Import radpajama (a redpajama.cpp fork) (#814)

This is the relevant commit: bfa6466199

Model download links:
https://huggingface.co/ceonlabs/radpajama/tree/main

third_party/radpajama/scripts/convert_gptneox_to_ggml.py

@@ -0,0 +1,144 @@
+# Convert Hugging Face fine-tuned gpt-neox-like models to ggml format
+
+import io
+import os
+import sys
+import struct
+import json
+import code
+import torch
+import numpy as np
+
+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 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 significant 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: python convert-hf-to-ggml.py model_name dir-output [use-f32]")
+    print("  model_name: name of the model to convert. Example: 'bigscience/bloomz-560m'")
+    print("  dir-output: directory where the output file will be written")
+    print("  use-f32:    if present, use float32 instead of float16")
+    sys.exit(1)
+
+model_name = sys.argv[1]
+dir_out = sys.argv[2]
+model_cache_dir = dir_out + "-cache"
+
+# make sure the output directory exists
+os.makedirs(dir_out, exist_ok=True)
+
+# possible data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+ftype = 1
+if len(sys.argv) > 3:
+    ftype = 0
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+print("Loading model: ", model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16 if ftype == 1 else torch.float32, 
+                                             cache_dir=model_cache_dir)
+model.eval()
+for p in model.parameters():
+    p.requires_grad = False
+hparams = model.config.to_dict()
+print("Model loaded: ", model_name)
+
+fn_bin = f"/ggml-{model_name.split('/')[-1]}-{ftype_str[ftype]}.bin"
+fn_out = dir_out + fn_bin
+fout = open(fn_out, "wb")
+
+ggml_file_magic = 0x67676d66 # 0x67676d6c is unversioned
+ggml_file_version = 0x00000001 # v1
+
+hparams["multiple_of"] = 1
+fout.write(struct.pack("i", ggml_file_magic)) # magic: ggmf in hex
+fout.write(struct.pack("i", ggml_file_version))
+fout.write(struct.pack("i", hparams["vocab_size"]))
+fout.write(struct.pack("i", hparams["max_position_embeddings"]))
+fout.write(struct.pack("i", hparams["hidden_size"]))
+fout.write(struct.pack("i", hparams["num_attention_heads"]))
+fout.write(struct.pack("i", hparams["num_hidden_layers"]))
+fout.write(struct.pack("i", int((hparams["hidden_size"] / hparams["num_attention_heads"]
+                             ) * hparams["rotary_pct"]))) # rotary_dim
+fout.write(struct.pack("i", int(hparams["use_parallel_residual"])))
+fout.write(struct.pack("i", ftype))
+
+# Is this correct??
+dot_token = tokenizer.encode(".")[0]
+for i in range(hparams["vocab_size"]):
+    text = tokenizer.decode([i]).encode('utf-8')
+    fout.write(struct.pack("i", len(text)))
+    fout.write(text)
+
+list_vars = model.state_dict()
+
+print(hparams)
+
+for name in list_vars.keys():
+    if name.startswith('gpt_neox.layers.'):
+        if 'attention.masked_bias' in name or \
+            'attention.rotary_emb.inv_freq' in name or \
+            'attention.bias' in name:
+            continue
+    # No gradients for these
+    list_vars[name].requires_grad = False
+    src = name
+    nn = name
+
+    print(src, ' -> ', name)
+    data = list_vars[src].squeeze().numpy()
+    data = data.astype(np.float32)
+
+    n_dims = len(data.shape)
+    print(name, n_dims, data.shape)
+
+    # default type is fp32
+    ftype_cur = 0
+    if ftype == 1 and n_dims > 1:
+        print("  Converting to float16", data.shape, data[:3, :3].tolist())
+        data = data.astype(np.float16)
+        ftype_cur = 1
+    else:
+        print("  Converting to float32", data.shape,
+              data[:3, :3].tolist() if n_dims > 1 else data[:3].tolist())
+        data = data.astype(np.float32)
+
+    # 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]))
+    print(str)
+    fout.write(str)
+
+    # data
+    data.tofile(fout)
+
+fout.close()
+
+print("Done. Output file: " + fn_out)
+print("")