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)