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274 lines
12 KiB
Python
274 lines
12 KiB
Python
'''
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Description :
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Author : Boxin Zhang
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Version : 0.1.0
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'''
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/cache_utils.py
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# Copyright 2018- The Hugging Face team. All rights reserved.
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# Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
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import torch
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import torch.nn as nn
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import transformers
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from transformers import Cache, PretrainedConfig
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from typing import List, Optional, Dict, Any, Tuple
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from ktransformers.server.balance_serve.settings import sched_ext
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class StaticCache(transformers.StaticCache):
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"""
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Static Cache class to be used with `torch.compile(model)`.
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Parameters:
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config (`PretrainedConfig):
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The configuration file defining the shape-related attributes required to initialize the static cache.
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max_batch_size (`int`):
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The maximum batch size with which the model will be used.
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max_cache_len (`int`):
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The maximum sequence length with which the model will be used.
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device (`torch.device` or `dict`):
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The device on which the cache should be initialized. Should be the same as the layer.
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If a `dict`, it should contain the `device` key with the device name as the value.
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dtype (*optional*, defaults to `torch.float32`):
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The default `dtype` to use when initializing the layer.
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"""
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def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device: torch.device| dict, dtype=None) -> None:
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Cache.__init__(self)
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self.max_batch_size = max_batch_size
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self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
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# Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
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if config.architectures[0] == "DeepseekV3ForCausalLM":
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self.head_dim = config.qk_rope_head_dim
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else:
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self.head_dim = (
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config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
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)
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self.dtype = dtype if dtype is not None else torch.float32
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self.num_key_value_heads = (
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config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
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)
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self.key_cache: List[torch.Tensor] = []
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self.value_cache: List[torch.Tensor] = []
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cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
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if config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM":
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# TODO: for deepseek, cache_shape is different whether using Absorbed MLA, check it automatically
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self.page_size = 64
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self.max_pages = (self.max_cache_len + self.page_size - 1) // self.page_size
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latent_shape = (self.max_pages, self.page_size, 1, config.kv_lora_rank + config.qk_rope_head_dim)
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self.kv_lora_rank = config.kv_lora_rank
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self.qk_rope_head_dim = config.qk_rope_head_dim
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# TODO: support real page table
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self.page_table_map = dict()
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self.page_table_list = []
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for idx in range(config.num_hidden_layers):
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if isinstance(device, dict):
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target_device = device[f"blk.{idx}.self_attn"]["generate_device"]
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else:
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target_device = device
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if target_device not in self.page_table_map:
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page_table = torch.zeros((max_batch_size, self.max_pages), dtype=torch.int32, device=target_device)
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for seq_id in range(max_batch_size):
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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)
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self.page_table_map[target_device] = page_table
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self.page_table_list.append(self.page_table_map[target_device])
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self.is_MLA = True
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self.is_page = True
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else:
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key_shape = cache_shape
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value_shape = cache_shape
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self.is_MLA = False
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self.past_tokens = []
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self.num_hidden_layers = config.num_hidden_layers
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for idx in range(self.num_hidden_layers):
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# Note: `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
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# breaks when updating the cache.
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if isinstance(device, dict):
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target_device = device[f"blk.{idx}.self_attn"]["generate_device"]
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else:
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target_device = device
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if self.is_MLA:
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new_layer_key_cache = torch.zeros(latent_shape, dtype=self.dtype, device=target_device)
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new_layer_value_cache = None
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torch._dynamo.mark_static_address(new_layer_key_cache)
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else:
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new_layer_key_cache = torch.zeros(key_shape, dtype=self.dtype, device=target_device)
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new_layer_value_cache = torch.zeros(value_shape, dtype=self.dtype, device=target_device)
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torch._dynamo.mark_static_address(new_layer_key_cache)
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torch._dynamo.mark_static_address(new_layer_value_cache)
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self.key_cache.append(new_layer_key_cache)
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self.value_cache.append(new_layer_value_cache)
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self.past_tokens.append(0)
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def update(
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self,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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layer_idx: int,
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cache_kwargs: Optional[Dict[str, Any]] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
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It is VERY important to index using a tensor, otherwise you introduce a copy to the device.
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Parameters:
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key_states (`torch.Tensor`):
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The new key states to cache.
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value_states (`torch.Tensor`):
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The new value states to cache.
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layer_idx (`int`):
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The index of the layer to cache the states for.
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cache_kwargs (`Dict[str, Any]`, `optional`):
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Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
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to know how where to write in the cache.
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Return:
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A tuple containing the updated key and value states.
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"""
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cache_position = cache_kwargs.get("cache_position")
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k_out = self.key_cache[layer_idx]
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v_out = self.value_cache[layer_idx]
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self.past_tokens[layer_idx] += cache_position.size(0)
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#print(cache_position)
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if self.is_MLA:
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page_idx = cache_position // self.page_size
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page_offset = cache_position % self.page_size
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# key shape (self.max_pages, self.page_size, 1, config.kv_lora_rank + config.qk_rope_head_dim)
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k_out[page_idx, page_offset, :, :self.kv_lora_rank] = key_states
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k_out[page_idx, page_offset, :, self.kv_lora_rank:] = value_states
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return k_out, self.page_table_list[layer_idx]
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else:
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k_out[:, :, cache_position] = key_states
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v_out[:, :, cache_position] = value_states
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return k_out, v_out
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def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
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"""Returns the sequence length of the cached states that were seen by the model."""
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# Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
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# limit the check to the first batch member and head dimension.
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# TODO: deprecate this function in favor of `cache_position`
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return self.past_tokens[layer_idx]
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def change_seq_length(self, bias: Optional[int] = 0) -> int:
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"""Returns the sequence length of the cached states that were seen by the model."""
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# Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
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# limit the check to the first batch member and head dimension.
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# TODO: deprecate this function in favor of `cache_position`
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for layer_idx in range(self.num_hidden_layers):
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self.past_tokens[layer_idx] += bias
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def get_max_length(self) -> Optional[int]:
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"""Returns the maximum sequence length of the cached states."""
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return self.max_cache_len
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def reset(self):
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"""Resets the cache values while preserving the objects"""
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for layer_idx in range(len(self.key_cache)):
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# In-place ops prevent breaking the static address
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self.key_cache[layer_idx].zero_()
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if self.value_cache[layer_idx] is not None:
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self.value_cache[layer_idx].zero_()
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self.past_tokens[layer_idx] = 0
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def remove_suffix(self, start_pos):
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for layer_idx in range(len(self.key_cache)):
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# In-place ops prevent breaking the static address
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if self.is_MLA:
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k_cache = self.key_cache[layer_idx]
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k_cache.view(-1, k_cache.shape[-1])[start_pos:].zero_()
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else:
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self.key_cache[layer_idx][..., start_pos:, :].zero_()
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self.value_cache[layer_idx][..., start_pos:, :].zero_()
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self.past_tokens[layer_idx] = start_pos
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def get_max_cache_shape(self) -> Tuple[int, int, int, int]:
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"""Returns the maximum shape of the cache."""
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return self.max_cache_len
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class KDeepSeekV3Cache(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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page_size: int = 256,
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dtype=torch.bfloat16,
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device=torch.device("cuda:0"),
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):
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super().__init__()
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self.config = config
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self.dtype = dtype
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self.device = device
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self.kv_lora_rank = config.kv_lora_rank
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self.page_size = page_size
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self.k_caches = []
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self.v_caches = []
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def load(self, inference_context: sched_ext.InferenceContext):
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for i in range(self.config.num_hidden_layers):
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self.k_caches.append(
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inference_context.k_cache[0][i]
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)
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self.max_cache_len = self.k_caches[0].shape[0]*self.k_caches[0].shape[1]
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def update(
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self,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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layer_idx: int,
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page_idx: torch.Tensor,
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page_offset: torch.Tensor,
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cache_kwargs: Optional[Dict[str, Any]] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
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It is VERY important to index using a tensor, otherwise you introduce a copy to the device.
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Parameters:
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key_states (`torch.Tensor`):
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The new key states to cache.
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value_states (`torch.Tensor`):
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The new value states to cache.
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layer_idx (`int`):
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The index of the layer to cache the states for.
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cache_kwargs (`Dict[str, Any]`, `optional`):
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Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
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to know how where to write in the cache.
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Return:
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A tuple containing the updated key and value states.
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"""
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k_out = self.k_caches[layer_idx]
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k_out[page_idx, page_offset, :, :self.kv_lora_rank] = key_states.reshape(-1, *key_states.shape[2:])
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k_out[page_idx, page_offset, :, self.kv_lora_rank:] = value_states.reshape(-1, *value_states.shape[2:])
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return k_out
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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):
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page_offset = cache_position % self.page_size
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page_idx_local = cache_position // self.page_size
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query_ids = torch.zeros_like(cache_position)
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for i in range(len(q_indptr) - 1):
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start_idx = q_indptr[i]
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end_idx = q_indptr[i + 1]
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query_ids[start_idx:end_idx] = i
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page_idx = torch.zeros_like(page_idx_local)
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for i in range(bsz_tensors[0]):
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query_id = query_ids[i]
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local_block = page_idx_local[i]
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start_block = kv_indptr[query_id]
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if local_block < kv_indptr[query_id + 1] - kv_indptr[query_id]:
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page_idx[i] = kv_indices[start_block + local_block]
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return page_idx, page_offset
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