Add Parameter Validation to trtllm-serve to Prevent Server Crashes

Description
Currently, the trtllm-serve lacks proper validation for client input parameters. When invalid values are provided (e.g., top_k = -1 or input sequences exceeding max_seq_len), the server crashes instead of returning an error response. This behavior severely impacts availability, as a single malformed request can disrupt service for all clients.

Reproduction Steps

• Send a request with top_k = -1 (default value in vLLM)
• Send a request with an input sequence longer than max_seq_len.

Expected Behavior

• The server should return a 400 Bad Request error with details (e.g., "top_k must be >= 0" or "Input length exceeds max_seq_len").
• The server must not crash and should continue processing other requests.

Actual Behavior
The server crashes with unhandled exceptions (e.g., RuntimeError or assertion failures), requiring a restart.

Proposed Solution
Implement validation checks:

1. Validation in OpenAI Protocol (tensorrt_llm/serve/openai_protocol.py)
   Add parameter checks(top_k, top_p, max_tokens, etc.) to CompletionRequest and ChatCompletionRequest dataclasses:

top_k: int = Field(default=0, ge=-1) 
top_p: Optional[float] = Field(default=1.0, gt=0.0, le=1.0)
...

2. Input Length Validation in LLM(tensorrt_llm/llmapi/llm.py)
   In BaseLLM.generate_async, add explicit input length checks before passing request to the executor:

def generate_async(
        self,
        inputs: PromptInputs,
        sampling_params: Optional[SamplingParams] = None,
        lora_request: Optional[LoRARequest] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        streaming: bool = False,
        kv_cache_retention_config: Optional[KvCacheRetentionConfig] = None,
        disaggregated_params: Optional[DisaggregatedParams] = None,
        _postproc_params: Optional[PostprocParams] = None,
    ) -> RequestOutput:
     ...
     if self.args.max_seq_len <= len(prompt_token_ids):
          raise Exception(f"Input length exceeds max sequence length, {len(prompt_token_ids)} >= {self.args.max_seq_len}")
     ...

Additional Context

• This issue is critical for production deployments where malformed requests are common (e.g., misconfigured clients).

Start with tensorrt_llm/serve/openai_protocol.py, reviewing CompletionRequest and ChatCompletionRequest validation, then inspect tensorrt_llm/llmapi/llm.py at BaseLLM.generate_async. Reproduce invalid top_k values and inputs exceeding max_seq_len. Done means invalid requests return detailed 400 errors without crashing the server or disrupting subsequent requests.