update scout example (#2310)

Signed-off-by: Mengni Wang <mengni.wang@intel.com>

Author: mengniwang95

examples/pytorch/multimodal-modeling/quantization/auto_round/llama4/README.md

@@ -11,6 +11,10 @@ docker run -d --gpus all -v ... --shm-size=100g --name llama4 -it nvcr.io/nvidia
 docker exec -it llama4 bash
 git clone https://github.com/intel/neural-compressor.git
 cd neural-compressor/examples/3.x_api/pytorch/multimodal-modeling/quantization/auto_round/llama4
+# Use `INC_PT_ONLY=1 pip install git+https://github.com/intel/neural-compressor.git@v3.6rc` for the latest updates before neural-compressor v3.6 release
+pip install neural-compressor-pt==3.6
+# Use `pip install git+https://github.com/intel/auto-round.git@v0.8.0rc2` for the latest updates before auto-round v0.8.0 release
+pip install auto-round==0.8.0
 bash setup.sh
 ```
 

examples/pytorch/multimodal-modeling/quantization/auto_round/llama4/main.py

@@ -0,0 +1,97 @@
+# 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 argparse
+
+import torch
+
+torch.use_deterministic_algorithms(True, warn_only=True)
+from transformers import AutoTokenizer, Llama4ForConditionalGeneration, AutoProcessor
+from neural_compressor.torch.quantization import (
+    AutoRoundConfig,
+    convert,
+    prepare,
+)
+
+
+class BasicArgumentParser(argparse.ArgumentParser):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.add_argument("--model", "--model_name", "--model_name_or_path",
+                          help="model name or path")
+
+        self.add_argument('--scheme', default="MXFP4", type=str,
+                          help="quantizaion scheme.")
+
+        self.add_argument("--device", "--devices", default="auto", type=str,
+                          help="the device to be used for tuning. The default is set to auto,"
+                               "allowing for automatic detection."
+                               "Currently, device settings support CPU, GPU, and HPU.")
+
+        self.add_argument("--export_format", default="llm_compressor", type=str,
+                          help="the format to save the model"
+                          )
+
+        self.add_argument("--output_dir", default="./tmp_autoround", type=str,
+                          help="the directory to save quantized model")
+
+        self.add_argument("--fp_layers", default="", type=str,
+                          help="layers to maintain original data type")
+
+
+def setup_parser():
+    parser = BasicArgumentParser()
+
+    parser.add_argument("--iters", "--iter", default=0, type=int,
+                        help=" iters")
+
+    args = parser.parse_args()
+    return args
+
+
+def tune(args):
+    model_name = args.model
+    if model_name[-1] == "/":
+        model_name = model_name[:-1]
+    print(f"start to quantize {model_name}")
+
+    layer_config = {}
+    model = Llama4ForConditionalGeneration.from_pretrained(args.model, device_map=None, torch_dtype="auto", trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
+    processor = AutoProcessor.from_pretrained(args.model, trust_remote_code=True)
+    fp_layers = args.fp_layers.replace(" ", "").split(",")
+    if len(fp_layers) > 0:
+        for n, m in model.named_modules():
+            if not isinstance(m, (torch.nn.Linear)):
+                continue
+            for name in fp_layers:
+                if name in n:
+                    layer_config[n] = {"bits": 16, "act_bits": 16}
+                    break
+
+    qconfig = AutoRoundConfig(
+        tokenizer=tokenizer,
+        iters=args.iters,
+        scheme=args.scheme,
+        layer_config=layer_config,
+        export_format="llm_compressor",
+        output_dir=args.output_dir,
+        processor=processor,
+    )
+    model = prepare(model, qconfig)
+    model = convert(model, qconfig)
+
+if __name__ == '__main__':
+    args = setup_parser()
+    tune(args)

examples/pytorch/multimodal-modeling/quantization/auto_round/llama4/requirements.txt

@@ -1,4 +1,3 @@
-auto-round @ git+https://github.com/intel/auto-round@v0.8.0rc
 lm-eval==0.4.9.1
 setuptools_scm
 torchao==0.12.0

examples/pytorch/multimodal-modeling/quantization/auto_round/llama4/run_quant.sh

@@ -47,12 +47,11 @@ function run_tuning {
         extra_cmd="--fp_layers lm-head,self_attn,router,vision_model,multi_modal_projector,shared_expert --scheme MXFP4"
     fi
 
-    python3 -m auto_round \
-        --model ${input_model} \
-        --iters ${iters}  \
-        --format "llm_compressor"  \
-        --output_dir ${tuned_checkpoint} \
-        ${extra_cmd}
+    python3 main.py \
+    	--model ${input_model} \
+		--iters ${iters} \
+		--output_dir ${tuned_checkpoint} \
+		${extra_cmd}
 }
 
 main "$@"