Simplify prepare_extend_after_decode (#6987)

python/sglang/srt/speculative/eagle_worker.py

@@ -23,6 +23,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardMode,
 )
 from sglang.srt.server_args import ServerArgs
+from sglang.srt.speculative.build_eagle_tree import build_tree_kernel_efficient
 from sglang.srt.speculative.eagle_draft_cuda_graph_runner import (
     EAGLEDraftCudaGraphRunner,
 )
@@ -69,7 +70,6 @@ class EAGLEWorker(TpModelWorker):
         self.server_args = server_args
         self.topk = server_args.speculative_eagle_topk
         self.speculative_num_steps = server_args.speculative_num_steps
-        self.padded_static_len = self.speculative_num_steps + 1
         self.enable_nan_detection = server_args.enable_nan_detection
         self.gpu_id = gpu_id
         self.device = server_args.device
@@ -78,6 +78,7 @@ class EAGLEWorker(TpModelWorker):
         self.speculative_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )
+        self.padded_static_len = -1
 
         # Override context length with target model's context length
         server_args.context_length = target_worker.model_runner.model_config.context_len
@@ -184,7 +185,6 @@ class EAGLEWorker(TpModelWorker):
                     self.draft_model_runner,
                     skip_prefill=False,
                 )
-            self.padded_static_len = self.speculative_num_steps + 1
             self.has_prefill_wrapper_verify = True
         elif self.server_args.attention_backend == "triton":
             from sglang.srt.layers.attention.triton_backend import (
@@ -201,7 +201,6 @@ class EAGLEWorker(TpModelWorker):
                 self.draft_model_runner,
                 skip_prefill=False,
             )
-            self.padded_static_len = self.speculative_num_steps + 1
             self.has_prefill_wrapper_verify = False
         elif self.server_args.attention_backend == "fa3":
             from sglang.srt.layers.attention.flashattention_backend import (
@@ -218,7 +217,6 @@ class EAGLEWorker(TpModelWorker):
                 self.draft_model_runner,
                 skip_prefill=False,
             )
-            self.padded_static_len = self.speculative_num_steps + 1
             self.has_prefill_wrapper_verify = False
         elif self.server_args.attention_backend == "flashmla":
             from sglang.srt.layers.attention.flashmla_backend import (
@@ -231,7 +229,6 @@ class EAGLEWorker(TpModelWorker):
                 self.speculative_num_steps,
             )
             self.draft_extend_attn_backend = None
-            self.padded_static_len = self.speculative_num_steps + 1
             self.has_prefill_wrapper_verify = False
         else:
             raise ValueError(
@@ -319,10 +316,12 @@ class EAGLEWorker(TpModelWorker):
 
             return logits_output, next_token_ids, model_worker_batch.bid, 0, False
         else:
-            logits_output, next_token_ids, bid = self.forward_target_extend(batch)
+            logits_output, next_token_ids, bid, seq_lens_cpu = (
+                self.forward_target_extend(batch)
+            )
             with self.draft_tp_context(self.draft_model_runner.tp_group):
                 self.forward_draft_extend(
-                    batch, logits_output.hidden_states, next_token_ids
+                    batch, logits_output.hidden_states, next_token_ids, seq_lens_cpu
                 )
             return logits_output, next_token_ids, bid, 0, False
 
@@ -346,7 +345,12 @@ class EAGLEWorker(TpModelWorker):
         logits_output, next_token_ids, _ = self.target_worker.forward_batch_generation(
             model_worker_batch
         )
-        return logits_output, next_token_ids, model_worker_batch.bid
+        return (
+            logits_output,
+            next_token_ids,
+            model_worker_batch.bid,
+            model_worker_batch.seq_lens_cpu,
+        )
 
     def draft(self, batch: ScheduleBatch):
         # Parse args
@@ -452,7 +456,14 @@ class EAGLEWorker(TpModelWorker):
 
         self.token_to_kv_pool_allocator.restore_state(token_to_kv_pool_state_backup)
 
-        ret = EagleVerifyInput.create(
+        (
+            tree_mask,
+            position,
+            retrive_index,
+            retrive_next_token,
+            retrive_next_sibling,
+            draft_tokens,
+        ) = build_tree_kernel_efficient(
             spec_info.verified_id,
             score_list,
             token_list,
@@ -463,7 +474,22 @@ class EAGLEWorker(TpModelWorker):
             self.speculative_num_steps,
             self.server_args.speculative_num_draft_tokens,
         )
-        return ret
+
+        return EagleVerifyInput(
+            draft_token=draft_tokens,
+            custom_mask=tree_mask,
+            positions=position,
+            retrive_index=retrive_index,
+            retrive_next_token=retrive_next_token,
+            retrive_next_sibling=retrive_next_sibling,
+            retrive_cum_len=None,
+            spec_steps=self.speculative_num_steps,
+            topk=self.topk,
+            draft_token_num=self.server_args.speculative_num_draft_tokens,
+            capture_hidden_mode=CaptureHiddenMode.FULL,
+            seq_lens_sum=forward_batch.seq_lens_sum,
+            seq_lens_cpu=forward_batch.seq_lens_cpu,
+        )
 
     def draft_forward(self, forward_batch: ForwardBatch):
         # Parse args
@@ -523,7 +549,9 @@ class EAGLEWorker(TpModelWorker):
         spec_info.prepare_for_verify(batch, self.page_size)
         batch.forward_mode = ForwardMode.TARGET_VERIFY
         batch.spec_info = spec_info
-        model_worker_batch = batch.get_model_worker_batch()
+        model_worker_batch = batch.get_model_worker_batch(
+            seq_lens_cpu_cache=spec_info.seq_lens_cpu
+        )
 
         if batch.has_grammar:
             retrieve_next_token_cpu = spec_info.retrive_next_token.cpu()
@@ -650,6 +678,7 @@ class EAGLEWorker(TpModelWorker):
         batch: ScheduleBatch,
         hidden_states: torch.Tensor,
         next_token_ids: List[int],
+        seq_lens_cpu: torch.Tensor,
     ):
         """Run draft model extend. This API modifies the states of the batch.
 
@@ -664,7 +693,9 @@ class EAGLEWorker(TpModelWorker):
         )
         batch.spec_info.prepare_for_extend(batch)
         batch.spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
-        model_worker_batch = batch.get_model_worker_batch()
+        model_worker_batch = batch.get_model_worker_batch(
+            seq_lens_cpu_cache=seq_lens_cpu
+        )
         forward_batch = ForwardBatch.init_new(
             model_worker_batch, self.draft_model_runner
         )
@@ -683,19 +714,18 @@ class EAGLEWorker(TpModelWorker):
         return_logprob_backup = batch.return_logprob
 
         # Prepare metadata
-        batch.forward_mode = ForwardMode.DRAFT_EXTEND
         batch.spec_info.prepare_extend_after_decode(
             batch,
             self.speculative_num_steps,
-            self.server_args.context_length,
-            pad_input=self.cuda_graph_runner_for_draft_extend is not None,
         )
-        batch.spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
-        batch.return_logprob = False
         model_worker_batch = batch.get_model_worker_batch()
         forward_batch = ForwardBatch.init_new(
             model_worker_batch, self.draft_model_runner
         )
+        if forward_batch.seq_lens_cpu is not None:
+            forward_batch.seq_lens_sum = forward_batch.seq_lens_cpu.sum().item()
+        else:
+            forward_batch.seq_lens_sum = batch.seq_lens.sum().item()
 
         # Run
         can_cuda_graph = (
@@ -706,14 +736,19 @@ class EAGLEWorker(TpModelWorker):
             logits_output = self.cuda_graph_runner_for_draft_extend.replay(
                 forward_batch
             )
+            forward_batch.spec_info.topk_p, forward_batch.spec_info.topk_index = (
+                logits_output.topk_p,
+                logits_output.topk_index,
+            )
+            forward_batch.spec_info.hidden_states = logits_output.hidden_states
         else:
             self.draft_model_runner.attn_backend.init_forward_metadata(forward_batch)
             logits_output = self.draft_model_runner.model.forward(
                 forward_batch.input_ids, forward_batch.positions, forward_batch
             )
+            self.capture_for_decode(logits_output, forward_batch.spec_info)
 
         self._detect_nan_if_needed(logits_output)
-        self.capture_for_decode(logits_output, forward_batch.spec_info)
 
         # Restore backup.
         # This is because `seq_lens` can be modified in `prepare_extend_after_decode`