RNNs vs Transformers: The Engineering Reality
Swopnil AcharyaWhat "One Word" Actually Means
When we say "processing one word," we're really talking about:
python
# A word is represented as a vector (array of numbers)
word_vector = [0.2, -0.1, 0.8, 0.3, -0.5, ...] # 512 or 1024 dimensions typically
# "cat" might be: [0.1, 0.9, -0.2, 0.4, ...]
# "dog" might be: [0.2, 0.8, -0.1, 0.5, ...]
# "sat" might be: [-0.3, 0.1, 0.7, -0.2, ...]
So when I say "process one word," I mean: feed one vector through the neural network.
The Old Way: RNNs - Step by Step Processing
The Data Structure:
python
# Input sentence: "The cat sat on mat"
input_sequence = [
[0.1, 0.2, 0.3, ...], # "The" as vector
[0.4, 0.5, 0.6, ...], # "cat" as vector
[0.7, 0.8, 0.9, ...], # "sat" as vector
[0.2, 0.1, 0.4, ...], # "on" as vector
[0.3, 0.6, 0.2, ...] # "mat" as vector
]
The RNN Processing Loop:
python
def process_with_rnn(input_sequence):
hidden_state = initialize_hidden_state() # [0, 0, 0, ..., 0]
for timestep, word_vector in enumerate(input_sequence):
# CRITICAL: You can only process ONE vector at a time
# You MUST wait for previous timestep to complete
print(f"Timestep {timestep}: Processing word vector {word_vector}")
# The RNN function takes:
# 1. Current word vector
# 2. Previous hidden state (memory from all previous words)
hidden_state = rnn_cell(word_vector, hidden_state)
print(f"Updated hidden state: {hidden_state}")
# This hidden state now contains "compressed memory"
# of all words seen so far
return hidden_state # Final understanding of entire sentence
What Actually Happens in Memory:
python
# Timestep 0: "The"
hidden_state = [0.1, 0.2, 0.05, ...] # Just "The" information
# Timestep 1: "cat"
# RNN tries to combine "cat" + memory of "The"
hidden_state = [0.3, 0.1, 0.4, ...] # "The cat" compressed
# Timestep 2: "sat"
# RNN tries to combine "sat" + memory of "The cat"
hidden_state = [0.2, 0.7, 0.1, ...] # "The cat sat" compressed
# Problem: The original "The" information is getting weaker and weaker
# It's like lossy compression - you lose details from early words
The New Way: Transformers - Parallel Processing
Same Input, Different Processing:
python
def process_with_transformer(input_sequence):
# KEY INSIGHT: Process ALL word vectors simultaneously
# Convert sequence to matrix (all words at once)
input_matrix = np.array([
[0.1, 0.2, 0.3, ...], # "The"
[0.4, 0.5, 0.6, ...], # "cat"
[0.7, 0.8, 0.9, ...], # "sat"
[0.2, 0.1, 0.4, ...], # "on"
[0.3, 0.6, 0.2, ...] # "mat"
])
# Shape: (5 words, 512 dimensions)
print(f"Processing matrix of shape: {input_matrix.shape}")
# ALL words are processed in parallel - no loop!
output_matrix = transformer_block(input_matrix)
return output_matrix
The Attention Mechanism (The Core Innovation):
python
def transformer_block(input_matrix):
# input_matrix shape: (num_words, embedding_dim)
# Let's say: (5, 512) for our 5-word sentence
# Step 1: Create Query, Key, Value matrices
Q = input_matrix @ W_query # (5, 512) @ (512, 64) = (5, 64)
K = input_matrix @ W_key # (5, 512) @ (512, 64) = (5, 64)
V = input_matrix @ W_value # (5, 512) @ (512, 64) = (5, 64)
# Step 2: Calculate attention scores
# This is WHERE THE MAGIC HAPPENS
attention_scores = Q @ K.T # (5, 64) @ (64, 5) = (5, 5)
# This 5x5 matrix tells us how much each word should
# pay attention to every other word:
#
# The cat sat on mat
# The [[0.1, 0.2, 0.05, 0.1, 0.05],
# cat [0.3, 0.8, 0.4, 0.1, 0.2 ],
# sat [0.1, 0.9, 0.6, 0.7, 0.8 ], # "sat" pays attention to cat(0.9) and mat(0.8)
# on [0.05, 0.2, 0.7, 0.4, 0.9 ],
# mat [0.1, 0.3, 0.8, 0.6, 0.7 ]]
# Step 3: Apply attention to values
attention_weights = softmax(attention_scores) # Normalize to probabilities
output = attention_weights @ V # (5, 5) @ (5, 64) = (5, 64)
return output
The Key Engineering Differences
1. Computational Complexity:
python
# RNN: Sequential - cannot parallelize the timestep loop
for i in range(sequence_length):
hidden_state = rnn_cell(input[i], hidden_state) # BLOCKS here
# Transformer: Parallel - matrix operations can use all GPU cores
output = transformer_block(input_matrix) # Single matrix operation
2. Memory Access Pattern:
python
# RNN: Each word can only access compressed memory of previous words
# Word 5 cannot directly "see" word 1 - it's been compressed away
# Transformer: Each word has direct access to every other word
# attention_scores[4][0] = how much word 5 attends to word 1
# No information loss!
3. Gradient Flow (Training):
python
# RNN: Gradients must flow backward through the entire sequence
# Error from word 5 → word 4 → word 3 → word 2 → word 1
# Gets weaker at each step (vanishing gradient problem)
# Transformer: Direct connections between all words
# Error from word 5 can flow directly to word 1
# Much better gradient flow = better training
What "All Sentence at Once" Really Means
Instead of this (RNN):
python
result = process_word_1(word1, empty_memory)
result = process_word_2(word2, result)
result = process_word_3(word3, result)
result = process_word_4(word4, result)
result = process_word_5(word5, result)
You do this (Transformer):
python
# All words in a single matrix operation
input_matrix = stack([word1, word2, word3, word4, word5])
result = transformer_function(input_matrix) # Processes all simultaneously
The "attention" mechanism is what allows each word to selectively focus on relevant other words, even though they're all processed in parallel.
Analogy for Engineers
RNN = Processing a linked list - you must traverse sequentially
python
current = head
while current:
process(current.data)
current = current.next # Must wait for each step
Transformer = Processing an array with random access + a smart indexing system
python
# Process all elements in parallel
results = parallel_map(process_function, array)
# But with a sophisticated attention mechanism that lets each element
# dynamically decide which other elements are relevant to it
The breakthrough insight: You don't need sequential processing for language understanding. You just need a way for each word to figure out which other words matter to it - and that's exactly what attention provides.