[None][fix] validate sampling params in trtllm-serve request models

tensorrt_llm/serve/openai_protocol.py

@@ -654,17 +654,30 @@ class CompletionRequest(OpenAIBaseModel):
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = None
     suffix: Optional[str] = None
-    temperature: Optional[float] = None
-    top_p: Optional[float] = None
+    temperature: Optional[float] = Field(
+        default=None, ge=0.0, description="Sampling temperature; must be >= 0.")
+    top_p: Optional[float] = Field(
+        default=None,
+        ge=0.0,
+        le=1.0,
+        description="Nucleus sampling probability; must be in [0, 1].")
     user: Optional[str] = None
     lora_request: Optional[LoRARequest] = None
     prompt_ignore_length: Optional[int] = 0
 
     # doc: begin-completion-sampling-params
     use_beam_search: bool = False
-    top_k: int = 0
+    top_k: int = Field(
+        default=0,
+        ge=0,
+        description="Top-k sampling; must be >= 0 (0 disables top-k).")
     top_p_min: float = 0.0
-    min_p: float = 0.0
+    min_p: float = Field(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="Minimum token probability scaled by the top token's "
+        "probability; must be in [0, 1] (0 disables min-p).")
     repetition_penalty: float = 1.0
     # Unset by default so the engine picks its own beam-search defaults, as it
     # does for requests coming through the Python API. Sending concrete values
@@ -1092,8 +1105,13 @@ class ChatCompletionRequest(OpenAIBaseModel):
     stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = None
-    temperature: Optional[float] = None
-    top_p: Optional[float] = None
+    temperature: Optional[float] = Field(
+        default=None, ge=0.0, description="Sampling temperature; must be >= 0.")
+    top_p: Optional[float] = Field(
+        default=None,
+        ge=0.0,
+        le=1.0,
+        description="Nucleus sampling probability; must be in [0, 1].")
     tools: Optional[List[ChatCompletionToolsParam]] = None
     tool_choice: Optional[Union[Literal["none", "auto", "required"],
                                 ChatCompletionNamedToolChoiceParam]] = "none"
@@ -1121,9 +1139,17 @@ class ChatCompletionRequest(OpenAIBaseModel):
     # doc: begin-chat-completion-sampling-params
     best_of: Optional[int] = None
     use_beam_search: bool = False
-    top_k: int = 0
+    top_k: int = Field(
+        default=0,
+        ge=0,
+        description="Top-k sampling; must be >= 0 (0 disables top-k).")
     top_p_min: float = 0.0
-    min_p: float = 0.0
+    min_p: float = Field(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="Minimum token probability scaled by the top token's "
+        "probability; must be in [0, 1] (0 disables min-p).")
     repetition_penalty: float = 1.0
     # Unset by default so the engine picks its own beam-search defaults, as it
     # does for requests coming through the Python API. Sending concrete values
@@ -1623,12 +1649,17 @@ def _materialize_lazy_item_content(cls, value):
                           "priority"] = "auto"
     store: Optional[bool] = True
     stream: Optional[bool] = False
-    temperature: Optional[float] = None
+    temperature: Optional[float] = Field(
+        default=None, ge=0.0, description="Sampling temperature; must be >= 0.")
     text: Optional[ResponseTextConfig] = None
     tool_choice: ToolChoice = "auto"
     tools: list[Tool] = Field(default_factory=list)
     top_logprobs: Optional[int] = 0
-    top_p: Optional[float] = None
+    top_p: Optional[float] = Field(
+        default=None,
+        ge=0.0,
+        le=1.0,
+        description="Nucleus sampling probability; must be in [0, 1].")
     truncation: Optional[Literal["auto", "disabled"]] = "disabled"
     user: Optional[str] = None
 

tests/integration/test_lists/test-db/l0_cpu.yml

@@ -190,6 +190,7 @@ l0_cpu:
   - unittest/llmapi/apps/test_responses_usage.py
   - unittest/llmapi/apps/test_serving_extensions.py
   - unittest/llmapi/apps/test_tool_parsers.py
+  - unittest/llmapi/apps/test_openai_protocol_validation.py
   - unittest/llmapi/apps/test_web_search_shared.py
   - unittest/llmapi/test_bench_async.py
   - unittest/llmapi/test_config_overrides.py

tests/unittest/llmapi/apps/test_openai_protocol_validation.py

@@ -0,0 +1,98 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Boundary validation for trtllm-serve request models (issue #6329).
+
+Invalid sampling parameters are rejected at the pydantic boundary with a
+field-specific ValidationError (surfaced to clients as HTTP 400 by the
+server's RequestValidationError handler). SamplingParams._validate already
+rejects these values later with a 400; the benefit here is the earlier,
+field-named error and schema-visible bounds, not a crash fix.
+"""
+
+import pytest
+from pydantic import ValidationError
+
+from tensorrt_llm.serve.openai_protocol import (
+    ChatCompletionRequest,
+    CompletionRequest,
+    ResponsesRequest,
+)
+
+
+def _completion(**kw):
+    return CompletionRequest(model="m", prompt="hi", **kw)
+
+
+def _chat(**kw):
+    return ChatCompletionRequest(model="m", messages=[{"role": "user", "content": "hi"}], **kw)
+
+
+def _responses(**kw):
+    return ResponsesRequest(model="m", input="hi", **kw)
+
+
+def _rejected_field(build, **kw):
+    """Return the set of top-level field names that failed validation."""
+    with pytest.raises(ValidationError) as exc_info:
+        build(**kw)
+    return {err["loc"][0] for err in exc_info.value.errors()}
+
+
+# top_k only exists on completion / chat requests.
+BUILDERS_WITH_TOP_K = [_completion, _chat]
+ALL_BUILDERS = [_completion, _chat, _responses]
+
+
+@pytest.mark.parametrize("build", ALL_BUILDERS)
+def test_valid_sampling_params_accepted(build):
+    # Boundary-valid values must pass unchanged.
+    req = build(temperature=0.0, top_p=1.0)
+    assert req.temperature == 0.0
+    assert req.top_p == 1.0
+
+
+@pytest.mark.parametrize("build", ALL_BUILDERS)
+def test_negative_temperature_rejected(build):
+    assert "temperature" in _rejected_field(build, temperature=-0.1)
+
+
+@pytest.mark.parametrize("build", ALL_BUILDERS)
+@pytest.mark.parametrize("bad_top_p", [-0.1, 1.1, 2.0])
+def test_out_of_range_top_p_rejected(build, bad_top_p):
+    assert "top_p" in _rejected_field(build, top_p=bad_top_p)
+
+
+@pytest.mark.parametrize("build", BUILDERS_WITH_TOP_K)
+@pytest.mark.parametrize("bad_min_p", [-0.1, 1.1])
+def test_out_of_range_min_p_rejected(build, bad_min_p):
+    assert "min_p" in _rejected_field(build, min_p=bad_min_p)
+
+
+@pytest.mark.parametrize("build", BUILDERS_WITH_TOP_K)
+def test_negative_top_k_rejected(build):
+    # top_k = -1 is vLLM's default; TRT-LLM's backend requires top_k >= 0, so
+    # reject it cleanly at the boundary rather than crashing.
+    assert "top_k" in _rejected_field(build, top_k=-1)
+
+
+@pytest.mark.parametrize("build", BUILDERS_WITH_TOP_K)
+@pytest.mark.parametrize("edge_min_p", [0.0, 1.0])
+def test_min_p_boundaries_accepted(build, edge_min_p):
+    assert build(min_p=edge_min_p).min_p == edge_min_p
+
+
+@pytest.mark.parametrize("build", BUILDERS_WITH_TOP_K)
+def test_top_k_zero_accepted(build):
+    assert build(top_k=0).top_k == 0