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@@ -1416,8 +1416,32 @@ class InternLM2Model(Model):
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self.gguf_writer.add_add_space_prefix(add_prefix)
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special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
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+ old_eos = special_vocab.special_token_ids["eos"]
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+ if "chat" in os.path.basename(self.dir_model.absolute()):
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+ # For the chat model, we replace the eos with '<|im_end|>'.
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+ special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
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+ print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
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+in chat mode so that the conversation can end normally.")
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+
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special_vocab.add_to_gguf(self.gguf_writer)
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+ def _try_get_sft_eos(self, tokenizer):
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+ unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]')
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+ im_end_list = tokenizer.encode('<|im_end|>')
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+ assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
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+ if len(unused_145_list) == 1:
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+ eos_token = unused_145_list[0]
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+ if len(im_end_list) == 1:
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+ eos_token = im_end_list[0]
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+ return eos_token
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+
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+ def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
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+ if n_head_kv is not None and n_head != n_head_kv:
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+ n_head = n_head_kv
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+ return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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+ .swapaxes(1, 2)
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+ .reshape(weights.shape))
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+
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def set_gguf_parameters(self):
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self.gguf_writer.add_name("InternLM2")
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self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
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@@ -1486,8 +1510,9 @@ class InternLM2Model(Model):
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qkv = data_torch
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qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
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q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
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- q = rearrange(q, " o g n i -> o (g n i)").T
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- k = rearrange(k, " o g n i -> o (g n i)").T
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+ # The model weights of q and k equire additional reshape.
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+ q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads)
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+ k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads)
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v = rearrange(v, " o g n i -> o (g n i)").T
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self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
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self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)
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