# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Datastructures defining a GPU input batch

from dataclasses import dataclass
from typing import Optional, cast

import numpy as np
import torch

from vllm import envs
from vllm.config import get_current_vllm_config
from vllm.lora.request import LoRARequest
from vllm.multimodal.inputs import MultiModalFeatureSpec
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingParams, SamplingType
from vllm.utils import length_from_prompt_token_ids_or_embeds
from vllm.utils.collection_utils import swap_dict_values
from vllm.v1.outputs import LogprobsTensors
from vllm.v1.pool.metadata import PoolingMetadata, PoolingStates
from vllm.v1.sample.logits_processor import (
    BatchUpdateBuilder,
    LogitsProcessors,
    MoveDirectionality,
)
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.utils import copy_slice
from vllm.v1.worker.block_table import MultiGroupBlockTable


@dataclass
class CachedRequestState:
    req_id: str
    prompt_token_ids: list[int] | None
    mm_features: list[MultiModalFeatureSpec]
    sampling_params: SamplingParams | None
    generator: torch.Generator | None

    block_ids: tuple[list[int], ...]
    num_computed_tokens: int
    output_token_ids: list[int]

    mrope_positions: torch.Tensor | None = None
    mrope_position_delta: int | None = None

    xdrope_positions: torch.Tensor | None = None

    lora_request: LoRARequest | None = None
    prompt_embeds: torch.Tensor | None = None

    # Used when both async_scheduling and spec_decode are enabled.
    prev_num_draft_len: int = 0

    # for pooling models
    pooling_params: PoolingParams | None = None
    pooling_states: PoolingStates | None = None

    def __post_init__(self):
        self.num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
            self.prompt_token_ids, self.prompt_embeds
        )

        if self.pooling_params is not None:
            self.pooling_states = PoolingStates()

    @property
    def num_tokens(self) -> int:
        return self.num_prompt_tokens + len(self.output_token_ids)

    def get_token_id(self, idx: int) -> int:
        if idx < self.num_prompt_tokens:
            if self.prompt_token_ids is None:
                raise ValueError(
                    f"Tried to access token index {idx}, but that token was "
                    "provided via prompt_embeds, and its ID is unknown."
                )
            return self.prompt_token_ids[idx]
        if idx - self.num_prompt_tokens < len(self.output_token_ids):
            return self.output_token_ids[idx - self.num_prompt_tokens]
        return -1


class InputBatch:
    def __init__(
        self,
        max_num_reqs: int,
        max_model_len: int,
        max_num_batched_tokens: int,
        device: torch.device,
        pin_memory: bool,
        vocab_size: int,
        block_sizes: list[int],  # The block_size of each kv cache group
        kernel_block_sizes: list[int],
        max_num_blocks_per_req: list[int] | None = None,
        logitsprocs: LogitsProcessors | None = None,
        logitsprocs_need_output_token_ids: bool = False,
        is_spec_decode: bool = False,
        is_pooling_model: bool = False,
        cp_kv_cache_interleave_size: int = 1,
    ):
        ori_max_num_reqs = max_num_reqs
        if is_spec_decode and envs.VLLM_REJECT_SAMPLE_OPT:
            vllm_config = get_current_vllm_config()
            max_num_reqs = max_num_reqs * (1 + vllm_config.speculative_config.num_speculative_tokens)

        self.is_pooling_model = is_pooling_model
        self.is_spec_decode = is_spec_decode
        self.max_num_reqs = max_num_reqs
        self.max_model_len = max_model_len
        self.max_num_batched_tokens = max_num_batched_tokens
        self.device = device
        self.pin_memory = pin_memory
        self.vocab_size = vocab_size

        self._req_ids: list[str | None] = []
        self.req_id_to_index: dict[str, int] = {}

        # TODO(woosuk): This buffer could be too large if max_model_len is big.
        # Find a way to reduce the CPU memory usage.
        # This buffer is not directly transferred to the GPU, so it does not
        # need to be pinned.
        self.token_ids_cpu_tensor = torch.zeros(
            (ori_max_num_reqs, max_model_len),
            device="cpu",
            dtype=torch.int32,
            pin_memory=False,
        )
        self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
        self.is_token_ids_tensor = torch.zeros(
            (max_num_reqs, max_model_len), device="cpu", dtype=bool, pin_memory=False
        )
        self.is_token_ids = self.is_token_ids_tensor.numpy()
        # Store prompt embeddings per request to avoid OOM from large upfront
        # allocation if max_model_len is big.
        # Maps req_index -> tensor of shape (num_prompt_tokens, hidden_size)
        self.req_prompt_embeds: dict[int, torch.Tensor] = {}
        self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
        self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
        self.num_computed_tokens_cpu_tensor = torch.zeros(
            (max_num_reqs,),
            device="cpu",
            dtype=torch.int32,
            pin_memory=pin_memory,
        )
        self.num_computed_tokens_cpu = self.num_computed_tokens_cpu_tensor.numpy()

        # Block table.
        self.block_table = MultiGroupBlockTable(
            max_num_reqs=max_num_reqs,
            max_model_len=max_model_len,
            max_num_batched_tokens=max_num_batched_tokens,
            pin_memory=pin_memory,
            device=device,
            block_sizes=block_sizes,
            kernel_block_sizes=kernel_block_sizes,
            max_num_blocks=max_num_blocks_per_req,
            cp_kv_cache_interleave_size=cp_kv_cache_interleave_size,
        )

        # Sampling-related.
        self.temperature = torch.empty(
            (max_num_reqs,), dtype=torch.float32, device=device
        )
        self.temperature_cpu_tensor = torch.empty(
            (max_num_reqs,), dtype=torch.float32, device="cpu", pin_memory=pin_memory
        )
        self.temperature_cpu = self.temperature_cpu_tensor.numpy()
        self.greedy_reqs: set[str] = set()
        self.random_reqs: set[str] = set()

        self.top_p = torch.empty((max_num_reqs,), dtype=torch.float32, device=device)
        self.top_p_cpu_tensor = torch.empty(
            (max_num_reqs,), dtype=torch.float32, device="cpu", pin_memory=pin_memory
        )
        self.top_p_cpu = self.top_p_cpu_tensor.numpy()
        self.top_p_reqs: set[str] = set()

        self.top_k = torch.empty((max_num_reqs,), dtype=torch.int32, device=device)
        self.top_k_cpu_tensor = torch.empty(
            (max_num_reqs,), dtype=torch.int32, device="cpu", pin_memory=pin_memory
        )
        self.top_k_cpu = self.top_k_cpu_tensor.numpy()
        self.top_k_reqs: set[str] = set()

        # Frequency penalty related data structures
        self.frequency_penalties = torch.empty(
            (max_num_reqs,), dtype=torch.float, device=device
        )
        self.frequency_penalties_cpu_tensor = torch.empty(
            (max_num_reqs,), dtype=torch.float, device="cpu", pin_memory=pin_memory
        )
        self.frequency_penalties_cpu = self.frequency_penalties_cpu_tensor.numpy()
        self.frequency_penalties_reqs: set[str] = set()

        # Presence penalty related data structures
        self.presence_penalties = torch.empty(
            (max_num_reqs,), dtype=torch.float, device=device
        )
        self.presence_penalties_cpu_tensor = torch.empty(
            (max_num_reqs,), dtype=torch.float, device="cpu", pin_memory=pin_memory
        )
        self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy()
        self.presence_penalties_reqs: set[str] = set()

        # Repetition penalty related data structures
        self.repetition_penalties = torch.empty(
            (max_num_reqs,), dtype=torch.float, device=device
        )
        self.repetition_penalties_cpu_tensor = torch.empty(
            (max_num_reqs,), dtype=torch.float, device="cpu", pin_memory=pin_memory
        )
        self.repetition_penalties_cpu = self.repetition_penalties_cpu_tensor.numpy()
        self.repetition_penalties_reqs: set[str] = set()

        # Speculative decoding
        self.num_accepted_tokens_cpu_tensor = torch.ones(
            (max_num_reqs,), dtype=torch.int64, device="cpu", pin_memory=pin_memory
        )
        self.num_accepted_tokens_cpu = self.num_accepted_tokens_cpu_tensor.numpy()

        # lora related
        self.request_lora_mapping = np.zeros((self.max_num_reqs,), dtype=np.int64)
        self.lora_id_to_request_ids: dict[int, set[str]] = {}
        self.lora_id_to_lora_request: dict[int, LoRARequest] = {}

        # req_index -> generator
        # NOTE(woosuk): The indices of the requests that do not have their own
        # generator should not be included in the dictionary.
        self.generators: dict[int, torch.Generator] = {}

        self.num_logprobs: dict[str, int] = {}

        # To accumulate prompt logprobs tensor chunks across prefill steps.
        self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}

        # Internal representation of per-step batch state changes, used for
        # reordering persistent batch and generating logitsprocs batch state
        # updates. Should reset each step.
        self.batch_update_builder = BatchUpdateBuilder()

        # TODO convert this to LogitsProcessor
        self.has_allowed_token_ids: set[str] = set()
        # NOTE(lufang): In the mask tensor, if the corresponding token allowed,
        # the value is False. Since we use masked_fill_ to set -inf.
        self.allowed_token_ids_mask: torch.Tensor | None = None
        self.allowed_token_ids_mask_cpu_tensor: torch.Tensor | None = None

        # req_index -> bad_words_token_ids
        self.bad_words_token_ids: dict[int, list[list[int]]] = {}

        self.logits_processing_needs_token_ids = np.zeros(max_num_reqs, dtype=bool)

        self.req_output_token_ids: list[list[int] | None] = []

        # Store provided logitsprocs. If none are provided, initialize empty
        # data structure
        self.logitsprocs = logitsprocs or LogitsProcessors()
        self.logitsprocs_need_output_token_ids = logitsprocs_need_output_token_ids

        # Store last speculative tokens for sampler.
        self.spec_token_ids: list[list[int]] = [[] for _ in range(max_num_reqs)]

        # This is updated each time the batch constituents change.
        self.sampling_metadata = self._make_sampling_metadata()

        # for pooling models
        self.pooling_params: dict[str, PoolingParams] = {}
        self.pooling_states: dict[str, PoolingStates] = {}

        # Cached reference to the GPU tensor of previously sampled tokens
        self.prev_sampled_token_ids: torch.Tensor | None = None
        self.prev_req_id_to_index: dict[str, int] | None = None
        # These are used to update output_token_ids with real sampled
        # ids from prior step, if required by current sampling params
        # (e.g. penalties).
        self.sampled_token_ids_cpu: torch.Tensor | None = None
        self.async_copy_ready_event: torch.Event | None = None

    @property
    def req_ids(self) -> list[str]:
        # None elements should only be present transiently
        # while performing state updates to the batch.
        return cast(list[str], self._req_ids)

    def _register_add_request(self, request: "CachedRequestState") -> int:
        """Track add-request operations for logits processors.
        Not applicable to pooling models.
        """

        # Fill the next empty index if there is one.
        if (new_req_index := self.batch_update_builder.pop_removed()) is None:
            # Append to end otherwise.
            new_req_index = self.num_reqs

        assert new_req_index < self.max_num_reqs
        self.batch_update_builder.batch_changed = True
        if request.sampling_params:
            # Detailed added request metadata is only required for non-pooling
            # models, to support logitsprocs.
            self.batch_update_builder.added.append(
                (
                    new_req_index,
                    request.sampling_params,
                    request.prompt_token_ids,
                    request.output_token_ids,
                )
            )

        return new_req_index

    def add_request(
        self,
        request: "CachedRequestState",
    ) -> int:
        req_index = self._register_add_request(request)

        req_id = request.req_id
        if req_index == len(self._req_ids):
            self._req_ids.append(req_id)
            self.req_output_token_ids.append(request.output_token_ids)
            self.spec_token_ids.append([])
        else:
            self._req_ids[req_index] = req_id
            self.req_output_token_ids[req_index] = request.output_token_ids
            self.spec_token_ids[req_index].clear()

        self.req_id_to_index[req_id] = req_index

        # Copy the prompt token ids and output token ids.
        num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
            request.prompt_token_ids, request.prompt_embeds
        )
        self.num_prompt_tokens[req_index] = num_prompt_tokens
        start_idx = num_prompt_tokens
        end_idx = start_idx + len(request.output_token_ids)
        if request.prompt_token_ids is not None:
            self.token_ids_cpu[req_index, :num_prompt_tokens] = request.prompt_token_ids
            self.is_token_ids[req_index, :num_prompt_tokens] = True
        else:
            self.is_token_ids[req_index, :num_prompt_tokens] = False
        if request.prompt_embeds is not None:
            self.req_prompt_embeds[req_index] = request.prompt_embeds
        self.token_ids_cpu[req_index, start_idx:end_idx] = request.output_token_ids
        self.is_token_ids[req_index, start_idx:end_idx] = True
        # Number of tokens without spec decode tokens.
        self.num_tokens_no_spec[req_index] = request.num_tokens

        self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
        self.block_table.add_row(request.block_ids, req_index)

        if sampling_params := request.sampling_params:
            if sampling_params.sampling_type == SamplingType.GREEDY:
                # Should avoid division by zero later when apply_temperature.
                self.temperature_cpu[req_index] = 0.0
                self.greedy_reqs.add(req_id)
            else:
                self.temperature_cpu[req_index] = sampling_params.temperature
                self.random_reqs.add(req_id)

            self.top_p_cpu[req_index] = sampling_params.top_p
            if sampling_params.top_p < 1:
                self.top_p_reqs.add(req_id)
            top_k = sampling_params.top_k
            if 0 < top_k < self.vocab_size:
                self.top_k_reqs.add(req_id)
            else:
                top_k = self.vocab_size
            self.top_k_cpu[req_index] = top_k
            self.frequency_penalties_cpu[req_index] = sampling_params.frequency_penalty
            if sampling_params.frequency_penalty != 0.0:
                self.frequency_penalties_reqs.add(req_id)
            self.presence_penalties_cpu[req_index] = sampling_params.presence_penalty
            if sampling_params.presence_penalty != 0.0:
                self.presence_penalties_reqs.add(req_id)
            self.repetition_penalties_cpu[req_index] = (
                sampling_params.repetition_penalty
            )
            if sampling_params.repetition_penalty != 1.0:
                self.repetition_penalties_reqs.add(req_id)

            # NOTE(woosuk): self.generators should not include the requests that
            # do not have their own generator.
            if request.generator is not None:
                self.generators[req_index] = request.generator

            if sampling_params.logprobs is not None:
                self.num_logprobs[req_id] = (
                    self.vocab_size
                    if sampling_params.logprobs == -1
                    else sampling_params.logprobs
                )

            if sampling_params.allowed_token_ids:
                self.has_allowed_token_ids.add(req_id)
                if self.allowed_token_ids_mask_cpu_tensor is None:
                    # Lazy allocation for this tensor, which can be large.
                    # False means we don't fill with -inf.
                    self.allowed_token_ids_mask = torch.zeros(
                        self.max_num_reqs,
                        self.vocab_size,
                        dtype=torch.bool,
                        device=self.device,
                    )
                    self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
                        self.max_num_reqs,
                        self.vocab_size,
                        dtype=torch.bool,
                        device="cpu",
                    )
                self.allowed_token_ids_mask_cpu_tensor[req_index] = True
                # False means we don't fill with -inf.
                self.allowed_token_ids_mask_cpu_tensor[req_index][
                    sampling_params.allowed_token_ids
                ] = False

            if sampling_params.bad_words_token_ids:
                self.bad_words_token_ids[req_index] = (
                    sampling_params.bad_words_token_ids
                )
        elif pooling_params := request.pooling_params:
            pooling_states = request.pooling_states
            assert pooling_states is not None

            self.pooling_params[req_id] = pooling_params
            self.pooling_states[req_id] = pooling_states
            self.logits_processing_needs_token_ids[req_index] = (
                pooling_params.requires_token_ids
            )
        else:
            raise NotImplementedError("Unrecognized request type")

        # Speculative decoding: by default 1 token is generated.
        self.num_accepted_tokens_cpu[req_index] = 1

        # Add request lora ID
        if request.lora_request:
            lora_id = request.lora_request.lora_int_id
            if lora_id not in self.lora_id_to_request_ids:
                self.lora_id_to_request_ids[lora_id] = set()

            self.request_lora_mapping[req_index] = lora_id
            self.lora_id_to_request_ids[lora_id].add(request.req_id)
            self.lora_id_to_lora_request[lora_id] = request.lora_request
        else:
            # No LoRA
            self.request_lora_mapping[req_index] = 0

        return req_index

    def update_req_spec_token_ids(
        self, request: CachedRequestState, scheduled_spec_tokens: dict[str, list[int]]
    ) -> None:
        req_id = request.req_id
        req_index = self.req_id_to_index[req_id]
        cur_spec_token_ids = self.spec_token_ids[req_index]
        # When speculative decoding is used with structured output,
        # the scheduler can drop draft tokens that do not
        # conform to the schema. This can result in
        # scheduler_output.scheduled_spec_decode_tokens being empty,
        # even when speculative decoding is enabled.
        cur_spec_token_ids.clear()
        spec_token_ids = scheduled_spec_tokens.get(req_id, ())
        num_spec_tokens = len(spec_token_ids)
        request.prev_num_draft_len = num_spec_tokens
        if not spec_token_ids:
            return

        # For async scheduling, token_ids_cpu assigned from
        # spec_token_ids are placeholders and will be overwritten in
        # _prepare_input_ids.
        start_index = self.num_tokens_no_spec[req_index]
        end_token_index = start_index + num_spec_tokens
        self.token_ids_cpu[req_index, start_index:end_token_index] = spec_token_ids
        cur_spec_token_ids.extend(spec_token_ids)

    def remove_request(self, req_id: str) -> int | None:
        """This method must always be followed by a call to condense().

        Args:
          req_id: request to remove

        Returns:
          Removed request index, or `None` if `req_id` not recognized
        """

        req_index = self.req_id_to_index.pop(req_id, None)
        if req_index is None:
            return None

        self.batch_update_builder.removed_append(req_index)
        self._req_ids[req_index] = None
        self.req_output_token_ids[req_index] = None
        self.spec_token_ids[req_index].clear()

        # LoRA
        lora_id = self.request_lora_mapping[req_index]
        if lora_id != 0:
            lora_req_ids = self.lora_id_to_request_ids[lora_id]
            lora_req_ids.discard(req_id)
            if not lora_req_ids:
                del self.lora_id_to_request_ids[lora_id]
                del self.lora_id_to_lora_request[lora_id]
            self.request_lora_mapping[req_index] = 0

        if self.is_pooling_model:
            self.pooling_params.pop(req_id, None)
            self.pooling_states.pop(req_id, None)
            return req_index

        self.greedy_reqs.discard(req_id)
        self.random_reqs.discard(req_id)
        self.top_p_reqs.discard(req_id)
        self.top_k_reqs.discard(req_id)
        self.frequency_penalties_reqs.discard(req_id)
        self.presence_penalties_reqs.discard(req_id)
        self.repetition_penalties_reqs.discard(req_id)
        self.generators.pop(req_index, None)
        self.num_logprobs.pop(req_id, None)
        self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
        if self.prev_req_id_to_index is not None:
            self.prev_req_id_to_index.pop(req_id, None)

        self.has_allowed_token_ids.discard(req_id)
        if self.allowed_token_ids_mask_cpu_tensor is not None:
            # False means we don't fill with -inf.
            self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
        self.bad_words_token_ids.pop(req_index, None)
        return req_index

    def swap_states(self, i1: int, i2: int) -> None:
        old_id_i1 = self._req_ids[i1]
        old_id_i2 = self._req_ids[i2]
        self._req_ids[i1], self._req_ids[i2] = self._req_ids[i2], self._req_ids[i1]  # noqa
        self.req_output_token_ids[i1], self.req_output_token_ids[i2] = (
            self.req_output_token_ids[i2],
            self.req_output_token_ids[i1],
        )
        self.spec_token_ids[i1], self.spec_token_ids[i2] = (
            self.spec_token_ids[i2],
            self.spec_token_ids[i1],
        )
        assert old_id_i1 is not None and old_id_i2 is not None
        self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] = (
            self.req_id_to_index[old_id_i2],
            self.req_id_to_index[old_id_i1],
        )
        self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] = (
            self.num_tokens_no_spec[i2],
            self.num_tokens_no_spec[i1],
        )
        self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] = (
            self.num_prompt_tokens[i2],
            self.num_prompt_tokens[i1],
        )
        self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] = (
            self.num_computed_tokens_cpu[i2],
            self.num_computed_tokens_cpu[i1],
        )

        # NOTE: the following is unsafe
        # self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\
        #     self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...]
        # instead, we need to temporarily copy the data for one of the indices
        # TODO(lucas): optimize this by only copying valid indices
        tmp = self.token_ids_cpu[i1, ...].copy()
        self.token_ids_cpu[i1, ...] = self.token_ids_cpu[i2, ...]
        self.token_ids_cpu[i2, ...] = tmp

        self.is_token_ids[[i1, i2], ...] = self.is_token_ids[[i2, i1], ...]

        # Swap prompt embeddings if they exist
        embeds_i1 = self.req_prompt_embeds.get(i1)
        embeds_i2 = self.req_prompt_embeds.get(i2)
        if embeds_i1 is not None:
            self.req_prompt_embeds[i2] = embeds_i1
        else:
            self.req_prompt_embeds.pop(i2, None)
        if embeds_i2 is not None:
            self.req_prompt_embeds[i1] = embeds_i2
        else:
            self.req_prompt_embeds.pop(i1, None)

        self.block_table.swap_row(i1, i2)

        self.request_lora_mapping[i1], self.request_lora_mapping[i2] = (
            self.request_lora_mapping[i2],
            self.request_lora_mapping[i1],
        )

        if self.is_pooling_model:
            # Sampling and logits parameters don't apply to pooling models.
            return

        # For autoregressive models, track detailed request reordering info
        # to support logitsprocs.
        self.batch_update_builder.moved.append((i1, i2, MoveDirectionality.SWAP))

        self.temperature_cpu[i1], self.temperature_cpu[i2] = (
            self.temperature_cpu[i2],
            self.temperature_cpu[i1],
        )
        self.top_p_cpu[i1], self.top_p_cpu[i2] = self.top_p_cpu[i2], self.top_p_cpu[i1]
        self.top_k_cpu[i1], self.top_k_cpu[i2] = self.top_k_cpu[i2], self.top_k_cpu[i1]
        self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] = (
            self.frequency_penalties_cpu[i2],
            self.frequency_penalties_cpu[i1],
        )
        self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] = (
            self.presence_penalties_cpu[i2],
            self.presence_penalties_cpu[i1],
        )
        self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] = (
            self.repetition_penalties_cpu[i2],
            self.repetition_penalties_cpu[i1],
        )
        self.num_accepted_tokens_cpu[i1], self.num_accepted_tokens_cpu[i2] = (
            self.num_accepted_tokens_cpu[i2],
            self.num_accepted_tokens_cpu[i1],
        )

        swap_dict_values(self.generators, i1, i2)
        swap_dict_values(self.bad_words_token_ids, i1, i2)

        if self.allowed_token_ids_mask_cpu_tensor is not None:
            (
                self.allowed_token_ids_mask_cpu_tensor[i1],
                self.allowed_token_ids_mask_cpu_tensor[i2],
            ) = (
                self.allowed_token_ids_mask_cpu_tensor[i2],
                self.allowed_token_ids_mask_cpu_tensor[i1],
            )

    def condense(self) -> None:
        """Slide non-empty requests down into lower, empty indices.

        Any consecutive empty indices at the very end of the list are not
        filled.

        Returns:
          swaps: list of (from,to) swap tuples for moved requests
          empty_req_indices: indices not filled by condensation
        """
        num_reqs = self.num_reqs

        if not (empty_req_indices := self.batch_update_builder.removed):
            # All removed requests were replaced by added requests, or else no
            # requests were removed at all. No condense() needed
            return
        if num_reqs == 0:
            # The batched states are empty.
            self._req_ids.clear()
            self.req_output_token_ids.clear()
            self.spec_token_ids.clear()
            return

        # NOTE(woosuk): This function assumes that the empty_req_indices
        # is sorted in descending order.
        last_req_index = num_reqs + len(empty_req_indices) - 1
        while empty_req_indices:
            # Find the largest non-empty index.
            while last_req_index in empty_req_indices:
                last_req_index -= 1

            # Find the smallest empty index.
            empty_index = self.batch_update_builder.peek_removed()
            assert empty_index is not None
            if empty_index >= last_req_index:
                break

            # Move active request down into empty request
            # index.
            self.batch_update_builder.pop_removed()
            req_id = self._req_ids[last_req_index]
            output_token_ids = self.req_output_token_ids[last_req_index]
            assert req_id is not None
            self._req_ids[empty_index] = req_id
            self._req_ids[last_req_index] = None
            self.req_output_token_ids[empty_index] = output_token_ids
            self.req_output_token_ids[last_req_index] = None
            self.req_id_to_index[req_id] = empty_index

            num_tokens = self.num_tokens_no_spec[last_req_index] + len(
                self.spec_token_ids[last_req_index]
            )

            (self.spec_token_ids[last_req_index], self.spec_token_ids[empty_index]) = (
                self.spec_token_ids[empty_index],
                self.spec_token_ids[last_req_index],
            )
            self.spec_token_ids[last_req_index].clear()

            self.token_ids_cpu[empty_index, :num_tokens] = self.token_ids_cpu[
                last_req_index, :num_tokens
            ]
            self.is_token_ids[empty_index, :num_tokens] = self.is_token_ids[
                last_req_index, :num_tokens
            ]
            if last_req_index in self.req_prompt_embeds:
                self.req_prompt_embeds[empty_index] = self.req_prompt_embeds.pop(
                    last_req_index
                )
            self.num_tokens_no_spec[empty_index] = self.num_tokens_no_spec[
                last_req_index
            ]
            self.num_prompt_tokens[empty_index] = self.num_prompt_tokens[last_req_index]
            self.num_computed_tokens_cpu[empty_index] = self.num_computed_tokens_cpu[
                last_req_index
            ]
            self.block_table.move_row(last_req_index, empty_index)

            self.request_lora_mapping[empty_index] = self.request_lora_mapping[
                last_req_index
            ]

            if self.is_pooling_model:
                last_req_index -= 1
                # Sampling state not used by pooling models.
                continue

            # Autoregressive models require detailed tracking of condense
            # operations to support logitsprocs
            self.batch_update_builder.moved.append(
                (last_req_index, empty_index, MoveDirectionality.UNIDIRECTIONAL)
            )

            self.temperature_cpu[empty_index] = self.temperature_cpu[last_req_index]
            self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index]
            self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index]
            self.frequency_penalties_cpu[empty_index] = self.frequency_penalties_cpu[
                last_req_index
            ]
            self.presence_penalties_cpu[empty_index] = self.presence_penalties_cpu[
                last_req_index
            ]
            self.repetition_penalties_cpu[empty_index] = self.repetition_penalties_cpu[
                last_req_index
            ]
            self.num_accepted_tokens_cpu[empty_index] = self.num_accepted_tokens_cpu[
                last_req_index
            ]
            generator = self.generators.pop(last_req_index, None)
            if generator is not None:
                self.generators[empty_index] = generator

            # TODO convert these to LogitsProcessors
            if self.allowed_token_ids_mask_cpu_tensor is not None:
                self.allowed_token_ids_mask_cpu_tensor[empty_index] = (
                    self.allowed_token_ids_mask_cpu_tensor[last_req_index]
                )

            bad_words_token_ids = self.bad_words_token_ids.pop(last_req_index, None)
            if bad_words_token_ids is not None:
                self.bad_words_token_ids[empty_index] = bad_words_token_ids

            # Decrement last_req_index since it is now empty.
            last_req_index -= 1

        # Trim lists to the batch size.
        del self._req_ids[num_reqs:]
        del self.req_output_token_ids[num_reqs:]
        del self.spec_token_ids[num_reqs:]

    def refresh_metadata(self, repeat_counts: Optional[torch.Tensor] = None):
        """Apply any batch updates to sampling metadata."""

        if self.is_pooling_model:
            batch_changed = self.batch_update_builder.reset()
            if batch_changed:
                self.sampling_metadata = self._make_sampling_metadata(repeat_counts)
            return

        # For non-pooling models - generate and apply logitsprocs update;
        # reset batch update tracking.
        # Update sampling metadata if batch state is changed.
        batch_update = self.batch_update_builder.get_and_reset(self.num_reqs)
        for logit_proc in self.logitsprocs.all:
            logit_proc.update_state(batch_update)
        if batch_update:
            self.sampling_metadata = self._make_sampling_metadata(repeat_counts)

    def _make_sampling_metadata(self, repeat_counts: Optional[torch.Tensor] = None) -> SamplingMetadata:
        num_reqs = self.num_reqs
        if not self.all_greedy:
            temperature = copy_slice(
                self.temperature_cpu_tensor, self.temperature, 
                num_reqs, repeat_counts
            )
        else:
            temperature = None

        if not self.no_top_p:
            top_p = copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs, repeat_counts)
        if not self.no_top_k:
            top_k = copy_slice(self.top_k_cpu_tensor, self.top_k, num_reqs, repeat_counts)

        if not self.no_penalties:
            # Since syncing these tensors is expensive only copy them
            # if necessary i.e. if there are requests which require
            # penalties to be applied during sampling.
            frequency_penalties = copy_slice(self.frequency_penalties_cpu_tensor,
                       self.frequency_penalties, num_reqs,
                       repeat_counts)
            presence_penalties = copy_slice(self.presence_penalties_cpu_tensor,
                       self.presence_penalties, num_reqs,
                       repeat_counts)
            repetition_penalties = copy_slice(self.repetition_penalties_cpu_tensor,
                       self.repetition_penalties, num_reqs,
                       repeat_counts)

        needs_prompt_token_ids = (
            not self.no_penalties
            or self.logits_processing_needs_token_ids[:num_reqs].any()
        )
        # The prompt tokens are used only for applying penalties or
        # step pooling during the sampling/pooling process.
        # Hence copy these tensors only when there are requests which
        # need penalties/step_pooler to be applied.
        prompt_token_ids = (
            self._make_prompt_token_ids_tensor() if needs_prompt_token_ids else None
        )

        # Only set output_token_ids if required by the current requests'
        # sampling parameters.
        needs_output_token_ids = (
            not self.no_penalties
            or bool(self.bad_words_token_ids)
            or self.logitsprocs_need_output_token_ids
        )
        output_token_ids = (
            cast(list[list[int]], self.req_output_token_ids)
            if needs_output_token_ids
            else []
        )

        allowed_token_ids_mask: torch.Tensor | None = None
        if not self.no_allowed_token_ids:
            assert self.allowed_token_ids_mask is not None
            allowed_token_ids_mask = copy_slice(self.allowed_token_ids_mask_cpu_tensor,
                       self.allowed_token_ids_mask, num_reqs,
                       repeat_counts)

        return SamplingMetadata(
            temperature=temperature,
            all_greedy=self.all_greedy,
            all_random=self.all_random,
            top_p=None if self.no_top_p else top_p,
            top_k=None if self.no_top_k else top_k,
            generators=self.generators,
            max_num_logprobs=self.max_num_logprobs,
            prompt_token_ids=prompt_token_ids,
            frequency_penalties=None if self.no_penalties else frequency_penalties,
            presence_penalties=None if self.no_penalties else presence_penalties,
            repetition_penalties=None if self.no_penalties else repetition_penalties,
            output_token_ids=output_token_ids,
            spec_token_ids=self.spec_token_ids,
            no_penalties=self.no_penalties,
            allowed_token_ids_mask=allowed_token_ids_mask,
            bad_words_token_ids=self.bad_words_token_ids,
            logitsprocs=self.logitsprocs,
        )

    def get_pooling_params(self) -> list[PoolingParams]:
        assert len(self.req_ids) == len(self.pooling_params)
        return [self.pooling_params[req_id] for req_id in self.req_ids]

    def get_pooling_states(self) -> list[PoolingStates]:
        assert len(self.req_ids) == len(self.pooling_states)
        return [self.pooling_states[req_id] for req_id in self.req_ids]

    def get_pooling_metadata(self) -> PoolingMetadata:
        pooling_params = self.get_pooling_params()
        pooling_states = self.get_pooling_states()

        return PoolingMetadata(
            prompt_lens=torch.from_numpy(self.num_prompt_tokens[: self.num_reqs]),
            prompt_token_ids=self.sampling_metadata.prompt_token_ids,
            pooling_params=pooling_params,
            pooling_states=pooling_states,
        )

    def _make_prompt_token_ids_tensor(self) -> torch.Tensor:
        num_reqs = self.num_reqs
        max_prompt_len = self.num_prompt_tokens[:num_reqs].max()
        prompt_token_ids_cpu_tensor = torch.empty(
            (self.num_reqs, max_prompt_len),
            device="cpu",
            dtype=torch.int64,
            pin_memory=self.pin_memory,
        )
        prompt_token_ids = prompt_token_ids_cpu_tensor.numpy()
        prompt_token_ids[:] = self.token_ids_cpu[:num_reqs, :max_prompt_len]
        # Use the value of vocab_size as a pad since we don't have a
        # token_id of this value.
        for i in range(num_reqs):
            prompt_token_ids[i, self.num_prompt_tokens[i] :] = self.vocab_size
        return prompt_token_ids_cpu_tensor.to(device=self.device, non_blocking=True)

    def make_lora_inputs(
        self, num_scheduled_tokens: np.ndarray, num_sampled_tokens: np.ndarray
    ) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]:
        """
        Given the num_scheduled_tokens for each request in the batch, return
        datastructures used to activate the current LoRAs.
        Returns:
            1. prompt_lora_mapping: A tuple of size np.sum(num_sampled_tokens)
               where, prompt_lora_mapping[i] is the LoRA id to use for the ith
               sampled token.
            2. token_lora_mapping: A tuple of size np.sum(num_scheduled_tokens)
               where, token_lora_mapping[i] is the LoRA id to use for ith token.
            3. lora_requests: Set of relevant LoRA requests.
        """

        req_lora_mapping = self.request_lora_mapping[: self.num_reqs]
        prompt_lora_mapping = tuple(req_lora_mapping.repeat(num_sampled_tokens))
        token_lora_mapping = tuple(req_lora_mapping.repeat(num_scheduled_tokens))

        active_lora_requests: set[LoRARequest] = set(
            self.lora_id_to_lora_request.values()
        )

        return prompt_lora_mapping, token_lora_mapping, active_lora_requests

    def set_async_sampled_token_ids(
        self,
        sampled_token_ids_cpu: torch.Tensor,
        async_copy_ready_event: torch.Event,
    ) -> None:
        """
        In async scheduling case, store ref to sampled_token_ids_cpu
        tensor and corresponding copy-ready event. Used to repair
        output_token_ids prior to sampling, if needed by logits processors.
        """
        if self.sampling_metadata.output_token_ids:
            self.sampled_token_ids_cpu = sampled_token_ids_cpu
            self.async_copy_ready_event = async_copy_ready_event
        else:
            self.sampled_token_ids_cpu = None
            self.async_copy_ready_event = None

    def update_async_output_token_ids(self) -> None:
        """
        In async scheduling case, update output_token_ids in sampling metadata
        from prior steps sampled token ids once they've finished copying to CPU.
        This is called right before they are needed by the logits processors.
        """
        output_token_ids = self.sampling_metadata.output_token_ids
        if self.sampled_token_ids_cpu is None or not output_token_ids:
            # Output token ids not needed or not async scheduling.
            return

        assert self.prev_req_id_to_index is not None
        sampled_token_ids = None
        for index, req_id in enumerate(self.req_ids):
            prev_index = self.prev_req_id_to_index.get(req_id)
            if prev_index is None:
                continue
            req_output_token_ids = output_token_ids[index]
            if not req_output_token_ids or req_output_token_ids[-1] != -1:
                # Final output id is not a placeholder, some tokens must have
                # been discarded after a kv-load failure.
                continue
            if sampled_token_ids is None:
                assert self.async_copy_ready_event is not None
                self.async_copy_ready_event.synchronize()
                sampled_token_ids = self.sampled_token_ids_cpu.tolist()
            # Replace placeholder token id(s) with actual sampled id(s).
            new_ids: list[int] = sampled_token_ids[prev_index]
            if not new_ids:
                continue
            num_sampled_ids = len(new_ids) if new_ids[-1] != -1 else new_ids.index(-1)
            # Also account for case where there may be a smaller number of
            # output placeholders (tokens can be discarded after a kv-load failure).
            first_placeholder = req_output_token_ids.index(-1)
            num_placeholders = len(req_output_token_ids) - first_placeholder
            num_to_replace = min(num_sampled_ids, num_placeholders)
            del new_ids[num_to_replace:]
            end_index = first_placeholder + num_to_replace
            req_output_token_ids[first_placeholder:end_index] = new_ids

    def update_async_spec_token_ids(self, draft_token_ids: list[list[int]]) -> None:
        """
        In async scheduling case, update spec_token_ids in sampling metadata with
        real draft token ids from prior step. This is called right before they are
        needed by the rejection sampler for penalty/bad_words computation.
        """
        if not draft_token_ids or not self.prev_req_id_to_index:
            return

        if (spec_token_ids := self.sampling_metadata.spec_token_ids) is not None:
            for req_id, spec_ids in zip(self.req_ids, spec_token_ids):
                if spec_ids:
                    prev_index = self.prev_req_id_to_index.get(req_id)
                    if prev_index is not None:
                        draft_ids = draft_token_ids[prev_index]
                        if draft_ids:
                            del draft_ids[len(spec_ids) :]
                            spec_ids.clear()
                            spec_ids.extend(draft_ids)

    @property
    def num_reqs(self) -> int:
        return len(self.req_id_to_index)

    @property
    def all_greedy(self) -> bool:
        return len(self.random_reqs) == 0

    @property
    def all_random(self) -> bool:
        return len(self.greedy_reqs) == 0

    @property
    def no_top_p(self) -> bool:
        return len(self.top_p_reqs) == 0

    @property
    def no_top_k(self) -> bool:
        return len(self.top_k_reqs) == 0

    @property
    def no_penalties(self) -> bool:
        return (
            len(self.presence_penalties_reqs) == 0
            and len(self.frequency_penalties_reqs) == 0
            and len(self.repetition_penalties_reqs) == 0
        )

    @property
    def max_num_logprobs(self) -> int | None:
        return max(self.num_logprobs.values()) if self.num_logprobs else None

    @property
    def no_allowed_token_ids(self) -> bool:
        return len(self.has_allowed_token_ids) == 0
