mirror of https://github.com/vllm-project/vllm.git
62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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This example shows how to run offline inference with an EAGLE speculative
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decoding model on neuron. To use EAGLE speculative decoding, you must use
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a draft model that is specifically fine-tuned for EAGLE speculation.
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Additionally, to use EAGLE with NxD Inference, the draft model must include
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the LM head weights from the target model. These weights are shared between
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the draft and target model.
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"""
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from vllm import LLM, SamplingParams
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# Sample prompts.
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prompts = [
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"What is annapurna labs?",
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]
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def main():
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# Create a sampling params object.
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sampling_params = SamplingParams(top_k=1, max_tokens=500, ignore_eos=True)
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# Create an LLM.
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llm = LLM(
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model="/home/ubuntu/model_hf/Meta-Llama-3.1-70B-Instruct",
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speculative_config={
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"model": "/home/ubuntu/model_hf/Llama-3.1-70B-Instruct-EAGLE-Draft",
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"num_speculative_tokens": 5,
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"max_model_len": 2048,
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},
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max_num_seqs=4,
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# The max_model_len and block_size arguments are required to be same as
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# max sequence length when targeting neuron device.
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# Currently, this is a known limitation in continuous batching support
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# in neuronx-distributed-inference.
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max_model_len=2048,
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block_size=2048,
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# The device can be automatically detected when AWS Neuron SDK is installed.
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# The device argument can be either unspecified for automated detection,
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# or explicitly assigned.
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device="neuron",
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tensor_parallel_size=32,
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override_neuron_config={
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"enable_eagle_speculation": True,
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"enable_fused_speculation": True,
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},
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)
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# Generate texts from the prompts. The output is a list of RequestOutput objects
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# that contain the prompt, generated text, and other information.
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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}, \n\n\n\ Generated text: {generated_text!r}")
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if __name__ == "__main__":
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main()
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