updated

auto_round/auto_quantizer.py

@@ -318,6 +318,11 @@ def convert_model(self, model: nn.Module):
 
     def _dynamic_import_inference_linear(self, bits, backend, device):
         if (str(device) == "cpu" and not torch.cuda.is_available()) or "qbits" in backend:
+            try:
+                from intel_extension_for_transformers import qbits  # noqa: F401
+            except Exception as e:
+                raise ImportError("Please install Intel Extension for Transformers via 'pip install "
+                                  "intel-extension-for-transformers' to  inference on Intel CPU")
             return qlinear_qbits.QuantLinear
         if bits == 4 and self.exllama2_available and "exllamav2" in backend:
             from auto_round_extension.cuda.qliner_exllamav2 import QuantLinear
@@ -366,17 +371,6 @@ def _replace_by_quant_layers(self, module: nn.Module, layer_configs, backend):
                 weight_dtype=layer.weight.dtype,
             )
 
-            # if new_layer.qweight.device.type == "cpu":  # fallback to qbits linear when qweight on cpu device
-            #     QuantLinear = qlinear_qbits.QuantLinear
-            #     new_layer = QuantLinear(  # pylint: disable=E1123
-            #         bits,
-            #         group_size,
-            #         in_features,
-            #         out_features,
-            #         bias,
-            #         weight_dtype=layer.weight.dtype,
-            #     )
-
             new_layer.device = device
             set_module(module, layer_name, new_layer)
 

auto_round_extension/qbits/qlinear_qbits.py

@@ -20,12 +20,6 @@
 import torch.nn as nn
 from auto_round.utils import convert_dtype_torch2str, logger
 QBITS_AVAILABLE = True
-try:
-    from intel_extension_for_transformers import qbits  # noqa: F401
-except Exception as e:
-    QBITS_AVAILABLE = False
-    # logger.warning(
-    #     "qlinear_qbits should be used with Intel Extension for Transformers.")
 
 BITS_DTYPE_MAPPING = {
     2: "int2_clip",