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https://github.com/ggerganov/llama.cpp.git
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convert-llama-h5-to-gguf.py : map tensor names
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klosax
GitHub
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@ -1,6 +1,8 @@
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# Quick and dirty HF llama --> gguf conversion, GQA/70b wont work
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import gguf
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import gguf_tensor_map as tmap
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import os
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import sys
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import struct
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import json
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@ -12,7 +14,6 @@ from sentencepiece import SentencePieceProcessor
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#NDArray = np.ndarray[Any, Any]
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# compatible with python < 3.9
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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@ -32,6 +33,7 @@ if len(sys.argv) < 3:
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# output in the same directory as the model
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dir_model = sys.argv[1]
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fname_out = sys.argv[1] + "/ggml-model.bin"
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last_dir = os.path.basename(os.path.normpath(dir_model))
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# possible tensor data types
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@ -48,6 +50,8 @@ if len(sys.argv) > 2:
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print("Invalid ftype: " + str(ftype))
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sys.exit(1)
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fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
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print("gguf: loading model "+last_dir)
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with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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@ -62,32 +66,34 @@ list_vars = model.state_dict()
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gguf_writer = gguf.GGUFWriter.open(fname_out)
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print("gguf: add key-values, metadata")
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print("gguf: get model metadata")
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llm_arch = "llama"
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llm_arch = "llama"
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head_count = hparams["num_attention_heads"]
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block_count = hparams["num_hidden_layers"]
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gguf_writer.add_name("llama2-7b")
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gguf_writer.add_description("gguf test model")
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gguf_writer.add_architecture(llm_arch)
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gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"])
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gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
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gguf_writer.add_layer_count(llm_arch, hparams["num_hidden_layers"])
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gguf_writer.add_layer_count(llm_arch, block_count)
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gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
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gguf_writer.add_rope_dimension_count(llm_arch, hparams["hidden_size"] // hparams["num_attention_heads"])
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gguf_writer.add_head_count(llm_arch, hparams["num_attention_heads"])
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gguf_writer.add_head_count(llm_arch, head_count)
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gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"])
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# TOKENIZATION
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print("gguf: add key-values, tokenizer")
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print("gguf: get tokenizer metadata")
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tokens: List[str] = []
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scores: List[float] = []
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if Path(dir_model + "/tokenizer.model").is_file():
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# vocab type sentencepiece
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print("gguf: adding sentencepiece tokenizer vocab")
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print("gguf: get sentencepiece tokenizer vocab and scores")
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tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
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@ -119,7 +125,7 @@ if Path(dir_model + "/tokenizer.json").is_file():
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tokenizer = json.load(f)
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if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
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print("gguf: adding special token ids")
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print("gguf: get special token ids")
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with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
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tokenizer_config = json.load(f)
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@ -154,8 +160,10 @@ if Path(dir_model + "/tokenizer.json").is_file():
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# TENSORS
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tensor_map = tmap.get_tensor_map(block_count)
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# tensor info
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print("gguf: add gguf tensor info")
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print("gguf: get tensor metadata")
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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@ -166,45 +174,16 @@ for name in list_vars.keys():
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# permute these
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if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
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data = permute(data, hparams["num_attention_heads"])
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data = permute(data,head_count)
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# chnage tensor name
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if name == "model.embed_tokens.weight":
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name = "tok_embeddings.weight"
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elif name == "model.norm.weight":
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name = "norm.weight"
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elif name == "lm_head.weight":
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name = "output.weight"
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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name = tensor_map[name[:-7]] + ".weight"
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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for i in range(80): # maximum number of layers
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if name == "model.layers." + str(i) + ".input_layernorm.weight":
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name = "layers." + str(i) + ".attention_norm.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.q_proj.weight":
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name = "layers." + str(i) + ".attention.wq.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.k_proj.weight":
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name = "layers." + str(i) + ".attention.wk.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.v_proj.weight":
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name = "layers." + str(i) + ".attention.wv.weight"
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break
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if name == "model.layers." + str(i) + ".self_attn.o_proj.weight":
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name = "layers." + str(i) + ".attention.wo.weight"
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break
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if name == "model.layers." + str(i) + ".post_attention_layernorm.weight":
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name = "layers." + str(i) + ".ffn_norm.weight"
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break
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if name == "model.layers." + str(i) + ".mlp.gate_proj.weight":
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name = "layers." + str(i) + ".feed_forward.w1.weight"
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break
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if name == "model.layers." + str(i) + ".mlp.down_proj.weight":
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name = "layers." + str(i) + ".feed_forward.w2.weight"
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break
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if name == "model.layers." + str(i) + ".mlp.up_proj.weight":
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name = "layers." + str(i) + ".feed_forward.w3.weight"
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break
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print( "Can not map tensor '" + name + "'" )
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sys.exit()
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n_dims = len(data.shape)
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@ -227,9 +206,9 @@ for name in list_vars.keys():
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print("gguf: write header")
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gguf_writer.write_header_to_file()
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print("gguf: write key-values")
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print("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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print("gguf: write tensor info")
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print("gguf: write tensor metadata")
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gguf_writer.write_ti_data_to_file()
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# tensor data
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@ -237,17 +216,14 @@ print("gguf: write tensor data")
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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# print("Process tensor: " + name + " with shape: ", data.shape)
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# we don't need these
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if name.endswith(".rotary_emb.inv_freq"):
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# print(" Skip tensor: " + name)
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continue
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# permute these
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if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
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# print(" Permute tensor: " + name)
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data = permute(data, hparams["num_attention_heads"])
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data = permute(data, head_count)
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n_dims = len(data.shape)
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@ -255,16 +231,13 @@ for name in list_vars.keys():
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ftype_cur = 0
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if ftype != 0:
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if name.endswith(".weight") and n_dims == 2:
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# print(" Converting to float16")
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data = data.astype(np.float16)
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ftype_cur = 1
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else:
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# print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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else:
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if data.dtype != np.float32:
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# print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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@ -273,5 +246,5 @@ for name in list_vars.keys():
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gguf_writer.close()
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print("gguf: conversion done, output file: " + fname_out)
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print("gguf: model successfully exported to '" + fname_out + "'" )
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print("")
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