[misc] improve entrypoint (#7345)

* 纯粹优化下入口代码，因为看到if else太多了

* Update cli.py

---------

Co-authored-by: hoshi-hiyouga <hiyouga@buaa.edu.cn>

src/llamafactory/cli.py

@@ -17,9 +17,11 @@ import subprocess
 import sys
 from copy import deepcopy
 from enum import Enum, unique
+from functools import partial
 
 from .extras import logging
 
+
 USAGE = (
     "-" * 70
     + "\n"
@@ -38,19 +40,6 @@ USAGE = (
 logger = logging.get_logger(__name__)
 
 
-@unique
-class Command(str, Enum):
-    API = "api"
-    CHAT = "chat"
-    ENV = "env"
-    EVAL = "eval"
-    EXPORT = "export"
-    TRAIN = "train"
-    WEBDEMO = "webchat"
-    WEBUI = "webui"
-    VER = "version"
-    HELP = "help"
-
 def main():
     from . import launcher
     from .api.app import run_api
@@ -73,67 +62,59 @@ def main():
         + "-" * 58
     )
 
-    command = sys.argv.pop(1) if len(sys.argv) != 1 else Command.HELP
-    if command == Command.API:
-        run_api()
-    elif command == Command.CHAT:
-        run_chat()
-    elif command == Command.ENV:
-        print_env()
-    elif command == Command.EVAL:
-        run_eval()
-    elif command == Command.EXPORT:
-        export_model()
-    elif command == Command.TRAIN:
-        force_torchrun = is_env_enabled("FORCE_TORCHRUN")
-        if force_torchrun or (get_device_count() > 1 and not use_ray()):
-            nnodes = os.getenv("NNODES", "1")
-            node_rank = os.getenv("NODE_RANK", "0")
-            nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
-            master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
-            master_port = os.getenv("MASTER_PORT", str(find_available_port()))
-            logger.info_rank0(f"Initializing {nproc_per_node} distributed tasks at: {master_addr}:{master_port}")
-            if int(nnodes) > 1:
-                print(f"Multi-node training enabled: num nodes: {nnodes}, node rank: {node_rank}")
+    COMMANDS = {
+        "api": run_api,
+        "chat": run_chat,
+        "env": print_env,
+        "eval": run_eval,
+        "export": export_model,
+        "train": run_exp,
+        "webchat": run_web_demo,
+        "webui": run_web_ui,
+        "version": partial(print, WELCOME),
+        "help": partial(print, USAGE),
+    }
 
-            env = deepcopy(os.environ)
-            if is_env_enabled("OPTIM_TORCH", "1"):
-                # optimize DDP, see https://zhuanlan.zhihu.com/p/671834539
-                env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-                env["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+    command = sys.argv.pop(1) if len(sys.argv) != 1 else "help"
+    force_torchrun = is_env_enabled("FORCE_TORCHRUN")
+    if command == "train" and (force_torchrun or (get_device_count() > 1 and not use_ray())):
+        nnodes = os.getenv("NNODES", "1")
+        node_rank = os.getenv("NODE_RANK", "0")
+        nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
+        master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
+        master_port = os.getenv("MASTER_PORT", str(find_available_port()))
+        logger.info_rank0(f"Initializing {nproc_per_node} distributed tasks at: {master_addr}:{master_port}")
+        if int(nnodes) > 1:
+            print(f"Multi-node training enabled: num nodes: {nnodes}, node rank: {node_rank}")
 
-            # NOTE: DO NOT USE shell=True to avoid security risk
-            process = subprocess.run(
-                (
-                    "torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
-                    "--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
-                )
-                .format(
-                    nnodes=nnodes,
-                    node_rank=node_rank,
-                    nproc_per_node=nproc_per_node,
-                    master_addr=master_addr,
-                    master_port=master_port,
-                    file_name=launcher.__file__,
-                    args=" ".join(sys.argv[1:]),
-                )
-                .split(),
-                env=env,
-                check=True,
+        env = deepcopy(os.environ)
+        if is_env_enabled("OPTIM_TORCH", "1"):
+            # optimize DDP, see https://zhuanlan.zhihu.com/p/671834539
+            env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+            env["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+        # NOTE: DO NOT USE shell=True to avoid security risk
+        process = subprocess.run(
+            (
+                "torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
+                "--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
             )
-            sys.exit(process.returncode)
-        else:
-            run_exp()
-    elif command == Command.WEBDEMO:
-        run_web_demo()
-    elif command == Command.WEBUI:
-        run_web_ui()
-    elif command == Command.VER:
-        print(WELCOME)
-    elif command == Command.HELP:
-        print(USAGE)
+            .format(
+                nnodes=nnodes,
+                node_rank=node_rank,
+                nproc_per_node=nproc_per_node,
+                master_addr=master_addr,
+                master_port=master_port,
+                file_name=launcher.__file__,
+                args=" ".join(sys.argv[1:]),
+            )
+            .split(),
+            env=env,
+            check=True,
+        )
+        sys.exit(process.returncode)
     else:
-        print(f"Unknown command: {command}.\n{USAGE}")
+        COMMANDS[command]()
 
 
 if __name__ == "__main__":