创建统一的参数配置基类

README.md

@@ -11,7 +11,7 @@
 - 编译并安装 `InfiniLM`
 
 ```bash
-xmake && xmake install
+
 ```
 
 - 运行模型推理测试

jiuge.sh

@@ -0,0 +1,46 @@
+featurize@featurize:~/work/InfiniLM$ cat jiuge.sh 
+#!/bin/bash
+
+# Jiuge模型运行脚本
+# 使用NVIDIA显卡运行9G4B模型
+
+set -e  # 遇到错误立即退出
+
+echo "=========================================="
+echo "🚀 启动 Jiuge 模型 (9G4B) - NVIDIA版本"
+echo "=========================================="
+export INFINI_ROOT=/home/featurize/.infini
+export LD_LIBRARY_PATH=$INFINI_ROOT/lib:$LD_LIBRARY_PATH
+# 设置参数
+MODEL_DIR="/home/featurize/work/InfiniFamily/9G4B"
+DEVICE="--nvidia"
+N_DEVICE=1
+SCRIPT_PATH="python scripts/jiuge.py"
+
+# 检查模型目录是否存在
+if [ ! -d "$MODEL_DIR" ]; then
+    echo "❌ 错误: 模型目录不存在: $MODEL_DIR"
+    echo "请检查路径是否正确"
+    exit 1
+fi
+
+# 检查Python脚本是否存在
+if [ ! -f "scripts/jiuge.py" ]; then
+    echo "❌ 错误: 未找到jiuge.py脚本: scripts/jiuge.py"
+    echo "请确保在当前目录下运行此脚本"
+    exit 1
+fi
+
+echo "📁 模型路径: $MODEL_DIR"
+echo "🎯 设备类型: NVIDIA GPU"
+echo "💻 设备数量: $N_DEVICE"
+echo ""
+
+# 运行模型
+echo "🔄 启动模型..."
+$SCRIPT_PATH $DEVICE $MODEL_DIR $N_DEVICE
+
+echo ""
+echo "=========================================="
+echo "✅ 模型运行完成"
+echo "=========================================="ß

python/infinilm/__init__.py

@@ -2,6 +2,7 @@
 from . import distributed
 from . import cache
 from . import llm
+from . import base_config
 
 from .llm import (
     LLM,
@@ -16,6 +17,7 @@
     "distributed",
     "cache",
     "llm",
+    "base_config",
     # LLM classes
     "LLM",
     "AsyncLLMEngine",

python/infinilm/base_config.py

@@ -0,0 +1,128 @@
+import argparse
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../scripts"))                                                           
+from libinfinicore_infer import DeviceType  
+
+
+class BaseConfig:
+    """InfiniLM Unified Config - Command line argument parser"""
+
+    def __init__(self):
+
+        self.parser = argparse.ArgumentParser(description="InfiniLM Unified Config")
+        self._add_common_args()
+        self.args, self.extra = self.parser.parse_known_args()
+
+
+        self.model = self.args.model
+        self.device_name = self.args.device
+        self.device_type = self._get_device_type(self.args.device)
+        self.tp = self.args.tp
+
+
+        self.attn = self.args.attn
+        self.enable_graph = self.args.enable_graph
+        self.cache_type = self.args.cache_type
+        self.enable_paged_attn = self.args.enable_paged_attn
+        self.paged_kv_block_size = self.args.paged_kv_block_size
+        self.kv_cache_dtype = self.args.kv_cache_dtype
+        self.skip_load = self.args.skip_load
+
+
+        self.batch_size = self.args.batch_size
+        self.input_len = self.args.input_len
+        self.output_len = self.args.output_len
+        self.max_new_tokens = self.args.max_new_tokens
+        self.top_k = self.args.top_k
+        self.top_p = self.args.top_p
+        self.temperature = self.args.temperature
+
+        self.warm_up = self.args.warm_up
+        self.verbose = self.args.verbose
+        self.log_evel = self.args.log_evel
+
+
+        # Evaluation parameters
+        self.bench = self.args.bench
+        self.backend = self.args.backend
+        self.ndev = self.args.ndev
+        self.subject = self.args.subject
+        self.split = self.args.split
+        self.num_samples = self.args.num_samples
+        self.output_csv = self.args.output_csv
+        self.cache_dir = self.args.cache_dir
+
+
+        # Quantization parameters
+        self.awq = self.args.awq
+        self.gptq = self.args.gptq
+
+    def _add_common_args(self):
+        # --- base configuration ---
+        self.parser.add_argument("--model", type=str, required=True)
+        self.parser.add_argument("--device", type=str, default="cpu")
+        self.parser.add_argument("--tp", "--tensor-parallel-size", type=int, default=1)
+
+
+        # --- Infer backend optimization ---
+        self.parser.add_argument("--attn", type=str, default="default", choices=["default", "paged-attn", "flash-attn"])
+        self.parser.add_argument("--enable-graph", action="store_true")
+        self.parser.add_argument("--cache-type", type=str, default="paged", choices=["paged",   "static"])
+        self.parser.add_argument("--enable-paged-attn", action="store_true", help="use paged cache",)
+        self.parser.add_argument("--paged-kv-block-size", type=int, default=256)
+        self.parser.add_argument("--kv-cache-dtype", type=str, default=None, choices=["int8"], help="KV cache data type")
+        self.parser.add_argument("--skip-load", action="store_true", help="skip loading model weights")
+
+
+        # --- Length and infer parameters ---
+        self.parser.add_argument("--batch-size", type=int, default=1)
+        self.parser.add_argument("--input-len", type=int, default=10, help="input sequence length")
+        self.parser.add_argument("--output-len", type=int, default=20, help="output sequence length")
+        self.parser.add_argument("--max-new-tokens", type=int, default=500, help="maximum number of new tokens to generate")
+        self.parser.add_argument("--top-k", type=int, default=1)
+        self.parser.add_argument("--top-p", type=float, default=1.0)
+        self.parser.add_argument("--temperature", type=float, default=1.0)
+
+        # --- debug ---
+        self.parser.add_argument("--warmup", action="store_true")
+        self.parser.add_argument("--verbose", action="store_true")
+        self.parser.add_argument("--log-evel", type=str, default="INFO")
+
+
+        # --- Evaluation parameters ---
+        self.parser.add_argument("--bench", type=str, default=None, choices=["ceval", "mmlu"], help="benchmark to evaluate")
+        self.parser.add_argument("--backend", type=str, default="cpp", choices=["python", "cpp", "torch", "vllm"], help="backend type")
+        self.parser.add_argument("--ndev", type=int, default=1, help="number of devices for tensor parallelism")
+        self.parser.add_argument("--subject", type=str, default="all", help="subject(s) to evaluate, comma-separated or 'all'")
+        self.parser.add_argument("--split", type=str, default="test", choices=["test", "val", "all"], help="dataset split to use")
+        self.parser.add_argument("--num-samples", type=int, default=None, help="number of samples to evaluate per subject")
+        self.parser.add_argument("--output-csv", type=str, default=None, help="path to output CSV file for results")
+        self.parser.add_argument("--cache-dir", type=str, default=None, help="directory for dataset cache")
+
+
+        # --- Quantization parameters ---
+        self.parser.add_argument("--awq", action="store_true", help="use AWQ quantization")
+        self.parser.add_argument("--gptq", action="store_true", help="use GPTQ quantization")
+
+
+    def _get_device_type(self, dev_str):
+        """Convert device string to DeviceType enum"""
+        DEVICE_TYPE_MAP = {
+            "cpu": DeviceType.DEVICE_TYPE_CPU,
+            "nvidia": DeviceType.DEVICE_TYPE_NVIDIA,
+            "qy": DeviceType.DEVICE_TYPE_QY,
+            "cambricon": DeviceType.DEVICE_TYPE_CAMBRICON,
+            "ascend": DeviceType.DEVICE_TYPE_ASCEND,
+            "metax": DeviceType.DEVICE_TYPE_METAX,
+            "moore": DeviceType.DEVICE_TYPE_MOORE,
+            "iluvatar": DeviceType.DEVICE_TYPE_ILUVATAR,
+            "kunlun": DeviceType.DEVICE_TYPE_KUNLUN,
+            "hygon": DeviceType.DEVICE_TYPE_HYGON,
+            "ali": DeviceType.DEVICE_TYPE_ALI
+        }
+        return DEVICE_TYPE_MAP.get(dev_str.lower(), DeviceType.DEVICE_TYPE_CPU)
+
+    def __repr__(self):
+        """String representation of configuration"""
+        return f"BaseConfig(model='{self.model}', device='{self.device_name}', tp={self.tp})"