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* Update TensorRT-LLM --------- Co-authored-by: Starrick Liu <73152103+StarrickLiu@users.noreply.github.com>
57 lines
2.6 KiB
Python
57 lines
2.6 KiB
Python
### Generate Text Using Medusa Decoding
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from tensorrt_llm import LLM, SamplingParams
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from tensorrt_llm.llmapi import (LLM, BuildConfig, KvCacheConfig,
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MedusaDecodingConfig, SamplingParams)
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from tensorrt_llm.models.modeling_utils import SpeculativeDecodingMode
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def main():
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The president of the United States 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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# The end user can customize the sampling configuration with the SamplingParams class
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# The end user can customize the build configuration with the BuildConfig class
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build_config = BuildConfig(
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max_batch_size=1,
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max_seq_len=1024,
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max_draft_len=63,
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speculative_decoding_mode=SpeculativeDecodingMode.MEDUSA)
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# The end user can customize the kv cache configuration with the KVCache class
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kv_cache_config = KvCacheConfig(enable_block_reuse=True)
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# The end user can customize the medusa decoding configuration by specifying the
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# medusa heads num and medusa choices with the MedusaDecodingConfig class
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speculative_config = MedusaDecodingConfig(num_medusa_heads=4,
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medusa_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], \
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[0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], \
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[0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], \
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[0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], \
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[6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], \
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[0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]
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)
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llm = LLM(model="lmsys/vicuna-7b-v1.3",
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speculative_model="FasterDecoding/medusa-vicuna-7b-v1.3",
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build_config=build_config,
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kv_cache_config=kv_cache_config,
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speculative_config=speculative_config)
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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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if __name__ == '__main__':
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main()
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