use ipex op in backward

bitsandbytes/autograd/_functions.py

@@ -517,7 +517,10 @@ def forward(ctx, A, B, out=None, bias=None, quant_state: Optional[F.QuantState]
 
         # 1. Dequantize
         # 2. MatmulnN
-        output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
+        if getattr(quant_state, "ipex", False):
+            output = F.gemv_4bit(A, B, out, state=quant_state)
+        else:
+            output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
 
         # 3. Save state
         ctx.state = quant_state
@@ -548,7 +551,10 @@ def backward(ctx, grad_output):
         # not supported by PyTorch. TODO: create work-around
         # if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
         if req_gradA:
-            grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype).t())
+            if getattr(ctx.state, "ipex", False):
+                grad_A = F.gemv_4bit(grad_output, B, None, state=ctx.state)
+            else:
+                grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype).t())
 
         return grad_A, grad_B, None, grad_bias, None
 
@@ -575,7 +581,7 @@ def matmul_4bit(
     bias=None,
 ):
     assert quant_state is not None
-    if (A.numel() == A.shape[-1] or A.device.type == "cpu") and A.requires_grad == False:
+    if A.numel() == A.shape[-1] and A.device.type != "cpu" and A.requires_grad == False:
         # CPU backend does not require A to be a vector
         if A.shape[-1] % quant_state.blocksize != 0:
             warn(