Generation with Quantization#
Source NVIDIA/TensorRT-LLM.
1### Generation with Quantization
2import logging
3
4import torch
5
6from tensorrt_llm import SamplingParams
7from tensorrt_llm._tensorrt_engine import LLM
8from tensorrt_llm.llmapi import CalibConfig, QuantAlgo, QuantConfig
9
10major, minor = torch.cuda.get_device_capability()
11enable_fp8 = major > 8 or (major == 8 and minor >= 9)
12enable_nvfp4 = major >= 10
13
14quant_and_calib_configs = []
15
16if not enable_nvfp4:
17 # Example 1: Specify int4 AWQ quantization to QuantConfig.
18 # We can skip specifying CalibConfig or leave a None as the default value.
19 quant_and_calib_configs.append(
20 (QuantConfig(quant_algo=QuantAlgo.W4A16_AWQ), None))
21
22if enable_fp8:
23 # Example 2: Specify FP8 quantization to QuantConfig.
24 # We can create a CalibConfig to specify the calibration dataset and other details.
25 # Note that the calibration dataset could be either HF dataset name or a path to local HF dataset.
26 quant_and_calib_configs.append(
27 (QuantConfig(quant_algo=QuantAlgo.FP8,
28 kv_cache_quant_algo=QuantAlgo.FP8),
29 CalibConfig(calib_dataset='cnn_dailymail',
30 calib_batches=256,
31 calib_max_seq_length=256)))
32else:
33 logging.error(
34 "FP8 quantization only works on post-ada GPUs. Skipped in the example.")
35
36if enable_nvfp4:
37 # Example 3: Specify NVFP4 quantization to QuantConfig.
38 quant_and_calib_configs.append(
39 (QuantConfig(quant_algo=QuantAlgo.NVFP4,
40 kv_cache_quant_algo=QuantAlgo.FP8),
41 CalibConfig(calib_dataset='cnn_dailymail',
42 calib_batches=256,
43 calib_max_seq_length=256)))
44else:
45 logging.error(
46 "NVFP4 quantization only works on Blackwell. Skipped in the example.")
47
48
49def main():
50
51 for quant_config, calib_config in quant_and_calib_configs:
52 # The built-in end-to-end quantization is triggered according to the passed quant_config.
53 llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
54 quant_config=quant_config,
55 calib_config=calib_config)
56
57 # Sample prompts.
58 prompts = [
59 "Hello, my name is",
60 "The president of the United States is",
61 "The capital of France is",
62 "The future of AI is",
63 ]
64
65 # Create a sampling params.
66 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
67
68 for output in llm.generate(prompts, sampling_params):
69 print(
70 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
71 )
72 llm.shutdown()
73
74 # Got output like
75 # 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?'
76 # Prompt: 'The president of the United States is', Generated text: 'considered the head of state, and the vice president of the United States is considered the head of state. President and Vice President of the United States (US)'
77 # 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'
78 # 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'
79
80
81if __name__ == '__main__':
82 main()