test_rejection_sampler.py

# SPDX-License-Identifier: Apache-2.0
from typing import Any, Optional

import pytest
import torch
import torch.nn.functional as F

from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.rejection_sampler import INVALID_TOKEN_ID, RejectionSampler

DEVICE = "cpu"


@pytest.fixture
def sampler():
    return RejectionSampler()


def create_logits_tensor(token_ids: list[list[int]],
                         vocab_size: int = 100) -> torch.Tensor:
    """Helper function to create logits tensor that 
       will produce desired token ids on argmax"""
    num_total_tokens = sum(len(tokens) for tokens in token_ids)
    logits = torch.full((num_total_tokens, vocab_size), -100.0, device=DEVICE)
    start_loc = 0
    for tokens in token_ids:
        for j, token_id in enumerate(tokens):
            logits[start_loc + j, token_id] = 100.0
        start_loc += len(tokens)
    return logits


def create_sampling_metadata(
        all_greedy: bool,
        generators: Optional[dict[int, Any]] = None) -> SamplingMetadata:
    """Create a v1 sampling metadata object with all_greedy set 
        to the given value. Either all greedy or all random sampling 
        is used.
    """
    generators = generators or {}
    return SamplingMetadata(
        temperature=torch.tensor([]),
        all_greedy=all_greedy,
        all_random=not all_greedy,
        top_p=None,
        top_k=None,
        min_p=torch.empty(1, ),
        generators=generators,
        max_num_logprobs=0,
        no_penalties=False,
        prompt_token_ids=None,
        frequency_penalties=torch.tensor([]),
        presence_penalties=torch.tensor([]),
        repetition_penalties=torch.tensor([]),
        output_token_ids=[],
        min_tokens={},
        logit_bias=[None],
        allowed_token_ids_mask=None,
        bad_words_token_ids={},
    )


########################### Tests for Greedy Sampling ###################
def test_perfect_match(sampler):
    """Test when output tokens perfectly match speculated tokens"""
    spec_tokens = [[1, 2, 3]]
    output_tokens = [[1, 2, 3, 4]]  # 4 is the bonus token

    metadata = create_sampling_metadata(all_greedy=True)
    logits = create_logits_tensor(output_tokens)
    bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
                                      device=logits.device)

    output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
    expected = torch.tensor([[1, 2, 3, 4]],
                            dtype=torch.int,
                            device=logits.device)
    assert torch.equal(output, expected)


def test_early_mismatch(sampler):
    """Test when there's an early mismatch in tokens"""
    spec_tokens = [[1, 2, 3]]
    output_tokens = [[1, 5, 3, 4]]  # Mismatch at position 1

    metadata = create_sampling_metadata(all_greedy=True)
    logits = create_logits_tensor(output_tokens)
    bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
                                      device=logits.device)

    output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
    expected = torch.tensor([[1, 5, INVALID_TOKEN_ID, INVALID_TOKEN_ID]],
                            dtype=torch.int,
                            device=logits.device)
    assert torch.equal(output, expected)


def test_multiple_sequences(sampler):
    """Test handling multiple sequences of speculated tokens"""
    spec_tokens = [[1, 2], [3]]
    output_tokens = [[1, 2, 5], [3,
                                 4]]  # Two sequences with bonus tokens 5 and 4

    metadata = create_sampling_metadata(all_greedy=True)
    logits = create_logits_tensor(output_tokens)
    bonus_token_tensor = torch.tensor(
        [output_tokens[0][-1], output_tokens[1][-1]], device=logits.device)

    output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
    expected = torch.tensor([[1, 2, 5], [3, 4, INVALID_TOKEN_ID]],
                            dtype=torch.int,
                            device=logits.device)
    assert torch.equal(output, expected)


def test_single_token_sequence(sampler):
    """Test handling sequences with single token"""
    spec_tokens = [[1]]
    output_tokens = [[1, 2]]  # Single token with bonus token 2

    metadata = create_sampling_metadata(all_greedy=True)
    logits = create_logits_tensor(output_tokens)
    bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
                                      device=logits.device)

    output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
    expected = torch.tensor([[1, 2]], dtype=torch.int, device=logits.device)
    assert torch.equal(output, expected)


def test_empty_sequence(sampler):
    """Test handling empty sequence of speculated tokens"""
    spec_tokens: list[list[int]] = [[]]
    output_tokens = [[5]]  # Just the bonus token

    metadata = create_sampling_metadata(all_greedy=True)
    logits = create_logits_tensor(output_tokens)
    bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
                                      device=logits.device)

    output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
    expected = torch.tensor([[5]], dtype=torch.int, device=logits.device)
    assert torch.equal(output, expected)


def test_multiple_mismatches(sampler):
    """Test handling multiple sequences with mismatches"""
    spec_tokens = [[1, 2, 3], [4, 5, 6]]
    output_tokens = [[1, 2, 7, 6], [4, 8, 6,
                                    9]]  # Mismatches in both sequences

    metadata = create_sampling_metadata(all_greedy=True)
    logits = create_logits_tensor(output_tokens)
    bonus_token_tensor = torch.tensor(
        [output_tokens[0][-1], output_tokens[1][-1]], device=logits.device)

    output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
    expected = torch.tensor([[1, 2, 7, INVALID_TOKEN_ID],
                             [4, 8, INVALID_TOKEN_ID, INVALID_TOKEN_ID]],
                            dtype=torch.int,
                            device=logits.device)
    assert torch.equal(output, expected)


@pytest.mark.parametrize(
    "spec_tokens,output_tokens,expected",
    [
        ([[1, 2]], [[1, 2, 3]], [[1, 2, 3]]),  # Perfect match with bonus
        ([[1]], [[2, 3]], [[2, INVALID_TOKEN_ID]]),  # First mismatch
        ([[1, 2], [3, 4]], [[1, 5, 6], [3, 4, 7]],
         [[1, 5, INVALID_TOKEN_ID], [3, 4, 7]]),  # Mixed matches
    ])
def test_parametrized_cases(sampler, spec_tokens, output_tokens, expected):
    """Parametrized test for various matching scenarios"""
    metadata = create_sampling_metadata(all_greedy=True)
    logits = create_logits_tensor(output_tokens)
    bonus_token_tensor = torch.tensor([tokens[-1] for tokens in output_tokens],
                                      device=logits.device)

    output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
    expected_tensor = torch.tensor(expected,
                                   dtype=torch.int,
                                   device=logits.device)
    assert torch.equal(output, expected_tensor)


########################### Tests for Random Sampling ###################
@pytest.mark.parametrize("k", [1, 3, 5])
@pytest.mark.parametrize("vocab_size", [1000])
@pytest.mark.parametrize("batch_size", [1, 4, 8])
@pytest.mark.parametrize("frac_seeded", [0.0, 0.5])
@pytest.mark.parametrize("n_rep", [20])
def test_deterministic_when_seeded(sampler, k: int, vocab_size: int,
                                   batch_size: int, frac_seeded: float,
                                   n_rep: int):
    draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
    target_probs = torch.rand(batch_size * (k + 1),
                              vocab_size,
                              dtype=torch.float32)
    bonus_token_ids = torch.randint(low=0,
                                    high=vocab_size,
                                    size=(batch_size, 1),
                                    dtype=torch.int64)
    draft_token_ids = torch.randint(low=0,
                                    high=vocab_size,
                                    size=(batch_size, k),
                                    dtype=torch.int64)

    seeded_mask = torch.rand(batch_size, dtype=torch.float32) <= frac_seeded

    results = []
    for _ in range(n_rep):
        seeded_seqs = {
            i: torch.Generator(device=DEVICE).manual_seed(i)
            for i in range(batch_size) if seeded_mask[i]
        }

        sampling_metadata = create_sampling_metadata(all_greedy=False,
                                                     generators=seeded_seqs)
        rep_result = sampler(draft_token_ids.tolist(), draft_probs,
                             bonus_token_ids, target_probs, sampling_metadata)

        results.append(rep_result)

    for i in range(batch_size):
        if seeded_mask[i]:
            for j in range(1, n_rep):
                assert torch.equal(results[j][i], results[0][i])


def test_rejection_sampling_approximates_target_distribution():
    """Verify rejection sampling approximates target distribution,
    despite sampling from a potentially distinct draft distribution.

    This is done by first creating a random target probability
    distribution and a random draft probability distribution. We then
    sample token ids from the rejection sampler using these draft
    and target distributions. The samples are used to estimate
    the output probability distribution, which we expect to approximate
    the target distribution.

    A basic distance metric is used to determine similarity between
    distributions.

    We expect that as we increase the number of samples,
    the distance between the observed distribution and the target
    distribution decreases. To measure this, we compare the distance
    of the observed distribution against both the target distribution
    and a uniform random distribution. We expect the distance between
    the observed distribution and the target distribution to improve
    much more than the distance improvement between the observed
    distribution and the random distribution.
    """
    torch.set_default_device(DEVICE)
    vocab_size = 10
    k = 2
    num_reference_probs = 100

    # Prepare draft, target, and reference probability distributions
    draft_probs, target_probs = (F.softmax(
        torch.rand(vocab_size, dtype=torch.float32),
        dim=-1,
    ) for _ in range(2))
    reference_probs = F.softmax(
        torch.rand(num_reference_probs, vocab_size, dtype=torch.float32),
        dim=-1,
    )

    sample_sizes = [10, 100, 1_000, 10_000, 100_000]
    distance_wrt_reference: list[float] = []
    distance_wrt_target: list[float] = []

    for num_samples in sample_sizes:
        # Sample using rejection sampling.
        rej_sample_probs = estimate_rejection_sampling_pdf(
            draft_probs, target_probs, k, vocab_size, num_samples)
        rej_sample_probs = rej_sample_probs.to(DEVICE)

        # Average distance from reference probs.
        reference_vs_rejsample_dist = torch.dist(
            reference_probs,
            rej_sample_probs).item() / reference_probs.shape[0]
        target_vs_rejsample_dist = torch.dist(target_probs,
                                              rej_sample_probs).item()

        distance_wrt_reference.append(reference_vs_rejsample_dist)
        distance_wrt_target.append(target_vs_rejsample_dist)

        relative_change_in_distance_wrt_target = get_ratio_first_to_last(
            distance_wrt_target)
        relative_change_in_distance_wrt_reference = get_ratio_first_to_last(
            distance_wrt_reference)

        print(f"{num_samples=} {target_vs_rejsample_dist=:.05f} "
              f"{reference_vs_rejsample_dist=:.05f}")
        print(f"{num_samples=} {relative_change_in_distance_wrt_target=:.02f} "
              f"{relative_change_in_distance_wrt_reference=:.02f}")

    relative_change_in_distance_wrt_target = get_ratio_first_to_last(
        distance_wrt_target)
    relative_change_in_distance_wrt_reference = get_ratio_first_to_last(
        distance_wrt_reference)

    expected_improvement_multiplier = 20
    assert (relative_change_in_distance_wrt_target
            > relative_change_in_distance_wrt_reference *
            expected_improvement_multiplier)


def get_ratio_first_to_last(elements: list[float]) -> float:
    return elements[0] / elements[-1]


def estimate_rejection_sampling_pdf(
    draft_probs: torch.Tensor,
    target_probs: torch.Tensor,
    k: int,
    vocab_size: int,
    num_samples: int,
) -> torch.Tensor:
    """Estimate the probability distribution of the output tokens
    using rejection sampling.

    Args:
        draft_probs: Draft probability distribution.
        target_probs: Target probability distribution.
        num_samples: Number of samples to draw.

    Returns:
        Estimated probability distribution of the output tokens.
    """
    sampler = RejectionSampler()
    # Repeat draft probs num_samples times.
    draft_probs = draft_probs.reshape(1, 1,
                                      vocab_size).repeat(num_samples, k, 1)

    # Repeat target probs num_samples * (k + 1) times.
    target_probs = target_probs.reshape(1, 1, vocab_size).repeat(
        num_samples, k + 1, 1).reshape(num_samples * (k + 1), vocab_size)

    # Randomly sample draft token ids from draft probs.
    draft_token_ids = torch.multinomial(draft_probs[:, 0, :],
                                        num_samples=k,
                                        replacement=True).reshape(
                                            num_samples, k)

    # Bonus tokens not used but required.
    bonus_token_ids = torch.zeros((1, 1), dtype=torch.int64,
                                  device=DEVICE).repeat(num_samples, 1)

    sampling_metadata = create_sampling_metadata(all_greedy=False)
    output_token_ids = sampler(draft_token_ids.tolist(), draft_probs,
                               bonus_token_ids, target_probs,
                               sampling_metadata)
    output_token_ids = output_token_ids[:, :-1].flatten()

    hist = torch.histogram(output_token_ids.to(dtype=torch.float,
                                               device="cpu"),
                           bins=vocab_size,
                           range=(0, vocab_size),
                           density=True)

    return hist.hist