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loading_from_pretrained.py
1837 lines (1654 loc) · 72 KB
/
loading_from_pretrained.py
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"""Loading Pretrained Models Utilities.
This module contains functions for loading pretrained models from the Hugging Face Hub.
"""
import dataclasses
import logging
import re
from typing import Dict, Optional
import einops
import torch
from huggingface_hub import HfApi
from transformers import AutoConfig, AutoModelForCausalLM, BertForPreTraining
import transformer_lens.utils as utils
from transformer_lens.HookedTransformerConfig import HookedTransformerConfig
OFFICIAL_MODEL_NAMES = [
"gpt2",
"gpt2-medium",
"gpt2-large",
"gpt2-xl",
"distilgpt2",
"facebook/opt-125m",
"facebook/opt-1.3b",
"facebook/opt-2.7b",
"facebook/opt-6.7b",
"facebook/opt-13b",
"facebook/opt-30b",
"facebook/opt-66b",
"EleutherAI/gpt-neo-125M",
"EleutherAI/gpt-neo-1.3B",
"EleutherAI/gpt-neo-2.7B",
"EleutherAI/gpt-j-6B",
"EleutherAI/gpt-neox-20b",
"stanford-crfm/alias-gpt2-small-x21",
"stanford-crfm/battlestar-gpt2-small-x49",
"stanford-crfm/caprica-gpt2-small-x81",
"stanford-crfm/darkmatter-gpt2-small-x343",
"stanford-crfm/expanse-gpt2-small-x777",
"stanford-crfm/arwen-gpt2-medium-x21",
"stanford-crfm/beren-gpt2-medium-x49",
"stanford-crfm/celebrimbor-gpt2-medium-x81",
"stanford-crfm/durin-gpt2-medium-x343",
"stanford-crfm/eowyn-gpt2-medium-x777",
"EleutherAI/pythia-14m",
"EleutherAI/pythia-31m",
"EleutherAI/pythia-70m",
"EleutherAI/pythia-160m",
"EleutherAI/pythia-410m",
"EleutherAI/pythia-1b",
"EleutherAI/pythia-1.4b",
"EleutherAI/pythia-2.8b",
"EleutherAI/pythia-6.9b",
"EleutherAI/pythia-12b",
"EleutherAI/pythia-70m-deduped",
"EleutherAI/pythia-160m-deduped",
"EleutherAI/pythia-410m-deduped",
"EleutherAI/pythia-1b-deduped",
"EleutherAI/pythia-1.4b-deduped",
"EleutherAI/pythia-2.8b-deduped",
"EleutherAI/pythia-6.9b-deduped",
"EleutherAI/pythia-12b-deduped",
"EleutherAI/pythia-70m-v0",
"EleutherAI/pythia-160m-v0",
"EleutherAI/pythia-410m-v0",
"EleutherAI/pythia-1b-v0",
"EleutherAI/pythia-1.4b-v0",
"EleutherAI/pythia-2.8b-v0",
"EleutherAI/pythia-6.9b-v0",
"EleutherAI/pythia-12b-v0",
"EleutherAI/pythia-70m-deduped-v0",
"EleutherAI/pythia-160m-deduped-v0",
"EleutherAI/pythia-410m-deduped-v0",
"EleutherAI/pythia-1b-deduped-v0",
"EleutherAI/pythia-1.4b-deduped-v0",
"EleutherAI/pythia-2.8b-deduped-v0",
"EleutherAI/pythia-6.9b-deduped-v0",
"EleutherAI/pythia-12b-deduped-v0",
"EleutherAI/pythia-160m-seed1",
"EleutherAI/pythia-160m-seed2",
"EleutherAI/pythia-160m-seed3",
"NeelNanda/SoLU_1L_v9_old",
"NeelNanda/SoLU_2L_v10_old",
"NeelNanda/SoLU_4L_v11_old",
"NeelNanda/SoLU_6L_v13_old",
"NeelNanda/SoLU_8L_v21_old",
"NeelNanda/SoLU_10L_v22_old",
"NeelNanda/SoLU_12L_v23_old",
"NeelNanda/SoLU_1L512W_C4_Code",
"NeelNanda/SoLU_2L512W_C4_Code",
"NeelNanda/SoLU_3L512W_C4_Code",
"NeelNanda/SoLU_4L512W_C4_Code",
"NeelNanda/SoLU_6L768W_C4_Code",
"NeelNanda/SoLU_8L1024W_C4_Code",
"NeelNanda/SoLU_10L1280W_C4_Code",
"NeelNanda/SoLU_12L1536W_C4_Code",
"NeelNanda/GELU_1L512W_C4_Code",
"NeelNanda/GELU_2L512W_C4_Code",
"NeelNanda/GELU_3L512W_C4_Code",
"NeelNanda/GELU_4L512W_C4_Code",
"NeelNanda/Attn_Only_1L512W_C4_Code",
"NeelNanda/Attn_Only_2L512W_C4_Code",
"NeelNanda/Attn_Only_3L512W_C4_Code",
"NeelNanda/Attn_Only_4L512W_C4_Code",
"NeelNanda/Attn-Only-2L512W-Shortformer-6B-big-lr",
"NeelNanda/SoLU_1L512W_Wiki_Finetune",
"NeelNanda/SoLU_4L512W_Wiki_Finetune",
"ArthurConmy/redwood_attn_2l",
"llama-7b-hf",
"llama-13b-hf",
"llama-30b-hf",
"llama-65b-hf",
"Llama-2-7b-hf",
"Llama-2-7b-chat-hf",
"Llama-2-13b-hf",
"Llama-2-13b-chat-hf",
# TODO Llama-2-70b-hf requires Grouped-Query Attention, see the paper https://arxiv.org/pdf/2307.09288.pdf
"Baidicoot/Othello-GPT-Transformer-Lens",
"bert-base-cased",
"roneneldan/TinyStories-1M",
"roneneldan/TinyStories-3M",
"roneneldan/TinyStories-8M",
"roneneldan/TinyStories-28M",
"roneneldan/TinyStories-33M",
"roneneldan/TinyStories-Instruct-1M",
"roneneldan/TinyStories-Instruct-3M",
"roneneldan/TinyStories-Instruct-8M",
"roneneldan/TinyStories-Instruct-28M",
"roneneldan/TinyStories-Instruct-33M",
"roneneldan/TinyStories-1Layer-21M",
"roneneldan/TinyStories-2Layers-33M",
"roneneldan/TinyStories-Instuct-1Layer-21M",
"roneneldan/TinyStories-Instruct-2Layers-33M",
"stabilityai/stablelm-base-alpha-3b",
"stabilityai/stablelm-base-alpha-7b",
"stabilityai/stablelm-tuned-alpha-3b",
"stabilityai/stablelm-tuned-alpha-7b",
"bigscience/bloom-560m",
"bigcode/santacoder",
]
"""Official model names for models on HuggingFace."""
# Model Aliases:
MODEL_ALIASES = {
"NeelNanda/SoLU_1L_v9_old": ["solu-1l-pile", "solu-1l-old"],
"NeelNanda/SoLU_2L_v10_old": ["solu-2l-pile", "solu-2l-old"],
"NeelNanda/SoLU_4L_v11_old": ["solu-4l-pile", "solu-4l-old"],
"NeelNanda/SoLU_6L_v13_old": ["solu-6l-pile", "solu-6l-old"],
"NeelNanda/SoLU_8L_v21_old": ["solu-8l-pile", "solu-8l-old"],
"NeelNanda/SoLU_10L_v22_old": ["solu-10l-pile", "solu-10l-old"],
"NeelNanda/SoLU_12L_v23_old": ["solu-12l-pile", "solu-12l-old"],
"NeelNanda/SoLU_1L512W_C4_Code": ["solu-1l", "solu-1l-new", "solu-1l-c4-code"],
"NeelNanda/SoLU_2L512W_C4_Code": ["solu-2l", "solu-2l-new", "solu-2l-c4-code"],
"NeelNanda/SoLU_3L512W_C4_Code": ["solu-3l", "solu-3l-new", "solu-3l-c4-code"],
"NeelNanda/SoLU_4L512W_C4_Code": ["solu-4l", "solu-4l-new", "solu-4l-c4-code"],
"NeelNanda/GELU_1L512W_C4_Code": ["gelu-1l", "gelu-1l-new", "gelu-1l-c4-code"],
"NeelNanda/GELU_2L512W_C4_Code": ["gelu-2l", "gelu-2l-new", "gelu-2l-c4-code"],
"NeelNanda/GELU_3L512W_C4_Code": ["gelu-3l", "gelu-3l-new", "gelu-3l-c4-code"],
"NeelNanda/GELU_4L512W_C4_Code": ["gelu-4l", "gelu-4l-new", "gelu-4l-c4-code"],
"NeelNanda/Attn_Only_1L512W_C4_Code": [
"attn-only-1l",
"attn-only-1l-new",
"attn-only-1l-c4-code",
],
"NeelNanda/Attn_Only_2L512W_C4_Code": [
"attn-only-2l",
"attn-only-2l-new",
"attn-only-2l-c4-code",
],
"NeelNanda/Attn_Only_3L512W_C4_Code": [
"attn-only-3l",
"attn-only-3l-new",
"attn-only-3l-c4-code",
],
"NeelNanda/Attn_Only_4L512W_C4_Code": [
"attn-only-4l",
"attn-only-4l-new",
"attn-only-4l-c4-code",
],
"NeelNanda/SoLU_6L768W_C4_Code": ["solu-6l", "solu-6l-new", "solu-6l-c4-code"],
"NeelNanda/SoLU_8L1024W_C4_Code": ["solu-8l", "solu-8l-new", "solu-8l-c4-code"],
"NeelNanda/SoLU_10L1280W_C4_Code": ["solu-10l", "solu-10l-new", "solu-10l-c4-code"],
"NeelNanda/SoLU_12L1536W_C4_Code": ["solu-12l", "solu-12l-new", "solu-12l-c4-code"],
"NeelNanda/Attn-Only-2L512W-Shortformer-6B-big-lr": [
"attn-only-2l-demo",
"attn-only-2l-shortformer-6b-big-lr",
"attn-only-2l-induction-demo",
"attn-only-demo",
],
"NeelNanda/SoLU_1L512W_Wiki_Finetune": [
"solu-1l-wiki",
"solu-1l-wiki-finetune",
"solu-1l-finetune",
],
"NeelNanda/SoLU_4L512W_Wiki_Finetune": [
"solu-4l-wiki",
"solu-4l-wiki-finetune",
"solu-4l-finetune",
],
"EleutherAI/pythia-14m": [
"pythia-14m",
],
"EleutherAI/pythia-31m": [
"pythia-31m",
],
"EleutherAI/pythia-70m": [
"pythia-70m",
"pythia",
"EleutherAI/pythia-19m",
"pythia-19m", # EleutherAI renamed this model
],
"EleutherAI/pythia-160m": [
"pythia-160m",
"EleutherAI/pythia-125m",
"pythia-125m", # EleutherAI renamed this model"
],
"EleutherAI/pythia-410m": [
"pythia-410m",
"EleutherAI/pythia-350m",
"pythia-350m", # EleutherAI renamed this model
],
"EleutherAI/pythia-1b": [
"pythia-1b",
"EleutherAI/pythia-800m",
"pythia-800m", # EleutherAI renamed this model
],
"EleutherAI/pythia-1.4b": [
"pythia-1.4b",
"EleutherAI/pythia-1.3b",
"pythia-1.3b", # EleutherAI renamed this model
],
"EleutherAI/pythia-2.8b": [
"pythia-2.8b",
"EleutherAI/pythia-2.7b",
"pythia-2.7b", # EleutherAI renamed this model
],
"EleutherAI/pythia-6.9b": [
"pythia-6.9b",
"EleutherAI/pythia-6.7b",
"pythia-6.7b", # EleutherAI renamed this model
],
"EleutherAI/pythia-12b": [
"pythia-12b",
"EleutherAI/pythia-13b",
"pythia-13b", # EleutherAI renamed this model
],
"EleutherAI/pythia-70m-deduped": [
"pythia-70m-deduped",
"EleutherAI/pythia-19m-deduped", # EleutherAI renamed this model
"pythia-19m-deduped",
],
"EleutherAI/pythia-160m-deduped": [
"pythia-160m-deduped",
"EleutherAI/pythia-125m-deduped", # EleutherAI renamed this model
"pythia-125m-deduped",
],
"EleutherAI/pythia-410m-deduped": [
"pythia-410m-deduped",
"EleutherAI/pythia-350m-deduped", # EleutherAI renamed this model
"pythia-350m-deduped",
],
"EleutherAI/pythia-1b-deduped": [
"pythia-1b-deduped",
"EleutherAI/pythia-800m-deduped", # EleutherAI renamed this model
"pythia-800m-deduped",
],
"EleutherAI/pythia-1.4b-deduped": [
"pythia-1.4b-deduped",
"EleutherAI/pythia-1.3b-deduped", # EleutherAI renamed this model
"pythia-1.3b-deduped",
],
"EleutherAI/pythia-2.8b-deduped": [
"pythia-2.8b-deduped",
"EleutherAI/pythia-2.7b-deduped", # EleutherAI renamed this model
"pythia-2.7b-deduped",
],
"EleutherAI/pythia-6.9b-deduped": [
"pythia-6.9b-deduped",
"EleutherAI/pythia-6.7b-deduped", # EleutherAI renamed this model
"pythia-6.7b-deduped",
],
"EleutherAI/pythia-12b-deduped": [
"pythia-12b-deduped",
"EleutherAI/pythia-13b-deduped", # EleutherAI renamed this model
"pythia-13b-deduped",
],
"EleutherAI/pythia-70m-v0": [
"pythia-70m-v0",
"pythia-v0",
"EleutherAI/pythia-19m-v0",
"pythia-19m-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-160m-v0": [
"pythia-160m-v0",
"EleutherAI/pythia-125m-v0",
"pythia-125m-v0", # EleutherAI renamed this model"
],
"EleutherAI/pythia-410m-v0": [
"pythia-410m-v0",
"EleutherAI/pythia-350m-v0",
"pythia-350m-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-1b-v0": [
"pythia-1b-v0",
"EleutherAI/pythia-800m-v0",
"pythia-800m-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-1.4b-v0": [
"pythia-1.4b-v0",
"EleutherAI/pythia-1.3b-v0",
"pythia-1.3b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-2.8b-v0": [
"pythia-2.8b-v0",
"EleutherAI/pythia-2.7b-v0",
"pythia-2.7b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-6.9b-v0": [
"pythia-6.9b-v0",
"EleutherAI/pythia-6.7b-v0",
"pythia-6.7b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-12b-v0": [
"pythia-12b-v0",
"EleutherAI/pythia-13b-v0",
"pythia-13b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-70m-deduped-v0": [
"pythia-70m-deduped-v0",
"EleutherAI/pythia-19m-deduped-v0", # EleutherAI renamed this model
"pythia-19m-deduped-v0",
],
"EleutherAI/pythia-160m-deduped-v0": [
"pythia-160m-deduped-v0",
"EleutherAI/pythia-125m-deduped-v0", # EleutherAI renamed this model
"pythia-125m-deduped-v0",
],
"EleutherAI/pythia-410m-deduped-v0": [
"pythia-410m-deduped-v0",
"EleutherAI/pythia-350m-deduped-v0", # EleutherAI renamed this model
"pythia-350m-deduped-v0",
],
"EleutherAI/pythia-1b-deduped-v0": [
"pythia-1b-deduped-v0",
"EleutherAI/pythia-800m-deduped-v0", # EleutherAI renamed this model
"pythia-800m-deduped-v0",
],
"EleutherAI/pythia-1.4b-deduped-v0": [
"pythia-1.4b-deduped-v0",
"EleutherAI/pythia-1.3b-deduped-v0", # EleutherAI renamed this model
"pythia-1.3b-deduped-v0",
],
"EleutherAI/pythia-2.8b-deduped-v0": [
"pythia-2.8b-deduped-v0",
"EleutherAI/pythia-2.7b-deduped-v0", # EleutherAI renamed this model
"pythia-2.7b-deduped-v0",
],
"EleutherAI/pythia-6.9b-deduped-v0": [
"pythia-6.9b-deduped-v0",
"EleutherAI/pythia-6.7b-deduped-v0", # EleutherAI renamed this model
"pythia-6.7b-deduped-v0",
],
"EleutherAI/pythia-12b-deduped-v0": [
"pythia-12b-deduped-v0",
"EleutherAI/pythia-13b-deduped-v0", # EleutherAI renamed this model
"pythia-13b-deduped-v0",
],
"EleutherAI/pythia-160m-seed1": [
"pythia-160m-seed1",
"EleutherAI/pythia-125m-seed1",
"pythia-125m-seed1", # EleutherAI renamed this model"
],
"EleutherAI/pythia-160m-seed2": [
"pythia-160m-seed2",
"EleutherAI/pythia-125m-seed2",
"pythia-125m-seed2", # EleutherAI renamed this model"
],
"EleutherAI/pythia-160m-seed3": [
"pythia-160m-seed3",
"EleutherAI/pythia-125m-seed3",
"pythia-125m-seed3", # EleutherAI renamed this model"
],
"gpt2": ["gpt2-small"],
"distilgpt2": ["distillgpt2", "distill-gpt2", "distil-gpt2", "gpt2-xs"],
"facebook/opt-125m": ["opt-125m", "opt-small", "opt"],
"facebook/opt-1.3b": ["opt-1.3b", "opt-medium"],
"facebook/opt-2.7b": ["opt-2.7b", "opt-large"],
"facebook/opt-6.7b": ["opt-6.7b", "opt-xl"],
"facebook/opt-13b": ["opt-13b", "opt-xxl"],
"facebook/opt-30b": ["opt-30b", "opt-xxxl"],
"facebook/opt-66b": ["opt-66b", "opt-xxxxl"],
"EleutherAI/gpt-neo-125M": ["gpt-neo-125M", "gpt-neo-small", "neo-small", "neo"],
"EleutherAI/gpt-neo-1.3B": ["gpt-neo-1.3B", "gpt-neo-medium", "neo-medium"],
"EleutherAI/gpt-neo-2.7B": ["gpt-neo-2.7B", "gpt-neo-large", "neo-large"],
"EleutherAI/gpt-j-6B": ["gpt-j-6B", "gpt-j", "gptj"],
"EleutherAI/gpt-neox-20b": ["gpt-neox-20b", "gpt-neox", "neox"],
"stanford-crfm/alias-gpt2-small-x21": [
"stanford-gpt2-small-a",
"alias-gpt2-small-x21",
"gpt2-mistral-small-a",
"gpt2-stanford-small-a",
],
"stanford-crfm/battlestar-gpt2-small-x49": [
"stanford-gpt2-small-b",
"battlestar-gpt2-small-x49",
"gpt2-mistral-small-b",
"gpt2-mistral-small-b",
],
"stanford-crfm/caprica-gpt2-small-x81": [
"stanford-gpt2-small-c",
"caprica-gpt2-small-x81",
"gpt2-mistral-small-c",
"gpt2-stanford-small-c",
],
"stanford-crfm/darkmatter-gpt2-small-x343": [
"stanford-gpt2-small-d",
"darkmatter-gpt2-small-x343",
"gpt2-mistral-small-d",
"gpt2-mistral-small-d",
],
"stanford-crfm/expanse-gpt2-small-x777": [
"stanford-gpt2-small-e",
"expanse-gpt2-small-x777",
"gpt2-mistral-small-e",
"gpt2-mistral-small-e",
],
"stanford-crfm/arwen-gpt2-medium-x21": [
"stanford-gpt2-medium-a",
"arwen-gpt2-medium-x21",
"gpt2-medium-small-a",
"gpt2-stanford-medium-a",
],
"stanford-crfm/beren-gpt2-medium-x49": [
"stanford-gpt2-medium-b",
"beren-gpt2-medium-x49",
"gpt2-medium-small-b",
"gpt2-stanford-medium-b",
],
"stanford-crfm/celebrimbor-gpt2-medium-x81": [
"stanford-gpt2-medium-c",
"celebrimbor-gpt2-medium-x81",
"gpt2-medium-small-c",
"gpt2-medium-small-c",
],
"stanford-crfm/durin-gpt2-medium-x343": [
"stanford-gpt2-medium-d",
"durin-gpt2-medium-x343",
"gpt2-medium-small-d",
"gpt2-stanford-medium-d",
],
"stanford-crfm/eowyn-gpt2-medium-x777": [
"stanford-gpt2-medium-e",
"eowyn-gpt2-medium-x777",
"gpt2-medium-small-e",
"gpt2-stanford-medium-e",
],
"ArthurConmy/redwood_attn_2l": ["redwood_attn_2l"],
"llama-7b-hf": ["llama-7b"],
"llama-13b-hf": ["llama-13b"],
"llama-30b-hf": ["llama-30b"],
"llama-65b-hf": ["llama-65b"],
"Llama-2-7b-hf": ["Llama-2-7b", "meta-llama/Llama-2-7b-hf"],
"Llama-2-7b-chat-hf": ["Llama-2-7b-chat", "meta-llama/Llama-2-7b-chat-hf"],
"Llama-2-13b-hf": ["Llama-2-13b", "meta-llama/Llama-2-13b-hf"],
"Llama-2-13b-chat-hf": ["Llama-2-13b-chat", "meta-llama/Llama-2-13b-chat-hf"],
# TODO Llama-2-70b-hf requires Grouped-Query Attention, see the paper https://arxiv.org/pdf/2307.09288.pdf
"Baidicoot/Othello-GPT-Transformer-Lens": ["othello-gpt"],
"roneneldan/TinyStories-1M": ["tiny-stories-1M"],
"roneneldan/TinyStories-3M": ["tiny-stories-3M"],
"roneneldan/TinyStories-8M": ["tiny-stories-8M"],
"roneneldan/TinyStories-28M": ["tiny-stories-28M"],
"roneneldan/TinyStories-33M": ["tiny-stories-33M"],
"roneneldan/TinyStories-Instruct-1M": ["tiny-stories-instruct-1M"],
"roneneldan/TinyStories-Instruct-3M": ["tiny-stories-instruct-3M"],
"roneneldan/TinyStories-Instruct-8M": ["tiny-stories-instruct-8M"],
"roneneldan/TinyStories-Instruct-28M": ["tiny-stories-instruct-28M"],
"roneneldan/TinyStories-Instruct-33M": ["tiny-stories-instruct-33M"],
"roneneldan/TinyStories-1Layer-21M": ["tiny-stories-1L-21M"],
"roneneldan/TinyStories-2Layers-33M": ["tiny-stories-2L-33M"],
"roneneldan/TinyStories-Instuct-1Layer-21M": ["tiny-stories-instruct-1L-21M"],
"roneneldan/TinyStories-Instruct-2Layers-33M": ["tiny-stories-instruct-2L-33M"],
"stabilityai/stablelm-base-alpha-3b": [
"stablelm-base-alpha-3b",
"stablelm-base-3b",
],
"stabilityai/stablelm-base-alpha-7b": [
"stablelm-base-alpha-7b",
"stablelm-base-7b",
],
"stabilityai/stablelm-tuned-alpha-3b": [
"stablelm-tuned-alpha-3b",
"stablelm-tuned-3b",
],
"stabilityai/stablelm-tuned-alpha-7b": [
"stablelm-tuned-alpha-7b",
"stablelm-tuned-7b",
],
"bigscience/bloom-560m": ["bloom-560m"],
"bigcode/santacoder": ["santacoder"],
}
"""Model aliases for models on HuggingFace."""
# Sets a default model alias, by convention the first one in the model alias table, else the official name if it has no aliases
DEFAULT_MODEL_ALIASES = [
MODEL_ALIASES[name][0] if name in MODEL_ALIASES else name
for name in OFFICIAL_MODEL_NAMES
]
def make_model_alias_map():
"""
Converts OFFICIAL_MODEL_NAMES (the list of actual model names on
HuggingFace) and MODEL_ALIASES (a dictionary mapping official model names to
aliases) into a dictionary mapping all aliases to the official model name.
"""
model_alias_map = {}
for official_model_name in OFFICIAL_MODEL_NAMES:
aliases = MODEL_ALIASES.get(official_model_name, [])
for alias in aliases:
model_alias_map[alias.lower()] = official_model_name
model_alias_map[official_model_name.lower()] = official_model_name
return model_alias_map
def get_official_model_name(model_name: str):
"""
Returns the official model name for a given model name (or alias).
"""
model_alias_map = make_model_alias_map()
official_model_name = model_alias_map.get(model_name.lower(), None)
if official_model_name is None:
raise ValueError(
f"{model_name} not found. Valid official model names (excl aliases): {OFFICIAL_MODEL_NAMES}"
)
return official_model_name
def convert_hf_model_config(model_name: str, **kwargs):
"""
Returns the model config for a HuggingFace model, converted to a dictionary
in the HookedTransformerConfig format.
Takes the official_model_name as an input.
"""
# In case the user passed in an alias
official_model_name = get_official_model_name(model_name)
# Load HuggingFace model config
if "llama" not in official_model_name.lower():
hf_config = AutoConfig.from_pretrained(official_model_name, **kwargs)
architecture = hf_config.architectures[0]
else:
architecture = "LlamaForCausalLM"
if official_model_name.startswith(
("llama-7b", "Llama-2-7b")
): # same architecture for LLaMA and Llama-2
cfg_dict = {
"d_model": 4096,
"d_head": 4096 // 32,
"n_heads": 32,
"d_mlp": 11008,
"n_layers": 32,
"n_ctx": 2048 if official_model_name.startswith("llama-7b") else 4096,
"eps": 1e-6 if official_model_name.startswith("llama-7b") else 1e-5,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_dim": 4096 // 32,
"final_rms": True,
"gated_mlp": True,
}
elif official_model_name.startswith(
("llama-13b", "Llama-2-13b")
): # same architecture for LLaMA and Llama-2
cfg_dict = {
"d_model": 5120,
"d_head": 5120 // 40,
"n_heads": 40,
"d_mlp": 13824,
"n_layers": 40,
"n_ctx": 2048 if official_model_name.startswith("llama-13b") else 4096,
"eps": 1e-6 if official_model_name.startswith("llama-13b") else 1e-5,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_dim": 5120 // 40,
"final_rms": True,
"gated_mlp": True,
}
elif "llama-30b" in official_model_name:
cfg_dict = {
"d_model": 6656,
"d_head": 6656 // 52,
"n_heads": 52,
"d_mlp": 17920,
"n_layers": 60,
"n_ctx": 2048,
"eps": 1e-6,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_dim": 6656 // 52,
"final_rms": True,
"gated_mlp": True,
}
elif "llama-65b" in official_model_name:
cfg_dict = {
"d_model": 8192,
"d_head": 8192 // 64,
"n_heads": 64,
"d_mlp": 22016,
"n_layers": 80,
"n_ctx": 2048,
"eps": 1e-6,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_dim": 8192 // 64,
"final_rms": True,
"gated_mlp": True,
}
elif architecture == "GPTNeoForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_heads,
"n_heads": hf_config.num_heads,
"d_mlp": hf_config.hidden_size * 4,
"n_layers": hf_config.num_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": hf_config.layer_norm_epsilon,
"d_vocab": hf_config.vocab_size,
"attn_types": hf_config.attention_layers,
"act_fn": hf_config.activation_function,
"use_attn_scale": False,
"use_local_attn": True,
"window_size": hf_config.window_size,
"scale_attn_by_inverse_layer_idx": False,
"normalization_type": "LN",
}
elif architecture == "GPT2LMHeadModel":
cfg_dict = {
"d_model": hf_config.n_embd,
"d_head": hf_config.n_embd // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": hf_config.n_embd * 4,
"n_layers": hf_config.n_layer,
"n_ctx": hf_config.n_ctx,
"eps": hf_config.layer_norm_epsilon,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": hf_config.scale_attn_by_inverse_layer_idx,
"normalization_type": "LN",
}
elif architecture == "OPTForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_attention_heads,
"n_heads": hf_config.num_attention_heads,
"d_mlp": hf_config.ffn_dim,
"n_layers": hf_config.num_hidden_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": 1e-5,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": False,
"normalization_type": "LN",
}
elif architecture == "GPTJForCausalLM":
cfg_dict = {
"d_model": hf_config.n_embd,
"d_head": hf_config.n_embd // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": 4 * hf_config.n_embd,
"n_layers": hf_config.n_layer,
"n_ctx": hf_config.n_positions,
"eps": 1e-5,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": False,
"parallel_attn_mlp": True,
"positional_embedding_type": "rotary",
"rotary_dim": hf_config.rotary_dim,
"normalization_type": "LN",
}
elif architecture == "GPTNeoXForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_attention_heads,
"n_heads": hf_config.num_attention_heads,
"d_mlp": hf_config.intermediate_size,
"n_layers": hf_config.num_hidden_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": hf_config.layer_norm_eps,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.hidden_act,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": False,
"parallel_attn_mlp": True,
"positional_embedding_type": "rotary",
"normalization_type": "LN",
}
rotary_pct = hf_config.rotary_pct
cfg_dict["rotary_dim"] = round(rotary_pct * cfg_dict["d_head"])
elif architecture == "BertForMaskedLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_attention_heads,
"n_heads": hf_config.num_attention_heads,
"d_mlp": hf_config.intermediate_size,
"n_layers": hf_config.num_hidden_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": hf_config.layer_norm_eps,
"d_vocab": hf_config.vocab_size,
"act_fn": "gelu",
"attention_dir": "bidirectional",
}
elif architecture == "BloomForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": hf_config.hidden_size * 4,
"n_layers": hf_config.n_layer,
"n_ctx": 2048, # Capped due to HF Tokenizer Constraints
"d_vocab": hf_config.vocab_size,
"act_fn": "gelu_fast",
"eps": hf_config.layer_norm_epsilon,
"normalization_type": "LN",
"post_embedding_ln": True,
"positional_embedding_type": "alibi",
}
elif architecture == "GPT2LMHeadCustomModel":
# santacoder
cfg_dict = {
"d_model": hf_config.n_embd,
"d_head": hf_config.n_embd // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": hf_config.n_embd * 4,
"n_layers": hf_config.n_layer,
"n_ctx": hf_config.n_positions,
"eps": hf_config.layer_norm_epsilon,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": hf_config.scale_attn_by_inverse_layer_idx,
"normalization_type": "LN",
}
else:
raise NotImplementedError(f"{architecture} is not currently supported.")
# All of these models use LayerNorm
cfg_dict["original_architecture"] = architecture
# The name such that AutoTokenizer.from_pretrained works
cfg_dict["tokenizer_name"] = official_model_name
return cfg_dict
def convert_neel_model_config(official_model_name: str, **kwargs):
"""
Loads the config for a model trained by me (NeelNanda), converted to a dictionary
in the HookedTransformerConfig format.
AutoConfig is not supported, because these models are in the HookedTransformer format, so we directly download and load the json.
"""
official_model_name = get_official_model_name(official_model_name)
cfg_json: dict = utils.download_file_from_hf(
official_model_name, "config.json", **kwargs
)
cfg_arch = cfg_json.get(
"architecture", "neel" if "_old" not in official_model_name else "neel-solu-old"
)
cfg_dict = {
"d_model": cfg_json["d_model"],
"n_layers": cfg_json["n_layers"],
"d_mlp": cfg_json["d_mlp"],
"d_head": cfg_json["d_head"],
"n_heads": cfg_json["n_heads"],
"n_ctx": cfg_json["n_ctx"],
"d_vocab": cfg_json["d_vocab"],
"tokenizer_name": cfg_json.get("tokenizer_name", None),
"act_fn": cfg_json["act_fn"],
"attn_only": cfg_json["attn_only"],
"final_rms": cfg_json.get("final_rms", False),
"original_architecture": cfg_arch,
}
if "normalization" in cfg_json:
cfg_dict["normalization_type"] = cfg_json["normalization"]
else:
cfg_dict["normalization_type"] = cfg_json["normalization_type"]
if "shortformer_pos" in cfg_json:
cfg_dict["positional_embedding_type"] = (
"shortformer" if cfg_json["shortformer_pos"] else "standard"
)
else:
cfg_dict["positional_embedding_type"] = "standard"
return cfg_dict
def get_pretrained_model_config(
model_name: str,
checkpoint_index: Optional[int] = None,
checkpoint_value: Optional[int] = None,
fold_ln: bool = False,
device: Optional[str] = None,
n_devices: int = 1,
default_prepend_bos: bool = True,
dtype: torch.dtype = torch.float32,
**kwargs,
):
"""Returns the pretrained model config as an HookedTransformerConfig object.
There are two types of pretrained models: HuggingFace models (where
AutoModel and AutoConfig work), and models trained by me (NeelNanda) which
aren't as integrated with HuggingFace infrastructure.
Args:
model_name: The name of the model. This can be either the official
HuggingFace model name, or the name of a model trained by me
(NeelNanda).
checkpoint_index (int, optional): If loading from a
checkpoint, the index of the checkpoint to load. Defaults to None.
checkpoint_value (int, optional): If loading from a checkpoint, the
value of
the checkpoint to load, ie the step or token number (each model has
checkpoints labelled with exactly one of these). Defaults to None.
fold_ln (bool, optional): Whether to fold the layer norm into the
subsequent linear layers (see HookedTransformer.fold_layer_norm for
details). Defaults to False.
device (str, optional): The device to load the model onto. By
default will load to CUDA if available, else CPU.
n_devices (int, optional): The number of devices to split the model across. Defaults to 1.
default_prepend_bos (bool, optional): Default behavior of whether to prepend the BOS token when the
methods of HookedTransformer process input text to tokenize (only when input is a string).
Defaults to True - even for models not explicitly trained with this, heads often use the
first position as a resting position and accordingly lose information from the first token,
so this empirically seems to give better results. To change the default behavior to False, pass in
default_prepend_bos=False. Note that you can also locally override the default behavior by passing
in prepend_bos=True/False when you call a method that processes the input string.
dtype (torch.dtype, optional): The dtype to load the TransformerLens model in.
kwargs: Other optional arguments passed to HuggingFace's from_pretrained.
Also given to other HuggingFace functions when compatible.
"""
official_model_name = get_official_model_name(model_name)
if (
official_model_name.startswith("NeelNanda")
or official_model_name.startswith("ArthurConmy")
or official_model_name.startswith("Baidicoot")
):
cfg_dict = convert_neel_model_config(official_model_name, **kwargs)
else:
cfg_dict = convert_hf_model_config(official_model_name, **kwargs)
# Processing common to both model types
# Remove any prefix, saying the organization who made a model.
cfg_dict["model_name"] = official_model_name.split("/")[-1]
# Don't need to initialize weights, we're loading from pretrained
cfg_dict["init_weights"] = False
if (
"positional_embedding_type" in cfg_dict
and cfg_dict["positional_embedding_type"] == "shortformer"
and fold_ln
):
logging.warning(
"You tried to specify fold_ln=True for a shortformer model, but this can't be done! Setting fold_ln=False instead."
)
fold_ln = False
if device is not None:
cfg_dict["device"] = device
cfg_dict["dtype"] = dtype
if fold_ln:
if cfg_dict["normalization_type"] in ["LN", "LNPre"]:
cfg_dict["normalization_type"] = "LNPre"
else:
logging.warning("Cannot fold in layer norm, normalization_type is not LN.")
if checkpoint_index is not None or checkpoint_value is not None:
checkpoint_labels, checkpoint_label_type = get_checkpoint_labels(
official_model_name,
**kwargs,
)
cfg_dict["from_checkpoint"] = True
cfg_dict["checkpoint_label_type"] = checkpoint_label_type
if checkpoint_index is not None:
cfg_dict["checkpoint_index"] = checkpoint_index
cfg_dict["checkpoint_value"] = checkpoint_labels[checkpoint_index]
elif checkpoint_value is not None:
assert (
checkpoint_value in checkpoint_labels
), f"Checkpoint value {checkpoint_value} is not in list of available checkpoints"
cfg_dict["checkpoint_value"] = checkpoint_value
cfg_dict["checkpoint_index"] = checkpoint_labels.index(checkpoint_value)
else:
cfg_dict["from_checkpoint"] = False
cfg_dict["device"] = device
cfg_dict["n_devices"] = n_devices
cfg_dict["default_prepend_bos"] = default_prepend_bos
cfg = HookedTransformerConfig.from_dict(cfg_dict)
return cfg
def get_num_params_of_pretrained(model_name):
"""
Returns the number of parameters of a pretrained model, used to filter to only run code for sufficiently small models.
"""
cfg = get_pretrained_model_config(model_name)
return cfg.n_params
# %% Load checkpointed model state dicts
# The steps for which there are checkpoints in the stanford crfm models
STANFORD_CRFM_CHECKPOINTS = (
list(range(0, 100, 10))
+ list(range(100, 2000, 50))
+ list(range(2000, 20000, 100))
+ list(range(20000, 400000 + 1, 1000))
)
# Linearly spaced checkpoints for Pythia models, taken every 1000 steps.
# Batch size 2,097,152 tokens, so checkpoints every 2.1B tokens
PYTHIA_CHECKPOINTS = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + list(
range(1000, 143000 + 1, 1000)
)
# Pythia V1 has log-spaced early checkpoints (see line above), but V0 doesn't
PYTHIA_V0_CHECKPOINTS = list(range(1000, 143000 + 1, 1000))
def get_checkpoint_labels(model_name: str, **kwargs):
"""Returns the checkpoint labels for a given model, and the label_type
(step or token). Raises an error for models that are not checkpointed."""
official_model_name = get_official_model_name(model_name)
if official_model_name.startswith("stanford-crfm/"):
return STANFORD_CRFM_CHECKPOINTS, "step"
elif official_model_name.startswith("EleutherAI/pythia"):
if "v0" in official_model_name:
return PYTHIA_V0_CHECKPOINTS, "step"
else:
logging.warning(
"Pythia models on HF were updated on 4/3/23! add '-v0' to model name to access the old models."
)
return PYTHIA_CHECKPOINTS, "step"
elif official_model_name.startswith("NeelNanda/"):
api = HfApi()
files_list = api.list_repo_files(
official_model_name,
**utils.select_compatible_kwargs(kwargs, api.list_repo_files),
)
labels = []
for file_name in files_list:
match = re.match(r"checkpoints/.*_(\d*)\.pth", file_name)
if match:
labels.append(int(match.group(1)))
if labels[-1] > 1e9:
label_type = "token"
else:
label_type = "step"
return labels, label_type
else:
raise ValueError(f"Model {official_model_name} is not checkpointed.")
# %% Loading state dicts
def get_pretrained_state_dict(
official_model_name: str,
cfg: HookedTransformerConfig,
hf_model=None,
dtype: torch.dtype = torch.float32,
**kwargs,
) -> Dict[str, torch.Tensor]:
"""
Loads in the model weights for a pretrained model, and processes them to
have the HookedTransformer parameter names and shapes. Supports checkpointed
models (and expects the checkpoint info to be stored in the config object)
hf_model: Optionally, a HuggingFace model object. If provided, we will use
these weights rather than reloading the model.
dtype: The dtype to load the HuggingFace model in.
kwargs: Other optional arguments passed to HuggingFace's from_pretrained.
Also given to other HuggingFace functions when compatible.
"""
if "torch_dtype" in kwargs: