fix skipping logic in worker plan (#1342)

the worker plan function had some skipping logic missing, leading to
double-submitting tasks.

src/exo/download/impl_shard_downloader.py

@@ -21,7 +21,7 @@ def exo_shard_downloader(max_parallel_downloads: int = 8) -> ShardDownloader:
 
 
 async def build_base_shard(model_id: ModelId) -> ShardMetadata:
-    model_card = await ModelCard.from_hf(model_id)
+    model_card = await ModelCard.load(model_id)
     return PipelineShardMetadata(
         model_card=model_card,
         device_rank=0,

src/exo/master/api.py

@@ -146,18 +146,6 @@ def chunk_to_response(
     )
 
 
-async def resolve_model_card(model_id: ModelId) -> ModelCard:
-    if model_id in MODEL_CARDS:
-        model_card = MODEL_CARDS[model_id]
-        return model_card
-
-    for card in MODEL_CARDS.values():
-        if card.model_id == ModelId(model_id):
-            return card
-
-    return await ModelCard.from_hf(model_id)
-
-
 class API:
     def __init__(
         self,
@@ -279,7 +267,7 @@ class API:
 
     async def place_instance(self, payload: PlaceInstanceParams):
         command = PlaceInstance(
-            model_card=await resolve_model_card(payload.model_id),
+            model_card=await ModelCard.load(payload.model_id),
             sharding=payload.sharding,
             instance_meta=payload.instance_meta,
             min_nodes=payload.min_nodes,
@@ -296,7 +284,7 @@ class API:
         self, payload: CreateInstanceParams
     ) -> CreateInstanceResponse:
         instance = payload.instance
-        model_card = await resolve_model_card(instance.shard_assignments.model_id)
+        model_card = await ModelCard.load(instance.shard_assignments.model_id)
         required_memory = model_card.storage_size
         available_memory = self._calculate_total_available_memory()
 
@@ -324,7 +312,7 @@ class API:
         instance_meta: InstanceMeta = InstanceMeta.MlxRing,
         min_nodes: int = 1,
     ) -> Instance:
-        model_card = await resolve_model_card(model_id)
+        model_card = await ModelCard.load(model_id)
 
         try:
             placements = get_instance_placements(
@@ -565,7 +553,7 @@ class API:
 
         text_parts: list[str] = []
         tool_calls: list[ToolCall] = []
-        model: str | None = None
+        model: ModelId | None = None
         finish_reason: FinishReason | None = None
         usage: Usage | None = None
 
@@ -624,7 +612,7 @@ class API:
     ) -> BenchChatCompletionResponse:
         text_parts: list[str] = []
         tool_calls: list[ToolCall] = []
-        model: str | None = None
+        model: ModelId | None = None
         finish_reason: FinishReason | None = None
 
         stats: GenerationStats | None = None
@@ -677,7 +665,7 @@ class API:
         )
         return resp
 
-    async def _trigger_notify_user_to_download_model(self, model_id: str) -> None:
+    async def _trigger_notify_user_to_download_model(self, model_id: ModelId) -> None:
         logger.warning(
             "TODO: we should send a notification to the user to download the model"
         )
@@ -686,7 +674,7 @@ class API:
         self, payload: ChatCompletionTaskParams
     ) -> ChatCompletionResponse | StreamingResponse:
         """Handle chat completions, supporting both streaming and non-streaming responses."""
-        model_card = await resolve_model_card(ModelId(payload.model))
+        model_card = await ModelCard.load(ModelId(payload.model))
         payload.model = model_card.model_id
 
         if not any(
@@ -713,7 +701,7 @@ class API:
     async def bench_chat_completions(
         self, payload: BenchChatCompletionTaskParams
     ) -> BenchChatCompletionResponse:
-        model_card = await resolve_model_card(ModelId(payload.model))
+        model_card = await ModelCard.load(ModelId(payload.model))
         payload.model = model_card.model_id
 
         if not any(
@@ -733,12 +721,12 @@ class API:
         response = await self._collect_chat_completion_with_stats(command.command_id)
         return response
 
-    async def _validate_image_model(self, model: str) -> ModelId:
+    async def _validate_image_model(self, model: ModelId) -> ModelId:
         """Validate model exists and return resolved model ID.
 
         Raises HTTPException 404 if no instance is found for the model.
         """
-        model_card = await resolve_model_card(ModelId(model))
+        model_card = await ModelCard.load(model)
         resolved_model = model_card.model_id
         if not any(
             instance.shard_assignments.model_id == resolved_model
@@ -784,7 +772,7 @@ class API:
         When stream=True and partial_images > 0, returns a StreamingResponse
         with SSE-formatted events for partial and final images.
         """
-        payload.model = await self._validate_image_model(payload.model)
+        payload.model = await self._validate_image_model(ModelId(payload.model))
 
         command = ImageGeneration(
             request_params=payload,
@@ -1029,7 +1017,7 @@ class API:
     async def bench_image_generations(
         self, request: Request, payload: BenchImageGenerationTaskParams
     ) -> BenchImageGenerationResponse:
-        payload.model = await self._validate_image_model(payload.model)
+        payload.model = await self._validate_image_model(ModelId(payload.model))
 
         payload.stream = False
         payload.partial_images = 0
@@ -1050,7 +1038,7 @@ class API:
         self,
         image: UploadFile,
         prompt: str,
-        model: str,
+        model: ModelId,
         n: int,
         size: str,
         response_format: Literal["url", "b64_json"],
@@ -1145,7 +1133,7 @@ class API:
         command = await self._send_image_edits_command(
             image=image,
             prompt=prompt,
-            model=model,
+            model=ModelId(model),
             n=n,
             size=size,
             response_format=response_format,
@@ -1201,7 +1189,7 @@ class API:
         command = await self._send_image_edits_command(
             image=image,
             prompt=prompt,
-            model=model,
+            model=ModelId(model),
             n=n,
             size=size,
             response_format=response_format,

src/exo/routing/router.py

@@ -216,6 +216,8 @@ def get_node_id_keypair(
     Obtains the :class:`Keypair` associated with this node-ID.
     Obtain the :class:`PeerId` by from it.
     """
+    # TODO(evan): bring back node id persistence once we figure out how to deal with duplicates
+    return Keypair.generate_ed25519()
 
     def lock_path(path: str | bytes | PathLike[str] | PathLike[bytes]) -> Path:
         return Path(str(path) + ".lock")

src/exo/shared/tests/test_node_id_persistence.py

@@ -8,7 +8,7 @@ from multiprocessing.synchronize import Event as EventT
 from multiprocessing.synchronize import Semaphore as SemaphoreT
 
 from loguru import logger
-from pytest import LogCaptureFixture
+from pytest import LogCaptureFixture, mark
 
 from exo.routing.router import get_node_id_keypair
 from exo.shared.constants import EXO_NODE_ID_KEYPAIR
@@ -74,6 +74,7 @@ def _delete_if_exists(p: str | bytes | os.PathLike[str] | os.PathLike[bytes]):
         os.remove(p)
 
 
+@mark.skip(reason="this functionality is currently disabled but may return in future")
 def test_node_id_fetching(caplog: LogCaptureFixture):
     reps = 10
 

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

@@ -3,6 +3,7 @@ from copy import deepcopy
 from typing import Any, cast
 
 import mlx.core as mx
+import psutil
 from mlx_lm.models.cache import (
     KVCache,
     QuantizedKVCache,
@@ -12,25 +13,29 @@ from mlx_lm.models.cache import (
 from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 
+from exo.shared.types.memory import Memory
 from exo.shared.types.mlx import KVCacheType
 from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.constants import CACHE_GROUP_SIZE, KV_CACHE_BITS
 from exo.worker.runner.bootstrap import logger
 
 # Fraction of device memory above which LRU eviction kicks in
-_DEFAULT_MEMORY_THRESHOLD = 0.85
+_DEFAULT_MEMORY_THRESHOLD = 0.9
 _MEMORY_THRESHOLD = float(
     os.environ.get("EXO_MEMORY_THRESHOLD", _DEFAULT_MEMORY_THRESHOLD)
 )
 
 
 class KVPrefixCache:
-    def __init__(self, tokenizer: TokenizerWrapper):
+    def __init__(
+        self, tokenizer: TokenizerWrapper, group: mx.distributed.Group | None = None
+    ):
         self.prompts: list[mx.array] = []  # mx array of tokens (ints)
         self.caches: list[KVCacheType] = []
         self._last_used: list[int] = []  # monotonic counter of last access per entry
         self._access_counter: int = 0
         self._tokenizer: TokenizerWrapper = tokenizer
+        self._group = group
 
     def clear(self):
         """Clear all cached prompts and caches."""
@@ -81,13 +86,13 @@ class KVPrefixCache:
         best_snapshot_index, best_snapshot_length = None, 0
 
         for i, cached_prompt in enumerate(self.prompts):
-            length = _get_prefix_length(tokenized_prompt, cached_prompt)
+            length = get_prefix_length(tokenized_prompt, cached_prompt)
 
             if length == max_length:
                 # Exact match - cached prompt starts with our entire prompt
                 # Trim cache to prompt length - 1, return last token for stream_generate
                 prompt_cache = deepcopy(self.caches[i])
-                cached_length = _cache_length(self.caches[i])
+                cached_length = cache_length(self.caches[i])
                 tokens_to_trim = cached_length - (max_length - 1)
                 if tokens_to_trim > 0:
                     trim_prompt_cache(cast(list[Any], prompt_cache), tokens_to_trim)
@@ -109,7 +114,7 @@ class KVPrefixCache:
             prompt_cache = deepcopy(self.caches[best_snapshot_index])
 
             # Trim removes tokens from the end, so we trim (cached_length - prefix_length) to keep the prefix
-            cached_length = _cache_length(self.caches[best_snapshot_index])
+            cached_length = cache_length(self.caches[best_snapshot_index])
             tokens_to_trim = cached_length - best_snapshot_length
             if tokens_to_trim > 0:
                 trim_prompt_cache(cast(list[Any], prompt_cache), tokens_to_trim)
@@ -131,29 +136,37 @@ class KVPrefixCache:
             return prompt_cache, tokenized_prompt, None
 
     def _evict_if_needed(self):
-        """Evict least recently used entries while memory pressure is high."""
+        """Evict least recently used entries while memory usage is high."""
         if len(self.caches) == 0:
             return
 
-        active: int = mx.metal.get_active_memory()
-        limit = int(mx.metal.device_info()["max_recommended_working_set_size"])
-        if active < limit * _MEMORY_THRESHOLD:
-            return
-
         # Evict LRU entries until below threshold or only one entry left
-        while len(self.caches) > 0:
+        while (
+            len(self.caches) > 1
+            and self.get_memory_used_percentage() > _MEMORY_THRESHOLD
+        ):
             lru_index = self._last_used.index(min(self._last_used))
             evicted_tokens = len(self.prompts[lru_index])
             self.prompts.pop(lru_index)
             self.caches.pop(lru_index)
             self._last_used.pop(lru_index)
             logger.info(
-                f"KV cache evicted LRU entry ({evicted_tokens} tokens) due to memory pressure"
+                f"KV cache evicted LRU entry ({evicted_tokens} tokens) due to memory usage"
             )
 
-            active = mx.metal.get_active_memory()
-            if active < limit * _MEMORY_THRESHOLD:
-                break
+    def get_memory_used_percentage(self) -> float:
+        local_pressure: float = get_memory_used_percentage()
+
+        if self._group is None:
+            return local_pressure
+
+        all_pressure = mx.distributed.all_gather(
+            mx.array([local_pressure], dtype=mx.float32),
+            group=self._group,
+        )
+        # .item() evals.
+        max_pressure = float(mx.max(all_pressure).item())
+        return max_pressure
 
 
 def encode_prompt(tokenizer: TokenizerWrapper, prompt: str) -> mx.array:
@@ -168,13 +181,13 @@ def encode_prompt(tokenizer: TokenizerWrapper, prompt: str) -> mx.array:
     return mx.array(tokenized_prompt)
 
 
-def _cache_length(cache: KVCacheType) -> int:
+def cache_length(cache: KVCacheType) -> int:
     """Get the number of tokens in a KV cache."""
     # Use .offset attribute which all cache types have (len() not implemented in older QuantizedKVCache)
     return max(c.offset for c in cache)  # type: ignore
 
 
-def _get_prefix_length(prompt: mx.array, cached_prompt: mx.array) -> int:
+def get_prefix_length(prompt: mx.array, cached_prompt: mx.array) -> int:
     """Find the length of the common prefix between two token arrays."""
     n = min(int(prompt.shape[0]), int(cached_prompt.shape[0]))
     if n == 0:
@@ -185,6 +198,17 @@ def _get_prefix_length(prompt: mx.array, cached_prompt: mx.array) -> int:
     return int(mx.sum(prefix_mask).item())
 
 
+def get_available_memory() -> Memory:
+    mem: int = psutil.virtual_memory().available
+    return Memory.from_bytes(mem)
+
+
+def get_memory_used_percentage() -> float:
+    mem = psutil.virtual_memory()
+    # percent is 0-100
+    return float(mem.percent / 100)
+
+
 def make_kv_cache(
     model: Model, max_kv_size: int | None = None, keep: int = 0
 ) -> KVCacheType:

src/exo/worker/runner/runner.py

@@ -37,6 +37,7 @@ from exo.shared.types.tasks import (
     Shutdown,
     StartWarmup,
     Task,
+    TaskId,
     TaskStatus,
 )
 from exo.shared.types.worker.instances import BoundInstance
@@ -111,8 +112,12 @@ def main(
     event_sender.send(
         RunnerStatusUpdated(runner_id=runner_id, runner_status=current_status)
     )
+    seen = set[TaskId]()
     with task_receiver as tasks:
         for task in tasks:
+            if task.task_id in seen:
+                logger.warning("repeat task - potential error")
+            seen.add(task.task_id)
             event_sender.send(
                 TaskStatusUpdated(task_id=task.task_id, task_status=TaskStatus.Running)
             )
@@ -163,7 +168,7 @@ def main(
                         logger.info(
                             f"model has_tool_calling={tokenizer.has_tool_calling}"
                         )
-                        kv_prefix_cache = KVPrefixCache(tokenizer)
+                        kv_prefix_cache = KVPrefixCache(tokenizer, group)
 
                     elif (
                         ModelTask.TextToImage in shard_metadata.model_card.tasks

src/exo/worker/runner/runner_supervisor.py

@@ -127,20 +127,25 @@ class RunnerSupervisor:
         self._tg.cancel_scope.cancel()
 
     async def start_task(self, task: Task):
+        if task.task_id in self.pending:
+            logger.warning(
+                f"Skipping invalid task {task} as it has already been submitted"
+            )
+            return
         if task.task_id in self.completed:
-            logger.info(
+            logger.warning(
                 f"Skipping invalid task {task} as it has already been completed"
             )
+            return
         logger.info(f"Starting task {task}")
         event = anyio.Event()
         self.pending[task.task_id] = event
         try:
-            self._task_sender.send(task)
+            await self._task_sender.send_async(task)
         except ClosedResourceError:
             logger.warning(f"Task {task} dropped, runner closed communication.")
             return
         await event.wait()
-        logger.info(f"Finished task {task}")
 
     async def _forward_events(self):
         with self._ev_recv as events:

src/exo/worker/tests/unittests/test_mlx/test_kv_prefix_cache.py

@@ -14,9 +14,9 @@ from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.cache import (
     KVPrefixCache,
-    _cache_length,
-    _get_prefix_length,
+    cache_length,
     encode_prompt,
+    get_prefix_length,
     make_kv_cache,
 )
 from exo.worker.engines.mlx.generator.generate import mlx_generate, prefill
@@ -35,47 +35,47 @@ class TestGetPrefixLength:
     def test_identical_arrays(self):
         a = mx.array([1, 2, 3, 4, 5])
         b = mx.array([1, 2, 3, 4, 5])
-        assert _get_prefix_length(a, b) == 5
+        assert get_prefix_length(a, b) == 5
 
     def test_no_common_prefix(self):
         a = mx.array([1, 2, 3])
         b = mx.array([4, 5, 6])
-        assert _get_prefix_length(a, b) == 0
+        assert get_prefix_length(a, b) == 0
 
     def test_partial_prefix(self):
         a = mx.array([1, 2, 3, 4, 5])
         b = mx.array([1, 2, 3, 7, 8])
-        assert _get_prefix_length(a, b) == 3
+        assert get_prefix_length(a, b) == 3
 
     def test_prompt_longer_than_cached(self):
         a = mx.array([1, 2, 3, 4, 5])
         b = mx.array([1, 2, 3])
-        assert _get_prefix_length(a, b) == 3
+        assert get_prefix_length(a, b) == 3
 
     def test_cached_longer_than_prompt(self):
         a = mx.array([1, 2, 3])
         b = mx.array([1, 2, 3, 4, 5])
-        assert _get_prefix_length(a, b) == 3
+        assert get_prefix_length(a, b) == 3
 
     def test_single_token_match(self):
         a = mx.array([1, 2, 3])
         b = mx.array([1, 5, 6])
-        assert _get_prefix_length(a, b) == 1
+        assert get_prefix_length(a, b) == 1
 
     def test_empty_prompt(self):
         a = mx.array([]).astype(mx.int32)
         b = mx.array([1, 2, 3])
-        assert _get_prefix_length(a, b) == 0
+        assert get_prefix_length(a, b) == 0
 
     def test_empty_cached(self):
         a = mx.array([1, 2, 3])
         b = mx.array([]).astype(mx.int32)
-        assert _get_prefix_length(a, b) == 0
+        assert get_prefix_length(a, b) == 0
 
     def test_both_empty(self):
         a = mx.array([]).astype(mx.int32)
         b = mx.array([]).astype(mx.int32)
-        assert _get_prefix_length(a, b) == 0
+        assert get_prefix_length(a, b) == 0
 
 
 class TestKVPrefix:
@@ -146,7 +146,7 @@ class TestKVPrefixCacheWithModel:
         prefill(model, tokenizer, make_sampler(0.0), tokens, cache)
 
         # Cache should now hold the prompt tokens
-        assert _cache_length(cache) == len(tokens)
+        assert cache_length(cache) == len(tokens)
 
     def test_add_and_get_exact_match(self, model_and_tokenizer):
         model, tokenizer = model_and_tokenizer
@@ -166,7 +166,7 @@ class TestKVPrefixCacheWithModel:
         kv_prefix_cache.add_kv_cache(prompt, cache)
 
         assert len(kv_prefix_cache.prompts) == 1
-        stored_length = _cache_length(kv_prefix_cache.caches[0])
+        stored_length = cache_length(kv_prefix_cache.caches[0])
         assert stored_length > 0
 
         # Retrieve with same prompt: exact match
@@ -209,7 +209,7 @@ class TestKVPrefixCacheWithModel:
         long_tokens = encode_prompt(tokenizer, long_prompt)
 
         # The prompts share a prefix (chat template preamble + "Hi")
-        expected_prefix = _get_prefix_length(long_tokens, short_tokens)
+        expected_prefix = get_prefix_length(long_tokens, short_tokens)
         assert expected_prefix > 0, (
             "Prompts should share a prefix from the chat template"
         )
@@ -243,7 +243,7 @@ class TestKVPrefixCacheWithModel:
         kv_prefix_cache = KVPrefixCache(tokenizer)
         kv_prefix_cache.add_kv_cache(prompt, cache)
 
-        stored_length = _cache_length(kv_prefix_cache.caches[0])
+        stored_length = cache_length(kv_prefix_cache.caches[0])
 
         # Get cache and mutate it (simulating what generation does)
         result_cache, _, matched_index = kv_prefix_cache.get_kv_cache(model, prompt)
@@ -259,7 +259,7 @@ class TestKVPrefixCacheWithModel:
         mx.eval([c.keys for c in result_cache])
 
         # Stored cache must be unchanged
-        assert _cache_length(kv_prefix_cache.caches[0]) == stored_length
+        assert cache_length(kv_prefix_cache.caches[0]) == stored_length
 
     def test_stored_cache_survives_repeated_get_mutate_cycles(
         self, model_and_tokenizer
@@ -281,7 +281,7 @@ class TestKVPrefixCacheWithModel:
         kv_prefix_cache = KVPrefixCache(tokenizer)
         kv_prefix_cache.add_kv_cache(prompt, cache)
 
-        stored_length = _cache_length(kv_prefix_cache.caches[0])
+        stored_length = cache_length(kv_prefix_cache.caches[0])
 
         for i in range(3):
             result_cache, _, _ = kv_prefix_cache.get_kv_cache(model, prompt)
@@ -293,7 +293,7 @@ class TestKVPrefixCacheWithModel:
                 layer_cache.update_and_fetch(extra, extra)
             mx.eval([c.keys for c in result_cache])
 
-            assert _cache_length(kv_prefix_cache.caches[0]) == stored_length, (
+            assert cache_length(kv_prefix_cache.caches[0]) == stored_length, (
                 f"Failed on loop {i}"
             )
 
@@ -325,7 +325,7 @@ class TestKVPrefixCacheWithModel:
         assert len(kv_prefix_cache.caches) == 1
         # Cache should contain prompt + generated tokens
         expected_length = len(prompt_tokens) + generated_tokens
-        assert _cache_length(kv_prefix_cache.caches[0]) == expected_length
+        assert cache_length(kv_prefix_cache.caches[0]) == expected_length
 
     def test_mlx_generate_second_call_gets_prefix_hit(self, model_and_tokenizer):
         """Second mlx_generate call with same prompt should get a prefix hit from stored cache."""
@@ -400,7 +400,7 @@ class TestKVPrefixCacheWithModel:
         first_gen_time = time.perf_counter() - t0
 
         assert len(kv_prefix_cache.prompts) == 1
-        first_cache_length = _cache_length(kv_prefix_cache.caches[0])
+        first_cache_length = cache_length(kv_prefix_cache.caches[0])
 
         # Second generation: same long prompt + extra content (simulating multi-turn)
         task2 = ChatCompletionTaskParams(
@@ -416,7 +416,7 @@ class TestKVPrefixCacheWithModel:
         prompt2_tokens = encode_prompt(tokenizer, prompt2)
 
         # Verify the prompts share a long prefix
-        prefix_len = _get_prefix_length(prompt2_tokens, prompt1_tokens)
+        prefix_len = get_prefix_length(prompt2_tokens, prompt1_tokens)
         assert prefix_len > 1000, "Prompts must share > 1000 token prefix"
 
         # Second generation should reuse the cached prefix (only prefill new tokens)
@@ -440,7 +440,7 @@ class TestKVPrefixCacheWithModel:
         # With prefix_hit > 1000, should update in-place (not add a second entry)
         assert len(kv_prefix_cache.prompts) == 1
         # Updated cache should be longer (prompt2 + generated > prompt1 + generated)
-        updated_cache_length = _cache_length(kv_prefix_cache.caches[0])
+        updated_cache_length = cache_length(kv_prefix_cache.caches[0])
         assert updated_cache_length > first_cache_length
 
     def test_mlx_generate_stored_cache_not_mutated(self, model_and_tokenizer):
@@ -465,7 +465,7 @@ class TestKVPrefixCacheWithModel:
         ):
             pass
 
-        first_cache_length = _cache_length(kv_prefix_cache.caches[0])
+        firstcache_length = cache_length(kv_prefix_cache.caches[0])
 
         # Second generation gets the cache and mutates it during generation
         for _response in mlx_generate(
@@ -478,7 +478,7 @@ class TestKVPrefixCacheWithModel:
             pass
 
         # The first stored cache must not have been mutated by the second generation
-        assert _cache_length(kv_prefix_cache.caches[0]) == first_cache_length
+        assert cache_length(kv_prefix_cache.caches[0]) == firstcache_length
 
     def test_evicts_lru_entry_under_memory_pressure(self, model_and_tokenizer):
         """Under memory pressure, adding a new cache entry evicts the least recently used one."""
@@ -540,6 +540,6 @@ class TestKVPrefixCacheWithModel:
         assert len(kv_prefix_cache.prompts) == 1
         # The surviving entry should be the newly added one
         new_tokens = encode_prompt(tokenizer, prompt)
-        assert _get_prefix_length(kv_prefix_cache.prompts[0], new_tokens) == len(
+        assert get_prefix_length(kv_prefix_cache.prompts[0], new_tokens) == len(
             new_tokens
         )

src/exo/worker/tests/unittests/test_runner/test_event_ordering.py

@@ -109,8 +109,8 @@ def assert_events_equal(test_events: Iterable[Event], true_events: Iterable[Even
 
 @pytest.fixture
 def patch_out_mlx(monkeypatch: pytest.MonkeyPatch):
-    # initialize_mlx returns a "group" equal to 1
-    monkeypatch.setattr(mlx_runner, "initialize_mlx", make_nothin(1))
+    # initialize_mlx returns a mock group
+    monkeypatch.setattr(mlx_runner, "initialize_mlx", make_nothin(MockGroup()))
     monkeypatch.setattr(mlx_runner, "load_mlx_items", make_nothin((1, MockTokenizer)))
     monkeypatch.setattr(mlx_runner, "warmup_inference", make_nothin(1))
     monkeypatch.setattr(mlx_runner, "_check_for_debug_prompts", nothin)
@@ -147,6 +147,14 @@ class MockTokenizer:
     has_tool_calling = False
 
 
+class MockGroup:
+    def rank(self) -> int:
+        return 0
+
+    def size(self) -> int:
+        return 1
+
+
 def _run(tasks: Iterable[Task]):
     bound_instance = get_bound_mlx_ring_instance(
         instance_id=INSTANCE_1_ID,