from __future__ import annotations import math import psutil import platform import torch from torch import einsum from ldm.util import default from einops import rearrange from modules import shared, errors, devices, sub_quadratic_attention from modules.hypernetworks import hypernetwork import ldm.modules.attention import ldm.modules.diffusionmodules.model import sgm.modules.attention import sgm.modules.diffusionmodules.model diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward sgm_diffusionmodules_model_AttnBlock_forward = sgm.modules.diffusionmodules.model.AttnBlock.forward class SdOptimization: name: str = None label: str | None = None cmd_opt: str | None = None priority: int = 0 def title(self): if self.label is None: return self.name return f"{self.name} - {self.label}" def is_available(self): return True def apply(self): pass def undo(self): ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward sgm.modules.diffusionmodules.model.AttnBlock.forward = sgm_diffusionmodules_model_AttnBlock_forward class SdOptimizationXformers(SdOptimization): name = "xformers" cmd_opt = "xformers" priority = 100 def is_available(self): return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)) def apply(self): ldm.modules.attention.CrossAttention.forward = xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward sgm.modules.attention.CrossAttention.forward = xformers_attention_forward sgm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward class SdOptimizationSdpNoMem(SdOptimization): name = "sdp-no-mem" label = "scaled dot product without memory efficient attention" cmd_opt = "opt_sdp_no_mem_attention" priority = 80 def is_available(self): return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention) def apply(self): ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward sgm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward class SdOptimizationSdp(SdOptimizationSdpNoMem): name = "sdp" label = "scaled dot product" cmd_opt = "opt_sdp_attention" priority = 70 def apply(self): ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward sgm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward class SdOptimizationSubQuad(SdOptimization): name = "sub-quadratic" cmd_opt = "opt_sub_quad_attention" @property def priority(self): return 1000 if shared.device.type == 'mps' else 10 def apply(self): ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward sgm.modules.attention.CrossAttention.forward = sub_quad_attention_forward sgm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward class SdOptimizationV1(SdOptimization): name = "V1" label = "original v1" cmd_opt = "opt_split_attention_v1" priority = 10 def apply(self): ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1 sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1 class SdOptimizationInvokeAI(SdOptimization): name = "InvokeAI" cmd_opt = "opt_split_attention_invokeai" @property def priority(self): return 1000 if shared.device.type != 'mps' and not torch.cuda.is_available() else 10 def apply(self): ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI class SdOptimizationDoggettx(SdOptimization): name = "Doggettx" cmd_opt = "opt_split_attention" priority = 90 def apply(self): ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward sgm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward def list_optimizers(res): res.extend([ SdOptimizationXformers(), SdOptimizationSdpNoMem(), SdOptimizationSdp(), SdOptimizationSubQuad(), SdOptimizationV1(), SdOptimizationInvokeAI(), SdOptimizationDoggettx(), ]) if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: try: import xformers.ops shared.xformers_available = True except Exception: errors.report("Cannot import xformers", exc_info=True) 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, **kwargs): 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 = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (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, **kwargs): 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 = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (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 for i in range(0, q.shape[1], slice_size): end = min(i + slice_size, q.shape[1]) 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, **kwargs): 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 = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (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, **kwargs): 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) if q.device.type == 'mps': q, k, v = q.contiguous(), k.contiguous(), v.contiguous() 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: if q.device.type == 'mps': chunk_threshold_bytes = 268435456 * (2 if platform.processor() == 'i386' else bytes_per_token) else: chunk_threshold_bytes = 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 kv_chunk_size = k_tokens with devices.without_autocast(disable=q.dtype == v.dtype): return sub_quadratic_attention.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, **kwargs): 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 = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (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, **kwargs): 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, **kwargs): 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 = (rearrange(t, 'b c h w -> b (h w) c') for t in (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 = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v)) dtype = q.dtype if shared.opts.upcast_attn: q, k, v = q.float(), k.float(), v.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 = (rearrange(t, 'b c h w -> b (h w) c') for t in (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