feat: eetq gemv optimization when batch_size <= 4 (#1502)

# What does this PR do?

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Add TensorRT-LLM weight-only GEMV kernel support. We extract GEMV kernel
from
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/cpp/tensorrt_llm/kernels/weightOnlyBatchedGemv)
to accelerate the decode speed of EETQ when batch_size is smaller or
equal to 4.

- Features

1. There is almost no loss of quantization accuracy.
2. The speed of decoding is 13% - 27% faster than original EETQ which
utilizes GEMM kernel.

- Test
Below is our test on 3090. Environment: torch=2.0.1, cuda=11.8, nvidia
driver: 525.78.01
prompt=1024, max_new_tokens=50

![image](https://github.com/huggingface/text-generation-inference/assets/139844877/98e63b23-23cd-452f-91bd-55ccdc9b7021)


![image](https://github.com/huggingface/text-generation-inference/assets/139844877/5c3132ff-fc1c-4b20-a83f-59b3d5f586b7)



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server/Makefile-eetq

@@ -1,4 +1,4 @@
-eetq_commit := 323827dd471458a84e9c840f614e4592b157a4b1
+eetq_commit := 71adb5e191bb8290069a580abff0355d7b2dd5c9
 
 eetq:
     # Clone eetq
@@ -6,7 +6,7 @@ eetq:
 	git clone https://github.com/NetEase-FuXi/EETQ.git eetq
 
 build-eetq: eetq
-	cd eetq && git fetch && git checkout $(eetq_commit)
+	cd eetq && git fetch && git checkout $(eetq_commit) && git submodule update --init --recursive
 	cd eetq && python setup.py build
 
 install-eetq: build-eetq