[ Misc ] non-uniform quantization via compressed-tensors for Llama

[robertgshaw2-redhat]

SUMMARY:

• add compressed-tensors integration to support for nonuniform quantization
• pipe layer_name though llama.py to enable selecting quantization scheme on layer-by-layer basis
• add lm-eval test case for an example non-uniform model

FOLLOW UP PRs:

• our config parsing is getting complicated. We should add compressed-tensors as a dependency
• expand non-uniform to the remaining models (currently only support llama, we will fail loudly but confusingly otherwise)

This enables us to run a model with fp8 for some layers and fp16 for other layers

from vllm import LLM
# this model has fp16, fp8, and int8!
model = LLM("nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test")

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[github-actions]

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🚀

[robertgshaw2-redhat]

/ready

[youkaichao]

add prefix="" for make_layers, and pass prefix=f"{prefix}.{_}" to layer_fn

[robertgshaw2-redhat]

okay sounds good

[youkaichao]

add default, and move it to the last arg

[youkaichao]

unify them to use prefix

[robertgshaw2-redhat]

Are you sure?

This is the Linear which is the root. So its no longer a prefix but rather this is the final layer_name

[youkaichao]

what's the difference?

[comaniac]

I also vote for prefix, because Linear is just another torch.nn.module. Unifying the naming might be more clear.

[robertgshaw2-redhat]

updated to prefix

[youkaichao]

LGTM, let's change to prefix for the argument name.

[youkaichao]

typing is not correct. you can create a proto for type checking:

class LayerFn(Protocol):
    def __call__(
        self, prefix="",
    ) -> torch.nn.Module:
        ...

[youkaichao]

update GPT2Block?

[youkaichao]

@robertgshaw2-neuralmagic please also merge the latest main and take care of the code in #6516 .