Follow on to #4332 to enable FP8 checkpoint loading for Mixtral and supersedes #4436.
This PR enables the following checkpoint loading features for Mixtral:
Supports loading fp8 checkpoints for Mixtral, such as this "nm-testing/Mixtral-8x7B-Instruct-v0.1-FP8" test model
Supports static or dynamic activation quantization with static weight quantization (all per tensor)
Supports different scales for each expert weight
Supports Fp8 in QKV layer
Notes:
The Expert Gate/Router always runs at half / full precision for now.
If there are different weight scales between QKV layer (for separate QKV weights), they are re-quantized using layer.weight_scale.max() so we can have a single gemm for performance.
This PR is the first step towards fixing https://github.com/vllm-project/vllm/pull/3208
It implements dynamic per-tensor scaling (see https://github.com/vllm-project/vllm/pull/4118), so users do not need to compute activation scales on a calibration dataset and they also don't need to convert their model checkpoints. It is enough to specify the `quantization="fp8"` argument. You can try out the PR like this:
```python
from vllm import LLM, SamplingParams
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)
llm = LLM(model="mistralai/Mixtral-8x7B-Instruct-v0.1", tensor_parallel_size=2, quantization="fp8")
outputs = llm.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}")
```
**Performance**: For this PR, the focus is on making the code clean (while still trying to get reasonable performance), there is a bunch of optimizations that we will submit as a follow up PR that significantly improve the performance (similar to the numbers in https://github.com/vllm-project/vllm/pull/3954). With this PR, the results are as follows:
<img width="725" alt="Screenshot 2024-04-21 at 1 31 50 PM" src="https://github.com/vllm-project/vllm/assets/113316/d8fe1118-07a0-4d4e-8530-37a77d465a03">
**Accuracy**: The accuracy with this PR on MMLU on `mistralai/Mixtral-8x7B-v0.1` is as follows:
```
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.7018|± |0.0036|
| - humanities |N/A |none | 5|acc |0.6472|± |0.0065|
| - other |N/A |none | 5|acc |0.7673|± |0.0072|
| - social_sciences|N/A |none | 5|acc |0.8099|± |0.0070|
| - stem |N/A |none | 5|acc |0.6131|± |0.0083|
```
this compares favorably with the fp16 results which are
```
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.7020|± |0.1313|
| - humanities |N/A |none | 5|acc |0.6425|± |0.1349|
| - other |N/A |none | 5|acc |0.7744|± |0.1038|
| - social_sciences|N/A |none | 5|acc |0.8131|± |0.0695|
| - stem |N/A |none | 5|acc |0.6108|± |0.1383|
```
Happy hacking!
* Fix AttributeError: MixtralModel object has no attribute org_vocab_size.
* Make LoRA logic for Mistral and Mixtral the same
---------
Co-authored-by: Pernekhan Utemuratov <pernekhan@deepinfra.com>
