[Core] More-efficient cross-attention parallel QKV computation

[afeldman-nm]

This PR makes QKV computation more efficient in the case of cross-attention layers.

QKVParallelLinear only performs self-attention QKV computation against a single hidden-state input from the previous decoder layer.

Cross-attention requires Q to be computed from the previous decoder layer output hidden state, and KV to be computed from the encoder output hidden state. Additionally,

• During prefill phase, both Q and KV must be computed
• During decode phase, only Q is compute because the encoder sequence is static so there are no new encoder KVs

The current, inefficient workaround is to construct a QKVParallelLinear layer & apply it up to twice, once to decoder_hidden_states to obtain Q, and (only during prefill) a second time to encoder_hidden_states to obtain KV:

# (afeldman-nm 2024/07/22) TODO:
# Need a more efficient solution for q/k/v
qkv_dec, _ = self.qkv_proj(decoder_hidden_states)
q, _, _ = qkv_dec.split([self.q_size, self.kv_size, self.kv_size],
                        dim=-1)
if encoder_hidden_states is None:
    k = None
    v = None
else:
    qkv_enc, _ = self.qkv_proj(encoder_hidden_states)
    _, k, v = qkv_enc.split([self.q_size, self.kv_size, self.kv_size],
                            dim=-1)

This PR introduces a cross-attention QKV parallel linear layer with the following characteristics

• Identical to QKVParallelLinear in terms of weight loading behavior
• Still distributes per-head computations across GPUs, like QKVParallelLinear
• forward() takes a decoder hidden states argument, and an optional encoder hidden states argument
• forward() always computes $(decoder\ hidden\ states) W_Q$
• forward() computes $(encoder\ hidden\ states) [W_K W_V]$ conditionally: only if the encoder hidden states argument is not None

The forward method of this new QKV parallel linear layer will return:

return (
    q_output,
    kv_output,
    q_bias if skip_bias_add else None,
    kv_bias if skip_bias_add else None,
)

The cross-attention layer will construct this new QKV parallel linear layer in __init__() as follows

self.qkv_proj = QCrossKVParallelLinear(
    self.d_model,
    self.d_model // self.total_num_heads,
    self.total_num_heads,
    self.total_num_kv_heads,
    bias=bias,
    quant_config=quant_config,
)

and then compute Q,K,V in forward() as follows

(
    q,
    kv,
    _,
    _,
) = self.qkv_proj(decoder_hidden_states, encoder_hidden_states)

if kv is None:
    k = None
    v = None
else:
    k, v = kv.split([self.kv_size, self.kv_size], dim=-1)

Note:

• This PR only impacts encoder/decoder models, not decoder-only models
• tests/models/test_bart serves as an end-to-end encoder/decoder model test, implicitly testing QCrossKVParallelLinear

FIX #7397 (link existing issues this PR will resolve)

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[sroy745]

LGTM. Left a couple of small comments. Thanks for the pr.

[sroy745]

Was wondering if in the encoder-decode e2e test (e.g. test_bart.py) did you observe any increase in the input token/s or the output token/s with this change?

[afeldman-nm]

Good question. I ran a very informal test to prove to myself that QCrossKVParallelLinear improved the total runtime of the encoder/decoder example script. I should run an actual benchmark in order compare tokens/s

[afeldman-nm]

Perf test:

Server:

Note the use of eager mode & the lack of tensor-/pipeline-parallelism.

The key change in QCrossKVParallelLinear has to do with skipping extra work involved in QKV computation & does not require parallelism to test effectively.

python  -m vllm.entrypoints.openai.api_server     --model facebook/bart-large-cnn --swap-space 16 --enforce-eager     --disable-log-requests   --tensor-parallel-size 1  --pipeline-parallel-siz
e 1  --gpu-memory-utilization 0.90  --worker-use-ray 2>&1 | tee output.log

Client:

python benchmarks/benchmark_serving.py     --backend vllm      --model facebook/bart-large-cnn     --dataset-name sharegpt     --dataset-path ../sharegpt.json

Testbench:

4x NVIDIA A4000 RTX 16GB

Baseline perf benchmark result (main, 029c71d)

============ Serving Benchmark Result ============
Successful requests:                     945       
Benchmark duration (s):                  39.58     
Total input tokens:                      197519    
Total generated tokens:                  18272     
Request throughput (req/s):              23.88     
Input token throughput (tok/s):          4990.44   
Output token throughput (tok/s):         461.65    
---------------Time to First Token----------------
Mean TTFT (ms):                          7176.60   
Median TTFT (ms):                        5528.61   
P99 TTFT (ms):                           19099.46  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          194.47    
Median TPOT (ms):                        208.84    
P99 TPOT (ms):                           303.55    
---------------Inter-token Latency----------------
Mean ITL (ms):                           151.25    
Median ITL (ms):                         188.21    
P99 ITL (ms):                            362.17    
==================================================

PR branch result (after merging in 029c71d)

============ Serving Benchmark Result ============
Successful requests:                     945       
Benchmark duration (s):                  38.30     
Total input tokens:                      197519    
Total generated tokens:                  18307     
Request throughput (req/s):              24.67     
Input token throughput (tok/s):          5156.99   
Output token throughput (tok/s):         477.97    
---------------Time to First Token----------------
Mean TTFT (ms):                          6972.37   
Median TTFT (ms):                        5344.80   
P99 TTFT (ms):                           18775.37  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          192.98    
Median TPOT (ms):                        205.76    
P99 TPOT (ms):                           328.27    
---------------Inter-token Latency----------------
Mean ITL (ms):                           148.30    
Median ITL (ms):                         186.53    
P99 ITL (ms):                            371.48    
==================================================

[afeldman-nm]

Based on the benchmarks it is not clear this change improves performance.

[heheda12345]

I'm trying mllama with QCrossKVParallelLinear. For batch_size 16 & H100 & Llama-3.2-11B-Vision-Instruct , this layer can reduce the time for preparing QKV from 46.5279ms to 21.7249ms. But it have negelible impact to the end to end performace compared with the thousands of ms to process such a batch, which is similar to the experiments in bart model.
The code is here

And I have a question: as you compute $wq$ and $w[k,v]$ sequentially, why not choosing QLinear + KVParallelLinear?

        q_output_parallel = self._apply_w_conditional_bias(
            self._cached_q_weights_wrapper, decoder_input_,
            self._cached_q_bias)
        kv_output_parallel = None if is_decode_phase else (
            self._apply_w_conditional_bias(self._cached_kv_weights_wrapper,
                                           encoder_input_,
                                           self._cached_kv_bias))

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