some users report that this repo is now being flagged as malicious?
no idea why, but I am removing all prebuilt binaries except libopenblas. windows users can still obtain it from /releases and osx and linux users can rebuild from source code.
This commit is contained in:
commit
4f5faf9612
6 changed files with 180 additions and 1 deletions
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koboldcpp.dll
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koboldcpp.dll
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@ -302,6 +302,12 @@ def main(args):
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elif not args.noblas:
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print("Attempting to use OpenBLAS library for faster prompt ingestion. A compatible libopenblas.dll will be required.")
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use_blas = True
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if args.psutil_set_threads:
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import psutil
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args.threads = psutil.cpu_count(logical=False)
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print("Overriding thread count, using " + str(args.threads) + " threads instead.")
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init_library() # Note: if blas does not exist and is enabled, program will crash.
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ggml_selected_file = args.model_file
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embedded_kailite = None
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@ -363,6 +369,7 @@ if __name__ == '__main__':
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physical_core_limit = int(os.cpu_count()/2)
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default_threads = (physical_core_limit if physical_core_limit<=3 else max(3,physical_core_limit-1))
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parser.add_argument("--threads", help="Use a custom number of threads if specified. Otherwise, uses an amount based on CPU cores", type=int, default=default_threads)
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parser.add_argument("--psutil_set_threads", help="Experimental flag. If set, uses psutils to determine thread count based on physical cores.", action='store_true')
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parser.add_argument("--stream", help="Uses pseudo streaming", action='store_true')
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parser.add_argument("--noblas", help="Do not use OpenBLAS for accelerated prompt ingestion", action='store_true')
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args = parser.parse_args()
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otherarch/convert_hf_gptj.py
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otherarch/convert_hf_gptj.py
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@ -0,0 +1,173 @@
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# Convert GPT-J-6B h5 transformer model to ggml format
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#
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# Load the model using GPTJForCausalLM.
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# Iterate over all variables and write them to a binary file.
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#
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# For each variable, write the following:
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# - Number of dimensions (int)
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# - Name length (int)
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# - Dimensions (int[n_dims])
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# - Name (char[name_length])
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# - Data (float[n_dims])
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#
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# By default, the bigger matrices are converted to 16-bit floats.
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# This can be disabled by adding the "use-f32" CLI argument.
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#
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# At the start of the ggml file we write the model parameters
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# and vocabulary.
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#
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import sys
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import struct
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import json
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import torch
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import numpy as np
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from transformers import GPTJForCausalLM
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a signficant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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if len(sys.argv) < 3:
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print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
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print(" ftype == 0 -> float32")
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print(" ftype == 1 -> float16")
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sys.exit(1)
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# output in the same directory as the model
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dir_model = sys.argv[1]
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fname_out = sys.argv[1] + "/ggml-model.bin"
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with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
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encoder = json.load(f)
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with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
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encoder_added = json.load(f)
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with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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# possible data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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#
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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ftype = 1
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if len(sys.argv) > 2:
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ftype = int(sys.argv[2])
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if ftype < 0 or ftype > 1:
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print("Invalid ftype: " + str(ftype))
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sys.exit(1)
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fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
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model = GPTJForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True)
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#print (model)
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list_vars = model.state_dict()
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#print (list_vars)
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fout = open(fname_out, "wb")
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fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
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fout.write(struct.pack("i", hparams["vocab_size"]))
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fout.write(struct.pack("i", hparams["n_positions"]))
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fout.write(struct.pack("i", hparams["n_embd"]))
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fout.write(struct.pack("i", hparams["n_head"]))
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fout.write(struct.pack("i", hparams["n_layer"]))
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fout.write(struct.pack("i", hparams["rotary_dim"]))
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fout.write(struct.pack("i", ftype))
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byte_encoder = bytes_to_unicode()
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byte_decoder = {v:k for k, v in byte_encoder.items()}
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fout.write(struct.pack("i", len(encoder) + len(encoder_added)))
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for key in encoder:
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text = bytearray([byte_decoder[c] for c in key])
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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for key in encoder_added:
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text = bytearray([byte_decoder[c] for c in key])
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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print("Processing variable: " + name + " with shape: ", data.shape)
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# we don't need these
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if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
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print(" Skipping variable: " + name)
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continue
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n_dims = len(data.shape);
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype_cur = 0;
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if ftype != 0:
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if name[-7:] == ".weight" and n_dims == 2:
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print(" Converting to float16")
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data = data.astype(np.float16)
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ftype_cur = 1
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else:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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else:
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if data.dtype != np.float32:
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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# for efficiency - transpose these matrices:
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# (note - with latest ggml this is no longer more efficient, so disabling it)
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# "transformer.h.*.mlp.fc_in.weight"
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# "transformer.h.*.attn.out_proj.weight"
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# "transformer.h.*.attn.q_proj.weight"
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# "transformer.h.*.attn.k_proj.weight"
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# "transformer.h.*.attn.v_proj.weight"
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#if name.endswith(".mlp.fc_in.weight") or \
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# name.endswith(".attn.out_proj.weight") or \
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# name.endswith(".attn.q_proj.weight") or \
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# name.endswith(".attn.k_proj.weight") or \
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# name.endswith(".attn.v_proj.weight"):
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# print(" Transposing")
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# data = data.transpose()
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# header
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str = name.encode('utf-8')
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fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
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for i in range(n_dims):
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fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
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fout.write(str);
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# data
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data.tofile(fout)
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fout.close()
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print("Done. Output file: " + fname_out)
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print("")
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@ -143,7 +143,6 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
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ctx_size += (5 + 10*n_layer)*256; // object overhead
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ctx_size = ctx_size * 3 / 2;
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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}
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BIN
quantize.exe
BIN
quantize.exe
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