Add fixes

## Motivation

The `download_progress_for_local_path` function and the "Handle local
paths" code block in `download_shard` are dead code that cannot be
reached in normal usage. The code checks if `model_id` (e.g.,
"mlx-community/Llama-3.2-3B-Instruct-4bit") exists as a filesystem path,
but model IDs are constrained to HuggingFace repo format and there's no
API pathway to pass local paths.

## Changes

- Removed `download_progress_for_local_path()` function (45 lines)
- Removed the "Handle local paths" block in `download_shard()` (7 lines)

## Why It Works

This code was added in PR #669 as part of a "feature-local-models"
branch, but the feature was never fully integrated. The check
`aios.path.exists(str(shard.model_card.model_id))` would only return
true if a directory literally named
"mlx-community/Llama-3.2-3B-Instruct-4bit" existed in the cwd, which
doesn't happen in practice. Offline caching is already handled by
`fetch_file_list_with_cache`.

## Test Plan

### Manual Testing
- Run exo normally and verify downloads still work

### Automated Testing
- Existing tests pass (this code had no test coverage)

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

src/exo/worker/download/download_utils.py

@@ -477,53 +477,6 @@ async def get_downloaded_size(path: Path) -> int:
     return 0
 
 
-async def download_progress_for_local_path(
-    repo_id: str, shard: ShardMetadata, local_path: Path
-) -> RepoDownloadProgress:
-    file_progress: dict[str, RepoFileDownloadProgress] = {}
-    total_files = 0
-    total_bytes = 0
-
-    if await aios.path.isdir(local_path):
-        for root, _, files in os.walk(local_path):
-            for f in files:
-                if f.endswith((".safetensors", ".bin", ".pt", ".gguf", ".json")):
-                    file_path = Path(root) / f
-                    size = (await aios.stat(file_path)).st_size
-                    rel_path = str(file_path.relative_to(local_path))
-                    file_progress[rel_path] = RepoFileDownloadProgress(
-                        repo_id=repo_id,
-                        repo_revision="local",
-                        file_path=rel_path,
-                        downloaded=Memory.from_bytes(size),
-                        downloaded_this_session=Memory.from_bytes(0),
-                        total=Memory.from_bytes(size),
-                        speed=0,
-                        eta=timedelta(0),
-                        status="complete",
-                        start_time=time.time(),
-                    )
-                    total_files += 1
-                    total_bytes += size
-    else:
-        raise ValueError(f"Local path {local_path} is not a directory")
-
-    return RepoDownloadProgress(
-        repo_id=repo_id,
-        repo_revision="local",
-        shard=shard,
-        completed_files=total_files,
-        total_files=total_files,
-        downloaded_bytes=Memory.from_bytes(total_bytes),
-        downloaded_bytes_this_session=Memory.from_bytes(0),
-        total_bytes=Memory.from_bytes(total_bytes),
-        overall_speed=0,
-        overall_eta=timedelta(0),
-        status="complete",
-        file_progress=file_progress,
-    )
-
-
 async def download_shard(
     shard: ShardMetadata,
     on_progress: Callable[[ShardMetadata, RepoDownloadProgress], Awaitable[None]],
@@ -534,14 +487,6 @@ async def download_shard(
     if not skip_download:
         logger.info(f"Downloading {shard.model_card.model_id=}")
 
-    # Handle local paths
-    if await aios.path.exists(str(shard.model_card.model_id)):
-        logger.info(f"Using local model path {shard.model_card.model_id}")
-        local_path = Path(str(shard.model_card.model_id))
-        return local_path, await download_progress_for_local_path(
-            str(shard.model_card.model_id), shard, local_path
-        )
-
     revision = "main"
     target_dir = await ensure_models_dir() / str(shard.model_card.model_id).replace(
         "/", "--"
@@ -552,7 +497,8 @@ async def download_shard(
     if not allow_patterns:
         allow_patterns = await resolve_allow_patterns(shard)
 
-    logger.info(f"Downloading {shard.model_card.model_id=} with {allow_patterns=}")
+    if not skip_download:
+        logger.info(f"Downloading {shard.model_card.model_id=} with {allow_patterns=}")
 
     all_start_time = time.time()
     # TODO: currently not recursive. Some models might require subdirectories - thus this will need to be changed.

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

@@ -4,7 +4,7 @@ from abc import ABC, abstractmethod
 from collections.abc import Callable
 from functools import partial
 from inspect import signature
-from typing import TYPE_CHECKING, Any, Protocol, cast
+from typing import TYPE_CHECKING, Any, cast
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -67,27 +67,16 @@ def eval_with_timeout(
         completed.set()
 
 
-class _LayerCallable(Protocol):
-    """Structural type that any compatible layer must satisfy.
-
-    We require a single positional input of type ``mx.array`` and an
-    ``mx.array`` output, while permitting arbitrary *args / **kwargs so this
-    protocol matches the vast majority of `mlx.nn.Module` subclasses.
-    """
-
-    def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array: ...
-
-
 class CustomMlxLayer(nn.Module):
     """Base class for replacing an MLX layer with a custom implementation."""
 
-    def __init__(self, original_layer: _LayerCallable):
+    def __init__(self, original_layer: nn.Module):
         super().__init__()
         object.__setattr__(self, "_original_layer", original_layer)
 
     @property
-    def original_layer(self) -> _LayerCallable:
+    def original_layer(self) -> nn.Module:
-        return cast(_LayerCallable, object.__getattribute__(self, "_original_layer"))
+        return cast(nn.Module, object.__getattribute__(self, "_original_layer"))
 
     # Calls __getattr__ for any attributes not found on nn.Module (e.g. use_sliding)
     if not TYPE_CHECKING:
@@ -100,52 +89,53 @@ class CustomMlxLayer(nn.Module):
                 return getattr(original_layer, name)
 
 
-class PipelineFirstLayer(CustomMlxLayer):
+def patch_pipeline_first_layer(
-    def __init__(
+    pipeline_layer: nn.Module, group: mx.distributed.Group
-        self,
+) -> nn.Module:
-        original_layer: _LayerCallable,
+    cls = type(pipeline_layer)
-        r: int,
+    orig_call = cast(Callable[..., mx.array], cls.__call__)
-        group: mx.distributed.Group,
-    ):
-        super().__init__(original_layer)
-        self.r: int = r
-        self.group = group
 
-    def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array:
+    rank = group.rank()
-        if self.r != 0:
+
-            x = mx.distributed.recv_like(x, (self.r - 1), group=self.group)
+    class PatchedFirstLayer(cls):
-        return self.original_layer(x, *args, **kwargs)
+        def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array:
+            if rank != 0:
+                x = mx.distributed.recv_like(x, (rank - 1), group=group)
+            return orig_call(self, x, *args, **kwargs)
+
+    pipeline_layer.__class__ = PatchedFirstLayer
+
+    return pipeline_layer
 
 
-class PipelineLastLayer(CustomMlxLayer):
+def patch_pipeline_last_layer(
-    def __init__(
+    pipeline_layer: nn.Module, group: mx.distributed.Group
-        self,
+) -> nn.Module:
-        original_layer: _LayerCallable,
+    cls = type(pipeline_layer)
-        r: int,
+    orig_call = cast(Callable[..., mx.array], cls.__call__)
-        s: int,
+    orig_call_sig = signature(orig_call)
-        group: mx.distributed.Group,
-    ):
-        super().__init__(original_layer)
-        self.r: int = r
-        self.s: int = s
-        self.group = group
-        self.original_layer_signature = signature(self.original_layer.__call__)
 
-    def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array:
+    rank = group.rank()
-        cache = self.original_layer_signature.bind_partial(
+    size = group.size()
-            x, *args, **kwargs
-        ).arguments.get("cache", None)
 
-        output: mx.array = self.original_layer(x, *args, **kwargs)
+    class PatchedLastLayer(cls):
-
+        def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array:
-        if self.r != self.s - 1:
+            cache = orig_call_sig.bind_partial(x, *args, **kwargs).arguments.get(
-            output = mx.distributed.send(
+                "cache", None
-                output, (self.r + 1) % self.s, group=self.group
             )
-            if cache is not None:
-                cache.keys = mx.depends(cache.keys, output)  # type: ignore[reportUnknownMemberType]
 
-        return output
+            output: mx.array = orig_call(self, x, *args, **kwargs)
+
+            if rank != size - 1:
+                output = mx.distributed.send(output, (rank + 1) % size, group=group)
+                if cache is not None:
+                    cache.keys = mx.depends(cache.keys, output)  # type: ignore[reportUnknownMemberType]
+
+            return output
+
+    pipeline_layer.__class__ = PatchedLastLayer
+
+    return pipeline_layer
 
 
 def _inner_model(model: nn.Module) -> nn.Module:
@@ -160,13 +150,13 @@ def _inner_model(model: nn.Module) -> nn.Module:
     raise ValueError("Model must either have a 'model' or 'transformer' attribute")
 
 
-def _get_layers(inner_model_instance: nn.Module) -> list[_LayerCallable]:
+def _get_layers(inner_model_instance: nn.Module) -> list[nn.Module]:
     # Handle both model.layers and model.h cases
-    layers: list[_LayerCallable]
+    layers: list[nn.Module]
     if hasattr(inner_model_instance, "layers"):
-        layers = cast(list[_LayerCallable], inner_model_instance.layers)
+        layers = cast(list[nn.Module], inner_model_instance.layers)
     elif hasattr(inner_model_instance, "h"):
-        layers = cast(list[_LayerCallable], inner_model_instance.h)
+        layers = cast(list[nn.Module], inner_model_instance.h)
     else:
         raise ValueError("Model must have either a 'layers' or 'h' attribute")
 
@@ -191,15 +181,12 @@ def pipeline_auto_parallel(
     layers = _get_layers(inner_model_instance)
 
     start_layer, end_layer = model_shard_meta.start_layer, model_shard_meta.end_layer
-    device_rank, world_size = model_shard_meta.device_rank, model_shard_meta.world_size
 
     layers = layers[start_layer:end_layer]
-    layers[0] = PipelineFirstLayer(layers[0], device_rank, group=group)
+    layers[0] = patch_pipeline_first_layer(layers[0], group)
-    layers[-1] = PipelineLastLayer(
+    layers[-1] = patch_pipeline_last_layer(
         layers[-1],
-        device_rank,
+        group,
-        world_size,
-        group=group,
     )
 
     if isinstance(inner_model_instance, GptOssMoeModel):
@@ -446,7 +433,7 @@ class LlamaShardingStrategy(TensorParallelShardingStrategy):
         return model
 
 
-def _set_layers(model: nn.Module, layers: list[_LayerCallable]) -> None:
+def _set_layers(model: nn.Module, layers: list[nn.Module]) -> None:
     inner_model_instance = _inner_model(model)
     if hasattr(inner_model_instance, "layers"):
         inner_model_instance.layers = layers
@@ -521,17 +508,17 @@ class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
 
 
 class ShardedDeepseekV3MoE(CustomMlxLayer):
-    def __init__(self, layer: _LayerCallable):
+    def __init__(self, layer: nn.Module):
         super().__init__(layer)
         self.sharding_group: mx.distributed.Group | None = None
 
     def __call__(self, x: mx.array) -> mx.array:
         if self.sharding_group is not None:
             x = sum_gradients(self.sharding_group)(x)
-        y = self.original_layer.__call__(x)
+        y = self.original_layer.__call__(x)  # type: ignore
         if self.sharding_group is not None:
-            y = mx.distributed.all_sum(y, group=self.sharding_group)
+            y = mx.distributed.all_sum(y, group=self.sharding_group)  # type: ignore
-        return y
+        return y  # type: ignore
 
 
 class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
@@ -565,7 +552,7 @@ class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
             self.all_to_sharded_linear_in_place(
                 layer.block_sparse_moe.switch_mlp.up_proj
             )
-            layer.block_sparse_moe = ShardedQwenMoE(layer.block_sparse_moe)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
+            layer.block_sparse_moe = ShardedQwenMoE(layer.block_sparse_moe)  # pyright: ignore[reportAttributeAccessIssue]
             layer.block_sparse_moe.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
 
         return model
@@ -599,7 +586,7 @@ class QwenShardingStrategy(TensorParallelShardingStrategy):
                 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)
-                layer.mlp = ShardedQwenMoE(layer.mlp)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
+                layer.mlp = ShardedQwenMoE(layer.mlp)  # pyright: ignore[reportAttributeAccessIssue]
                 layer.mlp.sharding_group = self.group
 
             # Shard the MLP
@@ -612,17 +599,17 @@ class QwenShardingStrategy(TensorParallelShardingStrategy):
 
 
 class ShardedQwenMoE(CustomMlxLayer):
-    def __init__(self, layer: _LayerCallable):
+    def __init__(self, layer: nn.Module):
         super().__init__(layer)
         self.sharding_group: mx.distributed.Group | None = None
 
     def __call__(self, x: mx.array) -> mx.array:
         if self.sharding_group is not None:
             x = sum_gradients(self.sharding_group)(x)
-        y = self.original_layer.__call__(x)
+        y = self.original_layer.__call__(x)  # type: ignore
         if self.sharding_group is not None:
-            y = mx.distributed.all_sum(y, group=self.sharding_group)
+            y = mx.distributed.all_sum(y, group=self.sharding_group)  # type: ignore
-        return y
+        return y  # type: ignore
 
 
 class GptOssShardingStrategy(TensorParallelShardingStrategy):
@@ -674,7 +661,7 @@ class ShardedGptOssMoE(CustomMlxLayer):
     def __call__(self, x: mx.array) -> mx.array:
         if self.sharding_group is not None:
             x = sum_gradients(self.sharding_group)(x)
-        y = self.original_layer(x)
+        y = self.original_layer(x)  # type: ignore
         if self.sharding_group is not None:
-            y = mx.distributed.all_sum(y, group=self.sharding_group)
+            y = mx.distributed.all_sum(y, group=self.sharding_group)  # type: ignore
-        return y
+        return y  # type: ignore

src/exo/worker/main.py

@@ -449,7 +449,7 @@ class Worker:
     async def _emit_existing_download_progress(self) -> None:
         try:
             while True:
-                logger.info("Fetching and emitting existing download progress...")
+                logger.debug("Fetching and emitting existing download progress...")
                 async for (
                     _,
                     progress,
@@ -480,7 +480,7 @@ class Worker:
                     await self.event_sender.send(
                         NodeDownloadProgress(download_progress=status)
                     )
-                logger.info("Done emitting existing download progress.")
+                logger.debug("Done emitting existing download progress.")
                 await anyio.sleep(5 * 60)  # 5 minutes
         except Exception as e:
             logger.error(f"Error emitting existing download progress: {e}")

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

@@ -18,7 +18,7 @@ from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.shards import PipelineShardMetadata, 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
+from exo.worker.engines.mlx.utils_mlx import shard_and_load, apply_chat_template
 
 
 class MockLayer(nn.Module):
@@ -116,12 +116,11 @@ def run_gpt_oss_pipeline_device(
             messages=[ChatCompletionMessage(role="user", content=prompt_text)],
             max_tokens=max_tokens,
         )
+        prompt = apply_chat_template(tokenizer, task)
 
         generated_text = ""
         for response in mlx_generate(
-            model=model,
+            model=model, tokenizer=tokenizer, task=task, prompt=prompt
-            tokenizer=tokenizer,
-            task=task,
         ):
             generated_text += response.text
             if response.finish_reason is not None:
@@ -183,11 +182,11 @@ def run_gpt_oss_tensor_parallel_device(
             max_tokens=max_tokens,
         )
 
+        prompt = apply_chat_template(tokenizer, task)
+
         generated_text = ""
         for response in mlx_generate(
-            model=model,
+            model=model, tokenizer=tokenizer, task=task, prompt=prompt
-            tokenizer=tokenizer,
-            task=task,
         ):
             generated_text += response.text
             if response.finish_reason is not None:

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

@@ -10,8 +10,8 @@ import pytest
 
 from exo.worker.engines.mlx.auto_parallel import (
     CustomMlxLayer,
-    PipelineFirstLayer,
+    patch_pipeline_first_layer,
-    PipelineLastLayer,
+    patch_pipeline_last_layer,
     patch_pipeline_model,
 )
 from exo.worker.tests.unittests.test_mlx.conftest import MockLayer
@@ -50,8 +50,8 @@ def run_pipeline_device(
         group = mx.distributed.init(backend="ring", strict=True)
 
         mock = MockLayerInner()
-        first = PipelineFirstLayer(mock, r=rank, group=group)
+        first = patch_pipeline_first_layer(mock, group)
-        composed = PipelineLastLayer(first, r=rank, s=world_size, group=group)
+        composed = patch_pipeline_last_layer(first, group)
 
         # Wrap in a mock model, then wrap in PipelineParallelModel for all_gather
         inner_model = MockModel([composed])
@@ -78,8 +78,8 @@ def test_composed_wrappers_delegate_attributes() -> None:
     mock = MockLayer()
     group = mx.distributed.init()
 
-    first = PipelineFirstLayer(mock, r=0, group=group)
+    first = patch_pipeline_first_layer(mock, group)
-    composed = PipelineLastLayer(first, r=0, s=1, group=group)
+    composed = patch_pipeline_last_layer(first, group)
 
     assert composed.custom_attr == "test_value"  # type: ignore[attr-defined]
     assert composed.use_sliding is True  # type: ignore[attr-defined]