How to Design Complex Deep Learning Tensor Pipelines Using Einops with Vision, Attention, and Multimodal Examples
In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and mathematically precise way. We demonstrate how rearrange, reduce, repeat, einsum, and pack/unpack let us reshape, aggregate, and combine tensors without relying on error-prone manual dimension handling. We focus on real deep-learning patterns, such as vision patchification, multi-head attention, and multimodal token mixing, and show how einops serves as a compact tensor manipulation language that integrates naturally with PyTorch. Check out the FULL CODES here.
import sys, subprocess, textwrap, math, time
def pip_install(pkg: str):
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", pkg])
pip_install("einops")
pip_install("torch")
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce, repeat, einsum, pack, unpack
from einops.layers.torch import Rearrange, Reduce
torch.manual_seed(0)
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Device:", device)
def section(title: str):
print("n" + "=" * 90)
print(title)
print("=" * 90)
def show_shape(name, x):
print(f"{name:>18} shape = {tuple(x.shape)} dtype={x.dtype} device={x.device}")We set up the execution environment and ensure all required dependencies are installed dynamically. We initialize PyTorch, einops, and utility helpers that standardize device selection and shape inspection. We also establish reusable printing utilities that help us track tensor shapes throughout the tutorial.
section("1) rearrange")
x = torch.randn(2, 3, 4, 5, device=device)
show_shape("x", x)
x_bhwc = rearrange(x, "b c h w -> b h w c")
show_shape("x_bhwc", x_bhwc)
x_split = rearrange(x, "b (g cg) h w -> b g cg h w", g=3)
show_shape("x_split", x_split)
x_tokens = rearrange(x, "b c h w -> b (h w) c")
show_shape("x_tokens", x_tokens)
y = torch.randn(2, 7, 11, 13, 17, device=device)
y2 = rearrange(y, "b ... c -> b c ...")
show_shape("y", y)
show_shape("y2", y2)
try:
_ = rearrange(torch.randn(2, 10, device=device), "b (h w) -> b h w", h=3)
except Exception as e:
print("Expected error (shape mismatch):", type(e).__name__, "-", str(e)[:140])We demonstrate how we use rearrange to express complex reshaping and axis-reordering operations in a readable, declarative way. We show how to split, merge, and permute dimensions while preserving semantic clarity. We also intentionally trigger a shape error to illustrate how Einops enforces shape safety at runtime.
section("2) reduce")
imgs = torch.randn(8, 3, 64, 64, device=device)
show_shape("imgs", imgs)
gap = reduce(imgs, "b c h w -> b c", "mean")
show_shape("gap", gap)
pooled = reduce(imgs, "b c (h ph) (w pw) -> b c h w", "mean", ph=2, pw=2)
show_shape("pooled", pooled)
chmax = reduce(imgs, "b c h w -> b c", "max")
show_shape("chmax", chmax)
section("3) repeat")
vec = torch.randn(5, device=device)
show_shape("vec", vec)
vec_batched = repeat(vec, "d -> b d", b=4)
show_shape("vec_batched", vec_batched)
q = torch.randn(2, 32, device=device)
q_heads = repeat(q, "b d -> b heads d", heads=8)
show_shape("q_heads", q_heads)We apply reduce and repeat to perform pooling, aggregation, and broadcasting operations without manual dimension handling. We compute global and local reductions directly within the transformation expression. We also show how repeating tensors across new dimensions simplifies batch and multi-head constructions.
section("4) patchify")
B, C, H, W = 4, 3, 32, 32
P = 8
img = torch.randn(B, C, H, W, device=device)
show_shape("img", img)
patches = rearrange(img, "b c (h p1) (w p2) -> b (h w) (p1 p2 c)", p1=P, p2=P)
show_shape("patches", patches)
img_rec = rearrange(
patches,
"b (h w) (p1 p2 c) -> b c (h p1) (w p2)",
h=H // P,
w=W // P,
p1=P,
p2=P,
c=C,
)
show_shape("img_rec", img_rec)
max_err = (img - img_rec).abs().max().item()
print("Reconstruction max abs error:", max_err)
assert max_err < 1e-6
section("5) attention")
B, T, D = 2, 64, 256
Hh = 8
Dh = D // Hh
x = torch.randn(B, T, D, device=device)
show_shape("x", x)
proj = nn.Linear(D, 3 * D, bias=False).to(device)
qkv = proj(x)
show_shape("qkv", qkv)
q, k, v = rearrange(qkv, "b t (three heads dh) -> three b heads t dh", three=3, heads=Hh, dh=Dh)
show_shape("q", q)
show_shape("k", k)
show_shape("v", v)
scale = Dh ** -0.5
attn_logits = einsum(q, k, "b h t dh, b h s dh -> b h t s") * scale
show_shape("attn_logits", attn_logits)
attn = attn_logits.softmax(dim=-1)
show_shape("attn", attn)
out = einsum(attn, v, "b h t s, b h s dh -> b h t dh")
show_shape("out (per-head)", out)
out_merged = rearrange(out, "b h t dh -> b t (h dh)")
show_shape("out_merged", out_merged)We implement vision and attention mechanisms that are commonly found in modern deep learning models. We convert images into patch sequences and reconstruct them to verify reversibility and correctness. We then reshape projected tensors into a multi-head attention format and compute attention using einops.einsum for clarity and correctness.
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)
packed, ps = pack([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):
super().__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):
x = self.to_patches(x)
return self.proj(x)
class SimpleVisionHead(nn.Module):
def __init__(self, emb_dim=192, num_classes=10):
super().__init__()
self.pool = Reduce("b t c -> b c", reduction="mean")
self.classifier = nn.Linear(emb_dim, num_classes)
def forward(self, tokens):
x = self.pool(tokens)
return 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
xg = rearrange(x, "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() < 1e-6
show_shape("z_flat", z_flat)
section("9) views")
a = torch.randn(2, 3, 4, 5, device=device)
b = rearrange(a, "b c h w -> 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", None) is a)
section("Done 
You now have reusable einops patterns for vision, attention, and multimodal token packing")We demonstrate reversible token packing and unpacking for multimodal and transformer-style workflows. We integrate Einops layers directly into PyTorch modules to build clean, composable model components. We conclude by applying practical tensor grouping and normalization patterns that reinforce how einops simplifies real-world model engineering.
In conclusion, we established Einops as a practical and expressive foundation for modern deep-learning code. We showed that complex operations like attention reshaping, reversible token packing, and spatial pooling can be written in a way that is both safer and more readable than traditional tensor operations. With these patterns, we reduced cognitive overhead and minimized shape bugs. We wrote models that are easier to extend, debug, and reason about while remaining fully compatible with high-performance PyTorch workflows.
Check out the FULL CODES here. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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