jikechao opened a new issue, #15089:
URL: https://github.com/apache/tvm/issues/15089
`grid_sample` operator in pytorch will lead a **divide by zero** error when
`padding_mode='reflection' and align_corners=True`
### Actual behavior
```
Traceback (most recent call last):
File "test.py", line 24, in <module>
exe = relay.create_executor('graph', mod=mod, params=params,
device=tvm.device('llvm', 0), target='llvm').evaluate()
File
"/workplace/software/tvm/tvm_/python/tvm/relay/backend/interpreter.py", line
171, in evaluate
return self._make_executor()
File "/workplace/software/tvm/tvm_/python/tvm/relay/build_module.py", line
519, in _make_executor
mod = build(self.mod, target=self.target)
File "/workplace/software/tvm/tvm_/python/tvm/relay/build_module.py", line
372, in build
mod_name=mod_name,
File "/workplace/software/tvm/tvm_/python/tvm/relay/build_module.py", line
169, in build
mod_name,
File
"/workplace/software/tvm/tvm_/python/tvm/_ffi/_ctypes/packed_func.py", line
237, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
43: TVMFuncCall
42:
tvm::relay::backend::RelayBuildModule::GetFunction(std::__cxx11::basic_string<char,
std::char_traits<char>, std::allocator<char> > const&,
tvm::runtime::ObjectPtr<tvm::runtime::Object>
const&)::{lambda(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)#3}::operator()(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*) const
41: tvm::relay::backend::RelayBuildModule::Build(tvm::IRModule,
tvm::runtime::Array<tvm::Target, void> const&, tvm::Target const&,
tvm::relay::Executor const&, tvm::relay::Runtime const&,
tvm::WorkspaceMemoryPools const&, tvm::ConstantMemoryPools const&,
tvm::runtime::String)
40: tvm::relay::backend::RelayBuildModule::BuildRelay(tvm::IRModule,
tvm::runtime::String const&)
39: tvm::relay::backend::ExecutorCodegen::Codegen(tvm::IRModule,
tvm::relay::Function const&, tvm::runtime::String)
38: void tvm::relay::backend::ExecutorCodegen::CallFunc<tvm::IRModule,
tvm::relay::Function, tvm::runtime::String>(std::__cxx11::basic_string<char,
std::char_traits<char>, std::allocator<char> > const&, tvm::IRModule,
tvm::relay::Function, tvm::runtime::String)
37:
tvm::relay::backend::GraphExecutorCodegenModule::GetFunction(std::__cxx11::basic_string<char,
std::char_traits<char>, std::allocator<char> > const&,
tvm::runtime::ObjectPtr<tvm::runtime::Object>
const&)::{lambda(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)#2}::operator()(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*) const
36: tvm::relay::backend::GraphExecutorCodegen::Codegen(tvm::IRModule,
tvm::relay::Function, tvm::runtime::String)
35: tvm::transform::Pass::operator()(tvm::IRModule) const
34: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
33: tvm::transform::SequentialNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
32: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
31: tvm::transform::ModulePassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
30:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::IRModule,
tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::tec::LowerTE(tvm::runtime::String,
tvm::CompilationConfig, std::function<void
(tvm::BaseFunc)>)::$_8>(tvm::relay::tec::LowerTE(tvm::runtime::String,
tvm::CompilationConfig, std::function<void
(tvm::BaseFunc)>)::$_8)::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
29: tvm::relay::tec::LowerTE(tvm::IRModule const&, tvm::runtime::String
const&, std::function<void (tvm::BaseFunc)>, tvm::CompilationConfig)
28: tvm::transform::Pass::operator()(tvm::IRModule) const
27: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
26: tvm::relay::transform::FunctionPassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
25:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::relay::Function
(tvm::relay::Function, tvm::IRModule,
tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::tec::LowerTensorExpr(tvm::relay::tec::TECompiler,
std::function<void (tvm::BaseFunc)>,
tvm::CompilationConfig)::$_7>(tvm::relay::tec::LowerTensorExpr(tvm::relay::tec::TECompiler,
std::function<void (tvm::BaseFunc)>,
tvm::CompilationConfig)::$_7)::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
24: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
23: tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr
const&)>::VisitExpr(tvm::RelayExpr const&)
22: tvm::NodeFunctor<tvm::RelayExpr (tvm::runtime::ObjectRef const&,
tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr
const&)>*)>::operator()(tvm::runtime::ObjectRef const&,
tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>*) const
21: _ZZN3tvm5relay11ExprFunc
20:
tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::FunctionNode
const*)
19:
tvm::relay::tec::LowerTensorExprMutator::DeviceAwareVisitExpr_(tvm::relay::FunctionNode
const*)
18: _ZN3tvm5relay9
17: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::FunctionNode const*)
16: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
15: tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr
const&)>::VisitExpr(tvm::RelayExpr const&)
14: tvm::NodeFunctor<tvm::RelayExpr (tvm::runtime::ObjectRef const&,
tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr
const&)>*)>::operator()(tvm::runtime::ObjectRef const&,
tvm::relay::ExprFunctor<tvm::RelayExpr (tvm::RelayExpr const&)>*) const
13: _ZZN3tvm5relay11ExprFunc
12:
tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::CallNode
const*)
11:
tvm::relay::tec::LowerTensorExprMutator::DeviceAwareVisitExpr_(tvm::relay::CallNode
const*)
10: tvm::relay::tec::TECompilerImpl::Lower(tvm::relay::tec::CCacheKey
const&)
9:
tvm::relay::tec::TECompilerImpl::LowerInternal(tvm::relay::tec::CCacheKey
const&, tvm::GlobalVarSupply)
8: tvm::relay::tec::PrimFuncFor(tvm::relay::Function const&, tvm::Target
const&, tvm::GlobalVarSupply, tvm::NameSupply)
7: tvm::relay::tec::ScheduleBuilder::Create(tvm::relay::Function const&,
tvm::GlobalVarSupply, tvm::NameSupply)
6: tvm::relay::tec::LowerToTECompute::Lower(tvm::relay::Function const&)
5:
tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor,
void> >::VisitExpr(tvm::RelayExpr const&)
4: tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void>
(tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&)
3: tvm::NodeFunctor<tvm::runtime::Array<tvm::te::Tensor, void>
(tvm::runtime::ObjectRef const&,
tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void>
(tvm::RelayExpr const&)>*)>::operator()(tvm::runtime::ObjectRef const&,
tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void>
(tvm::RelayExpr const&)>*) const
2: _ZZN3tvm5relay11ExprFunc
1: tvm::relay::tec::LowerToTECompute::VisitExpr_(tvm::relay::CallNode
const*)
0:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<TVMFuncCreateFromCFunc::$_2>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
File
"/workplace/software/tvm/tvm_/python/tvm/_ffi/_ctypes/packed_func.py", line 81,
in cfun
rv = local_pyfunc(*pyargs)
File
"/workplace/software/tvm/tvm_/python/tvm/relay/backend/te_compiler.py", line
320, in lower_call
best_impl, outputs = select_implementation(op, call.attrs, inputs,
ret_type, target)
File
"/workplace/software/tvm/tvm_/python/tvm/relay/backend/te_compiler.py", line
207, in select_implementation
outs = impl.compute(attrs, inputs, out_type)
File "/workplace/software/tvm/tvm_/python/tvm/relay/op/op.py", line 126,
in compute
return _OpImplementationCompute(self, attrs, inputs, out_type)
File
"/workplace/software/tvm/tvm_/python/tvm/_ffi/_ctypes/packed_func.py", line
237, in __call__
raise get_last_ffi_error()
3: TVMFuncCall
2:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::relay::$_3>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
1: tvm::relay::OpImplementation::Compute(tvm::Attrs const&,
tvm::runtime::Array<tvm::te::Tensor, void> const&, tvm::Type const&)
0:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<TVMFuncCreateFromCFunc::$_2>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
File
"/workplace/software/tvm/tvm_/python/tvm/_ffi/_ctypes/packed_func.py", line 81,
in cfun
rv = local_pyfunc(*pyargs)
File "/workplace/software/tvm/tvm_/python/tvm/relay/op/image/_image.py",
line 371, in compute_grid_sample
topi.image.grid_sample(inputs[0], inputs[1], method, layout,
padding_mode, align_corners)
File "/workplace/software/tvm/tvm_/python/tvm/topi/image/grid_sample.py",
line 529, in grid_sample
return compute(data, grid, method, layout, padding_mode, align_corners)
File "/workplace/software/tvm/tvm_/python/tvm/topi/image/grid_sample.py",
line 286, in _grid_sample_2d
return te.compute((batch, in_channel, out_height, out_width),
interpolation, tag="grid_sample")
File "/workplace/software/tvm/tvm_/python/tvm/te/operation.py", line 132,
in compute
body = fcompute(*[v.var for v in dim_var])
File "/workplace/software/tvm/tvm_/python/tvm/topi/image/grid_sample.py",
line 188, in _bilinear_sample
y, x = _compute_source_index(n, h, w)
File "/workplace/software/tvm/tvm_/python/tvm/topi/image/grid_sample.py",
line 154, in _compute_source_index
y = _reflect_coordinates(y, in_height)
File "/workplace/software/tvm/tvm_/python/tvm/topi/image/grid_sample.py",
line 182, in _reflect_coordinates
new_x = __refelection(x, size - 1, 0)
File "/workplace/software/tvm/tvm_/python/tvm/topi/image/grid_sample.py",
line 178, in __refelection
__reflect(x, size, corner_start),
File "/workplace/software/tvm/tvm_/python/tvm/topi/image/grid_sample.py",
line 168, in __reflect
size_times = te.truncdiv(index_align_corner.astype("int32"),
size).astype("int32")
File "/workplace/software/tvm/tvm_/python/tvm/tir/op.py", line 2736, in
truncdiv
return _ffi_api._OpTruncDiv(a, b, span) # type: ignore
File
"/workplace/software/tvm/tvm_/python/tvm/_ffi/_ctypes/packed_func.py", line
237, in __call__
raise get_last_ffi_error()
5: TVMFuncCall
4:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::PrimExpr
(tvm::PrimExpr, tvm::PrimExpr,
tvm::Span)>::AssignTypedLambda<tvm::$_11>(tvm::$_11,
std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>
>)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
3: tvm::truncdiv(tvm::PrimExpr, tvm::PrimExpr, tvm::Span)
2: tvm::div(tvm::PrimExpr, tvm::PrimExpr, tvm::Span)
1: tvm::runtime::Optional<tvm::PrimExpr>
tvm::arith::TryConstFold<tvm::tir::Div>(tvm::PrimExpr, tvm::PrimExpr)
0: _ZN3tvm7runtime6detail
File "/workplace/software/tvm/tvm_/src/arith/const_fold.h", line 222
TVMError:
---------------------------------------------------------------
An error occurred during the execution of TVM.
For more information, please see: https://tvm.apache.org/docs/errors.html
---------------------------------------------------------------
Check failed: pb->value != 0 (0 vs. 0) : Divide by zero
```
### Environment
Any environment details, such as: Operating System, TVM version, etc
### Steps to reproduce
```
import torch
from tvm import relay
import tvm
import numpy as np
from torch.nn import Module
input_data = torch.rand([5, 2, 1, 1], dtype=torch.float64)
para_1 = torch.rand([5, 4, 2, 2], dtype=torch.float64)
class grid_sample(Module):
def forward(self, *args):
return torch.nn.functional.grid_sample(args[0], para_1,
padding_mode='reflection',align_corners=True)
m = grid_sample().float().eval()
torch_outputs = m(input_data)
trace = torch.jit.trace(m, input_data)
input_shapes = [('input0', torch.Size([5, 2, 1, 1]))]
mod, params = relay.frontend.from_pytorch(trace, input_shapes)
print(mod)
with tvm.transform.PassContext(opt_level=3):
exe = relay.create_executor('graph', mod=mod, params=params,
device=tvm.device('llvm', 0), target='llvm').evaluate()
```
### Triage
* needs-triage
* frontend:torch
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