Repository: huggingface/transformers
Pull request: Fix int4 quantized model cannot work with cpu

# What does this PR do?
Currently the CPU can support int4 quantized model,  should not prevent its execution.

```
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int4WeightOnlyConfig
from torchao.dtypes import Int4CPULayout

quant_config = Int4WeightOnlyConfig(group_size=32, layout=Int4CPULayout())
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    torch_dtype="auto",
    device_map="cpu",
    quantization_config=quantization_config
)
# save the quantized model
output_dir = "llama-3-8b-torchao-int8"
#print(f"quantized_model:{quantized_model}")
quantized_model.save_pretrained(output_dir, safe_serialization=False)
# reload the quantized model
reloaded_model = AutoModelForCausalLM.from_pretrained(
    output_dir,
    device_map="auto",
    torch_dtype=torch.bfloat16
)
#print(f"reloaded_model:{reloaded_model}")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt")

# Move input to the same device as the model
if hasattr(reloaded_model, 'device'):
    input_ids = input_ids.to(reloaded_model.device)
elif next(reloaded_model.parameters()).device != torch.device('cpu'):
    input_ids = input_ids.to(next(reloaded_model.parameters()).device)

output = reloaded_model.generate(**input_ids, max_new_tokens=10)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```


Fix the following issue:

<img width="1314" height="415" alt="image" src="https://github.com/user-attachments/assets/680e93e8-6df3-4e64-9fcd-ad57234591a5" />






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Fixes # (issue)


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Changed files:
filename: src/transformers/modeling_utils.py
patch: @@ -954,7 +954,7 @@ def load_shard_file(args):
             or isinstance(hf_quantizer.quantization_config.quant_type, Int4WeightOnlyConfig)
         )
     ):
-        map_location = torch.device([d for d in device_map.values() if d not in ["cpu", "disk"]][0])
+        map_location = torch.device([d for d in device_map.values() if d not in ["disk"]][0])
 
     # If shard_file is "", we use the existing state_dict instead of loading it
     if shard_file != "":

filename: src/transformers/quantizers/quantizer_torchao.py
patch: @@ -100,14 +100,13 @@ def validate_environment(self, *args, **kwargs):
         self.offload = False
         device_map = kwargs.get("device_map")
         if isinstance(device_map, dict):
-            if "cpu" in device_map.values() or "disk" in device_map.values():
-                if self.pre_quantized:
+            if ("disk" in device_map.values() or "cpu" in device_map.values()) and len(device_map) > 1:
+                self.offload = True
+                if self.pre_quantized and "disk" in device_map.values():
                     raise ValueError(
-                        "You are attempting to perform cpu/disk offload with a pre-quantized torchao model "
-                        "This is not supported yet . Please remove the CPU or disk device from the device_map."
+                        "You are attempting to perform disk offload with a pre-quantized torchao model "
+                        "This is not supported yet . Please remove the disk device from the device_map."
                     )
-                else:
-                    self.offload = True
         if self.pre_quantized:
             weights_only = kwargs.get("weights_only")
             if weights_only: