aphrodite-engine/examples/offline_inference/encoder_decoder_inference.py

"""Prompting encoder-decoder models, specifically the BART model."""

from aphrodite import LLM, SamplingParams
from aphrodite.inputs import (ExplicitEncoderDecoderPrompt, TextPrompt,
                              TokensPrompt, zip_enc_dec_prompts)

dtype = "float"

# Create a BART encoder/decoder model instance
llm = LLM(
    model="facebook/bart-large-cnn",
    dtype=dtype,
)

# Get BART tokenizer
tokenizer = llm.llm_engine.get_tokenizer_group()

# Test prompts
#
# This section shows all of the valid ways to prompt an
# encoder/decoder model.
#
# - Helpers for building prompts
text_prompt_raw = "Hello, my name is"
text_prompt = TextPrompt(prompt="The president of the United States is")
tokens_prompt = TokensPrompt(prompt_token_ids=tokenizer.encode(
    prompt="The capital of France is"))
# - Pass a single prompt to encoder/decoder model
#   (implicitly encoder input prompt);
#   decoder input prompt is assumed to be None

single_text_prompt_raw = text_prompt_raw  # Pass a string directly
single_text_prompt = text_prompt  # Pass a TextPrompt
single_tokens_prompt = tokens_prompt  # Pass a TokensPrompt

# - Pass explicit encoder and decoder input prompts within one data structure.
#   Encoder and decoder prompts can both independently be text or tokens, with
#   no requirement that they be the same prompt type. Some example prompt-type
#   combinations are shown below, note that these are not exhaustive.

enc_dec_prompt1 = ExplicitEncoderDecoderPrompt(
    # Pass encoder prompt string directly, &
    # pass decoder prompt tokens
    encoder_prompt=single_text_prompt_raw,
    decoder_prompt=single_tokens_prompt,
)
enc_dec_prompt2 = ExplicitEncoderDecoderPrompt(
    # Pass TextPrompt to encoder, and
    # pass decoder prompt string directly
    encoder_prompt=single_text_prompt,
    decoder_prompt=single_text_prompt_raw,
)
enc_dec_prompt3 = ExplicitEncoderDecoderPrompt(
    # Pass encoder prompt tokens directly, and
    # pass TextPrompt to decoder
    encoder_prompt=single_tokens_prompt,
    decoder_prompt=single_text_prompt,
)

# - Finally, here's a useful helper function for zipping encoder and
#   decoder prompts together into a list of ExplicitEncoderDecoderPrompt
#   instances
zipped_prompt_list = zip_enc_dec_prompts(
    ['An encoder prompt', 'Another encoder prompt'],
    ['A decoder prompt', 'Another decoder prompt'])

# - Let's put all of the above example prompts together into one list
#   which we will pass to the encoder/decoder LLM.
prompts = [
    single_text_prompt_raw, single_text_prompt, single_tokens_prompt,
    enc_dec_prompt1, enc_dec_prompt2, enc_dec_prompt3
] + zipped_prompt_list

print(prompts)

# Create a sampling params object.
sampling_params = SamplingParams(
    temperature=0,
    top_p=1.0,
    min_tokens=0,
    max_tokens=20,
)

# Generate output tokens 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
    encoder_prompt = output.encoder_prompt
    generated_text = output.outputs[0].text
    print(f"Encoder prompt: {encoder_prompt!r}, "
          f"Decoder prompt: {prompt!r}, "
          f"Generated text: {generated_text!r}")