feat: Support bloom models

convert-bloom-hf-to-gguf.py

@@ -0,0 +1,238 @@
+#!/usr/bin/env python3
+# HF bloom --> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import re
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+ sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path) -> int:
+ num_parts = 0
+ for filename in os.listdir(dir_model):
+ if filename.startswith("pytorch_model-"):
+ num_parts += 1
+
+ if num_parts > 0:
+ print("gguf: found " + str(num_parts) + " model parts")
+ return num_parts
+
+
+# Supported Models:
+# https://huggingface.co/bigscience/bloom-1b7
+# https://huggingface.co/bigscience/bloom-3b
+# https://huggingface.co/bigscience/bloom-7b1
+# https://huggingface.co/Langboat/bloom-1b4-zh
+def parse_args() -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description="Convert a Bloom model to a GGML compatible file")
+ parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
+ parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
+ parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
+ parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
+ return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+ print(f'Error: {args.model} is not a directory', file = sys.stderr)
+ sys.exit(1)
+
+# possible tensor data types
+# ftype == 0 -> float32
+# ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+ fname_out = args.outfile
+else:
+ # output in the same directory as the model by default
+ fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+ hparams = json.load(f)
+
+if hparams["architectures"][0] != "BloomForCausalLM":
+ print("Model architecture not supported: " + hparams["architectures"][0])
+ sys.exit(1)
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.BLOOM
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layer"]
+
+gguf_writer.add_name("Bloom")
+n_embed = hparams.get("hidden_size", hparams.get("n_embed"))
+n_head = hparams.get("n_head", hparams.get("num_attention_heads"))
+gguf_writer.add_context_length(hparams.get("seq_length", n_embed))
+gguf_writer.add_embedding_length(n_embed)
+gguf_writer.add_feed_forward_length(4 * n_embed)
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(n_head)
+gguf_writer.add_head_count_kv(n_head)
+gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+assert max(tokenizer.vocab.values()) < vocab_size
+
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+ tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
+ scores.append(0.0) # dummy
+ toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_scores(scores)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
+
+# params for qkv transform
+n_head_kv = hparams.get("n_head_kv", n_head)
+head_dim = n_embed // n_head
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+ part_names = iter(("pytorch_model.bin",))
+else:
+ part_names = (
+ f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+ )
+
+for part_name in part_names:
+ if args.vocab_only:
+ break
+ print("gguf: loading model part '" + part_name + "'")
+ model_part = torch.load(dir_model / part_name, map_location="cpu")
+
+ has_lm_head = True
+ if "lm_head.weight" not in model_part.keys() and "output.weight" not in model_part.keys():
+ has_lm_head = False
+
+ for original_name in model_part.keys():
+ data = model_part[original_name]
+ name = re.sub(r'transformer\.', '', original_name)
+
+ old_dtype = data.dtype
+
+ # convert any unsupported data types to float32
+ if data.dtype != torch.float16 and data.dtype != torch.float32:
+ data = data.to(torch.float32)
+
+ data = data.squeeze().numpy()
+
+ if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
+ # Map bloom-style qkv_linear to gpt-style qkv_linear
+ # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
+ # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
+ qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
+ data = np.concatenate(
+ (qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
+ qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
+ qkv_weights[:, 2, :, :].reshape((-1, n_embed))),
+ axis=0
+ )
+ print("re-format attention.linear_qkv.weight")
+ elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
+ qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
+ data = np.concatenate(
+ (qkv_bias[:, 0, :].reshape((n_embed,)),
+ qkv_bias[:, 1, :].reshape((n_embed,)),
+ qkv_bias[:, 2, :].reshape((n_embed,))),
+ axis=0
+ )
+ print("re-format attention.linear_qkv.bias")
+
+ # map tensor names
+ new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+ if new_name is None:
+ print("Can not map tensor '" + name + "'")
+ sys.exit()
+
+ n_dims = len(data.shape)
+ data_dtype = data.dtype
+
+ # if f32 desired, convert any float16 to float32
+ if ftype == 0 and data_dtype == np.float16:
+ data = data.astype(np.float32)
+
+ # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+ if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+ data = data.astype(np.float32)
+
+ # if f16 desired, convert any float32 2-dim weight tensors to float16
+ if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+ data = data.astype(np.float16)
+
+ print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+ gguf_writer.add_tensor(new_name, data)
+
+ if not has_lm_head and name == "word_embeddings.weight":
+ gguf_writer.add_tensor("output.weight", data)
+ print(name, "=>", "output.weight" + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) # noqa
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+ print("gguf: write tensors")
+ gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")