#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# 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.
# This file is a part of the vllm-ascend project.
#
import os
import time

import pandas as pd
from vllm.config import VllmConfig
from vllm.distributed.kv_events import KVEventBatch
from vllm.logger import logger
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.v1.core.kv_cache_manager import KVCacheBlocks
from vllm.v1.core.sched.output import NewRequestData, SchedulerOutput
from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
from vllm.v1.core.sched.scheduler import Scheduler
from vllm.v1.engine import EngineCoreEventType
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.request import Request, RequestStatus
from vllm.v1.structured_output import StructuredOutputManager


class BudgetRefiner:
    """This budget refiner can make dynamic adjustment to the token budget
    in the chunked prefill scheduling strategy."""

    def __init__(self, default_budget, slo_limit=-1) -> None:
        self.enabled = slo_limit > 0
        if not self.enabled:
            return
        logger.info(
            "Dynamic batch is enabled with SLO limit: {}, and chunked prefill is "
            "forced to be activated because dynamic batch relies on it".format(str(slo_limit))
        )
        self.lookup: dict[tuple[int, int], int] = {}
        self.context_keys: set[int] = set()
        self.dnum_keys: set[int] = set()
        self.default_budget = default_budget
        self._read_lookup_table(slo_limit)

    def _read_lookup_table(self, slo_limit):
        """Load the lookup table for dynamic budget."""
        base_dir = os.path.dirname(os.path.abspath(__file__))
        table_file_path = os.path.join(base_dir, "profile_table.csv")
        if not os.path.exists(table_file_path):
            # proceed without dynamic batch
            logger.error(
                "The dynamic batching feature requires the lookup table "
                "'profile_table.csv', but it was not found at '%s'. "
                "Please download the corresponding table file.",
                table_file_path,
            )
            self.enabled = False
            return
        else:
            df = pd.read_csv(table_file_path)
        grouped = df.groupby(["ctx_len", "d_num"])
        for (ctx_len, d_num), group in grouped:
            valid = group[group["cost"] <= slo_limit]
            if not valid.empty:
                max_row = valid.loc[valid["chunk_size"].idxmax()]
                assert isinstance(ctx_len, int), "ctx_len must be an integer"
                assert isinstance(d_num, int), "d_num must be an integer"
                self.lookup[(ctx_len, d_num)] = int(max_row["chunk_size"])
                self.context_keys.add(ctx_len)
                self.dnum_keys.add(d_num)
        self.context_keys = set(sorted(self.context_keys))
        self.dnum_keys = set(sorted(self.dnum_keys))

    def _align_key(self, value, valid_keys):
        """Align the minimum value within the valid_keys that is greater than the value."""
        for k in valid_keys:
            if k >= value:
                return k
        return None

    def _get_max_budget(self, num_decode_tokens, num_decode):
        """Get the maximum budget according to the number of decoding tokens and the decoding requests."""
        aligned_ctx = self._align_key(num_decode_tokens, self.context_keys)
        aligned_dnum = self._align_key(num_decode, self.dnum_keys)
        if aligned_ctx is None or aligned_dnum is None:
            return self.default_budget
        budget = self.lookup.get((aligned_ctx, aligned_dnum), None)
        if budget is None:
            logger.warn(f"Table miss for ctx,dnum{aligned_ctx, aligned_dnum}")
            budget = self.default_budget
        # For debug.
        # logger.info(
        #     f"budget {budget}, ctx,dnum {aligned_ctx, aligned_dnum}, "
        #     f"raw ctx,dnum {num_decode_tokens, num_decode}"
        # )
        return budget

    def refine_budget(self, running_request, budget):
        """Dynamically refine the token budget according to the running request."""
        if not self.enabled:
            return budget
        # assume all running request will be scheduled.
        num_decode_token_lst = [
            req.num_tokens_with_spec for req in running_request if req.num_computed_tokens >= req.num_prompt_tokens
        ]
        num_decode = len(num_decode_token_lst)
        if num_decode <= 0:
            return budget
        num_decode_tokens = sum(num_decode_token_lst) / num_decode
        return self._get_max_budget(num_decode_tokens, num_decode)


class SchedulerDynamicBatch(Scheduler):
    """This Scheduler extends vllm's original v1 scheduler
    with dynamic batch."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        kv_cache_config: KVCacheConfig,
        structured_output_manager: StructuredOutputManager,
        block_size: int | None = None,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
        include_finished_set: bool = False,
        log_stats: bool = False,
    ) -> None:
        super().__init__(
            vllm_config,
            kv_cache_config,
            structured_output_manager,
            block_size,
            mm_registry,
            include_finished_set,
            log_stats,
        )
        self.running: list[Request] = []
        self.budget_refiner = BudgetRefiner(
            default_budget=self.scheduler_config.max_num_batched_tokens,
            slo_limit=self.scheduler_config.SLO_limits_for_dynamic_batch,
        )

    def schedule(self) -> SchedulerOutput:
        # NOTE: This scheduling algorithm is developed based on the "super.schedule()"
        # with the implementations of the dynamic batch and some modifications:
        # 1. Token budget can be dynamically refined according to the self.running
        # through the BudgetRefiner;
        # 2. This scheduling algorithm follows decode-first chunked prefills and FCFS
        # strategy, which is slightly different to the "super.schedule()"
        # 3. Similar to the "super.schedule()", at each step, the scheduler tries to
        # assign tokens to the requests so that each request's num_computed_tokens can
        # catch up its num_tokens_with_spec.
        # 4. So far, the dynamic batch only supports 910B3 NPU. Further work will include
        # more devices and finer optimization strategy.

        scheduled_new_reqs: list[Request] = []
        scheduled_resumed_reqs: list[Request] = []
        scheduled_running_reqs: list[Request] = []
        preempted_reqs: list[Request] = []

        req_to_new_blocks: dict[str, KVCacheBlocks] = {}
        num_scheduled_tokens: dict[str, int] = {}
        token_budget = self.max_num_scheduled_tokens
        token_budget = self.budget_refiner.refine_budget(self.running, token_budget)

        # NOTE: We move the prefill requests to the end of the self.running
        # list and keep the relative order unchanged. This rearrangement makes this
        # scheduling algorithm a strict decode-first chunked prefills.
        d_lst = [req for req in self.running if req.num_computed_tokens >= req.num_prompt_tokens]
        p_lst = [req for req in self.running if req.num_computed_tokens < req.num_prompt_tokens]
        self.running = d_lst + p_lst

        # Encoder-related.
        scheduled_encoder_inputs: dict[str, list[int]] = {}
        encoder_compute_budget = self.max_num_encoder_input_tokens
        # Spec decode-related.
        scheduled_spec_decode_tokens: dict[str, list[int]] = {}

        # For logging.
        scheduled_timestamp = time.monotonic()

        # First, schedule the RUNNING requests.
        req_index = 0
        while req_index < len(self.running) and token_budget > 0:
            request = self.running[req_index]

            num_new_tokens = (
                request.num_tokens_with_spec + request.num_output_placeholders - request.num_computed_tokens
            )
            if 0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens:
                num_new_tokens = self.scheduler_config.long_prefill_token_threshold
            num_new_tokens = min(num_new_tokens, token_budget)

            # Make sure the input position does not exceed the max model len.
            # This is necessary when using spec decoding.
            num_new_tokens = min(num_new_tokens, self.max_model_len - 1 - request.num_computed_tokens)

            # Schedule encoder inputs.
            encoder_inputs_to_schedule = None
            new_encoder_compute_budget = encoder_compute_budget
            if request.has_encoder_inputs:
                (encoder_inputs_to_schedule, num_new_tokens, new_encoder_compute_budget) = (
                    self._try_schedule_encoder_inputs(
                        request, request.num_computed_tokens, num_new_tokens, encoder_compute_budget
                    )
                )

            if num_new_tokens == 0:
                # The request cannot be scheduled because one of the following
                # reasons:
                # 1. No new tokens to schedule. This may happen when
                #    (1) PP>1 and we have already scheduled all prompt tokens
                #    but they are not finished yet.
                #    (2) Async scheduling and the request has reached to either
                #    its max_total_tokens or max_model_len.
                # 2. The encoder budget is exhausted.
                # 3. The encoder cache is exhausted.
                # NOTE(woosuk): Here, by doing `break` instead of `continue` as
                # in v1 scheduler, we strictly follow the FCFS scheduling policy.
                req_index += 1
                break

            while True:
                new_blocks = self.kv_cache_manager.allocate_slots(
                    request, num_new_tokens, num_lookahead_tokens=self.num_lookahead_tokens
                )
                if new_blocks is None:
                    # The request cannot be scheduled.
                    # Preempt the lowest-priority request.
                    if self.policy == SchedulingPolicy.PRIORITY:
                        preempted_req = max(
                            self.running,
                            key=lambda r: (r.priority, r.arrival_time),
                        )
                        self.running.remove(preempted_req)
                        if preempted_req in scheduled_running_reqs:
                            scheduled_running_reqs.remove(preempted_req)
                    else:
                        preempted_req = self.running.pop()

                    self.kv_cache_manager.free(preempted_req)
                    self.encoder_cache_manager.free(preempted_req)
                    preempted_req.status = RequestStatus.PREEMPTED
                    preempted_req.num_computed_tokens = 0
                    if self.log_stats:
                        preempted_req.record_event(EngineCoreEventType.PREEMPTED, scheduled_timestamp)

                    self.waiting.prepend_request(preempted_req)
                    preempted_reqs.append(preempted_req)
                    if preempted_req == request:
                        # No more request to preempt.
                        can_schedule = False
                        break
                else:
                    # The request can be scheduled.
                    can_schedule = True
                    break
            if not can_schedule:
                break
            assert new_blocks is not None

            # Schedule the request.
            scheduled_running_reqs.append(request)
            req_to_new_blocks[request.request_id] = new_blocks
            num_scheduled_tokens[request.request_id] = num_new_tokens
            token_budget -= num_new_tokens
            req_index += 1

            # Speculative decode related.
            if request.spec_token_ids:
                num_scheduled_spec_tokens = num_new_tokens + request.num_computed_tokens - request.num_tokens
                if num_scheduled_spec_tokens > 0:
                    # Trim spec_token_ids list to num_scheduled_spec_tokens.
                    del request.spec_token_ids[num_scheduled_spec_tokens:]
                    scheduled_spec_decode_tokens[request.request_id] = request.spec_token_ids

            # Encoder-related.
            if encoder_inputs_to_schedule:
                scheduled_encoder_inputs[request.request_id] = encoder_inputs_to_schedule
                # Allocate the encoder cache.
                for i in encoder_inputs_to_schedule:
                    self.encoder_cache_manager.allocate(request, i)
                encoder_compute_budget = new_encoder_compute_budget

        # Record the LoRAs in scheduled_running_reqs
        scheduled_loras: set[int] = set()
        if self.lora_config:
            scheduled_loras = set(
                req.lora_request.lora_int_id
                for req in scheduled_running_reqs
                if req.lora_request and req.lora_request.lora_int_id > 0
            )
            assert len(scheduled_loras) <= self.lora_config.max_loras

        # Use a temporary RequestQueue to collect requests that need to be
        # skipped and put back at the head of the waiting queue later
        skipped_waiting_requests = create_request_queue(self.policy)

        # Next, schedule the WAITING requests.
        if not preempted_reqs:
            while self.waiting and token_budget > 0:
                if len(self.running) == self.max_num_running_reqs:
                    break

                request = self.waiting.peek_request()

                # KVTransfer: skip request if still waiting for remote kvs.
                if request.status == RequestStatus.WAITING_FOR_REMOTE_KVS:
                    is_ready = self._update_waiting_for_remote_kv(request)
                    if is_ready:
                        request.status = RequestStatus.WAITING
                    else:
                        logger.debug("%s is still in WAITING_FOR_REMOTE_KVS state.", request.request_id)
                        self.waiting.pop_request()
                        skipped_waiting_requests.prepend_request(request)
                        continue

                # Skip request if the structured output request is still waiting
                # for FSM compilation.
                if request.status == RequestStatus.WAITING_FOR_FSM:
                    structured_output_req = request.structured_output_request
                    if structured_output_req and structured_output_req.grammar:
                        request.status = RequestStatus.WAITING
                    else:
                        self.waiting.pop_request()
                        skipped_waiting_requests.prepend_request(request)
                        continue

                # Check that adding the request still respects the max_loras
                # constraint.
                if (
                    self.lora_config
                    and request.lora_request
                    and (
                        len(scheduled_loras) == self.lora_config.max_loras
                        and request.lora_request.lora_int_id not in scheduled_loras
                    )
                ):
                    # Scheduling would exceed max_loras, skip.
                    self.waiting.pop_request()
                    skipped_waiting_requests.prepend_request(request)
                    continue

                num_external_computed_tokens = 0
                load_kv_async = False

                # Get already-cached tokens.
                if request.num_computed_tokens == 0:
                    # Get locally-cached tokens.
                    new_computed_blocks, num_new_local_computed_tokens = self.kv_cache_manager.get_computed_blocks(
                        request
                    )

                    # Get externally-cached tokens if using a KVConnector.
                    if self.connector is not None:
                        num_external_computed_tokens, load_kv_async = self.connector.get_num_new_matched_tokens(
                            request, num_new_local_computed_tokens
                        )

                        if num_external_computed_tokens is None:
                            # The request cannot be scheduled because
                            # the KVConnector couldn't determine
                            # the number of matched tokens.
                            self.waiting.pop_request()
                            skipped_waiting_requests.prepend_request(request)
                            continue

                    # Total computed tokens (local + external).
                    num_computed_tokens = num_new_local_computed_tokens + num_external_computed_tokens
                # KVTransfer: WAITING reqs have num_computed_tokens > 0
                # after async KV recvs are completed.
                else:
                    new_computed_blocks = self.kv_cache_manager.create_empty_block_list()
                    num_new_local_computed_tokens = 0
                    num_computed_tokens = request.num_computed_tokens

                encoder_inputs_to_schedule = None
                new_encoder_compute_budget = encoder_compute_budget

                # KVTransfer: loading remote KV, do not allocate for new work.
                if load_kv_async:
                    assert num_external_computed_tokens > 0
                    num_new_tokens = 0
                # Number of tokens to be scheduled.
                else:
                    # We use `request.num_tokens` instead of
                    # `request.num_prompt_tokens` to consider the resumed
                    # requests, which have output tokens.
                    num_new_tokens = request.num_tokens - num_computed_tokens
                    if 0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens:
                        num_new_tokens = self.scheduler_config.long_prefill_token_threshold

                    # chunked prefill has to be enabled explicitly to allow
                    # pooling requests to be chunked
                    if not self.scheduler_config.enable_chunked_prefill and num_new_tokens > token_budget:
                        self.waiting.pop_request()
                        skipped_waiting_requests.prepend_request(request)
                        continue

                    num_new_tokens = min(num_new_tokens, token_budget)
                    assert num_new_tokens > 0

                    # Schedule encoder inputs.
                    if request.has_encoder_inputs:
                        (encoder_inputs_to_schedule, num_new_tokens, new_encoder_compute_budget, _) = (
                            self._try_schedule_encoder_inputs(
                                request, num_computed_tokens, num_new_tokens, encoder_compute_budget
                            )
                        )
                        if num_new_tokens == 0:
                            # The request cannot be scheduled.
                            break

                # Handles an edge case when P/D Disaggregation
                # is used with Spec Decoding where an
                # extra block gets allocated which
                # creates a mismatch between the number
                # of local and remote blocks.
                effective_lookahead_tokens = 0 if request.num_computed_tokens == 0 else self.num_lookahead_tokens

                # Determine if we need to allocate cross-attention blocks.
                if self.is_encoder_decoder and request.has_encoder_inputs:
                    # TODO(russellb): For Whisper, we know that the input is
                    # always padded to the maximum length. If we support other
                    # encoder-decoder models, this will need to be updated if we
                    # want to only allocate what is needed.
                    num_encoder_tokens = self.scheduler_config.max_num_encoder_input_tokens
                else:
                    num_encoder_tokens = 0

                new_blocks = self.kv_cache_manager.allocate_slots(
                    request,
                    num_new_tokens + num_external_computed_tokens,
                    num_new_local_computed_tokens,
                    new_computed_blocks,
                    num_lookahead_tokens=effective_lookahead_tokens,
                    delay_cache_blocks=load_kv_async,
                    num_encoder_tokens=num_encoder_tokens,
                )

                if new_blocks is None:
                    # The request cannot be scheduled.
                    break

                # KVTransfer: the connector uses this info to determine
                # if a load is needed. Note that
                # This information is used to determine if a load is
                # needed for this request.
                if self.connector is not None:
                    self.connector.update_state_after_alloc(
                        request,
                        new_computed_blocks + new_blocks,
                        num_external_computed_tokens,
                    )

                # Request was already popped from self.waiting
                # unless it was re-added above due to new_blocks being None.
                request = self.waiting.pop_request()
                if load_kv_async:
                    # If loading async, allocate memory and put request
                    # into the WAITING_FOR_REMOTE_KV state.
                    skipped_waiting_requests.prepend_request(request)
                    request.status = RequestStatus.WAITING_FOR_REMOTE_KVS
                    continue

                req_index += 1
                self.running.append(request)
                if self.log_stats:
                    request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp)
                if request.status == RequestStatus.WAITING:
                    scheduled_new_reqs.append(request)
                elif request.status == RequestStatus.PREEMPTED:
                    scheduled_resumed_reqs.append(request)
                else:
                    raise RuntimeError(f"Invalid request status: {request.status}")

                if self.lora_config and request.lora_request:
                    scheduled_loras.add(request.lora_request.lora_int_id)
                req_to_new_blocks[request.request_id] = self.kv_cache_manager.get_blocks(request.request_id)
                num_scheduled_tokens[request.request_id] = num_new_tokens
                token_budget -= num_new_tokens
                request.status = RequestStatus.RUNNING
                request.num_computed_tokens = num_computed_tokens
                # Count the number of prefix cached tokens.
                if request.num_cached_tokens < 0:
                    request.num_cached_tokens = num_computed_tokens
                # Encoder-related.
                if encoder_inputs_to_schedule:
                    scheduled_encoder_inputs[request.request_id] = encoder_inputs_to_schedule
                    # Allocate the encoder cache.
                    for i in encoder_inputs_to_schedule:
                        self.encoder_cache_manager.allocate(request, i)
                    encoder_compute_budget = new_encoder_compute_budget

        # Put back any skipped requests at the head of the waiting queue
        if skipped_waiting_requests:
            self.waiting.prepend_requests(skipped_waiting_requests)

        # Check if the scheduling constraints are satisfied.
        total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
        assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens
        assert token_budget >= 0
        assert len(self.running) <= self.max_num_running_reqs
        # Since some requests in the RUNNING queue may not be scheduled in
        # this step, the total number of scheduled requests can be smaller than
        # len(self.running).
        assert len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + len(scheduled_running_reqs) <= len(self.running)

        # Get the longest common prefix among all requests in the running queue.
        # This can be potentially used for cascade attention.
        num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
        if self.running:
            any_request = self.running[0]
            num_common_prefix_blocks = self.kv_cache_manager.get_num_common_prefix_blocks(any_request.request_id)
        # Construct the scheduler output.
        new_reqs_data = [
            NewRequestData.from_request(req, req_to_new_blocks[req.request_id].get_block_ids())
            for req in scheduled_new_reqs
        ]
        cached_reqs_data = self._make_cached_request_data(
            scheduled_running_reqs,
            scheduled_resumed_reqs,
            num_scheduled_tokens,
            scheduled_spec_decode_tokens,
            req_to_new_blocks,
        )
        scheduler_output = SchedulerOutput(
            scheduled_new_reqs=new_reqs_data,
            scheduled_cached_reqs=cached_reqs_data,
            num_scheduled_tokens=num_scheduled_tokens,
            total_num_scheduled_tokens=total_num_scheduled_tokens,
            scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
            scheduled_encoder_inputs=scheduled_encoder_inputs,
            num_common_prefix_blocks=num_common_prefix_blocks,
            # finished_req_ids is an existing state in the scheduler,
            # instead of being newly scheduled in this step.
            # It contains the request IDs that are finished in between
            # the previous and the current steps.
            finished_req_ids=self.finished_req_ids,
            free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(),
        )

        # NOTE(Kuntai): this function is designed for multiple purposes:
        # 1. Plan the KV cache store
        # 2. Wrap up all the KV cache load / save ops into an opaque object
        # 3. Clear the internal states of the connector
        if self.connector is not None:
            meta = self.connector.build_connector_meta(scheduler_output)
            scheduler_output.kv_connector_metadata = meta

        # collect KV cache events from KV cache manager
        events = self.kv_cache_manager.take_events()

        # collect KV cache events from connector
        if self.connector is not None:
            connector_events = self.connector.take_events()
            if connector_events:
                if events is None:
                    events = list(connector_events)
                else:
                    events.extend(connector_events)

        # publish collected KV cache events
        if events:
            batch = KVEventBatch(ts=time.time(), events=events)
            self.kv_event_publisher.publish(batch)

        self._update_after_schedule(scheduler_output)
        return scheduler_output
