neuralmagic
diff --git a/‎src/compressed_tensors/quantization/lifecycle/initialize.py
Lines changed: 17 additions & 44 deletions b/‎src/compressed_tensors/quantization/lifecycle/initialize.py
Lines changed: 17 additions & 44 deletions
diff --git a/‎src/compressed_tensors/utils/helpers.py
Lines changed: 64 additions & 1 deletion b/‎src/compressed_tensors/utils/helpers.py
Lines changed: 64 additions & 1 deletion
@@ -29,7 +29,11 @@
 from compressed_tensors.quantization.quant_config import QuantizationStatus
 from compressed_tensors.quantization.quant_scheme import QuantizationScheme
 from compressed_tensors.quantization.utils import is_kv_cache_quant_scheme
-from compressed_tensors.utils import get_execution_device, is_module_offloaded
+from compressed_tensors.utils import (
+    disable_hf_hook,
+    has_offloaded_params,
+    register_offload_parameter,
+)
 from torch.nn import Module, Parameter
 
 
@@ -112,43 +116,10 @@ def initialize_module_for_quantization(
         module.quantization_scheme = scheme
         module.quantization_status = QuantizationStatus.INITIALIZED
 
-        offloaded = False
-        # What is this doing/why isn't this in the attn case?
-        if is_module_offloaded(module):
-            try:
-                from accelerate.hooks import add_hook_to_module, remove_hook_from_module
-                from accelerate.utils import PrefixedDataset
-            except ModuleNotFoundError:
-                raise ModuleNotFoundError(
-                    "Offloaded model detected. To use CPU offloading with "
-                    "compressed-tensors the `accelerate` package must be installed, "
-                    "run `pip install compressed-tensors[accelerate]`"
-                )
-
-            offloaded = True
-            hook = module._hf_hook
-            prefix_dict = module._hf_hook.weights_map
-            new_prefix = {}
-
-            # recreate the prefix dict (since it is immutable)
-            # and add quantization parameters
-            for key, data in module.named_parameters():
-                if key not in prefix_dict:
-                    new_prefix[f"{prefix_dict.prefix}{key}"] = data
-                else:
-                    new_prefix[f"{prefix_dict.prefix}{key}"] = prefix_dict[key]
-            new_prefix_dict = PrefixedDataset(new_prefix, prefix_dict.prefix)
-            remove_hook_from_module(module)
-
-        # wrap forward call of module to perform
-        # quantized actions based on calltime status
-        wrap_module_forward_quantized(module, scheme)
-
-        if offloaded:
-            # we need to re-add the hook for offloading now that we've wrapped forward
-            add_hook_to_module(module, hook)
-            if prefix_dict is not None:
-                module._hf_hook.weights_map = new_prefix_dict
+        with disable_hf_hook(module):
+            # wrap forward call of module to perform
+            # quantized actions based on calltime status
+            wrap_module_forward_quantized(module, scheme)
 
 
 def is_attention_module(module: Module):
@@ -169,9 +140,11 @@ def _initialize_scale_zero_point(
     if quantization_args.dynamic:
         return
 
-    device = next(module.parameters()).device
-    if is_module_offloaded(module):
-        device = get_execution_device(module)
+    # begin on the same device as other parameters or cpu if offloaded.
+    # in the offloaded case, there's no point moving tensors to the execution device
+    # if they're going to be immediately offloaded by `register_offload_parameter`
+    params_device = next(module.parameters()).device
+    device = "cpu" if has_offloaded_params(module) else params_device
 
     # infer expected scale/zero point shape
     if quantization_args.strategy == QuantizationStrategy.TOKEN:
@@ -196,15 +169,15 @@ def _initialize_scale_zero_point(
         torch.empty(expected_shape, dtype=scale_dtype, device=device),
         requires_grad=False,
     )
-    module.register_parameter(f"{base_name}_scale", init_scale)
+    register_offload_parameter(module, f"{base_name}_scale", init_scale)
 
     if force_zero_point or not quantization_args.symmetric:
         zp_dtype = quantization_args.pytorch_dtype()
         init_zero_point = Parameter(
             torch.zeros(expected_shape, device=device, dtype=zp_dtype),
             requires_grad=False,
         )
-        module.register_parameter(f"{base_name}_zero_point", init_zero_point)
+        register_offload_parameter(module, f"{base_name}_zero_point", init_zero_point)
 
     # only grouped activation ordering has g_idx
     if quantization_args.actorder == ActivationOrdering.GROUP:
@@ -214,7 +187,7 @@ def _initialize_scale_zero_point(
             torch.full(g_idx_shape, -1, device=device, dtype=g_idx_dtype),
             requires_grad=False,
         )
-        module.register_parameter(f"{base_name}_g_idx", init_g_idx)
+        register_offload_parameter(module, f"{base_name}_g_idx", init_g_idx)
 
 
 def _initialize_attn_scales(module: Module) -> None:
 
@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, Optional
+import warnings
+from functools import wraps
+from typing import Any, Callable, Dict, Optional
 
 import torch
 from transformers import AutoConfig
@@ -24,6 +26,8 @@
     "tensor_follows_mask_structure",
     "replace_module",
     "is_compressed_tensors_config",
+    "getattr_chain",
+    "deprecated",
     "Aliasable",
 ]
 
@@ -122,6 +126,65 @@ def is_compressed_tensors_config(compression_config: Any) -> bool:
         return False
 
 
+def getattr_chain(obj: Any, chain_str: str, *args, **kwargs) -> Any:
+    """
+    Chain multiple getattr calls, separated by `.`
+
+    :param obj: base object whose attributes are being retrieved
+    :param chain_str: attribute names separated by `.`
+    :param default: default value, throw error otherwise
+    """
+    if len(args) >= 1:
+        has_default = True
+        default = args[0]
+    elif "default" in kwargs:
+        has_default = True
+        default = kwargs["default"]
+    else:
+        has_default = False
+
+    attr_names = chain_str.split(".")
+
+    res = obj
+    for attr_name in attr_names:
+        if not hasattr(res, attr_name):
+            if has_default:
+                return default
+            else:
+                raise AttributeError(f"{res} object has no attribute {attr_name}")
+        res = getattr(res, attr_name)
+
+    return res
+
+
+def deprecated(future_name: Optional[str] = None, message: Optional[str] = None):
+    """
+    Decorator to mark functions as deprecated
+
+    :param new_function: Function called in place of depreciated function
+    :param message: Depreciation message, replaces default depreciation message
+    """
+
+    def decorator(func: Callable[[Any], Any]):
+        nonlocal message
+
+        if message is None:
+            message = (
+                f"{func.__name__} is deprecated and will be removed in a future release"
+            )
+            if future_name is not None:
+                message += f". Please use {future_name} instead."
+
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            warnings.warn(message, DeprecationWarning, stacklevel=2)
+            return func(*args, **kwargs)
+
+        return wrapped
+
+    return decorator
+
+
 class Aliasable:
     """
     A mixin for enums to allow aliasing of enum members