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Signed-off-by: Satyajith Chilappagari <satchill@amazon.com> Co-authored-by: Aaron Dou <yzdou@amazon.com> Co-authored-by: Shashwat Srijan <sssrijan@amazon.com> Co-authored-by: Chongming Ni <chongmni@amazon.com> Co-authored-by: Amulya Ballakur <amulyaab@amazon.com> Co-authored-by: Patrick Lange <patlange@amazon.com> Co-authored-by: Elaine Zhao <elaineyz@amazon.com> Co-authored-by: Lin Lin Pan <tailinpa@amazon.com> Co-authored-by: Navyadhara Gogineni <navyadha@amazon.com> Co-authored-by: Yishan McNabb <yishanm@amazon.com> Co-authored-by: Mrinal Shukla <181322398+mrinalks@users.noreply.github.com>
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to run offline inference with a speculative
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decoding model on neuron.
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"""
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import os
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from vllm import LLM, SamplingParams
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# Sample prompts.
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prompts = [
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"Hello, I am a language model and I can help",
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"The president of the United States is",
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"The capital of France is",
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]
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def config_buckets():
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"""Configure context length and token gen buckets."""
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# creates XLA hlo graphs for all the context length buckets.
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os.environ['NEURON_CONTEXT_LENGTH_BUCKETS'] = "128,512,1024,2048"
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# creates XLA hlo graphs for all the token gen buckets.
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os.environ['NEURON_TOKEN_GEN_BUCKETS'] = "128,512,1024,2048"
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def initialize_model():
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"""Create an LLM with speculative decoding."""
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return LLM(
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model="openlm-research/open_llama_7b",
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speculative_config={
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"model": "openlm-research/open_llama_3b",
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"num_speculative_tokens": 4,
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"max_model_len": 2048
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},
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max_num_seqs=4,
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max_model_len=2048,
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block_size=2048,
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use_v2_block_manager=True,
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device="neuron",
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tensor_parallel_size=32,
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)
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def process_requests(model: LLM, sampling_params: SamplingParams):
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"""Generate texts from prompts and print them."""
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outputs = model.generate(prompts, sampling_params)
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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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def main():
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"""Main function that sets up the model and processes prompts."""
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config_buckets()
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model = initialize_model()
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# Create a sampling params object.
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sampling_params = SamplingParams(max_tokens=100, top_k=1)
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process_requests(model, sampling_params)
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if __name__ == '__main__':
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main()
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