To compare the matmul performance of bfloat16 and fp8.

[AllenDou]

After quantizing the AutoModelForSequenceClassification model using AutoFP8 and serving it with vllm, I've observed a significant performance drop-off. So, I've created a PR to test bfloat16 and fp8 performance. @mgoin , could you give me some advice? As you can see from the charts, the scale + cutlass time is almost same with torch.mm time.

m=4096 n=4096 k=4096
pytest test_cutlass.py -k test_compare_cutlass_fp8_torch_mm -s
use_bias=False, torch_mm_time=0.0014884472 scale_time=0.0000164509 cutlass_mm_time=0.0008094311
use_bias=False, torch_mm_time=0.0014874935 scale_time=0.0000212193 cutlass_mm_time=0.0008101463
use_bias=False, torch_mm_time=0.0014891624 scale_time=0.0000171661 cutlass_mm_time=0.0008101463
use_bias=False, torch_mm_time=0.0014884472 scale_time=0.0000157356 cutlass_mm_time=0.0008111000
use_bias=True, torch_mm_time=0.0015590191 scale_time=0.0000166893 cutlass_mm_time=0.0013942719
use_bias=True, torch_mm_time=0.0015594959 scale_time=0.0000171661 cutlass_mm_time=0.0013976097
use_bias=True, torch_mm_time=0.0015587807 scale_time=0.0000176430 cutlass_mm_time=0.0013940334
use_bias=True, torch_mm_time=0.0015587807 scale_time=0.0000171661 cutlass_mm_time=0.0013928413

Weird thing is when use_bias=True, cutlass_mm_time increases significantly.

the GPU I used is L20.

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[mgoin]

Hey @AllenDou you can see some benchmarks we made of our tuned FP8 kernels against the pytorch FP8 kernels in this PR #6677

I think your current measurement setup is not accurate because you are switching between the kernels each iteration and measuring the time after each iteration. Usually we run a loop of iterations over a kernel, synchronize and take the time after all iterations, then take the average to get the per-iter time. For the most standard results we try to use the PyTorch benchmarking tools, example here for existing w8a8 benchmarking: https://github.com/vllm-project/vllm/blob/main/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

[fan-niu]

@AllenDou hi thanks for doing some research on AutoModelForSequenceClassification, Can you tell me how to use AutoModelForSequenceClassification on vllm? Thank you so much

[AllenDou]

@AllenDou hi thanks for doing some research on AutoModelForSequenceClassification, Can you tell me how to use AutoModelForSequenceClassification on vllm? Thank you so much

Please refer #6260

[fan-niu]

@AllenDou hi thanks for doing some research on AutoModelForSequenceClassification, Can you tell me how to use AutoModelForSequenceClassification on vllm? Thank you so much

Please refer #6260

Cool, thanks so much

[fan-niu]

@AllenDou hi, I downloaded your branch (branch: bge_onemb2) and then installed the source code usingpip install -e ., but when running the service, I got the following error, my most fundamental desire is to use LlamaForSequenceClassification but find the model support list just has XLMRobertaForSequenceClassification, looking forward to your reply, thank you so much.

error info:

raise ValueError( ValueError: Model architectures ['LlamaForSequenceClassification'] are not supported for now. Supported architectures: ['AquilaModel', 'AquilaForCausalLM', 'BaiChuanForCausalLM', 'BaichuanForCausalLM', 'BloomForCausalLM', 'ChatGLMModel', 'ChatGLMForConditionalGeneration', 'CohereForCausalLM', 'DbrxForCausalLM', 'DeciLMForCausalLM', 'DeepseekForCausalLM', 'DeepseekV2ForCausalLM', 'FalconForCausalLM', 'GemmaForCausalLM', 'Gemma2ForCausalLM', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTJForCausalLM', 'GPTNeoXForCausalLM', 'InternLMForCausalLM', 'InternLM2ForCausalLM', 'JAISLMHeadModel', 'LlamaForCausalLM', 'LLaMAForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'QuantMixtralForCausalLM', 'MptForCausalLM', 'MPTForCausalLM', 'MiniCPMForCausalLM', 'NemotronForCausalLM', 'OlmoForCausalLM', 'OPTForCausalLM', 'OrionForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'Phi3ForCausalLM', 'QWenLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RWForCausalLM', 'StableLMEpochForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'ArcticForCausalLM', 'XverseForCausalLM', 'Phi3SmallForCausalLM', 'MedusaModel', 'MLPSpeculatorPreTrainedModel', 'JambaForCausalLM', 'MistralModel', 'Blip2ForConditionalGeneration', 'ChameleonForConditionalGeneration', 'FuyuForCausalLM', 'InternVLChatModel', 'LlavaForConditionalGeneration', 'LlavaNextForConditionalGeneration', 'MiniCPMV', 'PaliGemmaForConditionalGeneration', 'Phi3VForCausalLM', 'BartModel', 'BartForConditionalGeneration', 'XLMRobertaForSequenceClassification']

Start service command:
export MODEL_PATH=llama3.1-8b_sequencelabeling/ export PORT=8081 export TP=1 export gpu_memory_utilization=0.95 export max_num_batched_tokens=130000 export max_model_len=64000 export max_num_seqs=32 vllm serve ${MODEL_PATH} \ --host 0.0.0.0 \ --port ${PORT} \ --tensor-parallel-size ${TP} \ --gpu-memory-utilization ${gpu_memory_utilization} \ --max-num-batched-tokens ${max_num_batched_tokens} \ --max-model-len ${max_model_len} \ --max-num-seqs ${max_num_seqs} \ --device cuda \

[AllenDou]

@fan-niu Yes, currently, #6260 only supports XLMRobertaForSequenceClassification. I will enable more model architectures once this PR is merged

[fan-niu]

@fan-niu Yes, currently, #6260 only supports XLMRobertaForSequenceClassification. I will enable more model architectures once this PR is merged

@AllenDou thanks for your reply, because llama3.1 is very popular now, I am very looking forward to the update with this new change.