test_batch_expansion.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import pytest
import torch

from vllm.spec_decode.batch_expansion import BatchExpansionTop1Scorer

from .utils import create_seq_group_metadata_from_prompts, mock_worker


@pytest.mark.parametrize('num_target_seq_ids', [100])
@pytest.mark.skip_global_cleanup
def test_create_target_seq_id_iterator(num_target_seq_ids: int):
    """Verify all new sequence ids are greater than all input
    seq ids.
    """
    scorer = BatchExpansionTop1Scorer(mock_worker(), 'cuda:0', 32_000)

    all_seq_ids = [
        [1, 3, 5, 7],
        list(range(100)) + [0],
        [100],
    ]

    for seq_ids in all_seq_ids:
        max_seq_id = max(seq_ids)
        iterator = scorer._create_target_seq_id_iterator(seq_ids)  # pylint: disable=protected-access
        for _ in range(num_target_seq_ids):
            assert next(iterator) > max_seq_id


@pytest.mark.parametrize('k', [1, 2, 6])
@pytest.mark.skip_global_cleanup
def test_get_token_ids_to_score(k: int):
    """Verify correct tokens are selected for scoring.
    """
    proposal_token_ids = torch.tensor(
        list(range(k)),
        dtype=torch.int64,
        device='cuda',
    )

    expected_output: list[list[int]] = [
        [],
    ]
    for i in range(proposal_token_ids.shape[0]):
        expected_output.append(proposal_token_ids[:i + 1].tolist())

    scorer = BatchExpansionTop1Scorer(mock_worker(), 'cuda:0', 32_000)
    actual_output = scorer._get_token_ids_to_score(proposal_token_ids.tolist())  # pylint: disable=protected-access

    actual_output = [
        x.tolist() if isinstance(x, torch.Tensor) else x for x in actual_output
    ]

    assert actual_output == expected_output


@pytest.mark.parametrize('k', [1, 2, 6])
@pytest.mark.skip_global_cleanup
def test_create_single_target_seq_group_metadata(k: int):
    """Verify correct creation of a batch-expanded seq group metadata.
    """

    prompt_tokens = [1, 2, 3]
    prev_output_tokens = [4, 5, 6]

    token_ids = list(range(k))

    num_tokens_processed = len(prompt_tokens) + len(prev_output_tokens) - 1

    final_seq_len = len(prompt_tokens) + len(prev_output_tokens) + len(
        token_ids)

    block_size = 32
    input_seq_group_metadata = create_seq_group_metadata_from_prompts(
        [prompt_tokens], 2048 // block_size, block_size, [final_seq_len],
        [prev_output_tokens], [num_tokens_processed])[0]

    input_seq_id = list(input_seq_group_metadata.seq_data.keys())[0]
    target_seq_id = 100

    scorer = BatchExpansionTop1Scorer(mock_worker(), 'cuda:0', 32_000)
    output = scorer._create_single_target_seq_group_metadata(  # pylint: disable=protected-access
        input_seq_group_metadata,
        input_seq_id,
        target_seq_id,
        token_ids,
        input_seq_group_metadata.sampling_params,
    )

    assert output.request_id == input_seq_group_metadata.request_id
    assert output.sampling_params.repetition_penalty == \
        input_seq_group_metadata.sampling_params.repetition_penalty
    assert output.sampling_params.temperature == \
        input_seq_group_metadata.sampling_params.temperature
    assert output.sampling_params.top_p == \
        input_seq_group_metadata.sampling_params.top_p
    assert output.sampling_params.top_k == \
        input_seq_group_metadata.sampling_params.top_k
    assert len(output.seq_data) == 1
    assert output.seq_data[target_seq_id].get_prompt_token_ids() == tuple(
        prompt_tokens)
    assert output.seq_data[target_seq_id].get_output_token_ids() == tuple(
        prev_output_tokens + token_ids)

    assert len(output.block_tables) == 1
    assert output.block_tables[
        target_seq_id] == input_seq_group_metadata.block_tables[input_seq_id]