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| import os | |
| import torch | |
| import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import | |
| import diffusers #0.24.0 # pylint: disable=import-error | |
| from diffusers.models.attention_processor import Attention | |
| from diffusers.utils import USE_PEFT_BACKEND | |
| from functools import cache | |
| # pylint: disable=protected-access, missing-function-docstring, line-too-long | |
| attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 4)) | |
| def find_slice_size(slice_size, slice_block_size): | |
| while (slice_size * slice_block_size) > attention_slice_rate: | |
| slice_size = slice_size // 2 | |
| if slice_size <= 1: | |
| slice_size = 1 | |
| break | |
| return slice_size | |
| def find_attention_slice_sizes(query_shape, query_element_size, query_device_type, slice_size=None): | |
| if len(query_shape) == 3: | |
| batch_size_attention, query_tokens, shape_three = query_shape | |
| shape_four = 1 | |
| else: | |
| batch_size_attention, query_tokens, shape_three, shape_four = query_shape | |
| if slice_size is not None: | |
| batch_size_attention = slice_size | |
| slice_block_size = query_tokens * shape_three * shape_four / 1024 / 1024 * query_element_size | |
| block_size = batch_size_attention * slice_block_size | |
| split_slice_size = batch_size_attention | |
| split_2_slice_size = query_tokens | |
| split_3_slice_size = shape_three | |
| do_split = False | |
| do_split_2 = False | |
| do_split_3 = False | |
| if query_device_type != "xpu": | |
| return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size | |
| if block_size > attention_slice_rate: | |
| do_split = True | |
| split_slice_size = find_slice_size(split_slice_size, slice_block_size) | |
| if split_slice_size * slice_block_size > attention_slice_rate: | |
| slice_2_block_size = split_slice_size * shape_three * shape_four / 1024 / 1024 * query_element_size | |
| do_split_2 = True | |
| split_2_slice_size = find_slice_size(split_2_slice_size, slice_2_block_size) | |
| if split_2_slice_size * slice_2_block_size > attention_slice_rate: | |
| slice_3_block_size = split_slice_size * split_2_slice_size * shape_four / 1024 / 1024 * query_element_size | |
| do_split_3 = True | |
| split_3_slice_size = find_slice_size(split_3_slice_size, slice_3_block_size) | |
| return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size | |
| class SlicedAttnProcessor: # pylint: disable=too-few-public-methods | |
| r""" | |
| Processor for implementing sliced attention. | |
| Args: | |
| slice_size (`int`, *optional*): | |
| The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and | |
| `attention_head_dim` must be a multiple of the `slice_size`. | |
| """ | |
| def __init__(self, slice_size): | |
| self.slice_size = slice_size | |
| def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, | |
| encoder_hidden_states=None, attention_mask=None) -> torch.FloatTensor: # pylint: disable=too-many-statements, too-many-locals, too-many-branches | |
| residual = hidden_states | |
| 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 | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| 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) | |
| dim = query.shape[-1] | |
| query = attn.head_to_batch_dim(query) | |
| 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) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| batch_size_attention, query_tokens, shape_three = query.shape | |
| hidden_states = torch.zeros( | |
| (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype | |
| ) | |
| #################################################################### | |
| # ARC GPUs can't allocate more than 4GB to a single block, Slice it: | |
| _, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_attention_slice_sizes(query.shape, query.element_size(), query.device.type, slice_size=self.slice_size) | |
| for i in range(batch_size_attention // split_slice_size): | |
| start_idx = i * split_slice_size | |
| end_idx = (i + 1) * split_slice_size | |
| if do_split_2: | |
| for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name | |
| start_idx_2 = i2 * split_2_slice_size | |
| end_idx_2 = (i2 + 1) * split_2_slice_size | |
| if do_split_3: | |
| for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name | |
| start_idx_3 = i3 * split_3_slice_size | |
| end_idx_3 = (i3 + 1) * split_3_slice_size | |
| query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] | |
| key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] | |
| attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] if attention_mask is not None else None | |
| attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) | |
| del query_slice | |
| del key_slice | |
| del attn_mask_slice | |
| attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3]) | |
| hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = attn_slice | |
| del attn_slice | |
| else: | |
| query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2] | |
| key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2] | |
| attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attention_mask is not None else None | |
| attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) | |
| del query_slice | |
| del key_slice | |
| del attn_mask_slice | |
| attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2]) | |
| hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice | |
| del attn_slice | |
| torch.xpu.synchronize(query.device) | |
| else: | |
| query_slice = query[start_idx:end_idx] | |
| key_slice = key[start_idx:end_idx] | |
| attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None | |
| attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) | |
| del query_slice | |
| del key_slice | |
| del attn_mask_slice | |
| attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) | |
| hidden_states[start_idx:end_idx] = attn_slice | |
| del attn_slice | |
| #################################################################### | |
| hidden_states = attn.batch_to_head_dim(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 AttnProcessor: | |
| r""" | |
| Default processor for performing attention-related computations. | |
| """ | |
| def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, | |
| encoder_hidden_states=None, attention_mask=None, | |
| temb=None, scale: float = 1.0) -> torch.Tensor: # pylint: disable=too-many-statements, too-many-locals, too-many-branches | |
| residual = hidden_states | |
| args = () if USE_PEFT_BACKEND else (scale,) | |
| 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 | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| 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, *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) | |
| key = attn.to_k(encoder_hidden_states, *args) | |
| value = attn.to_v(encoder_hidden_states, *args) | |
| query = attn.head_to_batch_dim(query) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| #################################################################### | |
| # ARC GPUs can't allocate more than 4GB to a single block, Slice it: | |
| batch_size_attention, query_tokens, shape_three = query.shape[0], query.shape[1], query.shape[2] | |
| hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype) | |
| do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_attention_slice_sizes(query.shape, query.element_size(), query.device.type) | |
| if do_split: | |
| for i in range(batch_size_attention // split_slice_size): | |
| start_idx = i * split_slice_size | |
| end_idx = (i + 1) * split_slice_size | |
| if do_split_2: | |
| for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name | |
| start_idx_2 = i2 * split_2_slice_size | |
| end_idx_2 = (i2 + 1) * split_2_slice_size | |
| if do_split_3: | |
| for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name | |
| start_idx_3 = i3 * split_3_slice_size | |
| end_idx_3 = (i3 + 1) * split_3_slice_size | |
| query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] | |
| key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] | |
| attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] if attention_mask is not None else None | |
| attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) | |
| del query_slice | |
| del key_slice | |
| del attn_mask_slice | |
| attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3]) | |
| hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = attn_slice | |
| del attn_slice | |
| else: | |
| query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2] | |
| key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2] | |
| attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attention_mask is not None else None | |
| attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) | |
| del query_slice | |
| del key_slice | |
| del attn_mask_slice | |
| attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2]) | |
| hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice | |
| del attn_slice | |
| else: | |
| query_slice = query[start_idx:end_idx] | |
| key_slice = key[start_idx:end_idx] | |
| attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None | |
| attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) | |
| del query_slice | |
| del key_slice | |
| del attn_mask_slice | |
| attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) | |
| hidden_states[start_idx:end_idx] = attn_slice | |
| del attn_slice | |
| torch.xpu.synchronize(query.device) | |
| else: | |
| 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) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states, *args) | |
| # 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 | |
| def ipex_diffusers(): | |
| #ARC GPUs can't allocate more than 4GB to a single block: | |
| diffusers.models.attention_processor.SlicedAttnProcessor = SlicedAttnProcessor | |
| diffusers.models.attention_processor.AttnProcessor = AttnProcessor | |