--- /dev/null
+# Convert GPT-2 h5 transformer model to ggml format
+#
+# Load the model using GPT2Model.
+# 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 numpy as np
+import re
+
+from transformers import GPT2Model
+
+# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a corresponding list of unicode strings.
+ 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 signficant percentage of your normal, say, 32K bpe vocab.
+ To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+ And avoids mapping to whitespace/control characters the bpe code barfs on.
+ """
+ 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) < 2:
+ print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
+ 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)
+
+# use 16-bit or 32-bit floats
+use_f16 = True
+if len(sys.argv) > 2:
+ use_f16 = False
+ fname_out = sys.argv[1] + "/ggml-model-f32.bin"
+
+model = GPT2Model.from_pretrained(dir_model, low_cpu_mem_usage=True)
+#print (model)
+
+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", use_f16))
+
+byte_encoder = bytes_to_unicode()
+byte_decoder = {v:k for k, v in byte_encoder.items()}
+
+fout.write(struct.pack("i", len(encoder) + len(encoder_added)))
+
+for key in encoder:
+ text = bytearray([byte_decoder[c] for c in key])
+ fout.write(struct.pack("i", len(text)))
+ fout.write(text)
+
+for key in encoder_added:
+ text = bytearray([byte_decoder[c] for c in key])
+ 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 = 0;
+ if use_f16:
+ if name[-7:] == ".weight" and n_dims == 2:
+ print(" Converting to float16")
+ data = data.astype(np.float16)
+ ftype = 1
+ else:
+ print(" Converting to float32")
+ data = data.astype(np.float32)
+ ftype = 0
+
+ # for efficiency - transpose these matrices:
+ # "transformer.h.*.mlp.c_proj.weight
+ if name.endswith(".mlp.c_proj.weight"):
+ print(" Transposing")
+ data = data.transpose()
+
+ # rename headers to keep compatibility
+ if name == "ln_f.weight":
+ name = "model/ln_f/g"
+ elif name == "ln_f.bias":
+ name = "model/ln_f/b"
+ elif name == "wte.weight":
+ name = "model/wte"
+ elif name == "wpe.weight":
+ name = "model/wpe"
+ elif re.match(r'h\.\d+\.ln_1\.weight', name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_1/g"
+ elif re.match(r"h\.\d+\.ln_1\.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_1/b"
+ elif re.match(r"h\.\d+\.attn\.c_attn\.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_attn/w"
+ elif re.match(r"h\.\d+\.attn\.c_attn\.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_attn/b"
+ elif re.match(r"h\.\d+\.attn\.c_proj\.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_proj/w"
+ elif re.match(r"h.\d+.attn.c_proj.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_proj/b"
+ elif re.match(r"h.\d+.ln_2.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_2/g"
+ elif re.match(r"h.\d+.ln_2.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_2/b"
+ elif re.match(r"h.\d+.mlp.c_fc.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_fc/w"
+ elif re.match(r"h.\d+.mlp.c_fc.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_fc/b"
+ elif re.match(r"h.\d+.mlp.c_proj.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_proj/w"
+ elif re.match(r"h.\d+.mlp.c_proj.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_proj/b"
+ else:
+ print("Unrecognized variable name. %s", name)
+
+ str = name.encode('utf-8')
+
+ fout.write(struct.pack("iii", n_dims, len(str), ftype))
+ 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("")
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