# Convert GPT-J-6B h5 transformer model to ggml format # # Load the model using GPTJForCausalLM. # Iterate over all variables and write them to a binary file. # # For each variable, write the following: # - Number of dimensions (int) # - Name length (int) # - Dimensions (int[n_dims]) # - Name (char[name_length]) # - Data (float[n_dims]) # # By default, the bigger matrices are converted to 16-bit floats. # This can be disabled by adding the "use-f32" CLI argument. # # At the start of the ggml file we write the model parameters # and vocabulary. # import sys import struct import json import torch import numpy as np from accelerate import init_empty_weights from transformers import AutoModelForCausalLM, AutoTokenizer # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) if len(sys.argv) < 3: print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n") print(" ftype == 0 -> float32") print(" ftype == 1 -> float16") sys.exit(1) # output in the same directory as the model dir_model = sys.argv[1] fname_out = sys.argv[1] + "/ggml-model.bin" with open(dir_model + "/vocab.json", "r") as f: encoder = json.load(f) with open(dir_model + "/added_tokens.json", "r") as f: encoder_added = json.load(f) with open(dir_model + "/config.json", "r") as f: hparams = json.load(f) # possible data types # ftype == 0 -> float32 # ftype == 1 -> float16 # # map from ftype to string ftype_str = ["f32", "f16"] ftype = 1 if len(sys.argv) > 2: ftype = int(sys.argv[2]) if ftype < 0 or ftype > 1: print("Invalid ftype: " + str(ftype)) sys.exit(1) fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True) print (model) tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-350M-multi') print(tokenizer) # config = AutoConfig.from_pretrained(sys.argv[1]) # model = AutoModelForCausalLM.from_pretrained("./codegen-2B-multi", torch_dtype=torch.float16).to("cuda") from accelerate import load_checkpoint_and_dispatch list_vars = model.state_dict() #print (list_vars) fout = open(fname_out, "wb") fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex fout.write(struct.pack("i", hparams['vocab_size'])) fout.write(struct.pack("i", hparams["n_positions"])) fout.write(struct.pack("i", hparams["n_embd"])) fout.write(struct.pack("i", hparams["n_head"])) fout.write(struct.pack("i", hparams["n_layer"])) fout.write(struct.pack("i", hparams["rotary_dim"])) fout.write(struct.pack("i", ftype)) byte_encoder = bytes_to_unicode() byte_decoder = {v:k for k, v in byte_encoder.items()} print(byte_encoder) fout.write(struct.pack("i", hparams['vocab_size']))#len(encoder) + len(encoder_added))) # replace key tokens in tokenizer for word,idx in sorted(tokenizer.vocab.items(), key=lambda x: x[1]) : #text = word.encode("utf8") # text = bytearray([byte_decoder[c] for c in word if c in byte_decoder]) if(len(text)) < 1: #print(f"'{word}'") #continue text = bytearray(word.encode('utf8')) # else: # print(text) fout.write(struct.pack("i", len(text))) fout.write(text) # for key in encoder: # #text = bytearray([byte_decoder[c] for c in key]) # text = key.encode("utf8") # fout.write(struct.pack("i", len(text))) # fout.write(text) # for key in encoder_added: # try: # #text = bytearray([byte_decoder[c] for c in key]) # text = key.encode("utf8") # except Exception as e: # print(e) # print(key) # print(text) # sys.exit(1) # fout.write(struct.pack("i", len(text))) # fout.write(text) empty_vocab = hparams['vocab_size'] - tokenizer.vocab_size print(f"Fill empty vocab for {empty_vocab} slots") for i in range( hparams['vocab_size'] - len(encoder) - len(encoder_added)): text = "<|endoftext|>".encode("utf8") fout.write(struct.pack("i", len(text))) fout.write(text) for name in list_vars.keys(): data = list_vars[name].squeeze().numpy() print("Processing variable: " + name + " with shape: ", data.shape) # we don't need these if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"): print(" Skipping variable: " + name) continue n_dims = len(data.shape); # ftype == 0 -> float32, ftype == 1 -> float16 ftype_cur = 0; if ftype != 0: if name[-7:] == ".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 # for efficiency - transpose these matrices: # (note - with latest ggml this is no longer more efficient, so disabling it) # "transformer.h.*.mlp.fc_in.weight" # "transformer.h.*.attn.out_proj.weight" # "transformer.h.*.attn.q_proj.weight" # "transformer.h.*.attn.k_proj.weight" # "transformer.h.*.attn.v_proj.weight" #if name.endswith(".mlp.fc_in.weight") or \ # name.endswith(".attn.out_proj.weight") or \ # name.endswith(".attn.q_proj.weight") or \ # name.endswith(".attn.k_proj.weight") or \ # name.endswith(".attn.v_proj.weight"): # print(" Transposing") # data = data.transpose() # header str = name.encode('utf-8') fout.write(struct.pack("iii", n_dims, len(str), ftype_cur)) for i in range(n_dims): fout.write(struct.pack("i", data.shape[n_dims - 1 - i])) fout.write(str); # data data.tofile(fout) fout.close() print("Done. Output file: " + fname_out) print("")