Previously FP8 static scaling works if the scales are overestimating the maxima of all activation tensors during computation. However this will not always be the case even if the scales were calibrated very carefully. For example, with the activations in my checkpoint
https://huggingface.co/pcmoritz/Mixtral-8x7B-v0.1-fp8-act-scale
(which was calibrated on https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k), I'm getting the following mostly random performance on MMLU:
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.2295|± |0.0035|
| - humanities |N/A |none | 5|acc |0.2421|± |0.0062|
| - other |N/A |none | 5|acc |0.2398|± |0.0076|
| - social_sciences|N/A |none | 5|acc |0.2171|± |0.0074|
| - stem |N/A |none | 5|acc |0.2125|± |0.0073|
With the fix in this PR where the scaled activations are clamped between [-std::numeric_limits<c10::Float8_e4m3fn>::max(), std::numeric_limits<c10::Float8_e4m3fn>::max()] to make sure there are no NaNs, the performance is
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.7008|± |0.0036|
| - humanities |N/A |none | 5|acc |0.6453|± |0.0065|
| - other |N/A |none | 5|acc |0.7692|± |0.0072|
| - social_sciences|N/A |none | 5|acc |0.8083|± |0.0070|
| - stem |N/A |none | 5|acc |0.6115|± |0.0083|
This is not perfect yet but is getting very close to the FP16 / dynamic activation scale performance.
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.
Antoni Baum