lazycal opened a new issue, #12008:
URL: https://github.com/apache/tvm/issues/12008
This model
```python
def @main(%x: Tensor[(1, 1, 1, 1), float32] /* ty=Tensor[(1, 1, 1, 1),
float32] */) -> Tensor[(1, 1, 1, 1), float32] {
%0 = image.resize2d(%x, size=[2, 4], roi=[0f, 0f, 0f, 0f],
rounding_method="") /* ty=Tensor[(1, 1, 2, 4), float32] */;
%1 = transpose(%0, axes=[0, 1, 3, 2]) /* ty=Tensor[(1, 1, 4, 2), float32]
*/;
%2 = nn.max_pool2d(%1, pool_size=[1, 1], padding=[0, 0, 0, 0]) /*
ty=Tensor[(1, 1, 4, 2), float32] */;
image.resize2d(%2, size=[1, 1], roi=[0f, 0f, 0f, 0f], rounding_method="")
/* ty=Tensor[(1, 1, 1, 1), float32] */
}
```
fails with error `ValueError: NCWH layout is not supported.`, and this
layout is introduced after AlterOpLayout (see below for the IR right after
AlterOpLayout):
```python
def @main(%x: Tensor[(1, 1, 1, 1), float32] /* ty=Tensor[(1, 1, 1, 1),
float32] */) -> Tensor[(1, 1, 1, 1), float32] {
%0 = image.resize2d(%x, size=[2, 4], roi=[0f, 0f, 0f, 0f],
rounding_method="") /* ty=Tensor[(1, 1, 2, 4), float32] */;
%1 = transpose(%0, axes=[0, 1, 3, 2]) /* ty=Tensor[(1, 1, 4, 2), float32]
*/;
%2 = layout_transform(%1, src_layout="NCHW", dst_layout="NCWH") /*
ty=Tensor[(1, 1, 2, 4), float32] */;
%3 = nn.max_pool2d(%2, pool_size=[1, 1], padding=[0, 0, 0, 0],
layout="NCWH") /* ty=Tensor[(1, 1, 2, 4), float32] */;
%4 = image.resize2d(%3, size=[1, 1], roi=[0f, 0f, 0f, 0f], layout="NCWH",
rounding_method="") /* ty=Tensor[(1, 1, 1, 1), float32] */;
layout_transform(%4, src_layout="NCWH", dst_layout="NCHW") /*
ty=Tensor[(1, 1, 1, 1), float32] */
}
```
where the max_pool2d outputs NCWH layout and resize2d accepts it during
AlterOpLayout but complains when lowering in this function:
https://github.com/apache/tvm/blob/5efe8b0bfdff4c9939185a7581dc77e23cbcb6d5/python/tvm/topi/image/resize.py#L807-L824
Not sure if this is a bug, but I guess ideally resize2d should not accept
unsupported layout? The current logic
https://github.com/apache/tvm/blob/5efe8b0bfdff4c9939185a7581dc77e23cbcb6d5/src/relay/op/image/resize.cc#L49-L53
does not check this.
### Expected behavior
Pass the compilation.
### Actual behavior
Pass with opt_level=2 but fail with opt_level=4
### Environment
Ubuntu 18.04
TVM: 1787cca3f90237dff001fba01ffdbaf9a510f886
### Steps to reproduce
Run this code:
```python
import tvm
from tvm import relay
from tvm.relay.transform import InferType
@tvm.instrument.pass_instrument
class PrintIR:
"""Print the name of the pass, the IR, only before passes execute."""
def __init__(self, print_mod=True, show_meta_data=False) -> None:
self.pass_cnt = 0
self.print_mod = print_mod
self.show_meta_data = show_meta_data
def run_before_pass(self, mod, info):
with tvm.transform.PassContext(instruments=[]):
global prev_mod
if self.print_mod:
print(relay.transform.InferType()(mod))
print('>' * 40, f'Running Pass#{self.pass_cnt}:', info)
self.pass_cnt += 1
x = relay.var("x", shape=(1, 1, 1, 1))
y = relay.image.resize2d(x, (2, 4))
z = relay.transpose(y, (0, 1, 3, 2))
a = relay.nn.max_pool2d(z)
b = relay.image.resize2d(a, (1, 1))
func = relay.Function((x,), b)
mod = tvm.IRModule.from_expr(func)
print(InferType()(mod))
with tvm.transform.PassContext(opt_level=4, instruments=[PrintIR()]):
relay.create_executor("graph", mod).evaluate()
```
Part of error log:
```
6: _ZZN3tvm5relay11ExprFunctorIFNS_7runtime5ArrayINS_2te6TensorEvEERKNS_
5: tvm::relay::tec::LowerToTECompute::VisitExpr_(tvm::relay::CallNode
const*)
4:
tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor,
void> >::VisitExpr(tvm::RelayExpr const&)
3: tvm::relay::ExprFunctor<tvm::runtime::Array<tvm::te::Tensor, void>
(tvm::RelayExpr const&)>::VisitExpr(tvm::RelayExpr const&)
2: _ZZN3tvm5relay11ExprFunctorIFNS_7runtime5ArrayINS_2te6TensorEvEERKNS_
1: tvm::relay::tec::LowerToTECompute::VisitExpr_(tvm::relay::CallNode
const*)
0:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<TVMFuncCreateFromCFunc::{lambda(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)#2}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in
tvm._ffi._cy3.core.tvm_callback
File
"/workspace/workspace/tvm-intact/python/tvm/relay/backend/te_compiler.py", line
317, in lower_call
best_impl, outputs = select_implementation(op, call.attrs, inputs,
ret_type, target)
File
"/workspace/workspace/tvm-intact/python/tvm/relay/backend/te_compiler.py", line
207, in select_implementation
outs = impl.compute(attrs, inputs, out_type)
File "/workspace/workspace/tvm-intact/python/tvm/relay/op/op.py", line
126, in compute
return _OpImplementationCompute(self, attrs, inputs, out_type)
File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in
tvm._ffi._cy3.core.PackedFuncBase.__call__
File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in
tvm._ffi._cy3.core.FuncCall
File "tvm/_ffi/_cython/./base.pxi", line 181, in
tvm._ffi._cy3.core.CHECK_CALL
3: TVMFuncCall
2:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::relay::{lambda(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)#4}> >::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::{lambda(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)#2}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in
tvm._ffi._cy3.core.tvm_callback
File
"/workspace/workspace/tvm-intact/python/tvm/relay/op/image/_image.py", line
156, in compute_resize2d
out_dtype,
File "/workspace/workspace/tvm-intact/python/tvm/topi/image/resize.py",
line 824, in resize2d
raise ValueError("%s layout is not supported." % layout)
ValueError: NCWH layout is not supported.
```
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