[Features] support hugging face qwen3 moe (#3649)

* split ut

* qwen3-30B-A3B

* fix

* add test

* add test_torch_model.py

* fix test_torch_model.py

* delete print

* fix moe

* delete init.py

* fix

* fix

---------

Co-authored-by: bukejiyu <395822456@qq.com>
Co-authored-by: bukejiyu <52310069+bukejiyu@users.noreply.github.com>

tests/model_loader/test_torch_model.py

@@ -0,0 +1,148 @@
+# 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.
+
+import os
+import sys
+
+import pytest
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.abspath(os.path.join(current_dir, ".."))
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+from tests.model_loader.utils import (
+    calculate_diff_rate,
+    form_model_get_output_topp0,
+    get_torch_model_path,
+    run_with_timeout,
+)
+
+FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8313))
+
+prompts = ["北京天安门在哪里?"]
+
+
+def check_result_against_baseline(outputs, baseline_file, threshold=0.05):
+    """
+    Check model outputs against baseline file.
+    """
+    try:
+        with open(baseline_file, "r", encoding="utf-8") as f:
+            baseline_content = f.read().strip()
+    except FileNotFoundError:
+        raise AssertionError(f"Baseline file not found: {baseline_file}")
+
+    # Combine all outputs into a single string for comparison
+    current_content = ""
+    for idx, output in enumerate(outputs):
+        # output format: (token_ids, text)
+        _, text = output
+
+        if isinstance(text, list):
+            text_str = "".join(text)
+        else:
+            text_str = text
+
+        current_content += text_str
+
+    temp_file = f"{os.path.basename(baseline_file)}-current"
+    with open(temp_file, "w", encoding="utf-8") as f:
+        f.write(current_content)
+
+    diff_rate = calculate_diff_rate(current_content, baseline_content)
+
+    if diff_rate >= threshold:
+        raise AssertionError(
+            f"Output differs from baseline file by too much ({diff_rate:.4%}):\n"
+            f"Current output: {current_content!r}\n"
+            f"Baseline content: {baseline_content!r}\n"
+            f"Current output saved to: {temp_file}"
+        )
+
+
+hugging_face_model_param_map = {
+    "Qwen2.5-7B-Instruct": {
+        "tensor_parallel_size": 2,
+        "quantizations": ["wint8"],
+    },
+    "Qwen3-30B-A3B": {
+        "tensor_parallel_size": 2,
+        "quantizations": ["wint8"],
+    },
+}
+
+hf_params = []
+for model, cfg in hugging_face_model_param_map.items():
+    for q in cfg["quantizations"]:
+        hf_params.append(
+            pytest.param(
+                model,
+                cfg.get("tensor_parallel_size", 2),
+                cfg.get("max_model_len", 1024),
+                q,
+                cfg.get("max_tokens", 100),
+                marks=[pytest.mark.core_model],
+            )
+        )
+
+
+@pytest.mark.parametrize(
+    "model_name_or_path,tensor_parallel_size,max_model_len,quantization,max_tokens",
+    hf_params,
+)
+def test_model_against_baseline(
+    fd_runner,
+    model_name_or_path: str,
+    tensor_parallel_size: int,
+    max_model_len: int,
+    max_tokens: int,
+    quantization: str,
+) -> None:
+    """
+    Test that model output matches baseline file.
+    """
+    torch_model_path = get_torch_model_path(model_name_or_path)
+
+    # Run model
+    hf_outputs = run_with_timeout(
+        target=form_model_get_output_topp0,
+        args=(
+            fd_runner,
+            torch_model_path,
+            tensor_parallel_size,
+            max_model_len,
+            max_tokens,
+            quantization,
+            "default_v1",
+            FD_ENGINE_QUEUE_PORT,
+            prompts,
+        ),
+    )
+
+    # Determine baseline file path based on model name
+    base_path = os.getenv("MODEL_PATH", "")
+
+    # Get baseline suffix from config
+    model_config = hugging_face_model_param_map.get(model_name_or_path, {})
+    baseline_suffix = model_config.get("baseline_suffix", "tp2")
+    baseline_filename = f"{model_name_or_path}-{baseline_suffix}"
+
+    if base_path:
+        baseline_file = os.path.join(base_path, baseline_filename)
+    else:
+        baseline_file = baseline_filename
+
+    # Compare against baseline file
+    check_result_against_baseline(hf_outputs, baseline_file, threshold=0.05)