/
_model_builders.py
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/
_model_builders.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from typing import List, Optional
from functools import partial
from torch import nn
from torchtune.models.llama3._component_builders import llama3, lora_llama3
from torchtune.models.llama3._model_utils import scale_hidden_dim_for_mlp
from torchtune.modules import TransformerDecoder
from torchtune.modules.tokenizers import TikTokenTokenizer
from torchtune.modules.peft import LORA_ATTN_MODULES
"""
Model builders build specific instantiations using component builders. For example
the llama3_8b model builder uses the llama3 component builder to create the
Llama3 8B model.
"""
def llama3_8b() -> TransformerDecoder:
"""
Builder for creating a Llama3 model initialized w/ the default 8b parameter values.
Returns:
TransformerDecoder: Instantiation of Llama3 8B model
"""
return llama3(
vocab_size=128_256,
num_layers=32,
num_heads=32,
num_kv_heads=8,
embed_dim=4096,
max_seq_len=8192,
intermediate_dim=14336,
attn_dropout=0.0,
norm_eps=1e-5,
rope_base=500000.0,
)
def llama3_70b() -> TransformerDecoder:
"""
Builder for creating a Llama3 model initialized w/ the default 70B parameter values.
Returns:
TransformerDecoder: Instantiation of Llama3 70 model
"""
return llama3(
vocab_size=128_256,
num_layers=80,
num_heads=64,
num_kv_heads=8,
embed_dim=8192,
max_seq_len=8192,
intermediate_dim=28672,
attn_dropout=0.0,
norm_eps=1e-5,
rope_base=500000.0,
)
def llama3_tokenizer(path: str) -> TikTokenTokenizer:
tiktoken = TikTokenTokenizer(path)
tiktoken.pad_id = 0
return tiktoken
def lora_llama3_8b(
lora_attn_modules: List[LORA_ATTN_MODULES],
apply_lora_to_mlp: bool = False,
apply_lora_to_output: bool = False,
lora_rank: int = 8,
lora_alpha: float = 16,
quantize_base: bool = False,
) -> TransformerDecoder:
"""
Builder for creating a Llama3 8B model with LoRA enabled.
The Llama3 defaults are the same as in :func:`~torchtune.models.llama3.llama3_8b`,
while LoRA default params are based on
https://github.com/tloen/alpaca-lora/blob/8bb8579e403dc78e37fe81ffbb253c413007323f/finetune.py#L41-L43.
Args:
lora_attn_modules (List[LORA_ATTN_MODULES]): list of which linear layers
LoRA should be applied to in each self-attention block. Options are
``{"q_proj", "k_proj", "v_proj", "output_proj"}``.
apply_lora_to_mlp (bool): whether to apply LoRA to the MLP in each transformer layer.
Default: False
apply_lora_to_output (bool): whether to apply LoRA to the model's final output projection.
Default: False
lora_rank (int): rank of each low-rank approximation
lora_alpha (float): scaling factor for the low-rank approximation
quantize_base (bool): Whether to quantize base model weights
Returns:
TransformerDecoder: Instantiation of Llama3 8B model with LoRA applied
"""
return lora_llama3(
lora_attn_modules=lora_attn_modules,
apply_lora_to_mlp=apply_lora_to_mlp,
apply_lora_to_output=apply_lora_to_output,
vocab_size=128_256,
num_layers=32,
num_heads=32,
num_kv_heads=8,
embed_dim=4096,
max_seq_len=8192,
intermediate_dim=14336,
attn_dropout=0.0,
norm_eps=1e-5,
rope_base=500000.0,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=0.05,
quantize_base=quantize_base,
)
def lora_llama3_70b(
lora_attn_modules: List[LORA_ATTN_MODULES],
apply_lora_to_mlp: bool = False,
apply_lora_to_output: bool = False,
lora_rank: int = 8,
lora_alpha: float = 16,
quantize_base: bool = False,
) -> TransformerDecoder:
"""
Builder for creating a Llama3 70B model with LoRA enabled.
The Llama3 defaults are the same as in :func:`~torchtune.models.llama3.llama3_70b`,
while LoRA default params are based on
https://github.com/tloen/alpaca-lora/blob/8bb8579e403dc78e37fe81ffbb253c413007323f/finetune.py#L41-L43.
Args:
lora_attn_modules (List[LORA_ATTN_MODULES]): list of which linear layers
LoRA should be applied to in each self-attention block. Options are
``{"q_proj", "k_proj", "v_proj", "output_proj"}``.
apply_lora_to_mlp (bool): whether to apply LoRA to the MLP in each transformer layer.
Default: False
apply_lora_to_output (bool): whether to apply LoRA to the model's final output projection.
Default: False
lora_rank (int): rank of each low-rank approximation
lora_alpha (float): scaling factor for the low-rank approximation
quantize_base (bool): Whether to quantize base model weights
Returns:
TransformerDecoder: Instantiation of Llama3 8B model with LoRA applied
"""
return lora_llama3(
lora_attn_modules=lora_attn_modules,
apply_lora_to_mlp=apply_lora_to_mlp,
apply_lora_to_output=apply_lora_to_output,
vocab_size=128_256,
num_layers=80,
num_heads=64,
num_kv_heads=8,
embed_dim=8192,
max_seq_len=8192,
intermediate_dim=28672,
attn_dropout=0.0,
norm_eps=1e-5,
rope_base=500000.0,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=0.05,
quantize_base=quantize_base,
)
qlora_llama3_8b = partial(lora_llama3_8b, quantize_base=True)
qlora_llama3_8b.__doc__ = """
Builder for creating a Llama3 model with QLoRA enabled. Base model weights in linear layers
that LoRA is applied to are quantized per the QLoRA paper: https://arxiv.org/abs/2305.14314.
Please see `lora_llama3_8b` for full API arguments.
"""