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[CORE] Quantized lm-head Framework #4442

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merged 177 commits into from
Jul 2, 2024

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Qubitium
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@Qubitium Qubitium commented Apr 29, 2024

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://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-neuralmagic
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@comaniac ready for re-review

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

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Overall LGTM

@@ -437,7 +437,7 @@ def __init__(
self.model = LLM(
model=model_name,
tokenizer=tokenizer_name,
trust_remote_code=True,
trust_remote_code="falcon" not in model_name,
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Why we have to re-download Falcon?

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trust_remote_code did not work for falcon, not sure why


MAX_MODEL_LEN = 1024

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

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

@robertgshaw2-neuralmagic robertgshaw2-neuralmagic enabled auto-merge (squash) July 2, 2024 21:07
@robertgshaw2-neuralmagic robertgshaw2-neuralmagic enabled auto-merge (squash) July 2, 2024 21:07
@robertgshaw2-neuralmagic robertgshaw2-neuralmagic merged commit ee93f4f into vllm-project:main Jul 2, 2024
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prashantgupta24 pushed a commit to opendatahub-io/vllm that referenced this pull request Jul 3, 2024
Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: ZX <zx@lbx.dev>
robertgshaw2-neuralmagic added a commit to neuralmagic/nm-vllm that referenced this pull request Jul 7, 2024
Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: ZX <zx@lbx.dev>
xjpang pushed a commit to xjpang/vllm that referenced this pull request Jul 8, 2024
Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: ZX <zx@lbx.dev>
@njhill
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njhill commented Jul 10, 2024

  • @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

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  • @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?

@Qubitium Qubitium deleted the autogptq-lm-head branch July 12, 2024 21:01
xjpang pushed a commit to xjpang/vllm that referenced this pull request Jul 24, 2024
Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: ZX <zx@lbx.dev>
@RanchiZhao
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hi!is 'look into quantized embeddings' available now? looking forward to this!

Alvant pushed a commit to compressa-ai/vllm that referenced this pull request Oct 26, 2024
Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: ZX <zx@lbx.dev>
Signed-off-by: Alvant <alvasian@yandex.ru>
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6 participants