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Update IPEX Libs

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Disty0
2024-01-01 12:44:17 +03:00
parent 5cae6db804
commit 15d5e78ac2
4 changed files with 489 additions and 281 deletions
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241
library/ipex/diffusers.py
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@@ -1,10 +1,59 @@
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))
@cache
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
@cache
def find_attention_slice_sizes(query_shape, query_element_size, 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 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.
@@ -18,7 +67,9 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
def __init__(self, slice_size):
self.slice_size = slice_size
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
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
@@ -54,49 +105,61 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
(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:
block_multiply = query.element_size()
slice_block_size = self.slice_size * shape_three / 1024 / 1024 * block_multiply
block_size = query_tokens * slice_block_size
split_2_slice_size = query_tokens
if block_size > 4:
do_split_2 = True
#Find something divisible with the query_tokens
while (split_2_slice_size * slice_block_size) > 4:
split_2_slice_size = split_2_slice_size // 2
if split_2_slice_size <= 1:
split_2_slice_size = 1
break
else:
do_split_2 = False
for i in range(batch_size_attention // self.slice_size):
start_idx = i * self.slice_size
end_idx = (i + 1) * self.slice_size
####################################################################
# 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(), 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]
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
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)
attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2])
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] = attn_slice
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
####################################################################
hidden_states = attn.batch_to_head_dim(hidden_states)
@@ -115,6 +178,130 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
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())
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
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