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76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
import pytest
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from tensorrt_llm.llmapi import LLM, KvCacheConfig, SamplingParams
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from tensorrt_llm.llmapi.llm_utils import CalibConfig, QuantAlgo, QuantConfig
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# isort: off
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from .test_llm import cnn_dailymail_path, llama_model_path, get_model_path
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from utils.util import skip_blackwell, skip_pre_blackwell, skip_pre_hopper
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# isort: on
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@skip_blackwell
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def test_llm_int4_awq_quantization():
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quant_config = QuantConfig(quant_algo=QuantAlgo.W4A16_AWQ)
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assert quant_config.quant_mode.has_any_quant()
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calib_config = CalibConfig(calib_dataset=cnn_dailymail_path)
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llm = LLM(llama_model_path,
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quant_config=quant_config,
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calib_config=calib_config)
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sampling_params = SamplingParams(max_tokens=6)
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for output in llm.generate(["A B C"], sampling_params=sampling_params):
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print(output)
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assert output.outputs[0].text == "D E F G H I"
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@skip_pre_hopper
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def test_llm_fp8_quantization():
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quant_config = QuantConfig(quant_algo=QuantAlgo.FP8,
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kv_cache_quant_algo=QuantAlgo.FP8)
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assert quant_config.quant_mode.has_any_quant()
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calib_config = CalibConfig(calib_dataset=cnn_dailymail_path)
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llm = LLM(llama_model_path,
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quant_config=quant_config,
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calib_config=calib_config)
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sampling_params = SamplingParams(max_tokens=6)
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for output in llm.generate(["A B C"], sampling_params=sampling_params):
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print(output)
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assert output.outputs[0].text == "D E F G H I"
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@skip_pre_blackwell
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def test_llm_nvfp4_quantization():
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quant_config = QuantConfig(quant_algo=QuantAlgo.NVFP4,
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kv_cache_quant_algo=QuantAlgo.FP8)
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assert quant_config.quant_mode.has_any_quant()
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calib_config = CalibConfig(calib_dataset=cnn_dailymail_path)
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llm = LLM(llama_model_path,
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quant_config=quant_config,
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calib_config=calib_config)
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sampling_params = SamplingParams(max_tokens=6)
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for output in llm.generate(["A B C"], sampling_params=sampling_params):
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print(output)
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assert output.outputs[0].text == "D E F G H I"
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@skip_pre_hopper
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@pytest.mark.skip("https://nvbugs/5027953")
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def test_llm_fp8_quantization_modelOpt_ckpt():
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llama_fp8_model_path = get_model_path(
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"llama-3.1-model/Llama-3.1-8B-Instruct-FP8")
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llm = LLM(llama_fp8_model_path,
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kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.4))
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sampling_params = SamplingParams(max_tokens=6)
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for output in llm.generate(["A B C"], sampling_params=sampling_params):
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print(output)
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assert output.outputs[0].text == " D E F G H I"
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if __name__ == "__main__":
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test_llm_int4_awq_quantization()
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test_llm_fp8_quantization_modelOpt_ckpt()
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