[CORE] Quantized lm-head Framework

[Qubitium]

MOTIVATION

• lm-head and be quantized with minimal loss in accuracy
• models like llama-3 have very large vocab sizes

SUMMARY:

• support quantized lm head (for models without tied word embeddings)
• update autogptq integration to enable quantized lm-head
• added tests for ~all models that can fit in our L4 CI to confirm this does not break anything

IMPLEMENTATION:

• refactored VocabParallelEmbedding to use create_weights to create the parameters
• refactored ParallelLMHead to use apply() to generate output
• plumb changes through all models
• plumb quant_config into ParallelLMHead
• updated AutoGPTQ integration to use lm-head if detected in config

FOLLOW UPS:

• support for fp8 and int8
• look into quantized embeddings

TEST MODEL: (quantized by auto-round and load tested with autogptq):

• https://huggingface.co/LnL-AI/TinyLlama-1.1B-intermediate-step-1341k-3T-autoround-lm_head-symFalse

https://github.com/intel/auto-round/blob/8a3da144423322dfedb0b3fa702ae35d242496d8/docs/Meta-Llama-3-8B-Instruct-acc.md?plain=1#L3

Metric	BF16	w4g128 w/o lm-head	w4g128 with quantized lm-head
Avg.	0.6352	0.6312	0.6303
mmlu	0.6386	0.6306	0.6318
winogrande	0.7143	0.7238	0.7269
truthfulqa_mc1	0.3623	0.3537	0.3525
rte	0.6751	0.6859	0.6679
piqa	0.7867	0.7797	0.7802
openbookqa	0.3400	0.3300	0.3320
lambada_openai	0.7182	0.7200	0.7173
hellaswag	0.5769	0.5699	0.5701
boolq	0.8297	0.8309	0.8284
arc_easy	0.8152	0.8089	0.8106
arc_challenge	0.5299	0.5102	0.5154

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[robertgshaw2-redhat]

@comaniac ready for re-review

(ignore spec decode test failure @njhill making a new model that avoids OOM at fp32)

[comaniac]

Overall LGTM

[comaniac]

Why we have to re-download Falcon?

[robertgshaw2-redhat]

trust_remote_code did not work for falcon, not sure why

[comaniac]

How long does it take to run all models listed here? Can some of them be removed to reduce the CI time?

[robertgshaw2-redhat]

I can just remove them. I just wanted to prove the accuracy was right

[njhill]

• @Yard1 for VocabParallelEmbedding weight_loading --> anything to be aware of for LoRA?

@robertgshaw2-neuralmagic @Yard1 it looks like there was some impact from this ... not sure if it actually exposed a latent bug where the lm_head for gpt_bigcode (and similar) was not previously adaptable: #6314

[robertgshaw2-redhat]

• @Yard1 for VocabParallelEmbedding weight_loading --> anything to be aware of for LoRA?

@robertgshaw2-neuralmagic @Yard1 it looks like there was some impact from this ... not sure if it actually exposed a latent bug where the lm_head for gpt_bigcode (and similar) was not previously adaptable: #6314

@njhill do you know if this was working before?

[RanchiZhao]

hi！is 'look into quantized embeddings' available now? looking forward to this!