From aede5dbc8b34411fc18dcca3e2332bea17d88510 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Fri, 24 Oct 2025 17:09:59 +0800
Subject: [PATCH 01/36] update

---
 auto_round/wrapper.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/auto_round/wrapper.py b/auto_round/wrapper.py
index 6a6227023..57618780d 100644
--- a/auto_round/wrapper.py
+++ b/auto_round/wrapper.py
@@ -138,8 +138,12 @@ def _init_tuning_params_and_quant_func(self):
         if type(self.orig_layer) == transformers.pytorch_utils.Conv1D:
             orig_weight = orig_weight.t()
         weight_reshape = reshape_and_pad_tensor(orig_weight.data, orig_layer.group_size)
-        self.weight_min = torch.clamp(weight_reshape.min(1)[0], max=0)
-        self.weight_max = torch.clamp(weight_reshape.max(1)[0], min=0)
+        if  self.enable_round_tuning:
+            self.weight_min = torch.clamp(weight_reshape.min(1)[0], max=0)
+            self.weight_max = torch.clamp(weight_reshape.max(1)[0], min=0)
+        else:
+            self.weight_min=None
+            self.weight_max=None
         self._init_params(
             "value", p_dtype, weight_reshape.shape, 0, self.enable_round_tuning and self.orig_layer.bits < 16
         )
@@ -449,6 +453,7 @@ def forward(self, x):
         Returns:
             torch.Tensor: Output tensor after applying the wrapped layer.
         """
+        # logger.info(self.orig_layer.tmp_name)
         x = x.to(self.device)
         weight_q, *_ = self._qdq_weight(self.value, self.min_scale, self.max_scale)
 

From 4b5630f8747e7d2ca8d06188a10c2a8e1f841ec2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 24 Oct 2025 09:14:47 +0000
Subject: [PATCH 02/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/wrapper.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/auto_round/wrapper.py b/auto_round/wrapper.py
index 57618780d..f0a8aaec6 100644
--- a/auto_round/wrapper.py
+++ b/auto_round/wrapper.py
@@ -138,12 +138,12 @@ def _init_tuning_params_and_quant_func(self):
         if type(self.orig_layer) == transformers.pytorch_utils.Conv1D:
             orig_weight = orig_weight.t()
         weight_reshape = reshape_and_pad_tensor(orig_weight.data, orig_layer.group_size)
-        if  self.enable_round_tuning:
+        if self.enable_round_tuning:
             self.weight_min = torch.clamp(weight_reshape.min(1)[0], max=0)
             self.weight_max = torch.clamp(weight_reshape.max(1)[0], min=0)
         else:
-            self.weight_min=None
-            self.weight_max=None
+            self.weight_min = None
+            self.weight_max = None
         self._init_params(
             "value", p_dtype, weight_reshape.shape, 0, self.enable_round_tuning and self.orig_layer.bits < 16
         )

From 272e9ea3d31094fed71e0b5239fae4022bb5ba6a Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Mon, 27 Oct 2025 15:05:36 +0800
Subject: [PATCH 03/36] refine device_map code

---
 auto_round/auto_scheme/gen_auto_scheme.py |   2 +
 auto_round/compressors/adam_comperssor.py | 147 ++++++++++
 auto_round/compressors/base.py            | 310 ++--------------------
 auto_round/data_type/int.py               |   2 +-
 auto_round/schemes.py                     |   1 +
 auto_round/utils_bk/__init__.py           |   0
 auto_round/utils_bk/device.py             | 153 +++++++++++
 7 files changed, 327 insertions(+), 288 deletions(-)
 create mode 100644 auto_round/compressors/adam_comperssor.py
 create mode 100644 auto_round/utils_bk/__init__.py
 create mode 100644 auto_round/utils_bk/device.py

diff --git a/auto_round/auto_scheme/gen_auto_scheme.py b/auto_round/auto_scheme/gen_auto_scheme.py
index 9ed0b21fa..84b8726ed 100644
--- a/auto_round/auto_scheme/gen_auto_scheme.py
+++ b/auto_round/auto_scheme/gen_auto_scheme.py
@@ -81,6 +81,8 @@ def _check_configs(self) -> None:
     def get_layer_config(self) -> dict[str, dict]:
         method_name = self.auto_scheme.method
         method_func = AUTO_SCHEME_METHODS[method_name]
+        if self.auto_scheme.low_gpu_mem_usage:
+            self.enable_torch_compile=False
         layer_config = method_func(
             self.auto_scheme,
             self.model,
diff --git a/auto_round/compressors/adam_comperssor.py b/auto_round/compressors/adam_comperssor.py
new file mode 100644
index 000000000..15b6caca2
--- /dev/null
+++ b/auto_round/compressors/adam_comperssor.py
@@ -0,0 +1,147 @@
+from typing import Union
+
+import torch
+
+from auto_round import QuantizationScheme
+from auto_round.compressors import BaseCompressor
+from auto_round.utils import check_is_cpu, is_hpex_available, htcore
+
+
+class AdamCompressor(BaseCompressor):
+    """Class for quantization with optimizers like adamw of a PyTorch model.
+
+    Args:
+        model: The PyTorch model to be quantized.
+        tokenizer: An optional tokenizer for processing input data.
+        scheme (str| dict | QuantizationScheme ): A preset scheme that defines the quantization configurations
+        bits (int): Number of bits for quantization (default is 4).
+        group_size (int): Size of the quantization group (default is 128).
+        sym (bool): Whether sym to be used (default is True).
+        layer_config (dict): Configuration for weight quantization (default is None).
+        batch_size (int): Batch size for training (default is 8).
+        amp (bool): Whether to use automatic mixed precision (default is True).
+        device: The device to be used for training (default is "auto").
+        lr_scheduler: The learning rate scheduler to be used.
+        dataset: The default dataset name (default is "NeelNanda/pile-10k").
+        enable_quanted_input (bool): Whether to use quantized input data (default is True).
+        enable_minmax_tuning (bool): Whether to enable min-max tuning (default is True).
+        lr (float): The learning rate (default is 0.005).
+        minmax_lr (float): The learning rate for min-max tuning (default is None).
+        low_gpu_mem_usage (bool): Whether to use low GPU memory (default is False).
+        iters (int): Number of iterations (default is 200).
+        seqlen (int): Length of the sequence.
+        nsamples (int): Number of samples (default is 128).
+        sampler (str): The sampling method (default is "rand").
+        seed (int): The random seed (default is 42).
+        nblocks (int): Number of blocks (default is 1).
+        gradient_accumulate_steps (int): Number of gradient accumulation steps (default is 1).
+        not_use_best_mse (bool): Whether to use mean squared error (default is False).
+        dynamic_max_gap (int): The dynamic maximum gap (default is -1).
+        data_type (str): The data type to be used (default is "int").
+        scale_dtype (str): The data type of quantization scale to be used (default is "float16"), different kernels
+                           have different choices.
+        act_bits (int): Number of bits for activation quantization. Default is 16.
+        act_group_size (int): Group size for activation quantization. Default is None.
+        act_sym (bool): Whether to use symmetric activation quantization. Default is None.
+        act_data_type (str): Specifies the data type for activations.
+                             Defaults to None, in which case it inherits the weight data type.
+        act_dynamic (bool): Whether to use dynamic activation quantization. Default is True.
+        to_quant_block_names (str|list): A string or list whose elements are list of
+                            block's layer names to be quantized.
+        enable_norm_bias_tuning (bool): Whether to enable fast norm/layer_bias tuning
+        enable_torch_compile (bool): Whether to enable torch compile to optimize quant_block/layer function
+        **kwargs: Additional keyword arguments.
+
+    Returns:
+        The quantized model.
+    """
+
+    bits: int | None
+    group_size: int | None
+    sym: bool | None
+    data_type: str | None
+    act_bits: int | None
+    act_group_size: int | None
+    act_sym: bool | None
+    act_data_type: str | None
+    act_dynamic: bool | None
+    super_bits: int | None
+    super_group_size: int | None
+
+    def __init__(
+        self,
+        model: Union[torch.nn.Module, str],
+        tokenizer=None,
+        scheme: Union[str, dict, QuantizationScheme] = "W4A16",
+        layer_config: dict[str, Union[str, dict, QuantizationScheme]] = None,
+        dataset: Union[str, list, tuple, torch.utils.data.DataLoader] = "NeelNanda/pile-10k",
+        iters: int = 200,
+        seqlen: int = 2048,
+        nsamples: int = 128,
+        batch_size: int = 8,
+        gradient_accumulate_steps: int = 1,
+        low_gpu_mem_usage: bool = False,
+        device_map: Union[str, int, torch.device, dict] = 0,
+        enable_torch_compile: bool = False,
+        seed: int = 42,
+        optimizer="AdamW",
+        **kwargs,
+    ):
+        super(AdamCompressor, self).__init__(
+            model=model,
+            tokenizer=tokenizer,
+            scheme=scheme,
+            layer_config=layer_config,
+            batch_size=batch_size,
+            dataset=dataset,
+            low_gpu_mem_usage=low_gpu_mem_usage,
+            iters=iters,
+            seqlen=seqlen,
+            nsamples=nsamples,
+            seed=seed,
+            gradient_accumulate_steps=gradient_accumulate_steps,
+            enable_torch_compile=enable_torch_compile,
+            device_map=device_map,
+            **kwargs,
+        )
+
+        self.optimizer = self._get_optimizer(optimizer)
+
+    def _get_optimizer(self, optimizer):
+        if optimizer is None:
+            optimizer = torch.optim.AdamW
+        elif isinstance(optimizer, str):
+            optimizer = getattr(torch.optim, optimizer)
+        else:
+            optimizer = optimizer
+        return optimizer
+
+    def _get_scaler(self):
+        scaler = None
+        if self.amp and not check_is_cpu(self.device):
+            from torch.cuda.amp import GradScaler
+
+            scaler = GradScaler(init_scale=1024, growth_interval=100000)
+        return scaler
+
+    def _scale_loss_and_backward(self, scaler, loss):
+        if scaler is not None:
+            loss = scaler.scale(loss)
+
+        loss.backward()
+        if is_hpex_available():
+            htcore.mark_step()
+        return loss
+
+    def _step(self, scaler, optimizer, lr_schedule):
+        if scaler is not None:
+            scaler.step(optimizer)
+            optimizer.zero_grad()
+            lr_schedule.step()
+            scaler.update()
+        else:
+            optimizer.step()
+            optimizer.zero_grad()
+            lr_schedule.step()
+        if is_hpex_available():
+            htcore.mark_step()
diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index 045fb697f..c20f26b36 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -20,7 +20,6 @@
 import traceback
 from collections import defaultdict
 from dataclasses import asdict, fields
-from enum import Enum
 from typing import Any, Callable, Union
 
 import accelerate
@@ -34,7 +33,7 @@
 from auto_round.data_type import QUANT_FUNC_WITH_DTYPE
 from auto_round.data_type.utils import reshape_pad_tensor_by_group_size
 from auto_round.export.export_to_autoround import AutoRoundFormat
-from auto_round.export.export_to_gguf.config import GGUF_CONFIG, GGUF_INNER_CONFIG, ModelType
+from auto_round.export.export_to_gguf.config import GGUF_INNER_CONFIG, ModelType
 from auto_round.logger import logger
 from auto_round.schemes import AutoScheme, QuantizationScheme, get_gguf_scheme, preset_name_to_scheme
 from auto_round.sign_sgd import SignSGD
@@ -64,17 +63,10 @@
     convert_fp8_model_to_16b_model,
     copy_python_files_from_model_cache,
     detect_device,
-    estimate_tuning_block_mem,
     find_matching_blocks,
     flatten_list,
     get_block_names,
-    get_device_memory,
-    get_fp_layer_names,
-    get_layer_config_by_gguf_format,
-    get_layer_features,
     get_layer_names_in_block,
-    get_lm_head_name,
-    get_max_vram,
     get_module,
     get_shared_keys,
     htcore,
@@ -97,6 +89,7 @@
     to_dtype,
     unsupported_meta_device,
 )
+from auto_round.utils_bk.device import get_major_device, set_non_auto_device_map, set_auto_device_map_for_block
 from auto_round.wrapper import WrapperLinear, WrapperMultiblock, unwrapper_block, unwrapper_layer, wrapper_block
 
 
@@ -232,17 +225,20 @@ def __init__(
         enable_deterministic_algorithms = kwargs.pop("enable_deterministic_algorithms", False)
         static_kv_dtype = kwargs.pop("static_kv_dtype", None)
         device = kwargs.pop("device", None)
+        # Scale factor for RAM usage per parameter.
+        mem_per_param_scale = kwargs.pop("mem_per_param_scale", None)
         self.quant_lm_head = kwargs.pop("quant_lm_head", False)
         self.mllm = kwargs.pop("mllm") if "mllm" in kwargs else False
         self.diffusion = kwargs.pop("diffusion") if "diffusion" in kwargs else False
-        # Scale factor for RAM usage per parameter.
-        self.mem_per_param_scale = kwargs.pop("mem_per_param_scale", None)
+
+
         self.fp_layers = kwargs.pop("fp_layers", "")
         self.layer_config = layer_config
         self.supported_types = SUPPORTED_LAYER_TYPES
         self.inner_supported_types = INNER_SUPPORTED_LAYER_TYPES
         self.scale_dtype = convert_dtype_str2torch(scale_dtype)
 
+
         if kwargs:
             logger.warning(f"unrecognized keys {list(kwargs.keys())} were passed. Please check them.")
         if "CUBLAS_WORKSPACE_CONFIG" not in os.environ:
@@ -292,21 +288,17 @@ def __init__(
         self.enable_torch_compile = enable_torch_compile
         self._adjust_torch_compile(enable_torch_compile)
 
+        self.device_map = device_map
+        if isinstance(self.device_map, str):
+            self.device_map = self.device_map.replace(" ", "")
+
         if isinstance(scheme, AutoScheme):
-            self.layer_config = self._gen_auto_scheme(model, scheme, dataset, device_map)
+            self.layer_config = self._gen_auto_scheme(model, scheme, dataset, self.device_map)
 
         # Set device, must place after model loading
-        self._set_device(device_map)
-
-        if (isinstance(device_map, dict) and device_map) or device_map == "auto":
-            self.device_map = device_map
-        elif isinstance(device_map, str) and "," in device_map:
-            device_map = device_map.replace(" ", "")  # Remove any spaces
-            self.device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
-            self.device_map = "auto"
-        else:
-            self.device_map = None
-        self._set_device_map_in_blocks(self.device_map)
+        self.device = get_major_device(device_map)
+
+        set_non_auto_device_map(self.device_map)
 
         # Tuning hyperparameters
         self.seed = seed
@@ -338,6 +330,10 @@ def __init__(
         self.optimizer = self._get_optimizer(None)
         self.disable_opt_rtn = disable_opt_rtn
         self.is_packing_immediate = False  # whether to pack the layer immediately after tuning
+        if mem_per_param_scale is None :
+            self.mem_per_param_scale = 13 if self.iters != 0 else 1
+        else:
+            self.mem_per_param_scale = mem_per_param_scale
 
         # KV cache, this one does not affect tuning but will collect some infos during tuning
         self.static_kv_dtype = static_kv_dtype
@@ -590,133 +586,11 @@ def _adjust_torch_compile(self, enable_torch_compile: bool) -> None:
                 "Enabling it can reduce tuning cost by 20%, but it might throw an exception."
             )
 
-        # if is_debug_mode() and self.enable_torch_compile:
-        #     self.enable_torch_compile = False
-        #     logger.warning("reset enable_torch_compile to `False` as debug mode is enabled")
 
         if (self.data_type.startswith("fp") or self.act_data_type.startswith("fp")) and self.enable_torch_compile:
             self.enable_torch_compile = False
             logger.warning("reset enable_torch_compile to `False` as fp8 is enabled")
 
-    def _set_device_map_in_blocks(self, device_map: Union[str, dict, None]) -> None:
-        """Sets the device map for specific blocks in the model.
-
-        Args:
-            device_map (Union[str, dict, None]): A mapping of module names to devices.
-                If provided as a string, it should be in the format
-                "module_name:device,module_name:device". Devices can be integers
-                (GPU IDs) or strings (e.g., 'cpu', 'cuda:0').
-        """
-        if self.device_map is None or len(self.device_map) == 0:
-            self.device_map = None
-        if not device_map:
-            return
-        if self.device_map == "auto" and device_map == "auto":
-            return
-        if isinstance(device_map, str):
-            device_map = device_map.replace(" ", "")
-            infos = device_map.split(",")
-            device_map_dict = {}
-            for info in infos:
-                index = info.find(":")
-                key = info[:index]
-                value = info[index + 1 :]
-                device_map_dict[key] = value
-            device_map = device_map_dict
-
-        names = [n for n, m in self.model.named_modules() if len(list(m.children())) == 0]
-
-        for key, device in device_map.items():
-            if isinstance(device, str) and device.isdigit():
-                device = int(device)
-            device = detect_device(device)
-            try:
-                module = get_module(self.model, key)
-                module.tuning_device = device
-            except:
-                matching_names = [name for name in names if re.match(key, name)]
-                if len(matching_names) > 0:
-                    for name in matching_names:
-                        self._set_device_for_matching_module(name, device)
-                else:
-                    for name in names:
-                        if key in name:
-                            self._set_device_for_matching_module(name, device)
-
-    def _set_device_for_matching_module(self, name: str, device: str) -> None:
-        """Sets the device for a module if it matches the given name."""
-        module = get_module(self.model, name)
-        if hasattr(module, "tuning_device") and module.tuning_device != device:
-            logger.warning(
-                f"multiple devices have been set for layer {name}, keeping original device {module.tuning_device}"
-            )
-        else:
-            module.tuning_device = device
-
-    def _set_auto_device_map_in_block(self, block: torch.nn.Module, input_ids: list[torch.Tensor]) -> None:
-        """Automatically sets the device map for the block based on available GPUs and memory constraints."""
-        if torch.cuda.is_available():
-            num_gpus = torch.cuda.device_count()
-        elif torch.xpu.is_available():
-            logger.warning_once("XPU does not support auto device map yet, using device 0 for tuning.")
-            return
-        else:
-            raise RuntimeError("No CUDA or XPU devices found.")
-        if num_gpus <= 1:
-            self.device_map = None
-            return
-
-        if hasattr(self, "device_list") and self.device_list:
-            cuda_devices = [f"cuda:{i}" for i in self.device_list]
-            device_0 = cuda_devices[0]
-        else:
-            cuda_devices = [f"cuda:{i}" for i in range(num_gpus)]
-            device_0 = "cuda:0"
-
-        device_0_memory = get_device_memory(
-            self.device_list[0] if hasattr(self, "device_list") and self.device_list else 0
-        )
-        block_memory, input_output_memory = estimate_tuning_block_mem(block, input_ids)
-        if self.low_gpu_mem_usage:
-            input_output_memory = 0
-
-        mem_per_param_scale = 13 if self.mem_per_param_scale is None else self.mem_per_param_scale
-        if self.iters == 0:
-            mem_per_param_scale = 1  # for rtn
-
-        if (block_memory * mem_per_param_scale + input_output_memory) < device_0_memory:
-            return  # fit in one GPU
-
-        device_map = {}
-        device_memory = {device: get_device_memory(int(device.split(":")[1])) for device in cuda_devices}
-        device_memory[device_0] = device_0_memory - input_output_memory
-
-        device_idx = 0
-        # First, fill device 0 to its maximum capacity, then distribute the remaining layers evenly across other devices
-        for n, m in block.named_modules():
-            if check_to_quantized(m):
-                layer_name = block.tmp_name + "." + n
-                layer_memory = m.weight.nbytes / 1024**3
-                if device_idx == 0 and layer_memory * mem_per_param_scale < device_memory[cuda_devices[device_idx]]:
-                    device_map[layer_name] = cuda_devices[device_idx]
-                    device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
-                elif device_idx == 0:
-                    device_idx += 1  # Move to the next device once device 0 is full
-                    device_map[layer_name] = cuda_devices[device_idx]
-                    device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
-                else:
-                    # Calculate the target device index based on even distribution
-                    sorted_devices = sorted(cuda_devices, key=lambda d: device_memory[d], reverse=True)
-                    device_idx = sorted_devices[0]
-                    if layer_memory * mem_per_param_scale < device_memory[device_idx]:
-                        device_map[layer_name] = device_idx
-                        device_memory[device_idx] -= layer_memory * mem_per_param_scale
-                    else:
-                        logger.warning_once(
-                            f"Block {block.tmp_name} not fit in available GPU memory. "
-                            "Consider using more GPUs or reducing mem_per_param_scale if OOM occurs."
-                        )
-        self._set_device_map_in_blocks(device_map)
 
     def _dq_check(self) -> None:
         """Reset the default value of super_bits and super_group_size"""
@@ -1561,8 +1435,8 @@ def _quantize_via_rtn_blockwise(self, all_to_quantized_module_names: list[str])
                 if _is_fp8_model(self.model):
                     convert_fp8_model_to_16b_model(block, dtype=self.amp_dtype)
 
-                if self.device_map == "auto":
-                    self._set_auto_device_map_in_block(block, input_ids)
+                if self.device_map == "auto" or (isinstance(self.device_map, str) and "," in self.device_map):
+                    set_auto_device_map_for_block(block,self.devcie_map,input_ids,  self.low_gpu_mem_usage,self.mem_per_param_scale)
 
                 # Dispatch model if needed
                 if self.device_map is not None:
@@ -2575,8 +2449,8 @@ def _quantize_block(
                     new_layer = convert_fp8_layer_to_linear(m, self.amp_dtype).to(device)
                     set_module(block, n, new_layer)
 
-        if self.device_map == "auto":
-            self._set_auto_device_map_in_block(block, input_ids)
+        if self.device_map == "auto" or (isinstance(self.device_map, str) and "," in self.device_map):
+            set_auto_device_map_for_block(block, self.device_map,input_ids,self.low_gpu_mem_usage,self.mem_per_param_scale)
 
         if self.device_map is not None:
             for n, m in block.named_modules():
@@ -3213,141 +3087,3 @@ class LLMCompressor(BaseCompressor):
     pass
 
 
-class AdamCompressor(BaseCompressor):
-    """Class for quantization with optimizers like adamw of a PyTorch model.
-
-    Args:
-        model: The PyTorch model to be quantized.
-        tokenizer: An optional tokenizer for processing input data.
-        scheme (str| dict | QuantizationScheme ): A preset scheme that defines the quantization configurations
-        bits (int): Number of bits for quantization (default is 4).
-        group_size (int): Size of the quantization group (default is 128).
-        sym (bool): Whether sym to be used (default is True).
-        layer_config (dict): Configuration for weight quantization (default is None).
-        batch_size (int): Batch size for training (default is 8).
-        amp (bool): Whether to use automatic mixed precision (default is True).
-        device: The device to be used for training (default is "auto").
-        lr_scheduler: The learning rate scheduler to be used.
-        dataset: The default dataset name (default is "NeelNanda/pile-10k").
-        enable_quanted_input (bool): Whether to use quantized input data (default is True).
-        enable_minmax_tuning (bool): Whether to enable min-max tuning (default is True).
-        lr (float): The learning rate (default is 0.005).
-        minmax_lr (float): The learning rate for min-max tuning (default is None).
-        low_gpu_mem_usage (bool): Whether to use low GPU memory (default is False).
-        iters (int): Number of iterations (default is 200).
-        seqlen (int): Length of the sequence.
-        nsamples (int): Number of samples (default is 128).
-        sampler (str): The sampling method (default is "rand").
-        seed (int): The random seed (default is 42).
-        nblocks (int): Number of blocks (default is 1).
-        gradient_accumulate_steps (int): Number of gradient accumulation steps (default is 1).
-        not_use_best_mse (bool): Whether to use mean squared error (default is False).
-        dynamic_max_gap (int): The dynamic maximum gap (default is -1).
-        data_type (str): The data type to be used (default is "int").
-        scale_dtype (str): The data type of quantization scale to be used (default is "float16"), different kernels
-                           have different choices.
-        act_bits (int): Number of bits for activation quantization. Default is 16.
-        act_group_size (int): Group size for activation quantization. Default is None.
-        act_sym (bool): Whether to use symmetric activation quantization. Default is None.
-        act_data_type (str): Specifies the data type for activations.
-                             Defaults to None, in which case it inherits the weight data type.
-        act_dynamic (bool): Whether to use dynamic activation quantization. Default is True.
-        to_quant_block_names (str|list): A string or list whose elements are list of
-                            block's layer names to be quantized.
-        enable_norm_bias_tuning (bool): Whether to enable fast norm/layer_bias tuning
-        enable_torch_compile (bool): Whether to enable torch compile to optimize quant_block/layer function
-        **kwargs: Additional keyword arguments.
-
-    Returns:
-        The quantized model.
-    """
-
-    bits: int | None
-    group_size: int | None
-    sym: bool | None
-    data_type: str | None
-    act_bits: int | None
-    act_group_size: int | None
-    act_sym: bool | None
-    act_data_type: str | None
-    act_dynamic: bool | None
-    super_bits: int | None
-    super_group_size: int | None
-
-    def __init__(
-        self,
-        model: Union[torch.nn.Module, str],
-        tokenizer=None,
-        scheme: Union[str, dict, QuantizationScheme] = "W4A16",
-        layer_config: dict[str, Union[str, dict, QuantizationScheme]] = None,
-        dataset: Union[str, list, tuple, torch.utils.data.DataLoader] = "NeelNanda/pile-10k",
-        iters: int = 200,
-        seqlen: int = 2048,
-        nsamples: int = 128,
-        batch_size: int = 8,
-        gradient_accumulate_steps: int = 1,
-        low_gpu_mem_usage: bool = False,
-        device_map: Union[str, int, torch.device, dict] = 0,
-        enable_torch_compile: bool = False,
-        seed: int = 42,
-        optimizer="AdamW",
-        **kwargs,
-    ):
-        super(AdamCompressor, self).__init__(
-            model=model,
-            tokenizer=tokenizer,
-            scheme=scheme,
-            layer_config=layer_config,
-            batch_size=batch_size,
-            dataset=dataset,
-            low_gpu_mem_usage=low_gpu_mem_usage,
-            iters=iters,
-            seqlen=seqlen,
-            nsamples=nsamples,
-            seed=seed,
-            gradient_accumulate_steps=gradient_accumulate_steps,
-            enable_torch_compile=enable_torch_compile,
-            device_map=device_map,
-            **kwargs,
-        )
-
-        self.optimizer = self._get_optimizer(optimizer)
-
-    def _get_optimizer(self, optimizer):
-        if optimizer is None:
-            optimizer = torch.optim.AdamW
-        elif isinstance(optimizer, str):
-            optimizer = getattr(torch.optim, optimizer)
-        else:
-            optimizer = optimizer
-        return optimizer
-
-    def _get_scaler(self):
-        scaler = None
-        if self.amp and not check_is_cpu(self.device):
-            from torch.cuda.amp import GradScaler
-
-            scaler = GradScaler(init_scale=1024, growth_interval=100000)
-        return scaler
-
-    def _scale_loss_and_backward(self, scaler, loss):
-        if scaler is not None:
-            loss = scaler.scale(loss)
-
-        loss.backward()
-        if is_hpex_available():
-            htcore.mark_step()
-        return loss
-
-    def _step(self, scaler, optimizer, lr_schedule):
-        if scaler is not None:
-            scaler.step(optimizer)
-            optimizer.zero_grad()
-            lr_schedule.step()
-            scaler.update()
-        else:
-            optimizer.step()
-            optimizer.zero_grad()
-            lr_schedule.step()
-        if is_hpex_available():
-            htcore.mark_step()
diff --git a/auto_round/data_type/int.py b/auto_round/data_type/int.py
index fb6c5f3db..bd0533581 100644
--- a/auto_round/data_type/int.py
+++ b/auto_round/data_type/int.py
@@ -22,7 +22,7 @@
 
 def search_scales(data: torch.Tensor, bits: int, qw: Union[None, torch.Tensor, float] = None) -> torch.Tensor:
     nmax = pow(2, bits - 1)
-    imax = abs(data).argmax(axis=-1, keepdims=True)
+    imax = torch.abs(data).argmax(dim=-1, keepdim=True)
     group_max = torch.take_along_dim(data, imax, dim=-1)
     iscales = -nmax * get_reciprocal(group_max)
     scales = get_reciprocal(iscales)
diff --git a/auto_round/schemes.py b/auto_round/schemes.py
index 96faf62c0..94eb17f2c 100644
--- a/auto_round/schemes.py
+++ b/auto_round/schemes.py
@@ -300,6 +300,7 @@ class AutoScheme:
     device_map: Optional[Union[str, torch.device, int, dict]] = None
     enable_torch_compile: Optional[bool] = None
     disable_opt_rtn: bool = True
+    low_gpu_mem_usage: bool = True
 
     def __post_init__(self):
         if isinstance(self.options, str):
diff --git a/auto_round/utils_bk/__init__.py b/auto_round/utils_bk/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
new file mode 100644
index 000000000..7ce247b42
--- /dev/null
+++ b/auto_round/utils_bk/device.py
@@ -0,0 +1,153 @@
+from typing import Union
+
+import torch
+
+from auto_round.logger import logger
+
+from auto_round.utils import detect_device, get_module, get_device_memory, estimate_tuning_block_mem, check_to_quantized
+import re
+
+
+def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
+    if isinstance(device_map, (str, torch.device, int)):
+        device = detect_device(device_map)
+        return device
+
+    if isinstance(device_map, dict) and device_map:
+        tmp_devices = []
+        for val in device_map.values():
+            if isinstance(val, (str, torch.device, int)):  # could optimize
+                tmp_device = detect_device(val)
+                tmp_device = tmp_device.split(":")[0]
+                tmp_devices.append(tmp_device)
+        tmp_devices = list(set(tmp_devices))
+        if len(tmp_devices) > 1:
+            logger.warning(
+                f"there are multiple device types in the device_map, "
+                f"please make sure they are correct,use the first none-cpu device {tmp_devices[0]} as the core device "
+            )
+
+        for device in tmp_devices:
+            if device != "cpu":
+                return device
+
+        device = tmp_devices[0]
+        return device
+    logger.warning(f"device_map should be [str, torch.device, int, dict], but got {type(device_map)}")
+    return "cpu"
+
+
+def set_tuning_device_for_layer(model, name: str, device: str) -> None:
+    """Sets the device for a module if it matches the given name."""
+    module = get_module(model, name)
+    if hasattr(module, "tuning_device") and module.tuning_device != device:
+        logger.warning(
+            f"multiple devices have been set for layer {name}, keeping original device {module.tuning_device}"
+        )
+    else:
+        module.tuning_device = device
+
+
+def set_non_auto_device_map(model: torch.nn.Module, device_map, quant_layer_names=None):
+    if not device_map:
+        return
+    if device_map == "auto":
+        return
+    if isinstance(device_map, str) and "," in device_map:  # auto device map
+        return
+    if isinstance(device_map, int):
+        return
+    if isinstance(device_map, str):
+        device_map = device_map.replace(" ", "")
+        infos = device_map.split(",")
+        device_map_dict = {}
+        for info in infos:
+            index = info.find(":")
+            key = info[:index]
+            value = info[index + 1:]
+            device_map_dict[key] = value
+        device_map = device_map_dict
+    if quant_layer_names is not None:
+        names = quant_layer_names
+    else:
+        names = [n for n, m in model.model.named_modules() if len(list(m.children())) == 0] # if it's a block, it will be incorrect
+    for key, device in device_map.items():
+        if isinstance(device, str) and device.isdigit():
+            device = int(device)
+        device = detect_device(device)
+        if key in names:
+            module = get_module(model, key)
+            module.tuning_device = device
+        else:
+            matching_names = [name for name in names if re.match(key, name)]
+            for name in matching_names:
+                set_non_auto_device_map(model, name, device)
+            if not matching_names:
+                logger.warning(f"{key} in `device_map` dose not match any modules, please have a check")
+
+
+
+def set_auto_device_map_for_block(block: torch.nn.Module, device_map, input_ids: list[torch.Tensor],
+                                  low_gpu_mem_usage=False,mem_per_param_scale=13.0) -> None:
+    """Automatically sets the device map for the block based on available GPUs and memory constraints."""
+    if torch.cuda.is_available():
+        num_gpus = torch.cuda.device_count()
+    elif torch.xpu.is_available():
+        logger.warning_once("XPU does not support auto device map yet, using device 0 for tuning.")
+        return
+    else:
+        raise RuntimeError("No CUDA or XPU devices found.")
+    device_list = None
+    if isinstance(device_map, str) and "," in device_map:
+        device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
+
+    if device_list:
+        cuda_devices = [f"cuda:{i}" for i in device_list]
+        device_0 = cuda_devices[0]
+    else:
+        cuda_devices = [f"cuda:{i}" for i in range(num_gpus)]
+        device_0 = "cuda:0"
+
+    device_0_memory = get_device_memory(
+    device_list[0] if device_list else 0
+    )
+    block_memory, input_output_memory = estimate_tuning_block_mem(block, input_ids)
+    if low_gpu_mem_usage:
+        input_output_memory = 0
+
+
+    if (block_memory * mem_per_param_scale + input_output_memory) < device_0_memory:
+        return  # fit in one GPU
+
+    device_map = {}
+    device_memory = {device: get_device_memory(int(device.split(":")[1])) for device in cuda_devices}
+    device_memory[device_0] = device_0_memory - input_output_memory
+
+    device_idx = 0
+    names = []
+    # First, fill device 0 to its maximum capacity, then distribute the remaining layers evenly across other devices
+    for n, m in block.named_modules():
+        if check_to_quantized(m):
+            layer_name = m.tmp_name
+            names.append(layer_name)
+            layer_memory = m.weight.nbytes / 1024**3
+            if device_idx == 0 and layer_memory * mem_per_param_scale < device_memory[cuda_devices[device_idx]]:
+                device_map[layer_name] = cuda_devices[device_idx]
+                device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
+            elif device_idx == 0:
+                device_idx += 1  # Move to the next device once device 0 is full
+                device_map[layer_name] = cuda_devices[device_idx]
+                device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
+            else:
+                # Calculate the target device index based on even distribution
+                sorted_devices = sorted(cuda_devices, key=lambda d: device_memory[d], reverse=True)
+                device_idx = sorted_devices[0]
+                if layer_memory * mem_per_param_scale < device_memory[device_idx]:
+                    device_map[layer_name] = device_idx
+                    device_memory[device_idx] -= layer_memory * mem_per_param_scale
+                else:
+                    logger.warning_once(
+                        f"Block {block.tmp_name} not fit in available GPU memory. "
+                        "Consider using more GPUs or reducing mem_per_param_scale if OOM occurs."
+                    )
+    set_non_auto_device_map(block,device_map,names)

From 2e46f391bbd1b8eed573bdab7701edcb13f55a90 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 27 Oct 2025 07:07:26 +0000
Subject: [PATCH 04/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/auto_scheme/gen_auto_scheme.py |  2 +-
 auto_round/compressors/adam_comperssor.py | 16 +++++++++-
 auto_round/compressors/base.py            | 18 +++++------
 auto_round/utils_bk/__init__.py           | 13 ++++++++
 auto_round/utils_bk/device.py             | 38 +++++++++++++++--------
 5 files changed, 62 insertions(+), 25 deletions(-)

diff --git a/auto_round/auto_scheme/gen_auto_scheme.py b/auto_round/auto_scheme/gen_auto_scheme.py
index 84b8726ed..dd7d0f0b7 100644
--- a/auto_round/auto_scheme/gen_auto_scheme.py
+++ b/auto_round/auto_scheme/gen_auto_scheme.py
@@ -82,7 +82,7 @@ def get_layer_config(self) -> dict[str, dict]:
         method_name = self.auto_scheme.method
         method_func = AUTO_SCHEME_METHODS[method_name]
         if self.auto_scheme.low_gpu_mem_usage:
-            self.enable_torch_compile=False
+            self.enable_torch_compile = False
         layer_config = method_func(
             self.auto_scheme,
             self.model,
diff --git a/auto_round/compressors/adam_comperssor.py b/auto_round/compressors/adam_comperssor.py
index 15b6caca2..37e14329b 100644
--- a/auto_round/compressors/adam_comperssor.py
+++ b/auto_round/compressors/adam_comperssor.py
@@ -1,10 +1,24 @@
+# Copyright (c) 2025 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import Union
 
 import torch
 
 from auto_round import QuantizationScheme
 from auto_round.compressors import BaseCompressor
-from auto_round.utils import check_is_cpu, is_hpex_available, htcore
+from auto_round.utils import check_is_cpu, htcore, is_hpex_available
 
 
 class AdamCompressor(BaseCompressor):
diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index c20f26b36..b97e4d463 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -89,7 +89,7 @@
     to_dtype,
     unsupported_meta_device,
 )
-from auto_round.utils_bk.device import get_major_device, set_non_auto_device_map, set_auto_device_map_for_block
+from auto_round.utils_bk.device import get_major_device, set_auto_device_map_for_block, set_non_auto_device_map
 from auto_round.wrapper import WrapperLinear, WrapperMultiblock, unwrapper_block, unwrapper_layer, wrapper_block
 
 
@@ -231,14 +231,12 @@ def __init__(
         self.mllm = kwargs.pop("mllm") if "mllm" in kwargs else False
         self.diffusion = kwargs.pop("diffusion") if "diffusion" in kwargs else False
 
-
         self.fp_layers = kwargs.pop("fp_layers", "")
         self.layer_config = layer_config
         self.supported_types = SUPPORTED_LAYER_TYPES
         self.inner_supported_types = INNER_SUPPORTED_LAYER_TYPES
         self.scale_dtype = convert_dtype_str2torch(scale_dtype)
 
-
         if kwargs:
             logger.warning(f"unrecognized keys {list(kwargs.keys())} were passed. Please check them.")
         if "CUBLAS_WORKSPACE_CONFIG" not in os.environ:
@@ -330,7 +328,7 @@ def __init__(
         self.optimizer = self._get_optimizer(None)
         self.disable_opt_rtn = disable_opt_rtn
         self.is_packing_immediate = False  # whether to pack the layer immediately after tuning
-        if mem_per_param_scale is None :
+        if mem_per_param_scale is None:
             self.mem_per_param_scale = 13 if self.iters != 0 else 1
         else:
             self.mem_per_param_scale = mem_per_param_scale
@@ -586,12 +584,10 @@ def _adjust_torch_compile(self, enable_torch_compile: bool) -> None:
                 "Enabling it can reduce tuning cost by 20%, but it might throw an exception."
             )
 
-
         if (self.data_type.startswith("fp") or self.act_data_type.startswith("fp")) and self.enable_torch_compile:
             self.enable_torch_compile = False
             logger.warning("reset enable_torch_compile to `False` as fp8 is enabled")
 
-
     def _dq_check(self) -> None:
         """Reset the default value of super_bits and super_group_size"""
         if self.data_type.endswith("_dq"):
@@ -1436,7 +1432,9 @@ def _quantize_via_rtn_blockwise(self, all_to_quantized_module_names: list[str])
                     convert_fp8_model_to_16b_model(block, dtype=self.amp_dtype)
 
                 if self.device_map == "auto" or (isinstance(self.device_map, str) and "," in self.device_map):
-                    set_auto_device_map_for_block(block,self.devcie_map,input_ids,  self.low_gpu_mem_usage,self.mem_per_param_scale)
+                    set_auto_device_map_for_block(
+                        block, self.device_map, input_ids, self.low_gpu_mem_usage, self.mem_per_param_scale
+                    )
 
                 # Dispatch model if needed
                 if self.device_map is not None:
@@ -2450,7 +2448,9 @@ def _quantize_block(
                     set_module(block, n, new_layer)
 
         if self.device_map == "auto" or (isinstance(self.device_map, str) and "," in self.device_map):
-            set_auto_device_map_for_block(block, self.device_map,input_ids,self.low_gpu_mem_usage,self.mem_per_param_scale)
+            set_auto_device_map_for_block(
+                block, self.device_map, input_ids, self.low_gpu_mem_usage, self.mem_per_param_scale
+            )
 
         if self.device_map is not None:
             for n, m in block.named_modules():
@@ -3085,5 +3085,3 @@ def _sampling_inputs(
 
 class LLMCompressor(BaseCompressor):
     pass
-
-
diff --git a/auto_round/utils_bk/__init__.py b/auto_round/utils_bk/__init__.py
index e69de29bb..0d2740cdb 100644
--- a/auto_round/utils_bk/__init__.py
+++ b/auto_round/utils_bk/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2025 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
index 7ce247b42..5ea42a5f4 100644
--- a/auto_round/utils_bk/device.py
+++ b/auto_round/utils_bk/device.py
@@ -1,11 +1,24 @@
+# Copyright (c) 2025 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
 from typing import Union
 
 import torch
 
 from auto_round.logger import logger
-
-from auto_round.utils import detect_device, get_module, get_device_memory, estimate_tuning_block_mem, check_to_quantized
-import re
+from auto_round.utils import check_to_quantized, detect_device, estimate_tuning_block_mem, get_device_memory, get_module
 
 
 def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
@@ -64,13 +77,15 @@ def set_non_auto_device_map(model: torch.nn.Module, device_map, quant_layer_name
         for info in infos:
             index = info.find(":")
             key = info[:index]
-            value = info[index + 1:]
+            value = info[index + 1 :]
             device_map_dict[key] = value
         device_map = device_map_dict
     if quant_layer_names is not None:
         names = quant_layer_names
     else:
-        names = [n for n, m in model.model.named_modules() if len(list(m.children())) == 0] # if it's a block, it will be incorrect
+        names = [
+            n for n, m in model.model.named_modules() if len(list(m.children())) == 0
+        ]  # if it's a block, it will be incorrect
     for key, device in device_map.items():
         if isinstance(device, str) and device.isdigit():
             device = int(device)
@@ -86,9 +101,9 @@ def set_non_auto_device_map(model: torch.nn.Module, device_map, quant_layer_name
                 logger.warning(f"{key} in `device_map` dose not match any modules, please have a check")
 
 
-
-def set_auto_device_map_for_block(block: torch.nn.Module, device_map, input_ids: list[torch.Tensor],
-                                  low_gpu_mem_usage=False,mem_per_param_scale=13.0) -> None:
+def set_auto_device_map_for_block(
+    block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False, mem_per_param_scale=13.0
+) -> None:
     """Automatically sets the device map for the block based on available GPUs and memory constraints."""
     if torch.cuda.is_available():
         num_gpus = torch.cuda.device_count()
@@ -108,14 +123,11 @@ def set_auto_device_map_for_block(block: torch.nn.Module, device_map, input_ids:
         cuda_devices = [f"cuda:{i}" for i in range(num_gpus)]
         device_0 = "cuda:0"
 
-    device_0_memory = get_device_memory(
-    device_list[0] if device_list else 0
-    )
+    device_0_memory = get_device_memory(device_list[0] if device_list else 0)
     block_memory, input_output_memory = estimate_tuning_block_mem(block, input_ids)
     if low_gpu_mem_usage:
         input_output_memory = 0
 
-
     if (block_memory * mem_per_param_scale + input_output_memory) < device_0_memory:
         return  # fit in one GPU
 
@@ -150,4 +162,4 @@ def set_auto_device_map_for_block(block: torch.nn.Module, device_map, input_ids:
                         f"Block {block.tmp_name} not fit in available GPU memory. "
                         "Consider using more GPUs or reducing mem_per_param_scale if OOM occurs."
                     )
-    set_non_auto_device_map(block,device_map,names)
+    set_non_auto_device_map(block, device_map, names)

From d26bc74aae8f3cc29e7102a2951adc6518eee535 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Mon, 27 Oct 2025 15:08:50 +0800
Subject: [PATCH 05/36] refine device_map code

---
 auto_round/compressors/base.py | 6 +++---
 auto_round/utils_bk/device.py  | 7 +++++--
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index c20f26b36..06bee1702 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -89,7 +89,7 @@
     to_dtype,
     unsupported_meta_device,
 )
-from auto_round.utils_bk.device import get_major_device, set_non_auto_device_map, set_auto_device_map_for_block
+from auto_round.utils_bk.device import get_major_device, set_non_auto_device_map, set_auto_device_map_for_block_with_tuning
 from auto_round.wrapper import WrapperLinear, WrapperMultiblock, unwrapper_block, unwrapper_layer, wrapper_block
 
 
@@ -1436,7 +1436,7 @@ def _quantize_via_rtn_blockwise(self, all_to_quantized_module_names: list[str])
                     convert_fp8_model_to_16b_model(block, dtype=self.amp_dtype)
 
                 if self.device_map == "auto" or (isinstance(self.device_map, str) and "," in self.device_map):
-                    set_auto_device_map_for_block(block,self.devcie_map,input_ids,  self.low_gpu_mem_usage,self.mem_per_param_scale)
+                    set_auto_device_map_for_block_with_tuning(block, self.devcie_map, input_ids, self.low_gpu_mem_usage, self.mem_per_param_scale)
 
                 # Dispatch model if needed
                 if self.device_map is not None:
@@ -2450,7 +2450,7 @@ def _quantize_block(
                     set_module(block, n, new_layer)
 
         if self.device_map == "auto" or (isinstance(self.device_map, str) and "," in self.device_map):
-            set_auto_device_map_for_block(block, self.device_map,input_ids,self.low_gpu_mem_usage,self.mem_per_param_scale)
+            set_auto_device_map_for_block_with_tuning(block, self.device_map, input_ids, self.low_gpu_mem_usage, self.mem_per_param_scale)
 
         if self.device_map is not None:
             for n, m in block.named_modules():
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
index 7ce247b42..29d8dcb63 100644
--- a/auto_round/utils_bk/device.py
+++ b/auto_round/utils_bk/device.py
@@ -87,8 +87,8 @@ def set_non_auto_device_map(model: torch.nn.Module, device_map, quant_layer_name
 
 
 
-def set_auto_device_map_for_block(block: torch.nn.Module, device_map, input_ids: list[torch.Tensor],
-                                  low_gpu_mem_usage=False,mem_per_param_scale=13.0) -> None:
+def set_auto_device_map_for_block_with_tuning(block: torch.nn.Module, device_map, input_ids: list[torch.Tensor],
+                                              low_gpu_mem_usage=False, mem_per_param_scale=13.0) -> None:
     """Automatically sets the device map for the block based on available GPUs and memory constraints."""
     if torch.cuda.is_available():
         num_gpus = torch.cuda.device_count()
@@ -151,3 +151,6 @@ def set_auto_device_map_for_block(block: torch.nn.Module, device_map, input_ids:
                         "Consider using more GPUs or reducing mem_per_param_scale if OOM occurs."
                     )
     set_non_auto_device_map(block,device_map,names)
+
+
+

From 04697c5df1edb2c5f0706e65824dd0ccaebe5374 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 27 Oct 2025 07:12:51 +0000
Subject: [PATCH 06/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/compressors/base.py | 8 +++++---
 auto_round/utils_bk/device.py  | 1 -
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index 012fc5d0e..92e366c81 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -88,9 +88,11 @@
     to_dtype,
     unsupported_meta_device,
 )
-
-from auto_round.utils_bk.device import get_major_device, set_auto_device_map_for_block_with_tuning, set_non_auto_device_map
-
+from auto_round.utils_bk.device import (
+    get_major_device,
+    set_auto_device_map_for_block_with_tuning,
+    set_non_auto_device_map,
+)
 from auto_round.wrapper import WrapperLinear, WrapperMultiblock, unwrapper_block, unwrapper_layer, wrapper_block
 
 
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
index bc092ca5a..53f8a0463 100644
--- a/auto_round/utils_bk/device.py
+++ b/auto_round/utils_bk/device.py
@@ -101,7 +101,6 @@ def set_non_auto_device_map(model: torch.nn.Module, device_map, quant_layer_name
                 logger.warning(f"{key} in `device_map` dose not match any modules, please have a check")
 
 
-
 def set_auto_device_map_for_block_with_tuning(
     block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False, mem_per_param_scale=13.0
 ):

From 3d73d47e59f80dd758d4a8c2935911c2c11d2a18 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Mon, 27 Oct 2025 15:17:47 +0800
Subject: [PATCH 07/36] fix adam issue

---
 auto_round/compressors/__init__.py                     | 3 ++-
 auto_round/compressors/{adam_comperssor.py => adam.py} | 0
 2 files changed, 2 insertions(+), 1 deletion(-)
 rename auto_round/compressors/{adam_comperssor.py => adam.py} (100%)

diff --git a/auto_round/compressors/__init__.py b/auto_round/compressors/__init__.py
index dbf47b9c2..396505214 100644
--- a/auto_round/compressors/__init__.py
+++ b/auto_round/compressors/__init__.py
@@ -12,7 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from auto_round.compressors.base import *
+from auto_round.compressors.adam import AdamCompressor
+from auto_round.compressors.base import BaseCompressor,LLMCompressor
 from auto_round.compressors.mllm.compressor import MLLMCompressor
 from auto_round.compressors.diffusion.compressor import DiffusionCompressor
 from auto_round.compressors.config import (
diff --git a/auto_round/compressors/adam_comperssor.py b/auto_round/compressors/adam.py
similarity index 100%
rename from auto_round/compressors/adam_comperssor.py
rename to auto_round/compressors/adam.py

From 3d9dbe824a95ae6269bb585c052b102c33ca54f9 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 27 Oct 2025 07:18:17 +0000
Subject: [PATCH 08/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/compressors/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/auto_round/compressors/__init__.py b/auto_round/compressors/__init__.py
index 396505214..35786b9b8 100644
--- a/auto_round/compressors/__init__.py
+++ b/auto_round/compressors/__init__.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from auto_round.compressors.adam import AdamCompressor
-from auto_round.compressors.base import BaseCompressor,LLMCompressor
+from auto_round.compressors.base import BaseCompressor, LLMCompressor
 from auto_round.compressors.mllm.compressor import MLLMCompressor
 from auto_round.compressors.diffusion.compressor import DiffusionCompressor
 from auto_round.compressors.config import (

From b261a2163c07f5aeba857aab29fbed4058f5b593 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Mon, 27 Oct 2025 15:21:27 +0800
Subject: [PATCH 09/36] tiny change

---
 auto_round/compressors/__init__.py | 3 ++-
 auto_round/compressors/base.py     | 4 ----
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/auto_round/compressors/__init__.py b/auto_round/compressors/__init__.py
index 35786b9b8..03983b2c7 100644
--- a/auto_round/compressors/__init__.py
+++ b/auto_round/compressors/__init__.py
@@ -13,7 +13,8 @@
 # limitations under the License.
 
 from auto_round.compressors.adam import AdamCompressor
-from auto_round.compressors.base import BaseCompressor, LLMCompressor
+from auto_round.compressors.base import BaseCompressor
+from auto_round.compressors.base import BaseCompressor as LLMCompressor
 from auto_round.compressors.mllm.compressor import MLLMCompressor
 from auto_round.compressors.diffusion.compressor import DiffusionCompressor
 from auto_round.compressors.config import (
diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index 92e366c81..f0f5051bb 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -3083,7 +3083,3 @@ def _sampling_inputs(
                 current_input_others[key] = input_others[key]
 
         return current_input_ids, current_input_others
-
-
-class LLMCompressor(BaseCompressor):
-    pass

From b6dd6fe5b2245243f753dc88f75ac62d3c3d3cff Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Mon, 27 Oct 2025 16:22:48 +0800
Subject: [PATCH 10/36] update

---
 auto_round/compressors/adam.py |   4 +-
 auto_round/compressors/base.py |   4 +-
 auto_round/utils_bk/device.py  | 107 ++++++++++++++++++++++++++++++++-
 3 files changed, 108 insertions(+), 7 deletions(-)

diff --git a/auto_round/compressors/adam.py b/auto_round/compressors/adam.py
index 37e14329b..2e6511899 100644
--- a/auto_round/compressors/adam.py
+++ b/auto_round/compressors/adam.py
@@ -16,8 +16,8 @@
 
 import torch
 
-from auto_round import QuantizationScheme
-from auto_round.compressors import BaseCompressor
+from auto_round.schemes import QuantizationScheme
+from auto_round.compressors.base import BaseCompressor
 from auto_round.utils import check_is_cpu, htcore, is_hpex_available
 
 
diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index f0f5051bb..f9bf48001 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -2260,8 +2260,8 @@ def _quantize_layer(
         best_loss = torch.finfo(torch.float).max
         scaler = self._get_scaler()  # pylint: disable=assignment-from-none
         init_loss = None
-        gradient_accumulate_steps = self.batch_size  ##Force to low gpu
-        batch_size = 1  ##Force to low gpu
+        gradient_accumulate_steps = self.batch_size  # Force to low gpu
+        batch_size = 1  # Force to low gpu
         pick_samples = batch_size * gradient_accumulate_steps
         pick_samples = min(nsamples, pick_samples)
         if self.sampler != "rand":
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
index 53f8a0463..a156aa99d 100644
--- a/auto_round/utils_bk/device.py
+++ b/auto_round/utils_bk/device.py
@@ -18,8 +18,9 @@
 import torch
 
 from auto_round.logger import logger
-from auto_round.utils import check_to_quantized, detect_device, estimate_tuning_block_mem, get_device_memory, get_module
-
+from auto_round.utils import check_to_quantized, detect_device, estimate_tuning_block_mem, get_device_memory, \
+    get_module, get_block_names, get_layer_features
+from itertools import combinations
 
 def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
     if isinstance(device_map, (str, torch.device, int)):
@@ -61,7 +62,9 @@ def set_tuning_device_for_layer(model, name: str, device: str) -> None:
         module.tuning_device = device
 
 
-def set_non_auto_device_map(model: torch.nn.Module, device_map, quant_layer_names=None):
+def set_non_auto_device_map(model: torch.nn.Module,
+                            device_map:Union[str,int,dict],
+                            quant_layer_names:Union[None,list,tuple]=None)->None:
     if not device_map:
         return
     if device_map == "auto":
@@ -164,3 +167,101 @@ def set_auto_device_map_for_block_with_tuning(
                     )
 
     set_non_auto_device_map(block, device_map, names)
+
+
+def set_device_map_for_auto_scheme(model, device_map):
+    if not device_map:
+        return
+    if device_map=="auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
+        set_avg_auto_device_map(model)
+    else:
+        set_non_auto_device_map(model,device_map)
+
+
+
+
+
+def partition_dict_numbers(number_dict, n):
+    """
+    Partition a dictionary of numbers into N groups with approximately equal sums
+
+    Args:
+        number_dict: dict with string keys and number values
+        n: number of groups to partition into
+
+    Returns:
+        list of dictionaries, each representing a group
+    """
+    total_sum = sum(number_dict.values())
+    target = total_sum // n
+
+    def find_subset(items, target):
+        # Find a subset with sum closest to but not exceeding target
+        keys, values = zip(*items) if items else ([], [])
+        for r in range(len(items), 0, -1):
+            for combo in combinations(range(len(items)), r):
+                subset_sum = sum(values[i] for i in combo)
+                if subset_sum <= target:
+                    return {keys[i]: values[i] for i in combo}
+        return {}
+
+    # Convert dict to list of (key, value) tuples for processing
+    items = list(number_dict.items())
+    result = []
+    remaining_items = items.copy()
+
+    for i in range(n - 1):
+        subset_dict = find_subset(remaining_items, target)
+        result.append(subset_dict)
+        # Remove allocated items from remaining list
+        for key in subset_dict.keys():
+            remaining_items = [(k, v) for k, v in remaining_items if k != key]
+
+    # Last group contains all remaining items
+    result.append(dict(remaining_items))
+    return result
+
+
+
+
+def set_avg_auto_device_map(model, device_map):
+    block_name_list = get_block_names(model)
+    params_dict = {}
+    if isinstance(device_map, str) and "," in device_map:
+        device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
+        num_devices = len(device_list)
+    else:
+        if torch.cuda.is_available():
+            num_devices = torch.cuda.device_count()
+        elif torch.xpu.is_available():
+            logger.warning_once("XPU does not support auto device map yet, using device 0 for tuning.")
+            return
+        else:
+            return
+
+    for block_names in block_name_list:
+        for block_name in block_names:
+            block_module = get_module(model,block_name)
+            for n,m in block_module.named_modules():
+                in_features,out_features = get_layer_features(m)
+                params_dict[n] = in_features*out_features
+            res = partition_dict_numbers(params_dict, num_devices)
+
+
+
+if __name__ == "__main__":
+    # Example usage
+    number_dict = {
+        "item1": 90,
+        "item2": 20,
+        "item3": 30,
+        "item4": 40,
+        "item5": 50,
+        "item6": 60
+    }
+
+    groups = partition_dict_numbers(number_dict, 3)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+

From 06beee364227e25037d164bf9dbb4832825b733a Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 27 Oct 2025 08:23:56 +0000
Subject: [PATCH 11/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/compressors/adam.py |  2 +-
 auto_round/utils_bk/device.py  | 49 +++++++++++++++-------------------
 2 files changed, 22 insertions(+), 29 deletions(-)

diff --git a/auto_round/compressors/adam.py b/auto_round/compressors/adam.py
index 2e6511899..4606eab3a 100644
--- a/auto_round/compressors/adam.py
+++ b/auto_round/compressors/adam.py
@@ -16,8 +16,8 @@
 
 import torch
 
-from auto_round.schemes import QuantizationScheme
 from auto_round.compressors.base import BaseCompressor
+from auto_round.schemes import QuantizationScheme
 from auto_round.utils import check_is_cpu, htcore, is_hpex_available
 
 
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
index a156aa99d..ee24dd1e1 100644
--- a/auto_round/utils_bk/device.py
+++ b/auto_round/utils_bk/device.py
@@ -13,14 +13,22 @@
 # limitations under the License.
 
 import re
+from itertools import combinations
 from typing import Union
 
 import torch
 
 from auto_round.logger import logger
-from auto_round.utils import check_to_quantized, detect_device, estimate_tuning_block_mem, get_device_memory, \
-    get_module, get_block_names, get_layer_features
-from itertools import combinations
+from auto_round.utils import (
+    check_to_quantized,
+    detect_device,
+    estimate_tuning_block_mem,
+    get_block_names,
+    get_device_memory,
+    get_layer_features,
+    get_module,
+)
+
 
 def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
     if isinstance(device_map, (str, torch.device, int)):
@@ -62,9 +70,9 @@ def set_tuning_device_for_layer(model, name: str, device: str) -> None:
         module.tuning_device = device
 
 
-def set_non_auto_device_map(model: torch.nn.Module,
-                            device_map:Union[str,int,dict],
-                            quant_layer_names:Union[None,list,tuple]=None)->None:
+def set_non_auto_device_map(
+    model: torch.nn.Module, device_map: Union[str, int, dict], quant_layer_names: Union[None, list, tuple] = None
+) -> None:
     if not device_map:
         return
     if device_map == "auto":
@@ -172,13 +180,10 @@ def set_auto_device_map_for_block_with_tuning(
 def set_device_map_for_auto_scheme(model, device_map):
     if not device_map:
         return
-    if device_map=="auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
+    if device_map == "auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
         set_avg_auto_device_map(model)
     else:
-        set_non_auto_device_map(model,device_map)
-
-
-
+        set_non_auto_device_map(model, device_map)
 
 
 def partition_dict_numbers(number_dict, n):
@@ -222,8 +227,6 @@ def find_subset(items, target):
     return result
 
 
-
-
 def set_avg_auto_device_map(model, device_map):
     block_name_list = get_block_names(model)
     params_dict = {}
@@ -241,27 +244,17 @@ def set_avg_auto_device_map(model, device_map):
 
     for block_names in block_name_list:
         for block_name in block_names:
-            block_module = get_module(model,block_name)
-            for n,m in block_module.named_modules():
-                in_features,out_features = get_layer_features(m)
-                params_dict[n] = in_features*out_features
+            block_module = get_module(model, block_name)
+            for n, m in block_module.named_modules():
+                in_features, out_features = get_layer_features(m)
+                params_dict[n] = in_features * out_features
             res = partition_dict_numbers(params_dict, num_devices)
 
 
-
 if __name__ == "__main__":
     # Example usage
-    number_dict = {
-        "item1": 90,
-        "item2": 20,
-        "item3": 30,
-        "item4": 40,
-        "item5": 50,
-        "item6": 60
-    }
+    number_dict = {"item1": 90, "item2": 20, "item3": 30, "item4": 40, "item5": 50, "item6": 60}
 
     groups = partition_dict_numbers(number_dict, 3)
     for i, group in enumerate(groups):
         print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
-
-

From 0a2bd4baf1c9feb9e82923a47770397300d19ede Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Mon, 27 Oct 2025 17:06:42 +0800
Subject: [PATCH 12/36] try to fix preci

---
 auto_round/compressors/base.py |   3 +-
 auto_round/utils_bk/device.py  | 138 +++++++++++++++++++++++----------
 2 files changed, 97 insertions(+), 44 deletions(-)

diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index f9bf48001..299c3c77e 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -298,8 +298,7 @@ def __init__(
 
         # Set device, must place after model loading
         self.device = get_major_device(device_map)
-
-        set_non_auto_device_map(self.device_map)
+        set_non_auto_device_map(self.model, self.device_map)
 
         # Tuning hyperparameters
         self.seed = seed
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
index a156aa99d..0639b98fd 100644
--- a/auto_round/utils_bk/device.py
+++ b/auto_round/utils_bk/device.py
@@ -169,64 +169,85 @@ def set_auto_device_map_for_block_with_tuning(
     set_non_auto_device_map(block, device_map, names)
 
 
-def set_device_map_for_auto_scheme(model, device_map):
-    if not device_map:
-        return
-    if device_map=="auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
-        set_avg_auto_device_map(model)
-    else:
-        set_non_auto_device_map(model,device_map)
-
-
-
+# def set_device_map_for_auto_scheme(model, device_map):
+#     if not device_map:
+#         return
+#     if device_map=="auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
+#         set_avg_auto_device_map(model)
+#     else:
+#         set_non_auto_device_map(model,device_map)
 
 
 def partition_dict_numbers(number_dict, n):
     """
     Partition a dictionary of numbers into N groups with approximately equal sums
-
-    Args:
-        number_dict: dict with string keys and number values
-        n: number of groups to partition into
-
-    Returns:
-        list of dictionaries, each representing a group
     """
+    # Edge cases
+    if n > len(number_dict):
+        groups = []
+        for key, value in number_dict.items():
+            groups.append({key: value})
+        for _ in range(n - len(number_dict)):
+            groups.append({})
+        return groups
+
+    if n == len(number_dict):
+        return [{key: value} for key, value in number_dict.items()]
+
     total_sum = sum(number_dict.values())
-    target = total_sum // n
-
-    def find_subset(items, target):
-        # Find a subset with sum closest to but not exceeding target
-        keys, values = zip(*items) if items else ([], [])
-        for r in range(len(items), 0, -1):
-            for combo in combinations(range(len(items)), r):
-                subset_sum = sum(values[i] for i in combo)
-                if subset_sum <= target:
-                    return {keys[i]: values[i] for i in combo}
-        return {}
-
-    # Convert dict to list of (key, value) tuples for processing
+    # target = total_sum / n  # Use float for better precision
+
     items = list(number_dict.items())
     result = []
-    remaining_items = items.copy()
+    remaining = items.copy()
+
+    def find_optimal_subset(arr, target):
+        """Find subset with sum closest to target"""
+        best_subset = []
+        best_diff = float('inf')
 
+        # Try all possible subset sizes
+        for r in range(1, len(arr) + 1):
+            for combo in combinations(arr, r):
+                current_sum = sum(value for _, value in combo)
+                current_diff = abs(current_sum - target)
+
+                # If we found a perfect match, return immediately
+                if current_diff == 0:
+                    return list(combo)
+
+                # Update the best subset if this is better
+                if current_diff < best_diff and current_sum <= total_sum:
+                    best_diff = current_diff
+                    best_subset = list(combo)
+
+        return best_subset
+
+    # Distribute items into n-1 groups
     for i in range(n - 1):
-        subset_dict = find_subset(remaining_items, target)
-        result.append(subset_dict)
-        # Remove allocated items from remaining list
-        for key in subset_dict.keys():
-            remaining_items = [(k, v) for k, v in remaining_items if k != key]
-
-    # Last group contains all remaining items
-    result.append(dict(remaining_items))
-    return result
+        if not remaining:
+            break
+
+        # Calculate dynamic target based on remaining items
+        remaining_target = sum(value for _, value in remaining) / (n - i)
+        subset = find_optimal_subset(remaining, remaining_target)
 
+        result.append(dict(subset))
+
+        # Remove allocated items
+        for item in subset:
+            remaining.remove(item)
+
+    # Last group gets all remaining items
+    result.append(dict(remaining))
+
+    return result
 
 
 
 def set_avg_auto_device_map(model, device_map):
     block_name_list = get_block_names(model)
-    params_dict = {}
+    device_list = None
     if isinstance(device_map, str) and "," in device_map:
         device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
         num_devices = len(device_list)
@@ -239,13 +260,29 @@ def set_avg_auto_device_map(model, device_map):
         else:
             return
 
+    if device_list:
+        cuda_devices = [f"cuda:{i}" for i in device_list]
+    else:
+        cuda_devices = [f"cuda:{i}" for i in range(num_devices)]
+
     for block_names in block_name_list:
         for block_name in block_names:
+            params_dict = {}
             block_module = get_module(model,block_name)
             for n,m in block_module.named_modules():
                 in_features,out_features = get_layer_features(m)
                 params_dict[n] = in_features*out_features
-            res = partition_dict_numbers(params_dict, num_devices)
+
+            res_list = partition_dict_numbers(params_dict, num_devices)
+            device_index = 0
+            for res in res_list:
+                for key in res.keys():
+                    tmp_m = get_module(model,key)
+                    set_tuning_device_for_layer(block_module,tmp_m, cuda_devices[device_index])
+
+
+
+
 
 
 
@@ -260,8 +297,25 @@ def set_avg_auto_device_map(model, device_map):
         "item6": 60
     }
 
+    groups = partition_dict_numbers(number_dict, 10)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+    groups = partition_dict_numbers(number_dict, 6)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+    groups = partition_dict_numbers(number_dict, 4)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
     groups = partition_dict_numbers(number_dict, 3)
     for i, group in enumerate(groups):
         print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
 
+    groups = partition_dict_numbers(number_dict, 2)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+
 

From 7cfc72b5112920ad3830ccb436a20dd9d957ee88 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 27 Oct 2025 09:09:53 +0000
Subject: [PATCH 13/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/utils_bk/device.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
index a073a9b0a..9eb197af2 100644
--- a/auto_round/utils_bk/device.py
+++ b/auto_round/utils_bk/device.py
@@ -187,7 +187,6 @@ def set_auto_device_map_for_block_with_tuning(
 #         set_non_auto_device_map(model, device_map)
 
 
-
 def partition_dict_numbers(number_dict, n):
     """
     Partition a dictionary of numbers into N groups with approximately equal sums
@@ -214,7 +213,7 @@ def partition_dict_numbers(number_dict, n):
     def find_optimal_subset(arr, target):
         """Find subset with sum closest to target"""
         best_subset = []
-        best_diff = float('inf')
+        best_diff = float("inf")
 
         # Try all possible subset sizes
         for r in range(1, len(arr) + 1):
@@ -253,6 +252,7 @@ def find_optimal_subset(arr, target):
 
     return result
 
+
 def set_avg_auto_device_map(model, device_map):
     block_name_list = get_block_names(model)
     device_list = None
@@ -276,18 +276,17 @@ def set_avg_auto_device_map(model, device_map):
     for block_names in block_name_list:
         for block_name in block_names:
             params_dict = {}
-            block_module = get_module(model,block_name)
-            for n,m in block_module.named_modules():
-                in_features,out_features = get_layer_features(m)
-                params_dict[n] = in_features*out_features
+            block_module = get_module(model, block_name)
+            for n, m in block_module.named_modules():
+                in_features, out_features = get_layer_features(m)
+                params_dict[n] = in_features * out_features
 
             res_list = partition_dict_numbers(params_dict, num_devices)
             device_index = 0
             for res in res_list:
                 for key in res.keys():
-                    tmp_m = get_module(model,key)
-                    set_tuning_device_for_layer(block_module,tmp_m, cuda_devices[device_index])
-
+                    tmp_m = get_module(model, key)
+                    set_tuning_device_for_layer(block_module, tmp_m, cuda_devices[device_index])
 
 
 if __name__ == "__main__":

From 8dbd3b6ff099edc5b66e64aa494a53e37678f1fd Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Mon, 27 Oct 2025 20:00:09 +0800
Subject: [PATCH 14/36] trigger ut

---
 auto_round/auto_scheme/gen_auto_scheme.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/auto_round/auto_scheme/gen_auto_scheme.py b/auto_round/auto_scheme/gen_auto_scheme.py
index dd7d0f0b7..1022e8d43 100644
--- a/auto_round/auto_scheme/gen_auto_scheme.py
+++ b/auto_round/auto_scheme/gen_auto_scheme.py
@@ -83,6 +83,7 @@ def get_layer_config(self) -> dict[str, dict]:
         method_func = AUTO_SCHEME_METHODS[method_name]
         if self.auto_scheme.low_gpu_mem_usage:
             self.enable_torch_compile = False
+
         layer_config = method_func(
             self.auto_scheme,
             self.model,

From 7c17f29aa077a8d1516c4cc9aca1cd66ca71c290 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Oct 2025 01:32:51 +0000
Subject: [PATCH 15/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/compressors/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index 44405ff05..3ea3989e6 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -65,8 +65,8 @@
     TORCH_VERSION_AT_LEAST_2_6,
     CpuInfo,
     check_and_mark_fp8_model,
-    check_need_act_calibration,
     check_is_cpu,
+    check_need_act_calibration,
     check_seqlen_compatible,
     check_to_quantized,
     clear_memory,

From c6396a9435cebd02e5439034858f092b431a35a2 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 09:35:53 +0800
Subject: [PATCH 16/36] fix merge issue

---
 auto_round/compressors/base.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index 3ea3989e6..e8a5fd176 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -35,8 +35,6 @@
     check_need_act_calibration,
     check_skippable_keywords,
     collect_best_params,
-    get_fp_layer_names,
-    get_layer_config_by_gguf_format,
     get_shared_keys,
     gguf_args_check,
     infer_bits_by_data_type,
@@ -65,8 +63,6 @@
     TORCH_VERSION_AT_LEAST_2_6,
     CpuInfo,
     check_and_mark_fp8_model,
-    check_is_cpu,
-    check_need_act_calibration,
     check_seqlen_compatible,
     check_to_quantized,
     clear_memory,
@@ -79,8 +75,6 @@
     find_matching_blocks,
     flatten_list,
     get_block_names,
-    get_device_memory,
-    get_layer_features,
     get_layer_names_in_block,
     get_module,
     htcore,

From abf94a4b1afe553ddd07a3cea179614411355afc Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 10:06:33 +0800
Subject: [PATCH 17/36] merge utils

---
 auto_round/compressors/base.py  |   2 +-
 auto_round/utils/device.py      | 303 +++++++++++++++++++++++++++++-
 auto_round/utils_bk/__init__.py |  13 --
 auto_round/utils_bk/device.py   | 314 --------------------------------
 4 files changed, 302 insertions(+), 330 deletions(-)
 delete mode 100644 auto_round/utils_bk/__init__.py
 delete mode 100644 auto_round/utils_bk/device.py

diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index e8a5fd176..70370b200 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -90,7 +90,7 @@
     to_dtype,
     unsupported_meta_device,
 )
-from auto_round.utils_bk.device import (
+from auto_round.utils.device import (
     get_major_device,
     set_auto_device_map_for_block_with_tuning,
     set_non_auto_device_map,
diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 53abc57aa..b840510d1 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -14,12 +14,28 @@
 import gc
 import os
 from functools import lru_cache
-from typing import Any, Callable, Dict, List, Tuple, Union
+from typing import  Callable
 
 import cpuinfo
+
+
+import re
+from itertools import combinations
+from typing import Union
+
 import torch
 
 from auto_round.logger import logger
+from auto_round.utils import (
+    check_to_quantized,
+    detect_device,
+    estimate_tuning_block_mem,
+    get_block_names,
+    get_device_memory,
+    get_layer_features,
+    get_module,
+)
+
 
 # Note on HPU usage:
 # There are two modes available for enabling auto-round on HPU:
@@ -262,7 +278,7 @@ def is_valid_digit(s):
     return device
 
 
-def get_device_and_parallelism(device: Union[str, torch.device, int]) -> Tuple[str, bool]:
+def get_device_and_parallelism(device: Union[str, torch.device, int]) -> tuple[str, bool]:
     if isinstance(device, str):
         devices = device.replace(" ", "").split(",")
     elif isinstance(device, int):
@@ -539,3 +555,286 @@ def get_device_memory(i: int = 0) -> int:
     else:
         raise RuntimeError("No supported device found (CUDA or XPU).")
     return total_memory
+
+def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
+    if isinstance(device_map, (str, torch.device, int)):
+        device = detect_device(device_map)
+        return device
+
+    if isinstance(device_map, dict) and device_map:
+        tmp_devices = []
+        for val in device_map.values():
+            if isinstance(val, (str, torch.device, int)):  # could optimize
+                tmp_device = detect_device(val)
+                tmp_device = tmp_device.split(":")[0]
+                tmp_devices.append(tmp_device)
+        tmp_devices = list(set(tmp_devices))
+        if len(tmp_devices) > 1:
+            logger.warning(
+                f"there are multiple device types in the device_map, "
+                f"please make sure they are correct,use the first none-cpu device {tmp_devices[0]} as the core device "
+            )
+
+        for device in tmp_devices:
+            if device != "cpu":
+                return device
+
+        device = tmp_devices[0]
+        return device
+    logger.warning(f"device_map should be [str, torch.device, int, dict], but got {type(device_map)}")
+    return "cpu"
+
+
+def set_tuning_device_for_layer(model, name: str, device: str) -> None:
+    """Sets the device for a module if it matches the given name."""
+    module = get_module(model, name)
+    if hasattr(module, "tuning_device") and module.tuning_device != device:
+        logger.warning(
+            f"multiple devices have been set for layer {name}, keeping original device {module.tuning_device}"
+        )
+    else:
+        module.tuning_device = device
+
+
+def set_non_auto_device_map(
+    model: torch.nn.Module, device_map: Union[str, int, dict], quant_layer_names: Union[None, list, tuple] = None
+) -> None:
+    if not device_map:
+        return
+    if device_map == "auto":
+        return
+    if isinstance(device_map, str) and "," in device_map:  # auto device map
+        return
+    if isinstance(device_map, int):
+        return
+    if isinstance(device_map, str):
+        device_map = device_map.replace(" ", "")
+        infos = device_map.split(",")
+        device_map_dict = {}
+        for info in infos:
+            index = info.find(":")
+            key = info[:index]
+            value = info[index + 1 :]
+            device_map_dict[key] = value
+        device_map = device_map_dict
+    if quant_layer_names is not None:
+        names = quant_layer_names
+    else:
+        names = [
+            n for n, m in model.model.named_modules() if len(list(m.children())) == 0
+        ]  # if it's a block, it will be incorrect
+    for key, device in device_map.items():
+        if isinstance(device, str) and device.isdigit():
+            device = int(device)
+        device = detect_device(device)
+        if key in names:
+            module = get_module(model, key)
+            module.tuning_device = device
+        else:
+            matching_names = [name for name in names if re.match(key, name)]
+            for name in matching_names:
+                set_non_auto_device_map(model, name, device)
+            if not matching_names:
+                logger.warning(f"{key} in `device_map` dose not match any modules, please have a check")
+
+
+def set_auto_device_map_for_block_with_tuning(
+    block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False, mem_per_param_scale=13.0
+):
+    """Automatically sets the device map for the block based on available GPUs and memory constraints."""
+    if torch.cuda.is_available():
+        num_gpus = torch.cuda.device_count()
+    elif torch.xpu.is_available():
+        logger.warning_once("XPU does not support auto device map yet, using device 0 for tuning.")
+        return
+    else:
+        raise RuntimeError("No CUDA or XPU devices found.")
+    device_list = None
+    if isinstance(device_map, str) and "," in device_map:
+        device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
+
+    if device_list:
+        cuda_devices = [f"cuda:{i}" for i in device_list]
+        device_0 = cuda_devices[0]
+    else:
+        cuda_devices = [f"cuda:{i}" for i in range(num_gpus)]
+        device_0 = "cuda:0"
+
+    device_0_memory = get_device_memory(device_list[0] if device_list else 0)
+    block_memory, input_output_memory = estimate_tuning_block_mem(block, input_ids)
+    if low_gpu_mem_usage:
+        input_output_memory = 0
+
+    if (block_memory * mem_per_param_scale + input_output_memory) < device_0_memory:
+        return  # fit in one GPU
+
+    device_map = {}
+    device_memory = {device: get_device_memory(int(device.split(":")[1])) for device in cuda_devices}
+    device_memory[device_0] = device_0_memory - input_output_memory
+
+    device_idx = 0
+    names = []
+    # First, fill device 0 to its maximum capacity, then distribute the remaining layers evenly across other devices
+    for n, m in block.named_modules():
+        if check_to_quantized(m):
+            layer_name = m.tmp_name
+            names.append(layer_name)
+            layer_memory = m.weight.nbytes / 1024**3
+            if device_idx == 0 and layer_memory * mem_per_param_scale < device_memory[cuda_devices[device_idx]]:
+                device_map[layer_name] = cuda_devices[device_idx]
+                device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
+            elif device_idx == 0:
+                device_idx += 1  # Move to the next device once device 0 is full
+                device_map[layer_name] = cuda_devices[device_idx]
+                device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
+            else:
+                # Calculate the target device index based on even distribution
+                sorted_devices = sorted(cuda_devices, key=lambda d: device_memory[d], reverse=True)
+                device_idx = sorted_devices[0]
+                if layer_memory * mem_per_param_scale < device_memory[device_idx]:
+                    device_map[layer_name] = device_idx
+                    device_memory[device_idx] -= layer_memory * mem_per_param_scale
+                else:
+                    logger.warning_once(
+                        f"Block {block.tmp_name} not fit in available GPU memory. "
+                        "Consider using more GPUs or reducing mem_per_param_scale if OOM occurs."
+                    )
+
+    set_non_auto_device_map(block, device_map, names)
+
+
+#
+# def set_device_map_for_auto_scheme(model, device_map):
+#     if not device_map:
+#         return
+#     if device_map == "auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
+#         set_avg_auto_device_map(model)
+#     else:
+#         set_non_auto_device_map(model, device_map)
+
+
+def partition_dict_numbers(number_dict, n):
+    """
+    Partition a dictionary of numbers into N groups with approximately equal sums
+    """
+    # Edge cases
+    if n > len(number_dict):
+        groups = []
+        for key, value in number_dict.items():
+            groups.append({key: value})
+        for _ in range(n - len(number_dict)):
+            groups.append({})
+        return groups
+
+    if n == len(number_dict):
+        return [{key: value} for key, value in number_dict.items()]
+
+    total_sum = sum(number_dict.values())
+    # target = total_sum / n  # Use float for better precision
+
+    items = list(number_dict.items())
+    result = []
+    remaining = items.copy()
+
+    def find_optimal_subset(arr, target):
+        """Find subset with sum closest to target"""
+        best_subset = []
+        best_diff = float("inf")
+
+        # Try all possible subset sizes
+        for r in range(1, len(arr) + 1):
+            for combo in combinations(arr, r):
+                current_sum = sum(value for _, value in combo)
+                current_diff = abs(current_sum - target)
+
+                # If we found a perfect match, return immediately
+                if current_diff == 0:
+                    return list(combo)
+
+                # Update the best subset if this is better
+                if current_diff < best_diff and current_sum <= total_sum:
+                    best_diff = current_diff
+                    best_subset = list(combo)
+
+        return best_subset
+
+    # Distribute items into n-1 groups
+    for i in range(n - 1):
+        if not remaining:
+            break
+
+        # Calculate dynamic target based on remaining items
+        remaining_target = sum(value for _, value in remaining) / (n - i)
+        subset = find_optimal_subset(remaining, remaining_target)
+
+        result.append(dict(subset))
+
+        # Remove allocated items
+        for item in subset:
+            remaining.remove(item)
+
+    # Last group gets all remaining items
+    result.append(dict(remaining))
+
+    return result
+
+
+def set_avg_auto_device_map(model, device_map):
+    block_name_list = get_block_names(model)
+    device_list = None
+    if isinstance(device_map, str) and "," in device_map:
+        device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
+        num_devices = len(device_list)
+    else:
+        if torch.cuda.is_available():
+            num_devices = torch.cuda.device_count()
+        elif torch.xpu.is_available():
+            logger.warning_once("XPU does not support auto device map yet, using device 0 for tuning.")
+            return
+        else:
+            return
+
+    if device_list:
+        cuda_devices = [f"cuda:{i}" for i in device_list]
+    else:
+        cuda_devices = [f"cuda:{i}" for i in range(num_devices)]
+
+    for block_names in block_name_list:
+        for block_name in block_names:
+            params_dict = {}
+            block_module = get_module(model, block_name)
+            for n, m in block_module.named_modules():
+                in_features, out_features = get_layer_features(m)
+                params_dict[n] = in_features * out_features
+
+            res_list = partition_dict_numbers(params_dict, num_devices)
+            device_index = 0
+            for res in res_list:
+                for key in res.keys():
+                    tmp_m = get_module(model, key)
+                    set_tuning_device_for_layer(block_module, tmp_m, cuda_devices[device_index])
+
+
+if __name__ == "__main__":
+    # Example usage
+    number_dict = {"item1": 90, "item2": 20, "item3": 30, "item4": 40, "item5": 50, "item6": 60}
+
+    groups = partition_dict_numbers(number_dict, 10)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+    groups = partition_dict_numbers(number_dict, 6)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+    groups = partition_dict_numbers(number_dict, 4)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+    groups = partition_dict_numbers(number_dict, 3)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
+
+    groups = partition_dict_numbers(number_dict, 2)
+    for i, group in enumerate(groups):
+        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
diff --git a/auto_round/utils_bk/__init__.py b/auto_round/utils_bk/__init__.py
deleted file mode 100644
index 0d2740cdb..000000000
--- a/auto_round/utils_bk/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2025 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/auto_round/utils_bk/device.py b/auto_round/utils_bk/device.py
deleted file mode 100644
index 9eb197af2..000000000
--- a/auto_round/utils_bk/device.py
+++ /dev/null
@@ -1,314 +0,0 @@
-# Copyright (c) 2025 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import re
-from itertools import combinations
-from typing import Union
-
-import torch
-
-from auto_round.logger import logger
-from auto_round.utils import (
-    check_to_quantized,
-    detect_device,
-    estimate_tuning_block_mem,
-    get_block_names,
-    get_device_memory,
-    get_layer_features,
-    get_module,
-)
-
-
-def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
-    if isinstance(device_map, (str, torch.device, int)):
-        device = detect_device(device_map)
-        return device
-
-    if isinstance(device_map, dict) and device_map:
-        tmp_devices = []
-        for val in device_map.values():
-            if isinstance(val, (str, torch.device, int)):  # could optimize
-                tmp_device = detect_device(val)
-                tmp_device = tmp_device.split(":")[0]
-                tmp_devices.append(tmp_device)
-        tmp_devices = list(set(tmp_devices))
-        if len(tmp_devices) > 1:
-            logger.warning(
-                f"there are multiple device types in the device_map, "
-                f"please make sure they are correct,use the first none-cpu device {tmp_devices[0]} as the core device "
-            )
-
-        for device in tmp_devices:
-            if device != "cpu":
-                return device
-
-        device = tmp_devices[0]
-        return device
-    logger.warning(f"device_map should be [str, torch.device, int, dict], but got {type(device_map)}")
-    return "cpu"
-
-
-def set_tuning_device_for_layer(model, name: str, device: str) -> None:
-    """Sets the device for a module if it matches the given name."""
-    module = get_module(model, name)
-    if hasattr(module, "tuning_device") and module.tuning_device != device:
-        logger.warning(
-            f"multiple devices have been set for layer {name}, keeping original device {module.tuning_device}"
-        )
-    else:
-        module.tuning_device = device
-
-
-def set_non_auto_device_map(
-    model: torch.nn.Module, device_map: Union[str, int, dict], quant_layer_names: Union[None, list, tuple] = None
-) -> None:
-    if not device_map:
-        return
-    if device_map == "auto":
-        return
-    if isinstance(device_map, str) and "," in device_map:  # auto device map
-        return
-    if isinstance(device_map, int):
-        return
-    if isinstance(device_map, str):
-        device_map = device_map.replace(" ", "")
-        infos = device_map.split(",")
-        device_map_dict = {}
-        for info in infos:
-            index = info.find(":")
-            key = info[:index]
-            value = info[index + 1 :]
-            device_map_dict[key] = value
-        device_map = device_map_dict
-    if quant_layer_names is not None:
-        names = quant_layer_names
-    else:
-        names = [
-            n for n, m in model.model.named_modules() if len(list(m.children())) == 0
-        ]  # if it's a block, it will be incorrect
-    for key, device in device_map.items():
-        if isinstance(device, str) and device.isdigit():
-            device = int(device)
-        device = detect_device(device)
-        if key in names:
-            module = get_module(model, key)
-            module.tuning_device = device
-        else:
-            matching_names = [name for name in names if re.match(key, name)]
-            for name in matching_names:
-                set_non_auto_device_map(model, name, device)
-            if not matching_names:
-                logger.warning(f"{key} in `device_map` dose not match any modules, please have a check")
-
-
-def set_auto_device_map_for_block_with_tuning(
-    block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False, mem_per_param_scale=13.0
-):
-    """Automatically sets the device map for the block based on available GPUs and memory constraints."""
-    if torch.cuda.is_available():
-        num_gpus = torch.cuda.device_count()
-    elif torch.xpu.is_available():
-        logger.warning_once("XPU does not support auto device map yet, using device 0 for tuning.")
-        return
-    else:
-        raise RuntimeError("No CUDA or XPU devices found.")
-    device_list = None
-    if isinstance(device_map, str) and "," in device_map:
-        device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
-
-    if device_list:
-        cuda_devices = [f"cuda:{i}" for i in device_list]
-        device_0 = cuda_devices[0]
-    else:
-        cuda_devices = [f"cuda:{i}" for i in range(num_gpus)]
-        device_0 = "cuda:0"
-
-    device_0_memory = get_device_memory(device_list[0] if device_list else 0)
-    block_memory, input_output_memory = estimate_tuning_block_mem(block, input_ids)
-    if low_gpu_mem_usage:
-        input_output_memory = 0
-
-    if (block_memory * mem_per_param_scale + input_output_memory) < device_0_memory:
-        return  # fit in one GPU
-
-    device_map = {}
-    device_memory = {device: get_device_memory(int(device.split(":")[1])) for device in cuda_devices}
-    device_memory[device_0] = device_0_memory - input_output_memory
-
-    device_idx = 0
-    names = []
-    # First, fill device 0 to its maximum capacity, then distribute the remaining layers evenly across other devices
-    for n, m in block.named_modules():
-        if check_to_quantized(m):
-            layer_name = m.tmp_name
-            names.append(layer_name)
-            layer_memory = m.weight.nbytes / 1024**3
-            if device_idx == 0 and layer_memory * mem_per_param_scale < device_memory[cuda_devices[device_idx]]:
-                device_map[layer_name] = cuda_devices[device_idx]
-                device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
-            elif device_idx == 0:
-                device_idx += 1  # Move to the next device once device 0 is full
-                device_map[layer_name] = cuda_devices[device_idx]
-                device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
-            else:
-                # Calculate the target device index based on even distribution
-                sorted_devices = sorted(cuda_devices, key=lambda d: device_memory[d], reverse=True)
-                device_idx = sorted_devices[0]
-                if layer_memory * mem_per_param_scale < device_memory[device_idx]:
-                    device_map[layer_name] = device_idx
-                    device_memory[device_idx] -= layer_memory * mem_per_param_scale
-                else:
-                    logger.warning_once(
-                        f"Block {block.tmp_name} not fit in available GPU memory. "
-                        "Consider using more GPUs or reducing mem_per_param_scale if OOM occurs."
-                    )
-
-    set_non_auto_device_map(block, device_map, names)
-
-
-#
-# def set_device_map_for_auto_scheme(model, device_map):
-#     if not device_map:
-#         return
-#     if device_map == "auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
-#         set_avg_auto_device_map(model)
-#     else:
-#         set_non_auto_device_map(model, device_map)
-
-
-def partition_dict_numbers(number_dict, n):
-    """
-    Partition a dictionary of numbers into N groups with approximately equal sums
-    """
-    # Edge cases
-    if n > len(number_dict):
-        groups = []
-        for key, value in number_dict.items():
-            groups.append({key: value})
-        for _ in range(n - len(number_dict)):
-            groups.append({})
-        return groups
-
-    if n == len(number_dict):
-        return [{key: value} for key, value in number_dict.items()]
-
-    total_sum = sum(number_dict.values())
-    # target = total_sum / n  # Use float for better precision
-
-    items = list(number_dict.items())
-    result = []
-    remaining = items.copy()
-
-    def find_optimal_subset(arr, target):
-        """Find subset with sum closest to target"""
-        best_subset = []
-        best_diff = float("inf")
-
-        # Try all possible subset sizes
-        for r in range(1, len(arr) + 1):
-            for combo in combinations(arr, r):
-                current_sum = sum(value for _, value in combo)
-                current_diff = abs(current_sum - target)
-
-                # If we found a perfect match, return immediately
-                if current_diff == 0:
-                    return list(combo)
-
-                # Update the best subset if this is better
-                if current_diff < best_diff and current_sum <= total_sum:
-                    best_diff = current_diff
-                    best_subset = list(combo)
-
-        return best_subset
-
-    # Distribute items into n-1 groups
-    for i in range(n - 1):
-        if not remaining:
-            break
-
-        # Calculate dynamic target based on remaining items
-        remaining_target = sum(value for _, value in remaining) / (n - i)
-        subset = find_optimal_subset(remaining, remaining_target)
-
-        result.append(dict(subset))
-
-        # Remove allocated items
-        for item in subset:
-            remaining.remove(item)
-
-    # Last group gets all remaining items
-    result.append(dict(remaining))
-
-    return result
-
-
-def set_avg_auto_device_map(model, device_map):
-    block_name_list = get_block_names(model)
-    device_list = None
-    if isinstance(device_map, str) and "," in device_map:
-        device_list = [int(dev) for dev in device_map.split(",") if dev.isdigit()]
-        num_devices = len(device_list)
-    else:
-        if torch.cuda.is_available():
-            num_devices = torch.cuda.device_count()
-        elif torch.xpu.is_available():
-            logger.warning_once("XPU does not support auto device map yet, using device 0 for tuning.")
-            return
-        else:
-            return
-
-    if device_list:
-        cuda_devices = [f"cuda:{i}" for i in device_list]
-    else:
-        cuda_devices = [f"cuda:{i}" for i in range(num_devices)]
-
-    for block_names in block_name_list:
-        for block_name in block_names:
-            params_dict = {}
-            block_module = get_module(model, block_name)
-            for n, m in block_module.named_modules():
-                in_features, out_features = get_layer_features(m)
-                params_dict[n] = in_features * out_features
-
-            res_list = partition_dict_numbers(params_dict, num_devices)
-            device_index = 0
-            for res in res_list:
-                for key in res.keys():
-                    tmp_m = get_module(model, key)
-                    set_tuning_device_for_layer(block_module, tmp_m, cuda_devices[device_index])
-
-
-if __name__ == "__main__":
-    # Example usage
-    number_dict = {"item1": 90, "item2": 20, "item3": 30, "item4": 40, "item5": 50, "item6": 60}
-
-    groups = partition_dict_numbers(number_dict, 10)
-    for i, group in enumerate(groups):
-        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
-
-    groups = partition_dict_numbers(number_dict, 6)
-    for i, group in enumerate(groups):
-        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
-
-    groups = partition_dict_numbers(number_dict, 4)
-    for i, group in enumerate(groups):
-        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
-
-    groups = partition_dict_numbers(number_dict, 3)
-    for i, group in enumerate(groups):
-        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")
-
-    groups = partition_dict_numbers(number_dict, 2)
-    for i, group in enumerate(groups):
-        print(f"Group {i + 1}: {group}, Sum: {sum(group.values())}")

From 7015847fd4335ca97ed1d42a6447f66ab264172b Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Oct 2025 02:07:29 +0000
Subject: [PATCH 18/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/utils/device.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index b840510d1..b449c700c 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -13,16 +13,12 @@
 # limitations under the License.
 import gc
 import os
-from functools import lru_cache
-from typing import  Callable
-
-import cpuinfo
-
-
 import re
+from functools import lru_cache
 from itertools import combinations
-from typing import Union
+from typing import Callable, Union
 
+import cpuinfo
 import torch
 
 from auto_round.logger import logger
@@ -36,7 +32,6 @@
     get_module,
 )
 
-
 # Note on HPU usage:
 # There are two modes available for enabling auto-round on HPU:
 # 1. Compile Mode
@@ -556,6 +551,7 @@ def get_device_memory(i: int = 0) -> int:
         raise RuntimeError("No supported device found (CUDA or XPU).")
     return total_memory
 
+
 def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
     if isinstance(device_map, (str, torch.device, int)):
         device = detect_device(device_map)

From 9bdedae1ecd54936d24e73c21b7e6eaf6873da35 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 10:14:32 +0800
Subject: [PATCH 19/36] fix import issues

---
 auto_round/utils/device.py | 11 ++---------
 1 file changed, 2 insertions(+), 9 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index b840510d1..f18d35708 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -26,15 +26,8 @@
 import torch
 
 from auto_round.logger import logger
-from auto_round.utils import (
-    check_to_quantized,
-    detect_device,
-    estimate_tuning_block_mem,
-    get_block_names,
-    get_device_memory,
-    get_layer_features,
-    get_module,
-)
+from auto_round.utils.model import get_block_names,get_module,get_layer_features,check_to_quantized
+
 
 
 # Note on HPU usage:

From 55fcd51ae554937d0767b99553d5b470e525df03 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Oct 2025 02:16:51 +0000
Subject: [PATCH 20/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/utils/device.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 3be483f4f..fcb1ff3d9 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -22,8 +22,7 @@
 import torch
 
 from auto_round.logger import logger
-from auto_round.utils.model import get_block_names,get_module,get_layer_features,check_to_quantized
-
+from auto_round.utils.model import check_to_quantized, get_block_names, get_layer_features, get_module
 
 # Note on HPU usage:
 # There are two modes available for enabling auto-round on HPU:

From ad6ba7a59eeaa9401cb5ebedd603dcc143b7e3be Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 11:45:37 +0800
Subject: [PATCH 21/36] update

---
 auto_round/auto_scheme/gen_auto_scheme.py |  1 +
 auto_round/utils/device.py                | 28 ++++++++++++-----------
 test/test_cpu/test_gguf_format.py         |  2 +-
 3 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/auto_round/auto_scheme/gen_auto_scheme.py b/auto_round/auto_scheme/gen_auto_scheme.py
index b2f3b1ab2..fb876da6d 100644
--- a/auto_round/auto_scheme/gen_auto_scheme.py
+++ b/auto_round/auto_scheme/gen_auto_scheme.py
@@ -95,6 +95,7 @@ def get_layer_config(self) -> dict[str, dict]:
             device_map=self.device_map,
             enable_torch_compile=self.enable_torch_compile,
             disable_opt_rtn=self.disable_opt_rtn,
+            low_gpu_mem_usage=self.auto_scheme.low_gpu_mem_usage
         )
         layer_config = self.fallback_gguf_layer_config(layer_config)
         return layer_config
diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 3be483f4f..be79b447b 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -203,7 +203,7 @@ def detect_device_count():
             return 0
 
 
-def detect_device(device: Union[str, int, torch.device] = None) -> str:
+def detect_device(device: Union[None, str, int, torch.device] = None) -> str:
     """Detects the appropriate computation device.
 
     This function determines the device to use for computations. It can take
@@ -545,8 +545,8 @@ def get_device_memory(i: int = 0) -> int:
     return total_memory
 
 
-def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
-    if isinstance(device_map, (str, torch.device, int)):
+def get_major_device(device_map: Union[None, str, torch.device, int, dict]) -> str:
+    if device_map is None or isinstance(device_map, (str, torch.device, int)):
         device = detect_device(device_map)
         return device
 
@@ -558,19 +558,21 @@ def get_major_device(device_map: Union[str, torch.device, int, dict]) -> str:
                 tmp_device = tmp_device.split(":")[0]
                 tmp_devices.append(tmp_device)
         tmp_devices = list(set(tmp_devices))
+        device=None
+        for tmp_device in tmp_devices:
+            if device != "cpu":
+                device = tmp_device
+                break
+        if device is None:
+            device = tmp_devices[0]
         if len(tmp_devices) > 1:
-            logger.warning(
+            logger.warning_once(
                 f"there are multiple device types in the device_map, "
-                f"please make sure they are correct,use the first none-cpu device {tmp_devices[0]} as the core device "
+                f"please make sure they are correct,use the first none-cpu device {device} as the core device "
             )
 
-        for device in tmp_devices:
-            if device != "cpu":
-                return device
-
-        device = tmp_devices[0]
         return device
-    logger.warning(f"device_map should be [str, torch.device, int, dict], but got {type(device_map)}")
+    logger.warning_once(f"device_map should be [str, torch.device, int, dict], but got {type(device_map)}")
     return "cpu"
 
 
@@ -622,7 +624,7 @@ def set_non_auto_device_map(
         else:
             matching_names = [name for name in names if re.match(key, name)]
             for name in matching_names:
-                set_non_auto_device_map(model, name, device)
+                set_tuning_device_for_layer(model, name, device)
             if not matching_names:
                 logger.warning(f"{key} in `device_map` dose not match any modules, please have a check")
 
@@ -768,7 +770,7 @@ def find_optimal_subset(arr, target):
     return result
 
 
-def set_avg_auto_device_map(model, device_map):
+def set_avg_auto_device_map(model:torch.nn.Module, device_map):
     block_name_list = get_block_names(model)
     device_list = None
     if isinstance(device_map, str) and "," in device_map:
diff --git a/test/test_cpu/test_gguf_format.py b/test/test_cpu/test_gguf_format.py
index c366e264a..be7d663cd 100644
--- a/test/test_cpu/test_gguf_format.py
+++ b/test/test_cpu/test_gguf_format.py
@@ -339,7 +339,7 @@ def test_qtype_setting(self):
         # Qwen3-0.6B output q6_k, token_embed q4_0  448M
         # Qwen3-8B output q6_k, token_embed q4_0 4.5G
         # Llama-3.2-1B-Instruct o output, token_embed q6_k 736M
-        from auto_round.compressors import get_layer_config_by_gguf_format, set_layer_config
+        from auto_round.compressors.utils import  set_layer_config
         from auto_round.export.export_to_gguf.config import ModelType
 
         model_name = "/tf_dataset/auto_round/models/Qwen/Qwen2.5-0.5B-Instruct"

From 9fe31012a1ec1f77a9f19723b851f95e5c74c181 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Oct 2025 03:47:50 +0000
Subject: [PATCH 22/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/auto_scheme/gen_auto_scheme.py | 2 +-
 auto_round/utils/device.py                | 4 ++--
 test/test_cpu/test_gguf_format.py         | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/auto_round/auto_scheme/gen_auto_scheme.py b/auto_round/auto_scheme/gen_auto_scheme.py
index fb876da6d..d650bccca 100644
--- a/auto_round/auto_scheme/gen_auto_scheme.py
+++ b/auto_round/auto_scheme/gen_auto_scheme.py
@@ -95,7 +95,7 @@ def get_layer_config(self) -> dict[str, dict]:
             device_map=self.device_map,
             enable_torch_compile=self.enable_torch_compile,
             disable_opt_rtn=self.disable_opt_rtn,
-            low_gpu_mem_usage=self.auto_scheme.low_gpu_mem_usage
+            low_gpu_mem_usage=self.auto_scheme.low_gpu_mem_usage,
         )
         layer_config = self.fallback_gguf_layer_config(layer_config)
         return layer_config
diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 2cbe6532a..00606b5cb 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -557,7 +557,7 @@ def get_major_device(device_map: Union[None, str, torch.device, int, dict]) -> s
                 tmp_device = tmp_device.split(":")[0]
                 tmp_devices.append(tmp_device)
         tmp_devices = list(set(tmp_devices))
-        device=None
+        device = None
         for tmp_device in tmp_devices:
             if device != "cpu":
                 device = tmp_device
@@ -769,7 +769,7 @@ def find_optimal_subset(arr, target):
     return result
 
 
-def set_avg_auto_device_map(model:torch.nn.Module, device_map):
+def set_avg_auto_device_map(model: torch.nn.Module, device_map):
     block_name_list = get_block_names(model)
     device_list = None
     if isinstance(device_map, str) and "," in device_map:
diff --git a/test/test_cpu/test_gguf_format.py b/test/test_cpu/test_gguf_format.py
index be7d663cd..91789e92a 100644
--- a/test/test_cpu/test_gguf_format.py
+++ b/test/test_cpu/test_gguf_format.py
@@ -339,7 +339,7 @@ def test_qtype_setting(self):
         # Qwen3-0.6B output q6_k, token_embed q4_0  448M
         # Qwen3-8B output q6_k, token_embed q4_0 4.5G
         # Llama-3.2-1B-Instruct o output, token_embed q6_k 736M
-        from auto_round.compressors.utils import  set_layer_config
+        from auto_round.compressors.utils import set_layer_config
         from auto_round.export.export_to_gguf.config import ModelType
 
         model_name = "/tf_dataset/auto_round/models/Qwen/Qwen2.5-0.5B-Instruct"

From 4af492444fb44abf2e24794460123b15d1358958 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 15:42:01 +0800
Subject: [PATCH 23/36] fix issues

---
 auto_round/schemes.py              |  1 +
 auto_round/utils/device.py         |  2 +-
 auto_round/utils/model.py          | 13 ++++++------
 auto_round/wrapper.py              |  2 +-
 test/test_cuda/test_auto_scheme.py | 34 ++++++++++++++++++++++++++++--
 5 files changed, 42 insertions(+), 10 deletions(-)

diff --git a/auto_round/schemes.py b/auto_round/schemes.py
index 94eb17f2c..1e4dcdf2b 100644
--- a/auto_round/schemes.py
+++ b/auto_round/schemes.py
@@ -294,6 +294,7 @@ class AutoScheme:
     shared_layers: Optional[Iterable[Iterable[str]]] = None
     method: str = "default"
     ignore_scale_zp_bits: bool = False
+    batch_size: Optional[int] = None
     nsamples: Optional[int] = None
     seqlen: Optional[int] = None
     dataset: Optional[str] = None  # Import Notice no comma for each item
diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 00606b5cb..f2d2b58f0 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -611,7 +611,7 @@ def set_non_auto_device_map(
         names = quant_layer_names
     else:
         names = [
-            n for n, m in model.model.named_modules() if len(list(m.children())) == 0
+            n for n, m in model.named_modules() if len(list(m.children())) == 0
         ]  # if it's a block, it will be incorrect
     for key, device in device_map.items():
         if isinstance(device, str) and device.isdigit():
diff --git a/auto_round/utils/model.py b/auto_round/utils/model.py
index 63cb24a9f..38c9f55b6 100644
--- a/auto_round/utils/model.py
+++ b/auto_round/utils/model.py
@@ -18,11 +18,12 @@
 from collections import UserDict
 from dataclasses import asdict, fields
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Tuple, Union
+from typing import Union
 
 import torch
 import transformers
 
+
 from auto_round.export.export_to_gguf.config import ModelType
 from auto_round.logger import logger
 from auto_round.schemes import QuantizationScheme
@@ -263,7 +264,6 @@ def mllm_load_model(
     **kwargs,
 ):
     import transformers
-    from huggingface_hub import HfApi, HfFileSystem, hf_hub_download
     from transformers import AutoModel, AutoModelForCausalLM, AutoProcessor, AutoTokenizer
 
     from auto_round.utils.device import get_device_and_parallelism, set_fake_cuda_device_capability
@@ -732,7 +732,7 @@ def set_nested_attr(module, attr_name: str, value):
     setattr(module, attrs[-1], value)
 
 
-def _pad_weight(weight: torch.Tensor, block_size: list) -> Tuple[torch.Tensor, int, int]:
+def _pad_weight(weight: torch.Tensor, block_size: list) -> tuple[torch.Tensor, int, int]:
     """Pads a matrix to make its dimensions multiples of block_size."""
     M, N = weight.shape[-2:]
     block_size_m, block_size_n = block_size
@@ -757,7 +757,7 @@ def _unpad_weight(weight: torch.Tensor, original_M: int, original_N: int, keep_f
 
 def pad_block_fp8_weight_naive(
     weight: torch.Tensor, weight_scale: torch.Tensor, block_size: list
-) -> Tuple[torch.Tensor, int, int]:
+) -> tuple[torch.Tensor, int, int]:
     assert len(block_size) == 2
 
     block_size_m, block_size_n = block_size
@@ -946,8 +946,9 @@ def set_module(model, key, new_module):
 
 def get_layer_features(layer):
     """Extracts input and output feature dimensions for supported layers."""
-    from auto_round.utils.common import LinearAllreduce, LinearLayer, deepspeed_exists
-
+    from auto_round.utils import deepspeed_exists
+    if deepspeed_exists:
+        from deepspeed.module_inject import LinearAllreduce, LinearLayer
     if type(layer) == torch.nn.Linear:
         return layer.in_features, layer.out_features
     elif type(layer) == transformers.pytorch_utils.Conv1D:  # TODO: Verify correctness
diff --git a/auto_round/wrapper.py b/auto_round/wrapper.py
index 7bb6381e2..48535aeda 100644
--- a/auto_round/wrapper.py
+++ b/auto_round/wrapper.py
@@ -366,7 +366,7 @@ def _set_dict_attr(attr_dict, attr_name):
             assert global_scale.numel() == 1
             self.orig_layer.weight_global_scale = global_scale.to("cpu")
 
-        ##unwrapper bias
+        # Unwrapper bias
         if self.enable_norm_bias_tuning and "bias_v" in best_params.keys():  ##fake quant
             bias_v = best_params["bias_v"].to(self.device)
             bias = self.orig_layer.bias
diff --git a/test/test_cuda/test_auto_scheme.py b/test/test_cuda/test_auto_scheme.py
index 5b7116768..ab740ae93 100644
--- a/test/test_cuda/test_auto_scheme.py
+++ b/test/test_cuda/test_auto_scheme.py
@@ -83,10 +83,40 @@ def test_shared_layers(self):
     #
     @multi_card
     def test_multi_card(self):
-        model_name = "/models/Qwen3-8B"
+        model_name = "/models/Qwen3-0.6B"
         target_bits = 5.254
-        # for device_map in ["auto", "0,1", "0", None]:
+        for device_map in ["auto", "0,1", "0", None]:
+            scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"))
+            ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1,device_map=device_map)
+            model, layer_config = ar.quantize()
+            avg_bits, _ = compute_avg_bits_for_model(model)
+            print(avg_bits)
+            assert target_bits - 0.1 < avg_bits <= target_bits + 1e-3
+
+    @multi_card
+    def test_multi_card_1(self):
+        model_name = "/models/Qwen3-0.6B"
+        target_bits = 5.254
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype="auto",
+            device_map="auto"
+        )
         scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"))
+        ar = AutoRound(model=model,tokenizer=tokenizer, scheme=scheme, iters=0, nsamples=1)
+        model, layer_config = ar.quantize()
+        avg_bits, _ = compute_avg_bits_for_model(model)
+        print(avg_bits)
+        assert target_bits - 0.1 < avg_bits <= target_bits + 1e-3
+
+
+    def test_non_low_gpu_mem_usage(self):
+        model_name = "/models/Qwen3-0.6B"
+        target_bits = 5.254
+        # for device_map in ["auto", "0,1", "0", None]:
+        scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"),low_gpu_mem_usage=False)
         ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1)
         model, layer_config = ar.quantize()
         avg_bits, _ = compute_avg_bits_for_model(model)

From 5057b5ef30826090dc4bb90577bfff4298e505e0 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Oct 2025 07:42:41 +0000
Subject: [PATCH 24/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/utils/model.py          |  2 +-
 test/test_cuda/test_auto_scheme.py | 14 +++++---------
 2 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/auto_round/utils/model.py b/auto_round/utils/model.py
index 38c9f55b6..7a1c66de8 100644
--- a/auto_round/utils/model.py
+++ b/auto_round/utils/model.py
@@ -23,7 +23,6 @@
 import torch
 import transformers
 
-
 from auto_round.export.export_to_gguf.config import ModelType
 from auto_round.logger import logger
 from auto_round.schemes import QuantizationScheme
@@ -947,6 +946,7 @@ def set_module(model, key, new_module):
 def get_layer_features(layer):
     """Extracts input and output feature dimensions for supported layers."""
     from auto_round.utils import deepspeed_exists
+
     if deepspeed_exists:
         from deepspeed.module_inject import LinearAllreduce, LinearLayer
     if type(layer) == torch.nn.Linear:
diff --git a/test/test_cuda/test_auto_scheme.py b/test/test_cuda/test_auto_scheme.py
index ab740ae93..663abcba5 100644
--- a/test/test_cuda/test_auto_scheme.py
+++ b/test/test_cuda/test_auto_scheme.py
@@ -87,7 +87,7 @@ def test_multi_card(self):
         target_bits = 5.254
         for device_map in ["auto", "0,1", "0", None]:
             scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"))
-            ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1,device_map=device_map)
+            ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1, device_map=device_map)
             model, layer_config = ar.quantize()
             avg_bits, _ = compute_avg_bits_for_model(model)
             print(avg_bits)
@@ -98,25 +98,21 @@ def test_multi_card_1(self):
         model_name = "/models/Qwen3-0.6B"
         target_bits = 5.254
         from transformers import AutoModelForCausalLM, AutoTokenizer
+
         tokenizer = AutoTokenizer.from_pretrained(model_name)
-        model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            torch_dtype="auto",
-            device_map="auto"
-        )
+        model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
         scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"))
-        ar = AutoRound(model=model,tokenizer=tokenizer, scheme=scheme, iters=0, nsamples=1)
+        ar = AutoRound(model=model, tokenizer=tokenizer, scheme=scheme, iters=0, nsamples=1)
         model, layer_config = ar.quantize()
         avg_bits, _ = compute_avg_bits_for_model(model)
         print(avg_bits)
         assert target_bits - 0.1 < avg_bits <= target_bits + 1e-3
 
-
     def test_non_low_gpu_mem_usage(self):
         model_name = "/models/Qwen3-0.6B"
         target_bits = 5.254
         # for device_map in ["auto", "0,1", "0", None]:
-        scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"),low_gpu_mem_usage=False)
+        scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"), low_gpu_mem_usage=False)
         ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1)
         model, layer_config = ar.quantize()
         avg_bits, _ = compute_avg_bits_for_model(model)

From dcb7ff2a3f5f4b2172d8680818911cc36d599164 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 16:56:27 +0800
Subject: [PATCH 25/36] fix issues

---
 auto_round/autoround.py            | 2 +-
 auto_round/wrapper.py              | 2 +-
 test/test_cuda/test_auto_scheme.py | 6 +++---
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/auto_round/autoround.py b/auto_round/autoround.py
index 4716f173b..a78c94737 100644
--- a/auto_round/autoround.py
+++ b/auto_round/autoround.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 from __future__ import annotations
 
-from typing import Any, Callable, Union
+from typing import Union
 
 import torch
 
diff --git a/auto_round/wrapper.py b/auto_round/wrapper.py
index 48535aeda..67072d762 100644
--- a/auto_round/wrapper.py
+++ b/auto_round/wrapper.py
@@ -236,7 +236,7 @@ def _qdq_weight(self, value, min_scale, max_scale):
             quant_kwargs["super_group_size"] = self.orig_layer.super_group_size
 
         weight_q, scale, zp = self.weight_quant_func(
-            weight,
+            weight.to(self.device),
             bits=self.orig_layer.bits,
             group_size=self.orig_layer.group_size,
             v=value,
diff --git a/test/test_cuda/test_auto_scheme.py b/test/test_cuda/test_auto_scheme.py
index ab740ae93..a6d339469 100644
--- a/test/test_cuda/test_auto_scheme.py
+++ b/test/test_cuda/test_auto_scheme.py
@@ -84,7 +84,7 @@ def test_shared_layers(self):
     @multi_card
     def test_multi_card(self):
         model_name = "/models/Qwen3-0.6B"
-        target_bits = 5.254
+        target_bits = 5.265
         for device_map in ["auto", "0,1", "0", None]:
             scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"))
             ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1,device_map=device_map)
@@ -96,7 +96,7 @@ def test_multi_card(self):
     @multi_card
     def test_multi_card_1(self):
         model_name = "/models/Qwen3-0.6B"
-        target_bits = 5.254
+        target_bits = 5.265
         from transformers import AutoModelForCausalLM, AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         model = AutoModelForCausalLM.from_pretrained(
@@ -114,7 +114,7 @@ def test_multi_card_1(self):
 
     def test_non_low_gpu_mem_usage(self):
         model_name = "/models/Qwen3-0.6B"
-        target_bits = 5.254
+        target_bits = 5.265
         # for device_map in ["auto", "0,1", "0", None]:
         scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"),low_gpu_mem_usage=False)
         ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1)

From c91bbaef3018b5f3ee713bf017df1a730d00a5b8 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 17:40:16 +0800
Subject: [PATCH 26/36] fix bug

---
 auto_round/utils/device.py         | 26 +++++++++++++++-----------
 test/test_cuda/test_auto_scheme.py |  2 +-
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index f2d2b58f0..19fad4888 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -24,6 +24,7 @@
 from auto_round.logger import logger
 from auto_round.utils.model import check_to_quantized, get_block_names, get_layer_features, get_module
 
+
 # Note on HPU usage:
 # There are two modes available for enabling auto-round on HPU:
 # 1. Compile Mode
@@ -82,7 +83,7 @@ def compile_func_on_cuda_or_cpu(func):
 
 
 def compile_func(
-    fun: Union[torch.nn.Module, Callable], device: Union[str, torch.device, int]
+        fun: Union[torch.nn.Module, Callable], device: Union[str, torch.device, int]
 ) -> Union[torch.nn.Module, Callable]:
     """Compile function on the specified device."""
     if "hpu" in str(device):
@@ -480,10 +481,10 @@ def estimate_tuning_block_mem(block: torch.nn.Module, input_ids: list[torch.Tens
         if check_to_quantized(module):
             param_size = module.weight.nbytes
             total_param_mem += param_size
-    block_memory = total_param_mem / 1024**3  # Convert to GB
+    block_memory = total_param_mem / 1024 ** 3  # Convert to GB
 
     # Assuming bfloat16 or float32, input and output
-    input_output_memory = 2 * sum(tensor.nbytes for tensor in input_ids) / 1024**3
+    input_output_memory = 2 * sum(tensor.nbytes for tensor in input_ids) / 1024 ** 3
 
     return block_memory, input_output_memory
 
@@ -511,13 +512,13 @@ def get_max_vram(ratio: float = 0.9) -> dict:
         num_devices = torch.cuda.device_count()
         for i in range(num_devices):
             total_mem = torch.cuda.get_device_properties(i).total_memory
-            max_mem_gb = int(total_mem / 1024**3 * ratio)
+            max_mem_gb = int(total_mem / 1024 ** 3 * ratio)
             max_memory[i] = f"{max_mem_gb}GiB"
     elif torch.xpu.is_available():  # TODO need verification
         num_devices = torch.xpu.device_count()
         for i in range(num_devices):
             total_mem = torch.xpu.get_device_properties(i).total_memory
-            max_mem_gb = int(total_mem / 1024**3 * ratio)
+            max_mem_gb = int(total_mem / 1024 ** 3 * ratio)
             max_memory[i] = f"{max_mem_gb}GiB"
 
     else:
@@ -587,7 +588,7 @@ def set_tuning_device_for_layer(model, name: str, device: str) -> None:
 
 
 def set_non_auto_device_map(
-    model: torch.nn.Module, device_map: Union[str, int, dict], quant_layer_names: Union[None, list, tuple] = None
+        model: torch.nn.Module, device_map: Union[str, int, dict], quant_layer_names: Union[None, list, tuple] = None
 ) -> None:
     if not device_map:
         return
@@ -604,7 +605,7 @@ def set_non_auto_device_map(
         for info in infos:
             index = info.find(":")
             key = info[:index]
-            value = info[index + 1 :]
+            value = info[index + 1:]
             device_map_dict[key] = value
         device_map = device_map_dict
     if quant_layer_names is not None:
@@ -629,7 +630,8 @@ def set_non_auto_device_map(
 
 
 def set_auto_device_map_for_block_with_tuning(
-    block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False, mem_per_param_scale=13.0
+        block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False,
+        mem_per_param_scale=13.0
 ):
     """Automatically sets the device map for the block based on available GPUs and memory constraints."""
     if torch.cuda.is_available():
@@ -669,7 +671,7 @@ def set_auto_device_map_for_block_with_tuning(
         if check_to_quantized(m):
             layer_name = m.tmp_name
             names.append(layer_name)
-            layer_memory = m.weight.nbytes / 1024**3
+            layer_memory = m.weight.nbytes / 1024 ** 3
             if device_idx == 0 and layer_memory * mem_per_param_scale < device_memory[cuda_devices[device_idx]]:
                 device_map[layer_name] = cuda_devices[device_idx]
                 device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
@@ -795,14 +797,16 @@ def set_avg_auto_device_map(model: torch.nn.Module, device_map):
             block_module = get_module(model, block_name)
             for n, m in block_module.named_modules():
                 in_features, out_features = get_layer_features(m)
+                if in_features is None:
+                    continue
                 params_dict[n] = in_features * out_features
 
             res_list = partition_dict_numbers(params_dict, num_devices)
             device_index = 0
             for res in res_list:
                 for key in res.keys():
-                    tmp_m = get_module(model, key)
-                    set_tuning_device_for_layer(block_module, tmp_m, cuda_devices[device_index])
+                    set_tuning_device_for_layer(block_module, key, cuda_devices[device_index])
+                device_index += 1
 
 
 if __name__ == "__main__":
diff --git a/test/test_cuda/test_auto_scheme.py b/test/test_cuda/test_auto_scheme.py
index a6d339469..498c0a3fd 100644
--- a/test/test_cuda/test_auto_scheme.py
+++ b/test/test_cuda/test_auto_scheme.py
@@ -116,7 +116,7 @@ def test_non_low_gpu_mem_usage(self):
         model_name = "/models/Qwen3-0.6B"
         target_bits = 5.265
         # for device_map in ["auto", "0,1", "0", None]:
-        scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"),low_gpu_mem_usage=False)
+        scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"),low_gpu_mem_usage=False,device_map="auto")
         ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1)
         model, layer_config = ar.quantize()
         avg_bits, _ = compute_avg_bits_for_model(model)

From 8e0bfa49b2ff42c181137cc30db218348b4fbcf5 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Oct 2025 09:41:52 +0000
Subject: [PATCH 27/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/utils/device.py         | 20 +++++++++-----------
 test/test_cuda/test_auto_scheme.py |  2 +-
 2 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 19fad4888..b92ab2074 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -24,7 +24,6 @@
 from auto_round.logger import logger
 from auto_round.utils.model import check_to_quantized, get_block_names, get_layer_features, get_module
 
-
 # Note on HPU usage:
 # There are two modes available for enabling auto-round on HPU:
 # 1. Compile Mode
@@ -83,7 +82,7 @@ def compile_func_on_cuda_or_cpu(func):
 
 
 def compile_func(
-        fun: Union[torch.nn.Module, Callable], device: Union[str, torch.device, int]
+    fun: Union[torch.nn.Module, Callable], device: Union[str, torch.device, int]
 ) -> Union[torch.nn.Module, Callable]:
     """Compile function on the specified device."""
     if "hpu" in str(device):
@@ -481,10 +480,10 @@ def estimate_tuning_block_mem(block: torch.nn.Module, input_ids: list[torch.Tens
         if check_to_quantized(module):
             param_size = module.weight.nbytes
             total_param_mem += param_size
-    block_memory = total_param_mem / 1024 ** 3  # Convert to GB
+    block_memory = total_param_mem / 1024**3  # Convert to GB
 
     # Assuming bfloat16 or float32, input and output
-    input_output_memory = 2 * sum(tensor.nbytes for tensor in input_ids) / 1024 ** 3
+    input_output_memory = 2 * sum(tensor.nbytes for tensor in input_ids) / 1024**3
 
     return block_memory, input_output_memory
 
@@ -512,13 +511,13 @@ def get_max_vram(ratio: float = 0.9) -> dict:
         num_devices = torch.cuda.device_count()
         for i in range(num_devices):
             total_mem = torch.cuda.get_device_properties(i).total_memory
-            max_mem_gb = int(total_mem / 1024 ** 3 * ratio)
+            max_mem_gb = int(total_mem / 1024**3 * ratio)
             max_memory[i] = f"{max_mem_gb}GiB"
     elif torch.xpu.is_available():  # TODO need verification
         num_devices = torch.xpu.device_count()
         for i in range(num_devices):
             total_mem = torch.xpu.get_device_properties(i).total_memory
-            max_mem_gb = int(total_mem / 1024 ** 3 * ratio)
+            max_mem_gb = int(total_mem / 1024**3 * ratio)
             max_memory[i] = f"{max_mem_gb}GiB"
 
     else:
@@ -588,7 +587,7 @@ def set_tuning_device_for_layer(model, name: str, device: str) -> None:
 
 
 def set_non_auto_device_map(
-        model: torch.nn.Module, device_map: Union[str, int, dict], quant_layer_names: Union[None, list, tuple] = None
+    model: torch.nn.Module, device_map: Union[str, int, dict], quant_layer_names: Union[None, list, tuple] = None
 ) -> None:
     if not device_map:
         return
@@ -605,7 +604,7 @@ def set_non_auto_device_map(
         for info in infos:
             index = info.find(":")
             key = info[:index]
-            value = info[index + 1:]
+            value = info[index + 1 :]
             device_map_dict[key] = value
         device_map = device_map_dict
     if quant_layer_names is not None:
@@ -630,8 +629,7 @@ def set_non_auto_device_map(
 
 
 def set_auto_device_map_for_block_with_tuning(
-        block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False,
-        mem_per_param_scale=13.0
+    block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False, mem_per_param_scale=13.0
 ):
     """Automatically sets the device map for the block based on available GPUs and memory constraints."""
     if torch.cuda.is_available():
@@ -671,7 +669,7 @@ def set_auto_device_map_for_block_with_tuning(
         if check_to_quantized(m):
             layer_name = m.tmp_name
             names.append(layer_name)
-            layer_memory = m.weight.nbytes / 1024 ** 3
+            layer_memory = m.weight.nbytes / 1024**3
             if device_idx == 0 and layer_memory * mem_per_param_scale < device_memory[cuda_devices[device_idx]]:
                 device_map[layer_name] = cuda_devices[device_idx]
                 device_memory[cuda_devices[device_idx]] -= layer_memory * mem_per_param_scale
diff --git a/test/test_cuda/test_auto_scheme.py b/test/test_cuda/test_auto_scheme.py
index f6ea23e85..ac486fef4 100644
--- a/test/test_cuda/test_auto_scheme.py
+++ b/test/test_cuda/test_auto_scheme.py
@@ -112,7 +112,7 @@ def test_non_low_gpu_mem_usage(self):
         model_name = "/models/Qwen3-0.6B"
         target_bits = 5.265
         # for device_map in ["auto", "0,1", "0", None]:
-        scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"),low_gpu_mem_usage=False,device_map="auto")
+        scheme = AutoScheme(avg_bits=target_bits, options=("NVFP4"), low_gpu_mem_usage=False, device_map="auto")
 
         ar = AutoRound(model=model_name, scheme=scheme, iters=0, nsamples=1)
         model, layer_config = ar.quantize()

From c5223a732dabdaa1a055119c2c8600bee327c257 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 21:03:25 +0800
Subject: [PATCH 28/36] update

---
 auto_round/auto_scheme/default_alg.abi3.so | Bin 346072 -> 499128 bytes
 auto_round/compressors/base.py             |  22 ++++++----------
 docs/step_by_step.md                       |  28 +++++++++++++++++----
 3 files changed, 30 insertions(+), 20 deletions(-)

diff --git a/auto_round/auto_scheme/default_alg.abi3.so b/auto_round/auto_scheme/default_alg.abi3.so
index 83da28647bce2eac676c827a3f2149eaf34e2b9a..02045ecd67c14cfa9ac335d0aec6e6bddab8fe5b 100644
GIT binary patch
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diff --git a/auto_round/compressors/base.py b/auto_round/compressors/base.py
index 70370b200..297f8a50e 100644
--- a/auto_round/compressors/base.py
+++ b/auto_round/compressors/base.py
@@ -431,7 +431,7 @@ def _gen_auto_scheme(
         # mainly using quant_layers and fixed by users
         from auto_round.auto_scheme.gen_auto_scheme import GenScheme
 
-        if not self.enable_torch_compile and self.super_bits is None:
+        if not self.enable_torch_compile and self.super_bits is None and not scheme.low_gpu_mem_usage:
             logger.warning("we strongly recommend to set `enable_torch_compile` to True for AutoScheme to save VRAM")
         gen_scheme = GenScheme(
             scheme,
@@ -1207,24 +1207,19 @@ def _quantize_layer_via_rtn(self, name: str) -> None:
         if is_fp8_linear(m):
             m = convert_fp8_layer_to_linear(m, self.amp_dtype)
             set_module(self.model, name, m)
-        #
-        # # Step 1: Use optimized RTN data type if available
-        # if not self.disable_opt_rtn:
-        #     rtn_data_type = self._check_rtn_dytpe(m.data_type, m.bits, m.sym)
-        #     if rtn_data_type is not None:
-        #         m.data_type = rtn_data_type
-        #         self.layer_config[name]["data_type"] = m.data_type
-
-        # Step 2: Try quantization on GPU first, fall back to CPU if OOM
+
+        # Step 1: Try quantization on GPU first, fall back to CPU if OOM
         # if only export gguf, using gguf-packing instead of rtn
         if self.is_packing_immediate and self.iters == 0 and "gguf" in self.formats[0] and not self.disable_opt_rtn:
             m.scale = None
             m.zp = None
         else:
             try:
-                m = m.to(m.tuning_device if hasattr(m, "tuning_device") else self.device)
+                tuning_device = m.tuning_device if hasattr(m, "tuning_device") else self.device
+                m = m.to(tuning_device)
                 m = WrapperLinear(
                     m,
+                    device=tuning_device,
                     enable_minmax_tuning=False,
                     enable_norm_bias_tuning=False,
                     enable_round_tuning=False,
@@ -1251,7 +1246,7 @@ def _quantize_layer_via_rtn(self, name: str) -> None:
                 except Exception as e:
                     raise
 
-        # Step 3: Optional immediate packing/export
+        # Step 2: Optional immediate packing/export
         if self.is_packing_immediate:
             from auto_round.export import PACKING_LAYER_WITH_FORMAT
 
@@ -1277,9 +1272,6 @@ def _quantize_layer_via_rtn(self, name: str) -> None:
                     )
                 else:
                     PACKING_LAYER_WITH_FORMAT[target_backend](name, self.model, self.formats[0], device=self.device)
-
-                # if self.low_gpu_mem_usage:
-                #     clear_memory()
         else:
             set_module(self.model, name, m)
 
diff --git a/docs/step_by_step.md b/docs/step_by_step.md
index 4f2a8e9bd..c28c7d9cf 100644
--- a/docs/step_by_step.md
+++ b/docs/step_by_step.md
@@ -306,16 +306,20 @@ ar.quantize_and_save()
 ~~~
 
 #### Hyperparameters in AutoScheme
-`avg_bits(float)`: Target average bits for the whole model, only to be quantized layer will be counted in the average bits calculation.
+`avg_bits(float)` Target average bits for the whole model, only to be quantized layer will be counted in the average bits calculation.
 
-`options(Union[str, list[Union[QuantizationScheme, str]])`: the options of quantization schemes to choose from. It could be a string like "W4A16", or a list of strings or QuantizationScheme objects.
+`options(Union[str, list[Union[QuantizationScheme, str]])` the options of quantization schemes to choose from. It could be a string like "W4A16", or a list of strings or QuantizationScheme objects.
 
-`ignore_scale_zp_bits(bool)`: Whether to ignore the bits of scale and zero point in average bits calculation. Default is False.
+`ignore_scale_zp_bits(bool)` Whether to ignore the bits of scale and zero point in average bits calculation. Default is False.
 
 `device_map (Optional[str,dict,torch.device])`  only supported in API now, as auto-scheme used more VRAM than auto-round tuning, so you could set a different device_map for it.
 
 `shared_layers (Optional[Iterable[Iterable[str]]])`  only supported in API now
 
+`batch_size (Optional[int])` could be set to 1 to reduce VRAM but increase time cost
+
+`low_gpu_mem_usage(bool=True)` whether to reduct gpu memory usage 
+
 In some serving frameworks, certain layers (e.g., QKV or MoE) are fused to accelerate inference. These fused layers may require the same data type and bit configuration. The shared_layers option simplifies this setup by supporting both regex and full-name matching. **Note that regex matching is applied in a block-wise manner.**
 
 
@@ -348,10 +352,24 @@ ar.quantize_and_save()
 ```
 
 #### AutoScheme Cost
-The tuning cost of AutoScheme is approximately 2 to 4 times that of model's bf16 size, depending on the options.
+
 We tested it on Nvidia A100 80G using torch v2.8.
 
-We will try to optimize the VRAM usage in the future.
+We will try to optimize the RAM usage in the future. The RAM usage is about 1.1-1.5x of the model's BF16 size
+
+<details>
+<summary>Cost w/o low_gpu_mem_usage </summary>
+
+| Models    | Scheme            | VRAM Cost <br />(torch compile) | Time Cost<br /> torch compile | VRAM Cost <br />wo torch compile | Time Cost<br /> wo torch compile |
+| --------- | ----------------- | ------------------------------- | ----------------------------- | -------------------------------- | -------------------------------- |
+| Qwen3-8B  | W2A16/W4A16/W8A16 | 34G                             | 30s * len of options          | 61G                              | 40s * len of options             |
+| Qwen3-8B  | MXFP4/MXFP8       | 36G                             | 60s * len of options          | 54G                              | 120s * len of options            |
+| Qwen3-8B  | GGUF*             | 54G                             | 30s * len of options          | 50G                              | 23S * len of options             |
+| Qwen3-32B | W2A16/W4A16/W8A16 | OOM with 240G                   | ---                           | OOM with 240G                    | ---                              |
+| Qwen3-32B | MXFP4/MXFP8       | 160G                            | 200s * len of options         | 200G                             | 240s * len of options            |
+| Qwen3-32B | GGUF*             | 210G                            | 80s * len of options          | 200G                             | 60s * len of options             |
+</details> 
+
 
 | Models    | Scheme            | VRAM Cost <br />(torch compile) | Time Cost<br /> torch compile | VRAM Cost <br />wo torch compile | Time Cost<br /> wo torch compile |
 | --------- | ----------------- | ------------------------------- | ----------------------------- | -------------------------------- | -------------------------------- |

From 3ab18ffd280f337d423905beccc67a6eab482fce Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 21:19:52 +0800
Subject: [PATCH 29/36] update

---
 auto_round/auto_scheme/default_alg.abi3.so | Bin 499128 -> 499128 bytes
 docs/step_by_step.md                       |  23 ++++++++++-----------
 2 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/auto_round/auto_scheme/default_alg.abi3.so b/auto_round/auto_scheme/default_alg.abi3.so
index 02045ecd67c14cfa9ac335d0aec6e6bddab8fe5b..48bccb692c8d3325f558f847596466f7817f3ef7 100644
GIT binary patch
delta 32582
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diff --git a/docs/step_by_step.md b/docs/step_by_step.md
index c28c7d9cf..02c99ead0 100644
--- a/docs/step_by_step.md
+++ b/docs/step_by_step.md
@@ -318,7 +318,7 @@ ar.quantize_and_save()
 
 `batch_size (Optional[int])` could be set to 1 to reduce VRAM but increase time cost
 
-`low_gpu_mem_usage(bool=True)` whether to reduct gpu memory usage 
+`low_gpu_mem_usage(bool=True)` whether to reduce gpu memory usage at the cost of more time cost
 
 In some serving frameworks, certain layers (e.g., QKV or MoE) are fused to accelerate inference. These fused layers may require the same data type and bit configuration. The shared_layers option simplifies this setup by supporting both regex and full-name matching. **Note that regex matching is applied in a block-wise manner.**
 
@@ -357,6 +357,16 @@ We tested it on Nvidia A100 80G using torch v2.8.
 
 We will try to optimize the RAM usage in the future. The RAM usage is about 1.1-1.5x of the model's BF16 size
 
+| Models        | Scheme                | VRAM Cost | Time Cost             |
+| ------------- | --------------------- | --------- | --------------------- |
+| Qwen3-8B      | W2A16 / W4A16 / W8A16 | 14G       | 60s * len of options  |
+| Qwen3-8B      | MXFP4 / MXFP8         | 18G       | 60s * len of options  |
+| Qwen3-8B      | GGUF*                 | 14G       | 80s * len of options  |
+| Qwen3-32B     | W2A16 / W4A16 / W8A16 | 29G       | 180s*  len of options |
+| Qwen3-32B     | MXFP4 / MXFP8         | 29G       | 180s*  len of options |
+| Qwen3-32B     | GGUF*                 | 18G       | 300s * len of options |
+| Llama-3.3-70B | W2A16 / W4A16 / W8A16 | 32G       | 420s * len of options |
+
 <details>
 <summary>Cost w/o low_gpu_mem_usage </summary>
 
@@ -371,17 +381,6 @@ We will try to optimize the RAM usage in the future. The RAM usage is about 1.1-
 </details> 
 
 
-| Models    | Scheme            | VRAM Cost <br />(torch compile) | Time Cost<br /> torch compile | VRAM Cost <br />wo torch compile | Time Cost<br /> wo torch compile |
-| --------- | ----------------- | ------------------------------- | ----------------------------- | -------------------------------- | -------------------------------- |
-| Qwen3-8B  | W2A16/W4A16/W8A16 | 34G                             | 30s * len of options          | 61G                              | 40s * len of options             |
-| Qwen3-8B  | MXFP4/MXFP8       | 36G                             | 60s * len of options          | 54G                              | 120s * len of options            |
-| Qwen3-8B  | GGUF*             | 54G                             | 30s * len of options          | 50G                              | 23S * len of options             |
-| Qwen3-32B | W2A16/W4A16/W8A16 | OOM with 240G                   | ---                           | OOM with 240G                    | ---                              |
-| Qwen3-32B | MXFP4/MXFP8       | 160G                            | 200s * len of options         | 200G                             | 240s * len of options            |
-| Qwen3-32B | GGUF*             | 210G                            | 80s * len of options          | 200G                             | 60s * len of options             |
-
-
-
 #### Limitations
 Embedding layer is not supported in AutoScheme, it will use the best scheme in options.
 

From 9e67c158a008bf92ac7ded77292f021bd1732024 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 21:22:33 +0800
Subject: [PATCH 30/36] clean code

---
 auto_round/utils/device.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index b92ab2074..374dfab00 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -693,16 +693,6 @@ def set_auto_device_map_for_block_with_tuning(
     set_non_auto_device_map(block, device_map, names)
 
 
-#
-# def set_device_map_for_auto_scheme(model, device_map):
-#     if not device_map:
-#         return
-#     if device_map == "auto" or (isinstance(device_map, str) and "," in device_map):  # auto device map
-#         set_avg_auto_device_map(model)
-#     else:
-#         set_non_auto_device_map(model, device_map)
-
-
 def partition_dict_numbers(number_dict, n):
     """
     Partition a dictionary of numbers into N groups with approximately equal sums

From 3fe8d087868066d898907736ef9baa92966a53e9 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 21:27:02 +0800
Subject: [PATCH 31/36] Update auto_round/utils/device.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 auto_round/utils/device.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 374dfab00..35a56ee6d 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -559,7 +559,7 @@ def get_major_device(device_map: Union[None, str, torch.device, int, dict]) -> s
         tmp_devices = list(set(tmp_devices))
         device = None
         for tmp_device in tmp_devices:
-            if device != "cpu":
+            if tmp_device != "cpu":
                 device = tmp_device
                 break
         if device is None:

From cb560f355f1786d32f940b7a11cc43318118a781 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 21:27:59 +0800
Subject: [PATCH 32/36] Update auto_round/utils/device.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 auto_round/utils/device.py | 23 ++++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 35a56ee6d..c5af8a269 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -631,7 +631,28 @@ def set_non_auto_device_map(
 def set_auto_device_map_for_block_with_tuning(
     block: torch.nn.Module, device_map, input_ids: list[torch.Tensor], low_gpu_mem_usage=False, mem_per_param_scale=13.0
 ):
-    """Automatically sets the device map for the block based on available GPUs and memory constraints."""
+    """
+    Automatically sets the device map for the block based on available GPUs and memory constraints.
+
+    Args:
+        block (torch.nn.Module): The model block whose device map is to be set.
+        device_map (str | int | dict): Specifies the device mapping. Can be a string (e.g., "0,1"), integer, or dictionary mapping layer names to devices.
+        input_ids (list[torch.Tensor]): List of input tensors used for estimating memory requirements.
+        low_gpu_mem_usage (bool, optional): If True, reduces memory usage estimation by ignoring input/output memory. Defaults to False.
+        mem_per_param_scale (float, optional): Scaling factor for estimating memory usage per parameter in the block. 
+            Typical values range from 10.0 to 20.0 depending on model size and GPU memory characteristics. 
+            Higher values are more conservative and help avoid out-of-memory errors. Defaults to 13.0.
+
+    Returns:
+        None
+
+    Raises:
+        RuntimeError: If no CUDA or XPU devices are found.
+
+    Note:
+        This function is intended for internal use in device memory management and tuning. 
+        The mem_per_param_scale parameter should be adjusted based on empirical memory usage observations.
+    """
     if torch.cuda.is_available():
         num_gpus = torch.cuda.device_count()
     elif torch.xpu.is_available():

From 8987254d02cf65bf65c336ec5486bbb71cc4cb54 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 21:28:35 +0800
Subject: [PATCH 33/36] Update docs/step_by_step.md

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 docs/step_by_step.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/step_by_step.md b/docs/step_by_step.md
index 02c99ead0..f692543b7 100644
--- a/docs/step_by_step.md
+++ b/docs/step_by_step.md
@@ -306,7 +306,7 @@ ar.quantize_and_save()
 ~~~
 
 #### Hyperparameters in AutoScheme
-`avg_bits(float)` Target average bits for the whole model, only to be quantized layer will be counted in the average bits calculation.
+`avg_bits(float)` Target average bits for the whole model; only layers to be quantized will be counted in the average bits calculation.
 
 `options(Union[str, list[Union[QuantizationScheme, str]])` the options of quantization schemes to choose from. It could be a string like "W4A16", or a list of strings or QuantizationScheme objects.
 

From c3c082399252de5ae81b15947e739dc4142a4fac Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Oct 2025 13:28:44 +0000
Subject: [PATCH 34/36] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 auto_round/utils/device.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index c5af8a269..58bb5f27d 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -639,8 +639,8 @@ def set_auto_device_map_for_block_with_tuning(
         device_map (str | int | dict): Specifies the device mapping. Can be a string (e.g., "0,1"), integer, or dictionary mapping layer names to devices.
         input_ids (list[torch.Tensor]): List of input tensors used for estimating memory requirements.
         low_gpu_mem_usage (bool, optional): If True, reduces memory usage estimation by ignoring input/output memory. Defaults to False.
-        mem_per_param_scale (float, optional): Scaling factor for estimating memory usage per parameter in the block. 
-            Typical values range from 10.0 to 20.0 depending on model size and GPU memory characteristics. 
+        mem_per_param_scale (float, optional): Scaling factor for estimating memory usage per parameter in the block.
+            Typical values range from 10.0 to 20.0 depending on model size and GPU memory characteristics.
             Higher values are more conservative and help avoid out-of-memory errors. Defaults to 13.0.
 
     Returns:
@@ -650,7 +650,7 @@ def set_auto_device_map_for_block_with_tuning(
         RuntimeError: If no CUDA or XPU devices are found.
 
     Note:
-        This function is intended for internal use in device memory management and tuning. 
+        This function is intended for internal use in device memory management and tuning.
         The mem_per_param_scale parameter should be adjusted based on empirical memory usage observations.
     """
     if torch.cuda.is_available():

From bde5f2096d61f6520959834897f04f5ac29f9096 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 21:35:26 +0800
Subject: [PATCH 35/36] fix line too long issue

---
 auto_round/utils/device.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/auto_round/utils/device.py b/auto_round/utils/device.py
index 58bb5f27d..850c95343 100644
--- a/auto_round/utils/device.py
+++ b/auto_round/utils/device.py
@@ -636,9 +636,9 @@ def set_auto_device_map_for_block_with_tuning(
 
     Args:
         block (torch.nn.Module): The model block whose device map is to be set.
-        device_map (str | int | dict): Specifies the device mapping. Can be a string (e.g., "0,1"), integer, or dictionary mapping layer names to devices.
+        device_map (str | int | dict): Specifies the device mapping.
         input_ids (list[torch.Tensor]): List of input tensors used for estimating memory requirements.
-        low_gpu_mem_usage (bool, optional): If True, reduces memory usage estimation by ignoring input/output memory. Defaults to False.
+        low_gpu_mem_usage (bool, optional): If True, ignoring input/output memory. Defaults to False.
         mem_per_param_scale (float, optional): Scaling factor for estimating memory usage per parameter in the block.
             Typical values range from 10.0 to 20.0 depending on model size and GPU memory characteristics.
             Higher values are more conservative and help avoid out-of-memory errors. Defaults to 13.0.

From 035c04648b6856f0c19bb37cfc1570e90c93b8e3 Mon Sep 17 00:00:00 2001
From: Wenhua Cheng <wenhua.cheng@intel.com>
Date: Tue, 28 Oct 2025 22:01:22 +0800
Subject: [PATCH 36/36] fix ut

---
 auto_round/auto_scheme/default_alg.abi3.so | Bin 499128 -> 499128 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/auto_round/auto_scheme/default_alg.abi3.so b/auto_round/auto_scheme/default_alg.abi3.so
index 48bccb692c8d3325f558f847596466f7817f3ef7..220fb3ce50652fb00a1beb87c87f2d20f4e9b2e8 100644
GIT binary patch
delta 141941
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