"""
Life cycle of a request in the decode server

1. PreallocQueue:
    a. Initialize a receiver for each request
    b. The request handshakes first, and pre-allocate kv once there is available kv.
    c. Move the request to TransferQueue.

2. TransferQueue:
    a. Poll the receiver to check the transfer state
    b. If the transfer has finished, move the request to waiting queue

3. WaitingQueue:
    a. Use the requests in the queue to construct a PrebuiltExtendBatch
    b. Skip the prefill forward but only populate metadata

4. RunningBatch:
    a. Merge the resolved PrebuiltExtendBatch into running batch to run decoding
"""

from __future__ import annotations

import logging
import os
from collections import deque
from dataclasses import dataclass
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np
import torch
from torch.distributed import ProcessGroup

from sglang.srt.disaggregation.base import BaseKVManager, BaseKVReceiver, KVArgs, KVPoll
from sglang.srt.disaggregation.utils import (
    DisaggregationMode,
    FakeBootstrapHost,
    KVClassType,
    ReqToMetadataIdxAllocator,
    TransferBackend,
    get_kv_class,
    is_mla_backend,
    kv_to_page_indices,
    poll_and_all_reduce,
)
from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
from sglang.srt.mem_cache.memory_pool import ReqToTokenPool, TokenToKVPoolAllocator
from sglang.srt.model_executor.forward_batch_info import ForwardMode
from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo

logger = logging.getLogger(__name__)

if TYPE_CHECKING:
    from sglang.srt.configs.model_config import ModelConfig
    from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
    from sglang.srt.managers.scheduler import Scheduler
    from sglang.srt.server_args import ServerArgs


@dataclass
class DecodeRequest:
    req: Req
    kv_receiver: BaseKVReceiver
    waiting_for_input: bool = False
    metadata_buffer_index: int = -1


class DecodePreallocQueue:
    """
    Store the requests that are preallocating.
    """

    def __init__(
        self,
        req_to_token_pool: ReqToTokenPool,
        token_to_kv_pool_allocator: TokenToKVPoolAllocator,
        req_to_metadata_buffer_idx_allocator: ReqToMetadataIdxAllocator,
        metadata_buffers: List[torch.Tensor],
        aux_dtype: torch.dtype,
        scheduler: Scheduler,
        transfer_queue: DecodeTransferQueue,
        tree_cache: BasePrefixCache,
        gloo_group: ProcessGroup,
        tp_rank: int,
        tp_size: int,
        bootstrap_port: int,
        transfer_backend: TransferBackend,
    ):
        self.req_to_token_pool = req_to_token_pool
        self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
        self.token_to_kv_pool = token_to_kv_pool_allocator.get_kvcache()
        self.is_mla_backend = is_mla_backend(self.token_to_kv_pool)
        self.aux_dtype = aux_dtype
        self.metadata_buffers = metadata_buffers
        self.req_to_metadata_buffer_idx_allocator = req_to_metadata_buffer_idx_allocator
        self.scheduler = scheduler
        self.transfer_queue = transfer_queue
        self.tree_cache = tree_cache  # this is always a chunk cache
        self.gloo_group = gloo_group
        self.tp_rank = tp_rank
        self.tp_size = tp_size
        self.bootstrap_port = bootstrap_port

        self.num_reserved_decode_tokens = int(
            os.environ.get("SGLANG_NUM_RESERVED_DECODE_TOKENS", "512")
        )

        # Queue for requests pending pre-allocation
        self.queue: List[DecodeRequest] = []
        self.transfer_backend = transfer_backend
        self.kv_manager = self._init_kv_manager()

    def _init_kv_manager(self) -> BaseKVManager:
        kv_args = KVArgs()
        kv_args.engine_rank = self.tp_rank
        kv_data_ptrs, kv_data_lens, kv_item_lens = (
            self.token_to_kv_pool.get_contiguous_buf_infos()
        )

        kv_args.kv_data_ptrs = kv_data_ptrs
        kv_args.kv_data_lens = kv_data_lens
        kv_args.kv_item_lens = kv_item_lens

        kv_args.aux_data_ptrs = [
            output_id_tensor.data_ptr() for output_id_tensor in self.metadata_buffers
        ]
        kv_args.aux_data_lens = [
            metadata_buffer.nbytes for metadata_buffer in self.metadata_buffers
        ]
        kv_args.aux_item_lens = [
            metadata_buffer[0].nbytes for metadata_buffer in self.metadata_buffers
        ]
        kv_args.ib_device = self.scheduler.server_args.disaggregation_ib_device
        kv_args.gpu_id = self.scheduler.gpu_id
        kv_manager_class = get_kv_class(self.transfer_backend, KVClassType.MANAGER)
        kv_manager = kv_manager_class(
            kv_args,
            DisaggregationMode.DECODE,
            self.scheduler.server_args,
            self.is_mla_backend,
        )
        return kv_manager

    def add(self, req: Req) -> None:
        """Add a request to the pending queue."""
        if req.bootstrap_host == FakeBootstrapHost:
            # Fake transfer for warmup reqs
            kv_receiver_class = get_kv_class(TransferBackend.FAKE, KVClassType.RECEIVER)
        else:
            kv_receiver_class = get_kv_class(
                self.transfer_backend, KVClassType.RECEIVER
            )
        kv_receiver = kv_receiver_class(
            mgr=self.kv_manager,
            bootstrap_addr=f"{req.bootstrap_host}:{req.bootstrap_port}",
            bootstrap_room=req.bootstrap_room,
        )
        self.queue.append(DecodeRequest(req=req, kv_receiver=kv_receiver))

    def extend(self, reqs: List[Req]) -> None:
        """Add a request to the pending queue."""
        for req in reqs:
            self.add(req)

    def _update_handshake_waiters(self) -> None:
        if not self.queue:
            return

        if all(decode_req.waiting_for_input for decode_req in self.queue):
            return

        polls = poll_and_all_reduce(
            [decode_req.kv_receiver for decode_req in self.queue], self.gloo_group
        )

        for i, (decode_req, poll) in enumerate(zip(self.queue, polls)):
            if poll == KVPoll.Bootstrapping:
                pass
            elif poll == KVPoll.WaitingForInput:
                decode_req.waiting_for_input = True
            elif poll == KVPoll.Failed:
                raise Exception("Handshake failed")

    def pop_preallocated(self) -> List[DecodeRequest]:
        """Pop the preallocated requests from the pending queue (FIFO)."""
        self._update_handshake_waiters()

        preallocated_reqs = []
        indices_to_remove = set()
        allocatable_tokens = self._allocatable_tokens()

        for i, decode_req in enumerate(self.queue):
            if not decode_req.waiting_for_input:
                continue

            if self.req_to_token_pool.available_size() <= 0:
                break

            if self.req_to_metadata_buffer_idx_allocator.available_size() <= 0:
                break

            required_tokens_for_request = (
                len(decode_req.req.origin_input_ids) + self.num_reserved_decode_tokens
            )

            if required_tokens_for_request > allocatable_tokens:
                break

            allocatable_tokens -= required_tokens_for_request
            self._pre_alloc(decode_req.req)

            kv_indices = (
                self.req_to_token_pool.req_to_token[decode_req.req.req_pool_idx][
                    : len(decode_req.req.origin_input_ids)
                ]
                .cpu()
                .numpy()
                .astype(np.int64)
            )

            decode_req.metadata_buffer_index = (
                self.req_to_metadata_buffer_idx_allocator.alloc()
            )
            assert decode_req.metadata_buffer_index is not None
            page_indices = kv_to_page_indices(
                kv_indices, self.token_to_kv_pool_allocator.page_size
            )
            decode_req.kv_receiver.init(page_indices, decode_req.metadata_buffer_index)
            preallocated_reqs.append(decode_req)
            indices_to_remove.add(i)

        self.queue = [
            entry for i, entry in enumerate(self.queue) if i not in indices_to_remove
        ]

        return preallocated_reqs

    def _allocatable_tokens(self) -> int:
        allocatable_tokens = (
            self.token_to_kv_pool_allocator.available_size()
            - self.num_reserved_decode_tokens
            * (
                len(self.scheduler.running_batch.reqs)
                + len(self.transfer_queue.queue)
                + len(self.scheduler.waiting_queue)
            )
        )

        # Note: if the last fake extend just finishes, and we enter `pop_preallocated` immediately in the next iteration
        #       the extend batch is not in any queue, so we need to explicitly add the tokens slots here
        if (
            self.scheduler.last_batch
            and self.scheduler.last_batch.forward_mode.is_extend()
        ):
            allocatable_tokens -= self.num_reserved_decode_tokens * len(
                self.scheduler.last_batch.reqs
            )

        return allocatable_tokens

    def _pre_alloc(self, req: Req) -> torch.Tensor:
        """Pre-allocate the memory for req_to_token and token_kv_pool"""
        req_pool_indices = self.req_to_token_pool.alloc(1)

        assert req_pool_indices is not None

        req.req_pool_idx = req_pool_indices[0]
        if self.token_to_kv_pool_allocator.page_size == 1:
            kv_loc = self.token_to_kv_pool_allocator.alloc(
                len(req.origin_input_ids) + max(len(req.output_ids) - 1, 0)
            )
        else:
            num_tokens = len(req.origin_input_ids) + max(len(req.output_ids) - 1, 0)
            kv_loc = self.token_to_kv_pool_allocator.alloc_extend(
                prefix_lens=torch.tensor(
                    [0],
                    dtype=torch.int64,
                    device=self.token_to_kv_pool_allocator.device,
                ),
                seq_lens=torch.tensor(
                    [num_tokens],
                    dtype=torch.int64,
                    device=self.token_to_kv_pool_allocator.device,
                ),
                last_loc=torch.tensor(
                    [-1],
                    dtype=torch.int64,
                    device=self.token_to_kv_pool_allocator.device,
                ),
                extend_num_tokens=num_tokens,
            )
        assert kv_loc is not None

        self.req_to_token_pool.write((req.req_pool_idx, slice(0, len(kv_loc))), kv_loc)

        # populate metadata
        req.fill_ids = req.origin_input_ids + req.output_ids
        req.extend_input_len = len(req.origin_input_ids)

        return kv_loc


class DecodeTransferQueue:
    """
    Store the requests that is polling kv
    """

    def __init__(
        self,
        gloo_group: ProcessGroup,
        req_to_metadata_buffer_idx_allocator: ReqToMetadataIdxAllocator,
        metadata_buffers: torch.Tensor,
    ):
        self.queue: List[DecodeRequest] = []
        self.gloo_group = gloo_group
        self.req_to_metadata_buffer_idx_allocator = req_to_metadata_buffer_idx_allocator
        self.metadata_buffers = metadata_buffers

    def add(self, req_conn: DecodeRequest) -> None:
        self.queue.append(req_conn)

    def extend(self, req_conns) -> None:
        self.queue.extend(req_conns)

    def pop_transferred(self) -> List[DecodeRequest]:
        if not self.queue:
            return []

        polls = poll_and_all_reduce(
            [decode_req.kv_receiver for decode_req in self.queue], self.gloo_group
        )

        transferred_reqs = []
        indices_to_remove = set()
        for i, (decode_req, poll) in enumerate(zip(self.queue, polls)):
            if poll == KVPoll.Failed:
                raise Exception("Transfer failed")
            elif poll == KVPoll.Success:
                # pop and push it to waiting queue
                idx = decode_req.metadata_buffer_index
                assert len(decode_req.req.output_ids) == 0
                output_id_buffer = self.metadata_buffers[0]
                # the last dimension is padded by the same values.
                output_id = output_id_buffer[idx][0].item()
                assert len(decode_req.req.output_ids) == 0
                assert decode_req.req.transferred_output_id is None
                decode_req.req.transferred_output_id = output_id
                transferred_reqs.append(decode_req)
                indices_to_remove.add(i)
            elif poll in [
                KVPoll.Bootstrapping,
                KVPoll.WaitingForInput,
                KVPoll.Transferring,
            ]:
                pass
            else:
                raise ValueError(f"Unexpected poll case: {poll}")

        for i in indices_to_remove:
            idx = self.queue[i].metadata_buffer_index
            assert idx != -1
            self.req_to_metadata_buffer_idx_allocator.free(idx)

        self.queue = [
            entry for i, entry in enumerate(self.queue) if i not in indices_to_remove
        ]

        return transferred_reqs


class ScheduleBatchDisaggregationDecodeMixin:

    def prepare_for_prebuilt_extend(self: ScheduleBatch):
        """
        Prepare a prebuilt extend by populate metadata
        Adapted from .prepare_for_extend().
        """

        self.forward_mode = ForwardMode.EXTEND
        reqs = self.reqs
        input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
        extend_num_tokens = sum(len(ids) for ids in input_ids)
        seq_lens = []
        pre_lens = []
        req_pool_indices = []

        # Pre-calculate total size
        total_size = sum(req.extend_input_len for req in reqs)
        out_cache_loc = torch.empty(total_size, dtype=torch.int64, device=self.device)

        # Fill the tensor in one pass
        offset = 0
        for i, req in enumerate(reqs):
            req_pool_indices.append(req.req_pool_idx)

            chunk = self.req_to_token_pool.req_to_token[req.req_pool_idx][
                : req.extend_input_len
            ]
            assert (
                offset + req.extend_input_len <= total_size
            ), f"Exceeds total size: offset={offset}, req.extend_input_len={req.extend_input_len}, total_size={total_size}"
            out_cache_loc[offset : offset + req.extend_input_len] = chunk
            offset += req.extend_input_len

            pre_len = len(req.prefix_indices)
            seq_len = len(req.origin_input_ids) + max(0, len(req.output_ids) - 1)
            seq_lens.append(seq_len)
            if len(req.output_ids) == 0:
                assert (
                    seq_len - pre_len == req.extend_input_len
                ), f"seq_len={seq_len}, pre_len={pre_len}, req.extend_input_len={req.extend_input_len}"

            req.cached_tokens += pre_len - req.already_computed
            req.already_computed = seq_len
            req.is_retracted = False
            pre_lens.append(pre_len)
            req.extend_logprob_start_len = 0

        extend_input_logprob_token_ids = None

        # Set fields
        self.input_ids = torch.tensor(
            sum(input_ids, []), dtype=torch.int32, device=self.device
        )
        self.req_pool_indices = torch.tensor(
            req_pool_indices, dtype=torch.int64, device=self.device
        )
        self.seq_lens = torch.tensor(seq_lens, dtype=torch.int64, device=self.device)
        self.out_cache_loc = out_cache_loc
        self.seq_lens_sum = sum(seq_lens)
        self.extend_num_tokens = extend_num_tokens
        self.prefix_lens = [len(r.prefix_indices) for r in reqs]
        self.extend_lens = [r.extend_input_len for r in reqs]
        self.extend_logprob_start_lens = [r.extend_logprob_start_len for r in reqs]
        self.extend_input_logprob_token_ids = extend_input_logprob_token_ids

        # Build sampling info
        self.sampling_info = SamplingBatchInfo.from_schedule_batch(
            self,
            self.model_config.vocab_size,
        )

    def process_prebuilt_extend(
        self: ScheduleBatch, server_args: ServerArgs, model_config: ModelConfig
    ):
        """Assign the buffered last input id to schedule batch"""
        self.output_ids = []
        for req in self.reqs:
            if req.output_ids and len(req.output_ids) > 0:
                # resumed retracted req
                self.output_ids.append(req.output_ids[-1])
            else:
                assert req.transferred_output_id is not None
                req.output_ids.append(req.transferred_output_id)
                self.output_ids.append(req.transferred_output_id)
            self.tree_cache.cache_unfinished_req(req)
        self.output_ids = torch.tensor(self.output_ids, device=self.device)


class SchedulerDisaggregationDecodeMixin:

    def _prepare_idle_batch_and_run(self, batch, delay_process=False):
        batch, _ = self.prepare_dp_attn_batch(batch)
        result = None
        if batch:
            result = self.run_batch(batch)
            if not delay_process:
                self.process_batch_result(batch, result)
        return batch, result

    @torch.no_grad()
    def event_loop_normal_disagg_decode(self: Scheduler):
        """A normal scheduler loop for decode worker in disaggregation mode."""

        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)
            # polling and allocating kv cache
            self.process_decode_queue()
            batch = self.get_next_disagg_decode_batch_to_run()
            self.cur_batch = batch

            prepare_dp_attn_flag = (
                self.server_args.enable_dp_attention
                or self.server_args.enable_sp_layernorm
            )

            if batch:
                # Generate fake extend output.
                if batch.forward_mode.is_extend():
                    # Note: Logprobs should be handled on the prefill engine.
                    self.stream_output(batch.reqs, False)
                    if prepare_dp_attn_flag:
                        self._prepare_idle_batch_and_run(None)
                else:
                    if prepare_dp_attn_flag:
                        self.prepare_dp_attn_batch(batch)
                    result = self.run_batch(batch)
                    self.process_batch_result(batch, result)
            elif prepare_dp_attn_flag:
                batch, _ = self._prepare_idle_batch_and_run(None)

            if batch is None and (
                len(self.disagg_decode_transfer_queue.queue)
                + len(self.disagg_decode_prealloc_queue.queue)
                == 0
            ):
                # When the server is idle, do self-check and re-init some states
                self.check_memory()
                self.new_token_ratio = self.init_new_token_ratio

            self.last_batch = batch

    @torch.no_grad()
    def event_loop_overlap_disagg_decode(self: Scheduler):
        result_queue = deque()
        self.last_batch: Optional[ScheduleBatch] = None
        self.last_batch_in_queue = False  # last batch is modified in-place, so we need another variable to track if it's extend

        while True:
            recv_reqs = self.recv_requests()
            self.process_input_requests(recv_reqs)
            # polling and allocating kv cache
            self.process_decode_queue()
            batch = self.get_next_disagg_decode_batch_to_run()
            self.cur_batch = batch
            last_batch_in_queue = False

            prepare_dp_attn_flag = (
                self.server_args.enable_dp_attention
                or self.server_args.enable_sp_layernorm
            )

            if batch:
                # Generate fake extend output.
                if batch.forward_mode.is_extend():
                    # Note: Logprobs should be handled on the prefill engine.
                    self.stream_output(batch.reqs, False)
                    if prepare_dp_attn_flag:
                        batch_, result = self._prepare_idle_batch_and_run(
                            None, delay_process=True
                        )
                        if batch_:
                            result_queue.append((batch_.copy(), result))
                            last_batch_in_queue = True
                else:
                    if prepare_dp_attn_flag:
                        self.prepare_dp_attn_batch(batch)
                    result = self.run_batch(batch)
                    result_queue.append((batch.copy(), result))
                    last_batch_in_queue = True
            elif prepare_dp_attn_flag:
                batch, result = self._prepare_idle_batch_and_run(
                    None, delay_process=True
                )
                if batch:
                    result_queue.append((batch.copy(), result))
                    last_batch_in_queue = True

            # Process the results of the previous batch but skip if the last batch is extend
            if self.last_batch and self.last_batch_in_queue:
                tmp_batch, tmp_result = result_queue.popleft()
                self.process_batch_result(tmp_batch, tmp_result)

            if batch is None and (
                len(self.disagg_decode_transfer_queue.queue)
                + len(self.disagg_decode_prealloc_queue.queue)
                == 0
            ):
                # When the server is idle, do self-check and re-init some states
                self.check_memory()
                self.new_token_ratio = self.init_new_token_ratio

            self.last_batch = batch
            self.last_batch_in_queue = last_batch_in_queue

    def get_next_disagg_decode_batch_to_run(
        self: Scheduler,
    ) -> Optional[Tuple[ScheduleBatch, bool]]:
        """Create fake completed prefill if possible and merge with running batch"""
        # Merge the prefill batch into the running batch
        last_batch = self.last_batch
        if last_batch and last_batch.forward_mode.is_extend():
            # chunked prefill doesn't happen in decode instance.
            assert self.chunked_req is None
            # Filter finished batches.
            last_batch.filter_batch()
            if not last_batch.is_empty():
                if self.running_batch.is_empty():
                    self.running_batch = last_batch
                else:
                    # merge running_batch with prefill batch
                    self.running_batch.merge_batch(last_batch)

        new_prebuilt_batch = self.get_new_prebuilt_batch()

        ret: Optional[ScheduleBatch] = None
        if new_prebuilt_batch:
            ret = new_prebuilt_batch
        else:
            if self.running_batch.is_empty():
                ret = None
            else:
                self.running_batch = self.update_running_batch(self.running_batch)
                ret = self.running_batch if not self.running_batch.is_empty() else None

        return ret

    def get_new_prebuilt_batch(self: Scheduler) -> Optional[ScheduleBatch]:
        """Create a schedulebatch for fake completed prefill"""
        if len(self.waiting_queue) == 0:
            return None

        curr_batch_size = self.running_batch.batch_size()

        batch_size = min(self.req_to_token_pool.size, self.max_running_requests)

        num_not_used_batch = batch_size - curr_batch_size

        # pop req from waiting queue
        can_run_list: List[Req] = []
        waiting_queue: List[Req] = []

        for i in range(len(self.waiting_queue)):
            req = self.waiting_queue[i]
            # we can only add at least `num_not_used_batch` new batch to the running queue
            if i < num_not_used_batch:
                can_run_list.append(req)
                req.init_next_round_input(self.tree_cache)
            else:
                waiting_queue.append(req)

        self.waiting_queue = waiting_queue
        if len(can_run_list) == 0:
            return None
        # local import to avoid circular import
        from sglang.srt.managers.schedule_batch import ScheduleBatch

        # construct a schedule batch with those requests and mark as decode
        new_batch = ScheduleBatch.init_new(
            can_run_list,
            self.req_to_token_pool,
            self.token_to_kv_pool_allocator,
            self.tree_cache,
            self.model_config,
            self.enable_overlap,
            self.spec_algorithm,
            self.server_args.enable_custom_logit_processor,
        )

        # construct fake completed prefill
        new_batch.prepare_for_prebuilt_extend()
        new_batch.process_prebuilt_extend(self.server_args, self.model_config)

        return new_batch

    def process_decode_queue(self: Scheduler):
        req_conns = self.disagg_decode_prealloc_queue.pop_preallocated()

        def _num_pre_alloc(req):
            return len(req.req.origin_input_ids) + max(len(req.req.output_ids) - 1, 0)

        self.num_tokens_pre_allocated += sum(_num_pre_alloc(req) for req in req_conns)
        self.disagg_decode_transfer_queue.extend(req_conns)
        alloc_reqs = (
            self.disagg_decode_transfer_queue.pop_transferred()
        )  # the requests which kv has arrived
        self.num_tokens_pre_allocated -= sum(_num_pre_alloc(req) for req in alloc_reqs)

        self.waiting_queue.extend([req.req for req in alloc_reqs])
