[TRTLLM-8958] and [TRTLLM-8960]: create ConfigurableMoE and support the TRTLLMGenFusedMoE as the backend in ConfigurableMoE

Signed-off-by: xxi <xxi@nvidia.com>

	modified:   tensorrt_llm/_torch/model_config.py
	modified:   tensorrt_llm/_torch/models/modeling_deepseekv3.py
	modified:   tensorrt_llm/_torch/models/modeling_gpt_oss.py
	modified:   tensorrt_llm/_torch/models/modeling_hunyuan_moe.py
	modified:   tensorrt_llm/_torch/models/modeling_utils.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/communication/__init__.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/communication/allgather_reducescatter.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/communication/base.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/communication/communication_factory.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/communication/deep_ep.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/communication/deep_ep_low_latency.py
	renamed:    tensorrt_llm/_torch/modules/fused_moe/communication/mnnvl_throughput.py -> tensorrt_llm/_torch/modules/fused_moe/communication/nvlink_one_sided.py
	renamed:    tensorrt_llm/_torch/modules/fused_moe/communication/mnnvl_latency.py -> tensorrt_llm/_torch/modules/fused_moe/communication/nvlink_two_sided.py
	new file:   tensorrt_llm/_torch/modules/fused_moe/configurable_moe.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/create_moe.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/fused_moe_vanilla.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py
	modified:   tensorrt_llm/_torch/modules/fused_moe/interface.py
	modified:   tests/unittest/_torch/modeling/test_modeling_nemotron_h.py
	modified:   tests/unittest/_torch/modules/test_fused_moe.py

Author: xxi-nv

tensorrt_llm/_torch/model_config.py

@@ -165,6 +165,11 @@ def get_all_reduce_strategy(strategy: str = "AUTO"):
             self.allreduce_strategy = get_all_reduce_strategy(
                 self.allreduce_strategy)
 
+        # Set default moe_max_num_tokens if not specified
+        # The maximum number of tokens in MoE are multiplied by DP size when attention DP is enabled
+        if self.moe_max_num_tokens is None:
+            self.moe_max_num_tokens = self.max_num_tokens * self.mapping.dp_size
+
     @property
     def torch_dtype(self) -> torch.dtype:
         """Get the torch dtype of the model."""

tensorrt_llm/_torch/models/modeling_deepseekv3.py

@@ -55,7 +55,7 @@
 from ..modules.attention import MLA
 from ..modules.decoder_layer import DecoderLayer
 from ..modules.embedding import Embedding
-from ..modules.fused_moe import (DeepSeekV3MoeRoutingMethod,
+from ..modules.fused_moe import (DeepSeekV3MoeRoutingMethod, MoE,
                                  MoEWeightLoadingMode, create_moe)
 from ..modules.fused_moe.fused_moe_wide_ep import WideEPMoE
 from ..modules.gated_mlp import GatedMLP
@@ -382,6 +382,21 @@ def split_kv_b_proj(kv_b_proj: torch.Tensor,
                         "gate_proj": "w1",
                     })
                     module.load_weights(weights=[module_weights])
+                elif names[-1] == "backend" and isinstance(module, MoE):
+                    # Special case: ConfigurableMoE.backend (TRTLLMGenFusedMoE)
+                    # Currently saved MoE weights don't include 'backend' in their names.
+                    # After MoE refactoring, ConfigurableMoE now has a backend submodule,
+                    # and weights loading is done in the backend, so module name includes '.backend'.
+                    # We need to use parent module name (without .backend) to match saved weight names.
+                    # After MoE refactoring is fully complete, all paths will follow this branch.
+                    parent_name = '.'.join(names[:-1])
+                    module_weights = filter_weights(parent_name, weights)
+                    module_weights = rename_moe_weight(module_weights, {
+                        "down_proj": "w2",
+                        "up_proj": "w3",
+                        "gate_proj": "w1",
+                    })
+                    module.load_weights(weights=[module_weights])
                 elif names[-1] == "self_attn":
                     continue
                 elif names[-1] == "next_layer_layernorm":

tensorrt_llm/_torch/models/modeling_gpt_oss.py

@@ -657,6 +657,18 @@ def load_hf_weights(self, weights: Dict):
             module_weights = {}
             for k, v in self.hf_params_map.items():
                 name = name.replace(k, v)
+
+            # Special case: ConfigurableMoE.backend (TRTLLMGenFusedMoE)
+            # Currently saved MoE weights don't include 'backend' in their names.
+            # After MoE refactoring, ConfigurableMoE now has a backend submodule,
+            # and weights loading is done in the backend, so module name includes '.backend'.
+            # We need to use parent module name (without .backend) to match saved weight names.
+            # After MoE refactoring is fully complete, all paths will follow this branch.
+            names = name.split('.')
+            if names[-1] == "backend" and isinstance(module, MoE):
+                # Backend is under experts module (ConfigurableMoE wrapper)
+                name = '.'.join(names[:-1])
+
             module_weights = filter_weights(name, weights)
 
             if isinstance(module, MoE):

tensorrt_llm/_torch/models/modeling_hunyuan_moe.py

@@ -15,8 +15,9 @@
 from ..modules.attention import Attention
 from ..modules.decoder_layer import DecoderLayer
 from ..modules.embedding import Embedding
-from ..modules.fused_moe import (CutlassFusedMoE, RenormalizeMoeRoutingMethod,
-                                 VanillaMoE, create_moe)
+from ..modules.fused_moe import (CutlassFusedMoE, MoE,
+                                 RenormalizeMoeRoutingMethod, VanillaMoE,
+                                 create_moe)
 from ..modules.gated_mlp import GatedMLP
 from ..modules.linear import Linear, TensorParallelMode
 from ..modules.multi_stream_utils import maybe_execute_in_parallel
@@ -364,6 +365,17 @@ def filter_weights(prefix, weights: Dict):
                         "lm_head"):
                     continue
                 names = name.split('.')
+
+                # Special case: ConfigurableMoE.backend (TRTLLMGenFusedMoE)
+                # Currently saved MoE weights don't include 'backend' in their names.
+                # After MoE refactoring, ConfigurableMoE now has a backend submodule,
+                # and weights loading is done in the backend, so module name includes '.backend'.
+                # We need to use parent module name (without .backend) to match saved weight names.
+                # After MoE refactoring is fully complete, all paths will follow this branch.
+                if names[-1] == "backend" and isinstance(module, MoE):
+                    name = '.'.join(names[:-1])
+                    names = name.split('.')
+
                 if names[-1] in params_map:
                     # model.layers.{idx}.mlp.shared_mlp.gate_up_proj or model.layers.{idx}.self_attn.qkv_proj
                     module_weights = []

tensorrt_llm/_torch/models/modeling_utils.py

@@ -868,6 +868,17 @@ def load_single_module(name, module):
                 return
 
             names = name.split('.')
+
+            # Special case: ConfigurableMoE.backend (TRTLLMGenFusedMoE)
+            # Currently saved MoE weights don't include 'backend' in their names.
+            # After MoE refactoring, ConfigurableMoE now has a backend submodule,
+            # and weights loading is done in the backend, so module name includes '.backend'.
+            # We need to use parent module name (without .backend) to match saved weight names.
+            # After MoE refactoring is fully complete, all paths will follow this branch.
+            if names[-1] == "backend" and isinstance(module, MoE):
+                name = '.'.join(names[:-1])
+                names = name.split('.')
+
             # WAR: better solution is that llama has its own load_weights function.
             if names[-1] == 'next_layer_layernorm':
                 return
@@ -968,6 +979,17 @@ def load_single_module(name, module):
                 return
 
             names = name.split('.')
+
+            # Special case: ConfigurableMoE.backend (TRTLLMGenFusedMoE)
+            # Currently saved MoE weights don't include 'backend' in their names.
+            # After MoE refactoring, ConfigurableMoE now has a backend submodule,
+            # and weights loading is done in the backend, so module name includes '.backend'.
+            # We need to use parent module name (without .backend) to match saved weight names.
+            # After MoE refactoring is fully complete, all paths will follow this branch.
+            if names[-1] == "backend" and isinstance(module, MoE):
+                name = '.'.join(names[:-1])
+                names = name.split('.')
+
             module_names_breakdown, module_name = names[:-1], names[-1]
 
             if weight_mapper.does_require_special_handling(module_name):

tensorrt_llm/_torch/modules/fused_moe/communication/__init__.py

@@ -20,8 +20,8 @@
 
 Available Communication Methods:
 - AllGatherReduceScatter: Default fallback method, always available
-- MnnvlLatency: MNNVL-optimized communication for latency
-- MNNVLThroughput: MNNVL-optimized communication for throughput
+- NVLinkTwoSided: NVLINK-optimized communication for latency (formerly MNNVLLatency)
+- NVLinkOneSided: NVLINK-optimized communication for throughput (formerly MNNVLThroughput)
 - DeepEP: Deep Expert Parallelism with support for large batches
 - DeepEPLowLatency: Deep Expert Parallelism optimized for low latency
 
@@ -34,16 +34,16 @@
 from .communication_factory import CommunicationFactory
 from .deep_ep import DeepEP
 from .deep_ep_low_latency import DeepEPLowLatency
-from .mnnvl_latency import MnnvlLatency
-from .mnnvl_throughput import MNNVLThroughput
+from .nvlink_one_sided import NVLinkOneSided
+from .nvlink_two_sided import NVLinkTwoSided
 
 __all__ = [
     # Base classes and types
     "Communication",
     # Communication strategies
     "AllGatherReduceScatter",
-    "MnnvlLatency",
-    "MNNVLThroughput",
+    "NVLinkTwoSided",
+    "NVLinkOneSided",
     "DeepEP",
     "DeepEPLowLatency",
     # Factory

tensorrt_llm/_torch/modules/fused_moe/communication/allgather_reducescatter.py

@@ -42,6 +42,13 @@ def __init__(
         # Initialize dispatch state
         self._dispatch_state = {}
 
+    @staticmethod
+    def is_platform_supported() -> bool:
+        """
+        AllGather + ReduceScatter is always supported as the fallback strategy
+        """
+        return True
+
     def is_workload_feasible(self, all_rank_num_tokens: List[int], num_chunks: int) -> bool:
         """
         Check if AllGather is feasible for the given workload at runtime.

tensorrt_llm/_torch/modules/fused_moe/communication/base.py

@@ -50,6 +50,31 @@ def __init__(
         self.ep_size = mapping.moe_ep_size
         self.ep_rank = mapping.moe_ep_rank
 
+        # Check platform support and raise error if not supported
+        if not self.is_platform_supported():
+            raise RuntimeError(
+                f"Communication strategy {self.__class__.__name__} "
+                f"is not supported on this platform."
+            )
+        self._is_platform_supported = True
+
+    @staticmethod
+    @abstractmethod
+    def is_platform_supported() -> bool:
+        """
+        Check if this communication strategy is supported on the current platform.
+
+        This method performs platform/hardware checks to determine if the strategy
+        can be used on the current system.
+
+        Returns:
+            True if platform is supported, False otherwise
+
+        Note: This is a static method that can be called before instantiation
+              to check compatibility without creating an instance.
+        """
+        raise NotImplementedError
+
     @abstractmethod
     def is_workload_feasible(
         self,