Fix dt proj bias reassigned (#33314)

* When we set self.dt_proj.bias = None, it removes the bias parameter from the model. When we later tried to assign a tensor to self.dt_proj.bias, it caused a TypeError because PyTorch expects a Parameter object.

* When we set self.dt_proj.bias = None, it removes the bias parameter from the model. When we later tried to assign a tensor to self.dt_proj.bias, it caused a TypeError because PyTorch expects a Parameter object.

* When we set self.dt_proj.bias = None, it removes the bias parameter from the model. When we later tried to assign a tensor to self.dt_proj.bias, it caused a TypeError because PyTorch expects a Parameter object.

src/transformers/models/jamba/modeling_jamba.py

@@ -713,11 +713,14 @@ class JambaMambaMixer(nn.Module):
         # This is a hack to apply dt_proj while still using the forward pass of `torch.nn.Linear`, which is needed
         # in order to make quantization work. Quantization code replaces `torch.nn.Linear` layers with quantized
         # linear layers, and requires to call the forward pass directly.
-        # The original code here was: ```discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)```
-        time_proj_bias = self.dt_proj.bias
-        self.dt_proj.bias = None
+        # Quantized model can't work with the original code:
+        # ```discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)```
+        time_proj_bias = self.dt_proj.bias.data
+        with torch.no_grad():
+            self.dt_proj.bias.data = torch.zeros_like(self.dt_proj.bias.data)
         discrete_time_step = self.dt_proj(time_step).transpose(1, 2)
-        self.dt_proj.bias = time_proj_bias
+        with torch.no_grad():
+            self.dt_proj.bias.data = time_proj_bias
 
         A = -torch.exp(self.A_log.float())
         # 3.c perform the recurrence y ← SSM(A, B, C)(x)