mirror of https://github.com/vladmandic/automatic
382 lines
15 KiB
Python
382 lines
15 KiB
Python
from collections.abc import Callable
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import math
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import torch
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import torch.nn.functional as F
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from diffusers.models.attention_processor import Attention
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from diffusers.utils import USE_PEFT_BACKEND
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from diffusers.utils.import_utils import is_xformers_available
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if is_xformers_available():
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import xformers
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import xformers.ops
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xformers_is_available = True
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else:
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xformers_is_available = False
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if hasattr(F, "scaled_dot_product_attention"):
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torch2_is_available = True
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else:
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torch2_is_available = False
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def init_generator(device: torch.device, fallback: torch.Generator = None):
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"""
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Forks the current default random generator given device.
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"""
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if device.type == "cpu":
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return torch.Generator(device="cpu").set_state(torch.get_rng_state())
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elif device.type == "cuda":
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return torch.Generator(device=device).set_state(torch.cuda.get_rng_state())
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elif device.type == "cuda":
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return torch.Generator(device=device).set_state(torch.mps.get_rng_state())
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else:
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if fallback is None:
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return init_generator(torch.device("cpu"))
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else:
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return fallback
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def do_nothing(x: torch.Tensor, mode: str = None): # pylint: disable=unused-argument
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return x
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def mps_gather_workaround(input, dim, index): # pylint: disable=redefined-builtin
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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 up_or_downsample(item, cur_w, cur_h, new_w, new_h, method):
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batch_size = item.shape[0]
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item = item.reshape(batch_size, cur_h, cur_w, -1)
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item = item.permute(0, 3, 1, 2)
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df = cur_h // new_h
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if method in "max_pool":
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item = F.max_pool2d(item, kernel_size=df, stride=df, padding=0)
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elif method in "avg_pool":
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item = F.avg_pool2d(item, kernel_size=df, stride=df, padding=0)
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else:
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item = F.interpolate(item, size=(new_h, new_w), mode=method)
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item = item.permute(0, 2, 3, 1)
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item = item.reshape(batch_size, new_h * new_w, -1)
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return item
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def compute_merge(x: torch.Tensor, tome_info):
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original_h, original_w = tome_info["size"]
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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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dim = x.shape[-1]
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if dim == 320:
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cur_level = "level_1"
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downsample_factor = tome_info['args']['downsample_factor']
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ratio = tome_info['args']['ratio']
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elif dim == 640:
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cur_level = "level_2"
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downsample_factor = tome_info['args']['downsample_factor_level_2']
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ratio = tome_info['args']['ratio_level_2']
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else:
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cur_level = "other"
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downsample_factor = 1
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ratio = 0.0
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args = tome_info["args"]
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cur_h, cur_w = original_h // downsample, original_w // downsample
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new_h, new_w = cur_h // downsample_factor, cur_w // downsample_factor
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if tome_info['timestep'] / 1000 > tome_info['args']['timestep_threshold_switch']:
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merge_method = args["merge_method"]
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else:
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merge_method = args["secondary_merge_method"]
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if cur_level != "other" and tome_info['timestep'] / 1000 > tome_info['args']['timestep_threshold_stop']:
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if merge_method == "downsample" and downsample_factor > 1:
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m = lambda x: up_or_downsample(x, cur_w, cur_h, new_w, new_h, args["downsample_method"]) # pylint: disable=unnecessary-lambda-assignment
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u = lambda x: up_or_downsample(x, new_w, new_h, cur_w, cur_h, args["downsample_method"]) # pylint: disable=unnecessary-lambda-assignment
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elif merge_method == "similarity" and ratio > 0.0:
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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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# Re-init the generator if it hasn't already been initialized or device has changed.
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if args["generator"] is None:
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args["generator"] = init_generator(x.device)
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elif args["generator"].device != x.device:
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args["generator"] = init_generator(x.device, fallback=args["generator"])
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# If the batch size is odd, then it's not possible for prompted and unprompted images to be in the same
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# batch, which causes artifacts with use_rand, so force it to be off.
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use_rand = False if x.shape[0] % 2 == 1 else args["use_rand"]
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m, u = bipartite_soft_matching_random2d(x, w, h, args["sx"], args["sy"], r,
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no_rand=not use_rand, generator=args["generator"])
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else:
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m, u = (do_nothing, do_nothing)
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else:
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m, u = (do_nothing, do_nothing)
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merge_fn, unmerge_fn = (m, u)
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return merge_fn, unmerge_fn
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def bipartite_soft_matching_random2d(metric: torch.Tensor,
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w: int,
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h: int,
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sx: int,
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sy: int,
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r: int,
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no_rand: bool = False,
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generator: torch.Generator = None) -> 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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- rand_seed: if no_rand is false, and if not None, sets random seed.
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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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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=generator.device, generator=generator).to(
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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 = torch.gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C))
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dst = torch.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 = torch.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 = torch.gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))
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src = torch.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 = torch.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,
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index=torch.gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c),
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src=unm)
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out.scatter_(dim=-2,
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index=torch.gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c),
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src=src)
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return out
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return merge, unmerge
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class TokenMergeAttentionProcessor:
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def __init__(self):
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# priortize torch2's flash attention, if not fall back to xformers then regular attention
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if torch2_is_available:
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self.attn_method = "torch2"
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elif xformers_is_available:
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self.attn_method = "xformers"
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else:
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self.attn_method = "regular"
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def torch2_attention(self, attn, query, key, value, attention_mask, batch_size):
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inner_dim=key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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return hidden_states
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def xformers_attention(self, attn, query, key, value, attention_mask, batch_size):
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query = attn.head_to_batch_dim(query).contiguous()
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key = attn.head_to_batch_dim(key).contiguous()
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value = attn.head_to_batch_dim(value).contiguous()
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if attention_mask is not None:
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attention_mask = attention_mask.reshape(batch_size * attn.heads, -1, attention_mask.shape[-1])
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hidden_states = xformers.ops.memory_efficient_attention(
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query, key, value, attn_bias=attention_mask, scale=attn.scale
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)
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hidden_states = attn.batch_to_head_dim(hidden_states)
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return hidden_states
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def regular_attention(self, attn, query, key, value, attention_mask, batch_size):
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query = attn.head_to_batch_dim(query)
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key = attn.head_to_batch_dim(key)
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value = attn.head_to_batch_dim(value)
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if attention_mask is not None:
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attention_mask = attention_mask.reshape(batch_size * attn.heads, -1, attention_mask.shape[-1])
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attention_probs = attn.get_attention_scores(query, key, attention_mask)
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hidden_states = torch.bmm(attention_probs, value)
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hidden_states = attn.batch_to_head_dim(hidden_states)
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return hidden_states
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.FloatTensor,
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encoder_hidden_states: torch.FloatTensor | None = None,
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attention_mask: torch.FloatTensor | None = None,
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temb: torch.FloatTensor | None = None,
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scale: float = 1.0,
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) -> torch.FloatTensor:
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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args = () if USE_PEFT_BACKEND else (scale,)
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if self._tome_info['args']['merge_tokens'] == "all": # pylint: disable=no-member
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merge_fn, unmerge_fn = compute_merge(hidden_states, self._tome_info) # pylint: disable=no-member
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hidden_states = merge_fn(hidden_states)
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query = attn.to_q(hidden_states, *args)
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
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if self._tome_info['args']['merge_tokens'] == "keys/values": # pylint: disable=no-member
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merge_fn, _ = compute_merge(encoder_hidden_states, self._tome_info) # pylint: disable=no-member
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encoder_hidden_states = merge_fn(encoder_hidden_states)
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key = attn.to_k(encoder_hidden_states, *args)
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value = attn.to_v(encoder_hidden_states, *args)
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if self.attn_method == "torch2":
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hidden_states = self.torch2_attention(attn, query, key, value, attention_mask, batch_size)
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elif self.attn_method == "xformers":
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hidden_states = self.xformers_attention(attn, query, key, value, attention_mask, batch_size)
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else:
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hidden_states = self.regular_attention(attn, query, key, value, attention_mask, batch_size)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states, *args)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if self._tome_info['args']['merge_tokens'] == "all": # pylint: disable=no-member
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hidden_states = unmerge_fn(hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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