[feat]支持mtp模型full_cuda_graph

vllm/v1/spec_decode/eagle.py

@@ -107,7 +107,7 @@ class EagleProposer:
         num_rejected_tokens: list[int],
         # [batch_size]
         sampling_metadata: SamplingMetadata
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         num_tokens = target_token_ids.shape[0]
         batch_size = next_token_ids.shape[0]
         last_token_indices = cu_num_tokens[1:] - 1
@@ -231,16 +231,13 @@ class EagleProposer:
                 last_hidden_states, hidden_states = ret_hidden_states
         sample_hidden_states = last_hidden_states[last_token_indices]
         logits = self.model.compute_logits(sample_hidden_states, None)
-        draft_token_ids = logits.argmax(dim=-1)
 
-        draft_prob = logits.softmax(dim=-1, dtype=torch.float32)
-
-        draft_probs_list = [draft_prob]
+        draft_token_ids = torch.argmax(logits, dim=-1)
 
         # Early exit if there is only one draft token to be generated.
         if self.num_speculative_tokens == 1:
             # [batch_size, 1]
-            return draft_token_ids.view(-1, 1), draft_prob.view(-1, 1, draft_prob.shape[-1])
+            return draft_token_ids.view(-1, 1)
 
         # TODO: Currently, MTP module released by deepseek only has
         # one layer. Adapt this code to support multiple layers once
@@ -383,18 +380,14 @@ class EagleProposer:
             logits = self.model.compute_logits(last_hidden_states[:batch_size],
                                                None)
 
-            # TODO(wenlong): get more than one token for tree attention
+            # # TODO(wenlong): get more than one token for tree attention
             draft_token_ids = logits.argmax(dim=-1)
             draft_token_ids_list.append(draft_token_ids)
 
-            draft_prob = logits.softmax(dim=-1, dtype=torch.float32)
-            draft_probs_list.append(draft_prob)
-
         # [batch_size, num_speculative_tokens]
         draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
-        draft_probs = torch.stack(draft_probs_list, dim=1).contiguous()
 
-        return draft_token_ids, draft_probs
+        return draft_token_ids
 
     @staticmethod
     def prepare_inputs(

vllm/v1/spec_decode/utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import msgspec
-from abc import ABC
-import torch
-
 from vllm.sampling_params import SamplingParams
 from vllm.triton_utils import tl, triton
 
@@ -43,41 +39,3 @@ def prepare_eagle_input_kernel(
             index_start + offset,
             mask=offset < num_tokens,
         )
-
-class DraftProbs(ABC):  # type: ignore[call-arg]
-    """Draft probs corresponding to in-progress sequences."""
-
-    # spec tokens probs. 
-    draft_probs: torch.Tensor
-
-    # The request id list.
-    _req_ids: list[str]
-
-    def __init__(self, draft_probs, req_ids):
-        assert len(req_ids) == len(draft_probs)
-        self.draft_probs = draft_probs
-        self._req_ids = req_ids
-
-    def update(self,
-               draft_probs: torch.Tensor,
-               tmp_req_ids: list[str]):
-        diff_req_ids = [item for item in self._req_ids if item not in tmp_req_ids]
-        index = [self._req_ids.index(req_id) for req_id in diff_req_ids]
-        self._req_ids = diff_req_ids
-        self.draft_probs = self.draft_probs[index]
-        self.draft_probs = torch.cat([self.draft_probs, draft_probs])
-        
-        self._req_ids.extend(tmp_req_ids)
-        assert len(self._req_ids) == len(self.draft_probs)
-
-    def prune(self, req_ids: list[str]):
-        new_req_ids = [req_id for req_id in self._req_ids if req_id not in req_ids]
-        if new_req_ids != self._req_ids:
-            # Batch contents changed - prune removed sequences.
-            index = [self._req_ids.index(req_id) for req_id in new_req_ids]
-            self.draft_probs = self.draft_probs[index]
-            self._req_ids = new_req_ids
-
-    def get_probs(self, req_ids: list[str]):
-        index = [self._req_ids.index(req_id) for req_id in req_ids]
-        return self.draft_probs[index]

vllm/v1/worker/gpu_model_runner.py

@@ -58,13 +58,11 @@ from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsTensors,
 from vllm.v1.pool.metadata import PoolingMetadata
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.rejection_sampler import RejectionSampler
-from vllm.v1.sample.rejection_sampler_mtp import MtpRejectionSampler
 from vllm.v1.sample.sampler import Sampler
 from vllm.v1.spec_decode.eagle import EagleProposer
 from vllm.v1.spec_decode.medusa import MedusaProposer
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
-from vllm.v1.spec_decode.utils import DraftProbs
 from vllm.v1.utils import bind_kv_cache
 from vllm.v1.worker.block_table import BlockTable
 from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
@@ -194,11 +192,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 raise ValueError("Unknown speculative decoding method: "
                                  f"{self.speculative_config.method}")
             
-            self.use_mtp = self.speculative_config.method == "deepseek_mtp"
-            if not self.use_mtp:
             self.rejection_sampler = RejectionSampler()
-            else:
-                self.rejection_sampler = MtpRejectionSampler()
 
         # Request states.
         self.requests: dict[str, CachedRequestState] = {}
@@ -325,8 +319,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # from the KV cache of `shared_kv_cache_layers[layer_name]`.
         self.shared_kv_cache_layers: dict[str, str] = {}
 
-        self.draft_probs : Optional[DraftProbs] = None
-
     def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
         """
         Update the order of requests in the batch based on the attention
@@ -386,10 +378,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         for req_id in scheduler_output.finished_req_ids:
             self.input_batch.remove_request(req_id)
 
-        # prune draft probs of finished requests
-        if self.use_mtp and self.draft_probs is not None and len(scheduler_output.finished_req_ids) > 0:
-            self.draft_probs.prune(list(scheduler_output.finished_req_ids))
-
         # Free the cached encoder outputs.
         for req_id, input_id in scheduler_output.free_encoder_input_ids:
             encoder_outputs = self.encoder_cache.get(req_id)
@@ -547,7 +535,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             # Add spec_token_ids to token_ids_cpu.
             spec_token_ids = (
                 scheduler_output.scheduled_spec_decode_tokens.get(req_id, ()))
-            
             if spec_token_ids:
                 num_spec_tokens = len(spec_token_ids)
                 start_index = self.input_batch.num_tokens_no_spec[req_index]
@@ -1465,8 +1452,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             target_logits = logits[spec_decode_metadata.target_logits_indices]
             output_token_ids = self.rejection_sampler(
                 spec_decode_metadata,
-                self.draft_probs.get_probs(self.input_batch.req_ids) \
-                    if self.draft_probs is not None else None,  # draft_probs
+                None,  # draft_probs
                 target_logits,
                 bonus_token_ids,
                 sampling_metadata,
@@ -1551,7 +1537,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             # Speculative decoding is not enabled.
             spec_token_ids = None
         else:
-            spec_token_ids, draft_probs = self.propose_draft_token_ids(
+            spec_token_ids = self.propose_draft_token_ids(
                 scheduler_output,
                 valid_sampled_token_ids,
                 sampling_metadata,
@@ -1562,13 +1548,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 attn_metadata,
             )
 
-            if self.use_mtp:
-                if self.draft_probs is None:
-                    self.draft_probs = DraftProbs(
-                    draft_probs, self.input_batch.req_ids)
-                else:
-                    self.draft_probs.update(draft_probs, self.input_batch.req_ids)
-
             spec_token_ids = spec_token_ids.tolist()
 
         # Clear KVConnector state after all KVs are generated.
@@ -1587,7 +1566,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             pooler_output=[],
             finished_sending=finished_sending,
             finished_recving=finished_recving,
-            num_nans_in_logits=num_nans_in_logits
+            num_nans_in_logits=num_nans_in_logits,
         )
 
     def propose_draft_token_ids(
@@ -1600,8 +1579,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         aux_hidden_states: Optional[torch.Tensor],
         spec_decode_metadata: Optional[SpecDecodeMetadata],
         attn_metadata: dict[str, Any],
-    ) -> tuple[list[list[int]], torch.Tensor]:
-        draft_probs = None
+    ) -> list[list[int]]:
         num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
         if self.speculative_config.method == "ngram":
             assert isinstance(self.drafter, NgramProposer)
@@ -1700,7 +1678,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                     target_hidden_states = hidden_states[token_indices]
                 target_slot_mapping = eagle_attn_metadata.slot_mapping[
                     token_indices]
-            spec_token_ids, draft_probs = self.drafter.propose(
+            draft_token_ids = self.drafter.propose(
                 target_token_ids=target_token_ids,
                 target_positions=target_positions,
                 target_hidden_states=target_hidden_states,
@@ -1711,8 +1689,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 sampling_metadata=sampling_metadata,
                 num_rejected_tokens=num_rejected_tokens
             )
+            spec_token_ids = draft_token_ids.tolist()
             
-        return spec_token_ids, draft_probs
+        return spec_token_ids
 
     def kv_connector_no_forward(
             self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
@@ -2168,10 +2147,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 draft_token_ids, self.device)
 
             num_tokens = sum(len(ids) for ids in draft_token_ids)
-            draft_probs = torch.randn(
-                num_tokens, logits.shape[-1], device=self.device,
-                dtype=logits.dtype)
-            # draft_probs = None
+            # draft_probs = torch.randn(
+            #     num_tokens, logits.shape[-1], device=self.device,
+            #     dtype=logits.dtype)
+            draft_probs = None
             target_logits = torch.randn(num_tokens,
                                         logits.shape[-1],
                                         device=self.device,