diff options
Diffstat (limited to 'convert_hf_to_gguf.py')
| -rwxr-xr-x | convert_hf_to_gguf.py | 372 |
1 files changed, 318 insertions, 54 deletions
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 7a74cc20..b470a088 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -251,12 +251,7 @@ class Model: return [(self.map_tensor_name(name), data_torch)] - def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: - del name, new_name, bid, n_dims # unused - - return False - - def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: del name, new_name, bid, n_dims # unused return False @@ -285,54 +280,46 @@ class Model: for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)): data: np.ndarray # type hint n_dims = len(data.shape) - data_dtype = data.dtype - data_qtype: gguf.GGMLQuantizationType | None = None - - # when both are True, f32 should win - extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims) - extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims) + data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims) # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors - # Conditions should closely match those in llama_model_quantize_internal in llama.cpp - extra_f32 = any(cond for cond in ( - extra_f32, - n_dims == 1, - new_name.endswith("_norm.weight"), - )) + if n_dims <= 1 or new_name.endswith("_norm.weight"): + data_qtype = gguf.GGMLQuantizationType.F32 + # Conditions should closely match those in llama_model_quantize_internal in llama.cpp # Some tensor types are always in float32 - extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in ( - gguf.MODEL_TENSOR.FFN_GATE_INP, - gguf.MODEL_TENSOR.POS_EMBD, - gguf.MODEL_TENSOR.TOKEN_TYPES, - )) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - extra_f16 = any(cond for cond in ( - extra_f16, - (name.endswith(".weight") and n_dims >= 2), - )) - - if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32: - if self.ftype == gguf.LlamaFileType.MOSTLY_BF16: - data = gguf.quantize_bf16(data) - assert data.dtype == np.int16 - data_qtype = gguf.GGMLQuantizationType.BF16 - - elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data): - data = gguf.quantize_q8_0(data) - assert data.dtype == np.uint8 - data_qtype = gguf.GGMLQuantizationType.Q8_0 + if data_qtype is False and ( + any( + self.match_model_tensor_name(new_name, key, bid) + for key in ( + gguf.MODEL_TENSOR.FFN_GATE_INP, + gguf.MODEL_TENSOR.POS_EMBD, + gguf.MODEL_TENSOR.TOKEN_TYPES, + ) + ) + or not name.endswith(".weight") + ): + data_qtype = gguf.GGMLQuantizationType.F32 - else: # default to float16 for quantized tensors - if data_dtype != np.float16: - data = data.astype(np.float16) + # No override (data_qtype is False), or wants to be quantized (data_qtype is True) + if isinstance(data_qtype, bool): + if self.ftype == gguf.LlamaFileType.ALL_F32: + data_qtype = gguf.GGMLQuantizationType.F32 + elif self.ftype == gguf.LlamaFileType.MOSTLY_F16: data_qtype = gguf.GGMLQuantizationType.F16 + elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16: + data_qtype = gguf.GGMLQuantizationType.BF16 + elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0: + data_qtype = gguf.GGMLQuantizationType.Q8_0 + else: + raise ValueError(f"Unknown file type: {self.ftype.name}") - if data_qtype is None: # by default, convert to float32 - if data_dtype != np.float32: - data = data.astype(np.float32) - data_qtype = gguf.GGMLQuantizationType.F32 + try: + data = gguf.quants.quantize(data, data_qtype) + except gguf.QuantError as e: + logger.warning("%s, %s", e, "falling back to F16") + data_qtype = gguf.GGMLQuantizationType.F16 + data = gguf.quants.quantize(data, data_qtype) shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape @@ -1570,6 +1557,34 @@ class LlamaModel(Model): return [(self.map_tensor_name(name), data_torch)] def prepare_tensors(self): + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32)) + super().prepare_tensors() if self._experts is not None: @@ -1754,7 +1769,7 @@ class DbrxModel(Model): return [(new_name, data_torch)] - def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: del name, new_name, bid # unused return n_dims > 1 @@ -2495,6 +2510,112 @@ class NomicBertModel(BertModel): self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) +@Model.register("XLMRobertaModel") +class XLMRobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'sentencepiece.bpe.model' + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + # realign tokens (see HF tokenizer code) + tokens = [b'<s>', b'<pad>', b'</s>', b'<unk>'] + tokens[3:-1] + scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1] + toktypes = [ + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.UNKNOWN, + ] + toktypes[3:-1] + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + self.gguf_writer.add_token_type_count(1) + self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + @Model.register("GemmaForCausalLM") class GemmaModel(Model): model_arch = gguf.MODEL_ARCH.GEMMA @@ -2669,18 +2790,22 @@ class MambaModel(Model): return [(new_name, data_torch)] - def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: - del n_dims # unused - - return bid is not None and new_name in ( - self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [ + def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: + if bid is not None and new_name in ( + self.format_tensor_name( + n, bid, ".weight" if name.endswith(".weight") else "" + ) + for n in [ gguf.MODEL_TENSOR.SSM_CONV1D, gguf.MODEL_TENSOR.SSM_X, gguf.MODEL_TENSOR.SSM_DT, gguf.MODEL_TENSOR.SSM_A, gguf.MODEL_TENSOR.SSM_D, ] - ) + ): + return gguf.GGMLQuantizationType.F32 + + return super().tensor_force_quant(name, new_name, bid, n_dims) @Model.register("CohereForCausalLM") @@ -3216,6 +3341,145 @@ class T5Model(Model): return [(self.map_tensor_name(name), data_torch)] +@Model.register("T5EncoderModel") +class T5EncoderModel(Model): + model_arch = gguf.MODEL_ARCH.T5ENCODER + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.shared_token_embeddings_found = False + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + # many older models use spiece.model tokenizer model filename + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'spiece.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + + # some models like Pile-T5 family use BPE tokenizer instead of Unigram + if sentencepiece_model.trainer_spec.model_type == 2: # BPE + # assure the tokenizer model file name is correct + assert tokenizer_path.name == 'tokenizer.model' + return self._set_vocab_sentencepiece() + else: + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = 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: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_bos_token(False) + self.gguf_writer.add_add_eos_token(True) + + def set_gguf_parameters(self): + if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: + logger.warning("Couldn't find context length in config.json, assuming default value of 512") + n_ctx = 512 + self.gguf_writer.add_context_length(n_ctx) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) + self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_head_count(self.hparams["num_heads"]) + self.gguf_writer.add_key_length(self.hparams["d_kv"]) + self.gguf_writer.add_value_length(self.hparams["d_kv"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", + # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored + # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder + # and decoder and ignore the remaining ones. + if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: + if not self.shared_token_embeddings_found: + name = "shared.weight" + self.shared_token_embeddings_found = True + else: + logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") + return [] + + return [(self.map_tensor_name(name), data_torch)] + + @Model.register("JAISLMHeadModel") class JaisModel(Model): model_arch = gguf.MODEL_ARCH.JAIS |