# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Request scheduler policy"""

import os
import random
from collections import defaultdict
from contextlib import contextmanager
from enum import Enum, auto
from typing import Dict, List, Optional, Set, Union

import torch

from sglang.srt.managers.schedule_batch import (
    Req,
    ScheduleBatch,
    global_server_args_dict,
)
from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
from sglang.srt.mem_cache.memory_pool import TokenToKVPoolAllocator
from sglang.srt.mem_cache.radix_cache import RadixCache, TreeNode

# Clip the estimation of max_new_tokens for the request whose max_new_tokens is very large.
# This can prevent the server from being too conservative.
# Note that this only clips the estimation in the scheduler but does not change the stop
# condition. The request can still generate tokens until it hits the unclipped max_new_tokens.
CLIP_MAX_NEW_TOKENS_ESTIMATION = int(
    os.environ.get("SGLANG_CLIP_MAX_NEW_TOKENS_ESTIMATION", "4096")
)

# Threshold for in-batch prefix cache.
# If a request has a matched prefix length (against existing cache) less than this value,
# the scheduler runs the in-batch prefix caching check for this request.
# If we set it to -1, it means we disable in-batch prefix caching.
IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD = int(
    os.environ.get("IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD", "32")
)

# Threshold for in-batch prefix cache.
# If a request has a matched prefix length (within the waiting queue) larger than this value,
# the scheduler deprioritizes this request
IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD = int(
    os.environ.get("IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD", "32")
)


class CacheAwarePolicy(Enum):
    """Scheduling policies that are aware of the tree cache."""

    LPM = "lpm"  # longest prefix match
    DFS_WEIGHT = "dfs-weight"  # depth-first search weighting


class CacheAgnosticPolicy(Enum):
    """Scheduling policies that are not aware of the tree cache."""

    FCFS = "fcfs"  # first come first serve
    LOF = "lof"  # longest output first
    RANDOM = "random"


class SchedulePolicy:
    Policy = Union[CacheAwarePolicy, CacheAgnosticPolicy]

    def __init__(
        self,
        policy: str,
        tree_cache: BasePrefixCache,
        enable_hierarchical_cache: bool,
    ):
        self.policy = self._validate_and_adjust_policy(policy, tree_cache)
        self.tree_cache = tree_cache
        self.enable_hierarchical_cache = enable_hierarchical_cache

        # It is used to find the matching prefix for in-batch prefix caching.
        self.waiting_queue_radix_tree = RadixCache(
            req_to_token_pool=None,
            token_to_kv_pool_allocator=None,
            page_size=1,
            disable=False,
        )

    def calc_priority(self, waiting_queue: List[Req]) -> bool:
        if self.policy == CacheAgnosticPolicy.FCFS:
            # A shortcut for FCFS
            return

        policy = self._determine_active_policy(waiting_queue)

        prefix_computed = False
        if isinstance(policy, CacheAwarePolicy):
            prefix_computed = True
            temporary_deprioritized = self._compute_prefix_matches(
                waiting_queue, policy
            )
            if policy == CacheAwarePolicy.LPM:
                SchedulePolicy._sort_by_longest_prefix(
                    waiting_queue, temporary_deprioritized
                )
            elif policy == CacheAwarePolicy.DFS_WEIGHT:
                SchedulePolicy._sort_by_dfs_weight(waiting_queue, self.tree_cache)
            else:
                raise ValueError(f"Unknown CacheAware Policy: {policy=}")
        else:
            if policy == CacheAgnosticPolicy.FCFS:
                pass
            elif policy == CacheAgnosticPolicy.LOF:
                SchedulePolicy._sort_by_longest_output(waiting_queue)
            elif policy == CacheAgnosticPolicy.RANDOM:
                SchedulePolicy._sort_randomly(waiting_queue)
            else:
                raise ValueError(f"Unknown CacheAgnostic Policy: {policy=}")

        return prefix_computed

    def _determine_active_policy(self, waiting_queue: List[Req]) -> Policy:
        if self.policy == CacheAwarePolicy.LPM and len(waiting_queue) > 128:
            # Turn off the expensive prefix matching and sorting when the #queue is large.
            return CacheAgnosticPolicy.FCFS
        return self.policy

    def _validate_and_adjust_policy(
        self, policy: str, tree_cache: BasePrefixCache
    ) -> Policy:
        """
        Validates the policy and adjusts it if necessary based on tree cache settings.
        """
        try:
            policy_enum = CacheAwarePolicy(policy)
            if tree_cache.disable:
                # If tree_cache is disabled, using CacheAgnosticPolicy policy
                return CacheAgnosticPolicy.FCFS
            return policy_enum
        except ValueError:
            try:
                return CacheAgnosticPolicy(policy)
            except ValueError:
                raise ValueError(f"Unknown schedule_policy: {policy=}")

    def _compute_prefix_matches(
        self, waiting_queue: List[Req], policy: CacheAwarePolicy
    ) -> Set[int]:
        """
        Computes and caches the matching prefixes for requests in the waiting queue,
            and handles in-batch prefix caching logic.
        """
        temporary_deprioritized: Set[int] = set()
        self.waiting_queue_radix_tree.reset()

        for r in waiting_queue:
            prefix_ids = r.adjust_max_prefix_ids()

            # NOTE: the prefix_indices must always be aligned with last_node
            if self.enable_hierarchical_cache:
                r.prefix_indices, r.last_node, r.last_node_global = (
                    self.tree_cache.match_prefix(key=prefix_ids, include_evicted=True)
                )
            else:
                r.prefix_indices, r.last_node = self.tree_cache.match_prefix(
                    rid=r.rid, key=prefix_ids
                )

            # NOTE(sang): This logic is for in-batch prefix caching;
            # If there are more than 1 request that have small matching prefix from
            # existing cache, but all those requests share the same prefix, we prefer
            # to schedule only one of them so that we can increase the cache hit rate.
            # We prefer to set IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD > 0 because too small
            # threshold means we cannot use in-batch prefix caching for short prefixes.
            # It is kind of common when the engine is long running (e.g., imagine the prefix "the").
            if len(r.prefix_indices) <= IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD:
                in_batch_matching_prefixes, _ = (
                    self.waiting_queue_radix_tree.match_prefix(
                        rid=r.rid, key=prefix_ids
                    )
                )
                if (
                    len(in_batch_matching_prefixes)
                    >= IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD
                ):
                    temporary_deprioritized.add(r.rid)
                else:
                    # Insert with a dummy key
                    self.waiting_queue_radix_tree.insert(
                        prefix_ids, torch.empty(len(prefix_ids), dtype=torch.bool)
                    )
        return temporary_deprioritized

    @staticmethod
    def _sort_by_longest_prefix(
        waiting_queue: List[Req], temporary_deprioritized: Set[int]
    ) -> None:
        """Sorts the waiting queue based on the longest prefix match."""
        waiting_queue.sort(
            key=lambda r: (
                -len(r.prefix_indices)
                if r.rid not in temporary_deprioritized
                else float("inf")
            )
        )

    @staticmethod
    def _sort_by_dfs_weight(
        waiting_queue: List[Req], tree_cache: BasePrefixCache
    ) -> None:
        """Sorts the waiting queue based on a depth-first search weighting."""
        last_node_to_reqs = defaultdict(list)
        for req in waiting_queue:
            last_node_to_reqs[req.last_node].append(req)

        node_to_weight = defaultdict(int)
        for node in last_node_to_reqs:
            node_to_weight[node] = len(last_node_to_reqs[node])
        SchedulePolicy._calc_weight(tree_cache.root_node, node_to_weight)

        waiting_queue.clear()
        SchedulePolicy._get_dfs_priority(
            tree_cache.root_node,
            node_to_weight,
            last_node_to_reqs,
            waiting_queue,
        )

    @staticmethod
    def _sort_by_longest_output(waiting_queue: List[Req]) -> None:
        """Sorts the waiting queue based on the longest output (max_new_tokens)."""
        waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)

    @staticmethod
    def _sort_randomly(waiting_queue: List[Req]) -> None:
        """Shuffles the waiting queue randomly."""
        random.shuffle(waiting_queue)

    @staticmethod
    def _calc_weight(cur_node: TreeNode, node_to_weight: Dict[TreeNode, int]) -> None:
        for child in cur_node.children.values():
            SchedulePolicy._calc_weight(child, node_to_weight)
            node_to_weight[cur_node] += node_to_weight[child]

    @staticmethod
    def _get_dfs_priority(
        cur_node: TreeNode,
        node_to_priority: Dict[TreeNode, int],
        last_node_to_reqs: Dict[TreeNode, List[Req]],
        q: List,
    ) -> None:
        childs = [child for child in cur_node.children.values()]
        childs.sort(key=lambda x: -node_to_priority[x])
        for child in childs:
            SchedulePolicy._get_dfs_priority(
                child, node_to_priority, last_node_to_reqs, q
            )
        q.extend(last_node_to_reqs[cur_node])


class AddReqResult(Enum):
    CONTINUE = auto()  # Continue to add requests
    NO_TOKEN = auto()  # No token left
    OTHER = auto()  # Other reasons to stop adding requests


class PrefillAdder:
    def __init__(
        self,
        tree_cache: BasePrefixCache,
        token_to_kv_pool_allocator: TokenToKVPoolAllocator,
        running_batch: ScheduleBatch,
        new_token_ratio: float,
        rem_input_tokens: int,
        rem_chunk_tokens: Optional[int],
        mixed_with_decode_tokens: int = 0,
    ):
        self.tree_cache = tree_cache
        self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
        self.running_batch = running_batch
        self.new_token_ratio = new_token_ratio
        self.rem_input_tokens = rem_input_tokens - mixed_with_decode_tokens
        self.rem_chunk_tokens = rem_chunk_tokens
        if self.rem_chunk_tokens is not None:
            self.rem_chunk_tokens -= mixed_with_decode_tokens

        self.rem_total_token_offset = mixed_with_decode_tokens
        self.cur_rem_token_offset = mixed_with_decode_tokens

        self.req_states = None
        self.can_run_list = []
        self.new_chunked_req = None
        self.log_hit_tokens = 0
        self.log_input_tokens = 0

        if running_batch is not None:
            self.rem_total_token_offset += sum(
                [
                    min(
                        (r.sampling_params.max_new_tokens - len(r.output_ids)),
                        CLIP_MAX_NEW_TOKENS_ESTIMATION,
                    )
                    * self.new_token_ratio
                    for r in running_batch.reqs
                ]
            )

    @property
    def rem_total_tokens(self):
        return (
            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
            - self.rem_total_token_offset
        )

    @property
    def cur_rem_tokens(self):
        return (
            self.token_to_kv_pool_allocator.available_size()
            + self.tree_cache.evictable_size()
            - self.cur_rem_token_offset
        )

    def budget_state(self):
        if self.rem_total_tokens <= 0 or self.cur_rem_tokens <= 0:
            return AddReqResult.NO_TOKEN

        if self.rem_input_tokens <= 0 or (
            self.rem_chunk_tokens is not None and self.rem_chunk_tokens <= 0
        ):
            return AddReqResult.OTHER

        return AddReqResult.CONTINUE

    def _prefill_one_req(
        self, prefix_len: int, extend_input_len: int, max_new_tokens: int
    ):
        self.rem_total_token_offset += extend_input_len + max_new_tokens
        self.cur_rem_token_offset += extend_input_len
        self.rem_input_tokens -= extend_input_len
        if self.rem_chunk_tokens is not None:
            self.rem_chunk_tokens -= extend_input_len

        self.log_hit_tokens += prefix_len
        self.log_input_tokens += extend_input_len

    def add_chunked_req(self, req: Req):
        truncated = req.extend_input_len > self.rem_chunk_tokens
        req.extend_input_len = min(req.extend_input_len, self.rem_chunk_tokens)
        req.fill_ids = req.fill_ids[: len(req.prefix_indices) + req.extend_input_len]
        self.can_run_list.append(req)
        self._prefill_one_req(
            0,
            req.extend_input_len,
            (
                min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS_ESTIMATION)
                if not truncated
                else 0
            ),
        )

        # Return if chunked prefill not finished
        return req if truncated else None

    @contextmanager
    def _lock_node(self, last_node: TreeNode):
        try:
            self.tree_cache.inc_lock_ref(last_node)
            yield None
        finally:
            self.tree_cache.dec_lock_ref(last_node)

    def add_one_req_ignore_eos(self, req: Req, has_chunked_req: bool):
        def add_req_state(r, insert_sort=False):
            new_token_ratio = (
                1.0 if r.sampling_params.ignore_eos else self.new_token_ratio
            )
            tokens_left = r.sampling_params.max_new_tokens * new_token_ratio - len(
                r.output_ids
            )
            tokens_occupied = len(r.origin_input_ids) + len(r.output_ids)

            if tokens_left > 0:
                if not insert_sort:
                    self.req_states.append((tokens_left, tokens_occupied))
                else:
                    i = 0
                    for i in range(len(self.req_states)):
                        if tokens_left <= self.req_states[i][0]:
                            break
                    self.req_states.insert(i, (tokens_left, tokens_occupied))

        if self.req_states is None:
            self.req_states = []
            add_req_state(req)
            if self.running_batch is not None:
                for r in self.running_batch.reqs:
                    add_req_state(r)
            for r in self.can_run_list:
                add_req_state(r)
            self.req_states.sort(key=lambda x: x[0])
        else:
            add_req_state(req, insert_sort=True)

        cur_rem_tokens = self.cur_rem_tokens - len(req.origin_input_ids)
        tokens_freed = 0
        for i, (tokens_left, tokens_occupied) in enumerate(self.req_states):
            decode_steps = (
                self.req_states[i + 1][0]
                if i + 1 < len(self.req_states)
                else tokens_left
            )
            bs = len(self.req_states) - i
            if cur_rem_tokens + tokens_freed - decode_steps * bs <= 0:
                return AddReqResult.NO_TOKEN
            tokens_freed += tokens_occupied

        if (
            self.rem_chunk_tokens is None  # chunked prefill is disabled
            or req.extend_input_len <= self.rem_chunk_tokens  # it is the last chunk
        ):
            # Non-chunked prefill
            self.can_run_list.append(req)
            self._prefill_one_req(
                0,
                req.extend_input_len,
                min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS_ESTIMATION),
            )
        else:
            if self.rem_chunk_tokens == 0:
                return AddReqResult.OTHER

            # Chunked prefill
            trunc_len = self.rem_chunk_tokens

            req.extend_input_len = trunc_len
            req.fill_ids = req.fill_ids[:trunc_len]
            self.can_run_list.append(req)
            self.new_chunked_req = req
            self._prefill_one_req(0, trunc_len, 0)

        return self.budget_state()

    def add_one_req(
        self, req: Req, has_chunked_req: bool, enable_hierarchical_cache: bool = False
    ):
        if req.sampling_params.ignore_eos and getattr(self.tree_cache, "disable", True):
            return self.add_one_req_ignore_eos(req, has_chunked_req)

        total_tokens = req.extend_input_len + min(
            req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS_ESTIMATION
        )
        input_tokens = req.extend_input_len
        prefix_len = len(req.prefix_indices)

        if total_tokens >= self.rem_total_tokens:
            return AddReqResult.NO_TOKEN

        if input_tokens > self.rem_input_tokens and len(self.can_run_list) != 0:
            return AddReqResult.OTHER

        with self._lock_node(req.last_node):
            if total_tokens > self.rem_total_tokens:
                return AddReqResult.NO_TOKEN

            if (
                enable_hierarchical_cache
                and req.last_node_global is not None
                and req.last_node_global.evicted
            ):
                req.last_node, req.prefix_indices = self.tree_cache.init_load_back(
                    req.last_node_global, req.prefix_indices
                )
                req.extend_input_len = len(req.fill_ids) - len(req.prefix_indices)
                input_tokens = req.extend_input_len
                prefix_len = len(req.prefix_indices)

            if self.rem_chunk_tokens is None or input_tokens <= self.rem_chunk_tokens:
                # Non-chunked prefill
                self.can_run_list.append(req)
                self.tree_cache.inc_lock_ref(req.last_node)
                self._prefill_one_req(
                    prefix_len,
                    input_tokens,
                    min(
                        req.sampling_params.max_new_tokens,
                        CLIP_MAX_NEW_TOKENS_ESTIMATION,
                    ),
                )
            else:
                if self.rem_chunk_tokens == 0:
                    return AddReqResult.OTHER

                # Chunked prefill
                trunc_len = self.rem_chunk_tokens

                req.extend_input_len = trunc_len
                req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len]

                self.can_run_list.append(req)
                self.new_chunked_req = req
                self.tree_cache.inc_lock_ref(req.last_node)
                self._prefill_one_req(prefix_len, trunc_len, 0)

        return self.budget_state()