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121 lines
5.1 KiB
Python
121 lines
5.1 KiB
Python
import torch
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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import diffusers #0.21.1 # pylint: disable=import-error
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from diffusers.models.attention_processor import Attention
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# pylint: disable=protected-access, missing-function-docstring, line-too-long
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class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
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r"""
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Processor for implementing sliced attention.
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Args:
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slice_size (`int`, *optional*):
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The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
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`attention_head_dim` must be a multiple of the `slice_size`.
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"""
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def __init__(self, slice_size):
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self.slice_size = slice_size
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def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): # pylint: disable=too-many-statements, too-many-locals, too-many-branches
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residual = hidden_states
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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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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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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)
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dim = query.shape[-1]
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query = attn.head_to_batch_dim(query)
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if encoder_hidden_states is None:
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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)
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value = attn.to_v(encoder_hidden_states)
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key = attn.head_to_batch_dim(key)
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value = attn.head_to_batch_dim(value)
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batch_size_attention, query_tokens, shape_three = query.shape
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hidden_states = torch.zeros(
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(batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
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)
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#ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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block_multiply = query.element_size()
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slice_block_size = self.slice_size * shape_three / 1024 / 1024 * block_multiply
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block_size = query_tokens * slice_block_size
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split_2_slice_size = query_tokens
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if block_size > 4:
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do_split_2 = True
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#Find something divisible with the query_tokens
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while (split_2_slice_size * slice_block_size) > 4:
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split_2_slice_size = split_2_slice_size // 2
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if split_2_slice_size <= 1:
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split_2_slice_size = 1
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break
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else:
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do_split_2 = False
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for i in range(batch_size_attention // self.slice_size):
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start_idx = i * self.slice_size
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end_idx = (i + 1) * self.slice_size
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if do_split_2:
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for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
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start_idx_2 = i2 * split_2_slice_size
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end_idx_2 = (i2 + 1) * split_2_slice_size
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query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2]
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key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2]
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attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attention_mask is not None else None
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attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
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attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2])
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hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice
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else:
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query_slice = query[start_idx:end_idx]
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key_slice = key[start_idx:end_idx]
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attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
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attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
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attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
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hidden_states[start_idx:end_idx] = attn_slice
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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)
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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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return hidden_states
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def ipex_diffusers():
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#ARC GPUs can't allocate more than 4GB to a single block:
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diffusers.models.attention_processor.SlicedAttnProcessor = SlicedAttnProcessor
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