Sure, this is the minimum code to reproduce:
from tvm import relay
import tensorflow as tf
#
#
# Model parameters
#
myTarget = "llvm"
layout = "NCHW"
model_name = "model.pb"
input_name = "input_1"
output_name = ["act_softmax/Softmax"]
dshape = (1, 224, 224, 3)
shape_dict = {input_name: dshape}
#
# Read frozen PB file from MobileNetV1
#
with tf.io.gfile.GFile("./" + model_name, 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
graph = tf.import_graph_def(graph_def, name='')
sym, params =\
relay.frontend.from_tensorflow(graph_def,
layout=layout,
shape={input_name: dshape},
outputs=output_name)
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(sym, myTarget, params=params)
lib.export_library("./model.so")
with open("./model.json", "w") as fo:
fo.write(graph)
with open("./params.params", "wb") as fo:
fo.write(relay.save_param_dict(params))
I am reading a frozen session file that has converted variables to constants. I can upload that too but its a 16M file. The model comes from (https://github.com/keras-team/keras-applications/blob/master/keras_applications/mobilenet.py) though.
Error log:
File "tvm_Opt_error.py", line 47, in <module>
graph, lib, params = relay.build(sym, myTarget, params=params)
File "/Users/alopez/Documents/Code/tvm/python/tvm/relay/build_module.py", line 251, in build
graph_json, mod, params = bld_mod.build(mod, target, target_host, params)
File "/Users/alopez/Documents/Code/tvm/python/tvm/relay/build_module.py", line 120, in build
self._build(mod, target, target_host)
File "/Users/alopez/Documents/Code/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 216, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
[bt] (8) 9 libtvm.dylib 0x000000012066a55e tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const + 1070
[bt] (7) 8 libtvm.dylib 0x000000012066a943 tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const + 179
[bt] (6) 7 libtvm.dylib 0x0000000120e7650f tvm::relay::transform::FunctionPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const + 1647
[bt] (5) 6 libtvm.dylib 0x0000000120649520 tvm::IRModuleNode::Add(tvm::GlobalVar const&, tvm::BaseFunc const&, bool) + 320
[bt] (4) 5 libtvm.dylib 0x0000000120648977 tvm::RunTypeCheck(tvm::IRModule const&, tvm::GlobalVar const&, tvm::relay::Function) + 1431
[bt] (3) 4 libtvm.dylib 0x0000000120dbfd85 tvm::relay::InferType(tvm::relay::Function const&, tvm::IRModule const&, tvm::GlobalVar const&) + 565
[bt] (2) 3 libtvm.dylib 0x0000000120dbedd1 tvm::relay::TypeInferencer::Infer(tvm::RelayExpr) + 145
[bt] (1) 2 libtvm.dylib 0x000000012063b125 tvm::ErrorReporter::RenderErrors(tvm::IRModule const&, bool) + 5477
[bt] (0) 1 libtvm.dylib 0x0000000120530909 dmlc::LogMessageFatal::~LogMessageFatal() + 57
[bt] (8) 9 libtvm.dylib 0x0000000120648977 tvm::RunTypeCheck(tvm::IRModule const&, tvm::GlobalVar const&, tvm::relay::Function) + 1431
[bt] (7) 8 libtvm.dylib 0x0000000120dbfd85 tvm::relay::InferType(tvm::relay::Function const&, tvm::IRModule const&, tvm::GlobalVar const&) + 565
[bt] (6) 7 libtvm.dylib 0x0000000120dbedb5 tvm::relay::TypeInferencer::Infer(tvm::RelayExpr) + 117
[bt] (5) 6 libtvm.dylib 0x0000000120c8e4af tvm::relay::TypeSolver::Solve() + 559
[bt] (4) 5 libtvm.dylib 0x0000000120c8ed75 tvm::TypedEnvFunc<bool (tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::operator()(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&) const + 325
[bt] (3) 4 libtvm.dylib 0x000000012099d53b std::__1::__function::__func<void tvm::runtime::TypedPackedFunc<bool (tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>(bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&))::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*), std::__1::allocator<void tvm::runtime::TypedPackedFunc<bool (tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>(bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&))::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)>, void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&) + 107
[bt] (2) 3 libtvm.dylib 0x000000012099d6e3 void tvm::runtime::detail::unpack_call_dispatcher<bool, 0, 4, bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::run<tvm::runtime::TVMMovableArgValue_, tvm::runtime::TVMMovableArgValue_, tvm::runtime::TVMMovableArgValue_, tvm::runtime::TVMMovableArgValue_>(bool (* const&)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&), tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*, tvm::runtime::TVMMovableArgValue_&&, tvm::runtime::TVMMovableArgValue_&&, tvm::runtime::TVMMovableArgValue_&&, tvm::runtime::TVMMovableArgValue_&&) + 323
[bt] (1) 2 libtvm.dylib 0x0000000120bbd75a tvm::relay::LayoutTransformRel(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&) + 234
[bt] (0) 1 libtvm.dylib 0x0000000120530909 dmlc::LogMessageFatal::~LogMessageFatal() + 57
[bt] (8) 9 libtvm.dylib 0x00000001207ea106 tvm::tir::BijectiveLayout::ForwardShape(tvm::Array<tvm::PrimExpr, void> const&) const + 182
[bt] (7) 8 libtvm.dylib 0x00000001207ead01 tvm::tir::TransformShape(tvm::Array<tvm::PrimExpr, void> const&, tvm::Array<tvm::tir::IterVar, void> const&, tvm::Array<tvm::tir::IterVar, void> const&, tvm::Array<tvm::PrimExpr, void> const&) + 2993
[bt] (6) 7 libtvm.dylib 0x0000000120867e67 tvm::tir::Substitute(tvm::PrimExpr, std::__1::unordered_map<tvm::tir::VarNode const*, tvm::PrimExpr, std::__1::hash<tvm::tir::VarNode const*>, std::__1::equal_to<tvm::tir::VarNode const*>, std::__1::allocator<std::__1::pair<tvm::tir::VarNode const* const, tvm::PrimExpr> > > const&) + 71
[bt] (5) 6 libtvm.dylib 0x0000000120553835 tvm::tir::ExprFunctor<tvm::PrimExpr (tvm::PrimExpr const&)>::VisitExpr(tvm::PrimExpr const&) + 53
[bt] (4) 5 libtvm.dylib 0x0000000120553c51 tvm::NodeFunctor<tvm::PrimExpr (tvm::runtime::ObjectRef const&, tvm::tir::ExprFunctor<tvm::PrimExpr (tvm::PrimExpr const&)>*)>::operator()(tvm::runtime::ObjectRef const&, tvm::tir::ExprFunctor<tvm::PrimExpr (tvm::PrimExpr const&)>*) const + 305
[bt] (3) 4 libtvm.dylib 0x0000000120557168 tvm::tir::ExprFunctor<tvm::PrimExpr (tvm::PrimExpr const&)>::InitVTable()::'lambda10'(tvm::runtime::ObjectRef const&, tvm::tir::ExprFunctor<tvm::PrimExpr (tvm::PrimExpr const&)>*)::__invoke(tvm::runtime::ObjectRef const&, tvm::tir::ExprFunctor<tvm::PrimExpr (tvm::PrimExpr const&)>*) + 24
[bt] (2) 3 libtvm.dylib 0x000000012080f745 tvm::tir::ExprMutator::VisitExpr_(tvm::tir::FloorDivNode const*) + 133
[bt] (1) 2 libtvm.dylib 0x00000001207fd86a tvm::tir::BinaryOpNode<tvm::tir::FloorDivNode>::make(tvm::PrimExpr, tvm::PrimExpr) + 394
[bt] (0) 1 libtvm.dylib 0x0000000120530909 dmlc::LogMessageFatal::~LogMessageFatal() + 57
File "/Users/alopez/Documents/Code/tvm/src/ir/error.cc", line 133
TVMError:
Error(s) have occurred. The program has been annotated with them:
In `main`:
v0.0.4
fn (%input_1: Tensor[(1, 224, 224, 3), float32]) -> Tensor[(1, 1000), float32] {
%0 = nn.pad(%input_1, pad_width=[[0, 0], [0, 1], [0, 1], [0, 0]]);
%1 = transpose(%0, axes=[0, 3, 1, 2]);
%2 = layout_transform(%1, src_layout="NCHW", dst_layout="NCHW3c");
%3 = layout_transform(meta[relay.Constant][0], src_layout="OIHW", dst_layout="OIHW3i8o");
%4 = nn.contrib_conv2d_NCHWc(%2, %3, strides=[2, 2], padding=[0, 0, 0, 0], channels=32, kernel_size=[3, 3], data_layout="NCHW3c", kernel_layout="OIHW3i8o", out_layout="NCHW8c");
%5 = layout_transform(%4, src_layout="NCHW8c", dst_layout="NCHW");
%6 = transpose(%5, axes=[0, 2, 3, 1]);
%7 = cast(%6, dtype="float32");
%8 = multiply(%7, meta[relay.Constant][1]);
%9 = add(%8, meta[relay.Constant][2]);
%10 = cast(%9, dtype="float32");
%11 = clip(%10, a_min=0f, a_max=6f);
%12 = transpose(%11, axes=[0, 3, 1, 2]);
%13 = layout_transform(%12, src_layout="NCHW", dst_layout="NCHW8c");
%14 = layout_transform(meta[relay.Constant][3], src_layout="OIHW", dst_layout="OIHW1i8o");
%15 = nn.contrib_depthwise_conv2d_NCHWc(%13, %14, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%16 = layout_transform(%15, src_layout="NCHW8c", dst_layout="NCHW");
%17 = transpose(%16, axes=[0, 2, 3, 1]);
%18 = cast(%17, dtype="float32");
%19 = multiply(%18, meta[relay.Constant][4]);
%20 = add(%19, meta[relay.Constant][5]);
%21 = cast(%20, dtype="float32");
%22 = clip(%21, a_min=0f, a_max=6f);
%23 = transpose(%22, axes=[0, 3, 1, 2]);
%24 = layout_transform(%23, src_layout="NCHW", dst_layout="NCHW8c");
%25 = layout_transform(meta[relay.Constant][6], src_layout="OIHW", dst_layout="OIHW8i8o");
%26 = nn.contrib_conv2d_NCHWc(%24, %25, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%27 = layout_transform(%26, src_layout="NCHW8c", dst_layout="NCHW");
%28 = transpose(%27, axes=[0, 2, 3, 1]);
%29 = cast(%28, dtype="float32");
%30 = multiply(%29, meta[relay.Constant][7]);
%31 = add(%30, meta[relay.Constant][8]);
%32 = cast(%31, dtype="float32");
%33 = clip(%32, a_min=0f, a_max=6f);
%34 = nn.pad(%33, pad_width=[[0, 0], [0, 1], [0, 1], [0, 0]]);
%35 = transpose(%34, axes=[0, 3, 1, 2]);
%36 = layout_transform(%35, src_layout="NCHW", dst_layout="NCHW8c");
%37 = layout_transform(meta[relay.Constant][9], src_layout="OIHW", dst_layout="OIHW1i8o");
%38 = nn.contrib_depthwise_conv2d_NCHWc(%36, %37, strides=[2, 2], padding=[0, 0, 0, 0], groups=64, channels=64, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%39 = layout_transform(%38, src_layout="NCHW8c", dst_layout="NCHW");
%40 = transpose(%39, axes=[0, 2, 3, 1]);
%41 = cast(%40, dtype="float32");
%42 = multiply(%41, meta[relay.Constant][10]);
%43 = add(%42, meta[relay.Constant][11]);
%44 = cast(%43, dtype="float32");
%45 = clip(%44, a_min=0f, a_max=6f);
%46 = transpose(%45, axes=[0, 3, 1, 2]);
%47 = layout_transform(%46, src_layout="NCHW", dst_layout="NCHW8c");
%48 = layout_transform(meta[relay.Constant][12], src_layout="OIHW", dst_layout="OIHW8i8o");
%49 = nn.contrib_conv2d_NCHWc(%47, %48, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%50 = layout_transform(%49, src_layout="NCHW8c", dst_layout="NCHW");
%51 = transpose(%50, axes=[0, 2, 3, 1]);
%52 = cast(%51, dtype="float32");
%53 = multiply(%52, meta[relay.Constant][13]);
%54 = add(%53, meta[relay.Constant][14]);
%55 = cast(%54, dtype="float32");
%56 = clip(%55, a_min=0f, a_max=6f);
%57 = transpose(%56, axes=[0, 3, 1, 2]);
%58 = layout_transform(%57, src_layout="NCHW", dst_layout="NCHW8c");
%59 = layout_transform(meta[relay.Constant][15], src_layout="OIHW", dst_layout="OIHW1i8o");
%60 = nn.contrib_depthwise_conv2d_NCHWc(%58, %59, padding=[1, 1, 1, 1], groups=128, channels=128, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%61 = layout_transform(%60, src_layout="NCHW8c", dst_layout="NCHW");
%62 = transpose(%61, axes=[0, 2, 3, 1]);
%63 = cast(%62, dtype="float32");
%64 = multiply(%63, meta[relay.Constant][16]);
%65 = add(%64, meta[relay.Constant][17]);
%66 = cast(%65, dtype="float32");
%67 = clip(%66, a_min=0f, a_max=6f);
%68 = transpose(%67, axes=[0, 3, 1, 2]);
%69 = layout_transform(%68, src_layout="NCHW", dst_layout="NCHW8c");
%70 = layout_transform(meta[relay.Constant][18], src_layout="OIHW", dst_layout="OIHW8i8o");
%71 = nn.contrib_conv2d_NCHWc(%69, %70, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%72 = layout_transform(%71, src_layout="NCHW8c", dst_layout="NCHW");
%73 = transpose(%72, axes=[0, 2, 3, 1]);
%74 = cast(%73, dtype="float32");
%75 = multiply(%74, meta[relay.Constant][19]);
%76 = add(%75, meta[relay.Constant][20]);
%77 = cast(%76, dtype="float32");
%78 = clip(%77, a_min=0f, a_max=6f);
%79 = nn.pad(%78, pad_width=[[0, 0], [0, 1], [0, 1], [0, 0]]);
%80 = transpose(%79, axes=[0, 3, 1, 2]);
%81 = layout_transform(%80, src_layout="NCHW", dst_layout="NCHW8c");
%82 = layout_transform(meta[relay.Constant][21], src_layout="OIHW", dst_layout="OIHW1i8o");
%83 = nn.contrib_depthwise_conv2d_NCHWc(%81, %82, strides=[2, 2], padding=[0, 0, 0, 0], groups=128, channels=128, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%84 = layout_transform(%83, src_layout="NCHW8c", dst_layout="NCHW");
%85 = transpose(%84, axes=[0, 2, 3, 1]);
%86 = cast(%85, dtype="float32");
%87 = multiply(%86, meta[relay.Constant][22]);
%88 = add(%87, meta[relay.Constant][23]);
%89 = cast(%88, dtype="float32");
%90 = clip(%89, a_min=0f, a_max=6f);
%91 = transpose(%90, axes=[0, 3, 1, 2]);
%92 = layout_transform(%91, src_layout="NCHW", dst_layout="NCHW8c");
%93 = layout_transform(meta[relay.Constant][24], src_layout="OIHW", dst_layout="OIHW8i8o");
%94 = nn.contrib_conv2d_NCHWc(%92, %93, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%95 = layout_transform(%94, src_layout="NCHW8c", dst_layout="NCHW");
%96 = transpose(%95, axes=[0, 2, 3, 1]);
%97 = cast(%96, dtype="float32");
%98 = multiply(%97, meta[relay.Constant][25]);
%99 = add(%98, meta[relay.Constant][26]);
%100 = cast(%99, dtype="float32");
%101 = clip(%100, a_min=0f, a_max=6f);
%102 = transpose(%101, axes=[0, 3, 1, 2]);
%103 = layout_transform(%102, src_layout="NCHW", dst_layout="NCHW8c");
%104 = layout_transform(meta[relay.Constant][27], src_layout="OIHW", dst_layout="OIHW1i8o");
%105 = nn.contrib_depthwise_conv2d_NCHWc(%103, %104, padding=[1, 1, 1, 1], groups=256, channels=256, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%106 = layout_transform(%105, src_layout="NCHW8c", dst_layout="NCHW");
%107 = transpose(%106, axes=[0, 2, 3, 1]);
%108 = cast(%107, dtype="float32");
%109 = multiply(%108, meta[relay.Constant][28]);
%110 = add(%109, meta[relay.Constant][29]);
%111 = cast(%110, dtype="float32");
%112 = clip(%111, a_min=0f, a_max=6f);
%113 = transpose(%112, axes=[0, 3, 1, 2]);
%114 = layout_transform(%113, src_layout="NCHW", dst_layout="NCHW8c");
%115 = layout_transform(meta[relay.Constant][30], src_layout="OIHW", dst_layout="OIHW8i8o");
%116 = nn.contrib_conv2d_NCHWc(%114, %115, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%117 = layout_transform(%116, src_layout="NCHW8c", dst_layout="NCHW");
%118 = transpose(%117, axes=[0, 2, 3, 1]);
%119 = cast(%118, dtype="float32");
%120 = multiply(%119, meta[relay.Constant][31]);
%121 = add(%120, meta[relay.Constant][32]);
%122 = cast(%121, dtype="float32");
%123 = clip(%122, a_min=0f, a_max=6f);
%124 = nn.pad(%123, pad_width=[[0, 0], [0, 1], [0, 1], [0, 0]]);
%125 = transpose(%124, axes=[0, 3, 1, 2]);
%126 = layout_transform(%125, src_layout="NCHW", dst_layout="NCHW8c");
%127 = layout_transform(meta[relay.Constant][33], src_layout="OIHW", dst_layout="OIHW1i8o");
%128 = nn.contrib_depthwise_conv2d_NCHWc(%126, %127, strides=[2, 2], padding=[0, 0, 0, 0], groups=256, channels=256, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%129 = layout_transform(%128, src_layout="NCHW8c", dst_layout="NCHW");
%130 = transpose(%129, axes=[0, 2, 3, 1]);
%131 = cast(%130, dtype="float32");
%132 = multiply(%131, meta[relay.Constant][34]);
%133 = add(%132, meta[relay.Constant][35]);
%134 = cast(%133, dtype="float32");
%135 = clip(%134, a_min=0f, a_max=6f);
%136 = transpose(%135, axes=[0, 3, 1, 2]);
%137 = layout_transform(%136, src_layout="NCHW", dst_layout="NCHW8c");
%138 = layout_transform(meta[relay.Constant][36], src_layout="OIHW", dst_layout="OIHW8i8o");
%139 = nn.contrib_conv2d_NCHWc(%137, %138, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%140 = layout_transform(%139, src_layout="NCHW8c", dst_layout="NCHW");
%141 = transpose(%140, axes=[0, 2, 3, 1]);
%142 = cast(%141, dtype="float32");
%143 = multiply(%142, meta[relay.Constant][37]);
%144 = add(%143, meta[relay.Constant][38]);
%145 = cast(%144, dtype="float32");
%146 = clip(%145, a_min=0f, a_max=6f);
%147 = transpose(%146, axes=[0, 3, 1, 2]);
%148 = layout_transform(%147, src_layout="NCHW", dst_layout="NCHW8c");
%149 = layout_transform(meta[relay.Constant][39], src_layout="OIHW", dst_layout="OIHW1i8o");
%150 = nn.contrib_depthwise_conv2d_NCHWc(%148, %149, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%151 = layout_transform(%150, src_layout="NCHW8c", dst_layout="NCHW");
%152 = transpose(%151, axes=[0, 2, 3, 1]);
%153 = cast(%152, dtype="float32");
%154 = multiply(%153, meta[relay.Constant][40]);
%155 = add(%154, meta[relay.Constant][41]);
%156 = cast(%155, dtype="float32");
%157 = clip(%156, a_min=0f, a_max=6f);
%158 = transpose(%157, axes=[0, 3, 1, 2]);
%159 = layout_transform(%158, src_layout="NCHW", dst_layout="NCHW8c");
%160 = layout_transform(meta[relay.Constant][42], src_layout="OIHW", dst_layout="OIHW8i8o");
%161 = nn.contrib_conv2d_NCHWc(%159, %160, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%162 = layout_transform(%161, src_layout="NCHW8c", dst_layout="NCHW");
%163 = transpose(%162, axes=[0, 2, 3, 1]);
%164 = cast(%163, dtype="float32");
%165 = multiply(%164, meta[relay.Constant][43]);
%166 = add(%165, meta[relay.Constant][44]);
%167 = cast(%166, dtype="float32");
%168 = clip(%167, a_min=0f, a_max=6f);
%169 = transpose(%168, axes=[0, 3, 1, 2]);
%170 = layout_transform(%169, src_layout="NCHW", dst_layout="NCHW8c");
%171 = layout_transform(meta[relay.Constant][45], src_layout="OIHW", dst_layout="OIHW1i8o");
%172 = nn.contrib_depthwise_conv2d_NCHWc(%170, %171, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%173 = layout_transform(%172, src_layout="NCHW8c", dst_layout="NCHW");
%174 = transpose(%173, axes=[0, 2, 3, 1]);
%175 = cast(%174, dtype="float32");
%176 = multiply(%175, meta[relay.Constant][46]);
%177 = add(%176, meta[relay.Constant][47]);
%178 = cast(%177, dtype="float32");
%179 = clip(%178, a_min=0f, a_max=6f);
%180 = transpose(%179, axes=[0, 3, 1, 2]);
%181 = layout_transform(%180, src_layout="NCHW", dst_layout="NCHW8c");
%182 = layout_transform(meta[relay.Constant][48], src_layout="OIHW", dst_layout="OIHW8i8o");
%183 = nn.contrib_conv2d_NCHWc(%181, %182, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%184 = layout_transform(%183, src_layout="NCHW8c", dst_layout="NCHW");
%185 = transpose(%184, axes=[0, 2, 3, 1]);
%186 = cast(%185, dtype="float32");
%187 = multiply(%186, meta[relay.Constant][49]);
%188 = add(%187, meta[relay.Constant][50]);
%189 = cast(%188, dtype="float32");
%190 = clip(%189, a_min=0f, a_max=6f);
%191 = transpose(%190, axes=[0, 3, 1, 2]);
%192 = layout_transform(%191, src_layout="NCHW", dst_layout="NCHW8c");
%193 = layout_transform(meta[relay.Constant][51], src_layout="OIHW", dst_layout="OIHW1i8o");
%194 = nn.contrib_depthwise_conv2d_NCHWc(%192, %193, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%195 = layout_transform(%194, src_layout="NCHW8c", dst_layout="NCHW");
%196 = transpose(%195, axes=[0, 2, 3, 1]);
%197 = cast(%196, dtype="float32");
%198 = multiply(%197, meta[relay.Constant][52]);
%199 = add(%198, meta[relay.Constant][53]);
%200 = cast(%199, dtype="float32");
%201 = clip(%200, a_min=0f, a_max=6f);
%202 = transpose(%201, axes=[0, 3, 1, 2]);
%203 = layout_transform(%202, src_layout="NCHW", dst_layout="NCHW8c");
%204 = layout_transform(meta[relay.Constant][54], src_layout="OIHW", dst_layout="OIHW8i8o");
%205 = nn.contrib_conv2d_NCHWc(%203, %204, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%206 = layout_transform(%205, src_layout="NCHW8c", dst_layout="NCHW");
%207 = transpose(%206, axes=[0, 2, 3, 1]);
%208 = cast(%207, dtype="float32");
%209 = multiply(%208, meta[relay.Constant][55]);
%210 = add(%209, meta[relay.Constant][56]);
%211 = cast(%210, dtype="float32");
%212 = clip(%211, a_min=0f, a_max=6f);
%213 = transpose(%212, axes=[0, 3, 1, 2]);
%214 = layout_transform(%213, src_layout="NCHW", dst_layout="NCHW8c");
%215 = layout_transform(meta[relay.Constant][57], src_layout="OIHW", dst_layout="OIHW1i8o");
%216 = nn.contrib_depthwise_conv2d_NCHWc(%214, %215, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%217 = layout_transform(%216, src_layout="NCHW8c", dst_layout="NCHW");
%218 = transpose(%217, axes=[0, 2, 3, 1]);
%219 = cast(%218, dtype="float32");
%220 = multiply(%219, meta[relay.Constant][58]);
%221 = add(%220, meta[relay.Constant][59]);
%222 = cast(%221, dtype="float32");
%223 = clip(%222, a_min=0f, a_max=6f);
%224 = transpose(%223, axes=[0, 3, 1, 2]);
%225 = layout_transform(%224, src_layout="NCHW", dst_layout="NCHW8c");
%226 = layout_transform(meta[relay.Constant][60], src_layout="OIHW", dst_layout="OIHW8i8o");
%227 = nn.contrib_conv2d_NCHWc(%225, %226, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%228 = layout_transform(%227, src_layout="NCHW8c", dst_layout="NCHW");
%229 = transpose(%228, axes=[0, 2, 3, 1]);
%230 = cast(%229, dtype="float32");
%231 = multiply(%230, meta[relay.Constant][61]);
%232 = add(%231, meta[relay.Constant][62]);
%233 = cast(%232, dtype="float32");
%234 = clip(%233, a_min=0f, a_max=6f);
%235 = transpose(%234, axes=[0, 3, 1, 2]);
%236 = layout_transform(%235, src_layout="NCHW", dst_layout="NCHW8c");
%237 = layout_transform(meta[relay.Constant][63], src_layout="OIHW", dst_layout="OIHW1i8o");
%238 = nn.contrib_depthwise_conv2d_NCHWc(%236, %237, padding=[1, 1, 1, 1], groups=512, channels=512, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%239 = layout_transform(%238, src_layout="NCHW8c", dst_layout="NCHW");
%240 = transpose(%239, axes=[0, 2, 3, 1]);
%241 = cast(%240, dtype="float32");
%242 = multiply(%241, meta[relay.Constant][64]);
%243 = add(%242, meta[relay.Constant][65]);
%244 = cast(%243, dtype="float32");
%245 = clip(%244, a_min=0f, a_max=6f);
%246 = transpose(%245, axes=[0, 3, 1, 2]);
%247 = layout_transform(%246, src_layout="NCHW", dst_layout="NCHW8c");
%248 = layout_transform(meta[relay.Constant][66], src_layout="OIHW", dst_layout="OIHW8i8o");
%249 = nn.contrib_conv2d_NCHWc(%247, %248, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%250 = layout_transform(%249, src_layout="NCHW8c", dst_layout="NCHW");
%251 = transpose(%250, axes=[0, 2, 3, 1]);
%252 = cast(%251, dtype="float32");
%253 = multiply(%252, meta[relay.Constant][67]);
%254 = add(%253, meta[relay.Constant][68]);
%255 = cast(%254, dtype="float32");
%256 = clip(%255, a_min=0f, a_max=6f);
%257 = nn.pad(%256, pad_width=[[0, 0], [0, 1], [0, 1], [0, 0]]);
%258 = transpose(%257, axes=[0, 3, 1, 2]);
%259 = layout_transform(%258, src_layout="NCHW", dst_layout="NCHW8c");
%260 = layout_transform(meta[relay.Constant][69], src_layout="OIHW", dst_layout="OIHW1i8o");
%261 = nn.contrib_depthwise_conv2d_NCHWc(%259, %260, strides=[2, 2], padding=[0, 0, 0, 0], groups=512, channels=512, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%262 = layout_transform(%261, src_layout="NCHW8c", dst_layout="NCHW");
%263 = transpose(%262, axes=[0, 2, 3, 1]);
%264 = cast(%263, dtype="float32");
%265 = multiply(%264, meta[relay.Constant][70]);
%266 = add(%265, meta[relay.Constant][71]);
%267 = cast(%266, dtype="float32");
%268 = clip(%267, a_min=0f, a_max=6f);
%269 = transpose(%268, axes=[0, 3, 1, 2]);
%270 = layout_transform(%269, src_layout="NCHW", dst_layout="NCHW8c");
%271 = layout_transform(meta[relay.Constant][72], src_layout="OIHW", dst_layout="OIHW8i8o");
%272 = nn.contrib_conv2d_NCHWc(%270, %271, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%273 = layout_transform(%272, src_layout="NCHW8c", dst_layout="NCHW");
%274 = transpose(%273, axes=[0, 2, 3, 1]);
%275 = cast(%274, dtype="float32");
%276 = multiply(%275, meta[relay.Constant][73]);
%277 = add(%276, meta[relay.Constant][74]);
%278 = cast(%277, dtype="float32");
%279 = clip(%278, a_min=0f, a_max=6f);
%280 = transpose(%279, axes=[0, 3, 1, 2]);
%281 = layout_transform(%280, src_layout="NCHW", dst_layout="NCHW8c");
%282 = layout_transform(meta[relay.Constant][75], src_layout="OIHW", dst_layout="OIHW1i8o");
%283 = nn.contrib_depthwise_conv2d_NCHWc(%281, %282, padding=[1, 1, 1, 1], groups=1024, channels=1024, kernel_size=[3, 3], data_layout="NCHW8c", kernel_layout="OIHW1i8o", out_layout="NCHW8c");
%284 = layout_transform(%283, src_layout="NCHW8c", dst_layout="NCHW");
%285 = transpose(%284, axes=[0, 2, 3, 1]);
%286 = cast(%285, dtype="float32");
%287 = multiply(%286, meta[relay.Constant][76]);
%288 = add(%287, meta[relay.Constant][77]);
%289 = cast(%288, dtype="float32");
%290 = clip(%289, a_min=0f, a_max=6f);
%291 = transpose(%290, axes=[0, 3, 1, 2]);
%292 = layout_transform(%291, src_layout="NCHW", dst_layout="NCHW8c");
%293 = layout_transform(meta[relay.Constant][78], src_layout="OIHW", dst_layout="OIHW8i8o");
%294 = nn.contrib_conv2d_NCHWc(%292, %293, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%295 = layout_transform(%294, src_layout="NCHW8c", dst_layout="NCHW");
%296 = transpose(%295, axes=[0, 2, 3, 1]);
%297 = cast(%296, dtype="float32");
%298 = multiply(%297, meta[relay.Constant][79]);
%299 = add(%298, meta[relay.Constant][80]);
%300 = cast(%299, dtype="float32");
%301 = clip(%300, a_min=0f, a_max=6f);
%302 = mean(%301, axis=[1, 2]);
%303 = reshape(%302, newshape=[1, 1, 1, 1024]);
%304 = transpose(%303, axes=[0, 3, 1, 2]);
%305 = layout_transform(%304, src_layout="NCHW", dst_layout="NCHW8c") an internal invariant was violated while typechecking your program [16:03:10] /Users/alopez/Documents/Code/tvm/include/tvm/tir/expr.h:143: Check failed: a.dtype() == b.dtype(): TypeError: mismatched types
; ;
%306 = layout_transform(meta[relay.Constant][81], src_layout="OIHW", dst_layout="OIHW8i8o");
%307 = nn.contrib_conv2d_NCHWc(%305, %306, padding=[0, 0, 0, 0], channels=1000, kernel_size=[1, 1], data_layout="NCHW8c", kernel_layout="OIHW8i8o", out_layout="NCHW8c");
%308 = layout_transform(%307, src_layout="NCHW8c", dst_layout="NCHW") an internal invariant was violated while typechecking your program [16:03:10] /Users/alopez/Documents/Code/tvm/src/relay/op/tensor/transform.cc:2328: Check failed: data != nullptr:
; ;
%309 = transpose(%308, axes=[0, 2, 3, 1]);
%310 = add(%309, meta[relay.Constant][82]);
%311 = reshape(%310, newshape=[1, 1000]);
nn.softmax(%311)
}
// meta data omitted. you can use show_meta_data=True to include meta data
Looking at the original source code I think the error is around here:
x = layers.Reshape(shape, name='reshape_1')(x)
x = layers.Dropout(dropout, name='dropout')(x)
x = layers.Conv2D(classes, (1, 1),
padding='same',
name='conv_preds')(x)
x = layers.Reshape((classes,), name='reshape_2')(x)
As you can see the error happens almost at the end of the model. If I set opt_level=2 the error does not take place. By the way the dropout is removed in my PB file.
