[https://nvbugs/6693991][fix] Restore verl rollout after sampler_type removal

docs/source/features/sampling.md

@@ -17,6 +17,8 @@ The PyTorch backend supports a wide variety of features, listed below:
 
 ## General usage
 
+TorchSampler is the only sampler; the `sampler_type` argument and the TRTLLM Sampler it selected have been removed. `sampler_type="auto"` and `sampler_type="TorchSampler"` are still accepted and ignored, while `sampler_type="TRTLLMSampler"` is rejected. When migrating off TRTLLM Sampler, also review the settings tuned for it, notably `logprobs`: under TorchSampler, `logprobs=0` returns the sampled token's log probability.
+
 Here is an example to run a model with basic usage of sampling parameters. This example prepares two identical prompts which will give different results due to the sampling parameters chosen:
 
 ```python

tensorrt_llm/llmapi/llm.py

@@ -57,7 +57,8 @@
 from ..logger import logger
 from ..sampling_params import LogitsProcessor, SamplingParams
 from ..scheduling_params import SchedulingParams
-from .llm_args import (TORCH_LLMARGS_EXPLICIT_DOCSTRING, TorchLlmArgs,
+from .llm_args import (TORCH_LLMARGS_EXPLICIT_DOCSTRING,
+                       TORCH_LLMARGS_REMOVED_ARGS, TorchLlmArgs,
                        validate_token_encoder_bucket_config)
 from .llm_utils import (CachedModelLoader, KvCacheRetentionConfig,
                         LlmBuildStats, ModelLoader)
@@ -403,6 +404,9 @@ def __init__(self,
             valid_keys = set(
                 list(llm_args_cls.model_fields.keys()) +
                 ['_mpi_session', 'backend'])
+            if issubclass(llm_args_cls, TorchLlmArgs):
+                # Values are vetted by TorchLlmArgs._drop_removed_args.
+                valid_keys |= TORCH_LLMARGS_REMOVED_ARGS
             for key in kwargs:
                 if key not in valid_keys:
                     raise ValueError(
@@ -2008,14 +2012,13 @@ def _build_model(self):
     def _validate_args_for_torch_backend(self, kwargs: dict) -> None:
         """Validate that only arguments supported by the PyTorch backend are passed.
         """
-        torchllm_fields = set(TorchLlmArgs.model_fields.keys())
+        # Values of removed args are vetted by TorchLlmArgs._drop_removed_args.
+        accepted_keys = (set(TorchLlmArgs.model_fields.keys())
+                         | TORCH_LLMARGS_REMOVED_ARGS
+                         | {'_mpi_session', 'backend'})
 
         # Check if any arguments not supported by the PyTorch backend are passed.
-        unsupported_args = [
-            key for key in kwargs
-            if key not in torchllm_fields and key not in ('_mpi_session',
-                                                          'backend')
-        ]
+        unsupported_args = [key for key in kwargs if key not in accepted_keys]
 
         if unsupported_args:
             raise ValueError(

tensorrt_llm/llmapi/llm_args.py

@@ -25,9 +25,9 @@
 from dataclasses import dataclass
 from enum import Enum, EnumMeta
 from pathlib import Path
-from typing import (TYPE_CHECKING, Annotated, Any, ClassVar, Dict, List,
-                    Literal, Optional, Set, Tuple, Type, TypeAlias, TypeVar,
-                    Union, get_args, get_origin)
+from typing import (TYPE_CHECKING, Annotated, Any, Callable, ClassVar, Dict,
+                    List, Literal, Optional, Set, Tuple, Type, TypeAlias,
+                    TypeVar, Union, get_args, get_origin)
 
 import torch
 import yaml
@@ -5330,6 +5330,39 @@ def validate_capture_num_tokens(cls, v):
         "The maximum number of CUDA streams to use for torch.compile.")
 
 
+def _check_removed_sampler_type(value: Any) -> None:
+    """Validate a value passed to the removed ``sampler_type`` argument.
+
+    ``auto`` and ``TorchSampler`` always resolved to TorchSampler, so dropping
+    the argument honors them. ``TRTLLMSampler`` cannot be honored: callers
+    asking for it tune the rest of their configuration to its conventions, so
+    substituting TorchSampler would silently run a mismatched configuration.
+    """
+    if value in ("auto", "TorchSampler"):
+        logger.warning(
+            f"'sampler_type' was removed (got {value!r}) and is ignored; "
+            "TorchSampler is the only sampler. Remove the argument to silence "
+            "this warning.")
+        return
+    raise ValueError(
+        f"'sampler_type' was removed and {value!r} is no longer available; "
+        "TorchSampler is the only sampler. Drop the argument, and make sure "
+        "the surrounding configuration targets TorchSampler -- notably "
+        "`logprobs=0` for the sampled token's logprob and a real `end_id`, "
+        "which differ from the TRTLLMSampler conventions.")
+
+
+# Arguments removed from `TorchLlmArgs` but still accepted on input, so that
+# integrations pinned to an older TRT-LLM keep constructing. Declaring one here
+# is enough: the pre-pydantic kwarg gates in `llm.py` consult
+# `TORCH_LLMARGS_REMOVED_ARGS`, and `_drop_removed_args` runs the check below.
+_TORCH_LLMARGS_REMOVED_ARG_CHECKS: Dict[str, Callable[[Any], None]] = {
+    "sampler_type": _check_removed_sampler_type,
+}
+
+TORCH_LLMARGS_REMOVED_ARGS = frozenset(_TORCH_LLMARGS_REMOVED_ARG_CHECKS)
+
+
 class TorchLlmArgs(BaseLlmArgs):
     # PyTorch backend specific configurations
     generation_config: Literal["auto", "trtllm"] = Field(
@@ -5454,6 +5487,20 @@ def init_multimodal_config(cls, v):
             "model's largest atomic item when necessary."),
         status="prototype")
 
+    @model_validator(mode="before")
+    @classmethod
+    def _drop_removed_args(cls, data: Any) -> Any:
+        """Drop removed arguments; their checks reject unserviceable values."""
+        if not isinstance(data, dict):
+            return data
+        removed = TORCH_LLMARGS_REMOVED_ARGS.intersection(data)
+        if not removed:
+            return data
+        data = dict(data)
+        for key in sorted(removed):
+            _TORCH_LLMARGS_REMOVED_ARG_CHECKS[key](data.pop(key))
+        return data
+
     @field_validator("encoder_max_batch_size", "encoder_max_num_tokens")
     @classmethod
     def validate_encoder_runtime_sizes(cls, v: Optional[int]) -> Optional[int]:

tests/integration/defs/verl/verl_config.yml

@@ -50,6 +50,10 @@ verl_config:
     - "PATH=\"${NVSHMEM_DIR}/bin:$PATH\""
     - "TRTLLM_TEST_MODEL_PATH_ROOT=/tmp/verl-models"
     - "TORCH_CUDA_ARCH_LIST="
+    # A verl-side switch that TRT-LLM never reads. Besides the removed
+    # sampler_type, it also selects verl's rollout config (end_id, detokenize,
+    # logprobs), so it must be set for that config to match TorchSampler.
+    - "TLLM_USE_TORCHSAMPLER=1"
 
   # Read-only CI model cache (flat layout: /scratch.../ModelName)
   ci_model_cache: "/scratch.trt_llm_data/llm-models"

tests/integration/test_lists/waives.txt

@@ -385,10 +385,4 @@ verl/test_verl_cases.py::test_async_generate SKIP (https://nvbugs/6683838)
 verl/test_verl_cases.py::test_async_memory_management SKIP (https://nvbugs/6683838)
 verl/test_verl_cases.py::test_init_without_device_mesh SKIP (https://nvbugs/6683838)
 verl/test_verl_cases.py::test_inter_node_trtllm_rollout SKIP (https://nvbugs/6683838)
-verl/test_verl_cases.py::test_multimodal_different_image_sizes SKIP (https://nvbugs/6693991)
-verl/test_verl_cases.py::test_multimodal_generate_with_image SKIP (https://nvbugs/6693991)
-verl/test_verl_cases.py::test_multimodal_text_only_fallback SKIP (https://nvbugs/6693991)
 verl/test_verl_cases.py::test_trtllm_abort SKIP (https://nvbugs/6272653)
-verl/test_verl_cases.py::test_unimodal_batch_generate SKIP (https://nvbugs/6693991)
-verl/test_verl_cases.py::test_unimodal_generate SKIP (https://nvbugs/6693991)
-verl/test_verl_cases.py::test_wake_sleep_cycle SKIP (https://nvbugs/6693991)