kvcache-ai-ktransformers/ktransformers/models/custom_cache.py

'''
Description  :  
Author       : Boxin Zhang
Version      : 0.1.0
'''
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/cache_utils.py
# Copyright 2018- The Hugging Face team. All rights reserved.
# Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
import torch
import torch.nn as nn
import transformers
from transformers import Cache, PretrainedConfig
from typing import List, Optional, Dict, Any, Tuple
try:
    from ktransformers.server.balance_serve.settings import sched_ext
except:
    print("no balance_serve")
class StaticCache(transformers.StaticCache):
    """
    Static Cache class to be used with `torch.compile(model)`.

    Parameters:
        config (`PretrainedConfig):
            The configuration file defining the shape-related attributes required to initialize the static cache.
        max_batch_size (`int`):
            The maximum batch size with which the model will be used.
        max_cache_len (`int`):
            The maximum sequence length with which the model will be used.
        device (`torch.device` or `dict`):
            The device on which the cache should be initialized. Should be the same as the layer.
            If a `dict`, it should contain the `device` key with the device name as the value.
        dtype (*optional*, defaults to `torch.float32`):
            The default `dtype` to use when initializing the layer.
    """

    def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device: torch.device| dict, dtype=None) -> None:
        Cache.__init__(self)
        self.max_batch_size = max_batch_size
        self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
        # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
        if config.architectures[0] == "DeepseekV3ForCausalLM":
            self.head_dim = config.qk_rope_head_dim
        else:
            self.head_dim = (
                config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
            )

        self.dtype = dtype if dtype is not None else torch.float32
        self.num_key_value_heads = (
            config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
        )

        self.key_cache: List[torch.Tensor] = []
        self.value_cache: List[torch.Tensor] = []
        cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
        if config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM":
            # TODO: for deepseek, cache_shape is different whether using Absorbed MLA, check it automatically
            self.page_size = 64
            self.max_pages = (self.max_cache_len + self.page_size - 1) // self.page_size
            latent_shape = (self.max_pages, self.page_size, 1, config.kv_lora_rank + config.qk_rope_head_dim)
            self.kv_lora_rank = config.kv_lora_rank
            self.qk_rope_head_dim = config.qk_rope_head_dim
            # TODO: support real page table
            self.page_table_map = dict()
            self.page_table_list = []
            for idx in range(config.num_hidden_layers):
                if isinstance(device, dict):
                    target_device = device[f"model.layers.{idx}.self_attn"]["generate_device"]
                else:
                    target_device = device
                
                if target_device not in self.page_table_map:
                    page_table = torch.zeros((max_batch_size, self.max_pages), dtype=torch.int32, device=target_device)
                    for seq_id in range(max_batch_size):
                        page_table[seq_id, :] = torch.arange(seq_id * self.max_pages, seq_id * self.max_pages + self.max_pages, dtype=torch.int32, device=target_device)
                    self.page_table_map[target_device] = page_table
                    
                self.page_table_list.append(self.page_table_map[target_device])
                    
            self.is_MLA = True
            self.is_page = True
        else:
            key_shape = cache_shape
            value_shape = cache_shape
            self.is_MLA = False

        self.past_tokens = []
        self.num_hidden_layers = config.num_hidden_layers
        for idx in range(self.num_hidden_layers):
            # Note: `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
            # breaks when updating the cache.
            if isinstance(device, dict):
                target_device = device[f"model.layers.{idx}.self_attn"]["generate_device"]
            else:
                target_device = device
            
            if self.is_MLA:
                new_layer_key_cache = torch.zeros(latent_shape, dtype=self.dtype, device=target_device)
                new_layer_value_cache = None
                torch._dynamo.mark_static_address(new_layer_key_cache)
            else:
                new_layer_key_cache = torch.zeros(key_shape, dtype=self.dtype, device=target_device)
                new_layer_value_cache = torch.zeros(value_shape, dtype=self.dtype, device=target_device)
                torch._dynamo.mark_static_address(new_layer_key_cache)
                torch._dynamo.mark_static_address(new_layer_value_cache)
                
            self.key_cache.append(new_layer_key_cache)
            self.value_cache.append(new_layer_value_cache)
            self.past_tokens.append(0)

    def update(
        self,
        key_states: torch.Tensor,
        value_states: torch.Tensor,
        layer_idx: int,
        cache_kwargs: Optional[Dict[str, Any]] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
        It is VERY important to index using a tensor, otherwise you introduce a copy to the device.

        Parameters:
            key_states (`torch.Tensor`):
                The new key states to cache.
            value_states (`torch.Tensor`):
                The new value states to cache.
            layer_idx (`int`):
                The index of the layer to cache the states for.
            cache_kwargs (`Dict[str, Any]`, `optional`):
                Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
                to know how where to write in the cache.

        Return:
            A tuple containing the updated key and value states.
        """
        cache_position = cache_kwargs.get("cache_position")
        k_out = self.key_cache[layer_idx]
        v_out = self.value_cache[layer_idx]
        self.past_tokens[layer_idx] += cache_position.size(0)
        #print(cache_position)
        if self.is_MLA:
            page_idx = cache_position // self.page_size
            page_offset = cache_position % self.page_size
            # key shape (self.max_pages, self.page_size, 1, config.kv_lora_rank + config.qk_rope_head_dim)
            k_out[page_idx, page_offset, :, :self.kv_lora_rank] = key_states
            k_out[page_idx, page_offset, :, self.kv_lora_rank:] = value_states
            return k_out, self.page_table_list[layer_idx]
        else:
            k_out[:, :, cache_position] = key_states
            v_out[:, :, cache_position] = value_states
            return k_out, v_out

    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
        """Returns the sequence length of the cached states that were seen by the model."""
        # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
        # limit the check to the first batch member and head dimension.
        # TODO: deprecate this function in favor of `cache_position`
        return self.past_tokens[layer_idx]
    
    def change_seq_length(self, bias: Optional[int] = 0) -> int:
        """Returns the sequence length of the cached states that were seen by the model."""
        # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
        # limit the check to the first batch member and head dimension.
        # TODO: deprecate this function in favor of `cache_position`
        for layer_idx in range(self.num_hidden_layers):
            self.past_tokens[layer_idx] += bias

    def get_max_length(self) -> Optional[int]:
        """Returns the maximum sequence length of the cached states."""
        return self.max_cache_len

    def reset(self):
        """Resets the cache values while preserving the objects"""
        for layer_idx in range(len(self.key_cache)):
            # In-place ops prevent breaking the static address
            self.key_cache[layer_idx].zero_()
            if self.value_cache[layer_idx] is not None:
                self.value_cache[layer_idx].zero_()
            self.past_tokens[layer_idx] = 0

    def remove_suffix(self, start_pos):
        for layer_idx in range(len(self.key_cache)):
            # In-place ops prevent breaking the static address
            if self.is_MLA:
                k_cache = self.key_cache[layer_idx]
                k_cache.view(-1, k_cache.shape[-1])[start_pos:].zero_()
            else:
                self.key_cache[layer_idx][..., start_pos:, :].zero_()
                self.value_cache[layer_idx][..., start_pos:, :].zero_()
            self.past_tokens[layer_idx] = start_pos
    
    def get_max_cache_shape(self) -> Tuple[int, int, int, int]:
        """Returns the maximum shape of the cache."""
        return self.max_cache_len

class KDeepSeekV3Cache(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        page_size: int = 256,
        dtype=torch.bfloat16,
        device=torch.device("cuda:0"),
        
    ):
        super().__init__()
        self.config = config
        self.dtype = dtype
        self.device = device
        self.kv_lora_rank = config.kv_lora_rank
        self.page_size = page_size
        self.k_caches = []
        self.v_caches = []
        

    def load(self, inference_context: "sched_ext.InferenceContext"): 
        
        for i in range(self.config.num_hidden_layers):
            self.k_caches.append(
                inference_context.k_cache[0][i] 
            )
        self.max_cache_len = self.k_caches[0].shape[0]*self.k_caches[0].shape[1]

    def update(
        self,
        key_states: torch.Tensor,
        value_states: torch.Tensor,
        layer_idx: int,

        page_idx: torch.Tensor,
        page_offset: torch.Tensor,

        cache_kwargs: Optional[Dict[str, Any]] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
        It is VERY important to index using a tensor, otherwise you introduce a copy to the device.

        Parameters:
            key_states (`torch.Tensor`):
                The new key states to cache.
            value_states (`torch.Tensor`):
                The new value states to cache.
            layer_idx (`int`):
                The index of the layer to cache the states for.
            cache_kwargs (`Dict[str, Any]`, `optional`):
                Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
                to know how where to write in the cache.

        Return:
            A tuple containing the updated key and value states.
        """
        k_out = self.k_caches[layer_idx]

        k_out[page_idx, page_offset, :, :self.kv_lora_rank] = key_states.reshape(-1, *key_states.shape[2:])
        k_out[page_idx, page_offset, :, self.kv_lora_rank:] = value_states.reshape(-1, *value_states.shape[2:])
        return k_out

        
    def get_page_table(self, cache_position: torch.Tensor, q_indptr: torch.Tensor, kv_indptr: torch.Tensor, kv_indices: torch.Tensor, bsz_tensors: torch.tensor):
        page_offset = cache_position % self.page_size  
        page_idx_local = cache_position // self.page_size  
        query_ids = torch.zeros_like(cache_position)
        for i in range(len(q_indptr) - 1):
            start_idx = q_indptr[i]
            end_idx = q_indptr[i + 1]
            query_ids[start_idx:end_idx] = i
        page_idx = torch.zeros_like(page_idx_local)
        for i in range(bsz_tensors[0]):
            query_id = query_ids[i]
            local_block = page_idx_local[i]
            start_block = kv_indptr[query_id]
            if local_block < kv_indptr[query_id + 1] - kv_indptr[query_id]:
                page_idx[i] = kv_indices[start_block + local_block]
        
        return page_idx, page_offset
    
class KGQACache(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        page_size: int = 256,
        dtype=torch.bfloat16,
        device=torch.device("cuda:0"),
        
    ):
        super().__init__()
        self.config = config
        self.dtype = dtype
        self.device = device
        self.page_size = page_size
        self.k_caches = []
        self.v_caches = []
        

    def load(self, inference_context: sched_ext.InferenceContext): 
        print(self.config.num_hidden_layers)
        for i in range(self.config.num_hidden_layers):
            self.k_caches.append(
                inference_context.k_cache[0][i] 
            )
            self.v_caches.append(
                inference_context.v_cache[0][i]
            )


        self.max_cache_len = self.k_caches[0].shape[0]*self.k_caches[0].shape[1]


        
    def get_page_table(self, cache_position: torch.Tensor, q_indptr: torch.Tensor, kv_indptr: torch.Tensor, kv_indices: torch.Tensor, bsz_tensors: torch.tensor):
        page_offset = cache_position % self.page_size  
        page_idx_local = cache_position // self.page_size  
        query_ids = torch.zeros_like(cache_position)
        for i in range(len(q_indptr) - 1):
            start_idx = q_indptr[i]
            end_idx = q_indptr[i + 1]
            query_ids[start_idx:end_idx] = i
        page_idx = torch.zeros_like(page_idx_local)
        for i in range(bsz_tensors[0]):
            query_id = query_ids[i]
            local_block = page_idx_local[i]
            start_block = kv_indptr[query_id]
            if local_block < kv_indptr[query_id + 1] - kv_indptr[query_id]:
                page_idx[i] = kv_indices[start_block + local_block]
        
        return page_idx, page_offset

    def get_k_cache(self, layer_idx):
        return self.k_caches[layer_idx]

    def get_v_cache(self, layer_idx):
        return self.v_caches[layer_idx]