lit_gpt/config.py

import json
from copy import deepcopy
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Literal, Optional, Type, Union

import torch
from typing_extensions import Self

import lit_gpt.model
from lit_gpt.utils import find_multiple


@dataclass
class Config:
    name: str = ""
    hf_config: dict = field(default_factory=dict)
    block_size: int = 4096
    vocab_size: int = 50254
    padding_multiple: int = 512
    padded_vocab_size: Optional[int] = None
    n_layer: int = 16
    n_head: int = 32
    n_embd: int = 4096
    rotary_percentage: float = 0.25
    parallel_residual: bool = True
    bias: bool = True
    lm_head_bias: bool = False
    # to use multi-head attention (MHA), set this to `n_head` (default)
    # to use multi-query attention (MQA), set this to 1
    # to use grouped-query attention (GQA), set this to a value in between
    # Example with `n_head=4`
    # ┌───┐┌───┐┌───┐┌───┐     ┌───┐    ┌───┐             ┌───┐
    # │ v ││ v ││ v ││ v │     │ v │    │ v │             │ v │
    # └───┘└───┘└───┘└───┘     └───┘    └───┘             └───┘
    #   │    │    │    │         │        │                 │
    # ┌───┐┌───┐┌───┐┌───┐     ┌───┐    ┌───┐             ┌───┐
    # │ k ││ k ││ k ││ k │     │ k │    │ k │             │ k │
    # └───┘└───┘└───┘└───┘     └───┘    └───┘             └───┘
    #   │    │    │    │      ┌──┴──┐  ┌──┴──┐      ┌────┬──┴─┬────┐
    # ┌───┐┌───┐┌───┐┌───┐  ┌───┐┌───┐┌───┐┌───┐  ┌───┐┌───┐┌───┐┌───┐
    # │ q ││ q ││ q ││ q │  │ q ││ q ││ q ││ q │  │ q ││ q ││ q ││ q │
    # └───┘└───┘└───┘└───┘  └───┘└───┘└───┘└───┘  └───┘└───┘└───┘└───┘
    # ◀──────────────────▶  ◀──────────────────▶  ◀──────────────────▶
    #         MHA                    GQA                   MQA
    #   n_query_groups=4       n_query_groups=2      n_query_groups=1
    #
    # credit https://arxiv.org/pdf/2305.13245.pdf
    n_query_groups: Optional[int] = None
    shared_attention_norm: bool = False
    _norm_class: Literal["LayerNorm", "RMSNorm"] = "LayerNorm"
    norm_eps: float = 1e-5
    _mlp_class: Literal["GptNeoxMLP", "LLaMAMLP"] = "GptNeoxMLP"
    gelu_approximate: str = "none"
    intermediate_size: Optional[int] = None
    rope_condense_ratio: int = 1
    rope_base: int = 10000

    def __post_init__(self):
        if not self.name:
            self.name = self.hf_config.get("name", self.name)

        assert self.n_embd % self.n_head == 0
        self.head_size = self.n_embd // self.n_head

        # vocab size should be a power of 2 to be optimal on hardware. compute the closest value
        if self.padded_vocab_size is None:
            self.padded_vocab_size = find_multiple(self.vocab_size, self.padding_multiple)
        else:
            # vocab size shouldn't be larger than padded vocab size
            self.vocab_size = min(self.vocab_size, self.padded_vocab_size)

        # compute the number of query groups
        if self.n_query_groups is not None:
            assert self.n_head % self.n_query_groups == 0
        else:
            self.n_query_groups = self.n_head

        # compute the intermediate size for MLP if not set
        if self.intermediate_size is None:
            if self._mlp_class == "LLaMAMLP":
                raise ValueError("The config needs to set the `intermediate_size`")
            self.intermediate_size = 4 * self.n_embd

        self.rope_n_elem = int(self.rotary_percentage * self.head_size)

    @classmethod
    def from_name(cls, name: str, **kwargs: Any) -> Self:
        if name not in name_to_config:
            # search through all `config['hf_config']['name']`
            try:
                conf_dict = next(config for config in configs if name == config["hf_config"]["name"])
            except StopIteration:
                raise ValueError(f"{name!r} is not a supported config name")
        else:
            conf_dict = name_to_config[name]

        conf_dict = conf_dict.copy()
        if "condense_ratio" in kwargs:  # legacy name
            kwargs["rope_condense_ratio"] = kwargs.pop("condense_ratio")
        conf_dict.update(kwargs)
        return cls(**conf_dict)

    @classmethod
    def from_json(cls, path: Union[str, Path], **kwargs: Any) -> Self:
        with open(path, encoding="utf-8") as fp:
            json_kwargs = json.load(fp)
        if "condense_ratio" in json_kwargs:  # legacy name
            json_kwargs["rope_condense_ratio"] = json_kwargs.pop("condense_ratio")
        if "condense_ratio" in kwargs:  # legacy name
            kwargs["rope_condense_ratio"] = kwargs.pop("condense_ratio")
        if "org" in json_kwargs:  # legacy name
            json_kwargs["hf_config"] = {"name": json_kwargs["name"], "org": json_kwargs.pop("org")}
        if "org" in kwargs:  # legacy name
            kwargs["hf_config"] = {"name": kwargs.get("name", json_kwargs["name"]), "org": kwargs.pop("org")}
        json_kwargs.update(kwargs)
        return cls(**json_kwargs)

    @classmethod
    def from_checkpoint(cls, path: Path, **kwargs: Any) -> Self:
        """Automatically load `lit_config.json` and if it doesn't exist - a matching config from `lit_gpt/config.py`."""
        if (config_path := path / "lit_config.json").is_file():
            return cls.from_json(config_path, **kwargs)
        if (model_name := path.name) in name_to_config:
            return cls.from_name(model_name, **kwargs)
        raise FileNotFoundError(f"For {str(path)!r} neither 'lit_config.json' nor matching config exists.")

    @property
    def mlp_class(self) -> Type:
        # `self._mlp_class` cannot be the type to keep the config json serializable
        return getattr(lit_gpt.model, self._mlp_class)

    @property
    def norm_class(self) -> Type:
        # `self._norm_class` cannot be the type to keep the config json serializable
        if self._norm_class == "RMSNorm":
            from lit_gpt.rmsnorm import RMSNorm

            return RMSNorm
        return getattr(torch.nn, self._norm_class)


########################
# Stability AI StableLM
########################
configs = [
    # https://huggingface.co/stabilityai/stablelm-base-alpha-3b/blob/main/config.json
    dict(name="stablelm-base-alpha-3b", hf_config=dict(org="stabilityai", name="stablelm-base-alpha-3b")),
    # https://huggingface.co/stabilityai/stablelm-base-alpha-7b/blob/main/config.json
    dict(
        name="stablelm-base-alpha-7b",
        hf_config=dict(org="stabilityai", name="stablelm-base-alpha-7b"),
        n_head=48,
        n_embd=6144,
        padding_multiple=256,
    ),
    # https://huggingface.co/stabilityai/stablelm-tuned-alpha-3b/blob/main/config.json
    dict(name="stablelm-tuned-alpha-3b", hf_config=dict(org="stabilityai", name="stablelm-tuned-alpha-3b"), n_head=32),
    # https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b/blob/main/config.json
    dict(
        name="stablelm-tuned-alpha-7b",
        hf_config=dict(org="stabilityai", name="stablelm-tuned-alpha-7b"),
        n_head=48,
        n_embd=6144,
        padding_multiple=256,
    ),
]

####################
# EleutherAI Pythia
####################
pythia = [
    # https://huggingface.co/EleutherAI/pythia-14m/blob/main/config.json
    dict(
        name="pythia-14m",
        hf_config=dict(org="EleutherAI", name="pythia-14m"),
        block_size=512,
        n_layer=6,
        n_embd=128,
        n_head=4,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-31m/blob/main/config.json
    dict(
        name="pythia-31m",
        hf_config=dict(org="EleutherAI", name="pythia-31m"),
        block_size=1024,
        n_layer=6,
        n_embd=256,
        n_head=8,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-70m/blob/main/config.json
    dict(
        name="pythia-70m",
        hf_config=dict(org="EleutherAI", name="pythia-70m"),
        block_size=2048,
        n_layer=6,
        n_embd=512,
        n_head=8,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-160m/blob/main/config.json
    dict(
        name="pythia-160m",
        hf_config=dict(org="EleutherAI", name="pythia-160m"),
        block_size=2048,
        n_layer=12,
        n_embd=768,
        n_head=12,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-410m/blob/main/config.json
    dict(
        name="pythia-410m",
        hf_config=dict(org="EleutherAI", name="pythia-410m"),
        block_size=2048,
        n_layer=24,
        n_embd=1024,
        n_head=16,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-1b/blob/main/config.json
    dict(
        name="pythia-1b",
        hf_config=dict(org="EleutherAI", name="pythia-1b"),
        block_size=2048,
        n_embd=2048,
        n_head=8,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-1.4b/blob/main/config.json
    dict(
        name="pythia-1.4b",
        hf_config=dict(org="EleutherAI", name="pythia-1.4b"),
        block_size=2048,
        n_layer=24,
        n_embd=2048,
        n_head=16,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-2.8b/blob/main/config.json
    dict(
        name="pythia-2.8b",
        hf_config=dict(org="EleutherAI", name="pythia-2.8b"),
        block_size=2048,
        n_layer=32,
        n_embd=2560,
        padding_multiple=128,
    ),
    # https://huggingface.co/EleutherAI/pythia-6.9b/blob/main/config.json
    dict(
        name="pythia-6.9b",
        hf_config=dict(org="EleutherAI", name="pythia-6.9b"),
        block_size=2048,
        n_layer=32,
        padding_multiple=256,
    ),
    # https://huggingface.co/EleutherAI/pythia-12b/blob/main/config.json
    dict(
        name="pythia-12b",
        hf_config=dict(org="EleutherAI", name="pythia-12b"),
        block_size=2048,
        n_layer=36,
        n_embd=5120,
        n_head=40,
    ),
]
configs.extend(pythia)
for c in pythia:
    # "pythia-14m" and "pythia-31m" don't have deduped version
    if c["name"] in ("pythia-14m", "pythia-31m"):
        continue
    copy = deepcopy(c)
    copy["name"] = f"{c['name']}-deduped"
    copy["hf_config"]["name"] = f"{c['hf_config']['name']}-deduped"
    configs.append(copy)


####################################
# togethercomputer RedPajama INCITE
####################################
redpajama_incite = [
    # https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1/blob/main/config.json
    dict(
        name="RedPajama-INCITE-{}-3B-v1",
        hf_config=dict(org="togethercomputer", name="RedPajama-INCITE-{}-3B-v1"),
        block_size=2048,
        n_layer=32,
        n_embd=2560,
        padding_multiple=256,
        rotary_percentage=1.0,
        parallel_residual=False,
    ),
    # https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/blob/main/config.json
    dict(
        name="RedPajama-INCITE-7B-{}",
        hf_config=dict(org="togethercomputer", name="RedPajama-INCITE-7B-{}"),
        block_size=2048,
        n_layer=32,
        padding_multiple=256,
        rotary_percentage=1.0,
        parallel_residual=False,
    ),
    # this redirects to the checkpoint above. kept for those who had the old weights already downloaded
    dict(
        name="RedPajama-INCITE-{}-7B-v0.1",
        hf_config=dict(org="togethercomputer", name="RedPajama-INCITE-{}-7B-v0.1"),
        block_size=2048,
        n_layer=32,
        padding_multiple=256,
        rotary_percentage=1.0,
        parallel_residual=False,
    ),
]
for c in redpajama_incite:
    for kind in ("Base", "Chat", "Instruct"):
        copy = deepcopy(c)
        copy["name"] = c["name"].format(kind)
        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
        configs.append(copy)


#################
# TII UAE Falcon
#################
falcon = [
    # https://huggingface.co/tiiuae/falcon-7b/blob/main/config.json
    dict(
        name="falcon-7b{}",
        hf_config=dict(org="tiiuae", name="falcon-7b{}"),
        block_size=2048,
        vocab_size=65024,
        padded_vocab_size=65024,
        n_layer=32,
        n_head=71,
        n_embd=4544,
        rotary_percentage=1.0,
        n_query_groups=1,
        bias=False,
        # this is not in the config, but in the original model implementation, only for this config
        shared_attention_norm=True,
    ),
    # https://huggingface.co/tiiuae/falcon-40b/blob/main/config.json
    dict(
        name="falcon-40b{}",
        hf_config=dict(org="tiiuae", name="falcon-40b{}"),
        block_size=2048,
        vocab_size=65024,
        padded_vocab_size=65024,
        n_layer=60,
        n_head=128,
        n_embd=8192,
        rotary_percentage=1.0,
        n_query_groups=8,
        bias=False,
    ),
]
for c in falcon:
    for kind in ("", "-instruct"):
        copy = deepcopy(c)
        copy["name"] = c["name"].format(kind)
        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
        configs.append(copy)

# https://huggingface.co/tiiuae/falcon-180b/blob/main/config.json
falcon180b = dict(
    name="falcon-180B{}",
    hf_config=dict(org="tiiuae", name="falcon-180B{}"),
    block_size=2048,
    vocab_size=65024,
    padded_vocab_size=65024,
    n_layer=80,
    n_head=232,
    n_embd=14848,
    rotary_percentage=1.0,
    n_query_groups=8,
    bias=False,
)

for kind in ("", "-chat"):
    copy = deepcopy(falcon180b)
    copy["name"] = falcon180b["name"].format(kind)
    copy["hf_config"]["name"] = falcon180b["hf_config"]["name"].format(kind)
    configs.append(copy)


#############################
# OpenLM Research Open LLaMA
#############################
open_LLaMA = [
    # https://huggingface.co/openlm-research/open_llama_3b/blob/main/config.json
    dict(
        name="open_llama_3b",
        hf_config=dict(org="openlm-research", name="open_llama_3b"),
        block_size=2048,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=26,
        n_embd=3200,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=8640,
    ),
    # https://huggingface.co/openlm-research/open_llama_7b/blob/main/config.json
    dict(
        name="open_llama_7b",
        hf_config=dict(org="openlm-research", name="open_llama_7b"),
        block_size=2048,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
    ),
    # https://huggingface.co/openlm-research/open_llama_13b/blob/main/config.json
    dict(
        name="open_llama_13b",
        hf_config=dict(org="openlm-research", name="open_llama_13b"),
        block_size=2048,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
]
configs.extend(open_LLaMA)


###############
# LMSYS Vicuna
###############
vicuna = [
    # https://huggingface.co/lmsys/vicuna-7b-v1.3/blob/main/config.json
    dict(
        name="vicuna-7b-v1.3",
        hf_config=dict(org="lmsys", name="vicuna-7b-v1.3"),
        block_size=2048,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
    ),
    # https://huggingface.co/lmsys/vicuna-13b-v1.3/blob/main/config.json
    dict(
        name="vicuna-13b-v1.3",
        hf_config=dict(org="lmsys", name="vicuna-13b-v1.3"),
        block_size=2048,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
    # https://huggingface.co/lmsys/vicuna-33b-v1.3/blob/main/config.json
    dict(
        name="vicuna-33b-v1.3",
        hf_config=dict(org="lmsys", name="vicuna-33b-v1.3"),
        block_size=2048,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=60,
        n_head=52,
        n_embd=6656,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=17920,
    ),
    # https://huggingface.co/lmsys/vicuna-7b-v1.5/blob/main/config.json
    dict(
        name="vicuna-7b-v1.5",
        hf_config=dict(org="lmsys", name="vicuna-7b-v1.5"),
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
    ),
    # https://huggingface.co/lmsys/vicuna-7b-v1.5-16k/blob/main/config.json
    dict(
        name="vicuna-7b-v1.5-16k",
        hf_config=dict(org="lmsys", name="vicuna-7b-v1.5-16k"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
        rope_condense_ratio=4,
    ),
    # https://huggingface.co/lmsys/vicuna-13b-v1.5/blob/main/config.json
    dict(
        name="vicuna-13b-v1.5",
        hf_config=dict(org="lmsys", name="vicuna-13b-v1.5"),
        vocab_size=32000,
        padding_multiple=64,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
    # https://huggingface.co/lmsys/vicuna-13b-v1.5-16k/blob/main/config.json
    dict(
        name="vicuna-13b-v1.5-16k",
        hf_config=dict(org="lmsys", name="vicuna-13b-v1.5-16k"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
        rope_condense_ratio=4,
    ),
]
configs.extend(vicuna)


#################
# LMSYS LongChat
#################
long_chat = [
    # https://huggingface.co/lmsys/longchat-7b-16k/blob/main/config.json
    dict(
        name="longchat-7b-16k",
        hf_config=dict(org="lmsys", name="longchat-7b-16k"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
        rope_condense_ratio=8,
    ),
    # https://huggingface.co/lmsys/longchat-13b-16k/blob/main/config.json
    dict(
        name="longchat-13b-16k",
        hf_config=dict(org="lmsys", name="longchat-13b-16k"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
        rope_condense_ratio=8,
    ),
]
configs.extend(long_chat)


######################
# NousResearch Hermes
######################
nous_research = [
    # https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b/blob/main/config.json
    dict(
        name="Nous-Hermes-llama-2-7b",
        hf_config=dict(org="NousResearch", name="Nous-Hermes-llama-2-7b"),
        padded_vocab_size=32000,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
    ),
    # https://huggingface.co/NousResearch/Nous-Hermes-13B/blob/main/config.json
    dict(
        name="Nous-Hermes-13b",
        hf_config=dict(org="NousResearch", name="Nous-Hermes-13b"),
        block_size=2048,
        vocab_size=32000,
        padded_vocab_size=32001,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-6,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
    # https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b
    dict(
        name="Nous-Hermes-Llama2-13b",
        hf_config=dict(org="NousResearch", name="Nous-Hermes-Llama2-13b"),
        vocab_size=32000,
        padded_vocab_size=32032,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
]
configs.extend(nous_research)


###############
# Meta LLaMA 2
###############
llama_2 = [
    # https://huggingface.co/meta-llama/Llama-2-7b-hf/blob/main/config.json
    dict(
        name="Llama-2-7b{}-hf",
        hf_config=dict(org="meta-llama", name="Llama-2-7b{}-hf"),
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
    ),
    # https://huggingface.co/meta-llama/Llama-2-13b-hf/blob/main/config.json
    dict(
        name="Llama-2-13b{}-hf",
        hf_config=dict(org="meta-llama", name="Llama-2-13b{}-hf"),
        vocab_size=32000,
        padding_multiple=64,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
    # https://huggingface.co/meta-llama/Llama-2-70b-hf/blob/main/config.json
    dict(
        name="Llama-2-70b{}-hf",
        hf_config=dict(org="meta-llama", name="Llama-2-70b{}-hf"),
        vocab_size=32000,
        padding_multiple=64,
        n_layer=80,
        n_head=64,
        n_embd=8192,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=28672,
    ),
]
for c in llama_2:
    for kind in ("", "-chat"):
        copy = deepcopy(c)
        copy["name"] = c["name"].format(kind)
        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
        configs.append(copy)


##########################
# Stability AI FreeWilly2
##########################
freewilly_2 = [
    # https://huggingface.co/stabilityai/FreeWilly2/blob/main/config.json
    dict(
        name="FreeWilly2",
        hf_config=dict(org="stabilityai", name="FreeWilly2"),
        vocab_size=32000,
        padding_multiple=64,
        n_layer=80,
        n_head=64,
        n_embd=8192,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=28672,
    )
]
configs.extend(freewilly_2)


##################
# Meta Code Llama
##################
code_llama = [
    # https://huggingface.co/codellama/CodeLlama-7b-hf/blob/main/config.json
    dict(
        name="CodeLlama-7b-hf",
        hf_config=dict(org="codellama", name="CodeLlama-7b-hf"),
        block_size=16384,
        vocab_size=32016,
        padding_multiple=16,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-13b-hf/blob/main/config.json
    dict(
        name="CodeLlama-13b-hf",
        hf_config=dict(org="codellama", name="CodeLlama-13b-hf"),
        block_size=16384,
        vocab_size=32016,
        padding_multiple=16,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-34b-hf/blob/main/config.json
    dict(
        name="CodeLlama-34b-hf",
        hf_config=dict(org="codellama", name="CodeLlama-34b-hf"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=48,
        n_head=64,
        n_embd=8192,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=22016,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-7b-Python-hf/blob/main/config.json
    dict(
        name="CodeLlama-7b-Python-hf",
        hf_config=dict(org="codellama", name="CodeLlama-7b-Python-hf"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-13b-Python-hf/blob/main/config.json
    dict(
        name="CodeLlama-13b-Python-hf",
        hf_config=dict(org="codellama", name="CodeLlama-13b-Python-hf"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-34b-Python-hf/blob/main/config.json
    dict(
        name="CodeLlama-34b-Python-hf",
        hf_config=dict(org="codellama", name="CodeLlama-34b-Python-hf"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=48,
        n_head=64,
        n_embd=8192,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=22016,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf/tree/main/config.json
    dict(
        name="CodeLlama-7b-Instruct-hf",
        hf_config=dict(org="codellama", name="CodeLlama-7b-Instruct-hf"),
        block_size=16384,
        vocab_size=32016,
        padding_multiple=16,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf/blob/main/config.json
    dict(
        name="CodeLlama-13b-Instruct-hf",
        hf_config=dict(org="codellama", name="CodeLlama-13b-Instruct-hf"),
        block_size=2048,
        vocab_size=32016,
        padding_multiple=16,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
        rope_base=1000000,
    ),
    # https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf/blob/main/config.json
    dict(
        name="CodeLlama-34b-Instruct-hf",
        hf_config=dict(org="codellama", name="CodeLlama-34b-Instruct-hf"),
        block_size=16384,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=48,
        n_head=64,
        n_embd=8192,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=22016,
        rope_base=1000000,
    ),
]
configs.extend(code_llama)


########################
# garage-bAInd Platypus
########################
platypus = [
    # https://huggingface.co/garage-bAInd/Platypus-30B/blob/main/config.json
    dict(
        name="Platypus-30B",
        hf_config=dict(org="garage-bAInd", name="Platypus-30B"),
        block_size=2048,
        padded_vocab_size=32000,
        n_layer=60,
        n_head=52,
        n_embd=6656,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-06,
        _mlp_class="LLaMAMLP",
        intermediate_size=17920,
    ),
    # https://huggingface.co/garage-bAInd/Platypus2-7B/blob/main/config.json
    dict(
        name="Platypus2-7B",
        hf_config=dict(org="garage-bAInd", name="Platypus2-7B"),
        padded_vocab_size=32000,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
    ),
    # https://huggingface.co/garage-bAInd/Platypus2-13B/blob/main/config.json
    dict(
        name="Platypus2-13B",
        hf_config=dict(org="garage-bAInd", name="Platypus2-13B"),
        padded_vocab_size=32000,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
    # https://huggingface.co/garage-bAInd/Platypus2-70B/blob/main/config.json
    dict(
        name="Platypus2-70B",
        hf_config=dict(org="garage-bAInd", name="Platypus2-70B"),
        padded_vocab_size=32000,
        n_layer=80,
        n_head=64,
        n_embd=8192,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=28672,
    ),
    # https://huggingface.co/garage-bAInd/Camel-Platypus2-13B/blob/main/config.json
    dict(
        name="Camel-Platypus2-13B",
        hf_config=dict(org="garage-bAInd", name="Camel-Platypus2-13B"),
        padded_vocab_size=32000,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
    # https://huggingface.co/garage-bAInd/Camel-Platypus2-70B/blob/main/config.json
    dict(
        name="Camel-Platypus2-70B",
        hf_config=dict(org="garage-bAInd", name="Camel-Platypus2-70B"),
        padded_vocab_size=32000,
        n_layer=80,
        n_head=64,
        n_embd=8192,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=28672,
    ),
    # https://huggingface.co/garage-bAInd/Stable-Platypus2-13B/blob/main/config.json
    dict(
        name="Stable-Platypus2-13B",
        hf_config=dict(org="garage-bAInd", name="Stable-Platypus2-13B"),
        padded_vocab_size=32000,
        n_layer=40,
        n_head=40,
        n_embd=5120,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=13824,
    ),
    # https://huggingface.co/garage-bAInd/Platypus2-70B-instruct/blob/main/config.json
    dict(
        name="Platypus2-70B-instruct",
        hf_config=dict(org="garage-bAInd", name="Platypus2-70B-instruct"),
        padded_vocab_size=32000,
        n_layer=80,
        n_head=64,
        n_embd=8192,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=28672,
    ),
]
configs.extend(platypus)


##########################
# Stability AI StableCode
##########################
stablecode = [
    # https://huggingface.co/stabilityai/stablecode-completion-alpha-3b/blob/main/config.json
    dict(
        name="stablecode-completion-alpha-3b",
        hf_config=dict(org="stabilityai", name="stablecode-completion-alpha-3b"),
        block_size=16384,
        vocab_size=49152,
        n_layer=32,
        n_embd=2560,
    ),
    # https://huggingface.co/stabilityai/stablecode-completion-alpha-3b-4k/blob/main/config.json
    dict(
        name="stablecode-completion-alpha-3b-4k",
        hf_config=dict(org="stabilityai", name="stablecode-completion-alpha-3b-4k"),
        vocab_size=49152,
        n_layer=32,
        n_embd=2560,
    ),
    # https://huggingface.co/stabilityai/stablecode-instruct-alpha-3b/blob/main/config.json
    dict(
        name="stablecode-instruct-alpha-3b",
        hf_config=dict(org="stabilityai", name="stablecode-instruct-alpha-3b"),
        vocab_size=49152,
        n_layer=32,
        n_embd=2560,
    ),
]
configs.extend(stablecode)


##################################
# togethercomputer LLaMA-2-7B-32K
##################################
together_llama2_32k = [
    # https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/config.json
    dict(
        name="LLaMA-2-7B-32K",
        hf_config=dict(org="togethercomputer", name="LLaMA-2-7B-32K"),
        vocab_size=32000,
        padding_multiple=64,
        n_layer=32,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        _mlp_class="LLaMAMLP",
        intermediate_size=11008,
        rope_condense_ratio=8,
    )
]
configs.extend(together_llama2_32k)


################
# Microsoft Phi
################
phi = [
    # https://huggingface.co/microsoft/phi-1_5/blob/main/config.json
    dict(
        name="phi-1_5",
        hf_config=dict(org="microsoft", name="phi-1_5"),
        vocab_size=50257,
        padded_vocab_size=51200,
        block_size=2048,
        n_embd=2048,
        n_layer=24,
        rotary_percentage=0.5,  # 32 / (n_embd / n_head) = 32 / 64
        shared_attention_norm=True,
        lm_head_bias=True,
        gelu_approximate="tanh",
    )
]
configs.extend(phi)


#############
# Mistral AI
#############
mistral = [
    # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
    dict(
        name="Mistral-7B-{}v0.1",
        hf_config=dict(org="mistralai", name="Mistral-7B-{}v0.1"),
        padded_vocab_size=32000,
        block_size=4096,  # should be 32768 but sliding window attention is not implemented
        n_layer=32,
        n_query_groups=8,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",
        norm_eps=1e-05,
        _mlp_class="LLaMAMLP",
        intermediate_size=14336,
    )
]
for c in mistral:
    for kind in ("", "Instruct-"):
        copy = deepcopy(c)
        copy["name"] = c["name"].format(kind)
        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
        configs.append(copy)


############
# TinyLlama
############
tiny_llama = [
    dict(
        name="tiny-llama-1.1b{}",
        hf_config=dict(org="TinyLlama", name="TinyLlama-1.1B{}"),
        block_size=2048,
        vocab_size=32000,
        padding_multiple=64,
        n_layer=22,
        n_head=32,
        n_embd=2048,
        rotary_percentage=1.0,
        parallel_residual=False,
        bias=False,
        _norm_class="RMSNorm",  # original TinyLlama uses FusedRMSNorm
        norm_eps=1e-5,
        _mlp_class="LLaMAMLP",
        intermediate_size=5632,
        n_query_groups=4,
    ),
]
for c in tiny_llama:
    for kind, hf_postfix in (("", "-intermediate-step-955k-token-2T"), ("chat", "-Chat-v0.6")):
        copy = deepcopy(c)
        copy["name"] = c["name"].format(kind)
        copy["hf_config"]["name"] = c["hf_config"]["name"].format(hf_postfix)
        configs.append(copy)


name_to_config = {config["name"]: config for config in configs}