# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from typing import Any

import numba
import numba.types as types
import numpy as np
import torch

from vllm.triton_utils import tl, triton
from vllm.utils import random_uuid
from vllm.utils.math_utils import cdiv
from vllm.v1.utils import CpuGpuBuffer


class InputBuffers:
    def __init__(
        self,
        max_num_reqs: int,
        max_num_tokens: int,
        hidden_size: int,
        vocab_size: int,
        dtype: torch.dtype,
        device: torch.device,
        pin_memory: bool,
    ):
        self.max_num_reqs = max_num_reqs
        self.max_num_tokens = max_num_tokens
        self.device = device
        self.pin_memory = pin_memory

        self.idx_mapping = self._make_buffer(max_num_reqs, dtype=torch.int32)
        self.input_ids = self._make_buffer(max_num_tokens, dtype=torch.int32)
        self.positions = torch.zeros(max_num_tokens, dtype=torch.int64, device=device)
        self.query_start_loc = self._make_buffer(max_num_reqs + 1, dtype=torch.int32)
        self.seq_lens = torch.zeros(max_num_reqs, dtype=torch.int32, device=device)

        # Structured outputs.
        self.bitmask_indices = self._make_buffer(max_num_reqs, dtype=torch.int32)
        self.grammar_bitmask = self._make_buffer(
            max_num_reqs, cdiv(vocab_size, 32), dtype=torch.int32
        )

    def _make_buffer(self, *args, dtype: torch.dtype) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *args, dtype=dtype, pin_memory=self.pin_memory, device=self.device
        )


@dataclass
class InputBatch:
    # batch_idx -> req_id
    req_ids: list[str]
    num_reqs: int

    # batch_idx -> req_state_idx
    idx_mapping: torch.Tensor
    idx_mapping_np: np.ndarray

    # [num_reqs]
    # batch_idx -> num_scheduled_tokens
    num_scheduled_tokens: np.ndarray
    # sum(num_scheduled_tokens)
    num_tokens: int
    num_tokens_after_padding: int

    # [num_reqs + 1]
    query_start_loc: torch.Tensor
    query_start_loc_np: np.ndarray
    # [num_reqs]
    seq_lens: torch.Tensor
    seq_lens_np: np.ndarray

    # [num_tokens_after_padding]
    input_ids: torch.Tensor
    # [num_tokens_after_padding]
    positions: torch.Tensor

    # layer_name -> Metadata
    attn_metadata: dict[str, Any]

    # [num_reqs]
    logits_indices: torch.Tensor

    @classmethod
    def make_dummy(
        cls,
        num_reqs: int,
        num_tokens: int,
        input_buffers: InputBuffers,
        device: torch.device,
    ) -> "InputBatch":
        assert 0 < num_reqs <= num_tokens
        req_ids = [f"req_{i}_{random_uuid()}" for i in range(num_reqs)]
        idx_mapping_np = np.arange(num_reqs, dtype=np.int32)
        idx_mapping = torch.arange(num_reqs, dtype=torch.int32, device=device)
        num_scheduled_tokens = np.full(num_reqs, num_tokens // num_reqs, dtype=np.int32)
        num_scheduled_tokens[-1] += num_tokens % num_reqs
        assert int(num_scheduled_tokens.sum()) == num_tokens

        input_buffers.query_start_loc.np[0] = 0
        input_buffers.query_start_loc.np[1 : num_reqs + 1] = np.cumsum(
            num_scheduled_tokens
        )
        input_buffers.query_start_loc.np[num_reqs + 1 :] = num_tokens
        query_start_loc_np = input_buffers.query_start_loc.np[: num_reqs + 1]
        query_start_loc = input_buffers.query_start_loc.copy_to_gpu()[: num_reqs + 1]
        # seq_len equals to query_len
        seq_lens_np = np.full(num_reqs, num_tokens // num_reqs, dtype=np.int32)
        seq_lens_np[-1] += num_tokens % num_reqs
        input_buffers.seq_lens[:num_reqs] = num_tokens // num_reqs
        input_buffers.seq_lens[num_reqs - 1] += num_tokens % num_reqs
        input_buffers.seq_lens[num_reqs:] = 0
        seq_lens = input_buffers.seq_lens[:num_reqs]

        input_ids = input_buffers.input_ids.copy_to_gpu(num_tokens)
        positions = input_buffers.positions[:num_tokens]
        # attn_metadata = defaultdict(lambda: None)
        logits_indices = query_start_loc[1:] - 1
        return cls(
            req_ids=req_ids,
            num_reqs=num_reqs,
            idx_mapping=idx_mapping,
            idx_mapping_np=idx_mapping_np,
            num_scheduled_tokens=num_scheduled_tokens,
            num_tokens=num_tokens,
            num_tokens_after_padding=num_tokens,
            query_start_loc=query_start_loc,
            query_start_loc_np=query_start_loc_np,
            seq_lens=seq_lens,
            seq_lens_np=seq_lens_np,
            input_ids=input_ids,
            positions=positions,
            attn_metadata=None,  # type: ignore
            logits_indices=logits_indices,
        )


# NOTE: With the type annotations, this function is pre-compiled
# before the first call.
@numba.jit(
    [
        types.none(
            types.int32[:],  # idx_mapping
            types.int32[:],  # num_scheduled_tokens
            types.int32[:, :],  # prefill_token_ids
            types.int32[:],  # num_computed_prefill_tokens
            types.int32[:],  # prefill_len
            types.int32[:],  # input_ids
            types.int32[:],  # query_start_loc
        )
    ],
    nopython=True,
    cache=True,
)
def _prepare_prefill_inputs(
    idx_mapping: np.ndarray,  # batch_idx -> req_idx
    num_scheduled_tokens: np.ndarray,  # [B]
    prefill_token_ids: np.ndarray,  # [N, max_model_len]
    num_computed_prefill_tokens: np.ndarray,  # [N]
    prefill_len: np.ndarray,  # [N]
    input_ids: np.ndarray,  # [num_input_tokens]
    query_start_loc: np.ndarray,  # [B + 1]
) -> None:
    num_reqs = num_scheduled_tokens.shape[0]
    query_start_loc[0] = 0

    cu_num_tokens = 0
    for i in range(num_reqs):
        req_idx = idx_mapping[i]
        query_len = num_scheduled_tokens[i]

        start = num_computed_prefill_tokens[req_idx]
        end = min(start + query_len, prefill_len[req_idx])
        n = end - start

        start_idx = cu_num_tokens
        input_ids[start_idx : start_idx + n] = prefill_token_ids[req_idx, start:end]

        cu_num_tokens = start_idx + query_len
        query_start_loc[i + 1] = cu_num_tokens

    # Pad the inputs for CUDA graphs.
    # Note: pad query_start_loc to be non-decreasing, as kernels
    # like FlashAttention requires that
    query_start_loc[num_reqs + 1 :].fill(cu_num_tokens)


def prepare_prefill_inputs(
    idx_mapping: np.ndarray,
    num_scheduled_tokens: np.ndarray,
    total_num_tokens: int,
    prefill_token_ids: np.ndarray,
    num_computed_prefill_tokens: np.ndarray,
    prefill_len: np.ndarray,
    input_ids: CpuGpuBuffer,
    query_start_loc: CpuGpuBuffer,
) -> None:
    _prepare_prefill_inputs(
        idx_mapping,
        num_scheduled_tokens,
        prefill_token_ids,
        num_computed_prefill_tokens,
        prefill_len,
        input_ids.np,
        query_start_loc.np,
    )
    input_ids.copy_to_gpu(total_num_tokens)
    # NOTE(woosuk): We should copy the whole query_start_loc and seq_lens
    # tensors from CPU to GPU, because they may include paddings needed
    # for full CUDA graph mode.
    query_start_loc.copy_to_gpu()


@triton.jit
def _prepare_pos_seq_lens_kernel(
    pos_ptr,
    seq_lens_ptr,
    idx_mapping_ptr,
    query_start_loc_ptr,
    num_computed_tokens_ptr,
    max_num_reqs,
    BLOCK_SIZE: tl.constexpr,
):
    req_id = tl.program_id(0)
    num_reqs = tl.num_programs(0) - 1
    if req_id == num_reqs:
        # Pad unused seq_lens as 0 for full CUDA graphs.
        for i in tl.range(num_reqs, max_num_reqs, BLOCK_SIZE):
            block = i + tl.arange(0, BLOCK_SIZE)
            mask = block < max_num_reqs
            tl.store(seq_lens_ptr + block, 0, mask=mask)
        return

    req_state_idx = tl.load(idx_mapping_ptr + req_id)
    num_computed_tokens = tl.load(num_computed_tokens_ptr + req_state_idx)

    start = tl.load(query_start_loc_ptr + req_id)
    end = tl.load(query_start_loc_ptr + req_id + 1)
    query_len = end - start

    seq_len = num_computed_tokens + query_len
    tl.store(seq_lens_ptr + req_id, seq_len)

    for i in tl.range(0, query_len, BLOCK_SIZE):
        block = i + tl.arange(0, BLOCK_SIZE)
        mask = block < query_len
        pos = num_computed_tokens + block
        tl.store(pos_ptr + start + block, pos, mask=mask)


def prepare_pos_seq_lens(
    idx_mapping: torch.Tensor,
    query_start_loc: torch.Tensor,
    num_computed_tokens: torch.Tensor,
    pos: torch.Tensor,
    seq_lens: torch.Tensor,
) -> None:
    num_reqs = idx_mapping.shape[0]
    # NOTE(woosuk): We do +1 because the last thread block is used
    # to pad unused seq_lens as 0 for full CUDA graphs.
    _prepare_pos_seq_lens_kernel[(num_reqs + 1,)](
        pos,
        seq_lens,
        idx_mapping,
        query_start_loc,
        num_computed_tokens,
        seq_lens.shape[0],
        BLOCK_SIZE=1024,
    )


@triton.jit
def _combine_sampled_and_draft_tokens_kernel(
    input_ids_ptr,
    idx_mapping_ptr,
    last_sampled_tokens_ptr,
    query_start_loc_ptr,
    seq_lens_ptr,
    prefill_len_ptr,
):
    batch_idx = tl.program_id(0)
    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)

    seq_len = tl.load(seq_lens_ptr + batch_idx)
    prefill_len = tl.load(prefill_len_ptr + req_state_idx)
    if seq_len <= prefill_len:
        # Handling prefill tokens.
        return

    last_token_id = tl.load(last_sampled_tokens_ptr + req_state_idx)
    end = tl.load(query_start_loc_ptr + batch_idx + 1)
    tl.store(input_ids_ptr + end - 1, last_token_id)


def combine_sampled_and_draft_tokens(
    input_ids: torch.Tensor,
    idx_mapping: torch.Tensor,
    last_sampled_tokens: torch.Tensor,
    query_start_loc: torch.Tensor,
    seq_lens: torch.Tensor,
    prefill_len: torch.Tensor,
) -> torch.Tensor:
    num_reqs = seq_lens.shape[0]
    _combine_sampled_and_draft_tokens_kernel[(num_reqs,)](
        input_ids,
        idx_mapping,
        last_sampled_tokens,
        query_start_loc,
        seq_lens,
        prefill_len,
    )
    return input_ids


@triton.jit
def _update_num_computed_tokens_kernel(
    idx_mapping_ptr,
    num_computed_tokens_ptr,
    query_start_loc_ptr,
):
    req_id = tl.program_id(0)
    req_state_idx = tl.load(idx_mapping_ptr + req_id)

    start = tl.load(query_start_loc_ptr + req_id)
    end = tl.load(query_start_loc_ptr + req_id + 1)
    query_len = end - start

    n = tl.load(num_computed_tokens_ptr + req_state_idx)
    tl.store(num_computed_tokens_ptr + req_state_idx, n + query_len)


def update_num_computed_tokens(
    idx_mapping: torch.Tensor,
    num_computed_tokens: torch.Tensor,
    query_start_loc: torch.Tensor,
) -> None:
    num_reqs = idx_mapping.shape[0]
    _update_num_computed_tokens_kernel[(num_reqs,)](
        idx_mapping,
        num_computed_tokens,
        query_start_loc,
    )