Add UI setting for upcasting attention to float32
Adds "Upcast cross attention layer to float32" option in Stable Diffusion settings. This allows for generating images using SD 2.1 models without --no-half or xFormers. In order to make upcasting cross attention layer optimizations possible it is necessary to indent several sections of code in sd_hijack_optimizations.py so that a context manager can be used to disable autocast. Also, even though Stable Diffusion (and Diffusers) only upcast q and k, unfortunately my findings were that most of the cross attention layer optimizations could not function unless v is upcast also.
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brkirch
parent
84d9ce30cb
commit
e3b53fd295
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@ -108,6 +108,10 @@ def autocast(disable=False):
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return torch.autocast("cuda")
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def without_autocast(disable=False):
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return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()
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class NansException(Exception):
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pass
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@ -125,7 +129,7 @@ def test_for_nans(x, where):
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message = "A tensor with all NaNs was produced in Unet."
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if not shared.cmd_opts.no_half:
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message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try using --no-half commandline argument to fix this."
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message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."
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elif where == "vae":
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message = "A tensor with all NaNs was produced in VAE."
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@ -611,7 +611,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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with devices.autocast(disable=devices.unet_needs_upcast):
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with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
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x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
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@ -9,7 +9,7 @@ from torch import einsum
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from ldm.util import default
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from einops import rearrange
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from modules import shared, errors
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from modules import shared, errors, devices
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from modules.hypernetworks import hypernetwork
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from .sub_quadratic_attention import efficient_dot_product_attention
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@ -52,18 +52,25 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
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for i in range(0, q.shape[0], 2):
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end = i + 2
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s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
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s1 *= self.scale
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dtype = q.dtype
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if shared.opts.upcast_attn:
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q, k, v = q.float(), k.float(), v.float()
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s2 = s1.softmax(dim=-1)
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del s1
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with devices.without_autocast(disable=not shared.opts.upcast_attn):
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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for i in range(0, q.shape[0], 2):
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end = i + 2
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s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
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s1 *= self.scale
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s2 = s1.softmax(dim=-1)
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del s1
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r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
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del s2
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del q, k, v
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r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
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del s2
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del q, k, v
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r1 = r1.to(dtype)
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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@ -82,45 +89,52 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
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k_in = self.to_k(context_k)
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v_in = self.to_v(context_v)
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k_in *= self.scale
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dtype = q_in.dtype
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if shared.opts.upcast_attn:
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q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()
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del context, x
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with devices.without_autocast(disable=not shared.opts.upcast_attn):
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k_in = k_in * self.scale
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del context, x
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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mem_free_total = get_available_vram()
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
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s2 = s1.softmax(dim=-1, dtype=q.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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del q, k, v
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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mem_free_total = get_available_vram()
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
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s2 = s1.softmax(dim=-1, dtype=q.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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del q, k, v
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r1 = r1.to(dtype)
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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@ -204,12 +218,20 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
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context = default(context, x)
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context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
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k = self.to_k(context_k) * self.scale
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k = self.to_k(context_k)
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v = self.to_v(context_v)
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del context, context_k, context_v, x
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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r = einsum_op(q, k, v)
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dtype = q.dtype
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if shared.opts.upcast_attn:
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q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()
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with devices.without_autocast(disable=not shared.opts.upcast_attn):
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k = k * self.scale
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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r = einsum_op(q, k, v)
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r = r.to(dtype)
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return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
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# -- End of code from https://github.com/invoke-ai/InvokeAI --
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@ -234,8 +256,14 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
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k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
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v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
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dtype = q.dtype
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if shared.opts.upcast_attn:
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q, k = q.float(), k.float()
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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)
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x = x.to(dtype)
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x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
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out_proj, dropout = self.to_out
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@ -268,15 +296,16 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
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query_chunk_size = q_tokens
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kv_chunk_size = k_tokens
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return efficient_dot_product_attention(
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q,
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k,
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v,
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query_chunk_size=q_chunk_size,
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kv_chunk_size=kv_chunk_size,
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kv_chunk_size_min = kv_chunk_size_min,
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use_checkpoint=use_checkpoint,
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)
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with devices.without_autocast(disable=q.dtype == v.dtype):
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return efficient_dot_product_attention(
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q,
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k,
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v,
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query_chunk_size=q_chunk_size,
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kv_chunk_size=kv_chunk_size,
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kv_chunk_size_min = kv_chunk_size_min,
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use_checkpoint=use_checkpoint,
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)
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def get_xformers_flash_attention_op(q, k, v):
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@ -306,8 +335,14 @@ def xformers_attention_forward(self, x, context=None, mask=None):
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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dtype = q.dtype
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if shared.opts.upcast_attn:
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q, k = q.float(), k.float()
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out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
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out = out.to(dtype)
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out = rearrange(out, 'b n h d -> b n (h d)', h=h)
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return self.to_out(out)
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@ -378,10 +413,14 @@ def xformers_attnblock_forward(self, x):
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v = self.v(h_)
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b, c, h, w = q.shape
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q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
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dtype = q.dtype
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if shared.opts.upcast_attn:
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q, k = q.float(), k.float()
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q = q.contiguous()
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k = k.contiguous()
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v = v.contiguous()
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out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
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out = out.to(dtype)
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out = rearrange(out, 'b (h w) c -> b c h w', h=h)
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out = self.proj_out(out)
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return x + out
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@ -410,6 +410,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
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"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
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"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
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}))
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options_templates.update(options_section(('compatibility', "Compatibility"), {
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@ -67,7 +67,7 @@ def _summarize_chunk(
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max_score, _ = torch.max(attn_weights, -1, keepdim=True)
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max_score = max_score.detach()
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exp_weights = torch.exp(attn_weights - max_score)
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exp_values = torch.bmm(exp_weights, value)
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exp_values = torch.bmm(exp_weights, value) if query.device.type == 'mps' else torch.bmm(exp_weights, value.to(exp_weights.dtype)).to(value.dtype)
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max_score = max_score.squeeze(-1)
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return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
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@ -129,7 +129,7 @@ def _get_attention_scores_no_kv_chunking(
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)
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attn_probs = attn_scores.softmax(dim=-1)
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del attn_scores
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hidden_states_slice = torch.bmm(attn_probs, value)
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hidden_states_slice = torch.bmm(attn_probs, value) if query.device.type == 'mps' else torch.bmm(attn_probs, value.to(attn_probs.dtype)).to(value.dtype)
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return hidden_states_slice
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