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README.md ADDED
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+ ---
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+ license: other
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+ language:
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+ - en
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+ - zh
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+ base_model: internlm/internlm2-7b
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+ ---
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+
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+ [internlm2-7b](https://huggingface.co/internlm/internlm2-7b) converted into Llama-format weights. Generates coherent text but not sure it's 100% correct yet - still testing to make sure it hasn't lost any smarts.
config.json ADDED
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+ {
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+ "architectures": ["LlamaForCausalLM"],
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+ "bias": false,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 32768,
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+ "model_type": "llama",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "pad_token_id": 2,
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 1000000,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.36.2",
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+ "use_cache": true,
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+ "vocab_size": 92544
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+ }
convert_weights.py ADDED
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+ #!/usr/bin/env python3
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+ # 1/17/2024
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+ # Charles O. Goddard
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+ """Convert internlm2 weights to Llama format."""
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+
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+ import json
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+ import os
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+ import einops
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+ import tqdm
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+ from mergekit.io import LazyTensorLoader, TensorWriter
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+ from mergekit.common import ModelReference
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+ from transformers import LlamaTokenizer
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+
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+ MODEL_IN = "internlm/internlm2-20b"
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+ OUT_PATH = "./internlm2-20b-llama"
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+
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+ model_ref = ModelReference.parse(MODEL_IN)
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+ cfg = model_ref.config(trust_remote_code=True)
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+ head_dim = cfg.hidden_size // cfg.num_attention_heads
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+ num_key_value_groups = cfg.num_attention_heads // cfg.num_key_value_heads
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+ loader = LazyTensorLoader(model_ref.tensor_index(), lazy_unpickle=True)
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+ writer = TensorWriter(OUT_PATH)
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+
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+ SIMPLE_REPLACEMENTS = {
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+ "feed_forward.w1": "mlp.gate_proj",
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+ "feed_forward.w2": "mlp.down_proj",
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+ "feed_forward.w3": "mlp.up_proj",
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+ "attention.wo": "self_attn.o_proj",
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+ "ffn_norm": "post_attention_layernorm",
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+ "attention_norm": "input_layernorm",
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+ "tok_embeddings": "embed_tokens",
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+ "output.weight": "lm_head.weight",
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+ }
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+
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+ for tensor_name in tqdm.tqdm(loader.index.tensor_paths):
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+ tensor = loader.get_tensor(tensor_name)
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+ if "attention.wqkv" in tensor_name:
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+ # make me think about tensor shapes will you >:(
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+
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+ # ((cfg.num_attention_heads + 2 * cfg.num_key_value_heads) * head_dim, cfg.hidden_size) x (batch_sz, sq_len, cfg.hidden_size)
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+ # -> (batch_sz, sq_len, (cfg.num_attention_heads + 2 * cfg.num_key_value_heads) * head_dim)
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+ # qkv_states = rearrange(
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+ # qkv_states,
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+ # "b q (h gs d) -> b q h gs d",
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+ # gs=2 + self.num_key_value_groups,
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+ # d=self.head_dim,
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+ # )
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+ # ->(batch_sz, sq_len, h, 2 + self.num_key_value_groups, head_dim)
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+ qkv_vecs = einops.rearrange(
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+ tensor, "(h gs d) z -> h gs d z", gs=2 + num_key_value_groups, d=head_dim
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+ )
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+ q_proj = (
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+ qkv_vecs[:, :num_key_value_groups, ...]
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+ .reshape(-1, cfg.hidden_size)
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+ .contiguous()
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+ )
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+ k_proj = qkv_vecs[:, -2, ...].reshape(-1, cfg.hidden_size).contiguous()
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+ v_proj = qkv_vecs[:, -1, ...].reshape(-1, cfg.hidden_size).contiguous()
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+ assert k_proj.shape == v_proj.shape
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+
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+ writer.save_tensor(
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+ tensor_name.replace("attention.wqkv", "self_attn.q_proj"),
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+ q_proj,
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+ clone=True,
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+ )
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+ writer.save_tensor(
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+ tensor_name.replace("attention.wqkv", "self_attn.k_proj"),
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+ k_proj,
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+ clone=True,
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+ )
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+ writer.save_tensor(
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+ tensor_name.replace("attention.wqkv", "self_attn.v_proj"),
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+ v_proj,
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+ clone=True,
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+ )
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+ continue
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+
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+ out_name = tensor_name
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+ for pattern, sub in SIMPLE_REPLACEMENTS.items():
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+ if pattern in out_name:
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+ out_name = out_name.replace(pattern, sub)
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+ writer.save_tensor(out_name, tensor)
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+ writer.finalize()
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+
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+ cfg_dict = json.loads(cfg.to_json_string())
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+ del cfg_dict["auto_map"]
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+ cfg_dict["architectures"] = ["LlamaForCausalLM"]
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+ cfg_dict["model_type"] = "llama"
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+ if "rope_scaling" in cfg_dict and cfg_dict["rope_scaling"]["factor"] == 1.0:
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+ del cfg_dict["rope_scaling"]
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+ with open(os.path.join(OUT_PATH, "config.json"), "w", encoding="utf-8") as fp:
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+ json.dump(cfg_dict, fp, indent=2)
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+
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+ # InternLMTokenizer differences:
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+ # 1. clean_up_tokenization() hardcoded to always be called
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+ # 2. might prepend a space to some tokens that LlamaTokenizer doesn't if they're the first token
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+ # 1 is easy to fix, 2... is not important
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+ tok = LlamaTokenizer.from_pretrained(MODEL_IN, trust_remote_code=False, legacy=True)
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+ tok.clean_up_tokenization_spaces = True
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+ tok.save_pretrained(OUT_PATH)
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "pad_token_id": 2,
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+ "transformers_version": "4.36.2"
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+ }
model.safetensors.index.json ADDED
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