opt: add batch waiting when scheduling, to accumulate batch

Signed-off-by: yunruis <[email protected]>

Author: yunruis

tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py

@@ -137,6 +137,8 @@ def __init__(
         self.pytorch_backend_config.attention_dp_time_out_iters = 50
         self.pytorch_backend_config.attention_dp_batching_wait_iters = 10
         self.pytorch_backend_config.batch_wait_timeout_ms = 0
+        self.pytorch_backend_config.batch_wait_timeout_iters = 0
+        self.pytorch_backend_config.batch_wait_max_tokens_ratio = 0.0
         self.iter_counter = 0
 
         # NOTE (lucaslie): not a declared base member in the base class; required by PyExecutor...

tensorrt_llm/_torch/pyexecutor/config.py

@@ -50,7 +50,11 @@ class PyTorchConfig:
     attention_dp_time_out_iters: int = 50
     attention_dp_batching_wait_iters: int = 10
 
+    max_num_tokens: int = 8192
+
     batch_wait_timeout_ms: float = 0
+    batch_wait_timeout_iters: int = 0
+    batch_wait_max_tokens_ratio: float = 0
 
     attn_backend: str = 'TRTLLM'
     moe_backend: str = 'CUTLASS'

tensorrt_llm/_torch/pyexecutor/py_executor.py

@@ -180,6 +180,7 @@ def __init__(self,
         self.active = True
         self.max_beam_width = max_beam_width
         self.max_draft_len = max_draft_len
+        self.max_num_tokens = model_engine.pytorch_backend_config.max_num_tokens
         self.print_log = model_engine.pytorch_backend_config.print_iter_log
         self.enable_iter_perf_stats = model_engine.pytorch_backend_config.enable_iter_perf_stats
         self.enable_iter_req_stats = model_engine.pytorch_backend_config.enable_iter_req_stats
@@ -188,6 +189,10 @@ def __init__(self,
         self.attention_dp_time_out_iters = model_engine.pytorch_backend_config.attention_dp_time_out_iters
         self.attention_dp_batching_wait_iters = model_engine.pytorch_backend_config.attention_dp_batching_wait_iters
         self.batch_wait_timeout_ms = model_engine.pytorch_backend_config.batch_wait_timeout_ms
+        self.batch_wait_timeout_iters = model_engine.pytorch_backend_config.batch_wait_timeout_iters
+        self.batch_wait_max_tokens_ratio = model_engine.pytorch_backend_config.batch_wait_max_tokens_ratio
+        self.enable_batch_waiting = self.batch_wait_timeout_iters > 0 or self.batch_wait_max_tokens_ratio > 0
+
         self.num_fetch_requests_cur_rank = 0
         self.num_fetch_requests = 0
         self.shutdown_event = threading.Event()
@@ -232,6 +237,7 @@ def __init__(self,
         self.max_batch_size = max_batch_size
         self.adp_ctx_waiting_iters_count = 0
         self.adp_ctx_batching_wait_iters_count = 0
+        self.batch_wait_iters_count = 0
 
         # request fetcher initialization
         self.executor_request_queue = ExecutorRequestQueue(
@@ -1266,6 +1272,27 @@ def _balance_adp_requests(self, context_requests: list[LlmRequest],
                     balanced_context_requests = context_requests
         return balanced_context_requests
 
+    def _waiting_requests(self, context_requests: list[LlmRequest],
+                          generation_requests: list[LlmRequest]):
+        if not self.enable_batch_waiting:
+            return context_requests
+
+        waited_context_requests = []
+        stop_waiting = False
+        num_scheduled_ctx_tokens = sum(
+            len(ctx_req.get_tokens(0)) for ctx_req in context_requests)
+        num_scheduled_gen_tokens = sum(
+            len(gen_req.get_tokens(0)) for gen_req in generation_requests)
+        num_scheduled_tokens = num_scheduled_ctx_tokens + num_scheduled_gen_tokens
+
+        stop_waiting = self.batch_wait_iters_count >= self.batch_wait_timeout_iters or num_scheduled_tokens >= self.batch_wait_max_tokens_ratio * self.max_num_tokens
+        if stop_waiting:
+            waited_context_requests = context_requests
+            self.batch_wait_iters_count = 0
+        else:
+            self.batch_wait_iters_count += 1
+        return waited_context_requests
+
     @nvtx_range("_schedule")
     def _schedule(self):
         scheduler_output = self.scheduler.schedule_request(
@@ -1276,6 +1303,14 @@ def _schedule(self):
                 scheduler_output.context_requests,
                 scheduler_output.generation_requests)
 
+        # if no generation requests, no need to wait, to avoid dead waiting
+        if not self.enable_attention_dp and self.enable_batch_waiting and len(
+                scheduler_output.context_requests) > 0 and len(
+                    scheduler_output.generation_requests) > 0:
+            scheduled_context_requests = self._waiting_requests(
+                scheduler_output.context_requests,
+                scheduler_output.generation_requests)
+
         scheduled_requests = ScheduledRequests()
         scheduled_requests.context_requests = scheduled_context_requests
         scheduled_requests.generation_requests = scheduler_output.generation_requests

tensorrt_llm/llmapi/llm_args.py

@@ -2240,6 +2240,18 @@ class TorchLlmArgs(BaseLlmArgs):
         "If greater than 0, the request queue might wait up to batch_wait_timeout_ms to receive max_batch_size requests, if fewer than max_batch_size requests are currently available. If 0, no waiting occurs.",
         status="prototype")
 
+    batch_wait_timeout_iters: int = Field(
+        default=0,
+        description=
+        "Maximum number of iterations the scheduler will wait to accumulate new coming requests for improved GPU utilization efficiency. If greater than 0, the scheduler will delay batch processing to gather more requests up to the specified iteration limit. If 0, disables timeout-iters-based batching delays.",
+        status="prototype")
+
+    batch_wait_max_tokens_ratio: float = Field(
+        default=0,
+        description=
+        "Token accumulation threshold ratio for batch scheduling optimization. If greater than 0, the scheduler will accumulate requests locally until the total token count reaches batch_wait_max_tokens_ratio * max_num_tokens. This mechanism enhances GPU utilization efficiency by ensuring adequate batch sizes.If 0 disables token-based batching delays.",
+        status="prototype")
+
     torch_compile_config: Optional[TorchCompileConfig] = Field(
         default=None, description="Torch compile config.", status="prototype")
 
@@ -2508,6 +2520,19 @@ def validate_batch_wait_timeout_ms(self) -> 'TorchLlmArgs':
             raise ValueError("batch_wait_timeout_ms must be greater than 0")
         return self
 
+    @model_validator(mode='after')
+    def validate_batch_wait_timeout_iters(self) -> 'TorchLlmArgs':
+        if self.batch_wait_timeout_iters < 0:
+            raise ValueError("batch_wait_timeout_iters must be greater than 0")
+        return self
+
+    @model_validator(mode='after')
+    def validate_batch_wait_max_tokens_ratio(self) -> 'TorchLlmArgs':
+        if self.batch_wait_max_tokens_ratio < 0 or self.batch_wait_max_tokens_ratio > 1:
+            raise ValueError(
+                "batch_wait_max_tokens_ratio must be between 0 and 1")
+        return self
+
     def get_executor_config(
         self,
         _hf_model_dir: Optional[Path] = None,
@@ -2583,7 +2608,10 @@ def get_pytorch_backend_config(self) -> "PyTorchConfig":
             attention_dp_batching_wait_iters=self.attention_dp_config.
             batching_wait_iters if self.attention_dp_config is not None else
             AttentionDpConfig.model_fields['batching_wait_iters'].default,
-            batch_wait_timeout_ms=self.batch_wait_timeout_ms)
+            batch_wait_timeout_ms=self.batch_wait_timeout_ms,
+            batch_wait_timeout_iters=self.batch_wait_timeout_iters,
+            batch_wait_max_tokens_ratio=self.batch_wait_max_tokens_ratio,
+        )
 
 
 def update_llm_args_with_extra_dict(

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -1483,6 +1483,49 @@ def test_nvfp4(self, fp8kv, attention_dp, cuda_graph, overlap_scheduler,
             task = GSM8K(self.MODEL_NAME)
             task.evaluate(llm)
 
+    @skip_pre_blackwell
+    @parametrize_with_ids("torch_compile", [False, True])
+    @parametrize_with_ids("fp8kv,cuda_graph,overlap_scheduler",
+                          [(False, False, False), (True, True, True)])
+    @parametrize_with_ids("mtp_nextn", [0, 2])
+    @parametrize_with_ids(
+        "batch_wait_timeout_iters,batch_wait_max_tokens_ratio", [(0, 0),
+                                                                 (10, 0.75),
+                                                                 (10, 0),
+                                                                 (0, 0.75)])
+    def test_nvfp4_batch_waiting(self, torch_compile, fp8kv, cuda_graph,
+                                 overlap_scheduler, mtp_nextn,
+                                 batch_wait_timeout_iters,
+                                 batch_wait_max_tokens_ratio):
+        moe_backend = "CUTLASS"
+        kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.75)
+        torch_compile_config = TorchCompileConfig(
+            enable_fullgraph=True,
+            enable_piecewise_cuda_graph=cuda_graph,
+            capture_num_tokens=[2048, 8192],
+            max_num_streams=3) if torch_compile else None
+        pytorch_config = dict(
+            disable_overlap_scheduler=not overlap_scheduler,
+            cuda_graph_config=CudaGraphConfig() if cuda_graph else None,
+            torch_compile_config=torch_compile_config,
+            batch_wait_timeout_iters=batch_wait_timeout_iters,
+            batch_wait_max_tokens_ratio=batch_wait_max_tokens_ratio,
+            moe_config=MoeConfig(backend=moe_backend))
+        mtp_config = None
+        if mtp_nextn > 0:
+            mtp_config = MTPDecodingConfig(num_nextn_predict_layers=mtp_nextn)
+        if fp8kv:
+            kv_cache_config.dtype = "fp8"
+        with LLM(f"{llm_models_root()}/DeepSeek-V3-Lite/nvfp4_moe_only_mtp",
+                 kv_cache_config=kv_cache_config,
+                 **pytorch_config,
+                 enable_attention_dp=False,
+                 speculative_config=mtp_config) as llm:
+            assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4
+
+            task = GSM8K(self.MODEL_NAME)
+            task.evaluate(llm)
+
     @pytest.mark.skip_less_device(4)
     @skip_pre_blackwell
     @parametrize_with_ids("torch_compile", [False, True])

tests/integration/test_lists/qa/llm_function_full.txt

@@ -501,6 +501,7 @@ accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16[mtp_nextn=0-
 accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_fp8_block_scales[mtp=disable-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False]
 accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4[moe_backend=CUTLASS-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_nvfp4[moe_backend=CUTLASS-mtp_nextn=2-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False]
+accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4_batch_waiting[batch_wait_timeout_iters=10-batch_wait_max_tokens_ratio=0.75-mtp_nextn=0-fp8kv=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False]
 accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_fp8_block_scales_4gpus_static_eplb
 accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_online_eplb[mtp_nextn=0]
 accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_bfloat16_4gpus_online_eplb[mtp_nextn=2]

tests/unittest/api_stability/references/llm.yaml

@@ -131,6 +131,14 @@ methods:
         annotation: float
         default: 0
         status: prototype
+      batch_wait_timeout_iters:
+        annotation: int
+        default: 0
+        status: prototype
+      batch_wait_max_tokens_ratio:
+        annotation: float
+        default: 0
+        status: prototype
       print_iter_log:
         annotation: bool
         default: False