Address comment

# Conflicts:
#	pyproject.toml
#	src/exo/worker/runner/runner.py
#	uv.lock

.mlx_typings/mlx_lm/models/glm_moe_dsa.pyi

@@ -0,0 +1,46 @@
+"""Type stubs for mlx_lm.models.glm_moe_dsa"""
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+from .base import BaseModelArgs
+from .deepseek_v32 import Model as DSV32Model
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    vocab_size: int
+    hidden_size: int
+    index_head_dim: int
+    index_n_heads: int
+    index_topk: int
+    intermediate_size: int
+    moe_intermediate_size: int
+    num_hidden_layers: int
+    num_attention_heads: int
+    num_key_value_heads: int
+    n_shared_experts: Optional[int]
+    n_routed_experts: Optional[int]
+    routed_scaling_factor: float
+    kv_lora_rank: int
+    q_lora_rank: int
+    qk_rope_head_dim: int
+    v_head_dim: int
+    qk_nope_head_dim: int
+    topk_method: str
+    scoring_func: str
+    norm_topk_prob: bool
+    n_group: int
+    topk_group: int
+    num_experts_per_tok: int
+    moe_layer_freq: int
+    first_k_dense_replace: int
+    max_position_embeddings: int
+    rms_norm_eps: float
+    rope_parameters: Dict[str, Any]
+    attention_bias: bool
+    rope_scaling: Dict[str, Any] | None
+    rope_theta: float | None
+
+class Model(DSV32Model):
+    def __init__(self, config: ModelArgs) -> None: ...

nix/mlx.nix

@@ -41,7 +41,7 @@ let
 
   mlx = stdenv.mkDerivation rec {
     pname = "mlx";
-    version = let v = "0.30.7.dev20260217+50487b41"; in
+    version = let v = "0.30.7.dev20260218+14841977"; in
       assert v == uvLockMlxVersion || throw "MLX version mismatch: nix/mlx.nix has ${v} but uv.lock has ${uvLockMlxVersion}. Update both the version and hash in nix/mlx.nix.";
       v;
     pyproject = true;
@@ -49,8 +49,8 @@ let
     src = fetchFromGitHub {
       owner = "rltakashige";
       repo = "mlx-jaccl-fix-small-recv";
-      rev = "50487b4141f3c951122655db3b83df5146c1fbeb";
-      hash = "sha256-IL4a9vMX5nocgJU1WG4zE8hArHkHJtnh4sdYh3od5zU=";
+      rev = "1484197707f35186ad3bd614357c7c47fdf86ebc";
+      hash = "sha256-FupCMoK/SF/ldfKuvMSAKECcOP8c+ANgkQlPZttDsLk=";
     };
 
     patches = [

resources/inference_model_cards/mlx-community--GLM-5-8bit.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/GLM-5-8bit-MXFP8"
+n_layers = 78
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "glm"
+quantization = "8bit"
+base_model = "GLM-5"
+capabilities = ["text", "thinking"]
+
+[storage_size]
+in_bytes = 790517400864

resources/inference_model_cards/mlx-community--GLM-5-MXFP4-Q8.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/GLM-5-MXFP4-Q8"
+n_layers = 78
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "glm"
+quantization = "MXFP4-Q8"
+base_model = "GLM-5"
+capabilities = ["text", "thinking"]
+
+[storage_size]
+in_bytes = 405478939008

resources/inference_model_cards/mlx-community--GLM-5-bf16.toml

@@ -0,0 +1,12 @@
+model_id = "mlx-community/GLM-5"
+n_layers = 78
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+family = "glm"
+quantization = "bf16"
+base_model = "GLM-5"
+capabilities = ["text", "thinking"]
+
+[storage_size]
+in_bytes = 1487822475264

src/exo/shared/models/model_cards.py

@@ -183,6 +183,7 @@ class ConfigData(BaseModel):
     def supports_tensor(self) -> bool:
         return self.architectures in [
             ["Glm4MoeLiteForCausalLM"],
+            ["GlmMoeDsaForCausalLM"],
             ["DeepseekV32ForCausalLM"],
             ["DeepseekV3ForCausalLM"],
             ["Qwen3NextForCausalLM"],

src/exo/worker/engines/mlx/auto_parallel.py

@@ -163,11 +163,14 @@ class PipelineLastLayer(CustomMlxLayer):
                 output, (self.r + 1) % self.s, group=self.group
             )
             if cache is not None:
-                cache.keys = mx.depends(cache.keys, output)  # type: ignore[reportUnknownMemberType]
+                # CacheList (used by MLA models like DeepSeekV32, GLM MoE DSA)
+                # doesn't have .keys directly; access via first sub-cache.
+                _cache = cache[0] if hasattr(cache, "caches") else cache  # type: ignore
+                _cache.keys = mx.depends(_cache.keys, output)  # type: ignore
             if self.is_prefill:
                 mx.eval(output)
                 if cache is not None:
-                    mx.eval(cache.keys)  # type: ignore
+                    mx.eval(_cache.keys)  # type: ignore
 
         if not self.is_prefill:
             output = mx.distributed.all_gather(output, group=self.group)[
@@ -307,7 +310,9 @@ def patch_pipeline_model[T](model: T, group: mx.distributed.Group) -> T:
 
         # Add dependency to last cache entry to ensure distributed ops are evaluated
         if cache is not None:
-            cache[-1].state = mx.depends(cache[-1].state, logits)  # type: ignore
+            last = cache[-1]  # type: ignore
+            dep_cache = last[0] if hasattr(last, "caches") else last  # type: ignore
+            dep_cache.keys = mx.depends(dep_cache.keys, logits)  # type: ignore
 
         return logits
 
@@ -333,7 +338,9 @@ def patch_tensor_model[T](model: T) -> T:
 
         # Add dependency to last cache entry to ensure distributed ops are evaluated
         if cache is not None and len(cache) > 0:  # pyright: ignore[reportAny]
-            cache[-1].state = mx.depends(cache[-1].state, logits)  # pyright: ignore[reportAny,reportUnknownMemberType]
+            last = cache[-1]  # pyright: ignore[reportAny]
+            dep_cache = last[0] if hasattr(last, "caches") else last  # pyright: ignore[reportAny]
+            dep_cache.keys = mx.depends(dep_cache.keys, logits)  # pyright: ignore[reportAny,reportUnknownMemberType]
 
         return logits
 
@@ -547,10 +554,12 @@ class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
         on_timeout: TimeoutCallback | None,
     ) -> nn.Module:
         model = cast(DeepseekV3Model, model)
+
         for layer in model.layers:
             eval_with_timeout(
                 layer.parameters(), timeout_seconds / len(model.layers), on_timeout
             )
+
             # Shard the self attention
             if layer.self_attn.q_lora_rank is None:
                 layer.self_attn.q_proj = self.all_to_sharded_linear(
@@ -581,12 +590,18 @@ class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
                 layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
                 layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
 
-            # Shard the MoE. Shard in place since the MoE should be responsible
-            # for aggregating the results.
+            # Shard the MoE.
             else:
-                self.all_to_sharded_linear_in_place(layer.mlp.shared_experts.gate_proj)
-                self.sharded_to_all_linear_in_place(layer.mlp.shared_experts.down_proj)
-                self.all_to_sharded_linear_in_place(layer.mlp.shared_experts.up_proj)
+                if getattr(layer.mlp, "shared_experts", None) is not None:
+                    self.all_to_sharded_linear_in_place(
+                        layer.mlp.shared_experts.gate_proj
+                    )
+                    self.sharded_to_all_linear_in_place(
+                        layer.mlp.shared_experts.down_proj
+                    )
+                    self.all_to_sharded_linear_in_place(
+                        layer.mlp.shared_experts.up_proj
+                    )
                 self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.gate_proj)
                 self.sharded_to_all_linear_in_place(layer.mlp.switch_mlp.down_proj)
                 self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.up_proj)
@@ -779,8 +794,7 @@ class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
 
             layer.self_attn = WrappedMiniMaxAttention(layer.self_attn, self.group)  # pyright: ignore[reportAttributeAccessIssue,reportArgumentType]
 
-            # Shard the MoE. Shard in place since the MoE should be responsible
-            # for aggregating the results.
+            # Shard the MoE.
             self.all_to_sharded_linear_in_place(
                 layer.block_sparse_moe.switch_mlp.gate_proj
             )
@@ -893,8 +907,7 @@ class QwenShardingStrategy(TensorParallelShardingStrategy):
                     layer.self_attn.num_attention_heads //= self.N
                     layer.self_attn.num_key_value_heads //= self.N
 
-            # Shard the MoE. Shard in place since the MoE should be responsible
-            # for aggregating the results.
+            # Shard the MoE.
             if isinstance(layer.mlp, (Qwen3MoeSparseMoeBlock, Qwen3NextSparseMoeBlock)):
                 self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.gate_proj)
                 self.sharded_to_all_linear_in_place(layer.mlp.switch_mlp.down_proj)

src/exo/worker/engines/mlx/generator/generate.py

@@ -57,6 +57,7 @@ def prefill(
     sampler: Callable[[mx.array], mx.array],
     prompt_tokens: mx.array,
     cache: KVCacheType,
+    group: mx.distributed.Group | None,
 ) -> tuple[float, int, list[CacheSnapshot]]:
     """Prefill the KV cache with prompt tokens.
 
@@ -86,6 +87,9 @@ def prefill(
 
     set_pipeline_prefill(model, is_prefill=True)
 
+    mx_barrier(group)
+    logger.info("Starting prefill")
+
     # Use max_tokens=1 because max_tokens=0 does not work.
     # We just throw away the generated token - we only care about filling the cache
     for _ in stream_generate(
@@ -305,16 +309,9 @@ def mlx_generate(
     )
     max_stop_len = max((len(s) for s in stop_sequences), default=0)
 
-    mx_barrier(group)
-    logger.info("Starting prefill")
-
     # Prefill cache with all tokens except the last one
     prefill_tps, prefill_tokens, ssm_snapshots_list = prefill(
-        model,
-        tokenizer,
-        sampler,
-        prompt_tokens[:-1],
-        caches,
+        model, tokenizer, sampler, prompt_tokens[:-1], caches, group
     )
     cache_snapshots: list[CacheSnapshot] | None = ssm_snapshots_list or None
 
@@ -331,6 +328,7 @@ def mlx_generate(
     think_start = tokenizer.think_start
     think_end = tokenizer.think_end
 
+    logger.info("Starting decode")
     mx_barrier(group)
 
     for completion_tokens, out in enumerate(

src/exo/worker/engines/mlx/utils_mlx.py

@@ -285,10 +285,12 @@ def get_eos_token_ids_for_model(model_id: ModelId) -> list[int] | None:
     model_id_lower = model_id.lower()
     if "kimi-k2" in model_id_lower:
         return [163586]
-    elif "glm-4.7-flash" in model_id_lower:
+    elif "glm-5" in model_id_lower or "glm-4.7" in model_id_lower:
+        # For GLM-5 and GLM-4.7
         # 154820: <|endoftext|>, 154827: <|user|>, 154829: <|observation|>
         return [154820, 154827, 154829]
     elif "glm" in model_id_lower:
+        # For GLM-4.5 and older
         return [151336, 151329, 151338]
     return None
 

src/exo/worker/runner/runner_supervisor.py

@@ -191,7 +191,7 @@ class RunnerSupervisor:
         logger.info("Checking runner's status")
         if self.runner_process.is_alive():
             logger.info("Runner was found to be alive, attempting to join process")
-            await to_thread.run_sync(self.runner_process.join, 1)
+            await to_thread.run_sync(self.runner_process.join, 5)
         rc = self.runner_process.exitcode
         logger.info(f"RunnerSupervisor exited with exit code {rc}")
         if rc == 0:

uv.lock

@@ -378,7 +378,7 @@ dependencies = [
     { name = "loguru", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mflux", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mlx", version = "0.30.6", source = { registry = "https://pypi.org/simple" }, extra = ["cpu"], marker = "sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.7.dev20260217+50487b41", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#50487b4141f3c951122655db3b83df5146c1fbeb" }, marker = "sys_platform == 'darwin'" },
+    { name = "mlx", version = "0.30.7.dev20260218+14841977", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#1484197707f35186ad3bd614357c7c47fdf86ebc" }, marker = "sys_platform == 'darwin'" },
     { name = "mlx-lm", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "msgspec", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "openai-harmony", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -1021,7 +1021,7 @@ dependencies = [
     { name = "huggingface-hub", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "matplotlib", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mlx", version = "0.30.6", source = { registry = "https://pypi.org/simple" }, extra = ["cuda13"], marker = "sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.7.dev20260217+50487b41", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#50487b4141f3c951122655db3b83df5146c1fbeb" }, marker = "sys_platform == 'darwin'" },
+    { name = "mlx", version = "0.30.7.dev20260218+14841977", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#1484197707f35186ad3bd614357c7c47fdf86ebc" }, marker = "sys_platform == 'darwin'" },
     { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "opencv-python", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "piexif", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -1068,8 +1068,8 @@ cuda13 = [
 
 [[package]]
 name = "mlx"
-version = "0.30.7.dev20260217+50487b41"
-source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#50487b4141f3c951122655db3b83df5146c1fbeb" }
+version = "0.30.7.dev20260218+14841977"
+source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#1484197707f35186ad3bd614357c7c47fdf86ebc" }
 resolution-markers = [
     "sys_platform == 'darwin'",
 ]
@@ -1104,7 +1104,7 @@ version = "0.30.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jinja2", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.7.dev20260217+50487b41", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#50487b4141f3c951122655db3b83df5146c1fbeb" }, marker = "sys_platform == 'darwin'" },
+    { name = "mlx", version = "0.30.7.dev20260218+14841977", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#1484197707f35186ad3bd614357c7c47fdf86ebc" }, marker = "sys_platform == 'darwin'" },
     { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "protobuf", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "pyyaml", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },