stable-diffusion-webui/modules/sd_hijack_optimizations.py
2023-03-24 16:29:16 +04:00

515 lines
19 KiB
Python

import math
import sys
import traceback
import psutil
import torch
from torch import einsum
from ldm.util import default
from einops import rearrange
from modules import shared, errors, devices
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:
import xformers.ops
shared.xformers_available = True
except Exception:
print("Cannot import xformers", file=sys.stderr)
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
q_in = self.to_q(x)
context = default(context, x)
context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
del context, context_k, context_v, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
dtype = q.dtype
if shared.opts.upcast_attn:
q, k, v = q.float(), k.float(), v.float()
with devices.without_autocast(disable=not shared.opts.upcast_attn):
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[0], 2):
end = i + 2
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
s1 *= self.scale
s2 = s1.softmax(dim=-1)
del s1
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
del s2
del q, k, v
r1 = r1.to(dtype)
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
# taken from https://github.com/Doggettx/stable-diffusion and modified
def split_cross_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
dtype = q_in.dtype
if shared.opts.upcast_attn:
q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()
with devices.without_autocast(disable=not shared.opts.upcast_attn):
k_in = k_in * self.scale
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
mem_free_total = get_available_vram()
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
if mem_required > mem_free_total:
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
s2 = s1.softmax(dim=-1, dtype=q.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
del q, k, v
r1 = r1.to(dtype)
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
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)
s = s.softmax(dim=-1, dtype=s.dtype)
return einsum('b i j, b j d -> b i d', s, v)
def einsum_op_slice_0(q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[0], slice_size):
end = i + slice_size
r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
return r
def einsum_op_slice_1(q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
return r
def einsum_op_mps_v1(q, k, v):
if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
return einsum_op_compvis(q, k, v)
else:
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
if slice_size % 4096 == 0:
slice_size -= 1
return einsum_op_slice_1(q, k, v, slice_size)
def einsum_op_mps_v2(q, k, v):
if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
return einsum_op_compvis(q, k, v)
else:
return einsum_op_slice_0(q, k, v, 1)
def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
if size_mb <= max_tensor_mb:
return einsum_op_compvis(q, k, v)
div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
if div <= q.shape[0]:
return einsum_op_slice_0(q, k, v, q.shape[0] // div)
return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
def einsum_op_cuda(q, k, v):
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(q.device)
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
# Divide factor of safety as there's copying and fragmentation
return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
def einsum_op(q, k, v):
if q.device.type == 'cuda':
return einsum_op_cuda(q, k, v)
if q.device.type == 'mps':
if mem_total_gb >= 32 and q.shape[0] % 32 != 0 and q.shape[0] * q.shape[1] < 2**18:
return einsum_op_mps_v1(q, k, v)
return einsum_op_mps_v2(q, k, v)
# Smaller slices are faster due to L2/L3/SLC caches.
# Tested on i7 with 8MB L3 cache.
return einsum_op_tensor_mem(q, k, v, 32)
def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
k = self.to_k(context_k)
v = self.to_v(context_v)
del context, context_k, context_v, x
dtype = q.dtype
if shared.opts.upcast_attn:
q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()
with devices.without_autocast(disable=not shared.opts.upcast_attn):
k = k * self.scale
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
r = einsum_op(q, k, v)
r = r.to(dtype)
return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
# -- 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
# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
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_hypernetworks(shared.loaded_hypernetworks, 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)
dtype = q.dtype
if shared.opts.upcast_attn:
q, k = q.float(), k.float()
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=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
x = x.to(dtype)
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=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
if chunk_threshold is None:
chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
elif chunk_threshold == 0:
chunk_threshold_bytes = None
else:
chunk_threshold_bytes = int(0.01 * chunk_threshold * get_available_vram())
if kv_chunk_size_min is None and chunk_threshold_bytes is not 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
with devices.without_autocast(disable=q.dtype == v.dtype):
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 get_xformers_flash_attention_op(q, k, v):
if not shared.cmd_opts.xformers_flash_attention:
return None
try:
flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
fw, bw = flash_attention_op
if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
return flash_attention_op
except Exception as e:
errors.display_once(e, "enabling flash attention")
return None
def xformers_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
dtype = q.dtype
if shared.opts.upcast_attn:
q, k, v = q.float(), k.float(), v.float()
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
out = out.to(dtype)
out = rearrange(out, 'b n h d -> b n (h d)', h=h)
return self.to_out(out)
# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
batch_size, sequence_length, inner_dim = x.shape
if mask is not None:
mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
head_dim = inner_dim // h
q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
del q_in, k_in, v_in
dtype = q.dtype
if shared.opts.upcast_attn:
q, k, v = q.float(), k.float(), v.float()
# the output of sdp = (batch, num_heads, seq_len, head_dim)
hidden_states = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
hidden_states = hidden_states.to(dtype)
# linear proj
hidden_states = self.to_out[0](hidden_states)
# dropout
hidden_states = self.to_out[1](hidden_states)
return hidden_states
def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
return scaled_dot_product_attention_forward(self, x, context, mask)
def cross_attention_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
q1 = self.q(h_)
k1 = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q1.shape
q2 = q1.reshape(b, c, h*w)
del q1
q = q2.permute(0, 2, 1) # b,hw,c
del q2
k = k1.reshape(b, c, h*w) # b,c,hw
del k1
h_ = torch.zeros_like(k, device=q.device)
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
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w2 = w1 * (int(c)**(-0.5))
del w1
w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
del w2
# attend to values
v1 = v.reshape(b, c, h*w)
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
del w3
h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
del v1, w4
h2 = h_.reshape(b, c, h, w)
del h_
h3 = self.proj_out(h2)
del h2
h3 += x
return h3
def xformers_attnblock_forward(self, x):
try:
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))
dtype = q.dtype
if shared.opts.upcast_attn:
q, k = q.float(), k.float()
q = q.contiguous()
k = k.contiguous()
v = v.contiguous()
out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
out = out.to(dtype)
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
out = self.proj_out(out)
return x + out
except NotImplementedError:
return cross_attention_attnblock_forward(self, x)
def sdp_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))
dtype = q.dtype
if shared.opts.upcast_attn:
q, k = q.float(), k.float()
q = q.contiguous()
k = k.contiguous()
v = v.contiguous()
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
out = out.to(dtype)
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
out = self.proj_out(out)
return x + out
def sdp_no_mem_attnblock_forward(self, x):
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
return sdp_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=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