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Merge pull request #477 from Disty0/united

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IPEX Optimizations
This commit is contained in:
henk717
2023-10-04 00:34:37 +02:00
committed by GitHub
parent caa040b416 5a3986fb06
commit 1ba20518fc
4 changed files with 33 additions and 22 deletions
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4
environments/ipex.yml
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@@ -23,7 +23,7 @@ dependencies:
- Pillow
- psutil
- pip:
- -f https://developer.intel.com/ipex-whl-stable-xpu
- --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
- torch==2.0.1a0; sys_platform == 'linux'
- torch==2.0.0a0; sys_platform == 'win32'
- intel_extension_for_pytorch==2.0.110+xpu; sys_platform == 'linux'
@@ -35,7 +35,7 @@ dependencies:
- lupa==1.10
- transformers[sentencepiece]==4.34.0
- huggingface_hub==0.16.4
- optimum[openvino,nncf,neural-compressor]==1.12.0
- optimum[onnxruntime,openvino,nncf,neural-compressor]==1.13.2
- safetensors==0.3.3
- accelerate==0.21.0
- git+https://github.com/VE-FORBRYDERNE/mkultra

9
modeling/ipex/__init__.py
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@@ -16,7 +16,6 @@ def ipex_init(): # pylint: disable=too-many-statements
torch.cuda.device = torch.xpu.device
torch.cuda.device_count = torch.xpu.device_count
torch.cuda.device_of = torch.xpu.device_of
torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
torch.cuda.get_device_name = torch.xpu.get_device_name
torch.cuda.get_device_properties = torch.xpu.get_device_properties
torch.cuda.init = torch.xpu.init
@@ -145,7 +144,7 @@ def ipex_init(): # pylint: disable=too-many-statements
ipex._C._DeviceProperties.minor = 2
#Fix functions with ipex:
torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_allocated(device)), torch.xpu.get_device_properties(device).total_memory]
torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_reserved(device)), torch.xpu.get_device_properties(device).total_memory]
torch._utils._get_available_device_type = lambda: "xpu"
torch.has_cuda = True
torch.cuda.has_half = True
@@ -157,6 +156,12 @@ def ipex_init(): # pylint: disable=too-many-statements
torch.cuda.get_device_properties.minor = 7
torch.cuda.ipc_collect = lambda *args, **kwargs: None
torch.cuda.utilization = lambda *args, **kwargs: 0
if hasattr(torch.xpu, 'getDeviceIdListForCard'):
torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
torch.cuda.get_device_id_list_per_card = torch.xpu.getDeviceIdListForCard
else:
torch.cuda.getDeviceIdListForCard = torch.xpu.get_device_id_list_per_card
torch.cuda.get_device_id_list_per_card = torch.xpu.get_device_id_list_per_card
ipex_hijacks()
attention_init()

33
modeling/ipex/attention.py
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@@ -10,13 +10,15 @@ def torch_bmm(input, mat2, *, out=None):
#ARC GPUs can't allocate more than 4GB to a single block, Slice it:
batch_size_attention, input_tokens, mat2_shape = input.shape[0], input.shape[1], mat2.shape[2]
block_multiply = 2.4 if input.dtype == torch.float32 else 1.2
block_size = (batch_size_attention * input_tokens * mat2_shape) / 1024 * block_multiply #MB
block_multiply = input.element_size()
slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply
block_size = batch_size_attention * slice_block_size
split_slice_size = batch_size_attention
if block_size >= 4000:
if block_size > 4:
do_split = True
#Find something divisible with the input_tokens
while ((split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply) > 4000:
while (split_slice_size * slice_block_size) > 4:
split_slice_size = split_slice_size // 2
if split_slice_size <= 1:
split_slice_size = 1
@@ -24,12 +26,12 @@ def torch_bmm(input, mat2, *, out=None):
else:
do_split = False
split_block_size = (split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply #MB
split_2_slice_size = input_tokens
if split_block_size >= 4000:
if split_slice_size * slice_block_size > 4:
slice_block_size2 = split_slice_size * mat2_shape / 1024 / 1024 * block_multiply
do_split_2 = True
#Find something divisible with the input_tokens
while ((split_slice_size * split_2_slice_size * mat2_shape) / 1024 * block_multiply) > 4000:
while (split_2_slice_size * slice_block_size2) > 4:
split_2_slice_size = split_2_slice_size // 2
if split_2_slice_size <= 1:
split_2_slice_size = 1
@@ -71,13 +73,16 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
else:
shape_one, batch_size_attention, query_tokens, shape_four = query.shape
no_shape_one = False
block_multiply = 3.6 if query.dtype == torch.float32 else 1.8
block_size = (shape_one * batch_size_attention * query_tokens * shape_four) / 1024 * block_multiply #MB
block_multiply = query.element_size()
slice_block_size = shape_one * query_tokens * shape_four / 1024 / 1024 * block_multiply
block_size = batch_size_attention * slice_block_size
split_slice_size = batch_size_attention
if block_size >= 4000:
if block_size > 4:
do_split = True
#Find something divisible with the shape_one
while ((shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply) > 4000:
while (split_slice_size * slice_block_size) > 4:
split_slice_size = split_slice_size // 2
if split_slice_size <= 1:
split_slice_size = 1
@@ -85,12 +90,12 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
else:
do_split = False
split_block_size = (shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply #MB
split_2_slice_size = query_tokens
if split_block_size >= 4000:
if split_slice_size * slice_block_size > 4:
slice_block_size2 = shape_one * split_slice_size * shape_four / 1024 / 1024 * block_multiply
do_split_2 = True
#Find something divisible with the batch_size_attention
while ((shape_one * split_slice_size * split_2_slice_size * shape_four) / 1024 * block_multiply) > 4000:
while (split_2_slice_size * slice_block_size2) > 4:
split_2_slice_size = split_2_slice_size // 2
if split_2_slice_size <= 1:
split_2_slice_size = 1

9
modeling/ipex/diffusers.py
View File

@@ -55,13 +55,14 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
)
#ARC GPUs can't allocate more than 4GB to a single block, Slice it:
block_multiply = 2.4 if query.dtype == torch.float32 else 1.2
block_size = (batch_size_attention * query_tokens * shape_three) / 1024 * block_multiply #MB
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 >= 4000:
if block_size > 4:
do_split_2 = True
#Find something divisible with the query_tokens
while ((self.slice_size * split_2_slice_size * shape_three) / 1024 * block_multiply) > 4000:
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