Support AWQ quantization with bias (#2117)

When the AWQ quantizer was used with a layer that uses a bias,
the bias tensor was not correctly passed/used. Instead, the
value `true`/`1.0` was added to the linear transformation.

Correctly pass through the bias when it is not `None`.

Fixes #2106.

server/text_generation_server/layers/awq/quantize/qmodule.py

 # Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
 
 import math
+from typing import Optional
 import torch
 import torch.nn as nn
 import awq_inference_engine  # with CUDA kernels
@@ -17,7 +18,9 @@ import awq_inference_engine  # with CUDA kernels
 
 
 class WQLinear(nn.Module):
-    def __init__(self, w_bit, group_size, qweight, qzeros, scales, bias):
+    def __init__(
+        self, w_bit, group_size, qweight, qzeros, scales, bias: Optional[torch.Tensor]
+    ):
         super().__init__()
 
         if w_bit not in [4]:
@@ -35,10 +38,7 @@ class WQLinear(nn.Module):
         self.qweight = qweight
         self.qzeros = qzeros
         self.scales = scales
-        if bias:
-            self.bias = bias
-        else:
-            self.bias = None
+        self.bias = bias
 
     @torch.no_grad()
     def forward(self, x):

server/text_generation_server/layers/linear.py

@@ -217,7 +217,7 @@ def get_linear(weight, bias, quantize):
                 qweight=weight.qweight,
                 qzeros=weight.qzeros,
                 scales=weight.scales,
-                bias=bias is not None,
+                bias=bias,
             )
         except ImportError:
             raise NotImplementedError(