[CORE] Allow loading of quantized lm_head (ParallelLMHead) #4442
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…d positional argument: 'params_dtype'
Let's handle this in a follow up PR since the scope of this is already big |
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…ut why marlin load is broken
…lm-head # Conflicts: # tests/quantization/test_quant_lm_head.py
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@robertgshaw2-neuralmagic I overwrote your test changes in commit 2f63a72 Will re-merge with your changes later. Fixed OPT model compat with New problem Marlin kernel loading with lm_head is broken. For now I disabled Marlin auto-upconvert when
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@Qubitium no worries - to make it easier for us to both work on it, im going to move the model testing refactor to another PR |
2. optimize opt vram usage by keeping only one copy of lm_head vs embed_tokens when possible
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@robertgshaw2-neuralmagic I do not plan to make any more changes unless pending CI build shows something broken. https://buildkite.com/vllm/ci/builds/6198 Feel free to add/mod. Changes and notes:
On last point, it may be a good idea to add an api (future PR) to model so there is an equivalent Extra notes:
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All the relevant tests are passing. Of note that the code changes for OPT works but is actually not efficient. There is negative advantage to have an OPT model enable quantization of |
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To align with |
Qubitium
changed the title
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Going to pick this back up this weekend |
Reason for PR:
Changes:
lm_headfrom quantize_configParallelLmHeadto be loaded quantizedQuantizeMethodBaseto more accurateQuantizableMethodBasesince non-quantized methods also inherit thisQUANTIZEDbool property toQuantizableMethodBaseto avoid all theisinstancecallsutils/skip_gptq_extra_paramTooling Cross Dependency (tools that make quantized lm_head using GPTQ):
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
Intel/auto-round project @wenhuach21 has demonostrated that
lm_headis a good candidate for quantization with minimal-loss.https://github.com/intel/auto-round/blob/8a3da144423322dfedb0b3fa702ae35d242496d8/docs/Meta-Llama-3-8B-Instruct-acc.md?plain=1#L3
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