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81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
### Generation with Quantization
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import logging
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import torch
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from tensorrt_llm import SamplingParams
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from tensorrt_llm._tensorrt_engine import LLM
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from tensorrt_llm.llmapi import CalibConfig, QuantAlgo, QuantConfig
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major, minor = torch.cuda.get_device_capability()
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enable_fp8 = major > 8 or (major == 8 and minor >= 9)
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enable_nvfp4 = major >= 10
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quant_and_calib_configs = []
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if not enable_nvfp4:
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# Example 1: Specify int4 AWQ quantization to QuantConfig.
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# We can skip specifying CalibConfig or leave a None as the default value.
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quant_and_calib_configs.append(
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(QuantConfig(quant_algo=QuantAlgo.W4A16_AWQ), None))
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if enable_fp8:
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# Example 2: Specify FP8 quantization to QuantConfig.
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# We can create a CalibConfig to specify the calibration dataset and other details.
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# Note that the calibration dataset could be either HF dataset name or a path to local HF dataset.
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quant_and_calib_configs.append(
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(QuantConfig(quant_algo=QuantAlgo.FP8,
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kv_cache_quant_algo=QuantAlgo.FP8),
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CalibConfig(calib_dataset='cnn_dailymail',
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calib_batches=256,
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calib_max_seq_length=256)))
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else:
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logging.error(
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"FP8 quantization only works on post-ada GPUs. Skipped in the example.")
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if enable_nvfp4:
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# Example 3: Specify NVFP4 quantization to QuantConfig.
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quant_and_calib_configs.append(
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(QuantConfig(quant_algo=QuantAlgo.NVFP4,
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kv_cache_quant_algo=QuantAlgo.FP8),
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CalibConfig(calib_dataset='cnn_dailymail',
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calib_batches=256,
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calib_max_seq_length=256)))
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else:
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logging.error(
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"NVFP4 quantization only works on Blackwell. Skipped in the example.")
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def main():
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for quant_config, calib_config in quant_and_calib_configs:
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# The built-in end-to-end quantization is triggered according to the passed quant_config.
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llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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quant_config=quant_config,
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calib_config=calib_config)
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The capital of France is",
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"The future of AI is",
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]
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# Create a sampling params.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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for output in llm.generate(prompts, sampling_params):
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print(
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f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
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)
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llm.shutdown()
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# Got output like
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# Prompt: 'Hello, my name is', Generated text: 'Jane Smith. I am a resident of the city. Can you tell me more about the public services provided in the area?'
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# Prompt: 'The capital of France is', Generated text: 'located in Paris, France. The population of Paris, France, is estimated to be 2 million. France is home to many famous artists, including Picasso'
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# Prompt: 'The future of AI is', Generated text: 'an open and collaborative project. The project is an ongoing effort, and we invite participation from members of the community.\n\nOur community is'
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if __name__ == '__main__':
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main()
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