Merge pull request #8367 from pamparamm/scaled-dot-product-attention

Add scaled dot product attention
2023-03-11 12:24:03 +03:00 · 2023-03-11 12:24:03 +03:00 · d81c503918
commit d81c503918
parent 1ace16e799 8d7fa2f67c
4 changed files with 303 additions and 0 deletions
--- a/html/licenses.html
+++ b/html/licenses.html
@ -417,3 +417,222 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 </pre>
 <h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
 <small>Some small amounts of code borrowed and reworked.</small>
 <pre>
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 </pre>
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@ -37,11 +37,23 @@ def apply_optimizations():
    optimization_method = None
    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
        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
        optimization_method = 'xformers'
    elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
        print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
        optimization_method = 'sdp-no-mem'
    elif cmd_opts.opt_sdp_attention and can_use_sdp:
        print("Applying scaled dot product cross attention optimization.")
        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
        optimization_method = 'sdp'
    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
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@ -346,6 +346,52 @@ def xformers_attention_forward(self, x, context=None, mask=None):
    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 = q.float(), k.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_)
@ -427,6 +473,30 @@ def xformers_attnblock_forward(self, x):
    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_)
--- a/modules/shared.py
+++ b/modules/shared.py
@ -69,6 +69,8 @@ parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size fo
 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)
 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("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
 parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 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")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)