forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtext_kv_cache.py
More file actions
173 lines (121 loc) 路 6.24 KB
/
Copy pathtext_kv_cache.py
File metadata and controls
173 lines (121 loc) 路 6.24 KB
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
# Copyright 2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
import torch
from .hooks import BaseState, HookRegistry, ModelHook, StateManager
_TEXT_KV_CACHE_TRANSFORMER_HOOK = "text_kv_cache_transformer"
_TEXT_KV_CACHE_BLOCK_HOOK = "text_kv_cache_block"
@dataclass
class TextKVCacheConfig:
"""Enable exact (lossless) text K/V caching for transformer models.
Pre-computes per-block text key and value projections once before the denoising loop and reuses them across all
steps. Positive and negative prompts are distinguished via a stable cache key captured by a transformer-level hook
before any intermediate tensor allocations.
"""
pass
class TextKVCacheState(BaseState):
"""Shared state between the transformer-level and block-level hooks.
The transformer hook writes the stable ``encoder_hidden_states`` ``data_ptr()`` (captured *before* ``txt_norm``) so
that block hooks can use it as a reliable cache key across denoising steps.
"""
def __init__(self):
self.key: int | None = None
def reset(self):
self.key = None
class TextKVCacheBlockState(BaseState):
"""Per-block state holding cached text key/value projections."""
def __init__(self):
self.kv_cache: dict[int, tuple[torch.Tensor, torch.Tensor]] = {}
def reset(self):
self.kv_cache.clear()
class TextKVCacheTransformerHook(ModelHook):
"""Captures ``encoder_hidden_states.data_ptr()`` before ``txt_norm``
and writes it to shared state for the block hooks to read."""
_is_stateful = True
def __init__(self, state_manager: StateManager):
super().__init__()
self.state_manager = state_manager
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
if self.state_manager._current_context is None:
self.state_manager.set_context("inference")
encoder_hidden_states = kwargs.get("encoder_hidden_states")
if encoder_hidden_states is not None:
state: TextKVCacheState = self.state_manager.get_state()
state.key = encoder_hidden_states.data_ptr()
return self.fn_ref.original_forward(*args, **kwargs)
def reset_state(self, module: torch.nn.Module):
self.state_manager.reset()
return module
class TextKVCacheBlockHook(ModelHook):
"""Caches ``(txt_key, txt_value)`` per block per unique prompt using
the stable cache key from the shared state."""
_is_stateful = True
def __init__(self, state_manager: StateManager, block_state_manager: StateManager):
super().__init__()
self.state_manager = state_manager
self.block_state_manager = block_state_manager
def new_forward(self, module: torch.nn.Module, *args, **kwargs):
from ..models.transformers.transformer_nucleusmoe_image import _apply_rotary_emb_nucleus
if self.state_manager._current_context is None:
self.state_manager.set_context("inference")
if self.block_state_manager._current_context is None:
self.block_state_manager.set_context("inference")
if "encoder_hidden_states" in kwargs:
encoder_hidden_states = kwargs["encoder_hidden_states"]
else:
encoder_hidden_states = args[1]
if "image_rotary_emb" in kwargs:
image_rotary_emb = kwargs["image_rotary_emb"]
elif len(args) > 3:
image_rotary_emb = args[3]
else:
image_rotary_emb = None
state: TextKVCacheState = self.state_manager.get_state()
cache_key = state.key
block_state: TextKVCacheBlockState = self.block_state_manager.get_state()
if cache_key not in block_state.kv_cache:
context = module.encoder_proj(encoder_hidden_states)
attn = module.attn
head_dim = attn.inner_dim // attn.heads
num_kv_heads = attn.inner_kv_dim // head_dim
txt_key = attn.add_k_proj(context).unflatten(-1, (num_kv_heads, -1))
txt_value = attn.add_v_proj(context).unflatten(-1, (num_kv_heads, -1))
if attn.norm_added_k is not None:
txt_key = attn.norm_added_k(txt_key)
if image_rotary_emb is not None:
_, txt_freqs = image_rotary_emb
txt_key = _apply_rotary_emb_nucleus(txt_key, txt_freqs, use_real=False)
block_state.kv_cache[cache_key] = (txt_key, txt_value)
txt_key, txt_value = block_state.kv_cache[cache_key]
attn_kwargs = kwargs.get("attention_kwargs") or {}
attn_kwargs["cached_txt_key"] = txt_key
attn_kwargs["cached_txt_value"] = txt_value
kwargs["attention_kwargs"] = attn_kwargs
return self.fn_ref.original_forward(*args, **kwargs)
def reset_state(self, module: torch.nn.Module):
self.block_state_manager.reset()
return module
def apply_text_kv_cache(module: torch.nn.Module, config: TextKVCacheConfig) -> None:
from ..models.transformers.transformer_nucleusmoe_image import NucleusMoEImageTransformerBlock
HookRegistry.check_if_exists_or_initialize(module)
state_manager = StateManager(TextKVCacheState)
transformer_hook = TextKVCacheTransformerHook(state_manager)
registry = HookRegistry.check_if_exists_or_initialize(module)
registry.register_hook(transformer_hook, _TEXT_KV_CACHE_TRANSFORMER_HOOK)
for _, submodule in module.named_modules():
if isinstance(submodule, NucleusMoEImageTransformerBlock):
block_state_manager = StateManager(TextKVCacheBlockState)
hook = TextKVCacheBlockHook(state_manager, block_state_manager)
block_registry = HookRegistry.check_if_exists_or_initialize(submodule)
block_registry.register_hook(hook, _TEXT_KV_CACHE_BLOCK_HOOK)