Support enabling partial non intrusive dump in dumper (#19069)

python/sglang/srt/debug_utils/dumper.py

@@ -102,6 +102,7 @@ class _DumperConfig(_FrozenConfig):
     cleanup_previous: bool = False
     collective_timeout: int = 60
     server_port: str = "-1"
+    non_intrusive_mode: str = "core"
 
     @classmethod
     def _env_prefix(cls) -> str:
@@ -234,8 +235,11 @@ class _Dumper:
     def register_non_intrusive_dumper(
         self,
         model: "torch.nn.Module",
-    ) -> "_NonIntrusiveDumper":
-        return _NonIntrusiveDumper(dumper=self, model=model)
+    ) -> Optional["_NonIntrusiveDumper"]:
+        mode = self._config.non_intrusive_mode
+        if mode == "off":
+            return None
+        return _NonIntrusiveDumper(dumper=self, model=model, mode=mode)
 
     # ------------------------------- public :: secondary ---------------------------------
 
@@ -475,39 +479,50 @@ class _Dumper:
 
 
 class _NonIntrusiveDumper:
-    """Registers forward hooks on model modules to non-invasively dump tensor outputs."""
-
     _NAME_PREFIX = "non_intrusive__"
+    _CORE_FIELDS: frozenset[str] = frozenset({"input_ids", "positions"})
 
     def __init__(
         self,
         dumper: _Dumper,
         model: "torch.nn.Module",
+        mode: str,
     ):
         self._dumper = dumper
+        self._mode = mode
 
         for module_name, module in model.named_modules():
             module.register_forward_hook(
-                self._make_forward_hook(module_name=module_name)
+                self._make_forward_hook(
+                    module_name=module_name,
+                    is_root=(module_name == ""),
+                )
             )
 
-    def _make_forward_hook(self, module_name: str):
+    def _make_forward_hook(self, *, module_name: str, is_root: bool):
         def _hook(_module, input, output):
             for i, item in enumerate(input):
-                self._dump_value(module_name, item, role=f"inputs.{i}")
+                self._dump_value(module_name, item, role=f"inputs.{i}", is_root=is_root)
 
             if output is not None:
-                self._dump_value(module_name, output, role="output")
+                self._dump_value(module_name, output, role="output", is_root=False)
 
         return _hook
 
-    def _dump_value(self, module_name: str, value, role: str) -> None:
-        for key, tensor in self._convert_value(value).items():
-            parts = [p for p in (module_name, role, key) if p]
-            self._dumper.dump(self._NAME_PREFIX + ".".join(parts), tensor)
+    def _dump_value(self, module_name: str, value, role: str, *, is_root: bool) -> None:
+        for key, tensor in self._convert_value(
+            value, skip_forward_batch=(not is_root)
+        ).items():
+            if key in self._CORE_FIELDS:
+                self._dumper.dump(key, tensor)
+            elif self._mode == "all":
+                parts = [p for p in (module_name, role, key) if p]
+                self._dumper.dump(self._NAME_PREFIX + ".".join(parts), tensor)
 
     @staticmethod
-    def _convert_value(value) -> dict[str, torch.Tensor]:
+    def _convert_value(
+        value, *, skip_forward_batch: bool = False
+    ) -> dict[str, torch.Tensor]:
         if isinstance(value, torch.Tensor):
             return {"": value}
 
@@ -528,6 +543,8 @@ class _NonIntrusiveDumper:
             if isinstance(value, LogitsProcessorOutput):
                 return {"next_token_logits": value.next_token_logits}
             if isinstance(value, ForwardBatch):
+                if skip_forward_batch:
+                    return {}
                 return {
                     "input_ids": value.input_ids,
                     "seq_lens": value.seq_lens,

test/registered/debug_utils/test_dumper.py

@@ -953,7 +953,7 @@ class TestDumperHttp:
         assert resp.status_code == 400
 
 
-class TestNonIntrusiveDumper:
+class _NonIntrusiveTestBase:
     _PREFIX = "non_intrusive__"
 
     @staticmethod
@@ -978,6 +978,14 @@ class TestNonIntrusiveDumper:
 
         return OuterModel()
 
+    @staticmethod
+    def _make_dumper(tmp_path, **overrides) -> "_Dumper":
+        return _make_test_dumper(tmp_path, non_intrusive_mode="all", **overrides)
+
+
+class TestNonIntrusiveDumper(_NonIntrusiveTestBase):
+    """Tests for mode='all' — hooks on every module, non_intrusive__ prefix."""
+
     def test_basic_inputs_and_outputs(self, tmp_path):
         class Inner(torch.nn.Module):
             def __init__(self):
@@ -988,7 +996,7 @@ class TestNonIntrusiveDumper:
             def forward(self, x):
                 return self.relu(self.linear(x))
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
 
@@ -1043,7 +1051,7 @@ class TestNonIntrusiveDumper:
                     x = layer(x)
                 return x
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
 
@@ -1084,7 +1092,7 @@ class TestNonIntrusiveDumper:
                 a, b = self.split(x)
                 return self.linear(a + b)
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
 
@@ -1111,7 +1119,7 @@ class TestNonIntrusiveDumper:
             def forward(self, x):
                 return self.wrap(x)[0]
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
 
@@ -1136,7 +1144,7 @@ class TestNonIntrusiveDumper:
                 mask = torch.ones_like(x)
                 return self.mul(x, mask)
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
 
@@ -1161,7 +1169,7 @@ class TestNonIntrusiveDumper:
                 self.sink(x)
                 return x
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
 
@@ -1188,7 +1196,7 @@ class TestNonIntrusiveDumper:
                 self.const(x)
                 return x
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
 
@@ -1202,7 +1210,7 @@ class TestNonIntrusiveDumper:
         )
 
     def test_root_module_name_no_malformed_dots(self, tmp_path):
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         model = torch.nn.Linear(4, 4)
         d.register_non_intrusive_dumper(model)
 
@@ -1227,7 +1235,7 @@ class TestNonIntrusiveDumper:
             def forward(self, x):
                 return self.relu(self.linear(x))
 
-        d = _make_test_dumper(
+        d = self._make_dumper(
             tmp_path, filter="name=non_intrusive__model.linear.output"
         )
         model = self._wrap_as_outer(Inner)
@@ -1250,7 +1258,7 @@ class TestNonIntrusiveDumper:
             def forward(self, x):
                 return self.linear(x)
 
-        d = _make_test_dumper(tmp_path)
+        d = self._make_dumper(tmp_path)
         d.configure(enable=False)
         model = self._wrap_as_outer(Inner)
         d.register_non_intrusive_dumper(model)
@@ -1262,5 +1270,105 @@ class TestNonIntrusiveDumper:
         assert len(captured) == 0
 
 
+def _make_forward_batch():
+    from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
+
+    return ForwardBatch(
+        forward_mode=ForwardMode.DECODE,
+        batch_size=2,
+        input_ids=torch.tensor([10, 20]),
+        req_pool_indices=torch.zeros(2, dtype=torch.long),
+        seq_lens=torch.tensor([5, 6]),
+        out_cache_loc=torch.zeros(2, dtype=torch.long),
+        seq_lens_sum=11,
+        positions=torch.tensor([0, 1]),
+    )
+
+
+class TestNonIntrusiveDumperConfigMode(_NonIntrusiveTestBase):
+
+    @staticmethod
+    def _build_model() -> torch.nn.Module:
+        class SubLayer(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = torch.nn.Linear(4, 4)
+
+            def forward(self, forward_batch):
+                return self.linear(
+                    forward_batch.input_ids.float().unsqueeze(-1).expand(-1, 4)
+                )
+
+        class Root(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.layer = SubLayer()
+
+            def forward(self, forward_batch):
+                return self.layer(forward_batch)
+
+        return Root()
+
+    def _run(self, tmp_path, mode: str) -> tuple:
+        d = _make_test_dumper(tmp_path, non_intrusive_mode=mode)
+        model = self._build_model()
+        d.register_non_intrusive_dumper(model)
+        forward_batch = _make_forward_batch()
+        with d.capture_output() as captured:
+            model(forward_batch)
+        return captured, forward_batch
+
+    def test_off_mode(self, tmp_path):
+        captured, _ = self._run(tmp_path, "off")
+        assert len(captured) == 0
+
+    def test_core_mode(self, tmp_path):
+        captured, fb = self._run(tmp_path, "core")
+
+        # core fields dumped with clean names
+        assert "input_ids" in captured
+        assert "positions" in captured
+        assert torch.equal(captured["input_ids"]["value"], fb.input_ids)
+        assert torch.equal(captured["positions"]["value"], fb.positions)
+
+        # nothing with non_intrusive__ prefix
+        assert not any(k.startswith("non_intrusive__") for k in captured)
+
+    def test_all_mode(self, tmp_path):
+        captured, fb = self._run(tmp_path, "all")
+
+        # core fields dumped with clean names
+        assert "input_ids" in captured
+        assert "positions" in captured
+        assert torch.equal(captured["input_ids"]["value"], fb.input_ids)
+        assert torch.equal(captured["positions"]["value"], fb.positions)
+
+        # non-core ForwardBatch fields dumped with prefix
+        assert "non_intrusive__inputs.0.seq_lens" in captured
+        assert torch.equal(
+            captured["non_intrusive__inputs.0.seq_lens"]["value"], fb.seq_lens
+        )
+
+        # core fields NOT duplicated with prefix
+        assert not any(
+            k.startswith("non_intrusive__") and k.endswith("input_ids")
+            for k in captured
+        )
+        assert not any(
+            k.startswith("non_intrusive__") and k.endswith("positions")
+            for k in captured
+        )
+
+        # ForwardBatch skipped on sub-modules (no duplication)
+        assert not any(
+            k.startswith("non_intrusive__layer.inputs.") and "seq_lens" in k
+            for k in captured
+        ), f"ForwardBatch skipped on sub-module, got: {list(captured.keys())}"
+
+        # regular tensor outputs on sub-modules still dumped
+        assert "non_intrusive__layer.linear.output" in captured
+        assert "non_intrusive__layer.output" in captured
+
+
 if __name__ == "__main__":
     sys.exit(pytest.main([__file__]))