diff --git a/convert-llama-h5-to-gguf.py b/convert-llama-h5-to-gguf.py index 67b3c55d4..0b477a133 100644 --- a/convert-llama-h5-to-gguf.py +++ b/convert-llama-h5-to-gguf.py @@ -1,6 +1,8 @@ # Quick and dirty HF llama --> gguf conversion, GQA/70b wont work import gguf +import gguf_tensor_map as tmap +import os import sys import struct import json @@ -12,7 +14,6 @@ from sentencepiece import SentencePieceProcessor #NDArray = np.ndarray[Any, Any] - # compatible with python < 3.9 NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' @@ -32,6 +33,7 @@ if len(sys.argv) < 3: # output in the same directory as the model dir_model = sys.argv[1] fname_out = sys.argv[1] + "/ggml-model.bin" +last_dir = os.path.basename(os.path.normpath(dir_model)) # possible tensor data types @@ -48,6 +50,8 @@ if len(sys.argv) > 2: print("Invalid ftype: " + str(ftype)) sys.exit(1) fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf" + +print("gguf: loading model "+last_dir) with open(dir_model + "/config.json", "r", encoding="utf-8") as f: hparams = json.load(f) @@ -62,32 +66,34 @@ list_vars = model.state_dict() gguf_writer = gguf.GGUFWriter.open(fname_out) -print("gguf: add key-values, metadata") +print("gguf: get model metadata") -llm_arch = "llama" +llm_arch = "llama" +head_count = hparams["num_attention_heads"] +block_count = hparams["num_hidden_layers"] gguf_writer.add_name("llama2-7b") gguf_writer.add_description("gguf test model") gguf_writer.add_architecture(llm_arch) gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"]) gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"]) -gguf_writer.add_layer_count(llm_arch, hparams["num_hidden_layers"]) +gguf_writer.add_layer_count(llm_arch, block_count) gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"]) gguf_writer.add_rope_dimension_count(llm_arch, hparams["hidden_size"] // hparams["num_attention_heads"]) -gguf_writer.add_head_count(llm_arch, hparams["num_attention_heads"]) +gguf_writer.add_head_count(llm_arch, head_count) gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"]) # TOKENIZATION -print("gguf: add key-values, tokenizer") +print("gguf: get tokenizer metadata") tokens: List[str] = [] scores: List[float] = [] if Path(dir_model + "/tokenizer.model").is_file(): # vocab type sentencepiece - print("gguf: adding sentencepiece tokenizer vocab") + print("gguf: get sentencepiece tokenizer vocab and scores") tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model") @@ -119,7 +125,7 @@ if Path(dir_model + "/tokenizer.json").is_file(): tokenizer = json.load(f) if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file(): - print("gguf: adding special token ids") + print("gguf: get special token ids") with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f: tokenizer_config = json.load(f) @@ -154,8 +160,10 @@ if Path(dir_model + "/tokenizer.json").is_file(): # TENSORS +tensor_map = tmap.get_tensor_map(block_count) + # tensor info -print("gguf: add gguf tensor info") +print("gguf: get tensor metadata") for name in list_vars.keys(): data = list_vars[name].squeeze().numpy() @@ -166,45 +174,16 @@ for name in list_vars.keys(): # permute these if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"): - data = permute(data, hparams["num_attention_heads"]) + data = permute(data,head_count) - # chnage tensor name - - if name == "model.embed_tokens.weight": - name = "tok_embeddings.weight" - elif name == "model.norm.weight": - name = "norm.weight" - elif name == "lm_head.weight": - name = "output.weight" + # map tensor names + if name.endswith(".weight") and name[:-7] in tensor_map: + name = tensor_map[name[:-7]] + ".weight" + elif name.endswith(".bias") and name[:-5] in tensor_map: + name = tensor_map[name[:-5]] + ".bias" else: - for i in range(80): # maximum number of layers - if name == "model.layers." + str(i) + ".input_layernorm.weight": - name = "layers." + str(i) + ".attention_norm.weight" - break - if name == "model.layers." + str(i) + ".self_attn.q_proj.weight": - name = "layers." + str(i) + ".attention.wq.weight" - break - if name == "model.layers." + str(i) + ".self_attn.k_proj.weight": - name = "layers." + str(i) + ".attention.wk.weight" - break - if name == "model.layers." + str(i) + ".self_attn.v_proj.weight": - name = "layers." + str(i) + ".attention.wv.weight" - break - if name == "model.layers." + str(i) + ".self_attn.o_proj.weight": - name = "layers." + str(i) + ".attention.wo.weight" - break - if name == "model.layers." + str(i) + ".post_attention_layernorm.weight": - name = "layers." + str(i) + ".ffn_norm.weight" - break - if name == "model.layers." + str(i) + ".mlp.gate_proj.weight": - name = "layers." + str(i) + ".feed_forward.w1.weight" - break - if name == "model.layers." + str(i) + ".mlp.down_proj.weight": - name = "layers." + str(i) + ".feed_forward.w2.weight" - break - if name == "model.layers." + str(i) + ".mlp.up_proj.weight": - name = "layers." + str(i) + ".feed_forward.w3.weight" - break + print( "Can not map tensor '" + name + "'" ) + sys.exit() n_dims = len(data.shape) @@ -227,9 +206,9 @@ for name in list_vars.keys(): print("gguf: write header") gguf_writer.write_header_to_file() -print("gguf: write key-values") +print("gguf: write metadata") gguf_writer.write_kv_data_to_file() -print("gguf: write tensor info") +print("gguf: write tensor metadata") gguf_writer.write_ti_data_to_file() # tensor data @@ -237,17 +216,14 @@ print("gguf: write tensor data") for name in list_vars.keys(): data = list_vars[name].squeeze().numpy() -# print("Process tensor: " + name + " with shape: ", data.shape) # we don't need these if name.endswith(".rotary_emb.inv_freq"): -# print(" Skip tensor: " + name) continue # permute these if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"): -# print(" Permute tensor: " + name) - data = permute(data, hparams["num_attention_heads"]) + data = permute(data, head_count) n_dims = len(data.shape) @@ -255,16 +231,13 @@ for name in list_vars.keys(): ftype_cur = 0 if ftype != 0: if name.endswith(".weight") and n_dims == 2: -# print(" Converting to float16") data = data.astype(np.float16) ftype_cur = 1 else: -# print(" Converting to float32") data = data.astype(np.float32) ftype_cur = 0 else: if data.dtype != np.float32: -# print(" Converting to float32") data = data.astype(np.float32) ftype_cur = 0 @@ -273,5 +246,5 @@ for name in list_vars.keys(): gguf_writer.close() -print("gguf: conversion done, output file: " + fname_out) +print("gguf: model successfully exported to '" + fname_out + "'" ) print("")