Fix random dataset mismatched token length with config. (#24937)

Signed-off-by: Weiliang Liu <weiliangl@nvidia.com>
Signed-off-by: Roger Wang <hey@rogerw.io>
Co-authored-by: Roger Wang <hey@rogerw.io>

vllm/benchmarks/datasets.py

@@ -366,11 +366,67 @@ def process_video(video: Any) -> Mapping[str, Any]:
         f"Invalid video input {video}. Must be a string of local path/remote url, or a dictionary with raw video bytes in the form of `{{'bytes': raw_video_bytes}}`."  # noqa: E501
     )
 
+
+def gen_prompt_decode_to_target_len(
+    tokenizer: PreTrainedTokenizerBase,
+    token_sequence: list[int],
+    target_token_len: int,
+    max_retry: int = 10,
+    add_special_tokens: bool = False,
+    rng: Optional[np.random.Generator] = None,
+) -> tuple[str, list[int]]:
+    """
+    Ensure decoded-then-encoded prompt length matches the target token length.
+
+    This function decodes an initial token sequence to text and re-encodes it
+    , iteratively adjusting the token sequence length to match a target. 
+    This is necessary because some tokenizers do not guarantee a 1:1 mapping 
+    between consecutive tokens and the decoded-then-encoded sequence length.
+    For example, for GPT2Tokenizer:
+    [6880, 6881] -> ['Ġcalls', 'here'] ->
+    [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
+
+    Returns a tuple of the final prompt string and the adjusted token sequence.
+    """
+    remain_num_try = max_retry
+    token_mismatch = 0
+    while True:
+        prompt = tokenizer.decode(token_sequence)
+        token_sequence = tokenizer.encode(
+            prompt, add_special_tokens=add_special_tokens
+        )
+        if remain_num_try <= 0:
+            if len(token_sequence) != target_token_len:
+                token_mismatch = len(token_sequence) - target_token_len
+            break
+        
+        if len(token_sequence) == target_token_len:
+            break
+        elif len(token_sequence) < target_token_len:
+            if rng is not None:
+                extra_tokens = rng.integers(
+                    0,
+                    tokenizer.vocab_size,
+                    size=target_token_len - len(token_sequence),
+                ).tolist()
+            else:
+                extra_tokens = np.random.randint(
+                    0,
+                    tokenizer.vocab_size,
+                    size=target_token_len - len(token_sequence),
+                ).tolist()
+            token_sequence.extend(extra_tokens)
+        elif len(token_sequence) > target_token_len:
+            token_sequence = token_sequence[:target_token_len]
+
+        remain_num_try -= 1
+
+    return prompt, token_sequence, token_mismatch
+
 # -----------------------------------------------------------------------------
 # Random Dataset Implementation (Synthetic Data)
 # -----------------------------------------------------------------------------
 
-
 class RandomDataset(BenchmarkDataset):
     """
     Synthetic text-only dataset for serving/throughput benchmarks.
@@ -420,8 +476,9 @@ class RandomDataset(BenchmarkDataset):
         vocab_size = tokenizer.vocab_size
 
         requests = []
+        token_mismatch_total = 0
         for i in range(num_requests):
-            prompt, total_input_len = self.generate_token_sequence(
+            prompt, total_input_len, token_mismatch = self.generate_token_sequence( # noqa: E501
                 tokenizer=tokenizer,
                 prefix_token_ids=prefix_token_ids,
                 prefix_len=prefix_len,
@@ -430,6 +487,7 @@ class RandomDataset(BenchmarkDataset):
                 offset=int(offsets[i]),
                 index=i,
             )
+            token_mismatch_total += token_mismatch
             requests.append(
                 SampleRequest(
                     prompt=prompt,
@@ -453,6 +511,18 @@ class RandomDataset(BenchmarkDataset):
                     )
                 )
             requests = batch_requests
+        
+        if token_mismatch_total != 0:
+            sign = "more" if token_mismatch_total > 0 else "fewer"
+            logger.warning(
+                "Across all generated prompts, there were %d %s tokens "
+                "than expected after decoding and re-encoding. This is "
+                "expected due to the imperfect nature of the sampling "
+                "procedure.",
+                abs(token_mismatch_total),
+                sign,
+            )
+
         return requests
 
     def get_prefix(
@@ -530,7 +600,7 @@ class RandomDataset(BenchmarkDataset):
         input_len: int,
         offset: int,
         index: int,
-    ) -> tuple[str, int]:
+    ) -> tuple[str, int, int]:
         """
         Returns (prompt, total_input_len).
 
@@ -549,15 +619,16 @@ class RandomDataset(BenchmarkDataset):
         token_sequence = prefix_token_ids + inner_seq
 
         # Decode, then re-encode and truncate to preserve token count invariants
-        prompt = tokenizer.decode(token_sequence)
         total_input_len = prefix_len + int(input_len)
-
-        re_encoded_sequence = tokenizer.encode(
-            prompt, add_special_tokens=False)[:total_input_len]
-        prompt = tokenizer.decode(re_encoded_sequence)
-        total_input_len = len(re_encoded_sequence)
-
-        return prompt, total_input_len
+        prompt, adjusted_token_sequence, token_mismatch = gen_prompt_decode_to_target_len( # noqa: E501
+            tokenizer=tokenizer,
+            token_sequence=token_sequence,
+            target_token_len=total_input_len,
+            add_special_tokens=False,
+            rng=self._rng,
+        )
+        total_input_len = len(adjusted_token_sequence)
+        return prompt, total_input_len, token_mismatch
 
 
 # -----------------------------------------------------------------------------
@@ -873,8 +944,9 @@ class RandomMultiModalDataset(RandomDataset):
         vocab_size = tokenizer.vocab_size
         # Add synthetic multimodal items to each request
         mm_requests = []
+        token_mismatch_total = 0
         for i in range(num_requests):
-            prompt, total_input_len = self.generate_token_sequence(
+            prompt, total_input_len, token_mismatch = self.generate_token_sequence( # noqa: E501
                 tokenizer=tokenizer,
                 prefix_token_ids=prefix_token_ids,
                 prefix_len=prefix_len,
@@ -883,6 +955,7 @@ class RandomMultiModalDataset(RandomDataset):
                 offset=int(offsets[i]),
                 index=i,
             )
+            token_mismatch_total += token_mismatch
             # Get multimodal item iterator for a given request
             mm_item_iterator = self.get_mm_item_iterator(
                 min_num_mm_items,
@@ -918,6 +991,18 @@ class RandomMultiModalDataset(RandomDataset):
                     request_id=request_id_prefix + str(i),
                 )
             mm_requests.append(sample_request)
+
+        if token_mismatch_total != 0:
+            sign = "more" if token_mismatch_total > 0 else "fewer"
+            logger.warning(
+                "Across all generated prompts, there were %d %s tokens "
+                "than expected after decoding and re-encoding. This is "
+                "expected due to the imperfect nature of the sampling "
+                "procedure.",
+                abs(token_mismatch_total),
+                sign,
+            )
+
         return mm_requests
 
 # -----------------------------------------------------------------------------
@@ -2694,27 +2779,23 @@ class PrefixRepetitionRandomDataset(BenchmarkDataset):
             # Generate random tokens
             tokens = np.random.randint(
                 0, vocab_size, size=target_length).tolist()
-            text = tokenizer.decode(tokens)
-            re_encoded = tokenizer.encode(text, add_special_tokens=False)
-
-            if len(re_encoded) == target_length:
-                return re_encoded
-            elif len(re_encoded) < target_length:
-                # Recursively generate additional consistent tokens
-                needed = target_length - len(re_encoded)
-                extra_tokens = _generate_exact_length_tokens(needed)
-                return re_encoded + extra_tokens
-            else:
-                # Truncate to target length
-                return re_encoded[:target_length]
+
+            _, adjusted_tokens, token_mismatch = gen_prompt_decode_to_target_len( # noqa: E501
+                tokenizer=tokenizer,
+                token_sequence=tokens,
+                target_token_len=target_length,
+                add_special_tokens=False,
+            )
+            return adjusted_tokens, token_mismatch
 
         requests = []
+        token_mismatch_total = 0
         for _ in range(num_prefixes):
             prefix_tokens = _generate_exact_length_tokens(prefix_len)
 
             for _ in range(prompts_per_prefix):
-                suffix_tokens = _generate_exact_length_tokens(suffix_len)
-
+                suffix_tokens, token_mistmatch = _generate_exact_length_tokens(suffix_len) # noqa: E501
+                token_mismatch_total += token_mistmatch
                 combined_tokens = prefix_tokens + suffix_tokens
                 prompt = tokenizer.decode(combined_tokens)
                 prompt_len = len(combined_tokens)
@@ -2726,6 +2807,16 @@ class PrefixRepetitionRandomDataset(BenchmarkDataset):
                     )
                 )
 
+        if token_mismatch_total != 0:
+            sign = "more" if token_mismatch_total > 0 else "fewer"
+            logger.warning(
+                "Across all generated prompts, there were %d %s tokens "
+                "than expected after decoding and re-encoding. This is "
+                "expected due to the imperfect nature of the sampling "
+                "procedure.",
+                abs(token_mismatch_total),
+                sign,
+            )
         random.shuffle(requests)
         return requests