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IPEX slice Diffusers AttnProcessor V1

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Disty0
2023-12-31 15:36:54 +03:00
parent db8b9650c9
commit ba168390de
2 changed files with 234 additions and 36 deletions
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modules/intel/ipex/attention.py
+35 -15
View File
@@ -44,13 +44,13 @@ def find_sdpa_slice_sizes(query_shape, query_element_size):
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_block_size_2 = split_slice_size * shape_three * shape_four / 1024 / 1024 * query_element_size
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_block_size_2)
if split_2_slice_size * slice_block_size_2 > attention_slice_rate:
slice_block_size_3 = split_slice_size * split_2_slice_size * shape_four / 1024 / 1024 * query_element_size
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_block_size_3)
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
@@ -63,28 +63,36 @@ def find_bmm_slice_sizes(input_shape, input_element_size, mat2_shape):
split_slice_size = batch_size_attention
split_2_slice_size = input_tokens
split_3_slice_size = mat2_atten_shape
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_block_size_2 = split_slice_size * mat2_atten_shape / 1024 / 1024 * input_element_size
slice_2_block_size = split_slice_size * mat2_atten_shape / 1024 / 1024 * input_element_size
do_split_2 = True
split_2_slice_size = find_slice_size(split_2_slice_size, slice_block_size_2)
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 / 1024 / 1024 * input_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, split_slice_size, split_2_slice_size
return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size
original_torch_bmm = torch.bmm
def torch_bmm_32_bit(input, mat2, *, out=None):
do_split, do_split_2, split_slice_size, split_2_slice_size = find_bmm_slice_sizes(input.shape, input.element_size(), mat2.shape)
if input.device.type != "xpu":
return original_torch_bmm(input, mat2, out=out)
do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_bmm_slice_sizes(input.shape, input.element_size(), mat2.shape)
# Slice BMM
if do_split:
batch_size_attention, input_tokens = input.shape[0], input.shape[1]
batch_size_attention, input_tokens, mat2_atten_shape = input.shape[0], input.shape[1], mat2.shape[2]
hidden_states = torch.zeros(input.shape[0], input.shape[1], mat2.shape[2], device=input.device, dtype=input.dtype)
for i in range(batch_size_attention // split_slice_size):
start_idx = i * split_slice_size
@@ -93,11 +101,21 @@ def torch_bmm_32_bit(input, mat2, *, out=None):
for i2 in range(input_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
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm(
input[start_idx:end_idx, start_idx_2:end_idx_2],
mat2[start_idx:end_idx, start_idx_2:end_idx_2],
out=out
)
if do_split_3:
for i3 in range(mat2_atten_shape // 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
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = original_torch_bmm(
input[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
mat2[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
out=out
)
else:
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm(
input[start_idx:end_idx, start_idx_2:end_idx_2],
mat2[start_idx:end_idx, start_idx_2:end_idx_2],
out=out
)
else:
hidden_states[start_idx:end_idx] = original_torch_bmm(
input[start_idx:end_idx],
@@ -110,6 +128,8 @@ def torch_bmm_32_bit(input, mat2, *, out=None):
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
if query.device.type != "xpu":
return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_sdpa_slice_sizes(query.shape, query.element_size())
# Slice SDPA
modules/intel/ipex/diffusers.py
+199 -21
View File
@@ -3,6 +3,7 @@ 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
@@ -18,6 +19,40 @@ def find_slice_size(slice_size, slice_block_size):
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"""
@@ -68,44 +103,61 @@ 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
split_2_slice_size = find_slice_size(split_2_slice_size, slice_block_size)
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, 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)
@@ -124,6 +176,132 @@ 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:
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())
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
####################################################################
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