mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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from paddleformers.trl.llm_utils import init_dist_env
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from fastdeploy.rl.rollout_config import RolloutModelConfig
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from fastdeploy.rl.rollout_model import RolloutModel
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_, ranks = init_dist_env()
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_path", type=str, required=True, help="Path to the model directory")
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parser.add_argument("--baseline_path", type=str, required=True, help="Path to the baseline path")
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parser.add_argument("--quantization", type=str, default=None, help="Quantization")
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parser.add_argument("--enable_mm", action="store_true", required=False, help="Flags to enable multi-modal model")
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args = parser.parse_args()
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# base result
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model_path = args.model_path
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# Usage example:
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init_kwargs = {
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"model_name_or_path": model_path,
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"max_model_len": 32768,
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"tensor_parallel_size": ranks,
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"dynamic_load_weight": True,
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"load_strategy": "ipc_snapshot",
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"quantization": args.quantization,
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}
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if args.enable_mm:
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init_kwargs["enable_mm"] = True
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rollout_config = RolloutModelConfig(**init_kwargs)
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actor_eval_model = RolloutModel(rollout_config)
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content = "".join(
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sorted(
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[f"{k}\n" for k, v in actor_eval_model.state_dict().items()]
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+ [f"{k}:{v}\n" for k, v in actor_eval_model.get_name_mappings_to_training().items()]
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)
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)
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def compare_strings_line_by_line(a: str, b: str) -> bool:
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"""
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Compare two multiline strings line by line.
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Returns:
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True if all lines match exactly in order and content.
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False if any line differs or the number of lines is not equal.
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"""
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a_lines = a.splitlines()
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b_lines = b.splitlines()
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if len(a_lines) != len(b_lines):
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print(f"❌ Mismatch in number of lines: expected {len(a_lines)}, but got {len(b_lines)}.")
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return False
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for i, (line_a, line_b) in enumerate(zip(a_lines, b_lines)):
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if line_a != line_b:
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print(f"❌ Difference found on line {i + 1}:")
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print(f" Expected: {repr(line_a)}")
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print(f" Actual : {repr(line_b)}")
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return False
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print("✅ All lines match exactly.")
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return True
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with open(args.baseline_path, "r", encoding="utf-8") as f:
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baseline = f.read()
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assert compare_strings_line_by_line(baseline, content), (
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"In the unittest of RL scenario, your modification "
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"caused inconsistency in the content before and after. Please fix it. "
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"Can request assistance from yuanlehome or gzy19990617 (github id)."
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)
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