[None][feat] Optimize CUDA graph memory usage for spec decode cases

Signed-off-by: Mike Iovine <[email protected]>

Author: mikeiovine

tensorrt_llm/_torch/pyexecutor/model_engine.py

@@ -722,8 +722,11 @@ def disable_optimization(backend: Backend):
             # For non-draft model, we also capture the CUDA graph instance for draft length 0,
             # so that when we disable spec decode at runtime, we can still run the captured graph.
             # Note that for one engine mode, we are not able to turn off spec decode at runtime.
-            if not self.is_draft_model and self.max_draft_len > 0 and not self.spec_config.spec_dec_mode.use_one_engine(
-            ):
+            if (not self.is_draft_model and self.max_draft_len > 0
+                    and not self.spec_config.spec_dec_mode.use_one_engine()
+                    # Assume that speculation is always on if the user didn't give us a max_concurrency
+                    # value. This will save on memory.
+                    and self.spec_config.max_concurrency is not None):
                 draft_lengths.append(0)
 
             for bs in cuda_graph_batch_sizes:

tensorrt_llm/_torch/speculative/drafter.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Optional
+from typing import List, Optional, final
 
 from ..pyexecutor.llm_request import LlmRequest
 from ..pyexecutor.resource_manager import ResourceManager
@@ -26,8 +26,13 @@ def prepare_draft_tokens(
         """
         raise NotImplementedError
 
+    @final
     def should_use_spec_decode(self, requests: List[LlmRequest]) -> bool:
-        """Check if spec decode should be used for the current iteration."""
+        """
+        You probably don't want to override this. ModelEngine
+        assumes that speculation is always on if max_concurrency
+        is not specified by the user's spec config.
+        """
         if self.max_concurrency is not None:
             return len(requests) <= self.max_concurrency
         return True