fix regression issue and command in mix-precision example

examples/pytorch/nlp/huggingface_models/language-modeling/quantization/mix-precision/README.md

@@ -27,7 +27,7 @@ pip install -r requirements.txt
 ### Demo (`MXFP4`, `MXFP8`, `NVFP4`, `uNVFP4`)
 
 ```bash
-python quantize.py  --model_name_or_path facebook/opt-125m --quantize --dtype MXFP4 --batch_size 8 --accuracy
+python quantize.py  --model_name_or_path facebook/opt-125m --quantize --dtype MXFP4 --batch_size 8 --accuracy --enable_torch_compile
 ```
 
 ### Mix-precision Quantization (`MXFP4 + MXFP8`)
@@ -41,10 +41,11 @@ python quantize.py  \
     --use_recipe \
     --recipe_file recipes/Meta-Llama-3.1-8B-Instruct_7bits.json \
     --accuracy \
-    --batch_size 32
+    --batch_size 32 \
+    --enable_torch_compile
 
 # Llama 3.3 70B
-deepspeed --include="localhost:4,5,6,7" --master_port=29500 python quantize.py  \
+deepspeed --include="localhost:0,1,2,3" --master_port=29500 quantize.py  \
     --model_name_or_path meta-llama/Llama-3.3-70B-Instruct/ \
     --quantize \
     --dtype MXFP4 \
@@ -111,13 +112,13 @@ Model with mixed precision is not supported in vLLM, but supported in transforme
 python quantize.py  \
     --model_name_or_path meta-llama/Llama-3.1-8B-Instruct \
     --quantize \
-    --iters 0 \
     --dtype MXFP4 \
     --use_recipe \
     --recipe_file recipes/Meta-Llama-3.1-8B-Instruct_7bits.json \
     --save \
     --save_format auto_round \
-    --save_path Llama-3.1-8B-Instruct-MXFP4-MXFP8-AR
+    --save_path Llama-3.1-8B-Instruct-MXFP4-MXFP8-AR \
+    --enable_torch_compile
 
 # Command to inference with transformer:
 python run_hf_inf.py Llama-3.1-8B-Instruct-MXFP4-MXFP8-AR

examples/pytorch/nlp/huggingface_models/language-modeling/quantization/mix-precision/quantize.py

@@ -71,12 +71,14 @@ def initialize_model_and_tokenizer(model_name_or_path):
     parser.add_argument("--device_map", type=str, default=None, help="device map for model")
     parser.add_argument("--use_recipe", action="store_true", help="whether to use recipe to quantize model")
     parser.add_argument("--recipe_file", type=str, default="recipes/Meta-Llama-3.1-8B-Instruct_6bits.json", help="path of recipe file")
+    parser.add_argument("--mem_per_param_scale", default=13, type=int, help="memory per param scale factor")
     parser.add_argument("--iters", default=200, type=int, help="iters for autoround.")
     parser.add_argument("--seqlen", default=2048, type=int, help="sequence length for autoround.")
     parser.add_argument("--nsamples", default=128, type=int, help="number of samples for autoround.")
     parser.add_argument("--save", action="store_true", help="whether to save the quantized model")
     parser.add_argument("--save_path", type=str, default="saved_results", help="path to save the quantized model")
     parser.add_argument("--save_format", type=str, default="auto_round", help="format to save the quantized model")
+    parser.add_argument("--enable_torch_compile", action="store_true", help="whether to enable torch.compile")
     parser.add_argument("--quant_lm_head", action="store_true", help="whether to quantize lm_head")
     parser.add_argument("--accuracy", action="store_true", help="accuracy measurement")
     parser.add_argument("--local_rank", type=int, default=0, metavar="N", help="Local process rank.")
@@ -103,21 +105,24 @@ def initialize_model_and_tokenizer(model_name_or_path):
     device="hpu" if is_hpex_available() else "cuda"
 
     if args.quantize:
-        autoround_dtype_mapping = {
-            "MXFP4": "mx_fp4",
-            "MXFP8": "mx_fp8",
-            "NVFP4": "nv_fp4",
-            "uNVFP4": "fp4_v2",
-            "NVFP4+": "fp4_v2",
-        }
-        args.dtype = autoround_dtype_mapping[args.dtype]
+        if args.dtype in ["uNVFP4", "NVFP4+"]:
+            from auto_round.schemes import QuantizationScheme
+
+            uNVFP4 = QuantizationScheme.from_dict(
+                {
+                    "bits": 4,
+                    "group_size": 16,
+                    "data_type": "fp4_v2",
+                    "act_bits": 4,
+                    "act_data_type": "fp4_v2",
+                    "act_group_size": 16,
+                    "act_sym": True,
+                }
+            )
+            args.dtype = uNVFP4
+
         if args.quant_lm_head:
-            lm_head_config = {
-                "group_size": 32 if "mx" in args.dtype else 16,
-                "data_type": args.dtype,
-                "act_data_type": "fp4_v2_with_global_scale" if "fp4_v2" in args.dtype else args.dtype,
-            }
-            layer_config = {"lm_head": lm_head_config}
+            layer_config = {"lm_head": args.dtype}
 
         autoround = AutoRound(
             model,
@@ -128,10 +133,10 @@ def initialize_model_and_tokenizer(model_name_or_path):
             seqlen=args.seqlen,
             nsamples=args.nsamples,
             low_gpu_mem_usage=True,
-            group_size=32 if "mx" in args.dtype else 16,
-            data_type=args.dtype,
-            act_data_type="fp4_v2_with_global_scale" if "fp4_v2" in args.dtype else args.dtype,
+            scheme=args.dtype,
             layer_config=layer_config if args.quant_lm_head else None,
+            enable_torch_compile=args.enable_torch_compile,
+            mem_per_param_scale=args.mem_per_param_scale,
         )
 
         if args.use_recipe:
@@ -140,20 +145,16 @@ def load_recipe_results(file_path):
                 import json
                 with open(file_path, "r") as f:
                     return json.load(f)
-                
+
             layer_config = load_recipe_results(args.recipe_file)
             if args.quant_lm_head:
-                mxfp8_config = {
-                    "bits": 8,
-                    "group_size": 32,
-                    "data_type": "mx_fp8",
-                    "act_data_type": "mx_fp8",
-                }
                 # ensure lm_head is quantized with mxfp8_config
-                layer_config.update({"lm_head": mxfp8_config})
+                layer_config.update({"lm_head": "MXFP8"})
                 print("In recipe mode, lm_head is quantized with MXFP8.")
             autoround.layer_config = layer_config
 
+        # A placeholder, to pass assertion in AutoRound
+        autoround.formats = "auto_round"
         autoround.quantize()
         model = autoround.model
 
@@ -192,7 +193,6 @@ def load_recipe_results(file_path):
         else:
             # CUDA evaluation support all tasks.
             # gsm8k requires add_bos_token=False for better accuracy for llama model.
-            # model = torch.compile(model)
             args.tasks = ["piqa", "hellaswag", "mmlu", "gsm8k"]
             all_accuracy = {}
             test_gsm8k = False
@@ -243,7 +243,7 @@ def load_recipe_results(file_path):
             print(f"Overall accuracy: {sum(all_accuracy.values())/len(all_accuracy):.4f}")
 
     if args.save:
-        if args.dtype == "nv_fp4":
+        if args.dtype == "NVFP4":
             # using llm_compressor format to save nv_fp4 model
             autoround.save_quantized(args.save_path, format=args.save_format)
         else: