Merge branch 'v0.6.2-dev_wm' into 'v0.6.2-dev'

优化medusa 推理

See merge request dcutoolkit/deeplearing/vllm!41

benchmarks/kernels/benchmark_moe.py

@@ -304,7 +304,9 @@ def main(args: argparse.Namespace):
     else:
         batch_sizes = [args.batch_size]
 
-    ray.init()
+    ray.init(address=None,
+                 ignore_reinit_error=True,
+                 num_gpus=args.tp_size)
     num_gpus = int(ray.available_resources()["GPU"])
     workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
 

examples/medusa/README.md

@@ -17,9 +17,9 @@ Vllm medusa model的实现在[vllm/model_executor/models/medusa.py]
 # 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,)]"
+python medusa_weight_converter.py --medusa_num_heads 4 --medusa_num_layers 1 --medusa_model_path /work/model.bin --vocab_size 152064 --hidden_size 8192 --output_dir /work/medusa/vllm-medusa-qwen2-72b-head-4 --medusa_choices="[(0), (0, 0), (0, 0, 0), (0, 1), (1), (1, 0), (0, 0, 0, 0), (0, 0, 1), (0, 2), (0, 1, 0), (2), (0, 0, 2), (0, 3), (1, 0, 0), (2, 0), (0, 2, 0), (0, 4), (0, 0, 3), (3), (0, 0, 0, 1), (0, 5), (0, 0, 1, 0), (0, 0, 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
+此处model.bin是训练后保存的medusa head权重
 
 
 ### Run
@@ -28,30 +28,34 @@ python medusa_weight_converter.py --medusa_num_heads 4 --medusa_num_layers 1 --m
 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 \
+  --max-model-len 1024 --max-num-seqs 8 --gpu-memory-utilization 0.8 \
   --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 \
+  --dtype float16 --trust-remote-code --port 8086\
   --tree-style-spec-decoding True\
-  --num-speculative-heads 4 --num-speculative-tokens 33
+  --num-speculative-heads 4 --num-speculative-tokens 24
 ```
+
 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
+num-speculative-tokens和medusa choices的个数相关，num_speculative_tokens = len(medusa_choices) + 1
 
 # do request
 ```bash
 curl http://localhost:8086/v1/completions \
 -H "Content-Type: application/json" \
 -d '{
-"model": "medusa-lora",
+"model": "qwen_medusa",
 "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
+```
+
+### benchmark
+python medusa_benchmark_throughput.py --model /data/llm-models/qwen2/Qwen2-72B-Instruct/ -tp 4 --dtype float16 --trust-remote-code --max-num-seqs 1 --dataset /work/test/medusa_benchmark_data.json --max-model-len 4096 --gpu-memory-utilization 0.9
+
+可设置max-num-seqs对不同的batch进行性能测试
+

examples/medusa/medusa_benchmark_throughput.py

+"""Benchmark offline inference throughput."""
+import argparse
+import json
+import random
+import time
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+import uvloop
+from tqdm import tqdm
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          PreTrainedTokenizerBase)
+
+from vllm.inputs import PromptInputs
+from vllm.engine.arg_utils import DEVICE_OPTIONS, AsyncEngineArgs, EngineArgs
+from vllm.entrypoints.openai.api_server import (
+    build_async_engine_client_from_engine_args)
+from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.utils import FlexibleArgumentParser, merge_async_iterators
+from vllm.lora.request import LoRARequest
+
+
+def nullable_str(val: str):
+    if not val or val == "None":
+        return None
+    return val
+
+
+def sample_requests(
+    dataset_path: str,
+    num_requests: int,
+    tokenizer: PreTrainedTokenizerBase,
+    fixed_output_len: Optional[int],
+) -> List[Tuple[str, int, int]]:
+    if fixed_output_len is not None and fixed_output_len < 4:
+        raise ValueError("output_len too small")
+
+    # Load the dataset.
+    with open(dataset_path) as f:
+        dataset = json.load(f)
+    # Only keep the first two turns of each conversation.
+    dataset = [data["prompt"] for data in dataset]
+
+    # Shuffle the dataset.
+    random.shuffle(dataset)
+
+    # Filter out sequences that are too long or too short
+    filtered_dataset: List[Tuple[str, int, int]] = []
+    for i in range(len(dataset)):
+        if len(filtered_dataset) == num_requests:
+            break
+
+        # Tokenize the prompts and completions.
+        prompt = dataset[i]
+        prompt_token_ids = tokenizer(prompt).input_ids
+        prompt_len = len(prompt_token_ids)
+        output_len = fixed_output_len
+        filtered_dataset.append((prompt, prompt_len, output_len))
+
+    return filtered_dataset
+
+
+def run_vllm(
+    warmup_requests: List[Tuple[str, int, int]],
+    requests: List[Tuple[str, int, int]],
+    model: str,
+    tokenizer: str,
+    quantization: Optional[str],
+    tensor_parallel_size: int,
+    seed: int,
+    n: int,
+    use_beam_search: bool,
+    trust_remote_code: bool,
+    dtype: str,
+    max_model_len: Optional[int],
+    enforce_eager: bool,
+    kv_cache_dtype: str,
+    quantization_param_path: Optional[str],
+    device: str,
+    enable_prefix_caching: bool,
+    enable_chunked_prefill: bool,
+    max_num_batched_tokens: int,
+    distributed_executor_backend: Optional[str],
+    gpu_memory_utilization: float = 0.9,
+    num_scheduler_steps: int = 1,
+    use_v2_block_manager: bool = False,
+    download_dir: Optional[str] = None,
+    load_format: str = EngineArgs.load_format,
+    disable_async_output_proc: bool = False,
+    max_num_seqs: int = 8,
+    speculative_model: str=None,
+    speculative_draft_tensor_parallel_size: int = 1,
+    speculative_disable_by_batch_size: int = 4,
+    spec_decoding_acceptance_method: str = None,
+    enable_lora: bool = False,
+    max_lora_rank: int = 32,
+    merge_lora: bool = False,
+    lora_extra_vocab_size: int = 0,
+    lora_target_modules: List[str] = None,
+    tree_style_spec_decoding: bool = False, 
+    num_speculative_heads: int = 5,
+    num_speculative_tokens: int = 64,
+    use_new_beam_search_impl: bool = False,
+    lora_modules: str = None
+) -> float:
+    from vllm import LLM, SamplingParams
+    llm = LLM(
+        model=model,
+        tokenizer=tokenizer,
+        quantization=quantization,
+        tensor_parallel_size=tensor_parallel_size,
+        seed=seed,
+        trust_remote_code=trust_remote_code,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_memory_utilization,
+        enforce_eager=enforce_eager,
+        kv_cache_dtype=kv_cache_dtype,
+        quantization_param_path=quantization_param_path,
+        device=device,
+        enable_prefix_caching=enable_prefix_caching,
+        download_dir=download_dir,
+        enable_chunked_prefill=enable_chunked_prefill,
+        max_num_batched_tokens=max_num_batched_tokens,
+        distributed_executor_backend=distributed_executor_backend,
+        load_format=load_format,
+        num_scheduler_steps=num_scheduler_steps,
+        use_v2_block_manager=use_v2_block_manager,
+        disable_async_output_proc=disable_async_output_proc,
+        max_num_seqs=max_num_seqs,
+        speculative_model=speculative_model,
+        speculative_draft_tensor_parallel_size=speculative_draft_tensor_parallel_size,
+        speculative_disable_by_batch_size=speculative_disable_by_batch_size,
+        spec_decoding_acceptance_method=spec_decoding_acceptance_method,
+        enable_lora=enable_lora,
+        max_lora_rank=max_lora_rank,
+        merge_lora=merge_lora,
+        lora_extra_vocab_size=lora_extra_vocab_size,
+        lora_target_modules=lora_target_modules,
+        tree_style_spec_decoding=tree_style_spec_decoding,
+        num_speculative_heads=num_speculative_heads,
+        num_speculative_tokens=num_speculative_tokens
+    )
+
+    # Add the requests to the engine.
+    prompts: List[str] = []
+    sampling_params: List[SamplingParams] = []
+    for prompt, _, output_len in requests:
+        prompts.append(prompt)
+        sampling_params.append(
+            SamplingParams(
+                n=n,
+                temperature=0.0,
+                top_p=1.0,
+                use_beam_search=use_beam_search,
+                ignore_eos=False,
+                max_tokens=output_len,
+            ))
+
+    # warmup
+    warmup_prompts = []
+    warmup_sampling_params = []
+    for prompt, _, output_len in warmup_requests:
+        warmup_prompts.append(prompt)
+        warmup_sampling_params.append(
+            SamplingParams(
+                n=n,
+                temperature=0.0,
+                top_p=1.0,
+                use_beam_search=use_beam_search,
+                ignore_eos=False,
+                max_tokens=output_len,
+            ))
+        
+    print("Warming up...")
+    for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
+        if lora_modules is None:
+            llm.generate(warmup_prompts, warmup_sampling_params, use_tqdm=True)
+        else:
+            llm.generate(warmup_prompts, warmup_sampling_params, use_tqdm=True,
+                         lora_request=LoRARequest("medusa-lora", 1, lora_modules))
+
+    total_out_tokens = 0
+    start = time.perf_counter()
+
+    if lora_modules is None:
+        outputs = llm.generate(prompts, sampling_params, use_tqdm=False)
+    else:
+        outputs = llm.generate(prompts, sampling_params, use_tqdm=False, 
+                               lora_request=LoRARequest("medusa-lora", 1, lora_modules))
+    for output in outputs:
+        print("token_ids len:{} text:{}".format(len(output.outputs[0].token_ids), output.outputs[0].text))
+        total_out_tokens += len(output.outputs[0].token_ids)
+
+    end = time.perf_counter()
+    return end - start, total_out_tokens
+
+
+async def run_vllm_async(
+    requests: List[Tuple[str, int, int]],
+    model: str,
+    tokenizer: str,
+    quantization: Optional[str],
+    tensor_parallel_size: int,
+    seed: int,
+    n: int,
+    use_beam_search: bool,
+    trust_remote_code: bool,
+    dtype: str,
+    max_model_len: Optional[int],
+    enforce_eager: bool,
+    kv_cache_dtype: str,
+    quantization_param_path: Optional[str],
+    device: str,
+    enable_prefix_caching: bool,
+    enable_chunked_prefill: bool,
+    max_num_batched_tokens: int,
+    distributed_executor_backend: Optional[str],
+    gpu_memory_utilization: float = 0.9,
+    num_scheduler_steps: int = 1,
+    use_v2_block_manager: bool = False,
+    download_dir: Optional[str] = None,
+    load_format: str = EngineArgs.load_format,
+    disable_async_output_proc: bool = False,
+    disable_frontend_multiprocessing: bool = False,
+    max_num_seqs: int = 8,
+    speculative_model: str=None,
+    speculative_draft_tensor_parallel_size: int = 1,
+    speculative_disable_by_batch_size: int = 4,
+    spec_decoding_acceptance_method: str = None,
+    enable_lora: bool = False,
+    max_lora_rank: int = 32,
+    merge_lora: bool = False,
+    lora_extra_vocab_size: int = 0,
+    lora_target_modules: List[str] = None,
+    tree_style_spec_decoding: bool = False, 
+    num_speculative_heads: int = 5,
+    num_speculative_tokens: int = 64,
+    use_new_beam_search_impl: bool = False,
+    lora_modules: str = None
+) -> float:
+    from vllm import SamplingParams
+    engine_args = AsyncEngineArgs(
+        model=model,
+        tokenizer=tokenizer,
+        quantization=quantization,
+        tensor_parallel_size=tensor_parallel_size,
+        seed=seed,
+        trust_remote_code=trust_remote_code,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_memory_utilization,
+        enforce_eager=enforce_eager,
+        kv_cache_dtype=kv_cache_dtype,
+        quantization_param_path=quantization_param_path,
+        device=device,
+        enable_prefix_caching=enable_prefix_caching,
+        download_dir=download_dir,
+        enable_chunked_prefill=enable_chunked_prefill,
+        max_num_batched_tokens=max_num_batched_tokens,
+        distributed_executor_backend=distributed_executor_backend,
+        load_format=load_format,
+        num_scheduler_steps=num_scheduler_steps,
+        use_v2_block_manager=use_v2_block_manager,
+        disable_async_output_proc=disable_async_output_proc,
+        worker_use_ray=False,
+        disable_log_requests=True,
+        max_num_seqs=max_num_seqs,
+        speculative_model=speculative_model,
+        speculative_draft_tensor_parallel_size=speculative_draft_tensor_parallel_size,
+        speculative_disable_by_batch_size=speculative_disable_by_batch_size,
+        spec_decoding_acceptance_method=spec_decoding_acceptance_method,
+        enable_lora=enable_lora,
+        max_lora_rank=max_lora_rank,
+        merge_lora=merge_lora,
+        lora_extra_vocab_size=lora_extra_vocab_size,
+        lora_target_modules=lora_target_modules,
+        tree_style_spec_decoding=tree_style_spec_decoding,
+        num_speculative_heads=num_speculative_heads,
+        num_speculative_tokens=num_speculative_tokens
+    )
+
+    async with build_async_engine_client_from_engine_args(
+            engine_args, disable_frontend_multiprocessing) as llm:
+
+        # Add the requests to the engine.
+        prompts: List[str] = []
+        sampling_params: List[SamplingParams] = []
+        for prompt, _, output_len in requests:
+            prompts.append(prompt)
+            sampling_params.append(
+                SamplingParams(
+                    n=n,
+                    temperature=0.0 if use_beam_search else 1.0,
+                    top_p=1.0,
+                    use_beam_search=use_beam_search,
+                    ignore_eos=False,
+                    max_tokens=output_len,
+                ))
+
+        generators = []
+        start = time.perf_counter()
+        for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
+            generator = llm.generate(prompt, sp, request_id=f"test{i}")
+            generators.append(generator)
+        all_gens = merge_async_iterators(*generators)
+        out_dict = {}
+        async for i, res in all_gens:
+            #print("res:", res)
+            out_dict[res.request_id] = len(res.outputs[0].token_ids)
+        end = time.perf_counter()
+        total_out_tokens = 0
+        for token_num in out_dict.values():
+            total_out_tokens += token_num
+        return end - start, total_out_tokens
+
+
+def main(args: argparse.Namespace):
+    print(args)
+    random.seed(args.seed)
+
+    # Sample the requests.
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.tokenizer, trust_remote_code=args.trust_remote_code)
+    warmup_prompt = "hi" * 10
+    warmup_requests = [(warmup_prompt, 10, 10)
+                for _ in range(1)]
+    if args.dataset is None:
+        # Synthesize a prompt with the given input length.
+        prompt = "hi" * (args.input_len - 1)
+        requests = [(prompt, args.input_len, args.output_len)
+                    for _ in range(args.num_prompts)]
+    else:
+        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
+                                   args.output_len)
+
+    if args.async_engine:
+        run_args = [
+            requests, args.model, args.tokenizer, args.quantization,
+            args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
+            args.trust_remote_code, args.dtype, args.max_model_len,
+            args.enforce_eager, args.kv_cache_dtype,
+            args.quantization_param_path, args.device,
+            args.enable_prefix_caching, args.enable_chunked_prefill,
+            args.max_num_batched_tokens, args.distributed_executor_backend,
+            args.gpu_memory_utilization, args.num_scheduler_steps,
+            args.use_v2_block_manager, args.download_dir, args.load_format,
+            args.disable_async_output_proc, False, args.max_num_seqs,
+            args.speculative_model, args.speculative_draft_tensor_parallel_size,
+            args.speculative_disable_by_batch_size, args.spec_decoding_acceptance_method,
+            args.enable_lora, args.max_lora_rank, args.merge_lora, args.lora_extra_vocab_size,
+            args.lora_target_modules, args.tree_style_spec_decoding, args.num_speculative_heads,
+            args.num_speculative_tokens
+        ]
+    else:
+        run_args = [
+            warmup_requests, requests, args.model, args.tokenizer, args.quantization,
+            args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
+            args.trust_remote_code, args.dtype, args.max_model_len,
+            args.enforce_eager, args.kv_cache_dtype,
+            args.quantization_param_path, args.device,
+            args.enable_prefix_caching, args.enable_chunked_prefill,
+            args.max_num_batched_tokens, args.distributed_executor_backend,
+            args.gpu_memory_utilization, args.num_scheduler_steps,
+            args.use_v2_block_manager, args.download_dir, args.load_format,
+            args.disable_async_output_proc, args.max_num_seqs,
+            args.speculative_model, args.speculative_draft_tensor_parallel_size,
+            args.speculative_disable_by_batch_size, args.spec_decoding_acceptance_method,
+            args.enable_lora, args.max_lora_rank, args.merge_lora, args.lora_extra_vocab_size,
+            args.lora_target_modules, args.tree_style_spec_decoding, args.num_speculative_heads,
+            args.num_speculative_tokens
+        ]
+    
+    if args.async_engine:
+        run_args.append(args.disable_frontend_multiprocessing)
+        elapsed_time, total_out_tokens = uvloop.run(run_vllm_async(*run_args))
+    else:
+        elapsed_time, total_out_tokens = run_vllm(*run_args, args.use_new_beam_search_impl, args.lora_modules)
+
+    total_num_tokens = total_out_tokens + sum(prompt_len
+                           for _, prompt_len, _ in requests)
+    
+    print(f"Latency: {elapsed_time:.2f} s")
+    print(f"All Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+          f"{total_num_tokens / elapsed_time:.2f} tokens/s")
+    print(f"Generate Throughput: {total_out_tokens / elapsed_time:.2f} tokens/s")
+
+    # Output JSON results if specified
+    if args.output_json:
+        results = {
+            "elapsed_time": elapsed_time,
+            "num_requests": len(requests),
+            "total_num_tokens": total_num_tokens,
+            "requests_per_second": len(requests) / elapsed_time,
+            "tokens_per_second": total_num_tokens / elapsed_time,
+        }
+        with open(args.output_json, "w") as f:
+            json.dump(results, f, indent=4)
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(description="Benchmark the throughput.")
+    parser.add_argument("--dataset",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset.")
+    parser.add_argument("--input-len",
+                        type=int,
+                        default=None,
+                        help="Input prompt length for each request")
+    parser.add_argument("--output-len",
+                        type=int,
+                        default=256,
+                        help="Output length for each request. Overrides the "
+                        "output length from the dataset.")
+    parser.add_argument("--model", type=str, default="facebook/opt-125m")
+    parser.add_argument("--tokenizer", type=str, default=None)
+    parser.add_argument('--quantization',
+                        '-q',
+                        choices=[*QUANTIZATION_METHODS, None],
+                        default=None)
+    parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
+    parser.add_argument("--n",
+                        type=int,
+                        default=1,
+                        help="Number of generated sequences per prompt.")
+    parser.add_argument("--use-beam-search", action="store_true")
+    parser.add_argument('--num-iters-warmup',
+                        type=int,
+                        default=1,
+                        help='Number of iterations to run for warmup.')
+    parser.add_argument("--use-new-beam-search-impl", action="store_true")
+    parser.add_argument("--num-prompts",
+                        type=int,
+                        default=1000,
+                        help="Number of prompts to process.")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument('--trust-remote-code',
+                        action='store_true',
+                        help='trust remote code from huggingface')
+    parser.add_argument(
+        '--max-model-len',
+        type=int,
+        default=None,
+        help='Maximum length of a sequence (including prompt and output). '
+        'If None, will be derived from the model.')
+    parser.add_argument(
+        '--dtype',
+        type=str,
+        default='auto',
+        choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
+        help='data type for model weights and activations. '
+        'The "auto" option will use FP16 precision '
+        'for FP32 and FP16 models, and BF16 precision '
+        'for BF16 models.')
+    parser.add_argument('--gpu-memory-utilization',
+                        type=float,
+                        default=0.9,
+                        help='the fraction of GPU memory to be used for '
+                        'the model executor, which can range from 0 to 1.'
+                        'If unspecified, will use the default value of 0.9.')
+    parser.add_argument("--enforce-eager",
+                        action="store_true",
+                        help="enforce eager execution")
+    parser.add_argument(
+        '--kv-cache-dtype',
+        type=str,
+        choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
+        default="auto",
+        help='Data type for kv cache storage. If "auto", will use model '
+        'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
+        'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
+    parser.add_argument(
+        '--quantization-param-path',
+        type=str,
+        default=None,
+        help='Path to the JSON file containing the KV cache scaling factors. '
+        'This should generally be supplied, when KV cache dtype is FP8. '
+        'Otherwise, KV cache scaling factors default to 1.0, which may cause '
+        'accuracy issues. FP8_E5M2 (without scaling) is only supported on '
+        'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is '
+        'instead supported for common inference criteria.')
+    parser.add_argument("--device",
+                        type=str,
+                        default="auto",
+                        choices=DEVICE_OPTIONS,
+                        help='device type for vLLM execution')
+    parser.add_argument(
+        "--num-scheduler-steps",
+        type=int,
+        default=1,
+        help="Maximum number of forward steps per scheduler call.")
+    parser.add_argument("--use-v2-block-manager",
+                        action='store_true',
+                        help="Enable block manager v2.")
+    parser.add_argument(
+        "--enable-prefix-caching",
+        action='store_true',
+        help="Enable automatic prefix caching for vLLM backend.")
+    parser.add_argument("--enable-chunked-prefill",
+                        action='store_true',
+                        help="enable chunked prefill for vLLM backend.")
+    parser.add_argument('--max-num-batched-tokens',
+                        type=int,
+                        default=None,
+                        help='maximum number of batched tokens per '
+                        'iteration')
+    parser.add_argument('--download-dir',
+                        type=str,
+                        default=None,
+                        help='directory to download and load the weights, '
+                        'default to the default cache dir of huggingface')
+    parser.add_argument(
+        '--output-json',
+        type=str,
+        default=None,
+        help='Path to save the throughput results in JSON format.')
+    parser.add_argument(
+        '--distributed-executor-backend',
+        choices=['ray', 'mp'],
+        default=None,
+        help='Backend to use for distributed serving. When more than 1 GPU '
+        'is used, will be automatically set to "ray" if installed '
+        'or "mp" (multiprocessing) otherwise.')
+    parser.add_argument(
+        '--load-format',
+        type=str,
+        default=EngineArgs.load_format,
+        choices=[
+            'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer',
+            'bitsandbytes'
+        ],
+        help='The format of the model weights to load.\n\n'
+        '* "auto" will try to load the weights in the safetensors format '
+        'and fall back to the pytorch bin format if safetensors format '
+        'is not available.\n'
+        '* "pt" will load the weights in the pytorch bin format.\n'
+        '* "safetensors" will load the weights in the safetensors format.\n'
+        '* "npcache" will load the weights in pytorch format and store '
+        'a numpy cache to speed up the loading.\n'
+        '* "dummy" will initialize the weights with random values, '
+        'which is mainly for profiling.\n'
+        '* "tensorizer" will load the weights using tensorizer from '
+        'CoreWeave. See the Tensorize vLLM Model script in the Examples'
+        'section for more information.\n'
+        '* "bitsandbytes" will load the weights using bitsandbytes '
+        'quantization.\n')
+    parser.add_argument(
+        "--disable-async-output-proc",
+        action='store_true',
+        default=False,
+        help="Disable async output processor for vLLM backend.")
+    parser.add_argument("--async-engine",
+                        action='store_true',
+                        default=False,
+                        help="Use vLLM async engine rather than LLM class.")
+    parser.add_argument("--disable-frontend-multiprocessing",
+                        action='store_true',
+                        default=False,
+                        help="Disable decoupled async engine frontend.")
+    parser.add_argument('--max-num-seqs',
+                            type=int,
+                            default=EngineArgs.max_num_seqs,
+                            help='Maximum number of sequences per iteration.')
+    parser.add_argument(
+            '--speculative-model',
+            type=nullable_str,
+            default=EngineArgs.speculative_model,
+            help=
+            'The name of the draft model to be used in speculative decoding.')
+    parser.add_argument(
+            '--speculative-draft-tensor-parallel-size',
+            '-spec-draft-tp',
+            type=int,
+            default=EngineArgs.speculative_draft_tensor_parallel_size,
+            help='Number of tensor parallel replicas for '
+            'the draft model in speculative decoding.')
+    
+    parser.add_argument(
+            '--speculative-disable-by-batch-size',
+            type=int,
+            default=EngineArgs.speculative_disable_by_batch_size,
+            help='Disable speculative decoding for new incoming requests '
+            'if the number of enqueue requests is larger than this value.')
+    
+    parser.add_argument(
+            '--spec-decoding-acceptance-method',
+            type=str,
+            default=EngineArgs.spec_decoding_acceptance_method,
+            choices=['rejection_sampler', 'typical_acceptance_sampler'],
+            help='Specify the acceptance method to use during draft token '
+            'verification in speculative decoding. Two types of acceptance '
+            'routines are supported: '
+            '1) RejectionSampler which does not allow changing the '
+            'acceptance rate of draft tokens, '
+            '2) TypicalAcceptanceSampler which is configurable, allowing for '
+            'a higher acceptance rate at the cost of lower quality, '
+            'and vice versa.')
+
+    # LoRA related configs
+    parser.add_argument('--enable-lora',
+                            action='store_true',
+                            help='If True, enable handling of LoRA adapters.')
+    
+    parser.add_argument('--max-lora-rank',
+                            type=int,
+                            default=EngineArgs.max_lora_rank,
+                            help='Max LoRA rank.')
+    parser.add_argument('--merge-lora',
+                        type=bool,
+                        default=False,
+                        help='If set to True, the weights of the base layer will be merged with the weights of Lora.')
+    
+    parser.add_argument(
+            '--lora-extra-vocab-size',
+            type=int,
+            default=EngineArgs.lora_extra_vocab_size,
+            help=('Maximum size of extra vocabulary that can be '
+                  'present in a LoRA adapter (added to the base '
+                  'model vocabulary).'))
+    
+    parser.add_argument('--lora-target-modules',
+                            nargs='*',
+                            default=None,
+                            help='List of lora module name, If not specified, modules will be chosen according to the model architecture.')
+    
+    parser.add_argument('--tree-style-spec-decoding',
+                            type=bool,
+                            default=False,
+                            help='If set to True, tree-style generation will be activated.')
+    
+    parser.add_argument(
+            '--num-speculative-heads',
+            type=int,
+            default=EngineArgs.num_speculative_heads,
+            help='The number of speculative heads to sample from '
+                 'the draft model in speculative decoding.')
+    
+    parser.add_argument(
+            '--num-speculative-tokens',
+            type=int,
+            default=EngineArgs.num_speculative_tokens,
+            help='The number of speculative tokens to sample from '
+            'the draft model in speculative decoding.')
+    
+    parser.add_argument(
+            '--lora-modules',
+            type=nullable_str,
+            default=None,
+            help=
+            'Path of lora model.')
+    
+
+    args = parser.parse_args()
+    if args.tokenizer is None:
+        args.tokenizer = args.model
+    if args.dataset is None:
+        assert args.input_len is not None
+        assert args.output_len is not None
+    else:
+        assert args.input_len is None
+
+    main(args)
\ No newline at end of file

examples/medusa/medusa_weight_converter.py

@@ -20,9 +20,9 @@ from vllm.model_executor.utils import set_weight_attrs
 
 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'
+TRAINED_BLOCK_WEIGHT_NAME_TEMPLATE = 'medusa_head.{}.{}.linear.weight'
+TRAINED_MEDUSA_HEADS_NEMA_TEMPLATE = 'medusa_head.{}.1.weight'
+TRAINED_BLOCK_BIAS_NAME_TEMPLATE = 'medusa_head.{}.{}.linear.bias'
 
 VLLM_BLOCK_WEIGHT_NAME_TEMPLATE = 'blocks.{}.layers.{}.weight'
 VLLM_BLOCK_BIAS_NAME_TEMPLATE = 'blocks.{}.layers.{}.bias'

vllm/attention/backends/blocksparse_attn.py

@@ -238,6 +238,8 @@ class BlocksparseFlashAttentionMetadata(AttentionMetadata):
     
     tree_attention_masks_tensor: Optional[torch.Tensor] = None
 
+    block_tables_list: Optional[List[int]] = None
+
     @property
     def prefill_metadata(
             self) -> Optional["BlocksparseFlashAttentionMetadata"]:
@@ -269,7 +271,8 @@ class BlocksparseFlashAttentionMetadata(AttentionMetadata):
             context_lens_tensor=self.context_lens_tensor[:self.num_prefills],
             block_tables=self.block_tables[:self.num_prefills],
             use_cuda_graph=False,
-            tree_attention_masks_tensor=self.tree_attention_masks_tensor
+            tree_attention_masks_tensor=self.tree_attention_masks_tensor,
+            block_tables_list=self.block_tables_list
         )
         return self._cached_prefill_metadata
 
@@ -298,7 +301,8 @@ class BlocksparseFlashAttentionMetadata(AttentionMetadata):
             context_lens_tensor=None,
             block_tables=self.block_tables[self.num_prefills:],
             use_cuda_graph=self.use_cuda_graph,
-            tree_attention_masks_tensor=self.tree_attention_masks_tensor
+            tree_attention_masks_tensor=self.tree_attention_masks_tensor,
+            block_tables_list=self.block_tables_list
         )
         return self._cached_decode_metadata
 

vllm/attention/backends/rocm_flash_attn.py

@@ -168,6 +168,7 @@ class ROCmFlashAttentionMetadata(AttentionMetadata, PagedAttentionMetadata):
     _cached_decode_metadata: Optional["ROCmFlashAttentionMetadata"] = None
 
     tree_attention_masks_tensor: Optional[torch.Tensor] = None
+    block_tables_list: Optional[List[int]] = None
 
     @property
     def prefill_metadata(self) -> Optional["ROCmFlashAttentionMetadata"]:
@@ -199,7 +200,8 @@ class ROCmFlashAttentionMetadata(AttentionMetadata, PagedAttentionMetadata):
             context_lens_tensor=self.context_lens_tensor[:self.num_prefills],
             block_tables=self.block_tables[:self.num_prefills],
             use_cuda_graph=False,
-            tree_attention_masks_tensor=self.tree_attention_masks_tensor
+            tree_attention_masks_tensor=self.tree_attention_masks_tensor,
+            block_tables_list=self.block_tables_list
         )
         return self._cached_prefill_metadata
 
@@ -228,7 +230,8 @@ class ROCmFlashAttentionMetadata(AttentionMetadata, PagedAttentionMetadata):
             context_lens_tensor=None,
             block_tables=self.block_tables[self.num_prefills:],
             use_cuda_graph=self.use_cuda_graph,
-            tree_attention_masks_tensor=self.tree_attention_masks_tensor
+            tree_attention_masks_tensor=self.tree_attention_masks_tensor,
+            block_tables_list=self.block_tables_list
         )
         return self._cached_decode_metadata
 

vllm/attention/backends/utils.py

@@ -272,7 +272,8 @@ class CommonMetadataBuilder(AttentionMetadataBuilder[TAttentionMetadata]):
             context_lens_tensor=context_lens_tensor,
             block_tables=block_tables,
             use_cuda_graph=use_captured_graph,
-            tree_attention_masks_tensor=tree_attention_masks_tensor
+            tree_attention_masks_tensor=tree_attention_masks_tensor,
+            block_tables_list=self.block_tables
         )
 
 

vllm/attention/backends/xformers.py

@@ -190,6 +190,7 @@ class XFormersMetadata(AttentionMetadata, PagedAttentionMetadata):
     cross_block_tables: Optional[torch.Tensor] = None
 
     tree_attention_masks_tensor: Optional[torch.Tensor] = None
+    block_tables_list: Optional[List[int]] = None
 
     def __post_init__(self):
         # Set during the execution of the first attention op.
@@ -271,7 +272,8 @@ class XFormersMetadata(AttentionMetadata, PagedAttentionMetadata):
             max_encoder_seq_len=self.max_encoder_seq_len,
             cross_slot_mapping=self.cross_slot_mapping,
             cross_block_tables=self.cross_block_tables,
-            tree_attention_masks_tensor=self.tree_attention_masks_tensor)
+            tree_attention_masks_tensor=self.tree_attention_masks_tensor,
+            block_tables_list=self.block_tables_list)
         return self._cached_prefill_metadata
 
     @property
@@ -311,7 +313,8 @@ class XFormersMetadata(AttentionMetadata, PagedAttentionMetadata):
             max_encoder_seq_len=self.max_encoder_seq_len,
             cross_slot_mapping=self.cross_slot_mapping,
             cross_block_tables=self.cross_block_tables,
-            tree_attention_masks_tensor=self.tree_attention_masks_tensor)
+            tree_attention_masks_tensor=self.tree_attention_masks_tensor,
+            block_tables_list=self.block_tables_list)
         return self._cached_decode_metadata
 
 

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, VLLM_INVALID_TOKEN_ID)
+                           SequenceStatus, CompletionSequenceGroupOutput, 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
@@ -989,7 +989,7 @@ class LLMEngine:
                 output = [outputs_by_sequence_group[0][i]]
 
             # tree style speculative decoding may generate empty output in first step
-            if outputs and isinstance(output[0], SamplerOutput):
+            if outputs and isinstance(output[0], CompletionSequenceGroupOutput):
                 samples = [o.samples[0] for o in output]
                 valid_samples = [
                     sample for sample in samples

vllm/model_executor/layers/sampler.py

@@ -235,7 +235,6 @@ class Sampler(nn.Module):
             sampling_metadata: Metadata for sampling.
         """
         assert logits is not None
-        original_logits = logits.clone()
         _, vocab_size = logits.shape
 
         # Prepare sampling tensors with pinned memory to avoid blocking.
@@ -320,7 +319,7 @@ class Sampler(nn.Module):
             sample_logprobs,
             on_device_tensors=on_device_tensors,
             skip_sampler_cpu_output=sampling_metadata.skip_sampler_cpu_output,
-            logits=original_logits)
+            logits=logits)
 
     @property
     def _should_modify_greedy_probs_inplace(self) -> bool:

vllm/model_executor/layers/typical_acceptance_sampler.py

@@ -198,10 +198,11 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
         k = draft_token_ids.shape[-1]
         output_token_id_list = []
 
-        logger.info("accept_length:%s", accept_length)
+        accept_length_list = accept_length.cpu().tolist()
+        logger.info("accept_length:%s", accept_length_list)
         for i in range(batch_size):
             output_best_candidates.append(best_candidate[i])
-            accept_lengths.append(accept_length[i])
+            accept_lengths.append(accept_length_list[i])
 
             if not first_step_flags[i]:
                 select_indices = cart_candidates[i, best_candidate[i], : accept_length[i] + 1]

vllm/sequence.py

@@ -996,9 +996,6 @@ class SequenceGroupMetadata(
     # TODO: We should maintain this states out of the sequence group.
     num_speculative_tokens: Optional[int] = None
 
-    tree_attn_masks : Optional[torch.Tensor] = None
-    tree_position_ids : Optional[torch.Tensor] = None
-
     def __post_init__(self):
         if self.seq_data is not None and self.token_chunk_size is None:
             if self.is_prompt:
@@ -1036,11 +1033,6 @@ class SequenceGroupMetadata(
         assert self.state.current_step < self.state.num_steps
         self.state.current_step += 1
 
-    def set_tree_style_args(self, tree_attn_masks: Optional[torch.Tensor], 
-                            tree_position_ids: Optional[torch.Tensor]):
-        self.tree_attn_masks = tree_attn_masks
-        self.tree_position_ids = tree_position_ids
-
 
 class SequenceOutput(
         msgspec.Struct,

vllm/spec_decode/medusa_worker.py

@@ -343,7 +343,7 @@ class MedusaWorker(NonLLMProposerWorkerBase, Worker):
         
         # Move the tensors in the dictionary to the specified device
         medusa_buffers = {
-            k: (v.clone().to(device) if k != "tree_position_ids" else v.clone())
+            k: v.clone().to(device)
             if isinstance(v, torch.Tensor)
             else torch.tensor(v,  device=device)
             for k, v in medusa_buffers.items()

vllm/spec_decode/spec_decode_worker.py

@@ -318,8 +318,11 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         """
         (self.scorer_worker.model_runner.model.sampler.include_gpu_probs_tensor
          ) = True
-        (self.scorer_worker.model_runner.model.sampler.
-         should_modify_greedy_probs_inplace) = True
+        
+        # tree_style decoding modify probs in _verify_tokens
+        if not self.tree_style_spec_decoding:
+            (self.scorer_worker.model_runner.model.sampler.
+            should_modify_greedy_probs_inplace) = True
         self.proposer_worker.set_include_gpu_probs_tensor()
         self.proposer_worker.set_should_modify_greedy_probs_inplace()
 
@@ -694,7 +697,7 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         proposal_scores: SpeculativeScores,
         proposals: SpeculativeProposals,
         max_proposal_len: int,
-    ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor], List[int]]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[int]], List[int]]:
         """Determine which speculative tokens are accepted using the
         probabilities of each token according to the proposer and scorer models.
 
@@ -712,7 +715,10 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         original_indices = spec_indices + non_spec_indices
 
         # Get probabilities of target model, including bonus tokens.
-        proposal_verifier_probs = proposal_scores.probs[spec_indices]
+        if non_spec_indices:
+            proposal_verifier_probs = proposal_scores.probs[spec_indices]
+        else:
+            proposal_verifier_probs = proposal_scores.probs
 
         if self.tree_style_spec_decoding:
             retrieve_indices = proposals.retrieve_indices
@@ -722,17 +728,24 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         non_spec_token_ids = proposal_scores.token_ids[non_spec_indices]
 
         # Get bonus tokens from target model.
-        bonus_token_ids = proposal_scores.token_ids[spec_indices, -1:]
+        bonus_token_ids = proposal_scores.token_ids[:, -1:]
+        if non_spec_indices:
+            bonus_token_ids = bonus_token_ids[spec_indices, :]
 
         # Get probabilities according to proposal method.
-        proposal_probs = proposals.proposal_probs[spec_indices] \
-            if proposals.proposal_probs is not None else None
+        proposal_probs = proposals.proposal_probs if proposals.proposal_probs is not None else None
+        if non_spec_indices:
+            proposal_probs = proposal_probs[spec_indices]
 
         # Get proposed tokens.
-        proposal_token_ids = proposals.proposal_token_ids[spec_indices]
+        proposal_token_ids = proposals.proposal_token_ids
+        if non_spec_indices:
+            proposal_token_ids = proposal_token_ids[spec_indices] 
 
         # Get tree buffers.
-        cart_candidates = proposals.cart_candidates[spec_indices] if proposals.cart_candidates is not None else None
+        cart_candidates = proposals.cart_candidates if proposals.cart_candidates is not None else None
+        if non_spec_indices:
+            cart_candidates = cart_candidates[spec_indices] 
 
         # Sampler arguments
         sampler_extra_kwargs: Dict[str, Any] = {}
@@ -820,6 +833,8 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
             previous_logits_list = []
 
             previous_hidden_state_list = []
+
+            retrieve_indices = retrieve_indices.cpu()
             
             for i in range(batch_size):
                 logit = logits[i, best_candidates[i], accept_lengths[i]].unsqueeze(0)
@@ -865,13 +880,11 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
 
         model_input = self.scorer._scorer_worker.model_input
         block_tables = None
-        if hasattr(model_input, 'attn_metadata') and hasattr(model_input.attn_metadata, 'block_tables'):
-            block_tables = model_input.attn_metadata.block_tables
+        if hasattr(model_input, 'attn_metadata') and hasattr(model_input.attn_metadata, 'block_tables_list'):
+            block_tables = model_input.attn_metadata.block_tables_list
 
         if block_tables is None:
             raise RuntimeError("Can not get block_tables from model_input.")
-        
-        block_tables = block_tables.cpu().tolist()
 
         cache_engine = self.scorer._scorer_worker.cache_engines[execute_model_req.virtual_engine]
         block_size = cache_engine.block_size
@@ -885,10 +898,12 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
 
             if accept_legth > 0:
                 select_indices = select_indices_list[i][1:] + seq_lens[i]
+                select_indices = select_indices.tolist()
                 self.compute_slot_mapping(select_indices_slot_mapping, i*block_table_stride,
                                             select_indices, block_size, block_tables)
 
                 target_indices = torch.arange(accept_legth+1)[1:] + seq_lens[i]
+                target_indices = target_indices.tolist()
                 self.compute_slot_mapping(target_slot_mapping, i*block_table_stride, 
                                             target_indices, block_size, block_tables)
 
@@ -900,12 +915,7 @@ 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*4, head_size)
+
             self.kvcache_slot_to_be_moved = src_dst_tensor
     
     def compute_slot_mapping(self, slot_mapping: List[int],

vllm/worker/model_runner.py

@@ -469,6 +469,15 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
                 self.sliding_window + self.block_size - 1) // self.block_size
             self.block_aligned_sliding_window = \
                 self.sliding_window_blocks * self.block_size
+            
+        if hasattr(self.runner, "tree_attn_masks"):
+            self.tree_attn_masks = self.runner.tree_attn_masks
+            self.tree_position_ids = self.runner.tree_position_ids
+        else:
+            self.tree_attn_masks = None
+            self.tree_position_ids = None
+
+        self.is_encoder_decoder_model = self.runner.model_config.is_encoder_decoder_model
 
     def _compute_lens(self, inter_data: InterDataForSeqGroup, seq_idx: int,
                       seq_group_metadata: SequenceGroupMetadata):
@@ -511,10 +520,6 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         
         inter_data.input_positions[seq_idx] = list(range(context_len, seq_len))
 
-        if seq_group_metadata.tree_position_ids is not None:
-            inter_data.input_positions[seq_idx] = seq_group_metadata.tree_position_ids.contiguous().tolist()
-            inter_data.tree_attn_masks[seq_idx] = seq_group_metadata.tree_attn_masks
-
         inter_data.query_lens[
             seq_idx] = seq_len - context_len if inter_data.is_prompt else 1
 
@@ -718,7 +723,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
 
         encoder_seq_len = 0
 
-        if self.runner.model_config.is_encoder_decoder_model:
+        if self.is_encoder_decoder_model:
             encoder_seq_len = seq_group_metadata.encoder_seq_data.get_len()
 
         inter_data = self.init_cached_inter_data(
@@ -796,7 +801,7 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
             if not inter_data.is_prompt:
                 max_decode_seq_len = max(max_decode_seq_len,
                                          max(inter_data.seq_lens))
-                if self.runner.model_config.is_encoder_decoder_model:
+                if self.is_encoder_decoder_model:
                     max_encoder_seq_len = max(max_encoder_seq_len,
                                               inter_data.encoder_seq_len)
 
@@ -847,22 +852,12 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
         # Sequence and query lengths.
         if cuda_graph_pad_size:
             seq_lens.extend(itertools.repeat(1, cuda_graph_pad_size))
-        
+
         # prepare tree attention masks
-        max_context_len = 0
-        for inter_data in self.inter_data_list:
-            max_context_len = max(max_context_len, max(inter_data.context_lens))
-        tree_attention_masks_list = []
-        for inter_data in self.inter_data_list:
-            for i in range(len(inter_data.seq_lens)):
-                if inter_data.tree_attn_masks:
-                    tree_attn_masks = inter_data.tree_attn_masks[i]
-                    if tree_attn_masks is not None:
-                        tree_attention_masks_list.append(tree_attn_masks)
-        tree_attention_masks_tensor = None
-        if tree_attention_masks_list:
-            tree_attention_masks_tensor = torch.stack(tree_attention_masks_list, dim=0)
+        tree_attention_masks_tensor = self.tree_attn_masks
+        if tree_attention_masks_tensor is not None:
             tree_attention_masks_tensor = tree_attention_masks_tensor.contiguous()
+            input_positions_tensor = self.tree_position_ids.contiguous()
 
         # Attention metadata.
         attn_metadata = self.attn_metadata_builder.build(
@@ -1038,6 +1033,9 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         self.inter_data_cache: Dict[int, PyObjectCache] = {}
         self.sampling_metadata_cache: SamplingMetadataCache = \
             SamplingMetadataCache()
+        
+        self.tree_attn_masks: Optional[torch.Tensor] = None
+        self.tree_position_ids : Optional[torch.Tensor] = None
 
     def load_model(self) -> None:
         logger.info("Starting to load model %s...", self.model_config.model)
@@ -1505,6 +1503,11 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
     @property
     def vocab_size(self) -> int:
         return self.model_config.get_vocab_size()
+    
+    def set_tree_style_args(self, tree_attn_masks: Optional[torch.Tensor], 
+                            tree_position_ids: Optional[torch.Tensor]):
+        self.tree_attn_masks = tree_attn_masks
+        self.tree_position_ids = tree_position_ids
 
 
 class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):

vllm/worker/worker_base.py

@@ -283,11 +283,9 @@ class LocalOrDistributedWorkerBase(WorkerBase):
         worker_input: WorkerInput = self.prepare_worker_input(
             execute_model_req=execute_model_req)
         
-        # 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.set_tree_style_args(tree_attn_masks=execute_model_req.tree_attn_masks[i], 
-                                                       tree_position_ids=execute_model_req.tree_position_ids[i])
+        if hasattr(self.model_runner, "set_tree_style_args"):
+            self.model_runner.set_tree_style_args(tree_attn_masks=execute_model_req.tree_attn_masks,
+                                                  tree_position_ids=execute_model_req.tree_position_ids)
 
         model_input: ModelRunnerInputBase = (
             self.model_runner.prepare_model_input(