[Kernel] Support Fp8 Checkpoints (Dynamic + Static)

[robertgshaw2-neuralmagic]

This PR does two things:

• Supports loading serialized fp8 models with static weight and act scales
• Supports loading serialized fp8 models with static weight scales and dynamic activations scales

For loading serialized models, vllm auto-detects if merged Layers (QKV / Gate-UpProj) have a shared weight_scale

As a result, vLLM now supports the following cases on H100:

• Loading fp16 checkpoints and converting to dynamic per tensor activation scaling (but without memory savings)
• (new) Loading fp8 checkpoints with static dynamic activation scales
• (new) Loading fp8 checkpoints with dynamic activations scales

Performance will be bad if merged layers (QKV / GateUp) do not have shared weight scales. We are working on cutlass kernels to replace the naive for loop

Next steps (to be handled in another PR):

• Fused Cutlass GEMM_DQ for QKV and GateUpProj (needed for performance)
• Update fp16 loading to support memory reduction
• Add support to Mixtral ([Kernel] Support Fp8 Checkpoints for Mixtral (Dynamic + Static) #4436)
• Add support to all other MoEs
• Refactor weight loading

Usage

from vllm import LLM

# fp16 model at fp8
model = LLM("mistralai/Mistral-7B-Instruct-v0.2", quantization="fp8")

# fp8 static act_scale model
model = LLM("nm-testing/mistral-fp8-static")

# fp8 dynamic act_scale model
model = LLM("nm-testing/mistral-fp8-dynamic")

Performance

Note: all models have shared weight scales for merged layers (QKV, GateUpProj), so there is no naive for loop

Benchmarking shapes

We can see that fp8 is faster end-to-end for prefills, but is slower than fp16 for decode

Benchmark serving (1xH100, ShareGPT, Request Rate=5.0, Num Prompts=1000:

• fp16: meta-llama/Meta-Llama-3-8B-Instruct

python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-8B-Instruct --disable-log-requests

python3 benchmark_serving.py --backend openai --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --request-rate 5.0 --num-prompts 1000

============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  205.16    
Total input tokens:                      215196    
Total generated tokens:                  186204    
Request throughput (req/s):              4.87      
Input token throughput (tok/s):          1048.94   
Output token throughput (tok/s):         907.62    
---------------Time to First Token----------------
Mean TTFT (ms):                          20.49     
Median TTFT (ms):                        19.88     
P99 TTFT (ms):                           42.07     
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          10.42     
Median TPOT (ms):                        10.36     
P99 TPOT (ms):                           13.23     
==================================================

• fp8 static with shared scales: nm-testing/llama-3-instruct-fp8-static-shared-scales

python3 -m vllm.entrypoints.openai.api_server --model nm-testing/llama-3-instruct-fp8-static-shared-scales --disable-log-requests

python3 benchmark_serving.py --backend openai --model nm-testing/llama-3-instruct-fp8-static-shared-scales --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --request-rate 5.0 --num-prompts 1000

============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  205.65    
Total input tokens:                      215196    
Total generated tokens:                  142560    
Request throughput (req/s):              4.86      
Input token throughput (tok/s):          1046.41   
Output token throughput (tok/s):         693.21    
---------------Time to First Token----------------
Mean TTFT (ms):                          22.48     
Median TTFT (ms):                        22.34     
P99 TTFT (ms):                           37.28     
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          27.67     
Median TPOT (ms):                        11.58     
P99 TPOT (ms):                           94.82     
==================================================

^not sure why P99 TPOT is so bad here

• fp8 static with dynamic scales: meta-llama/Meta-Llama-3-8B-Instruct --quantization fp8

python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-8B-Instruct --quantization fp8 --disable-log-requests

python3 benchmark_serving.py --backend openai --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --request-rate 5.0 --num-prompts 1000

============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  206.00    
Total input tokens:                      215196    
Total generated tokens:                  186083    
Request throughput (req/s):              4.85      
Input token throughput (tok/s):          1044.65   
Output token throughput (tok/s):         903.32    
---------------Time to First Token----------------
Mean TTFT (ms):                          23.52     
Median TTFT (ms):                        24.31     
P99 TTFT (ms):                           40.56     
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          11.80     
Median TPOT (ms):                        11.93     
P99 TPOT (ms):                           14.11     
==================================================

Experimental checkpoint structure

Here we detail the experimental structure for the fp8 checkpoints.

We plan to expand upon this as we start adding support for int8, sparsity, etc

The following is added to config.json

"quantization_config": {
    "quant_method": "fp8",
    "activation_scheme": "static" or "dynamic"
  },

Each quantized layer in the state_dict will have:

If the config has "activation_scheme": "static":

model.layers.0.mlp.down_proj.weight              < F8_E4M3
model.layers.0.mlp.down_proj.act_scale           < F32
model.layers.0.mlp.down_proj.weight_scale        < F32

If config has "activation_scheme": "dynamic":

model.layers.0.mlp.down_proj.weight              < F8_E4M3
model.layers.0.mlp.down_proj.weight_scale        < F32

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[mgoin]

Aren't you meant to use the static input scale stored in the model state dict?

[robertgshaw2-neuralmagic]

This is the next step I'm working on

[comaniac]

LGTM

[pcmoritz]

There is a simplification we can make to this PR, which is when loading the checkpoint, always rescale the different parts of the tensor so the resulting tensor has only one scale, for example in the case of qkv weigts (wq, wk, wv) it would be:

w_scale = max(wq_scale, wk_scale, wv_scale)
wq' = (wq_scale / w_scale) * wq
wk' = (wk_scale / w_scale) * wk
wv' = (wv_scale / w_scale) * wv

so the common scale w_scale can be used for the whole tensor.

This way we wouldn't need custom kernels to treat the different parts of the tensor differently.

Since the weights don't have much variance and fp8 is actually a decent amount of accuracy for the weights, this would likely not impact the accuracy (but we would need to test that of course).

[pcmoritz]

Note that at the moment the "nm-testing/mistral-fp8-static" checkpoint doesn't seem to be working well, these are the results I got:

In [1]: from vllm import LLM, SamplingParams

In [2]: model = LLM("nm-testing/mistral-fp8-static")
fp8 quantization is not fully optimized yet. The speed can be slower than non-quantized models.
Detected fp8 checkpoint. Please note that the format is experimental and subject to change.

In [3]: prompts = [
   ...:     "Hello, my name is",
   ...:     "The president of the United States is",
   ...:     "The capital of France is",
   ...:     "The future of AI is",
   ...: ]
   ...: sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

In [4]: 

In [4]: outputs = model.generate(prompts, sampling_params)
   ...: 
   ...: # Print the outputs.
   ...: for output in outputs:
   ...:     prompt = output.prompt
   ...:     generated_text = output.outputs[0].text
   ...:     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
   ...: 
Processed prompts: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 19.62it/s]
Prompt: 'Hello, my name is', Generated text: ' Marissa'
Prompt: 'The president of the United States is', Generated text: ''
Prompt: 'The capital of France is', Generated text: ' an iconic city that is famous for its landmarks, museums, caf'
Prompt: 'The future of AI is', Generated text: ' bright and the potential of AI to revolutionize industries, improve lives and drive eff'

[robertgshaw2-neuralmagic]

There is a simplification we can make to this PR, which is when loading the checkpoint, always rescale the different parts of the tensor so the resulting tensor has only one scale, for example in the case of qkv weigts (wq, wk, wv) it would be:

w_scale = max(wq_scale, wk_scale, wv_scale)
wq' = (wq_scale / w_scale) * wq
wk' = (wk_scale / w_scale) * wk
wv' = (wv_scale / w_scale) * wv

so the common scale w_scale can be used for the whole tensor.

This way we wouldn't need custom kernels to treat the different parts of the tensor differently.

Since the weights don't have much variance and fp8 is actually a decent amount of accuracy for the weights, this would likely not impact the accuracy (but we would need to test that of course).

This is a good idea. I will make this change

[robertgshaw2-neuralmagic]

Note that at the moment the "nm-testing/mistral-fp8-static" checkpoint doesn't seem to be working well, these are the results I got:

In [1]: from vllm import LLM, SamplingParams

In [2]: model = LLM("nm-testing/mistral-fp8-static")
fp8 quantization is not fully optimized yet. The speed can be slower than non-quantized models.
Detected fp8 checkpoint. Please note that the format is experimental and subject to change.

In [3]: prompts = [
   ...:     "Hello, my name is",
   ...:     "The president of the United States is",
   ...:     "The capital of France is",
   ...:     "The future of AI is",
   ...: ]
   ...: sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

In [4]: 

In [4]: outputs = model.generate(prompts, sampling_params)
   ...: 
   ...: # Print the outputs.
   ...: for output in outputs:
   ...:     prompt = output.prompt
   ...:     generated_text = output.outputs[0].text
   ...:     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
   ...: 
Processed prompts: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 19.62it/s]
Prompt: 'Hello, my name is', Generated text: ' Marissa'
Prompt: 'The president of the United States is', Generated text: ''
Prompt: 'The capital of France is', Generated text: ' an iconic city that is famous for its landmarks, museums, caf'
Prompt: 'The future of AI is', Generated text: ' bright and the potential of AI to revolutionize industries, improve lives and drive eff'

Yes, this checkpoint was only calibrated with 10 datapoints so I could work on the implementation - Its not intended to be an accurate model

[WoosukKwon]

There is a simplification we can make to this PR, which is when loading the checkpoint, always rescale the different parts of the tensor so the resulting tensor has only one scale, for example in the case of qkv weigts (wq, wk, wv) it would be:

w_scale = max(wq_scale, wk_scale, wv_scale)
wq' = (wq_scale / w_scale) * wq
wk' = (wk_scale / w_scale) * wk
wv' = (wv_scale / w_scale) * wv

so the common scale w_scale can be used for the whole tensor.

This way we wouldn't need custom kernels to treat the different parts of the tensor differently.

Since the weights don't have much variance and fp8 is actually a decent amount of accuracy for the weights, this would likely not impact the accuracy (but we would need to test that of course).

Agreed. IIRC, TRT-LLM also takes this approach.

[robertgshaw2-neuralmagic]

We will need to eval the accuracy of this (especially for big models), but I think good for now