change to greedy

benchmarks/benchmark_latency.py

@@ -5,18 +5,21 @@ import argparse
 import dataclasses
 import json
 import os
+import random
 import time
 from pathlib import Path
-from typing import Any, Optional
+from typing import Any, Optional, Union
 
 import numpy as np
 import torch
-from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
+from benchmark_utils import (convert_to_pytorch_benchmark_format, get_requests,
+                             validate_dataset, write_to_json)
 from tqdm import tqdm
+from transformers import AutoTokenizer
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.inputs import PromptType
+from vllm.inputs import TextPrompt, TokensPrompt
 from vllm.sampling_params import BeamSearchParams
 from vllm.utils import FlexibleArgumentParser
 
@@ -48,28 +51,34 @@ def main(args: argparse.Namespace):
 
     sampling_params = SamplingParams(
         n=args.n,
-        temperature=1.0,
+        temperature=0,
         top_p=1.0,
         ignore_eos=True,
         max_tokens=args.output_len,
         detokenize=not args.disable_detokenize,
     )
     print(sampling_params)
-    dummy_prompt_token_ids = np.random.randint(10000,
-                                               size=(args.batch_size,
-                                                     args.input_len))
-    dummy_prompts: list[PromptType] = [{
-        "prompt_token_ids": batch
-    } for batch in dummy_prompt_token_ids.tolist()]
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.tokenizer, trust_remote_code=args.trust_remote_code)
+    requests = get_requests(args.batch_size, args, tokenizer)
+    prompts: list[Union[TextPrompt, TokensPrompt]] = []
+    for request in requests:
+        prompts.append(
+            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                       multi_modal_data=request.multi_modal_data)
+            if "prompt_token_ids" in request.prompt else \
+            TextPrompt(prompt=request.prompt,
+                       multi_modal_data=request.multi_modal_data))
 
     def llm_generate():
         if not args.use_beam_search:
-            llm.generate(dummy_prompts,
+            llm.generate(prompts,
                          sampling_params=sampling_params,
                          use_tqdm=False)
         else:
             llm.beam_search(
-                dummy_prompts,
+                prompts,
                 BeamSearchParams(
                     beam_width=args.n,
                     max_tokens=args.output_len,
@@ -180,7 +189,44 @@ if __name__ == "__main__":
         help=("Do not detokenize responses (i.e. do not include "
               "detokenization time in the latency measurement)"),
     )
+    parser.add_argument(
+        "--dataset-name",
+        type=str,
+        choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
+        help="Name of the dataset to benchmark on.",
+        default="sharegpt")
+    # random dataset
+    parser.add_argument(
+        "--random-range-ratio",
+        type=float,
+        default=None,
+        help="Range of sampled ratio of input/output length, "
+        "used only for RandomDataSet.",
+    )
+    parser.add_argument("--dataset-path",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset")
+
+    # LoRA
+    parser.add_argument(
+        "--lora-path",
+        type=str,
+        default=None,
+        help="Path to the lora adapters to use. This can be an absolute path, "
+        "a relative path, or a Hugging Face model identifier.")
+
+    parser.add_argument("--prefix-len",
+                        type=int,
+                        default=None,
+                        help="Number of prefix tokens per request."
+                        "This is for the RandomDataset and SonnetDataset")
 
     parser = EngineArgs.add_cli_args(parser)
     args = parser.parse_args()
+    if args.tokenizer is None:
+        args.tokenizer = args.model
+    args.backend = "vllm"
+    validate_dataset(args)
+    random.seed(0)
     main(args)

benchmarks/benchmark_throughput.py

@@ -11,11 +11,9 @@ from typing import Any, Optional, Union
 
 import torch
 import uvloop
-from benchmark_dataset import (BurstGPTDataset, ConversationDataset,
-                               InstructCoderDataset, RandomDataset,
-                               SampleRequest, ShareGPTDataset, SonnetDataset,
-                               VisionArenaDataset)
-from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
+from benchmark_dataset import SampleRequest
+from benchmark_utils import (convert_to_pytorch_benchmark_format, get_requests,
+                             validate_dataset, write_to_json)
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
@@ -287,59 +285,6 @@ def save_to_pytorch_benchmark_format(args: argparse.Namespace,
         write_to_json(pt_file, pt_records)
 
 
-def get_requests(args, tokenizer):
-    # Common parameters for all dataset types.
-    common_kwargs = {
-        "dataset_path": args.dataset_path,
-        "random_seed": args.seed,
-    }
-    sample_kwargs = {
-        "tokenizer": tokenizer,
-        "lora_path": args.lora_path,
-        "max_loras": args.max_loras,
-        "num_requests": args.num_prompts,
-        "input_len": args.input_len,
-        "output_len": args.output_len,
-    }
-
-    if args.dataset_path is None or args.dataset_name == "random":
-        sample_kwargs["range_ratio"] = args.random_range_ratio
-        sample_kwargs["prefix_len"] = args.prefix_len
-        dataset_cls = RandomDataset
-    elif args.dataset_name == "sharegpt":
-        dataset_cls = ShareGPTDataset
-        if args.backend == "vllm-chat":
-            sample_kwargs["enable_multimodal_chat"] = True
-    elif args.dataset_name == "sonnet":
-        assert tokenizer.chat_template or tokenizer.default_chat_template, (
-            "Tokenizer/model must have chat template for sonnet dataset.")
-        dataset_cls = SonnetDataset
-        sample_kwargs["prefix_len"] = args.prefix_len
-        sample_kwargs["return_prompt_formatted"] = True
-    elif args.dataset_name == "burstgpt":
-        dataset_cls = BurstGPTDataset
-    elif args.dataset_name == "hf":
-        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
-            dataset_cls = VisionArenaDataset
-            common_kwargs['dataset_subset'] = None
-            common_kwargs['dataset_split'] = "train"
-            sample_kwargs["enable_multimodal_chat"] = True
-        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
-            dataset_cls = InstructCoderDataset
-            common_kwargs['dataset_split'] = "train"
-        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
-            dataset_cls = ConversationDataset
-            common_kwargs['dataset_subset'] = args.hf_subset
-            common_kwargs['dataset_split'] = args.hf_split
-            sample_kwargs["enable_multimodal_chat"] = True
-
-    else:
-        raise ValueError(f"Unknown dataset name: {args.dataset_name}")
-    # Remove None values
-    sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
-    return dataset_cls(**common_kwargs).sample(**sample_kwargs)
-
-
 def main(args: argparse.Namespace):
     if args.seed is None:
         args.seed = 0
@@ -348,7 +293,7 @@ def main(args: argparse.Namespace):
     # Sample the requests.
     tokenizer = AutoTokenizer.from_pretrained(
         args.tokenizer, trust_remote_code=args.trust_remote_code)
-    requests = get_requests(args, tokenizer)
+    requests = get_requests(args.num_prompts, args, tokenizer)
     is_multi_modal = any(request.multi_modal_data is not None
                          for request in requests)
     request_outputs: Optional[list[RequestOutput]] = None
@@ -449,47 +394,8 @@ def validate_args(args):
     if args.backend not in valid_backends:
         raise ValueError(f"Unsupported backend: {args.backend}")
 
-    # === Dataset Configuration ===
-    if not args.dataset and not args.dataset_path:
-        print(
-            "When dataset path is not set, it will default to random dataset")
-        args.dataset_name = 'random'
-        if args.input_len is None:
-            raise ValueError("input_len must be provided for a random dataset")
-
-    # === Dataset Name Specific Checks ===
-    # --hf-subset and --hf-split: only used
-    # when dataset_name is 'hf'
-    if args.dataset_name != "hf" and (
-            getattr(args, "hf_subset", None) is not None
-            or getattr(args, "hf_split", None) is not None):
-        warnings.warn("--hf-subset and --hf-split will be ignored \
-                since --dataset-name is not 'hf'.",
-                      stacklevel=2)
-    elif args.dataset_name == "hf":
-        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
-            assert args.backend == "vllm-chat", "VisionArenaDataset needs to use vllm-chat as the backend."  #noqa: E501
-        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
-            assert args.backend == "vllm", "InstructCoder dataset needs to use vllm as the backend."  #noqa: E501
-        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
-            assert args.backend == "vllm-chat", "ConversationDataset needs to use vllm-chat as the backend."  #noqa: E501
-        else:
-            raise ValueError(
-                f"{args.dataset_path} is not supported by hf dataset.")
-
-    # --random-range-ratio: only used when dataset_name is 'random'
-    if args.dataset_name != 'random' and args.random_range_ratio is not None:
-        warnings.warn("--random-range-ratio will be ignored since \
-                --dataset-name is not 'random'.",
-                      stacklevel=2)
-
-    # --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
-    # set.
-    if args.dataset_name not in {"random", "sonnet", None
-                                 } and args.prefix_len is not None:
-        warnings.warn("--prefix-len will be ignored since --dataset-name\
-                 is not 'random', 'sonnet', or not set.",
-                      stacklevel=2)
+    # === Dataset Validation ===
+    validate_dataset(args)
 
     # === LoRA Settings ===
     if getattr(args, "enable_lora", False) and args.backend != "vllm":
@@ -529,14 +435,6 @@ if __name__ == "__main__":
         choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
         help="Name of the dataset to benchmark on.",
         default="sharegpt")
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default=None,
-        help="Path to the ShareGPT dataset, will be deprecated in\
-            the next release. The dataset is expected to "
-        "be a json in form of list[dict[..., conversations: "
-        "list[dict[..., value: <prompt_or_response>]]]]")
     parser.add_argument("--dataset-path",
                         type=str,
                         default=None,

benchmarks/benchmark_utils.py

@@ -4,8 +4,14 @@ import argparse
 import json
 import math
 import os
+import warnings
 from typing import Any
 
+from benchmark_dataset import (BurstGPTDataset, ConversationDataset,
+                               InstructCoderDataset, RandomDataset,
+                               SampleRequest, ShareGPTDataset, SonnetDataset,
+                               VisionArenaDataset)
+
 
 def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
                                         metrics: dict[str, list],
@@ -67,3 +73,113 @@ class InfEncoder(json.JSONEncoder):
 def write_to_json(filename: str, records: list) -> None:
     with open(filename, "w") as f:
         json.dump(records, f, cls=InfEncoder)
+
+
+def get_requests(num_requests: int, args: argparse.Namespace,
+                 tokenizer: Any) -> list[SampleRequest]:
+    """
+    Sample the requests for the benchmark.
+    """
+    # Common parameters for all dataset types.
+    common_kwargs = {
+        "dataset_path": args.dataset_path,
+        "random_seed": args.seed,
+    }
+    sample_kwargs = {
+        "tokenizer": tokenizer,
+        "lora_path": args.lora_path,
+        "max_loras": args.max_loras,
+        "num_requests": num_requests,
+        "input_len": args.input_len,
+        "output_len": args.output_len,
+    }
+
+    if args.dataset_path is None or args.dataset_name == "random":
+        sample_kwargs["range_ratio"] = args.random_range_ratio
+        sample_kwargs["prefix_len"] = args.prefix_len
+        dataset_cls = RandomDataset
+    elif args.dataset_name == "sharegpt":
+        dataset_cls = ShareGPTDataset
+        if getattr(args, "backend", False) and args.backend == "vllm-chat":
+            sample_kwargs["enable_multimodal_chat"] = True
+    elif args.dataset_name == "sonnet":
+        assert tokenizer.chat_template or tokenizer.default_chat_template, (
+            "Tokenizer/model must have chat template for sonnet dataset.")
+        dataset_cls = SonnetDataset
+        sample_kwargs["prefix_len"] = args.prefix_len
+        sample_kwargs["return_prompt_formatted"] = True
+    elif args.dataset_name == "burstgpt":
+        dataset_cls = BurstGPTDataset
+    elif args.dataset_name == "hf":
+        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = VisionArenaDataset
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
+            sample_kwargs["enable_multimodal_chat"] = True
+        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = InstructCoderDataset
+            common_kwargs['dataset_split'] = "train"
+        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = ConversationDataset
+            common_kwargs['dataset_subset'] = args.hf_subset
+            common_kwargs['dataset_split'] = args.hf_split
+            sample_kwargs["enable_multimodal_chat"] = True
+
+    else:
+        raise ValueError(f"Unknown dataset name: {args.dataset_name}")
+    # Remove None values
+    sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
+    return dataset_cls(**common_kwargs).sample(**sample_kwargs)
+
+
+def validate_dataset(args: argparse.Namespace, ):
+    """
+    Validate the dataset arguments.
+    """
+    # === Dataset Configuration ===
+    if not args.dataset_path:
+        print(
+            "When dataset path is not set, it will default to random dataset")
+        args.dataset_name = 'random'
+        if args.input_len is None:
+            raise ValueError("input_len must be provided for a random dataset")
+
+    # === Dataset Name Specific Checks ===
+    # --hf-subset and --hf-split: only used
+    # when dataset_name is 'hf'
+    if args.dataset_name != "hf" and (
+            getattr(args, "hf_subset", None) is not None
+            or getattr(args, "hf_split", None) is not None):
+        warnings.warn("--hf-subset and --hf-split will be ignored \
+                since --dataset-name is not 'hf'.",
+                      stacklevel=2)
+    elif args.dataset_name == "hf":
+        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
+            assert getattr(
+                args, 'backend', None
+            ) and args.backend == "vllm-chat", "VisionArenaDataset needs to use vllm-chat as the backend."  #noqa: E501
+        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
+            assert getattr(
+                args, 'backend', None
+            ) and args.backend == "vllm", "InstructCoder dataset needs to use vllm as the backend."  #noqa: E501
+        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
+            assert getattr(
+                args, 'backend', None
+            ) and args.backend == "vllm-chat", "ConversationDataset needs to use vllm-chat as the backend."  #noqa: E501
+        else:
+            raise ValueError(
+                f"{args.dataset_path} is not supported by hf dataset.")
+
+    # --random-range-ratio: only used when dataset_name is 'random'
+    if args.dataset_name != 'random' and args.random_range_ratio is not None:
+        warnings.warn("--random-range-ratio will be ignored since \
+                --dataset-name is not 'random'.",
+                      stacklevel=2)
+
+    # --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
+    # set.
+    if args.dataset_name not in {"random", "sonnet", None
+                                 } and args.prefix_len is not None:
+        warnings.warn("--prefix-len will be ignored since --dataset-name\
+                 is not 'random', 'sonnet', or not set.",
+                      stacklevel=2)

vllm/v1/sample/rejection_sampler.py

@@ -81,6 +81,7 @@ class RejectionSampler(nn.Module):
         Returns:
             output_token_ids (torch.Tensor):
                 A tensor containing the final output token IDs.
+            acceptance_rate: min(p, q)
         '''
         assert metadata.max_spec_len <= MAX_SPEC_LEN
         # [num_tokens, vocab_size]
@@ -92,7 +93,7 @@ class RejectionSampler(nn.Module):
             sampling_metadata,
         )
 
-        output_token_ids = rejection_sample(
+        output_token_ids, output_probs = rejection_sample(
             metadata.draft_token_ids,
             metadata.num_draft_tokens,
             metadata.max_spec_len,
@@ -102,7 +103,9 @@ class RejectionSampler(nn.Module):
             bonus_token_ids,
             sampling_metadata,
         )
-        return output_token_ids
+        mask = output_probs != PLACEHOLDER_TOKEN_ID
+        acceptance_rate = output_probs[mask].mean()
+        return output_token_ids, acceptance_rate
 
     @staticmethod
     def parse_output(
@@ -170,6 +173,8 @@ def rejection_sample(
         device=device,
     )
     output_token_ids.fill_(PLACEHOLDER_TOKEN_ID)
+    output_probs = torch.empty_like(output_token_ids, dtype=torch.float32)
+    output_probs.fill_(PLACEHOLDER_TOKEN_ID)
 
     if sampling_metadata.all_greedy:
         is_greedy = None
@@ -180,6 +185,7 @@ def rejection_sample(
         target_argmax = target_probs.argmax(dim=-1)
         rejection_greedy_sample_kernel[(batch_size, )](
             output_token_ids,
+            output_probs,
             cu_num_draft_tokens,
             draft_token_ids,
             target_argmax,
@@ -189,7 +195,7 @@ def rejection_sample(
             num_warps=1,
         )
         if sampling_metadata.all_greedy:
-            return output_token_ids
+            return output_token_ids, output_probs
 
     # Generate uniform probabilities for rejection sampling.
     # [num_tokens]
@@ -216,6 +222,7 @@ def rejection_sample(
     # Rejection sampling for random sampling requests.
     rejection_random_sample_kernel[(batch_size, )](
         output_token_ids,
+        output_probs,
         cu_num_draft_tokens,
         draft_token_ids,
         draft_probs,
@@ -229,7 +236,7 @@ def rejection_sample(
         IS_NGRAM=draft_probs is None,
         num_warps=1,
     )
-    return output_token_ids
+    return output_token_ids, output_probs
 
 
 def compute_probs(
@@ -432,6 +439,7 @@ def sample_recovered_tokens(
 @triton.jit(do_not_specialize=["max_spec_len"])
 def rejection_greedy_sample_kernel(
     output_token_ids_ptr,  # [batch_size, max_spec_len + 1]
+    output_probs_ptr,  # [batch_size, max_spec_len + 1]
     cu_num_draft_tokens_ptr,  # [batch_size]
     draft_token_ids_ptr,  # [num_tokens]
     target_argmax_ptr,  # [num_tokens]
@@ -459,14 +467,16 @@ def rejection_greedy_sample_kernel(
 
     rejected = False
     for pos in range(num_draft_tokens):
+        draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
+        target_argmax_id = tl.load(target_argmax_ptr + start_idx + pos)
         if not rejected:
-            draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
-            target_argmax_id = tl.load(target_argmax_ptr + start_idx + pos)
             tl.store(output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos,
                      target_argmax_id)
             if draft_token_id != target_argmax_id:
                 # Reject.
                 rejected = True
+        tl.store(output_probs_ptr + req_idx * (max_spec_len + 1) + pos,
+                 draft_token_id == target_argmax_id)
 
     if not rejected:
         # If all tokens are accepted, append the bonus token.
@@ -480,6 +490,7 @@ def rejection_greedy_sample_kernel(
 @triton.jit(do_not_specialize=["max_spec_len"])
 def rejection_random_sample_kernel(
     output_token_ids_ptr,  # [batch_size, max_spec_len + 1]
+    output_probs_ptr,  # [batch_size, max_spec_len + 1]
     cu_num_draft_tokens_ptr,  # [batch_size]
     draft_token_ids_ptr,  # [num_tokens]
     draft_probs_ptr,  # [num_tokens, vocab_size] or None
@@ -507,17 +518,16 @@ def rejection_random_sample_kernel(
 
     rejected = False
     for pos in range(num_draft_tokens):
+        draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
+        if IS_NGRAM:
+            draft_prob = 1
+        else:
+            draft_prob = tl.load(draft_probs_ptr +
+                                 (start_idx + pos) * vocab_size +
+                                 draft_token_id)
+        target_prob = tl.load(target_probs_ptr +
+                              (start_idx + pos) * vocab_size + draft_token_id)
         if not rejected:
-            draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
-            if IS_NGRAM:
-                draft_prob = 1
-            else:
-                draft_prob = tl.load(draft_probs_ptr +
-                                     (start_idx + pos) * vocab_size +
-                                     draft_token_id)
-            target_prob = tl.load(target_probs_ptr +
-                                  (start_idx + pos) * vocab_size +
-                                  draft_token_id)
             uniform_prob = tl.load(uniform_probs_ptr + start_idx + pos)
             # NOTE(woosuk): While the draft probability should never be 0,
             # we check it to avoid NaNs. If it happens to be 0, we reject.
@@ -530,6 +540,8 @@ def rejection_random_sample_kernel(
                 token_id = tl.load(recovered_token_ids_ptr + start_idx + pos)
             tl.store(output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos,
                      token_id)
+        tl.store(output_probs_ptr + req_idx * (max_spec_len + 1) + pos,
+                 min(draft_prob, target_prob))
 
     if not rejected:
         # If all tokens are accepted, append the bonus token.

vllm/v1/spec_decode/auto_tuner.py

@@ -0,0 +1,133 @@
+# SPDX-License-Identifier: Apache-2.0
+from vllm.v1.worker.gpu_input_batch import CachedRequestState
+
+
+class AutoTuner:
+
+    def __init__(self):
+        # Some tracking metrics
+        # for the auto-tuning process.
+        # metrics specific to ngram_proposer.
+        self.step_cnt = 0
+        self.match_cnt = 0
+        self.total_cnt = 0
+        self.past_acceptance_rates = []
+        self.past_match_ratios = []
+
+        # config
+        self.update_interval = 100
+        self.window_size = 10000
+        self.c_kv_load = 0.1
+        self.c_computation = 0.2
+        self.c_overhead = 0.3
+
+        # some cached values
+        self.last_verified_len = 0
+
+    def get_verified_len(self, batch_size: int, match_cnt: int,
+                         num_kv_tokens: int, max_draft_len: int) -> int:
+        if self.step_cnt % self.update_interval != 0:
+            return self.last_verified_len
+
+        best_verified_len = 0
+        max_goodput = -1.0
+        for i in range(max_draft_len):
+            cur_goodput, draft_time, target_time = self._predict_goodput(
+                batch_size, match_cnt, num_kv_tokens, i)
+            # print(f"Goodput for proposal len {i}: {cur_goodput}")
+            if cur_goodput > max_goodput:
+                max_goodput = cur_goodput
+                best_verified_len = i
+            else:
+                break
+
+        self.last_verified_len = best_verified_len
+        return best_verified_len
+
+    def adjust_draft_len(self, req_states: dict[str, CachedRequestState],
+                         draft_token_ids: list[list[int]]):
+        """
+        Adjust the draft length based on the verified length.
+        """
+
+        # Calculate parameters used for goodput prediction.
+        num_kv_tokens = 0
+        for req_id in req_states:
+            num_kv_tokens += req_states[req_id].num_tokens
+        batch_size = len(draft_token_ids)
+        match_cnt = 0
+        max_draft_len = 0
+
+        for i in range(batch_size):
+            if len(draft_token_ids[i]) == 0:
+                continue
+            match_cnt += 1
+            max_draft_len = max(max_draft_len, len(draft_token_ids[i]))
+        self.total_cnt += batch_size
+        self.match_cnt += match_cnt
+        self.past_match_ratios.append(match_cnt * 1.0 / (batch_size))
+
+        return draft_token_ids
+        # Use goodput prediction to get the verified length.
+        verified_len = self.get_verified_len(batch_size, match_cnt,
+                                             num_kv_tokens, max_draft_len)
+
+        draft_token_ids = [draft[:verified_len] for draft in draft_token_ids]
+        return draft_token_ids
+
+    def update_stats(self, acceptance_rate: float):
+        self.step_cnt += 1
+        if self.step_cnt % 20 == 0:
+            print(
+                f"Step {self.step_cnt}: "
+                f"Last acceptance rate: {acceptance_rate:.2f}",
+                f"Last match ratio: {self.past_match_ratios[-1]:.2f}",
+                f"Global acceptance rate: {self.acceptance_rate:.2f}",
+                "Global match ratio:",
+                f"{self.match_cnt / (self.total_cnt + 1e-5):.2f}",
+            )
+
+        self.past_acceptance_rates.append(acceptance_rate)
+
+    @property
+    def acceptance_rate(self):
+        window_acceptance_rates = self.past_acceptance_rates[-self.
+                                                             window_size:]
+        return sum(window_acceptance_rates) / len(window_acceptance_rates)
+
+    def _predict_goodput(self, batch_size: int, match_cnt: int,
+                         num_kv_tokens: int,
+                         verified_len: int) -> tuple[float, float, float]:
+        """
+        Predict the goodput for a given verified length.
+        """
+        generated_len = self._predict_generated_len(batch_size, match_cnt,
+                                                    verified_len)
+        draft_time = self._predict_draft_time()
+        target_time = self._predict_target_time(batch_size, match_cnt,
+                                                num_kv_tokens, verified_len)
+        batch_time = draft_time + target_time
+        return generated_len / batch_time, draft_time, target_time
+
+    def _predict_generated_len(self, batch_size: int, match_cnt: int,
+                               verified_len: int):
+        spec_gen_len = float((1 - self.acceptance_rate**(verified_len + 1)) /
+                             (1 - self.acceptance_rate))
+        non_spec_gen_len = batch_size - match_cnt
+        return spec_gen_len + non_spec_gen_len
+
+    def _predict_draft_time(self):
+        # TODO: We need to benchmark and model this.
+        return 0
+
+    def _predict_target_time(self, batch_size: int, match_cnt: int,
+                             num_kv_tokens: int, verified_len: int):
+        kv_load_time = num_kv_tokens * self.c_kv_load
+
+        # Computation time
+        # +1 for the input token.
+        num_batched_tokens = match_cnt * (verified_len + 1) + (batch_size -
+                                                               match_cnt)
+        computation_time = num_batched_tokens * self.c_computation
+
+        return kv_load_time + computation_time + self.c_overhead

vllm/v1/worker/gpu_model_runner.py

@@ -34,6 +34,7 @@ from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsTensors,
                              ModelRunnerOutput)
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.rejection_sampler import RejectionSampler
+from vllm.v1.spec_decode.auto_tuner import AutoTuner
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
 from vllm.v1.spec_decode.utils import is_spec_decode_supported
@@ -156,6 +157,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         self.use_spec_decode = False
         if self.speculative_config:
             self.use_spec_decode = True
+            self.auto_tuner = AutoTuner()
             assert self.speculative_config.method == "ngram", \
                     "Currently, only ngram spec decode is supported in V1."
             if get_pp_group().is_last_rank:
@@ -1087,13 +1089,14 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             # separate storage from the original `logits` tensor. Therefore,
             # it is safe to update `target_logits` in place.
             target_logits = logits[spec_decode_metadata.target_logits_indices]
-            output_token_ids = self.rejection_sampler(
+            output_token_ids, acceptance_rate = self.rejection_sampler(
                 spec_decode_metadata,
                 None,  # draft_probs
                 target_logits,
                 bonus_token_ids,
                 sampling_metadata,
             )
+            self.auto_tuner.update_stats(acceptance_rate)
             sampler_output.sampled_token_ids = output_token_ids
 
         # TODO(woosuk): The following loop can be slow since it iterates over
@@ -1191,6 +1194,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 draft_token_ids.append([])
             else:
                 draft_token_ids.append(drafter_output.tolist())
+
+        draft_token_ids = self.auto_tuner.adjust_draft_len(
+            self.requests, draft_token_ids)
         return draft_token_ids
 
     def load_model(self) -> None: