[Bugfix] Allow fallback to AWQ from AWQMarlin at per-layer granularity

[mgoin]

Models like TechxGenus/DeepSeek-Coder-V2-Lite-Instruct-AWQ have some layers that can't satisfy the constraints of marlin even for TP=1.

[rank0]: ValueError: Weight input_size_per_partition = 10944 is not divisible by min_thread_k = 128. Consider reducing tensor_parallel_size or running with --quantization awq.

This PR makes it so we check if the layer shapes are compatible with the marlin kernel and routes to the unoptimized kernel as a last effort to support that layer.

Tested with lm-eval

lm_eval --model vllm --model_args pretrained=TechxGenus/DeepSeek-Coder-V2-Lite-Instruct-AWQ --trust_remote_code --tasks gsm8k --num_fewshot 5 --batch_size auto
...
WARNING 02-11 22:26:57 awq_marlin.py:131] Layer 'model.layers.0.mlp.down_proj' is not supported by AWQMarlin. Falling back to unoptimized AWQ kernels.
...
vllm (pretrained=TechxGenus/DeepSeek-Coder-V2-Lite-Instruct-AWQ,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.7111|±  |0.0125|
|     |       |strict-match    |     5|exact_match|↑  |0.6816|±  |0.0128|

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