''' 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]