From f4f545b7243435116f3cedc4f42cb39bfed3331e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 30 四月 2024 00:06:43 +0800
Subject: [PATCH] batch
---
funasr/models/sense_voice/decoder.py | 202 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 202 insertions(+), 0 deletions(-)
diff --git a/funasr/models/sense_voice/decoder.py b/funasr/models/sense_voice/decoder.py
index 133508f..4986d50 100644
--- a/funasr/models/sense_voice/decoder.py
+++ b/funasr/models/sense_voice/decoder.py
@@ -335,3 +335,205 @@
x = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
return x
+
+
+class MultiHeadedAttentionSANMDecoder(nn.Module):
+ """Multi-Head Attention layer.
+
+ Args:
+ n_head (int): The number of heads.
+ n_feat (int): The number of features.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, n_feat, dropout_rate, kernel_size, sanm_shfit=0):
+ """Construct an MultiHeadedAttention object."""
+ super().__init__()
+
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ self.fsmn_block = nn.Conv1d(
+ n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False
+ )
+ # padding
+ # padding
+ left_padding = (kernel_size - 1) // 2
+ if sanm_shfit > 0:
+ left_padding = left_padding + sanm_shfit
+ right_padding = kernel_size - 1 - left_padding
+ self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
+ self.kernel_size = kernel_size
+
+ def forward(self, inputs, mask, cache=None, mask_shfit_chunk=None):
+ """
+ :param x: (#batch, time1, size).
+ :param mask: Mask tensor (#batch, 1, time)
+ :return:
+ """
+ # print("in fsmn, inputs", inputs.size())
+ b, t, d = inputs.size()
+ # logging.info(
+ # "mask: {}".format(mask.size()))
+ if mask is not None:
+ mask = torch.reshape(mask, (b, -1, 1))
+ # logging.info("in fsmn, mask: {}, {}".format(mask.size(), mask[0:100:50, :, :]))
+ if mask_shfit_chunk is not None:
+ # logging.info("in fsmn, mask_fsmn: {}, {}".format(mask_shfit_chunk.size(), mask_shfit_chunk[0:100:50, :, :]))
+ mask = mask * mask_shfit_chunk
+ # logging.info("in fsmn, mask_after_fsmn: {}, {}".format(mask.size(), mask[0:100:50, :, :]))
+ # print("in fsmn, mask", mask.size())
+ # print("in fsmn, inputs", inputs.size())
+ inputs = inputs * mask
+
+ x = inputs.transpose(1, 2)
+ b, d, t = x.size()
+ if cache is None:
+ # print("in fsmn, cache is None, x", x.size())
+
+ x = self.pad_fn(x)
+ if not self.training:
+ cache = x
+ else:
+ # print("in fsmn, cache is not None, x", x.size())
+ # x = torch.cat((x, cache), dim=2)[:, :, :-1]
+ # if t < self.kernel_size:
+ # x = self.pad_fn(x)
+ x = torch.cat((cache[:, :, 1:], x), dim=2)
+ x = x[:, :, -(self.kernel_size + t - 1) :]
+ # print("in fsmn, cache is not None, x_cat", x.size())
+ cache = x
+ x = self.fsmn_block(x)
+ x = x.transpose(1, 2)
+ # print("in fsmn, fsmn_out", x.size())
+ if x.size(1) != inputs.size(1):
+ inputs = inputs[:, -1, :]
+
+ x = x + inputs
+ x = self.dropout(x)
+ if mask is not None:
+ x = x * mask
+ return x, cache
+
+
+class ResidualAttentionBlockFSMN(nn.Module):
+ def __init__(self, n_state: int, n_head: int, cross_attention: bool = False, **kwargs):
+ super().__init__()
+
+ self.attn = MultiHeadedAttentionSANMDecoder(
+ n_state,
+ kwargs.get("self_attention_dropout_rate"),
+ kwargs.get("kernel_size", 20),
+ kwargs.get("sanm_shfit", 10),
+ )
+ 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,
+ **kwargs,
+ ):
+ is_pad_mask = kwargs.get("is_pad_mask", False)
+ is_pad_memory_mask = kwargs.get("is_pad_memory_mask", False)
+ x = x + self.attn(self.attn_ln(x), mask=None, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
+ if self.cross_attn:
+ x = (
+ x
+ + self.cross_attn(
+ self.cross_attn_ln(x), xa, kv_cache=kv_cache, is_pad_mask=is_pad_memory_mask
+ )[0]
+ )
+ x = x + self.mlp(self.mlp_ln(x))
+ return x
+
+
+@tables.register("decoder_classes", "SenseVoiceDecoderFSMN")
+class SenseVoiceDecoderFSMN(nn.Module):
+ def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int, **kwargs):
+ super().__init__()
+
+ self.token_embedding = nn.Embedding(n_vocab, n_state)
+ self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+ self.blocks = nn.ModuleList(
+ [
+ ResidualAttentionBlockFSMN(
+ n_state, n_head, cross_attention=True, layer_id=i, **kwargs
+ )
+ for i 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)
+
+ self.use_padmask = kwargs.get("use_padmask", True)
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ xa: torch.Tensor,
+ kv_cache: Optional[dict] = None,
+ **kwargs,
+ ):
+ """Forward decoder.
+
+ Args:
+ hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
+ hlens: (batch)
+ 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)
+ Returns:
+ (tuple): tuple containing:
+
+ x: decoded token score before softmax (batch, maxlen_out, token)
+ if use_output_layer is True,
+ olens: (batch, )
+ """
+ # import pdb;pdb.set_trace()
+ use_padmask = self.use_padmask
+ hlens = kwargs.get("hlens", None)
+
+ ys_in_lens = kwargs.get("ys_in_lens", None)
+
+ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+ tgt, memory = x, xa
+ tgt[tgt == -1] = 0
+ tgt = self.token_embedding(tgt) + self.positional_embedding[offset : offset + tgt.size(1)]
+ # tgt = self.dropout(tgt)
+
+ x = tgt.to(memory.dtype)
+
+ if use_padmask and hlens is not None:
+ memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
+ else:
+ memory_mask = None
+
+ for layer, block in enumerate(self.blocks):
+ x = block(
+ x,
+ memory,
+ mask=self.mask,
+ memory_mask=memory_mask,
+ is_pad_mask=False,
+ is_pad_memory_mask=True,
+ )
+
+ x = self.ln(x)
+ x = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+ return x
--
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