[V1] support min_tokens for detokener (#22014)

Signed-off-by: calvin chen <wen.chen@dynamia.ai>
Co-authored-by: Nick Hill <nhill@redhat.com>

tests/detokenizer/test_min_tokens.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from transformers import AutoTokenizer
+
+from vllm import SamplingParams
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.engine.detokenizer import FastIncrementalDetokenizer
+
+PROMPT = "Hello, my name is Lee, and I'm a student in the " + \
+         "college of engineering"
+
+
+@pytest.mark.parametrize("min_tokens,stop,truth", [
+    (0, None, " is Lee, and I'm a student in the college of engineering"),
+    (0, "e", " is L"),
+    (5, "e", " is Lee, and I'm a stud"),
+])
+def test_min_tokens_with_stop(min_tokens: int, stop: str, truth: str):
+    """Test for a specific min_tokens and stop.
+
+    See https://github.com/vllm-project/vllm/pull/22014
+    """
+    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
+    all_prompt_ids = tokenizer(PROMPT, add_special_tokens=False).input_ids
+
+    # The prompt is "Hello, my name is"
+    prompt_token_ids = all_prompt_ids[:4]
+    params = SamplingParams(
+        stop=stop,
+        min_tokens=min_tokens,
+    )
+    request = EngineCoreRequest("",
+                                prompt_token_ids,
+                                None,
+                                None,
+                                None,
+                                params,
+                                None,
+                                None,
+                                0.0,
+                                None,
+                                cache_salt=None,
+                                data_parallel_rank=None)
+
+    detokenizer = FastIncrementalDetokenizer(tokenizer, request)
+
+    detokenizer.update(all_prompt_ids[4:], False)
+    assert detokenizer.output_text == truth

vllm/v1/engine/detokenizer.py

@@ -74,6 +74,7 @@ class BaseIncrementalDetokenizer(IncrementalDetokenizer, ABC):
         params = request.sampling_params
         assert params is not None
         self.stop = stop = params.stop
+        self.min_tokens = params.min_tokens
         self.include_stop_str_in_output = params.include_stop_str_in_output
 
         # Number of chars to hold back when stop strings are to be excluded
@@ -111,10 +112,14 @@ class BaseIncrementalDetokenizer(IncrementalDetokenizer, ABC):
         # 1) Detokenize the new token ids incrementally.
         # TODO(woosuk): This method becomes very inefficient when the number of
         # new_token_ids is more than 1. We need to optimize this.
-        offset_before = len(self.output_text)
+        stop_check_offset = len(self.output_text)
         for new_token_id in new_token_ids:
             self.token_ids.append(new_token_id)
             self.output_text += self.decode_next(new_token_id)
+            # Support min_tokens, see https://github.com/vllm-project/vllm/pull/22014
+            if self.min_tokens and len(
+                    self.output_token_ids) <= self.min_tokens:
+                stop_check_offset = len(self.output_text)
 
         if stop_terminated:
             if skipped_stop_token_id is not None:
@@ -125,10 +130,10 @@ class BaseIncrementalDetokenizer(IncrementalDetokenizer, ABC):
 
         # 2) Evaluate stop strings.
         stop_string = None
-        if self.stop:
+        if self.stop and len(self.output_token_ids) > self.min_tokens:
             stop = StopChecker.check_stop_strings(
                 output_text=self.output_text,
-                new_char_count=len(self.output_text) - offset_before,
+                new_char_count=len(self.output_text) - stop_check_offset,
                 stop=self.stop,
                 include_in_output=self.include_stop_str_in_output,
             )