import copy
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from typing import Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.register import tables
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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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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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def sense_voice_decode_forward(
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self,
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x: torch.Tensor,
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xa: torch.Tensor,
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kv_cache: Optional[dict] = None,
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**kwargs,
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):
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"""Forward decoder.
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Args:
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hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
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hlens: (batch)
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ys_in_pad:
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input token ids, int64 (batch, maxlen_out)
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if input_layer == "embed"
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input tensor (batch, maxlen_out, #mels) in the other cases
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ys_in_lens: (batch)
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Returns:
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(tuple): tuple containing:
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x: decoded token score before softmax (batch, maxlen_out, token)
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if use_output_layer is True,
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olens: (batch, )
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"""
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# import pdb;pdb.set_trace()
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use_padmask = self.use_padmask
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hlens = kwargs.get("hlens", None)
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ys_in_lens = kwargs.get("ys_in_lens", None)
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offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
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tgt, memory = x, xa
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tgt[tgt == -1] = 0
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tgt = (
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self.token_embedding(tgt)
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+ self.positional_embedding[offset: offset + tgt.size(1)]
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)
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# tgt = self.dropout(tgt)
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x = tgt.to(memory.dtype)
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if use_padmask and hlens is not None:
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memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
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else:
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memory_mask = None
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for layer, block in enumerate(self.blocks):
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x = block(x, memory, mask=self.mask, memory_mask=memory_mask, is_pad_mask=False, is_pad_memory_mask=True)
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x = self.ln(x)
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x = (
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x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
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).float()
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return x
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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, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None, **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(
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np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min
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)
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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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from funasr.models.sense_voice.rwkv_v6 import RWKVLayer
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from omegaconf import OmegaConf
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class ResidualAttentionBlockRWKV(nn.Module):
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def __init__(self, n_state: int, n_head: int, cross_attention: bool = False, layer_id=0, **kwargs):
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super().__init__()
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rwkv_cfg = kwargs.get("rwkv_cfg", {})
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args = OmegaConf.create(rwkv_cfg)
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self.attn = RWKVLayer(args=args, layer_id=layer_id)
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if args.get("datatype", "bf16") == "bf16":
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self.attn.to(torch.bfloat16)
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self.ln0 = None
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if layer_id == 0 and not args.get("ln0", True):
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self.ln0 = LayerNorm(args.n_embd)
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if args.get("init_rwkv", True):
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print("init_rwkv")
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layer_id = 0
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scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
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nn.init.constant_(self.ln0.weight, scale)
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self.layer_id = layer_id
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self.args = args
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self.ln1 = None
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if not args.get("ln1", True):
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self.ln1 = LayerNorm(args.n_embd)
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# init
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if args.get("init_rwkv", True):
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print("init_rwkv")
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scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
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nn.init.constant_(self.ln1.weight, scale)
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self.cross_attn = (
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MultiHeadAttention(n_state, n_head) if cross_attention else None
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)
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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(
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Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
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)
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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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if self.layer_id == 0 and self.ln0 is not None:
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x = self.ln0(x)
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if self.ln1 is None:
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x = x + self.attn(x, mask=mask, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
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else:
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x = x + self.attn(self.ln1(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 = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache, is_pad_mask=is_pad_memory_mask)[0]
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x = x + self.mlp(self.mlp_ln(x))
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return x
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@tables.register("decoder_classes", "SenseVoiceDecoder")
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class SenseVoiceDecoder(nn.Module):
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def __init__(
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self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int, **kwargs
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):
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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 = nn.ModuleList(
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[
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ResidualAttentionBlockRWKV(n_state, n_head, cross_attention=True, layer_id=i, **kwargs)
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for i in range(n_layer)
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]
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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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self.use_padmask = kwargs.get("use_padmask", True)
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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 = (
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# self.token_embedding(x)
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# + self.positional_embedding[offset: offset + x.shape[-1]]
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# )
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# x = x.to(xa.dtype)
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#
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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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#
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# x = self.ln(x)
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# logits = (
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# x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
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# ).float()
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#
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# return logits
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def forward(
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self,
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x: torch.Tensor,
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xa: torch.Tensor,
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kv_cache: Optional[dict] = None,
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**kwargs,
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):
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"""Forward decoder.
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|
Args:
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hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
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hlens: (batch)
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ys_in_pad:
|
input token ids, int64 (batch, maxlen_out)
|
if input_layer == "embed"
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
ys_in_lens: (batch)
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Returns:
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(tuple): tuple containing:
|
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x: decoded token score before softmax (batch, maxlen_out, token)
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if use_output_layer is True,
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olens: (batch, )
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"""
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# import pdb;pdb.set_trace()
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use_padmask = self.use_padmask
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hlens = kwargs.get("hlens", None)
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ys_in_lens = kwargs.get("ys_in_lens", None)
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offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
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tgt, memory = x, xa
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tgt[tgt == -1] = 0
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tgt = (
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self.token_embedding(tgt)
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+ self.positional_embedding[offset: offset + tgt.size(1)]
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)
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# tgt = self.dropout(tgt)
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x = tgt.to(memory.dtype)
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if use_padmask and hlens is not None:
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memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
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else:
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memory_mask = None
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for layer, block in enumerate(self.blocks):
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x = block(x, memory, mask=self.mask, memory_mask=memory_mask, is_pad_mask=False, is_pad_memory_mask=True)
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x = self.ln(x)
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x = (
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x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
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).float()
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return x
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