falcon7b example

examples/CMakeLists.txt

@@ -26,3 +26,4 @@ add_subdirectory(dolly-v2)
 add_subdirectory(replit)
 add_subdirectory(mpt)
 add_subdirectory(starcoder)
+add_subdirectory(falcon)

examples/falcon/CMakeLists.txt

@@ -0,0 +1,13 @@
+#
+# falcon
+
+set(TEST_TARGET falcon)
+add_executable(${TEST_TARGET} main.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml)
+
+#
+# falcon-quantize
+
+set(TEST_TARGET falcon-quantize)
+add_executable(${TEST_TARGET} quantize.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml)

examples/falcon/README.md

@@ -0,0 +1,9 @@
+# falcon
+
+Transformer architecture: falcon-7b
+
+## Notes
+
+- No guarantees for correctness
+- The tokenizer is currently hacked - probably works only for English
+- Non-parallel residual is not supported

examples/falcon/convert-hf-to-ggml.py

@@ -0,0 +1,122 @@
+# Convert Hugging Face fine-tuned bloom-like models to ggml format
+#
+# Usage:
+#
+#   python3 models/convert-h5-to-ggml.py 
+#
+# This script is similar to "convert-pt-to-ggml.py"
+#
+
+import io
+import os
+import sys
+import struct
+import json
+import code
+import torch
+import numpy as np
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+
+# 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]
+
+# 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)
+config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+hparams = config.to_dict()
+print("Loading model: ", model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True, trust_remote_code=True)
+print("Model loaded: ", model_name)
+
+
+fname_out = dir_out + f"/ggml-model-{model_name.split('/')[-1]}-{ftype_str[ftype]}.bin"
+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["hidden_size"]))
+fout.write(struct.pack("i", hparams["n_head"]))
+fout.write(struct.pack("i", hparams["n_layer"]))
+fout.write(struct.pack("i", ftype))
+
+# Is this correct?
+# 
+# No. Multibyte characters that span multiple tokens like emoji 🤖 won't be
+# decoded properly.
+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()
+for name in list_vars.keys():
+    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 = data.astype(np.float16)
+        ftype_cur = 1
+
+    # 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("")