""" Modernized MisoTTS models.py — identical Model logic, torchtune removed. The ONLY change vs the upstream models.py is the transformer source: upstream: from torchtune.models import llama3_2 (drags torch==2.4, deprecated) here: import csm_llama (plain torch, parity-verified Δ=0) Everything else (generate_frame, _embed_*, setup_caches, sampling) is byte-for-byte the upstream logic, so the published checkpoint loads with identical keys. """ from dataclasses import dataclass from typing import Tuple import torch import torch.nn as nn from . import csm_llama def llama3_2_8B(): return csm_llama.llama3_2( vocab_size=128_256, num_layers=32, num_heads=32, num_kv_heads=8, embed_dim=4096, max_seq_len=2048, intermediate_dim=14_336, attn_dropout=0.1, norm_eps=1e-5, rope_base=500_000, scale_factor=32, ) def llama3_2_300M(): return csm_llama.llama3_2( vocab_size=128_256, num_layers=8, num_heads=24, num_kv_heads=6, embed_dim=1536, max_seq_len=2048, intermediate_dim=6912, attn_dropout=0.1, norm_eps=1e-5, rope_base=500_000, scale_factor=32, ) FLAVORS = {"llama-8B": llama3_2_8B, "llama-300M": llama3_2_300M} def _prepare_transformer(model): # csm_llama decoders are already "prepared" (no tok_embeddings/output params). return model, 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): return mask[input_pos, :] def _multinomial_sample_one_no_sync(probs): 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("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) return _multinomial_sample_one_no_sync(probs) @dataclass class ModelArgs: backbone_flavor: str decoder_flavor: str text_vocab_size: int audio_vocab_size: int audio_num_codebooks: int MISO_TTS_8B_CONFIG = ModelArgs( backbone_flavor="llama-8B", decoder_flavor="llama-300M", text_vocab_size=128_256, audio_vocab_size=2051, audio_num_codebooks=32, ) class Model(nn.Module): def __init__(self, config: ModelArgs): super().__init__() self.config = config self.backbone, backbone_dim = _prepare_transformer(FLAVORS[config.backbone_flavor]()) self.decoder, decoder_dim = _prepare_transformer(FLAVORS[config.decoder_flavor]()) self.text_embeddings = nn.Embedding(config.text_vocab_size, backbone_dim) self.audio_embeddings = nn.Embedding(config.audio_vocab_size * config.audio_num_codebooks, backbone_dim) self.projection = nn.Linear(backbone_dim, decoder_dim, bias=False) self.codebook0_head = nn.Linear(backbone_dim, config.audio_vocab_size, bias=False) self.audio_head = nn.Parameter(torch.empty(config.audio_num_codebooks - 1, decoder_dim, config.audio_vocab_size)) def setup_caches(self, max_batch_size: int) -> None: 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.config.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.config.audio_num_codebooks, device)) def generate_frame(self, tokens, tokens_mask, input_pos, temperature, topk): 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) self.decoder.reset_caches() for i in range(1, self.config.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.config.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.config.audio_vocab_size * torch.arange(self.config.audio_num_codebooks, device=tokens.device) ) audio_embeds = self.audio_embeddings(audio_tokens.view(-1)).reshape( tokens.size(0), tokens.size(1), self.config.audio_num_codebooks, -1 ) return torch.cat([audio_embeds, text_embeds], dim=-2)