Deprecate TRTLLM-build in examples

.github/CODEOWNERS

@@ -50,6 +50,5 @@ modelopt/torch/utils @NVIDIA/modelopt-torch-utils-codeowners
 /examples/onnx_ptq @NVIDIA/modelopt-onnx-codeowners
 /examples/pruning @NVIDIA/modelopt-torch-nas-prune-codeowners
 /examples/speculative_decoding @NVIDIA/modelopt-torch-speculative-codeowners
-/examples/vlm_eval @NVIDIA/modelopt-examples-vlm-codeowners
 /examples/vlm_ptq @NVIDIA/modelopt-examples-vlm-codeowners
 /examples/windows @NVIDIA/modelopt-windows-codeowners

CHANGELOG.rst

@@ -5,12 +5,17 @@ Model Optimizer Changelog (Linux)
 ^^^^^^^^^^^^^^^^^
 
 **Deprecations**
-- Deprecated ``quantize_mode`` argument in ``examples/onnx_ptq/evaluate.py`` to support strongly typing. Use ``engine_precision`` instead.
 
-**Bug Fixes**
+- Deprecated ``quantize_mode`` argument in ``examples/onnx_ptq/evaluate.py`` to support strongly typing. Use ``engine_precision`` instead.
+- Deprecated TRT-LLM's TRT backend in ``examples/llm_ptq`` and ``examples/vlm_ptq``. Tasks ``build`` and ``benchmark`` support are removed and replaced with ``quant``. For performance evaluation, please use ``trtllm-bench`` directly.
+- ``--export_fmt`` flag in ``examples/llm_ptq`` is removed. By default we export to the unified Hugging Face checkpoint format.
+- ``int8_sq`` quantization format is deprecated from the ``examples/vlm_ptq`` with respect to the TensorRT-LLM's torch backend switch. Please refer to the previous releases if this quantization format is needed.
+- Deprecated ``examples/vlm_eval`` as it depends on the deprecated TRT-LLM's TRT backend.
 
 **New Features**
+
 - ``high_precision_dtype`` default to fp16 in ONNX quantization, i.e. quantized output model weights are now FP16 by default.
+- Upgrade TensorRT-LLM dependency to 1.1.0rc2.
 
 0.35 (2025-09-04)
 ^^^^^^^^^^^^^^^^^
@@ -23,7 +28,6 @@ Model Optimizer Changelog (Linux)
 **Bug Fixes**
 
 - Fix attention head ranking logic for pruning Megatron Core GPT models.
-- Upgrade TensorRT-LLM dependency to 1.1.0rc2.
 
 **New Features**
 

docs/source/getting_started/_installation_for_Linux.rst

@@ -18,7 +18,7 @@ Latest Model Optimizer (``nvidia-modelopt``) currently has the following system
 +-------------------------+-----------------------------+
 | PyTorch                 |  >=2.6                      |
 +-------------------------+-----------------------------+
-| TensorRT-LLM (Optional) |  1.0.0rc6                   |
+| TensorRT-LLM (Optional) |  1.1.0rc2.post2             |
 +-------------------------+-----------------------------+
 | ONNX Runtime (Optional) |  1.22                       |
 +-------------------------+-----------------------------+

examples/gpt-oss/requirements.txt

@@ -3,7 +3,7 @@ datasets
 deepspeed
 kernels>=0.9.0
 peft>=0.17.0
-torch >= 2.8.0
+torch>2.7.1
 trackio
 transformers>=4.55.0
 trl>=0.21.0

examples/llm_eval/README.md

@@ -93,7 +93,7 @@ If `trust_remote_code` needs to be true, please append the command with the `--t
 ### TensorRT-LLM
 
 ```sh
-python lm_eval_tensorrt_llm.py --model trt-llm --model_args tokenizer=<HF model folder>,engine_dir=<TRT LLM engine dir> --tasks <comma separated tasks> --batch_size <engine batch size>
+python lm_eval_tensorrt_llm.py --model trt-llm --model_args tokenizer=<HF model folder>,engine_dir=<Quantized checkpoint dir> --tasks <comma separated tasks> --batch_size <engine batch size>
 ```
 
 ## MMLU
@@ -140,7 +140,7 @@ python mmlu.py --model_name causal --model_path <HF model folder or model card>
 ### Evaluate the TensorRT-LLM engine
 
 ```bash
-python mmlu.py --model_name causal --model_path <HF model folder or model card> --engine_dir <built TensorRT-LLM folder>
+python mmlu.py --model_name causal --model_path <HF model folder or model card> --engine_dir <Quantized checkpoint dir>
 ```
 
 ## MT-Bench
@@ -163,7 +163,7 @@ bash run_fastchat.sh -h <HF model folder or model card> --quant_cfg MODELOPT_QUA
 ### Evaluate the TensorRT-LLM engine
 
 ```bash
-bash run_fastchat.sh -h <HF model folder or model card> <built TensorRT-LLM folder>
+bash run_fastchat.sh -h <HF model folder or model card> <Quantized checkpoint dir>
 ```
 
 ### Judging the responses

examples/llm_ptq/README.md

@@ -203,7 +203,7 @@ scripts/huggingface_example.sh --type llama --model $HF_PATH --quant w4a8_awq,fp
 The above example perform `AutoQuantize` where the less quantization accuracy sensitive layers are quantized with `w4a8_awq` (specified by `--quant w4a8_awq`) and the more sensitive layers
 are kept un-quantized such that the effective bits is 4.8 (specified by `--auto_quantize_bits 4.8`).
 
-The example scripts above also have an additional flag `--tasks`, where the actual tasks run in the script can be customized. The allowed tasks are `build,mmlu,benchmark,lm_eval,livecodebench` specified in the script [parser](./scripts/parser.sh). The tasks combo can be specified with a comma-separated task list. Some tasks like mmlu can take a long time to run. To run lm_eval tasks, please also specify the `--lm_eval_tasks` flag with comma separated lm_eval tasks [here](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks).
+The example scripts above also have an additional flag `--tasks`, where the actual tasks run in the script can be customized. The allowed tasks are `quant,mmlu,lm_eval,livecodebench` specified in the script [parser](./scripts/parser.sh). The tasks combo can be specified with a comma-separated task list. Some tasks like mmlu can take a long time to run. To run lm_eval tasks, please also specify the `--lm_eval_tasks` flag with comma separated lm_eval tasks [here](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks).
 
 > *If GPU out-of-memory error is reported running the scripts, please try editing the scripts and reducing the max batch size to save GPU memory.*
 
@@ -251,7 +251,7 @@ scripts/huggingface_example.sh --model $HF_PATH --quant [fp8|nvfp4|int8_sq|int4_
 
 > *If a GPU OOM error occurs during model quantization despite sufficient memory, setting the --use_seq_device_map flag can help. This enforces sequential device mapping, distributing the model across GPUs and utilizing up to 80% of each GPU's memory.*
 
-> *You can now add `--low_memory_mode` to the command when setting `--export_fmt=hf` to lower the memory requirements of the PTQ process. With this mode, the script will compress model weights to low precision before calibration. This mode is only supported for FP8 and NVFP4 with max calibration.*
+> *You can add `--low_memory_mode` to the command to lower the memory requirements of the PTQ process. With this mode, the script will compress model weights to low precision before calibration. This mode is only supported for FP8 and NVFP4 with max calibration.*
 
 #### Deepseek R1
 
@@ -301,15 +301,15 @@ with torch.inference_mode():
 ### Quantize and Export
 
 ```bash
-python hf_ptq.py --pyt_ckpt_path <huggingface_model_card> --qformat fp8 --export_fmt hf --export_path <quantized_ckpt_path> --trust_remote_code
+python hf_ptq.py --pyt_ckpt_path <huggingface_model_card> --qformat fp8 --export_path <quantized_ckpt_path> --trust_remote_code
 ```
 
 ### Hugging Face framework [Script](./scripts/huggingface_example.sh)
 
 Alternatively, the framework script `huggingface_example.sh` also supports quantize and export:
 
 ```bash
-scripts/huggingface_example.sh --model <huggingface_model_card> --quant fp8 --export_fmt hf
+scripts/huggingface_example.sh --model <huggingface_model_card> --quant fp8
 ```
 
 ### Deployment

examples/llm_ptq/hf_ptq.py

@@ -89,28 +89,20 @@ def auto_quantize(
     qformat_list = qformat.split(",")
     assert qformat_list, "No quantization formats provided"
     # Check if all provided quantization formats are supported
-    if args.export_fmt == "hf":
-        assert all(
-            qformat
-            in [
-                "fp8",
-                "int4_awq",
-                "nvfp4",
-                "nvfp4_awq",
-                "w4a8_awq",
-                "fp8_pb_wo",
-                "w4a8_mxfp4_fp8",
-                "nvfp4_mlp_only",
-            ]
-            for qformat in qformat_list
-        ), (
-            "One or more quantization formats provided are not supported for unified checkpoint export"
-        )
-    else:
-        assert all(
-            qformat in ["fp8", "int8_sq", "int4_awq", "w4a8_awq", "nvfp4", "nvfp4_awq"]
-            for qformat in qformat_list
-        ), "One or more quantization formats provided are not supported for tensorrt llm export"
+    assert all(
+        qformat
+        in [
+            "fp8",
+            "int4_awq",
+            "nvfp4",
+            "nvfp4_awq",
+            "w4a8_awq",
+            "fp8_pb_wo",
+            "w4a8_mxfp4_fp8",
+            "nvfp4_mlp_only",
+        ]
+        for qformat in qformat_list
+    ), "One or more quantization formats provided are not supported for unified checkpoint export"
 
     def loss_func(output, data):
         # For transformers AutoModelForCausalLM models, the outputs are wrapped in `CausalLMOutputWithPast`
@@ -219,27 +211,21 @@ def main(args):
             "Quantization supports only one quantization format."
         )
 
-    # Check arguments for unified_hf export format and set to default if unsupported arguments are provided
-    if args.export_fmt == "hf":
-        assert args.sparsity_fmt == "dense", (
-            f"Sparsity format {args.sparsity_fmt} not supported by unified export api."
-        )
-
-        if not args.auto_quantize_bits:
-            assert (
-                args.qformat
-                in [
-                    "int4_awq",
-                    "fp8",
-                    "nvfp4",
-                    "nvfp4_awq",
-                    "w4a8_awq",
-                    "fp8_pb_wo",
-                    "w4a8_mxfp4_fp8",
-                    "nvfp4_mlp_only",
-                ]
-                or args.kv_cache_qformat in KV_QUANT_CFG_CHOICES
-            ), f"Quantization format {args.qformat} not supported for HF export path"
+    if not args.auto_quantize_bits:
+        assert (
+            args.qformat
+            in [
+                "int4_awq",
+                "fp8",
+                "nvfp4",
+                "nvfp4_awq",
+                "w4a8_awq",
+                "fp8_pb_wo",
+                "w4a8_mxfp4_fp8",
+                "nvfp4_mlp_only",
+            ]
+            or args.kv_cache_qformat in KV_QUANT_CFG_CHOICES
+        ), f"Quantization format {args.qformat} not supported for HF export path"
 
     # If low memory mode is enabled, we compress the model while loading the HF checkpoint.
     calibration_only = False
@@ -253,9 +239,6 @@ def main(args):
             attn_implementation=args.attn_implementation,
         )
     else:
-        assert args.export_fmt == "hf", (
-            "Low memory mode is only supported for exporting HF checkpoint."
-        )
         assert args.qformat in QUANT_CFG_CHOICES, (
             f"Quantization format is not supported for low memory mode. Supported formats: {QUANT_CFG_CHOICES.keys()}"
         )
@@ -600,34 +583,41 @@ def output_decode(generated_ids, input_shape):
             setattr(model.config, "architectures", full_model_config.architectures)
 
         start_time = time.time()
-        if args.export_fmt == "tensorrt_llm":
+        if (
+            model_type in ["t5", "bart", "whisper"]
+            or args.sparsity_fmt != "dense"
+            or "int8_sq" in args.qformat
+        ):
+            warnings.warn(
+                "Still exporting TensorRT-LLM checkpoints for models not supported by the TensorRT-LLM torch runtime."
+            )
+
             # Move meta tensor back to device before exporting.
             remove_hook_from_module(model, recurse=True)
 
-            dtype = None
-            if "w4a8_awq" in args.qformat:
-                # TensorRT-LLM w4a8 only support fp16 as the dtype.
-                dtype = torch.float16
-
-            # For Gemma2-27B, TRT-LLM only works with bfloat16 as the dtype.
-            if model_type == "gemma2":
-                dtype = torch.bfloat16
-
             export_tensorrt_llm_checkpoint(
                 model,
                 model_type,
-                dtype=dtype,
                 export_dir=export_path,
                 inference_tensor_parallel=args.inference_tensor_parallel,
                 inference_pipeline_parallel=args.inference_pipeline_parallel,
             )
-        elif args.export_fmt == "hf":
+        else:
+            # Check arguments for unified_hf export format and set to default if unsupported arguments are provided
+            assert args.sparsity_fmt == "dense", (
+                f"Sparsity format {args.sparsity_fmt} not supported by unified export api."
+            )
+
+            if args.inference_tensor_parallel != 1 or args.inference_pipeline_parallel != 1:
+                warnings.warn(
+                    "Unified HF export format does not specify inference tensor parallel or pipeline parallel. "
+                    "They will be set at deployment time."
+                )
+
             export_hf_checkpoint(
                 full_model,
                 export_dir=export_path,
             )
-        else:
-            raise NotImplementedError(f"{args.export_fmt} not supported")
 
         # Restore default padding and export the tokenizer as well.
         if tokenizer is not None:
@@ -710,9 +700,9 @@ def output_decode(generated_ids, input_shape):
     parser.add_argument(
         "--export_fmt",
         required=False,
-        default="tensorrt_llm",
+        default="hf",
         choices=["tensorrt_llm", "hf"],
-        help=("Checkpoint export format"),
+        help="Deprecated. Please avoid using this argument.",
     )
     parser.add_argument(
         "--trust_remote_code",
@@ -767,6 +757,9 @@ def output_decode(generated_ids, input_shape):
 
     args = parser.parse_args()
 
+    if args.export_fmt != "hf":
+        warnings.warn("Deprecated. --export_fmt forced to hf.")
+
     args.dataset = args.dataset.split(",") if args.dataset else None
     args.calib_size = [int(num_sample) for num_sample in args.calib_size.split(",")]
     main(args)