[feat] Optional base64 encoding for the flat prompt top logprob arrays (#31960)

python/sglang/srt/managers/io_struct.py

@@ -211,6 +211,8 @@ class GenerateReqInput:
     # Return prompt top logprobs as flat arrays plus shape metadata instead of
     # the nested per-position [logprob, token_id, text] lists.
     return_flat_raw_top_logprobs: bool = False
+    # Base64-encode the flat arrays. Requires return_flat_raw_top_logprobs.
+    return_flat_raw_top_logprobs_b64: bool = False
     # Whether to stream output.
     stream: bool = False
     # Whether to log metrics for this request (e.g. health_generate calls do not log metrics)
@@ -381,6 +383,13 @@ class GenerateReqInput:
                 "scoring: delimiter-sparse top logprob rows have no contiguous "
                 "position mapping."
             )
+        if (
+            self.return_flat_raw_top_logprobs_b64
+            and not self.return_flat_raw_top_logprobs
+        ):
+            raise ValueError(
+                "return_flat_raw_top_logprobs_b64 requires return_flat_raw_top_logprobs."
+            )
 
     def _determine_batch_size(self):
         """Determine if this is a single example or a batch and the batch size."""
@@ -737,6 +746,7 @@ class GenerateReqInput:
             return_sampling_mask=self.return_sampling_mask[i],
             return_text_in_logprobs=self.return_text_in_logprobs,
             return_flat_raw_top_logprobs=self.return_flat_raw_top_logprobs,
+            return_flat_raw_top_logprobs_b64=self.return_flat_raw_top_logprobs_b64,
             stream=self.stream,
             log_metrics=self.log_metrics,
             return_hidden_states=(

python/sglang/srt/managers/tokenizer_manager.py

@@ -37,6 +37,7 @@ from http import HTTPStatus
 from typing import Any, Awaitable, Dict, Iterable, List, Optional, Tuple, Union
 
 import fastapi
+import numpy as np
 import pybase64
 import torch
 import uvloop
@@ -263,6 +264,7 @@ def _build_flat_input_top_logprobs_fields(
     input_top_logprobs_val: List[Optional[List[float]]],
     input_top_logprobs_idx: List[Optional[List[int]]],
     top_logprobs_num: int,
+    return_b64: bool = False,
 ) -> Dict[str, Any]:
     """Build the flat raw prompt top logprob response fields.
 
@@ -270,7 +272,9 @@ def _build_flat_input_top_logprobs_fields(
     arrays. The leading null positions (counted by
     `input_top_logprobs_null_prefix`) precede the arrays, so covered position
     i, entry j lives at flat[(i - null_prefix) * k + j] and the covered range
-    spans null_prefix + rows positions.
+    spans null_prefix + rows positions. With ``return_b64``, the arrays are
+    base64 contiguous little-endian binary; the dtype marker fields let the
+    widths change later without a wire break.
     """
     num_rows = len(input_top_logprobs_val)
     null_prefix = 0
@@ -289,6 +293,18 @@ def _build_flat_input_top_logprobs_fields(
             )
 
     fields: Dict[str, Any] = {}
+    if return_b64:
+        val_arr = np.asarray(val_rows, dtype=np.float32)
+        idx_arr = np.asarray(idx_rows, dtype=np.int32)
+        fields["input_top_logprobs_val_flat_b64"] = pybase64.b64encode(
+            val_arr.tobytes()
+        ).decode("utf-8")
+        fields["input_top_logprobs_idx_flat_b64"] = pybase64.b64encode(
+            idx_arr.tobytes()
+        ).decode("utf-8")
+        fields["input_top_logprobs_val_flat_b64_dtype"] = "float32"
+        fields["input_top_logprobs_idx_flat_b64_dtype"] = "int32"
+    else:
         fields["input_top_logprobs_val_flat"] = [v for row in val_rows for v in row]
         fields["input_top_logprobs_idx_flat"] = [i for row in idx_rows for i in row]
     fields["input_top_logprobs_shape"] = [len(val_rows), k]
@@ -2301,6 +2317,7 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
                                 state.input_top_logprobs_val,
                                 state.input_top_logprobs_idx,
                                 top_logprobs_num,
+                                return_b64=state.obj.return_flat_raw_top_logprobs_b64,
                             )
                         )
                     except ValueError as e:

test/registered/unit/managers/test_flat_raw_top_logprobs.py

@@ -1,8 +1,9 @@
 """Unit tests for the flat raw prompt top logprob response format
-(`return_flat_raw_top_logprobs`).
+(`return_flat_raw_top_logprobs` / `return_flat_raw_top_logprobs_b64`).
 """
 
 import asyncio
+import base64
 import json
 import os
 import time
@@ -90,6 +91,21 @@ class TestFlatRawTopLogprobsValidation(CustomTestCase):
         for i in range(2):
             self.assertTrue(req[i].return_flat_raw_top_logprobs)
 
+    def test_b64_requires_flat_flag(self):
+        req = GenerateReqInput(text="hello", return_flat_raw_top_logprobs_b64=True)
+        with self.assertRaisesRegex(ValueError, "return_flat_raw_top_logprobs"):
+            req.normalize_batch_and_arguments()
+
+    def test_b64_flag_propagates_to_batch_items(self):
+        req = GenerateReqInput(
+            text=["a", "b"],
+            return_flat_raw_top_logprobs=True,
+            return_flat_raw_top_logprobs_b64=True,
+        )
+        req.normalize_batch_and_arguments()
+        for i in range(2):
+            self.assertTrue(req[i].return_flat_raw_top_logprobs_b64)
+
 
 class TestFlatAssembly(CustomTestCase):
     def test_flat_matches_nested_rows(self):
@@ -116,6 +132,26 @@ class TestFlatAssembly(CustomTestCase):
             start = (i - null_prefix) * k
             self.assertEqual(flat_val[start : start + k], _VAL_ROWS[i])
 
+    def test_b64_roundtrip(self):
+        fields = _build_flat_input_top_logprobs_fields(
+            _VAL_ROWS, _IDX_ROWS, top_logprobs_num=2, return_b64=True
+        )
+        self.assertEqual(fields["input_top_logprobs_shape"], [3, 2])
+        self.assertEqual(fields["input_top_logprobs_null_prefix"], 1)
+        self.assertEqual(fields["input_top_logprobs_val_flat_b64_dtype"], "float32")
+        self.assertEqual(fields["input_top_logprobs_idx_flat_b64_dtype"], "int32")
+        # shape is the literal array shape, so the b64 buffer reshapes with it.
+        val = np.frombuffer(
+            base64.b64decode(fields["input_top_logprobs_val_flat_b64"]),
+            dtype=np.dtype(fields["input_top_logprobs_val_flat_b64_dtype"]),
+        ).reshape(fields["input_top_logprobs_shape"])
+        idx = np.frombuffer(
+            base64.b64decode(fields["input_top_logprobs_idx_flat_b64"]),
+            dtype=np.dtype(fields["input_top_logprobs_idx_flat_b64_dtype"]),
+        ).reshape(fields["input_top_logprobs_shape"])
+        np.testing.assert_array_equal(val, np.asarray(_VAL_ROWS[1:], dtype=np.float32))
+        np.testing.assert_array_equal(idx, np.asarray(_IDX_ROWS[1:], dtype=np.int32))
+
     def test_all_null_rows(self):
         fields = _build_flat_input_top_logprobs_fields(
             [None], [None], top_logprobs_num=2
@@ -237,6 +273,29 @@ class TestAddLogprobToMetaInfo(CustomTestCase):
         )
 
 
+class TestB64MetaInfo(CustomTestCase):
+    def test_b64_fields_replace_flat_and_cache_reused(self):
+        state = _make_state(
+            return_logprob=True,
+            top_logprobs_num=2,
+            return_flat_raw_top_logprobs=True,
+            return_flat_raw_top_logprobs_b64=True,
+        )
+        state.input_top_logprobs_val.extend(_VAL_ROWS)
+        state.input_top_logprobs_idx.extend(_IDX_ROWS)
+        meta_info = _add_logprob_meta_info(state)
+        self.assertNotIn("input_top_logprobs", meta_info)
+        self.assertNotIn("input_top_logprobs_val_flat", meta_info)
+        self.assertIn("input_top_logprobs_val_flat_b64", meta_info)
+        self.assertEqual(meta_info["input_top_logprobs_shape"], [3, 2])
+        # No new rows -> the encoded payload is reused, not rebuilt.
+        again = _add_logprob_meta_info(state)
+        self.assertIs(
+            again["input_top_logprobs_val_flat_b64"],
+            meta_info["input_top_logprobs_val_flat_b64"],
+        )
+
+
 @unittest.skipUnless(
     os.environ.get("SGLANG_BENCH_FLAT_RAW_TOP_LOGPROBS"),
     "Serialization microbenchmark; set SGLANG_BENCH_FLAT_RAW_TOP_LOGPROBS=1 to run.",
@@ -301,6 +360,28 @@ class BenchFlatRawTopLogprobsSerialization(CustomTestCase):
             decode_flat,
         )
 
+        def decode_b64(payload):
+            d = json.loads(payload)
+            shape = d["input_top_logprobs_shape"]
+            return (
+                np.frombuffer(
+                    base64.b64decode(d["input_top_logprobs_val_flat_b64"]),
+                    np.dtype(d["input_top_logprobs_val_flat_b64_dtype"]),
+                ).reshape(shape),
+                np.frombuffer(
+                    base64.b64decode(d["input_top_logprobs_idx_flat_b64"]),
+                    np.dtype(d["input_top_logprobs_idx_flat_b64_dtype"]),
+                ).reshape(shape),
+            )
+
+        bench(
+            "flat b64",
+            lambda: _build_flat_input_top_logprobs_fields(
+                val_rows, idx_rows, top_logprobs_num=k, return_b64=True
+            ),
+            decode_b64,
+        )
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)