Beta spec-overlap for EAGLE (#11398)

Co-authored-by: Lianmin Zheng <15100009+merrymercy@users.noreply.github.com>
Co-authored-by: Hanming Lu <69857889+hanming-lu@users.noreply.github.com>

python/sglang/srt/managers/overlap_utils.py

@@ -1,11 +1,18 @@
+from __future__ import annotations
+
 from dataclasses import dataclass
-from typing import Optional
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
-from sglang.srt.managers.schedule_batch import ModelWorkerBatch
 from sglang.srt.utils import get_compiler_backend
 
+if TYPE_CHECKING:
+    from sglang.srt.managers.schedule_batch import ModelWorkerBatch
+    from sglang.srt.managers.scheduler import GenerationBatchResult
+    from sglang.srt.speculative.eagle_info import EagleDraftInput
+    from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
+
 
 @torch.compile(dynamic=True, backend=get_compiler_backend())
 def _resolve_future_token_ids(input_ids, future_token_ids_map):
@@ -27,6 +34,7 @@ class FutureMap:
         self,
         max_running_requests: int,
         device: torch.device,
+        spec_algo: Optional[SpeculativeAlgorithm] = None,
     ):
         self.future_ct = 0
         # A factor of 3 is used to avoid collision in the circular buffer.
@@ -34,9 +42,51 @@ class FutureMap:
         # A factor of 5 is used to ensure the buffer is large enough.
         self.future_buffer_len = max_running_requests * 5
         self.device = device
+        self.spec_algo = spec_algo
+        self.buf_initialized = False
 
-        self.token_ids_buf = torch.empty(
-            (self.future_buffer_len,), dtype=torch.int64, device=self.device
+        if self.spec_algo.is_none():
+            self.token_ids_buf = torch.empty(
+                (self.future_buffer_len,), dtype=torch.int64, device=self.device
+            )
+
+    def _lazy_init_buf(self, draft_input: EagleDraftInput):
+        if self.buf_initialized or not self.spec_algo.is_eagle():
+            return
+
+        self.buf_initialized = True
+
+        # get the template for each tensor
+        topk_p0 = draft_input.topk_p[0]
+        topk_index0 = draft_input.topk_index[0]
+        hidden_states0 = draft_input.hidden_states[0]
+        verified_id0 = draft_input.verified_id[0]
+        new_seq_lens0 = draft_input.new_seq_lens[0]
+
+        self.topk_p_buf = torch.empty(
+            (self.future_buffer_len, *topk_p0.shape),
+            dtype=topk_p0.dtype,
+            device=self.device,
+        )
+        self.topk_index_buf = torch.empty(
+            (self.future_buffer_len, *topk_index0.shape),
+            dtype=topk_index0.dtype,
+            device=self.device,
+        )
+        self.hidden_states_buf = torch.empty(
+            (self.future_buffer_len, *hidden_states0.shape),
+            dtype=hidden_states0.dtype,
+            device=self.device,
+        )
+        self.verified_id_buf = torch.empty(
+            (self.future_buffer_len, *verified_id0.shape),
+            dtype=verified_id0.dtype,
+            device=self.device,
+        )
+        self.new_seq_lens_buf = torch.empty(
+            (self.future_buffer_len, *new_seq_lens0.shape),
+            dtype=new_seq_lens0.dtype,
+            device=self.device,
         )
 
     def alloc_future_indices(self, bs: int) -> FutureIndices:
@@ -49,7 +99,32 @@ class FutureMap:
         return FutureIndices(indices=indices, interval=slice(start, end))
 
     def resolve_future(self, model_worker_batch: ModelWorkerBatch):
-        _resolve_future_token_ids(model_worker_batch.input_ids, self.token_ids_buf)
+        if self.spec_algo.is_eagle():
+            # TODO(lsyin): write future indices into spec_info.future_indices
+            draft_input: EagleDraftInput = model_worker_batch.spec_info
+            if draft_input is None:
+                # FIXME(lsyin): No future exists, only for prefill batch, not compatible with mixed mode
+                return
+            indices = draft_input.future_indices.indices
+            draft_input.topk_p = self.topk_p_buf[indices]
+            draft_input.topk_index = self.topk_index_buf[indices]
+            draft_input.hidden_states = self.hidden_states_buf[indices]
+            draft_input.verified_id = self.verified_id_buf[indices]
+            draft_input.new_seq_lens = self.new_seq_lens_buf[indices]
+        else:
+            _resolve_future_token_ids(model_worker_batch.input_ids, self.token_ids_buf)
 
-    def store_to_map(self, future_indices: FutureIndices, next_token_ids: torch.Tensor):
-        self.token_ids_buf[future_indices.interval] = next_token_ids
+    def store_to_map(
+        self, future_indices: FutureIndices, batch_result: GenerationBatchResult
+    ):
+        intv = future_indices.interval
+        if self.spec_algo.is_eagle():
+            draft_input: EagleDraftInput = batch_result.next_draft_input
+            self._lazy_init_buf(draft_input)
+            self.topk_p_buf[intv] = draft_input.topk_p
+            self.topk_index_buf[intv] = draft_input.topk_index
+            self.hidden_states_buf[intv] = draft_input.hidden_states
+            self.verified_id_buf[intv] = draft_input.verified_id
+            self.new_seq_lens_buf[intv] = draft_input.new_seq_lens
+        else:
+            self.token_ids_buf[intv] = batch_result.next_token_ids