super().__init__()
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self.heads = heads
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hidden_dim = dim_head * heads
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self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
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self.to_out = nn.Conv2d(hidden_dim, dim, 1)
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def forward(self, x):
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b, c, h, w = x.shape # noqa
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qkv = self.to_qkv(x)
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q, k, v = rearrange(
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qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
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)
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k = k.softmax(dim=-1)
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context = torch.einsum("bhdn,bhen->bhde", k, v)
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out = torch.einsum("bhde,bhdn->bhen", context, q)
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out = rearrange(
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out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
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)
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return self.to_out(out)
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class SpatialSelfAttention(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape # noqa
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q = rearrange(q, "b c h w -> b (h w) c")
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k = rearrange(k, "b c h w -> b c (h w)")
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w_ = torch.einsum("bij,bjk->bik", q, k)
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w_ = w_ * (int(c) ** (-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = rearrange(v, "b c h w -> b c (h w)")
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w_ = rearrange(w_, "b i j -> b j i")
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h_ = torch.einsum("bij,bjk->bik", v, w_)
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h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
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h_ = self.proj_out(h_)
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return x + h_
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class CrossAttention(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = context_dim if context_dim is not None else query_dim
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self.scale = dim_head**-0.5
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self.heads = heads
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
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)
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def forward(self, x, context=None, mask=None):
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if get_device() == "cuda":
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return self.forward_cuda(x, context=context, mask=mask)
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h = self.heads
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q = self.to_q(x)
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context = context if context is not None else x
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k = self.to_k(context)
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v = self.to_v(context)
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q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
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sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
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if mask is not None:
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mask = rearrange(mask, "b ... -> b (...)")
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_max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, "b j -> (b h) () j", h=h)
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sim.masked_fill_(~mask, _max_neg_value)
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# attention, what we cannot get enough of
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attn = sim.softmax(dim=-1)
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out = einsum("b i j, b j d -> b i d", attn, v)
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out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
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return self.to_out(out)
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def forward_cuda(self, x, context=None, mask=None): # noqa
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h = self.heads
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q_in = self.to_q(x)
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context = context if context is not None else x
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k_in = self.to_k(context)
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v_in = self.to_v(context)
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del context, x
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q, k, v = map(
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lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q_in, k_in, v_in)
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)
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats["active_bytes.all.current"]
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mem_reserved = stats["reserved_bytes.all.current"]
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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gb = 1024**3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(
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f"Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). "
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f"Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free"
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)
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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s1 = einsum("b i d, b j d -> b i j", q[:, i:end], k) * self.scale
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s2 = s1.softmax(dim=-1, dtype=q.dtype)
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del s1
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r1[:, i:end] = einsum("b i j, b j d -> b i d", s2, v)
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del s2
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del q, k, v
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r2 = rearrange(r1, "(b h) n d -> b n (h d)", h=h)
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del r1
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return self.to_out(r2)
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class BasicTransformerBlock(nn.Module):
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def __init__(
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self,
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dim,
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n_heads,
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d_head,
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dropout=0.0,
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context_dim=None,
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gated_ff=True,
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checkpoint=True, # noqa
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):
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super().__init__()
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self.attn1 = CrossAttention(
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query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
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) # is a self-attention
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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self.attn2 = CrossAttention(
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query_dim=dim,
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context_dim=context_dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.norm3 = nn.LayerNorm(dim)
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self.checkpoint = checkpoint
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def forward(self, x, context=None):
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return checkpoint(
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self._forward, (x, context), self.parameters(), self.checkpoint
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)
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def _forward(self, x, context=None):
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x = x.contiguous() if x.device.type == "mps" else x
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x = self.attn1(self.norm1(x)) + x
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x = self.attn2(self.norm2(x), context=context) + x
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x = self.ff(self.norm3(x)) + x
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return x
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class SpatialTransformer(nn.Module):
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"""
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Transformer block for image-like data.
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First, project the input (aka embedding)
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and reshape to b, t, d.
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Then apply standard transformer action.
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Finally, reshape to image
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"""
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def __init__(
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self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None
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):
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super().__init__()
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self.in_channels = in_channels
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inner_dim = n_heads * d_head
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self.norm = Normalize(in_channels)
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self.proj_in = nn.Conv2d(
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in_channels, inner_dim, kernel_size=1, stride=1, padding=0
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)
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self.transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim
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)
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for d in range(depth)
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]
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)
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self.proj_out = zero_module(
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nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
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)
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def forward(self, x, context=None):
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# note: if no context is given, cross-attention defaults to self-attention
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b, c, h, w = x.shape # noqa
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x_in = x
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x = self.norm(x)
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x = self.proj_in(x)
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x = rearrange(x, "b c h w -> b (h w) c")
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for block in self.transformer_blocks:
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x = block(x, context=context)
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x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
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x = self.proj_out(x)
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return x + x_in
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