[TRTLLM-8821][feat] Apply AutoTuner to AllReduce Op for strategy tuning.

cpp/tensorrt_llm/common/customAllReduceUtils.h

@@ -40,50 +40,12 @@ inline size_t getMaxRequiredWorkspaceSize(int worldSize) noexcept
     {
         return common::getEnvAllReduceWorkspaceSize();
     }
-    if (worldSize <= 2)
+    char const* envWorkspaceSize = std::getenv("TRTLLM_ALLREDUCE_FUSION_WORKSPACE_SIZE");
+    if (envWorkspaceSize != nullptr)
     {
-        return 16 * 1000 * 1000;
-    }
-    return 8 * 1000 * 1000;
-}
-
-// (SM major_version, TP_size) -> (NCCL_num_token_threshold, TWO_SHOT_numel_threshold)
-inline std::unordered_map<int, std::unordered_map<int, std::pair<size_t, size_t>>> HeuristicThresholdLP{
-    {90,
-        {
-            {2, {4096, 4096 * 4096}},
-            {4, {4096, 1024 * 1024}},
-            {8, {2048, 512 * 512}},
-        }},
-    {100,
-        {
-            {2, {4096, 4096 * 4096}},
-            {4, {4096, 1024 * 2048}},
-            {8, {4096, 1024 * 1024}},
-        }},
-};
-
-inline AllReduceStrategyType SelectStrategyLP(size_t seq_len, size_t hidden_size, int world_size, AllReduceFusionOp op)
-{
-    // The heuristic is based on the following assumptions:
-    //  __________________________________
-    // |              \ TWO-SHOT zone |
-    // | ONE-SHOT zone    \           | NCCL zone
-    // |_______________________\______|___
-    // sm_major is 90 or 100
-
-    auto const sm_major = std::min(100, std::max(90, tensorrt_llm::common::getSMVersion()));
-
-    auto const [nccl_num_token_threshold, two_shot_numel_threshold] = HeuristicThresholdLP[sm_major][world_size];
-    auto const message_size = seq_len * hidden_size;
-    if (message_size >= two_shot_numel_threshold)
-    {
-        return AllReduceStrategyType::TWOSHOT;
-    }
-    else
-    {
-        return AllReduceStrategyType::ONESHOT;
+        return static_cast<size_t>(std::atoi(envWorkspaceSize));
     }
+    return 67108864; // 64 MiB
 }
 
 // use 1D vector to store the best strategy instead of a map for each sm version

tensorrt_llm/_torch/auto_deploy/shim/ad_executor.py

@@ -23,6 +23,7 @@
 
 from tensorrt_llm._torch.attention_backend.interface import AttentionRuntimeFeatures
 from tensorrt_llm._torch.auto_deploy.utils._graph import get_input_embeddings, get_lm_head_weights
+from tensorrt_llm._torch.autotuner import AutoTuner
 from tensorrt_llm._torch.models.modeling_speculative import Eagle3ForCausalLM
 from tensorrt_llm._torch.pyexecutor._util import (
     _create_kv_cache_manager,
@@ -1008,6 +1009,10 @@ def create_autodeploy_executor(ad_config: LlmArgs, tokenizer: Optional[Tokenizer
     torch.cuda.set_device(rank)
     port = mpi_dist.broadcast(dist.get_free_port())  # use MPI broadcast to pick a free port
     dist.initialize_or_skip(rank, world_size, port)
+
+    # Setup AutoTuner with distributed state for allreduce autotuning
+    AutoTuner.get().setup_distributed_state(dist_mapping, mpi_dist)
+
     # some config
     assert ad_config.max_beam_width <= 1, "_autodeploy + beam_search is not supported"
 

tensorrt_llm/_torch/autotuner.py

@@ -18,6 +18,7 @@
 
 import tensorrt_llm
 from tensorrt_llm._torch.distributed import Distributed
+from tensorrt_llm._utils import nvtx_range
 from tensorrt_llm.bindings.internal.runtime import delay_kernel
 from tensorrt_llm.logger import logger
 from tensorrt_llm.mapping import Mapping
@@ -844,8 +845,10 @@ def choose_one(
                     custom_op, runners, p.get_opt_shapes(), tuning_config)
                 if not is_cache_hit:
                     # Initialize runner and tactic as None in case of no valid tactic or runners are found
-                    best_runner_id, best_tactic, min_time, has_tuning_failure_occurred = self._profile_runners(
-                        custom_op, runners, tensors, p, tuning_config, **kwargs)
+                    with nvtx_range(f"{custom_op}, shape {p.get_opt_shapes()}"):
+                        best_runner_id, best_tactic, min_time, has_tuning_failure_occurred = self._profile_runners(
+                            custom_op, runners, tensors, p, tuning_config,
+                            **kwargs)
                     new_tuning_failure_occurred = new_tuning_failure_occurred or has_tuning_failure_occurred
 
         self._maybe_sync_cache_data(tuning_config.distributed_tuning_strategy,
@@ -882,6 +885,13 @@ def _profile_runners(
         tuning_config: TuningConfig,
         **kwargs,
     ) -> float:
+        """Profile runners and select the best tactic.
+
+        For multi-rank profiling, only rank 0 performs the actual profiling
+        to avoid sync issues when different ranks select different tactics.
+        The results are then broadcasted to all other ranks.
+        """
+
         min_time = float('inf')
         has_tuning_failure_occurred = False
         best_runner_id, best_tactic = None, None
@@ -909,14 +919,15 @@ def _profile_runners(
 
             for tac in valid_tactics:
                 try:
-                    time_measured = self._profile_single_kernel(
-                        runner=runner,
-                        inputs=input_tensors,
-                        tactic=tac,
-                        tuning_config=tuning_config,
-                        use_cuda_graph=tuning_config.use_cuda_graph,
-                        **kwargs,
-                    )
+                    with nvtx_range(f"r{runner_id}, tactic {tac}"):
+                        time_measured = self._profile_single_kernel(
+                            runner=runner,
+                            inputs=input_tensors,
+                            tactic=tac,
+                            tuning_config=tuning_config,
+                            use_cuda_graph=tuning_config.use_cuda_graph,
+                            **kwargs,
+                        )
                 except Exception as e:
                     # Handle None tensors for optional inputs
                     shapes = self._get_input_sizes(input_tensors)
@@ -1026,10 +1037,13 @@ def pure_profile(stream: torch.cuda.Stream, repeat: int):
                             )
 
                 stream.synchronize()
+                if tuning_config.distributed_tuning_strategy == DistributedTuningStrategy.MERGE:
+                    # Currently only AllReduce will use this strategy, and only MPI parallel will enable tuning.
+                    # TODO: Unified tp barrier for both MPIDist and TorchDist.
+                    if hasattr(self._dist, "tp_comm"):
+                        self._dist.tp_comm.barrier()
 
                 # Delay the profiled kernel launch to eliminate affects of host time overhead in profiling.
-                # TODO: This is build time sensitive, O(tactic_num * impl_num * num_profile * tunable_ops)
-                # Consider apply a preprofiling to estimate the kernel execution time, then decide the necessity.
                 if use_cuda_graph:
                     delay_kernel(self._CUDA_GRAPH_DELAY_MICRO_SECS, stream)
                 else:
@@ -1052,6 +1066,7 @@ def pure_profile(stream: torch.cuda.Stream, repeat: int):
 
                 return start.elapsed_time(end) / repeat
 
+        # warm up, no timing
         for _ in range(self.warmup):
             runner(input_tensor_batches[-1], tactic=tactic, **kwargs)
 

tensorrt_llm/_torch/compilation/patterns/ar_residual_norm.py

@@ -1,5 +1,5 @@
 from operator import getitem
-from typing import List, Optional
+from typing import Callable, List, Optional
 
 import torch
 from torch._inductor.pattern_matcher import (MULTIPLE, CallFunction, Ignored,
@@ -14,13 +14,13 @@
 from tensorrt_llm.mapping import Mapping
 
 
-def register_ar_residual_norm(custom_pass: PatternMatcherPass,
-                              mapping: Mapping):
+def register_ar_residual_norm(custom_pass: PatternMatcherPass, mapping: Mapping,
+                              allreduce_func: Callable):
     residual_key = KeywordArg("residual")
     trtllm_allreduce_default = CallFunction(
-        torch.ops.trtllm.allreduce.default, KeywordArg("input"), None, None,
-        None, None, KeywordArg("workspace"), mapping.tp_group,
-        KeywordArg("strategy"), int(AllReduceFusionOp.NONE), Ignored(),
+        allreduce_func.default, KeywordArg("input"), None, None, None, None,
+        KeywordArg("workspace"), mapping.tp_group, KeywordArg("strategy"),
+        int(AllReduceFusionOp.NONE), Ignored(),
         KeywordArg("trigger_completion_at_end"))
     getitem_x = CallFunction(getitem, trtllm_allreduce_default, 0)
     add_Tensor = CallFunction(aten.add.Tensor,
@@ -56,7 +56,7 @@ def target_pattern(
         eps: float,
         trigger_completion_at_end: bool,
     ):
-        all_reduce_output = torch.ops.trtllm.allreduce(
+        all_reduce_output = allreduce_func(
             input, residual, norm_weight, None, None, workspace,
             mapping.tp_group, int(strategy),
             int(AllReduceFusionOp.RESIDUAL_RMS_NORM), float(eps),
@@ -111,10 +111,11 @@ def check_non_ub_strategy(match, strategy_node) -> bool:
 
 
 def register_ar_residual_norm_out_fp8_quant(custom_pass: PatternMatcherPass,
-                                            mapping: Mapping):
+                                            mapping: Mapping,
+                                            allreduce_func: Callable):
     input_node = KeywordArg("input")
     strategy_node = KeywordArg("strategy")
-    allreduce_default = CallFunction(torch.ops.trtllm.allreduce.default,
+    allreduce_default = CallFunction(allreduce_func.default,
                                      input_node,
                                      KeywordArg("residual"),
                                      KeywordArg("gamma"),
@@ -165,7 +166,7 @@ def target_pattern(
         scale: torch.Tensor,
         trigger_completion_at_end: bool,
     ):
-        allreduce = torch.ops.trtllm.allreduce(
+        allreduce = allreduce_func(
             input, residual, gamma, scale, None, workspace, mapping.tp_group,
             int(strategy),
             int(AllReduceFusionOp.RESIDUAL_RMS_NORM_OUT_QUANT_FP8), float(eps),
@@ -188,10 +189,11 @@ def extra_check(match: Match) -> bool:
 
 
 def register_ar_residual_norm_fp8_quant(custom_pass: PatternMatcherPass,
-                                        mapping: Mapping):
+                                        mapping: Mapping,
+                                        allreduce_func: Callable):
     input_node = KeywordArg("input")
     strategy_node = KeywordArg("strategy")
-    allreduce_default = CallFunction(torch.ops.trtllm.allreduce.default,
+    allreduce_default = CallFunction(allreduce_func.default,
                                      input_node,
                                      KeywordArg("residual"),
                                      KeywordArg("gamma"),
@@ -242,7 +244,7 @@ def target_pattern(
         scale: torch.Tensor,
         trigger_completion_at_end: bool,
     ):
-        allreduce = torch.ops.trtllm.allreduce(
+        allreduce = allreduce_func(
             input, residual, gamma, scale, None, workspace, mapping.tp_group,
             int(strategy), int(AllReduceFusionOp.RESIDUAL_RMS_NORM_QUANT_FP8),
             float(eps), trigger_completion_at_end)
@@ -264,10 +266,11 @@ def extra_check(match: Match) -> bool:
 
 
 def register_ar_residual_norm_out_fp4_quant(custom_pass: PatternMatcherPass,
-                                            mapping: Mapping):
+                                            mapping: Mapping,
+                                            allreduce_func: Callable):
     input_node = KeywordArg("input")
     strategy_node = KeywordArg("strategy")
-    allreduce_default = CallFunction(torch.ops.trtllm.allreduce.default,
+    allreduce_default = CallFunction(allreduce_func.default,
                                      input_node,
                                      KeywordArg("residual"),
                                      KeywordArg("gamma"),
@@ -313,7 +316,7 @@ def target_pattern(
         scale: torch.Tensor,
         trigger_completion_at_end: bool,
     ):
-        allreduce = torch.ops.trtllm.allreduce(
+        allreduce = allreduce_func(
             input, residual, gamma, scale, None, workspace, mapping.tp_group,
             int(strategy),
             int(AllReduceFusionOp.RESIDUAL_RMS_NORM_OUT_QUANT_NVFP4),
@@ -336,10 +339,11 @@ def extra_check(match: Match) -> bool:
 
 
 def register_ar_residual_norm_fp4_quant(custom_pass: PatternMatcherPass,
-                                        mapping: Mapping):
+                                        mapping: Mapping,
+                                        allreduce_func: Callable):
     input_node = KeywordArg("input")
     strategy_node = KeywordArg("strategy")
-    allreduce_default = CallFunction(torch.ops.trtllm.allreduce.default,
+    allreduce_default = CallFunction(allreduce_func.default,
                                      input_node,
                                      KeywordArg("residual"),
                                      KeywordArg("gamma"),
@@ -385,7 +389,7 @@ def target_pattern(
         scale: torch.Tensor,
         trigger_completion_at_end: bool,
     ):
-        allreduce = torch.ops.trtllm.allreduce(
+        allreduce = allreduce_func(
             input, residual, gamma, scale, None, workspace, mapping.tp_group,
             int(strategy), int(AllReduceFusionOp.RESIDUAL_RMS_NORM_QUANT_NVFP4),
             float(eps), trigger_completion_at_end)
@@ -407,17 +411,20 @@ def extra_check(match: Match) -> bool:
 
 
 def register_ub_patterns(custom_passes: List[PatternMatcherPass],
-                         mapping: Mapping):
+                         mapping: Mapping, allreduce_func: Callable):
 
     def register_convert_supported_ar_to_ub(custom_pass: PatternMatcherPass):
         strategy = int(AllReduceStrategy.AUTO)
         input_node = KeywordArg('input')
         fusion = KeywordArg('fusion_op')
-        trtllm_allreduce_default = CallFunction(
-            torch.ops.trtllm.allreduce.default, input_node,
-            KeywordArg('residual_in'), KeywordArg('gamma'), KeywordArg('scale'),
-            None, Ignored(), mapping.tp_group, strategy, fusion,
-            KeywordArg('eps'), Ignored())
+        trtllm_allreduce_default = CallFunction(allreduce_func.default,
+                                                input_node,
+                                                KeywordArg('residual_in'),
+                                                KeywordArg('gamma'),
+                                                KeywordArg('scale'), None,
+                                                Ignored(), mapping.tp_group,
+                                                strategy, fusion,
+                                                KeywordArg('eps'), Ignored())
 
         def empty_convert_supported_ar_to_ub(
             input: torch.Tensor,
@@ -667,7 +674,7 @@ def register_ub_finalize_patterns(custom_pass: PatternMatcherPass):
             torch.ops.trtllm.userbuffers_allreduce_finalize.default,
             KeywordArg("sharded_residual"), False)
         trtllm_allreduce_default = CallFunction(
-            torch.ops.trtllm.allreduce.default, KeywordArg("input"),
+            torch.ops.trtllm.allreduce, KeywordArg("input"),
             trtllm_userbuffers_allreduce_finalize_default, KeywordArg("gamma"),
             KeywordArg("scale"), Ignored(), Ignored(), mapping.tp_group,
             int(AllReduceStrategy.UB), KeywordArg("fusion_op"),
@@ -718,15 +725,28 @@ def target_finalize_pattern(
 
 def register_ar_fusions(custom_passes: List[PatternMatcherPass],
                         mapping: Mapping, enable_ub: bool):
-    register_ar_residual_norm(custom_passes[-1], mapping)
+    register_ar_residual_norm(custom_passes[-1], mapping,
+                              torch.ops.trtllm.allreduce)
+    register_ar_residual_norm(custom_passes[-1], mapping,
+                              torch.ops.trtllm.tunable_allreduce)
 
     custom_passes.append(PatternMatcherPass())
-    register_ar_residual_norm_fp8_quant(custom_passes[-1], mapping)
-    register_ar_residual_norm_fp4_quant(custom_passes[-1], mapping)
-    # AR-Residual-Norm-Out-Quant-X is not supported by Userbuffers kernel.
-    if not enable_ub:
-        register_ar_residual_norm_out_fp8_quant(custom_passes[-1], mapping)
-        register_ar_residual_norm_out_fp4_quant(custom_passes[-1], mapping)
+    for allreduce_func in [
+            torch.ops.trtllm.allreduce, torch.ops.trtllm.tunable_allreduce
+    ]:
+        register_ar_residual_norm_fp8_quant(custom_passes[-1], mapping,
+                                            allreduce_func)
+        register_ar_residual_norm_fp4_quant(custom_passes[-1], mapping,
+                                            allreduce_func)
+
+        # AR-Residual-Norm-Out-Quant-X is not supported by Userbuffers kernel.
+        if not enable_ub:
+            register_ar_residual_norm_out_fp8_quant(custom_passes[-1], mapping,
+                                                    allreduce_func)
+            register_ar_residual_norm_out_fp4_quant(custom_passes[-1], mapping,
+                                                    allreduce_func)
 
     if enable_ub:
-        register_ub_patterns(custom_passes, mapping)
+        register_ub_patterns(custom_passes, mapping, torch.ops.trtllm.allreduce)
+        register_ub_patterns(custom_passes, mapping,
+                             torch.ops.trtllm.tunable_allreduce)