Fix gibberish outputs

## Motivation

With a single pipeline layer, PipelineFirstLayer gets composed with
PipelineLastLayer.

## Changes

<!-- Describe what you changed in detail -->

## Why It Works

<!-- Explain why your approach solves the problem -->

## Test Plan

### Manual Testing


### Automated Testing
Made failing tests. Fixed them!

AGENTS.md

@@ -40,6 +40,31 @@ uv run ruff check
 nix fmt
 ```
 
+## Pre-Commit Checks (REQUIRED)
+
+**IMPORTANT: Always run these checks before committing code. CI will fail if these don't pass.**
+
+```bash
+# 1. Type checking - MUST pass with 0 errors
+uv run basedpyright
+
+# 2. Linting - MUST pass
+uv run ruff check
+
+# 3. Formatting - MUST be applied
+nix fmt
+
+# 4. Tests - MUST pass
+uv run pytest
+```
+
+Run all checks in sequence:
+```bash
+uv run basedpyright && uv run ruff check && nix fmt && uv run pytest
+```
+
+If `nix fmt` changes any files, stage them before committing. The CI runs `nix flake check` which verifies formatting, linting, and runs Rust tests.
+
 ## Architecture
 
 ### Node Composition

README.md

@@ -27,6 +27,15 @@ exo connects all your devices into an AI cluster. Not only does exo enable runni
 - **Tensor Parallelism**: exo supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
 - **MLX Support**: exo uses [MLX](https://github.com/ml-explore/mlx) as an inference backend and [MLX distributed](https://ml-explore.github.io/mlx/build/html/usage/distributed.html) for distributed communication.
 
+## Dashboard
+
+exo includes a built-in dashboard for managing your cluster and chatting with models.
+
+<p align="center">
+  <img src="docs/imgs/dashboard-cluster-view.png" alt="exo dashboard - cluster view showing 4 x M3 Ultra Mac Studio with DeepSeek v3.1 and Kimi-K2-Thinking loaded" width="80%" />
+</p>
+<p align="center"><em>4 × 512GB M3 Ultra Mac Studio running DeepSeek v3.1 (8-bit) and Kimi-K2-Thinking (4-bit)</em></p>
+
 ## Benchmarks
 
 <details>

bench/exo_bench.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import argparse
+import contextlib
 import http.client
 import json
 import os
@@ -26,7 +27,7 @@ class ExoHttpError(RuntimeError):
 
 
 class ExoClient:
-    def __init__(self, host: str, port: int, timeout_s: float = 2400.0):
+    def __init__(self, host: str, port: int, timeout_s: float = 600.0):
         self.host = host
         self.port = port
         self.timeout_s = timeout_s
@@ -104,22 +105,46 @@ def runner_ready(runner: dict[str, Any]) -> bool:
     return "RunnerReady" in runner
 
 
+def runner_failed(runner: dict[str, Any]) -> bool:
+    return "RunnerFailed" in runner
+
+
+def get_runner_failed_message(runner: dict[str, Any]) -> str | None:
+    if "RunnerFailed" in runner:
+        return runner["RunnerFailed"].get("errorMessage")
+    return None
+
+
 def wait_for_instance_ready(
     client: ExoClient, instance_id: str, timeout: float = 24000.0
 ) -> None:
     start_time = time.time()
+    instance_existed = False
     while time.time() - start_time < timeout:
         state = client.request_json("GET", "/state")
         instances = state.get("instances", {})
 
         if instance_id not in instances:
+            if instance_existed:
+                # Instance was deleted after being created - likely due to runner failure
+                raise RuntimeError(
+                    f"Instance {instance_id} was deleted (runner may have failed)"
+                )
             time.sleep(0.1)
             continue
 
+        instance_existed = True
         instance = instances[instance_id]
         runner_ids = runner_ids_from_instance(instance)
         runners = state.get("runners", {})
 
+        # Check for failed runners first
+        for rid in runner_ids:
+            runner = runners.get(rid, {})
+            if runner_failed(runner):
+                error_msg = get_runner_failed_message(runner) or "Unknown error"
+                raise RuntimeError(f"Runner {rid} failed: {error_msg}")
+
         if all(runner_ready(runners.get(rid, {})) for rid in runner_ids):
             return
 
@@ -299,6 +324,12 @@ def main() -> int:
         default=4,
         help="Only consider placements using <= this many nodes.",
     )
+    ap.add_argument(
+        "--min-nodes",
+        type=int,
+        default=1,
+        help="Only consider placements using >= this many nodes.",
+    )
     ap.add_argument(
         "--instance-meta", choices=["ring", "jaccl", "both"], default="both"
     )
@@ -320,7 +351,7 @@ def main() -> int:
         help="Warmup runs per placement (uses first pp/tg).",
     )
     ap.add_argument(
-        "--timeout", type=float, default=2400.0, help="HTTP timeout (seconds)."
+        "--timeout", type=float, default=600.0, help="HTTP timeout (seconds)."
     )
     ap.add_argument(
         "--json-out",
@@ -399,7 +430,7 @@ def main() -> int:
         ):
             continue
 
-        if 0 < n <= args.max_nodes:
+        if args.min_nodes <= n <= args.max_nodes:
             selected.append(p)
 
     if not selected:
@@ -441,7 +472,13 @@ def main() -> int:
         )
 
         client.request_json("POST", "/instance", body={"instance": instance})
-        wait_for_instance_ready(client, instance_id)
+        try:
+            wait_for_instance_ready(client, instance_id)
+        except (RuntimeError, TimeoutError) as e:
+            logger.error(f"Failed to initialize placement: {e}")
+            with contextlib.suppress(ExoHttpError):
+                client.request_json("DELETE", f"/instance/{instance_id}")
+            continue
 
         time.sleep(1)
 
@@ -453,17 +490,17 @@ def main() -> int:
                 logger.debug(f"  warmup {i + 1}/{args.warmup} done")
 
             for pp in pp_list:
-                if (
-                    pp * n_nodes > 2048
-                    and "ring" in instance_meta.lower()
-                    and "tensor" in sharding.lower()
-                ):
-                    model_card = MODEL_CARDS[short_id]
-                    if model_card.metadata.storage_size > Memory.from_gb(10):
-                        logger.info(
-                            f"Skipping tensor ring as this is too slow for model of size {model_card.metadata.storage_size} on {n_nodes=}"
-                        )
-                        continue
+                # if (
+                #     pp * n_nodes > 2048
+                #     and "ring" in instance_meta.lower()
+                #     and "tensor" in sharding.lower()
+                # ):
+                #     model_card = MODEL_CARDS[short_id]
+                #     if model_card.metadata.storage_size > Memory.from_gb(10):
+                #         logger.info(
+                #             f"Skipping tensor ring as this is too slow for model of size {model_card.metadata.storage_size} on {n_nodes=}"
+                #         )
+                #         continue
                 for tg in tg_list:
                     runs: list[dict[str, Any]] = []
                     for r in range(args.repeat):

pyproject.toml

@@ -126,3 +126,6 @@ env = [
   "EXO_TESTS=1"
 ]
 addopts = "-m 'not slow'"
+filterwarnings = [
+    "ignore:builtin type Swig:DeprecationWarning",
+]

src/exo/main.py

@@ -205,6 +205,14 @@ def main():
     logger.info("Starting EXO")
     logger.info(f"EXO_LIBP2P_NAMESPACE: {os.getenv('EXO_LIBP2P_NAMESPACE')}")
 
+    # Set FAST_SYNCH override env var for runner subprocesses
+    if args.fast_synch is True:
+        os.environ["EXO_FAST_SYNCH"] = "on"
+        logger.info("FAST_SYNCH forced ON")
+    elif args.fast_synch is False:
+        os.environ["EXO_FAST_SYNCH"] = "off"
+        logger.info("FAST_SYNCH forced OFF")
+
     node = anyio.run(Node.create, args)
     anyio.run(node.run)
     logger.info("EXO Shutdown complete")
@@ -218,6 +226,7 @@ class Args(CamelCaseModel):
     api_port: PositiveInt = 52415
     tb_only: bool = False
     no_worker: bool = False
+    fast_synch: bool | None = None  # None = auto, True = force on, False = force off
 
     @classmethod
     def parse(cls) -> Self:
@@ -259,6 +268,20 @@ class Args(CamelCaseModel):
             "--no-worker",
             action="store_true",
         )
+        fast_synch_group = parser.add_mutually_exclusive_group()
+        fast_synch_group.add_argument(
+            "--fast-synch",
+            action="store_true",
+            dest="fast_synch",
+            default=None,
+            help="Force MLX FAST_SYNCH on (for JACCL backend)",
+        )
+        fast_synch_group.add_argument(
+            "--no-fast-synch",
+            action="store_false",
+            dest="fast_synch",
+            help="Force MLX FAST_SYNCH off",
+        )
 
         args = parser.parse_args()
         return cls(**vars(args))  # pyright: ignore[reportAny] - We are intentionally validating here, we can't do it statically

src/exo/master/api.py

@@ -1,13 +1,14 @@
 import time
 from collections.abc import AsyncGenerator
+from http import HTTPStatus
 from typing import cast
 
 import anyio
-from anyio import create_task_group
+from anyio import BrokenResourceError, create_task_group
 from anyio.abc import TaskGroup
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 from fastapi.staticfiles import StaticFiles
 from hypercorn.asyncio import serve  # pyright: ignore[reportUnknownVariableType]
 from hypercorn.config import Config
@@ -29,6 +30,8 @@ from exo.shared.types.api import (
     CreateInstanceParams,
     CreateInstanceResponse,
     DeleteInstanceResponse,
+    ErrorInfo,
+    ErrorResponse,
     FinishReason,
     GenerationStats,
     ModelList,
@@ -49,7 +52,12 @@ from exo.shared.types.commands import (
     TaskFinished,
 )
 from exo.shared.types.common import CommandId, NodeId, SessionId
-from exo.shared.types.events import ChunkGenerated, Event, ForwarderEvent, IndexedEvent
+from exo.shared.types.events import (
+    ChunkGenerated,
+    Event,
+    ForwarderEvent,
+    IndexedEvent,
+)
 from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.state import State
@@ -115,6 +123,7 @@ class API:
         self.paused_ev: anyio.Event = anyio.Event()
 
         self.app = FastAPI()
+        self._setup_exception_handlers()
         self._setup_cors()
         self._setup_routes()
 
@@ -145,6 +154,21 @@ class API:
         self.paused_ev.set()
         self.paused_ev = anyio.Event()
 
+    def _setup_exception_handlers(self) -> None:
+        self.app.exception_handler(HTTPException)(self.http_exception_handler)
+
+    async def http_exception_handler(
+        self, _: Request, exc: HTTPException
+    ) -> JSONResponse:
+        err = ErrorResponse(
+            error=ErrorInfo(
+                message=exc.detail,
+                type=HTTPStatus(exc.status_code).phrase,
+                code=exc.status_code,
+            )
+        )
+        return JSONResponse(err.model_dump(), status_code=exc.status_code)
+
     def _setup_cors(self) -> None:
         self.app.add_middleware(
             CORSMiddleware,
@@ -406,6 +430,18 @@ class API:
         """Generate chat completion stream as JSON strings."""
 
         async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                error_response = ErrorResponse(
+                    error=ErrorInfo(
+                        message=chunk.error_message or "Internal server error",
+                        type="InternalServerError",
+                        code=500,
+                    )
+                )
+                yield f"data: {error_response.model_dump_json()}\n\n"
+                yield "data: [DONE]\n\n"
+                return
+
             chunk_response: ChatCompletionResponse = chunk_to_response(
                 chunk, command_id
             )
@@ -426,6 +462,12 @@ class API:
         finish_reason: FinishReason | None = None
 
         async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                raise HTTPException(
+                    status_code=500,
+                    detail=chunk.error_message or "Internal server error",
+                )
+
             if model is None:
                 model = chunk.model
 
@@ -463,6 +505,12 @@ class API:
         stats: GenerationStats | None = None
 
         async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                raise HTTPException(
+                    status_code=500,
+                    detail=chunk.error_message or "Internal server error",
+                )
+
             if model is None:
                 model = chunk.model
 
@@ -607,14 +655,14 @@ class API:
                 for idx, event in self.event_buffer.drain_indexed():
                     self._event_log.append(event)
                     self.state = apply(self.state, IndexedEvent(event=event, idx=idx))
-                    if (
-                        isinstance(event, ChunkGenerated)
-                        and event.command_id in self._chat_completion_queues
-                    ):
+                    if isinstance(event, ChunkGenerated):
                         assert isinstance(event.chunk, TokenChunk)
-                        await self._chat_completion_queues[event.command_id].send(
-                            event.chunk
-                        )
+                        queue = self._chat_completion_queues.get(event.command_id)
+                        if queue is not None:
+                            try:
+                                await queue.send(event.chunk)
+                            except BrokenResourceError:
+                                self._chat_completion_queues.pop(event.command_id, None)
 
     async def _pause_on_new_election(self):
         with self.election_receiver as ems:

src/exo/master/placement_utils.py

@@ -49,33 +49,83 @@ def get_smallest_cycles(cycles: list[list[NodeInfo]]) -> list[list[NodeInfo]]:
     return [cycle for cycle in cycles if len(cycle) == min_nodes]
 
 
+def allocate_layers_proportionally(
+    total_layers: int,
+    memory_fractions: list[float],
+) -> list[int]:
+    n = len(memory_fractions)
+    if n == 0:
+        raise ValueError("Cannot allocate layers to an empty node list")
+    if total_layers < n:
+        raise ValueError(
+            f"Cannot distribute {total_layers} layers across {n} nodes "
+            "(need at least 1 layer per node)"
+        )
+
+    # Largest remainder: floor each, then distribute remainder by fractional part
+    raw = [f * total_layers for f in memory_fractions]
+    result = [int(r) for r in raw]
+    by_remainder = sorted(range(n), key=lambda i: raw[i] - result[i], reverse=True)
+    for i in range(total_layers - sum(result)):
+        result[by_remainder[i]] += 1
+
+    # Ensure minimum 1 per node by taking from the largest
+    for i in range(n):
+        if result[i] == 0:
+            max_idx = max(range(n), key=lambda j: result[j])
+            assert result[max_idx] > 1
+            result[max_idx] -= 1
+            result[i] = 1
+
+    return result
+
+
 def get_shard_assignments_for_pipeline_parallel(
     model_meta: ModelMetadata,
     selected_cycle: list[NodeWithProfile],
 ):
+    if not selected_cycle:
+        raise ValueError("Cannot create shard assignments for empty node cycle")
+
     cycle_memory = sum(
         (node.node_profile.memory.ram_available for node in selected_cycle),
         start=Memory(),
     )
+
+    if cycle_memory.in_bytes == 0:
+        raise ValueError("Cannot create shard assignments: total available memory is 0")
+
     total_layers = model_meta.n_layers
     world_size = len(selected_cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
 
-    layers_assigned = 0
-    for i, node in enumerate(selected_cycle):
-        if i == len(selected_cycle) - 1:
-            node_layers = total_layers - layers_assigned
-        else:
-            node_layers = round(
-                total_layers
-                * (
-                    node.node_profile.memory.ram_available.in_bytes
-                    / cycle_memory.in_bytes
-                )
-            )
-            node_layers = max(1, node_layers)
+    layer_allocations = allocate_layers_proportionally(
+        total_layers=total_layers,
+        memory_fractions=[
+            node.node_profile.memory.ram_available.in_bytes / cycle_memory.in_bytes
+            for node in selected_cycle
+        ],
+    )
 
+    # Validate each node has sufficient memory for its assigned layers
+    memory_per_layer = model_meta.storage_size.in_bytes / total_layers
+    for i, (node, node_layers) in enumerate(
+        zip(selected_cycle, layer_allocations, strict=True)
+    ):
+        required_memory = node_layers * memory_per_layer
+        available_memory = node.node_profile.memory.ram_available.in_bytes
+        if required_memory > available_memory:
+            raise ValueError(
+                f"Node {i} ({node.node_id}) has insufficient memory: "
+                f"requires {required_memory / (1024**3):.2f} GB for {node_layers} layers, "
+                f"but only has {available_memory / (1024**3):.2f} GB available"
+            )
+
+    layers_assigned = 0
+    for i, (node, node_layers) in enumerate(
+        zip(selected_cycle, layer_allocations, strict=True)
+    ):
         runner_id = RunnerId()
 
         shard = PipelineShardMetadata(

src/exo/master/tests/test_api_error_handling.py

@@ -0,0 +1,107 @@
+# pyright: reportUnusedFunction=false, reportAny=false
+from typing import Any, get_args
+
+from fastapi import FastAPI, HTTPException
+from fastapi.testclient import TestClient
+
+from exo.shared.types.api import ErrorInfo, ErrorResponse, FinishReason
+from exo.shared.types.chunks import TokenChunk
+from exo.worker.tests.constants import MODEL_A_ID
+
+
+def test_http_exception_handler_formats_openai_style() -> None:
+    """Test that HTTPException is converted to OpenAI-style error format."""
+    from exo.master.api import API
+
+    app = FastAPI()
+
+    # Setup exception handler
+    api = object.__new__(API)
+    api.app = app
+    api._setup_exception_handlers()  # pyright: ignore[reportPrivateUsage]
+
+    # Add test routes that raise HTTPException
+    @app.get("/test-error")
+    async def _test_error() -> None:
+        raise HTTPException(status_code=500, detail="Test error message")
+
+    @app.get("/test-not-found")
+    async def _test_not_found() -> None:
+        raise HTTPException(status_code=404, detail="Resource not found")
+
+    client = TestClient(app)
+
+    # Test 500 error
+    response = client.get("/test-error")
+    assert response.status_code == 500
+    data: dict[str, Any] = response.json()
+    assert "error" in data
+    assert data["error"]["message"] == "Test error message"
+    assert data["error"]["type"] == "Internal Server Error"
+    assert data["error"]["code"] == 500
+
+    # Test 404 error
+    response = client.get("/test-not-found")
+    assert response.status_code == 404
+    data = response.json()
+    assert "error" in data
+    assert data["error"]["message"] == "Resource not found"
+    assert data["error"]["type"] == "Not Found"
+    assert data["error"]["code"] == 404
+
+
+def test_finish_reason_includes_error() -> None:
+    valid_reasons = get_args(FinishReason)
+    assert "error" in valid_reasons
+
+
+def test_token_chunk_with_error_fields() -> None:
+    chunk = TokenChunk(
+        idx=0,
+        model=MODEL_A_ID,
+        text="",
+        token_id=0,
+        finish_reason="error",
+        error_message="Something went wrong",
+    )
+
+    assert chunk.finish_reason == "error"
+    assert chunk.error_message == "Something went wrong"
+
+
+def test_token_chunk_without_error() -> None:
+    chunk = TokenChunk(
+        idx=1,
+        model=MODEL_A_ID,
+        text="Hello",
+        token_id=42,
+        finish_reason=None,
+    )
+
+    assert chunk.finish_reason is None
+    assert chunk.error_message is None
+
+
+def test_error_response_construction() -> None:
+    error_response = ErrorResponse(
+        error=ErrorInfo(
+            message="Generation failed",
+            type="InternalServerError",
+            code=500,
+        )
+    )
+
+    assert error_response.error.message == "Generation failed"
+    assert error_response.error.code == 500
+
+
+def test_normal_finish_reasons_still_work() -> None:
+    for reason in ["stop", "length", "tool_calls", "content_filter", "function_call"]:
+        chunk = TokenChunk(
+            idx=0,
+            model=MODEL_A_ID,
+            text="done",
+            token_id=100,
+            finish_reason=reason,  # type: ignore[arg-type]
+        )
+        assert chunk.finish_reason == reason

src/exo/master/tests/test_placement.py

@@ -70,7 +70,7 @@ def place_instance_command(model_meta: ModelMetadata) -> PlaceInstance:
     [
         ((500, 500, 1000), 12, (3, 3, 6)),
         ((500, 500, 500), 12, (4, 4, 4)),
-        ((312, 518, 1024), 12, (2, 3, 7)),
+        ((312, 468, 1092), 12, (2, 3, 7)),
     ],
 )
 def test_get_instance_placements_create_instance(

src/exo/master/tests/test_placement_utils.py

@@ -3,6 +3,7 @@ from typing import Callable
 import pytest
 
 from exo.master.placement_utils import (
+    allocate_layers_proportionally,
     filter_cycles_by_memory,
     get_hosts_from_subgraph,
     get_mlx_jaccl_coordinators,
@@ -165,6 +166,9 @@ def test_get_smallest_cycles(
         ((500, 500, 1000), 12, (3, 3, 6)),
         ((500, 500, 500), 12, (4, 4, 4)),
         ((312, 518, 1024), 12, (2, 3, 7)),
+        # Edge case: one node has ~90% of memory - should not over-allocate.
+        # Each node must have enough memory for at least 1 layer (50 KB = 1000/20).
+        ((900, 50, 50), 20, (18, 1, 1)),
     ],
 )
 def test_get_shard_assignments(
@@ -397,3 +401,96 @@ def test_get_mlx_jaccl_coordinators(
     assert coordinators[node_c_id] == (
         f"{conn_c_a.send_back_multiaddr.ip_address}:5000"
     ), "node_c should use the IP from conn_c_a"
+
+
+class TestAllocateLayersProportionally:
+    def test_empty_node_list_raises(self):
+        with pytest.raises(ValueError, match="empty node list"):
+            allocate_layers_proportionally(total_layers=10, memory_fractions=[])
+
+    def test_zero_layers_raises(self):
+        with pytest.raises(ValueError, match="need at least 1 layer per node"):
+            allocate_layers_proportionally(total_layers=0, memory_fractions=[0.5, 0.5])
+
+    def test_negative_layers_raises(self):
+        with pytest.raises(ValueError, match="need at least 1 layer per node"):
+            allocate_layers_proportionally(total_layers=-1, memory_fractions=[0.5, 0.5])
+
+    def test_fewer_layers_than_nodes_raises(self):
+        with pytest.raises(ValueError, match="need at least 1 layer per node"):
+            allocate_layers_proportionally(
+                total_layers=2, memory_fractions=[0.33, 0.33, 0.34]
+            )
+
+    def test_equal_distribution(self):
+        result = allocate_layers_proportionally(
+            total_layers=12, memory_fractions=[0.25, 0.25, 0.25, 0.25]
+        )
+        assert result == [3, 3, 3, 3]
+        assert sum(result) == 12
+
+    def test_proportional_distribution(self):
+        result = allocate_layers_proportionally(
+            total_layers=12, memory_fractions=[0.25, 0.25, 0.50]
+        )
+        assert result == [3, 3, 6]
+        assert sum(result) == 12
+
+    def test_extreme_imbalance_ensures_minimum(self):
+        result = allocate_layers_proportionally(
+            total_layers=20, memory_fractions=[0.975, 0.0125, 0.0125]
+        )
+        assert all(layers >= 1 for layers in result)
+        assert sum(result) == 20
+        # Small nodes get minimum 1 layer
+        assert result == [18, 1, 1]
+
+    def test_single_node_gets_all_layers(self):
+        result = allocate_layers_proportionally(total_layers=10, memory_fractions=[1.0])
+        assert result == [10]
+
+    def test_minimum_viable_allocation(self):
+        result = allocate_layers_proportionally(
+            total_layers=3, memory_fractions=[0.33, 0.33, 0.34]
+        )
+        assert result == [1, 1, 1]
+        assert sum(result) == 3
+
+
+def test_get_shard_assignments_insufficient_memory_raises(
+    topology: Topology,
+    create_node: Callable[[int, NodeId | None], NodeInfo],
+    create_connection: Callable[[NodeId, NodeId], Connection],
+):
+    """Test that ValueError is raised when a node has insufficient memory for its layers."""
+    node_a_id = NodeId()
+    node_b_id = NodeId()
+    node_c_id = NodeId()
+
+    # Node C has only 10 KB but would need 50 KB for 1 layer (1000 KB / 20 layers)
+    node_a = create_node(900 * 1024, node_a_id)
+    node_b = create_node(50 * 1024, node_b_id)
+    node_c = create_node(10 * 1024, node_c_id)  # Insufficient memory
+
+    topology.add_node(node_a)
+    topology.add_node(node_b)
+    topology.add_node(node_c)
+
+    topology.add_connection(create_connection(node_a_id, node_b_id))
+    topology.add_connection(create_connection(node_b_id, node_c_id))
+    topology.add_connection(create_connection(node_c_id, node_a_id))
+    topology.add_connection(create_connection(node_b_id, node_a_id))
+
+    model_meta = ModelMetadata(
+        model_id=ModelId("test-model"),
+        pretty_name="Test Model",
+        n_layers=20,
+        storage_size=Memory.from_kb(1000),
+        hidden_size=1000,
+        supports_tensor=True,
+    )
+    cycles = topology.get_cycles()
+    selected_cycle = cycles[0]
+
+    with pytest.raises(ValueError, match="insufficient memory"):
+        get_shard_assignments(model_meta, selected_cycle, Sharding.Pipeline)

src/exo/shared/types/api.py

@@ -11,10 +11,21 @@ from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 
 FinishReason = Literal[
-    "stop", "length", "tool_calls", "content_filter", "function_call"
+    "stop", "length", "tool_calls", "content_filter", "function_call", "error"
 ]
 
 
+class ErrorInfo(BaseModel):
+    message: str
+    type: str
+    param: str | None = None
+    code: int
+
+
+class ErrorResponse(BaseModel):
+    error: ErrorInfo
+
+
 class ModelListModel(BaseModel):
     id: str
     object: str = "model"

src/exo/shared/types/chunks.py

@@ -22,6 +22,7 @@ class TokenChunk(BaseChunk):
     token_id: int
     finish_reason: FinishReason | None = None
     stats: GenerationStats | None = None
+    error_message: str | None = None
 
 
 class ImageChunk(BaseChunk):

src/exo/worker/download/download_utils.py

@@ -245,12 +245,15 @@ def create_http_session(
         sock_read_timeout = 1800
         sock_connect_timeout = 60
 
-    ssl_context = ssl.create_default_context(cafile=certifi.where())
+    ssl_context = ssl.create_default_context(
+        cafile=os.getenv("SSL_CERT_FILE") or certifi.where()
+    )
     connector = aiohttp.TCPConnector(ssl=ssl_context)
 
     return aiohttp.ClientSession(
         auto_decompress=auto_decompress,
         connector=connector,
+        proxy=os.getenv("HTTPS_PROXY") or os.getenv("HTTP_PROXY") or None,
         timeout=aiohttp.ClientTimeout(
             total=total_timeout,
             connect=connect_timeout,

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

@@ -41,14 +41,16 @@ class _LayerCallable(Protocol):
     def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array: ...
 
 
-class CustomMlxLayer(nn.Module):
+class CustomMlxModule(nn.Module):
     """Base class for replacing an MLX layer with a custom implementation."""
 
     def __init__(self, original_layer: _LayerCallable):
         super().__init__()
-        # Set twice to avoid __setattr__ recursion
         object.__setattr__(self, "_original_layer", original_layer)
-        self.original_layer: _LayerCallable = original_layer
+
+    @property
+    def original_layer(self) -> _LayerCallable:
+        return cast(_LayerCallable, object.__getattribute__(self, "_original_layer"))
 
     # Calls __getattr__ for any attributes not found on nn.Module (e.g. use_sliding)
     if not TYPE_CHECKING:
@@ -58,10 +60,10 @@ class CustomMlxLayer(nn.Module):
                 return super().__getattr__(name)
             except AttributeError:
                 original_layer = object.__getattribute__(self, "_original_layer")
-                return object.__getattribute__(original_layer, name)
+                return getattr(original_layer, name)
 
 
-class PipelineFirstLayer(CustomMlxLayer):
+class PipelineFirstLayer(CustomMlxModule):
     def __init__(
         self,
         original_layer: _LayerCallable,
@@ -78,7 +80,7 @@ class PipelineFirstLayer(CustomMlxLayer):
         return self.original_layer(x, *args, **kwargs)
 
 
-class PipelineLastLayer(CustomMlxLayer):
+class PipelineLastLayer(CustomMlxModule):
     def __init__(
         self,
         original_layer: _LayerCallable,
@@ -168,11 +170,21 @@ def pipeline_auto_parallel(
         inner_model_instance.layer_types = inner_model_instance.layer_types[  # type: ignore
             start_layer:end_layer
         ]
-        inner_model_instance.swa_idx = inner_model_instance.layer_types.index(  # type: ignore
-            "sliding_attention"
+        # We can assume the model has at least one layer thanks to placement.
+        # If a layer type doesn't exist, we can set it to 0.
+        inner_model_instance.swa_idx = (
+            0
+            if "sliding_attention" not in inner_model_instance.layer_types  # type: ignore
+            else inner_model_instance.layer_types.index(  # type: ignore
+                "sliding_attention"
+            )
         )
-        inner_model_instance.ga_idx = inner_model_instance.layer_types.index(  # type: ignore
-            "full_attention"
+        inner_model_instance.ga_idx = (
+            0
+            if "full_attention" not in inner_model_instance.layer_types  # type: ignore
+            else inner_model_instance.layer_types.index(  # type: ignore
+                "full_attention"
+            )
         )
 
     _set_layers(model, layers)
@@ -181,7 +193,32 @@ def pipeline_auto_parallel(
         "Expected a list of layers after auto-parallel initialisation"
     )
 
-    return model
+    return PipelineParallelModel(model, group)
+
+
+class PipelineParallelModel(CustomMlxModule):
+    def __init__(self, model: nn.Module, group: mx.distributed.Group):
+        super().__init__(model)
+        self.original_call_signature = signature(self.original_layer.__call__)
+        self.group = group
+        dict.__setitem__(self, "original_layer", model)
+
+    def __call__(
+        self,
+        *args: object,
+        **kwargs: object,
+    ) -> mx.array:
+        logits: mx.array = self.original_layer(*args, **kwargs)  # type: ignore
+        cache = self.original_call_signature.bind_partial(
+            *args, **kwargs
+        ).arguments.get("cache", None)
+
+        if cache is not None:
+            for c in cache:  # type: ignore
+                if hasattr(c, "state") and c.state is not None:  # type: ignore
+                    c.state = mx.depends(c.state, logits)  # type: ignore
+
+        return logits
 
 
 def tensor_auto_parallel(
@@ -389,7 +426,7 @@ class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
         return model
 
 
-class ShardedDeepseekV3MoE(CustomMlxLayer):
+class ShardedDeepseekV3MoE(CustomMlxModule):
     def __init__(self, layer: _LayerCallable):
         super().__init__(layer)
         self.sharding_group: mx.distributed.Group | None = None
@@ -464,7 +501,7 @@ class QwenShardingStrategy(TensorParallelShardingStrategy):
         return model
 
 
-class ShardedQwenMoE(CustomMlxLayer):
+class ShardedQwenMoE(CustomMlxModule):
     def __init__(self, layer: _LayerCallable):
         super().__init__(layer)
         self.sharding_group: mx.distributed.Group | None = None
@@ -511,7 +548,7 @@ class GptOssShardingStrategy(TensorParallelShardingStrategy):
         return model
 
 
-class ShardedGptOssMoE(CustomMlxLayer):
+class ShardedGptOssMoE(CustomMlxModule):
     def __init__(self, layer: nn.Module):
         super().__init__(layer)
         self.sharding_group: mx.distributed.Group | None = None

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

@@ -2,7 +2,9 @@ import json
 import os
 import resource
 import sys
+import threading
 import time
+from collections.abc import Callable
 from pathlib import Path
 from typing import Any, cast
 
@@ -82,6 +84,45 @@ def get_weights_size(model_shard_meta: ShardMetadata) -> Memory:
     )
 
 
+class ModelLoadingTimeoutError(Exception):
+    pass
+
+
+TimeoutCallback = Callable[[], None]
+
+
+def eval_with_timeout(
+    mlx_item: Any,  # pyright: ignore[reportAny]
+    timeout_seconds: float = 60.0,
+    on_timeout: TimeoutCallback | None = None,
+) -> None:
+    """Evaluate MLX item with a hard timeout.
+
+    If on_timeout callback is provided, it will be called before terminating
+    the process. This allows the runner to send a failure event before exit.
+    """
+    completed = threading.Event()
+
+    def watchdog() -> None:
+        if not completed.wait(timeout=timeout_seconds):
+            logger.error(
+                f"mlx_item evaluation timed out after {timeout_seconds:.0f}s. "
+                "This may indicate an issue with FAST_SYNCH and tensor parallel sharding. "
+                "Terminating process."
+            )
+            if on_timeout is not None:
+                on_timeout()
+            os._exit(1)
+
+    watchdog_thread = threading.Thread(target=watchdog, daemon=True)
+    watchdog_thread.start()
+
+    try:
+        mx.eval(mlx_item)  # pyright: ignore[reportAny]
+    finally:
+        completed.set()
+
+
 def mx_barrier(group: Group | None = None):
     mx.eval(
         mx.distributed.all_sum(
@@ -188,7 +229,9 @@ def initialize_mlx(
 
 
 def load_mlx_items(
-    bound_instance: BoundInstance, group: Group | None
+    bound_instance: BoundInstance,
+    group: Group | None,
+    on_timeout: TimeoutCallback | None = None,
 ) -> tuple[Model, TokenizerWrapper]:
     if group is None:
         logger.info(f"Single device used for {bound_instance.instance}")
@@ -202,7 +245,9 @@ def load_mlx_items(
     else:
         logger.info("Starting distributed init")
         start_time = time.perf_counter()
-        model, tokenizer = shard_and_load(bound_instance.bound_shard, group=group)
+        model, tokenizer = shard_and_load(
+            bound_instance.bound_shard, group=group, on_timeout=on_timeout
+        )
         end_time = time.perf_counter()
         logger.info(
             f"Time taken to shard and load model: {(end_time - start_time):.2f}s"
@@ -216,6 +261,7 @@ def load_mlx_items(
 def shard_and_load(
     shard_metadata: ShardMetadata,
     group: Group,
+    on_timeout: TimeoutCallback | None = None,
 ) -> tuple[nn.Module, TokenizerWrapper]:
     model_path = build_model_path(shard_metadata.model_meta.model_id)
 
@@ -252,7 +298,15 @@ def shard_and_load(
             logger.info(f"loading model from {model_path} with pipeline parallelism")
             model = pipeline_auto_parallel(model, group, shard_metadata)
 
-    mx.eval(model.parameters())
+    # Estimate timeout based on model size
+    base_timeout = float(os.environ.get("EXO_MODEL_LOAD_TIMEOUT", "60"))
+    model_size_gb = get_weights_size(shard_metadata).in_bytes / (1024**3)
+    timeout_seconds = base_timeout + model_size_gb / 5
+    logger.info(
+        f"Evaluating model parameters with timeout of {timeout_seconds:.0f}s "
+        f"(model size: {model_size_gb:.1f}GB)"
+    )
+    eval_with_timeout(model.parameters(), timeout_seconds, on_timeout)
 
     # TODO: Do we need this?
     mx.eval(model)

src/exo/worker/runner/bootstrap.py

@@ -17,15 +17,23 @@ def entrypoint(
     task_receiver: MpReceiver[Task],
     _logger: "loguru.Logger",
 ) -> None:
-    if (
-        isinstance(bound_instance.instance, MlxJacclInstance)
-        and len(bound_instance.instance.ibv_devices) >= 2
+    fast_synch_override = os.environ.get("EXO_FAST_SYNCH")
+    if fast_synch_override == "on" or (
+        fast_synch_override != "off"
+        and (
+            isinstance(bound_instance.instance, MlxJacclInstance)
+            and len(bound_instance.instance.ibv_devices) >= 2
+        )
     ):
         os.environ["MLX_METAL_FAST_SYNCH"] = "1"
+    else:
+        os.environ["MLX_METAL_FAST_SYNCH"] = "0"
 
     global logger
     logger = _logger
 
+    logger.info(f"Fast synch flag: {os.environ['MLX_METAL_FAST_SYNCH']}")
+
     # Import main after setting global logger - this lets us just import logger from this module
     try:
         from exo.worker.runner.runner import main

src/exo/worker/runner/runner.py

@@ -67,6 +67,7 @@ def main(
         bound_instance.bound_runner_id,
         bound_instance.bound_shard,
     )
+    device_rank = shard_metadata.device_rank
     logger.info("hello from the runner")
     if getattr(shard_metadata, "immediate_exception", False):
         raise Exception("Fake exception - runner failed to spin up.")
@@ -118,7 +119,20 @@ def main(
                         )
                     )
 
-                    model, tokenizer = load_mlx_items(bound_instance, group)
+                    def on_model_load_timeout() -> None:
+                        event_sender.send(
+                            RunnerStatusUpdated(
+                                runner_id=runner_id,
+                                runner_status=RunnerFailed(
+                                    error_message="Model loading timed out"
+                                ),
+                            )
+                        )
+                        time.sleep(0.5)
+
+                    model, tokenizer = load_mlx_items(
+                        bound_instance, group, on_timeout=on_model_load_timeout
+                    )
 
                     current_status = RunnerLoaded()
                     logger.info("runner loaded")
@@ -148,8 +162,6 @@ def main(
                 case ChatCompletion(task_params=task_params, command_id=command_id) if (
                     isinstance(current_status, RunnerReady)
                 ):
-                    assert model
-                    assert tokenizer
                     logger.info(f"received chat request: {str(task)[:500]}")
                     current_status = RunnerRunning()
                     logger.info("runner running")
@@ -158,41 +170,61 @@ def main(
                             runner_id=runner_id, runner_status=current_status
                         )
                     )
+                    assert model
+                    assert tokenizer
                     assert task_params.messages[0].content is not None
-                    _check_for_debug_prompts(task_params.messages[0].content)
 
-                    # Generate responses using the actual MLX generation
-                    mlx_generator = mlx_generate(
-                        model=model,
-                        tokenizer=tokenizer,
-                        task=task_params,
-                    )
+                    try:
+                        _check_for_debug_prompts(task_params.messages[0].content)
 
-                    # GPT-OSS specific parsing to match other model formats.
-                    if isinstance(model, GptOssModel):
-                        mlx_generator = parse_gpt_oss(mlx_generator)
+                        # Generate responses using the actual MLX generation
+                        mlx_generator = mlx_generate(
+                            model=model,
+                            tokenizer=tokenizer,
+                            task=task_params,
+                        )
 
-                    # TODO: Add tool call parser here
+                        # GPT-OSS specific parsing to match other model formats.
+                        if isinstance(model, GptOssModel):
+                            mlx_generator = parse_gpt_oss(mlx_generator)
 
-                    for response in mlx_generator:
-                        match response:
-                            case GenerationResponse():
-                                if shard_metadata.device_rank == 0:
-                                    event_sender.send(
-                                        ChunkGenerated(
-                                            command_id=command_id,
-                                            chunk=TokenChunk(
-                                                idx=response.token,
-                                                model=shard_metadata.model_meta.model_id,
-                                                text=response.text,
-                                                token_id=response.token,
-                                                finish_reason=response.finish_reason,
-                                                stats=response.stats,
-                                            ),
+                        # TODO: Add tool call parser here
+
+                        for response in mlx_generator:
+                            match response:
+                                case GenerationResponse():
+                                    if device_rank == 0:
+                                        event_sender.send(
+                                            ChunkGenerated(
+                                                command_id=command_id,
+                                                chunk=TokenChunk(
+                                                    idx=response.token,
+                                                    model=shard_metadata.model_meta.model_id,
+                                                    text=response.text,
+                                                    token_id=response.token,
+                                                    finish_reason=response.finish_reason,
+                                                    stats=response.stats,
+                                                ),
+                                            )
                                         )
-                                    )
-                                # case TokenizedResponse():
-                                # TODO: something here ig
+
+                    # can we make this more explicit?
+                    except Exception as e:
+                        if device_rank == 0:
+                            event_sender.send(
+                                ChunkGenerated(
+                                    command_id=command_id,
+                                    chunk=TokenChunk(
+                                        idx=0,
+                                        model=shard_metadata.model_meta.model_id,
+                                        text="",
+                                        token_id=0,
+                                        finish_reason="error",
+                                        error_message=str(e),
+                                    ),
+                                )
+                            )
+                        raise
 
                     current_status = RunnerReady()
                     logger.info("runner ready")

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

@@ -0,0 +1,220 @@
+# type: ignore
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, cast
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from exo.shared.constants import EXO_MODELS_DIR
+
+
+class MockLayer(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.custom_attr = "test_value"
+        self.use_sliding = True
+
+    def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array:
+        return x * 2
+
+
+@dataclass(frozen=True)
+class PipelineTestConfig:
+    model_path: Path
+    total_layers: int
+    base_port: int
+    max_tokens: int
+
+
+def create_hostfile(world_size: int, base_port: int) -> tuple[str, list[str]]:
+    import json
+    import tempfile
+
+    hosts = [f"127.0.0.1:{base_port + i}" for i in range(world_size)]
+
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+        json.dump(hosts, f)
+        hostfile_path = f.name
+
+    return hostfile_path, hosts
+
+
+# Use GPT OSS 20b to test as it is a model with a lot of strange behaviour
+
+DEFAULT_GPT_OSS_CONFIG = PipelineTestConfig(
+    model_path=EXO_MODELS_DIR / "mlx-community--gpt-oss-20b-MXFP4-Q8",
+    total_layers=24,
+    base_port=29600,
+    max_tokens=200,
+)
+
+
+DEFAULT_GPT_OSS_MODEL_ID = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+
+
+def run_gpt_oss_pipeline_device(
+    rank: int,
+    world_size: int,
+    hostfile_path: str,
+    layer_splits: list[tuple[int, int]],
+    prompt_tokens: int,
+    prefill_step_size: int,
+    result_queue: Any,  # pyright: ignore[reportAny]
+    max_tokens: int = 200,
+) -> None:
+    import os
+    import traceback
+
+    os.environ["MLX_HOSTFILE"] = hostfile_path
+    os.environ["MLX_RANK"] = str(rank)
+
+    import mlx.core as mlx_core
+
+    from exo.shared.types.api import ChatCompletionMessage
+    from exo.shared.types.memory import Memory
+    from exo.shared.types.models import ModelId, ModelMetadata
+    from exo.shared.types.tasks import ChatCompletionTaskParams
+    from exo.shared.types.worker.shards import PipelineShardMetadata
+    from exo.worker.engines.mlx import Model
+    from exo.worker.engines.mlx.generator.generate import mlx_generate
+    from exo.worker.engines.mlx.utils_mlx import shard_and_load
+
+    try:
+        group = mlx_core.distributed.init(backend="ring", strict=True)
+
+        start_layer, end_layer = layer_splits[rank]
+
+        shard_meta = PipelineShardMetadata(
+            model_meta=ModelMetadata(
+                model_id=ModelId(DEFAULT_GPT_OSS_MODEL_ID),
+                pretty_name="GPT-OSS 20B",
+                storage_size=Memory.from_gb(12),
+                n_layers=24,
+                hidden_size=2880,
+                supports_tensor=False,
+            ),
+            device_rank=rank,
+            world_size=world_size,
+            start_layer=start_layer,
+            end_layer=end_layer,
+            n_layers=24,
+        )
+
+        model, tokenizer = shard_and_load(shard_meta, group)
+        model = cast(Model, model)
+
+        # Generate a prompt of exact token length
+        base_text = "The quick brown fox jumps over the lazy dog. "
+        base_tokens = tokenizer.encode(base_text)
+        base_len = len(base_tokens)
+
+        # Build prompt with approximate target length
+        repeats = (prompt_tokens // base_len) + 2
+        long_text = base_text * repeats
+        tokens = tokenizer.encode(long_text)
+        # Truncate to exact target length
+        tokens = tokens[:prompt_tokens]
+        prompt_text = tokenizer.decode(tokens)
+
+        task = ChatCompletionTaskParams(
+            model=DEFAULT_GPT_OSS_MODEL_ID,
+            messages=[ChatCompletionMessage(role="user", content=prompt_text)],
+            max_tokens=max_tokens,
+        )
+
+        generated_text = ""
+        for response in mlx_generate(
+            model=model,
+            tokenizer=tokenizer,
+            task=task,
+        ):
+            generated_text += response.text
+            if response.finish_reason is not None:
+                break
+
+        result_queue.put((rank, True, generated_text))  # pyright: ignore[reportAny]
+
+    except Exception as e:
+        result_queue.put((rank, False, f"{e}\n{traceback.format_exc()}"))  # pyright: ignore[reportAny]
+
+
+def run_gpt_oss_tensor_parallel_device(
+    rank: int,
+    world_size: int,
+    hostfile_path: str,
+    prompt_tokens: int,
+    prefill_step_size: int,
+    result_queue: Any,  # pyright: ignore[reportAny]
+    max_tokens: int = 10,
+) -> None:
+    import os
+    import traceback
+
+    os.environ["MLX_HOSTFILE"] = hostfile_path
+    os.environ["MLX_RANK"] = str(rank)
+
+    import mlx.core as mlx_core
+
+    from exo.shared.types.api import ChatCompletionMessage
+    from exo.shared.types.memory import Memory
+    from exo.shared.types.models import ModelId, ModelMetadata
+    from exo.shared.types.tasks import ChatCompletionTaskParams
+    from exo.shared.types.worker.shards import TensorShardMetadata
+    from exo.worker.engines.mlx import Model
+    from exo.worker.engines.mlx.generator.generate import mlx_generate
+    from exo.worker.engines.mlx.utils_mlx import shard_and_load
+
+    try:
+        group = mlx_core.distributed.init(backend="ring", strict=True)
+
+        # For tensor parallelism, all devices run all layers
+        shard_meta = TensorShardMetadata(
+            model_meta=ModelMetadata(
+                model_id=ModelId(DEFAULT_GPT_OSS_MODEL_ID),
+                pretty_name="GPT-OSS 20B",
+                storage_size=Memory.from_gb(12),
+                n_layers=24,
+                hidden_size=2880,
+                supports_tensor=True,
+            ),
+            device_rank=rank,
+            world_size=world_size,
+            start_layer=0,
+            end_layer=24,
+            n_layers=24,
+        )
+
+        model, tokenizer = shard_and_load(shard_meta, group)
+        model = cast(Model, model)
+
+        base_text = "The quick brown fox jumps over the lazy dog. "
+        base_tokens = tokenizer.encode(base_text)
+        base_len = len(base_tokens)
+
+        repeats = (prompt_tokens // base_len) + 2
+        long_text = base_text * repeats
+        tokens = tokenizer.encode(long_text)
+        tokens = tokens[:prompt_tokens]
+        prompt_text = tokenizer.decode(tokens)
+
+        task = ChatCompletionTaskParams(
+            model=DEFAULT_GPT_OSS_MODEL_ID,
+            messages=[ChatCompletionMessage(role="user", content=prompt_text)],
+            max_tokens=max_tokens,
+        )
+
+        generated_text = ""
+        for response in mlx_generate(
+            model=model,
+            tokenizer=tokenizer,
+            task=task,
+        ):
+            generated_text += response.text
+            if response.finish_reason is not None:
+                break
+
+        result_queue.put((rank, True, generated_text))  # pyright: ignore[reportAny]
+
+    except Exception as e:
+        result_queue.put((rank, False, f"{e}\n{traceback.format_exc()}"))  # pyright: ignore[reportAny]

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

@@ -0,0 +1,154 @@
+import multiprocessing as mp
+from typing import Any
+
+import mlx.core as mx
+import pytest
+
+from exo.worker.engines.mlx.auto_parallel import (
+    CustomMlxModule,
+    PipelineFirstLayer,
+    PipelineLastLayer,
+    PipelineParallelModel,
+)
+from exo.worker.tests.unittests.test_mlx.conftest import MockLayer
+
+
+def run_pipeline_device(
+    rank: int,
+    world_size: int,
+    hostfile_path: str,
+    result_queue: Any,  # pyright: ignore[reportAny]
+) -> None:
+    import os
+
+    os.environ["MLX_HOSTFILE"] = hostfile_path
+    os.environ["MLX_RANK"] = str(rank)
+
+    import mlx.core as mlx_core
+    import mlx.nn as mlx_nn
+
+    class MockLayerInner(mlx_nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self.custom_attr = "test_value"
+
+        def __call__(
+            self, x: mlx_core.array, *args: object, **kwargs: object
+        ) -> mlx_core.array:
+            return x * 2
+
+    class MockModel(mlx_nn.Module):
+        def __init__(self, layers: list[mlx_nn.Module]) -> None:
+            super().__init__()
+            self.layers = layers
+
+        def __call__(
+            self, x: mlx_core.array, *args: object, **kwargs: object
+        ) -> mlx_core.array:
+            for layer in self.layers:
+                x = layer(x, *args, **kwargs)  # pyright: ignore[reportUnknownVariableType]
+            return x  # pyright: ignore[reportUnknownVariableType]
+
+    try:
+        group = mlx_core.distributed.init(backend="ring", strict=True)
+
+        mock = MockLayerInner()
+        first = PipelineFirstLayer(mock, r=rank, group=group)
+        composed = PipelineLastLayer(first, r=rank, s=world_size, group=group)
+
+        # Wrap in a mock model, then wrap in PipelineParallelModel for all_gather
+        inner_model = MockModel([composed])
+        model = PipelineParallelModel(inner_model, group)
+
+        x = mlx_core.ones((1, 4))
+        result = model(x)
+        mlx_core.eval(result)
+
+        success = result.shape == x.shape
+        result_queue.put((rank, success, result))  # pyright: ignore[reportAny]
+    except Exception as e:
+        result_queue.put((rank, False, str(e)))  # pyright: ignore[reportAny]
+
+
+def test_single_wrapper_delegates_attributes() -> None:
+    mock = MockLayer()
+    wrapped = CustomMlxModule(mock)
+
+    assert wrapped.custom_attr == "test_value"  # type: ignore[attr-defined]
+    assert wrapped.use_sliding is True  # type: ignore[attr-defined]
+
+
+def test_composed_wrappers_delegate_attributes() -> None:
+    mock = MockLayer()
+    group = mx.distributed.init()
+
+    first = PipelineFirstLayer(mock, r=0, group=group)
+    composed = PipelineLastLayer(first, r=0, s=1, group=group)
+
+    assert composed.custom_attr == "test_value"  # type: ignore[attr-defined]
+    assert composed.use_sliding is True  # type: ignore[attr-defined]
+
+
+def test_missing_attribute_raises() -> None:
+    mock = MockLayer()
+    wrapped = CustomMlxModule(mock)
+
+    with pytest.raises(AttributeError):
+        _ = wrapped.nonexistent_attr  # type: ignore[attr-defined]
+
+
+def test_composed_call_works() -> None:
+    import json
+    import os
+    import tempfile
+
+    ctx = mp.get_context("spawn")
+
+    world_size = 2
+    base_port = 29500
+
+    hosts = [f"127.0.0.1:{base_port + i}" for i in range(world_size)]
+
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+        json.dump(hosts, f)
+        hostfile_path = f.name
+
+    try:
+        result_queue: Any = ctx.Queue()
+
+        processes: list[Any] = []
+        for rank in range(world_size):
+            p = ctx.Process(
+                target=run_pipeline_device,
+                args=(rank, world_size, hostfile_path, result_queue),
+            )
+            p.start()
+            processes.append(p)
+
+        for p in processes:  # pyright: ignore[reportAny]
+            p.join(timeout=10)  # pyright: ignore[reportAny]
+
+        results: dict[int, Any] = {}
+        errors: dict[int, str] = {}
+        while not result_queue.empty():  # pyright: ignore[reportAny]
+            rank, success, value = result_queue.get()  # pyright: ignore[reportAny]
+            if success:
+                results[rank] = value
+            else:
+                errors[rank] = value
+
+        assert len(results) == world_size, (
+            f"Expected {world_size} results, got {len(results)}. Errors: {errors}"
+        )
+
+        for rank in range(world_size):
+            assert rank in results, (
+                f"Device {rank} failed: {errors.get(rank, 'unknown')}"
+            )
+            result_array = results[rank]
+            # Both devices see the final result (4.0) after all_gather
+            assert (result_array == 4.0).all(), (
+                f"Device {rank}: expected 4.0, got {result_array}"
+            )
+    finally:
+        os.unlink(hostfile_path)

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

@@ -0,0 +1,230 @@
+import multiprocessing as mp
+import os
+from dataclasses import dataclass
+from typing import Any, Callable
+
+import pytest
+
+from exo.worker.tests.unittests.test_mlx.conftest import (
+    DEFAULT_GPT_OSS_CONFIG,
+    create_hostfile,
+    run_gpt_oss_pipeline_device,
+    run_gpt_oss_tensor_parallel_device,
+)
+
+
+def _check_model_exists() -> bool:
+    return DEFAULT_GPT_OSS_CONFIG.model_path.exists()
+
+
+pytestmark = [
+    pytest.mark.skipif(
+        not _check_model_exists(),
+        reason=f"GPT-OSS model not found at {DEFAULT_GPT_OSS_CONFIG.model_path}",
+    ),
+]
+
+
+@dataclass
+class DistributedTestResult:
+    timed_out: bool
+    world_size: int
+    results: dict[int, tuple[bool, str]]
+
+    @property
+    def all_success(self) -> bool:
+        if len(self.results) != self.world_size:
+            return False
+        return all(r[0] for r in self.results.values())
+
+
+def run_distributed_test(
+    world_size: int,
+    port_offset: int,
+    process_timeout: int,
+    target: Callable[..., None],
+    make_args: Callable[[int], tuple[Any, ...]],
+) -> DistributedTestResult:
+    ctx = mp.get_context("spawn")
+    hostfile_path, _ = create_hostfile(
+        world_size, DEFAULT_GPT_OSS_CONFIG.base_port + port_offset
+    )
+
+    try:
+        result_queue: Any = ctx.Queue()
+        processes: list[Any] = []
+
+        for rank in range(world_size):
+            args = make_args(rank)
+            p = ctx.Process(
+                target=target,
+                args=(rank, world_size, hostfile_path, *args, result_queue),
+            )
+            p.start()
+            processes.append(p)
+
+        for p in processes:  # pyright: ignore[reportAny]
+            p.join(timeout=process_timeout)  # pyright: ignore[reportAny]
+
+        timed_out = any(p.is_alive() for p in processes)  # pyright: ignore[reportAny]
+
+        for p in processes:  # pyright: ignore[reportAny]
+            if p.is_alive():  # pyright: ignore[reportAny]
+                p.terminate()  # pyright: ignore[reportAny]
+                p.join(timeout=5)  # pyright: ignore[reportAny]
+
+        results: dict[int, tuple[bool, str]] = {}
+        while not result_queue.empty():  # pyright: ignore[reportAny]
+            rank, success, value = result_queue.get()  # pyright: ignore[reportAny]
+            results[rank] = (success, value)
+
+        return DistributedTestResult(
+            timed_out=timed_out, world_size=world_size, results=results
+        )
+
+    finally:
+        os.unlink(hostfile_path)
+
+
+def run_pipeline_test(
+    layer_splits: list[tuple[int, int]],
+    prompt_tokens: int,
+    prefill_step_size: int,
+    port_offset: int = 0,
+    process_timeout: int = 60,
+) -> DistributedTestResult:
+    def make_args(rank: int) -> tuple[Any, ...]:
+        return (
+            layer_splits,
+            prompt_tokens,
+            prefill_step_size,
+        )
+
+    return run_distributed_test(
+        world_size=len(layer_splits),
+        port_offset=port_offset,
+        process_timeout=process_timeout,
+        target=run_gpt_oss_pipeline_device,
+        make_args=make_args,
+    )
+
+
+def run_tensor_test(
+    prompt_tokens: int,
+    prefill_step_size: int,
+    port_offset: int = 0,
+    process_timeout: int = 60,
+) -> DistributedTestResult:
+    def make_args(rank: int) -> tuple[Any, ...]:
+        return (
+            prompt_tokens,
+            prefill_step_size,
+        )
+
+    return run_distributed_test(
+        world_size=2,
+        port_offset=port_offset,
+        process_timeout=process_timeout,
+        target=run_gpt_oss_tensor_parallel_device,
+        make_args=make_args,
+    )
+
+
+class TestPipelineParallelFix:
+    BUG_TRIGGER_SPLITS: list[tuple[int, int]] = [(0, 1), (1, 24)]
+
+    def test_pipeline_single_layer_first_device(self) -> None:
+        result = run_pipeline_test(
+            layer_splits=self.BUG_TRIGGER_SPLITS,
+            prompt_tokens=100,
+            prefill_step_size=64,
+            process_timeout=60,
+        )
+        assert not result.timed_out, "Unexpected timeout - fix may not be working"
+        assert result.all_success, f"Failures: {result.results}"
+
+
+class TestPipelineSplitConfigurations:
+    @pytest.mark.parametrize(
+        "layer_splits",
+        [
+            [(0, 1), (1, 24)],
+            [(0, 6), (6, 24)],
+            [(0, 12), (12, 24)],
+        ],
+        ids=["1_23", "6_18", "12_12"],
+    )
+    def test_pipeline_splits(
+        self,
+        layer_splits: list[tuple[int, int]],
+    ) -> None:
+        result = run_pipeline_test(
+            layer_splits=layer_splits,
+            prompt_tokens=600,
+            prefill_step_size=512,
+            port_offset=100,
+        )
+        assert not result.timed_out, f"Timeout with {layer_splits}"
+        assert result.all_success, f"Failures with {layer_splits}: {result.results}"
+
+
+class TestPrefillStepSizeBoundaries:
+    @pytest.mark.parametrize(
+        "prefill_step_size,prompt_tokens",
+        [
+            (512, 511),
+            (512, 512),
+            (512, 513),
+            (512, 1024),
+        ],
+        ids=["under", "exact", "over", "double"],
+    )
+    def test_boundary_conditions(
+        self,
+        prefill_step_size: int,
+        prompt_tokens: int,
+    ) -> None:
+        result = run_pipeline_test(
+            layer_splits=[(0, 12), (12, 24)],
+            prompt_tokens=prompt_tokens,
+            prefill_step_size=prefill_step_size,
+            port_offset=200,
+        )
+        assert not result.timed_out, f"Timeout: {prompt_tokens=}, {prefill_step_size=}"
+        assert result.all_success, f"Failures: {result.results}"
+
+
+class TestTensorParallelFix:
+    def test_tensor_parallel(self) -> None:
+        result = run_tensor_test(
+            prompt_tokens=100,
+            prefill_step_size=64,
+            port_offset=400,
+        )
+        assert not result.timed_out, "Unexpected timeout"
+        assert result.all_success, f"Failures: {result.results}"
+
+
+class TestTensorParallelBoundaries:
+    @pytest.mark.parametrize(
+        "prefill_step_size,prompt_tokens",
+        [
+            (512, 511),
+            (512, 512),
+            (512, 513),
+            (512, 1024),
+        ],
+        ids=["under", "exact", "over", "double"],
+    )
+    def test_tensor_parallel_boundaries(
+        self,
+        prefill_step_size: int,
+        prompt_tokens: int,
+    ) -> None:
+        result = run_tensor_test(
+            prompt_tokens=prompt_tokens,
+            prefill_step_size=prefill_step_size,
+            port_offset=500,
+        )
+        assert not result.timed_out, f"Timeout: {prompt_tokens=}, {prefill_step_size=}"
+        assert result.all_success, f"Failures: {result.results}"

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

@@ -121,6 +121,21 @@ def patch_out_mlx(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.setattr(mlx_runner, "mlx_generate", fake_generate)
 
 
+# Use a fake event_sender to remove test flakiness.
+class EventCollector:
+    def __init__(self) -> None:
+        self.events: list[Event] = []
+
+    def send(self, event: Event) -> None:
+        self.events.append(event)
+
+    def close(self) -> None:
+        pass
+
+    def join(self) -> None:
+        pass
+
+
 def _run(tasks: Iterable[Task]):
     bound_instance = get_bound_mlx_ring_instance(
         instance_id=INSTANCE_1_ID,
@@ -130,22 +145,20 @@ def _run(tasks: Iterable[Task]):
     )
 
     task_sender, task_receiver = mp_channel[Task]()
-    event_sender, event_receiver = mp_channel[Event]()
+    event_sender = EventCollector()
 
-    with task_sender, event_receiver:
+    with task_sender:
         for t in tasks:
             task_sender.send(t)
 
         # worst monkeypatch known to man
         # this is some c++ nonsense
-        event_sender.close = nothin
-        event_sender.join = nothin
         task_receiver.close = nothin
         task_receiver.join = nothin
 
-        mlx_runner.main(bound_instance, event_sender, task_receiver)
+        mlx_runner.main(bound_instance, event_sender, task_receiver)  # type: ignore[arg-type]
 
-        return event_receiver.collect()
+        return event_sender.events
 
 
 def test_events_processed_in_correct_order(patch_out_mlx: pytest.MonkeyPatch):