59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
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import gc
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import time
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from typing import List
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from vllm import LLM, SamplingParams
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def time_generation(llm: LLM, prompts: List[str],
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sampling_params: SamplingParams):
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# Generate texts from the prompts. The output is a list of RequestOutput
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# objects that contain the prompt, generated text, and other information.
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# Warmup first
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llm.generate(prompts, sampling_params)
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llm.generate(prompts, sampling_params)
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start = time.time()
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outputs = llm.generate(prompts, sampling_params)
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end = time.time()
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print((end - start) / sum([len(o.outputs[0].token_ids) for o in outputs]))
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# Print the outputs.
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for output in outputs:
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generated_text = output.outputs[0].text
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print(f"text: {generated_text!r}")
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if __name__ == "__main__":
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template = (
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"Below is an instruction that describes a task. Write a response "
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"that appropriately completes the request.\n\n### Instruction:\n{}"
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"\n\n### Response:\n")
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# Sample prompts.
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prompts = [
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"Write about the president of the United States.",
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]
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prompts = [template.format(prompt) for prompt in prompts]
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# Create a sampling params object.
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sampling_params = SamplingParams(temperature=0.0, max_tokens=200)
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# Create an LLM without spec decoding
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llm = LLM(model="meta-llama/Llama-2-13b-chat-hf")
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print("Without speculation")
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time_generation(llm, prompts, sampling_params)
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del llm
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gc.collect()
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# Create an LLM with spec decoding
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llm = LLM(
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model="meta-llama/Llama-2-13b-chat-hf",
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speculative_model="ibm-fms/llama-13b-accelerator",
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# These are currently required for MLPSpeculator decoding
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use_v2_block_manager=True,
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)
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print("With speculation")
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time_generation(llm, prompts, sampling_params)
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