1.解决medusa模型多卡推理结果异常问题

2.examples中添加medusa readme
3.修复model_runner中input_positions配置错误的笔误，解决多个模型运行失败问题

csrc/attention/attention_kernels.cu

@@ -297,18 +297,19 @@ __device__ void paged_attention_kernel(
       // Add the ALiBi bias if slopes are given.
       qk += (alibi_slope != 0) ? alibi_slope * (token_idx - seq_len + 1) : 0;
 
-      if (thread_group_offset == 0) {
-        // Store the partial reductions to shared memory.
-        // NOTE(woosuk): It is required to zero out the masked logits.
-        bool mask = token_idx >= seq_len;
-
       // used for tree-style attention
       if (attn_masks != nullptr) {
         const int* attn_masks_ptr = attn_masks + seq_idx * attn_masks_stride;
-          mask |= attn_masks_ptr[token_idx] == 0;
+        if (attn_masks_ptr[token_idx] == 0) {
+          qk = -FLT_MAX;
+        }
       }
 
-        logits[token_idx - start_token_idx] = mask ? 0.f : qk;
+      if (thread_group_offset == 0) {
+        // Store the partial reductions to shared memory.
+        // NOTE(woosuk): It is required to zero out the masked logits.
+        const bool mask = token_idx >= seq_len;
+        logits[token_idx - start_token_idx] = mask ? 0.f: qk;
         // Update the max value.
         qk_max = mask ? qk_max : fmaxf(qk_max, qk);
       }

csrc/attention/attention_kernels_opt.cu

@@ -327,17 +327,26 @@ __device__ void paged_attention_kernel_opt(
       float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(q_vecs[reuse_kv_idx*THREAD_GROUP_SIZE + thread_group_offset], k_vecs);
       // Add the ALiBi bias if slopes are given.
       qk += (alibi_slope != 0) ? alibi_slope * (token_idx - seq_len + 1) : 0;
+
+      // used for tree-style attention
+      if (attn_masks != nullptr) {
+        const int* attn_masks_ptr = attn_masks + seq_idx * attn_masks_stride;
+        if (attn_masks_ptr[token_idx] == 0) {
+          qk = -FLT_MAX;
+        }
+      }
+
       __builtin_amdgcn_sched_barrier(0);
       if (thread_group_offset == 0) {
         // Store the partial reductions to shared memory.
         // NOTE(woosuk): It is required to zero out the masked logits.
-        bool mask = token_idx >= seq_len;
+        const bool mask = token_idx >= seq_len;
 
         // used for tree-style attention
-        if (attn_masks != nullptr) {
+        /*if (attn_masks != nullptr) {
           const int* attn_masks_ptr = attn_masks + seq_idx * attn_masks_stride;
           mask |= attn_masks_ptr[token_idx] == 0;
-        }
+        }*/
         logits[(reuse_kv_idx * partition_size) + (token_idx - start_token_idx)] = mask ? 0.f : qk;
         // Update the max value.
         qk_max[reuse_kv_idx] = mask ? qk_max[reuse_kv_idx] : fmaxf(qk_max[reuse_kv_idx], qk);

csrc/attention/attention_kernels_opt_tc.cu

@@ -290,15 +290,18 @@ __device__ void paged_attention_kernel_TC(
         const int token_idx = block_idx * BLOCK_SIZE+rowid;
         if(alibi_slope[i] != 0){
           float alibi=alibi_slope[i]* (token_idx - seq_len + 1);
-          qk_vec[i] += alibi;
+          qk_vec[i] = alibi;
         }
-        bool mask = (token_idx >= seq_len);
 
         // used for tree-style attention
         if (attn_masks != nullptr) {
           const int* attn_masks_ptr = attn_masks + seq_idx * attn_masks_stride;
-          mask |= attn_masks_ptr[token_idx] == 0;
+          if (attn_masks_ptr[token_idx] == 0) {
+            qk_vec[i] = -FLT_MAX;
           }
+        }
+
+        const bool mask = (token_idx >= seq_len);
         if(mask){
           from_float(logits[partition_size*reuse_kv_idx+token_idx - start_token_idx] , 0.f);
         }

examples/medusa/README.md

+# Medusa Decoding
+
+本文说明如何使用vllm构建和运行medusa模型，目前medusa支持tree-style generation，target model和draft model均可多卡推理
+
+## Overview
+与其他模型不同，medusa解码需要一个base model和若干Medusa heads.
+
+Vllm medusa model的实现在[vllm/model_executor/models/medusa.py]
+
+## Support Matrix
+  * FP16
+  * BF16
+  * PAGED_KV_CACHE
+  * Tensor Parallel
+
+### convert Medusa model weights
+# medusa 模型需要转换为vllm中Medusa的模型格式
+
+```bash
+python medusa_weight_converter.py --medusa_num_heads 4 --medusa_num_layers 1 --medusa_model_path /work/medusa/qwen2_72b_head_4/adapter_model.bin --vocab_size 152064 --hidden_size 8192 --output_dir /work/medusa/sugon/vllm-medusa-qwen2-72b-head-4 --medusa_choices="[(0,), (0, 0), (0, 0, 0), (0, 0, 0, 0), (0, 1), (1,), (1, 0), (0, 0, 1), (0, 1, 0), (0, 2), (1, 0, 0), (2,), (2, 0), (0, 3), (0, 0, 2), (0, 2, 0), (0, 4), (0, 0, 1, 0), (0, 1, 0, 0), (2, 0, 0), (3,), (0, 5), (0, 0, 0, 1), (3, 0), (0, 0, 3), (1, 0, 0, 0), (0, 3, 0), (0, 6), (0, 0, 4), (0, 4, 0), (1, 1), (4,)]"
+```
+此处qwen2_72b_head_4是medusa模型使用peft lora训练后保存的权重，其他格式也可参考[medusa_weight_converter.py]修改进行权重转换
+
+
+### Run
+
+```bash
+python3 -m vllm.entrypoints.openai.api_server \
+  --served-model-name qwen_medusa \
+  --model /models/Qwen2-72B-Instruct/ -tp 4 \
+  --max-model-len 1024 --max-num-seqs 8 --gpu-memory-utilization 0.7 \
+  --speculative-model /work/medusa/vllm-medusa-qwen2-72b-head-4 \
+  --speculative-draft-tensor-parallel-size 4 \
+  --speculative-disable-by-batch-size 4 \
+  --use-v2-block-manager \
+  --spec-decoding-acceptance-method typical_acceptance_sampler \
+  --enforce-eager --dtype float16 --trust-remote-code --port 8086\
+  --enable-lora --lora-modules medusa-lora=/work/qwen2_72b_head_4 \
+  --max-lora-rank 32 --lora-extra-vocab-size 0 --merge-lora True \
+  --lora-target-modules qkv_proj \
+  --tree-style-spec-decoding True\
+  --num-speculative-heads 4 --num-speculative-tokens 33
+```
+merge-lora可以将lora权重和base model权重融合，提升整体推理速度，若对精度有严格要求，可不设置此参数
+num-speculative-tokens和medusa choices的个数相关，num_speculative_tokens = len(medusa_choices) + 2
+
+# do request
+```bash
+curl http://localhost:8086/v1/completions \
+-H "Content-Type: application/json" \
+-d '{
+"model": "medusa-lora",
+"prompt": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n帮我写一个C++的快速排序算法<|im_end|>\n<|im_start|>assistant\n",
+"max_tokens": 256,
+"temperature": 0.0
+}'
+```bash

examples/medusa/medusa_weight_converter.py

 import os
+import ast
 from pathlib import Path
 from typing import Iterable, List, Optional, Tuple, Union
 from addict import Dict
@@ -21,12 +22,12 @@ DEFAULT_VOCAB_PADDING_SIZE = 64
 
 TRAINED_BLOCK_WEIGHT_NAME_TEMPLATE = 'base_model.model.medusa_head.{}.{}.linear.weight'
 TRAINED_MEDUSA_HEADS_NEMA_TEMPLATE = 'base_model.model.medusa_head.{}.1.weight'
+TRAINED_BLOCK_BIAS_NAME_TEMPLATE = 'base_model.model.medusa_head.{}.{}.linear.bias'
 
 VLLM_BLOCK_WEIGHT_NAME_TEMPLATE = 'blocks.{}.layers.{}.weight'
+VLLM_BLOCK_BIAS_NAME_TEMPLATE = 'blocks.{}.layers.{}.bias'
 VLLM_MEDUSA_HEADS_WEIGHT_NAME_TEMPLATE = 'lm_heads.{}.weight'
 
-MEDUSA_CHOICES = [(0,), (0, 0), (0, 0, 0), (1,), (0, 1), (1, 0), (0, 2), (2,), (0, 0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 3), (2, 0), (1, 0, 0), (3,), (0, 0, 2), (0, 4), (0, 2, 0), (0, 5), (4,), (1, 1), (0, 0, 3), (3, 0), (0, 6), (0, 0, 0, 1), (0, 3, 0), (0, 0, 4), (0, 0, 1, 0), (2, 0, 0), (5,), (0, 1, 0, 0), (0, 7)]
-
 
 def default_weight_loader(param: torch.Tensor,
                           loaded_weight: torch.Tensor) -> None:
@@ -218,7 +219,7 @@ class ResidualBlock(nn.Module):
         super().__init__()
 
         self.layers = nn.ModuleList([
-            nn.Linear(hidden_size, hidden_size, bias=False)
+            nn.Linear(hidden_size, hidden_size)
             for _ in range(num_layers)
         ])
         self.act = nn.SiLU()
@@ -319,13 +320,8 @@ class CustomMedusaConfig(PretrainedConfig):
 
 
 def main(args):
-    # load the medusa config from the yaml file
-    medusa_config_path=args.medusa_config_path
-    with open(medusa_config_path, encoding="utf-8") as file:
-        medusa_cfg: Dict = Dict(yaml.safe_load(file))
-
-    medusa_head_num = medusa_cfg.medusa_num_heads
-    medusa_num_layers = medusa_cfg.medusa_num_layers
+    medusa_head_num = args.medusa_num_heads
+    medusa_num_layers = args.medusa_num_layers
 
     config = MedusaConfig(hidden_size=args.hidden_size, vocab_size=args.vocab_size, num_heads=medusa_head_num)
     medusa_model = Medusa(config)
@@ -346,6 +342,7 @@ def main(args):
 
     for i in range(medusa_head_num):
         for j in range(medusa_num_layers):
+            # load linear weight
             vllm_medusa_block_weight_name = VLLM_BLOCK_WEIGHT_NAME_TEMPLATE.format(i, j)
             trained_medusa_block_weight_name = TRAINED_BLOCK_WEIGHT_NAME_TEMPLATE.format(i, j)
 
@@ -356,38 +353,59 @@ def main(args):
                                     default_weight_loader)
             weight_loader(vllm_medusa_block_param, trained_medusa_block_param)
 
+            # load linear bias
+            vllm_medusa_block_bias_name = VLLM_BLOCK_BIAS_NAME_TEMPLATE.format(i, j)
+            trained_medusa_block_bias_name = TRAINED_BLOCK_BIAS_NAME_TEMPLATE.format(i, j)
+
+            vllm_medusa_block_bias_param = params_dict[vllm_medusa_block_bias_name]
+            trained_medusa_block_bias_param = trained_medusa_model[trained_medusa_block_bias_name]
+
+            weight_loader = getattr(vllm_medusa_block_bias_param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(vllm_medusa_block_bias_param, trained_medusa_block_bias_param)
+    
 
     if not Path(args.output_dir).is_dir():
         os.makedirs(args.output_dir, exist_ok=True)
     save_model(medusa_model, os.path.join(args.output_dir, "model.safetensors"))
     
+    medusa_choices = ast.literal_eval(args.medusa_choices)
     to_save_config = CustomMedusaConfig(name_or_path=os.path.join(args.output_dir, "config.json"),
                                         hidden_size=args.hidden_size,
                                         num_heads=medusa_head_num,
                                         num_hidden_layers=medusa_num_layers,
                                         vocab_size=args.vocab_size,
-                                        medusa_choices=MEDUSA_CHOICES)
+                                        medusa_choices=medusa_choices)
     to_save_config.save_pretrained(args.output_dir)
 
     # validate weight
-    # with safe_open("model.safetensors", framework="pt") as f:
-    #     param = f.get_tensor(VLLM_BLOCK_WEIGHT_NAME_TEMPLATE.format(0, 0))
-    #     trained_param = trained_medusa_model[TRAINED_BLOCK_WEIGHT_NAME_TEMPLATE.format(0, 0)]
+    # with safe_open(os.path.join(args.output_dir, "model.safetensors"), framework="pt") as f:
+    #     param = f.get_tensor(VLLM_BLOCK_WEIGHT_NAME_TEMPLATE.format(3, 0))
+    #     trained_param = trained_medusa_model[TRAINED_BLOCK_WEIGHT_NAME_TEMPLATE.format(3, 0)]
     #     mse_value = torch.nn.functional.mse_loss(param.cpu(), trained_param.cpu())
     #     print("weight mes:", mse_value)
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Medusa Model Evaluator")
-    parser.add_argument("--medusa_config_path", type=str, required=True,
-                        help="Path to the medusa config file.")
     parser.add_argument("--medusa_model_path", type=str, required=True,
                         help="Path to the medusa model file.")
     parser.add_argument("--vocab_size", type=int, required=True,
                         help="Vocab size")
+    parser.add_argument("--medusa_num_heads", type=int, required=True,
+                        help="Number of Medusa heads")
+    parser.add_argument("--medusa_num_layers", type=int, required=True,
+                        help="Number of Medusa layers")
     parser.add_argument("--hidden_size", type=int, required=True,
                         help="Hidden size")
     parser.add_argument("--output_dir", type=str, required=True,
                         help="Output dir")
+    parser.add_argument(
+        '--medusa_choices',
+        type=str,
+        required=True,
+        help="Medusa choice to use, if not none, will use Medusa decoding."
+        "   E.g.: [[0, 0, 0, 0], [0, 1, 0], [1, 0], [1, 1]] for 9 medusa tokens."
+    )
     args = parser.parse_args()
     main(args)

vllm/config.py

@@ -1386,11 +1386,11 @@ class SpeculativeConfig:
             else:
                 speculative_draft_tensor_parallel_size = \
                     target_parallel_config.tensor_parallel_size
-        elif speculative_draft_tensor_parallel_size != 1:
-            # TODO(wooyeon): allow tp values larger than 1
-            raise ValueError(
-                f"{speculative_draft_tensor_parallel_size=} cannot be "
-                f"other value than 1")
+        # elif speculative_draft_tensor_parallel_size != 1:
+        #     # TODO(wooyeon): allow tp values larger than 1
+        #     raise ValueError(
+        #         f"{speculative_draft_tensor_parallel_size=} cannot be "
+        #         f"other value than 1")
 
         draft_parallel_config = ParallelConfig(
             pipeline_parallel_size=target_parallel_config.

vllm/engine/llm_engine.py

@@ -41,7 +41,7 @@ from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import RequestOutputKind, SamplingParams
 from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest,
                            Sequence, SequenceGroup, SequenceGroupMetadata,
-                           SequenceStatus)
+                           SequenceStatus, VLLM_INVALID_TOKEN_ID)
 from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
                           init_tracer)
 from vllm.transformers_utils.config import try_get_generation_config
@@ -945,8 +945,10 @@ class LLMEngine:
         if len(outputs) > 1:
             outputs_by_sequence_group = create_output_by_sequence_group(
                 outputs, num_seq_groups=len(seq_group_metadata_list))
-        else:
+        elif len(outputs) == 1:
             outputs_by_sequence_group = outputs
+        else:
+            return None
 
         # Determine the requests we need to operate on
         if request_id:
@@ -967,6 +969,7 @@ class LLMEngine:
 
         finished_before: List[int] = []
         finished_now: List[int] = []
+        empty_seq_indices: List[int] = []
         for i in indices:
             if i in skip:
                 continue
@@ -985,6 +988,16 @@ class LLMEngine:
             else:
                 output = [outputs_by_sequence_group[0][i]]
 
+            # tree style speculative decoding may generate empty output in first step
+            samples = [o.samples[0] for o in output]
+            valid_samples = [
+                sample for sample in samples
+                if sample.output_token != VLLM_INVALID_TOKEN_ID
+            ]
+            if len(valid_samples) == 0:
+                empty_seq_indices.append(i)
+                continue
+
             if not is_async:
                 seq_group.update_num_computed_tokens(
                     scheduled_seq_group.token_chunk_size)
@@ -1056,7 +1069,7 @@ class LLMEngine:
 
         # Create the outputs
         for i in indices:
-            if i in skip or i in finished_before or i in finished_now:
+            if i in skip or i in finished_before or i in finished_now or i in empty_seq_indices:
                 continue  # Avoids double processing
 
             scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i]

vllm/lora/layers.py

@@ -131,7 +131,6 @@ class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
         self.base_layer = base_layer
         self.embeddings_slice: Optional[Tuple[int, int]]
         self.embeddings_weights: Optional[torch.Tensor]
-        self.device = _get_lora_device(self.base_layer)
 
     def create_lora_weights(
             self,
@@ -208,18 +207,6 @@ class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
         self.lora_b_stacked[index,
                             0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                 lora_b.T, non_blocking=True)
-        
-        self.lora_a = lora_a.to(self.device)
-        self.lora_b = lora_b.to(self.device)
-
-        if self.lora_config.merge_lora:
-            merged_weights = torch.matmul(self.lora_a, self.lora_b)
-            if merged_weights.shape != self.base_layer.weight.data:
-                merged_weights = merged_weights.T + self.base_layer.weight
-            else:
-                merged_weights = merged_weights + self.base_layer.weight
-            self.base_layer.weight.data.copy_(merged_weights)
-
         if embeddings_tensor is not None:
             self.embeddings_tensors[
                 index, :embeddings_tensor.shape[0], :embeddings_tensor.
@@ -238,15 +225,12 @@ class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         added_tokens_mask = x > self.base_layer.org_vocab_size - 1
         embeddings_indices = self.punica_wrapper.embeddings_indices
-        indices = embeddings_indices[0].view_as(x)
-
-        if not self.lora_config.merge_lora:
-            indices_0 = embeddings_indices[1].view_as(x)
+        indices = embeddings_indices[1].view_as(x)
         full_lora_a_embeddings = F.embedding(
-                x + indices_0,
+            x + indices,
             self.lora_a_stacked_2d,
         )
-
+        indices = embeddings_indices[0].view_as(x)
         full_output = self.base_layer.forward(
             x.add_(indices * added_tokens_mask))
 
@@ -267,10 +251,6 @@ class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
                                        self.lora_b_stacked,
                                        add_input=True)
         return full_output.view_as(full_output_org)
-        else:
-            full_output = self.base_layer.forward(
-                x.add_(indices * added_tokens_mask))
-            return full_output
 
     @classmethod
     def can_replace_layer(
@@ -729,7 +709,7 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
         self.q_shard_id = self.tp_rank
         self.kv_shard_id = self.tp_rank // self.base_layer.num_kv_head_replicas
 
-        lora_a_output_size_per_partition = (
+        self.lora_a_output_size_per_partition = (
             lora_config.max_lora_rank if not lora_config.fully_sharded_loras
             else divide(lora_config.max_lora_rank, self.tp_size))
         # q, k, v
@@ -737,7 +717,7 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
             torch.zeros(
                 max_loras,
                 1,
-                lora_a_output_size_per_partition,
+                self.lora_a_output_size_per_partition,
                 self.input_size,
                 dtype=lora_config.lora_dtype,
                 device=self.device,
@@ -745,7 +725,7 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
             torch.zeros(
                 max_loras,
                 1,
-                lora_a_output_size_per_partition,
+                self.lora_a_output_size_per_partition,
                 self.input_size,
                 dtype=lora_config.lora_dtype,
                 device=self.device,
@@ -753,7 +733,7 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
             torch.zeros(
                 max_loras,
                 1,
-                lora_a_output_size_per_partition,
+                self.lora_a_output_size_per_partition,
                 self.input_size,
                 dtype=lora_config.lora_dtype,
                 device=self.device,
@@ -869,6 +849,7 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
                     qkv_weights = qkv_weights + self.base_layer.weight.data
                 
                 self.base_layer.weight.data.copy_(qkv_weights)
+                qkv_weights_list.clear()
         else:
             if lora_b[0] is not None:
                 lora_b_q = lora_b[0]

vllm/model_executor/layers/typical_acceptance_sampler.py

@@ -5,6 +5,9 @@ import torch.nn.functional as F
 
 from vllm.model_executor.layers.spec_decode_base_sampler import (
     SpecDecodeDeterministicBaseSampler)
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
 
 
 class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
@@ -195,7 +198,7 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
         k = draft_token_ids.shape[-1]
         output_token_id_list = []
 
-        print("####################################accept_length:", accept_length)
+        logger.info("accept_length:%s", accept_length)
         for i in range(batch_size):
             output_best_candidates.append(best_candidate[i])
             accept_lengths.append(accept_length[i])

vllm/model_executor/models/medusa.py

@@ -12,6 +12,8 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.transformers_utils.configs.medusa import MedusaConfig
 from vllm import _custom_ops as ops
+from vllm.distributed import (tensor_model_parallel_all_gather,
+                              tensor_model_parallel_gather)
 
 TOPK=10 # topk for sparse tree (10 is a placeholder and it is sufficient)
 
@@ -22,7 +24,7 @@ class ResidualBlock(nn.Module):
         super().__init__()
 
         self.layers = nn.ModuleList([
-            nn.Linear(hidden_size, hidden_size, bias=False)
+            nn.Linear(hidden_size, hidden_size)
             for _ in range(num_layers)
         ])
         self.act = nn.SiLU()
@@ -89,12 +91,13 @@ class Medusa(nn.Module):
         return [block(hidden_states) for block in self.blocks]
 
     def compute_logits(
-            self, hidden_states: List[torch.Tensor],
-            sampling_metadata: SamplingMetadata) -> List[torch.Tensor]:
+            self, hidden_states: List[torch.Tensor]) -> List[torch.Tensor]:
         logits_lst: List[torch.Tensor] = []
 
         for hs, lm_head in zip(hidden_states, self.lm_heads):
-            _logits = self.logits_processor(lm_head, hs, sampling_metadata)
+            #_logits = self.logits_processor(lm_head, hs, sampling_metadata)
+            _logits = lm_head.linear_method.apply(lm_head, hs, bias=None)
+            _logits = tensor_model_parallel_all_gather(_logits)
 
             if _logits is None:
                 # _logits should only be None on rank > 0, in which case
@@ -117,7 +120,7 @@ class Medusa(nn.Module):
     def sample(
         self,
         logits: List[torch.Tensor],
-        sampling_metadata: SamplingMetadata,
+        sample_indices_list: List[List[int]],
     ) -> List[SamplerOutput]:
         logits = torch.stack(logits, dim=0).float()
         logprobs = torch.log_softmax(logits, dim=-1)
@@ -128,13 +131,13 @@ class Medusa(nn.Module):
         token_prob_list = []
         token_logprob_list = []
 
-        for idx, seq_group in enumerate(sampling_metadata.seq_groups):
-            token_id_list.append(token_ids[:, seq_group.sample_indices])
-            token_prob_list.append(probs[:, seq_group.sample_indices])
-            token_logprob_list.append(logprobs[:, seq_group.sample_indices])
+        for idx, sample_indices in enumerate(sample_indices_list):
+            token_id_list.append(token_ids[:, sample_indices])
+            token_prob_list.append(probs[:, sample_indices])
+            token_logprob_list.append(logprobs[:, sample_indices])
 
         outputs: List[Optional[SamplerOutput]] = []
-        for idx in range(len(sampling_metadata.seq_groups)):
+        for idx in range(len(sample_indices_list)):
             outputs.append(
                 SamplerOutput(
                     outputs=None,
@@ -148,7 +151,7 @@ class Medusa(nn.Module):
     def medusa_sample(
         self,
         medusa_logits: List[torch.Tensor],
-        sampling_metadata: SamplingMetadata,
+        sample_indices_list: List[List[int]],
         logits: torch.Tensor,
         medusa_buffers: Dict[str, Any]
     ) -> List[SamplerOutput]:
@@ -178,12 +181,12 @@ class Medusa(nn.Module):
         token_id_list = []
         cart_candidate_list = []
 
-        for idx, seq_group in enumerate(sampling_metadata.seq_groups):
-            token_id_list.append(tree_candidates[seq_group.sample_indices, :])
-            cart_candidate_list.append(cart_candidates[seq_group.sample_indices, :])
+        for sample_indices in sample_indices_list:
+            token_id_list.append(tree_candidates[sample_indices, :])
+            cart_candidate_list.append(cart_candidates[sample_indices, :])
 
         outputs: List[Optional[SamplerOutput]] = []
-        for idx in range(len(sampling_metadata.seq_groups)):
+        for idx in range(len(sample_indices_list)):
             outputs.append(
                 SamplerOutput(
                     outputs=None,
@@ -196,24 +199,22 @@ class Medusa(nn.Module):
     def generate_proposals(
         self,
         previous_hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
+        sample_indices_list: List[List[int]],
         previous_logits: torch.Tensor=None,
         medusa_buffers: Dict[str, Any]=None
     ) -> List[SamplerOutput]:
         if medusa_buffers is None:
             return self.sample(
                 logits=self.compute_logits(
-                    hidden_states=self.forward(previous_hidden_states),
-                    sampling_metadata=sampling_metadata,
+                    hidden_states=self.forward(previous_hidden_states)
                 ),
-                sampling_metadata=sampling_metadata,
+                sample_indices_list=sample_indices_list,
             )
         else:
             return self.medusa_sample(medusa_logits=self.compute_logits(
-                    hidden_states=self.forward(previous_hidden_states),
-                    sampling_metadata=sampling_metadata,
+                    hidden_states=self.forward(previous_hidden_states)
                 ),
-                sampling_metadata=sampling_metadata,
+                sample_indices_list=sample_indices_list,
                 logits=previous_logits,
                 medusa_buffers=medusa_buffers)
 

vllm/sequence.py

@@ -172,6 +172,8 @@ class SequenceData(msgspec.Struct,
     # It is used to compute mrope_position_ids.
     _mrope_position_delta: Optional[int] = None
 
+    _first_step_flag: bool = True
+
     @staticmethod
     def from_token_counts(*token_counts: Tuple[int, int]) -> "SequenceData":
         if len(token_counts) == 0:
@@ -351,6 +353,12 @@ class SequenceData(msgspec.Struct,
     def stage(self) -> SequenceStage:
         return self._stage
     
+    def get_first_step_flag(self):
+        return self._first_step_flag
+    
+    def set_first_step_flag(self, flag: bool):
+        self._first_step_flag = flag
+
     def __repr__(self) -> str:
         return (f"SequenceData("
                 f"prompt_token_ids={self._prompt_token_ids}, "
@@ -1371,6 +1379,9 @@ class ExecuteModelRequest(
     # Optional tree position ids from draft model.
     tree_position_ids: Optional[torch.Tensor] = None
 
+    # Optional slot mapping of kvcache that pending to be moved generated from draft model.
+    kvcache_slot_to_be_moved: Optional[torch.Tensor] = None
+
     @property
     def is_first_multi_step(self) -> bool:
         # TODO(will) make this be able to handle batches with variable number of
@@ -1419,4 +1430,5 @@ class ExecuteModelRequest(
             if self.last_sampled_token_ids is not None else None,
             async_callback=self.async_callback,
             tree_attn_masks=self.tree_attn_masks,
-            tree_position_ids=self.tree_position_ids)
+            tree_position_ids=self.tree_position_ids,
+            kvcache_slot_to_be_moved=self.kvcache_slot_to_be_moved)

vllm/spec_decode/batch_expansion.py

@@ -592,7 +592,7 @@ class BatchExpansionTreeStyleScorer(BatchExpansionTop1Scorer):
         prompt_token_ids = seq_data.prompt_token_ids_array
 
         # first step need to ignore output token generated by prefill phase
-        if len(seq_data.get_output_token_ids()) == 1:
+        if seq_data.get_first_step_flag():
             new_output_token_ids = [*seq_data.get_output_token_ids()[:-1], *token_ids]
         else:
             new_output_token_ids = [*seq_data.get_output_token_ids(), *token_ids]

vllm/spec_decode/medusa_worker.py

 import weakref
-from typing import List, Optional, Set, Tuple
+from typing import List, Optional, Set, Tuple, Dict
 
 import torch
 import torch.nn.functional as F
@@ -12,6 +12,7 @@ from vllm.spec_decode.proposer_worker_base import NonLLMProposerWorkerBase
 from vllm.spec_decode.top1_proposer import Top1Proposer
 from vllm.spec_decode.tree_style_proposer import TreeStyleProposer
 from vllm.worker.worker import Worker
+from vllm.distributed import broadcast_tensor_dict
 
 TOPK=10 # topk for sparse tree (10 is a placeholder and it is sufficient)
 
@@ -66,6 +67,51 @@ class MedusaWorker(NonLLMProposerWorkerBase, Worker):
     def set_should_modify_greedy_probs_inplace(self):
         pass
 
+    def _get_driver_input_and_broadcast(
+        self, execute_model_req: ExecuteModelRequest
+    ) -> Dict[str, torch.Tensor]:
+        
+        seq_group_metadata_list = execute_model_req.seq_group_metadata_list
+
+        seq_lens, query_lens = self._prepare_input_tensors(
+            seq_group_metadata_list)
+
+        generators = self.model_runner.get_generators(
+            execute_model_req.finished_requests_ids)
+        sampling_metadata = SamplingMetadata.prepare(
+            seq_group_metadata_list, seq_lens, query_lens, self.device,
+            self.model_runner.pin_memory, generators)
+        
+        sample_indices_list = []
+        for seq_group in sampling_metadata.seq_groups:
+            sample_indices_list.append(seq_group.sample_indices)
+        
+        previous_hidden_states = execute_model_req.previous_hidden_states.hidden_states
+        previous_logits = execute_model_req.previous_logits.logits if \
+            execute_model_req.previous_logits is not None else None
+
+        tensor_dict = {
+            "previous_hidden_states": previous_hidden_states,
+            "previous_logits": previous_logits,
+            "sample_indices_list": sample_indices_list,
+            "seq_lens": seq_lens
+        }
+
+        if self.do_metadata_broadcast:
+            broadcast_tensor_dict(tensor_dict, src=0)
+
+        return tensor_dict
+    
+    def _get_worker_input_from_broadcast(
+        self
+    ) -> Optional[Dict[str, torch.Tensor]]:
+        """ Get the worker input from the broadcasted tensor dict. """
+        assert self.do_metadata_broadcast
+        assert not self.is_driver_worker
+        broadcast_data = broadcast_tensor_dict(src=0)
+
+        return broadcast_data
+
     @torch.inference_mode()
     def sampler_output(
         self,
@@ -83,26 +129,22 @@ class MedusaWorker(NonLLMProposerWorkerBase, Worker):
         """
         self._raise_if_unsupported(execute_model_req)
         
-        seq_group_metadata_list = execute_model_req.seq_group_metadata_list
-
-        seq_lens, query_lens = self._prepare_input_tensors(
-            seq_group_metadata_list)
-
-        generators = self.model_runner.get_generators(
-            execute_model_req.finished_requests_ids)
-        sampling_metadata = SamplingMetadata.prepare(
-            seq_group_metadata_list, seq_lens, query_lens, self.device,
-            self.model_runner.pin_memory, generators)
+        if self.is_driver_worker:
+            tensor_dict = self._get_driver_input_and_broadcast(execute_model_req)
+        else:
+            tensor_dict = self._get_worker_input_from_broadcast()
+            if tensor_dict is None:
+                raise ValueError("Can not get inputs of medusa worker!!!")
 
         model_outputs = self.model_runner.model.generate_proposals(
-            previous_hidden_states=execute_model_req.previous_hidden_states.
-            hidden_states,
-            sampling_metadata=sampling_metadata,
-            previous_logits=execute_model_req.previous_logits.logits if execute_model_req.previous_logits is not None else None,
+            previous_hidden_states=tensor_dict["previous_hidden_states"],
+            sample_indices_list=tensor_dict["sample_indices_list"],
+            previous_logits=tensor_dict["previous_logits"],
             medusa_buffers=self.medusa_buffers)
         
         # create tree attn masks
         if self.medusa_buffers is not None:
+            seq_lens = tensor_dict["seq_lens"]
             max_context_len = max(seq_lens)
             for sampler_output, seq_len in zip(model_outputs, seq_lens):
                 context_len = seq_len
@@ -142,7 +184,7 @@ class MedusaWorker(NonLLMProposerWorkerBase, Worker):
                     query_lens.append(seq_len - context_len)
                 else:
                     # first step of tree decoding need to ignore first token
-                    if self.medusa_buffers is not None and seq_data.get_output_len() == 1:
+                    if self.medusa_buffers is not None and seq_data.get_first_step_flag():
                         seq_data_len -= 1
                     seq_lens.append(seq_data_len)
                     query_lens.append(1)
@@ -168,6 +210,9 @@ class MedusaWorker(NonLLMProposerWorkerBase, Worker):
         """MedusaWorker does not yet implement support for cache swap
         operations or beam search.
         """
+        if execute_model_req is None:
+            return None
+
         if any([
                 execute_model_req.blocks_to_swap_in,
                 execute_model_req.blocks_to_swap_out,

vllm/spec_decode/spec_decode_worker.py

@@ -260,6 +260,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         # in the subsequent step.
         self.previous_hidden_states: Optional[HiddenStates] = None
         self.previous_logits: Optional[Logits] = None
+        self.kvcache_slot_to_be_moved: Optional[torch.Tensor] = None
         self._disable_logprobs = disable_logprobs
         self._disable_log_stats = disable_log_stats
 
@@ -600,6 +601,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
             self.scorer_worker.execute_model()
 
         if not data["disable_all_speculation"]:
+            if not self.tree_style_spec_decoding:
                 # Even if num_lookahead_slots is zero, we want to run the
                 # proposer model as it may have KV.
                 #
@@ -607,6 +609,9 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
                 # should delegate how many times it runs to the proposer.
                 for _ in range(max(num_lookahead_slots, 1)):
                     self.proposer_worker.execute_model()
+            else:
+                if not data["no_spec"]:
+                    self.proposer_worker.sampler_output(None, None, None)
 
         if not data["no_spec"]:
             self.scorer_worker.execute_model()
@@ -635,6 +640,9 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         execute_model_req.previous_logits = self.previous_logits
         self.previous_logits = None
 
+        execute_model_req.kvcache_slot_to_be_moved = self.kvcache_slot_to_be_moved
+        self.kvcache_slot_to_be_moved = None
+
         with Timer() as proposal_timer:
             # Generate proposals using draft worker.
             proposals = self.proposer_worker.get_spec_proposals(
@@ -743,7 +751,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
             first_step_flags = []
             for i, sgm in enumerate(seq_group_metadata_list):
                 seq = next(iter(sgm.seq_data.values()))
-                first_step_flags.append(True if seq.get_output_len() == 1 else False)
+                first_step_flags.append(True if seq.get_first_step_flag() else False)
             
             sampler_extra_kwargs["first_step_flags"] = first_step_flags
 
@@ -847,8 +855,11 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
                 seq_len = min(seq_len, context_len + token_chunk_size)
 
                 # first step of tree-style decoding need to ignore first generated token
-                if seq_data.get_output_len() == 1:
+                if seq_data.get_first_step_flag():
                     seq_len -= 1
+
+                # move cache is the last step of tree decoding, so set first_step_flag to false
+                seq_data.set_first_step_flag(False)   
                 seq_lens.append(seq_len)
 
         model_input = self.scorer._scorer_worker.model_input
@@ -888,12 +899,13 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
                                             dtype=torch.long,
                                             device=self.device).view(-1, 1)
             src_dst_tensor = torch.cat([select_indices_slot_tensor, target_slot_mapping_tensor], dim=-1) #[batch_size*T, 2]
-            kv_caches = self.scorer._scorer_worker.kv_cache[execute_model_req.virtual_engine]
-            kv_cache_dtype = cache_engine.cache_config.cache_dtype
-            backend = cache_engine.attn_backend
-            num_kv_heads = cache_engine.num_kv_heads
-            head_size = cache_engine.head_size
-            backend.move_cache(kv_caches, src_dst_tensor, kv_cache_dtype, num_kv_heads, head_size)
+            # kv_caches = self.scorer._scorer_worker.kv_cache[execute_model_req.virtual_engine]
+            # kv_cache_dtype = cache_engine.cache_config.cache_dtype
+            # backend = cache_engine.attn_backend
+            # num_kv_heads = cache_engine.num_kv_heads
+            # head_size = cache_engine.head_size
+            # backend.move_cache(kv_caches, src_dst_tensor, kv_cache_dtype, num_kv_heads*4, head_size)
+            self.kvcache_slot_to_be_moved = src_dst_tensor
     
     def compute_slot_mapping(self, slot_mapping: List[int],
                          seq_id: int, select_indices: List[int], block_size: int,
@@ -996,7 +1008,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
                                                 accepted_token_ids_by_step)
         maybe_rejsample_metrics = (
             self._metrics.maybe_collect_rejsample_metrics(k))
-        if maybe_rejsample_metrics is not None:
+        if maybe_rejsample_metrics is not None and sampler_output_list:
             sampler_output_list[
                 0].spec_decode_worker_metrics = maybe_rejsample_metrics
 

vllm/spec_decode/tree_style_proposer.py

@@ -115,7 +115,7 @@ class TreeStyleProposer(SpeculativeProposer):
         tree_position_ids_list = []
         for seq_group_metadata in seq_group_metadata_list:
             seq_data = next(iter(seq_group_metadata.seq_data.values()))
-            if seq_data.get_output_len() == 1:
+            if seq_data.get_first_step_flag():
                 seq_len = seq_data.get_len() -1
             else:
                 seq_len = seq_data.get_len()

vllm/worker/cache_engine.py

@@ -101,6 +101,14 @@ class CacheEngine:
     def copy(self, src_to_dsts: torch.Tensor) -> None:
         self.attn_backend.copy_blocks(self.gpu_cache, src_to_dsts)
 
+    def move_caches(self, kv_caches: List[torch.Tensor],
+                    src_to_dsts: torch.Tensor) -> None:
+        self.attn_backend.move_cache(kv_caches, 
+                                     src_to_dsts, 
+                                     self.cache_config.cache_dtype,
+                                     self.num_kv_heads,
+                                     self.head_size)
+
     @staticmethod
     def get_cache_block_size(
         cache_config: CacheConfig,

vllm/worker/model_runner.py

@@ -509,20 +509,10 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         else:
             inter_data.input_tokens[seq_idx].append(tokens)
         
-        if (seq_len - context_len) == 1:
-            inter_data.input_positions[seq_idx].append(seq_len - 1)
-        else:
-            inter_data.input_positions[seq_idx].extend(
-                range(context_len, seq_len))
+        inter_data.input_positions[seq_idx] = list(range(context_len, seq_len))
 
-        if seq_group_metadata.tree_position_ids is None:
-            if (seq_len - context_len) == 1:
-                inter_data.input_positions[seq_idx].append(seq_len - 1)
-            else:
-                inter_data.input_positions[seq_idx].extend(
-                    range(context_len, seq_len))
-        else:
-            inter_data.input_positions[seq_idx] = seq_group_metadata.tree_position_ids.cpu().tolist()
+        if seq_group_metadata.tree_position_ids is not None:
+            inter_data.input_positions[seq_idx] = seq_group_metadata.tree_position_ids.contiguous().cpu().tolist()
             inter_data.tree_attn_masks[seq_idx] = seq_group_metadata.tree_attn_masks
 
         inter_data.query_lens[
@@ -871,6 +861,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         tree_attention_masks_tensor = None
         if len(tree_attention_masks_list) > 0:
             tree_attention_masks_tensor = torch.stack(tree_attention_masks_list, dim=0)
+            tree_attention_masks_tensor = tree_attention_masks_tensor.contiguous()
 
         # Attention metadata.
         attn_metadata = self.attn_metadata_builder.build(

vllm/worker/worker.py

@@ -324,6 +324,7 @@ class Worker(LocalOrDistributedWorkerBase):
             blocks_to_copy=blocks_to_copy,
             virtual_engine=virtual_engine,
             num_steps=num_steps,
+            kvcache_slot_to_be_moved=execute_model_req.kvcache_slot_to_be_moved
         )
 
     @torch.inference_mode()
@@ -342,6 +343,11 @@ class Worker(LocalOrDistributedWorkerBase):
                 and worker_input.blocks_to_copy.numel() > 0):
             self.cache_engine[virtual_engine].copy(worker_input.blocks_to_copy)
 
+        # tree-style generation need to move kvcache to correct position
+        if worker_input.kvcache_slot_to_be_moved is not None:
+            self.cache_engine[virtual_engine].move_caches(self.kv_cache[virtual_engine], 
+                                                          worker_input.kvcache_slot_to_be_moved)
+
     def _get_cached_seq_group_metadata(
             self,
             seq_group_metadata_list: List[Union[SequenceGroupMetadata,

vllm/worker/worker_base.py

@@ -161,6 +161,9 @@ class WorkerInput:
     virtual_engine: int = 0
     num_steps: int = 1
 
+    # Optional slot mapping of kvcache that pending to be moved generated from draft model.
+    kvcache_slot_to_be_moved: Optional[torch.Tensor] = None
+
     @classmethod
     def from_broadcasted_tensor_dict(
         cls: Type["WorkerInput"],
@@ -177,6 +180,7 @@ class WorkerInput:
             blocks_to_copy=tensor_dict.pop("blocks_to_copy"),
             virtual_engine=tensor_dict["virtual_engine"],
             num_steps=tensor_dict.pop("num_steps"),
+            kvcache_slot_to_be_moved=tensor_dict.pop("kvcache_slot_to_be_moved"),
         )
 
     def as_broadcastable_tensor_dict(
@@ -191,6 +195,7 @@ class WorkerInput:
             "blocks_to_copy": self.blocks_to_copy,
             "virtual_engine": self.virtual_engine,
             "num_steps": self.num_steps,
+            "kvcache_slot_to_be_moved": self.kvcache_slot_to_be_moved
         }
 
         return tensor_dict
@@ -281,8 +286,6 @@ class LocalOrDistributedWorkerBase(WorkerBase):
         # set tree_attn_masks and position ids to seq_group_metadata_list
         if execute_model_req.tree_attn_masks is not None:
             for i, seq_group_metadata in enumerate(execute_model_req.seq_group_metadata_list):
-                # seq_group_metadata.tree_attn_masks = execute_model_req.tree_attn_masks[i]
-                # seq_group_metadata.tree_position_ids = execute_model_req.tree_position_ids[i]
                 seq_group_metadata.set_tree_style_args(tree_attn_masks=execute_model_req.tree_attn_masks[i], 
                                                        tree_position_ids=execute_model_req.tree_position_ids[i])