diff options
| -rwxr-xr-x | convert_hf_to_gguf.py | 23 | ||||
| -rwxr-xr-x | convert_lora_to_gguf.py | 4 | ||||
| -rw-r--r-- | gguf-py/gguf/constants.py | 2 | ||||
| -rw-r--r-- | gguf-py/gguf/tensor_mapping.py | 3 |
4 files changed, 27 insertions, 5 deletions
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 16f97ab0..966cfcd3 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -14,6 +14,7 @@ from enum import IntEnum from pathlib import Path from hashlib import sha256 from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast +from itertools import chain import math import numpy as np @@ -256,10 +257,14 @@ class Model: return False + # some models need extra generated tensors (like rope_freqs) + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + return () + def prepare_tensors(self): max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") - for name, data_torch in self.get_tensors(): + for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()): # we don't need these if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): continue @@ -1559,7 +1564,7 @@ class LlamaModel(Model): return [(self.map_tensor_name(name), data_torch)] - def prepare_tensors(self): + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: 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) @@ -1586,8 +1591,9 @@ class LlamaModel(Model): 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)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + def prepare_tensors(self): super().prepare_tensors() if self._experts is not None: @@ -2307,6 +2313,13 @@ class Phi3MiniModel(Model): self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"])) + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rope_dims = n_embd // n_head + # write rope scaling for long context (128k) model rope_scaling = self.find_hparam(['rope_scaling'], True) if rope_scaling is None: @@ -2336,8 +2349,8 @@ class Phi3MiniModel(Model): if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') - self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG] + ".weight", np.array(long_factors, dtype=np.float32)) - self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) @Model.register("PlamoForCausalLM") diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index a88d0d4a..ef088034 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -331,6 +331,10 @@ if __name__ == '__main__': self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha) super().set_gguf_parameters() + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + # Never add extra tensors (e.g. rope_freqs) for LoRA adapters + return () + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: tensor_map: dict[str, PartialLoraTensor] = {} diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index e2f4eb1a..6819979f 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -806,6 +806,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FACTORS_LONG, + MODEL_TENSOR.ROPE_FACTORS_SHORT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_QKV, MODEL_TENSOR.ATTN_Q, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 3ff70cd7..9688b02c 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -82,6 +82,9 @@ class TensorNameMap: "rope.freqs", # llama-pth "rotary_pos_emb.inv_freq", # chatglm ), + + MODEL_TENSOR.ROPE_FACTORS_LONG: (), + MODEL_TENSOR.ROPE_FACTORS_SHORT: (), } block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { |