[PD] Support structured output (sgl-project#6560)

Author: ByronHsu

python/sglang/srt/disaggregation/decode.py

@@ -45,19 +45,16 @@
     poll_and_all_reduce,
     prepare_abort,
 )
-from sglang.srt.managers.schedule_batch import FINISH_ABORT
+from sglang.srt.managers.schedule_batch import FINISH_ABORT, ScheduleBatch
 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 CaptureHiddenMode, ForwardMode
-from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
+from sglang.srt.model_executor.forward_batch_info import ForwardMode
 
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
-    from sglang.srt.configs.model_config import ModelConfig
-    from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
+    from sglang.srt.managers.schedule_batch import Req
     from sglang.srt.managers.scheduler import Scheduler
-    from sglang.srt.server_args import ServerArgs
 
 
 @dataclass
@@ -531,7 +528,18 @@ def event_loop_overlap_disagg_decode(self: Scheduler):
                         self.prepare_dp_attn_batch(batch)
                     result = self.run_batch(batch)
                     result_queue.append((batch.copy(), result))
+
+                    if (self.last_batch is None) or (not self.last_batch_in_queue):
+                        # Create a dummy first batch to start the pipeline for overlap schedule.
+                        # It is now used for triggering the sampling_info_done event.
+                        tmp_batch = ScheduleBatch(
+                            reqs=None,
+                            forward_mode=ForwardMode.DUMMY_FIRST,
+                            next_batch_sampling_info=self.tp_worker.cur_sampling_info,
+                        )
+                        self.set_next_batch_sampling_info_done(tmp_batch)
                     last_batch_in_queue = True
+
             elif prepare_dp_attn_flag:
                 batch, result = self._prepare_idle_batch_and_run(
                     None, delay_process=True
@@ -543,6 +551,9 @@ def event_loop_overlap_disagg_decode(self: Scheduler):
             # Process the results of the previous batch but skip if the last batch is extend
             if self.last_batch and self.last_batch_in_queue:
                 tmp_batch, tmp_result = result_queue.popleft()
+                tmp_batch.next_batch_sampling_info = (
+                    self.tp_worker.cur_sampling_info if batch else None
+                )
                 self.process_batch_result(tmp_batch, tmp_result)
 
             if batch is None and (
@@ -591,6 +602,9 @@ def get_next_disagg_decode_batch_to_run(
 
     def get_new_prebuilt_batch(self: Scheduler) -> Optional[ScheduleBatch]:
         """Create a schedulebatch for fake completed prefill"""
+        if self.grammar_queue:
+            self.move_ready_grammar_requests()
+
         if len(self.waiting_queue) == 0:
             return None
 
@@ -616,8 +630,6 @@ def get_new_prebuilt_batch(self: Scheduler) -> Optional[ScheduleBatch]:
         self.waiting_queue = waiting_queue
         if len(can_run_list) == 0:
             return None
-        # local import to avoid circular import
-        from sglang.srt.managers.schedule_batch import ScheduleBatch
 
         # construct a schedule batch with those requests and mark as decode
         new_batch = ScheduleBatch.init_new(

python/sglang/srt/disaggregation/decode_schedule_batch_mixin.py

@@ -101,6 +101,9 @@ def process_prebuilt_extend(
         for req in self.reqs:
             self.output_ids.append(req.output_ids[-1])
             self.tree_cache.cache_unfinished_req(req)
+            if req.grammar is not None:
+                req.grammar.accept_token(req.output_ids[-1])
+                req.grammar.finished = req.finished()
         self.output_ids = torch.tensor(self.output_ids, device=self.device)
 
         # Simulate the eagle run. We add mock data to hidden states for the

python/sglang/srt/disaggregation/prefill.py

@@ -43,6 +43,7 @@
     prepare_abort,
 )
 from sglang.srt.managers.schedule_batch import FINISH_LENGTH, Req, ScheduleBatch
+from sglang.srt.model_executor.forward_batch_info import ForwardMode
 
 if TYPE_CHECKING:
     from torch.distributed import ProcessGroup
@@ -143,6 +144,10 @@ def add(self, req: Req) -> None:
         self._process_req(req)
         self.queue.append(req)
 
+    def extend(self, reqs: List[Req]) -> None:
+        for req in reqs:
+            self.add(req)
+
     def _process_req(self, req: Req) -> None:
         """
         Set max_new_tokens = 1, so PrefillAdder memory estimation is accurate
@@ -269,6 +274,16 @@ def event_loop_overlap_disagg_prefill(self: Scheduler):
                 result = self.run_batch(batch)
                 self.result_queue.append((batch.copy(), result))
 
+                if self.last_batch is None:
+                    # Create a dummy first batch to start the pipeline for overlap schedule.
+                    # It is now used for triggering the sampling_info_done event.
+                    tmp_batch = ScheduleBatch(
+                        reqs=None,
+                        forward_mode=ForwardMode.DUMMY_FIRST,
+                        next_batch_sampling_info=self.tp_worker.cur_sampling_info,
+                    )
+                    self.set_next_batch_sampling_info_done(tmp_batch)
+
             if self.last_batch:
                 tmp_batch, tmp_result = self.result_queue.popleft()
                 self.process_batch_result_disagg_prefill(tmp_batch, tmp_result)

python/sglang/srt/managers/scheduler.py

@@ -1087,8 +1087,11 @@ def _add_request_to_queue(self, req: Req):
         else:
             self.waiting_queue.append(req)
 
-    def _extend_requests_to_queue(self, reqs: List[Req], is_retracted: bool = False):
-        if self.disaggregation_mode == DisaggregationMode.DECODE:
+    def _extend_requests_to_queue(self, reqs: List[Req]):
+        if self.disaggregation_mode == DisaggregationMode.PREFILL:
+            self.disagg_prefill_bootstrap_queue.extend(reqs)
+        elif self.disaggregation_mode == DisaggregationMode.DECODE:
+            # If this is a decode server, we put the request to the decode pending prealloc queue
             self.disagg_decode_prealloc_queue.extend(reqs)
         else:
             self.waiting_queue.extend(reqs)

scripts/playground/disaggregation/cli-so.py

@@ -0,0 +1,34 @@
+import json
+
+import requests
+
+port = 8000
+
+json_schema = json.dumps(
+    {
+        "type": "object",
+        "properties": {
+            "name": {"type": "string", "pattern": "^[\\w]+$"},
+            "population": {"type": "integer"},
+        },
+        "required": ["name", "population"],
+    }
+)
+
+# JSON
+response = requests.post(
+    f"http://localhost:{port}/generate",
+    json={
+        "text": "Here is the information of the capital of France in the JSON format.\n",
+        "sampling_params": {
+            "temperature": 0,
+            "max_new_tokens": 64,
+            "json_schema": json_schema,
+        },
+    },
+)
+
+print(response.json())
+
+
+# python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --trust-remote-code --disaggregation-mode prefill --tp 2 --disaggregation-ib-device mlx5_roce0,mlx5_roce1 --speculative-algorithm EAGLE --speculative-draft-model-path lmsys/sglang-EAGLE-llama2-chat-7B --speculative-num-steps 3 --speculative-eagle-topk 4 --speculative-num-draft-tokens 16 --cuda-graph-max-bs 8 --host 127.0.0.1 --port 8100

test/srt/test_disaggregation.py

@@ -1,3 +1,4 @@
+import json
 import os
 import subprocess
 import time
@@ -17,12 +18,9 @@
     DEFAULT_URL_FOR_TEST,
     CustomTestCase,
     popen_launch_pd_server,
-    run_with_timeout,
 )
 
 
-# skip the test because we have different_tp test
-@unittest.skip("skip the test because we have different_tp test")
 class TestDisaggregationAccuracy(CustomTestCase):
     @classmethod
     def setUpClass(cls):
@@ -172,6 +170,34 @@ def test_logprob(self):
             len(input_logprobs) > 0
         ), f"input_logprobs should have at least one token, but got {len(input_logprobs)}"
 
+    def test_structured_output(self):
+        json_schema = json.dumps(
+            {
+                "type": "object",
+                "properties": {
+                    "name": {"type": "string", "pattern": "^[\\w]+$"},
+                    "population": {"type": "integer"},
+                },
+                "required": ["name", "population"],
+            }
+        )
+
+        # JSON
+        response = requests.post(
+            f"{self.lb_url}/generate",
+            json={
+                "text": "Here is the information of the capital of France in the JSON format.\n",
+                "sampling_params": {
+                    "temperature": 0,
+                    "max_new_tokens": 64,
+                    "json_schema": json_schema,
+                },
+            },
+        )
+        output = response.json()["text"]
+        # ensure the output is a valid JSON
+        json.loads(output)
+
 
 class TestDisaggregationMooncakeFailure(CustomTestCase):
     @classmethod