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Add vocab padding to embedding bias in gptj.json
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@ -9,11 +9,11 @@
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},
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"static_weights": {
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"transformer.wte.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}},
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"transformer.wte.bias": {"mtj": {"module": "embedding_shard/~/linear", "param": "b"}},
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"transformer.wte.bias": {"mtj": {"module": "embedding_shard/~/linear", "param": "b", "transforms": ["vocab_pad"]}},
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"transformer.ln_f.weight": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale"}},
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"transformer.ln_f.bias": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset"}},
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"lm_head.weight": {"mtj": {"module": "projection_shard/~/linear", "param": "w", "transforms": ["vocab_pad"]}},
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"lm_head.bias": {"mtj": {"module": "projection_shard/~/linear", "param": "b"}}
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"lm_head.bias": {"mtj": {"module": "projection_shard/~/linear", "param": "b", "transforms": ["vocab_pad"]}}
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},
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"layer_weights": {
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"transformer.h.{layer}.attn.bias": {},
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@ -1304,7 +1304,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
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if "divide_by_shards" in transforms:
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tensor /= params["cores_per_replica"]
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if "vocab_pad" in transforms:
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tensor = torch.nn.functional.pad(tensor, (0, 0, 0, params["n_vocab_padding"]))
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tensor = torch.nn.functional.pad(tensor, (0,) * (tensor.ndim * 2 - 1) + (params["n_vocab_padding"],))
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if "no_transpose" not in transforms and tensor.ndim == 2:
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tensor = tensor.T
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tensor.unsqueeze_(0)
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