import os
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from funasr.export import models
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import torch
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import torch.nn as nn
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from funasr.export.utils.torch_function import MakePadMask
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from funasr.export.utils.torch_function import sequence_mask
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from funasr.modules.attention import MultiHeadedAttentionSANMDecoder
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from funasr.export.models.modules.multihead_att import MultiHeadedAttentionSANMDecoder as MultiHeadedAttentionSANMDecoder_export
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from funasr.modules.attention import MultiHeadedAttentionCrossAtt, MultiHeadedAttention
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from funasr.export.models.modules.multihead_att import MultiHeadedAttentionCrossAtt as MultiHeadedAttentionCrossAtt_export
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from funasr.export.models.modules.multihead_att import OnnxMultiHeadedAttention
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from funasr.modules.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
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from funasr.export.models.modules.feedforward import PositionwiseFeedForwardDecoderSANM as PositionwiseFeedForwardDecoderSANM_export
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from funasr.export.models.modules.decoder_layer import DecoderLayer as DecoderLayer_export
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class ParaformerDecoderSAN(nn.Module):
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def __init__(self, model,
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max_seq_len=512,
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model_name='decoder',
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onnx: bool = True,):
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super().__init__()
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# self.embed = model.embed #Embedding(model.embed, max_seq_len)
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self.model = model
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if onnx:
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self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
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else:
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self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
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for i, d in enumerate(self.model.decoders):
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if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
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d.feed_forward = PositionwiseFeedForwardDecoderSANM_export(d.feed_forward)
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if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
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d.self_attn = MultiHeadedAttentionSANMDecoder_export(d.self_attn)
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# if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt):
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# d.src_attn = MultiHeadedAttentionCrossAtt_export(d.src_attn)
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if isinstance(d.src_attn, MultiHeadedAttention):
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d.src_attn = OnnxMultiHeadedAttention(d.src_attn)
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self.model.decoders[i] = DecoderLayer_export(d)
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self.output_layer = model.output_layer
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self.after_norm = model.after_norm
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self.model_name = model_name
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def prepare_mask(self, mask):
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mask_3d_btd = mask[:, :, None]
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if len(mask.shape) == 2:
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mask_4d_bhlt = 1 - mask[:, None, None, :]
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elif len(mask.shape) == 3:
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mask_4d_bhlt = 1 - mask[:, None, :]
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mask_4d_bhlt = mask_4d_bhlt * -10000.0
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return mask_3d_btd, mask_4d_bhlt
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def forward(
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self,
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hs_pad: torch.Tensor,
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hlens: torch.Tensor,
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ys_in_pad: torch.Tensor,
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ys_in_lens: torch.Tensor,
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):
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tgt = ys_in_pad
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tgt_mask = self.make_pad_mask(ys_in_lens)
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tgt_mask, _ = self.prepare_mask(tgt_mask)
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# tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
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memory = hs_pad
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memory_mask = self.make_pad_mask(hlens)
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_, memory_mask = self.prepare_mask(memory_mask)
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# memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
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x = tgt
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x, tgt_mask, memory, memory_mask = self.model.decoders(
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x, tgt_mask, memory, memory_mask
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)
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x = self.after_norm(x)
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x = self.output_layer(x)
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return x, ys_in_lens
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def get_dummy_inputs(self, enc_size):
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tgt = torch.LongTensor([0]).unsqueeze(0)
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memory = torch.randn(1, 100, enc_size)
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pre_acoustic_embeds = torch.randn(1, 1, enc_size)
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cache_num = len(self.model.decoders) + len(self.model.decoders2)
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cache = [
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torch.zeros((1, self.model.decoders[0].size, self.model.decoders[0].self_attn.kernel_size))
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for _ in range(cache_num)
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]
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return (tgt, memory, pre_acoustic_embeds, cache)
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def is_optimizable(self):
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return True
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def get_input_names(self):
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cache_num = len(self.model.decoders) + len(self.model.decoders2)
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return ['tgt', 'memory', 'pre_acoustic_embeds'] \
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+ ['cache_%d' % i for i in range(cache_num)]
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def get_output_names(self):
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cache_num = len(self.model.decoders) + len(self.model.decoders2)
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return ['y'] \
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+ ['out_cache_%d' % i for i in range(cache_num)]
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def get_dynamic_axes(self):
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ret = {
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'tgt': {
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0: 'tgt_batch',
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1: 'tgt_length'
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},
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'memory': {
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0: 'memory_batch',
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1: 'memory_length'
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},
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'pre_acoustic_embeds': {
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0: 'acoustic_embeds_batch',
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1: 'acoustic_embeds_length',
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}
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}
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cache_num = len(self.model.decoders) + len(self.model.decoders2)
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ret.update({
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'cache_%d' % d: {
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0: 'cache_%d_batch' % d,
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2: 'cache_%d_length' % d
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}
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for d in range(cache_num)
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})
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return ret
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def get_model_config(self, path):
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return {
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"dec_type": "XformerDecoder",
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"model_path": os.path.join(path, f'{self.model_name}.onnx'),
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"n_layers": len(self.model.decoders) + len(self.model.decoders2),
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"odim": self.model.decoders[0].size
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}
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