178 lines
6.5 KiB
Python
178 lines
6.5 KiB
Python
#Taken from: https://github.com/dbolya/tomesd
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import torch
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from typing import Tuple, Callable
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import math
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def do_nothing(x: torch.Tensor, mode:str=None):
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return x
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def mps_gather_workaround(input, dim, index):
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if input.shape[-1] == 1:
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return torch.gather(
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input.unsqueeze(-1),
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dim - 1 if dim < 0 else dim,
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index.unsqueeze(-1)
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).squeeze(-1)
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else:
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return torch.gather(input, dim, index)
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def bipartite_soft_matching_random2d(metric: torch.Tensor,
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w: int, h: int, sx: int, sy: int, r: int,
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no_rand: bool = False) -> Tuple[Callable, Callable]:
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"""
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Partitions the tokens into src and dst and merges r tokens from src to dst.
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Dst tokens are partitioned by choosing one randomy in each (sx, sy) region.
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Args:
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- metric [B, N, C]: metric to use for similarity
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- w: image width in tokens
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- h: image height in tokens
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- sx: stride in the x dimension for dst, must divide w
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- sy: stride in the y dimension for dst, must divide h
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- r: number of tokens to remove (by merging)
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- no_rand: if true, disable randomness (use top left corner only)
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"""
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B, N, _ = metric.shape
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if r <= 0 or w == 1 or h == 1:
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return do_nothing, do_nothing
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gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather
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with torch.no_grad():
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hsy, wsx = h // sy, w // sx
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# For each sy by sx kernel, randomly assign one token to be dst and the rest src
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if no_rand:
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rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64)
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else:
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rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device)
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# The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead
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idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64)
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idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype))
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idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx)
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# Image is not divisible by sx or sy so we need to move it into a new buffer
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if (hsy * sy) < h or (wsx * sx) < w:
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idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64)
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idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view
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else:
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idx_buffer = idx_buffer_view
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# We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices
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rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1)
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# We're finished with these
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del idx_buffer, idx_buffer_view
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# rand_idx is currently dst|src, so split them
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num_dst = hsy * wsx
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a_idx = rand_idx[:, num_dst:, :] # src
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b_idx = rand_idx[:, :num_dst, :] # dst
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def split(x):
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C = x.shape[-1]
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src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C))
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dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C))
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return src, dst
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# Cosine similarity between A and B
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metric = metric / metric.norm(dim=-1, keepdim=True)
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a, b = split(metric)
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scores = a @ b.transpose(-1, -2)
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# Can't reduce more than the # tokens in src
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r = min(a.shape[1], r)
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# Find the most similar greedily
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node_max, node_idx = scores.max(dim=-1)
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edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]
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unm_idx = edge_idx[..., r:, :] # Unmerged Tokens
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src_idx = edge_idx[..., :r, :] # Merged Tokens
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dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx)
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def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
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src, dst = split(x)
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n, t1, c = src.shape
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unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))
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src = gather(src, dim=-2, index=src_idx.expand(n, r, c))
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dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)
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return torch.cat([unm, dst], dim=1)
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def unmerge(x: torch.Tensor) -> torch.Tensor:
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unm_len = unm_idx.shape[1]
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unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]
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_, _, c = unm.shape
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src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c))
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# Combine back to the original shape
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out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype)
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out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst)
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out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm)
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out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src)
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return out
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return merge, unmerge
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def get_functions(x, ratio, original_shape):
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b, c, original_h, original_w = original_shape
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original_tokens = original_h * original_w
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downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1])))
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stride_x = 2
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stride_y = 2
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max_downsample = 1
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if downsample <= max_downsample:
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w = int(math.ceil(original_w / downsample))
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h = int(math.ceil(original_h / downsample))
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r = int(x.shape[1] * ratio)
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no_rand = False
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m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand)
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return m, u
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nothing = lambda y: y
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return nothing, nothing
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class TomePatchModel:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "_for_testing"
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def patch(self, model, ratio):
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self.u = None
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def tomesd_m(q, k, v, extra_options):
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#NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q
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#however from my basic testing it seems that using q instead gives better results
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m, self.u = get_functions(q, ratio, extra_options["original_shape"])
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return m(q), k, v
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def tomesd_u(n, extra_options):
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return self.u(n)
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m = model.clone()
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m.set_model_attn1_patch(tomesd_m)
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m.set_model_attn1_output_patch(tomesd_u)
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return (m, )
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NODE_CLASS_MAPPINGS = {
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"TomePatchModel": TomePatchModel,
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}
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