rejection_sampler_opt.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional

import torch
import torch.nn as nn

from vllm.logger import init_logger
from vllm.triton_utils import tl, triton
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata

logger = init_logger(__name__)

PLACEHOLDER_TOKEN_ID: tl.constexpr = -1
GREEDY_TEMPERATURE: tl.constexpr = -1
# Maximum number of speculative draft tokens allowed per request in a single
# step. This value is chosen to be large enough to handle typical use cases.
MAX_SPEC_LEN = 32


class OptRejectionSampler(nn.Module):
    """
    The implementation strictly follows the algorithm described in
        https://arxiv.org/abs/2211.17192.
    However, we want to clarify the terminology used in the implementation:
    accepted tokens: tokens that are accepted based on the relationship
            between the "raw" draft and target probabilities.
    recovered tokens: tokens that are sampled based on the adjusted probability
        distribution, which is derived from both the draft and target
        probabilities.
    bonus tokens:
        If all proposed tokens are accepted, the bonus token is added to the
        end of the sequence. The bonus token is only sampled from the target
        probabilities. We pass in the bonus tokens instead of sampling them
        in the rejection sampler to allow for more flexibility in the
        sampling process. For example, we can use top_p, top_k sampling for
        bonus tokens, while spec decode does not support these sampling
        strategies.
    output tokens:
        Tokens are finally generated with the rejection sampler.
        output tokens = accepted tokens + recovered tokens + bonus tokens
    """

    def forward(
        self,
        metadata: SpecDecodeMetadata,
        # [num_tokens, vocab_size]
        draft_probs: Optional[torch.Tensor],
        # [num_tokens, vocab_size]
        target_logits: torch.Tensor,
        # [num_tokens, vocab_size]
        target_tokens: torch.Tensor,
        # [batch_size, 1]
        bonus_token_ids: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> torch.Tensor:
        '''
        Args:
            metadata:
                Metadata for spec decoding.
            draft_probs (Optional[torch.Tensor]):
                Probability distribution for the draft tokens. Shape is
                [num_tokens, vocab_size]. Can be None if probabilities are
                not provided, which is the case for ngram spec decode.
            target_logits (torch.Tensor):
                Target model's logits probability distribution.
                Shape is [num_tokens, vocab_size]. Here, probabilities from
                different requests are flattened into a single tensor because
                this is the shape of the output logits.
                NOTE: `target_logits` can be updated in place to save memory.
            bonus_token_ids_tensor (torch.Tensor):
                A tensor containing bonus tokens. Shape is [batch_size, 1].
                Bonus tokens are added to the end of the sequence if all
                proposed tokens are accepted. We generate the bonus tokens
                outside of the rejection sampler with the default sampling
                strategy. It allows for more flexibility in the sampling
                process such as top_p, top_k sampling.
            sampling_metadata (vllm.v1.sample.metadata.SamplingMetadata):
                Additional metadata needed for sampling, such as temperature,
                top-k/top-p parameters, or other relevant information.
        Returns:
            output_token_ids (torch.Tensor):
                A tensor containing the final output token IDs.
        '''
        assert metadata.max_spec_len <= MAX_SPEC_LEN
        target_probs = target_logits.softmax(dim=-1, dtype=torch.float32)

        output_token_ids = rejection_sample(
            metadata.draft_token_ids,
            metadata.num_draft_tokens,
            metadata.max_spec_len,
            metadata.cu_num_draft_tokens,
            draft_probs,
            target_probs,
            target_tokens,
            bonus_token_ids,
            sampling_metadata,
        )
        return output_token_ids

    @staticmethod
    def parse_output(
        output_token_ids: torch.Tensor,
        vocab_size: int,
    ) -> list[list[int]]:
        """Parse the output of the rejection sampler.

        Args:
            output_token_ids: The sampled token IDs in shape
                [batch_size, max_spec_len + 1]. The rejected tokens are
                replaced with `PLACEHOLDER_TOKEN_ID` by the rejection sampler
                and will be filtered out in this function.
            vocab_size: The size of the vocabulary.

        Returns:
            A list of lists of token IDs.
        """
        output_token_ids_np = output_token_ids.cpu().numpy()
        # Create mask for valid tokens.
        valid_mask = ((output_token_ids_np != PLACEHOLDER_TOKEN_ID) &
                      (output_token_ids_np < vocab_size))
        outputs = [
            row[valid_mask[i]].tolist()
            for i, row in enumerate(output_token_ids_np)
        ]
        return outputs


def rejection_sample(
    # [num_tokens]
    draft_token_ids: torch.Tensor,
    # [batch_size]
    num_draft_tokens: list[int],
    max_spec_len: int,
    # [batch_size]
    cu_num_draft_tokens: torch.Tensor,
    # [num_tokens, vocab_size]
    draft_probs: Optional[torch.Tensor],
    # [num_tokens, vocab_size]
    target_probs: torch.Tensor,
    # [num_tokens, vocab_size]
    target_tokens,
    # [batch_size, 1]
    bonus_token_ids: torch.Tensor,
    sampling_metadata: SamplingMetadata,
) -> torch.Tensor:
    assert draft_token_ids.ndim == 1
    assert draft_probs is None or draft_probs.ndim == 3
    assert cu_num_draft_tokens.ndim == 1
    assert target_probs.ndim == 2

    batch_size = len(num_draft_tokens)
    num_tokens = draft_token_ids.shape[0]
    vocab_size = target_probs.shape[-1]
    device = target_probs.device
    assert draft_token_ids.is_contiguous()
    assert draft_probs is None or draft_probs.is_contiguous()
    assert target_probs.is_contiguous()
    assert bonus_token_ids.is_contiguous()
    assert target_probs.shape == (num_tokens, vocab_size)

    # Create output buffer.
    output_token_ids = torch.full(
        (batch_size, max_spec_len + 1),
        dtype=torch.int32,  # Consistent with SamplerOutput.sampled_token_ids.
        fill_value=PLACEHOLDER_TOKEN_ID,
        device=device,
    )

    uniform_probs = torch.rand(
        (num_tokens, ),
        dtype=torch.float32,
        device=device,
    )
    uniform_probs = uniform_probs * 0.1 + 0.1

    # Rejection sampling for random sampling requests.
    rejection_random_sample_kernel[(batch_size, )](
        output_token_ids,
        cu_num_draft_tokens,
        draft_token_ids,
        draft_probs,
        target_probs,
        target_tokens,
        bonus_token_ids,
        uniform_probs,
        max_spec_len,
        vocab_size,
        NO_DRAFT_PROBS=draft_probs is None,
        num_warps=1,
    )
    return output_token_ids

# NOTE(woosuk): Avoid specialization to prevent unnecessary recompilation.
@triton.jit(do_not_specialize=["max_spec_len"])
def rejection_random_sample_kernel(
    output_token_ids_ptr,  # [batch_size, max_spec_len + 1]
    cu_num_draft_tokens_ptr,  # [batch_size]
    draft_token_ids_ptr,  # [num_tokens]
    draft_probs_ptr,  # [num_tokens, vocab_size] or None
    target_probs_ptr,  # [num_tokens, vocab_size]
    target_token_ids_ptr, # [num_tokens, vocab_size]
    bonus_token_ids_ptr,  # [batch_size]
    uniform_probs_ptr,  # [num_tokens]
    max_spec_len,
    vocab_size,
    NO_DRAFT_PROBS: tl.constexpr,
):
    req_idx = tl.program_id(0)

    if req_idx == 0:
        start_idx = 0
    else:
        start_idx = tl.load(cu_num_draft_tokens_ptr + req_idx - 1)
    end_idx = tl.load(cu_num_draft_tokens_ptr + req_idx)
    num_draft_tokens = end_idx - start_idx

    rejected = False
    for pos in range(num_draft_tokens):
        if not rejected:
            draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
            if draft_token_id < 0:
                draft_token_id = 0
            if NO_DRAFT_PROBS:
                draft_prob = 1
            else:
                draft_prob = tl.load(draft_probs_ptr +
                                     (start_idx + pos) * vocab_size +
                                     draft_token_id)
            target_prob = tl.load(target_probs_ptr +
                                  (start_idx + pos) * vocab_size +
                                  draft_token_id)
            
            draft_token_id = draft_token_id.to(tl.int64)
            target_token_id = tl.load(target_token_ids_ptr + (start_idx + pos))
            target_token_id = target_token_id.to(tl.int64)
            uniform_prob = tl.load(uniform_probs_ptr + start_idx + pos)
            
            # NOTE(woosuk): While the draft probability should never be 0,
            # we check it to avoid NaNs. If it happens to be 0, we reject.
            if (draft_token_id == target_token_id) or (target_prob / draft_prob >= uniform_prob and draft_prob > 0):
                token_id = draft_token_id
            else:
                rejected = True
                token_id = target_token_id
            tl.store(output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos,
                    token_id)

    if not rejected:
        # If all tokens are accepted, append the bonus token.
        bonus_token_id = tl.load(bonus_token_ids_ptr + req_idx)
        tl.store(
            output_token_ids_ptr + req_idx * (max_spec_len + 1) +
            num_draft_tokens, bonus_token_id)