7f00899ff7
Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import os
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from vllm import LLM, SamplingParams
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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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# Quantizes neuron model weight to int8 ,
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# The default config for quantization is int8 dtype.
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os.environ['NEURON_QUANT_DTYPE'] = "s8"
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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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# Create a sampling params object.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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def main():
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# Create an LLM.
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llm = LLM(
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model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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max_num_seqs=8,
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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 transformers-neuronx.
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# TODO(liangfu): Support paged-attention in transformers-neuronx.
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max_model_len=2048,
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block_size=2048,
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# ruff: noqa: E501
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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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quantization="neuron_quant",
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override_neuron_config={
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"cast_logits_dtype": "bfloat16",
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},
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tensor_parallel_size=2)
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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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print("-" * 50)
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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}\nGenerated text: {generated_text!r}")
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print("-" * 50)
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if __name__ == "__main__":
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main()
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