Revert "fix a bug of global cuda graph dummy request" (NVIDIA#4970)

Author: QiJune

examples/pytorch/quickstart_advanced.py

@@ -78,13 +78,6 @@ def add_llm_args(parser):
                         default=False,
                         action='store_true')
     parser.add_argument('--use_cuda_graph', default=False, action='store_true')
-    parser.add_argument('--cuda_graph_padding_enabled',
-                        default=False,
-                        action='store_true')
-    parser.add_argument('--cuda_graph_batch_sizes',
-                        nargs='+',
-                        type=int,
-                        default=[])
     parser.add_argument('--print_iter_log',
                         default=False,
                         action='store_true',
@@ -167,8 +160,6 @@ def setup_llm(args):
               kv_cache_config=kv_cache_config,
               attn_backend=args.attention_backend,
               use_cuda_graph=args.use_cuda_graph,
-              cuda_graph_padding_enabled=args.cuda_graph_padding_enabled,
-              cuda_graph_batch_sizes=args.cuda_graph_batch_sizes,
               load_format=args.load_format,
               print_iter_log=args.print_iter_log,
               enable_iter_perf_stats=args.print_iter_log,

tensorrt_llm/_torch/pyexecutor/model_engine.py

@@ -478,10 +478,6 @@ def warmup(self, resource_manager: ResourceManager) -> None:
             logger.info("Skipping warm up as no KV Cache manager allocated.")
             return
 
-        # The lifetime of model engine and kv cache manager can be different.
-        # Reset the global cuda graph dummy request to None in warmup.
-        self.cuda_graph_dummy_request = None
-
         def get_cuda_graph_warmup_request(batch_size):
             available_blocks = kv_cache_manager.get_num_free_blocks()
             if available_blocks >= batch_size:

tests/integration/defs/test_e2e.py

@@ -1569,31 +1569,6 @@ def test_ptq_quickstart_advanced_mtp(llm_root, llm_venv, model_name,
         _check_mem_usage(running_log, [54.50, 0, 0, 0])
 
 
-@pytest.mark.skip_less_device(4)
-def test_ptq_quickstart_advanced_bs1(llm_root, llm_venv):
-    model_name = "DeepSeek-V3-Lite-FP8"
-    model_path = "DeepSeek-V3-Lite/fp8"
-    print(f"Testing {model_name}.")
-    example_root = Path(os.path.join(llm_root, "examples", "pytorch"))
-    llm_venv.run_cmd([
-        str(example_root / "quickstart_advanced.py"),
-        "--use_cuda_graph",
-        "--cuda_graph_padding_enabled",
-        "--cuda_graph_batch_sizes",
-        "8",
-        "--disable_overlap_scheduler",
-        "--enable_attention_dp",
-        "--tp_size",
-        "4",
-        "--moe_ep_size",
-        "4",
-        "--prompt",
-        "\"NVIDIA is a great company because\"",
-        "--model_dir",
-        f"{llm_models_root()}/{model_path}",
-    ])
-
-
 @pytest.mark.skip_less_device_memory(80000)
 @pytest.mark.skip_less_device(8)
 @skip_pre_hopper

tests/integration/test_lists/test-db/l0_dgx_h100.yml

@@ -53,7 +53,6 @@ l0_dgx_h100:
       auto_trigger: deepseek
   tests:
   - unittest/_torch/multi_gpu_modeling -k "deepseek"
-  - test_e2e.py::test_ptq_quickstart_advanced_bs1
   - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_fp8_block_scales_4gpus[tp4-mtp_nextn=0-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False]
   - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_fp8_block_scales_4gpus[tp4-mtp_nextn=0-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=True]
   - accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_fp8_block_scales_4gpus[tp4-mtp_nextn=0-fp8kv=True-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False]