IPEX Torch 2.1
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Disty0
parent
e6afb766af
commit
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@ -25,10 +25,8 @@ dependencies:
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- ffmpeg
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- pip:
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- --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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- torch==2.0.1a0; sys_platform == 'linux'
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- torch==2.0.0a0; sys_platform == 'win32'
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- intel_extension_for_pytorch==2.0.110+xpu; sys_platform == 'linux'
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- intel_extension_for_pytorch==2.0.110+gitba7f6c1; sys_platform == 'win32'
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- torch==2.1.0a0
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- intel-extension-for-pytorch==2.1.10+xpu
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- openvino
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- onnxruntime-openvino
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- flask-cloudflared==0.0.10
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@ -4,13 +4,12 @@ import contextlib
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import torch
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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from .hijacks import ipex_hijacks
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from .attention import attention_init
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# pylint: disable=protected-access, missing-function-docstring, line-too-long
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def ipex_init(): # pylint: disable=too-many-statements
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try:
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#Replace cuda with xpu:
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# Replace cuda with xpu:
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torch.cuda.current_device = torch.xpu.current_device
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torch.cuda.current_stream = torch.xpu.current_stream
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torch.cuda.device = torch.xpu.device
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@ -91,9 +90,9 @@ def ipex_init(): # pylint: disable=too-many-statements
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torch.cuda.CharStorage = torch.xpu.CharStorage
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torch.cuda.__file__ = torch.xpu.__file__
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torch.cuda._is_in_bad_fork = torch.xpu.lazy_init._is_in_bad_fork
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#torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing
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# torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing
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#Memory:
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# Memory:
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torch.cuda.memory = torch.xpu.memory
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if 'linux' in sys.platform and "WSL2" in os.popen("uname -a").read():
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torch.xpu.empty_cache = lambda: None
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@ -113,7 +112,7 @@ def ipex_init(): # pylint: disable=too-many-statements
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torch.cuda.memory_stats_as_nested_dict = torch.xpu.memory_stats_as_nested_dict
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torch.cuda.reset_accumulated_memory_stats = torch.xpu.reset_accumulated_memory_stats
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#RNG:
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# RNG:
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torch.cuda.get_rng_state = torch.xpu.get_rng_state
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torch.cuda.get_rng_state_all = torch.xpu.get_rng_state_all
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torch.cuda.set_rng_state = torch.xpu.set_rng_state
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@ -124,7 +123,7 @@ def ipex_init(): # pylint: disable=too-many-statements
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torch.cuda.seed_all = torch.xpu.seed_all
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torch.cuda.initial_seed = torch.xpu.initial_seed
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#AMP:
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# AMP:
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torch.cuda.amp = torch.xpu.amp
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if not hasattr(torch.cuda.amp, "common"):
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torch.cuda.amp.common = contextlib.nullcontext()
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@ -139,12 +138,12 @@ def ipex_init(): # pylint: disable=too-many-statements
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except Exception: # pylint: disable=broad-exception-caught
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torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
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#C
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# C
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torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream
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ipex._C._DeviceProperties.major = 2023
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ipex._C._DeviceProperties.minor = 2
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#Fix functions with ipex:
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# Fix functions with ipex:
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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]
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torch._utils._get_available_device_type = lambda: "xpu"
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torch.has_cuda = True
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@ -157,20 +156,14 @@ def ipex_init(): # pylint: disable=too-many-statements
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torch.cuda.get_device_properties.minor = 7
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torch.cuda.ipc_collect = lambda *args, **kwargs: None
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torch.cuda.utilization = lambda *args, **kwargs: 0
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if hasattr(torch.xpu, 'getDeviceIdListForCard'):
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torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
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torch.cuda.get_device_id_list_per_card = torch.xpu.getDeviceIdListForCard
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else:
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torch.cuda.getDeviceIdListForCard = torch.xpu.get_device_id_list_per_card
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torch.cuda.get_device_id_list_per_card = torch.xpu.get_device_id_list_per_card
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ipex_hijacks()
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attention_init()
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try:
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from .diffusers import ipex_diffusers
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ipex_diffusers()
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except Exception: # pylint: disable=broad-exception-caught
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pass
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if not torch.xpu.has_fp64_dtype():
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try:
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from .diffusers import ipex_diffusers
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ipex_diffusers()
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except Exception: # pylint: disable=broad-exception-caught
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pass
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except Exception as e:
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return False, e
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return True, None
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@ -4,11 +4,8 @@ import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unuse
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# pylint: disable=protected-access, missing-function-docstring, line-too-long
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original_torch_bmm = torch.bmm
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def torch_bmm(input, mat2, *, out=None):
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if input.dtype != mat2.dtype:
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mat2 = mat2.to(input.dtype)
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#ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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def torch_bmm_32_bit(input, mat2, *, out=None):
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# ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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batch_size_attention, input_tokens, mat2_shape = input.shape[0], input.shape[1], mat2.shape[2]
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block_multiply = input.element_size()
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slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply
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@ -17,7 +14,7 @@ def torch_bmm(input, mat2, *, out=None):
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split_slice_size = batch_size_attention
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if block_size > 4:
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do_split = True
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#Find something divisible with the input_tokens
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# Find something divisible with the input_tokens
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while (split_slice_size * slice_block_size) > 4:
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split_slice_size = split_slice_size // 2
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if split_slice_size <= 1:
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@ -30,7 +27,7 @@ def torch_bmm(input, mat2, *, out=None):
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if split_slice_size * slice_block_size > 4:
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slice_block_size2 = split_slice_size * mat2_shape / 1024 / 1024 * block_multiply
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do_split_2 = True
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#Find something divisible with the input_tokens
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# Find something divisible with the input_tokens
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while (split_2_slice_size * slice_block_size2) > 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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@ -64,8 +61,8 @@ def torch_bmm(input, mat2, *, out=None):
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return hidden_states
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original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
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def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
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#ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
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# ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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if len(query.shape) == 3:
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batch_size_attention, query_tokens, shape_four = query.shape
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shape_one = 1
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@ -74,11 +71,6 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
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shape_one, batch_size_attention, query_tokens, shape_four = query.shape
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no_shape_one = False
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if query.dtype != key.dtype:
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key = key.to(dtype=query.dtype)
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if query.dtype != value.dtype:
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value = value.to(dtype=query.dtype)
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block_multiply = query.element_size()
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slice_block_size = shape_one * query_tokens * shape_four / 1024 / 1024 * block_multiply
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block_size = batch_size_attention * slice_block_size
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@ -86,7 +78,7 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
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split_slice_size = batch_size_attention
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if block_size > 4:
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do_split = True
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#Find something divisible with the shape_one
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# Find something divisible with the shape_one
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while (split_slice_size * slice_block_size) > 4:
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split_slice_size = split_slice_size // 2
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if split_slice_size <= 1:
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@ -99,7 +91,7 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
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if split_slice_size * slice_block_size > 4:
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slice_block_size2 = shape_one * split_slice_size * shape_four / 1024 / 1024 * block_multiply
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do_split_2 = True
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#Find something divisible with the batch_size_attention
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# Find something divisible with the batch_size_attention
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while (split_2_slice_size * slice_block_size2) > 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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@ -155,8 +147,3 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
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query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal
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)
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return hidden_states
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def attention_init():
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#ARC GPUs can't allocate more than 4GB to a single block:
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torch.bmm = torch_bmm
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torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
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@ -1,6 +1,6 @@
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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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import diffusers #0.24.0 # 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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@ -5,6 +5,7 @@ import intel_extension_for_pytorch._C as core # pylint: disable=import-error, un
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# pylint: disable=protected-access, missing-function-docstring, line-too-long
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device_supports_fp64 = torch.xpu.has_fp64_dtype()
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OptState = ipex.cpu.autocast._grad_scaler.OptState
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_MultiDeviceReplicator = ipex.cpu.autocast._grad_scaler._MultiDeviceReplicator
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_refresh_per_optimizer_state = ipex.cpu.autocast._grad_scaler._refresh_per_optimizer_state
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@ -96,7 +97,10 @@ def unscale_(self, optimizer):
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# FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
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assert self._scale is not None
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inv_scale = self._scale.to("cpu").double().reciprocal().float().to(self._scale.device)
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if device_supports_fp64:
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inv_scale = self._scale.double().reciprocal().float()
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else:
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inv_scale = self._scale.to("cpu").double().reciprocal().float().to(self._scale.device)
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found_inf = torch.full(
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(1,), 0.0, dtype=torch.float32, device=self._scale.device
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)
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@ -92,7 +92,7 @@ def ipex_autocast(*args, **kwargs):
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else:
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return original_autocast(*args, **kwargs)
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#Embedding BF16
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# Embedding BF16
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original_torch_cat = torch.cat
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def torch_cat(tensor, *args, **kwargs):
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if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype):
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@ -100,7 +100,7 @@ def torch_cat(tensor, *args, **kwargs):
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else:
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return original_torch_cat(tensor, *args, **kwargs)
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#Latent antialias:
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# Latent antialias:
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original_interpolate = torch.nn.functional.interpolate
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def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
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if antialias or align_corners is not None:
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@ -120,6 +120,32 @@ def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name
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else:
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return original_linalg_solve(A, B, *args, **kwargs)
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if torch.xpu.has_fp64_dtype():
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original_torch_bmm = torch.bmm
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original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
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else:
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# 64 bit attention workarounds for Alchemist:
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try:
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from .attention import torch_bmm_32_bit as original_torch_bmm
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from .attention import scaled_dot_product_attention_32_bit as original_scaled_dot_product_attention
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except Exception: # pylint: disable=broad-exception-caught
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original_torch_bmm = torch.bmm
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original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
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# dtype errors:
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def torch_bmm(input, mat2, *, out=None):
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if input.dtype != mat2.dtype:
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mat2 = mat2.to(input.dtype)
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return original_torch_bmm(input, mat2, out=out)
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def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
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if query.dtype != key.dtype:
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key = key.to(dtype=query.dtype)
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if query.dtype != value.dtype:
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value = value.to(dtype=query.dtype)
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return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
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@property
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def is_cuda(self):
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return self.device.type == 'xpu'
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@ -158,12 +184,12 @@ def ipex_hijacks():
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lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs:
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orig_func(orig_func, f, map_location=return_xpu(map_location), pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs),
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lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs: check_device(map_location))
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if hasattr(torch.xpu, "Generator"):
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CondFunc('torch.Generator',
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lambda orig_func, device=None: torch.xpu.Generator(return_xpu(device)),
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lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
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CondFunc('torch.Generator',
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lambda orig_func, device=None: torch.xpu.Generator(return_xpu(device)),
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lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
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#TiledVAE and ControlNet:
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# TiledVAE and ControlNet:
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CondFunc('torch.batch_norm',
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lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
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weight if weight is not None else torch.ones(input.size()[1], device=input.device),
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@ -175,46 +201,51 @@ def ipex_hijacks():
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bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
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lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
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#Functions with dtype errors:
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# Functions with dtype errors:
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CondFunc('torch.nn.modules.GroupNorm.forward',
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lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
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lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
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#Training:
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# Training:
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CondFunc('torch.nn.modules.linear.Linear.forward',
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lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
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lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
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CondFunc('torch.nn.modules.conv.Conv2d.forward',
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lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
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lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
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#BF16:
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# BF16:
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CondFunc('torch.nn.functional.layer_norm',
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lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
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orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
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lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
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weight is not None and input.dtype != weight.data.dtype)
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#SwinIR BF16:
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# SwinIR BF16:
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CondFunc('torch.nn.functional.pad',
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lambda orig_func, input, pad, mode='constant', value=None: orig_func(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16),
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lambda orig_func, input, pad, mode='constant', value=None: mode == 'reflect' and input.dtype == torch.bfloat16)
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#Diffusers Float64 (ARC GPUs doesn't support double or Float64):
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# Diffusers Float64 (Alchemist GPUs doesn't support 64 bit):
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if not torch.xpu.has_fp64_dtype():
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CondFunc('torch.from_numpy',
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lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
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lambda orig_func, ndarray: ndarray.dtype == float)
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#Broken functions when torch.cuda.is_available is True:
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#Pin Memory:
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# Broken functions when torch.cuda.is_available is True:
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# Pin Memory:
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CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
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lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
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lambda orig_func, *args, **kwargs: True)
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#Functions that make compile mad with CondFunc:
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torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
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# Functions that make compile mad with CondFunc:
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torch.nn.DataParallel = DummyDataParallel
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torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
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torch.autocast = ipex_autocast
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torch.cat = torch_cat
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torch.linalg.solve = linalg_solve
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torch.UntypedStorage.is_cuda = is_cuda
|
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torch.nn.functional.interpolate = interpolate
|
||||
torch.backends.cuda.sdp_kernel = return_null_context
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torch.UntypedStorage.is_cuda = is_cuda
|
||||
|
||||
torch.nn.functional.interpolate = interpolate
|
||||
torch.linalg.solve = linalg_solve
|
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|
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torch.bmm = torch_bmm
|
||||
torch.cat = torch_cat
|
||||
torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
|
||||
|
|
|
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Reference in New Issue