Clean mt5 #419
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Clean mt5 #419
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xiezipeng-ML
commented
Nov 4, 2022
| # [bsz, tgt_len, num_heads, head_size] -> [bsz, tgt_len, num_heads * head_size] | ||
| # SBP sign: [S(0), S(2)] | ||
| # [S(0), S(2)] x [B, S(0)] = [S(0), P] -> [S(0), B] | ||
| context = flow._C.transpose(context, perm=(2, 0, 1, 3)) |
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关于今天提到的这里有个transpose操作的原因：
attention_scores, value = flow._C.fused_self_attention(...)，这个fused_self_attention返回的tensor的shape是attention_scores：[batch_size, num_head, seq_len1, seq_len2]以及value：[bsz, num_head, seq_len, head_size]，所以这里0维就是batch_size- 然后这里的context是由context = flow.matmul(attention_weights, value)得到，所以context的shape是 [bsz, num_head, seq_len, head_size]，所以最后需要有这个transpose的操作，没改之前也有transpose的操作：
context = context.transpose(1, 2)，只不过现在交换了3个维度( context = flow._C.transpose(context, perm=(2, 0, 1, 3)))
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