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Create gguf_namemap.py : tensor name map changes
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klosax
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gguf_namemap.py
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95
gguf_namemap.py
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# Recommended mapping of model tensor names for storage in gguf
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def get_tensor_namemap( n_blocks : int):
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tensor_map = {}
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# Token embeddings
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mapped_to = "token_embd"
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tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
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tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
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tensor_map["transformer.word_embeddings"] = mapped_to # falcon
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tensor_map["model.embed_tokens"] = mapped_to # llama-hf
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tensor_map["tok_embeddings"] = mapped_to # llama-pth
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# Position embeddings
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mapped_to = "pos_embd"
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tensor_map["transformer.wpe"] = mapped_to # gpt2
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# Output norm
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mapped_to = "output_norm"
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tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
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tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
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tensor_map["transformer.norm_f"] = mapped_to # mpt
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tensor_map["model.norm"] = mapped_to # llama-hf
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tensor_map["norm"] = mapped_to # llama-pth
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# Output
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mapped_to = "output"
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tensor_map["embed_out"] = mapped_to # gptneox
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tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
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tensor_map["output"] = mapped_to # llama-pth
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# Attention and fee-forward layer blocks
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for i in range(0,n_blocks):
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# Attention norm
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mapped_to = "blk."+str(i)+".attn_norm"
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tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
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tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
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tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
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# Attention norm 2
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mapped_to = "blk."+str(i)+".attn_norm_2"
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tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
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# Attention query-key-value
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mapped_to = "blk."+str(i)+".attn_qkv"
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tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
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# Attention query
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mapped_to = "blk."+str(i)+".attn_q"
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tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
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# Attention key
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mapped_to = "blk."+str(i)+".attn_k"
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tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
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# Attention value
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mapped_to = "blk."+str(i)+".attn_v"
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tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
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# Attention output
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mapped_to = "blk."+str(i)+".attn_output"
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tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
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tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
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# Feed-forward norm
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mapped_to = "blk."+str(i)+".ffn_norm"
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tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
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tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
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# Feed-forward up
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mapped_to = "blk."+str(i)+".ffn_up"
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tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
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tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
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# Feed-forward gate
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mapped_to = "blk."+str(i)+".ffn_gate"
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tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
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# Feed-forward down
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mapped_to = "blk."+str(i)+".ffn_down"
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tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
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tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
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return tensor_map
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