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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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127 lines
5.4 KiB
Python
127 lines
5.4 KiB
Python
from modules import shared
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from modules.sd_hijack_utils import CondFunc
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has_ipex = False
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try:
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import torch
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import intel_extension_for_pytorch as ipex # noqa: F401
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has_ipex = True
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except Exception:
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pass
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def check_for_xpu():
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return has_ipex and hasattr(torch, 'xpu') and torch.xpu.is_available()
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def get_xpu_device_string():
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if shared.cmd_opts.device_id is not None:
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return f"xpu:{shared.cmd_opts.device_id}"
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return "xpu"
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def torch_xpu_gc():
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with torch.xpu.device(get_xpu_device_string()):
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torch.xpu.empty_cache()
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has_xpu = check_for_xpu()
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# Arc GPU cannot allocate a single block larger than 4GB: https://github.com/intel/compute-runtime/issues/627
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# Here we implement a slicing algorithm to split large batch size into smaller chunks,
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# so that SDPA of each chunk wouldn't require any allocation larger than ARC_SINGLE_ALLOCATION_LIMIT.
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# The heuristic limit (TOTAL_VRAM // 8) is tuned for Intel Arc A770 16G and Arc A750 8G,
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# which is the best trade-off between VRAM usage and performance.
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ARC_SINGLE_ALLOCATION_LIMIT = {}
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orig_sdp_attn_func = torch.nn.functional.scaled_dot_product_attention
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def torch_xpu_scaled_dot_product_attention(
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query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, *args, **kwargs
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):
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# cast to same dtype first
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key = key.to(query.dtype)
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value = value.to(query.dtype)
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if attn_mask is not None and attn_mask.dtype != torch.bool:
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attn_mask = attn_mask.to(query.dtype)
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N = query.shape[:-2] # Batch size
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L = query.size(-2) # Target sequence length
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E = query.size(-1) # Embedding dimension of the query and key
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S = key.size(-2) # Source sequence length
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Ev = value.size(-1) # Embedding dimension of the value
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total_batch_size = torch.numel(torch.empty(N))
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device_id = query.device.index
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if device_id not in ARC_SINGLE_ALLOCATION_LIMIT:
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ARC_SINGLE_ALLOCATION_LIMIT[device_id] = min(torch.xpu.get_device_properties(device_id).total_memory // 8, 4 * 1024 * 1024 * 1024)
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batch_size_limit = max(1, ARC_SINGLE_ALLOCATION_LIMIT[device_id] // (L * S * query.element_size()))
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if total_batch_size <= batch_size_limit:
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return orig_sdp_attn_func(
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query,
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key,
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value,
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attn_mask,
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dropout_p,
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is_causal,
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*args, **kwargs
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)
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query = torch.reshape(query, (-1, L, E))
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key = torch.reshape(key, (-1, S, E))
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value = torch.reshape(value, (-1, S, Ev))
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if attn_mask is not None:
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attn_mask = attn_mask.view(-1, L, S)
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chunk_count = (total_batch_size + batch_size_limit - 1) // batch_size_limit
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outputs = []
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for i in range(chunk_count):
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attn_mask_chunk = (
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None
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if attn_mask is None
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else attn_mask[i * batch_size_limit : (i + 1) * batch_size_limit, :, :]
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)
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chunk_output = orig_sdp_attn_func(
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query[i * batch_size_limit : (i + 1) * batch_size_limit, :, :],
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key[i * batch_size_limit : (i + 1) * batch_size_limit, :, :],
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value[i * batch_size_limit : (i + 1) * batch_size_limit, :, :],
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attn_mask_chunk,
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dropout_p,
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is_causal,
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*args, **kwargs
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)
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outputs.append(chunk_output)
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result = torch.cat(outputs, dim=0)
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return torch.reshape(result, (*N, L, Ev))
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if has_xpu:
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# W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device
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CondFunc('torch.Generator',
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lambda orig_func, device=None: torch.xpu.Generator(device),
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lambda orig_func, device=None: device is not None and device.type == "xpu")
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# W/A for some OPs that could not handle different input dtypes
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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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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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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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CondFunc('torch.bmm',
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lambda orig_func, input, mat2, out=None: orig_func(input.to(mat2.dtype), mat2, out=out),
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lambda orig_func, input, mat2, out=None: input.dtype != mat2.dtype)
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CondFunc('torch.cat',
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lambda orig_func, tensors, dim=0, out=None: orig_func([t.to(tensors[0].dtype) for t in tensors], dim=dim, out=out),
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lambda orig_func, tensors, dim=0, out=None: not all(t.dtype == tensors[0].dtype for t in tensors))
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CondFunc('torch.nn.functional.scaled_dot_product_attention',
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lambda orig_func, *args, **kwargs: torch_xpu_scaled_dot_product_attention(*args, **kwargs),
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lambda orig_func, query, *args, **kwargs: query.is_xpu)
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