[feat]1.支持mtp模型 full_cuda_graph; 2.优化mtp拒绝采样

vllm/model_executor/models/deepseek_mtp.py

@@ -152,7 +152,7 @@ class DeepSeekMultiTokenPredictor(nn.Module):
         return logits
 
 
-#@support_torch_compile
+@support_torch_compile
 class DeepSeekMTP(nn.Module, SupportsPP):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm/v1/spec_decode/eagle.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any, Optional
 import numpy as np
+
 import torch
 import torch.nn as nn
 
@@ -57,6 +59,9 @@ class EagleProposer:
         self.use_cuda_graph = (self.vllm_config.compilation_config.level
                                == CompilationLevel.PIECEWISE and
                                not self.vllm_config.model_config.enforce_eager)
+        self.use_full_cuda_graph = (
+            self.use_cuda_graph
+            and vllm_config.compilation_config.full_cuda_graph)
         self.cudagraph_batch_sizes = list(
             reversed(
                 self.vllm_config.compilation_config.cudagraph_capture_sizes))
@@ -72,6 +77,8 @@ class EagleProposer:
             (self.max_num_tokens, self.hidden_size),
             dtype=self.dtype,
             device=device)
+        # attention metadata captured in full cudagraph mode
+        self.attn_metadata_cudagraph = None
         # We need +1 here because the arange is used to set query_start_loc,
         # which has one more element than batch_size.
         self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
@@ -131,6 +138,38 @@ class EagleProposer:
         # copy inputs to buffer for cudagraph
         self.positions[:num_tokens] = target_positions
         self.hidden_states[:num_tokens] = target_hidden_states
+        
+        if (self.use_full_cuda_graph
+                and num_tokens <= self.cudagraph_batch_sizes[-1]):
+            assert self.attn_metadata_cudagraph
+            if self.method in ["eagle", "eagle3"]:
+                self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
+                    attn_metadata.seq_lens)
+                self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
+                    attn_metadata.slot_mapping)
+                self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
+                    attn_metadata.query_start_loc)
+                self.attn_metadata_cudagraph.block_table[:batch_size] = (
+                    attn_metadata.block_table)
+            elif self.method == "deepseek_mtp":
+                self.attn_metadata_cudagraph.num_actual_tokens = (
+                    attn_metadata.num_actual_tokens)
+                self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
+                    attn_metadata.query_start_loc)
+                self.attn_metadata_cudagraph.slot_mapping[:num_tokens] = (
+                    attn_metadata.slot_mapping)
+                self.attn_metadata_cudagraph.num_decodes = (
+                    attn_metadata.num_decodes)
+                self.attn_metadata_cudagraph.num_decode_tokens = (
+                    attn_metadata.num_decode_tokens)
+                self.attn_metadata_cudagraph.num_prefills = (
+                    attn_metadata.num_prefills)
+                
+                if attn_metadata.decode is not None:
+                    self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.decode.block_table)
+                    self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.decode.seq_lens)
 
         with set_forward_context(per_layer_attn_metadata,
                                  self.vllm_config,
@@ -147,11 +186,15 @@ class EagleProposer:
         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]
 
         # 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)
+            return draft_token_ids.view(-1, 1), draft_probs_list
 
         # TODO: Currently, MTP module released by deepseek only has
         # one layer. Adapt this code to support multiple layers once
@@ -191,7 +234,7 @@ class EagleProposer:
                 seq_lens=(seq_lens + 1),
             )
 
-        for _ in range(self.num_speculative_tokens - 1):
+        for i in range(self.num_speculative_tokens - 1):
             # Update the inputs.
             # cast to int32 is crucial when eagle model is compiled.
             # tensor.argmax() returns int64 by default.
@@ -242,6 +285,43 @@ class EagleProposer:
             self.input_ids[:batch_size] = input_ids
             self.positions[:batch_size] = clamped_positions
             self.hidden_states[:batch_size] = hidden_states
+            
+            if (self.use_full_cuda_graph
+                    and batch_size <= self.cudagraph_batch_sizes[-1]):
+                assert self.attn_metadata_cudagraph
+                if self.method in ["eagle", "eagle3"]:
+                    self.attn_metadata_cudagraph.seq_lens[:batch_size] = (
+                        attn_metadata.seq_lens)
+                    self.attn_metadata_cudagraph.slot_mapping[:batch_size] = (
+                        attn_metadata.slot_mapping)
+                    if i == 0:
+                        self.attn_metadata_cudagraph.query_start_loc[:batch_size +
+                                                                    1] = (
+                                                                        attn_metadata
+                                                                        .
+                                                                        query_start_loc
+                                                                    )
+                        self.attn_metadata_cudagraph.block_table[:batch_size] = (
+                            attn_metadata.block_table)
+                elif self.method == "deepseek_mtp":
+                    self.attn_metadata_cudagraph.num_actual_tokens = (
+                        attn_metadata.num_actual_tokens)
+                    self.attn_metadata_cudagraph.slot_mapping[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.slot_mapping)
+                    self.attn_metadata_cudagraph.num_decodes = (
+                        attn_metadata.num_decodes)
+                    self.attn_metadata_cudagraph.num_decode_tokens = (
+                        attn_metadata.num_decode_tokens)
+                    self.attn_metadata_cudagraph.num_prefills = (
+                        attn_metadata.num_prefills)
+                    self.attn_metadata_cudagraph.decode.seq_lens[:attn_metadata.num_decode_tokens] = (
+                        attn_metadata.decode.seq_lens)
+                    
+                    if i == 0:
+                        self.attn_metadata_cudagraph.query_start_loc[:batch_size + 1] = (
+                            attn_metadata.query_start_loc)
+                        self.attn_metadata_cudagraph.decode.block_table[:attn_metadata.num_decode_tokens] = (
+                            attn_metadata.decode.block_table)
 
             # Run the model.
             with set_forward_context(per_layer_attn_metadata,
@@ -265,10 +345,15 @@ class EagleProposer:
             # 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)
-        return draft_token_ids
+        draft_probs = torch.stack(draft_probs_list, dim=1).contiguous()
+
+        return draft_token_ids, draft_probs
 
     def prepare_inputs(
         self,
@@ -418,8 +503,13 @@ class EagleProposer:
     def dummy_run(
         self,
         num_tokens: int,
+        attn_metadata: Optional[dict[str, Any]] = None,
     ) -> None:
-        with set_forward_context(None, self.vllm_config,
+        if attn_metadata is not None and self.attn_metadata_cudagraph is None:
+            self.attn_metadata_cudagraph = attn_metadata[
+                self.attn_layer_names[0]]
+        with set_forward_context(attn_metadata,
+                                 self.vllm_config,
                                  num_tokens=num_tokens):
             self.model(
                 self.input_ids[:num_tokens],

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
 
 _SAMPLING_EPS = 1e-5
@@ -12,3 +16,41 @@ def is_spec_decode_unsupported(sampling_params: SamplingParams) -> bool:
             or sampling_params.repetition_penalty != 1.0
             or sampling_params.min_p > _SAMPLING_EPS
             or sampling_params.logprobs is not None)
+    
+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

@@ -60,11 +60,13 @@ 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.worker.gpu_input_batch import CachedRequestState, InputBatch
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
 from vllm.platforms import current_platform
@@ -194,7 +196,13 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             else:
                 raise ValueError("Unknown speculative decoding method: "
                                  f"{self.speculative_config.method}")
-            self.rejection_sampler = RejectionSampler()
+                
+            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] = {}
@@ -320,6 +328,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # means this layer will perform attention using the keys and values
         # 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:
         """
@@ -379,6 +389,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         for req_id in scheduler_output.finished_req_ids:
             self.requests.pop(req_id, None)
             self.encoder_cache.pop(req_id, None)
+            
         # Remove the finished requests from the persistent batch.
         # NOTE(woosuk): There could be an edge case where finished_req_ids and
         # scheduled_req_ids overlap. This happens when a request is aborted and
@@ -387,6 +398,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # and handling the second as a new request.
         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:
@@ -1541,7 +1556,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             target_logits = logits[spec_decode_metadata.target_logits_indices]
             output_token_ids = self.rejection_sampler(
                 spec_decode_metadata,
-                None,  # draft_probs
+                self.draft_probs.get_probs(self.input_batch.req_ids) \
+                    if self.draft_probs is not None else None,  # draft_probs
                 target_logits,
                 bonus_token_ids,
                 sampling_metadata,
@@ -1627,7 +1643,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             spec_token_ids = None
         else:
             assert spec_decode_common_attn_metadata is not None
-            spec_token_ids = self.propose_draft_token_ids(
+            spec_token_ids, draft_probs = self.propose_draft_token_ids(
                 scheduler_output,
                 valid_sampled_token_ids,
                 sampling_metadata,
@@ -1637,6 +1653,14 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 spec_decode_metadata,
                 spec_decode_common_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()
 
         self.eplb_step()
 
@@ -1743,7 +1767,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                         [h[token_indices] for h in aux_hidden_states], dim=-1)
                 else:
                     target_hidden_states = hidden_states[token_indices]
-            draft_token_ids = self.drafter.propose(
+            spec_token_ids, draft_probs = self.drafter.propose(
                 target_token_ids=target_token_ids,
                 target_positions=target_positions,
                 target_hidden_states=target_hidden_states,
@@ -1752,8 +1776,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 common_attn_metadata=common_attn_metadata,
                 num_rejected_tokens=num_rejected_tokens
             )
-            spec_token_ids = draft_token_ids.tolist()
-        return spec_token_ids
+        
+        return spec_token_ids, draft_probs
 
     @staticmethod
     def maybe_setup_kv_connector(scheduler_output: "SchedulerOutput"):
@@ -2200,7 +2224,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
 
             if self.speculative_config and self.speculative_config.use_eagle():
                 assert isinstance(self.drafter, EagleProposer)
-                self.drafter.dummy_run(num_tokens)
+                self.drafter.dummy_run(num_tokens, attn_metadata)
 
         # This is necessary to avoid blocking DP.
         # For dummy runs, we typically skip EPLB since we don't have any real
@@ -2267,10 +2291,11 @@ 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,