#
Caching

Imagine you’re having a conversation with someone, and instead of remembering what they previously said, they have to start from scratch every time you respond. This would be slow and inefficient, right?

You can extend this analogy to transformer models. Autoregressive model generation can be slow because it makes a prediction one token at a time. Each new prediction is dependent on all the previous context.

To predict the 1000th token, the model requires information from the previous 999 tokens. The information is represented as matrix multiplications across the token representations.

To predict the 1001th token, you need the same information from the previous 999 tokens in addition to any information from the 1000th token. This is a lot of matrix multiplications a model has to compute over and over for each token!

A key-value (KV) cache eliminates this inefficiency by storing kv pairs derived from the attention layers of previously processed tokens. The stored kv pairs are retrieved from the cache and reused for subsequent tokens, avoiding the need to recompute.

Caching should only be used for inference. It may cause unexpected errors if it’s enabled during training.

To better understand how and why caching works, let’s take a closer look at the structure of the attention matrices.

##
Attention matrices

The scaled dot-product attention is calculated as shown below for a batch of size `b`, number of attention heads `h`, sequence length so far `T`, and dimension per attention head `d_head`. Attention(Q,K,V)=softmax(Q K⊤d head×mask)V \text{Attention}(Q, K, V) = \text{softmax}\left( \frac{Q K^\top}{\sqrt{d_{\text{head}}}} \times \text{mask} \right) V Attention(Q,K,V)=softmax(d head​
​Q K⊤​×mask)V

The query (`Q`), key (`K`), and value (`V`) matrices are projections from the input embeddings of shape `(b, h, T, d_head)`.

For causal attention, the mask prevents the model from attending to future tokens. Once a token is processed, its representation never changes with respect to future tokens, which meansK past K_{\text{past}} K past​ andV past V_{\text{past}} V past​ can be cached and reused to compute the last token’s representation. Attention(q t,[k 1,k 2,…,k t−1⏟cached,k t],[v 1,v 2,…,v t−1⏟cached,v t]) \text{Attention}(q_t, [\underbrace{k_1, k_2, \dots, k_{t-1}}_{\text{cached}}, k_{t}], [\underbrace{v_1, v_2, \dots, v_{t-1}}_{\text{cached}}, v_{t}]) Attention(q t​,[cached
k 1​,k 2​,…,k t−1​​​,k t​],[cached
v 1​,v 2​,…,v t−1​​​,v t​])

At inference time, you only need the last token’s query to compute the representationx t x_t x t​ that predicts the next tokent+1 t+1 t+1. At each step, the new key and value vectors are stored in the cache and appended to the past keys and values. K cache←concat(K past,k t),V cache←concat(V past,v t) K_{\text{cache}} \leftarrow \text{concat}(K_{\text{past}}, k_t), \quad V_{\text{cache}} \leftarrow \text{concat}(V_{\text{past}}, v_t) K cache​←concat(K past​,k t​),V cache​←concat(V past​,v t​)

Attention is calculated independently in each layer of the model, and caching is done on a per-layer basis.

Refer to the table below to compare how caching improves efficiency.

without caching | with caching
--- | ---
for each step, recompute all previous `K` and `V` | for each step, only compute current `K` and `V`
attention cost per step is quadratic with sequence length | attention cost per step is linear with sequence length (memory grows linearly, but compute/token remains low)

##
Cache class

A basic KV cache interface takes a key and value tensor for the current token and returns the updated `K` and `V` tensors. This is internally managed by a model’s `forward` method.

    new_K, new_V = cache.update(k_t, v_t, layer_idx)
    attn_output = attn_layer_idx_fn(q_t, new_K, new_V)

When you use Transformers’ Cache class, the self-attention module performs several critical steps to integrate past and present information.

1. The attention module concatenates current kv pairs with past kv pairs stored in the cache. This creates attentions weights with the shape `(new_tokens_length, past_kv_length + new_tokens_length)`. The current and past kv pairs are essentially combined to compute the attention scores, ensuring a model is aware of previous context and the current input.

2. When the `forward` method is called iteratively, it’s crucial that the attention mask shape matches the combined length of the past and current kv pairs. The attention mask should have the shape `(batch_size, past_kv_length + new_tokens_length)`. This is typically handled internally in generate(), but if you want to implement your own generation loop with Cache, keep this in mind! The attention mask should hold the past and current token values.

3. It is also important to be aware of the `cache_position`. This is important if you want to reuse a prefilled Cache with the `forward` method because you have to pass a valid `cache_position` value. This indicates the input positions in a sequence. `cache_position` is unaffected by padding, and it always adds one more position for each token. For example, if a kv cache contains 10 tokens - regardless of pad tokens - the cache position for the next token should be `torch.tensor([10])`.

##
Cache storage implementation

The actual storage of key-value pairs varies between cache implementations. As an example, consider the DynamicCache.

In DynamicCache, the key-value pairs are stored as two lists of tensors. Each tensor in the lists have the shape `[batch_size, num_heads, seq_len, head_dim]`.

- `key_cache`: A list of tensors, one for each layer.
- `value_cache`: A list of tensors, one for each layer.

When new tokens are processed:

1. For each layer, the new key and value states are concatenated with the existing cache.

    self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
    self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)

2. The cache grows dynamically as more tokens are processed. The sequence length dimension (`seq_len`) increases with each new token.

3. The cache maintains a count of seen tokens through `self._seen_tokens`. This is updated when the first layer processes a new token.

The example below demonstrates how to create a generation loop with DynamicCache. As discussed, the attention mask is a concatenation of past and current token values and `1` is added to the cache position for the next token.

    import torch
    from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache

    model_id = "meta-llama/Llama-2-7b-chat-hf"
    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda:0")
    tokenizer = AutoTokenizer.from_pretrained(model_id)

    past_key_values = DynamicCache()
    messages = [{"role": "user", "content": "Hello, what's your name."}]
    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda:0")

    generated_ids = inputs.input_ids
    cache_position = torch.arange(inputs.input_ids.shape[1], dtype=torch.int64, device="cuda:0")
    max_new_tokens = 10

    for _ in range(max_new_tokens):
        outputs = model(**inputs, cache_position=cache_position, past_key_values=past_key_values, use_cache=True)
        # Greedily sample one next token
        next_token_ids = outputs.logits[:, -1:].argmax(-1)
        generated_ids = torch.cat([generated_ids, next_token_ids], dim=-1)
        # Prepare inputs for the next generation step by leaving unprocessed tokens, in our case we have only one new token
        # and expanding attn mask for the new token, as explained above
        attention_mask = inputs["attention_mask"]
        attention_mask = torch.cat([attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1)
        inputs = {"input_ids": next_token_ids, "attention_mask": attention_mask}
        cache_position = cache_position[-1:] + 1 # add one more position for the next token

    print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
    "[INST] Hello, what's your name. [/INST]  Hello! My name is LLaMA,"

##
Legacy cache format

Before the Cache class, the cache used to be stored as a tuple of tuples of tensors. This format is dynamic because it grows as text is generated, similar to DynamicCache.

The legacy format is essentially the same data structure but organized differently.

- It’s a tuple of tuples, where each inner tuple contains the key and value tensors for a layer.
- The tensors have the same shape `[batch_size, num_heads, seq_len, head_dim]`.
- The format is less flexible and doesn’t support features like quantization or offloading.

If your project depends on this legacy format, you can convert between DynamicCache and a tuple of tuples as shown below with the from_legacy_cache() and DynamicCache.to_legacy_cache() functions. This is helpful if you have custom logic for manipulating a cache in a specific format.

    import torch
    from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache

    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
    model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
    inputs = tokenizer("Hello, my name is", return_tensors="pt").to(model.device)

    # `return_dict_in_generate=True` is required to return the cache and `return_legacy_cache` forces the returned cache
    # in the legacy format
    generation_outputs = model.generate(**inputs, return_dict_in_generate=True, return_legacy_cache=True, max_new_tokens=5)

    cache = DynamicCache.from_legacy_cache(generation_outputs.past_key_values)
    legacy_format_cache = cache.to_legacy_cache()

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