Update utils.py

unsloth/kernels/utils.py

@@ -63,6 +63,25 @@ def get_lora_parameters(proj):
 pass
 
 
+def get_lora_parameters_bias(proj):
+    # For DPO or disabled adapters
+    base_layer = (proj.base_layer if hasattr(proj, "base_layer") else proj)
+    W = base_layer.weight
+    bias = base_layer.bias
+
+    if not hasattr(proj, "disable_adapters") or proj.disable_adapters or proj.merged:
+        return W, QUANT_STATE(W), None, None, None, bias
+    pass
+
+    active_adapter = proj.active_adapters[0] if \
+        hasattr(proj, "active_adapters") else proj.active_adapter
+    A = proj.lora_A [active_adapter].weight
+    B = proj.lora_B [active_adapter].weight
+    s = proj.scaling[active_adapter]
+    return W, QUANT_STATE(W), A, B, s, bias
+pass
+
+
 def fast_dequantize(W, quant_state = None, out = None):
     if quant_state is None: return W
     if type(quant_state) is not list:
@@ -181,7 +200,7 @@ def fast_gemv(X, W, quant_state, out = None):
 
 def fast_linear_forward(proj, X, temp_lora = None, out = None):
 
-    W, W_quant, lora_A, lora_B, lora_S = get_lora_parameters(proj)
+    W, W_quant, lora_A, lora_B, lora_S, bias = get_lora_parameters_bias(proj)
     bsz, q_len, in_dim = X.shape
     if q_len != 1: return matmul_lora(X, W, W_quant, lora_A, lora_B, lora_S)
 
@@ -216,6 +235,8 @@ def fast_linear_forward(proj, X, temp_lora = None, out = None):
         out = out.view(bsz, 1, out_dim)
     pass
 
+    if bias is not None: out += bias
+
     return out
 pass