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https://github.com/vladmandic/automatic
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IPEX fixes
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@@ -183,6 +183,7 @@ def ipex_init(): # pylint: disable=too-many-statements
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torch.cuda.is_fp16_supported = lambda *args, **kwargs: True
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torch.backends.cuda.is_built = lambda *args, **kwargs: True
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torch.version.cuda = "12.1"
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torch.cuda.get_arch_list = lambda: ["ats-m150", "pvc"]
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torch.cuda.get_device_capability = lambda *args, **kwargs: [12,1]
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torch.cuda.get_device_properties.major = 12
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torch.cuda.get_device_properties.minor = 1
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@@ -96,14 +96,14 @@ def torch_bmm(input, mat2, *, out=None):
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return original_torch_bmm(input, mat2, out=out)
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@wraps(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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def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, **kwargs):
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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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if attn_mask is not None and query.dtype != attn_mask.dtype:
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attn_mask = attn_mask.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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return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, **kwargs)
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# A1111 FP16
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original_functional_group_norm = torch.nn.functional.group_norm
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