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Running
on
Zero
| import torch | |
| from typing import Tuple, Callable | |
| from diffusers.models.attention_processor import XFormersAttnProcessor, Attention | |
| import xformers, xformers.ops | |
| from typing import Optional | |
| import math | |
| import torch.nn.functional as F | |
| from diffusers.utils import USE_PEFT_BACKEND | |
| from diffusers.utils.import_utils import is_xformers_available | |
| if is_xformers_available(): | |
| import xformers | |
| import xformers.ops | |
| xformers_is_available = True | |
| else: | |
| xformers_is_available = False | |
| if hasattr(F, "scaled_dot_product_attention"): | |
| torch2_is_available = True | |
| else: | |
| torch2_is_available = False | |
| def init_generator(device: torch.device, fallback: torch.Generator = None): | |
| """ | |
| Forks the current default random generator given device. | |
| """ | |
| if device.type == "cpu": | |
| return torch.Generator(device="cpu").set_state(torch.get_rng_state()) | |
| elif device.type == "cuda": | |
| return torch.Generator(device=device).set_state(torch.cuda.get_rng_state()) | |
| else: | |
| if fallback is None: | |
| return init_generator(torch.device("cpu")) | |
| else: | |
| return fallback | |
| def do_nothing(x: torch.Tensor, mode: str = None): | |
| return x | |
| def mps_gather_workaround(input, dim, index): | |
| if input.shape[-1] == 1: | |
| return torch.gather( | |
| input.unsqueeze(-1), | |
| dim - 1 if dim < 0 else dim, | |
| index.unsqueeze(-1) | |
| ).squeeze(-1) | |
| else: | |
| return torch.gather(input, dim, index) | |
| def up_or_downsample(item, cur_w, cur_h, new_w, new_h, method): | |
| batch_size = item.shape[0] | |
| item = item.reshape(batch_size, cur_h, cur_w, -1) | |
| item = item.permute(0, 3, 1, 2) | |
| df = cur_h // new_h | |
| if method in "max_pool": | |
| item = F.max_pool2d(item, kernel_size=df, stride=df, padding=0) | |
| elif method in "avg_pool": | |
| item = F.avg_pool2d(item, kernel_size=df, stride=df, padding=0) | |
| else: | |
| item = F.interpolate(item, size=(new_h, new_w), mode=method) | |
| item = item.permute(0, 2, 3, 1) | |
| item = item.reshape(batch_size, new_h * new_w, -1) | |
| return item | |
| def compute_merge(x: torch.Tensor, tome_info): | |
| original_h, original_w = tome_info["size"] | |
| original_tokens = original_h * original_w | |
| downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1]))) | |
| dim = x.shape[-1] | |
| if dim == 320: | |
| cur_level = "level_1" | |
| downsample_factor = tome_info['args']['downsample_factor'] | |
| ratio = tome_info['args']['ratio'] | |
| elif dim == 640: | |
| cur_level = "level_2" | |
| downsample_factor = tome_info['args']['downsample_factor_level_2'] | |
| ratio = tome_info['args']['ratio_level_2'] | |
| else: | |
| cur_level = "other" | |
| downsample_factor = 1 | |
| ratio = 0.0 | |
| args = tome_info["args"] | |
| cur_h, cur_w = original_h // downsample, original_w // downsample | |
| new_h, new_w = cur_h // downsample_factor, cur_w // downsample_factor | |
| if tome_info['timestep'] / 1000 > tome_info['args']['timestep_threshold_switch']: | |
| merge_method = args["merge_method"] | |
| else: | |
| merge_method = args["secondary_merge_method"] | |
| if cur_level != "other" and tome_info['timestep'] / 1000 > tome_info['args']['timestep_threshold_stop']: | |
| if merge_method == "downsample" and downsample_factor > 1: | |
| m = lambda x: up_or_downsample(x, cur_w, cur_h, new_w, new_h, args["downsample_method"]) | |
| u = lambda x: up_or_downsample(x, new_w, new_h, cur_w, cur_h, args["downsample_method"]) | |
| elif merge_method == "similarity" and ratio > 0.0: | |
| w = int(math.ceil(original_w / downsample)) | |
| h = int(math.ceil(original_h / downsample)) | |
| r = int(x.shape[1] * ratio) | |
| # Re-init the generator if it hasn't already been initialized or device has changed. | |
| if args["generator"] is None: | |
| args["generator"] = init_generator(x.device) | |
| elif args["generator"].device != x.device: | |
| args["generator"] = init_generator(x.device, fallback=args["generator"]) | |
| # If the batch size is odd, then it's not possible for prompted and unprompted images to be in the same | |
| # batch, which causes artifacts with use_rand, so force it to be off. | |
| use_rand = False if x.shape[0] % 2 == 1 else args["use_rand"] | |
| m, u = bipartite_soft_matching_random2d(x, w, h, args["sx"], args["sy"], r, | |
| no_rand=not use_rand, generator=args["generator"]) | |
| else: | |
| m, u = (do_nothing, do_nothing) | |
| else: | |
| m, u = (do_nothing, do_nothing) | |
| merge_fn, unmerge_fn = (m, u) | |
| return merge_fn, unmerge_fn | |
| def bipartite_soft_matching_random2d(metric: torch.Tensor, | |
| w: int, | |
| h: int, | |
| sx: int, | |
| sy: int, | |
| r: int, | |
| no_rand: bool = False, | |
| generator: torch.Generator = None) -> Tuple[Callable, Callable]: | |
| """ | |
| Partitions the tokens into src and dst and merges r tokens from src to dst. | |
| Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. | |
| Args: | |
| - metric [B, N, C]: metric to use for similarity | |
| - w: image width in tokens | |
| - h: image height in tokens | |
| - sx: stride in the x dimension for dst, must divide w | |
| - sy: stride in the y dimension for dst, must divide h | |
| - r: number of tokens to remove (by merging) | |
| - no_rand: if true, disable randomness (use top left corner only) | |
| - rand_seed: if no_rand is false, and if not None, sets random seed. | |
| """ | |
| B, N, _ = metric.shape | |
| if r <= 0: | |
| return do_nothing, do_nothing | |
| with torch.no_grad(): | |
| hsy, wsx = h // sy, w // sx | |
| # For each sy by sx kernel, randomly assign one token to be dst and the rest src | |
| if no_rand: | |
| rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) | |
| else: | |
| rand_idx = torch.randint(sy * sx, size=(hsy, wsx, 1), device=generator.device, generator=generator).to( | |
| metric.device) | |
| # 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 | |
| idx_buffer_view = torch.zeros(hsy, wsx, sy * sx, device=metric.device, dtype=torch.int64) | |
| idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) | |
| idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) | |
| # Image is not divisible by sx or sy so we need to move it into a new buffer | |
| if (hsy * sy) < h or (wsx * sx) < w: | |
| idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) | |
| idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view | |
| else: | |
| idx_buffer = idx_buffer_view | |
| # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices | |
| rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) | |
| # We're finished with these | |
| del idx_buffer, idx_buffer_view | |
| # rand_idx is currently dst|src, so split them | |
| num_dst = hsy * wsx | |
| a_idx = rand_idx[:, num_dst:, :] # src | |
| b_idx = rand_idx[:, :num_dst, :] # dst | |
| def split(x): | |
| C = x.shape[-1] | |
| src = torch.gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) | |
| dst = torch.gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) | |
| return src, dst | |
| # Cosine similarity between A and B | |
| metric = metric / metric.norm(dim=-1, keepdim=True) | |
| a, b = split(metric) | |
| scores = a @ b.transpose(-1, -2) | |
| # Can't reduce more than the # tokens in src | |
| r = min(a.shape[1], r) | |
| # Find the most similar greedily | |
| node_max, node_idx = scores.max(dim=-1) | |
| edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] | |
| unm_idx = edge_idx[..., r:, :] # Unmerged Tokens | |
| src_idx = edge_idx[..., :r, :] # Merged Tokens | |
| dst_idx = torch.gather(node_idx[..., None], dim=-2, index=src_idx) | |
| def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: | |
| src, dst = split(x) | |
| n, t1, c = src.shape | |
| unm = torch.gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) | |
| src = torch.gather(src, dim=-2, index=src_idx.expand(n, r, c)) | |
| dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) | |
| return torch.cat([unm, dst], dim=1) | |
| def unmerge(x: torch.Tensor) -> torch.Tensor: | |
| unm_len = unm_idx.shape[1] | |
| unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] | |
| _, _, c = unm.shape | |
| src = torch.gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) | |
| # Combine back to the original shape | |
| out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) | |
| out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) | |
| out.scatter_(dim=-2, | |
| index=torch.gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), | |
| src=unm) | |
| out.scatter_(dim=-2, | |
| index=torch.gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), | |
| src=src) | |
| return out | |
| return merge, unmerge | |
| class TokenMergeAttentionProcessor: | |
| def __init__(self): | |
| # priortize torch2's flash attention, if not fall back to xformers then regular attention | |
| if torch2_is_available: | |
| self.attn_method = "torch2" | |
| elif xformers_is_available: | |
| self.attn_method = "xformers" | |
| else: | |
| self.attn_method = "regular" | |
| def torch2_attention(self, attn, query, key, value, attention_mask, batch_size): | |
| inner_dim=key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| return hidden_states | |
| def xformers_attention(self, attn, query, key, value, attention_mask, batch_size): | |
| query = attn.head_to_batch_dim(query).contiguous() | |
| key = attn.head_to_batch_dim(key).contiguous() | |
| value = attn.head_to_batch_dim(value).contiguous() | |
| if attention_mask is not None: | |
| attention_mask = attention_mask.reshape(batch_size * attn.heads, -1, attention_mask.shape[-1]) | |
| hidden_states = xformers.ops.memory_efficient_attention( | |
| query, key, value, attn_bias=attention_mask, scale=attn.scale | |
| ) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| return hidden_states | |
| def regular_attention(self, attn, query, key, value, attention_mask, batch_size): | |
| query = attn.head_to_batch_dim(query) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| if attention_mask is not None: | |
| attention_mask = attention_mask.reshape(batch_size * attn.heads, -1, attention_mask.shape[-1]) | |
| attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| return hidden_states | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.FloatTensor, | |
| encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| temb: Optional[torch.FloatTensor] = None, | |
| scale: float = 1.0, | |
| ) -> torch.FloatTensor: | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| args = () if USE_PEFT_BACKEND else (scale,) | |
| if self._tome_info['args']['merge_tokens'] == "all": | |
| merge_fn, unmerge_fn = compute_merge(hidden_states, self._tome_info) | |
| hidden_states = merge_fn(hidden_states) | |
| query = attn.to_q(hidden_states, *args) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| if self._tome_info['args']['merge_tokens'] == "keys/values": | |
| merge_fn, _ = compute_merge(encoder_hidden_states, self._tome_info) | |
| encoder_hidden_states = merge_fn(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states, *args) | |
| value = attn.to_v(encoder_hidden_states, *args) | |
| if self.attn_method == "torch2": | |
| hidden_states = self.torch2_attention(attn, query, key, value, attention_mask, batch_size) | |
| elif self.attn_method == "xformers": | |
| hidden_states = self.xformers_attention(attn, query, key, value, attention_mask, batch_size) | |
| else: | |
| hidden_states = self.regular_attention(attn, query, key, value, attention_mask, batch_size) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states, *args) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if self._tome_info['args']['merge_tokens'] == "all": | |
| hidden_states = unmerge_fn(hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |