"""
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.
"""

"""ModelRunner runs the forward passes of the models."""
from dataclasses import dataclass
from enum import IntEnum, auto
from typing import TYPE_CHECKING, List

import numpy as np
import torch

from sglang.srt.managers.schedule_batch import ScheduleBatch
from sglang.srt.mem_cache.memory_pool import BaseTokenToKVPool, ReqToTokenPool

if TYPE_CHECKING:
    from sglang.srt.model_executor.model_runner import ModelRunner


class ForwardMode(IntEnum):
    # Prefill a new sequence. This is deprecated now. "EXTEND" covers this case.
    PREFILL = auto()
    # Extend a sequence. The KV cache of the first part of the sequence is already computed (e.g., system prompt).
    EXTEND = auto()
    # Decode one token.
    DECODE = auto()


@dataclass
class InputMetadata:
    """Store all inforamtion of a forward pass."""

    forward_mode: ForwardMode
    batch_size: int
    req_pool_indices: torch.Tensor
    seq_lens: torch.Tensor
    req_to_token_pool: ReqToTokenPool
    token_to_kv_pool: BaseTokenToKVPool

    # Output location of the KV cache
    out_cache_loc: torch.Tensor

    total_num_tokens: int = None

    # Position information
    positions: torch.Tensor = None

    # For extend
    extend_seq_lens: torch.Tensor = None
    extend_start_loc: torch.Tensor = None
    extend_no_prefix: bool = None

    # Output options
    return_logprob: bool = False
    top_logprobs_nums: List[int] = None

    # For multimodal
    pixel_values: List[torch.Tensor] = None
    image_sizes: List[List[int]] = None
    image_offsets: List[int] = None

    # Trition attention backend
    triton_max_seq_len: int = 0
    triton_max_extend_len: int = 0
    triton_start_loc: torch.Tensor = None
    triton_prefix_lens: torch.Tensor = None

    # FlashInfer attention backend
    flashinfer_prefill_wrapper_ragged: "BatchPrefillWithRaggedKVCacheWrapper" = None
    flashinfer_prefill_wrapper_paged: "BatchPrefillWithPagedKVCacheWrapper" = None
    flashinfer_decode_wrapper: "BatchDecodeWithPagedKVCacheWrapper" = None
    flashinfer_use_ragged: bool = False

    def init_multimuldal_info(self, batch: ScheduleBatch):
        reqs = batch.reqs
        self.pixel_values = [r.pixel_values for r in reqs]
        self.image_sizes = [r.image_size for r in reqs]
        self.image_offsets = [
            (
                (r.image_offset - len(r.prefix_indices))
                if r.image_offset is not None
                else 0
            )
            for r in reqs
        ]

    def compute_positions(self, batch: ScheduleBatch):
        position_ids_offsets = batch.position_ids_offsets

        if self.forward_mode == ForwardMode.DECODE:
            if True:
                self.positions = self.seq_lens - 1
            else:
                # Deprecated
                self.positions = (self.seq_lens - 1) + position_ids_offsets
        else:
            if True:
                self.positions = torch.tensor(
                    np.concatenate(
                        [
                            np.arange(len(req.prefix_indices), len(req.input_ids))
                            for req in batch.reqs
                        ],
                        axis=0,
                    ),
                    device="cuda",
                )
            else:
                # Deprecated
                position_ids_offsets_cpu = position_ids_offsets.cpu().numpy()
                self.positions = torch.tensor(
                    np.concatenate(
                        [
                            np.arange(
                                len(req.prefix_indices) + position_ids_offsets_cpu[i],
                                len(req.input_ids) + position_ids_offsets_cpu[i],
                            )
                            for i, req in enumerate(batch.reqs)
                        ],
                        axis=0,
                    ),
                    device="cuda",
                )

        # Positions should be in long type
        self.positions = self.positions.to(torch.int64)

    def compute_extend_infos(self, batch: ScheduleBatch):
        if self.forward_mode == ForwardMode.DECODE:
            self.extend_seq_lens = self.extend_start_loc = self.extend_no_prefix = None
        else:
            prefix_lens_cpu = [
                len(r.input_ids) - len(r.prefix_indices) for r in batch.reqs
            ]
            self.extend_seq_lens = torch.tensor(prefix_lens_cpu, device="cuda")
            self.extend_start_loc = torch.zeros_like(self.seq_lens)
            self.extend_start_loc[1:] = torch.cumsum(self.extend_seq_lens[:-1], dim=0)
            self.extend_no_prefix = all(x == 0 for x in prefix_lens_cpu)

    def init_total_num_tokens(self, batch: ScheduleBatch):
        self.total_num_tokens = sum(len(req.input_ids) for req in batch.reqs)

    @classmethod
    def from_schedule_batch(
        cls,
        model_runner: "ModelRunner",
        batch: ScheduleBatch,
        forward_mode: ForwardMode,
    ):
        ret = cls(
            forward_mode=forward_mode,
            batch_size=batch.batch_size(),
            req_pool_indices=batch.req_pool_indices,
            seq_lens=batch.seq_lens,
            req_to_token_pool=model_runner.req_to_token_pool,
            token_to_kv_pool=model_runner.token_to_kv_pool,
            out_cache_loc=batch.out_cache_loc,
            return_logprob=batch.return_logprob,
            top_logprobs_nums=batch.top_logprobs_nums,
        )

        ret.compute_positions(batch)

        ret.compute_extend_infos(batch)

        ret.init_total_num_tokens(batch)

        if forward_mode != ForwardMode.DECODE:
            ret.init_multimuldal_info(batch)

        prefix_lens = None
        if forward_mode != ForwardMode.DECODE:
            prefix_lens = torch.tensor(
                [len(r.prefix_indices) for r in batch.reqs], device="cuda"
            )

        if model_runner.server_args.disable_flashinfer:
            ret.init_triton_args(batch, prefix_lens)

        flashinfer_use_ragged = False
        if not model_runner.server_args.disable_flashinfer:
            if (
                forward_mode != ForwardMode.DECODE
                and int(torch.sum(ret.seq_lens)) > 4096
            ):
                flashinfer_use_ragged = True
            ret.init_flashinfer_handlers(
                model_runner, prefix_lens, flashinfer_use_ragged
            )

        return ret

    def init_triton_args(self, batch: ScheduleBatch, prefix_lens):
        """Init auxiliary variables for triton attention backend."""
        self.triton_max_seq_len = max(len(r.input_ids) for r in batch.reqs)
        self.triton_prefix_lens = prefix_lens
        self.triton_start_loc = torch.zeros_like(self.seq_lens, dtype=torch.int32)
        self.triton_start_loc[1:] = torch.cumsum(self.seq_lens[:-1], dim=0)

        if self.forward_mode == ForwardMode.DECODE:
            self.triton_max_extend_len = None
        else:
            extend_seq_lens = self.seq_lens - prefix_lens
            self.triton_max_extend_len = int(torch.max(extend_seq_lens))

    def init_flashinfer_handlers(
        self, model_runner, prefix_lens, flashinfer_use_ragged
    ):
        update_flashinfer_indices(
            self.forward_mode,
            model_runner,
            self.req_pool_indices,
            self.seq_lens,
            prefix_lens,
            flashinfer_use_ragged=flashinfer_use_ragged,
        )

        (
            self.flashinfer_prefill_wrapper_ragged,
            self.flashinfer_prefill_wrapper_paged,
            self.flashinfer_decode_wrapper,
            self.flashinfer_use_ragged,
        ) = (
            model_runner.flashinfer_prefill_wrapper_ragged,
            model_runner.flashinfer_prefill_wrapper_paged,
            model_runner.flashinfer_decode_wrapper,
            flashinfer_use_ragged,
        )


def update_flashinfer_indices(
    forward_mode,
    model_runner,
    req_pool_indices,
    seq_lens,
    prefix_lens,
    flashinfer_decode_wrapper=None,
    flashinfer_use_ragged=False,
):
    """Init auxiliary variables for FlashInfer attention backend."""
    num_qo_heads = model_runner.model_config.num_attention_heads // model_runner.tp_size
    num_kv_heads = model_runner.model_config.get_num_kv_heads(model_runner.tp_size)
    head_dim = model_runner.model_config.head_dim
    batch_size = len(req_pool_indices)

    if flashinfer_use_ragged:
        paged_kernel_lens = prefix_lens
    else:
        paged_kernel_lens = seq_lens

    kv_indptr = torch.zeros((batch_size + 1,), dtype=torch.int32, device="cuda")
    kv_indptr[1:] = torch.cumsum(paged_kernel_lens, dim=0)
    req_pool_indices_cpu = req_pool_indices.cpu().numpy()
    paged_kernel_lens_cpu = paged_kernel_lens.cpu().numpy()
    kv_indices = torch.cat(
        [
            model_runner.req_to_token_pool.req_to_token[
                req_pool_indices_cpu[i], : paged_kernel_lens_cpu[i]
            ]
            for i in range(batch_size)
        ],
        dim=0,
    ).contiguous()
    kv_last_page_len = torch.ones((batch_size,), dtype=torch.int32, device="cuda")

    if forward_mode == ForwardMode.DECODE:
        # CUDA graph uses different flashinfer_decode_wrapper
        if flashinfer_decode_wrapper is None:
            flashinfer_decode_wrapper = model_runner.flashinfer_decode_wrapper

        flashinfer_decode_wrapper.end_forward()
        flashinfer_decode_wrapper.begin_forward(
            kv_indptr,
            kv_indices,
            kv_last_page_len,
            num_qo_heads,
            num_kv_heads,
            head_dim,
            1,
        )
    else:
        # extend part
        qo_indptr = torch.zeros((batch_size + 1,), dtype=torch.int32, device="cuda")
        qo_indptr[1:] = torch.cumsum(seq_lens - prefix_lens, dim=0)

        if flashinfer_use_ragged:
            model_runner.flashinfer_prefill_wrapper_ragged.end_forward()
            model_runner.flashinfer_prefill_wrapper_ragged.begin_forward(
                qo_indptr,
                qo_indptr,
                num_qo_heads,
                num_kv_heads,
                head_dim,
            )

        # cached part
        model_runner.flashinfer_prefill_wrapper_paged.end_forward()
        model_runner.flashinfer_prefill_wrapper_paged.begin_forward(
            qo_indptr,
            kv_indptr,
            kv_indices,
            kv_last_page_len,
            num_qo_heads,
            num_kv_heads,
            head_dim,
            1,
        )
