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from einops import rearrange
from einops.layers.torch import Rearrange
from torch import nn
from transformers import PreTrainedModel
import math
import torch
from .configuration_moonshine import MoonshineConfig
class RotaryEmbedding(nn.Module):
def __init__(self, dim, base=10000):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, t):
freqs = torch.einsum("i , j -> i j", t.type_as(self.inv_freq), self.inv_freq)
freqs = torch.stack((freqs, freqs), dim=-1)
return rearrange(freqs, "... d r -> ... (d r)")
def rotate_half(x):
x = rearrange(x, "... (d r) -> ... d r", r=2)
x1, x2 = x.unbind(dim=-1)
x = torch.stack((-x2, x1), dim=-1)
return rearrange(x, "... d r -> ... (d r)")
def apply_rotary_pos_emb(t, freqs):
rot_dim, seq_len, orig_dtype = freqs.shape[-1], t.shape[-2], t.dtype
freqs = freqs[-seq_len:, :]
# partial rotary embeddings, Wang et al. GPT-J
t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
t = t * freqs.cos() + rotate_half(t) * freqs.sin()
out = torch.cat((t, t_unrotated), dim=-1)
return out.type(orig_dtype)
class MultiHeadAttention(nn.Module):
def __init__(self, dim, inner_dim, n_head):
super().__init__()
self.n_head = n_head
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_k = nn.Linear(dim, inner_dim, bias=False)
self.to_v = nn.Linear(dim, inner_dim, bias=False)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
self.softmax = nn.Softmax(dim=-1)
# Scaled dot product attention
def sdp_attention(self, q, k_t, v, mask=None):
d_tensor = v.shape[3]
op = (q @ k_t) / math.sqrt(d_tensor)
if mask is not None:
op = op.masked_fill(mask, -torch.finfo(op.dtype).max)
score = self.softmax(op)
out = score @ v
# concat and pass to linear layer
out = rearrange(out, "b h n d -> b n (h d)")
return self.to_out(out)
def forward(self, q, k, v, rot_pos_emb=None, mask=None):
# dot product with weight matrices
q, k, v = self.to_q(q), self.to_k(k), self.to_v(v)
q = rearrange(q, "b n (h d) -> b h n d", h=self.n_head)
k = rearrange(k, "b n (h d) -> b h n d", h=self.n_head)
v = rearrange(v, "b n (h d) -> b h n d", h=self.n_head)
# apply RoPE
if rot_pos_emb is not None:
q = apply_rotary_pos_emb(q, rot_pos_emb)
k = apply_rotary_pos_emb(k, rot_pos_emb)
k_t = k.transpose(2, 3)
return self.sdp_attention(q, k_t, v, mask), k_t, v
class MultiHeadCausalSelfAttentionWithKVCache(MultiHeadAttention):
def __init__(self, dim, inner_dim, n_head):
super().__init__(dim, inner_dim, n_head)
def forward(self, q, k, v, k_cache, v_cache, rot_pos_emb, mask):
# dot product with weight matrices
q, k, v = self.to_q(q), self.to_k(k), self.to_v(v)
q = rearrange(q, "b n (h d) -> b h n d", h=self.n_head)
k = rearrange(k, "b n (h d) -> b h n d", h=self.n_head)
v = rearrange(v, "b n (h d) -> b h n d", h=self.n_head)
# apply RoPE
q = apply_rotary_pos_emb(q, rot_pos_emb)
k = apply_rotary_pos_emb(k, rot_pos_emb)
k_t = k.transpose(2, 3)
# Append new rows to K and V caches.
k_t = torch.concat((k_cache, k_t), dim=3)
v = torch.concat((v_cache, v), dim=2)
return super().sdp_attention(q, k_t, v, mask=mask), k_t, v
class MultiHeadCrossAttentionWithKVCache(MultiHeadAttention):
def __init__(self, dim, inner_dim, n_head):
super().__init__(dim, inner_dim, n_head)
def forward(self, q, k_cache, v_cache):
q = self.to_q(q)
q = rearrange(q, "b n (h d) -> b h n d", h=self.n_head)
return super().sdp_attention(q, k_cache, v_cache)
class FFLinearGelu(nn.Module):
def __init__(self, dim, ff_mult=4):
super().__init__()
self.ff = nn.Sequential(
nn.Linear(dim, dim * ff_mult, bias=True),
nn.GELU(),
nn.Linear(dim * ff_mult, dim, bias=True),
)
def forward(self, x):
return self.ff(x)
class FFSwiGLU(nn.Module):
def __init__(self, dim, ff_mult=4):
super().__init__()
self.ff_proj = nn.Linear(dim, dim * ff_mult, bias=True)
self.ff_noact = nn.Linear(dim, dim * ff_mult, bias=True)
self.ff_act = nn.SiLU()
self.ff_out = nn.Linear(dim * ff_mult, dim, bias=True)
def forward(self, x):
gate = self.ff_act(self.ff_proj(x))
x_noact = self.ff_noact(x)
x = x_noact * gate
return self.ff_out(x)
class EncoderLayer(nn.Module):
def __init__(self, dim, inner_dim, n_head, ff_swiglu, ff_mult=4):
super().__init__()
self.norm1 = nn.LayerNorm(dim, bias=False)
self.attention = MultiHeadAttention(dim, inner_dim=inner_dim, n_head=n_head)
self.norm2 = nn.LayerNorm(dim, bias=False)
self.ff = FFSwiGLU(dim, ff_mult) if ff_swiglu else FFLinearGelu(dim, ff_mult)
def forward(self, x, rot_pos_emb):
_x = x
x = self.norm1(x)
x, _, _ = self.attention(q=x, k=x, v=x, rot_pos_emb=rot_pos_emb)
x = x + _x
_x = x
x = self.norm2(x)
x = self.ff(x)
x = x + _x
return x
class Encoder(nn.Module):
def __init__(self, dim, inner_dim, n_head, n_layers, ff_swiglu):
super().__init__()
rot_embed_dim = max(inner_dim / n_head / 2, 32)
self.rot_pos_emb = RotaryEmbedding(rot_embed_dim)
self.layers = nn.ModuleList(
[EncoderLayer(dim, inner_dim, n_head, ff_swiglu) for _ in range(n_layers)]
)
self.post_norm = nn.LayerNorm(dim, bias=False)
def forward(self, x):
pos = torch.arange(x.shape[1], device=x.device)
rot_pos_emb = self.rot_pos_emb(pos)
for layer in self.layers:
x = layer(x, rot_pos_emb=rot_pos_emb)
return self.post_norm(x)
class DecoderLayer(nn.Module):
def __init__(self, dim, inner_dim, n_head, ff_swiglu, ff_mult=4):
super().__init__()
self.norm1 = nn.LayerNorm(dim, bias=False)
self.self_attention = MultiHeadCausalSelfAttentionWithKVCache(
dim, inner_dim=inner_dim, n_head=n_head
)
self.norm2 = nn.LayerNorm(dim, bias=False)
self.cross_attention = MultiHeadCrossAttentionWithKVCache(
dim, inner_dim=inner_dim, n_head=n_head
)
self.norm3 = nn.LayerNorm(dim, bias=False)
self.ff = FFSwiGLU(dim, ff_mult) if ff_swiglu else FFLinearGelu(dim, ff_mult)
def forward(self, x, k_cache, v_cache, x_attn_k_cache, x_attn_v_cache, rot_pos_emb):
dim = x.size()[1]
causal_mask = torch.ones((dim, dim), dtype=torch.bool).triu(1).to(x.device)
_x = x
x = self.norm1(x)
x, new_k_cache, new_v_cache = self.self_attention(
q=x,
k=x,
v=x,
k_cache=k_cache,
v_cache=v_cache,
rot_pos_emb=rot_pos_emb,
mask=causal_mask,
)
x = x + _x
_x = x
x = self.norm2(x)
x = self.cross_attention(q=x, k_cache=x_attn_k_cache, v_cache=x_attn_v_cache)
x = x + _x
_x = x
x = self.norm3(x)
x = self.ff(x)
x = x + _x
return x, new_k_cache, new_v_cache
class Decoder(nn.Module):
def __init__(self, dim, inner_dim, n_head, n_layers, dec_voc_size, ff_swiglu):
super().__init__()
self.n_head = n_head
self.d_head = inner_dim // n_head
rot_embed_dim = max(inner_dim / n_head / 2, 32)
self.rot_pos_emb = RotaryEmbedding(rot_embed_dim)
self.layers = nn.ModuleList(
[DecoderLayer(dim, inner_dim, n_head, ff_swiglu) for _ in range(n_layers)]
)
self.final_norm = nn.LayerNorm(dim, bias=False)
self.token_embedding = nn.Embedding(dec_voc_size, dim)
def forward(self, x, *args):
pos = torch.arange(x.shape[1], device=x.device)
rot_pos_emb = self.rot_pos_emb(pos)
x = self.token_embedding(x)
k_cache_new = []
v_cache_new = []
n_layer = len(self.layers)
k_cache, v_cache, x_attn_k_cache, x_attn_v_cache = [
args[i : i + n_layer] for i in range(0, 4 * n_layer, n_layer)
]
for idx, layer in enumerate(self.layers):
x, new_k_line, new_v_line = layer(
x[:, -1:],
k_cache=k_cache[idx],
v_cache=v_cache[idx],
x_attn_k_cache=x_attn_k_cache[idx],
x_attn_v_cache=x_attn_v_cache[idx],
rot_pos_emb=rot_pos_emb,
)
k_cache_new.append(new_k_line)
v_cache_new.append(new_v_line)
x = self.final_norm(x)
return x @ self.token_embedding.weight.t(), *k_cache_new, *v_cache_new
class InitialDecoderLayer(nn.Module):
def __init__(self, dim, inner_dim, n_head, ff_swiglu, ff_mult=4):
super().__init__()
self.norm1 = nn.LayerNorm(dim, bias=False)
self.self_attention = MultiHeadAttention(
dim, inner_dim=inner_dim, n_head=n_head
)
self.norm2 = nn.LayerNorm(dim, bias=False)
self.cross_attention = MultiHeadAttention(
dim, inner_dim=inner_dim, n_head=n_head
)
self.norm3 = nn.LayerNorm(dim, bias=False)
self.ff = FFSwiGLU(dim, ff_mult) if ff_swiglu else FFLinearGelu(dim, ff_mult)
def forward(self, x, context, rot_pos_emb):
dim = x.size()[1]
causal_mask = torch.ones((dim, dim), dtype=torch.bool).triu(1).to(x.device)
_x = x
x = self.norm1(x)
x, new_k_cache, new_v_cache = self.self_attention(
q=x,
k=x,
v=x,
rot_pos_emb=rot_pos_emb,
mask=causal_mask,
)
x = x + _x
_x = x
x = self.norm2(x)
x, x_attn_k_cache, x_attn_v_cache = self.cross_attention(
q=x, k=context, v=context
)
x = x + _x
_x = x
x = self.norm3(x)
x = self.ff(x)
x = x + _x
return x, new_k_cache, new_v_cache, x_attn_k_cache, x_attn_v_cache
class DecoderInitial(Decoder):
def __init__(self, dim, inner_dim, n_head, n_layers, dec_voc_size, ff_swiglu):
super().__init__(dim, inner_dim, n_head, n_layers, dec_voc_size, ff_swiglu)
self.layers = nn.ModuleList(
[
InitialDecoderLayer(dim, inner_dim, n_head, ff_swiglu)
for _ in range(n_layers)
]
)
def forward(self, x, enc_src):
pos = torch.arange(x.shape[1], device=x.device)
rot_pos_emb = self.rot_pos_emb(pos)
x = self.token_embedding(x)
# Shape [n_layers, batch_size, n_head, seq_len, inner_dim]. Cache K transposed.
n_layer = len(self.layers)
k_cache = []
v_cache = []
x_attn_k_cache = []
x_attn_v_cache = []
for idx, layer in enumerate(self.layers):
x, new_k_line, new_v_line, new_x_attn_k_line, new_x_attn_v_line = layer(
x,
enc_src,
rot_pos_emb,
)
k_cache.append(new_k_line)
v_cache.append(new_v_line)
x_attn_k_cache.append(new_x_attn_k_line)
x_attn_v_cache.append(new_x_attn_v_line)
x = self.final_norm(x)
return (
x @ self.token_embedding.weight.t(),
*k_cache,
*v_cache,
*x_attn_k_cache,
*x_attn_v_cache,
)
class AudioPreprocessor(nn.Module):
def __init__(self, dim):
super().__init__()
self.audio_preprocess = nn.Sequential(
nn.Conv1d(1, dim, 127, 64, bias=False),
nn.Tanh(),
nn.GroupNorm(1, dim),
nn.Conv1d(dim, 2 * dim, 7, 3),
nn.GELU(),
nn.Conv1d(2 * dim, dim, 3, 2),
nn.GELU(),
Rearrange("... c s -> ... s c"),
)
def forward(self, src):
assert (
src.shape[-1] >= 1023
), f"src shape[-1] {src.shape[-1]} should be at least 1023"
src = src.unsqueeze(-2)
return self.audio_preprocess(src)
class MoonshineModelTorch(nn.Module):
def __init__(
self,
dim,
inner_dim,
enc_depth,
dec_depth,
n_head=8,
dec_voc_size=32768,
enc_ff_swiglu=False,
dec_ff_swiglu=False,
):
super().__init__()
self.preprocessor = AudioPreprocessor(dim)
self.encoder = Encoder(
dim, inner_dim, n_head, enc_depth, ff_swiglu=enc_ff_swiglu
)
self.decoder_initial = DecoderInitial(
dim, inner_dim, n_head, dec_depth, dec_voc_size, ff_swiglu=dec_ff_swiglu
)
self.decoder = Decoder(
dim, inner_dim, n_head, dec_depth, dec_voc_size, ff_swiglu=dec_ff_swiglu
)
self.dec_depth = dec_depth
self.n_head = n_head
self.d_head = inner_dim // n_head
def generate(self, src):
preprocessed = self.preprocessor(src)
enc = self.encoder(preprocessed)
sot_token = 1
eot_token = 2
seq = torch.as_tensor([[sot_token]]).to(src.device)
vals = self.decoder_initial(x=seq, enc_src=enc)
logits = vals[0]
k_cache, v_cache, x_attn_k_cache, x_attn_v_cache = [
vals[i : i + self.dec_depth]
for i in range(1, 1 + self.dec_depth * 4, self.dec_depth)
]
sample = logits[:, -1].argmax(dim=-1, keepdim=True)
seq = torch.cat((seq, sample), dim=-1)
seq_len = int(src.shape[-1] * 6.5 / 16000)
while sample != eot_token and len(seq.flatten()) <= seq_len:
vals = self.decoder(
seq,
*k_cache,
*v_cache,
*x_attn_k_cache,
*x_attn_v_cache,
)
logits = vals[0]
k_cache = vals[1 : self.dec_depth + 1]
v_cache = vals[self.dec_depth + 1 :]
logits = logits[:, -1] # get last token
sample = logits.argmax(dim=-1, keepdim=True)
seq = torch.cat((seq, sample), dim=-1)
return seq
class MoonshineModel(PreTrainedModel):
config_class = MoonshineConfig
def __init__(self, config):
super().__init__(config)
self.model = MoonshineModelTorch(
dim = config.dim,
inner_dim = config.inner_dim,
enc_depth = config.enc_depth,
dec_depth = config.dec_depth,
n_head = config.n_head,
dec_voc_size = config.dec_voc_size,
enc_ff_swiglu = config.enc_ff_swiglu,
dec_ff_swiglu = config.dec_ff_swiglu,
)
def forward(self, tensor):
return self.model.generate(tensor)
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