j-paulus opened a new issue #6441:
URL: https://github.com/apache/incubator-tvm/issues/6441
If strided slice is used in a model, the stride argument is ignored and the
result is wrong.
I encountered the problem when trying to compile an ONNX model created by
pytorch conversion. Similar problem was present in the pytorch frontend
(#6414), and was fixed by #6418.
Possibly related issue #6316.
Code to reproduce the problem:
```
import torch
import tvm
from tvm import relay
import onnx
class TriggerBug(torch.nn.Module):
def __init__(self):
super(TriggerBug, self).__init__()
def forward(self, x):
return x[..., 0::2] + x[..., 1::2]
x_in = torch.randn(1, 4)
torch_model = TriggerBug()
onnx_name = 'strided_slice.onnx'
example_output = torch_model(x_in)
# convert to ONNX
torch.onnx.export(torch_model, (x_in,), onnx_name,
verbose=True,
example_outputs=example_output,
input_names=['x'],
output_names=['y'],
opset_version=10,
enable_onnx_checker=True)
onnx_model = onnx.load(onnx_name)
target = 'llvm'
shape_dict = {'x': x_in.shape}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
with tvm.transform.PassContext(opt_level=1):
intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0),
target)
```
The traceback:
> mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
> File
"/Users/name/opt/anaconda3/envs/tvm/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.9-x86_64.egg/tvm/relay/frontend/onnx.py",
line 2456, in from_onnx
> mod, params = g.from_onnx(graph, opset)
> File
"/Users/name/opt/anaconda3/envs/tvm/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.9-x86_64.egg/tvm/relay/frontend/onnx.py",
line 2302, in from_onnx
> return IRModule.from_expr(func), self._params
> File
"/Users/name/opt/anaconda3/envs/tvm/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.9-x86_64.egg/tvm/ir/module.py",
line 236, in from_expr
> return _ffi_api.Module_FromExpr(expr, funcs, defs)
> File
"/Users/name/opt/anaconda3/envs/tvm/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.9-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py",
line 225, in __call__
> raise get_last_ffi_error()
> tvm._ffi.base.TVMError: Traceback (most recent call last):
> [bt] (8) 9 libtvm.dylib 0x0000000122684df8
TVMFuncCall + 72
> [bt] (7) 8 libtvm.dylib 0x0000000121b8e452
std::__1::__function::__func<void tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::RelayExpr, tvm::Map<tvm::GlobalVar, tvm::BaseFunc, void, void>,
tvm::Map<tvm::GlobalTypeVar, tvm::TypeData, void,
void>)>::AssignTypedLambda<tvm::$_9>(tvm::$_9)::'lambda'(tvm::runtime::TVMArgs
const&, tvm::runtime::TVMRetValue*), std::__1::allocator<void
tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::RelayExpr,
tvm::Map<tvm::GlobalVar, tvm::BaseFunc, void, void>,
tvm::Map<tvm::GlobalTypeVar, tvm::TypeData, void,
void>)>::AssignTypedLambda<tvm::$_9>(tvm::$_9)::'lambda'(tvm::runtime::TVMArgs
const&, tvm::runtime::TVMRetValue*)>, void (tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&,
tvm::runtime::TVMRetValue*&&) + 610
> [bt] (6) 7 libtvm.dylib 0x0000000121b7f810
tvm::IRModule::FromExpr(tvm::RelayExpr const&, tvm::Map<tvm::GlobalVar,
tvm::BaseFunc, void, void> const&, tvm::Map<tvm::GlobalTypeVar, tvm::TypeData,
void, void> const&) + 1040
> [bt] (5) 6 libtvm.dylib 0x0000000121b7ca47
tvm::IRModuleNode::Add(tvm::GlobalVar const&, tvm::BaseFunc const&, bool) + 183
> [bt] (4) 5 libtvm.dylib 0x0000000121b7c4ef
tvm::RunTypeCheck(tvm::IRModule const&, tvm::GlobalVar const&,
tvm::relay::Function) + 1103
> [bt] (3) 4 libtvm.dylib 0x00000001224dca20
tvm::relay::InferType(tvm::relay::Function const&, tvm::IRModule const&,
tvm::GlobalVar const&) + 544
> [bt] (2) 3 libtvm.dylib 0x00000001224dbbc7
tvm::relay::TypeInferencer::Infer(tvm::RelayExpr) + 119
> [bt] (1) 2 libtvm.dylib 0x0000000121b6d87c
tvm::ErrorReporter::RenderErrors(tvm::IRModule const&, bool) + 5308
> [bt] (0) 1 libtvm.dylib 0x00000001219917bf
dmlc::LogMessageFatal::~LogMessageFatal() + 111
> File "/Users/name/code/python/tvm/src/ir/error.cc", line 132
> TVMError:
> Error(s) have occurred. The program has been annotated with them:
>
> In `main`:
> #[version = "0.0.5"]
> fn (%x: Tensor[(1, 4), float32]) {
> %0 = strided_slice(%x, begin=[0, 0], end=[2147483647,
9223372036854775807], strides=[1]);
> %1 = strided_slice(%x, begin=[0, 1], end=[2147483647,
9223372036854775807], strides=[1]);
> add(%0, %1) Incompatible broadcast type TensorType([1, 4], float32) and
TensorType([1, 3], float32);
> }
The intermediate ONNX graph is:
> graph(%x : Float(1:4, 4:1, requires_grad=0, device=cpu)):
> %1 : Tensor = onnx::Constant[value={1}]()
> %2 : Tensor = onnx::Constant[value={0}]()
> %3 : Tensor = onnx::Constant[value={9223372036854775807}]()
> %4 : Tensor = onnx::Constant[value={2}]()
> %5 : Float(1:4, 2:2, requires_grad=0, device=cpu) = onnx::Slice(%x, %2,
%3, %1, %4)
> %6 : Tensor = onnx::Constant[value={1}]()
> %7 : Tensor = onnx::Constant[value={1}]()
> %8 : Tensor = onnx::Constant[value={9223372036854775807}]()
> %9 : Tensor = onnx::Constant[value={2}]()
> %10 : Float(1:4, 2:2, requires_grad=0, device=cpu) = onnx::Slice(%x, %7,
%8, %6, %9)
> %y : Float(1:2, 2:1, requires_grad=0, device=cpu) = onnx::Add(%5, %10)
> return (%y)
Here the stride length is correctly present.
Versions:
- pytorch: 1.7.0.dev20200908
- TVM: 0.7.dev1 git revision 84fa626
- onnx: 1.7.0
If you are asking why am I going this route via ONNX and not use directly
pytorch frontend: The compilation of my real model from pytorch does not
currently work, but I have verified that the converted ONNX version works. I
was hoping that the ONNX frontend could then compile the full model.
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