offline_inference_neuron_int8_quantization.py

import os

from vllm import LLM, SamplingParams

# creates XLA hlo graphs for all the context length buckets.
os.environ['NEURON_CONTEXT_LENGTH_BUCKETS'] = "128,512,1024,2048"
# creates XLA hlo graphs for all the token gen buckets.
os.environ['NEURON_TOKEN_GEN_BUCKETS'] = "128,512,1024,2048"
# Quantizes neuron model weight to int8 ,
# The default config for quantization is int8 dtype.
os.environ['NEURON_QUANT_DTYPE'] = "s8"

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(
    model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    max_num_seqs=8,
    # The max_model_len and block_size arguments are required to be same as
    # max sequence length when targeting neuron device.
    # Currently, this is a known limitation in continuous batching support
    # in transformers-neuronx.
    # TODO(liangfu): Support paged-attention in transformers-neuronx.
    max_model_len=2048,
    block_size=2048,
    # The device can be automatically detected when AWS Neuron SDK is installed.
    # The device argument can be either unspecified for automated detection,
    # or explicitly assigned.
    device="neuron",
    quantization="neuron_quant",
    override_neuron_config={
        "cast_logits_dtype": "bfloat16",
    },
    tensor_parallel_size=2)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")