check device before sync (ModelCloud#796)

* check device before sync

* cleanup

gptqmodel/models/loader.py

@@ -12,7 +12,6 @@
 from transformers.modeling_utils import no_init_weights
 from transformers.utils.generic import ContextManagers
 
-from ._const import DEVICE, SUPPORTED_MODELS, get_best_device, is_torch_support_xpu
 from ..nn_modules.qlinear.exllamav2 import ExllamaV2QuantLinear
 from ..nn_modules.qlinear.ipex import IPEXQuantLinear, ipex_dtype
 from ..quantization import QuantizeConfig
@@ -25,6 +24,7 @@
 from ..utils.model import (auto_dtype_from_config, convert_gptq_v1_to_v2_format, find_layers,
                            get_checkpoints, get_moe_layer_modules, gptqmodel_post_init, make_quant,
                            simple_dispatch_model, verify_model_hash, verify_sharded_model_hashes)
+from ._const import DEVICE, SUPPORTED_MODELS, get_best_device, is_torch_support_xpu
 
 logger = setup_logger()
 

gptqmodel/quantization/gptq.py

@@ -183,10 +183,11 @@ def fasterquant(
                 logger.debug(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
                 logger.debug(torch.sum(Losses))
 
-        if torch.cuda.is_available():
+        if self.dev.type == "cuda":
             torch.cuda.synchronize()
-        if hasattr(torch, "xpu") and torch.xpu.is_available():
+        elif self.dev.type == "xpu":
             torch.xpu.synchronize()
+
         duration = time.time() - tick
         avg_loss = torch.sum(Losses).item() / self.nsamples
 

gptqmodel/utils/model.py

@@ -20,17 +20,17 @@
 from transformers import AutoConfig, PretrainedConfig
 from transformers.utils.hub import cached_file
 
-from .backend import BACKEND
-from .importer import select_quant_linear
-from .logger import setup_logger
-from .progress import ProgressBar
 from ..models._const import CPU, EXLLAMA_DEFAULT_MAX_INPUT_LENGTH, EXPERT_INDEX_PLACEHOLDER, SUPPORTED_MODELS
 from ..nn_modules.qlinear import BaseQuantLinear
 from ..nn_modules.qlinear.exllama import ExllamaQuantLinear
 from ..nn_modules.qlinear.exllamav2 import ExllamaV2QuantLinear
 from ..nn_modules.qlinear.ipex import IPEXQuantLinear
 from ..nn_modules.qlinear.torch import TorchQuantLinear
 from ..quantization import FORMAT, QuantizeConfig
+from .backend import BACKEND
+from .importer import select_quant_linear
+from .logger import setup_logger
+from .progress import ProgressBar
 
 logger = setup_logger()