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| # modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py | |
| # and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/attention_processor.py | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def minmax_normalize(batch_maps): | |
| min_val = batch_maps.min(dim=-1, keepdim=True)[0].min(dim=-2, keepdim=True)[0] | |
| max_val = batch_maps.max(dim=-1, keepdim=True)[0].max(dim=-2, keepdim=True)[0] | |
| return (batch_maps - min_val) / (max_val - min_val + 1e-5) | |
| class AttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| ): | |
| super().__init__() | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| boxes=None, | |
| phrase_idxes=None, | |
| eot_idxes=None, | |
| ): | |
| 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) | |
| query = attn.to_q(hidden_states) | |
| 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) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| 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) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| 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) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](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 | |
| class MaskedIPAttnProcessor2_0(nn.Module): | |
| def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, text_tokens=77, | |
| need_text_attention_map=False, need_image_attention_map=True, num_dummy_tokens=4, mask_threshold=0.5, | |
| use_psuedo_attention_mask=False, subject_scales=None, start_step=5): | |
| super().__init__() | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") | |
| self.hidden_size = hidden_size | |
| self.cross_attention_dim = cross_attention_dim | |
| self.scale = scale | |
| self.num_tokens = num_tokens | |
| self.text_tokens = text_tokens | |
| self.num_dummy_tokens = num_dummy_tokens | |
| self.mask_threshold = mask_threshold | |
| self.subject_scales = subject_scales | |
| self.start_step = start_step | |
| self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| self.need_text_attention_map = need_text_attention_map | |
| self.need_image_attention_map = need_image_attention_map | |
| self.use_psuedo_attention_mask = use_psuedo_attention_mask | |
| self.attention_maps = [] | |
| def prepare_attention_mask_qk(self, boxes, phrase_idxes, sequence_length_q, sequence_length_k, | |
| batch_size, head_size, dtype, device, use_masked_text_attention=False): | |
| if boxes is None: | |
| return None, None | |
| # TODO: only support square image now | |
| num_patches_per_row = int(sequence_length_q ** 0.5) | |
| box_idxes_start = torch.floor(boxes[:, :, 0:2] * num_patches_per_row) | |
| box_idxes_end = torch.ceil(boxes[:, :, 2:4] * num_patches_per_row) | |
| box_idxes = torch.cat([box_idxes_start, box_idxes_end], dim=-1) | |
| box_masks = [] | |
| dummy_attention_mask = torch.ones((batch_size, sequence_length_q), dtype=dtype, device=device) | |
| for box_idx in box_idxes.unbind(dim=1): | |
| x_start_patch_idx, y_start_patch_idx, x_end_patch_idx, y_end_patch_idx = box_idx.unbind(dim=1) | |
| x_indices = torch.arange(num_patches_per_row).unsqueeze(0).expand(batch_size, -1).to(device) | |
| y_indices = torch.arange(num_patches_per_row).unsqueeze(0).expand(batch_size, -1).to(device) | |
| x_mask = ((x_indices >= x_start_patch_idx.unsqueeze(1)) & (x_indices < x_end_patch_idx.unsqueeze(1))).to(dtype) | |
| y_mask = ((y_indices >= y_start_patch_idx.unsqueeze(1)) & (y_indices < y_end_patch_idx.unsqueeze(1))).to(dtype) | |
| box_mask = torch.bmm(y_mask.unsqueeze(2), x_mask.unsqueeze(1)).reshape(batch_size, -1) | |
| box_masks.append(box_mask) | |
| dummy_attention_mask = torch.clamp(dummy_attention_mask - box_mask, min=0) | |
| # post mask | |
| post_dummy_attention_mask = dummy_attention_mask.to(torch.bool) | |
| post_dummy_attention_mask = post_dummy_attention_mask.repeat_interleave(head_size, dim=0) | |
| attention_mask_qk_image = torch.stack(box_masks, dim=-1) | |
| attention_mask_qk_image = attention_mask_qk_image.repeat_interleave(self.num_tokens, dim=-1) | |
| attention_mask_qk_image = (1 - attention_mask_qk_image.to(dtype)) * -10000.0 # mask to bias | |
| # use dummy image tokens to process the background | |
| dummy_attention_mask = dummy_attention_mask.unsqueeze(-1).repeat_interleave(self.num_dummy_tokens, dim=-1) | |
| dummy_attention_mask = (1 - dummy_attention_mask) * -10000.0 | |
| attention_mask_qk_image = torch.cat([dummy_attention_mask, attention_mask_qk_image], dim=-1) | |
| if attention_mask_qk_image.shape[0] < batch_size*head_size: | |
| attention_mask_qk_image = attention_mask_qk_image.repeat_interleave(head_size, dim=0) | |
| if use_masked_text_attention: | |
| attention_mask_qk_text = torch.ones((batch_size, sequence_length_q, sequence_length_k), dtype=dtype, device=device) | |
| for i in range(batch_size): | |
| for j in range(len(box_masks)): | |
| start_idx, end_idx = int(phrase_idxes[i, j, 0].item()), int(phrase_idxes[i, j, 1].item()) | |
| if start_idx == 0 and end_idx == 0: | |
| continue | |
| attention_mask_qk_text[i, :, start_idx:end_idx] = box_masks[j][i, ...].unsqueeze(-1) | |
| attention_mask_qk_text = (1 - attention_mask_qk_text) * -10000.0 | |
| if attention_mask_qk_text.shape[0] < batch_size*head_size: | |
| attention_mask_qk_text = attention_mask_qk_text.repeat_interleave(head_size, dim=0) | |
| else: | |
| attention_mask_qk_text = None | |
| return attention_mask_qk_image, attention_mask_qk_text, post_dummy_attention_mask | |
| def get_text_attention_maps(self, attention_probs, boxes, phrase_idxes, head_size): | |
| bsz = boxes.shape[0] | |
| _, num_tokens_q, num_tokens_k = attention_probs.shape | |
| attention_probs = attention_probs.view(bsz, head_size, num_tokens_q, num_tokens_k) | |
| num_ref = boxes.shape[1] | |
| h = w = int(num_tokens_q ** 0.5) | |
| batch_attention_maps = [] | |
| for i in range(bsz): | |
| sample_attention_maps = [] | |
| for j in range(num_ref): | |
| start_idx, end_idx = int(phrase_idxes[i, j, 0].item()), int(phrase_idxes[i, j, 1].item()) | |
| if start_idx == 0 and end_idx == 0: | |
| sample_attention_maps.append( | |
| torch.zeros(num_tokens_q, dtype=attention_probs.dtype, device=attention_probs.device)) | |
| else: | |
| attention_map = attention_probs[i, :, :, | |
| start_idx:end_idx] # [num_heads, num_tokens_q, num_tokens_phrase] | |
| attention_map = torch.mean(torch.mean(attention_map, dim=-1), dim=0) # [num_tokens_q] | |
| sample_attention_maps.append(attention_map) | |
| batch_attention_maps.append(torch.stack(sample_attention_maps)) | |
| self.attention_maps.append(torch.stack(batch_attention_maps).reshape(bsz, num_ref, h, w)) | |
| def get_psuedo_attention_mask(self, head_size): | |
| # text_attention_maps = self.attention_maps[-1] # [bsz, num_ref, h, w] | |
| if not self.use_psuedo_attention_mask or len(self.attention_maps) < self.start_step: | |
| return None, None | |
| text_attention_maps = torch.stack(self.attention_maps).mean(dim=0) # [bsz, num_ref, h, w] | |
| text_attention_maps = minmax_normalize(text_attention_maps) | |
| dtype, device = text_attention_maps.dtype, text_attention_maps.device | |
| bsz, num_ref, h, w = text_attention_maps.shape | |
| seq_len_q = h * w | |
| text_attention_maps = text_attention_maps.view(bsz, num_ref, -1) | |
| text_attention_maps = text_attention_maps.transpose(1, 2) # [bsz, h*w, num_ref] | |
| # use threshold to get the mask | |
| psuedo_attention_mask = (text_attention_maps > self.mask_threshold).to(dtype) | |
| psuedo_dummy_attention_mask = torch.ones((bsz, seq_len_q), dtype=dtype, device=device) | |
| for i in range(num_ref): | |
| psuedo_box_mask = psuedo_attention_mask[..., i] | |
| psuedo_dummy_attention_mask = torch.clamp(psuedo_dummy_attention_mask - psuedo_box_mask, min=0) | |
| # post mask | |
| post_psuedo_dummy_attention_mask = psuedo_dummy_attention_mask.to(torch.bool) | |
| post_psuedo_dummy_attention_mask = post_psuedo_dummy_attention_mask.repeat_interleave(head_size, dim=0) | |
| psuedo_attention_mask = psuedo_attention_mask.repeat_interleave(self.num_tokens, dim=-1) | |
| psuedo_attention_mask = (1 - psuedo_attention_mask) * -10000.0 # mask to bias | |
| psuedo_dummy_attention_mask = psuedo_dummy_attention_mask.unsqueeze(-1).repeat_interleave(self.num_dummy_tokens, dim=-1) | |
| psuedo_dummy_attention_mask = (1 - psuedo_dummy_attention_mask) * -10000.0 | |
| psuedo_attention_mask = torch.cat([psuedo_dummy_attention_mask, psuedo_attention_mask], dim=-1) | |
| if psuedo_attention_mask.shape[0] < bsz * head_size: | |
| psuedo_attention_mask = psuedo_attention_mask.repeat_interleave(head_size, dim=0) | |
| return psuedo_attention_mask, post_psuedo_dummy_attention_mask | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| boxes=None, | |
| phrase_idxes=None, | |
| eot_idxes=None, | |
| ): | |
| 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]) | |
| rf_attention_mask = None | |
| custom_attention_masks = self.prepare_attention_mask_qk(boxes, phrase_idxes, hidden_states.shape[1], | |
| self.text_tokens, batch_size, attn.heads, | |
| hidden_states.dtype, hidden_states.device, | |
| use_masked_text_attention=False) | |
| attention_mask_qk_image, attention_mask_qk_text, dummy_attention_mask = custom_attention_masks | |
| if attention_mask_qk_image is not None: | |
| attention_mask_qk_image = attention_mask_qk_image.view(batch_size, attn.heads, -1, attention_mask_qk_image.shape[-1]) | |
| if attention_mask_qk_text is not None: | |
| attention_mask_qk_text = attention_mask_qk_text.view(batch_size, attn.heads, -1, attention_mask_qk_text.shape[-1]) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| else: | |
| # get encoder_hidden_states, ip_hidden_states | |
| # end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
| end_pos = self.text_tokens | |
| encoder_hidden_states, ip_hidden_states = ( | |
| encoder_hidden_states[:, :end_pos, :], | |
| encoder_hidden_states[:, end_pos:, :], | |
| ) | |
| attention_mask, rf_attention_mask = ( | |
| attention_mask[:, :, :, :end_pos], | |
| attention_mask[:, :, :, end_pos:], | |
| ) if attention_mask is not None else (None, None) | |
| if attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| attention_mask = attention_mask_qk_text if attention_mask_qk_text is not None else attention_mask | |
| if not self.need_text_attention_map: | |
| # original attention 2.0 | |
| new_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) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| hidden_states = F.scaled_dot_product_attention( | |
| new_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) | |
| hidden_states = hidden_states.to(query.dtype) | |
| else: | |
| # we need get the attention map, so use the previous attention | |
| new_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.view(batch_size*attn.heads, -1, attention_mask.shape[-1]) | |
| attention_probs = attn.get_attention_scores(new_query, key, attention_mask) | |
| self.get_text_attention_maps(attention_probs, boxes, phrase_idxes, attn.heads) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| # get psuedo attention mask for image: better start after some timesteps | |
| psuedo_attention_mask, psuedo_dummy_attention_mask = self.get_psuedo_attention_mask(attn.heads) | |
| if psuedo_attention_mask is not None: | |
| psuedo_attention_mask = psuedo_attention_mask.view(batch_size, attn.heads, -1, | |
| psuedo_attention_mask.shape[-1]) | |
| ip_key = self.to_k_ip(ip_hidden_states) | |
| ip_value = self.to_v_ip(ip_hidden_states) | |
| rf_attention_mask = attention_mask_qk_image if attention_mask_qk_image is not None else rf_attention_mask | |
| rf_attention_mask = psuedo_attention_mask if psuedo_attention_mask is not None else rf_attention_mask | |
| dummy_attention_mask = psuedo_dummy_attention_mask if psuedo_dummy_attention_mask is not None else dummy_attention_mask | |
| if not self.need_image_attention_map: | |
| new_query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| ip_hidden_states = F.scaled_dot_product_attention( | |
| new_query, ip_key, ip_value, attn_mask=rf_attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| ip_hidden_states = ip_hidden_states.to(query.dtype) | |
| else: | |
| new_query = attn.head_to_batch_dim(query) | |
| ip_key = attn.head_to_batch_dim(ip_key) | |
| ip_value = attn.head_to_batch_dim(ip_value) | |
| if rf_attention_mask is not None: | |
| rf_attention_mask = rf_attention_mask.view(batch_size*attn.heads, -1, rf_attention_mask.shape[-1]) | |
| ip_attention_probs = attn.get_attention_scores(new_query, ip_key, rf_attention_mask) | |
| # mask attention_probs in background | |
| ip_attention_probs = torch.where(dummy_attention_mask.unsqueeze(-1), torch.zeros_like(ip_attention_probs), ip_attention_probs) | |
| if self.subject_scales is not None: | |
| # apply different scales to different subjects | |
| subject_scales = torch.tensor(self.subject_scales, dtype=ip_attention_probs.dtype, device=ip_attention_probs.device) | |
| subject_scales = subject_scales.unsqueeze(0).unsqueeze(0).repeat_interleave(self.num_tokens, dim=-1) | |
| dummy_subject_scales = torch.ones((1, 1, 1), dtype=ip_attention_probs.dtype, device=ip_attention_probs.device).repeat_interleave(self.num_dummy_tokens, dim=-1) | |
| subject_scales = torch.cat([dummy_subject_scales, subject_scales], dim=-1) | |
| ip_attention_probs = ip_attention_probs * subject_scales | |
| ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) | |
| ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states) | |
| if self.subject_scales is None: | |
| hidden_states = hidden_states + self.scale * ip_hidden_states | |
| else: | |
| hidden_states = hidden_states + ip_hidden_states | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](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 | |
| class CNAttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__(self, num_tokens=4, text_tokens=77): | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") | |
| self.num_tokens = num_tokens | |
| self.text_tokens = text_tokens | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| 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]) | |
| rf_attention_mask = None | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| else: | |
| # end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
| end_pos = self.text_tokens | |
| encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text | |
| attention_mask = attention_mask[:, :, :end_pos] | |
| if attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| 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) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| 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) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](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 | |