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(self.device) 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) self.mu_mert_proj = nn.Linear(1024, 4096) 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) self.mu_mert_f1_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.mu_mert_f2_1 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.mu_mert_f3_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.mu_mert_norm_2 = bridge_norm_layer(4096) self.mu_mert_f1_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.mu_mert_f2_2 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.mu_mert_f3_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.mu_mert_norm_3 = bridge_norm_layer(4096) self.mu_mert_f1_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.mu_mert_f2_3 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.mu_mert_f3_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) 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(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) self.iu_vit_proj = nn.Linear(768, 4096) self.iu_vit_norm_1 = bridge_norm_layer(4096) self.iu_vit_f1_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vit_f2_1 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.iu_vit_f3_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vit_norm_2 = bridge_norm_layer(4096) self.iu_vit_f1_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vit_f2_2 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.iu_vit_f3_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vit_norm_3 = bridge_norm_layer(4096) self.iu_vit_f1_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vit_f2_3 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.iu_vit_f3_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) 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(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) self.iu_vivit_proj = nn.Linear(768, 4096) self.iu_vivit_norm_1 = bridge_norm_layer(4096) self.iu_vivit_f1_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vivit_f2_1 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.iu_vivit_f3_1 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vivit_norm_2 = bridge_norm_layer(4096) self.iu_vivit_f1_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vivit_f2_2 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.iu_vivit_f3_2 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vivit_norm_3 = bridge_norm_layer(4096) self.iu_vivit_f1_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) self.iu_vivit_f2_3 = nn.Linear(4096 * self.feature_scaler, 4096, bias=bridge_bias) self.iu_vivit_f3_3 = nn.Linear(4096, 4096 * self.feature_scaler, bias=bridge_bias) 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) 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) # 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") 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) self.generation_model.eval() print(f'MusicGen initialized...') self.music_decoder = self.args.music_decoder.lower() # 4. prefix self.query_layer = 20 self.query_len = 1 self.prefix_query = nn.Embedding(self.query_layer * self.query_len, self.model_args.dim) # 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(self.device)).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(self.device)).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(self.device)).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) freqs_cis = self.llama.freqs_cis.to(h.device) freqs_cis = freqs_cis[start_pos:start_pos + seqlen] feats = torch.zeros((1, 1, 4096)).to(self.device) if audio_feats is not None: feats += audio_feats if video_feats is not None: feats += video_feats if image_feats is not None: feats += image_feats mask = None mask = torch.full((1, 1, seqlen, seqlen), float("-inf"), device=h.device) mask = torch.triu(mask, diagonal=start_pos + 1).type_as(h) music_output_embedding = [] for layer in self.llama.layers[:-1 * 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, 1, 4096).unsqueeze(1) prefix_index = 0 for layer in self.llama.layers[-1 * self.query_layer:]: h = layer(h, 0, freqs_cis, mask, feats + prefix_query[prefix_index]) 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): feats = torch.zeros((1, 1, 4096)).to(self.device) if audios is not None: feats += self.forward_audio({'Audio': [audios, 1]}) if videos is not None: feats += self.forward_video({'Video': [videos, 1]}) if imgs is not None: 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 = None 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[:-1 * self.query_layer]: h = layer(h, 0, freqs_cis, mask) prefix_query = self.prefix_query.weight.reshape(self.query_layer, 1, 4096).unsqueeze(1) prefix_index = 0 for layer in self.llama.layers[-1 * self.query_layer:]: h = layer(h, 0, freqs_cis, mask, feats + 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( self.device) gen_prefix_embs = self.llama.tok_embeddings(gen_prefx_ids) if self.music_decoder == "audioldm2": gen_emb = self.output_projector(embeddings.float().to("cuda"), 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...") gen_emb = 0.1 * self.output_projector(embeddings.float().to("cuda"), gen_prefix_embs) / 10 gen_inputs = self.generation_processor(text=music_caption, padding='max_length', max_length=128, truncation=True, return_tensors="pt").to( self.device) #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).cuda().long() for k, t in enumerate(prompts): tokens[k, : len(t)] = torch.tensor(t).cuda().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)