modeling_whisper.py

import base64
import gzip
from contextlib import contextmanager
from dataclasses import dataclass
from typing import Dict, Iterable, Optional, Tuple

import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn

try:
    from torch.nn.functional import scaled_dot_product_attention

    SDPA_AVAILABLE = True
except (ImportError, RuntimeError, OSError):
    scaled_dot_product_attention = None
    SDPA_AVAILABLE = False


@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)


@contextmanager
def disable_sdpa():
    prev_state = MultiHeadAttention.use_sdpa
    try:
        MultiHeadAttention.use_sdpa = False
        yield
    finally:
        MultiHeadAttention.use_sdpa = prev_state


class MultiHeadAttention(nn.Module):
    use_sdpa = True

    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
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        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)
        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)

        if SDPA_AVAILABLE and MultiHeadAttention.use_sdpa:
            a = scaled_dot_product_attention(
                q, k, v, is_causal=mask is not None and n_ctx > 1
            )
            out = a.permute(0, 2, 1, 3).flatten(start_dim=2)
            qk = None
        else:
            qk = (q * scale) @ (k * scale).transpose(-1, -2)
            if mask is not None:
                qk = qk + mask[:n_ctx, :n_ctx]
            qk = qk.float()

            w = F.softmax(qk, dim=-1).to(q.dtype)
            out = (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2)
            qk = qk.detach()

        return out, qk


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)

        assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
        x = (x + self.positional_embedding).to(x.dtype)

        for block in self.blocks:
            x = block(x)

        x = self.ln_post(x)
        return x