[Bug]

[DaTouJun]

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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)...);

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)...);

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(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(*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)...);

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)...);

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(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

Please refer to the list of label tags here to find the relevant tags and add them below in a bullet format (example below).

• needs-triage

[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()

[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()

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);

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)...);

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)...);

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(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(*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)...);

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)...);

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(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.
[22:56:24] /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

And I also tried different torch version vary from 2.9.1+cu130 to 2.8.0+cu129. And the tvm is build from source which might have some problem but current I upgrade the system so taht I could not test various tvm build.

[ConvolutedDog]

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.)

Perhaps the problem lies here. It typically occurs when using torch.export.load() to load a model, because PyTorch internally uses torch.frombuffer() for data deserialization.

Therefore, you can try the version without serializing the model:

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)
mod = from_exported_program(exported_program)
mod.show()

If this program runs without issues, it would indicate that the problem is specifically related to the deserialization process (i.e., loading from the .pt2 file) rather than the export or TVM conversion steps themselves.