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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Refactor attention.CrossAttention to remove duplicate code and apply optimizations
Apply ~6% speedup by moving * self.scale to earlier on a smaller tensor. When we have enough VRAM don't make a useless zeros tensor. Switch between cuda/mps/cpu based on q.device.type to allow cleaner per architecture future optimizations. For cuda and cpu keep VRAM usage and faster slicing consistent. For cpu use smaller slices. Tested ~20% faster on i7, 9.8 to 7.7 s/it. Fix = typo to self.mem_total >= 8 in einsum_op_mps_v2 as per #582 discussion.
This commit is contained in:
Mihail Dumitrescu
Mihai
parent
100f2e8f57
commit
e0951f28cf
@ -90,7 +90,7 @@ class LinearAttention(nn.Module):
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b, c, h, w = x.shape
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qkv = self.to_qkv(x)
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q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
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k = k.softmax(dim=-1)
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k = k.softmax(dim=-1)
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context = torch.einsum('bhdn,bhen->bhde', k, v)
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out = torch.einsum('bhde,bhdn->bhen', context, q)
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out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
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@ -167,101 +167,85 @@ class CrossAttention(nn.Module):
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nn.Linear(inner_dim, query_dim),
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nn.Dropout(dropout)
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)
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if torch.cuda.is_available():
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self.einsum_op = self.einsum_op_cuda
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else:
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self.mem_total = psutil.virtual_memory().total / (1024**3)
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self.einsum_op = self.einsum_op_mps_v1 if self.mem_total >= 32 else self.einsum_op_mps_v2
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def einsum_op_compvis(self, q, k, v, r1):
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s1 = einsum('b i d, b j d -> b i j', q, k) * self.scale # faster
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s2 = s1.softmax(dim=-1, dtype=q.dtype)
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del s1
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r1 = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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return r1
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self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
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def einsum_op_mps_v1(self, q, k, v, r1):
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def einsum_op_compvis(self, q, k, v):
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s = einsum('b i d, b j d -> b i j', q, k)
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s = s.softmax(dim=-1, dtype=s.dtype)
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return einsum('b i j, b j d -> b i d', s, v)
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def einsum_op_slice_0(self, q, k, v, slice_size):
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r = 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], slice_size):
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end = i + slice_size
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r[i:end] = self.einsum_op_compvis(q[i:end], k[i:end], v[i:end])
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return r
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def einsum_op_slice_1(self, q, k, v, slice_size):
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r = 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[1], slice_size):
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end = i + slice_size
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r[:, i:end] = self.einsum_op_compvis(q[:, i:end], k, v)
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return r
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def einsum_op_mps_v1(self, q, k, v):
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if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
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r1 = self.einsum_op_compvis(q, k, v, r1)
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return self.einsum_op_compvis(q, k, v)
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else:
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slice_size = math.floor(2**30 / (q.shape[0] * 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) * self.scale
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s2 = s1.softmax(dim=-1, dtype=r1.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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return r1
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return self.einsum_op_slice_1(q, k, v, slice_size)
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def einsum_op_mps_v2(self, q, k, v, r1):
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if self.mem_total >= 8 and q.shape[1] <= 4096:
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r1 = self.einsum_op_compvis(q, k, v, r1)
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def einsum_op_mps_v2(self, q, k, v):
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if self.mem_total_gb > 8 and q.shape[1] <= 4096:
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return self.einsum_op_compvis(q, k, v)
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else:
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slice_size = 1
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for i in range(0, q.shape[0], slice_size):
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end = min(q.shape[0], i + slice_size)
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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, dtype=r1.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[i:end])
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del s2
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return r1
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def einsum_op_cuda(self, q, k, v, r1):
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return self.einsum_op_slice_0(q, k, v, 1)
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def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
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size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
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if size_mb <= max_tensor_mb:
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return self.einsum_op_compvis(q, k, v)
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div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
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if div <= q.shape[0]:
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return self.einsum_op_slice_0(q, k, v, q.shape[0] // div)
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return self.einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
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def einsum_op_cuda(self, q, k, v):
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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# Divide factor of safety as there's copying and fragmentation
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return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
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mem_required = tensor_size * 2.5
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steps = 1
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def einsum_op(self, q, k, v):
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if q.device.type == 'cuda':
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return self.einsum_op_cuda(q, k, v)
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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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if q.device.type == 'mps':
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if self.mem_total_gb >= 32:
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return self.einsum_op_mps_v1(q, k, v)
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return self.einsum_op_mps_v2(q, k, v)
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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 = min(q.shape[1], i + slice_size)
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
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s2 = s1.softmax(dim=-1, dtype=r1.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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return r1
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# Smaller slices are faster due to L2/L3/SLC caches.
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# Tested on i7 with 8MB L3 cache.
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return self.einsum_op_tensor_mem(q, k, v, 32)
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def forward(self, x, context=None, mask=None):
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h = self.heads
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q_in = self.to_q(x)
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q = self.to_q(x)
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context = default(context, x)
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k_in = self.to_k(context)
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v_in = self.to_v(context)
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device_type = 'mps' if x.device.type == 'mps' else 'cuda'
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k = self.to_k(context) * self.scale
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v = self.to_v(context)
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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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r1 = self.einsum_op(q, k, v, r1)
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del q, k, v
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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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return self.to_out(r2)
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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 = self.einsum_op(q, k, v)
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return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
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class BasicTransformerBlock(nn.Module):
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