[None][fix] Enable LoRA in EAGLE3 speculative decoding

tensorrt_llm/_torch/peft/lora/adapter_slot_manager.py

@@ -71,10 +71,13 @@ def get_or_assign_task(self, task_id: int) -> tuple[int, Optional[int]]:
                 self.task2slot[task_id] = evicted_slot
         return self.task2slot[task_id], evicted_task
 
-    def remove_evicted_slots_in_cpp(self, peft_cache_manager: PeftCacheManager):
+    def remove_evicted_slots_in_cpp(self, peft_cache_manager: Optional[PeftCacheManager]):
         """
         Validate slots by removing tasks that are not cached in PeftCacheManager.
         """
+        if peft_cache_manager is None:
+            return
+
         for task_id in self.slot2task:
             if task_id is not None:
                 if not peft_cache_manager.is_task_cached_device(task_id):

tensorrt_llm/_torch/peft/lora/cuda_graph_lora_params.py

@@ -213,14 +213,17 @@ def update_sorted_indices(self, slot_ids: List[int], tokens_per_seq: int = 1):
         self.sorted_ids[:num_tokens].copy_(sorted_ids_host, non_blocking=True)
 
     def update_weight_pointers(
-        self, peft_table: Dict[int, List], slot_to_task_mapping: tuple[Optional[int], ...]
+        self,
+        peft_table: Optional[Dict[int, List]],
+        slot_to_task_mapping: tuple[Optional[int], ...],
     ):
         """
         Update weight pointers from PEFT cache manager.
 
         Args:
             peft_table: PEFT table from cache manager containing weight pointers, map task id to list of layer
-                        module configs
+                        module configs. Can be None when slot membership changes without any newly prepared PEFT
+                        entries in the current batch.
             slot_to_task_mapping: Mapping from slot_id to task_id, tuple of None for empty slots
         """
 
@@ -241,9 +244,9 @@ def zero_out_weight_pointers(slot_id: int):
             if task_id is None:  # empty slot
                 self.slot_ranks_host[slot_id] = 0
                 zero_out_weight_pointers(slot_id)
-            elif (
-                task_id not in peft_table
-            ):  # task has not changed in the slot, retain old rank / weight pointers
+            elif peft_table is None or task_id not in peft_table:
+                # No new PEFT entry was prepared for this task in the current batch, so retain
+                # the existing rank and weight pointers for the occupied slot.
                 continue
             else:  # task might have changed in the slot, update its rank
                 task_configs = peft_table[task_id]

tests/integration/test_lists/test-db/l0_h100.yml

@@ -19,6 +19,7 @@ l0_h100:
   - unittest/_torch/compilation
   - unittest/_torch/debugger
   - unittest/_torch/executor
+  - unittest/_torch/lora
   - unittest/_torch/misc
   # ------------- modules (non-MoE) ---------------
   - unittest/_torch/modules/test_mla_helix.py

tests/unittest/_torch/lora/test_lora.py

@@ -0,0 +1,38 @@
+import torch
+
+from tensorrt_llm._torch.peft.lora.adapter_slot_manager import AdapterSlotManager
+from tensorrt_llm._torch.peft.lora.cuda_graph_lora_params import CudaGraphLoraParams
+
+
+def test_cuda_graph_lora_params_handle_missing_peft_table():
+    layer_key = CudaGraphLoraParams.LoraLayerKey(layer_idx=0, module_ids=(1, 2))
+    layer_info = {layer_key: CudaGraphLoraParams.LoraLayerInfo(module_num=2, output_sizes=[16, 32])}
+    params = CudaGraphLoraParams(
+        max_batch_size=2, max_lora_size=2, max_rank=8, layer_info=layer_info
+    )
+    layer_params = params.layer_params[layer_key]
+
+    layer_params.h_b_ptrs[:, 0] = torch.tensor([11, 22], dtype=torch.int64)
+    layer_params.h_b_prime_ptrs[:, 0] = torch.tensor([33, 44], dtype=torch.int64)
+    layer_params.h_b_ptrs[:, 1] = torch.tensor([55, 66], dtype=torch.int64)
+    layer_params.h_b_prime_ptrs[:, 1] = torch.tensor([77, 88], dtype=torch.int64)
+    params.slot_ranks_host[:] = torch.tensor([4, 7], dtype=torch.int32)
+
+    params.update_weight_pointers(None, (123, None))
+
+    assert params.slot_ranks_host.tolist() == [4, 0]
+    assert layer_params.h_b_ptrs[:, 0].tolist() == [11, 22]
+    assert layer_params.h_b_prime_ptrs[:, 0].tolist() == [33, 44]
+    assert layer_params.h_b_ptrs[:, 1].tolist() == [0, 0]
+    assert layer_params.h_b_prime_ptrs[:, 1].tolist() == [0, 0]
+
+
+def test_adapter_slot_manager_handles_missing_peft_cache_manager():
+    manager = AdapterSlotManager(max_num_adapters=2)
+    manager.slot2task[0] = 123
+    manager.task2slot[123] = 0
+
+    manager.remove_evicted_slots_in_cpp(None)
+
+    assert manager.get_slot_to_task_mapping() == (123, None)
+    assert manager.task2slot[123] == 0

tests/unittest/_torch/speculative/test_eagle3.py

@@ -15,8 +15,10 @@
 from tensorrt_llm import LLM, SamplingParams
 from tensorrt_llm._torch.attention_backend.trtllm import TrtllmAttentionMetadata
 from tensorrt_llm._torch.metadata import KVCacheParams
+from tensorrt_llm.executor.request import LoRARequest
 from tensorrt_llm.llmapi import (CudaGraphConfig, Eagle3DecodingConfig,
                                  KvCacheConfig)
+from tensorrt_llm.lora_helper import LoraConfig
 
 sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
 
@@ -757,8 +759,9 @@ def test_eagle3_cuda_graph_padding(disable_overlap_scheduler: bool):
     llm_spec = LLM(**llm_common_config, speculative_config=spec_config)
 
     prompts = [
-        "The capital of France is", "The president of the United States is",
-        "The future of AI is"
+        "The capital of France is",
+        "The president of the United States is",
+        "The future of AI is",
     ]
 
     sampling_params = SamplingParams(max_tokens=2048, temperature=0)
@@ -906,5 +909,68 @@ def test_llama_eagle3_dynamic_tree(use_cuda_graph: bool,
         assert text_spec == text_ref
 
 
+@pytest.mark.parametrize("use_cuda_graph", [True, False])
+def test_eagle3_lora(use_cuda_graph: bool):
+    """Test LoRA with 3 requests and max_batch_size=4.
+
+    This test verifies that when using LoRA modules,
+    the system properly applies the LoRA configurations.
+    """
+    attn_backend = "TRTLLM"
+    enable_block_reuse = False
+    use_one_model = True
+    enable_chunked_prefill = False
+
+    total_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
+    if total_mem_gb < 35:
+        pytest.skip("Not enough memory to load target + draft model")
+
+    models_path = llm_models_root()
+
+    eagle_model_dir = f"{models_path}/EAGLE3-LLaMA3.1-Instruct-8B"
+    target_model_dir = f"{models_path}/llama-3.1-model/Llama-3.1-8B-Instruct"
+    hf_lora_dir = f"{models_path}/llama-models/luotuo-lora-7b-0.1"
+
+    # Test with 3 requests and max_batch_size=4 to trigger padding
+    max_batch_size = 4
+    max_draft_len = 4
+    kv_cache_config = KvCacheConfig(enable_block_reuse=enable_block_reuse,
+                                    max_tokens=8192)
+    cuda_graph_config = CudaGraphConfig(
+        batch_sizes=[1, 2, 4], enable_padding=True) if use_cuda_graph else None
+    lora_config = LoraConfig(max_lora_rank=64, max_loras=2, max_cpu_loras=2)
+
+    llm_common_config = dict(
+        model=target_model_dir,
+        attn_backend=attn_backend,
+        cuda_graph_config=cuda_graph_config,
+        max_batch_size=max_batch_size,
+        kv_cache_config=kv_cache_config,
+        max_seq_len=1024,
+        enable_chunked_prefill=enable_chunked_prefill,
+        lora_config=lora_config,
+    )
+
+    spec_config = Eagle3DecodingConfig(
+        max_draft_len=max_draft_len,
+        speculative_model=eagle_model_dir,
+        eagle3_one_model=use_one_model,
+    )
+
+    # Create the LLM instance
+    llm_spec = LLM(**llm_common_config, speculative_config=spec_config)
+
+    prompts = [
+        "The capital of France is",
+        "The president of the United States is",
+        "The future of AI is",
+    ]
+    lora_requests = [LoRARequest("luotuo", 1, hf_lora_dir)] * len(prompts)
+
+    sampling_params = SamplingParams(max_tokens=20, temperature=0)
+    llm_spec.generate(prompts, sampling_params, lora_request=lora_requests)
+    llm_spec.shutdown()
+
+
 if __name__ == "__main__":
     unittest.main()