added MPT support

otherarch/tools/convert_hf_mpt.py

@@ -0,0 +1,158 @@
+import sys
+import struct
+import json
+import numpy as np
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import sentencepiece.sentencepiece_model_pb2 as model
+
+# 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) < 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 + "/config.json", "r", encoding="utf-8") 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"
+
+
+tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    dir_model, low_cpu_mem_usage=True, trust_remote_code=True
+)
+# print (model)
+
+# print(tokenizer.encode('I believe the meaning of life is'))
+
+list_vars = model.state_dict()
+for name in list_vars.keys():
+    print(name, list_vars[name].shape, list_vars[name].dtype)
+
+fout = open(fname_out, "wb")
+
+print(hparams)
+
+fout.write(struct.pack("i", 0x67676D6C))  # magic: ggml in hex
+fout.write(struct.pack("i", hparams["d_model"]))
+fout.write(struct.pack("i", hparams["max_seq_len"]))
+fout.write(struct.pack("i", hparams["n_heads"]))
+fout.write(struct.pack("i", hparams["n_layers"]))
+fout.write(struct.pack("i", hparams["vocab_size"]))
+fout.write(struct.pack("f", hparams["attn_config"]["alibi_bias_max"]))
+fout.write(struct.pack("f", hparams["attn_config"]["clip_qkv"] or 0.0))
+fout.write(struct.pack("i", ftype))
+
+vocab_size = hparams["vocab_size"]
+
+encoder = tokenizer.vocab
+# Add added_tokens (special tokens) to the encoder
+encoder.update(tokenizer.get_added_vocab())
+
+byte_encoder = bytes_to_unicode()
+byte_decoder = {v:k for k, v in byte_encoder.items()}
+
+counter = 0
+# sort by value
+for key in sorted(encoder, key=encoder.get):
+    # workaround for key error when c not found
+    text=""
+    for c in key:
+        if c not in byte_decoder:
+            text += c
+        else:
+            text += chr(byte_decoder[c] )
+    text = bytearray( text, encoding="utf-8" )
+    fout.write(struct.pack("i", len(text)))
+    fout.write(text)
+    counter += 1
+
+# Repeat last token until vocab_size
+while counter < vocab_size:
+    fout.write(struct.pack("i", len(text)))
+    fout.write(text)
+    counter += 1
+
+# assert counter == config.vocab_size
+
+for name in list_vars.keys():
+    data = list_vars[name].squeeze().numpy()
+    print("Processing variable: " + name + " with shape: ", data.shape)
+
+    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
+
+    # 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("")