Qwen quantize and hf export support in examples (#311)

Signed-off-by: Riyad Islam <[email protected]>

Author: i-riyad

examples/llm_ptq/hf_ptq.py

@@ -742,7 +742,7 @@ def output_decode(generated_ids, input_shape):
     )
     parser.add_argument(
         "--verbose",
-        help="Print verbose output (e.g. quantization summary). Disable by --no_verbose.",
+        help="Print verbose output (e.g. quantization summary). Disable by --no-verbose.",
         default=True,
         action=argparse.BooleanOptionalAction,
     )

examples/vlm_ptq/README.md

@@ -41,6 +41,7 @@ Please refer to the [llm_ptq/README.md](../llm_ptq/README.md#current-out-of-the-
 | Llava | llava | ✅ | ✅ | ✅ | ✅ | ❌ |
 | VILA | vila | ✅ | ✅ | ✅ | ✅ | ❌ |
 | Phi-3-vision | phi | ✅ | ✅ | ✅ | ✅ | ❌ |
+| Qwen2.5-VL | qwen | ✅ | ✅ | ✅ | ✅ | ❌ |
 
 > *<sup>1.</sup>The w4a8_awq is an experimental quantization scheme that may result in a higher accuracy penalty.* \
 > *<sup>2.</sup>A selective set of the popular models are internally tested. The actual model support list may be longer. NVFP4 inference requires Blackwell GPUs and TensorRT-LLM v0.17 or later.*
@@ -51,7 +52,7 @@ Please refer to the [llm_ptq/README.md](../llm_ptq/README.md#current-out-of-the-
 
 Please refer to the [llm_ptq/README.md](../llm_ptq/README.md) about the details of model quantization.
 
-The following scripts provide an all-in-one and step-by-step model quantization example for Llava, VILA and Phi-3-vision models. The quantization format and the number of GPUs will be supplied as inputs to these scripts. By default, we build the engine for the fp8 format and 1 GPU.
+The following scripts provide an all-in-one and step-by-step model quantization example for Llava, VILA, Phi-3-vision and Qwen2.5-VL models. The quantization format and the number of GPUs will be supplied as inputs to these scripts. By default, we build the engine for the fp8 format and 1 GPU.
 
 ### Hugging Face Example [Script](./scripts/huggingface_example.sh)
 
@@ -76,6 +77,13 @@ git clone https://huggingface.co/microsoft/Phi-3-vision-128k-instruct
 scripts/huggingface_example.sh --type phi --model Phi-3-vision-128k-instruct --quant [fp8|int8_sq|int4_awq|w4a8_awq]
 ```
 
+For [Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct):
+
+```bash
+git clone https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct
+scripts/huggingface_example.sh --type qwen --model Qwen2.5-VL-7B-Instruct --export_fmt hf --quant [fp8|nvfp4|int8_sq|int4_awq|w4a8_awq]
+```
+
 The example scripts above also have an additional flag `--tasks gqa`, which will trigger evaluation of the built TensorRT engine using GQA benchmark. Details of the evaluation is explained in this [tutorial](../vlm_eval/README.md).
 
 If you encounter Out of Memory (OOM) issues during inference or evaluation, you can try lowering the `--kv_cache_free_gpu_memory_fraction` argument (default is 0.8) to reduce GPU memory usage for kv_cache:

examples/vlm_ptq/scripts/huggingface_example.sh

@@ -30,10 +30,10 @@ for i in $(env | grep ^PMI_ | cut -d"=" -f 1); do unset -v $i; done
 for i in $(env | grep ^PMIX_ | cut -d"=" -f 1); do unset -v $i; done
 
 case $MODEL_TYPE in
-    llava|phi|vila|mllama)
+    llava|phi|vila|mllama|qwen)
         ;;
     *)
-        echo "Unsupported type argument: Expected one of: [llava, phi, vila, mllama]" >&2
+        echo "Unsupported type argument: Expected one of: [llava, phi, vila, mllama, qwen]" >&2
         exit 1
 esac
 
@@ -58,10 +58,10 @@ case $SPARSITY_FMT in
 esac
 
 case $QFORMAT in
-    fp8|int8_sq|int4_awq|w4a8_awq|fp16|bf16)
+    fp8|nvfp4|int8_sq|int4_awq|w4a8_awq|fp16|bf16)
         ;;
     *)
-        echo "Unknown quant argument: Expected one of: [fp8, int8_sq, int4_awq, w4a8_awq, fp16, bf16]" >&2
+        echo "Unknown quant argument: Expected one of: [fp8, nvfp4, int8_sq, int4_awq, w4a8_awq, fp16, bf16]" >&2
         exit 1
 esac
 
@@ -91,7 +91,7 @@ fi
 
 BUILD_MAX_OUTPUT_LEN=512
 
-if [ "$MODEL_TYPE" = "llava" ] || [ "$MODEL_TYPE" = "vila" ]; then
+if [ "$MODEL_TYPE" = "llava" ] || [ "$MODEL_TYPE" = "vila" ] || [ "$MODEL_TYPE" = "qwen" ]; then
     BUILD_MAX_BATCH_SIZE=20
 else
     BUILD_MAX_BATCH_SIZE=4
@@ -149,6 +149,9 @@ case "${MODEL_TYPE}" in
         PTQ_ARGS+=" --kv_cache_qformat none "
         VLM_ARGS=" --max_encoder_input_len=6404 --skip_run"
         ;;
+    "qwen")
+        PTQ_ARGS+=" --kv_cache_qformat none "
+        ;;
 esac
 
 if [ "${MODEL_TYPE}" = "vila" ]; then
@@ -177,6 +180,7 @@ if [[ $TASKS =~ "build" ]] || [[ ! -d "$ENGINE_DIR" ]] || [[ ! $(ls -A $ENGINE_D
             --inference_tensor_parallel=$TP \
             --inference_pipeline_parallel=$PP \
             --export_fmt=$EXPORT_FORMAT \
+            --no-verbose \
             $PTQ_ARGS
     else
         echo "Quantized model config $MODEL_CONFIG exists, skipping the quantization stage"
@@ -213,6 +217,10 @@ case "${MODEL_TYPE}" in
     "phi")
         VISUAL_MODEL_TYPE="phi-3-vision"
         ;;
+    "qwen")
+        # Map generic type to TRT-LLM multimodal model type
+        VISUAL_MODEL_TYPE="qwen2_vl"
+        ;;
 esac
 
 

modelopt/torch/export/model_config_export.py

@@ -362,6 +362,7 @@ def torch_to_tensorrt_llm_checkpoint(
                     "glm",
                     "llama",
                     "mllama",
+                    "qwen",
                 ], f"lm_head not available for decoder {decoder_type}"
                 config.share_embedding_table = True
 

modelopt/torch/export/plugins/hf_spec_export.py

@@ -82,7 +82,12 @@ def rename_and_prune_if_spec_decoding(model: nn.Module, post_state_dict: dict):
 
 def set_config_if_spec_decoding(model: nn.Module, config_data: dict):
     """Return the config of draft model in official format."""
-    if len(model._modelopt_state) != 1 or model._modelopt_state[0][0] != "eagle":
+    opt_modes = getattr(model, "_modelopt_state", None)
+    if (
+        not isinstance(opt_modes, (list, tuple))
+        or len(opt_modes) != 1
+        or opt_modes[0][0] != "eagle"
+    ):
         # return as is
         return config_data