[PD] Fix failure abort (#6535)

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)