modules/xpu_specific.py

# from modules import shared
# from modules.sd_hijack_utils import CondFunc
#
# has_ipex = False
# try:
#     import torch
#     import intel_extension_for_pytorch as ipex # noqa: F401
#     has_ipex = True
# except Exception:
#     pass
#
#
# def check_for_xpu():
#     return has_ipex and hasattr(torch, 'xpu') and torch.xpu.is_available()
#
#
# def get_xpu_device_string():
#     if shared.cmd_opts.device_id is not None:
#         return f"xpu:{shared.cmd_opts.device_id}"
#     return "xpu"
#
#
# def torch_xpu_gc():
#     with torch.xpu.device(get_xpu_device_string()):
#         torch.xpu.empty_cache()
#
#
# has_xpu = check_for_xpu()
#
#
# # Arc GPU cannot allocate a single block larger than 4GB: https://github.com/intel/compute-runtime/issues/627
# # Here we implement a slicing algorithm to split large batch size into smaller chunks,
# # so that SDPA of each chunk wouldn't require any allocation larger than ARC_SINGLE_ALLOCATION_LIMIT.
# # The heuristic limit (TOTAL_VRAM // 8) is tuned for Intel Arc A770 16G and Arc A750 8G,
# # which is the best trade-off between VRAM usage and performance.
# ARC_SINGLE_ALLOCATION_LIMIT = {}
# orig_sdp_attn_func = torch.nn.functional.scaled_dot_product_attention
# def torch_xpu_scaled_dot_product_attention(
#     query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, *args, **kwargs
# ):
#     # cast to same dtype first
#     key = key.to(query.dtype)
#     value = value.to(query.dtype)
#     if attn_mask is not None and attn_mask.dtype != torch.bool:
#         attn_mask = attn_mask.to(query.dtype)
#
#     N = query.shape[:-2]  # Batch size
#     L = query.size(-2)  # Target sequence length
#     E = query.size(-1)  # Embedding dimension of the query and key
#     S = key.size(-2)  # Source sequence length
#     Ev = value.size(-1)  # Embedding dimension of the value
#
#     total_batch_size = torch.numel(torch.empty(N))
#     device_id = query.device.index
#     if device_id not in ARC_SINGLE_ALLOCATION_LIMIT:
#         ARC_SINGLE_ALLOCATION_LIMIT[device_id] = min(torch.xpu.get_device_properties(device_id).total_memory // 8, 4 * 1024 * 1024 * 1024)
#     batch_size_limit = max(1, ARC_SINGLE_ALLOCATION_LIMIT[device_id] // (L * S * query.element_size()))
#
#     if total_batch_size <= batch_size_limit:
#         return orig_sdp_attn_func(
#             query,
#             key,
#             value,
#             attn_mask,
#             dropout_p,
#             is_causal,
#             *args, **kwargs
#         )
#
#     query = torch.reshape(query, (-1, L, E))
#     key = torch.reshape(key, (-1, S, E))
#     value = torch.reshape(value, (-1, S, Ev))
#     if attn_mask is not None:
#         attn_mask = attn_mask.view(-1, L, S)
#     chunk_count = (total_batch_size + batch_size_limit - 1) // batch_size_limit
#     outputs = []
#     for i in range(chunk_count):
#         attn_mask_chunk = (
#             None
#             if attn_mask is None
#             else attn_mask[i * batch_size_limit : (i + 1) * batch_size_limit, :, :]
#         )
#         chunk_output = orig_sdp_attn_func(
#             query[i * batch_size_limit : (i + 1) * batch_size_limit, :, :],
#             key[i * batch_size_limit : (i + 1) * batch_size_limit, :, :],
#             value[i * batch_size_limit : (i + 1) * batch_size_limit, :, :],
#             attn_mask_chunk,
#             dropout_p,
#             is_causal,
#             *args, **kwargs
#         )
#         outputs.append(chunk_output)
#     result = torch.cat(outputs, dim=0)
#     return torch.reshape(result, (*N, L, Ev))
#
#
# def is_xpu_device(device: str | torch.device = None):
#     if device is None:
#         return False
#     if isinstance(device, str):
#         return device.startswith("xpu")
#     return device.type == "xpu"
#
#
# if has_xpu:
#     try:
#         # torch.Generator supports "xpu" device since 2.1
#         torch.Generator("xpu")
#     except RuntimeError:
#         # W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device (for torch < 2.1)
#         CondFunc('torch.Generator',
#             lambda orig_func, device=None: torch.xpu.Generator(device),
#             lambda orig_func, device=None: is_xpu_device(device))
#
#     # W/A for some OPs that could not handle different input dtypes
#     CondFunc('torch.nn.functional.layer_norm',
#         lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
#         orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
#         lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
#         weight is not None and input.dtype != weight.data.dtype)
#     CondFunc('torch.nn.modules.GroupNorm.forward',
#         lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
#         lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
#     CondFunc('torch.nn.modules.linear.Linear.forward',
#         lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
#         lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
#     CondFunc('torch.nn.modules.conv.Conv2d.forward',
#         lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
#         lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
#     CondFunc('torch.bmm',
#         lambda orig_func, input, mat2, out=None: orig_func(input.to(mat2.dtype), mat2, out=out),
#         lambda orig_func, input, mat2, out=None: input.dtype != mat2.dtype)
#     CondFunc('torch.cat',
#         lambda orig_func, tensors, dim=0, out=None: orig_func([t.to(tensors[0].dtype) for t in tensors], dim=dim, out=out),
#         lambda orig_func, tensors, dim=0, out=None: not all(t.dtype == tensors[0].dtype for t in tensors))
#     CondFunc('torch.nn.functional.scaled_dot_product_attention',
#         lambda orig_func, *args, **kwargs: torch_xpu_scaled_dot_product_attention(*args, **kwargs),
#         lambda orig_func, query, *args, **kwargs: query.is_xpu)