Fix stale GLM MoE routing after runtime weight updates (#35883)

Co-authored-by: Jiajun Li <jiajun.li@radixark.ai>

python/sglang/srt/models/glm4_moe.py

@@ -368,19 +368,16 @@ class Glm4MoeGate(nn.Module):
     ):
         super().__init__()
         self.weight = nn.Parameter(
-            torch.empty((config.n_routed_experts, config.hidden_size))
+            torch.empty(
+                (config.n_routed_experts, config.hidden_size), dtype=torch.float32
+            )
         )
         self.e_score_correction_bias = nn.Parameter(
             torch.empty((config.n_routed_experts), dtype=torch.float32)
         )
-        # GLM requires FP32 gate projection; cache to avoid per-forward cast.
-        # FIXME: if gate weight is updated at runtime (e.g. expert rebalancing), _weight_fp32 must be invalidated.
-        self.register_buffer("_weight_fp32", None, persistent=False)
 
     def forward(self, hidden_states):
-        if self._weight_fp32 is None:
-            self._weight_fp32 = self.weight.data.to(torch.float32)
-        logits = F.linear(hidden_states.to(torch.float32), self._weight_fp32, None)
+        logits = F.linear(hidden_states.to(torch.float32), self.weight, None)
         return logits
 
 

python/sglang/srt/models/glm4_moe_lite.py

@@ -156,19 +156,16 @@ class Glm4MoeLiteGate(nn.Module):
         super().__init__()
         self.is_nextn = is_nextn
         self.weight = nn.Parameter(
-            torch.empty((config.n_routed_experts, config.hidden_size))
+            torch.empty(
+                (config.n_routed_experts, config.hidden_size), dtype=torch.float32
+            )
         )
         self.e_score_correction_bias = nn.Parameter(
             torch.empty((config.n_routed_experts), dtype=torch.float32)
         )
-        # GLM requires FP32 gate projection; cache to avoid per-forward cast.
-        # FIXME: if gate weight is updated at runtime (e.g. expert rebalancing), _weight_fp32 must be invalidated.
-        self.register_buffer("_weight_fp32", None, persistent=False)
 
     def forward(self, hidden_states):
-        if self._weight_fp32 is None:
-            self._weight_fp32 = self.weight.data.to(torch.float32)
-        logits = F.linear(hidden_states.to(torch.float32), self._weight_fp32, None)
+        logits = F.linear(hidden_states.to(torch.float32), self.weight, None)
         return logits
 
 

python/sglang/srt/utils/weight_checker.py

@@ -55,7 +55,6 @@ _NON_PERSISTENT_BUFFER_PATTERNS = (
     "cos_sin_cache",
     "inv_freq",
     "freqs_cis",
-    "_weight_fp32",
     "expert_mask_gpu",
 )
 

test/registered/rl/test_weight_checker_e2e.py

@@ -211,7 +211,6 @@ class TestWeightCheckerE2E(CustomTestCase):
                 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."""

test/registered/unit/models/test_glm_moe_gate_fp32.py

@@ -0,0 +1,54 @@
+"""Regression tests for GLM MoE gate weights used by FP32 routing."""
+
+from sglang.test.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=4, suite="base-a-test-cpu")
+
+import unittest
+from types import SimpleNamespace
+
+import torch
+import torch.nn.functional as F
+
+from sglang.srt.model_executor.model_runner_components.weight_updater import (
+    _model_load_weights_direct,
+)
+from sglang.srt.model_loader.utils import set_default_torch_dtype
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.glm4_moe import Glm4MoeGate
+from sglang.srt.models.glm4_moe_lite import Glm4MoeLiteGate
+from sglang.test.test_utils import CustomTestCase
+
+_CONFIG = SimpleNamespace(n_routed_experts=3, hidden_size=4)
+
+
+class TestGlmMoeGateFp32Weight(CustomTestCase):
+    def test_bf16_load_updates_fp32_weight_in_place(self):
+        """BF16 runtime updates must overwrite the canonical FP32 gate weight."""
+        hidden_states = torch.arange(8, dtype=torch.bfloat16).reshape(2, 4)
+        initial = torch.arange(12, dtype=torch.bfloat16).reshape(3, 4)
+        updated = initial + 1
+
+        for gate_cls in (Glm4MoeGate, Glm4MoeLiteGate):
+            with self.subTest(gate=gate_cls.__name__):
+                with set_default_torch_dtype(torch.bfloat16):
+                    gate = gate_cls(_CONFIG)
+
+                self.assertEqual(gate.weight.dtype, torch.float32)
+                self.assertFalse(hasattr(gate, "_weight_fp32"))
+                weight_ptr = gate.weight.data_ptr()
+
+                default_weight_loader(gate.weight, initial)
+                torch.testing.assert_close(gate.weight, initial.float())
+
+                _model_load_weights_direct(gate, [("weight", updated)])
+                self.assertEqual(gate.weight.data_ptr(), weight_ptr)
+                torch.testing.assert_close(gate.weight, updated.float())
+                torch.testing.assert_close(
+                    gate(hidden_states),
+                    F.linear(hidden_states.float(), updated.float()),
+                )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/utils/test_weight_checker.py

@@ -111,7 +111,7 @@ class _TinyModel(nn.Module):
         self.w = nn.Parameter(torch.randn(4, 4), requires_grad=False)
         self.b = nn.Parameter(torch.zeros(4), requires_grad=False)
         self.register_buffer("running_mean", torch.zeros(4))
-        # Buffer names that match weight_checker's hard-coded skip patterns.
+        # Buffer names used to exercise weight checker's hard-coded filters.
         self.register_buffer("rotary_emb_cos_sin_cache", torch.full((8,), 3.14))
         self.register_buffer("rotary_emb_freqs_cis", torch.full((8,), 2.71))
         self.register_buffer("gate_proj_weight_fp32_cache", torch.full((8,), 1.41))
@@ -252,13 +252,6 @@ class TestPostprocessTensors(CustomTestCase):
             [("model.rotary_emb.inv_freq", False, RawComparable(t))],
         )
 
-    def test_skips_weight_fp32_substring(self):
-        t = torch.randn(4)
-        _assert_entries_close(
-            _build_check_entries({"model.layers.0.mlp.gate._weight_fp32": t}, set()),
-            [("model.layers.0.mlp.gate._weight_fp32", False, RawComparable(t))],
-        )
-
     def test_substring_match_not_endswith(self):
         # Pattern can appear anywhere in the name, not just at the end.
         t = torch.randn(4)
@@ -545,10 +538,12 @@ class TestResetTensors(_WeightCheckerTestBase):
         self.checker._reset_tensors()
         torch.testing.assert_close(self.model.rotary_emb_freqs_cis, before)
 
-    def test_skips_weight_fp32(self):
+    def test_poisons_weight_fp32_cache(self):
         before = self.model.gate_proj_weight_fp32_cache.clone()
+        before_ptr = self.model.gate_proj_weight_fp32_cache.data_ptr()
         self.checker._reset_tensors()
-        torch.testing.assert_close(self.model.gate_proj_weight_fp32_cache, before)
+        self.assertEqual(self.model.gate_proj_weight_fp32_cache.data_ptr(), before_ptr)
+        self.assertFalse(torch.equal(self.model.gate_proj_weight_fp32_cache, before))
 
 
 class TestCompare(_WeightCheckerTestBase):
@@ -645,11 +640,6 @@ class TestIsNonPersistentBufferName(CustomTestCase):
     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"))
@@ -723,10 +713,10 @@ class TestComputeChecksum(_ChecksumTestBase):
         self.assertIn("w", names)
         self.assertIn("b", names)
         self.assertIn("running_mean", names)
+        self.assertIn("gate_proj_weight_fp32_cache", 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()