detok修改

benchmarks/benchmark_throughput.py

@@ -7,7 +7,7 @@ import random
 import time
 from functools import cache
 from typing import Dict, List, Optional, Tuple
-
+import os
 import numpy as np
 import torch
 import uvloop
@@ -179,7 +179,7 @@ def run_vllm(
     sampling_params: List[SamplingParams] = []
     for request in requests:
         prompts.append(
-            TextPrompt(prompt=request.prompt,
+            TextPrompt(prompt="helloworld",
                        multi_modal_data=request.multi_modal_data))
         sampling_params.append(
             SamplingParams(
@@ -205,21 +205,25 @@ def run_vllm(
     dummy_prompts: List[PromptType] = [{
         "prompt_token_ids": batch
     } for batch in dummy_prompt_token_ids.tolist()]
-    
+    print(f'{os.environ.get("VLLM_ZERO_OVERHEAD") == "1"}')
     print("Warming up...")
     for _ in tqdm(range(num_iters_warmup), desc="Warmup iterations"):
         llm.generate(dummy_prompts,
                         sampling_params=warmup_sampling_params,
                         use_tqdm=False)
         
-    use_beam_search = False
 
+    use_beam_search = False
+    print("testing")
     if not use_beam_search:
         start = time.perf_counter()
-        llm.generate(prompts,
+        outputs=llm.generate(prompts,
                      sampling_params,
                      lora_request=lora_requests,
                      use_tqdm=True)
+        for output in outputs:
+            generated_text=output.outputs[0].text
+            print(f"test生成的文本: {generated_text}")
         end = time.perf_counter()
     else:
         assert lora_requests is None, "BeamSearch API does not support LoRA"

vllm/engine/output_processor/stop_checker.py

@@ -42,6 +42,57 @@ class StopChecker:
 
         # Check if the minimum number of tokens has been generated yet;
         # skip the stop string/token checks if not
+        if self.zero_overhead:
+            if seq.zero_overhead_get_output_len() < sampling_params.min_tokens:
+                return
+        #new char count的 暂时未修改逻辑
+        # Check if the sequence has generated the EOS token.
+            if ((not sampling_params.ignore_eos)
+                    and seq.zero_overhead_get_last_token_id() == seq.eos_token_id):
+                # Remove the last EOS token unless explicitly specified
+                # This prevents unintended exposure of the EOS token
+                if new_char_count and (
+                        not sampling_params.include_stop_str_in_output):
+                    seq.output_text = seq.output_text[:-new_char_count]
+                seq.status = SequenceStatus.FINISHED_STOPPED
+                return
+
+            # Check if a stop token was encountered.
+            # This assumes a single token produced per step.
+            last_token_id = seq.zero_overhead_get_last_token_id()
+            if last_token_id in (sampling_params.stop_token_ids or ()):
+                if new_char_count and (
+                        not sampling_params.include_stop_str_in_output):
+                    # Remove last token
+                    seq.output_text = seq.output_text[:-new_char_count]
+                seq.status = SequenceStatus.FINISHED_STOPPED
+                seq.stop_reason = last_token_id
+                return
+
+            # Check if any stop strings are matched.
+            stop = self.check_stop_strings(
+                seq.output_text, new_char_count, sampling_params.stop,
+                sampling_params.include_stop_str_in_output)
+            if stop is not None:
+                stop_str, truncate_to = stop
+                if truncate_to != -1:
+                    seq.output_text = seq.output_text[:truncate_to]
+                seq.status = SequenceStatus.FINISHED_STOPPED
+                seq.stop_reason = stop_str
+                return
+
+            # Check if the sequence has reached max_model_len.
+            if seq.zero_overhead_get_len() > self._get_max_model_len(lora_req):
+                seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
+                return
+        
+            # Check if the sequence has reached max_tokens.
+            if seq.zero_overhead_get_output_len() >= sampling_params.max_tokens:
+                seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
+                return
+            # Check if the minimum number of tokens has been generated yet;
+            # skip the stop string/token checks if not
+        else:
             if seq.get_output_len() < sampling_params.min_tokens:
                 return
 

vllm/sequence.py

@@ -177,7 +177,7 @@ class SequenceData(msgspec.Struct,
     _mrope_position_delta: Optional[int] = None
 
     _first_step_flag: bool = True
-
+    _effective_length:int =0
     @staticmethod
     def from_prompt_token_counts(
             *token_counts: Tuple[int, int]) -> "SequenceData":
@@ -311,11 +311,15 @@ class SequenceData(msgspec.Struct,
     def get_len(self) -> int:
         return len(self._output_token_ids) + len(self._prompt_token_ids)
     
+    def zero_overhead_get_len(self) -> int:
+        return self._effective_length + len(self._prompt_token_ids)
     def get_prompt_len(self) -> int:
         return len(self._prompt_token_ids)
 
     def get_output_len(self) -> int:
         return len(self._output_token_ids)
+    def zero_overhead_get_output_len(self) -> Tuple[int, ...]:
+        return self._effective_length
 
     def get_token_ids(self) -> List[int]:
         return self._cached_all_token_ids
@@ -372,6 +376,10 @@ class SequenceData(msgspec.Struct,
         if not self._output_token_ids:
             return self._prompt_token_ids[-1]
         return self._output_token_ids[-1]
+    def zero_overhead_get_last_token_id(self) -> int:
+        if self._effective_length==0:
+            return self._prompt_token_ids[-1]
+        return self._output_token_ids[self._effective_length-1]
     
     def get_prompt_token_ids(self) -> Tuple[int, ...]:
         return self.prompt_token_ids
@@ -590,12 +598,16 @@ class Sequence:
     def get_len(self) -> int:
         return self.data.get_len()
     
+    def zero_overhead_get_len(self) -> int:
+        return self.data.zero_overhead_get_len()
+
     def get_prompt_len(self) -> int:
         return self.data.get_prompt_len()
 
     def get_output_len(self) -> int:
         return self.data.get_output_len()
-
+    def zero_overhead_get_output_len(self) -> int:
+        return self.data.zero_overhead_get_output_len()
     def get_token_ids(self) -> List[int]:
         return self.data.get_token_ids()
 
@@ -604,7 +616,8 @@ class Sequence:
 
     def get_last_token_id(self) -> int:
         return self.data.get_last_token_id()
-
+    def zero_overhead_get_last_token_id(self) -> int:
+        return self.data.zero_overhead_get_last_token_id()
     def get_output_token_ids(self) -> Tuple[int, ...]:
         return self.data.get_output_token_ids()
 

vllm/transformers_utils/detokenizer.py

@@ -108,6 +108,9 @@ class Detokenizer:
             The number of characters added to the output text.
         """
         all_input_ids = seq.get_token_ids()
+        if self.zero_overhead:
+            all_input_ids = seq.get_token_ids()[:seq.get_prompt_len()+self.data._effective_length]
+        print(f'{all_input_ids=}')
         token_id_generated_this_iteration = all_input_ids[-1]
         tokenizer = self.get_tokenizer_for_seq(seq)