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import torch
class KVCache:
"""
A key-value cache for the model.
This class provides a mechanism to maintain a growing cache of keys and values,
particularly useful for models that benefit from caching previous states,
like transformers during autoregressive decoding.
Attributes:
data (torch.Tensor): The tensor storing keys and values.
current_length (int): Current length of the data being stored.
"""
def __init__(self, data, current_length):
"""
Initialize the KVCache.
Args:
data (torch.Tensor): Initial tensor to store the keys and values.
current_length (int): Initial length of the data.
"""
self.data = data
self.current_length = current_length
@property
def shape(self):
"""Return the shape of the data tensor with updated length."""
return (
self.data.shape[0],
self.data.shape[1],
self.current_length.item(),
self.data.shape[3],
)
def copy(self, indices: torch.Tensor, prev_length: int, dim: int = 2):
"""
Copy values from the current data at specified indices to a new location.
Args:
indices (torch.Tensor): Indices of the data tensor to be copied.
prev_length (int): Previous length before adding new data.
dim (int, optional): Dimension along which copying should be performed. Default is 2.
"""
tgt = self.data.index_select(dim, indices)
dst = self.data.narrow(dim, prev_length, tgt.shape[dim])
dst.copy_(tgt, non_blocking=True)
self.current_length.fill_(prev_length + tgt.shape[dim])
def cat(self, tensor: torch.Tensor, dim: int = 2):
"""
Concatenate the given tensor with the current data.
Args:
tensor (torch.Tensor): The tensor to be concatenated.
dim (int, optional): The dimension along which concatenation should be done. Default is 2.
Returns:
torch.Tensor: The data tensor after concatenation up to the current length.
"""
dst = self.data.narrow(dim, self.current_length, tensor.shape[dim])
dst.copy_(tensor)
self.current_length.add_(tensor.shape[dim])
return torch.narrow(self.data, 2, 0, self.current_length)
def initialize_past_key_values(model):
"""
Initialize past key and value states for a given transformer model.
This function prepares key-value cache structures for the model, allowing it to store and reuse
past key and value states during autoregressive decoding, which can improve efficiency.
Args:
model (nn.Module): The transformer model for which past key-value states need to be initialized.
Returns:
tuple:
- past_key_values (list): A list of KVCache objects for each layer in the model.
- past_key_values_data (torch.Tensor): The tensor that will store all keys and values.
- current_length_data (torch.Tensor): A tensor tracking the current length of keys/values in the cache.
"""
# Extracting configuration from the model
config = model.config
# Initializing the batch size to 1, this can be modified if different batch sizes are required
batch_size = 1
# Initializing a tensor to store past keys and values for all layers
devices=[]
for i in range(config.num_hidden_layers):
try:
device = model.model.layers[i].self_attn.q_proj.weight.device
except:
device=model.layers[i].self_attn.q_proj.weight.device
devices.append(device)
past_key_values_data_list=[]
startnum=0
startdevice=devices[0]
for id,i in enumerate(devices):
if startdevice!=i:
past_key_values_data = torch.zeros(
startnum * 2,
batch_size,
config.num_key_value_heads,
config.max_position_embeddings,
config.hidden_size // config.num_attention_heads,
device=startdevice,
dtype=model.dtype,
)
past_key_values_data_list.append(past_key_values_data)
startdevice = i
startnum=0
startnum += 1
past_key_values_data = torch.zeros(
startnum * 2,
batch_size,
config.num_key_value_heads,
config.max_position_embeddings,
config.hidden_size // config.num_attention_heads,
device=startdevice,
dtype=model.dtype,
)
past_key_values_data_list.append(past_key_values_data)
# Initialize tensor to store the current length of the cached data for all layers.
# [IMPORTANT] It needs to be kept on CPU for quick access and updates.
current_length_data = torch.zeros(
config.num_hidden_layers * 2, dtype=torch.long, device="cpu"
)
# Creating a KVCache for each pair of key and value in all layers
past_key_values = [] * config.num_hidden_layers
bias=0
start_data_m=devices[0].index
for i in range(config.num_hidden_layers):
data_m=devices[i].index
if data_m!=start_data_m:
bias=0
start_data_m=data_m
try:
past_key_values.append(
[
KVCache(past_key_values_data_list[data_m-devices[0].index][2*bias + j], current_length_data[i * 2 + j])
for j in range(2)
]
)
except:
past_key_values.append(
[
KVCache(past_key_values_data_list[0][2 * bias + j],
current_length_data[i * 2 + j])
for j in range(2)
]
)
bias+=1
return past_key_values, past_key_values_data_list, current_length_data
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