Support qlora in CPU (#9233)

* support qlora in CPU

* revert example

* fix style

python/llm/src/bigdl/llm/transformers/low_bit_linear.py

@@ -323,6 +323,37 @@ class MatMulLowBit(torch.autograd.Function):
         return grad_A, grad_weight, None
 
 
+class MatMulLowBitCPU(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, A, weight):
+        if torch.is_autocast_enabled():
+            A = A.to(torch.get_autocast_dtype())
+        ctx.is_empty = False
+        x0_fp32 = ggml_int4_convert_fp32(weight.data, weight._shape,
+                                         weight._shape[0] * weight._shape[1])
+        result = torch.matmul(A, x0_fp32.T)
+        if any(ctx.needs_input_grad[:2]):
+            ctx.tensors = (A, weight)
+        else:
+            ctx.tensors = (None, None)
+        return result
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.is_empty:
+            bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
+        req_gradA, _, = ctx.needs_input_grad
+        A, weight = ctx.tensors
+        grad_A, grad_weight = None, None
+        if req_gradA:
+            x0_fp32 = ggml_int4_convert_fp32(weight.data, weight._shape,
+                                             weight._shape[0] * weight._shape[1])
+            grad_A = torch.matmul(grad_output, x0_fp32.to(grad_output.dtype))
+        return grad_A, grad_weight, None
+
+
 class LowBitLinear(nn.Linear):
     def __init__(self, input_features, output_features, qtype, bias=True,
                  conver_to_half=True, mp_group=None):
@@ -388,16 +419,21 @@ class LowBitLinear(nn.Linear):
                               and self.qtype != FP4,
                               "NF3, NF4, FP4 and FP8 quantization are currently not"
                               " supported on CPU")
-            if IS_SERVER and (not IS_SPR) and \
-                    self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
-                x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)
-                result = F.linear(x, x0_fp32, self.bias)
-            else:
-                result = ggml_matmul_src1_x_src0_t(x0, x_2d, self.weight_shape, self.qtype)
-                new_shape = x_shape[:-1] + (self.out_len,)
-                result = result.view(new_shape)
+            if self.training and x.requires_grad:
+                result = MatMulLowBitCPU.apply(x, self.weight)
                 if self.bias is not None:
-                    result += self.bias
+                    result = result + self.bias
+            else:
+                if IS_SERVER and (not IS_SPR) and \
+                        self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
+                    x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)
+                    result = F.linear(x, x0_fp32, self.bias)
+                else:
+                    result = ggml_matmul_src1_x_src0_t(x0, x_2d, self.weight_shape, self.qtype)
+                    new_shape = x_shape[:-1] + (self.out_len,)
+                    result = result.view(new_shape)
+                    if self.bias is not None:
+                        result += self.bias
         return result