Replace einops.rearrange with torch native
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1c0a0c4c26
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@ -486,7 +486,19 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
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k_in = self.to_k(context_k)
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v_in = self.to_v(context_v)
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q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
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def _reshape(t):
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"""rearrange(t, 'b n (h d) -> b n h d', h=h).
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Using torch native operations to avoid overhead as this function is
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called frequently. (70 times/it for SDXL)
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"""
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b, n, _ = t.shape # Get the batch size (b) and sequence length (n)
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d = t.shape[2] // h # Determine the depth per head
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return t.reshape(b, n, h, d)
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q = _reshape(q_in)
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k = _reshape(k_in)
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v = _reshape(v_in)
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del q_in, k_in, v_in
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dtype = q.dtype
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@ -497,7 +509,9 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
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out = out.to(dtype)
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out = rearrange(out, 'b n h d -> b n (h d)', h=h)
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# out = rearrange(out, 'b n h d -> b n (h d)', h=h)
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b, n, h, d = out.shape
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out = out.reshape(b, n, h * d)
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return self.to_out(out)
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