[Core] Optimizing cross-attention QKVParallelLinear computation

[NickLucche]

TL;DR: Basically another take at #7448 based on the work on the Whisper model, with sugar on top to provide a drop-in replacement module.

Addressing TODOs https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/bart.py#L352 and https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/mllama.py#L750.

Current cross-attention QKV projection is sub-optimal as we're wasting cycles on bigger-than-necessary matrices, especially important in the compute-bound stage. That is because QKVParallellLinear layers are being used to only compute the q and kv projection, separately in two sequential calls.

I propose adopting the solution we make use of here https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/whisper.py#L173, where q\kv are being split into a ColumnParallelLinear and QKVParallelLinear layer, respectively, instantiating and sharding only the matrices we actually make use of. Support of tensor parallelism should be unscathed.

I also provide a drop-in replacement util layer QKVCrossParallellLinear to use in substitution of QKVParallellLinear layers such that loading code remains the same, especially the usual stacked_params_mapping.

==>Let me know what you think about the util Module interface/API, otherwise I can just substitute in its optimized code inline.

Early benchmarking results (single L4 24gb, running facebook/bart-large-cnn):

PRE-PR b197a5cc

============ Serving Benchmark Result ============
Successful requests:                     12        
Benchmark duration (s):                  3.64      
Total input tokens:                      790       
Total generated tokens:                  374       
Request throughput (req/s):              3.29      
Output token throughput (tok/s):         102.67    
Total Token throughput (tok/s):          319.55    
---------------Time to First Token----------------
Mean TTFT (ms):                          73.88     
Median TTFT (ms):                        69.12     
P99 TTFT (ms):                           129.42    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          10.50     
Median TPOT (ms):                        9.94      
P99 TPOT (ms):                           14.84     
---------------Inter-token Latency----------------
Mean ITL (ms):                           8.93      
Median ITL (ms):                         8.70      
P99 ITL (ms):                            10.50     
==================================================

POST-PR

============ Serving Benchmark Result ============
Successful requests:                     12        
Benchmark duration (s):                  3.62      
Total input tokens:                      790       
Total generated tokens:                  374       
Request throughput (req/s):              3.31      
Output token throughput (tok/s):         103.29    
Total Token throughput (tok/s):          321.46    
---------------Time to First Token----------------
Mean TTFT (ms):                          74.35     
Median TTFT (ms):                        69.84     
P99 TTFT (ms):                           129.07    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          10.37     
Median TPOT (ms):                        9.81      
P99 TPOT (ms):                           14.53     
---------------Inter-token Latency----------------
Mean ITL (ms):                           8.84      
Median ITL (ms):                         8.75      
P99 ITL (ms):                            9.85      
==================================================

TODO:

• Document QKVCrossParallellLinear both in code and docs in "how to add model"
• Replace in other encoder decoder models ([Model] Add T5 model (2/2)  #11901?)

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[mgoin]

This looks like a great improvement to me! Cc @afeldman-nm

[DarkLight1337]

Please fix the error in distributed tests.

[afeldman-nm]

Thanks for the PR! I think this will be a valuable fix for encoder/decoder models. Just had one or two pieces of feedback.

A general observation - the original QKVParallelLinear has pretty limited test-coverage; the only unit-tests I could find were LoRA-oriented tests in tests/lora. So in #7448 I did not bother adding additional unit tests for QCrossKVParallelLinear, and I see that that is also the case in this PR.

That said, I observe that QKVParallelLinear has pretty complicated weight loading logic, with a lot of special cases for different weight representation formats i.e. GGUF, "bitsandbytes_4bit", etc.:

vllm/vllm/model_executor/layers/linear.py

Line 824 in 4c82229

def weight_loader(self,

In fact, there appear to be two different weight loading methods, weight_loader() and weight_loader_v2() (I actually don't know what the difference between these methods is):

vllm/vllm/model_executor/layers/linear.py

Line 798 in 4c82229

def weight_loader_v2(self,

So I am wondering, how many of these different weight-loading scenarios are supported by QKVCrossParallelLinear? All of them, or just a minimal set which covers typical cases? Personally I think it is fine to cover only the most commonly-used configurations in this PR.

[NickLucche]

Thanks for reviewing!

how many of these different weight-loading scenarios are supported by QKVCrossParallelLinear?

Given I am only re-using the QKVParallel and ColumnParallel pre-instantiated weight loaders, I would expect it to work with any format already covered by the two layers.
The weight_loader v1/v2 creation happens here https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/linear.py#L322 conditioned on the quant_config which I forward to the two layers here https://github.com/vllm-project/vllm/pull/12325/files#diff-b0ba1095e9881e5c87e33dfd20958d1e1ceafe8a4433aa692f468e61be130b21R676.

I did not bother adding additional unit tests for QCrossKVParallelLinear, and I see that that is also the case in this PR.

I've given some thought to this but I couldn't find a way to add meaningful tests, other than writing a "test model loading" function, which is already covered by all other correctness tests. Let me know if I've overlooked something.