scaled dot product attention
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@ -417,3 +417,222 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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</pre>
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<h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
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<small>Some small amounts of code borrowed and reworked.</small>
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<pre>
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Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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</pre>
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@ -42,6 +42,10 @@ def apply_optimizations():
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
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optimization_method = 'xformers'
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elif cmd_opts.opt_sdp_attention and (hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention"))):
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print("Applying scaled dot product cross attention optimization.")
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
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optimization_method = 'sdp'
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elif cmd_opts.opt_sub_quad_attention:
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print("Applying sub-quadratic cross attention optimization.")
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
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@ -346,6 +346,48 @@ def xformers_attention_forward(self, x, context=None, mask=None):
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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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# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
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# 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
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def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
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batch_size, sequence_length, inner_dim = x.shape
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if mask is not None:
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mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
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mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
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h = self.heads
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q_in = self.to_q(x)
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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_in = self.to_k(context_k)
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v_in = self.to_v(context_v)
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head_dim = inner_dim // h
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q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
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k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
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v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
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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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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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hidden_states = torch.nn.functional.scaled_dot_product_attention(
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q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
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hidden_states = hidden_states.to(dtype)
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# linear proj
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hidden_states = self.to_out[0](hidden_states)
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# dropout
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hidden_states = self.to_out[1](hidden_states)
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return hidden_states
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def cross_attention_attnblock_forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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@ -69,6 +69,7 @@ parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size fo
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parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
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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.")
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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")
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parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
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parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
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parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
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parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
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