Feat: Pre-quantized LLM model support #3740
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| return model | ||
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| class TensorRTQuantizedLinear(torch.nn.Module): |
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@peri044 Is this something we might want to upstream to ModelOpt in the future?
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Or pull into main torch-tensorrt as a pass?
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I guess its somewhat HF specific, so remaining in this tool would make sense but are there some parts we could make generic for any sort of quantization workflow (e.g. torchao)?
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| hf_quant_algo = hf_quant_config.pop("quant_algo", None) | ||
| if hf_quant_algo != "FP8" and hf_quant_algo != "NVFP4": | ||
| raise RuntimeError("Only FP8 or NVFP4 quantization is supported") |
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How would it be different for MXFP4?
| ) | ||
| .eval() | ||
| .cuda() | ||
| ) | ||
| if args.pre_quantized: |
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Is this something we could determine automatically?
| @@ -234,13 +239,36 @@ def measure_perf(trt_model, input_signature, backend_name): | |||
| arg_parser.add_argument( | |||
| "--benchmark", action="store_true", help="Enable benchmark (default: False)" | |||
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| arg_parser.add_argument( | |||
| "--qformat", | |||
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For the sake of clarity, this should be something like quant-[scheme/format]
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Description
Support pre-quantized HF models and post-training quantization (PTQ) option for run_llm.py
Fixes # (issue)
Type of change
Checklist: