from dataclasses import dataclass import torch import torch.nn as nn import torchtune from torchtune.models import llama3_2 def llama3_2_1B() -> torchtune.modules.transformer.TransformerDecoder: return llama3_2.llama3_2( vocab_size=128_256, num_layers=16, num_heads=32, num_kv_heads=8, embed_dim=2048, max_seq_len=2048, intermediate_dim=8192, attn_dropout=0.0, norm_eps=1e-5, rope_base=500_000, scale_factor=32, ) def llama3_2_100M() -> torchtune.modules.transformer.TransformerDecoder: return llama3_2.llama3_2( vocab_size=128_256, num_layers=4, num_heads=8, num_kv_heads=2, embed_dim=1024, max_seq_len=2048, intermediate_dim=8192, attn_dropout=0.0, norm_eps=1e-5, rope_base=500_000, scale_factor=32, ) FLAVORS = { "llama-1B": llama3_2_1B, "llama-100M": llama3_2_100M, } def _prepare_transformer(model): embed_dim = model.tok_embeddings.embedding_dim model.tok_embeddings = nn.Identity() model.output = nn.Identity() return model, embed_dim def _create_causal_mask(seq_len: int, device: torch.device): return torch.tril(torch.ones(seq_len, seq_len, dtype=torch.bool, device=device)) def _index_causal_mask(mask: torch.Tensor, input_pos: torch.Tensor): """ Args: mask: (max_seq_len, max_seq_len) input_pos: (batch_size, seq_len) Returns: (batch_size, seq_len, max_seq_len) """ r = mask[input_pos, :] return r def _multinomial_sample_one_no_sync(probs): # Does multinomial sampling without a cuda synchronization q = torch.empty_like(probs).exponential_(1) return torch.argmax(probs / q, dim=-1, keepdim=True).to(dtype=torch.int) def sample_topk(logits: torch.Tensor, topk: int, temperature: float): logits = logits / temperature filter_value: float = -float("Inf") indices_to_remove = logits < torch.topk(logits, topk)[0][..., -1, None] scores_processed = logits.masked_fill(indices_to_remove, filter_value) scores_processed = torch.nn.functional.log_softmax(scores_processed, dim=-1) probs = torch.nn.functional.softmax(scores_processed, dim=-1) sample_token = _multinomial_sample_one_no_sync(probs) return sample_token @dataclass class ModelArgs: backbone_flavor: str decoder_flavor: str text_vocab_size: int audio_vocab_size: int audio_num_codebooks: int class Model(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.args = args self.backbone, backbone_dim = _prepare_transformer(FLAVORS[args.backbone_flavor]()) self.decoder, decoder_dim = _prepare_transformer(FLAVORS[args.decoder_flavor]()) self.text_embeddings = nn.Embedding(args.text_vocab_size, backbone_dim) self.audio_embeddings = nn.Embedding(args.audio_vocab_size * args.audio_num_codebooks, backbone_dim) self.projection = nn.Linear(backbone_dim, decoder_dim, bias=False) self.codebook0_head = nn.Linear(backbone_dim, args.audio_vocab_size, bias=False) self.audio_head = nn.Parameter(torch.empty(args.audio_num_codebooks - 1, decoder_dim, args.audio_vocab_size)) def setup_caches(self, max_batch_size: int) -> torch.Tensor: """Setup KV caches and return a causal mask.""" dtype = next(self.parameters()).dtype device = next(self.parameters()).device with device: self.backbone.setup_caches(max_batch_size, dtype) self.decoder.setup_caches(max_batch_size, dtype, decoder_max_seq_len=self.args.audio_num_codebooks) self.register_buffer("backbone_causal_mask", _create_causal_mask(self.backbone.max_seq_len, device)) self.register_buffer("decoder_causal_mask", _create_causal_mask(self.args.audio_num_codebooks, device)) def generate_frame( self, tokens: torch.Tensor, tokens_mask: torch.Tensor, input_pos: torch.Tensor, temperature: float, topk: int, ) -> torch.Tensor: """ Args: tokens: (batch_size, seq_len, audio_num_codebooks+1) tokens_mask: (batch_size, seq_len, audio_num_codebooks+1) input_pos: (batch_size, seq_len) positions for each token mask: (batch_size, seq_len, max_seq_len Returns: (batch_size, audio_num_codebooks) sampled tokens """ dtype = next(self.parameters()).dtype b, s, _ = tokens.size() assert self.backbone.caches_are_enabled(), "backbone caches are not enabled" curr_backbone_mask = _index_causal_mask(self.backbone_causal_mask, input_pos) embeds = self._embed_tokens(tokens) masked_embeds = embeds * tokens_mask.unsqueeze(-1) h = masked_embeds.sum(dim=2) h = self.backbone(h, input_pos=input_pos, mask=curr_backbone_mask).to(dtype=dtype) last_h = h[:, -1, :] c0_logits = self.codebook0_head(last_h) c0_sample = sample_topk(c0_logits, topk, temperature) c0_embed = self._embed_audio(0, c0_sample) curr_h = torch.cat([last_h.unsqueeze(1), c0_embed], dim=1) curr_sample = c0_sample.clone() curr_pos = torch.arange(0, curr_h.size(1), device=curr_h.device).unsqueeze(0).repeat(curr_h.size(0), 1) # Decoder caches must be reset every frame. self.decoder.reset_caches() for i in range(1, self.args.audio_num_codebooks): curr_decoder_mask = _index_causal_mask(self.decoder_causal_mask, curr_pos) decoder_h = self.decoder(self.projection(curr_h), input_pos=curr_pos, mask=curr_decoder_mask).to( dtype=dtype ) ci_logits = torch.mm(decoder_h[:, -1, :], self.audio_head[i - 1]) ci_sample = sample_topk(ci_logits, topk, temperature) ci_embed = self._embed_audio(i, ci_sample) curr_h = ci_embed curr_sample = torch.cat([curr_sample, ci_sample], dim=1) curr_pos = curr_pos[:, -1:] + 1 return curr_sample def reset_caches(self): self.backbone.reset_caches() self.decoder.reset_caches() def _embed_audio(self, codebook: int, tokens: torch.Tensor) -> torch.Tensor: return self.audio_embeddings(tokens + codebook * self.args.audio_vocab_size) def _embed_tokens(self, tokens: torch.Tensor) -> torch.Tensor: text_embeds = self.text_embeddings(tokens[:, :, -1]).unsqueeze(-2) audio_tokens = tokens[:, :, :-1] + ( self.args.audio_vocab_size * torch.arange(self.args.audio_num_codebooks, device=tokens.device) ) audio_embeds = self.audio_embeddings(audio_tokens.view(-1)).reshape( tokens.size(0), tokens.size(1), self.args.audio_num_codebooks, -1 ) return torch.cat([audio_embeds, text_embeds], dim=-2)