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Remove pos_emb schedule
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@ -1014,8 +1014,6 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
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query_head_dim: dimension of the query (and key), per head. e.g. 24.
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pos_head_dim: dimension of the projected positional encoding per head, e.g. 4.
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dropout: dropout probability for attn_output_weights. Default: 0.0.
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pos_emb_skip: probability for skipping the pos_emb part of the scores on
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any given call to forward(), in training time.
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"""
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def __init__(
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@ -1026,8 +1024,6 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
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query_head_dim: int,
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pos_head_dim: int,
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dropout: float = 0.0,
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pos_emb_skip: FloatLike = ScheduledFloat((0.0, 0.5),
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(4000.0, 0.025), default=0.0)
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) -> None:
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super().__init__()
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self.embed_dim = embed_dim
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@ -1035,7 +1031,6 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
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self.query_head_dim = query_head_dim
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self.pos_head_dim = pos_head_dim
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self.dropout = dropout
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self.pos_emb_skip = copy.deepcopy(pos_emb_skip)
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key_head_dim = query_head_dim
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in_proj_dim = (query_head_dim + key_head_dim + pos_head_dim) * num_heads
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@ -1120,7 +1115,6 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
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attn_scores = torch.matmul(q, k)
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if not self.training or random.random() >= float(self.pos_emb_skip):
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pos_emb = self.linear_pos(pos_emb)
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seq_len2 = 2 * seq_len - 1
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pos_emb = pos_emb.reshape(-1, seq_len2, num_heads, pos_head_dim).permute(2, 0, 3, 1)
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