first commit for takway.ai
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import base64
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import gzip
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from dataclasses import dataclass
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from typing import Dict, Iterable, Optional
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch import Tensor, nn
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from .decoding import decode as decode_function
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from .decoding import detect_language as detect_language_function
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from .transcribe import transcribe as transcribe_function
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@dataclass
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class ModelDimensions:
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n_mels: int
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n_audio_ctx: int
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n_audio_state: int
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n_audio_head: int
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n_audio_layer: int
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n_vocab: int
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n_text_ctx: int
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n_text_state: int
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n_text_head: int
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n_text_layer: int
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class LayerNorm(nn.LayerNorm):
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def forward(self, x: Tensor) -> Tensor:
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return super().forward(x.float()).type(x.dtype)
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class Linear(nn.Linear):
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def forward(self, x: Tensor) -> Tensor:
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return F.linear(
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x,
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self.weight.to(x.dtype),
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None if self.bias is None else self.bias.to(x.dtype),
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)
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class Conv1d(nn.Conv1d):
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def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
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return super()._conv_forward(
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x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
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)
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def sinusoids(length, channels, max_timescale=10000):
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"""Returns sinusoids for positional embedding"""
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assert channels % 2 == 0
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log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
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inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
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scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
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return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
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class MultiHeadAttention(nn.Module):
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def __init__(self, n_state: int, n_head: int):
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super().__init__()
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self.n_head = n_head
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self.query = Linear(n_state, n_state)
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self.key = Linear(n_state, n_state, bias=False)
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self.value = Linear(n_state, n_state)
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self.out = Linear(n_state, n_state)
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def forward(
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self,
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x: Tensor,
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xa: Optional[Tensor] = None,
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mask: Optional[Tensor] = None,
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kv_cache: Optional[dict] = None,
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**kwargs,
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):
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is_pad_mask = kwargs.get("is_pad_mask", False)
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q = self.query(x)
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if kv_cache is None or xa is None or self.key not in kv_cache:
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# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
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# otherwise, perform key/value projections for self- or cross-attention as usual.
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k = self.key(x if xa is None else xa)
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v = self.value(x if xa is None else xa)
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else:
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# for cross-attention, calculate keys and values once and reuse in subsequent calls.
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k = kv_cache[self.key]
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v = kv_cache[self.value]
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wv, qk = self.qkv_attention(q, k, v, mask, is_pad_mask=is_pad_mask)
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return self.out(wv), qk
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def qkv_attention(
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self,
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q: Tensor,
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k: Tensor,
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v: Tensor,
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mask: Optional[Tensor] = None,
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**kwargs,
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):
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is_pad_mask = kwargs.get("is_pad_mask", False)
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n_batch, n_ctx, n_state = q.shape
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scale = (n_state // self.n_head) ** -0.25
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q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
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k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
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v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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qk = q @ k
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if mask is not None:
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if not is_pad_mask:
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qk = qk + mask[:n_ctx, :n_ctx]
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else:
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
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min_value = float(np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min)
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qk = qk.masked_fill(mask, min_value)
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qk = qk.float()
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w = F.softmax(qk, dim=-1).to(q.dtype)
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if mask is not None and is_pad_mask:
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w = w.masked_fill(mask, 0.0)
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return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
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class ResidualAttentionBlock(nn.Module):
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def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
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super().__init__()
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self.attn = MultiHeadAttention(n_state, n_head)
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self.attn_ln = LayerNorm(n_state)
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self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
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self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
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n_mlp = n_state * 4
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self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
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self.mlp_ln = LayerNorm(n_state)
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def forward(
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self,
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x: Tensor,
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xa: Optional[Tensor] = None,
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mask: Optional[Tensor] = None,
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kv_cache: Optional[dict] = None,
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**kwargs,
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):
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is_pad_mask = kwargs.get("is_pad_mask", False)
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is_pad_memory_mask = kwargs.get("is_pad_memory_mask", False)
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x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
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if self.cross_attn:
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x = (
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x
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+ self.cross_attn(
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self.cross_attn_ln(x), xa, kv_cache=kv_cache, is_pad_mask=is_pad_memory_mask
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)[0]
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)
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x = x + self.mlp(self.mlp_ln(x))
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return x
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class AudioEncoder(nn.Module):
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def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
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super().__init__()
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self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, stride=2, padding=1)
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self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
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self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
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self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
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[ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
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)
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self.ln_post = LayerNorm(n_state)
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def forward(self, x: Tensor):
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"""
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x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
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the mel spectrogram of the audio
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"""
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x = F.gelu(self.conv1(x))
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x = F.gelu(self.conv2(x))
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x = x.permute(0, 2, 1)
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# assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
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# x = (x + self.positional_embedding).to(x.dtype)
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x = (x + self.positional_embedding[: x.size(1), :]).to(x.dtype)
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for block in self.blocks:
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x = block(x)
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x = self.ln_post(x)
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return x
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class TextDecoder(nn.Module):
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def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
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super().__init__()
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self.token_embedding = nn.Embedding(n_vocab, n_state)
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self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
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self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
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[ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
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)
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self.ln = LayerNorm(n_state)
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mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
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self.register_buffer("mask", mask, persistent=False)
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def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
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"""
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x : torch.LongTensor, shape = (batch_size, <= n_ctx)
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the text tokens
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xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state)
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the encoded audio features to be attended on
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"""
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offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
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x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
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x = x.to(xa.dtype)
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for block in self.blocks:
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x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
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x = self.ln(x)
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logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
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return logits
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class Whisper(nn.Module):
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def __init__(self, dims: ModelDimensions):
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super().__init__()
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self.dims = dims
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self.encoder = AudioEncoder(
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self.dims.n_mels,
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self.dims.n_audio_ctx,
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self.dims.n_audio_state,
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self.dims.n_audio_head,
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self.dims.n_audio_layer,
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)
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self.decoder = TextDecoder(
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self.dims.n_vocab,
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self.dims.n_text_ctx,
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self.dims.n_text_state,
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self.dims.n_text_head,
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self.dims.n_text_layer,
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)
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# use the last half among the decoder layers for time alignment by default;
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# to use a specific set of heads, see `set_alignment_heads()` below.
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all_heads = torch.zeros(self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool)
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all_heads[self.dims.n_text_layer // 2 :] = True
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# self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False)
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# alignment_heads_dense = model.get_buffer("alignment_heads").to_dense()
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# model.register_buffer("alignment_heads", alignment_heads_dense, persistent=False)
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def set_alignment_heads(self, dump: bytes):
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array = np.frombuffer(gzip.decompress(base64.b85decode(dump)), dtype=bool).copy()
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mask = torch.from_numpy(array).reshape(self.dims.n_text_layer, self.dims.n_text_head)
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self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False)
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def embed_audio(self, mel: torch.Tensor):
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return self.encoder(mel)
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def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
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return self.decoder(tokens, audio_features)
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def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
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return self.decoder(tokens, self.encoder(mel))
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@property
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def device(self):
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return next(self.parameters()).device
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@property
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def is_multilingual(self):
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return self.dims.n_vocab >= 51865
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@property
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def num_languages(self):
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return self.dims.n_vocab - 51765 - int(self.is_multilingual)
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def install_kv_cache_hooks(self, cache: Optional[dict] = None):
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"""
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The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
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tensors calculated for the previous positions. This method returns a dictionary that stores
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all caches, and the necessary hooks for the key and value projection modules that save the
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intermediate tensors to be reused during later calculations.
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Returns
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-------
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cache : Dict[nn.Module, torch.Tensor]
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A dictionary object mapping the key/value projection modules to its cache
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hooks : List[RemovableHandle]
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List of PyTorch RemovableHandle objects to stop the hooks to be called
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"""
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cache = {**cache} if cache is not None else {}
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hooks = []
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def save_to_cache(module, _, output):
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if module not in cache or output.shape[1] > self.dims.n_text_ctx:
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# save as-is, for the first token or cross attention
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cache[module] = output
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else:
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cache[module] = torch.cat([cache[module], output], dim=1).detach()
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return cache[module]
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def install_hooks(layer: nn.Module):
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if isinstance(layer, MultiHeadAttention):
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hooks.append(layer.key.register_forward_hook(save_to_cache))
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hooks.append(layer.value.register_forward_hook(save_to_cache))
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self.decoder.apply(install_hooks)
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return cache, hooks
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detect_language = detect_language_function
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transcribe = transcribe_function
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decode = decode_function
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