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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,238 @@ | ||
| #!/usr/bin/env python3 | ||
| # HF bloom --> gguf conversion | ||
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| from __future__ import annotations | ||
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| import argparse | ||
| import json | ||
| import os | ||
| import re | ||
| import struct | ||
| import sys | ||
| from pathlib import Path | ||
| from typing import Any | ||
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| import numpy as np | ||
| import torch | ||
| from transformers import AutoTokenizer # type: ignore[import] | ||
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| if 'NO_LOCAL_GGUF' not in os.environ: | ||
| sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) | ||
| import gguf | ||
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| 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 | ||
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| if num_parts > 0: | ||
| print("gguf: found " + str(num_parts) + " model parts") | ||
| return num_parts | ||
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| # 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() | ||
|
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| args = parse_args() | ||
|
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| 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) | ||
|
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| # possible tensor data types | ||
| # ftype == 0 -> float32 | ||
| # ftype == 1 -> float16 | ||
|
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| # map from ftype to string | ||
| ftype_str = ["f32", "f16"] | ||
|
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| 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' | ||
|
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| print("gguf: loading model "+dir_model.name) | ||
|
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| with open(dir_model / "config.json", "r", encoding="utf-8") as f: | ||
| hparams = json.load(f) | ||
|
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| 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) | ||
|
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| ARCH=gguf.MODEL_ARCH.BLOOM | ||
| gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) | ||
|
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| print("gguf: get model metadata") | ||
|
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| block_count = hparams["n_layer"] | ||
|
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| 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) | ||
|
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| # TOKENIZATION | ||
|
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| print("gguf: get tokenizer metadata") | ||
|
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| tokens: list[bytearray] = [] | ||
| scores: list[float] = [] | ||
| toktypes: list[int] = [] | ||
|
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| # gpt2 tokenizer | ||
| gguf_writer.add_tokenizer_model("gpt2") | ||
|
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| print("gguf: get gpt2 tokenizer vocab") | ||
|
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| # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py | ||
| tokenizer = AutoTokenizer.from_pretrained(dir_model) | ||
|
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||
| # 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 | ||
|
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||
| reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} | ||
|
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| 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) | ||
|
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| gguf_writer.add_token_list(tokens) | ||
| gguf_writer.add_token_scores(scores) | ||
| gguf_writer.add_token_types(toktypes) | ||
|
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| special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) | ||
| special_vocab.add_to_gguf(gguf_writer) | ||
|
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| # TENSORS | ||
|
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| tensor_map = gguf.get_tensor_name_map(ARCH, block_count) | ||
|
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| # params for qkv transform | ||
| n_head_kv = hparams.get("n_head_kv", n_head) | ||
| head_dim = n_embed // n_head | ||
|
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| # 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") | ||
|
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||
| 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 | ||
|
|
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| for original_name in model_part.keys(): | ||
| data = model_part[original_name] | ||
| name = re.sub(r'transformer\.', '', original_name) | ||
|
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| old_dtype = data.dtype | ||
|
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| # convert any unsupported data types to float32 | ||
| if data.dtype != torch.float16 and data.dtype != torch.float32: | ||
| data = data.to(torch.float32) | ||
|
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| data = data.squeeze().numpy() | ||
|
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| 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) | ||
|
|
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| print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) | ||
|
|
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| gguf_writer.add_tensor(new_name, data) | ||
|
|
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| 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 | ||
|
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|
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| 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() | ||
|
|
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| gguf_writer.close() | ||
|
|
||
| print(f"gguf: model successfully exported to '{fname_out}'") | ||
| print("") |
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