#!/usr/bin/env python3 from __future__ import annotations import argparse import contextlib import json import os import re import sys from enum import IntEnum from pathlib import Path from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional import numpy as np import torch if TYPE_CHECKING: from torch import Tensor if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf ###### MODEL DEFINITIONS ###### class SentencePieceTokenTypes(IntEnum): NORMAL = 1 UNKNOWN = 2 CONTROL = 3 USER_DEFINED = 4 UNUSED = 5 BYTE = 6 class Model: def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): self.dir_model = dir_model self.ftype = ftype self.fname_out = fname_out self.is_big_endian = is_big_endian self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE self.is_safetensors = self._is_model_safetensors() self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") self.part_names = self._get_part_names() self.hparams = Model.load_hparams(self.dir_model) self.model_arch = self._get_model_architecture() self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess) def set_vocab(self): self._set_vocab_gpt2() def get_tensors(self) -> Iterator[tuple[str, Tensor]]: for part_name in self.part_names: print(f"gguf: loading model part '{part_name}'") ctx: ContextManager[Any] if self.is_safetensors: from safetensors import safe_open ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) else: ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) with ctx as model_part: for name in model_part.keys(): data = model_part.get_tensor(name) if self.is_safetensors else model_part[name] yield name, data def set_gguf_parameters(self): self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_block_count(self.hparams.get( "n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")), )) if (n_ctx := self.hparams.get("max_position_embeddings")) is not None: self.gguf_writer.add_context_length(n_ctx) if (n_embd := self.hparams.get("hidden_size")) is not None: self.gguf_writer.add_embedding_length(n_embd) if (n_ff := self.hparams.get("intermediate_size")) is not None: self.gguf_writer.add_feed_forward_length(n_ff) if (n_head := self.hparams.get("num_attention_heads")) is not None: self.gguf_writer.add_head_count(n_head) if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None: self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps) if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: self.gguf_writer.add_expert_used_count(n_experts_used) self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) def write_tensors(self): block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): # we don't need these if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): continue old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) data = data_torch.squeeze().numpy() # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype # if f32 desired, convert any float16 to float32 if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) def write(self): self.write_tensors() self.gguf_writer.write_header_to_file() self.gguf_writer.write_kv_data_to_file() self.gguf_writer.write_tensors_to_file() self.gguf_writer.close() def write_vocab(self): self.gguf_writer.write_header_to_file() self.gguf_writer.write_kv_data_to_file() self.gguf_writer.close() @staticmethod def count_model_parts(dir_model: Path, prefix: str) -> int: num_parts = 0 for filename in os.listdir(dir_model): if filename.endswith(prefix): num_parts += 1 return num_parts @staticmethod def load_hparams(dir_model): with open(dir_model / "config.json", "r", encoding="utf-8") as f: return json.load(f) @staticmethod def from_model_architecture(model_architecture): if model_architecture == "GPTNeoXForCausalLM": return GPTNeoXModel if model_architecture == "BloomForCausalLM": return BloomModel if model_architecture == "MPTForCausalLM": return MPTModel if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): return BaichuanModel if model_architecture in ("FalconForCausalLM", "RWForCausalLM"): return FalconModel if model_architecture == "GPTBigCodeForCausalLM": return StarCoderModel if model_architecture == "GPTRefactForCausalLM": return RefactModel if model_architecture == "PersimmonForCausalLM": return PersimmonModel if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): return StableLMModel if model_architecture == "QWenLMHeadModel": return QwenModel if model_architecture == "MixtralForCausalLM": return MixtralModel if model_architecture == "PhiForCausalLM": return Phi2Model return Model def _is_model_safetensors(self) -> bool: return Model.count_model_parts(self.dir_model, ".safetensors") > 0 def _get_part_names(self): if self.is_safetensors: if self.num_parts == 1: # there's only one .safetensors file return ("model.safetensors",) return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1)) if self.num_parts == 1: # there's only one .bin file return ("pytorch_model.bin",) return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) def _get_model_architecture(self) -> gguf.MODEL_ARCH: arch = self.hparams["architectures"][0] if arch == "GPTNeoXForCausalLM": return gguf.MODEL_ARCH.GPTNEOX if arch == "BloomForCausalLM": return gguf.MODEL_ARCH.BLOOM if arch == "MPTForCausalLM": return gguf.MODEL_ARCH.MPT if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): return gguf.MODEL_ARCH.BAICHUAN if arch in ("FalconForCausalLM", "RWForCausalLM"): return gguf.MODEL_ARCH.FALCON if arch == "GPTBigCodeForCausalLM": return gguf.MODEL_ARCH.STARCODER if arch == "GPTRefactForCausalLM": return gguf.MODEL_ARCH.REFACT if arch == "PersimmonForCausalLM": return gguf.MODEL_ARCH.PERSIMMON if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): return gguf.MODEL_ARCH.STABLELM if arch == "QWenLMHeadModel": return gguf.MODEL_ARCH.QWEN if arch == "MixtralForCausalLM": return gguf.MODEL_ARCH.LLAMA if arch == "PhiForCausalLM": return gguf.MODEL_ARCH.PHI2 raise NotImplementedError(f'Architecture "{arch}" not supported!') def _set_vocab_gpt2(self): dir_model = self.dir_model hparams = self.hparams tokens: list[bytearray] = [] toktypes: list[int] = [] from transformers import AutoTokenizer # type: ignore[attr-defined] tokenizer = AutoTokenizer.from_pretrained(dir_model) 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()} added_vocab = tokenizer.get_added_vocab() for i in range(vocab_size): if i not in reverse_vocab: pad_token = f"[PAD{i}]".encode('utf-8') tokens.append(bytearray(pad_token)) toktypes.append(gguf.TokenType.USER_DEFINED) elif reverse_vocab[i] in added_vocab: tokens.append(reverse_vocab[i]) if hasattr(tokenizer, "added_tokens_decoder") and tokenizer.added_tokens_decoder[i].special: toktypes.append(gguf.TokenType.CONTROL) else: toktypes.append(gguf.TokenType.USER_DEFINED) else: tokens.append(reverse_vocab[i]) toktypes.append(gguf.TokenType.NORMAL) self.gguf_writer.add_tokenizer_model("gpt2") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) special_vocab.add_to_gguf(self.gguf_writer) def _set_vocab_sentencepiece(self): from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' tokens: list[bytes] = [] scores: list[float] = [] toktypes: list[int] = [] if not tokenizer_path.is_file(): print(f'Error: Missing {tokenizer_path}', file=sys.stderr) sys.exit(1) tokenizer = SentencePieceProcessor(str(tokenizer_path)) vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) for token_id in range(vocab_size): piece = tokenizer.id_to_piece(token_id) text = piece.encode("utf-8") score = tokenizer.get_score(token_id) toktype = SentencePieceTokenTypes.NORMAL if tokenizer.is_unknown(token_id): toktype = SentencePieceTokenTypes.UNKNOWN elif tokenizer.is_control(token_id): toktype = SentencePieceTokenTypes.CONTROL elif tokenizer.is_unused(token_id): toktype = SentencePieceTokenTypes.UNUSED elif tokenizer.is_byte(token_id): toktype = SentencePieceTokenTypes.BYTE tokens.append(text) scores.append(score) toktypes.append(toktype) added_tokens_file = self.dir_model / 'added_tokens.json' if added_tokens_file.is_file(): with open(added_tokens_file, "r", encoding="utf-8") as f: added_tokens_json = json.load(f) for key in added_tokens_json: tokens.append(key.encode("utf-8")) scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.USER_DEFINED) self.gguf_writer.add_tokenizer_model("llama") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_scores(scores) self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) class GPTNeoXModel(Model): def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count( int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), ) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) class BloomModel(Model): def set_gguf_parameters(self): self.gguf_writer.add_name("Bloom") n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) self.gguf_writer.add_embedding_length(n_embed) self.gguf_writer.add_feed_forward_length(4 * n_embed) self.gguf_writer.add_block_count(self.hparams["n_layer"]) self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count_kv(n_head) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) def write_tensors(self): block_count = self.hparams["n_layer"] tensors = dict(self.get_tensors()) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) has_lm_head = True n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) for name, data_torch in tensors.items(): if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys(): has_lm_head = False name = re.sub(r'transformer\.', '', name) old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) data = data_torch.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(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype # if f32 desired, convert any float16 to float32 if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) if not has_lm_head and name == "word_embeddings.weight": self.gguf_writer.add_tensor("output.weight", data) print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") class MPTModel(Model): def set_gguf_parameters(self): block_count = self.hparams["n_layers"] self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) self.gguf_writer.add_embedding_length(self.hparams["d_model"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"]) self.gguf_writer.add_head_count(self.hparams["n_heads"]) if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"): self.gguf_writer.add_head_count_kv(kv_n_heads) self.gguf_writer.add_layer_norm_eps(1e-5) if self.hparams["attn_config"]["clip_qkv"] is not None: self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) def write_tensors(self): block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers")) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): # we don't need these if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): continue old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) data = data_torch.squeeze().numpy() # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype # if f32 desired, convert any float16 to float32 if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) # note: MPT output is tied to (same as) wte in original model; # for easier implementation in llama.cpp it's duplicated in GGUF, though :/ if new_name == "token_embd.weight": self.gguf_writer.add_tensor("output.weight", data) class BaichuanModel(Model): def set_vocab(self): self._set_vocab_sentencepiece() def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] head_count = self.hparams["num_attention_heads"] head_count_kv = self.hparams.get("num_key_value_heads", head_count) hf_repo = self.hparams.get("_name_or_path", "") ctx_length = 0 if "max_sequence_length" in self.hparams: ctx_length = self.hparams["max_sequence_length"] elif "max_position_embeddings" in self.hparams: ctx_length = self.hparams["max_position_embeddings"] elif "model_max_length" in self.hparams: ctx_length = self.hparams["model_max_length"] else: print("gguf: can not find ctx length parameter.") sys.exit() self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_source_hf_repo(hf_repo) self.gguf_writer.add_tensor_data_layout("Meta AI original pth") self.gguf_writer.add_context_length(ctx_length) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count(head_count) self.gguf_writer.add_head_count_kv(head_count_kv) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: if self.hparams["rope_scaling"].get("type") == "linear": self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) def write_tensors(self): # Collect tensors from generator object model_kv = dict(self.get_tensors()) block_count = self.hparams["num_hidden_layers"] head_count = self.hparams["num_attention_heads"] tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) head_count_kv = self.hparams.get("num_key_value_heads", head_count) for i in range(block_count): if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None: print(f"Unpacking and permuting layer {i}") model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \ self._reverse_hf_permute_part(w, 0, head_count, head_count) model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \ self._reverse_hf_permute_part(w, 1, head_count, head_count_kv) model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \ self._reverse_hf_part(w, 2) del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"] for name, data_torch in model_kv.items(): # we don't need these if name.endswith(".rotary_emb.inv_freq"): continue old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) data = data_torch.squeeze().numpy() # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype # if f32 desired, convert any float16 to float32 if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: if n_kv_head is not None and n_head != n_kv_head: n_head //= n_kv_head return ( weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) .swapaxes(1, 2) .reshape(weights.shape) ) def _reverse_hf_permute_part( self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None, ) -> Tensor: r = weights.shape[0] // 3 return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv) def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: r = weights.shape[0] // 3 return weights[r * n_part:r * n_part + r, ...] class FalconModel(Model): def set_gguf_parameters(self): block_count = self.hparams.get("num_hidden_layers") if block_count is None: block_count = self.hparams["n_layer"] # old name n_head = self.hparams.get("num_attention_heads") if n_head is None: n_head = self.hparams["n_head"] # old name n_head_kv = self.hparams.get("num_kv_heads") if n_head_kv is None: n_head_kv = self.hparams.get("n_head_kv", 1) # old name self.gguf_writer.add_name("Falcon") self.gguf_writer.add_context_length(2048) # not in config.json self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) def write_tensors(self): block_count = self.hparams.get("num_hidden_layers") if block_count is None: block_count = self.hparams["n_layer"] # old name n_head = self.hparams.get("num_attention_heads") if n_head is None: n_head = self.hparams["n_head"] # old name n_head_kv = self.hparams.get("num_kv_heads") if n_head_kv is None: n_head_kv = self.hparams.get("n_head_kv", 1) # old name head_dim = self.hparams["hidden_size"] // n_head tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) # QKV tensor transform # The original query_key_value tensor contains n_head_kv "kv groups", # each consisting of n_head/n_head_kv query weights followed by one key # and one value weight (shared by all query heads in the kv group). # This layout makes it a big pain to work with in GGML. # So we rearrange them here,, so that we have n_head query weights # followed by n_head_kv key weights followed by n_head_kv value weights, # in contiguous fashion. # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py if "query_key_value" in name: qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) data_torch = torch.cat((q, k, v)).reshape_as(data_torch) data = data_torch.squeeze().numpy() # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype # if f32 desired, convert any float16 to float32 if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) class StarCoderModel(Model): def set_gguf_parameters(self): block_count = self.hparams["n_layer"] self.gguf_writer.add_name("StarCoder") self.gguf_writer.add_context_length(self.hparams["n_positions"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(self.hparams["n_head"]) self.gguf_writer.add_head_count_kv(1) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) class RefactModel(Model): def set_gguf_parameters(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim hidden_dim = int(2 * inner_dim / 3) multiple_of = 256 ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) block_count = self.hparams["n_layer"] self.gguf_writer.add_name("Refact") # refact uses Alibi. So this is from config.json which might be used by training. self.gguf_writer.add_context_length(self.hparams["n_positions"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(ff_dim) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(self.hparams["n_head"]) self.gguf_writer.add_head_count_kv(1) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) def write_tensors(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim hidden_dim = int(2 * inner_dim / 3) multiple_of = 256 ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) n_head = self.hparams["n_head"] n_head_kv = 1 head_dim = self.hparams["n_embd"] // n_head block_count = self.hparams["n_layer"] tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) tensors = dict(self.get_tensors()) for i in range(block_count): if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None: tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim] tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:] del tensors[f"transformer.h.{i}.attn.kv.weight"] if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None: tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w del tensors[f"transformer.h.{i}.attn.q.weight"] if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None: tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim] tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:] del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"] for name, data_torch in tensors.items(): old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) data = data_torch.squeeze().numpy() # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype # if f32 desired, convert any float16 to float32 if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) class PersimmonModel(Model): def set_gguf_parameters(self): block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) head_count = self.hparams["num_attention_heads"] head_count_kv = head_count hidden_size = self.hparams["hidden_size"] self.gguf_writer.add_name('persimmon-8b-chat') self.gguf_writer.add_embedding_length(hidden_size) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) self.gguf_writer.add_head_count(head_count) self.gguf_writer.add_head_count_kv(head_count_kv) self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) def set_vocab(self): self._set_vocab_sentencepiece() # self.gguf_writer.add_bos_token_id(71013) # self.gguf_writer.add_eos_token_id(71013) def write_tensors(self): block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): if name.endswith(".self_attention.rotary_emb.inv_freq"): continue old_dtype = data_torch.dtype # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) data = data_torch.to(torch.float32).squeeze().numpy() new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) class StableLMModel(Model): def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] self.gguf_writer.add_name(dir_model.name) self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"] * (hparams["hidden_size"] // hparams["num_attention_heads"]))) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) self.gguf_writer.add_layer_norm_eps(1e-5) class MixtralModel(Model): def set_vocab(self): self._set_vocab_sentencepiece() class QwenModel(Model): @staticmethod def token_bytes_to_string(b): from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode byte_encoder = bytes_to_unicode() return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) @staticmethod def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]: parts = [bytes([b]) for b in token] while True: min_idx = None min_rank = None for i, pair in enumerate(zip(parts[:-1], parts[1:])): rank = mergeable_ranks.get(pair[0] + pair[1]) if rank is not None and (min_rank is None or rank < min_rank): min_idx = i min_rank = rank if min_rank is None or (max_rank is not None and min_rank >= max_rank): break assert min_idx is not None parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] return parts def set_vocab(self): dir_model = self.dir_model hparams = self.hparams tokens: list[bytearray] = [] toktypes: list[int] = [] from transformers import AutoTokenizer # type: ignore[attr-defined] tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) vocab_size = hparams["vocab_size"] assert max(tokenizer.get_vocab().values()) < vocab_size merges = [] vocab = {} mergeable_ranks = tokenizer.mergeable_ranks for token, rank in mergeable_ranks.items(): vocab[self.token_bytes_to_string(token)] = rank if len(token) == 1: continue merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) assert len(merged) == 2 merges.append(' '.join(map(self.token_bytes_to_string, merged))) reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()} added_vocab = tokenizer.special_tokens for i in range(vocab_size): if i not in reverse_vocab: pad_token = f"[PAD{i}]".encode("utf-8") tokens.append(bytearray(pad_token)) toktypes.append(gguf.TokenType.USER_DEFINED) elif reverse_vocab[i] in added_vocab: tokens.append(reverse_vocab[i]) toktypes.append(gguf.TokenType.CONTROL) else: tokens.append(reverse_vocab[i]) toktypes.append(gguf.TokenType.NORMAL) self.gguf_writer.add_tokenizer_model("gpt2") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) special_vocab = gguf.SpecialVocab(dir_model, load_merges=False) special_vocab.merges = merges special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"]) special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"]) special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"]) special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): self.gguf_writer.add_name("Qwen") self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) def write_tensors(self): block_count = self.hparams["num_hidden_layers"] model_kv = dict(self.get_tensors()) tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in model_kv.items(): # we don't need these if name.endswith(".rotary_emb.inv_freq"): continue old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) data = data_torch.squeeze().numpy() # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: print(f"Can not map tensor {name!r}") sys.exit() n_dims = len(data.shape) data_dtype = data.dtype # if f32 desired, convert any float16 to float32 if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) class Phi2Model(Model): def set_gguf_parameters(self): block_count = self.hparams["n_layer"] self.gguf_writer.add_name("Phi2") self.gguf_writer.add_context_length(self.hparams["n_positions"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(self.hparams["n_head"]) self.gguf_writer.add_head_count_kv(self.hparams["n_head"]) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"]) self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_add_bos_token(False) ###### CONVERSION LOGIC ###### def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Convert a huggingface 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( "--outtype", type=str, choices=["f32", "f16"], default="f16", help="output format - use f32 for float32, f16 for float16", ) parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") parser.add_argument( "model", type=Path, help="directory containing model file", ) return parser.parse_args() args = parse_args() dir_model = args.model if not dir_model.is_dir(): print(f'Error: {args.model} is not a directory', file=sys.stderr) sys.exit(1) ftype_map = { "f32": gguf.GGMLQuantizationType.F32, "f16": gguf.GGMLQuantizationType.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-{args.outtype}.gguf' print(f"Loading model: {dir_model.name}") hparams = Model.load_hparams(dir_model) with torch.inference_mode(): model_class = Model.from_model_architecture(hparams["architectures"][0]) model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) print("Set model parameters") model_instance.set_gguf_parameters() print("Set model tokenizer") model_instance.set_vocab() if args.vocab_only: print(f"Exporting model vocab to '{fname_out}'") model_instance.write_vocab() else: print(f"Exporting model to '{fname_out}'") model_instance.write() print(f"Model successfully exported to '{fname_out}'")