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# caching.py
import functools
import unittest
import contextlib
import dataclasses
from collections import defaultdict
from typing import DefaultDict, Dict
import torch
from diffusers import DiffusionPipeline, FluxTransformer2DModel
@dataclasses.dataclass
class CacheContext:
buffers: Dict[str, torch.Tensor] = dataclasses.field(default_factory=dict)
incremental_name_counters: DefaultDict[str, int] = dataclasses.field(default_factory=lambda: defaultdict(int))
def get_incremental_name(self, name=None):
if name is None:
name = "default"
idx = self.incremental_name_counters[name]
self.incremental_name_counters[name] += 1
return f"{name}_{idx}"
def reset_incremental_names(self):
self.incremental_name_counters.clear()
@torch.compiler.disable
def get_buffer(self, name):
return self.buffers.get(name)
@torch.compiler.disable
def set_buffer(self, name, buffer):
self.buffers[name] = buffer
def clear_buffers(self):
self.buffers.clear()
@torch.compiler.disable
def get_buffer(name):
cache_context = get_current_cache_context()
assert cache_context is not None, "cache_context must be set before"
return cache_context.get_buffer(name)
@torch.compiler.disable
def set_buffer(name, buffer):
cache_context = get_current_cache_context()
assert cache_context is not None, "cache_context must be set before"
cache_context.set_buffer(name, buffer)
_current_cache_context = None
def create_cache_context():
return CacheContext()
def get_current_cache_context():
return _current_cache_context
def set_current_cache_context(cache_context=None):
global _current_cache_context
_current_cache_context = cache_context
@contextlib.contextmanager
def cache_context(cache_context):
global _current_cache_context
old_cache_context = _current_cache_context
_current_cache_context = cache_context
try:
yield
finally:
_current_cache_context = old_cache_context
@torch.compiler.disable
def apply_prev_hidden_states_residual(hidden_states, encoder_hidden_states):
hidden_states_residual = get_buffer("hidden_states_residual")
assert hidden_states_residual is not None, "hidden_states_residual must be set before"
hidden_states = hidden_states_residual + hidden_states
encoder_hidden_states_residual = get_buffer("encoder_hidden_states_residual")
assert encoder_hidden_states_residual is not None, "encoder_hidden_states_residual must be set before"
encoder_hidden_states = encoder_hidden_states_residual + encoder_hidden_states
hidden_states = hidden_states.contiguous()
encoder_hidden_states = encoder_hidden_states.contiguous()
return hidden_states, encoder_hidden_states
def are_two_tensors_similar(t1, t2, *, threshold=0.85):
mean_diff = (t1 - t2).abs().mean()
mean_t1 = t1.abs().mean()
diff = mean_diff / mean_t1
return diff.item() < threshold
@torch.compiler.disable
def get_can_use_cache(first_hidden_states_residual, threshold, parallelized=False):
prev_first_hidden_states_residual = get_buffer("first_hidden_states_residual")
can_use_cache = prev_first_hidden_states_residual is not None and are_two_tensors_similar(
prev_first_hidden_states_residual,
first_hidden_states_residual,
)
return can_use_cache
class CachedTransformerBlocks(torch.nn.Module):
def __init__(
self,
transformer_blocks,
single_transformer_blocks=None,
*,
transformer=None,
residual_diff_threshold,
return_hidden_states_first=True,
):
super().__init__()
self.transformer = transformer
self.transformer_blocks = transformer_blocks
self.single_transformer_blocks = single_transformer_blocks
self.residual_diff_threshold = residual_diff_threshold
self.return_hidden_states_first = return_hidden_states_first
def forward(self, hidden_states, encoder_hidden_states, *args, **kwargs):
if self.residual_diff_threshold <= 0.0:
for block in self.transformer_blocks:
hidden_states, encoder_hidden_states = block(hidden_states, encoder_hidden_states, *args, **kwargs)
if not self.return_hidden_states_first:
hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states
if self.single_transformer_blocks is not None:
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
for block in self.single_transformer_blocks:
hidden_states = block(hidden_states, *args, **kwargs)
hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :]
return (
(hidden_states, encoder_hidden_states)
if self.return_hidden_states_first
else (encoder_hidden_states, hidden_states)
)
original_hidden_states = hidden_states
first_transformer_block = self.transformer_blocks[0]
hidden_states, encoder_hidden_states = first_transformer_block(
hidden_states, encoder_hidden_states, *args, **kwargs
)
if not self.return_hidden_states_first:
hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states
first_hidden_states_residual = hidden_states - original_hidden_states
del original_hidden_states
can_use_cache = get_can_use_cache(
first_hidden_states_residual,
threshold=self.residual_diff_threshold,
parallelized=self.transformer is not None and getattr(self.transformer, "_is_parallelized", False),
)
torch._dynamo.graph_break()
if can_use_cache:
del first_hidden_states_residual
hidden_states, encoder_hidden_states = apply_prev_hidden_states_residual(
hidden_states, encoder_hidden_states
)
else:
set_buffer("first_hidden_states_residual", first_hidden_states_residual)
del first_hidden_states_residual
(
hidden_states,
encoder_hidden_states,
hidden_states_residual,
encoder_hidden_states_residual,
) = self.call_remaining_transformer_blocks(hidden_states, encoder_hidden_states, *args, **kwargs)
set_buffer("hidden_states_residual", hidden_states_residual)
set_buffer("encoder_hidden_states_residual", encoder_hidden_states_residual)
torch._dynamo.graph_break()
return (
(hidden_states, encoder_hidden_states)
if self.return_hidden_states_first
else (encoder_hidden_states, hidden_states)
)
def call_remaining_transformer_blocks(self, hidden_states, encoder_hidden_states, *args, **kwargs):
original_hidden_states = hidden_states
original_encoder_hidden_states = encoder_hidden_states
for block in self.transformer_blocks[1:]:
hidden_states, encoder_hidden_states = block(hidden_states, encoder_hidden_states, *args, **kwargs)
if not self.return_hidden_states_first:
hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states
if self.single_transformer_blocks is not None:
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
for block in self.single_transformer_blocks:
hidden_states = block(hidden_states, *args, **kwargs)
encoder_hidden_states, hidden_states = hidden_states.split(
[encoder_hidden_states.shape[1], hidden_states.shape[1] - encoder_hidden_states.shape[1]], dim=1
)
# hidden_states_shape = hidden_states.shape
# encoder_hidden_states_shape = encoder_hidden_states.shape
hidden_states = hidden_states.reshape(-1).contiguous().reshape(original_hidden_states.shape)
encoder_hidden_states = (
encoder_hidden_states.reshape(-1).contiguous().reshape(original_encoder_hidden_states.shape)
)
# hidden_states = hidden_states.contiguous()
# encoder_hidden_states = encoder_hidden_states.contiguous()
hidden_states_residual = hidden_states - original_hidden_states
encoder_hidden_states_residual = encoder_hidden_states - original_encoder_hidden_states
hidden_states_residual = hidden_states_residual.reshape(-1).contiguous().reshape(original_hidden_states.shape)
encoder_hidden_states_residual = (
encoder_hidden_states_residual.reshape(-1).contiguous().reshape(original_encoder_hidden_states.shape)
)
return hidden_states, encoder_hidden_states, hidden_states_residual, encoder_hidden_states_residual
def apply_cache_on_transformer(
transformer: FluxTransformer2DModel,
*,
residual_diff_threshold=0.15,
):
cached_transformer_blocks = torch.nn.ModuleList(
[
CachedTransformerBlocks(
transformer.transformer_blocks,
transformer.single_transformer_blocks,
transformer=transformer,
residual_diff_threshold=residual_diff_threshold,
return_hidden_states_first=False,
)
]
)
dummy_single_transformer_blocks = torch.nn.ModuleList()
original_forward = transformer.forward
@functools.wraps(original_forward)
def new_forward(
self,
*args,
**kwargs,
):
with unittest.mock.patch.object(
self,
"transformer_blocks",
cached_transformer_blocks,
), unittest.mock.patch.object(
self,
"single_transformer_blocks",
dummy_single_transformer_blocks,
):
return original_forward(
*args,
**kwargs,
)
transformer.forward = new_forward.__get__(transformer)
return transformer
def apply_cache_on_pipe(
pipe: DiffusionPipeline,
*,
shallow_patch: bool = False,
**kwargs,
):
original_call = pipe.__class__.__call__
if not getattr(original_call, "_is_cached", False):
@functools.wraps(original_call)
def new_call(self, *args, **kwargs):
with cache_context(create_cache_context()):
return original_call(self, *args, **kwargs)
pipe.__class__.__call__ = new_call
new_call._is_cached = True
if not shallow_patch:
apply_cache_on_transformer(pipe.transformer, **kwargs)
pipe._is_cached = True
return pipe
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