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1d813fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import torch
from typing import Optional, Tuple, Dict, Any, List
class HybridMambaAttentionDynamicCache:
def __init__(self, config, batch_size, dtype=torch.bfloat16, device=None):
self.dtype = dtype
self.layers_block_type = config.layers_block_type
self.device = device
self.has_previous_state = False
self.max_batch_size = batch_size
self.sequence_len_offset = 0
self.batch_size_offset = 0
self.transformer_layers = []
for i in range(len(self.layers_block_type)):
name = self.layers_block_type[i]
layer_type = name.split("_")[0] if "_" in name else name
if layer_type in ("Transformer", "HelixSeekMLA"):
self.transformer_layers.append(i)
num_layers = len(config.layers_block_type)
self.conv_states = [None for _ in range(num_layers)]
self.recurrent_states = [None for _ in range(num_layers)]
self.key_cache = [None for _ in range(num_layers)]
self.value_cache = [None for _ in range(num_layers)]
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]:
if self.key_cache[layer_idx] is None:
self.key_cache[layer_idx] = key_states
self.value_cache[layer_idx] = value_states
else:
self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
return self.key_cache[layer_idx], self.value_cache[layer_idx]
def reorder_cache(self, beam_idx: torch.LongTensor):
for layer_idx in range(len(self.key_cache)):
if self.key_cache[layer_idx] is not None:
device = self.key_cache[layer_idx].device
beam_idx_device = beam_idx.to(device)
self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx_device)
self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx_device)
if self.conv_states[layer_idx] is not None:
device = self.conv_states[layer_idx][0].device
beam_idx_device = beam_idx.to(device)
q_conv, k_conv, v_conv = self.conv_states[layer_idx]
self.conv_states[layer_idx] = (
q_conv.index_select(0, beam_idx_device),
k_conv.index_select(0, beam_idx_device),
v_conv.index_select(0, beam_idx_device),
)
if self.recurrent_states[layer_idx] is not None:
device = self.recurrent_states[layer_idx].device
beam_idx_device = beam_idx.to(device)
self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx_device)
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
if self.transformer_layers:
if layer_idx is None or layer_idx not in self.transformer_layers:
layer_idx = self.transformer_layers[0]
if layer_idx >= len(self.key_cache) or self.key_cache[layer_idx] is None:
return 0
return self.key_cache[layer_idx].shape[-2]
for states in (self.conv_states, self.recurrent_states):
for s in states:
if s is not None:
if isinstance(s, tuple):
s = s[0]
return s.shape[1] if s.dim() >= 2 else s.shape[0]
return 0
def get_max_length(self) -> Optional[int]:
return None
def __len__(self) -> int:
return len(self.layers_block_type)
def to_legacy_cache(self):
raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.")
@classmethod
def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None):
raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.")
class HelixSeekDeltaDynamicCache:
is_compileable = False
def __init__(self, config, batch_size=1, dtype=torch.bfloat16, device=None):
self.config = config
self.dtype = dtype
self.device = device
self.batch_size = batch_size
self._has_previous_state = False
num_layers = len(config.layers_block_type)
layer_types = []
for i in range(num_layers):
layer_name = config.layers_block_type[i] if hasattr(config, 'layers_block_type') else ""
if layer_name.startswith("HelixSeekDelta"):
layer_types.append("linear_attention")
else:
layer_types.append("full_attention")
self.layer_types = layer_types
self.transformer_layers = [
i for i in range(num_layers) if self.layer_types[i] == "full_attention"
]
linear_layers = [i for i in range(num_layers) if self.layer_types[i] == "linear_attention"]
self.last_linear_layer = linear_layers[-1] if linear_layers else -1
self.conv_states = [None for _ in range(num_layers)]
self.recurrent_states = [None for _ in range(num_layers)]
self.key_cache = [None for _ in range(num_layers)]
self.value_cache = [None for _ in range(num_layers)]
def __len__(self):
return len(self.layer_types)
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]:
self.key_cache[layer_idx] = key_states if self.key_cache[layer_idx] is None else torch.cat([self.key_cache[layer_idx], key_states], dim=2)
self.value_cache[layer_idx] = value_states if self.value_cache[layer_idx] is None else torch.cat([self.value_cache[layer_idx], value_states], dim=2)
return self.key_cache[layer_idx], self.value_cache[layer_idx]
def reorder_cache(self, beam_idx: torch.LongTensor):
for layer_idx in range(len(self.key_cache)):
if self.key_cache[layer_idx] is not None:
device = self.key_cache[layer_idx].device
beam_idx_device = beam_idx.to(device)
self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx_device)
self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx_device)
if self.conv_states[layer_idx] is not None:
device = self.conv_states[layer_idx][0].device
beam_idx_device = beam_idx.to(device)
q_conv, k_conv, v_conv = self.conv_states[layer_idx]
self.conv_states[layer_idx] = (
q_conv.index_select(0, beam_idx_device),
k_conv.index_select(0, beam_idx_device),
v_conv.index_select(0, beam_idx_device),
)
if self.recurrent_states[layer_idx] is not None:
device = self.recurrent_states[layer_idx].device
beam_idx_device = beam_idx.to(device)
self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx_device)
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
if layer_idx is None:
layer_idx = 0
if not self.transformer_layers:
layer_idx = 0
elif layer_idx not in self.transformer_layers:
layer_idx = self.transformer_layers[0]
if layer_idx is None:
return 0
if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
return 0
return self.key_cache[layer_idx].shape[-2]
def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> Tuple[int, int]:
kv_offset = 0
query_length = cache_position.shape[0]
past_seen_tokens = self.get_seq_length(layer_idx)
kv_length = query_length + past_seen_tokens
return kv_length, kv_offset
@property
def has_previous_state(self):
if self.last_linear_layer == -1:
return False
return self.conv_states[self.last_linear_layer] is not None
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