Add sequential_weight_offload_device to skip unnecessary weight offloading

src/llmcompressor/args/dataset_arguments.py

@@ -242,6 +242,14 @@ class DatasetArguments(CustomDatasetArguments):
             "than one gpu. Default is cpu."
         },
     )
+    sequential_weight_offload_device: str = field(
+        default="cpu",
+        metadata={
+            "help": "Device used to offload model weights in the sequential "
+            "pipeline. Set to `none` to disable weight offloading and keep "
+            "weights on the main execution device. Default is cpu."
+        },
+    )
     quantization_aware_calibration: bool = field(
         default=True,
         metadata={

src/llmcompressor/entrypoints/oneshot.py

@@ -298,6 +298,7 @@ def oneshot(
     ],
     sequential_targets: list[str] | None = None,
     sequential_offload_device: str = "cpu",
+    sequential_weight_offload_device: str = "cpu",
     quantization_aware_calibration: bool = True,
     sequential_prefetch: bool = False,
     # Miscellaneous arguments
@@ -388,6 +389,9 @@ def oneshot(
     :param sequential_offload_device: Device used to offload intermediate activations
         between sequential layers. It is recommended to use `cuda:1` if using more
         than one gpu. Default is cpu.
+    :param sequential_weight_offload_device: Device used to offload model weights
+        in the sequential pipeline. Set to `none` to disable weight offloading and
+        keep weights on the main execution device. Default is cpu.
     :param quantization_aware_calibration: Whether to enable quantization-aware
         calibration in the sequential pipeline. When True, quantization is applied
         during forward pass in calibration. When False, quantization is disabled

src/llmcompressor/pipelines/sequential/helpers.py

@@ -527,10 +527,19 @@ def dispatch_for_sequential(
     if available. Removes any existing hooks.
 
     :param model: model to dispatch
+    :param onload_device: device used for forward passes
+    :param offload_device: device to offload weights to. ``"none"`` or ``None``
+        disables weight offloading and keeps the model on ``onload_device``.
     :return: dispatched model
     """
     if onload_device is None:
         onload_device = get_main_device()
+    if isinstance(offload_device, str) and offload_device.strip().lower() == "none":
+        offload_device = None
+    if offload_device is None:
+        remove_hook_from_module(model, recurse=True)
+        model.to(onload_device)
+        return model
     return offload_model(model, onload_device, offload_device)
 
 

src/llmcompressor/pipelines/sequential/pipeline.py

@@ -96,8 +96,10 @@ def __call__(
 
         # prepare model for sequential onloading
         onload_device = get_main_device()
-        offload_device = torch.device(dataset_args.sequential_offload_device)
-        dispatch_for_sequential(model, onload_device)
+        activation_offload_device = torch.device(dataset_args.sequential_offload_device)
+        dispatch_for_sequential(
+            model, onload_device, dataset_args.sequential_weight_offload_device
+        )
 
         # prepare to trace subgraphs
         modifiers = session.lifecycle.recipe.modifiers
@@ -126,7 +128,7 @@ def __call__(
 
             # prepare intermediates cache
             activations = IntermediatesCache.from_dataloader(
-                dataloader, onload_device, offload_device
+                dataloader, onload_device, activation_offload_device
             )
 
             # Populate loss_masks once from cached activations for AWQ masking support

tests/llmcompressor/pipelines/sequential/test_helpers.py

@@ -1,6 +1,10 @@
+import pytest
 import torch
 
-from llmcompressor.pipelines.sequential.helpers import get_sequential_ancestors
+from llmcompressor.pipelines.sequential.helpers import (
+    dispatch_for_sequential,
+    get_sequential_ancestors,
+)
 
 
 class DummyModel(torch.nn.Module):
@@ -22,3 +26,18 @@ def test_get_sequential_ancestors():
     assert get_sequential_ancestors(model, {model.fc}) == {model}
     assert get_sequential_ancestors(model, {model.seq[0]}) == {model, model.seq}
     assert get_sequential_ancestors(model, {model.seq[1]}) == {model, model.seq}
+
+
+@pytest.mark.parametrize("offload_device", [None, "none", "None"])
+def test_dispatch_for_sequential_no_offload(offload_device):
+    """When offload_device is None or 'none', the model should stay on the
+    onload device without any offloading hooks."""
+    model = DummyModel()
+    result = dispatch_for_sequential(
+        model, onload_device="cpu", offload_device=offload_device
+    )
+    assert result is model
+    for name, param in model.named_parameters():
+        assert param.device == torch.device(
+            "cpu"
+        ), f"{name} on {param.device}, expected cpu"