[nvbug 5304752][fix] enhance _check_arguments to filter illegal requests for pytorch backend

tensorrt_llm/llmapi/llm.py

@@ -529,8 +529,13 @@ def _check_arguments(self, prompt_len: int, query_len: int,
                 raise ValueError(
                     f"PyTorch backend currently only supports `logprobs=1`. Received `logprobs={sampling_params.logprobs}` (Top{sampling_params.logprobs} logprobs). Please set `logprobs=1` in `sampling_params` instead."
                 )
-            return
-        elif self.args.backend == "_autodeploy":
+            # Check prompt length and query length against max_num_tokens to filter illegal requests.
+            if self.args.backend == "pytorch" and not self.args.enable_chunked_prefill:
+                max_num_tokens = self.args.max_num_tokens
+                if max_num_tokens and prompt_len / self.args.parallel_config.cp_size + query_len > max_num_tokens:
+                    raise ValueError(
+                        f"The sum of prompt length ({prompt_len/self.args.parallel_config.cp_size}), query length ({query_len}) and max_tokens ({sampling_params.max_tokens}) should not exceed "
+                        f"max_num_tokens ({max_num_tokens})")
             return
 
         build_config = self.args.build_config
@@ -547,7 +552,7 @@ def _check_arguments(self, prompt_len: int, query_len: int,
             (sampling_params.max_tokens or 0) > max_seq_len):
             raise ValueError(
                 f"The sum of prompt length ({prompt_len/self.args.parallel_config.cp_size}) and query length ({query_len}) max_tokens ({sampling_params.max_tokens}) should not exceed "
-                f"max_seq_len ({build_config.max_seq_len})")
+                f"max_seq_len ({max_seq_len})")
 
         if sampling_params.use_beam_search and sampling_params.best_of > build_config.max_beam_width:
             if sampling_params.n == sampling_params.best_of:

tests/unittest/llmapi/test_llm.py

@@ -2061,24 +2061,37 @@ def success_path():
     success_path()
 
 
-def _test_llm_capture_request_error(tp_size: int = 1):
-    build_config = BuildConfig()
-    build_config.max_num_tokens = 64
+def _test_llm_capture_request_error(pytorch_backend: bool, tp_size: int = 1):
+    llm_args_extra = {}
+    if pytorch_backend:
+        from tensorrt_llm._torch import LLM as LLM_torch
+        LLM_CLASS = LLM_torch
+        llm_args_extra["max_num_tokens"] = 64
+    else:
+        LLM_CLASS = LLM
+        build_config = BuildConfig()
+        build_config.max_num_tokens = 64
+        llm_args_extra["fast_build"] = True
+        llm_args_extra["build_config"] = build_config
 
-    llm = LLM(
+    llm = LLM_CLASS(
         model=llama_model_path,
-        build_config=build_config,
-        fast_build=True,
+        tensor_parallel_size=tp_size,
+        **llm_args_extra,
     )
 
     prompt = 'A ' * 65  # the minimum max_num_tokens is 64
-
-    with pytest.raises(RequestError):
-        llm.generate(prompt)
+    if pytorch_backend:
+        # pytorch backend will raise ValueError for max_num_tokens
+        with pytest.raises(ValueError):
+            llm.generate(prompt)
+    else:
+        with pytest.raises(RequestError):
+            llm.generate(prompt)
 
 
 def test_llm_capture_request_error():
-    _test_llm_capture_request_error(tp_size=1)
+    _test_llm_capture_request_error(pytorch_backend=False, tp_size=1)
 
 
 def test_llm_api_jupyter_scenario():

tests/unittest/llmapi/test_llm_multi_gpu.py

@@ -452,7 +452,7 @@ def test_llm_get_stats_async_tp2(pytorch_backend):
 
 
 def test_llm_capture_request_error():
-    _test_llm_capture_request_error(tp_size=2)
+    _test_llm_capture_request_error(pytorch_backend=False, tp_size=2)
 
 
 def test_llm_with_postprocess_parallel_tp2():

tests/unittest/llmapi/test_llm_multi_gpu_pytorch.py

@@ -5,12 +5,17 @@
 from tensorrt_llm.llmapi import KvCacheConfig
 from .test_llm_pytorch import (llama_v2_13b_lora_test_harness,
                                llama_7b_multi_lora_test_harness)
-
+from .test_llm import _test_llm_capture_request_error
 # isort: on
 
 global_kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.4)
 
 
+@pytest.mark.gpu2
+def test_llm_capture_request_error():
+    _test_llm_capture_request_error(pytorch_backend=True, tp_size=2)
+
+
 @pytest.mark.gpu4
 def test_tinyllama_logits_processor_tp2pp2():
     tinyllama_logits_processor_test_harness(backend="pytorch",

tests/unittest/llmapi/test_llm_pytorch.py

@@ -4,12 +4,11 @@
 from tensorrt_llm.sampling_params import SamplingParams
 
 # isort: off
-from .test_llm import (get_model_path, global_kvcache_config, llama_model_path,
-                       llm_get_stats_async_test_harness,
-                       llm_get_stats_test_harness, prompts,
-                       run_llm_abort_request,
-                       run_llm_with_postprocess_parallel_and_result_handler,
-                       tinyllama_logits_processor_test_harness)
+from .test_llm import (
+    get_model_path, global_kvcache_config, llama_model_path,
+    llm_get_stats_async_test_harness, llm_get_stats_test_harness, prompts,
+    run_llm_abort_request, run_llm_with_postprocess_parallel_and_result_handler,
+    tinyllama_logits_processor_test_harness, _test_llm_capture_request_error)
 from utils.util import force_ampere, similar, skip_gpu_memory_less_than_40gb, skip_gpu_memory_less_than_80gb, skip_gpu_memory_less_than_138gb
 from utils.llm_data import llm_models_root
 from tensorrt_llm.lora_manager import LoraConfig
@@ -64,6 +63,10 @@ def test_llm_get_stats_async(return_context_logits, use_overlap,
         enable_iter_req_stats=enable_iter_req_stats)
 
 
+def test_llm_capture_request_error():
+    _test_llm_capture_request_error(pytorch_backend=True, tp_size=1)
+
+
 @force_ampere
 @pytest.mark.parametrize(
     "sampling_params",