DaTouJun opened a new issue, #18573: URL: https://github.com/apache/tvm/issues/18573
Thanks for participating in the TVM community! We use https://discuss.tvm.ai for any general usage questions and discussions. The issue tracker is used for actionable items such as feature proposals discussion, roadmaps, and bug tracking. You are always welcomed to post on the forum first :smile_cat: Issues that are inactive for a period of time may get closed. We adopt this policy so that we won't lose track of actionable issues that may fall at the bottom of the pile. Feel free to reopen a new one if you feel there is an additional problem that needs attention when an old one gets closed. ### 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 <module> 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_map[func_name](node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 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_v<tvm::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::ObjectPtr<tvm::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::ObjectPtr<tvm::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 Please refer to the list of label tags [here](https://github.com/apache/tvm/wiki/Issue-Triage-Labels) to find the relevant tags and add them below in a bullet format (example below). * needs-triage -- This is an automated message from the Apache Git Service. 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