Attention

This document details the implementation of multi-head attention (MHA), multi-query attention (MQA), and group-query attention (GQA) for autoregressive models in TensorRT-LLM’s PyTorch backend. As a quick reminder, multi-head attention involves a sequence of batched matrix multiplications, a softmax operation, and another batched matrix multiplication, as described in the Attention Is All You Need paper. Multi-query Attention (MQA) and Group-query Attention (GQA) are variants of MHA that use fewer KV heads than the number of query heads. TensorRT-LLM provides several implementations using different backends in tensorrt_llm/_torch/attention_backend/. The following sections explain how to use these implementations and provide a brief guide on implementing new backends.

Attention Backends

There are currently three available attention backends: the vanilla backend, the TRT-LLM backend, and the Flashinfer backend. You can specify the desired attention backend using PyTorchConfig.attn_backend. For instance, to utilize the Flashinfer backend, you can create a PyTorchConfig with attn_backend = "flashinfer" and then pass it to the LLM constructor as follows: LLM(pytorch_backend_config=pytorch_config). This will enable the use of the Flashinfer backend for your model.

The vanilla backend, VanillaAttention, is a reference implementation designed primarily for inflight batching and linear KV cache support. While it serves as a useful baseline, it is not recommended for production use due to its limited optimizations.

In contrast, the Flashinfer backend, FlashInferAttention, is performance-optimized and supports both inflight batching and paged KV cache. It also includes the following advanced features:

1. FP8 Quantization: This feature enables the quantization of inputs and KV cache into FP8 format, significantly reducing memory usage and improving computational throughput.

2. RoPE Fusion: By integrating rotary position embedding (RoPE) directly into the attention computation, this feature enhances efficiency and reduces overhead.

The TRT-LLM backend, TrtllmAttention, serves as the default backend and supports all the features available in the Flashinfer backend while being further optimized for enhanced performance. It is the recommended choice for production environments. Additionally, it offers the following advanced features:

1. Fused QKV Input: It can accept a single QKV tensor as input, which is more efficient compared to using separate Q, K, and V tensors.

2. FP8 Output: It supports outputting the attention result in FP8 format, fusing quantization into the attention computation process.

Implement a New Attention Backend

You can implement a new attention backend to integrate other attention libraries. An attention backend consists of an AttentionBackend class and an AttentionMetadata class. There are three stages in the PyTorch that involve the attention backend:

1. Model construction: During the model’s __init__, call AttentionBackend.__init__ to create an attention backend for each layer.

2. Metadata preparation: Before each forward step of the model:

   1. If the metadata is uninitialized, call AttentionMetadata.__init__ to create the attention metadata.

   2. If using CUDA graphs, call AttentionMetadata.create_cuda_graph_metadata to convert the metadata to CUDA graph metadata, which pre-allocates all tensors and can be used to capture CUDA graphs. Do not re-allocate any tensors stored inside AttentionMetadata after the initial warmup run when using CUDA graphs.

   3. To prepare parameters of the input and KV cache, call AttentionMetadata.prepare to convert from existing metadata and KV cache manager.

3. Single step forward: During the forward pass of each attention layer, call AttentionBackend.forward to perform the attention operation. The AttentionMetadata will be provided as a forward argument.

Implement AttentionMetadata

The AttentionMetadata class stores metadata from the batched input and KV cache for the attention backend. It contains the following predefined fields:

Field	Type	Description
max_num_requests	int	The max number of requests in a single batch.
num_contexts	int	The number of context-phase sequences in the batch.
num_generations	int	The number of generation-phase sequences in the batch.
max_num_tokens	int	The max number of tokens in all requests in a single batch.
num_tokens	int	Number of tokens in the batch.
num_ctx_tokens	int	Number of tokens in sequences in the context phase.
kv_cache_manager	KVCacheManager	The KV cache manager.
is_cuda_graph	bool	Whether CUDA graph is enabled.
seq_lens	Tensor	The length of each sequence in the batch. The shape is (batch_size), and located on CPU memory.
seq_lens_cuda	Tensor	A copy of seq_lens store on the GPU.
context_lens	Tensor	The length of each context-phase sequence in the batch. The shape is (num_contexts).
position_ids	Optional[Tensor]	The position of each token in each sequence. May be None if positional embedding is applied outside of the backend.
request_ids	List[int]	The request ID of each sequence in the batch.
prompt_lens	List[int]	The prompt length of each sequence in the batch.
kv_cache_params	KVCacheParams	The parameters for the KV cache.

During AttentionMetadata.__init__, you can initialize additional fields for the new attention metadata. For example, the Flashinfer metadata initializes decode_wrapper here. During AttentionMetadata.prepare, the runtime will fill all predefined fields, and you can fill your customized fields according to these predefined fields. For example, the Flashinfer metadata fills qo_indptr by combining context_lens and num_generations here.

Implement AttentionBackend

The AttentionBackend delegates the attention operation to the backend implementation.

Its __init__ accepts the following arguments:

Field	Type	Description
layer_idx	int	The index of the attention layer in the model.
num_heads	int	The number of query heads.
head_dim	int	The size of each attention head (hidden_size // num_heads).
num_kv_heads	Optional[int]	The number of KV heads. Defaults to num_heads if None.
quant_config	QuantConfig	Optional quantization configuration. If None, no quantization is applied.
pos_embd_params	PositionalEmbeddingParams	Optional parameters defining how positional embedding should be applied. If None, positional embedding should be applied by the model before calling the backend. Otherwise, the backend is in-charge of applying positional embedding and may cache K without embedding it first.

Its forward accepts the following arguments:

Field	Type	Description
q	Tensor	Query tensor with shape (num_tokens, num_heads * head_dim).
k	Tensor	Key tensor with shape (num_tokens, num_kv_heads * head_dim).
v	Tensor	Value tensor with shape (num_tokens, num_kv_heads * head_dim).
metadata	AttentionMetadata	Metadata for the attention operation.
attention_mask	AttentionMask	Optional attention mask. If None, causal mask is applied.

For example, the Flashinfer backend calls append_paged_kv_cache and then wrapper’s run to perform the attention operation here.