From 820f1dc96b1979d7e92170c161db281ee8bd988b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 15:03:39 +0300
Subject: initial support for training textual inversion

---
 modules/sd_hijack_optimizations.py | 164 +++++++++++++++++++++++++++++++++++++
 1 file changed, 164 insertions(+)
 create mode 100644 modules/sd_hijack_optimizations.py

(limited to 'modules/sd_hijack_optimizations.py')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
new file mode 100644
index 00000000..9c079e57
--- /dev/null
+++ b/modules/sd_hijack_optimizations.py
@@ -0,0 +1,164 @@
+import math
+import torch
+from torch import einsum
+
+from ldm.util import default
+from einops import rearrange
+
+
+# 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 = self.to_q(x)
+    context = default(context, x)
+    k = self.to_k(context)
+    v = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
+    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
+
+    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
+def split_cross_attention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q_in = self.to_q(x)
+    context = default(context, x)
+    k_in = self.to_k(context) * self.scale
+    v_in = self.to_v(context)
+    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)
+
+    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(torch.cuda.current_device())
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+
+    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
+
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    del r1
+
+    return self.to_out(r2)
+
+def nonlinearity_hijack(x):
+    # swish
+    t = torch.sigmoid(x)
+    x *= t
+    del t
+
+    return x
+
+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)
+
+        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(torch.cuda.current_device())
+        mem_free_torch = mem_reserved - mem_active
+        mem_free_total = mem_free_cuda + mem_free_torch
+
+        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
-- 
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