feat: add qwen3.5 moe

Author: gufengc

src/backend/server/static_config.py

@@ -84,7 +84,11 @@
     "Qwen/Qwen3-Next-80B-A3B-Instruct-FP8": "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit",
     "Qwen/Qwen3-Next-80B-A3B-Thinking": "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit",
     "Qwen/Qwen3-Next-80B-A3B-Thinking-FP8": "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit",
+    # Qwen 3.5 MoE Series
+    "Qwen/Qwen3.5-0.8B": "Qwen/Qwen3.5-0.8B",
+    "Qwen/Qwen3.5-35B-A3B": "mlx-community/Qwen3.5-35B-A3B-4bit",
     # Qwen 3.6 Series
+    "Qwen/Qwen3.6-35B-A3B": "mlx-community/Qwen3.6-35B-A3B-4bit",
     "Qwen/Qwen3.6-27B": "mlx-community/Qwen3.6-27B-mxfp4",
     # Qwen 3 Large MoE Models
     "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8": "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit",

src/parallax/server/shard_loader.py

@@ -33,10 +33,12 @@
 MODEL_CLASS_MAP = {
     "kimi_k2": "mlx_lm.models.deepseek_v3",
     "minimax_m2": "mlx_lm.models.minimax",
+    "qwen3_5_moe": "mlx_lm.models.qwen3_5",
 }
 
 ARCHITECTURE_CLASS_ALIASES = {
     "GlmMoeDsaForCausalLM": "DeepseekV32ForCausalLM",
+    "Qwen3_5MoeForConditionalGeneration": "Qwen3_5ForConditionalGeneration",
 }
 
 
@@ -310,6 +312,21 @@ def _cast_weight_array(weight_array: mx.array, dtype: mx.Dtype) -> mx.array:
             return weight_array.astype(dtype)
         return weight_array
 
+    @staticmethod
+    def _load_mlx_lm_module_and_args(model_type: str, config: Dict[str, Any]):
+        if model_type in MODEL_CLASS_MAP:
+            model_class = MODEL_CLASS_MAP[model_type]
+        else:
+            model_class = f"mlx_lm.models.{model_type}"
+
+        arch_module = importlib.import_module(model_class)
+        if hasattr(arch_module, "TextModelArgs"):
+            model_args_class = getattr(arch_module, "TextModelArgs")
+        else:
+            model_args_class = getattr(arch_module, "ModelArgs")
+
+        return arch_module, model_args_class.from_dict(config)
+
     def load(
         self, lazy: bool = False, strict: bool = False, use_selective_download: bool = True
     ) -> Tuple[nn.Module, Dict[str, Any], Any]:
@@ -366,18 +383,8 @@ def load(
         if not model_type:
             raise ValueError("model_type not found in config.json")
 
-        if model_type in MODEL_CLASS_MAP:
-            model_class = MODEL_CLASS_MAP[model_type]
-        else:
-            model_class = f"mlx_lm.models.{model_type}"
-
         try:
-            arch_module = importlib.import_module(model_class)
-            if model_type == "qwen3_5" and hasattr(arch_module, "TextModelArgs"):
-                model_args_class = getattr(arch_module, "TextModelArgs")
-            else:
-                model_args_class = getattr(arch_module, "ModelArgs")
-            model_args = model_args_class.from_dict(config)
+            arch_module, model_args = self._load_mlx_lm_module_and_args(model_type, config)
             self.arch_module = arch_module
             self.model_args = model_args
 

src/parallax/utils/model_download.py

@@ -1,15 +1,17 @@
+import json
 import logging
 import os
 from pathlib import Path
-from typing import Optional
+from typing import Dict, List, Optional, Set
 
 from huggingface_hub import hf_hub_download as _hf_hub_download
 from huggingface_hub import snapshot_download as _snapshot_download
 from modelscope import snapshot_download as _ms_snapshot_download
 from modelscope.hub.file_download import model_file_download as _ms_model_file_download
 
 from parallax.utils.weight_filter_utils import (
-    determine_needed_weight_files_for_download,
+    normalize_language_model_weight_key,
+    should_include_weight_key,
 )
 
 logger = logging.getLogger(__name__)
@@ -96,7 +98,7 @@ def selective_model_download(
     if start_layer is not None and end_layer is not None:
         logger.debug(f"Determining required weight files for layers [{start_layer}, {end_layer})")
 
-        needed_weight_files = determine_needed_weight_files_for_download(
+        needed_weight_files = _determine_needed_weight_files_for_download(
             model_path=model_path,
             start_layer=start_layer,
             end_layer=end_layer,
@@ -149,5 +151,80 @@ def selective_model_download(
 ]
 
 
+def _determine_needed_weight_files_for_download(
+    model_path: Path,
+    start_layer: int,
+    end_layer: int,
+    config: Optional[Dict] = None,
+) -> List[str]:
+    is_first_shard = start_layer == 0
+
+    is_last_shard = False
+    if config:
+        num_hidden_layers = config.get("num_hidden_layers", 0)
+        is_last_shard = end_layer >= num_hidden_layers
+    else:
+        config_file = model_path / "config.json"
+        if config_file.exists():
+            from parallax.utils.utils import normalize_model_config
+
+            with open(config_file, "r") as f:
+                cfg = normalize_model_config(json.load(f))
+                num_hidden_layers = cfg.get("num_hidden_layers", 0)
+                is_last_shard = end_layer >= num_hidden_layers
+
+    index_file = model_path / "model.safetensors.index.json"
+
+    if not index_file.exists():
+        logger.debug(f"Index file not found at {index_file}, checking for single weight file")
+        # For non-sharded models, look for single weight file
+        single_weight_files = [
+            "model.safetensors",
+            "pytorch_model.bin",
+            "model.bin",
+        ]
+        for weight_file in single_weight_files:
+            if (model_path / weight_file).exists():
+                logger.debug(f"Found single weight file: {weight_file}")
+                return [weight_file]
+
+        logger.debug("No weight files found (neither index nor single file)")
+        return []
+
+    with open(index_file, "r") as f:
+        index_data = json.load(f)
+
+    weight_map = index_data.get("weight_map", {})
+    if not weight_map:
+        logger.debug("weight_map is empty in index file")
+        return []
+
+    tie_word_embeddings = False
+    if config:
+        tie_word_embeddings = config.get("tie_word_embeddings", False)
+
+    needed_files: Set[str] = set()
+
+    for key, filename in weight_map.items():
+        if filename in needed_files:
+            continue
+        key = normalize_language_model_weight_key(key)
+        if should_include_weight_key(
+            key=key,
+            start_layer=start_layer,
+            end_layer=end_layer,
+            is_first_shard=is_first_shard,
+            is_last_shard=is_last_shard,
+            tie_word_embeddings=tie_word_embeddings,
+        ):
+            needed_files.add(filename)
+
+    result = sorted(list(needed_files))
+    logger.debug(
+        f"Determined {len(result)} weight files needed for layers [{start_layer}, {end_layer})"
+    )
+    return result
+
+
 def _use_modelscope() -> bool:
     return _USE_MODELSCOPE_ENV in os.environ

src/parallax/utils/utils.py

@@ -303,7 +303,9 @@ def load_config_only(name: str, local_files_only: bool = False):
 def normalize_model_config(config: dict) -> dict:
     """Expose nested text model fields at the top level for VLM-style configs."""
     text_config = config.get("text_config")
-    if config.get("model_type") == "qwen3_5" and isinstance(text_config, dict):
+    if config.get("model_type") in {"qwen3_5", "qwen3_5_moe"} and isinstance(
+        text_config, dict
+    ):
         normalized = {**config, **text_config}
         normalized["model_type"] = config["model_type"]
         normalized["architectures"] = config.get("architectures", normalized.get("architectures"))

src/parallax/utils/weight_filter_utils.py

@@ -116,76 +116,3 @@ def filter_weight_files_by_layer_range_for_load(
     )
 
     return filtered_files
-
-
-def determine_needed_weight_files_for_download(
-    model_path: Path,
-    start_layer: int,
-    end_layer: int,
-    config: Optional[Dict] = None,
-) -> List[str]:
-    is_first_shard = start_layer == 0
-
-    is_last_shard = False
-    if config:
-        num_hidden_layers = config.get("num_hidden_layers", 0)
-        is_last_shard = end_layer >= num_hidden_layers
-    else:
-        config_file = model_path / "config.json"
-        if config_file.exists():
-            with open(config_file, "r") as f:
-                cfg = json.load(f)
-                num_hidden_layers = cfg.get("num_hidden_layers", 0)
-                is_last_shard = end_layer >= num_hidden_layers
-
-    index_file = model_path / "model.safetensors.index.json"
-
-    if not index_file.exists():
-        logger.debug(f"Index file not found at {index_file}, checking for single weight file")
-        # For non-sharded models, look for single weight file
-        single_weight_files = [
-            "model.safetensors",
-            "pytorch_model.bin",
-            "model.bin",
-        ]
-        for weight_file in single_weight_files:
-            if (model_path / weight_file).exists():
-                logger.debug(f"Found single weight file: {weight_file}")
-                return [weight_file]
-
-        logger.debug("No weight files found (neither index nor single file)")
-        return []
-
-    with open(index_file, "r") as f:
-        index_data = json.load(f)
-
-    weight_map = index_data.get("weight_map", {})
-    if not weight_map:
-        logger.debug("weight_map is empty in index file")
-        return []
-
-    tie_word_embeddings = False
-    if config:
-        tie_word_embeddings = config.get("tie_word_embeddings", False)
-
-    needed_files: Set[str] = set()
-
-    for key, filename in weight_map.items():
-        if filename in needed_files:
-            continue
-        key = normalize_language_model_weight_key(key)
-        if should_include_weight_key(
-            key=key,
-            start_layer=start_layer,
-            end_layer=end_layer,
-            is_first_shard=is_first_shard,
-            is_last_shard=is_last_shard,
-            tie_word_embeddings=tie_word_embeddings,
-        ):
-            needed_files.add(filename)
-
-    result = sorted(list(needed_files))
-    logger.debug(
-        f"Determined {len(result)} weight files needed for layers [{start_layer}, {end_layer})"
-    )
-    return result

tests/test_shard_loader.py

@@ -2,6 +2,7 @@
 Tests for the shard_loader module.
 """
 
+import json
 import sys
 from unittest.mock import Mock, patch
 
@@ -14,6 +15,8 @@
     MLXModelLoader,
     normalize_language_model_weight_key,
 )
+from parallax.utils.model_download import _determine_needed_weight_files_for_download
+from parallax.utils.utils import normalize_model_config
 from parallax.utils.weight_filter_utils import should_include_weight_key
 
 
@@ -27,10 +30,20 @@ def test_normalize_nested_language_model_weight_keys():
         normalize_language_model_weight_key("model.language_model.layers.12.mlp.up_proj.weight")
         == "model.layers.12.mlp.up_proj.weight"
     )
+    assert (
+        normalize_language_model_weight_key(
+            "language_model.model.layers.12.mlp.switch_mlp.up_proj.weight"
+        )
+        == "model.layers.12.mlp.switch_mlp.up_proj.weight"
+    )
     assert (
         normalize_language_model_weight_key("model.language_model.norm.weight")
         == "model.norm.weight"
     )
+    assert (
+        normalize_language_model_weight_key("language_model.model.norm.weight")
+        == "model.norm.weight"
+    )
     assert (
         normalize_language_model_weight_key("model.language_model.lm_head.weight")
         == "lm_head.weight"
@@ -111,6 +124,80 @@ def test_mlx_lm_sanitize_uses_local_layer_keys_for_shards():
     ) == ["layers.0.linear_attn.conv1d.weight"]
 
 
+def test_qwen35_moe_uses_qwen35_text_args_and_sanitizer_module():
+    loader = MLXModelLoader("test_model_path")
+    config = normalize_model_config(
+        {
+            "model_type": "qwen3_5_moe",
+            "architectures": ["Qwen3_5MoeForConditionalGeneration"],
+            "text_config": {
+                "model_type": "qwen3_5_moe_text",
+                "hidden_size": 2048,
+                "num_hidden_layers": 40,
+                "num_attention_heads": 16,
+                "num_key_value_heads": 2,
+                "vocab_size": 248320,
+                "num_experts": 256,
+                "num_experts_per_tok": 8,
+                "moe_intermediate_size": 512,
+            },
+        }
+    )
+
+    sanitizer_module, model_args = loader._load_mlx_lm_module_and_args(
+        "qwen3_5_moe", config
+    )
+
+    assert MODEL_CLASS_MAP["qwen3_5_moe"] == "mlx_lm.models.qwen3_5"
+    assert sanitizer_module.__name__ == "mlx_lm.models.qwen3_5"
+    assert model_args.num_hidden_layers == 40
+    assert model_args.hidden_size == 2048
+    assert model_args.num_experts == 256
+    assert model_args.num_experts_per_tok == 8
+    assert model_args.moe_intermediate_size == 512
+
+
+def test_register_block_class_includes_qwen35_moe():
+    loader = MLXModelLoader("test_model_path")
+
+    assert "Qwen3_5MoeForConditionalGeneration" in loader.block_class_map
+
+
+def test_selective_download_uses_nested_qwen35_moe_num_layers(tmp_path):
+    (tmp_path / "config.json").write_text(
+        json.dumps(
+            {
+                "model_type": "qwen3_5_moe",
+                "text_config": {
+                    "num_hidden_layers": 40,
+                    "tie_word_embeddings": False,
+                },
+            }
+        )
+    )
+    (tmp_path / "model.safetensors.index.json").write_text(
+        json.dumps(
+            {
+                "weight_map": {
+                    "language_model.model.layers.39.linear_attn.in_proj_qkv.weight": (
+                        "layers-39.safetensors"
+                    ),
+                    "language_model.model.norm.weight": "final.safetensors",
+                    "language_model.lm_head.weight": "final.safetensors",
+                }
+            }
+        )
+    )
+
+    needed_files = _determine_needed_weight_files_for_download(
+        tmp_path,
+        start_layer=39,
+        end_layer=40,
+    )
+
+    assert needed_files == ["final.safetensors", "layers-39.safetensors"]
+
+
 @pytest.mark.skipif(sys.platform != "darwin", reason="MLX tests require macOS")
 class TestMLXModelLoader:
     """Test MLXModelLoader functionality."""