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_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 torchtune.models.llama2._component_builders import llama2, lora_llama2, llama2_classifier, lora_llama2_classifier
from torchtune.modules import TransformerDecoder
from torchtune.models.llama2._tokenizer import Llama2Tokenizer
from torchtune.modules.peft import LORA_ATTN_MODULES
"""
Model builders build specific instantiations using component builders. For example
the llama2_7b model builder uses the llama2 component builder to create the
llama2 7B model.
"""
def llama2_7b() -> TransformerDecoder:
"""
Builder for creating a Llama2 model initialized w/ the default 7B parameter values
from https://arxiv.org/abs/2307.09288
Returns:
TransformerDecoder: Instantiation of Llama2 7B model
"""
return llama2(
vocab_size=32_000,
num_layers=32,
num_heads=32,
num_kv_heads=32,
embed_dim=4096,
max_seq_len=4096,
attn_dropout=0.0,
norm_eps=1e-5,
)
def llama2_tokenizer(path: str, max_seq_len: Optional[int] = None) -> Llama2Tokenizer:
"""
Tokenizer for Llama2.
Args:
path (str): path to the tokenizer
max_seq_len (Optional[int]): maximum sequence length for tokenizing a single list of messages,
after which the input will be truncated. Default is None.
Returns:
Llama2Tokenizer: Instantiation of the Llama2 tokenizer
"""
return Llama2Tokenizer(path=path, max_seq_len=max_seq_len)
def lora_llama2_7b(
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,
lora_dropout: float = 0.05,
quantize_base: bool = False,
) -> TransformerDecoder:
"""
Builder for creating a Llama2 7B model with LoRA enabled.
The Llama2 defaults are the same as in :func:`~torchtune.models.llama2.llama2_7b`,
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
lora_dropout (float): dropout probability for LoRA linear layers. Default: 0.05
Returns:
TransformerDecoder: Instantiation of Llama2 7B model with LoRA applied
"""
return lora_llama2(
lora_attn_modules=lora_attn_modules,
apply_lora_to_mlp=apply_lora_to_mlp,
apply_lora_to_output=apply_lora_to_output,
vocab_size=32_000,
num_layers=32,
num_heads=32,
num_kv_heads=32,
embed_dim=4096,
max_seq_len=4096,
attn_dropout=0.0,
norm_eps=1e-5,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
quantize_base=quantize_base,
)
qlora_llama2_7b = partial(lora_llama2_7b, quantize_base=True)
qlora_llama2_7b.__doc__ = """
Builder for creating a Llama2 7B 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_llama2_7b` for full API arguments.
"""
def llama2_13b() -> TransformerDecoder:
"""
Builder for creating a Llama2 model initialized w/ the default 13B parameter values
from https://arxiv.org/abs/2307.09288
Returns:
TransformerDecoder: Instantiation of Llama2 13B model
"""
return llama2(
vocab_size=32_000,
num_layers=40,
num_heads=40,
num_kv_heads=40,
embed_dim=5120,
intermediate_dim=13824,
max_seq_len=4096,
attn_dropout=0.0,
norm_eps=1e-5,
)
def lora_llama2_13b(
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,
lora_dropout: float = 0.05,
quantize_base: bool = False,
) -> TransformerDecoder:
"""
Builder for creating a Llama2 13B model with LoRA enabled.
The Llama2 defaults are the same as in :func:`~torchtune.models.llama2.llama2_13b`,
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
lora_dropout (float): dropout probability for LoRA linear layers. Default: 0.05
quantize_base (bool): Whether to quantize base model weights
Returns:
TransformerDecoder: Instantiation of Llama2 13B model with LoRA applied
"""
return lora_llama2(
lora_attn_modules=lora_attn_modules,
apply_lora_to_mlp=apply_lora_to_mlp,
apply_lora_to_output=apply_lora_to_output,
vocab_size=32_000,
num_layers=40,
num_heads=40,
num_kv_heads=40,
embed_dim=5120,
intermediate_dim=13824,
max_seq_len=4096,
attn_dropout=0.0,
norm_eps=1e-5,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
quantize_base=quantize_base,
)
qlora_llama2_13b = partial(lora_llama2_13b, quantize_base=True)
qlora_llama2_13b.__doc__ = """
Builder for creating a Llama2 13B 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_llama2_13b` for full API arguments.
"""
def llama2_70b() -> TransformerDecoder:
"""
Builder for creating a Llama2 model initialized w/ the default 70B parameter values
from https://arxiv.org/abs/2307.09288
Returns:
TransformerDecoder: Instantiation of Llama2 70B model
"""
return llama2(
vocab_size=32_000,
num_layers=80,
num_heads=64,
num_kv_heads=8,
embed_dim=8192,
intermediate_dim=28672,
max_seq_len=4096,
attn_dropout=0.0,
norm_eps=1e-5,
)
def lora_llama2_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,
lora_dropout: float = 0.05,
quantize_base: bool = False,
) -> TransformerDecoder:
"""
Builder for creating a Llama2 70B model with LoRA enabled.
The Llama2 defaults are the same as in :func:`~torchtune.models.llama2.llama2_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
lora_dropout (float): dropout probability for LoRA linear layers. Default: 0.05
quantize_base (bool): Whether to quantize base model weights
Returns:
TransformerDecoder: Instantiation of Llama2 70B model with LoRA applied
"""
return lora_llama2(
lora_attn_modules=lora_attn_modules,
apply_lora_to_mlp=apply_lora_to_mlp,
apply_lora_to_output=apply_lora_to_output,
vocab_size=32_000,
num_layers=80,
num_heads=64,
num_kv_heads=8,
embed_dim=8192,
max_seq_len=4096,
intermediate_dim=28672,
attn_dropout=0.0,
norm_eps=1e-5,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
quantize_base=quantize_base,
)
qlora_llama2_70b = partial(lora_llama2_70b, quantize_base=True)
qlora_llama2_70b.__doc__ = """
Builder for creating a Llama2 70B 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_llama2_70b` for full API arguments.
"""
def llama2_reward_7b() -> TransformerDecoder:
"""
Builder for creating a Llama2 model initialized w/ the default 7B parameter values
from https://arxiv.org/abs/2307.09288, where the output layer is a classification layer
projecting to a single class for reward modelling.
Returns:
TransformerDecoder: Instantiation of Llama2 7B model
"""
return llama2_classifier(
num_classes=1,
vocab_size=32_000,
num_layers=32,
num_heads=32,
num_kv_heads=32,
embed_dim=4096,
max_seq_len=4096,
attn_dropout=0.0,
norm_eps=1e-5,
)
def lora_llama2_reward_7b(
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,
lora_dropout: float = 0.05,
quantize_base: bool = False,
) -> TransformerDecoder:
"""
Builder for creating a Llama2 7B reward model with LoRA enabled.
The Llama2 classifier defaults are the same as in :func:`~torchtune.models.llama2.llama2_reward_7b`,
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 Llama2 7B model with LoRA applied
"""
return lora_llama2_classifier(
lora_attn_modules=lora_attn_modules,
apply_lora_to_mlp=apply_lora_to_mlp,
apply_lora_to_output=apply_lora_to_output,
num_classes=1,
vocab_size=32_000,
num_layers=32,
num_heads=32,
num_kv_heads=32,
embed_dim=4096,
max_seq_len=4096,
attn_dropout=0.0,
norm_eps=1e-5,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
quantize_base=quantize_base,
)
qlora_llama2_reward_7b = partial(lora_llama2_7b, quantize_base=True)
qlora_llama2_reward_7b.__doc__ = """
Builder for creating a Llama2 reward 7b 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_llama2_reward_7b` for full API arguments.
"""