mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
convert : refactor rope_freqs generation
This should also fix vocab-only conversion for Phi-3.
This commit is contained in:
Francis Couture-Harpin
parent
daa9623ab0
commit
141dd55e53
@ -15,6 +15,7 @@ from enum import IntEnum
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from pathlib import Path
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from hashlib import sha256
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from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
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from itertools import chain
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import math
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import numpy as np
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@ -64,7 +65,6 @@ class Model:
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model_name: str | None
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metadata_override: Path | None
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dir_model_card: Path
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is_lora: bool
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# subclasses should define this!
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model_arch: gguf.MODEL_ARCH
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@ -72,7 +72,7 @@ class Model:
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def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False,
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use_temp_file: bool = False, eager: bool = False,
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metadata_override: Path | None = None, model_name: str | None = None,
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split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False, is_lora: bool = False):
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split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
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if type(self) is Model:
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raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
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@ -94,7 +94,6 @@ class Model:
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self.metadata_override = metadata_override
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self.model_name = model_name
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self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py
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self.is_lora = is_lora # true if model is used inside convert_lora_to_gguf.py
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# Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
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if self.ftype == gguf.LlamaFileType.GUESSED:
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@ -259,10 +258,14 @@ class Model:
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return False
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# some models need extra generated tensors (like rope_freqs)
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def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
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return ()
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def prepare_tensors(self):
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max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
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for name, data_torch in self.get_tensors():
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for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()):
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# we don't need these
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if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
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continue
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@ -1590,7 +1593,7 @@ class LlamaModel(Model):
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return [(self.map_tensor_name(name), data_torch)]
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def prepare_tensors(self):
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def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
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if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
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if rope_scaling.get("rope_type", '').lower() == "llama3":
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base = self.hparams.get("rope_theta", 10000.0)
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@ -1617,9 +1620,9 @@ class LlamaModel(Model):
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smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
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rope_factors.append(1 / ((1 - smooth) / factor + smooth))
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if not self.is_lora:
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self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
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yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
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def prepare_tensors(self):
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super().prepare_tensors()
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if self._experts is not None:
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@ -2135,6 +2138,13 @@ class Phi3MiniModel(Model):
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self.gguf_writer.add_file_type(self.ftype)
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self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"]))
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def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
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n_embd = self.find_hparam(["hidden_size", "n_embd"])
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n_head = self.find_hparam(["num_attention_heads", "n_head"])
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max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
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orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
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rope_dims = n_embd // n_head
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# write rope scaling for long context (128k) model
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rope_scaling = self.find_hparam(['rope_scaling'], True)
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if rope_scaling is None:
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@ -2164,9 +2174,8 @@ class Phi3MiniModel(Model):
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if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
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raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
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if not self.is_lora:
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self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG] + ".weight", np.array(long_factors, dtype=np.float32))
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self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32))
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yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
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yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
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@Model.register("PlamoForCausalLM")
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@ -3915,7 +3924,7 @@ class ExaoneModel(Model):
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
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self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
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def prepare_tensors(self):
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def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
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if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
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if rope_scaling.get("rope_type", '').lower() == "llama3":
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base = self.hparams.get("rope_theta", 10000.0)
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@ -3942,10 +3951,7 @@ class ExaoneModel(Model):
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smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
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rope_factors.append(1 / ((1 - smooth) / factor + smooth))
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if not self.is_lora:
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self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
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super().prepare_tensors()
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yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
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###### CONVERSION LOGIC ######
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@ -331,6 +331,10 @@ if __name__ == '__main__':
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self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
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super().set_gguf_parameters()
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def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
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# Never add extra tensors (e.g. rope_freqs) for LoRA adapters
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return ()
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def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
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tensor_map: dict[str, PartialLoraTensor] = {}
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@ -386,7 +390,6 @@ if __name__ == '__main__':
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dry_run=args.dry_run,
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dir_lora_model=dir_lora,
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lora_alpha=alpha,
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is_lora=True,
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)
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logger.info("Exporting model...")
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@ -793,6 +793,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FACTORS_LONG,
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MODEL_TENSOR.ROPE_FACTORS_SHORT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_QKV,
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MODEL_TENSOR.ATTN_Q,
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