# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from collections import defaultdict
from typing import List

import torch


class Block(object):

    def __init__(self, block_idx, k_ptrs, v_ptrs):
        self.idx = block_idx
        self.k_ptrs = k_ptrs
        self.v_ptrs = v_ptrs
        self.ref_count = 0

    def add_link(self):
        self.ref_count += 1

    def remove_link(self):
        self.ref_count -= 1

    def has_link(self) -> bool:
        return self.ref_count > 0

    def is_shared(self) -> bool:
        return self.ref_count > 1

    def get_k_ptr(self, idx) -> int:
        return self.k_ptrs[idx]

    def get_v_ptr(self, idx) -> int:
        return self.v_ptrs[idx]


class GenerationSequence(object):

    def __init__(self, seq_idx, batch_idx):
        self.seq_idx = seq_idx
        self.batch_idx = batch_idx

    def get_batch_idx(self) -> int:
        """
        Returns idx of sequence in batch
        """
        return self.batch_idx

    def get_seq_idx(self) -> int:
        """
        Returns sequence idx
        """
        return self.seq_idx

    def __eq__(self, another):
        return hasattr(another, 'seq_idx') and self.seq_idx == another.seq_idx and \
            hasattr(another, 'batch_idx') and self.batch_idx == another.batch_idx

    def __hash__(self):
        return self.seq_idx


class BlocksManager(object):
    _sizeof = {
        torch.float32: 4,
        torch.float16: 2,
        torch.bfloat16: 2,
        torch.int8: 1
    }

    def __init__(self,
                 memory_pools: List[torch.Tensor],
                 blocks: int,
                 max_blocks_per_seq: int = 128,
                 beam_width: int = 1):
        self.max_blocks_per_seq = max_blocks_per_seq

        self.pointer_array = None
        self.memory_pools = memory_pools
        self.blocks = blocks
        self.beam_width = beam_width

        self.elts_per_blocks = []
        for pool in memory_pools:
            # Pool consists of memory for K and V caches
            self.elts_per_blocks.append(pool.nelement() // (2 * blocks))

        self.free_blocks = []
        for bi in range(blocks):
            k_ptrs = []
            v_ptrs = []
            for pool, elts_per_block in zip(memory_pools, self.elts_per_blocks):
                k_ptrs.append(self.get_mempool_pointer(bi, pool,
                                                       elts_per_block))
                v_ptrs.append(
                    self.get_mempool_pointer(bi, pool, elts_per_block) +
                    self.blocks * elts_per_block * self._sizeof[pool.dtype])
            self.free_blocks.append(Block(bi, k_ptrs, v_ptrs))

        beam_width = self.beam_width
        # Here use beam_width instead of self.beam_width to remove cyclic reference between self and
        # self.allocated_blocks by preventing capture self, which may cause memory leak.
        self.allocated_blocks = defaultdict(
            lambda: [[] for _ in range(beam_width)])

    def has_free_block(self) -> bool:
        """
        Returns True if we have at least 1 free block
        """
        return len(self.free_blocks) > 0

    def allocate(self,
                 owner: GenerationSequence,
                 share_across_beam: bool = False):
        """
        Add block to owner and increase ref count
        """
        # Add blocks for whole beam width
        block = None
        for bi in range(self.beam_width):
            if not self.has_free_block():
                raise RuntimeError("Can't allocate new block for KV cache")

            # Use the same block for all seqs in beam if share_across_beam
            if block is None or share_across_beam == False:
                block = self.free_blocks.pop(0)
            # Add one reference to the block
            block.add_link()
            self.allocated_blocks[owner][bi].append(block)

    def replace_shared_block(self, owner: GenerationSequence, block_idx: int):
        """
        Replace the shared block.
        Free the shared block, and allocate blocks with share_across_beam=False
        """
        if not self.allocated_blocks[owner][0][block_idx].is_shared():
            return

        # Free shared block
        for bi in range(self.beam_width):
            block = self.allocated_blocks[owner][bi][block_idx]
            block.remove_link()
            if not block.has_link():
                self.free_blocks.append(block)

        # Allocate new block
        for bi in range(self.beam_width):
            if not self.has_free_block():
                raise RuntimeError("Can't allocate new block for KV cache")
            block = self.free_blocks.pop(0)
            block.add_link()
            self.allocated_blocks[owner][bi][block_idx] = block
        return

    def free(self, owner: GenerationSequence):
        """
        Unlink all blocks of given owner.
        Moves blocks with ref_count == 0 to free.
        Removes owner from allocated blocks.
        """
        for bi in range(self.beam_width):
            for block in self.allocated_blocks[owner][bi]:
                # Move block to free if no one refers to it
                block.remove_link()

                # Move block to free if no one refers to it
                if not block.has_link():
                    self.free_blocks.append(block)
        # Remove owner from allocated blocks
        self.allocated_blocks.pop(owner)

    def get_number_blocks(self, owner: GenerationSequence) -> int:
        """
        Returns number of blocks allocated to the sequence owner
        """
        return len(self.allocated_blocks[owner][0])

    def get_mempool_pointer(self, block_idx: int, pool: torch.Tensor,
                            elts_per_block: int) -> int:
        """
        Computes linear pointer
        """
        return pool.data_ptr(
        ) + block_idx * elts_per_block * self._sizeof[pool.dtype]

    def get_pointer_array(self, pool_idx: int, beam_width: int) -> torch.Tensor:
        """
        Returns array of [batch size, beam_width, 2, max_blocks_per_seq] of pointers
        to the allocated blocks in memory pool
        """
        assert (beam_width <= self.beam_width)

        def create_nested_list(dims):
            """Recursive function to generate nested list."""
            if len(dims) == 1:
                return [0 for _ in range(dims[0])]
            return [create_nested_list(dims[1:]) for _ in range(dims[0])]

        pointer_array = create_nested_list(
            (len(self.allocated_blocks), beam_width, 2,
             self.max_blocks_per_seq))

        for owner, beams_blocks in self.allocated_blocks.items():
            for bi in range(beam_width):
                for block_linear_idx, block in enumerate(beams_blocks[bi]):
                    # K cache pointers
                    pointer_array[owner.get_batch_idx(
                    )][bi][0][block_linear_idx] = block.get_k_ptr(pool_idx)
                    # V cache pointers
                    pointer_array[owner.get_batch_idx(
                    )][bi][1][block_linear_idx] = block.get_v_ptr(pool_idx)

        self.pointer_array = torch.tensor(pointer_array, dtype=torch.int64)
        return self.pointer_array

    def get_continuous_caches(self, pool_idx: int) -> torch.Tensor:
        """
        Returns continuous KV caches.
        Used only for debug purposes.
        """
        assert self.beam_width == 1

        elts_per_block = self.elts_per_blocks[pool_idx]
        pool = self.memory_pools[pool_idx].flatten()
        continuous_kv_cache = torch.zeros(len(self.allocated_blocks),
                                          2,
                                          self.max_blocks_per_seq *
                                          elts_per_block,
                                          dtype=pool.dtype,
                                          device="cuda")
        for owner, beam_blocks in self.allocated_blocks.items():
            for bi in range(self.beam_width):
                for block_linear_idx, block in enumerate(beam_blocks[bi]):
                    # The batch index.
                    batch_idx = owner.get_batch_idx()
                    # The first index in the sequence.
                    block_offset = block_linear_idx * elts_per_block
                    # The first index in the pool for K.
                    k_start = block.idx * elts_per_block
                    # The first index in the pool for V.
                    v_start = k_start + self.blocks * elts_per_block

                    continuous_kv_cache[batch_idx][0][
                        block_offset:block_offset +
                        elts_per_block] = pool[k_start:k_start + elts_per_block]
                    continuous_kv_cache[batch_idx][1][
                        block_offset:block_offset +
                        elts_per_block] = pool[v_start:v_start + elts_per_block]

        return continuous_kv_cache


class KVCacheManager(object):

    def __init__(self,
                 memory_pools: List[torch.Tensor],
                 blocks: int,
                 tokens_per_block: int,
                 max_blocks_per_seq: int,
                 max_attention_window_size: int,
                 sink_token_len: int,
                 beam_width: int = 1,
                 use_one_more_block: bool = False):

        self.blocks_manager = BlocksManager(
            memory_pools=memory_pools,
            blocks=blocks,
            max_blocks_per_seq=max_blocks_per_seq,
            beam_width=beam_width)
        self.num_pools = len(memory_pools)
        self.tokens_per_block = tokens_per_block
        self.max_attention_window_size = max_attention_window_size
        self.sink_token_len = sink_token_len
        self.beam_width = beam_width

        # The sink tokens are not stored into the same block with other tokens.
        # Need to add the bubble after the sink tokens.
        if sink_token_len % tokens_per_block == 0:
            self.bubble_len = 0
        else:
            self.bubble_len = tokens_per_block - sink_token_len % tokens_per_block

        # Token num in the sink blocks
        self.sink_block_token_num = self.sink_token_len + self.bubble_len

        # Max token num in the cache
        self.max_token_num = self.max_attention_window_size + self.bubble_len
        if use_one_more_block:
            self.max_token_num += self.tokens_per_block

        self.lens = []
        self.sequences = []

    def step(self, finished: List[bool]):
        """
        Iterate to the next generation step.
        Add new blocks where needed and clear finished sequences.
        """
        for seq in self.sequences:
            batch_idx = seq.get_batch_idx()
            # Enable cyclic kv cache when it exceeds the max_token_num
            cyclic_token_num = self.max_token_num - self.sink_block_token_num
            next_token_idx_in_cache = self.sink_block_token_num + \
                        (self.lens[batch_idx] - self.sink_block_token_num) % cyclic_token_num
            if not finished[batch_idx] and (
                    next_token_idx_in_cache % self.tokens_per_block == 0 or
                (next_token_idx_in_cache - self.sink_block_token_num) %
                    cyclic_token_num == 0):
                if self.lens[batch_idx] < self.max_token_num:
                    self.blocks_manager.allocate(seq)
                elif self.beam_width > 1:
                    # Get next block index
                    next_block_idx = next_token_idx_in_cache // self.tokens_per_block
                    # Replace the shared block with the unshared ones
                    self.blocks_manager.replace_shared_block(
                        seq, next_block_idx)

            self.lens[batch_idx] += 1

        # Remove finished sequences
        for fi in range(len(finished)):
            if finished[fi]:
                self.blocks_manager.free(self.sequences[fi])
        self.lens = [l for l, f in zip(self.lens, finished) if not f]

        # Remap sequence ids
        new_sequences = []
        batch_idx = 0
        for seq, finish in zip(self.sequences, finished):
            if not finish:
                seq.batch_idx = batch_idx
                new_sequences.append(seq)
                batch_idx += 1
        self.sequences = new_sequences

    def add_sequence(self, sequence: GenerationSequence, context_len: int):
        """
        Add sequence to the manager and allocate minimum amount of blocks for context
        """
        seq_len = context_len + self.bubble_len
        self.lens.append(seq_len)
        self.sequences.append(sequence)

        # Get the final token index in kv cache
        final_token_kv_index = self.sink_block_token_num + (
            (seq_len - 1 - self.sink_block_token_num) %
            (self.max_token_num - self.sink_block_token_num))

        # Get block index that with shareAmongBeams=False.
        unshared_block_idx = -1
        if final_token_kv_index % self.tokens_per_block > 0:
            unshared_block_idx = final_token_kv_index // self.tokens_per_block

        # Get context block num.
        # Allocate one more block if there are tokens that can't be shared across beams.
        seq_len = min(seq_len, self.max_token_num)
        context_blocks = seq_len // self.tokens_per_block
        if seq_len % self.tokens_per_block > 0:
            context_blocks += 1

        # Allocate blocks
        for i in range(context_blocks):
            self.blocks_manager.allocate(
                sequence, share_across_beam=i != unshared_block_idx)

    def get_block_pointers(self, beam_width: int) -> torch.Tensor:
        """
        Returns arrays of pointers for all memory pools
        """
        pointer_arrays = []
        for pool in range(self.num_pools):
            pointer_arrays.append(
                self.blocks_manager.get_pointer_array(
                    pool, beam_width).view(dtype=torch.int64))

        return torch.stack(pointer_arrays, dim=0)


class KVCacheUpdater:

    def __init__(self):
        self.use_paged_kv_cache = None
        self.num_kv_heads = None
        self.head_dim = None
        self.elt_size = None
        self.past_key_value_list = None
        self.max_kv_cache_length = None
        self.kv_cache_manager = None

    def init_linear_kv_cache(self, num_kv_heads, head_dim, kv_cache_type,
                             past_key_value_list):
        self.use_paged_kv_cache = False
        self.num_kv_heads = num_kv_heads
        self.head_dim = head_dim
        self.past_key_value_list = past_key_value_list
        self.elt_size = torch.zeros(1, dtype=kv_cache_type).element_size()
        self.max_kv_cache_length = past_key_value_list[0].shape[3]

    def init_paged_kv_cache(self, num_kv_heads, head_dim, kv_cache_type,
                            kv_cache_manager):
        self.use_paged_kv_cache = True
        self.num_kv_heads = num_kv_heads
        self.head_dim = head_dim
        self.kv_cache_manager = kv_cache_manager
        self.elt_size = torch.zeros(1, dtype=kv_cache_type).element_size()

    def update(self, accepted_draft_token_offsets,
               packed_accepted_draft_tokens_indices, sequence_length_buffer,
               rewind_tokens):
        assert isinstance(rewind_tokens, torch.Tensor) or isinstance(
            rewind_tokens, int)
        rewind_tokens_tensor = rewind_tokens if isinstance(
            rewind_tokens, torch.Tensor) else None
        rewind_tokens_count = rewind_tokens if isinstance(rewind_tokens,
                                                          int) else 0
        assert self.use_paged_kv_cache is not None
        if self.use_paged_kv_cache:
            host_kv_cache_block_pointers = self.kv_cache_manager.get_block_pointers(
                1)
            kv_cache_block_pointers = host_kv_cache_block_pointers.to('cuda')
            torch.ops.tensorrt_llm.update_kv_cache_draft_token_location(
                accepted_draft_token_offsets,
                packed_accepted_draft_tokens_indices,
                sequence_length_buffer,
                True,
                self.num_kv_heads,
                self.head_dim * self.elt_size,
                rewind_tokens_count,
                self.kv_cache_manager.max_attention_window_size,
                rewind_tokens_tensor,
                None,
                kv_cache_block_pointers,
                self.kv_cache_manager.blocks_manager.max_blocks_per_seq,
                self.kv_cache_manager.tokens_per_block,
                None,
            )
        else:
            torch.ops.tensorrt_llm.update_kv_cache_draft_token_location(
                accepted_draft_token_offsets,
                packed_accepted_draft_tokens_indices,
                sequence_length_buffer,
                False,
                self.num_kv_heads,
                self.head_dim * self.elt_size,
                rewind_tokens_count,
                self.max_kv_cache_length,
                rewind_tokens_tensor,
                self.past_key_value_list,
                None,
                None,
                None,
                None,
            )