Doing the proposed changes:
Starting VIA server in release mode
2025-03-05 11:02:42,031 INFO Initializing VIA Stream Handler
2025-03-05 11:02:42,032 INFO Initializing VLM pipeline
2025-03-05 11:02:42,036 INFO Using model cached at /tmp/via-ngc-model-cache/nim_nvidia_vila-1.5-40b_vila-yi-34b-siglip-stage3_1003_video_v8_vila-llama-3-8b-lita
2025-03-05 11:02:42,040 INFO TRT-LLM Engine not found. Generating engines âŠ
Selecting INT4 AWQ mode
Converting Checkpoint âŠ
[2025-03-05 11:02:45,486] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[TensorRT-LLM] TensorRT-LLM version: 0.18.0.dev2025020400
Traceback (most recent call last):
File â/opt/nvidia/via/via-engine/models/vila15/trt_helper/quantize.pyâ, line 156, in
quantize_and_export(
File â/usr/local/lib/python3.10/dist-packages/tensorrt_llm/quantization/quantize_by_modelopt.pyâ, line 669, in quantize_and_export
hf_config = get_hf_config(model_dir)
File â/usr/local/lib/python3.10/dist-packages/tensorrt_llm/quantization/quantize_by_modelopt.pyâ, line 265, in get_hf_config
return AutoConfig.from_pretrained(ckpt_path, trust_remote_code=True)
File â/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.pyâ, line 1053, in from_pretrained
raise ValueError(
ValueError: Unrecognized model in /tmp/tmp.vila.GW09JQvm. Should have a model_type key in its config.json, or contain one of the following strings in its name: albert, align, altclip, audio-spectrogram-transformer, autoformer, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot-small, blip, blip-2, bloom, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, conditional_detr, convbert, convnext, convnextv2, cpmant, ctrl, cvt, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deformable_detr, deit, depth_anything, deta, detr, dinat, dinov2, distilbert, donut-swin, dpr, dpt, efficientformer, efficientnet, electra, encodec, encoder-decoder, ernie, ernie_m, esm, falcon, falcon_mamba, fastspeech2_conformer, flaubert, flava, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, git, glm, glpn, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gptj, gptsan-japanese, granite, granitemoe, graphormer, grounding-dino, groupvit, hiera, hubert, ibert, idefics, idefics2, idefics3, ijepa, imagegpt, informer, instructblip, instructblipvideo, jamba, jetmoe, jukebox, kosmos-2, layoutlm, layoutlmv2, layoutlmv3, led, levit, lilt, llama, llava, llava_next, llava_next_video, llava_onevision, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, mgp-str, mimi, mistral, mixtral, mllama, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, owlv2, owlvit, paligemma, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, persimmon, phi, phi3, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rwkv, sam, seamless_m4t, seamless_m4t_v2, segformer, seggpt, sew, sew-d, siglip, siglip_vision_model, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, table-transformer, tapas, time_series_transformer, timesformer, timm_backbone, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vits, vivit, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xmod, yolos, yoso, zamba, zoedepth, intern_vit_6b, v2l_projector, llava_llama, llava_mistral, llava_mixtral
ERROR: Failed to convert checkpoint
2025-03-05 11:02:52,138 ERROR Failed to load VIA stream handler - Failed to generate TRT-LLM engine
Traceback (most recent call last):
File â/tmp/via/via-engine/via_server.pyâ, line 1211, in run
self._stream_handler = ViaStreamHandler(self._args)
File â/opt/nvidia/via/via-engine/via_stream_handler.pyâ, line 373, in init
self._vlm_pipeline = VlmPipeline(args.asset_dir, args)
File â/opt/nvidia/via/via-engine/vlm_pipeline/vlm_pipeline.pyâ, line 965, in init
raise Exception(âFailed to generate TRT-LLM engineâ)
Exception: Failed to generate TRT-LLM engine
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File â/tmp/via/via-engine/via_server.pyâ, line 2572, in
server.run()
File â/tmp/via/via-engine/via_server.pyâ, line 1213, in run
raise ViaException(f"Failed to load VIA stream handler - {str(ex)}")
via_exception.ViaException: ViaException - code: InternalServerError message: Failed to load VIA stream handler - Failed to generate TRT-LLM engine