vllm/tests/core/utils.py

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

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


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,
) -> Tuple[Sequence, SequenceGroup]:
    if not block_size:
        block_size = prompt_length

    # 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".
    decoder_prompt_tokens = list(range(decoder_prompt_length))
    decoder_prompt_str = " ".join([str(t) for t in decoder_prompt_tokens])

    decoder_prompt = Sequence(int(request_id),
                              inputs={
                                  "prompt": decoder_prompt_str,
                                  "prompt_token_ids": decoder_prompt_tokens,
                                  "multi_modal_data": None,
                              },
                              block_size=block_size)

    encoder_prompt_tokens = list(reversed(list(range(encoder_prompt_length))))
    encoder_prompt_str = " ".join([str(t) for t in encoder_prompt_tokens])
    encoder_prompt = Sequence(int(request_id),
                              inputs={
                                  "prompt": encoder_prompt_str,
                                  "prompt_token_ids": encoder_prompt_tokens,
                                  "multi_modal_data": None,
                              },
                              block_size=block_size)
    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

    seqs = []
    for seq_id_offset, output_len in enumerate(seq_output_lens):
        seq = Sequence(
            seq_id=seq_id_start + seq_id_offset,
            inputs={
                "prompt": "",
                "prompt_token_ids": prompt_token_ids,
                "multi_modal_data": None,
            },
            block_size=16,
        )

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

    # Encoder sequence
    encoder_seq = Sequence(
        seq_id=seq_id_start + len(seq_output_lens),
        inputs={
            "prompt": "",
            "prompt_token_ids": prompt_token_ids,
            "multi_modal_data": None,
        },
        block_size=16,
    )

    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