Support getting checksums in weight checker (#24537)

python/sglang/srt/entrypoints/http_server.py

@@ -1295,11 +1295,13 @@ async def resume_memory_occupation(
 @app.post("/weights_checker")
 @auth_level(AuthLevel.ADMIN_OPTIONAL)
 async def check_weights(obj: CheckWeightsReqInput, request: Request):
-    success, message = await _global_state.tokenizer_manager.check_weights(obj, request)
+    success, message, ranks = await _global_state.tokenizer_manager.check_weights(
-    return ORJSONResponse(
+        obj, request
-        {"success": success, "message": message},
-        status_code=200 if success else HTTPStatus.BAD_REQUEST,
     )
+    body = {"success": success, "message": message}
+    if ranks is not None:
+        body["ranks"] = ranks
+    return ORJSONResponse(body, status_code=200 if success else HTTPStatus.BAD_REQUEST)
 
 
 @app.api_route("/slow_down", methods=["GET", "POST"])

python/sglang/srt/managers/io_struct.py

@@ -1625,6 +1625,7 @@ class CheckWeightsReqInput(BaseReq):
 class CheckWeightsReqOutput(BaseReq):
     success: bool
     message: str
+    payload: Optional[Dict] = None
 
 
 @dataclass

python/sglang/srt/managers/scheduler_update_weights_mixin.py

@@ -196,8 +196,10 @@ class SchedulerUpdateWeightsMixin:
 
     def check_weights(self: Scheduler, recv_req: CheckWeightsReqInput):
         try:
-            self.tp_worker.model_runner.check_weights(action=recv_req.action)
+            payload = self.tp_worker.model_runner.check_weights(action=recv_req.action)
-            return CheckWeightsReqOutput(success=True, message="Success.")
+            return CheckWeightsReqOutput(
+                success=True, message="Success.", payload=payload
+            )
         except Exception as e:
             logger.warning(f"check_weights see error: {e}")
             traceback.print_exc()

python/sglang/srt/managers/tokenizer_control_mixin.py

@@ -764,10 +764,14 @@ class TokenizerControlMixin:
         self: TokenizerManager,
         obj: CheckWeightsReqInput,
         request: Optional[fastapi.Request] = None,
-    ) -> CheckWeightsReqOutput:
+    ) -> Tuple[bool, str, Optional[List[Dict]]]:
         self.auto_create_handle_loop()
         results = await self.check_weights_communicator(obj)
-        return FanOutCommunicator.merge_results(results)
+        success, message = FanOutCommunicator.merge_results(results)
+        ranks: Optional[List[Dict]] = None
+        if any(r.payload is not None for r in results):
+            ranks = [r.payload for r in results]
+        return success, message, ranks
 
     async def slow_down(
         self: TokenizerManager,

python/sglang/srt/model_executor/model_runner.py

@@ -3484,7 +3484,7 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         ShardedStateLoader.save_model(self.model, path, pattern, max_size)
 
     def check_weights(self, action: str):
-        self._weight_checker.handle(action=action)
+        return self._weight_checker.handle(action=action)
 
     def update_weights_from_ipc(self, recv_req):
         """Update weights from IPC for checkpoint-engine integration."""

python/sglang/srt/utils/weight_checker.py

@@ -1,16 +1,40 @@
 import logging
-from typing import Dict, Iterable, Tuple
+import time
+from typing import Dict, Iterable, Optional, Tuple
 
 import torch
+import torch.distributed as dist
+from pydantic import BaseModel, ConfigDict
 
 from sglang.srt.layers.quantization.fp8_utils import (
     block_quant_dequant,
     inverse_transform_scale_ue8m0,
 )
+from sglang.srt.managers.mm_utils import tensor_hash
 
 logger = logging.getLogger(__name__)
 
 
+class _StrictBaseModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+
+class ParallelismInfo(_StrictBaseModel):
+    tp_rank: int
+    tp_size: int
+    dp_rank: int
+    dp_size: int
+    pp_rank: int
+    pp_size: int
+    rank: int
+    size: int
+
+
+class ChecksumInfo(_StrictBaseModel):
+    checksums: Dict[str, str]
+    parallelism_info: ParallelismInfo
+
+
 _NON_PERSISTENT_BUFFER_PATTERNS = (
     "cos_sin_cache",
     "inv_freq",
@@ -28,14 +52,16 @@ class WeightChecker:
         self._model_runner = model_runner
         self._snapshot_tensors = None
 
-    def handle(self, action: str):
+    def handle(self, action: str) -> Optional[Dict]:
         logger.info(f"[WeightChecker] handle action={action}")
         if action == "snapshot":
-            self._snapshot()
+            return self._snapshot()
         elif action == "reset_tensors":
-            self._reset_tensors()
+            return self._reset_tensors()
         elif action == "compare":
-            self._compare()
+            return self._compare()
+        elif action == "checksum":
+            return self._compute_checksum()
         else:
             raise Exception(f"Unsupported {action=}")
 
@@ -62,12 +88,56 @@ class WeightChecker:
             actual_tensors=_postprocess_tensors(dict(self._model_state())),
         )
 
+    def _compute_checksum(self) -> Dict:
+        torch.cuda.synchronize()
+        start = time.perf_counter()
+
+        # Reuse the snapshot/compare postprocess pipeline so fp8 weights are
+        # dequantized to bf16 before hashing — two (qweight, scale) pairs that
+        # produce the same bf16 must produce the same checksum.
+        checksums = {
+            name: _hash_tensor(tensor.data)
+            for name, should_compare, tensor in _postprocess_tensors(
+                dict(self._model_state())
+            )
+            if should_compare
+        }
+
+        torch.cuda.synchronize()
+        elapsed = time.perf_counter() - start
+        logger.info(
+            f"[WeightChecker] checksum computed for {len(checksums)} tensors in {elapsed:.3f}s"
+        )
+
+        info = ChecksumInfo(
+            checksums=checksums,
+            parallelism_info=self._parallelism_info(),
+        )
+        return info.model_dump()
+
+    def _parallelism_info(self) -> ParallelismInfo:
+        mr = self._model_runner
+        return ParallelismInfo(
+            tp_rank=mr.tp_rank,
+            tp_size=mr.tp_size,
+            dp_rank=mr.dp_rank if mr.dp_rank is not None else 0,
+            dp_size=mr.dp_size,
+            pp_rank=mr.pp_rank,
+            pp_size=mr.pp_size,
+            rank=dist.get_rank() if dist.is_initialized() else 0,
+            size=dist.get_world_size() if dist.is_initialized() else 1,
+        )
+
     def _model_state(self):
         # TODO: support EAGLE etc (e.g. yield from both main model and draft model)
         yield from self._model_runner.model.named_parameters()
         yield from self._model_runner.model.named_buffers()
 
 
+def _hash_tensor(t: torch.Tensor) -> str:
+    return f"{tensor_hash(t):016x}"
+
+
 def _check_tensors(
     expect_tensors: Iterable[Tuple[str, bool, torch.Tensor]],
     actual_tensors: Iterable[Tuple[str, bool, torch.Tensor]],

test/registered/rl/test_weight_checker_e2e.py

@@ -138,6 +138,74 @@ class TestWeightCheckerE2E(CustomTestCase):
         self.assertEqual(resp.status_code, 200)
         self.assertTrue(resp.json()["success"])
 
+    def test_e_checksum_returns_ranks_with_hashes(self):
+        """checksum action must yield a ranks list with hex hashes per rank."""
+        resp = self._post("checksum")
+        self.assertEqual(resp.status_code, 200)
+        body = resp.json()
+        self.assertTrue(body["success"])
+        self.assertIn("ranks", body)
+        ranks = body["ranks"]
+        self.assertIsInstance(ranks, list)
+        self.assertGreaterEqual(len(ranks), 1)
+
+        first = ranks[0]
+        self.assertIn("checksums", first)
+        self.assertIn("parallelism_info", first)
+
+        info = first["parallelism_info"]
+        for key in (
+            "tp_rank",
+            "tp_size",
+            "dp_rank",
+            "dp_size",
+            "pp_rank",
+            "pp_size",
+            "rank",
+            "size",
+        ):
+            self.assertIn(key, info)
+
+        checksums = first["checksums"]
+        self.assertGreater(len(checksums), 0)
+        for name, h in checksums.items():
+            self.assertIsInstance(h, str)
+            self.assertEqual(len(h), 16, f"unexpected hash length for {name!r}: {h!r}")
+            int(h, 16)
+
+    def test_e_checksum_is_stable_across_calls(self):
+        """Two consecutive checksum calls with no weight update must match."""
+        first = self._post("checksum").json()["ranks"]
+        second = self._post("checksum").json()["ranks"]
+        self.assertEqual(first, second)
+
+    def test_e_checksum_changes_after_weight_update(self):
+        """Updating a tensor must change its corresponding hash."""
+        param_name = "model.layers.7.mlp.up_proj.weight"
+        fused_name = "model.layers.7.mlp.gate_up_proj.weight"
+
+        before = self._post("checksum").json()["ranks"][0]["checksums"]
+        before_hash = before.get(fused_name)
+        self.assertIsNotNone(before_hash, f"missing {fused_name!r} in checksum keys")
+
+        new_tensor = torch.full(_UP_PROJ_SHAPE, 0.5, device="cuda")
+        self.assertTrue(
+            self._update_weights([(param_name, new_tensor)]).json()["success"]
+        )
+
+        after = self._post("checksum").json()["ranks"][0]["checksums"]
+        self.assertNotEqual(after[fused_name], before_hash)
+
+    def test_e_checksum_skips_non_persistent_buffers(self):
+        """No checksum entry should contain a non-persistent-buffer substring."""
+        ranks = self._post("checksum").json()["ranks"]
+        for rank in ranks:
+            for name in rank["checksums"]:
+                self.assertNotIn("cos_sin_cache", name)
+                self.assertNotIn("inv_freq", name)
+                self.assertNotIn("freqs_cis", name)
+                self.assertNotIn("_weight_fp32", name)
+
     def test_z_snapshot_reset_compare_detects_diff(self):
         """Destructive: leaves weights randomized. Named test_z_* so it runs last."""
         self.assertEqual(self._post("snapshot").status_code, 200)

test/registered/unit/utils/test_weight_checker.py

@@ -26,8 +26,12 @@ from sglang.srt.layers.quantization.fp8_utils import (
     transform_scale_ue8m0,
 )
 from sglang.srt.utils.weight_checker import (
+    ChecksumInfo,
+    ParallelismInfo,
     WeightChecker,
     _check_tensors,
+    _hash_tensor,
+    _is_non_persistent_buffer_name,
     _postprocess_tensors,
     _random_like,
 )
@@ -99,11 +103,26 @@ class _TinyModel(nn.Module):
 
 
 class _FakeModelRunner:
-    """Minimal stand-in: WeightChecker only touches `.model.named_parameters()` and
+    """Minimal stand-in: WeightChecker touches `.model.named_parameters()`,
-    `.model.named_buffers()`, nothing else."""
+    `.model.named_buffers()`, plus parallelism attributes for the checksum action."""
 
-    def __init__(self, model: nn.Module):
+    def __init__(
+        self,
+        model: nn.Module,
+        tp_rank: int = 0,
+        tp_size: int = 1,
+        dp_rank: int = 0,
+        dp_size: int = 1,
+        pp_rank: int = 0,
+        pp_size: int = 1,
+    ):
         self.model = model
+        self.tp_rank = tp_rank
+        self.tp_size = tp_size
+        self.dp_rank = dp_rank
+        self.dp_size = dp_size
+        self.pp_rank = pp_rank
+        self.pp_size = pp_size
 
 
 # ---------------------------------------------------------------------------
@@ -449,13 +468,27 @@ class TestHandle(_WeightCheckerTestBase):
     def test_routes_to_actions(self):
         with patch.object(self.checker, "_snapshot") as m_snap, patch.object(
             self.checker, "_reset_tensors"
-        ) as m_reset, patch.object(self.checker, "_compare") as m_compare:
+        ) as m_reset, patch.object(self.checker, "_compare") as m_compare, patch.object(
+            self.checker, "_compute_checksum", return_value={"checksums": {}}
+        ) as m_checksum:
             self.checker.handle("snapshot")
             self.checker.handle("reset_tensors")
             self.checker.handle("compare")
+            self.checker.handle("checksum")
             m_snap.assert_called_once()
             m_reset.assert_called_once()
             m_compare.assert_called_once()
+            m_checksum.assert_called_once()
+
+    def test_returns_none_for_non_checksum_actions(self):
+        self.assertIsNone(self.checker.handle("snapshot"))
+        self.assertIsNone(self.checker.handle("compare"))
+
+    def test_returns_dict_for_checksum_action(self):
+        out = self.checker.handle("checksum")
+        self.assertIsInstance(out, dict)
+        self.assertIn("checksums", out)
+        self.assertIn("parallelism_info", out)
 
     def test_unknown_action_raises(self):
         with self.assertRaises(Exception) as ctx:
@@ -463,5 +496,138 @@ class TestHandle(_WeightCheckerTestBase):
         self.assertIn("Unsupported", str(ctx.exception))
 
 
+# ---------------------------------------------------------------------------
+# _is_non_persistent_buffer_name
+# ---------------------------------------------------------------------------
+
+
+class TestIsNonPersistentBufferName(CustomTestCase):
+
+    def test_matches_cos_sin_cache_substring(self):
+        self.assertTrue(
+            _is_non_persistent_buffer_name("model.rotary_emb.cos_sin_cache")
+        )
+
+    def test_matches_inv_freq_substring(self):
+        self.assertTrue(_is_non_persistent_buffer_name("model.rotary_emb.inv_freq"))
+
+    def test_matches_freqs_cis_substring(self):
+        self.assertTrue(_is_non_persistent_buffer_name("model.rotary_emb.freqs_cis"))
+
+    def test_matches_weight_fp32_substring(self):
+        self.assertTrue(
+            _is_non_persistent_buffer_name("model.layers.0.mlp.gate._weight_fp32")
+        )
+
+    def test_does_not_match_normal_param_names(self):
+        self.assertFalse(_is_non_persistent_buffer_name("model.layers.0.mlp.weight"))
+        self.assertFalse(_is_non_persistent_buffer_name("model.embed_tokens.weight"))
+
+
+# ---------------------------------------------------------------------------
+# _hash_tensor
+# ---------------------------------------------------------------------------
+
+
+class TestHashTensor(CustomTestCase):
+
+    def test_stable_for_same_input(self):
+        t = torch.arange(64, dtype=torch.float32).cuda()
+        self.assertEqual(_hash_tensor(t), _hash_tensor(t.clone()))
+
+    def test_changes_with_data(self):
+        a = torch.zeros(64, dtype=torch.float32).cuda()
+        b = torch.ones(64, dtype=torch.float32).cuda()
+        self.assertNotEqual(_hash_tensor(a), _hash_tensor(b))
+
+    def test_returns_16_char_hex(self):
+        t = torch.zeros(64, dtype=torch.float32).cuda()
+        h = _hash_tensor(t)
+        self.assertEqual(len(h), 16)
+        int(h, 16)  # raises if not hex
+
+    def test_does_not_mutate_input(self):
+        t = torch.arange(64, dtype=torch.float32).cuda()
+        before = t.clone()
+        _hash_tensor(t)
+        torch.testing.assert_close(t, before)
+
+
+# ---------------------------------------------------------------------------
+# _compute_checksum
+# ---------------------------------------------------------------------------
+
+
+class _ChecksumTestBase(CustomTestCase):
+
+    def setUp(self):
+        torch.manual_seed(0)
+        self.model = _TinyModel().cuda()
+        self.runner = _FakeModelRunner(
+            self.model,
+            tp_rank=2,
+            tp_size=4,
+            dp_rank=1,
+            dp_size=2,
+            pp_rank=0,
+            pp_size=1,
+        )
+        self.checker = WeightChecker(model_runner=self.runner)
+
+
+class TestComputeChecksum(_ChecksumTestBase):
+
+    def test_returns_dict_with_expected_top_level_keys(self):
+        out = self.checker._compute_checksum()
+        self.assertEqual(set(out.keys()), {"checksums", "parallelism_info"})
+
+    def test_skips_non_persistent_buffers(self):
+        out = self.checker._compute_checksum()
+        names = set(out["checksums"].keys())
+        # Normal params and buffers are present.
+        self.assertIn("w", names)
+        self.assertIn("b", names)
+        self.assertIn("running_mean", names)
+        # Non-persistent buffer patterns are filtered out.
+        self.assertNotIn("rotary_emb_cos_sin_cache", names)
+        self.assertNotIn("rotary_emb_freqs_cis", names)
+        self.assertNotIn("gate_proj_weight_fp32_cache", names)
+
+    def test_hashes_are_hex_strings(self):
+        out = self.checker._compute_checksum()
+        for name, h in out["checksums"].items():
+            self.assertEqual(len(h), 16, f"unexpected hash length for {name!r}")
+            int(h, 16)
+
+    def test_parallelism_info_reflects_runner_state(self):
+        info = self.checker._compute_checksum()["parallelism_info"]
+        self.assertEqual(info["tp_rank"], 2)
+        self.assertEqual(info["tp_size"], 4)
+        self.assertEqual(info["dp_rank"], 1)
+        self.assertEqual(info["dp_size"], 2)
+        self.assertEqual(info["pp_rank"], 0)
+        self.assertEqual(info["pp_size"], 1)
+        # rank/size come from torch.distributed; default to 0/1 when uninitialized.
+        self.assertIn("rank", info)
+        self.assertIn("size", info)
+
+    def test_checksum_is_stable_for_unchanged_weights(self):
+        first = self.checker._compute_checksum()
+        second = self.checker._compute_checksum()
+        self.assertEqual(first, second)
+
+    def test_checksum_changes_after_param_mutation(self):
+        first = self.checker._compute_checksum()["checksums"]["w"]
+        with torch.no_grad():
+            self.model.w.data.fill_(99.0)
+        second = self.checker._compute_checksum()["checksums"]["w"]
+        self.assertNotEqual(first, second)
+
+    def test_validates_against_pydantic_schema(self):
+        out = self.checker._compute_checksum()
+        info = ChecksumInfo.model_validate(out)
+        self.assertIsInstance(info.parallelism_info, ParallelismInfo)
+
+
 if __name__ == "__main__":
     unittest.main()