stacked_lstm_torchscript.txt

ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
graph(%self : __torch__.custom_lstms.StackedLSTM,
      %input.1 : Tensor,
      %states.1 : (Tensor, Tensor)[]):
  %i.2 : int = prim::Constant[value=0]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:80:8
  %i.3 : int = prim::Constant[value=1]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:80:8
  %output_states.1 : (Tensor, Tensor)[] = prim::ListConstruct()
  %6 : __torch__.torch.nn.modules.container.ModuleList = prim::GetAttr[name="layers"](%self)
  %7 : __torch__.custom_lstms.LSTMLayer = prim::GetAttr[name="0"](%6)
  %8 : __torch__.custom_lstms.LSTMLayer = prim::GetAttr[name="1"](%6)
  %state.1 : (Tensor, Tensor) = aten::__getitem__(%states.1, %i.2) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:81:20
  %22 : bool = prim::Constant[value=1]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:8
  %23 : int = prim::Constant[value=0]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:34
  %outputs.2 : Tensor[] = prim::ListConstruct()
  %25 : int = aten::size(%input.1, %23) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:23
  %outputs.4 : Tensor[], %state.2 : (Tensor, Tensor) = prim::Loop(%25, %22, %outputs.2, %state.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:8
    block0(%i.4 : int, %outputs.7 : Tensor[], %state.7 : (Tensor, Tensor)):
      %31 : __torch__.custom_lstms.LayerNormLSTMCell = prim::GetAttr[name="cell"](%7)
      %32 : Tensor = aten::select(%input.1, %23, %i.4) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:55:35
      %33 : int = prim::Constant[value=4]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:32:60
      %34 : int = prim::Constant[value=1]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:32:63
      %hx.2 : Tensor, %cx.2 : Tensor = prim::TupleUnpack(%state.7)
      %37 : __torch__.torch.nn.modules.normalization.LayerNorm = prim::GetAttr[name="layernorm_i"](%31)
      %38 : Tensor = prim::GetAttr[name="weight_ih"](%31)
      %39 : Tensor = aten::t(%38) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:29:50
      %40 : Tensor = aten::mm(%32, %39) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:29:34
      %41 : Function = prim::Constant[name="layer_norm"]()
      %42 : float = prim::Constant[value=1.0000000000000001e-05]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:66
      %43 : int = prim::Constant[value=16]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:19
      %44 : Tensor = prim::GetAttr[name="weight"](%37)
      %45 : Tensor = prim::GetAttr[name="bias"](%37)
      %46 : int[] = prim::ListConstruct(%43)
      %47 : bool = prim::Constant[value=1]() # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1938:28
      %igates.2 : Tensor = aten::layer_norm(%40, %46, %44, %45, %42, %47) # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1937:11
      %49 : __torch__.torch.nn.modules.normalization.LayerNorm = prim::GetAttr[name="layernorm_h"](%31)
      %50 : Tensor = prim::GetAttr[name="weight_hh"](%31)
      %51 : Tensor = aten::t(%50) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:30:47
      %52 : Tensor = aten::mm(%hx.2, %51) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:30:34
      %53 : Function = prim::Constant[name="layer_norm"]()
      %54 : float = prim::Constant[value=1.0000000000000001e-05]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:66
      %55 : int = prim::Constant[value=16]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:19
      %56 : Tensor = prim::GetAttr[name="weight"](%49)
      %57 : Tensor = prim::GetAttr[name="bias"](%49)
      %58 : int[] = prim::ListConstruct(%55)
      %59 : bool = prim::Constant[value=1]() # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1938:28
      %hgates.2 : Tensor = aten::layer_norm(%52, %58, %56, %57, %54, %59) # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1937:11
      %gates.2 : Tensor = aten::add(%igates.2, %hgates.2, %34) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:31:16
      %62 : Tensor[] = aten::chunk(%gates.2, %33, %34) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:32:48
      %ingate.2 : Tensor, %forgetgate.2 : Tensor, %cellgate.2 : Tensor, %outgate.2 : Tensor = prim::ListUnpack(%62)
      %ingate.4 : Tensor = aten::sigmoid(%ingate.2) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:34:17
      %forgetgate.4 : Tensor = aten::sigmoid(%forgetgate.2) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:35:21
      %cellgate.4 : Tensor = aten::tanh(%cellgate.2) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:36:19
      %outgate.4 : Tensor = aten::sigmoid(%outgate.2) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:37:18
      %71 : __torch__.torch.nn.modules.normalization.___torch_mangle_0.LayerNorm = prim::GetAttr[name="layernorm_c"](%31)
      %72 : Tensor = aten::mul(%forgetgate.4, %cx.2) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:39:31
      %73 : Tensor = aten::mul(%ingate.4, %cellgate.4) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:39:51
      %74 : Tensor = aten::add(%72, %73, %34) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:39:31
      %75 : Function = prim::Constant[name="layer_norm"]()
      %76 : float = prim::Constant[value=1.0000000000000001e-05]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:66
      %77 : int = prim::Constant[value=4]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:19
      %78 : Tensor = prim::GetAttr[name="weight"](%71)
      %79 : Tensor = prim::GetAttr[name="bias"](%71)
      %80 : int[] = prim::ListConstruct(%77)
      %81 : bool = prim::Constant[value=1]() # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1938:28
      %cy.2 : Tensor = aten::layer_norm(%74, %80, %78, %79, %76, %81) # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1937:11
      %83 : Tensor = aten::tanh(%cy.2) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:40:23
      %hy.2 : Tensor = aten::mul(%outgate.4, %83) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:40:13
      %85 : (Tensor, Tensor) = prim::TupleConstruct(%hy.2, %cy.2)
      %86 : (Tensor, (Tensor, Tensor)) = prim::TupleConstruct(%hy.2, %85)
      %out.2 : Tensor, %state.5 : (Tensor, Tensor) = prim::TupleUnpack(%86)
      %89 : Tensor[] = prim::ListConstruct(%out.2)
      %outputs.5 : Tensor[] = aten::add_(%outputs.7, %89) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:56:12
      -> (%22, %outputs.5, %state.5)
  %91 : Tensor = aten::stack(%outputs.4, %23) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:57:15
  %92 : (Tensor, (Tensor, Tensor)) = prim::TupleConstruct(%91, %state.2)
  %output.2 : Tensor, %out_state.1 : (Tensor, Tensor) = prim::TupleUnpack(%92)
  %13 : (Tensor, Tensor)[] = prim::ListConstruct(%out_state.1)
  %output_states.3 : (Tensor, Tensor)[] = aten::add_(%output_states.1, %13) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:83:12
  %state.4 : (Tensor, Tensor) = aten::__getitem__(%states.1, %i.3) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:81:20
  %93 : bool = prim::Constant[value=1]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:8
  %94 : int = prim::Constant[value=0]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:34
  %outputs.1 : Tensor[] = prim::ListConstruct()
  %96 : int = aten::size(%output.2, %94) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:23
  %outputs : Tensor[], %state : (Tensor, Tensor) = prim::Loop(%96, %93, %outputs.1, %state.4) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:54:8
    block0(%i.1 : int, %outputs.6 : Tensor[], %state.6 : (Tensor, Tensor)):
      %102 : __torch__.custom_lstms.LayerNormLSTMCell = prim::GetAttr[name="cell"](%8)
      %103 : Tensor = aten::select(%output.2, %94, %i.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:55:35
      %104 : int = prim::Constant[value=4]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:32:60
      %105 : int = prim::Constant[value=1]() # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:32:63
      %hx.1 : Tensor, %cx.1 : Tensor = prim::TupleUnpack(%state.6)
      %108 : __torch__.torch.nn.modules.normalization.LayerNorm = prim::GetAttr[name="layernorm_i"](%102)
      %109 : Tensor = prim::GetAttr[name="weight_ih"](%102)
      %110 : Tensor = aten::t(%109) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:29:50
      %111 : Tensor = aten::mm(%103, %110) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:29:34
      %112 : Function = prim::Constant[name="layer_norm"]()
      %113 : float = prim::Constant[value=1.0000000000000001e-05]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:66
      %114 : int = prim::Constant[value=16]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:19
      %115 : Tensor = prim::GetAttr[name="weight"](%108)
      %116 : Tensor = prim::GetAttr[name="bias"](%108)
      %117 : int[] = prim::ListConstruct(%114)
      %118 : bool = prim::Constant[value=1]() # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1938:28
      %igates.1 : Tensor = aten::layer_norm(%111, %117, %115, %116, %113, %118) # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1937:11
      %120 : __torch__.torch.nn.modules.normalization.LayerNorm = prim::GetAttr[name="layernorm_h"](%102)
      %121 : Tensor = prim::GetAttr[name="weight_hh"](%102)
      %122 : Tensor = aten::t(%121) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:30:47
      %123 : Tensor = aten::mm(%hx.1, %122) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:30:34
      %124 : Function = prim::Constant[name="layer_norm"]()
      %125 : float = prim::Constant[value=1.0000000000000001e-05]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:66
      %126 : int = prim::Constant[value=16]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:19
      %127 : Tensor = prim::GetAttr[name="weight"](%120)
      %128 : Tensor = prim::GetAttr[name="bias"](%120)
      %129 : int[] = prim::ListConstruct(%126)
      %130 : bool = prim::Constant[value=1]() # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1938:28
      %hgates.1 : Tensor = aten::layer_norm(%123, %129, %127, %128, %125, %130) # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1937:11
      %gates.1 : Tensor = aten::add(%igates.1, %hgates.1, %105) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:31:16
      %133 : Tensor[] = aten::chunk(%gates.1, %104, %105) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:32:48
      %ingate.1 : Tensor, %forgetgate.1 : Tensor, %cellgate.1 : Tensor, %outgate.1 : Tensor = prim::ListUnpack(%133)
      %ingate.3 : Tensor = aten::sigmoid(%ingate.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:34:17
      %forgetgate.3 : Tensor = aten::sigmoid(%forgetgate.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:35:21
      %cellgate.3 : Tensor = aten::tanh(%cellgate.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:36:19
      %outgate.3 : Tensor = aten::sigmoid(%outgate.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:37:18
      %142 : __torch__.torch.nn.modules.normalization.___torch_mangle_0.LayerNorm = prim::GetAttr[name="layernorm_c"](%102)
      %143 : Tensor = aten::mul(%forgetgate.3, %cx.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:39:31
      %144 : Tensor = aten::mul(%ingate.3, %cellgate.3) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:39:51
      %145 : Tensor = aten::add(%143, %144, %105) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:39:31
      %146 : Function = prim::Constant[name="layer_norm"]()
      %147 : float = prim::Constant[value=1.0000000000000001e-05]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:66
      %148 : int = prim::Constant[value=4]() # /home/masa/projects/deep/pytorch/torch/nn/modules/normalization.py:153:19
      %149 : Tensor = prim::GetAttr[name="weight"](%142)
      %150 : Tensor = prim::GetAttr[name="bias"](%142)
      %151 : int[] = prim::ListConstruct(%148)
      %152 : bool = prim::Constant[value=1]() # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1938:28
      %cy.1 : Tensor = aten::layer_norm(%145, %151, %149, %150, %147, %152) # /home/masa/projects/deep/pytorch/torch/nn/functional.py:1937:11
      %154 : Tensor = aten::tanh(%cy.1) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:40:23
      %hy.1 : Tensor = aten::mul(%outgate.3, %154) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:40:13
      %156 : (Tensor, Tensor) = prim::TupleConstruct(%hy.1, %cy.1)
      %157 : (Tensor, (Tensor, Tensor)) = prim::TupleConstruct(%hy.1, %156)
      %out.1 : Tensor, %state.3 : (Tensor, Tensor) = prim::TupleUnpack(%157)
      %160 : Tensor[] = prim::ListConstruct(%out.1)
      %outputs.3 : Tensor[] = aten::add_(%outputs.6, %160) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:56:12
      -> (%93, %outputs.3, %state.3)
  %162 : Tensor = aten::stack(%outputs, %94) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:57:15
  %163 : (Tensor, (Tensor, Tensor)) = prim::TupleConstruct(%162, %state)
  %output.4 : Tensor, %out_state.3 : (Tensor, Tensor) = prim::TupleUnpack(%163)
  %19 : (Tensor, Tensor)[] = prim::ListConstruct(%out_state.3)
  %output_states.5 : (Tensor, Tensor)[] = aten::add_(%output_states.3, %19) # /home/masa/projects/dev/torchscript-to-tvm/custom_lstms.py:83:12
  %21 : (Tensor, (Tensor, Tensor)[]) = prim::TupleConstruct(%output.4, %output_states.5)
  return (%21)