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smthemex
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from importlib import import_module
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from typing import Callable, Optional, Union
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from collections import deque
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import torch
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import torch.nn.functional as F
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from torch import nn
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from diffusers.models.attention_processor import Attention
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from diffusers.utils import USE_PEFT_BACKEND, deprecate, logging
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from diffusers.utils.import_utils import is_xformers_available
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from diffusers.utils.torch_utils import maybe_allow_in_graph
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from diffusers.models.lora import LoRACompatibleLinear, LoRALinearLayer
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from .utils import get_nn_feats, random_bipartite_soft_matching
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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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else:
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xformers = None
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class CachedSTAttnProcessor2_0:
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
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"""
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def __init__(self, name=None, use_feature_injection=False,
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feature_injection_strength=0.8,
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feature_similarity_threshold=0.98,
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interval=4,
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max_frames=1,
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use_tome_cache=False,
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tome_metric="keys",
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use_grid=False,
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tome_ratio=0.5):
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
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self.name = name
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self.use_feature_injection = use_feature_injection
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self.fi_strength = feature_injection_strength
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self.threshold = feature_similarity_threshold
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self.zero_tensor = torch.tensor(0)
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self.frame_id = torch.tensor(0)
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self.interval = torch.tensor(interval)
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self.max_frames = max_frames
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self.cached_key = None
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self.cached_value = None
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self.cached_output = None
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self.use_tome_cache = use_tome_cache
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self.tome_metric = tome_metric
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self.use_grid = use_grid
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self.tome_ratio = tome_ratio
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def _tome_step_kvout(self, keys, values, outputs):
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keys = torch.cat([self.cached_key, keys], dim=1)
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values = torch.cat([self.cached_value, values], dim=1)
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outputs = torch.cat([self.cached_output, outputs], dim=1)
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m_kv_out, _, _= random_bipartite_soft_matching(metric=keys, use_grid=self.use_grid, ratio=self.tome_ratio)
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compact_keys, compact_values, compact_outputs = m_kv_out(keys, values, outputs)
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self.cached_key = compact_keys
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self.cached_value = compact_values
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self.cached_output = compact_outputs
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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: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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temb: Optional[torch.FloatTensor] = 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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query = attn.to_q(hidden_states, *args)
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is_selfattn = False
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if encoder_hidden_states is None:
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is_selfattn = True
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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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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 is_selfattn:
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cached_key = key.clone()
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cached_value = value.clone()
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# Avoid if statement -> replace the dynamic graph to static graph
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if torch.equal(self.frame_id, self.zero_tensor):
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# ONNX
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self.cached_key = cached_key
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self.cached_value = cached_value
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key = torch.cat([key, self.cached_key], dim=1)
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value = torch.cat([value, self.cached_value], dim=1)
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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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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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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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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 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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if is_selfattn:
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cached_output = hidden_states.clone()
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if torch.equal(self.frame_id, self.zero_tensor):
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self.cached_output = cached_output
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if self.use_feature_injection and ("up_blocks.0" in self.name or "up_blocks.1" in self.name or 'mid_block' in self.name):
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nn_hidden_states = get_nn_feats(hidden_states, self.cached_output, threshold=self.threshold)
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hidden_states = hidden_states * (1-self.fi_strength) + self.fi_strength * nn_hidden_states
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mod_result = torch.remainder(self.frame_id, self.interval)
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if torch.equal(mod_result, self.zero_tensor) and is_selfattn:
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self._tome_step_kvout(cached_key, cached_value, cached_output)
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self.frame_id = self.frame_id + 1
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return hidden_states
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class CachedSTXFormersAttnProcessor:
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r"""
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Processor for implementing memory efficient attention using xFormers.
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Args:
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attention_op (`Callable`, *optional*, defaults to `None`):
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The base
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[operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
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use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
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operator.
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"""
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def __init__(self, attention_op: Optional[Callable] = None, name=None,
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use_feature_injection=False, feature_injection_strength=0.8, feature_similarity_threshold=0.98,
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interval=4, max_frames=4, use_tome_cache=False, tome_metric="keys", use_grid=False, tome_ratio=0.5):
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self.attention_op = attention_op
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self.name = name
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self.use_feature_injection = use_feature_injection
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self.fi_strength = feature_injection_strength
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self.threshold = feature_similarity_threshold
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self.frame_id = 0
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self.interval = interval
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self.cached_key = deque(maxlen=max_frames)
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self.cached_value = deque(maxlen=max_frames)
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self.cached_output = deque(maxlen=max_frames)
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self.use_tome_cache = use_tome_cache
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self.tome_metric = tome_metric
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self.use_grid = use_grid
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self.tome_ratio = tome_ratio
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def _tome_step_kvout(self, keys, values, outputs):
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if len(self.cached_value) == 1:
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keys = torch.cat(list(self.cached_key) + [keys], dim=1)
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values = torch.cat(list(self.cached_value) + [values], dim=1)
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outputs = torch.cat(list(self.cached_output) + [outputs], dim=1)
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m_kv_out, _, _= random_bipartite_soft_matching(metric=eval(self.tome_metric), use_grid=self.use_grid, ratio=self.tome_ratio)
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compact_keys, compact_values, compact_outputs = m_kv_out(keys, values, outputs)
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self.cached_key.append(compact_keys)
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self.cached_value.append(compact_values)
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self.cached_output.append(compact_outputs)
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else:
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self.cached_key.append(keys)
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self.cached_value.append(values)
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self.cached_output.append(outputs)
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def _tome_step_kv(self, keys, values):
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if len(self.cached_value) == 1:
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keys = torch.cat(list(self.cached_key) + [keys], dim=1)
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values = torch.cat(list(self.cached_value) + [values], dim=1)
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_, m_kv, _= random_bipartite_soft_matching(metric=eval(self.tome_metric), use_grid=self.use_grid, ratio=self.tome_ratio)
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compact_keys, compact_values = m_kv(keys, values)
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self.cached_key.append(compact_keys)
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self.cached_value.append(compact_values)
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else:
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self.cached_key.append(keys)
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self.cached_value.append(values)
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def _tome_step_out(self, outputs):
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if len(self.cached_value) == 1:
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outputs = torch.cat(list(self.cached_output) + [outputs], dim=1)
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_, _, m_out= random_bipartite_soft_matching(metric=outputs, use_grid=self.use_grid, ratio=self.tome_ratio)
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compact_outputs = m_out(outputs)
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self.cached_output.append(compact_outputs)
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else:
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self.cached_output.append(outputs)
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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: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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temb: Optional[torch.FloatTensor] = 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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args = () if USE_PEFT_BACKEND else (scale,)
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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, key_tokens, _ = (
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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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attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
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if attention_mask is not None:
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# expand our mask's singleton query_tokens dimension:
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# [batch*heads, 1, key_tokens] ->
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# [batch*heads, query_tokens, key_tokens]
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# so that it can be added as a bias onto the attention scores that xformers computes:
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# [batch*heads, query_tokens, key_tokens]
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# we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
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_, query_tokens, _ = hidden_states.shape
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attention_mask = attention_mask.expand(-1, query_tokens, -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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query = attn.to_q(hidden_states, *args)
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is_selfattn = False
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if encoder_hidden_states is None:
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is_selfattn = True
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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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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 is_selfattn:
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cached_key = key.clone()
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cached_value = value.clone()
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if len(self.cached_key) > 0:
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key = torch.cat([key] + list(self.cached_key), dim=1)
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value = torch.cat([value] + list(self.cached_value), dim=1)
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## Code for storing and visualizing features
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# if self.frame_id % self.interval == 0:
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# # if "down_blocks.0" in self.name or "up_blocks.3" in self.name:
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# # feats = {
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# # "hidden_states": hidden_states.clone().cpu(),
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# # "query": query.clone().cpu(),
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# # "key": cached_key.cpu(),
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# # "value": cached_value.cpu(),
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# # }
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# # torch.save(feats, f'./outputs/self_attn_feats_SD/{self.name}.frame{self.frame_id}.pt')
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# if self.use_tome_cache:
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# cached_key, cached_value = self._tome_step(cached_key, cached_value)
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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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hidden_states = xformers.ops.memory_efficient_attention(
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query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
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)
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hidden_states = hidden_states.to(query.dtype)
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hidden_states = attn.batch_to_head_dim(hidden_states)
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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 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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if is_selfattn:
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cached_output = hidden_states.clone()
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if self.use_feature_injection and ("up_blocks.0" in self.name or "up_blocks.1" in self.name or 'mid_block' in self.name):
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if len(self.cached_output) > 0:
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nn_hidden_states = get_nn_feats(hidden_states, self.cached_output, threshold=self.threshold)
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hidden_states = hidden_states * (1-self.fi_strength) + self.fi_strength * nn_hidden_states
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if self.frame_id % self.interval == 0:
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if is_selfattn:
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if self.use_tome_cache:
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self._tome_step_kvout(cached_key, cached_value, cached_output)
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else:
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self.cached_key.append(cached_key)
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self.cached_value.append(cached_value)
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self.cached_output.append(cached_output)
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self.frame_id += 1
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return hidden_states
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@@ -0,0 +1,127 @@
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from collections import deque
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from typing import Tuple, Callable
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from einops import rearrange
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import torch
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import torch.nn.functional as F
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def get_nn_feats(x, y, threshold=0.9):
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if type(x) is deque:
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x = torch.cat(list(x), dim=1)
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if type(y) is deque:
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y = torch.cat(list(y), dim=1)
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x_norm = F.normalize(x, p=2, dim=-1)
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y_norm = F.normalize(y, p=2, dim=-1)
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cosine_similarity = torch.matmul(x_norm, y_norm.transpose(1, 2))
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max_cosine_values, nearest_neighbors_indices = torch.max(cosine_similarity, dim=-1)
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mask = max_cosine_values < threshold
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# print('mask ratio', torch.sum(mask)/x.shape[0]/x.shape[1])
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indices_expanded = nearest_neighbors_indices.unsqueeze(-1).expand(-1, -1, x_norm.size(-1))
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nearest_neighbor_tensor = torch.gather(y, 1, indices_expanded)
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selected_tensor = torch.where(mask.unsqueeze(-1), x, nearest_neighbor_tensor)
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return selected_tensor
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def get_nn_latent(x, y, threshold=0.9):
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assert len(x.shape) == 4
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_, c, h, w = x.shape
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x_ = rearrange(x, 'n c h w -> n (h w) c')
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y_ = []
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for i in range(len(y)):
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y_.append(rearrange(y[i], 'n c h w -> n (h w) c'))
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y_ = torch.cat(y_, dim=1)
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x_norm = F.normalize(x_, p=2, dim=-1)
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y_norm = F.normalize(y_, p=2, dim=-1)
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cosine_similarity = torch.matmul(x_norm, y_norm.transpose(1, 2))
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max_cosine_values, nearest_neighbors_indices = torch.max(cosine_similarity, dim=-1)
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mask = max_cosine_values < threshold
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indices_expanded = nearest_neighbors_indices.unsqueeze(-1).expand(-1, -1, x_norm.size(-1))
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nearest_neighbor_tensor = torch.gather(y_, 1, indices_expanded)
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# Use values from x where the cosine similarity is below the threshold
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x_expanded = x_.expand_as(nearest_neighbor_tensor)
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selected_tensor = torch.where(mask.unsqueeze(-1), x_expanded, nearest_neighbor_tensor)
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||||
selected_tensor = rearrange(selected_tensor, 'n (h w) c -> n c h w', h=h, w=w, c=c)
|
||||
|
||||
return selected_tensor
|
||||
|
||||
|
||||
def random_bipartite_soft_matching(
|
||||
metric: torch.Tensor, use_grid: bool = False, ratio: float = 0.5
|
||||
) -> Tuple[Callable, Callable]:
|
||||
"""
|
||||
Applies ToMe with the two sets as (r chosen randomly, the rest).
|
||||
Input size is [batch, tokens, channels].
|
||||
|
||||
This will reduce the number of tokens by a ratio of ratio/2.
|
||||
"""
|
||||
|
||||
with torch.no_grad():
|
||||
B, N, _ = metric.shape
|
||||
if use_grid:
|
||||
assert ratio == 0.5
|
||||
sample = torch.randint(2, size=(B, N//2, 1), device=metric.device)
|
||||
sample_alternate = 1 - sample
|
||||
grid = torch.arange(0, N, 2).view(1, N//2, 1).to(device=metric.device)
|
||||
grid = grid.repeat(4, 1, 1)
|
||||
rand_idx = torch.cat([sample + grid, sample_alternate + grid], dim = 1)
|
||||
else:
|
||||
rand_idx = torch.rand(B, N, 1, device=metric.device).argsort(dim=1)
|
||||
r = int(ratio * N)
|
||||
a_idx = rand_idx[:, :r, :]
|
||||
b_idx = rand_idx[:, r:, :]
|
||||
def split(x):
|
||||
C = x.shape[-1]
|
||||
a = x.gather(dim=1, index=a_idx.expand(B, r, C))
|
||||
b = x.gather(dim=1, index=b_idx.expand(B, N - r, C))
|
||||
return a, b
|
||||
|
||||
metric = metric / metric.norm(dim=-1, keepdim=True)
|
||||
a, b = split(metric)
|
||||
scores = a @ b.transpose(-1, -2)
|
||||
|
||||
_, dst_idx = scores.max(dim=-1)
|
||||
dst_idx = dst_idx[..., None]
|
||||
|
||||
def merge_kv_out(keys: torch.Tensor, values: torch.Tensor, outputs: torch.Tensor, mode="mean") -> torch.Tensor:
|
||||
src_keys, dst_keys = split(keys)
|
||||
C_keys = src_keys.shape[-1]
|
||||
dst_keys = dst_keys.scatter_reduce(-2, dst_idx.expand(B, r, C_keys), src_keys, reduce=mode)
|
||||
|
||||
src_values, dst_values = split(values)
|
||||
C_values = src_values.shape[-1]
|
||||
dst_values = dst_values.scatter_reduce(-2, dst_idx.expand(B, r, C_values), src_values, reduce=mode)
|
||||
|
||||
src_outputs, dst_outputs = split(outputs)
|
||||
C_outputs = src_outputs.shape[-1]
|
||||
dst_outputs = dst_outputs.scatter_reduce(-2, dst_idx.expand(B, r, C_outputs), src_outputs, reduce=mode)
|
||||
|
||||
return dst_keys, dst_values, dst_outputs
|
||||
|
||||
def merge_kv(keys: torch.Tensor, values: torch.Tensor, mode="mean") -> torch.Tensor:
|
||||
src_keys, dst_keys = split(keys)
|
||||
C_keys = src_keys.shape[-1]
|
||||
dst_keys = dst_keys.scatter_reduce(-2, dst_idx.expand(B, r, C_keys), src_keys, reduce=mode)
|
||||
|
||||
src_values, dst_values = split(values)
|
||||
C_values = src_values.shape[-1]
|
||||
dst_values = dst_values.scatter_reduce(-2, dst_idx.expand(B, r, C_values), src_values, reduce=mode)
|
||||
|
||||
return dst_keys, dst_values
|
||||
|
||||
def merge_out(outputs: torch.Tensor, mode="mean") -> torch.Tensor:
|
||||
src_outputs, dst_outputs = split(outputs)
|
||||
C_outputs = src_outputs.shape[-1]
|
||||
dst_outputs = dst_outputs.scatter_reduce(-2, dst_idx.expand(B, r, C_outputs), src_outputs, reduce=mode)
|
||||
|
||||
return dst_outputs
|
||||
|
||||
return merge_kv_out, merge_kv, merge_out
|
||||
Reference in New Issue
Block a user