[Apple Silicon] [MLX] MLX decode partial overlap scheduling for generation (async eval) (#22416)

Co-authored-by: R0CKSTAR <yeahdongcn@gmail.com>
Co-authored-by: Alex Nails <alex.nails@radixark.ai>

docs/platforms/apple_metal.md

@@ -1,6 +1,6 @@
-# Apple Silicon with Metal
+# Apple Silicon with Metal (MLX)
 
-This document describes how run SGLang on Apple Silicon using [Metal](https://developer.apple.com/metal/). If you encounter issues or have questions, please [open an issue](https://github.com/sgl-project/sglang/issues).
+This document describes how run SGLang on Apple Silicon using [Metal (MLX)](https://opensource.apple.com/projects/mlx/). If you encounter issues or have questions, please [open an issue](https://github.com/sgl-project/sglang/issues).
 
 ## Install SGLang
 
@@ -18,3 +18,57 @@ pip install --upgrade pip
 rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
 uv pip install -e "python[all_mps]"
 ```
+
+## Launch of the Serving Engine
+
+Launch the server with:
+
+```bash
+SGLANG_USE_MLX=1 python -m sglang.launch_server \
+  --model <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --host 0.0.0.0
+```
+
+**Key Parameters Explained:**
+
+1. `SGLANG_USE_MLX=1` - Enables the use of MLX as the SGLang runtime backend (if disabled, SGLang will fall back to `torch.mps`, which has less support)
+2. `--disable-cuda-graph` - Disables usage of CUDA graph, which is not relevant for Apple Metal.
+3. `--disable-overlap-schedule` - Disables overlap scheduling (enabled/not present by default) achieved using MLX's `async_eval()`
+
+
+## Benchmarking with Requests
+
+`sglang.benchmark_one_batch` calls the synchronous prefill/decode methods directly without going through the scheduler and the overlap code path.
+
+`sglang.benchmark_offline_throughput` can toggle overlap scheduling as it uses the scheduler and the overlap code path by using the flag `--disable-overlap-schedule`.
+
+### Throughput Testing
+
+Basic synchronous one batch throughput:
+```bash
+SGLANG_USE_MLX=1 python -m sglang.bench_one_batch \
+  --model-path <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --tp-size 1 \
+  --batch-size 1 \
+  --input-len 60 \
+  --output-len 10
+```
+
+Synchronous offline throughput:
+```bash
+SGLANG_USE_MLX=1 python -m sglang.bench_offline_throughput \
+  --model-path <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --num-prompts 1 \
+  --disable-overlap-schedule
+```
+
+Asynchronous offline throughput:
+```bash
+SGLANG_USE_MLX=1 python -m sglang.bench_offline_throughput \
+  --model-path <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --num-prompts 1
+```

docs_new/docs/hardware-platforms/apple_metal.mdx

@@ -4,7 +4,7 @@ metatags:
     description: "Run SGLang on Apple Silicon using the Metal backend."
 ---
 
-This document describes how run SGLang on Apple Silicon using [Metal](https://developer.apple.com/metal/). If you encounter issues or have questions, please [open an issue](https://github.com/sgl-project/sglang/issues).
+This document describes how run SGLang on Apple Silicon using [Metal (MLX)](https://opensource.apple.com/projects/mlx/). If you encounter issues or have questions, please [open an issue](https://github.com/sgl-project/sglang/issues).
 
 ## Install SGLang
 
@@ -22,3 +22,57 @@ pip install --upgrade pip
 rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
 uv pip install -e "python[all_mps]"
 ```
+
+## Launch of the Serving Engine
+
+Launch the server with:
+
+```bash
+SGLANG_USE_MLX=1 python -m sglang.launch_server \
+  --model <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --host 0.0.0.0
+```
+
+**Key Parameters Explained:**
+
+1. `SGLANG_USE_MLX=1` - Enables the use of MLX as the SGLang runtime backend (if disabled, SGLang will fall back to `torch.mps`, which has less support)
+2. `--disable-cuda-graph` - Disables usage of CUDA graph, which is not relevant for Apple Metal.
+3. `--disable-overlap-schedule` - Disables overlap scheduling (enabled/not present by default) achieved using MLX's `async_eval()`
+
+
+## Benchmarking with Requests
+
+`sglang.benchmark_one_batch` calls the synchronous prefill/decode methods directly without going through the scheduler and the overlap code path.
+
+`sglang.benchmark_offline_throughput` can toggle overlap scheduling as it uses the scheduler and the overlap code path by using the flag `--disable-overlap-schedule`.
+
+### Throughput Testing
+
+Basic synchronous one batch throughput:
+```bash
+SGLANG_USE_MLX=1 python -m sglang.bench_one_batch \
+  --model-path <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --tp-size 1 \
+  --batch-size 1 \
+  --input-len 60 \
+  --output-len 10
+```
+
+Synchronous offline throughput:
+```bash
+SGLANG_USE_MLX=1 python -m sglang.bench_offline_throughput \
+  --model-path <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --num-prompts 1 \
+  --disable-overlap-schedule
+```
+
+Asynchronous offline throughput:
+```bash
+SGLANG_USE_MLX=1 python -m sglang.bench_offline_throughput \
+  --model-path <MODEL_ID_OR_PATH> \
+  --disable-cuda-graph \
+  --num-prompts 1
+```

python/sglang/srt/hardware_backend/mlx/kv_cache/attention_wrapper.py

@@ -14,6 +14,7 @@ from sglang.srt.hardware_backend.mlx.kv_cache.contiguous_cache import Contiguous
 _thread_local = threading.local()
 
 
+# TODO: Move from threading to multiprocessing or asyncio
 @dataclass
 class BatchedDecodeContext:
     """Context set before batched decode, read by attention wrappers."""

python/sglang/srt/hardware_backend/mlx/model_runner.py

@@ -5,10 +5,18 @@ scheduler (``TokenToKVPoolAllocator`` / ``RadixCache``).  This runner
 reads cached KV from ``MlxKVPool``, runs the forward pass, and writes
 new KV back.  Each request also keeps a ``ContiguousKVCache`` for
 decode-time attention.
+
+The module also exposes a lazy-eval (`*_start` / `*_finalize`) surface
+used by the MLX overlap scheduler to pipeline CPU bookkeeping with
+GPU execution.  The lazy API is a thin split of the synchronous API:
+``*_start`` builds the compute graph without materialising outputs,
+``*_finalize`` blocks on the lazy token(s) and commits per-request
+state.
 """
 
 import logging
 import time
+from dataclasses import dataclass
 
 import mlx.core as mx
 import psutil
@@ -32,6 +40,56 @@ from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class MlxPendingPrefill:
+    """Lazy prefill state, finalised after ``mx.eval``/``async_eval``.
+
+    ``cache`` is the per-layer list of ``ContiguousKVCache`` that will
+    become ``_req_caches[req_id]`` once the request is committed.  It
+    may have been converted from a transient ``PoolBackedCache`` list
+    already (so its ``state`` arrays are safe to hand to ``async_eval``).
+    """
+
+    lazy_token: mx.array
+    cache: list  # list[ContiguousKVCache]
+    req_id: str
+    full_token_ids: list[int]
+    req_pool_idx: int
+    synced_offset: int
+
+
+@dataclass
+class MlxPendingExtend:
+    """Lazy chunked-prefill-continuation state for an existing request.
+
+    Mirrors :meth:`MlxModelRunner.extend` split into launch/finalize
+    halves.  ``cache`` is the request's existing per-layer cache (not a
+    fresh one) so the graph writes extend onto the already-materialised
+    prefix.
+    """
+
+    lazy_token: mx.array
+    req_id: str
+    new_token_ids: list[int]
+    new_synced_offset: int
+
+
+@dataclass
+class MlxPendingDecode:
+    """Lazy decode state, finalised after ``mx.eval``/``async_eval``.
+
+    ``caches`` is a per-request list of per-layer ``ContiguousKVCache``
+    references (``caches[req_idx][layer_idx]``).  These are the same
+    objects the attention wrapper writes into during the forward pass,
+    so :meth:`decode_batch_start_chained` can launch the next step on
+    top of the same caches without materialising this step first.
+    """
+
+    lazy_tokens: mx.array
+    req_ids: list[str]
+    caches: list  # list[list[ContiguousKVCache]]
+
+
 class MlxModelRunner:
     """MLX model runner with radix-cache prefix sharing."""
 
@@ -48,6 +106,8 @@ class MlxModelRunner:
         self.model = None
         self.disable_radix_cache = disable_radix_cache
         self._mem_fraction_static = mem_fraction_static
+        # Counter used to trigger periodic mx.clear_cache() calls.
+        self._decode_step_ct: int = 0
 
         self._load_model()
 
@@ -104,6 +164,21 @@ class MlxModelRunner:
         """Evaluate token result and all cache buffers in one mx.eval call."""
         mx.eval(token_result, *[s for c in cache for s in c.state])
 
+    @staticmethod
+    def _cache_state_arrays(
+        pending_caches: list[list[ContiguousKVCache | PoolBackedCache]],
+    ) -> list[mx.array]:
+        """Flatten pending decode cache state list into an array list.
+
+        Safe to hand to ``mx.async_eval``.
+        """
+        return [
+            s
+            for cache_list in pending_caches
+            for cache in cache_list
+            for s in cache.state
+        ]
+
     def _load_model(self):
         """Load model using mlx_lm."""
         logger.info(f"Loading MLX model: {self.model_path}")
@@ -203,74 +278,16 @@ class MlxModelRunner:
         req_pool_idx: int,
     ) -> int:
         """Prefill a request.  Returns next_token_id."""
-        num_layers = self._num_layers
-        prefix_len = len(prefix_slot_ids)
-
-        if self.disable_radix_cache:
-            cache = self._acquire_cache()
-            input_ids = mx.array([new_token_ids], dtype=mx.int32)
-            model_output = self.model(input_ids, cache=cache)
-            logits = self._extract_logits(model_output)
-            next_token_mlx = mx.argmax(logits[:, -1, :], axis=-1)
-            self._eval_with_cache(next_token_mlx, cache)
-            next_token = int(next_token_mlx.item())
-
-            self._req_token_ids[req_id] = list(full_token_ids) + [next_token]
-            self._req_caches[req_id] = cache
-            self._req_pool_idx[req_id] = req_pool_idx
-            self._req_synced_offset[req_id] = 0
-            return next_token
-
-        assert self._kv_pool is not None
-
-        new_token_count = len(new_token_ids)
-
-        if prefix_len > 0:
-            slot_ids_mx = mx.array(prefix_slot_ids, dtype=mx.int32)
-            cache = [
-                PoolBackedCache(self._kv_pool, i, slot_ids_mx, prefix_len)
-                for i in range(num_layers)
-            ]
-        else:
-            cache = self._acquire_cache()
-
-        if new_token_count > 0:
-            extend_tokens = new_token_ids
-        else:
-            # Full cache hit — rerun last token to get next-token logits
-            extend_tokens = full_token_ids[-1:]
-            for c in cache:
-                c.offset = max(c.offset - 1, 0)
-
-        input_ids = mx.array([extend_tokens], dtype=mx.int32)
-        model_output = self.model(input_ids, cache=cache)
-        logits = self._extract_logits(model_output)
-
-        last_logits = logits[:, -1, :]
-        next_token_mlx = mx.argmax(last_logits, axis=-1)
-
-        # Convert PoolBackedCache → ContiguousKVCache for decode
-        if prefix_len > 0:
-            contiguous_cache = self._acquire_cache()
-            for layer_idx in range(num_layers):
-                pbc = cache[layer_idx]
-                contiguous_cache[layer_idx].update_and_fetch(
-                    pbc._full_keys, pbc._full_values
-                )
-            cache = contiguous_cache
-
-        self._eval_with_cache(next_token_mlx, cache)
-        next_token = int(next_token_mlx.item())
-
-        if new_slot_ids:
-            self._sync_new_kv_to_pool(cache, prefix_len, new_slot_ids)
-
-        self._req_token_ids[req_id] = list(full_token_ids) + [next_token]
-        self._req_caches[req_id] = cache
-        self._req_pool_idx[req_id] = req_pool_idx
-        self._req_synced_offset[req_id] = prefix_len + len(new_slot_ids)
-
-        return next_token
+        pending = self.prefill_start(
+            req_id=req_id,
+            new_token_ids=new_token_ids,
+            full_token_ids=full_token_ids,
+            prefix_slot_ids=prefix_slot_ids,
+            new_slot_ids=new_slot_ids,
+            req_pool_idx=req_pool_idx,
+        )
+        self._eval_with_cache(pending.lazy_token, pending.cache)
+        return self.prefill_finalize(pending)
 
     def extend(
         self,
@@ -279,32 +296,9 @@ class MlxModelRunner:
         new_slot_ids: list[int],
     ) -> int:
         """Continue prefill for a chunked request.  Returns next_token_id."""
-        assert req_id in self._req_caches, f"extend called for unknown request {req_id}"
-
-        cache = self._req_caches[req_id]
-
-        input_ids = mx.array([new_token_ids], dtype=mx.int32)
-        model_output = self.model(input_ids, cache=cache)
-        logits = self._extract_logits(model_output)
-
-        last_logits = logits[:, -1, :]
-        next_token_mlx = mx.argmax(last_logits, axis=-1)
-        self._eval_with_cache(next_token_mlx, cache)
-        next_token = int(next_token_mlx.item())
-
-        prev_tokens = self._req_token_ids[req_id]
-        if prev_tokens:
-            prev_tokens.pop()  # remove stale intermediate token
-        prev_tokens.extend(new_token_ids)
-        prev_tokens.append(next_token)
-
-        # Sync new chunk KV to pool immediately
-        if not self.disable_radix_cache and new_slot_ids:
-            synced = self._req_synced_offset[req_id]
-            self._sync_new_kv_to_pool(cache, synced, new_slot_ids)
-            self._req_synced_offset[req_id] = synced + len(new_slot_ids)
-
-        return next_token
+        pending = self.extend_start(req_id, new_token_ids, new_slot_ids)
+        self._eval_with_cache(pending.lazy_token, self._req_caches[req_id])
+        return self.extend_finalize(pending)
 
     def _sync_new_kv_to_pool(
         self,
@@ -318,6 +312,7 @@ class MlxModelRunner:
         num_layers = len(cache)
         end = cache_start + len(slot_ids)
         slot_ids_mx = mx.array(slot_ids, dtype=mx.int32)
+        # TODO: Standardize ContiguousKVCache size to avoid transpose
         # Transpose cache (1, n_kv_heads, S, head_dim) → pool (S, n_kv_heads, head_dim)
         k_all = mx.stack(
             [
@@ -370,11 +365,172 @@ class MlxModelRunner:
         req_ids: list[str],
     ) -> list[int]:
         """Decode one token per request."""
+        pending = self.decode_batch_start(req_ids)
+        # Evaluate lazy_tokens together with every affected cache buffer so
+        # the attention write-then-read ordering is materialised in one
+        # kernel submission.
+        cache_arrays = self._cache_state_arrays(pending.caches)
+        mx.eval(pending.lazy_tokens, *cache_arrays)
+        return self.decode_batch_finalize(pending)
+
+    def prefill_start(
+        self,
+        req_id: str,
+        new_token_ids: list[int],
+        full_token_ids: list[int],
+        prefix_slot_ids: list[int],
+        new_slot_ids: list[int],
+        req_pool_idx: int,
+    ) -> MlxPendingPrefill:
+        """Queue a prefill forward pass without evaluating.
+
+        Returns an :class:`MlxPendingPrefill` containing the lazy
+        next-token ``mx.array`` plus everything needed to commit the
+        request in :meth:`prefill_finalize`.  The caller drives the GPU
+        by handing ``lazy_token`` (and cache state) to ``mx.async_eval``.
+        """
+        num_layers = self._num_layers
+        prefix_len = len(prefix_slot_ids)
+
+        if self.disable_radix_cache:
+            cache = self._acquire_cache()
+            input_ids = mx.array([new_token_ids], dtype=mx.int32)
+            model_output = self.model(input_ids, cache=cache)
+            logits = self._extract_logits(model_output)
+            lazy_token = mx.argmax(logits[:, -1, :], axis=-1)
+            return MlxPendingPrefill(
+                lazy_token=lazy_token,
+                cache=cache,
+                req_id=req_id,
+                full_token_ids=list(full_token_ids),
+                req_pool_idx=req_pool_idx,
+                synced_offset=0,
+            )
+
+        assert self._kv_pool is not None
+
+        new_token_count = len(new_token_ids)
+
+        if prefix_len > 0:
+            slot_ids_mx = mx.array(prefix_slot_ids, dtype=mx.int32)
+            cache = [
+                PoolBackedCache(self._kv_pool, i, slot_ids_mx, prefix_len)
+                for i in range(num_layers)
+            ]
+        else:
+            cache = self._acquire_cache()
+
+        if new_token_count > 0:
+            extend_tokens = new_token_ids
+        else:
+            # Full cache hit — rerun last token to get next-token logits
+            extend_tokens = full_token_ids[-1:]
+            for c in cache:
+                c.offset = max(c.offset - 1, 0)
+
+        input_ids = mx.array([extend_tokens], dtype=mx.int32)
+        model_output = self.model(input_ids, cache=cache)
+        logits = self._extract_logits(model_output)
+
+        last_logits = logits[:, -1, :]
+        lazy_token = mx.argmax(last_logits, axis=-1)
+
+        # Convert PoolBackedCache → ContiguousKVCache for decode.
+        # This appends a lazy slice-assign onto the forward graph; the
+        # arrays get materialised when the caller evaluates lazy_token.
+        if prefix_len > 0:
+            contiguous_cache = self._acquire_cache()
+            for layer_idx in range(num_layers):
+                pbc = cache[layer_idx]
+                contiguous_cache[layer_idx].update_and_fetch(
+                    pbc._full_keys, pbc._full_values
+                )
+            cache = contiguous_cache
+
+        if new_slot_ids:
+            self._sync_new_kv_to_pool(cache, prefix_len, new_slot_ids)
+
+        return MlxPendingPrefill(
+            lazy_token=lazy_token,
+            cache=cache,
+            req_id=req_id,
+            full_token_ids=list(full_token_ids),
+            req_pool_idx=req_pool_idx,
+            synced_offset=prefix_len + len(new_slot_ids),
+        )
+
+    def prefill_finalize(self, pending: MlxPendingPrefill) -> int:
+        """Materialise a pending prefill and commit per-request state.
+
+        Must be called *after* ``pending.lazy_token`` has been handed to
+        ``mx.async_eval`` / ``mx.eval``.  ``.item()`` here is blocking on
+        that specific lazy scalar.
+        """
+        next_token = int(pending.lazy_token.item())
+        self._req_token_ids[pending.req_id] = list(pending.full_token_ids) + [
+            next_token
+        ]
+        self._req_caches[pending.req_id] = pending.cache
+        self._req_pool_idx[pending.req_id] = pending.req_pool_idx
+        self._req_synced_offset[pending.req_id] = pending.synced_offset
+        return next_token
+
+    def extend_start(
+        self,
+        req_id: str,
+        new_token_ids: list[int],
+        new_slot_ids: list[int],
+    ) -> MlxPendingExtend:
+        """Queue chunked-prefill continuation without evaluating."""
+        assert (
+            req_id in self._req_caches
+        ), f"extend_start called for unknown request {req_id}"
+
+        cache = self._req_caches[req_id]
+
+        input_ids = mx.array([new_token_ids], dtype=mx.int32)
+        model_output = self.model(input_ids, cache=cache)
+        logits = self._extract_logits(model_output)
+        lazy_token = mx.argmax(logits[:, -1, :], axis=-1)
+
+        if not self.disable_radix_cache and new_slot_ids:
+            synced = self._req_synced_offset[req_id]
+            self._sync_new_kv_to_pool(cache, synced, new_slot_ids)
+            new_synced_offset = synced + len(new_slot_ids)
+        else:
+            new_synced_offset = self._req_synced_offset.get(req_id, 0)
+
+        return MlxPendingExtend(
+            lazy_token=lazy_token,
+            req_id=req_id,
+            new_token_ids=list(new_token_ids),
+            new_synced_offset=new_synced_offset,
+        )
+
+    def extend_finalize(self, pending: MlxPendingExtend) -> int:
+        """Materialise a pending extend and commit per-request state."""
+        next_token = int(pending.lazy_token.item())
+
+        prev_tokens = self._req_token_ids[pending.req_id]
+        if prev_tokens:
+            prev_tokens.pop()  # remove stale intermediate token
+        prev_tokens.extend(pending.new_token_ids)
+        prev_tokens.append(next_token)
+
+        self._req_synced_offset[pending.req_id] = pending.new_synced_offset
+        return next_token
+
+    def decode_batch_start(self, req_ids: list[str]) -> MlxPendingDecode:
+        """Queue a decode forward pass without evaluating.
+
+        The caller is responsible for calling ``mx.async_eval`` on the
+        returned ``lazy_tokens`` (and optionally per-cache state arrays)
+        to kick off GPU work before :meth:`decode_batch_finalize`.
+        """
         batch_size = len(req_ids)
         num_layers = self._num_layers
 
         caches = [self._req_caches[rid] for rid in req_ids]
-        seq_lens = [caches[i][0].offset for i in range(batch_size)]
 
         if batch_size == 1:
             cache = caches[0]
@@ -382,42 +538,141 @@ class MlxModelRunner:
             input_ids = mx.array([[last_token]], dtype=mx.int32)
             model_output = self.model(input_ids, cache=cache)
             logits = self._extract_logits(model_output)
-            next_tokens_mlx = mx.argmax(logits[:, -1, :], axis=-1)
-            self._eval_with_cache(next_tokens_mlx, cache)
-        else:
-            layer_caches = [
-                [caches[i][layer_idx] for i in range(batch_size)]
-                for layer_idx in range(num_layers)
-            ]
-            ctx = BatchedDecodeContext(
-                batch_size=batch_size,
-                seq_lens=seq_lens,
-                layer_caches=layer_caches,
+            lazy_tokens = mx.argmax(logits[:, -1, :], axis=-1)
+            return MlxPendingDecode(
+                lazy_tokens=lazy_tokens,
+                req_ids=list(req_ids),
+                caches=caches,
             )
-            set_context(ctx)
-            try:
-                max_offset = max(seq_lens)
-                shim_cache = [OffsetCache(offset=max_offset) for _ in range(num_layers)]
-                last_tokens = [self._req_token_ids[rid][-1] for rid in req_ids]
-                batched_input = mx.array(last_tokens, dtype=mx.int32)[:, None]
-                model_output = self.model(batched_input, cache=shim_cache)
-                logits = self._extract_logits(model_output)
-                next_tokens_mlx = mx.argmax(logits[:, -1, :], axis=-1)
 
-                eval_targets = [next_tokens_mlx]
-                for c_list in caches:
-                    for c in c_list:
-                        eval_targets.append(c.keys)
-                        eval_targets.append(c.values)
-                mx.eval(*eval_targets)
-            finally:
-                clear_context()
+        seq_lens = [caches[i][0].offset for i in range(batch_size)]
+        layer_caches = [
+            [caches[i][layer_idx] for i in range(batch_size)]
+            for layer_idx in range(num_layers)
+        ]
+        ctx = BatchedDecodeContext(
+            batch_size=batch_size,
+            seq_lens=seq_lens,
+            layer_caches=layer_caches,
+        )
+        set_context(ctx)
+        try:
+            max_offset = max(seq_lens)
+            shim_cache = [OffsetCache(offset=max_offset) for _ in range(num_layers)]
+            last_tokens = [self._req_token_ids[rid][-1] for rid in req_ids]
+            batched_input = mx.array(last_tokens, dtype=mx.int32)[:, None]
+            model_output = self.model(batched_input, cache=shim_cache)
+            logits = self._extract_logits(model_output)
+            lazy_tokens = mx.argmax(logits[:, -1, :], axis=-1)
+        finally:
+            clear_context()
 
-        next_tokens = next_tokens_mlx.tolist()
+        return MlxPendingDecode(
+            lazy_tokens=lazy_tokens,
+            req_ids=list(req_ids),
+            caches=caches,
+        )
 
-        for i, rid in enumerate(req_ids):
+    def decode_batch_start_chained(
+        self,
+        prev: MlxPendingDecode,
+    ) -> MlxPendingDecode:
+        """Build the next decode step on top of a still-lazy previous decode.
+
+        Feeds ``prev.lazy_tokens`` (an unevaluated ``mx.array`` of shape
+        ``(B,)``) as the next step's input ids, reusing
+        ``prev.caches`` in-place so that the per-layer ``ContiguousKVCache``
+        writes from step N and step N+1 land in the same buffers.  MLX
+        tracks the full dependency graph, so once ``mx.async_eval`` is
+        called the GPU executes N+1 immediately after N with no gap.
+
+        Caller contract:
+
+        * ``prev`` MUST refer to the same set of requests (same order) as
+          the batch the caller intends to run next.  Composition changes
+          (finished reqs, new prefills) must break the chain instead.
+        * After calling this, finalise ``prev`` BEFORE finalising the
+          returned pending: state bookkeeping for step N has to happen
+          before step N+1's bookkeeping.
+        """
+        batch_size = len(prev.req_ids)
+        num_layers = self._num_layers
+        caches = prev.caches
+
+        # TODO (changminbark): Need to fix ContiguousKVCache.write_token
+        # to accommodate dynamic growing like ContiguousKVCache.update_and_fetch.
+
+        # After prev's graph ran, each ContiguousKVCache.offset was
+        # bumped by one per layer — attention wrapper's `write_token`
+        # mutates the Python offset synchronously at graph-build time.
+        # So layer-0 offsets reflect the position the NEW token will
+        # be written at in step N+1 (and equivalently the RoPE offset).
+        seq_lens = [caches[i][0].offset for i in range(batch_size)]
+
+        if batch_size == 1:
+            cache = caches[0]
+            batched_input = prev.lazy_tokens[:, None]
+            model_output = self.model(batched_input, cache=cache)
+            logits = self._extract_logits(model_output)
+            lazy_tokens = mx.argmax(logits[:, -1, :], axis=-1)
+            return MlxPendingDecode(
+                lazy_tokens=lazy_tokens,
+                req_ids=prev.req_ids,
+                caches=caches,
+            )
+
+        layer_caches = [
+            [caches[i][layer_idx] for i in range(batch_size)]
+            for layer_idx in range(num_layers)
+        ]
+        ctx = BatchedDecodeContext(
+            batch_size=batch_size,
+            seq_lens=seq_lens,
+            layer_caches=layer_caches,
+        )
+        set_context(ctx)
+        try:
+            max_offset = max(seq_lens)
+            shim_cache = [OffsetCache(offset=max_offset) for _ in range(num_layers)]
+            batched_input = prev.lazy_tokens[:, None]
+            model_output = self.model(batched_input, cache=shim_cache)
+            logits = self._extract_logits(model_output)
+            lazy_tokens = mx.argmax(logits[:, -1, :], axis=-1)
+        finally:
+            clear_context()
+
+        return MlxPendingDecode(
+            lazy_tokens=lazy_tokens,
+            req_ids=prev.req_ids,
+            caches=caches,
+        )
+
+    def decode_batch_finalize(
+        self,
+        pending: MlxPendingDecode,
+    ) -> list[int]:
+        """Materialise a pending decode and update per-request token lists.
+
+        ``pending.lazy_tokens.tolist()`` implicitly blocks until that
+        specific lazy array (and its graph ancestors, including the
+        per-request cache writes for this step) is evaluated.  The
+        caller should have previously handed this pending's lazy_tokens
+        to ``mx.async_eval`` (or to a subsequent chained step that will
+        be async_eval'd).
+        """
+        raw = pending.lazy_tokens.tolist()
+        if not isinstance(raw, list):
+            raw = [raw]
+        next_tokens = [int(t) for t in raw]
+
+        for i, rid in enumerate(pending.req_ids):
             self._req_token_ids[rid].append(next_tokens[i])
 
+        self._decode_step_ct += 1
+        # TODO (changminbark): allow for flag configuration for clearing mx cache
+        if self._decode_step_ct % 256 == 0:
+            mx.clear_cache()
+
         return next_tokens
 
     def has_request(self, req_id: str) -> bool:

python/sglang/srt/hardware_backend/mlx/scheduler_mixin.py

@@ -0,0 +1,234 @@
+"""MLX overlap scheduling mixin for the SGLang scheduler.
+
+Provides ``event_loop_overlap_mlx``, which pipelines MLX forward
+passes by keeping two in-flight lazy graphs queued on the GPU while
+the scheduler runs its CPU-side bookkeeping on the tokens of the
+older one.  The lazy-graph primitives live in
+``hardware_backend/mlx/tp_worker.py`` and ``model_runner.py``.
+
+Each request's KV lives ina set of per-request, per-layer ``ContiguousKVCache``
+objects that the ``MLXAttentionWrapper`` mutates in place during the forward pass.
+Chained decodes reuse the same cache objects: step N+1's graph reads
+step N's lazy writes via MLX's dependency tracking, so the GPU runs
+both steps back-to-back with no idle gap.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional
+
+import mlx.core as mx
+
+from sglang.srt.environ import envs
+from sglang.srt.utils import DynamicGradMode
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from sglang.srt.hardware_backend.mlx.model_runner import (
+        MlxPendingDecode,
+        MlxPendingExtend,
+        MlxPendingPrefill,
+    )
+    from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
+    from sglang.srt.managers.scheduler import Scheduler
+
+
+@dataclass
+class MlxPendingJob:
+    """Unfinished MLX work and graphs queued on the GPU.
+
+    Attributes:
+        lazy_tokens: Lazily evaluated token IDs produced by the forward
+            pass.  Unevaluated; calling ``.tolist()`` / ``.item()`` /
+            ``mx.eval`` on it will block until the Metal kernel finishes.
+            ``None`` for idle batches.
+        prefills: MLX prefill state returned by the model worker — one
+            entry per new request in an extend batch.  Used by
+            ``finalize_mlx_result`` to commit per-request caches.  Empty
+            list for pure-decode steps.
+        extends: Chunked-prefill-continuation state, one entry per
+            already-active request whose extend seq_len > 1.  Also empty
+            for pure-decode steps.
+        decode: Decode state covering full-decode mode AND mixed
+            single-token decodes inside an extend batch.  Used as the
+            chaining root by :meth:`async_chained_decode_mlx`.
+        mode: One of ``"decode"``, ``"extend"``, ``"idle"`` describing
+            which forward pass produced this job.  Drives finalise
+            dispatch and whether chaining is safe.
+        batch_copy: Snapshot of the :class:`ScheduleBatch` at launch
+            time.  Decoupled from the live batch so
+            ``process_batch_result`` can update request state without
+            racing against the next scheduling decision.
+        reqs: Snapshot of ``batch.reqs`` at launch time.  The overlap
+            loop uses this to check ``req.finished()`` on the previous
+            step's request list without holding a reference to the
+            mutable batch object.
+    """
+
+    lazy_tokens: Optional[mx.array]
+    prefills: list["MlxPendingPrefill"]
+    extends: list["MlxPendingExtend"]
+    decode: Optional["MlxPendingDecode"]
+    mode: str
+    batch_copy: "ScheduleBatch"
+    reqs: List[Req]
+
+
+class SchedulerMlxOverlapMixin:
+    """Mixin that adds MLX overlap scheduling to :class:`Scheduler`."""
+
+    @DynamicGradMode()
+    def event_loop_overlap_mlx(self: "Scheduler"):
+        """MLX-specific overlap loop modelled on ``mlx_lm.generate.generate_step``.
+
+        At steady state we keep TWO in-flight MLX graphs queued on the
+        GPU:
+
+        * ``pending_curr`` — the step whose tokens we are about to block
+          on and feed into the scheduler's bookkeeping.
+        * ``pending_next`` — the step that was built on top of
+          ``pending_curr``'s still-lazy output tokens via
+          ``async_chained_decode_mlx`` and has already been handed to
+          ``mx.async_eval``.  Because MLX tracks the full dependency
+          graph, the GPU will execute ``pending_next`` back-to-back
+          with ``pending_curr`` — there is no scheduling gap on the
+          device.
+
+        Bookkeeping timeline for a steady-state decode loop:
+
+            iter k:
+              build pending_next  (CPU graph build + mx.async_eval; cheap)
+              block on pending_curr via .tolist() (wait only on curr's tokens)
+              process_batch_result(pending_curr)   <-- GPU is running pending_next
+              pending_curr = pending_next
+
+        The chain is broken (we fall back to a "schedule + launch" step)
+        whenever any of the following holds:
+
+        * ``pending_curr`` is not a pure decode (e.g. prefill/extend).
+        * The waiting queue has new requests that need prefill.
+        * Any req in ``pending_curr`` just finished this iteration, so
+          the composition for ``pending_next`` would need to shrink.
+
+        When the chain breaks mid-flight we still finalise the
+        already-launched ``pending_next`` normally (its tokens are
+        valid for all surviving reqs).  With RadixCache-backed caches
+        (#21509) there is no ``extract_cache`` step: per-request caches
+        are the source of truth and are never merged into a shared
+        batched buffer.
+        """
+        pending_curr: Optional[MlxPendingJob] = None
+        pending_next: Optional[MlxPendingJob] = None
+
+        def _finalize(pending: MlxPendingJob):
+            result = self.tp_worker.finalize_mlx_result(
+                pending.prefills,
+                pending.extends,
+                pending.decode,
+                pending.mode,
+                pending.reqs,
+            )
+            if result.next_token_ids is not None:
+                pending.batch_copy.output_ids = result.next_token_ids
+            self.process_batch_result(pending.batch_copy, result)
+
+        def _launch_fresh(batch: "ScheduleBatch") -> MlxPendingJob:
+            mwb = batch.get_model_worker_batch()
+            lazy_tokens, prefills, extends, decode, mode = (
+                self.tp_worker.async_forward_batch_generation_mlx(mwb)
+            )
+            return MlxPendingJob(
+                lazy_tokens=lazy_tokens,
+                prefills=prefills,
+                extends=extends,
+                decode=decode,
+                mode=mode,
+                batch_copy=batch.copy(),
+                reqs=list(batch.reqs),
+            )
+
+        def _launch_chained(prev: MlxPendingJob) -> MlxPendingJob:
+            assert prev.decode is not None
+            lazy_tokens, prefills, extends, decode, mode = (
+                self.tp_worker.async_chained_decode_mlx(prev.decode)
+            )
+            # Composition is identical to prev: reuse a fresh batch copy
+            # of the same underlying ScheduleBatch so process_batch_result
+            # updates the same req objects with the new token.
+            return MlxPendingJob(
+                lazy_tokens=lazy_tokens,
+                prefills=prefills,
+                extends=extends,
+                decode=decode,
+                mode=mode,
+                batch_copy=prev.batch_copy.copy(),
+                reqs=prev.reqs,
+            )
+
+        while True:
+            recv_reqs = self.recv_requests()
+            self.process_input_requests(recv_reqs)
+            if self._engine_paused:
+                continue
+
+            # 1. If pending_curr is a pure decode AND no new prefill is waiting,
+            #    build pending_next on top of it NOW — before we block on curr.
+            can_chain = (
+                pending_curr is not None
+                and pending_curr.mode == "decode"
+                and pending_curr.decode is not None
+                and not self.waiting_queue
+            )
+            if can_chain and pending_next is None:
+                # Build + launch the chained step BEFORE we block on
+                # pending_curr — this is the "no idle gap" trick.
+                # GPU now has 2 steps queued.
+                pending_next = _launch_chained(pending_curr)
+                self.result_queue.append(pending_next)
+
+            # 2. Finalize/process on pending_curr's tokens.  (GPU is already
+            #    executing pending_next at this point.)
+            if pending_curr is not None:
+                _finalize(pending_curr)
+                self.result_queue.popleft()
+                pending_curr = None
+
+            # 3. Decide whether pending_next is still valid (if no reqs finished)
+            #    and promote it.
+            finished_any = any(
+                req.finished() for req in (pending_next.reqs if pending_next else [])
+            )
+            new_prefill_waiting = bool(self.waiting_queue)
+            if (
+                pending_next is not None
+                and not finished_any
+                and not new_prefill_waiting
+            ):
+                pending_curr = pending_next
+                pending_next = None
+                self.cur_batch = pending_curr.batch_copy
+                self.last_batch = pending_curr.batch_copy
+                if envs.SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_BUSY.get():
+                    self.self_check_during_busy()
+                continue
+
+            # 4. Chain is broken. Finalise pending_next (if any), then
+            #    schedule fresh.
+            if pending_next is not None:
+                _finalize(pending_next)
+                self.result_queue.popleft()
+                pending_next = None
+            next_batch = self.get_next_batch_to_run()
+            self.cur_batch = next_batch
+            if next_batch:
+                pending_curr = _launch_fresh(next_batch)
+                self.result_queue.append(pending_curr)
+            else:
+                self.on_idle()
+
+            self.last_batch = next_batch
+            if envs.SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_BUSY.get():
+                self.self_check_during_busy()

python/sglang/srt/hardware_backend/mlx/tp_worker.py

@@ -3,13 +3,26 @@
 Routes forward passes through the MLX model runner, bypassing PyTorch
 MPS.  A lightweight stub provides scheduler bookkeeping; the actual
 KV data lives in MlxKVPool.
+
+The worker also exposes an async (lazy-eval) surface used by the MLX
+overlap scheduler: ``async_forward_batch_generation_mlx`` launches a
+batch without blocking on the GPU, ``async_chained_decode_mlx`` builds
+the next decode step on top of a still-lazy previous decode, and
+``finalize_mlx_result`` blocks on the lazy outputs and produces a
+normal ``GenerationBatchResult``.
 """
 
 import logging
-from typing import Optional
+from typing import Optional, Union
 
+import mlx.core as mx
 import torch
 
+from sglang.srt.hardware_backend.mlx.model_runner import (
+    MlxPendingDecode,
+    MlxPendingExtend,
+    MlxPendingPrefill,
+)
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch
 from sglang.srt.managers.tp_worker import TpModelWorker
 from sglang.srt.managers.utils import GenerationBatchResult
@@ -100,6 +113,16 @@ class MlxTpModelWorker(TpModelWorker):
             skip_attn_backend_init,
         )
 
+    def _cleanup_stale_rids(self, forward_mode, current_rids: set[str]) -> None:
+        """Remove MLX state for decode-mode requests that dropped out of the batch."""
+        if forward_mode.is_decode():
+            stale_rids = self._mlx_active_rids - current_rids
+            for rid in stale_rids:
+                self._mlx_runner.remove_request(rid)
+            self._mlx_active_rids = current_rids
+        else:
+            self._mlx_active_rids |= current_rids
+
     def _forward_batch_generation_mlx(
         self,
         model_worker_batch: ModelWorkerBatch,
@@ -116,17 +139,9 @@ class MlxTpModelWorker(TpModelWorker):
                 can_run_cuda_graph=False,
             )
 
-        # Auto-cleanup: remove MLX state for requests no longer in the batch.
-        current_rids = {req.rid for req in reqs}
-        if forward_mode.is_decode():
-            stale_rids = self._mlx_active_rids - current_rids
-            for rid in stale_rids:
-                self._mlx_runner.remove_request(rid)
-            self._mlx_active_rids = current_rids
-        else:
-            self._mlx_active_rids |= current_rids
+        self._cleanup_stale_rids(forward_mode, {req.rid for req in reqs})
 
-        next_token_ids_list = []
+        next_token_ids_list: list[int] = []
 
         if forward_mode.is_extend():
             # Ensure pool is up-to-date before PoolBackedCache reads it
@@ -138,9 +153,9 @@ class MlxTpModelWorker(TpModelWorker):
 
             offset = 0  # into input_ids_cpu
             slot_offset = 0  # into out_cache_loc_cpu
-            prefill_rids = []
-            extend_rids = []
-            decode_rids = []
+            prefill_rids: list[tuple[str, int]] = []
+            extend_rids: list[tuple[str, int]] = []
+            decode_rids: list[str] = []
 
             for i, req in enumerate(reqs):
                 seq_len = extend_seq_lens[i]
@@ -209,3 +224,262 @@ class MlxTpModelWorker(TpModelWorker):
             next_token_ids=next_token_ids,
             can_run_cuda_graph=False,
         )
+
+    def async_forward_batch_generation_mlx(
+        self,
+        model_worker_batch: ModelWorkerBatch,
+    ) -> tuple[
+        Union[mx.array, None],
+        list[MlxPendingPrefill],
+        list[MlxPendingExtend],
+        Optional[MlxPendingDecode],
+        str,
+    ]:
+        """Start an async (lazy) forward pass through the MLX model runner.
+
+        Returns ``(lazy_result, prefills, extends, decode, mode)``:
+
+        * ``lazy_result`` — an ``mx.array`` that, when evaluated, forces
+          materialisation of the whole batch's outputs.  ``None`` for
+          idle batches.
+        * ``prefills`` — list of :class:`MlxPendingPrefill` for new
+          requests in an extend batch.
+        * ``extends`` — list of :class:`MlxPendingExtend` for chunked
+          prefill continuations in an extend batch.
+        * ``decode`` — :class:`MlxPendingDecode` for the decode
+          sub-batch (covers full decode mode AND mixed decodes inside
+          an extend batch).
+        * ``mode`` — one of ``"idle"``, ``"decode"``, ``"extend"``.
+
+        The caller must make sure the returned pendings are fed into a
+        subsequent ``mx.async_eval`` or ``.item()`` / ``.tolist()`` call
+        — :meth:`finalize_mlx_result` does that.
+        """
+        self._ensure_mlx_pool_initialized()
+
+        forward_mode = model_worker_batch.forward_mode
+        reqs = model_worker_batch.reqs
+
+        if forward_mode.is_idle():
+            return None, [], [], None, "idle"
+
+        self._cleanup_stale_rids(forward_mode, {req.rid for req in reqs})
+
+        if forward_mode.is_decode():
+            req_ids = [req.rid for req in reqs]
+            pending_decode = self._mlx_runner.decode_batch_start(req_ids)
+            mx.async_eval(pending_decode.lazy_tokens)
+            return pending_decode.lazy_tokens, [], [], pending_decode, "decode"
+
+        if forward_mode.is_extend():
+            # TODO (changminbark): Implement per-batch flushing using prefix_slot_ids
+            # Ensure the pool is up-to-date before any PoolBackedCache
+            # reads it for prefix-cached prefills. Mirror the sync path.
+            self._mlx_runner.flush_all_decode_kv()
+            return self._async_extend_batch(model_worker_batch)
+
+        raise ValueError(
+            f"MLX async runner does not support forward mode: {forward_mode}"
+        )
+
+    def _async_extend_batch(
+        self,
+        model_worker_batch: ModelWorkerBatch,
+    ) -> tuple[
+        Union[mx.array, None],
+        list[MlxPendingPrefill],
+        list[MlxPendingExtend],
+        Optional[MlxPendingDecode],
+        str,
+    ]:
+        """Launch each request in an EXTEND batch lazily and kick GPU work."""
+        reqs = model_worker_batch.reqs
+        input_ids_cpu = model_worker_batch.input_ids.cpu().tolist()
+        out_cache_loc_cpu = model_worker_batch.out_cache_loc.cpu().tolist()
+        extend_seq_lens = model_worker_batch.extend_seq_lens
+
+        offset = 0
+        slot_offset = 0
+        pending_prefills: list[MlxPendingPrefill] = []
+        pending_extends: list[MlxPendingExtend] = []
+        mixed_decode_rids: list[str] = []
+
+        for i, req in enumerate(reqs):
+            seq_len = extend_seq_lens[i]
+            req_token_ids = input_ids_cpu[offset : offset + seq_len]
+            req_new_slots = out_cache_loc_cpu[slot_offset : slot_offset + seq_len]
+            offset += seq_len
+            slot_offset += seq_len
+
+            if self._mlx_runner.has_request(req.rid):
+                if seq_len > 1:
+                    # Chunked prefill continuation
+                    pending_extends.append(
+                        self._mlx_runner.extend_start(
+                            req_id=req.rid,
+                            new_token_ids=req_token_ids,
+                            new_slot_ids=req_new_slots,
+                        )
+                    )
+                else:
+                    # MIXED mode: single-token decode
+                    mixed_decode_rids.append(req.rid)
+            else:
+                # New prefill
+                prefix_slot_ids = req.prefix_indices.tolist()
+                full_token_ids = list(req.fill_ids)
+                pending_prefills.append(
+                    self._mlx_runner.prefill_start(
+                        req_id=req.rid,
+                        new_token_ids=req_token_ids,
+                        full_token_ids=full_token_ids,
+                        prefix_slot_ids=prefix_slot_ids,
+                        new_slot_ids=req_new_slots,
+                        req_pool_idx=req.req_pool_idx,
+                    )
+                )
+
+        pending_mixed_decode: Optional[MlxPendingDecode] = None
+        if mixed_decode_rids:
+            pending_mixed_decode = self._mlx_runner.decode_batch_start(
+                mixed_decode_rids
+            )
+
+        # Stack lazy tokens so the caller has a single handle to evaluate
+        # after CPU scheduling work.  We also hand every cache buffer
+        # (and the decode cache arrays) to mx.async_eval so the GPU
+        # kernel-launch stream sees everything the next step depends on
+        # before we actually block on anything.
+        prefill_ext_tokens: list[mx.array] = [p.lazy_token for p in pending_prefills]
+        prefill_ext_tokens.extend(e.lazy_token for e in pending_extends)
+
+        async_args: list[mx.array] = []
+        if prefill_ext_tokens:
+            lazy_stacked = mx.stack(prefill_ext_tokens, axis=0)
+            async_args.append(lazy_stacked)
+        else:
+            lazy_stacked = None
+
+        for p in pending_prefills:
+            async_args.extend(self._cache_state(p.cache))
+        for e in pending_extends:
+            async_args.extend(self._cache_state(self._mlx_runner._req_caches[e.req_id]))
+        if pending_mixed_decode is not None:
+            async_args.append(pending_mixed_decode.lazy_tokens)
+            for c_list in pending_mixed_decode.caches:
+                async_args.extend(self._cache_state(c_list))
+
+        if async_args:
+            mx.async_eval(*async_args)
+
+        return (
+            lazy_stacked,
+            pending_prefills,
+            pending_extends,
+            pending_mixed_decode,
+            "extend",
+        )
+
+    @staticmethod
+    def _cache_state(cache_list) -> list[mx.array]:
+        """Flatten a per-layer cache list to its ``state`` arrays."""
+        return [s for c in cache_list for s in c.state]
+
+    def async_chained_decode_mlx(
+        self,
+        prev_pending: MlxPendingDecode,
+    ) -> tuple[mx.array, list, list, MlxPendingDecode, str]:
+        """Launch a decode step that chains off a still-lazy previous decode.
+
+        This is the "no idle gap" pipelining primitive: build the next
+        decode's compute graph using ``prev_pending.lazy_tokens`` (still
+        unevaluated) as its input ids, hand the combined graph to
+        ``mx.async_eval``, and return.  The GPU runs the new step
+        immediately after ``prev_pending`` with no scheduling gap, while
+        the caller is free to block on ``prev_pending`` and run CPU-side
+        bookkeeping.
+
+        Preconditions (caller must ensure):
+
+        * ``prev_pending`` was produced by a previous decode start
+          (either :meth:`async_forward_batch_generation_mlx` in decode
+          mode or a previous :meth:`async_chained_decode_mlx`).
+        * The batch composition for this step is identical to
+          ``prev_pending`` — same requests, same order.  Composition
+          changes (finished reqs, new prefills) must break the chain.
+        * ``prev_pending`` should be finalised BEFORE the returned
+          pending, so per-request token lists are appended in order.
+
+        Returns a 5-tuple matching
+        :meth:`async_forward_batch_generation_mlx` for the decode case:
+        ``(lazy_tokens, [], [], pending_decode, "decode")``.  The empty
+        prefill/extend lists are always absent for chained decodes.
+        """
+        pending = self._mlx_runner.decode_batch_start_chained(prev_pending)
+        mx.async_eval(pending.lazy_tokens)
+        return pending.lazy_tokens, [], [], pending, "decode"
+
+    def finalize_mlx_result(
+        self,
+        prefills: list[MlxPendingPrefill],
+        extends: list[MlxPendingExtend],
+        decode: Optional[MlxPendingDecode],
+        mode: str,
+        reqs: list,
+    ) -> GenerationBatchResult:
+        """Materialise a lazy MLX result into a :class:`GenerationBatchResult`.
+
+        The blocking wait happens inside ``decode_batch_finalize`` /
+        ``prefill_finalize`` / ``extend_finalize`` via ``.tolist()`` /
+        ``.item()`` on the specific lazy outputs.
+        """
+        from sglang.srt.layers.logits_processor import LogitsProcessorOutput
+
+        if mode == "idle":
+            return GenerationBatchResult(
+                logits_output=LogitsProcessorOutput(next_token_logits=None),
+                can_run_cuda_graph=False,
+            )
+
+        if mode == "decode":
+            assert decode is not None
+            next_tokens_list = self._mlx_runner.decode_batch_finalize(decode)
+
+        elif mode == "extend":
+            prefill_map: dict[str, int] = {}
+            for pending_p in prefills:
+                prefill_map[pending_p.req_id] = self._mlx_runner.prefill_finalize(
+                    pending_p
+                )
+
+            extend_map: dict[str, int] = {}
+            for pending_e in extends:
+                extend_map[pending_e.req_id] = self._mlx_runner.extend_finalize(
+                    pending_e
+                )
+
+            decode_map: dict[str, int] = {}
+            if decode is not None:
+                mixed_tokens = self._mlx_runner.decode_batch_finalize(decode)
+                decode_map = {
+                    rid: tok for rid, tok in zip(decode.req_ids, mixed_tokens)
+                }
+
+            next_tokens_list = []
+            for req in reqs:
+                if req.rid in decode_map:
+                    next_tokens_list.append(decode_map[req.rid])
+                elif req.rid in extend_map:
+                    next_tokens_list.append(extend_map[req.rid])
+                else:
+                    next_tokens_list.append(prefill_map[req.rid])
+
+        else:
+            raise ValueError(f"Unknown MLX async mode: {mode}")
+
+        next_token_ids = torch.tensor(next_tokens_list, dtype=torch.long, device="cpu")
+        return GenerationBatchResult(
+            logits_output=LogitsProcessorOutput(next_token_logits=None),
+            next_token_ids=next_token_ids,
+            can_run_cuda_graph=False,
+        )

python/sglang/srt/managers/scheduler.py

@@ -239,9 +239,14 @@ from sglang.utils import TypeBasedDispatcher, get_exception_traceback
 
 if is_mps():
     CudaStreamContext = nullcontext
+    from sglang.srt.hardware_backend.mlx.scheduler_mixin import SchedulerMlxOverlapMixin
 else:
     from torch.cuda import StreamContext as CudaStreamContext
 
+    class SchedulerMlxOverlapMixin:
+        pass
+
+
 logger = logging.getLogger(__name__)
 
 # Test retract decode for debugging purposes
@@ -326,6 +331,7 @@ class Scheduler(
     SchedulerPPMixin,
     SchedulerDPAttnMixin,
     SchedulerDllmMixin,
+    SchedulerMlxOverlapMixin,
 ):
     """A scheduler that manages a tensor parallel GPU worker."""
 
@@ -373,7 +379,8 @@ class Scheduler(
         self.enable_lora = server_args.enable_lora
         self.enable_lora_overlap_loading = server_args.enable_lora_overlap_loading
         self.max_loras_per_batch = server_args.max_loras_per_batch
-        self.enable_overlap = not server_args.disable_overlap_schedule
+        self.enable_overlap = not server_args.disable_overlap_schedule and not use_mlx()
+        self.enable_overlap_mlx = not server_args.disable_overlap_schedule and use_mlx()
         self.enable_pdmux = server_args.enable_pdmux
         self.skip_tokenizer_init = server_args.skip_tokenizer_init
         self.stream_interval = server_args.stream_interval
@@ -1247,6 +1254,15 @@ class Scheduler(
     def init_overlap(self):
         self.device_module = torch.get_device_module(self.device)
 
+        if use_mlx():
+            # MLX overlap scheduling uses mx.async_eval / mx.eval for
+            # synchronisation so no CUDA/MPS streams or FutureMap needed.
+            self.future_map = None
+            # Empty result_queue is needed because idle-check references it
+            # when enable_overlap is True.
+            self.result_queue: Deque = deque()
+            return
+
         self.forward_stream_ctx: CudaStreamContext = self.device_module.stream(
             self.forward_stream
         )
@@ -1437,6 +1453,12 @@ class Scheduler(
         Sets up the schedule stream and dispatches to the appropriate event loop.
         The event loop blocks until shutdown.
         """
+        if use_mlx():
+            # MLX overlap uses mx.async_eval for CPU/GPU overlap,
+            # not PyTorch MPS streams.
+            dispatch_event_loop(self)
+            return
+
         self.schedule_stream = self.device_module.Stream(priority=0)
         if self.device == "cpu":
             self.schedule_stream.synchronize = lambda: None  # No-op for CPU
@@ -1523,6 +1545,7 @@ class Scheduler(
 
             # Update last_batch
             self.last_batch = batch
+
             if envs.SGLANG_ENABLE_STRICT_MEM_CHECK_DURING_BUSY.get():
                 self.self_check_during_busy()
 
@@ -2874,7 +2897,6 @@ class Scheduler(
                 model_worker_batch.sampling_info = (
                     model_worker_batch.sampling_info.copy_for_forward()
                 )
-
                 bs = len(model_worker_batch.seq_lens)
                 future_indices = self.future_map.alloc_future_indices(bs)
 
@@ -3741,6 +3763,8 @@ def dispatch_event_loop(scheduler: Scheduler):
             scheduler.event_loop_pdmux()
         elif server_args.pp_size > 1:
             scheduler.event_loop_pp()
+        elif scheduler.enable_overlap_mlx:
+            scheduler.event_loop_overlap_mlx()
         elif scheduler.enable_overlap:
             scheduler.event_loop_overlap()
         else:

python/sglang/srt/managers/scheduler_output_processor_mixin.py

@@ -409,6 +409,8 @@ class SchedulerOutputProcessorMixin:
         if batch.spec_algorithm.is_none() or batch.is_spec_v2:
             if batch.is_spec_v2:
                 next_token_ids = self._resolve_spec_overlap_token_ids(result, batch)
+            elif isinstance(next_token_ids, list):
+                pass  # MLX path: already a list[int], skip torch round-trip
             else:
                 next_token_ids = next_token_ids.tolist()
 
@@ -447,7 +449,9 @@ class SchedulerOutputProcessorMixin:
         for i, req in enumerate(batch.reqs):
             req: Req
 
-            if self.enable_overlap and (req.finished() or req.is_retracted):
+            if (self.enable_overlap or self.enable_overlap_mlx) and (
+                req.finished() or req.is_retracted
+            ):
                 # NOTE: This (req.finished() or req.is_retracted) should only happen when overlap scheduling is enabled.
                 # And all the over-allocated tokens will be freed in `release_kv_cache`.
                 continue

python/sglang/srt/server_args.py

@@ -70,6 +70,7 @@ from sglang.srt.utils.common import (
 from sglang.srt.utils.hf_transformers_utils import check_gguf_file
 from sglang.srt.utils.network import NetworkAddress, get_free_port, wait_port_available
 from sglang.srt.utils.runai_utils import ObjectStorageModel, is_runai_obj_uri
+from sglang.srt.utils.tensor_bridge import use_mlx
 from sglang.utils import is_in_ci
 
 logger = logging.getLogger(__name__)
@@ -1164,7 +1165,8 @@ class ServerArgs:
 
     def _handle_mps_backends(self):
         if self.device == "mps":
-            self.disable_overlap_schedule = True
+            if not use_mlx():
+                self.disable_overlap_schedule = True
 
     def _handle_xpu_backends(self):
         if self.device == "xpu":