[PD] Fix failure abort (#6535)

python/sglang/srt/disaggregation/decode.py

@@ -44,6 +44,7 @@ from sglang.srt.disaggregation.utils import (
     poll_and_all_reduce,
     prepare_abort,
 )
+from sglang.srt.managers.schedule_batch import FINISH_ABORT
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool, TokenToKVPoolAllocator
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
@@ -321,11 +322,15 @@ class DecodeTransferQueue:
         gloo_group: ProcessGroup,
         req_to_metadata_buffer_idx_allocator: ReqToMetadataIdxAllocator,
         metadata_buffers: torch.Tensor,
+        scheduler: Scheduler,
+        tree_cache: BasePrefixCache,
     ):
         self.queue: List[DecodeRequest] = []
         self.gloo_group = gloo_group
         self.req_to_metadata_buffer_idx_allocator = req_to_metadata_buffer_idx_allocator
         self.metadata_buffers = metadata_buffers
+        self.scheduler = scheduler
+        self.tree_cache = tree_cache
 
     def add(self, req_conn: DecodeRequest) -> None:
         self.queue.append(req_conn)
@@ -341,6 +346,14 @@ class DecodeTransferQueue:
             [decode_req.kv_receiver for decode_req in self.queue], self.gloo_group
         )
 
+        # First, remove all failed requests from the queue
+        for i, decode_req in enumerate(self.queue):
+            if isinstance(decode_req.req.finished_reason, FINISH_ABORT):
+                self.scheduler.stream_output(
+                    [decode_req.req], decode_req.req.return_logprob
+                )
+                indices_to_remove.add(i)
+
         transferred_reqs = []
         indices_to_remove = set()
         for i, (decode_req, poll) in enumerate(zip(self.queue, polls)):
@@ -396,95 +409,6 @@ class DecodeTransferQueue:
         return transferred_reqs
 
 
-class ScheduleBatchDisaggregationDecodeMixin:
-
-    def prepare_for_prebuilt_extend(self: ScheduleBatch):
-        """
-        Prepare a prebuilt extend by populate metadata
-        Adapted from .prepare_for_extend().
-        """
-
-        self.forward_mode = ForwardMode.EXTEND
-        reqs = self.reqs
-        input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
-        extend_num_tokens = sum(len(ids) for ids in input_ids)
-        seq_lens = []
-        pre_lens = []
-        req_pool_indices = []
-
-        # Pre-calculate total size
-        total_size = sum(req.extend_input_len for req in reqs)
-        out_cache_loc = torch.empty(total_size, dtype=torch.int64, device=self.device)
-
-        # Fill the tensor in one pass
-        offset = 0
-        for i, req in enumerate(reqs):
-            req_pool_indices.append(req.req_pool_idx)
-
-            chunk = self.req_to_token_pool.req_to_token[req.req_pool_idx][
-                : req.extend_input_len
-            ]
-            assert (
-                offset + req.extend_input_len <= total_size
-            ), f"Exceeds total size: offset={offset}, req.extend_input_len={req.extend_input_len}, total_size={total_size}"
-            out_cache_loc[offset : offset + req.extend_input_len] = chunk
-            offset += req.extend_input_len
-
-            pre_len = len(req.prefix_indices)
-            seq_len = len(req.origin_input_ids) + max(0, len(req.output_ids) - 1)
-            seq_lens.append(seq_len)
-            if len(req.output_ids) == 0:
-                assert (
-                    seq_len - pre_len == req.extend_input_len
-                ), f"seq_len={seq_len}, pre_len={pre_len}, req.extend_input_len={req.extend_input_len}"
-
-            req.cached_tokens += pre_len - req.already_computed
-            req.already_computed = seq_len
-            req.is_retracted = False
-            pre_lens.append(pre_len)
-            req.extend_logprob_start_len = 0
-
-        extend_input_logprob_token_ids = None
-
-        # Set fields
-        self.input_ids = torch.tensor(
-            sum(input_ids, []), dtype=torch.int32, device=self.device
-        )
-        self.req_pool_indices = torch.tensor(
-            req_pool_indices, dtype=torch.int64, device=self.device
-        )
-        self.seq_lens = torch.tensor(seq_lens, dtype=torch.int64, device=self.device)
-        self.out_cache_loc = out_cache_loc
-        self.seq_lens_sum = sum(seq_lens)
-        self.extend_num_tokens = extend_num_tokens
-        self.prefix_lens = [len(r.prefix_indices) for r in reqs]
-        self.extend_lens = [r.extend_input_len for r in reqs]
-        self.extend_logprob_start_lens = [r.extend_logprob_start_len for r in reqs]
-        self.extend_input_logprob_token_ids = extend_input_logprob_token_ids
-
-        # Build sampling info
-        self.sampling_info = SamplingBatchInfo.from_schedule_batch(
-            self,
-            self.model_config.vocab_size,
-        )
-
-    def process_prebuilt_extend(
-        self: ScheduleBatch, server_args: ServerArgs, model_config: ModelConfig
-    ):
-        """Assign the buffered last input id to schedule batch"""
-        self.output_ids = []
-        for req in self.reqs:
-            if req.output_ids and len(req.output_ids) > 0:
-                # resumed retracted req
-                self.output_ids.append(req.output_ids[-1])
-            else:
-                assert req.transferred_output_id is not None
-                req.output_ids.append(req.transferred_output_id)
-                self.output_ids.append(req.transferred_output_id)
-            self.tree_cache.cache_unfinished_req(req)
-        self.output_ids = torch.tensor(self.output_ids, device=self.device)
-
-
 class SchedulerDisaggregationDecodeMixin:
 
     def _prepare_idle_batch_and_run(self, batch, delay_process=False):

python/sglang/srt/disaggregation/decode_schedule_batch_mixin.py

@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING
+
+import torch
+
+from sglang.srt.model_executor.forward_batch_info import ForwardMode
+from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from sglang.srt.configs.model_config import ModelConfig
+    from sglang.srt.managers.schedule_batch import ScheduleBatch
+    from sglang.srt.server_args import ServerArgs
+
+
+class ScheduleBatchDisaggregationDecodeMixin:
+
+    def prepare_for_prebuilt_extend(self: ScheduleBatch):
+        """
+        Prepare a prebuilt extend by populate metadata
+        Adapted from .prepare_for_extend().
+        """
+
+        self.forward_mode = ForwardMode.EXTEND
+        reqs = self.reqs
+        input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
+        extend_num_tokens = sum(len(ids) for ids in input_ids)
+        seq_lens = []
+        pre_lens = []
+        req_pool_indices = []
+
+        # Pre-calculate total size
+        total_size = sum(req.extend_input_len for req in reqs)
+        out_cache_loc = torch.empty(total_size, dtype=torch.int64, device=self.device)
+
+        # Fill the tensor in one pass
+        offset = 0
+        for i, req in enumerate(reqs):
+            req_pool_indices.append(req.req_pool_idx)
+
+            chunk = self.req_to_token_pool.req_to_token[req.req_pool_idx][
+                : req.extend_input_len
+            ]
+            assert (
+                offset + req.extend_input_len <= total_size
+            ), f"Exceeds total size: offset={offset}, req.extend_input_len={req.extend_input_len}, total_size={total_size}"
+            out_cache_loc[offset : offset + req.extend_input_len] = chunk
+            offset += req.extend_input_len
+
+            pre_len = len(req.prefix_indices)
+            seq_len = len(req.origin_input_ids) + max(0, len(req.output_ids) - 1)
+            seq_lens.append(seq_len)
+            if len(req.output_ids) == 0:
+                assert (
+                    seq_len - pre_len == req.extend_input_len
+                ), f"seq_len={seq_len}, pre_len={pre_len}, req.extend_input_len={req.extend_input_len}"
+
+            req.cached_tokens += pre_len - req.already_computed
+            req.already_computed = seq_len
+            req.is_retracted = False
+            pre_lens.append(pre_len)
+            req.extend_logprob_start_len = 0
+
+        extend_input_logprob_token_ids = None
+
+        # Set fields
+        self.input_ids = torch.tensor(
+            sum(input_ids, []), dtype=torch.int32, device=self.device
+        )
+        self.req_pool_indices = torch.tensor(
+            req_pool_indices, dtype=torch.int64, device=self.device
+        )
+        self.seq_lens = torch.tensor(seq_lens, dtype=torch.int64, device=self.device)
+        self.out_cache_loc = out_cache_loc
+        self.seq_lens_sum = sum(seq_lens)
+        self.extend_num_tokens = extend_num_tokens
+        self.prefix_lens = [len(r.prefix_indices) for r in reqs]
+        self.extend_lens = [r.extend_input_len for r in reqs]
+        self.extend_logprob_start_lens = [r.extend_logprob_start_len for r in reqs]
+        self.extend_input_logprob_token_ids = extend_input_logprob_token_ids
+
+        # Build sampling info
+        self.sampling_info = SamplingBatchInfo.from_schedule_batch(
+            self,
+            self.model_config.vocab_size,
+        )
+
+    def process_prebuilt_extend(
+        self: ScheduleBatch, server_args: ServerArgs, model_config: ModelConfig
+    ):
+        """Assign the buffered last input id to schedule batch"""
+        self.output_ids = []
+        for req in self.reqs:
+            if req.output_ids and len(req.output_ids) > 0:
+                # resumed retracted req
+                self.output_ids.append(req.output_ids[-1])
+            else:
+                assert req.transferred_output_id is not None
+                req.output_ids.append(req.transferred_output_id)
+                self.output_ids.append(req.transferred_output_id)
+            self.tree_cache.cache_unfinished_req(req)
+        self.output_ids = torch.tensor(self.output_ids, device=self.device)

python/sglang/srt/disaggregation/utils.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import dataclasses
+import os
+import random
 import warnings
 from collections import deque
 from enum import Enum
@@ -15,6 +17,9 @@ from sglang.srt.utils import get_ip
 
 FakeBootstrapHost = "2.2.2.2"
 
+# env var for testing failure, convert to float explicitly
+FAILURE_PROB = float(os.getenv("DISAGGREGATION_TEST_FAILURE_PROB", 0))
+
 
 class DisaggregationMode(Enum):
     NULL = "null"
@@ -23,6 +28,15 @@ class DisaggregationMode(Enum):
 
 
 def poll_and_all_reduce(pollers, gloo_group):
+    # at a certain prob, the poll is failed to simulate failure
+    if FAILURE_PROB > 0:
+        from sglang.srt.disaggregation.base import KVPoll
+
+        polls = [
+            int(KVPoll.Failed) if random.random() < FAILURE_PROB else int(poller.poll())
+            for poller in pollers
+        ]
+    else:
         polls = [int(poller.poll()) for poller in pollers]
     tensor_to_reduce = torch.tensor(polls, dtype=torch.uint8, device="cpu")
     dist.all_reduce(tensor_to_reduce, op=dist.ReduceOp.MIN, group=gloo_group)

python/sglang/srt/managers/schedule_batch.py

@@ -48,7 +48,9 @@ from sglang.global_config import global_config
 from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.constrained.base_grammar_backend import BaseGrammarObject
 from sglang.srt.disaggregation.base import BaseKVSender
-from sglang.srt.disaggregation.decode import ScheduleBatchDisaggregationDecodeMixin
+from sglang.srt.disaggregation.decode_schedule_batch_mixin import (
+    ScheduleBatchDisaggregationDecodeMixin,
+)
 from sglang.srt.layers.multimodal import gpu_tensor_hash
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.chunk_cache import ChunkCache

python/sglang/srt/managers/scheduler.py

@@ -582,6 +582,8 @@ class Scheduler(
                 gloo_group=self.attn_tp_cpu_group,
                 req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator,
                 metadata_buffers=metadata_buffers,
+                scheduler=self,
+                tree_cache=self.tree_cache,
             )
 
             # The decode requests pending for pre-allocation

python/sglang/srt/mem_cache/chunk_cache.py

@@ -38,7 +38,9 @@ class ChunkCache(BasePrefixCache):
 
     def cache_finished_req(self, req: Req):
         kv_indices = self.req_to_token_pool.req_to_token[
-            req.req_pool_idx, : len(req.origin_input_ids) + len(req.output_ids) - 1
+            req.req_pool_idx,
+            # For decode server: if req.output_ids is empty, we want to free all req.origin_input_ids
+            : len(req.origin_input_ids) + max(len(req.output_ids) - 1, 0),
         ]
         self.req_to_token_pool.free(req.req_pool_idx)
         self.token_to_kv_pool_allocator.free(kv_indices)