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144 lines
5.4 KiB
144 lines
5.4 KiB
#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: |
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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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