aphrodite-engine/tests/core/utils.py

import time
from typing import List, Optional
from typing import Sequence as GenericSequence
from typing import Tuple

from aphrodite import SamplingParams
from aphrodite.common.sequence import Logprob, Sequence, SequenceGroup
from aphrodite.lora.request import LoRARequest


def create_dummy_prompt(
    request_id: str,
    prompt_length: int,
    block_size: Optional[int] = None,
    lora_request: Optional[LoRARequest] = None,
    use_beam_search: bool = False,
    best_of: int = 1,
    prompt_tokens: Optional[List[int]] = None,
) -> Tuple[Sequence, SequenceGroup]:
    if not block_size:
        block_size = prompt_length

    if prompt_tokens is None:
        # Create dummy prompt sequence with tokens 0...block_size-1
        # and prompt "0 ... block_size".
        prompt_tokens = list(range(prompt_length))
    prompt_str = " ".join([str(t) for t in prompt_tokens])
    prompt = Sequence(int(request_id),
                      inputs={
                          "prompt": prompt_str,
                          "prompt_token_ids": prompt_tokens,
                      },
                      block_size=block_size)
    seq_group = SequenceGroup(request_id=request_id,
                              seqs=[prompt],
                              arrival_time=time.time(),
                              sampling_params=SamplingParams(
                                  use_beam_search=use_beam_search,
                                  best_of=best_of),
                              lora_request=lora_request)

    return prompt, seq_group


def create_dummy_prompt_encoder_decoder(
    request_id: str,
    decoder_prompt_length: int,
    encoder_prompt_length: int,
    block_size: Optional[int] = None,
    lora_request: Optional[LoRARequest] = None,
    use_beam_search: bool = False,
    best_of: int = 1,
) -> Tuple[Sequence, Sequence, SequenceGroup]:
    if not block_size:
        block_size = decoder_prompt_length

    # Create dummy prompt sequence with tokens 0...block_size-1
    # and prompt "0 ... block_size". Note that the prompt string
    # doesn't actually match the tokens
    decoder_prompt_tokens = list(range(decoder_prompt_length))
    decoder_prompt_str = " ".join([str(t) for t in decoder_prompt_tokens])
    encoder_prompt_tokens = list(reversed(list(range(encoder_prompt_length))))
    encoder_prompt_str = " ".join([str(t) for t in encoder_prompt_tokens])

    inputs = {
        "prompt": decoder_prompt_str,
        "prompt_token_ids": decoder_prompt_tokens,
        "encoder_prompt": encoder_prompt_str,
        "encoder_prompt_token_ids": encoder_prompt_tokens,
        "multi_modal_data": None,
    }

    decoder_prompt = Sequence(int(request_id),
                              inputs=inputs,
                              block_size=block_size,
                              from_decoder_prompt=True)

    encoder_prompt = Sequence(int(request_id),
                              inputs=inputs,
                              block_size=block_size,
                              from_decoder_prompt=False)
    seq_group = SequenceGroup(request_id=request_id,
                              seqs=[decoder_prompt],
                              sampling_params=SamplingParams(
                                  use_beam_search=use_beam_search,
                                  best_of=best_of),
                              arrival_time=time.time(),
                              lora_request=lora_request,
                              encoder_seq=encoder_prompt)

    return decoder_prompt, encoder_prompt, seq_group


def create_seq_group(
        seq_prompt_len: int = 1024,
        seq_output_lens: GenericSequence[int] = (128, ),
        request_id: str = '0',
        seq_id_start: int = 0,
        sampling_params: Optional[SamplingParams] = None) -> SequenceGroup:

    assert len(seq_output_lens) > 0

    if sampling_params is None:
        sampling_params = SamplingParams()

    prompt_token_ids = [0] * seq_prompt_len

    seqs: List[Sequence] = []
    for seq_id_offset, output_len in enumerate(seq_output_lens):
        seq = Sequence(
            seq_id=seq_id_start + seq_id_offset,
            inputs={"prompt_token_ids": prompt_token_ids},
            block_size=16,
        )

        for i in range(output_len):
            seq.append_token_id(
                token_id=i,
                logprobs={i: Logprob(0.0)},
            )
        seqs.append(seq)

    seq_group = SequenceGroup(
        request_id=request_id,
        seqs=seqs,
        sampling_params=sampling_params,
        arrival_time=time.time(),
    )

    return seq_group


def create_seq_group_encoder_decoder(
        seq_prompt_len: int = 1024,
        seq_output_lens: GenericSequence[int] = (128, ),
        request_id: str = '0',
        seq_id_start: int = 0,
        sampling_params: Optional[SamplingParams] = None) -> SequenceGroup:

    assert len(seq_output_lens) > 0

    if sampling_params is None:
        sampling_params = SamplingParams()

    prompt_token_ids = [0] * seq_prompt_len

    inputs = {
        "prompt": "",
        "prompt_token_ids": prompt_token_ids,
        "encoder_prompt": "",
        "encoder_prompt_token_ids": prompt_token_ids,
        "multi_modal_data": None,
    }

    seqs = []
    for seq_id_offset, output_len in enumerate(seq_output_lens):
        # Construct decoder input sequences
        seq = Sequence(seq_id=seq_id_start + seq_id_offset,
                       inputs=inputs,
                       block_size=16,
                       from_decoder_prompt=True)

        for i in range(output_len):
            seq.append_token_id(
                token_id=i,
                logprobs={i: Logprob(0.0)},
            )
        seqs.append(seq)

    # Encoder input sequence
    encoder_seq = Sequence(seq_id=seq_id_start + len(seq_output_lens),
                           inputs=inputs,
                           block_size=16,
                           from_decoder_prompt=False)

    return SequenceGroup(request_id=request_id,
                         seqs=seqs,
                         sampling_params=sampling_params,
                         arrival_time=time.time(),
                         encoder_seq=encoder_seq)


def round_up_to_next_block(seq_len: int, block_size: int) -> int:
    return (seq_len + block_size - 1) // block_size


# Helper functions for scheduler tests


def get_sequence_groups(scheduler_output):
    return [s.seq_group for s in scheduler_output.scheduled_seq_groups]


def append_new_token(out, token_id: int):
    seq_groups = get_sequence_groups(out)
    for seq_group in seq_groups:
        for seq in seq_group.get_seqs():
            seq.append_token_id(token_id, {token_id: Logprob(token_id)})


def schedule_and_update_computed_tokens(scheduler):
    metas, out, _ = scheduler.schedule()
    for s, meta in zip(out.scheduled_seq_groups, metas):
        s.seq_group.update_num_computed_tokens(meta.token_chunk_size)
    return metas, out


def append_new_token_seq_group(token_chunk_size, seq_group, token_id: int):
    seq_group.update_num_computed_tokens(token_chunk_size)
    for seq in seq_group.get_seqs():
        seq.append_token_id(token_id, {token_id: Logprob(token_id)})