Flash Attention: A New Way to Compute Self-Attention for Large Input Lengths

in

First, we split the input sequence into smaller chunks (usually around 512 tokens each). This is because self-attention can become computationally expensive for long sequences due to its quadratic time complexity. By breaking up the input into smaller pieces, we can reduce the number of computations required and make things faster overall.

2. Next, we compute a “query” vector for each chunk using an embedding layer (which converts words or characters into numerical vectors). This query vector is then used to calculate attention scores between different positions in the input sequence.

3. The attention scores are calculated by multiplying the query vector with a matrix that contains information about how important each position in the input sequence is for generating the output. This matrix is called the “key-value” matrix, and it’s essentially just a fancy way of storing all the relevant information from previous layers (like word embeddings or hidden states) so we can use them later on when computing attention scores.

4. Once we have our attention scores, we multiply each position in the input sequence by its corresponding score to get a weighted sum of values for that chunk. This is essentially what allows us to “focus” on certain parts of the input sequence and ignore others (which can be really helpful when dealing with long sequences or noisy data).

5. Finally, we repeat this process for each chunk in the input sequence, which gives us a set of weighted sums that represent how important each position is for generating the output. These weights are then used to calculate the final output (which could be anything from a sentence prediction to a sentiment analysis score).

Flash Attention: A New Way to Compute Self-Attention for Large Input Lengths, in simpler terms. It’s basically just a fancy way of breaking up long sequences into smaller chunks and then using attention scores to focus on the most important parts when generating an output. Pretty cool, right?

As for script or command examples, here are some code snippets that might help you get started:

# Load data and preprocess it as needed (e.g., tokenization)
data = load_data() # Load data from a source and store it in a variable called "data"

# Split input sequence into smaller chunks using a sliding window approach
chunks = []
for i in range(len(input_sequence)-window_size+1):
    chunk = input_sequence[i:i+window_size] # Use a sliding window approach to split the input sequence into smaller chunks
    # Add any necessary preprocessing steps (e.g., padding or truncation)
    chunks.append(preprocess_chunk(chunk)) # Preprocess each chunk and add it to the list of chunks

# Compute query vectors for each chunk using an embedding layer
queries = []
for chunk in chunks:
    queries.append(embedding_layer(chunk)) # Use an embedding layer to convert each chunk into a query vector

# Calculate attention scores between different positions in the input sequence
attention_scores = []
for i, q in enumerate(queries):
    for j, k in enumerate(key_value_matrix[i]):
        # Compute dot product of query and key vectors (which gives us an attention score)
        score = torch.matmul(q, k).squeeze() # Calculate the dot product of the query and key vectors to get an attention score
        # Add any necessary normalization or activation steps (e.g., softmax or ReLU)
        scores[i].append(score) # Add the score to the list of attention scores for the current chunk

# Calculate weighted sum of values for each chunk using attention scores
outputs = []
for i, scores in enumerate(attention_scores):
    # Compute weighted sum by multiplying each position's value with its corresponding score (which gives us a new vector representing the importance of that position)
    output = torch.sum([values[j] * scores[i][j] for j in range(len(key_value_matrix[0]))], dim=1) # Calculate the weighted sum of values for each chunk using the attention scores
    # Add any necessary postprocessing steps (e.g., softmax or sigmoid)
    outputs.append(output) # Add the output to the list of outputs for each chunk

Of course, this is just a basic example and there are many variations you can try depending on your specific use case!

SICORPS