269 lines
9.4 KiB
Python
269 lines
9.4 KiB
Python
from dataclasses import dataclass
|
|
from typing import Dict, Iterable, Optional
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch import Tensor, nn
|
|
|
|
from .decoding import decode as decode_function
|
|
from .decoding import detect_language as detect_language_function
|
|
|
|
|
|
@dataclass
|
|
class ModelDimensions:
|
|
n_mels: int
|
|
n_audio_ctx: int
|
|
n_audio_state: int
|
|
n_audio_head: int
|
|
n_audio_layer: int
|
|
n_vocab: int
|
|
n_text_ctx: int
|
|
n_text_state: int
|
|
n_text_head: int
|
|
n_text_layer: int
|
|
|
|
|
|
class LayerNorm(nn.LayerNorm):
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return super().forward(x.float()).type(x.dtype)
|
|
|
|
|
|
class Linear(nn.Linear):
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return F.linear(
|
|
x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype)
|
|
)
|
|
|
|
|
|
class Conv1d(nn.Conv1d):
|
|
def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
|
|
return super()._conv_forward(
|
|
x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
|
|
)
|
|
|
|
|
|
def sinusoids(length, channels, max_timescale=10000):
|
|
"""Returns sinusoids for positional embedding"""
|
|
assert channels % 2 == 0
|
|
log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
|
|
inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
|
|
scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
|
|
return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
|
|
|
|
|
|
class MultiHeadAttention(nn.Module):
|
|
def __init__(self, n_state: int, n_head: int):
|
|
super().__init__()
|
|
self.n_head = n_head
|
|
self.query = Linear(n_state, n_state)
|
|
self.key = Linear(n_state, n_state, bias=False)
|
|
self.value = Linear(n_state, n_state)
|
|
self.out = Linear(n_state, n_state)
|
|
|
|
def forward(
|
|
self,
|
|
x: Tensor,
|
|
xa: Optional[Tensor] = None,
|
|
mask: Optional[Tensor] = None,
|
|
kv_cache: Optional[dict] = None,
|
|
):
|
|
q = self.query(x)
|
|
|
|
if kv_cache is None or xa is None or self.key not in kv_cache:
|
|
# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
|
|
# otherwise, perform key/value projections for self- or cross-attention as usual.
|
|
k = self.key(x if xa is None else xa)
|
|
v = self.value(x if xa is None else xa)
|
|
else:
|
|
# for cross-attention, calculate keys and values once and reuse in subsequent calls.
|
|
k = kv_cache[self.key]
|
|
v = kv_cache[self.value]
|
|
|
|
wv, qk = self.qkv_attention(q, k, v, mask)
|
|
return self.out(wv), qk
|
|
|
|
def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
|
|
n_batch, n_ctx, n_state = q.shape
|
|
scale = (n_state // self.n_head) ** -0.25
|
|
q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
|
|
k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
|
|
v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
|
|
|
|
qk = q @ k
|
|
if mask is not None:
|
|
qk = qk + mask[:n_ctx, :n_ctx]
|
|
qk = qk.float()
|
|
|
|
w = F.softmax(qk, dim=-1).to(q.dtype)
|
|
return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
|
|
|
|
|
|
class ResidualAttentionBlock(nn.Module):
|
|
def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
|
|
super().__init__()
|
|
|
|
self.attn = MultiHeadAttention(n_state, n_head)
|
|
self.attn_ln = LayerNorm(n_state)
|
|
|
|
self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
|
|
self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
|
|
|
|
n_mlp = n_state * 4
|
|
self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
|
|
self.mlp_ln = LayerNorm(n_state)
|
|
|
|
def forward(
|
|
self,
|
|
x: Tensor,
|
|
xa: Optional[Tensor] = None,
|
|
mask: Optional[Tensor] = None,
|
|
kv_cache: Optional[dict] = None,
|
|
):
|
|
x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
|
|
if self.cross_attn:
|
|
x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
|
|
x = x + self.mlp(self.mlp_ln(x))
|
|
return x
|
|
|
|
|
|
class AudioEncoder(nn.Module):
|
|
def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
|
|
super().__init__()
|
|
self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
|
|
self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
|
|
self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
|
|
|
|
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
|
|
[ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
|
|
)
|
|
self.ln_post = LayerNorm(n_state)
|
|
|
|
def forward(self, x: Tensor):
|
|
"""
|
|
x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
|
|
the mel spectrogram of the audio
|
|
"""
|
|
x = F.gelu(self.conv1(x))
|
|
x = F.gelu(self.conv2(x))
|
|
x = x.permute(0, 2, 1)
|
|
|
|
len_x = x.shape[1]
|
|
len_e = self.positional_embedding.shape[0]
|
|
assert len_x <= len_e, "incorrect audio shape"
|
|
pos_e = self.positional_embedding[:len_x, :]
|
|
x = (x + pos_e).to(x.dtype)
|
|
|
|
for block in self.blocks:
|
|
x = block(x)
|
|
|
|
x = self.ln_post(x)
|
|
return x
|
|
|
|
|
|
class TextDecoder(nn.Module):
|
|
def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
|
|
super().__init__()
|
|
|
|
self.token_embedding = nn.Embedding(n_vocab, n_state)
|
|
self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
|
|
|
|
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
|
|
[ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
|
|
)
|
|
self.ln = LayerNorm(n_state)
|
|
|
|
mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
|
|
self.register_buffer("mask", mask, persistent=False)
|
|
|
|
def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
|
|
"""
|
|
x : torch.LongTensor, shape = (batch_size, <= n_ctx)
|
|
the text tokens
|
|
xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
|
|
the encoded audio features to be attended on
|
|
"""
|
|
offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
|
|
x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
|
|
x = x.to(xa.dtype)
|
|
|
|
for block in self.blocks:
|
|
x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
|
|
|
|
x = self.ln(x)
|
|
logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
|
|
|
|
return logits
|
|
|
|
|
|
class Whisper(nn.Module):
|
|
def __init__(self, dims: ModelDimensions):
|
|
super().__init__()
|
|
self.dims = dims
|
|
self.encoder = AudioEncoder(
|
|
self.dims.n_mels,
|
|
self.dims.n_audio_ctx,
|
|
self.dims.n_audio_state,
|
|
self.dims.n_audio_head,
|
|
self.dims.n_audio_layer,
|
|
)
|
|
self.decoder = TextDecoder(
|
|
self.dims.n_vocab,
|
|
self.dims.n_text_ctx,
|
|
self.dims.n_text_state,
|
|
self.dims.n_text_head,
|
|
self.dims.n_text_layer,
|
|
)
|
|
|
|
def embed_audio(self, mel: torch.Tensor):
|
|
return self.encoder(mel)
|
|
|
|
def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
|
|
return self.decoder(tokens, audio_features)
|
|
|
|
def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
|
|
return self.decoder(tokens, self.encoder(mel))
|
|
|
|
@property
|
|
def device(self):
|
|
return next(self.parameters()).device
|
|
|
|
@property
|
|
def is_multilingual(self):
|
|
return self.dims.n_vocab == 51865
|
|
|
|
def install_kv_cache_hooks(self, cache: Optional[dict] = None):
|
|
"""
|
|
The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
|
|
tensors calculated for the previous positions. This method returns a dictionary that stores
|
|
all caches, and the necessary hooks for the key and value projection modules that save the
|
|
intermediate tensors to be reused during later calculations.
|
|
|
|
Returns
|
|
-------
|
|
cache : Dict[nn.Module, torch.Tensor]
|
|
A dictionary object mapping the key/value projection modules to its cache
|
|
hooks : List[RemovableHandle]
|
|
List of PyTorch RemovableHandle objects to stop the hooks to be called
|
|
"""
|
|
cache = {**cache} if cache is not None else {}
|
|
hooks = []
|
|
|
|
def save_to_cache(module, _, output):
|
|
if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]:
|
|
cache[module] = output # save as-is, for the first token or cross attention
|
|
else:
|
|
cache[module] = torch.cat([cache[module], output], dim=1).detach()
|
|
return cache[module]
|
|
|
|
def install_hooks(layer: nn.Module):
|
|
if isinstance(layer, MultiHeadAttention):
|
|
hooks.append(layer.key.register_forward_hook(save_to_cache))
|
|
hooks.append(layer.value.register_forward_hook(save_to_cache))
|
|
|
|
self.decoder.apply(install_hooks)
|
|
return cache, hooks
|
|
|
|
detect_language = detect_language_function
|
|
decode = decode_function
|