Support pass python file as config.

llm/config/llama/pretrain_argument.py

@@ -0,0 +1,67 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+# models
+model_name_or_path = "meta-llama/Meta-Llama-3-8B"
+tokenizer_name_or_path = "meta-llama/Meta-Llama-3-8B"
+
+# data
+checkpoint_dirs = {
+    "input_dir": "./data",
+    "output_dir": "./checkpoints/pretrain_ckpts",
+    "unified_checkpoint": True,
+    "save_total_limit": 2,
+}
+
+training_contronl = {
+    "do_train": True,
+    "do_eval": True,
+    "do_predict": True,
+    "disable_tqdm": True,
+    "recompute": False,
+    "distributed_dataloader": 1,
+    "recompute_granularity": "full",
+}
+
+
+training_args = {
+    "per_device_train_batch_size": 1,
+    "gradient_accumulation_steps": 16,
+    "per_device_eval_batch_size": 2,
+    "tensor_parallel_degree": 2,
+    "pipeline_parallel_degree": 1,
+    "sharding": "stage2",
+    "virtual_pp_degree": 1,
+    "sequence_parallel": 0,
+    "max_seq_length": 4096,
+    "learning_rate": 3e-05,
+    "min_learning_rate": 3e-06,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "max_steps": 10000,
+    "save_steps": 5000,
+    "eval_steps": 1000,
+    "weight_decay": 0.01,
+    "warmup_ratio": 0.01,
+    "max_grad_norm": 1.0,
+    "dataloader_num_workers": 1,
+    "continue_training": 0,
+}
+accelerate = {
+    "use_flash_attention": True,
+    "use_fused_rms_norm": True,
+    "use_fused_rope": True,
+    "bf16": True,
+    "fp16_opt_level": "O2",
+}

llm/run_finetune.py

@@ -112,6 +112,8 @@ def main():
         gen_args, model_args, reft_args, data_args, training_args = parser.parse_json_file_and_cmd_lines()
     elif len(sys.argv) >= 2 and sys.argv[1].endswith(".yaml"):
         gen_args, model_args, reft_args, data_args, training_args = parser.parse_yaml_file_and_cmd_lines()
+    elif len(sys.argv) >= 2 and sys.argv[1].endswith(".py"):
+        gen_args, model_args, reft_args, data_args, training_args = parser.parse_python_file_and_cmd_lines()
     else:
         gen_args, model_args, reft_args, data_args, training_args = parser.parse_args_into_dataclasses()
 

llm/run_pretrain.py

@@ -354,6 +354,8 @@ def main():
         model_args, data_args, training_args = parser.parse_json_file_and_cmd_lines()
     elif len(sys.argv) >= 2 and sys.argv[1].endswith(".yaml"):
         model_args, data_args, training_args = parser.parse_yaml_file_and_cmd_lines()
+    elif len(sys.argv) >= 2 and sys.argv[1].endswith(".py"):
+        model_args, data_args, training_args = parser.parse_python_file_and_cmd_lines()
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 

paddlenlp/trainer/argparser.py

@@ -342,6 +342,64 @@
         args = yaml_args + sys.argv[2:]
         return self.common_parse(args, return_remaining_strings)
 
+    def read_python(self, python_file: str) -> list:
+
+        python_file = Path(python_file)
+
+        def get_variables_exec(file_path):
+            def flatten(config):
+                ret = {}
+                for k, v in config.items():
+                    if type(v) is dict:
+                        sub = flatten(v)
+                        for sk, sv in sub.items():
+                            ret[sk] = sv
+                    else:
+                        ret[k] = v
+                return ret
+
+            with open(file_path, "r", encoding="utf-8") as f:
+                code = compile(f.read(), file_path, "exec")
+                globals_dict = {}
+                exec(code, globals_dict)
+                ret_dict = {k: globals_dict[k] for k in globals_dict if not k.startswith("__")}
+                return flatten(ret_dict)
+
+        if python_file.exists():
+            data = get_variables_exec(python_file)
+
+            python_args = []
+            for key, value in data.items():
+                if isinstance(value, list):
+                    python_args.extend([f"--{key}", *[str(v) for v in value]])
+                elif isinstance(value, dict):
+                    python_args.extend([f"--{key}", json.dumps(value)])
+                else:
+                    python_args.extend([f"--{key}", str(value)])
+            return python_args
+        else:
+            raise FileNotFoundError(f"The argument file {python_file} does not exist.")
+
+    def parse_python_file_and_cmd_lines(self, return_remaining_strings=False) -> Tuple[DataClass, ...]:
+        """
+        Extend the functionality of `parse_python_file` to handle command line arguments in addition to loading a python
+        file.
+
+        When there is a conflict between the command line arguments and the YAML file configuration,
+        the command line arguments will take precedence.
+
+        Returns:
+            Tuple consisting of:
+
+                - the dataclass instances in the same order as they were passed to the initializer.abspath
+        """
+        if not sys.argv[1].endswith(".py"):
+            raise ValueError(f"The first argument should be a PYTHON file, but it is {sys.argv[1]}")
+        python_args = self.read_python(sys.argv[1])
+        # In case of conflict, command line arguments take precedence
+        args = python_args + sys.argv[2:]
+        return self.common_parse(args, return_remaining_strings)
+
     def parse_dict(self, args: dict) -> Tuple[DataClass, ...]:
         """
         Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass