|
| 1 | +importmath |
1 | 2 | importtorch |
| 3 | +importdiffusers |
2 | 4 |
|
3 | 5 |
|
4 | 6 | iftorch.backends.mps.is_available(): |
@@ -61,3 +63,150 @@ def new_torch_interpolate(input, size=None, scale_factor=None, mode='nearest', a |
61 | 63 | return_torch_interpolate(input,size,scale_factor,mode,align_corners,recompute_scale_factor,antialias) |
62 | 64 |
|
63 | 65 | torch.nn.functional.interpolate=new_torch_interpolate |
| 66 | + |
| 67 | +# TODO: refactor it |
| 68 | +_SlicedAttnProcessor=diffusers.models.attention_processor.SlicedAttnProcessor |
| 69 | +classChunkedSlicedAttnProcessor: |
| 70 | +r""" |
| 71 | + Processor for implementing sliced attention. |
| 72 | +
|
| 73 | + Args: |
| 74 | + slice_size (`int`, *optional*): |
| 75 | + The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and |
| 76 | + `attention_head_dim` must be a multiple of the `slice_size`. |
| 77 | + """ |
| 78 | + |
| 79 | +def__init__(self,slice_size): |
| 80 | +assertisinstance(slice_size,int) |
| 81 | +slice_size=1# TODO: maybe implement chunking in batches too when enough memory |
| 82 | +self.slice_size=slice_size |
| 83 | +self._sliced_attn_processor=_SlicedAttnProcessor(slice_size) |
| 84 | + |
| 85 | +def__call__(self,attn,hidden_states,encoder_hidden_states=None,attention_mask=None): |
| 86 | +ifself.slice_size!=1: |
| 87 | +returnself._sliced_attn_processor(attn,hidden_states,encoder_hidden_states,attention_mask) |
| 88 | + |
| 89 | +residual=hidden_states |
| 90 | + |
| 91 | +input_ndim=hidden_states.ndim |
| 92 | + |
| 93 | +ifinput_ndim==4: |
| 94 | +batch_size,channel,height,width=hidden_states.shape |
| 95 | +hidden_states=hidden_states.view(batch_size,channel,height*width).transpose(1,2) |
| 96 | + |
| 97 | +batch_size,sequence_length,_= ( |
| 98 | +hidden_states.shapeifencoder_hidden_statesisNoneelseencoder_hidden_states.shape |
| 99 | + ) |
| 100 | +attention_mask=attn.prepare_attention_mask(attention_mask,sequence_length,batch_size) |
| 101 | + |
| 102 | +ifattn.group_normisnotNone: |
| 103 | +hidden_states=attn.group_norm(hidden_states.transpose(1,2)).transpose(1,2) |
| 104 | + |
| 105 | +query=attn.to_q(hidden_states) |
| 106 | +dim=query.shape[-1] |
| 107 | +query=attn.head_to_batch_dim(query) |
| 108 | + |
| 109 | +ifencoder_hidden_statesisNone: |
| 110 | +encoder_hidden_states=hidden_states |
| 111 | +elifattn.norm_cross: |
| 112 | +encoder_hidden_states=attn.norm_encoder_hidden_states(encoder_hidden_states) |
| 113 | + |
| 114 | +key=attn.to_k(encoder_hidden_states) |
| 115 | +value=attn.to_v(encoder_hidden_states) |
| 116 | +key=attn.head_to_batch_dim(key) |
| 117 | +value=attn.head_to_batch_dim(value) |
| 118 | + |
| 119 | +batch_size_attention,query_tokens,_=query.shape |
| 120 | +hidden_states=torch.zeros( |
| 121 | + (batch_size_attention,query_tokens,dim//attn.heads),device=query.device,dtype=query.dtype |
| 122 | + ) |
| 123 | + |
| 124 | +chunk_tmp_tensor=torch.empty(self.slice_size,query.shape[1],key.shape[1],dtype=query.dtype,device=query.device) |
| 125 | + |
| 126 | +foriinrange(batch_size_attention//self.slice_size): |
| 127 | +start_idx=i*self.slice_size |
| 128 | +end_idx= (i+1)*self.slice_size |
| 129 | + |
| 130 | +query_slice=query[start_idx:end_idx] |
| 131 | +key_slice=key[start_idx:end_idx] |
| 132 | +attn_mask_slice=attention_mask[start_idx:end_idx]ifattention_maskisnotNoneelseNone |
| 133 | + |
| 134 | +self.get_attention_scores_chunked(attn,query_slice,key_slice,attn_mask_slice,hidden_states[start_idx:end_idx],value[start_idx:end_idx],chunk_tmp_tensor) |
| 135 | + |
| 136 | +hidden_states=attn.batch_to_head_dim(hidden_states) |
| 137 | + |
| 138 | +# linear proj |
| 139 | +hidden_states=attn.to_out[0](hidden_states) |
| 140 | +# dropout |
| 141 | +hidden_states=attn.to_out[1](hidden_states) |
| 142 | + |
| 143 | +ifinput_ndim==4: |
| 144 | +hidden_states=hidden_states.transpose(-1,-2).reshape(batch_size,channel,height,width) |
| 145 | + |
| 146 | +ifattn.residual_connection: |
| 147 | +hidden_states=hidden_states+residual |
| 148 | + |
| 149 | +hidden_states=hidden_states/attn.rescale_output_factor |
| 150 | + |
| 151 | +returnhidden_states |
| 152 | + |
| 153 | + |
| 154 | +defget_attention_scores_chunked(self,attn,query,key,attention_mask,hidden_states,value,chunk): |
| 155 | +# batch size = 1 |
| 156 | +assertquery.shape[0]==1 |
| 157 | +assertkey.shape[0]==1 |
| 158 | +assertvalue.shape[0]==1 |
| 159 | +asserthidden_states.shape[0]==1 |
| 160 | + |
| 161 | +dtype=query.dtype |
| 162 | +ifattn.upcast_attention: |
| 163 | +query=query.float() |
| 164 | +key=key.float() |
| 165 | + |
| 166 | +#out_item_size = query.dtype.itemsize |
| 167 | +#if attn.upcast_attention: |
| 168 | +# out_item_size = torch.float32.itemsize |
| 169 | +out_item_size=query.element_size() |
| 170 | +ifattn.upcast_attention: |
| 171 | +out_item_size=4 |
| 172 | + |
| 173 | +chunk_size=2**29 |
| 174 | + |
| 175 | +out_size=query.shape[1]*key.shape[1]*out_item_size |
| 176 | +chunks_count=min(query.shape[1],math.ceil((out_size-1)/chunk_size)) |
| 177 | +chunk_step=max(1,int(query.shape[1]/chunks_count)) |
| 178 | + |
| 179 | +key=key.transpose(-1,-2) |
| 180 | + |
| 181 | +def_get_chunk_view(tensor,start,length): |
| 182 | +ifstart+length>tensor.shape[1]: |
| 183 | +length=tensor.shape[1]-start |
| 184 | +#print(f"view: [{tensor.shape[0]},{tensor.shape[1]},{tensor.shape[2]}] - start: {start}, length: {length}") |
| 185 | +returntensor[:,start:start+length] |
| 186 | + |
| 187 | +forchunk_posinrange(0,query.shape[1],chunk_step): |
| 188 | +ifattention_maskisnotNone: |
| 189 | +torch.baddbmm( |
| 190 | +_get_chunk_view(attention_mask,chunk_pos,chunk_step), |
| 191 | +_get_chunk_view(query,chunk_pos,chunk_step), |
| 192 | +key, |
| 193 | +beta=1, |
| 194 | +alpha=attn.scale, |
| 195 | +out=chunk, |
| 196 | + ) |
| 197 | +else: |
| 198 | +torch.baddbmm( |
| 199 | +torch.zeros((1,1,1),device=query.device,dtype=query.dtype), |
| 200 | +_get_chunk_view(query,chunk_pos,chunk_step), |
| 201 | +key, |
| 202 | +beta=0, |
| 203 | +alpha=attn.scale, |
| 204 | +out=chunk, |
| 205 | + ) |
| 206 | +chunk=chunk.softmax(dim=-1) |
| 207 | +torch.bmm(chunk,value,out=_get_chunk_view(hidden_states,chunk_pos,chunk_step)) |
| 208 | + |
| 209 | +#del chunk |
| 210 | + |
| 211 | + |
| 212 | +diffusers.models.attention_processor.SlicedAttnProcessor=ChunkedSlicedAttnProcessor |