address comments

.mlx_typings/mlx_lm/generate.pyi

@@ -73,9 +73,11 @@ class GenerationResponse:
     finish_reason: Optional[str] = ...
 
 def maybe_quantize_kv_cache(
-    prompt_cache, quantized_kv_start, kv_group_size, kv_bits
-):  # -> None:
-    ...
+    prompt_cache: Any,
+    quantized_kv_start: int | None,
+    kv_group_size: int | None,
+    kv_bits: int | None,
+) -> None: ...
 def generate_step(
     prompt: mx.array,
     model: nn.Module,

.mlx_typings/mlx_lm/models/cache.pyi

@@ -16,7 +16,7 @@ class Cache(Protocol):
         self, keys: mx.array, values: mx.array
     ) -> tuple[mx.array, mx.array]: ...
     @property
-    def state(self) -> tuple[mx.array, mx.array]: ...
+    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
     @state.setter
     def state(self, v) -> None: ...
 
@@ -92,13 +92,14 @@ class _BaseCache(Cache):
     values: mx.array
     offset: int
     @property
-    def state(self) -> tuple[mx.array, mx.array]: ...
+    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
     @state.setter
     def state(self, v) -> None: ...
     @property
     def meta_state(self) -> Literal[""]: ...
     @meta_state.setter
     def meta_state(self, v) -> None: ...
+    def trim(self, n: int) -> int: ...
     def is_trimmable(self) -> Literal[False]: ...
     @classmethod
     def from_state(cls, state, meta_state) -> Self: ...
@@ -114,15 +115,13 @@ class ConcatenateKVCache(_BaseCache):
     def update_and_fetch(self, keys, values):  # -> tuple[Any | array, Any | array]:
         ...
     @property
-    def state(self):  # -> tuple[Any | array | None, Any | array | None]:
-        ...
+    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
     @state.setter
     def state(self, v):  # -> None:
         ...
     def is_trimmable(self):  # -> Literal[True]:
         ...
-    def trim(self, n):  # -> int:
-        ...
+    def trim(self, n: int) -> int: ...
     def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
         ...
 
@@ -132,10 +131,7 @@ class QuantizedKVCache(_BaseCache):
     def update_and_fetch(self, keys, values):  # -> Any:
         ...
     @property
-    def state(
-        self,
-    ):  # -> tuple[Any | tuple[array, array, array] | None, Any | tuple[array, array, array] | None] | Any:
-        ...
+    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
     @state.setter
     def state(self, v):  # -> None:
         ...
@@ -147,8 +143,7 @@ class QuantizedKVCache(_BaseCache):
         ...
     def is_trimmable(self):  # -> Literal[True]:
         ...
-    def trim(self, n):  # -> int:
-        ...
+    def trim(self, n: int) -> int: ...
     def make_mask(self, *args, **kwargs):  # -> array | Literal['causal'] | None:
         ...
 
@@ -160,13 +155,12 @@ class KVCache(_BaseCache):
     @property
     def state(
         self,
-    ) -> tuple[array, array]: ...
+    ) -> tuple[mx.array | None, mx.array | None]: ...
     @state.setter
     def state(self, v) -> None: ...
     def is_trimmable(self):  # -> Literal[True]:
         ...
-    def trim(self, n):  # -> int:
-        ...
+    def trim(self, n: int) -> int: ...
     def to_quantized(
         self, group_size: int = ..., bits: int = ...
     ) -> QuantizedKVCache: ...
@@ -183,8 +177,7 @@ class RotatingKVCache(_BaseCache):
     @property
     def state(
         self,
-    ):  # -> tuple[Any | array, Any | array] | tuple[Any | array | None, Any | array | None]:
-        ...
+    ) -> tuple[mx.array | None, mx.array | None]: ...
     @state.setter
     def state(self, v):  # -> None:
         ...
@@ -196,8 +189,7 @@ class RotatingKVCache(_BaseCache):
         ...
     def is_trimmable(self):  # -> bool:
         ...
-    def trim(self, n):  # -> int:
-        ...
+    def trim(self, n: int) -> int: ...
     def to_quantized(
         self, group_size: int = ..., bits: int = ...
     ) -> QuantizedKVCache: ...
@@ -212,8 +204,7 @@ class ArraysCache(_BaseCache):
         ...
     def __getitem__(self, idx): ...
     @property
-    def state(self):  # -> list[Any | array] | list[array]:
-        ...
+    def state(self) -> tuple[mx.array | None, mx.array | None]: ...
     @state.setter
     def state(self, v):  # -> None:
         ...
@@ -239,8 +230,7 @@ class ChunkedKVCache(KVCache):
         ...
     def update_and_fetch(self, keys, values):  # -> tuple[array, array]:
         ...
-    def trim(self, n):  # -> int:
-        ...
+    def trim(self, n: int) -> int: ...
     @property
     def meta_state(self):  # -> tuple[str, ...]:
         ...
@@ -253,10 +243,9 @@ class CacheList(_BaseCache):
     def __getitem__(self, idx): ...
     def is_trimmable(self):  # -> bool:
         ...
-    def trim(self, n): ...
+    def trim(self, n: int) -> int: ...
     @property
-    def state(self):  # -> list[Any]:
-        ...
+    def state(self) -> list[tuple[mx.array | None, mx.array | None]]: ...
     @state.setter
     def state(self, v):  # -> None:
         ...

src/exo/shared/types/mlx.py

@@ -2,6 +2,8 @@
 
 from collections.abc import Sequence
 
+from mlx import core as mx
+from mlx import nn as nn
 from mlx_lm.models.cache import (
     ArraysCache,
     CacheList,
@@ -14,3 +16,16 @@ from mlx_lm.models.cache import (
 KVCacheType = Sequence[
     KVCache | RotatingKVCache | QuantizedKVCache | ArraysCache | CacheList
 ]
+
+
+# Model is a wrapper function to fix the fact that mlx is not strongly typed in the same way that EXO is.
+# For example - MLX has no guarantee of the interface that nn.Module will expose. But we need a guarantee that it has a __call__() function
+class Model(nn.Module):
+    layers: list[nn.Module]
+
+    def __call__(
+        self,
+        x: mx.array,
+        cache: KVCacheType | None,
+        input_embeddings: mx.array | None = None,
+    ) -> mx.array: ...

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

@@ -1,17 +0,0 @@
-import mlx.core as mx
-import mlx.nn as nn
-from mlx_lm.models.cache import KVCache
-
-# These are wrapper functions to fix the fact that mlx is not strongly typed in the same way that EXO is.
-# For example - MLX has no guarantee of the interface that nn.Module will expose. But we need a guarantee that it has a __call__() function
-
-
-class Model(nn.Module):
-    layers: list[nn.Module]
-
-    def __call__(
-        self,
-        x: mx.array,
-        cache: list[KVCache] | None,
-        input_embeddings: mx.array | None = None,
-    ) -> mx.array: ...

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

@@ -49,6 +49,21 @@ if TYPE_CHECKING:
 TimeoutCallback = Callable[[], None]
 
 
+_pending_prefill_sends: list[tuple[mx.array, int, mx.distributed.Group]] = []
+
+
+def flush_prefill_sends() -> None:
+    for output, dst, group in _pending_prefill_sends:
+        sent = mx.distributed.send(output, dst, group=group)
+        mx.async_eval(sent)
+    _pending_prefill_sends.clear()
+
+
+def clear_prefill_sends() -> None:
+    # Discard pending sends (e.g. on cancellation).
+    _pending_prefill_sends.clear()
+
+
 def eval_with_timeout(
     mlx_item: Any,  # pyright: ignore[reportAny]
     timeout_seconds: float = 60.0,
@@ -159,18 +174,21 @@ class PipelineLastLayer(CustomMlxLayer):
         output: mx.array = self.original_layer(x, *args, **kwargs)
 
         if self.r != self.s - 1:
-            output = mx.distributed.send(
-                output, (self.r + 1) % self.s, group=self.group
-            )
-            if cache is not None:
-                # CacheList (used by MLA models like DeepSeekV32, GLM MoE DSA)
-                # doesn't have .keys directly; access via first sub-cache.
-                _cache = cache[0] if hasattr(cache, "caches") else cache  # type: ignore
-                _cache.keys = mx.depends(_cache.keys, output)  # type: ignore
             if self.is_prefill:
                 mx.eval(output)
                 if cache is not None:
+                    _cache = cache[0] if hasattr(cache, "caches") else cache  # type: ignore
                     mx.eval(_cache.keys)  # type: ignore
+                _pending_prefill_sends.append(
+                    (output, (self.r + 1) % self.s, self.group)
+                )
+            else:
+                output = mx.distributed.send(
+                    output, (self.r + 1) % self.s, group=self.group
+                )
+                if cache is not None:
+                    _cache = cache[0] if hasattr(cache, "caches") else cache  # type: ignore
+                    _cache.keys = mx.depends(_cache.keys, output)  # type: ignore
 
         if not self.is_prefill:
             output = mx.distributed.all_gather(output, group=self.group)[

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

@@ -13,8 +13,7 @@ from mlx_lm.models.cache import (
 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.shared.types.mlx import KVCacheType, Model
 from exo.worker.engines.mlx.constants import CACHE_GROUP_SIZE, KV_CACHE_BITS
 from exo.worker.runner.bootstrap import logger
 
@@ -254,9 +253,9 @@ def trim_cache(
             if snapshot is not None and snapshot.states[i] is not None:
                 cache[i] = deepcopy(snapshot.states[i])  # type: ignore
             else:
-                c.state = [None] * len(c.state)  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]
+                c.state = [None] * len(c.state)
         else:
-            c.trim(num_tokens)  # pyright: ignore[reportUnknownMemberType]
+            c.trim(num_tokens)
 
 
 def encode_prompt(tokenizer: TokenizerWrapper, prompt: str) -> mx.array:

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

@@ -1,9 +1,13 @@
+import functools
 import time
 from copy import deepcopy
 from typing import Callable, Generator, cast, get_args
 
 import mlx.core as mx
-from mlx_lm.generate import stream_generate
+from mlx_lm.generate import (
+    maybe_quantize_kv_cache,
+    stream_generate,
+)
 from mlx_lm.models.cache import ArraysCache, RotatingKVCache
 from mlx_lm.sample_utils import make_sampler
 from mlx_lm.tokenizer_utils import TokenizerWrapper
@@ -18,13 +22,18 @@ from exo.shared.types.api import (
 )
 from exo.shared.types.common import ModelId
 from exo.shared.types.memory import Memory
-from exo.shared.types.mlx import KVCacheType
+from exo.shared.types.mlx import KVCacheType, Model
 from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
 from exo.shared.types.worker.runner_response import (
     GenerationResponse,
 )
-from exo.worker.engines.mlx import Model
-from exo.worker.engines.mlx.auto_parallel import set_pipeline_prefill
+from exo.worker.engines.mlx.auto_parallel import (
+    PipelineFirstLayer,
+    PipelineLastLayer,
+    clear_prefill_sends,
+    flush_prefill_sends,
+    set_pipeline_prefill,
+)
 from exo.worker.engines.mlx.cache import (
     CacheSnapshot,
     KVPrefixCache,
@@ -55,6 +64,127 @@ class PrefillCancelled(BaseException):
     """Raised when prefill is cancelled via the progress callback."""
 
 
+def _has_pipeline_communication_layer(model: Model):
+    for layer in model.layers:
+        if isinstance(layer, (PipelineFirstLayer, PipelineLastLayer)):
+            return True
+    return False
+
+
+def pipeline_parallel_prefill(
+    model: Model,
+    prompt: mx.array,
+    prompt_cache: KVCacheType,
+    prefill_step_size: int,
+    kv_group_size: int | None,
+    kv_bits: int | None,
+    prompt_progress_callback: Callable[[int, int], None],
+    distributed_prompt_progress_callback: Callable[[], None] | None,
+    group: mx.distributed.Group,
+) -> None:
+    """Prefill the KV cache for pipeline parallel with overlapping stages.
+
+    Each rank processes the full prompt through its real cache, offset by leading
+    and trailing dummy iterations.
+
+    Total iterations per rank = N_real_chunks + world_size - 1:
+      - rank r leading dummies  (skip_pipeline_io, throwaway cache)
+      - N_real_chunks real      (pipeline IO active, real cache)
+      - (world_size-1-r) trailing dummies (skip_pipeline_io, throwaway cache)
+
+    e.g.
+    Timeline (2 ranks, 3 chunks of 10240 tokens @ step=4096):
+        iter 0: R0 real[0:4096]     R1 dummy
+        iter 1: R0 real[4096:8192]  R1 real[0:4096]
+        iter 2: R0 real[8192:10240] R1 real[4096:8192]
+        iter 3: R0 dummy            R1 real[8192:10240]
+
+    This function is designed to match mlx_lm's stream_generate exactly in terms of side effects.
+    """
+    quantize_cache_fn: Callable[..., None] = functools.partial(
+        maybe_quantize_kv_cache,
+        quantized_kv_start=0,
+        kv_group_size=kv_group_size,
+        kv_bits=kv_bits,
+    )
+
+    _prompt_cache: KVCacheType = prompt_cache
+    rank = group.rank()
+    world_size = group.size()
+
+    # Build list of real prompt chunk sizes
+    total = len(prompt)
+    real_chunk_sizes: list[int] = []
+    remaining = total - 1
+    while remaining:
+        n = min(prefill_step_size, remaining)
+        real_chunk_sizes.append(n)
+        remaining -= n
+    n_real = len(real_chunk_sizes)
+
+    # Each rank does: [rank leading dummies] [N real chunks] [world_size-1-rank trailing dummies]
+    n_leading = rank
+    n_trailing = world_size - 1 - rank
+    n_total = n_leading + n_real + n_trailing
+
+    t_start = time.perf_counter()
+    processed = 0
+    logger.info(
+        f"[R{rank}] Pipeline prefill: {n_real} real + {n_leading} leading + {n_trailing} trailing = {n_total} iterations"
+    )
+    clear_prefill_sends()
+
+    # Initial callback matching generate_step
+    prompt_progress_callback(0, total)
+
+    try:
+        with mx.stream(generation_stream):
+            for _ in range(n_leading):
+                if distributed_prompt_progress_callback is not None:
+                    distributed_prompt_progress_callback()
+
+            for i in range(n_real):
+                chunk_size = real_chunk_sizes[i]
+                model(
+                    prompt[processed : processed + chunk_size][None],
+                    cache=_prompt_cache,
+                )
+                quantize_cache_fn(_prompt_cache)
+                processed += chunk_size
+
+                if distributed_prompt_progress_callback is not None:
+                    distributed_prompt_progress_callback()
+
+                flush_prefill_sends()
+
+                prompt_progress_callback(processed, total)
+
+            for _ in range(n_leading):
+                if distributed_prompt_progress_callback is not None:
+                    distributed_prompt_progress_callback()
+
+    finally:
+        clear_prefill_sends()
+
+    # Post-loop: process remaining 1 token + add +1 entry to match stream_generate.
+    for _ in range(2):
+        with mx.stream(generation_stream):
+            model(prompt[-1:][None], cache=_prompt_cache)
+            quantize_cache_fn(_prompt_cache)
+        flush_prefill_sends()
+
+    assert _prompt_cache is not None
+    mx.eval([c.state for c in _prompt_cache])  # type: ignore
+
+    # Final callback matching generate_step
+    prompt_progress_callback(total, total)
+
+    logger.info(
+        f"[R{rank}] Prefill: {n_real} real + {n_leading}+{n_trailing} dummy iterations, "
+        f"Processed {processed} tokens in {(time.perf_counter() - t_start) * 1000:.1f}ms"
+    )
+
+
 def prefill(
     model: Model,
     tokenizer: TokenizerWrapper,
@@ -63,6 +193,7 @@ def prefill(
     cache: KVCacheType,
     group: mx.distributed.Group | None,
     on_prefill_progress: Callable[[int, int], None] | None,
+    distributed_prompt_progress_callback: Callable[[], None] | None,
 ) -> tuple[float, int, list[CacheSnapshot]]:
     """Prefill the KV cache with prompt tokens.
 
@@ -94,27 +225,48 @@ def prefill(
         if on_prefill_progress is not None:
             on_prefill_progress(processed, total)
 
+    def combined_progress_callback(processed: int, total: int) -> None:
+        if distributed_prompt_progress_callback is not None:
+            distributed_prompt_progress_callback()
+        progress_callback(processed, total)
+
     set_pipeline_prefill(model, is_prefill=True)
 
     mx_barrier(group)
     logger.info("Starting prefill")
 
-    # Use max_tokens=1 because max_tokens=0 does not work.
-    # We just throw away the generated token - we only care about filling the cache
+    is_pipeline = _has_pipeline_communication_layer(model)
+
     try:
-        for _ in stream_generate(
-            model=model,
-            tokenizer=tokenizer,
-            prompt=prompt_tokens,
-            max_tokens=1,
-            sampler=sampler,
-            prompt_cache=cache,
-            prefill_step_size=4096,
-            kv_group_size=KV_GROUP_SIZE,
-            kv_bits=KV_BITS,
-            prompt_progress_callback=progress_callback,
-        ):
-            break  # Stop after first iteration - cache is now filled
+        if is_pipeline:
+            assert group is not None, "Pipeline prefill requires a distributed group"
+            pipeline_parallel_prefill(
+                model=model,
+                prompt=prompt_tokens,
+                prompt_cache=cache,
+                prefill_step_size=4096,
+                kv_group_size=KV_GROUP_SIZE,
+                kv_bits=KV_BITS,
+                prompt_progress_callback=progress_callback,
+                distributed_prompt_progress_callback=distributed_prompt_progress_callback,
+                group=group,
+            )
+        else:
+            # Use max_tokens=1 because max_tokens=0 does not work.
+            # We just throw away the generated token - we only care about filling the cache
+            for _ in stream_generate(
+                model=model,
+                tokenizer=tokenizer,
+                prompt=prompt_tokens,
+                max_tokens=1,
+                sampler=sampler,
+                prompt_cache=cache,
+                prefill_step_size=4096,
+                kv_group_size=KV_GROUP_SIZE,
+                kv_bits=KV_BITS,
+                prompt_progress_callback=combined_progress_callback,
+            ):
+                break  # Stop after first iteration - cache is now filled
     except PrefillCancelled:
         set_pipeline_prefill(model, is_prefill=False)
         raise
@@ -131,7 +283,7 @@ def prefill(
                 cache[i] = deepcopy(pre_gen.states[i])  # type: ignore
         else:
             assert not isinstance(c, (ArraysCache, RotatingKVCache))
-            c.trim(2)  # pyright: ignore[reportUnknownMemberType]
+            c.trim(2)
 
     elapsed = time.perf_counter() - start_time
     tokens_per_sec = num_tokens / elapsed if elapsed > 0 else 0.0
@@ -271,6 +423,7 @@ def mlx_generate(
     kv_prefix_cache: KVPrefixCache | None,
     group: mx.distributed.Group | None,
     on_prefill_progress: Callable[[int, int], None] | None = None,
+    distributed_prompt_progress_callback: Callable[[], None] | None = None,
 ) -> Generator[GenerationResponse]:
     # Ensure that generation stats only contains peak memory for this generation
     mx.reset_peak_memory()
@@ -332,6 +485,7 @@ def mlx_generate(
         caches,
         group,
         on_prefill_progress,
+        distributed_prompt_progress_callback,
     )
     cache_snapshots: list[CacheSnapshot] | None = ssm_snapshots_list or None
 

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

@@ -41,6 +41,7 @@ from pydantic import RootModel
 from exo.download.download_utils import build_model_path
 from exo.shared.types.common import Host
 from exo.shared.types.memory import Memory
+from exo.shared.types.mlx import Model
 from exo.shared.types.text_generation import TextGenerationTaskParams
 from exo.shared.types.worker.instances import (
     BoundInstance,
@@ -53,7 +54,6 @@ from exo.shared.types.worker.shards import (
     ShardMetadata,
     TensorShardMetadata,
 )
-from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.auto_parallel import (
     TimeoutCallback,
     eval_with_timeout,

src/exo/worker/runner/llm_inference/runner.py

@@ -31,6 +31,7 @@ from exo.shared.types.events import (
     TaskAcknowledged,
     TaskStatusUpdated,
 )
+from exo.shared.types.mlx import Model
 from exo.shared.types.tasks import (
     ConnectToGroup,
     LoadModel,
@@ -63,7 +64,6 @@ from exo.shared.types.worker.runners import (
     RunnerWarmingUp,
 )
 from exo.utils.channels import MpReceiver, MpSender
-from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.cache import KVPrefixCache
 from exo.worker.engines.mlx.generator.generate import (
     PrefillCancelled,
@@ -255,8 +255,6 @@ def main(
                     def on_prefill_progress(
                         processed: int,
                         total: int,
-                        _task_id: TaskId = task.task_id,
-                        _group: mx.distributed.Group | None = group,
                     ) -> None:
                         if device_rank == 0:
                             event_sender.send(
@@ -269,6 +267,11 @@ def main(
                                     ),
                                 )
                             )
+
+                    def distributed_prompt_progress_callback(
+                        _task_id: TaskId = task.task_id,
+                        _group: mx.distributed.Group | None = group,
+                    ) -> None:
                         cancelled_tasks.update(cancel_receiver.collect())
                         want_to_cancel = (_task_id in cancelled_tasks) or (
                             TaskId("CANCEL_CURRENT_TASK") in cancelled_tasks
@@ -290,6 +293,7 @@ def main(
                             prompt=prompt,
                             kv_prefix_cache=kv_prefix_cache,
                             on_prefill_progress=on_prefill_progress,
+                            distributed_prompt_progress_callback=distributed_prompt_progress_callback,
                             group=group,
                         )
 

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

@@ -14,9 +14,9 @@ from exo.shared.constants import EXO_MODELS_DIR
 from exo.shared.models.model_cards import ModelCard, ModelTask
 from exo.shared.types.common import ModelId
 from exo.shared.types.memory import Memory
+from exo.shared.types.mlx import Model
 from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
 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 apply_chat_template, shard_and_load
 

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

@@ -9,8 +9,8 @@ from mlx_lm.models.cache import KVCache
 from mlx_lm.sample_utils import make_sampler
 
 from exo.shared.types.common import ModelId
+from exo.shared.types.mlx import Model
 from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
-from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.cache import (
     KVPrefixCache,
     cache_length,

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

@@ -0,0 +1,500 @@
+# type: ignore
+"""Test that pipeline prefill callbacks and output exactly match stream_generate.
+
+Spins up a single-device (non-pipeline) run and a distributed pipeline run,
+then verifies that the prompt_progress_callback sequences are identical
+and that generated text matches.
+"""
+
+import json
+import multiprocessing as mp
+import os
+import tempfile
+import traceback
+from typing import Any, cast
+
+import pytest
+
+from exo.shared.constants import EXO_MODELS_DIR
+from exo.shared.models.model_cards import ModelCard, ModelTask
+from exo.shared.types.common import ModelId
+from exo.shared.types.memory import Memory
+from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
+
+MODEL_ID = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+MODEL_PATH = EXO_MODELS_DIR / "mlx-community--gpt-oss-20b-MXFP4-Q8"
+TOTAL_LAYERS = 24
+MAX_TOKENS = 10
+SEED = 42
+TEMPERATURE = 0.0
+
+
+def _model_card() -> ModelCard:
+    return ModelCard(
+        model_id=ModelId(MODEL_ID),
+        storage_size=Memory.from_gb(12),
+        n_layers=TOTAL_LAYERS,
+        hidden_size=2880,
+        supports_tensor=False,
+        tasks=[ModelTask.TextGeneration],
+    )
+
+
+def _build_prompt(tokenizer: Any, prompt_tokens: int) -> tuple[str, Any]:
+    """Build a prompt with the given number of user-content tokens, return (chat_prompt, task)."""
+    from exo.worker.engines.mlx.utils_mlx import apply_chat_template
+
+    base_text = "The quick brown fox jumps over the lazy dog. "
+    base_toks = tokenizer.encode(base_text)
+    repeats = (prompt_tokens // len(base_toks)) + 2
+    long_text = base_text * repeats
+    tokens = tokenizer.encode(long_text)[:prompt_tokens]
+    prompt_text = tokenizer.decode(tokens)
+
+    task = TextGenerationTaskParams(
+        model=MODEL_ID,
+        input=[InputMessage(role="user", content=prompt_text)],
+        max_output_tokens=MAX_TOKENS,
+        temperature=TEMPERATURE,
+        seed=SEED,
+    )
+
+    prompt = apply_chat_template(tokenizer, task)
+    return prompt, task
+
+
+# ---------------------------------------------------------------------------
+# Single-device process: uses stream_generate path (no pipeline layers)
+# ---------------------------------------------------------------------------
+def _run_single_device(
+    prompt_tokens: int,
+    result_queue: Any,
+) -> None:
+    """Load full model without pipeline sharding, run mlx_generate, record callbacks."""
+    try:
+        import mlx.core as mx
+        from mlx_lm.utils import load_model
+
+        from exo.shared.types.worker.shards import PipelineShardMetadata
+        from exo.worker.engines.mlx.cache import encode_prompt
+        from exo.worker.engines.mlx.generator.generate import mlx_generate
+        from exo.worker.engines.mlx.utils_mlx import (
+            build_model_path,
+            get_tokenizer,
+        )
+
+        model_path = build_model_path(ModelId(MODEL_ID))
+        model, _ = load_model(model_path, lazy=True, strict=False)
+        mx.eval(model)
+
+        # Use PipelineShardMetadata just for get_tokenizer (needs model_card), but
+        # do NOT apply pipeline sharding — the model keeps all layers unwrapped.
+        dummy_meta = PipelineShardMetadata(
+            model_card=_model_card(),
+            device_rank=0,
+            world_size=1,
+            start_layer=0,
+            end_layer=TOTAL_LAYERS,
+            n_layers=TOTAL_LAYERS,
+        )
+        tokenizer = get_tokenizer(model_path, dummy_meta)
+
+        prompt, task = _build_prompt(tokenizer, prompt_tokens)
+
+        callbacks: list[tuple[int, int]] = []
+
+        def on_progress(processed: int, total: int) -> None:
+            callbacks.append((processed, total))
+
+        generated_text = ""
+        for response in mlx_generate(
+            model=model,
+            tokenizer=tokenizer,
+            task=task,
+            prompt=prompt,
+            kv_prefix_cache=None,
+            group=None,
+            on_prefill_progress=on_progress,
+        ):
+            generated_text += response.text
+            if response.finish_reason is not None:
+                break
+
+        # Also record the token count that prefill() received (prompt_tokens[:-1])
+        all_tokens = encode_prompt(tokenizer, prompt)
+        prefill_token_count = len(all_tokens) - 1
+
+        result_queue.put(
+            (
+                True,
+                {
+                    "callbacks": callbacks,
+                    "text": generated_text,
+                    "prefill_token_count": prefill_token_count,
+                },
+            )
+        )
+
+    except Exception as e:
+        result_queue.put((False, f"{e}\n{traceback.format_exc()}"))
+
+
+# ---------------------------------------------------------------------------
+# Pipeline device process: uses _pipeline_prefill_cache path
+# ---------------------------------------------------------------------------
+def _run_pipeline_device(
+    rank: int,
+    world_size: int,
+    hostfile_path: str,
+    layer_splits: list[tuple[int, int]],
+    prompt_tokens: int,
+    result_queue: Any,
+) -> None:
+    """Load model with pipeline sharding, run mlx_generate, record callbacks."""
+    os.environ["MLX_HOSTFILE"] = hostfile_path
+    os.environ["MLX_RANK"] = str(rank)
+
+    try:
+        import mlx.core as mx
+
+        from exo.shared.types.worker.shards import PipelineShardMetadata
+        from exo.worker.engines.mlx.cache import encode_prompt
+        from exo.worker.engines.mlx.generator.generate import mlx_generate
+        from exo.worker.engines.mlx.utils_mlx import shard_and_load
+
+        group = mx.distributed.init(backend="ring", strict=True)
+
+        start_layer, end_layer = layer_splits[rank]
+        shard_meta = PipelineShardMetadata(
+            model_card=_model_card(),
+            device_rank=rank,
+            world_size=world_size,
+            start_layer=start_layer,
+            end_layer=end_layer,
+            n_layers=TOTAL_LAYERS,
+        )
+
+        model, tokenizer = shard_and_load(shard_meta, group)
+        model = cast(Any, model)
+
+        prompt, task = _build_prompt(tokenizer, prompt_tokens)
+
+        callbacks: list[tuple[int, int]] = []
+
+        def on_progress(processed: int, total: int) -> None:
+            callbacks.append((processed, total))
+
+        def distributed_prompt_progress_callback(_group: Any = group) -> None:
+            from exo.worker.engines.mlx.utils_mlx import mx_any
+
+            mx_any(False, _group)
+
+        generated_text = ""
+        for response in mlx_generate(
+            model=model,
+            tokenizer=tokenizer,
+            task=task,
+            prompt=prompt,
+            kv_prefix_cache=None,
+            group=group,
+            on_prefill_progress=on_progress,
+            distributed_prompt_progress_callback=distributed_prompt_progress_callback,
+        ):
+            generated_text += response.text
+            if response.finish_reason is not None:
+                break
+
+        all_tokens = encode_prompt(tokenizer, prompt)
+        prefill_token_count = len(all_tokens) - 1
+
+        result_queue.put(
+            (
+                rank,
+                True,
+                {
+                    "callbacks": callbacks,
+                    "text": generated_text,
+                    "prefill_token_count": prefill_token_count,
+                },
+            )
+        )
+
+    except Exception as e:
+        result_queue.put((rank, False, f"{e}\n{traceback.format_exc()}"))
+
+
+# ---------------------------------------------------------------------------
+# Test helpers
+# ---------------------------------------------------------------------------
+def _create_hostfile(world_size: int, base_port: int) -> str:
+    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)
+        return f.name
+
+
+def _run_single_device_test(prompt_tokens: int, timeout: int = 120) -> dict[str, Any]:
+    """Run single-device (stream_generate) prefill and return results."""
+    ctx = mp.get_context("spawn")
+    result_queue: Any = ctx.Queue()
+
+    p = ctx.Process(target=_run_single_device, args=(prompt_tokens, result_queue))
+    p.start()
+    p.join(timeout=timeout)
+
+    if p.is_alive():
+        p.terminate()
+        p.join(timeout=5)
+        pytest.fail("Single-device process timed out")
+
+    assert not result_queue.empty(), "Single-device process produced no result"
+    success, data = result_queue.get()
+    assert success, f"Single-device process failed:\n{data}"
+    return data
+
+
+def _run_pipeline_test(
+    layer_splits: list[tuple[int, int]],
+    prompt_tokens: int,
+    base_port: int,
+    timeout: int = 120,
+) -> dict[int, dict[str, Any]]:
+    """Run pipeline prefill across ranks and return per-rank results."""
+    world_size = len(layer_splits)
+    hostfile_path = _create_hostfile(world_size, base_port)
+    ctx = mp.get_context("spawn")
+    result_queue: Any = ctx.Queue()
+
+    try:
+        processes: list[Any] = []
+        for rank in range(world_size):
+            p = ctx.Process(
+                target=_run_pipeline_device,
+                args=(
+                    rank,
+                    world_size,
+                    hostfile_path,
+                    layer_splits,
+                    prompt_tokens,
+                    result_queue,
+                ),
+            )
+            p.start()
+            processes.append(p)
+
+        for p in processes:
+            p.join(timeout=timeout)
+
+        timed_out = any(p.is_alive() for p in processes)
+        for p in processes:
+            if p.is_alive():
+                p.terminate()
+                p.join(timeout=5)
+
+        assert not timed_out, "Pipeline processes timed out"
+
+        results: dict[int, dict[str, Any]] = {}
+        while not result_queue.empty():
+            rank, success, data = result_queue.get()
+            assert success, f"Pipeline rank {rank} failed:\n{data}"
+            results[rank] = data
+
+        assert len(results) == world_size, (
+            f"Expected {world_size} results, got {len(results)}: missing ranks {set(range(world_size)) - results.keys()}"
+        )
+        return results
+
+    finally:
+        os.unlink(hostfile_path)
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+pytestmark = [
+    pytest.mark.slow,
+    pytest.mark.skipif(
+        not MODEL_PATH.exists(),
+        reason=f"GPT-OSS model not found at {MODEL_PATH}",
+    ),
+]
+
+LAYER_SPLITS_4WAY: list[tuple[int, int]] = [(0, 6), (6, 12), (12, 18), (18, 24)]
+LAYER_SPLITS_2WAY: list[tuple[int, int]] = [(0, 12), (12, 24)]
+
+
+class TestPipelineNoDeadlock:
+    """Pipeline prefill must not deadlock at any rank count or prompt length."""
+
+    @pytest.mark.parametrize(
+        "layer_splits,prompt_tokens",
+        [
+            (LAYER_SPLITS_2WAY, 128),
+            (LAYER_SPLITS_2WAY, 4096),
+            (LAYER_SPLITS_2WAY, 8192),
+            (LAYER_SPLITS_2WAY, 16384),
+            (LAYER_SPLITS_4WAY, 128),
+            (LAYER_SPLITS_4WAY, 4096),
+            (LAYER_SPLITS_4WAY, 8192),
+            (LAYER_SPLITS_4WAY, 16384),
+        ],
+        ids=[
+            "2rank_128tok",
+            "2rank_4096tok",
+            "2rank_8192tok",
+            "2rank_16384tok",
+            "4rank_128tok",
+            "4rank_4096tok",
+            "4rank_8192tok",
+            "4rank_16384tok",
+        ],
+    )
+    def test_no_deadlock(
+        self,
+        layer_splits: list[tuple[int, int]],
+        prompt_tokens: int,
+    ) -> None:
+        """Pipeline must complete without deadlock at various prompt lengths."""
+        pipeline_results = _run_pipeline_test(
+            layer_splits=layer_splits,
+            prompt_tokens=prompt_tokens,
+            base_port=29650,
+            timeout=60,
+        )
+        # If we get here, no deadlock. Verify all ranks produced output.
+        for rank, pipe_data in sorted(pipeline_results.items()):
+            assert pipe_data["text"], f"Rank {rank} produced no output text"
+
+
+class TestPipelinePrefillCallbacks:
+    """Verify that pipeline prefill callbacks exactly match stream_generate callbacks."""
+
+    @pytest.mark.parametrize(
+        "prompt_tokens",
+        [50, 500, 5000],
+        ids=["short_50", "medium_500", "long_5000"],
+    )
+    def test_callbacks_match(self, prompt_tokens: int) -> None:
+        """Pipeline and stream_generate must produce identical callback sequences."""
+        # Run single-device (stream_generate path)
+        single = _run_single_device_test(prompt_tokens, timeout=180)
+
+        # Run 4-rank pipeline
+        pipeline_results = _run_pipeline_test(
+            layer_splits=LAYER_SPLITS_4WAY,
+            prompt_tokens=prompt_tokens,
+            base_port=29700,
+            timeout=180,
+        )
+
+        single_callbacks = single["callbacks"]
+        prefill_count = single["prefill_token_count"]
+
+        # Every rank must produce the same callback sequence as stream_generate
+        for rank, pipe_data in sorted(pipeline_results.items()):
+            pipe_callbacks = pipe_data["callbacks"]
+
+            assert pipe_data["prefill_token_count"] == prefill_count, (
+                f"Rank {rank} prefill token count mismatch: "
+                f"{pipe_data['prefill_token_count']} vs {prefill_count}"
+            )
+
+            assert pipe_callbacks == single_callbacks, (
+                f"Rank {rank} callback mismatch for {prompt_tokens} prompt tokens "
+                f"(prefill M={prefill_count}):\n"
+                f"  stream_generate ({len(single_callbacks)} callbacks): {single_callbacks}\n"
+                f"  pipeline R{rank} ({len(pipe_callbacks)} callbacks): {pipe_callbacks}"
+            )
+
+    @pytest.mark.parametrize(
+        "prompt_tokens",
+        [50, 500],
+        ids=["short_50", "medium_500"],
+    )
+    def test_output_matches(self, prompt_tokens: int) -> None:
+        """Pipeline-generated text must match single-device output."""
+        single = _run_single_device_test(prompt_tokens, timeout=180)
+
+        pipeline_results = _run_pipeline_test(
+            layer_splits=LAYER_SPLITS_4WAY,
+            prompt_tokens=prompt_tokens,
+            base_port=29800,
+            timeout=180,
+        )
+
+        single_text = single["text"]
+
+        # The last rank produces the final logits, so its output should match.
+        # Due to SDPA tiling non-determinism, allow minor differences in text.
+        last_rank = max(pipeline_results.keys())
+        pipe_text = pipeline_results[last_rank]["text"]
+
+        # For deterministic sampling (temp=0.0), outputs should match exactly
+        # or be very close. Log both for debugging even if they match.
+        if single_text != pipe_text:
+            # Find first divergence point
+            min_len = min(len(single_text), len(pipe_text))
+            diverge_idx = next(
+                (i for i in range(min_len) if single_text[i] != pipe_text[i]),
+                min_len,
+            )
+            pytest.fail(
+                f"Output text diverged at character {diverge_idx} for {prompt_tokens} prompt tokens:\n"
+                f"  single-device: {single_text!r}\n"
+                f"  pipeline R{last_rank}: {pipe_text!r}"
+            )
+
+
+class TestPipelineCallbacksStructure:
+    """Verify structural properties of callbacks independent of model output."""
+
+    def test_callback_structure_matches_generate_step(self) -> None:
+        """Verify callbacks follow generate_step's pattern: (0,M), chunks up to M-1, (M,M)."""
+        prompt_tokens = 200
+        pipeline_results = _run_pipeline_test(
+            layer_splits=LAYER_SPLITS_4WAY,
+            prompt_tokens=prompt_tokens,
+            base_port=29900,
+            timeout=180,
+        )
+
+        for rank, pipe_data in sorted(pipeline_results.items()):
+            callbacks = pipe_data["callbacks"]
+            m = pipe_data["prefill_token_count"]
+            assert m > 0, f"Rank {rank}: prefill token count is 0"
+
+            assert callbacks[0] == (0, m), (
+                f"Rank {rank}: first callback should be (0, {m}), got {callbacks[0]}"
+            )
+
+            assert callbacks[-1] == (m, m), (
+                f"Rank {rank}: last callback should be ({m}, {m}), got {callbacks[-1]}"
+            )
+
+            if len(callbacks) > 2:
+                second_to_last = callbacks[-2]
+                assert second_to_last[0] < m, (
+                    f"Rank {rank}: second-to-last callback should report < {m}, "
+                    f"got {second_to_last}"
+                )
+
+            # All callbacks must have total == M
+            for i, (_, total) in enumerate(callbacks):
+                assert total == m, (
+                    f"Rank {rank}: callback {i} has total={total}, expected {m}"
+                )
+
+            # processed values must be non-decreasing
+            processed_vals = [p for p, _ in callbacks]
+            for i in range(1, len(processed_vals)):
+                assert processed_vals[i] >= processed_vals[i - 1], (
+                    f"Rank {rank}: callbacks not non-decreasing at index {i}: "
+                    f"{processed_vals}"
+                )
+
+            # No duplicate consecutive callbacks (pipeline dummies must not emit callbacks)
+            for i in range(1, len(callbacks)):
+                assert callbacks[i] != callbacks[i - 1], (
+                    f"Rank {rank}: duplicate consecutive callback at index {i}: "
+                    f"{callbacks[i]} (this suggests dummy iterations are emitting callbacks)"
+                )

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

@@ -15,8 +15,8 @@ from mlx.utils import tree_flatten, tree_unflatten
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 
 from exo.shared.types.common import ModelId
+from exo.shared.types.mlx import Model
 from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
-from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.cache import KVPrefixCache
 from exo.worker.engines.mlx.generator.generate import mlx_generate
 from exo.worker.engines.mlx.utils_mlx import (