scripts/convert_dance_diffusion_to_diffusers.py

#!/usr/bin/env python3
import argparse
import math
import os
from copy import deepcopy

import requests
import torch
from audio_diffusion.models import DiffusionAttnUnet1D
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel


MODELS_MAP = {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48000,
        "sample_size": 131072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
}


def alpha_sigma_to_t(alpha, sigma):
    """Returns a timestep, given the scaling factors for the clean image and for
    the noise."""
    return torch.atan2(sigma, alpha) / math.pi * 2


def get_crash_schedule(t):
    sigma = torch.sin(t * math.pi / 2) ** 2
    alpha = (1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(alpha, sigma)


class Object(object):
    pass


class DiffusionUncond(nn.Module):
    def __init__(self, global_args):
        super().__init__()

        self.diffusion = DiffusionAttnUnet1D(global_args, n_attn_layers=4)
        self.diffusion_ema = deepcopy(self.diffusion)
        self.rng = torch.quasirandom.SobolEngine(1, scramble=True)


def download(model_name):
    url = MODELS_MAP[model_name]["url"]
    r = requests.get(url, stream=True)

    local_filename = f"./{model_name}.ckpt"
    with open(local_filename, "wb") as fp:
        for chunk in r.iter_content(chunk_size=8192):
            fp.write(chunk)

    return local_filename


DOWN_NUM_TO_LAYER = {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
UP_NUM_TO_LAYER = {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
MID_NUM_TO_LAYER = {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
DEPTH_0_TO_LAYER = {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

RES_CONV_MAP = {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

ATTN_MAP = {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}


def convert_resconv_naming(name):
    if name.startswith("skip"):
        return name.replace("skip", RES_CONV_MAP["skip"])

    # name has to be of format main.{digit}
    if not name.startswith("main."):
        raise ValueError(f"ResConvBlock error with {name}")

    return name.replace(name[:6], RES_CONV_MAP[name[:6]])


def convert_attn_naming(name):
    for key, value in ATTN_MAP.items():
        if name.startswith(key) and not isinstance(value, list):
            return name.replace(key, value)
        elif name.startswith(key):
            return [name.replace(key, v) for v in value]
    raise ValueError(f"Attn error with {name}")


def rename(input_string, max_depth=13):
    string = input_string

    if string.split(".")[0] == "timestep_embed":
        return string.replace("timestep_embed", "time_proj")

    depth = 0
    if string.startswith("net.3."):
        depth += 1
        string = string[6:]
    elif string.startswith("net."):
        string = string[4:]

    while string.startswith("main.7."):
        depth += 1
        string = string[7:]

    if string.startswith("main."):
        string = string[5:]

    # mid block
    if string[:2].isdigit():
        layer_num = string[:2]
        string_left = string[2:]
    else:
        layer_num = string[0]
        string_left = string[1:]

    if depth == max_depth:
        new_layer = MID_NUM_TO_LAYER[layer_num]
        prefix = "mid_block"
    elif depth > 0 and int(layer_num) < 7:
        new_layer = DOWN_NUM_TO_LAYER[layer_num]
        prefix = f"down_blocks.{depth}"
    elif depth > 0 and int(layer_num) > 7:
        new_layer = UP_NUM_TO_LAYER[layer_num]
        prefix = f"up_blocks.{max_depth - depth - 1}"
    elif depth == 0:
        new_layer = DEPTH_0_TO_LAYER[layer_num]
        prefix = f"up_blocks.{max_depth - 1}" if int(layer_num) > 3 else "down_blocks.0"

    if not string_left.startswith("."):
        raise ValueError(f"Naming error with {input_string} and string_left: {string_left}.")

    string_left = string_left[1:]

    if "resnets" in new_layer:
        string_left = convert_resconv_naming(string_left)
    elif "attentions" in new_layer:
        new_string_left = convert_attn_naming(string_left)
        string_left = new_string_left

    if not isinstance(string_left, list):
        new_string = prefix + "." + new_layer + "." + string_left
    else:
        new_string = [prefix + "." + new_layer + "." + s for s in string_left]
    return new_string


def rename_orig_weights(state_dict):
    new_state_dict = {}
    for k, v in state_dict.items():
        if k.endswith("kernel"):
            # up- and downsample layers, don't have trainable weights
            continue

        new_k = rename(k)

        # check if we need to transform from Conv => Linear for attention
        if isinstance(new_k, list):
            new_state_dict = transform_conv_attns(new_state_dict, new_k, v)
        else:
            new_state_dict[new_k] = v

    return new_state_dict


def transform_conv_attns(new_state_dict, new_k, v):
    if len(new_k) == 1:
        if len(v.shape) == 3:
            # weight
            new_state_dict[new_k[0]] = v[:, :, 0]
        else:
            # bias
            new_state_dict[new_k[0]] = v
    else:
        # qkv matrices
        trippled_shape = v.shape[0]
        single_shape = trippled_shape // 3
        for i in range(3):
            if len(v.shape) == 3:
                new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape, :, 0]
            else:
                new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape]
    return new_state_dict


def main(args):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    model_name = args.model_path.split("/")[-1].split(".")[0]
    if not os.path.isfile(args.model_path):
        assert (
            model_name == args.model_path
        ), f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
        args.model_path = download(model_name)

    sample_rate = MODELS_MAP[model_name]["sample_rate"]
    sample_size = MODELS_MAP[model_name]["sample_size"]

    config = Object()
    config.sample_size = sample_size
    config.sample_rate = sample_rate
    config.latent_dim = 0

    diffusers_model = UNet1DModel(sample_size=sample_size, sample_rate=sample_rate)
    diffusers_state_dict = diffusers_model.state_dict()

    orig_model = DiffusionUncond(config)
    orig_model.load_state_dict(torch.load(args.model_path, map_location=device)["state_dict"])
    orig_model = orig_model.diffusion_ema.eval()
    orig_model_state_dict = orig_model.state_dict()
    renamed_state_dict = rename_orig_weights(orig_model_state_dict)

    renamed_minus_diffusers = set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())
    diffusers_minus_renamed = set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())

    assert len(renamed_minus_diffusers) == 0, f"Problem with {renamed_minus_diffusers}"
    assert all(k.endswith("kernel") for k in list(diffusers_minus_renamed)), f"Problem with {diffusers_minus_renamed}"

    for key, value in renamed_state_dict.items():
        assert (
            diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
        ), f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
        if key == "time_proj.weight":
            value = value.squeeze()

        diffusers_state_dict[key] = value

    diffusers_model.load_state_dict(diffusers_state_dict)

    steps = 100
    seed = 33

    diffusers_scheduler = IPNDMScheduler(num_train_timesteps=steps)

    generator = torch.manual_seed(seed)
    noise = torch.randn([1, 2, config.sample_size], generator=generator).to(device)

    t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
    step_list = get_crash_schedule(t)

    pipe = DanceDiffusionPipeline(unet=diffusers_model, scheduler=diffusers_scheduler)

    generator = torch.manual_seed(33)
    audio = pipe(num_inference_steps=steps, generator=generator).audios

    generated = sampling.iplms_sample(orig_model, noise, step_list, {})
    generated = generated.clamp(-1, 1)

    diff_sum = (generated - audio).abs().sum()
    diff_max = (generated - audio).abs().max()

    if args.save:
        pipe.save_pretrained(args.checkpoint_path)

    print("Diff sum", diff_sum)
    print("Diff max", diff_max)

    assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/"

    print(f"Conversion for {model_name} successful!")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
    parser.add_argument(
        "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
    )
    parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
    args = parser.parse_args()

    main(args)