Replit + MPT

examples/CMakeLists.txt

@@ -11,3 +11,5 @@ add_subdirectory(whisper)
 add_subdirectory(mnist)
 add_subdirectory(stablelm)
 add_subdirectory(dolly-v2)
+add_subdirectory(replit)
+add_subdirectory(mpt)

examples/mpt/CMakeLists.txt

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

examples/mpt/convert-h5-to-ggml.py

@@ -0,0 +1,109 @@
+import sys
+import struct
+import json
+import numpy as np
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import sentencepiece.sentencepiece_model_pb2 as model
+
+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("i", ftype))
+
+
+# TODO: temporary hack to not deal with implementing the tokenizer
+dot_token = tokenizer.encode(".")[0]
+for i in range(hparams["vocab_size"]):
+    text = tokenizer.decode([dot_token, i]).encode("utf-8")
+    # remove the first byte (it's always '.')
+    text = text[1:]
+    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)
+
+    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("")