Einops: How to Create Complex Tensor-Based Deep Learning Pipelines with Multimodal, Vision and Attention Examples

section("6) pack unpack")
B. Cemb = 2 128


class_token = torch.randn(B, 1, Cemb, device=device)
image_tokens = torch.randn(B, 196, Cemb, device=device)
text_tokens = torch.randn(B, 32, Cemb, device=device)
show_shape("class_token", class_token)
show_shape("image_tokens", image_tokens)
show_shape("text_tokens", text_tokens)


Pack(ps) = packed[class_token, image_tokens, text_tokens], "b * c")
show_shape("packed", packed)
print("packed_shapes (ps):", ps)


mixer = nn.Sequential(
   nn.LayerNorm(Cemb),
   nn.Linear(Cemb, 4 * Cemb),
   nn.GELU(),
   nn.Linear(4 * Cemb, Cemb),
).to(device)


mixed = mixer(packed)
show_shape("mixed", mixed)


class_out, image_out, text_out = unpack(mixed, ps, "b * c")
show_shape("class_out", class_out)
show_shape("image_out", image_out)
show_shape("text_out", text_out)
assert class_out.shape == class_token.shape
assert image_out.shape == image_tokens.shape
assert text_out.shape == text_tokens.shape


section("7) layers")
class PatchEmbed(nn.Module):
   def __init__(self, in_channels=3, emb_dim=192, patch=8):
 You can also check out our supersized().__init__()
 Self-patch = Patch
       self.to_patches = Rearrange("b c (h p1) (w p2) -> b (h w) (p1 p2 c)", p1=patch, p2=patch)
       self.proj = nn.Linear(in_channels * patch * patch, emb_dim)


   def forward(self, x):
 Self.to_patches()
 Self-proj.x


class SimpleVisionHead(nn.Module):
   def __init__(self, emb_dim=192, num_classes=10):
 You can also check out our supersized().__init__()
       self.pool = Reduce("b t c -> b c", reduction="mean")
       self.classifier = nn.Linear(emb_dim, num_classes)


   def forward(self, tokens):
 Self.pool.tokens
 Self-classifier(x).


patch_embed = PatchEmbed(in_channels=3, emb_dim=192, patch=8).to(device)
head = SimpleVisionHead(emb_dim=192, num_classes=10).to(device)


imgs = torch.randn(4, 3, 32, 32, device=device)
tokens = patch_embed(imgs)
Logits = Head (tokens).
show_shape("tokens", tokens)
show_shape("logits", logits)


section("8) practical")
x = torch.randn(2, 32, 16, 16, device=device)
G = 8
Rearrange (x) = xg "b (g cg) h w -> (b g) cg h w", g=g)
show_shape("x", x)
show_shape("xg", xg)


Mean = Reduce(xg) "bg cg h w -> bg 1 1 1", "mean")
var = reduce((xg - mean) ** 2, "bg cg h w -> bg 1 1 1", "mean")
xg_norm = (xg - mean) / torch.sqrt(var + 1e-5)
x_norm = rearrange(xg_norm, "(b g) cg h w -> b (g cg) h w", b=2, g=g)
show_shape("x_norm", x_norm)


z = torch.randn(3, 64, 20, 30, device=device)
z_flat = rearrange(z, "b c h w -> b c (h w)")
z_unflat = rearrange(z_flat, "b c (h w) -> b c h w", h=20, w=30)
assert (z - z_unflat).abs().max().item()  b h w c")
print("a.is_contiguous():", a.is_contiguous())
print("b.is_contiguous():", b.is_contiguous())
print("b._base is a:", getattr(b, "_base"It is not a.


section("Done ✅ You now have reusable einops patterns for vision, attention, and multimodal token packing")