280 lines
12 KiB
Python
280 lines
12 KiB
Python
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Convert Jukebox checkpoints"""
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import argparse
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import json
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import os
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from pathlib import Path
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import requests
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import torch
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from transformers import JukeboxConfig, JukeboxModel
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from transformers.utils import logging
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logging.set_verbosity_info()
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logger = logging.get_logger(__name__)
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PREFIX = "https://openaipublic.azureedge.net/jukebox/models/"
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MODEL_MAPPING = {
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"jukebox-1b-lyrics": [
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"5b/vqvae.pth.tar",
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"5b/prior_level_0.pth.tar",
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"5b/prior_level_1.pth.tar",
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"1b_lyrics/prior_level_2.pth.tar",
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],
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"jukebox-5b-lyrics": [
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"5b/vqvae.pth.tar",
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"5b/prior_level_0.pth.tar",
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"5b/prior_level_1.pth.tar",
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"5b_lyrics/prior_level_2.pth.tar",
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],
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}
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def replace_key(key):
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if key.endswith(".model.1.bias") and len(key.split(".")) > 10:
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key = key.replace(".model.1.bias", ".conv1d_1.bias")
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elif key.endswith(".model.1.weight") and len(key.split(".")) > 10:
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key = key.replace(".model.1.weight", ".conv1d_1.weight")
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elif key.endswith(".model.3.bias") and len(key.split(".")) > 10:
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key = key.replace(".model.3.bias", ".conv1d_2.bias")
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elif key.endswith(".model.3.weight") and len(key.split(".")) > 10:
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key = key.replace(".model.3.weight", ".conv1d_2.weight")
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if "conditioner_blocks.0." in key:
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key = key.replace("conditioner_blocks.0", "conditioner_blocks")
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if "prime_prior" in key:
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key = key.replace("prime_prior", "encoder")
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if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
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key = key.replace(".emb.", ".")
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if key.endswith("k"): # replace vqvae.X.k with vqvae.X.codebook
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return key.replace(".k", ".codebook")
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if "y_emb." in key:
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return key.replace("y_emb.", "metadata_embedding.")
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if "x_emb.emb." in key:
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key = key.replace("0.x_emb.emb", "embed_tokens")
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if "prime_state_ln" in key:
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return key.replace("prime_state_ln", "encoder.final_layer_norm")
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if ".ln" in key:
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return key.replace(".ln", ".layer_norm")
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if "_ln" in key:
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return key.replace("_ln", "_layer_norm")
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if "prime_state_proj" in key:
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return key.replace("prime_state_proj", "encoder.proj_in")
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if "prime_x_out" in key:
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return key.replace("prime_x_out", "encoder.lm_head")
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if "prior.x_out" in key:
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return key.replace("x_out", "fc_proj_out")
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if "x_emb" in key:
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return key.replace("x_emb", "embed_tokens")
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return key
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def fix_jukebox_keys(state_dict, model_state_dict, key_prefix, mapping):
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new_dict = {}
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import re
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re_encoder_block_conv_in = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
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re_encoder_block_resnet = re.compile(
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r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
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)
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re_encoder_block_proj_out = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
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re_decoder_block_conv_out = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
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re_decoder_block_resnet = re.compile(
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r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
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)
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re_decoder_block_proj_in = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
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re_prior_cond_conv_out = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)")
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re_prior_cond_resnet = re.compile(
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r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
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)
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re_prior_cond_proj_in = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)")
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for original_key, value in state_dict.items():
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# rename vqvae.encoder keys
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if re_encoder_block_conv_in.fullmatch(original_key):
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regex_match = re_encoder_block_conv_in.match(original_key)
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groups = regex_match.groups()
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block_index = int(groups[2]) * 2 + int(groups[3])
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re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
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key = re_encoder_block_conv_in.sub(re_new_key, original_key)
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elif re_encoder_block_resnet.fullmatch(original_key):
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regex_match = re_encoder_block_resnet.match(original_key)
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groups = regex_match.groups()
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block_index = int(groups[2]) * 2 + int(groups[3])
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conv_index = {"1": 1, "3": 2}[groups[-2]]
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prefix = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
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resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
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re_new_key = prefix + resnet_block
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key = re_encoder_block_resnet.sub(re_new_key, original_key)
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elif re_encoder_block_proj_out.fullmatch(original_key):
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regex_match = re_encoder_block_proj_out.match(original_key)
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groups = regex_match.groups()
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re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
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key = re_encoder_block_proj_out.sub(re_new_key, original_key)
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# rename vqvae.decoder keys
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elif re_decoder_block_conv_out.fullmatch(original_key):
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regex_match = re_decoder_block_conv_out.match(original_key)
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groups = regex_match.groups()
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block_index = int(groups[2]) * 2 + int(groups[3]) - 2
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re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
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key = re_decoder_block_conv_out.sub(re_new_key, original_key)
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elif re_decoder_block_resnet.fullmatch(original_key):
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regex_match = re_decoder_block_resnet.match(original_key)
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groups = regex_match.groups()
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block_index = int(groups[2]) * 2 + int(groups[3]) - 2
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conv_index = {"1": 1, "3": 2}[groups[-2]]
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prefix = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
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resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
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re_new_key = prefix + resnet_block
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key = re_decoder_block_resnet.sub(re_new_key, original_key)
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elif re_decoder_block_proj_in.fullmatch(original_key):
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regex_match = re_decoder_block_proj_in.match(original_key)
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groups = regex_match.groups()
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re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
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key = re_decoder_block_proj_in.sub(re_new_key, original_key)
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# rename prior cond.model to upsampler.upsample_block and resnet
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elif re_prior_cond_conv_out.fullmatch(original_key):
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regex_match = re_prior_cond_conv_out.match(original_key)
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groups = regex_match.groups()
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block_index = int(groups[1]) * 2 + int(groups[2]) - 2
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re_new_key = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
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key = re_prior_cond_conv_out.sub(re_new_key, original_key)
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elif re_prior_cond_resnet.fullmatch(original_key):
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regex_match = re_prior_cond_resnet.match(original_key)
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groups = regex_match.groups()
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block_index = int(groups[1]) * 2 + int(groups[2]) - 2
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conv_index = {"1": 1, "3": 2}[groups[-2]]
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prefix = f"conditioner_blocks.upsampler.upsample_block.{block_index}."
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resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
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re_new_key = prefix + resnet_block
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key = re_prior_cond_resnet.sub(re_new_key, original_key)
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elif re_prior_cond_proj_in.fullmatch(original_key):
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regex_match = re_prior_cond_proj_in.match(original_key)
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groups = regex_match.groups()
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re_new_key = f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
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key = re_prior_cond_proj_in.sub(re_new_key, original_key)
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# keep original key
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else:
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key = original_key
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key = replace_key(key)
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if f"{key_prefix}.{key}" not in model_state_dict or key is None:
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print(f"failed converting {original_key} to {key}, does not match")
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# handle missmatched shape
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elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
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val = model_state_dict[f"{key_prefix}.{key}"]
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print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match")
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key = original_key
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mapping[key] = original_key
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new_dict[key] = value
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return new_dict
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@torch.no_grad()
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def convert_openai_checkpoint(model_name=None, pytorch_dump_folder_path=None):
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"""
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Copy/paste/tweak model's weights to our Jukebox structure.
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"""
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for file in MODEL_MAPPING[model_name]:
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if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}"):
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r = requests.get(f"{PREFIX}{file}", allow_redirects=True)
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os.makedirs(f"{pytorch_dump_folder_path}/", exist_ok=True)
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open(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}", "wb").write(r.content)
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model_to_convert = MODEL_MAPPING[model_name.split("/")[-1]]
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config = JukeboxConfig.from_pretrained(model_name)
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model = JukeboxModel(config)
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weight_dict = []
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mapping = {}
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for i, dict_name in enumerate(model_to_convert):
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old_dic = torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/')[-1]}")["model"]
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new_dic = {}
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for k in old_dic.keys():
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if k.endswith(".b"):
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new_dic[k.replace("b", "bias")] = old_dic[k]
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elif k.endswith(".w"):
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new_dic[k.replace("w", "weight")] = old_dic[k]
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elif "level_2" not in dict_name and "cond.model." in k:
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new_dic[k.replace(".blocks.", ".model.")] = old_dic[k]
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else:
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new_dic[k] = old_dic[k]
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key_prefix = "vqvae" if i == 0 else f"priors.{3 - i}"
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new_dic = fix_jukebox_keys(new_dic, model.state_dict(), key_prefix, mapping)
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weight_dict.append(new_dic)
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vqvae_state_dict = weight_dict.pop(0)
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model.vqvae.load_state_dict(vqvae_state_dict)
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for i in range(len(weight_dict)):
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model.priors[i].load_state_dict(weight_dict[2 - i])
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Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
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with open(f"{pytorch_dump_folder_path}/mapping.json", "w") as txtfile:
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json.dump(mapping, txtfile)
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print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
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model.save_pretrained(pytorch_dump_folder_path)
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return weight_dict
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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# Required parameters
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parser.add_argument(
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"--model_name",
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default="jukebox-5b-lyrics",
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type=str,
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help="Name of the model you'd like to convert.",
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)
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parser.add_argument(
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"--pytorch_dump_folder_path",
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default="jukebox-5b-lyrics-converted",
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type=str,
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help="Path to the output PyTorch model directory.",
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)
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args = parser.parse_args()
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convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
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