import json
import os
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .llama import Transformer, ModelArgs, RMSNorm
from .projector import ProjectionLayer
from util.misc import download
from .utils import sample_top_p
from .musicgen.musicgen import MusicgenForConditionalGeneration
from .audioldm2 import AudioLDM2Pipeline
from transformers import LlamaTokenizer
from transformers import Wav2Vec2FeatureExtractor, AutoModel
from transformers import ViTImageProcessor, ViTModel
from transformers import VivitImageProcessor, VivitModel
from transformers import AutoProcessor
import torchaudio
class M2UGen(nn.Module):
""" Masked Autoencoder with VisionTransformer backbone
"""
def __init__(self, llama_ckpt_dir, llama_tokenizer, model_args, knn=False, knn_dir="./ckpts", stage=1,
legacy_bridge=False, load_llama=True, device=None):
super().__init__()
self.args = model_args
if device is None:
self.device = "cuda" if torch.cuda.is_available() else "cpu"
else:
self.device = device
# 1. MERT Encoder
# The model files for MERT can be downloaded here in case of network issues:
# https://huggingface.co/m-a-p/MERT-v1-330M
# And set the mert_path argument to directory with the model files
print(f'Initialize MERT...')
self.mert_model = AutoModel.from_pretrained(self.args.mert_path, trust_remote_code=True).to("cuda:0")
self.mert_processor = Wav2Vec2FeatureExtractor.from_pretrained(self.args.mert_path, trust_remote_code=True)
self.mu_mert_agg = nn.Conv1d(in_channels=25, out_channels=1, kernel_size=1).to("cuda:0")
self.mu_mert_proj = nn.Linear(1024, 4096).to("cuda:0")
if legacy_bridge:
bridge_norm_layer = nn.LayerNorm
bridge_bias = True
else:
bridge_norm_layer = RMSNorm
bridge_bias = False
self.feature_scaler = 1
self.mu_mert_norm_1 = bridge_norm_layer(4096).to("cuda:0")
self.mu_mert_f1_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.mu_mert_f2_1 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.mu_mert_f3_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.mu_mert_norm_2 = bridge_norm_layer(4096).to("cuda:0")
self.mu_mert_f1_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.mu_mert_f2_2 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.mu_mert_f3_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.mu_mert_norm_3 = bridge_norm_layer(4096).to("cuda:0")
self.mu_mert_f1_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.mu_mert_f2_3 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.mu_mert_f3_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
print(f'MERT initialized...')
# 2. ViT Encoder
# The model files for ViT can be downloaded here in case of network issues:
# https://huggingface.co/google/vit-base-patch16-224-in21k
# And set the vit_path argument to directory with the model files
print(f'Initialize ViT...')
self.vit_model = ViTModel.from_pretrained(self.args.vit_path).to("cuda:0") # .to(self.device)
self.vit_model.eval()
self.vit_processor = ViTImageProcessor.from_pretrained(self.args.vit_path, do_rescale=False)
self.iu_vit_agg = nn.Conv1d(in_channels=197, out_channels=1, kernel_size=1).to("cuda:0")
self.iu_vit_proj = nn.Linear(768, 4096).to("cuda:0")
self.iu_vit_norm_1 = bridge_norm_layer(4096).to("cuda:0")
self.iu_vit_f1_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vit_f2_1 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.iu_vit_f3_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vit_norm_2 = bridge_norm_layer(4096).to("cuda:0")
self.iu_vit_f1_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vit_f2_2 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.iu_vit_f3_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vit_norm_3 = bridge_norm_layer(4096).to("cuda:0")
self.iu_vit_f1_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vit_f2_3 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.iu_vit_f3_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
print(f'ViT initialized...')
# 3. ViViT Encoder
# The model files for ViViT can be downloaded here in case of network issues:
# https://huggingface.co/google/vivit-b-16x2-kinetics400
# And set the vivit_path argument to directory with the model files
print(f'Initialize ViViT...')
self.vivit_model = VivitModel.from_pretrained(self.args.vivit_path).to("cuda:0") # .to(self.device)
self.vivit_model.eval()
self.vivit_processor = VivitImageProcessor.from_pretrained(self.args.vivit_path)
self.iu_vivit_agg = nn.Conv1d(in_channels=3137, out_channels=1, kernel_size=1).to("cuda:0")
self.iu_vivit_proj = nn.Linear(768, 4096).to("cuda:0")
self.iu_vivit_norm_1 = bridge_norm_layer(4096).to("cuda:0")
self.iu_vivit_f1_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vivit_f2_1 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.iu_vivit_f3_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vivit_norm_2 = bridge_norm_layer(4096).to("cuda:0")
self.iu_vivit_f1_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vivit_f2_2 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.iu_vivit_f3_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vivit_norm_3 = bridge_norm_layer(4096).to("cuda:0")
self.iu_vivit_f1_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
self.iu_vivit_f2_3 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias).to("cuda:0")
self.iu_vivit_f3_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias).to("cuda:0")
print(f'ViViT initialized...')
# 4. llama
with open(os.path.join(llama_ckpt_dir, "params.json"), "r") as f:
params = json.loads(f.read())
bias_lora = True
if self.args.music_decoder.lower() == "audioldm2":
self.model_args: ModelArgs = ModelArgs(
max_seq_len=1024, max_batch_size=1, w_bias=bias_lora, w_lora=bias_lora,
num_output_tokens=1, output_dim_tokens=137216,
**params) # max_batch_size only affects inference
else:
self.model_args: ModelArgs = ModelArgs(
max_seq_len=1024, max_batch_size=1, w_bias=bias_lora, w_lora=bias_lora,
num_output_tokens=128, output_dim_tokens=768,
**params) # max_batch_size only affects inference
print(f"model args: {self.model_args}")
# 5. tokenizer
self.tokenizer = LlamaTokenizer.from_pretrained(
llama_tokenizer) # Tokenizer(model_path=llama_tokenizer, num_aud_tokens=self.model_args.num_gen_audio_tokens)
self._add_audio_token()
self.model_args.vocab_size = len(self.tokenizer)
if torch.cuda.is_available():
torch.set_default_tensor_type(torch.cuda.HalfTensor)
self.llama = Transformer(self.model_args).to("cuda:0")
torch.set_default_tensor_type(torch.FloatTensor)
if load_llama:
print(f"Loading LLaMA Checkpoint...")
ckpts = sorted(Path(llama_ckpt_dir).glob("*.pth"))
"""
Adapted from https://github.com/cedrickchee/llama/blob/main/chattyllama/combined/inference.py
"""
key_to_dim = {
"w1": 0,
"w2": -1,
"w3": 0,
"wo": -1,
"wq": 0,
"wk": 0,
"wv": 0,
"output": 0,
"tok_embeddings": 2,
"ffn_norm": None,
"attention_norm": None,
"norm": None,
"rope": None,
}
for i, ckpt in enumerate(ckpts):
checkpoint = torch.load(ckpt, map_location="cpu")
for parameter_name, parameter in self.llama.named_parameters():
short_name = parameter_name.split(".")[-2]
if "gate" in parameter_name or "lora" in parameter_name or "bias" in parameter_name:
continue
if key_to_dim[short_name] is None and i == 0:
parameter.data = checkpoint[parameter_name]
elif key_to_dim[short_name] == 0:
size = checkpoint[parameter_name].size(0)
parameter.data[size * i: size * (i + 1), :] = checkpoint[
parameter_name
]
elif key_to_dim[short_name] == -1:
size = checkpoint[parameter_name].size(-1)
parameter.data[:, size * i: size * (i + 1)] = checkpoint[
parameter_name
]
elif key_to_dim[short_name] == 2:
size = checkpoint[parameter_name].size(-1)
parameter.data[:-self.model_args.num_gen_audio_tokens, size * i: size * (i + 1)] = checkpoint[
parameter_name
]
parameter.data[-self.model_args.num_gen_audio_tokens:, :] = 1
del checkpoint
print(f"LLaMA Checkpoint Loaded")
# 5. projector
self.output_projector = ProjectionLayer(4096, self.model_args.output_dim_tokens,
num_input_tokens=self.model_args.num_gen_audio_tokens,
num_output_tokens=self.model_args.num_output_tokens).to("cuda:1")
# 6. Generator
if self.args.music_decoder.lower() == "audioldm2":
# The model files for AudioLDM2 can be downloaded here in case of network issues:
# https://huggingface.co/cvssp/audioldm2-music
# And set the music_decoder_path argument to directory with the model files
print(f'Initialize AudioLDM2...')
dtype = torch.float16 if torch.cuda.is_available() else torch.float32
self.generation_model = AudioLDM2Pipeline.from_pretrained(self.args.music_decoder_path, torch_dtype=dtype)
self.generation_model.to("cuda:1")
print(f'AudioLDM2 initialized...')
else:
# The model files for MusicGen can be downloaded here in case of network issues:
# https://huggingface.co/facebook/musicgen-medium
# And set the music_decoder_path argument to directory with the model files
print(f'Initialize MusicGen...')
self.generation_processor = AutoProcessor.from_pretrained(self.args.music_decoder_path)
self.generation_model = MusicgenForConditionalGeneration.from_pretrained(self.args.music_decoder_path).to("cuda:1")
self.generation_model.eval()
print(f'MusicGen initialized...')
self.music_decoder = self.args.music_decoder.lower()
# 4. prefix
self.query_layer = 6
self.query_len = 1
self.prefix_query = nn.Embedding(self.query_layer * 3 * self.query_len, self.model_args.dim).to("cuda:0")
# 5. knn
self.knn = knn
if knn:
import faiss
self.index = faiss.read_index(download("https://huggingface.co/csuhan/knn/resolve/main/knn.index", knn_dir))
# 6. training criterion
self.criterion = torch.nn.CrossEntropyLoss(ignore_index=0)
self.l2_loss = torch.nn.MSELoss()
self.stage = stage
self.set_default_trainability(self.stage)
def get_trainable_params(self, stage=1):
trainable = {}
if stage == 1:
for name, para in self.named_parameters():
if "llama." in name:
if 'norm' in name or 'bias' in name or 'lora' in name:
trainable[name] = para
if "mu_mert_" in name:
trainable[name] = para
if "iu_vivit_" in name:
trainable[name] = para
if "iu_vit_" in name:
trainable[name] = para
if "prefix_query" in name:
trainable[name] = para
if "output_projector" in name:
trainable[name] = para
if "tok_embeddings" in name:
trainable[name] = para
elif stage == 2:
for name, para in self.named_parameters():
if "llama." in name:
if 'norm' in name or 'bias' in name or 'lora' in name:
trainable[name] = para
if "output_projector" in name:
trainable[name] = para
if "prefix_query" in name:
trainable[name] = para
if "tok_embeddings" in name:
trainable[name] = para
elif stage == 3:
for name, para in self.named_parameters():
if "llama." in name:
if 'norm' in name or 'bias' in name or 'lora' in name:
trainable[name] = para
elif "prefix_query" in name:
trainable[name] = para
elif "tok_embeddings" in name:
trainable[name] = para
return trainable
def set_default_trainability(self, stage=1):
for key, value in self.named_parameters():
value.requires_grad = False
trainable_params = self.get_trainable_params(stage)
print(f"Trainable Params: {trainable_params.keys()}")
for key, value in trainable_params.items():
value.data = value.data.float()
value.requires_grad = True
def _add_audio_token(self):
self.audio_tokens = []
for i in range(self.model_args.num_gen_audio_tokens):
print(f'Adding [AUD{i}] token to vocabulary.')
print(f'Before adding new token, tokenizer("[AUD{i}]") =',
self.tokenizer(f'[AUD{i}]', add_special_tokens=False))
num_added_tokens = self.tokenizer.add_tokens([f'[AUD{i}]'])
print(f'After adding {num_added_tokens} new tokens, tokenizer("[AUD{i}]") =',
self.tokenizer(f'[AUD{i}]', add_special_tokens=False), ' Number of tokens: ', len(self.tokenizer))
gen_token_idx = self.tokenizer(f'[AUD{i}]', add_special_tokens=False).input_ids
assert len(gen_token_idx) == 1, gen_token_idx
self.audio_tokens.append(gen_token_idx[0])
def load_audio(self, audio_path, target_sr=16000):
y, sr = torchaudio.load(audio_path)
resampler = torchaudio.transforms.Resample(sr, target_sr, dtype=y.dtype)
audio = resampler(y)
return audio, target_sr
def encode_audio(self, x):
xs = []
for sub_x in x:
all_inputs = [self.mert_processor(sub_x[ix * self.mert_processor.sampling_rate:min(
(ix + 60) * self.mert_processor.sampling_rate, len(sub_x))],
sampling_rate=self.mert_processor.sampling_rate,
return_tensors="pt").to(self.mert_model.device) for ix in
range(0, len(sub_x) // (self.mert_processor.sampling_rate * 60) + 1, 60)]
aggoutputs = torch.zeros(1, 25, 1024).to(self.mert_model.device)
for inputs in all_inputs:
with torch.no_grad():
outputs = self.mert_model(**inputs, output_hidden_states=True)
all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
sub_x = all_layer_hidden_states.mean(-2).unsqueeze(0)
aggoutputs += sub_x
aggoutputs /= len(all_inputs)
sub_x = self.mu_mert_agg(aggoutputs.to("cuda:0")).squeeze()
del aggoutputs
xs.append(sub_x)
x = torch.stack(xs, dim=0)
return x
def encode_image(self, x):
xs = []
for sub_x in x:
inputs = self.vit_processor(images=sub_x, return_tensors="pt").to(self.vit_model.device)
with torch.no_grad():
outputs = self.vit_model(**inputs)
last_hidden_states = outputs.last_hidden_state
sub_x = self.iu_vit_agg(last_hidden_states.to("cuda:0")).squeeze()
xs.append(sub_x)
return torch.stack(xs, dim=0)
def encode_video(self, x):
xs = []
for sub_x in x:
inputs = self.vivit_processor(list(sub_x), padding=True, return_tensors="pt").to(self.vivit_model.device)
with torch.no_grad():
outputs = self.vivit_model(**inputs)
last_hidden_states = outputs.last_hidden_state
sub_x = self.iu_vivit_agg(last_hidden_states.to("cuda:0")).squeeze()
xs.append(sub_x)
return torch.stack(xs, dim=0)
def forward_audio(self, inputs, cache_size=10, cache_t=20, cache_weight=0.5):
outputs = []
outputs_weights = []
for input_type, (input, input_weight) in inputs.items():
outputs.append(F.normalize(self.encode_audio(input), dim=-1))
outputs_weights.append(input_weight)
outputs_weights = [x / (sum(outputs_weights) + 1e-6) for x in outputs_weights]
audio_feats = sum([output * output_weight for output, output_weight in zip(outputs, outputs_weights)])
device = audio_feats.device
if self.knn:
audio_feats_ori = audio_feats
sims, indices = self.index.search(audio_feats.cpu(), int(cache_size))
B = sims.shape[0]
prototypes = [self.index.reconstruct(x) for x in indices.reshape(-1, ).tolist()]
prototypes = np.vstack(prototypes).reshape(B, int(cache_size), -1) # [N, top_k, 1024]
sims = torch.tensor(sims, device=device)
prototypes = torch.tensor(prototypes, device=device)
sims = (sims * cache_t).softmax(dim=-1)
audio_feats = sims @ prototypes
audio_feats = audio_feats / audio_feats.norm(dim=-1, keepdim=True)
audio_feats = (1 - cache_weight) * audio_feats_ori + cache_weight * audio_feats
audio_feats = audio_feats / audio_feats.norm(dim=-1, keepdim=True)
audio_feats = audio_feats.unsqueeze(1) # B, 1, D
audio_feats = self.mu_mert_proj(audio_feats)
audio_feats_norm = self.mu_mert_norm_1(audio_feats)
audio_feats = audio_feats + self.mu_mert_f2_1(
F.silu(self.mu_mert_f1_1(audio_feats_norm)) * self.mu_mert_f3_1(audio_feats_norm))
audio_feats_norm = self.mu_mert_norm_2(audio_feats)
audio_feats = audio_feats + self.mu_mert_f2_2(
F.silu(self.mu_mert_f1_2(audio_feats_norm)) * self.mu_mert_f3_2(audio_feats_norm))
audio_feats_norm = self.mu_mert_norm_3(audio_feats)
audio_feats = audio_feats + self.mu_mert_f2_3(
F.silu(self.mu_mert_f1_3(audio_feats_norm)) * self.mu_mert_f3_3(audio_feats_norm))
return audio_feats
def forward_image(self, inputs, cache_size=10, cache_t=20, cache_weight=0.5):
outputs = []
outputs_weights = []
for input_type, (input, input_weight) in inputs.items():
outputs.append(F.normalize(self.encode_image(input), dim=-1))
outputs_weights.append(input_weight)
outputs_weights = [x / (sum(outputs_weights) + 1e-6) for x in outputs_weights]
image_feats = sum([output * output_weight for output, output_weight in zip(outputs, outputs_weights)])
device = image_feats.device
if self.knn:
image_feats_ori = image_feats
sims, indices = self.index.search(image_feats.cpu(), int(cache_size))
B = sims.shape[0]
prototypes = [self.index.reconstruct(x) for x in indices.reshape(-1, ).tolist()]
prototypes = np.vstack(prototypes).reshape(B, int(cache_size), -1) # [N, top_k, 1024]
sims = torch.tensor(sims, device=device)
prototypes = torch.tensor(prototypes, device=device)
sims = (sims * cache_t).softmax(dim=-1)
image_feats = sims @ prototypes
image_feats = image_feats / image_feats.norm(dim=-1, keepdim=True)
image_feats = (1 - cache_weight) * image_feats_ori + cache_weight * image_feats
image_feats = image_feats / image_feats.norm(dim=-1, keepdim=True)
image_feats = image_feats.unsqueeze(1) # B, 1, D
image_feats = self.iu_vit_proj(image_feats)
image_feats_norm = self.iu_vit_norm_1(image_feats)
image_feats = image_feats + self.iu_vit_f2_1(
F.silu(self.iu_vit_f1_1(image_feats_norm)) * self.iu_vit_f3_1(image_feats_norm))
image_feats_norm = self.iu_vit_norm_2(image_feats)
image_feats = image_feats + self.iu_vit_f2_2(
F.silu(self.iu_vit_f1_2(image_feats_norm)) * self.iu_vit_f3_2(image_feats_norm))
image_feats_norm = self.iu_vit_norm_3(image_feats)
image_feats = image_feats + self.iu_vit_f2_3(
F.silu(self.iu_vit_f1_3(image_feats_norm)) * self.iu_vit_f3_3(image_feats_norm))
return image_feats
def forward_video(self, inputs, cache_size=10, cache_t=20, cache_weight=0.5):
outputs = []
outputs_weights = []
for input_type, (input, input_weight) in inputs.items():
outputs.append(F.normalize(self.encode_video(input), dim=-1))
outputs_weights.append(input_weight)
outputs_weights = [x / (sum(outputs_weights) + 1e-6) for x in outputs_weights]
video_feats = sum([output * output_weight for output, output_weight in zip(outputs, outputs_weights)])
device = video_feats.device
if self.knn:
video_feats_ori = video_feats
sims, indices = self.index.search(video_feats.cpu(), int(cache_size))
B = sims.shape[0]
prototypes = [self.index.reconstruct(x) for x in indices.reshape(-1, ).tolist()]
prototypes = np.vstack(prototypes).reshape(B, int(cache_size), -1) # [N, top_k, 1024]
sims = torch.tensor(sims, device=device)
prototypes = torch.tensor(prototypes, device=device)
sims = (sims * cache_t).softmax(dim=-1)
video_feats = sims @ prototypes
video_feats = video_feats / video_feats.norm(dim=-1, keepdim=True)
video_feats = (1 - cache_weight) * video_feats_ori + cache_weight * video_feats
video_feats = video_feats / video_feats.norm(dim=-1, keepdim=True)
video_feats = video_feats.unsqueeze(1) # B, 1, D
video_feats = self.iu_vivit_proj(video_feats)
video_feats_norm = self.iu_vivit_norm_1(video_feats)
video_feats = video_feats + self.iu_vivit_f2_1(
F.silu(self.iu_vivit_f1_1(video_feats_norm)) * self.iu_vivit_f3_1(video_feats_norm))
video_feats_norm = self.iu_vivit_norm_2(video_feats)
video_feats = video_feats + self.iu_vivit_f2_2(
F.silu(self.iu_vivit_f1_2(video_feats_norm)) * self.iu_vivit_f3_2(video_feats_norm))
video_feats_norm = self.iu_vivit_norm_3(video_feats)
video_feats = video_feats + self.iu_vivit_f2_3(
F.silu(self.iu_vivit_f1_3(video_feats_norm)) * self.iu_vivit_f3_3(video_feats_norm))
return video_feats
@torch.inference_mode()
def forward_inference(self, tokens, start_pos: int, audio_feats=None, image_feats=None, video_feats=None):
_bsz, seqlen = tokens.shape
h = self.llama.tok_embeddings(tokens).to("cuda:0")
freqs_cis = self.llama.freqs_cis.to("cuda:0")
freqs_cis = freqs_cis[start_pos:start_pos + seqlen]
mask = None
mask = torch.full((1, 1, seqlen, seqlen), float("-inf"), device="cuda:0")
mask = torch.triu(mask, diagonal=start_pos + 1).type_as(h)
music_output_embedding = []
for layer in self.llama.layers[:-3 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask)
music_output_embedding.append(h)
prefix_query = self.prefix_query.weight.reshape(
self.query_layer * 3, 1, 4096).unsqueeze(1)
prefix_index = 0
if audio_feats is not None:
for layer in self.llama.layers[-3 * self.query_layer:-2 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, audio_feats + prefix_query[prefix_index])
music_output_embedding.append(h)
prefix_index = prefix_index + 1
else:
for layer in self.llama.layers[-3 * self.query_layer:-2 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, prefix_query[prefix_index])
music_output_embedding.append(h)
prefix_index = prefix_index + 1
if image_feats is not None:
for layer in self.llama.layers[-2 * self.query_layer:-1 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, image_feats + prefix_query[prefix_index])
music_output_embedding.append(h)
prefix_index = prefix_index + 1
else:
for layer in self.llama.layers[-2 * self.query_layer:-1 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, prefix_query[prefix_index])
music_output_embedding.append(h)
prefix_index = prefix_index + 1
if video_feats is not None:
for layer in self.llama.layers[-1 * self.query_layer:]:
h = layer(h, 0, freqs_cis, mask, video_feats + prefix_query[prefix_index])
music_output_embedding.append(h)
prefix_index = prefix_index + 1
else:
for layer in self.llama.layers[-1 * self.query_layer:]:
h = layer(h, 0, freqs_cis, mask, prefix_query[prefix_index])
music_output_embedding.append(h)
prefix_index = prefix_index + 1
h = self.llama.norm(h)
output = self.llama.output(h[:, -1, :])
return output.float(), torch.cat(music_output_embedding[-1:], dim=1)
def forward(self, tokens, labels, audios=None, imgs=None, videos=None, music_caption=None):
audio_feats, video_feats, image_feats = None, None, None
if audios is not None:
audio_feats = self.forward_audio({'Audio': [audios, 1]})
if videos is not None:
video_feats = self.forward_video({'Video': [videos, 1]})
if imgs is not None:
image_feats = self.forward_image({'Image': [imgs, 1]})
_bsz, seqlen = tokens.shape
h = self.llama.tok_embeddings(tokens.to(self.device))
freqs_cis = self.llama.freqs_cis.to(h.device)
freqs_cis = freqs_cis[:seqlen]
mask = torch.full((1, 1, seqlen, seqlen), float("-inf"), device=h.device)
mask = torch.triu(mask, diagonal=0 + 1).type_as(h)
for layer in self.llama.layers[:-3 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask)
prefix_query = self.prefix_query.weight.reshape(
self.query_layer * 3, 1, 4096).unsqueeze(1)
prefix_index = 0
if audio_feats is not None:
for layer in self.llama.layers[-3 * self.query_layer:-2 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, audio_feats + prefix_query[prefix_index])
prefix_index = prefix_index + 1
else:
for layer in self.llama.layers[-3 * self.query_layer:-2 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, prefix_query[prefix_index])
prefix_index = prefix_index + 1
if image_feats is not None:
for layer in self.llama.layers[-2 * self.query_layer:-1 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, image_feats + prefix_query[prefix_index])
prefix_index = prefix_index + 1
else:
for layer in self.llama.layers[-2 * self.query_layer:-1 * self.query_layer]:
h = layer(h, 0, freqs_cis, mask, prefix_query[prefix_index])
prefix_index = prefix_index + 1
if video_feats is not None:
for layer in self.llama.layers[-1 * self.query_layer:]:
h = layer(h, 0, freqs_cis, mask, video_feats + prefix_query[prefix_index])
prefix_index = prefix_index + 1
else:
for layer in self.llama.layers[-1 * self.query_layer:]:
h = layer(h, 0, freqs_cis, mask, prefix_query[prefix_index])
prefix_index = prefix_index + 1
final_hidden = h
h = self.llama.norm(h)
output = self.llama.output(h)
output = output[:, :-1, :]
labels = labels[:, 1:]
if labels.sum() == 0:
c_loss = output.mean() * 0
else:
assert self.llama.vocab_size == 32000 + self.model_args.num_gen_audio_tokens, self.llama.vocab_size
c_loss = self.criterion(output.reshape(-1, self.llama.vocab_size), labels.flatten().to(self.device))
if music_caption is not None and any([mc != '' for mc in music_caption]):
if not all([i in output for i in range(32000, 32008)]):
c_loss += 100
if self.music_decoder == "audioldm2":
prompt_embeds, generated_prompt_embeds = self.generation_model(prompt=list(music_caption),
guidance_scale=1,
return_prompts_only=True)
prompt_embeds = prompt_embeds.reshape(prompt_embeds.shape[0], -1)
generated_prompt_embeds = generated_prompt_embeds.reshape(generated_prompt_embeds.shape[0], -1)
out_embed = torch.cat([prompt_embeds, generated_prompt_embeds], dim=1)
out_embed = 10 * out_embed.view(out_embed.size(0), 1, out_embed.size(1)).to(self.device)
else:
gen_inputs = self.generation_processor(text=music_caption, padding='max_length',
max_length=128, truncation=True, return_tensors="pt").to(
self.device)
out_embed = 10 * self.generation_model.generate(**gen_inputs, guidance_scale=1, encoder_only=True)
del gen_inputs
start_pos = (labels == self.audio_tokens[0]).nonzero(as_tuple=False)[:, 1].tolist()
assert len(start_pos) != 0, (self.tokenizer.batch_decode(labels), music_caption)
hidden_states = []
hidden_embedding = []
input_embedding = []
for b, s in enumerate(start_pos):
hidden_embedding.append(final_hidden[b, s:s + self.model_args.num_gen_audio_tokens, :])
input_embedding.append(
self.llama.tok_embeddings(labels[b, s:s + self.model_args.num_gen_audio_tokens].to(self.device)))
hidden_embedding = torch.stack(hidden_embedding, dim=0).to(self.device)
input_embedding = torch.stack(input_embedding, dim=0).to(self.device)
hidden_states.append(self.output_projector(hidden_embedding, input_embedding))
embeddings = torch.stack(hidden_states, dim=-1).sum(dim=-1)
mse_loss = self.l2_loss(embeddings, out_embed)
del hidden_states, input_embedding, hidden_embedding, out_embed, embeddings
# c_loss += mse_loss
else:
if any([i in output for i in range(32000, 32008)]):
c_loss += 100
mse_loss = torch.tensor(0.0)
del feats
return c_loss, mse_loss
@torch.inference_mode()
def generate_music(self, embeddings, audio_length_in_s, music_caption):
gen_prefix = ''.join([f'[AUD{i}]' for i in range(len(self.audio_tokens))])
gen_prefx_ids = self.tokenizer(gen_prefix, add_special_tokens=False, return_tensors="pt").input_ids.to("cuda:1")
# gen_prefix_embs = self.llama.tok_embeddings(gen_prefx_ids)
if self.music_decoder == "audioldm2":
gen_emb = self.output_projector(embeddings.float().to("cuda:1"), gen_prefix_embs).squeeze(dim=0) / 10
prompt_embeds, generated_prompt_embeds = gen_emb[:, :128 * 1024], gen_emb[:, 128 * 1024:]
prompt_embeds = prompt_embeds.reshape(prompt_embeds.shape[0], 128, 1024)
generated_prompt_embeds = generated_prompt_embeds.reshape(generated_prompt_embeds.shape[0], 8, 768)
print("Generating Music...")
print(music_caption)
audio_outputs = self.generation_model(music_caption,
num_inference_steps=200,
num_waveforms_per_prompt=3,
negative_prompt='Low quality.',
audio_length_in_s=audio_length_in_s).audios
return audio_outputs
else:
print("Generating Music...")
print(music_caption)
gen_inputs = self.generation_processor(text=music_caption, return_tensors="pt").to("cuda:1")
#gen_emb = self.generation_model.generate(**gen_inputs, guidance_scale=3.5, encoder_only=True)
audio_outputs = self.generation_model.generate(**gen_inputs, guidance_scale=3.5,
max_new_tokens=int(256 / 5 * audio_length_in_s))
#encoder_outputs=(gen_emb,))
return audio_outputs[0][0].cpu().detach().numpy()
@torch.inference_mode()
def generate(
self,
prompts,
audios=None,
imgs=None,
videos=None,
max_gen_len: int = 100,
temperature: float = 0.1,
top_p: float = 0.75,
cache_size=10,
cache_t=20,
cache_weight=0.5,
audio_length_in_s=10
):
bsz = len(prompts)
params = self.llama.params
assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
with torch.cuda.amp.autocast():
if audios is not None:
audio_feats = self.forward_audio({'Audio': [[audios], 1]}, cache_size, cache_t, cache_weight)
else:
audio_feats = None
if videos is not None:
video_feats = self.forward_video({'Video': [[videos], 1]}, cache_size, cache_t, cache_weight)
else:
video_feats = None
if imgs is not None:
image_feats = self.forward_image({'Image': [[imgs], 1]}, cache_size, cache_t, cache_weight)
else:
image_feats = None
if isinstance(prompts[0], str):
prompts = [self.tokenizer(x).input_ids[:, 1:] for x in prompts]
min_prompt_size = min([len(t) for t in prompts])
max_prompt_size = max([len(t) for t in prompts])
total_len = min(params.max_seq_len, max_gen_len + max_prompt_size)
tokens = torch.full((bsz, total_len), 0).to("cuda:0").long()
for k, t in enumerate(prompts):
tokens[k, : len(t)] = torch.tensor(t).to("cuda:0").long()
input_text_mask = tokens != 0
start_pos = min_prompt_size
prev_pos = 0
music_output_embeddings = []
start_gather = 0
for cur_pos in range(start_pos, total_len):
with torch.cuda.amp.autocast():
logits, music_output_embedding = self.forward_inference(tokens[:, prev_pos:cur_pos], prev_pos,
audio_feats, image_feats, video_feats)
if temperature > 0:
probs = torch.softmax(logits / temperature, dim=-1)
next_token = sample_top_p(probs, top_p)
else:
next_token = torch.argmax(logits, dim=-1)
next_token = next_token.reshape(-1)
next_token = torch.where(
input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
)
tokens[:, cur_pos] = next_token
if next_token[0] == self.audio_tokens[start_gather]:
if start_gather == 0:
music_output_embeddings = []
music_output_embeddings.append(music_output_embedding[:, -1:, :])
start_gather += 1
if start_gather >= len(self.audio_tokens):
start_gather = 0
# trick: early stop if bsz==1
if bsz == 1 and self.tokenizer.decode(tokens[0, cur_pos - 2:cur_pos + 1]) == "\n###":
break
# prev_pos = cur_pos
decoded = []
for i, t in enumerate(tokens.tolist()):
# cut to max gen len
t = t[len(prompts[i]): len(prompts[i]) + max_gen_len]
# cut to eos tok if any
try:
t = t[: t.index(13)]
except ValueError:
pass
decoded.append(self.tokenizer.decode(t))
if len(music_output_embeddings) == len(self.audio_tokens):
music_output_embeddings = torch.cat(music_output_embeddings, dim=1)
return [decoded[0], {'aud': [self.generate_music(music_output_embeddings, audio_length_in_s, decoded[0])]}]
return [decoded[0]]
def load(model_path, llama_dir, mert_path="m-a-p/MERT-v1-330M", device="cuda" if torch.cuda.is_available() else "cpu",
knn=False, knn_dir="./ckpts", llama_type="7B", stage=3):
llama_ckpt_dir = os.path.join(llama_dir, llama_type)
llama_tokenzier_path = llama_dir
# load M2UGen weights and model_cfg
print(f'Loading LLaMA-Adapter from {model_path}')
adapter_ckpt = torch.load(model_path, map_location='cpu')
model_cfg = adapter_ckpt.get('config', {})
# The model files for MERT can be downloaded here in case of network issues:
# https://huggingface.co/m-a-p/MERT-v1-330M
# And set the MERT argument to directory with the model files
model = M2UGen(
llama_ckpt_dir, llama_tokenzier_path, mert_path, knn=knn, knn_dir=knn_dir, stage=stage)
load_result = model.load_state_dict(adapter_ckpt['model'], strict=False)
assert len(load_result.unexpected_keys) == 0, f"Unexpected keys: {load_result.unexpected_keys}"
return model.to(device)