[TRTLLM-9736][feat] AsyncLLM and verl integ

tensorrt_llm/__init__.py

@@ -84,7 +84,7 @@ def _preload_python_lib():
 from .builder import BuildConfig, Builder, BuilderConfig, build
 from .disaggregated_params import DisaggregatedParams
 from .functional import Tensor, constant
-from .llmapi import LLM, MultimodalEncoder
+from .llmapi import LLM, AsyncLLM, MultimodalEncoder
 from .llmapi.llm_args import LlmArgs, TorchLlmArgs, TrtLlmArgs
 from .logger import logger
 from .mapping import Mapping
@@ -136,6 +136,7 @@ def _preload_python_lib():
     'quantization',
     'tools',
     'LLM',
+    'AsyncLLM',
     'MultimodalEncoder',
     'LlmArgs',
     'TorchLlmArgs',

tensorrt_llm/_torch/async_llm.py

@@ -0,0 +1,106 @@
+from typing import Any, List, Optional
+
+from ..llmapi.llm import LLM
+from ..llmapi.llm_args import RayPlacementConfig
+
+
+class AsyncLLM(LLM):
+    """AsyncLLM is a subclass of LLM that supports asynchronous setup, release and
+    resume operations that are necessary for RL or agentic scenarios.
+
+    Currently, RL APIs are only supported with Ray orchestrator.
+    """
+
+    def __init__(
+        self,
+        placement_groups: Optional[List[Any]] = None,
+        placement_bundle_indices: Optional[List[List[int]]] = None,
+        per_worker_gpu_share: Optional[float] = None,
+        *args,
+        **kwargs,
+    ):
+        kwargs["orchestrator_type"] = "ray"
+        kwargs["ray_placement_config"] = RayPlacementConfig(
+            defer_workers_init=True,
+            placement_groups=placement_groups,
+            placement_bundle_indices=placement_bundle_indices,
+            per_worker_gpu_share=per_worker_gpu_share,
+        )
+
+        # WAR: RL integration needs to use NCCL AllReduce for TP>1 due to a bug in TRTLLM's AllReduce
+        # which will cause convergence issue when using multiple rollout instances.
+        kwargs["allreduce_strategy"] = "NCCL"
+
+        if "ray_worker_extension_cls" not in kwargs:
+            kwargs["ray_worker_extension_cls"] = "tensorrt_llm.llmapi.rlhf_utils.WorkerExtension"
+
+        super().__init__(*args, **kwargs)
+        self._async_initialized = False
+
+    async def setup_async(self):
+        """Setup the LLM asynchronously."""
+        if not self._async_initialized:
+            await self._executor.init_workers_async()
+            await self._executor.setup_engine_remote_async()
+            self._async_initialized = True
+        return self
+
+    async def release(self, tags: list[str]):
+        """Release the GPU memory used by the LLM asynchronously.
+
+        Args:
+            tags: List of memory tag strings to release (e.g., ["model", "kv_cache"]).
+        """
+        await self.collective_rpc("sleep", args=(tags,))
+
+    async def resume(self, tags: list[str]):
+        """Resume the GPU memory used by the LLM asynchronously.
+
+        Args:
+            tags: List of memory tag strings to resume (e.g., ["model", "kv_cache"]).
+        """
+        await self.collective_rpc("wakeup", args=(tags,))
+
+    async def update_weights(self, weights: dict[str, str]):
+        """Update the weights of the LLM asynchronously.
+
+
+        Args:
+            weights: Dictionary mapping device UUIDs to IPC handles for weight tensors.
+        """
+        await self.collective_rpc("update_weights", args=(weights,))
+
+    async def collective_rpc(
+        self,
+        method: str,
+        args: tuple[Any, ...] = (),
+        kwargs: Optional[dict] = None,
+        unique_reply_rank: Optional[int] = None,
+    ) -> list[Any]:
+        """Execute an asynchronous RPC call on all GPU workers. Currently, this is only supported for RayExecutor.
+
+        Args:
+            method (str): The name of the worker method to execute.
+            args (tuple[Any, ...]): Positional arguments to pass to the worker method. Defaults to ().
+            kwargs (dict, optional): Keyword arguments to pass to the worker method. Defaults to None.
+            unique_reply_rank (int, optional): The rank of the worker that will be used to send the reply.
+
+        Returns:
+            list[Any]: A list of results from each worker.
+        """
+        return await self._executor.collective_rpc_async(
+            method, args, kwargs, unique_reply_rank=unique_reply_rank
+        )
+
+    def __await__(self):
+        return self.setup_async().__await__()
+
+    def __enter__(self):
+        raise RuntimeError("Please use 'async with AsyncLLM' instead")
+
+    async def __aenter__(self):
+        await self.setup_async()
+        return super().__enter__()
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        return super().__exit__(exc_type, exc_val, exc_tb)

tensorrt_llm/_torch/pyexecutor/sampler.py

@@ -2897,7 +2897,7 @@ def update_requests_single_beam_single_step(self, state: SampleStateTRTLLM):
         new_tokens_host = state.host.new_tokens.flatten().tolist()
         sequence_lengths_host_data = state.host.sequence_lengths.flatten().tolist()
         finish_reasons = state.host.finish_reasons.flatten().tolist()
-        log_probs_host = state.host.log_probs.tolist() if state.host.log_probs is not None else None
+        log_probs_host_tensor = state.host.log_probs
         cum_log_probs_host = (
             state.host.cum_log_probs.tolist() if state.host.cum_log_probs is not None else None
         )
@@ -2919,24 +2919,31 @@ def update_requests_single_beam_single_step(self, state: SampleStateTRTLLM):
         add_new_tokens_to_requests(reqs_with_new_tokens, new_tokens, 0)
 
         # Log probs
-        for request in reqs_with_new_tokens:
-            if request.py_return_log_probs:
-                seq_slot = request.py_seq_slot
-                seq_len = sequence_lengths_host_data[seq_slot]
-                begin_log_probs_offset = request.prompt_len
-                current_token = seq_len - request.prompt_len - 1
-                log_probs = [
-                    {
-                        new_tokens_host[seq_slot]: Logprob(
-                            logprob=log_probs_host[seq_slot][0][
-                                begin_log_probs_offset + current_token
-                            ],
-                            rank=1,
-                        )
-                    }
-                ]
-                cum_log_probs = [cum_log_probs_host[seq_slot]]
-                request.py_result.append_log_probs([log_probs], cum_log_probs)
+        if log_probs_host_tensor is not None:
+            # Log probs
+            seq_slots = []
+            seq_lens = []
+            for request in reqs_with_new_tokens:
+                if request.py_return_log_probs:
+                    seq_slot = request.py_seq_slot
+                    seq_slots.append(seq_slot)
+                    seq_lens.append(sequence_lengths_host_data[seq_slot] - 1)
+
+            log_probs_host = log_probs_host_tensor[seq_slots, 0, seq_lens].tolist()
+            idx = 0
+            for request in reqs_with_new_tokens:
+                if request.py_return_log_probs:
+                    log_probs = [
+                        {
+                            new_tokens_host[seq_slot]: Logprob(
+                                logprob=log_probs_host[idx],
+                                rank=1,
+                            )
+                        }
+                    ]
+                    cum_log_probs = [cum_log_probs_host[seq_slot]]
+                    request.py_result.append_log_probs([log_probs], cum_log_probs)
+                    idx += 1
 
         for request in reqs:
             request.py_decoding_iter += 1

tensorrt_llm/_torch/virtual_memory.py

@@ -74,7 +74,8 @@ class ExecutorMemoryType(StrEnum):
     SPEC_RESOURCES = "spec_resource_manager"
     INIT_KV_CACHE = "_no_capture_init_kv_cache"
     INIT_EXTRA_RESOURCES = "_no_capture_init_extra_resources"
-    MODEL_EXTRA = "_no_capture_model_extra"  # TODO: remove _no_capture after torch fix crash on torch.cuda.empty_cache()
+    # MODEL_EXTRA = "_no_capture_model_extra"  # TODO: remove _no_capture after torch fix crash on torch.cuda.empty_cache()
+    MODEL_EXTRA = "model_extra"
     EXTRA_RESOURCES = "executor_extra"
     KV_CACHE = "kv_cache"
     MODEL_ENGINE_MAIN = "model"