I used PyTorch-1.9.0 and Python 3.6：
I work with a PyTorch porject, and find the following ops from Torchscript haven’t been support by TVM frontend.
‘aten::dim’, ‘prim::dtype’, ‘prim::unchecked_cast’, ‘aten::append’, ‘aten::warn’, ‘prim::TupleIndex’, ‘prim::Uninitialized’, ‘aten::copy_’, ‘aten::__is__’, ‘aten::format’, ‘aten::item’, ‘aten::__derive_index’, ‘aten::list’, ‘prim::data’
Part of my Torchscript is (limited by 40000 max text length):
graph(%self.1 : __torch__.decoders.ScriptGreedyDecoder,
%x.1 : Tensor,
%out_lens.1 : Tensor):
%3 : NoneType = prim::Constant()
%4 : bool = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:66:8
%5 : int = prim::Constant[value=0]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:66:43
%6 : int = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:67:54
%7 : __torch__.model_separable_rnnt.RNNT = prim::GetAttr[name="_model"](%self.1)
%8 : __torch__.model_separable_rnnt.___torch_mangle_4.Encoder = prim::GetAttr[name="encoder"](%7)
%23 : bool = prim::Constant[value=0]() # :0:0
%24 : bool = prim::Constant[value=1]() # /python3.6/site-packages/torch/nn/modules/rnn.py:679:61
%25 : NoneType = prim::Constant() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:83:45
%26 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%27 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%28 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%29 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%30 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%31 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%32 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%33 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%34 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%35 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%36 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%37 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%38 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%39 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%40 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%41 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%42 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%43 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%44 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%45 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%46 : str = prim::Constant[value="Expected hidden[1] size {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:624:31
%47 : str = prim::Constant[value="Expected hidden[0] size {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:622:31
%48 : str = prim::Constant[value="input.size(-1) must be equal to input_size. Expected {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:206:16
%49 : str = prim::Constant[value="input must have {} dimensions, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:202:16
%50 : int = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:88:41
%51 : int = prim::Constant[value=2]() # :0:0
%52 : int = prim::Constant[value=1024]() # /python3.6/site-packages/torch/nn/modules/rnn.py:664:73
%53 : int = prim::Constant[value=3]() # /python3.6/site-packages/torch/nn/modules/rnn.py:199:63
%54 : int = prim::Constant[value=240]() # /python3.6/site-packages/torch/nn/modules/rnn.py:204:11
%55 : int = prim::Constant[value=-1]() # /python3.6/site-packages/torch/nn/modules/rnn.py:204:41
%56 : float = prim::Constant[value=0.32000000000000001]() # /python3.6/site-packages/torch/nn/modules/rnn.py:680:30
%57 : int = prim::Constant[value=0]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:88:38
%58 : int = prim::Constant[value=6]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:105:28
%59 : int = prim::Constant[value=2048]() # /python3.6/site-packages/torch/nn/modules/rnn.py:204:11
%self_pre_rnn_lstm__flat_weights.1 : Tensor[] = prim::ListConstruct(%45, %44, %43, %42, %41, %40, %39, %38)
%self_post_rnn_lstm__flat_weights.1 : Tensor[] = prim::ListConstruct(%37, %36, %35, %34, %33, %32, %31, %30, %29, %28, %27, %26)
%max_batch_size.3 : int = aten::size(%x.1, %50) # /python3.6/site-packages/torch/nn/modules/rnn.py:658:68
%63 : int = prim::dtype(%x.1)
%64 : Device = prim::device(%x.1)
%65 : int[] = prim::ListConstruct(%51, %max_batch_size.3, %52)
%h_zeros.3 : Tensor = aten::zeros(%65, %63, %25, %64, %25) # /python3.6/site-packages/torch/nn/modules/rnn.py:665:22
%67 : int = prim::dtype(%x.1)
%68 : Device = prim::device(%x.1)
%c_zeros.3 : Tensor = aten::zeros(%65, %67, %25, %68, %25) # /python3.6/site-packages/torch/nn/modules/rnn.py:668:22
%70 : int = aten::dim(%x.1) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
%71 : bool = aten::ne(%70, %53) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
= prim::If(%71) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:8
block0():
%72 : int = aten::dim(%x.1) # /python3.6/site-packages/torch/nn/modules/rnn.py:203:40
%73 : str = aten::format(%49, %53, %72) # /python3.6/site-packages/torch/nn/modules/rnn.py:202:16
= prim::RaiseException(%73) # /python3.6/site-packages/torch/nn/modules/rnn.py:201:12
-> ()
block1():
-> ()
%74 : int = aten::size(%x.1, %55) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:30
%75 : bool = aten::ne(%54, %74) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:11
= prim::If(%75) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:8
block0():
%76 : int = aten::size(%x.1, %55) # /python3.6/site-packages/torch/nn/modules/rnn.py:207:37
%77 : str = aten::format(%48, %54, %76) # /python3.6/site-packages/torch/nn/modules/rnn.py:206:16
= prim::RaiseException(%77) # /python3.6/site-packages/torch/nn/modules/rnn.py:205:12
-> ()
block1():
-> ()
%mini_batch.1 : int = aten::size(%x.1, %50) # /python3.6/site-packages/torch/nn/modules/rnn.py:213:64
%expected_hidden_size.3 : (int, int, int) = prim::TupleConstruct(%51, %mini_batch.1, %52)
%80 : int[] = aten::size(%h_zeros.3) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%81 : int[] = prim::ListConstruct(%51, %mini_batch.1, %52)
%82 : bool = aten::ne(%80, %81) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%82) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%83 : int[] = aten::size(%h_zeros.3) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%84 : int[] = aten::list(%83) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%85 : str = aten::format(%47, %expected_hidden_size.3, %84) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%85) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%mini_batch0.1 : int = aten::size(%x.1, %50) # /python3.6/site-packages/torch/nn/modules/rnn.py:607:64
%expected_hidden_size0.1 : (int, int, int) = prim::TupleConstruct(%51, %mini_batch0.1, %52)
%88 : int[] = aten::size(%c_zeros.3) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%89 : int[] = prim::ListConstruct(%51, %mini_batch0.1, %52)
%90 : bool = aten::ne(%88, %89) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%90) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%91 : int[] = aten::size(%c_zeros.3) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%92 : int[] = aten::list(%91) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%93 : str = aten::format(%46, %expected_hidden_size0.1, %92) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%93) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%94 : Tensor[] = prim::ListConstruct(%h_zeros.3, %c_zeros.3)
%95 : Tensor, %96 : Tensor, %97 : Tensor = aten::lstm(%x.1, %94, %self_pre_rnn_lstm__flat_weights.1, %24, %51, %56, %23, %23, %23) # /python3.6/site-packages/torch/nn/modules/rnn.py:679:21
%98 : Tensor = prim::data(%95)
%99 : Tensor = aten::dropout_(%98, %56, %23) # /python3.6/site-packages/torch/nn/functional.py:1168:11
%seq.1 : Tensor[] = prim::ListConstruct(%95)
= prim::Loop(%50, %24) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:100:8
block0(%101 : int):
%i.1 : int = aten::__derive_index(%101, %50, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:100:8
%tmp.1 : Tensor = aten::zeros_like(%95, %25, %25, %25, %25, %25) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:102:18
%104 : Tensor = aten::slice(%95, %57, %i.1, %25, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:29
%105 : Tensor = aten::slice(%104, %50, %25, %25, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:29
%106 : Tensor = aten::slice(%105, %51, %25, %25, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:29
%107 : int = aten::neg(%i.1) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:17
%108 : Tensor = aten::slice(%tmp.1, %57, %25, %107, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:12
%109 : Tensor = aten::slice(%108, %50, %25, %25, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:12
%110 : Tensor = aten::slice(%109, %51, %25, %25, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:12
%111 : Tensor = aten::copy_(%110, %106, %23) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:103:12
%112 : Tensor[] = aten::append(%seq.1, %tmp.1) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:104:12
-> (%24)
%113 : Tensor = aten::to(%out_lens.1, %58, %23, %23, %25) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:105:28
%114 : Tensor = aten::div(%113, %51) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:105:28
%115 : Tensor = aten::ceil(%114) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:105:17
%x_lens0.1 : Tensor = aten::to(%115, %53, %23, %23, %25) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:105:17
%117 : Tensor = aten::cat(%seq.1, %51) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:107:15
%118 : Tensor = aten::slice(%117, %57, %25, %25, %51) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:107:15
%119 : Tensor = aten::slice(%118, %50, %25, %25, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:107:15
%120 : Tensor = aten::slice(%119, %51, %25, %25, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/rnn.py:107:15
%max_batch_size0.1 : int = aten::size(%120, %50) # /python3.6/site-packages/torch/nn/modules/rnn.py:658:68
%122 : int = prim::dtype(%120)
%123 : Device = prim::device(%120)
%124 : int[] = prim::ListConstruct(%53, %max_batch_size0.1, %52)
%h_zeros0.1 : Tensor = aten::zeros(%124, %122, %25, %123, %25) # /python3.6/site-packages/torch/nn/modules/rnn.py:665:22
%126 : int = prim::dtype(%120)
%127 : Device = prim::device(%120)
%c_zeros0.1 : Tensor = aten::zeros(%124, %126, %25, %127, %25) # /python3.6/site-packages/torch/nn/modules/rnn.py:668:22
%129 : int = aten::dim(%120) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
%130 : bool = aten::ne(%129, %53) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
= prim::If(%130) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:8
block0():
%131 : int = aten::dim(%120) # /python3.6/site-packages/torch/nn/modules/rnn.py:203:40
%132 : str = aten::format(%49, %53, %131) # /python3.6/site-packages/torch/nn/modules/rnn.py:202:16
= prim::RaiseException(%132) # /python3.6/site-packages/torch/nn/modules/rnn.py:201:12
-> ()
block1():
-> ()
%133 : int = aten::size(%120, %55) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:30
%134 : bool = aten::ne(%59, %133) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:11
= prim::If(%134) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:8
block0():
%135 : int = aten::size(%120, %55) # /python3.6/site-packages/torch/nn/modules/rnn.py:207:37
%136 : str = aten::format(%48, %59, %135) # /python3.6/site-packages/torch/nn/modules/rnn.py:206:16
= prim::RaiseException(%136) # /python3.6/site-packages/torch/nn/modules/rnn.py:205:12
-> ()
block1():
-> ()
%mini_batch1.1 : int = aten::size(%120, %50) # /python3.6/site-packages/torch/nn/modules/rnn.py:213:64
%expected_hidden_size1.1 : (int, int, int) = prim::TupleConstruct(%53, %mini_batch1.1, %52)
%139 : int[] = aten::size(%h_zeros0.1) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%140 : int[] = prim::ListConstruct(%53, %mini_batch1.1, %52)
%141 : bool = aten::ne(%139, %140) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%141) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%142 : int[] = aten::size(%h_zeros0.1) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%143 : int[] = aten::list(%142) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%144 : str = aten::format(%47, %expected_hidden_size1.1, %143) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%144) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%mini_batch2.1 : int = aten::size(%120, %50) # /python3.6/site-packages/torch/nn/modules/rnn.py:607:64
%expected_hidden_size2.1 : (int, int, int) = prim::TupleConstruct(%53, %mini_batch2.1, %52)
%147 : int[] = aten::size(%c_zeros0.1) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%148 : int[] = prim::ListConstruct(%53, %mini_batch2.1, %52)
%149 : bool = aten::ne(%147, %148) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%149) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%150 : int[] = aten::size(%c_zeros0.1) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%151 : int[] = aten::list(%150) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%152 : str = aten::format(%46, %expected_hidden_size2.1, %151) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%152) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%153 : Tensor[] = prim::ListConstruct(%h_zeros0.1, %c_zeros0.1)
%154 : Tensor, %155 : Tensor, %156 : Tensor = aten::lstm(%120, %153, %self_post_rnn_lstm__flat_weights.1, %24, %53, %56, %23, %23, %23) # /python3.6/site-packages/torch/nn/modules/rnn.py:679:21
%157 : Tensor = prim::data(%154)
%158 : Tensor = aten::dropout_(%157, %56, %23) # /python3.6/site-packages/torch/nn/functional.py:1168:11
%x_padded0.1 : Tensor = aten::transpose(%154, %57, %50) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:88:19
%160 : (Tensor, Tensor) = prim::TupleConstruct(%x_padded0.1, %x_lens0.1)
%logits.2 : Tensor, %logits_lens.1 : Tensor = prim::TupleUnpack(%160)
%output.1 : int[][] = prim::ListConstruct()
%13 : int = aten::size(%logits.2, %5) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:66:31
= prim::Loop(%13, %4) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:66:8
block0(%batch_idx.1 : int):
%15 : Tensor = aten::select(%logits.2, %5, %batch_idx.1) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:67:20
%16 : Tensor = aten::slice(%15, %5, %3, %3, %6) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:67:20
%17 : Tensor = aten::slice(%16, %6, %3, %3, %6) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:67:20
%inseq.1 : Tensor = aten::unsqueeze(%17, %6) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:67:20
%logitlen.1 : Tensor = aten::select(%logits_lens.1, %5, %batch_idx.1) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:69:23
%161 : bool = prim::Constant[value=0]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:89:60
%162 : bool = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:78:8
%hidden.11 : NoneType = prim::Constant() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:76:62
%164 : int = prim::Constant[value=0]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:79:44
%165 : int = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:100:33
%166 : int = prim::Constant[value=9223372036854775807]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:84:12
%sentence.1 : int[] = prim::ListConstruct()
%168 : Scalar = aten::item(%logitlen.1) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:78:34
%169 : int = aten::Int(%168) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:78:30
%hidden : (Tensor, Tensor)? = prim::Loop(%169, %162, %hidden.11) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:78:8
block0(%time_idx.1 : int, %hidden.9 : (Tensor, Tensor)?):
%173 : Tensor = aten::select(%inseq.1, %164, %time_idx.1) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:79:16
%174 : Tensor = aten::slice(%173, %164, %hidden.11, %hidden.11, %165) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:79:16
%175 : Tensor = aten::slice(%174, %165, %hidden.11, %hidden.11, %165) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:79:16
%f.1 : Tensor = aten::unsqueeze(%175, %164) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:79:16
%177 : int = prim::GetAttr[name="_max_symbols_per_step"](%self.1)
%178 : bool = aten::lt(%164, %177) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:84:32
%hidden0 : (Tensor, Tensor)?, %not_blank : bool, %symbols_added : int = prim::Loop(%166, %178, %hidden.9, %162, %164) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:84:12
block0(%182 : int, %hidden0.9 : (Tensor, Tensor)?, %not_blank.7 : bool, %symbols_added.5 : int):
%186 : int = prim::Constant[value=0]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:122:43
%187 : int = prim::Constant[value=-1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:122:57
%188 : int = aten::len(%sentence.1) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:122:28
%189 : bool = aten::eq(%188, %186) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:122:28
%190 : int = prim::If(%189) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:122:15
block0():
%191 : int = prim::GetAttr[name="_SOS"](%self.1)
-> (%191)
block1():
%192 : int = aten::__getitem__(%sentence.1, %187) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:122:50
-> (%192)
%193 : bool = prim::Constant[value=0]()
%194 : NoneType = prim::Constant() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:106:42
%195 : int = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:108:21
%196 : int = prim::Constant[value=4]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:109:46
%197 : int = prim::GetAttr[name="_SOS"](%self.1)
%198 : bool = aten::eq(%190, %197) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:105:11
%199 : (Tensor, (Tensor, Tensor)) = prim::If(%198) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:105:8
block0():
%200 : __torch__.model_separable_rnnt.RNNT = prim::GetAttr[name="_model"](%self.1)
%201 : __torch__.model_separable_rnnt.___torch_mangle_13.Prediction = prim::GetAttr[name="prediction"](%200)
%202 : bool = prim::Constant[value=1]() # /python3.6/site-packages/torch/nn/modules/rnn.py:679:61
%203 : bool = prim::Constant[value=0]()
%204 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%y0.2 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%206 : str = prim::Constant[value="AssertionError: "]() # :0:0
%207 : NoneType = prim::Constant() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:126:16
%208 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%209 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%210 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%211 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%212 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%213 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%214 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%215 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%216 : str = prim::Constant[value="Expected hidden[1] size {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:624:31
%217 : str = prim::Constant[value="Expected hidden[0] size {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:622:31
%218 : str = prim::Constant[value="input.size(-1) must be equal to input_size. Expected {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:206:16
%219 : str = prim::Constant[value="input must have {} dimensions, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:202:16
%220 : int = prim::Constant[value=0]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:144:24
%221 : int = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:131:16
%222 : int = prim::Constant[value=2]() # :0:0
%223 : int = prim::Constant[value=320]() # :0:0
%224 : int = prim::Constant[value=3]() # /python3.6/site-packages/torch/nn/modules/rnn.py:199:63
%225 : int = prim::Constant[value=-1]() # /python3.6/site-packages/torch/nn/functional.py:2030:22
%226 : float = prim::Constant[value=0.32000000000000001]() # /python3.6/site-packages/torch/nn/modules/rnn.py:680:30
%self_dec_rnn_lstm__flat_weights.2 : Tensor[] = prim::ListConstruct(%215, %214, %213, %212, %211, %210, %209, %208)
%228 : (Tensor, Tensor)? = prim::Uninitialized() # :0:0
%229 : bool = aten::__is__(%194, %207) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:126:11
%y0.4 : Tensor, %state0.1 : (Tensor, Tensor)? = prim::If(%229) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:126:8
block0():
%232 : bool = aten::__is__(%hidden0.9, %207) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:129:19
%state1.1 : (Tensor, Tensor)? = prim::If(%232) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:129:12
block0():
-> (%hidden0.9)
block1():
= prim::RaiseException(%206) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:129:12
-> (%228)
-> (%y0.2, %state1.1)
block1():
%y1.2 : Tensor = prim::unchecked_cast(%194)
%y2.2 : Tensor = aten::embedding(%204, %y1.2, %225, %203, %203) # /python3.6/site-packages/torch/nn/functional.py:2043:11
-> (%y2.2, %hidden0.9)
%y3.2 : Tensor = aten::transpose(%y0.4, %220, %221) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:144:12
%max_batch_size.2 : int = aten::size(%y3.2, %221) # /python3.6/site-packages/torch/nn/modules/rnn.py:658:68
%238 : bool = aten::__is__(%state0.1, %207) # /python3.6/site-packages/torch/nn/modules/rnn.py:662:11
%hx.2 : (Tensor, Tensor) = prim::If(%238) # /python3.6/site-packages/torch/nn/modules/rnn.py:662:8
block0():
%240 : int = prim::dtype(%y3.2)
%241 : Device = prim::device(%y3.2)
%242 : int[] = prim::ListConstruct(%222, %max_batch_size.2, %223)
%h_zeros.2 : Tensor = aten::zeros(%242, %240, %207, %241, %207) # /python3.6/site-packages/torch/nn/modules/rnn.py:665:22
%c_zeros.2 : Tensor = aten::zeros(%242, %240, %207, %241, %207) # /python3.6/site-packages/torch/nn/modules/rnn.py:668:22
%hx.4 : (Tensor, Tensor) = prim::TupleConstruct(%h_zeros.2, %c_zeros.2)
-> (%hx.4)
block1():
%hx0.2 : (Tensor, Tensor) = prim::unchecked_cast(%state0.1)
-> (%hx0.2)
%247 : int = aten::dim(%y3.2) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
%248 : bool = aten::ne(%247, %224) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
= prim::If(%248) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:8
block0():
%249 : str = aten::format(%219, %224, %247) # /python3.6/site-packages/torch/nn/modules/rnn.py:202:16
= prim::RaiseException(%249) # /python3.6/site-packages/torch/nn/modules/rnn.py:201:12
-> ()
block1():
-> ()
%250 : int = aten::size(%y3.2, %225) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:30
%251 : bool = aten::ne(%223, %250) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:11
= prim::If(%251) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:8
block0():
%252 : str = aten::format(%218, %223, %250) # /python3.6/site-packages/torch/nn/modules/rnn.py:206:16
= prim::RaiseException(%252) # /python3.6/site-packages/torch/nn/modules/rnn.py:205:12
-> ()
block1():
-> ()
%253 : Tensor = prim::TupleIndex(%hx.2, %220)
%expected_hidden_size.2 : (int, int, int) = prim::TupleConstruct(%222, %max_batch_size.2, %223)
%255 : int[] = aten::size(%253) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%256 : int[] = prim::ListConstruct(%222, %max_batch_size.2, %223)
%257 : bool = aten::ne(%255, %256) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%257) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%258 : int[] = aten::size(%253) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%259 : int[] = aten::list(%258) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%260 : str = aten::format(%217, %expected_hidden_size.2, %259) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%260) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%261 : Tensor = prim::TupleIndex(%hx.2, %221)
%262 : int[] = aten::size(%261) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%263 : bool = aten::ne(%262, %256) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%263) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%264 : int[] = aten::size(%261) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%265 : int[] = aten::list(%264) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%266 : str = aten::format(%216, %expected_hidden_size.2, %265) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%266) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%267 : Tensor, %268 : Tensor = prim::TupleUnpack(%hx.2)
%269 : Tensor[] = prim::ListConstruct(%267, %268)
%270 : Tensor, %271 : Tensor, %272 : Tensor = aten::lstm(%y3.2, %269, %self_dec_rnn_lstm__flat_weights.2, %202, %222, %226, %203, %203, %203) # /python3.6/site-packages/torch/nn/modules/rnn.py:679:21
%hidden.7 : (Tensor, Tensor) = prim::TupleConstruct(%271, %272)
%274 : Tensor = prim::data(%270)
%275 : Tensor = aten::dropout_(%274, %226, %203) # /python3.6/site-packages/torch/nn/functional.py:1168:11
%g.2 : Tensor = aten::transpose(%270, %220, %221) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:146:12
%277 : (Tensor, (Tensor, Tensor)) = prim::TupleConstruct(%g.2, %hidden.7)
-> (%277)
block1():
%278 : int = prim::GetAttr[name="_blank_id"](%self.1)
%279 : bool = aten::gt(%190, %278) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:107:11
%label0 : int = prim::If(%279) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:107:8
block0():
%label0.1 : int = aten::sub(%190, %195) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:108:12
-> (%label0.1)
block1():
-> (%190)
%282 : int[] = prim::ListConstruct(%label0)
%283 : int[][] = prim::ListConstruct(%282)
%label1.1 : Tensor = aten::tensor(%283, %196, %194, %193) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/decoders.py:109:16
%285 : __torch__.model_separable_rnnt.RNNT = prim::GetAttr[name="_model"](%self.1)
%286 : __torch__.model_separable_rnnt.___torch_mangle_13.Prediction = prim::GetAttr[name="prediction"](%285)
%287 : bool = prim::Constant[value=1]() # /python3.6/site-packages/torch/nn/modules/rnn.py:679:61
%288 : bool = prim::Constant[value=0]()
%289 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%y0.1 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%291 : str = prim::Constant[value="AssertionError: "]() # :0:0
%292 : NoneType = prim::Constant() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:126:16
%293 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%294 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%295 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%296 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%297 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%298 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%299 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%300 : Tensor = prim::Constant[value=<Tensor>]() # :0:0
%301 : str = prim::Constant[value="Expected hidden[1] size {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:624:31
%302 : str = prim::Constant[value="Expected hidden[0] size {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:622:31
%303 : str = prim::Constant[value="input.size(-1) must be equal to input_size. Expected {}, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:206:16
%304 : str = prim::Constant[value="input must have {} dimensions, got {}"]() # /python3.6/site-packages/torch/nn/modules/rnn.py:202:16
%305 : int = prim::Constant[value=0]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:144:24
%306 : int = prim::Constant[value=1]() # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:131:16
%307 : int = prim::Constant[value=2]() # :0:0
%308 : int = prim::Constant[value=320]() # :0:0
%309 : int = prim::Constant[value=3]() # /python3.6/site-packages/torch/nn/modules/rnn.py:199:63
%310 : int = prim::Constant[value=-1]() # /python3.6/site-packages/torch/nn/functional.py:2030:22
%311 : float = prim::Constant[value=0.32000000000000001]() # /python3.6/site-packages/torch/nn/modules/rnn.py:680:30
%self_dec_rnn_lstm__flat_weights.1 : Tensor[] = prim::ListConstruct(%300, %299, %298, %297, %296, %295, %294, %293)
%313 : (Tensor, Tensor)? = prim::Uninitialized() # :0:0
%314 : bool = aten::__is__(%label1.1, %292) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:126:11
%y0 : Tensor, %state0 : (Tensor, Tensor)? = prim::If(%314) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:126:8
block0():
%317 : bool = aten::__is__(%hidden0.9, %292) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:129:19
%state1 : (Tensor, Tensor)? = prim::If(%317) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:129:12
block0():
-> (%hidden0.9)
block1():
= prim::RaiseException(%291) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:129:12
-> (%313)
-> (%y0.1, %state1)
block1():
%y1.1 : Tensor = prim::unchecked_cast(%label1.1)
%y2.1 : Tensor = aten::embedding(%289, %y1.1, %310, %288, %288) # /python3.6/site-packages/torch/nn/functional.py:2043:11
-> (%y2.1, %hidden0.9)
%y3.1 : Tensor = aten::transpose(%y0, %305, %306) # /mlperf-rnnt/inference/speech_recognition/rnnt/pytorch/model_separable_rnnt.py:144:12
%max_batch_size.1 : int = aten::size(%y3.1, %306) # /python3.6/site-packages/torch/nn/modules/rnn.py:658:68
%323 : bool = aten::__is__(%state0, %292) # /python3.6/site-packages/torch/nn/modules/rnn.py:662:11
%hx : (Tensor, Tensor) = prim::If(%323) # /python3.6/site-packages/torch/nn/modules/rnn.py:662:8
block0():
%325 : int = prim::dtype(%y3.1)
%326 : Device = prim::device(%y3.1)
%327 : int[] = prim::ListConstruct(%307, %max_batch_size.1, %308)
%h_zeros.1 : Tensor = aten::zeros(%327, %325, %292, %326, %292) # /python3.6/site-packages/torch/nn/modules/rnn.py:665:22
%c_zeros.1 : Tensor = aten::zeros(%327, %325, %292, %326, %292) # /python3.6/site-packages/torch/nn/modules/rnn.py:668:22
%hx.1 : (Tensor, Tensor) = prim::TupleConstruct(%h_zeros.1, %c_zeros.1)
-> (%hx.1)
block1():
%hx0.1 : (Tensor, Tensor) = prim::unchecked_cast(%state0)
-> (%hx0.1)
%332 : int = aten::dim(%y3.1) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
%333 : bool = aten::ne(%332, %309) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:11
= prim::If(%333) # /python3.6/site-packages/torch/nn/modules/rnn.py:200:8
block0():
%334 : str = aten::format(%304, %309, %332) # /python3.6/site-packages/torch/nn/modules/rnn.py:202:16
= prim::RaiseException(%334) # /python3.6/site-packages/torch/nn/modules/rnn.py:201:12
-> ()
block1():
-> ()
%335 : int = aten::size(%y3.1, %310) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:30
%336 : bool = aten::ne(%308, %335) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:11
= prim::If(%336) # /python3.6/site-packages/torch/nn/modules/rnn.py:204:8
block0():
%337 : str = aten::format(%303, %308, %335) # /python3.6/site-packages/torch/nn/modules/rnn.py:206:16
= prim::RaiseException(%337) # /python3.6/site-packages/torch/nn/modules/rnn.py:205:12
-> ()
block1():
-> ()
%338 : Tensor = prim::TupleIndex(%hx, %305)
%expected_hidden_size.1 : (int, int, int) = prim::TupleConstruct(%307, %max_batch_size.1, %308)
%340 : int[] = aten::size(%338) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%341 : int[] = prim::ListConstruct(%307, %max_batch_size.1, %308)
%342 : bool = aten::ne(%340, %341) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%342) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%343 : int[] = aten::size(%338) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%344 : int[] = aten::list(%343) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%345 : str = aten::format(%302, %expected_hidden_size.1, %344) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%345) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%346 : Tensor = prim::TupleIndex(%hx, %306)
%347 : int[] = aten::size(%346) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
%348 : bool = aten::ne(%347, %341) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:11
= prim::If(%348) # /python3.6/site-packages/torch/nn/modules/rnn.py:225:8
block0():
%349 : int[] = aten::size(%346) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:69
%350 : int[] = aten::list(%349) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:64
%351 : str = aten::format(%301, %expected_hidden_size.1, %350) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:31
= prim::RaiseException(%351) # /python3.6/site-packages/torch/nn/modules/rnn.py:226:12
-> ()
block1():
-> ()
%352 : Tensor, %353 : Tensor = prim::TupleUnpack(%hx)
%354 : Tensor[] = prim::ListConstruct(%352, %353)
%355 : Tensor, %356 : Tensor, %357 : Tensor = aten::lstm(%y3.1, %354, %self_dec_rnn_lstm__flat_weights.1, %287, %307, %311, %288, %288, %288) # /python3.6/site-packages/torch/nn/modules/rnn.py:679:21
%hidden.1 : (Tensor, Tensor) = prim::TupleConstruct(%356, %357)