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improve comments
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@ -11,7 +11,7 @@ def convert_weights(official_state_dict: dict[str, Tensor]) -> dict[str, Tensor]
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r"multifieldcrossatt.attention.5",
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r"dec_blk\d+\.linear[12]",
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r"dec_blk[1234]\.attention\.[4567]",
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# We don't need the sideout weights
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# We don't need the sideout weights for inference
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r"sideout\d+",
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]
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state_dict = {k: v for k, v in official_state_dict.items() if not any(re.match(rm, k) for rm in rm_list)}
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@ -132,6 +132,8 @@ class MCLM(fl.Chain):
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positional_embedding = PositionEmbeddingSine(num_pos_feats=emb_dim // 2, device=device)
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# LayerNorms in MCLM share their weights.
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# We use the `proxy` trick below so they can be present only
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# once in the tree but called in two different places.
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ln1 = fl.LayerNorm(emb_dim, device=device)
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ln2 = fl.LayerNorm(emb_dim, device=device)
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