tvm [Bug]

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Expected behavior

Works proper with the loaded export model

Actual behavior

/home/guan/miniconda3/envs/tvm/bin/python /home/guan/dev/pycharm/TVM/tvm2/helloworld.py /home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/torch/export/pt2_archive/_package.py:682: UserWarning: The given buffer is not writable, and PyTorch does not support non-writable tensors. This means you can write to the underlying (supposedly non-writable) buffer using the tensor. You may want to copy the buffer to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_new.cpp:1581.) tensor = torch.frombuffer( Traceback (most recent call last): File "/home/guan/dev/pycharm/TVM/tvm2/helloworld.py", line 8, in mod = from_exported_program(exported_program) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/frontend/torch/exported_program_translator.py", line 1261, in from_exported_program return ExportedProgramImporter().from_exported_program( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/frontend/torch/exported_program_translator.py", line 1156, in from_exported_program self.env[node] = self.convert_mapfunc_name ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/frontend/torch/base_fx_graph_translator.py", line 1109, in _linear return self.block_builder.emit(relax.op.linear(x, weight, bias, "float32")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/block_builder.py", line 328, in emit return _ffi_api.BlockBuilderEmit(self, expr, name_hint) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "python/tvm_ffi/cython/function.pxi", line 904, in tvm_ffi.core.Function.call File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 1068, in operator() return builder->Emit(expr, name_hint);

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 243, in tvm::relax::BlockBuilderImpl::Emit(tvm::RelaxExpr, tvm::ffi::String) return this->Emit(expr, CurrentBlockFrame()->is_dataflow, name_hint);

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 395, in tvm::relax::BlockBuilderImpl::Emit(tvm::RelaxExpr, bool, tvm::ffi::String) expr = this->Normalize(expr);

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 532, in tvm::relax::Normalizer::Normalize(tvm::RelaxExpr const&) Expr normalized = this->VisitExpr(expr);

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 615, in tvm::relax::Normalizer::VisitExpr(tvm::RelaxExpr const&) return ExprFunctor::VisitExpr(expr);

File "/home/guan/dev/tvm/include/tvm/relax/expr_functor.h", line 132, in tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>::VisitExpr(tvm::RelaxExpr const&) return vtable(n, this, std::forward<Args>(args)...);

File "/home/guan/dev/tvm/include/tvm/node/functor.h", line 102, in tvm::NodeFunctor<tvm::RelaxExpr (tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>)>::operator()(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>) const return (*func_[n->type_index() - begin_type_index_])(n, std::forward<Args>(args)...);

File "/home/guan/dev/tvm/include/tvm/relax/expr_functor.h", line 171, in tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>)#9}::_FUN(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>) RELAX_EXPR_FUNCTOR_DISPATCH(CallNode);

File "/home/guan/dev/tvm/include/tvm/relax/expr_functor.h", line 171, in tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>)#9}::operator()(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>) const RELAX_EXPR_FUNCTOR_DISPATCH(CallNode);

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 654, in tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*) op->args.Map([this](const Expr& arg) { return NormalizeArgument(arg); });

File "/home/guan/dev/tvm/3rdparty/tvm-ffi/include/tvm/ffi/container/array.h", line 799, in tvm::ffi::Array<tvm::RelaxExpr, std::enable_if<storage_enabled_vtvm::RelaxExpr, void>::type> tvm::ffi::Array<tvm::RelaxExpr, void>::Map<tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*)::{lambda(tvm::RelaxExpr const&)#1}, tvm::RelaxExpr>(tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*)::{lambda(tvm::RelaxExpr const&)#1}) const return Array<U>(MapHelper(data_, fmap));

File "/home/guan/dev/tvm/3rdparty/tvm-ffi/include/tvm/ffi/container/array.h", line 975, in tvm::ffi::ObjectPtrtvm::ffi::Object tvm::ffi::Array<tvm::RelaxExpr, void>::MapHelper<tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*)::{lambda(tvm::RelaxExpr const&)#1}, tvm::RelaxExpr>(tvm::ffi::ObjectPtrtvm::ffi::Object, tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*)::{lambda(tvm::RelaxExpr const&)#1}) U mapped = fmap(details::AnyUnsafe::CopyFromAnyViewAfterCheck<T>(*it));

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 654, in tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*)::{lambda(tvm::RelaxExpr const&)#1}::operator()(tvm::RelaxExpr const&) const op->args.Map([this](const Expr& arg) { return NormalizeArgument(arg); });

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 563, in tvm::relax::Normalizer::NormalizeArgument(tvm::RelaxExpr const&) Expr post = ExprFunctor::VisitExpr(arg);

File "/home/guan/dev/tvm/include/tvm/relax/expr_functor.h", line 132, in tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>::VisitExpr(tvm::RelaxExpr const&) return vtable(n, this, std::forward<Args>(args)...);

File "/home/guan/dev/tvm/include/tvm/node/functor.h", line 102, in tvm::NodeFunctor<tvm::RelaxExpr (tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>)>::operator()(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>) const return (*func_[n->type_index() - begin_type_index_])(n, std::forward<Args>(args)...);

File "/home/guan/dev/tvm/include/tvm/relax/expr_functor.h", line 171, in tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>)#9}::_FUN(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>) RELAX_EXPR_FUNCTOR_DISPATCH(CallNode);

File "/home/guan/dev/tvm/include/tvm/relax/expr_functor.h", line 171, in tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>)#9}::operator()(tvm::ffi::ObjectRef const&, tvm::relax::ExprFunctor<tvm::RelaxExpr (tvm::RelaxExpr const&)>) const RELAX_EXPR_FUNCTOR_DISPATCH(CallNode);

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 664, in tvm::relax::Normalizer::VisitExpr_(tvm::relax::CallNode const*) auto inferred_sinfo = InferStructInfo(call);

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 847, in tvm::relax::Normalizer::InferStructInfo(tvm::relax::Call const&) return op_map_infer_struct_info_[op](call, ffi::GetRef<BlockBuilder>(this));

File "/home/guan/dev/tvm/src/relax/op/tensor/linear_algebra.cc", line 141, in tvm::relax::InferStructInfoMatmul(tvm::relax::Call const&, tvm::relax::BlockBuilder const&) ctx->ReportFatal(Diagnostic::Error(call)

File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 157, in tvm::relax::BlockBuilderImpl::ReportFatal(tvm::Diagnostic const&) LOG(FATAL) << diagnostic->message;

File "/home/guan/dev/tvm/include/tvm/runtime/logging.h", line 321, in tvm::runtime::detail::LogFatal::~LogFatal() GetEntry().Finalize();

File "/home/guan/dev/tvm/include/tvm/runtime/logging.h", line 337, in tvm::runtime::detail::LogFatal::Entry::Finalize() InternalError error(file_, lineno_, stream_.str());

tvm.error.InternalError: Matmul requires the reduction length of the operands to be equal. However, the LHS lv has shape R.shape([1, 10]), while the RHS lv1 has shape R.shape([784, 128]). The reduction dimensions of T.int64(10) and T.int64(784) are not equal. [16:08:40] /home/guan/dev/tvm/src/relax/ir/block_builder.cc:64: Warning: BlockBuilder destroyed with remaining blocks!

进程已结束，退出代码为 1

Environment

python 3.11 tvm v0.22.0

Steps to reproduce

import torch import os os.environ['TVM_LIBRARY_PATH'] = '/home/guan/dev/tvm/build' import tvm as t from tvm.relax.frontend.torch import from_exported_program

exported_program = torch.export.load("model.pt2") mod = from_exported_program(exported_program)

Model from: import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms import os os.environ['TVM_LIBRARY_PATH'] = '/home/guan/dev/tvm/build' from tvm.relax.frontend.torch import from_exported_program

transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])

train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform) test_dataset = datasets.MNIST('./data', train=False, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=1000, shuffle=False)

input_size = 28 * 28 num_classes = 10

class SimpleNet(nn.Module): def init(self): super(SimpleNet, self).init() self.fc1 = nn.Linear(input_size, 128) self.fc2 = nn.Linear(128, num_classes)

def forward(self, x):
    x = x.view(x.size(0), -1)
    x = torch.relu(self.fc1(x))
    x = self.fc2(x)
    return x

model = SimpleNet() print(model)

criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001)

print("--- 开始训练 (1 Epoch) ---")

def train(model, device, train_loader, optimizer, criterion, epoch): model.train()
for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step()

    if batch_idx % 100 == 0:
        print(f'Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} '
              f'({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device)

num_epochs = 1 for epoch in range(1, num_epochs + 1): train(model, device, train_loader, optimizer, criterion, epoch)

model.cpu() model.eval() example_args = (torch.randn(1, 1, 28, 28).to(torch.device("cpu")),)

exported_program = torch.export.export(model, example_args) output_path = "model.pt2" torch.export.save(exported_program, output_path)

mod = from_exported_program(exported_program) print(mod)

Triage

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needs-triage

Dec 09 '25 08:12 DaTouJun

I have checked the following minimal reproduction example, and it runs without errors in my environment.

import torch
import torch.nn as nn
from tvm.relax.frontend.torch import from_exported_program

input_size = 28 * 28
num_classes = 10


class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(input_size, 128)
        self.fc2 = nn.Linear(128, num_classes)

    def forward(self, x: torch.Tensor):
        x = x.view(x.size(0), -1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x


model = SimpleNet()
example_args = (torch.randn(1, 1, 28, 28).to(torch.device("cpu")),)
exported_program = torch.export.export(model, example_args)
output_path = "model.pt2"
torch.export.save(exported_program, output_path)


##############################

exported_program = torch.export.load("model.pt2")
mod = from_exported_program(exported_program)
mod.show()

Dec 09 '25 14:12 ConvolutedDog

I have checked the following minimal reproduction example, and it runs without errors in my environment.

import torch import torch.nn as nn from tvm.relax.frontend.torch import from_exported_program

input_size = 28 * 28 num_classes = 10

class SimpleNet(nn.Module): def init(self): super(SimpleNet, self).init() self.fc1 = nn.Linear(input_size, 128) self.fc2 = nn.Linear(128, num_classes)
def forward(self, x: torch.Tensor):
    x = x.view(x.size(0), -1)
    x = torch.relu(self.fc1(x))
    x = self.fc2(x)
    return x
model = SimpleNet() example_args = (torch.randn(1, 1, 28, 28).to(torch.device("cpu")),) exported_program = torch.export.export(model, example_args) output_path = "model.pt2" torch.export.save(exported_program, output_path)

##############################

exported_program = torch.export.load("model.pt2") mod = from_exported_program(exported_program) mod.show()

This example is also not work. /home/guan/miniconda3/envs/tvm/bin/python /home/guan/dev/pycharm/TVM/debuggit/demo.py /home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/torch/export/pt2_archive/_package.py:682: UserWarning: The given buffer is not writable, and PyTorch does not support non-writable tensors. This means you can write to the underlying (supposedly non-writable) buffer using the tensor. You may want to copy the buffer to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_new.cpp:1581.) tensor = torch.frombuffer( Traceback (most recent call last): File "/home/guan/dev/pycharm/TVM/debuggit/demo.py", line 34, in mod = from_exported_program(exported_program) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/frontend/torch/exported_program_translator.py", line 1261, in from_exported_program return ExportedProgramImporter().from_exported_program( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/frontend/torch/exported_program_translator.py", line 1156, in from_exported_program self.env[node] = self.convert_mapfunc_name ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/frontend/torch/base_fx_graph_translator.py", line 1109, in _linear return self.block_builder.emit(relax.op.linear(x, weight, bias, "float32")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/guan/miniconda3/envs/tvm/lib/python3.11/site-packages/tvm/relax/block_builder.py", line 328, in emit return _ffi_api.BlockBuilderEmit(self, expr, name_hint) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "python/tvm_ffi/cython/function.pxi", line 904, in tvm_ffi.core.Function.call File "/home/guan/dev/tvm/src/relax/ir/block_builder.cc", line 1068, in operator() return builder->Emit(expr, name_hint);