[Step1 ]new architecture for auto_round

auto_round/__main__.py

@@ -38,6 +38,7 @@
 
 
 class BasicArgumentParser(argparse.ArgumentParser):
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.add_argument(
@@ -729,45 +730,10 @@ def tune(args):
         trust_remote_code=not args.disable_trust_remote_code,
     )
 
-    model_name = args.model.rstrip("/")
-
-    if model_name.split("/")[-1].strip(".") == "" and "gguf" not in args.format:
-        if autoround.group_size <= 0:
-            if "fp" in autoround.act_data_type:
-                suffix = f"afp{autoround.act_bits}"
-            else:
-                suffix = f"a{autoround.act_bits}"
-        else:
-            suffix = f"g{autoround.group_size}"
-        export_dir = os.path.join(args.output_dir, f"w{autoround.bits}{suffix}")
-    elif model_name.split("/")[-1].strip(".") == "" and "gguf" in args.format:
-        export_dir = args.output_dir
-    elif model_name.split("./")[-1].strip("./") != "" and "gguf" in args.format:
-        export_dir = os.path.join(args.output_dir, model_name.split("/")[-1] + "-gguf")
-    else:
-        if isinstance(autoround.group_size, tuple):
-            assert len(autoround.group_size) == 2, f"Only support 2D group_size, but get {autoround.group_size}"
-            suffix = f"g{autoround.group_size[0]}x{autoround.group_size[1]}"
-        else:
-            if autoround.group_size <= 0:
-                if "fp" in autoround.act_data_type:
-                    suffix = f"afp{autoround.act_bits}"
-                else:
-                    suffix = f"a{autoround.act_bits}"
-            else:
-                suffix = f"g{autoround.group_size}"
-        prefix = (
-            autoround.data_type.lower().replace("_", "")
-            if "int" not in autoround.data_type or "mx" in autoround.data_type
-            else ""
-        )
-        export_dir = os.path.join(
-            args.output_dir,
-            model_name.split("/")[-1] + (f"-{prefix}" if prefix else "") + f"-w{autoround.bits}{suffix}",
-        )
-
     # ======================= Quantize and save model =======================
-    model, folders = autoround.quantize_and_save(export_dir, format=args.format)  # pylint: disable=E1101
+    # Export directory is now derived automatically inside quantize_and_save via
+    # BaseCompressor._get_export_dir(), so we only need to pass the base output_dir.
+    model, folders = autoround.quantize_and_save(args.output_dir, format=args.format)  # pylint: disable=E1101
     tokenizer = autoround.tokenizer  # pylint: disable=E1101
 
     model.eval()

auto_round/algorithms/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2026 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.

auto_round/algorithms/alg_config.py

@@ -0,0 +1,18 @@
+# Copyright (c) 2026 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.
+
+
+class AlgConfig:
+    def __init__(self):
+        pass

auto_round/algorithms/base.py

@@ -0,0 +1,17 @@
+# Copyright (c) 2026 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.
+
+
+class BaseAlgorithm:
+    pass

auto_round/algorithms/quantization/__init__.py

@@ -0,0 +1,21 @@
+# Copyright (c) 2026 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 auto_round.algorithms.quantization.base import BaseQuantizers
+from auto_round.algorithms.quantization.config import QuantizationConfig
+from auto_round.algorithms.quantization.sign_round.config import SignRoundConfig
+from auto_round.algorithms.quantization.sign_round.quantizer import SignRoundQuantizer
+from auto_round.algorithms.quantization.adam_round.adam import AdamRoundQuantizer
+from auto_round.algorithms.quantization.rtn.config import RTNConfig
+from auto_round.algorithms.quantization.rtn.quantizer import RTNQuantizer, OptimizedRTNQuantizer

auto_round/algorithms/quantization/adam_round/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2026 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.

auto_round/algorithms/quantization/adam_round/adam.py

@@ -0,0 +1,66 @@
+# Copyright (c) 2026 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.algorithms.quantization.sign_round.quantizer import SignRoundQuantizer
+from auto_round.schemes import QuantizationScheme
+from auto_round.utils import check_is_cpu, htcore, is_hpex_available
+
+
+class AdamRoundQuantizer(SignRoundQuantizer):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.momentum = None  # AdamW handles momentum internally
+
+    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.model_context.amp and not check_is_cpu(self.compress_context.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()