Update attention.py

Performance improvements to generate larger images in M1 invoke-ai#431

Update attention.py

Added dtype=r1.dtype to softmax

ldm/modules/attention.py

@@ -168,30 +168,72 @@ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.
  nn.Dropout(dropout)
  )
 
- def 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)
- v_in = self.to_v(context)
- device_type = 'mps' if x.device.type == 'mps' else 'cuda'
- 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)
+ if not torch.cuda.is_available():
+ mem_av = psutil.virtual_memory().available / (1024**3)
+ if mem_av > 32:
+ self.einsum_op = self.einsum_op_v1
+ elif mem_av > 12:
+ self.einsum_op = self.einsum_op_v2
+ else:
+ self.einsum_op = self.einsum_op_v3 
+ del mem_av 
+ else:
+ self.einsum_op = self.einsum_op_v4
 
- if device_type == 'mps':
- mem_free_total = psutil.virtual_memory().available
+ # mps 64-128 GB
+ def einsum_op_v1(self, q, k, v, r1):
+ if q.shape[1] <= 4096: # for 512x512: the max q.shape[1] is 4096
+ s1 = einsum('b i d, b j d -> b i j', q, k) * self.scale # aggressive/faster: operation in one go
+ s2 = s1.softmax(dim=-1, dtype=q.dtype)
+ del s1
+ r1 = einsum('b i j, b j d -> b i d', s2, v)
+ del s2
  else:
- 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
+ # q.shape[0] * q.shape[1] * slice_size >= 2**31 throws err
+ # needs around half of that slice_size to not generate noise
+ slice_size = math.floor(2**30 / (q.shape[0] * 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) * self.scale
+ s2 = s1.softmax(dim=-1, dtype=r1.dtype)
+ del s1 
+ r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+ del s2
+ return r1
+
+ # mps 16-32 GB (can be optimized)
+ def einsum_op_v2(self, q, k, v, r1):
+ slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+ for i in range(0, q.shape[1], slice_size): # conservative/less mem: operation in steps
+ end = i + slice_size
+ s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
+ s2 = s1.softmax(dim=-1, dtype=r1.dtype)
+ del s1 
+ r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+ del s2
+ return r1
+
+ # mps 8 GB
+ def einsum_op_v3(self, q, k, v, r1):
+ slice_size = 1
+ for i in range(0, q.shape[0], slice_size): # iterate over q.shape[0]
+ end = min(q.shape[0], i + slice_size)
+ s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end]) # adapted einsum for mem
+ s1 *= self.scale
+ s2 = s1.softmax(dim=-1, dtype=r1.dtype)
+ del s1
+ r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end]) # adapted einsum for mem
+ del s2
+ return r1
+
+ # cuda
+ def einsum_op_v4(self, q, k, v, r1):
+ 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] * 4
@@ -200,25 +242,36 @@ def forward(self, x, context=None, mask=None):
 
  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]
+ 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
+ end = min(q.shape[1], i + slice_size)
  s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
-
  s2 = s1.softmax(dim=-1, dtype=r1.dtype)
  del s1
-
  r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
- del s2
+ del s2 
+ return r1
+
+ def 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)
+ v_in = self.to_v(context)
+ device_type = 'mps' if x.device.type == 'mps' else 'cuda'
+ 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)
+ r1 = self.einsum_op(q, k, v, r1)
  del q, k, v
 
  r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)