More detailed weighted attention error logs
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@@ -280,6 +280,7 @@ class FABRICPatcher:
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def get_weights(pos_weight, neg_weight, q, num_pos, num_neg, cond_uncond_idxs):
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"""
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Prepare weights for the weighted attention
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:return: Weights of shape [batch_size, nk] where nk is the size of the Key sequence length. batch_size = len(cond_uncond_idxs)
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"""
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input_dim = q.shape[1]
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hs_dim = max(num_pos, num_neg) * input_dim
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@@ -26,6 +26,7 @@ class Weighted_Attn_Patcher:
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except Exception as e:
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print(e)
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print("[FABRIC] Encountered an exception. If this is not a memory issue, please report this issue.")
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print(f"[FABRIC] weights b: {weights.shape[0]}, weights nk: {weights.shape[1]}, nq: {nq}, nk: {nk}, h: {h}, B: {B}")
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self.unpatch()
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raise e
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@@ -45,6 +46,7 @@ class Weighted_Attn_Patcher:
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except Exception as e:
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print(e)
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print("[FABRIC] Encountered an exception. If this is not a memory issue, please report this issue.")
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print(f"[FABRIC] weights b: {weights.shape[0]}, weights nk: {weights.shape[1]}, nq: {nq}, nk: {nk}, h: {h}, bs: {bs}")
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self.unpatch()
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raise e
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torch.nn.functional.scaled_dot_product_attention = pt_sdp
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@@ -81,6 +83,7 @@ class Weighted_Attn_Patcher:
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except Exception as e:
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print(e)
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print("[FABRIC] Encountered an exception. If this is not a memory issue, please report this issue.")
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print(f"[FABRIC] weights b: {weights.shape[0]}, weights nk: {weights.shape[1]}, nq: {nq}, nk: {nk}, h: {h}, B: {B}")
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self.unpatch()
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raise e
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torch.Tensor.softmax = softmax_method
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