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Add Birch-san's sub-quadratic attention implementation

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brkirch 2022-12-27 08:50:55 -05:00
parent 4af3ca5393
commit d782a95967
6 changed files with 312 additions and 35 deletions
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1
README.md
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@ -139,6 +139,7 @@ The documentation was moved from this README over to the project's [wiki](https:
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san), Amin Rezaei (https://github.com/AminRezaei0x443)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot

15
modules/sd_hijack.py
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@ -7,8 +7,6 @@ from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
from modules.sd_hijack_optimizations import invokeAI_mps_available
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
import ldm.modules.diffusionmodules.openaimodel
@ -40,17 +38,16 @@ def apply_optimizations():
print("Applying xformers cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
elif cmd_opts.opt_sub_quad_attention:
print("Applying sub-quadratic cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
elif cmd_opts.opt_split_attention_v1:
print("Applying v1 cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
if not invokeAI_mps_available and shared.device.type == 'mps':
print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
print("Applying v1 cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
else:
print("Applying cross attention optimization (InvokeAI).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
print("Applying cross attention optimization (InvokeAI).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
print("Applying cross attention optimization (Doggettx).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward

124
modules/sd_hijack_optimizations.py
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@ -1,7 +1,7 @@
import math
import sys
import traceback
import importlib
import psutil
import torch
from torch import einsum
@ -12,6 +12,8 @@ from einops import rearrange
from modules import shared
from modules.hypernetworks import hypernetwork
from .sub_quadratic_attention import efficient_dot_product_attention
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
try:
@ -22,6 +24,19 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
print(traceback.format_exc(), file=sys.stderr)
def get_available_vram():
if shared.device.type == 'cuda':
stats = torch.cuda.memory_stats(shared.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
return mem_free_total
else:
return psutil.virtual_memory().available
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
h = self.heads
@ -76,12 +91,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
mem_free_total = get_available_vram()
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
@ -118,19 +128,8 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
return self.to_out(r2)
def check_for_psutil():
try:
spec = importlib.util.find_spec('psutil')
return spec is not None
except ModuleNotFoundError:
return False
invokeAI_mps_available = check_for_psutil()
# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
if invokeAI_mps_available:
import psutil
mem_total_gb = psutil.virtual_memory().total // (1 << 30)
mem_total_gb = psutil.virtual_memory().total // (1 << 30)
def einsum_op_compvis(q, k, v):
s = einsum('b i d, b j d -> b i j', q, k)
@ -215,6 +214,70 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
# -- End of code from https://github.com/invoke-ai/InvokeAI --
# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
def sub_quad_attention_forward(self, x, context=None, mask=None):
assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
h = self.heads
q = self.to_q(x)
context = default(context, x)
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
k = self.to_k(context_k)
v = self.to_v(context_v)
del context, context_k, context_v, x
q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold_bytes=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
out_proj, dropout = self.to_out
x = out_proj(x)
x = dropout(x)
return x
def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold_bytes=None, use_checkpoint=True):
bytes_per_token = torch.finfo(q.dtype).bits//8
batch_x_heads, q_tokens, _ = q.shape
_, k_tokens, _ = k.shape
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
available_vram = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
if chunk_threshold_bytes is None:
chunk_threshold_bytes = available_vram
elif chunk_threshold_bytes == 0:
chunk_threshold_bytes = None
if kv_chunk_size_min is None:
kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))
elif kv_chunk_size_min == 0:
kv_chunk_size_min = None
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
# the big matmul fits into our memory limit; do everything in 1 chunk,
# i.e. send it down the unchunked fast-path
query_chunk_size = q_tokens
kv_chunk_size = k_tokens
return efficient_dot_product_attention(
q,
k,
v,
query_chunk_size=q_chunk_size,
kv_chunk_size=kv_chunk_size,
kv_chunk_size_min = kv_chunk_size_min,
use_checkpoint=use_checkpoint,
)
def xformers_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = self.to_q(x)
@ -252,12 +315,7 @@ def cross_attention_attnblock_forward(self, x):
h_ = torch.zeros_like(k, device=q.device)
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
mem_free_total = get_available_vram()
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
mem_required = tensor_size * 2.5
@ -312,3 +370,19 @@ def xformers_attnblock_forward(self, x):
return x + out
except NotImplementedError:
return cross_attention_attnblock_forward(self, x)
def sub_quad_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
b, c, h, w = q.shape
q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
q = q.contiguous()
k = k.contiguous()
v = v.contiguous()
out = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold_bytes=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
out = self.proj_out(out)
return x + out

4
modules/shared.py
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@ -56,6 +56,10 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the size threshold in bytes for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

201
modules/sub_quadratic_attention.py Normal file
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@ -0,0 +1,201 @@
# original source:
# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
# license:
# unspecified
# credit:
# Amin Rezaei (original author)
# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
# implementation of:
# Self-attention Does Not Need O(n2) Memory":
# https://arxiv.org/abs/2112.05682v2
from functools import partial
import torch
from torch import Tensor
from torch.utils.checkpoint import checkpoint
import math
from typing import Optional, NamedTuple, Protocol, List
def dynamic_slice(
x: Tensor,
starts: List[int],
sizes: List[int],
) -> Tensor:
slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
return x[slicing]
class AttnChunk(NamedTuple):
exp_values: Tensor
exp_weights_sum: Tensor
max_score: Tensor
class SummarizeChunk(Protocol):
@staticmethod
def __call__(
query: Tensor,
key: Tensor,
value: Tensor,
) -> AttnChunk: ...
class ComputeQueryChunkAttn(Protocol):
@staticmethod
def __call__(
query: Tensor,
key: Tensor,
value: Tensor,
) -> Tensor: ...
def _summarize_chunk(
query: Tensor,
key: Tensor,
value: Tensor,
scale: float,
) -> AttnChunk:
attn_weights = torch.baddbmm(
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
query,
key.transpose(1,2),
alpha=scale,
beta=0,
)
max_score, _ = torch.max(attn_weights, -1, keepdim=True)
max_score = max_score.detach()
exp_weights = torch.exp(attn_weights - max_score)
exp_values = torch.bmm(exp_weights, value)
max_score = max_score.squeeze(-1)
return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
def _query_chunk_attention(
query: Tensor,
key: Tensor,
value: Tensor,
summarize_chunk: SummarizeChunk,
kv_chunk_size: int,
) -> Tensor:
batch_x_heads, k_tokens, k_channels_per_head = key.shape
_, _, v_channels_per_head = value.shape
def chunk_scanner(chunk_idx: int) -> AttnChunk:
key_chunk = dynamic_slice(
key,
(0, chunk_idx, 0),
(batch_x_heads, kv_chunk_size, k_channels_per_head)
)
value_chunk = dynamic_slice(
value,
(0, chunk_idx, 0),
(batch_x_heads, kv_chunk_size, v_channels_per_head)
)
return summarize_chunk(query, key_chunk, value_chunk)
chunks: List[AttnChunk] = [
chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
]
acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
chunk_values, chunk_weights, chunk_max = acc_chunk
global_max, _ = torch.max(chunk_max, 0, keepdim=True)
max_diffs = torch.exp(chunk_max - global_max)
chunk_values *= torch.unsqueeze(max_diffs, -1)
chunk_weights *= max_diffs
all_values = chunk_values.sum(dim=0)
all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
return all_values / all_weights
# TODO: refactor CrossAttention#get_attention_scores to share code with this
def _get_attention_scores_no_kv_chunking(
query: Tensor,
key: Tensor,
value: Tensor,
scale: float,
) -> Tensor:
attn_scores = torch.baddbmm(
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
query,
key.transpose(1,2),
alpha=scale,
beta=0,
)
attn_probs = attn_scores.softmax(dim=-1)
del attn_scores
hidden_states_slice = torch.bmm(attn_probs, value)
return hidden_states_slice
class ScannedChunk(NamedTuple):
chunk_idx: int
attn_chunk: AttnChunk
def efficient_dot_product_attention(
query: Tensor,
key: Tensor,
value: Tensor,
query_chunk_size=1024,
kv_chunk_size: Optional[int] = None,
kv_chunk_size_min: Optional[int] = None,
use_checkpoint=True,
):
"""Computes efficient dot-product attention given query, key, and value.
This is efficient version of attention presented in
https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
Args:
query: queries for calculating attention with shape of
`[batch * num_heads, tokens, channels_per_head]`.
key: keys for calculating attention with shape of
`[batch * num_heads, tokens, channels_per_head]`.
value: values to be used in attention with shape of
`[batch * num_heads, tokens, channels_per_head]`.
query_chunk_size: int: query chunks size
kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
Returns:
Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
"""
batch_x_heads, q_tokens, q_channels_per_head = query.shape
_, k_tokens, _ = key.shape
scale = q_channels_per_head ** -0.5
kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
if kv_chunk_size_min is not None:
kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
def get_query_chunk(chunk_idx: int) -> Tensor:
return dynamic_slice(
query,
(0, chunk_idx, 0),
(batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
)
summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
_get_attention_scores_no_kv_chunking,
scale=scale
) if k_tokens <= kv_chunk_size else (
# fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
partial(
_query_chunk_attention,
kv_chunk_size=kv_chunk_size,
summarize_chunk=summarize_chunk,
)
)
if q_tokens <= query_chunk_size:
# fast-path for when there's just 1 query chunk
return compute_query_chunk_attn(
query=query,
key=key,
value=value,
)
# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
# and pass slices to be mutated, instead of torch.cat()ing the returned slices
res = torch.cat([
compute_query_chunk_attn(
query=get_query_chunk(i * query_chunk_size),
key=key,
value=value,
) for i in range(math.ceil(q_tokens / query_chunk_size))
], dim=1)
return res

2
requirements.txt
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@ -30,4 +30,4 @@ inflection
GitPython
torchsde
safetensors
psutil; sys_platform == 'darwin'
psutil