Johnson9009 opened a new issue #8432:
URL: https://github.com/apache/tvm/issues/8432
## Standard Ouput and Error Message
```
[00:32:22] /home/zhaqia01/workspaces/tvm/src/ir/transform.cc:616: PrintIR():
#[version = "0.0.5"]
def @main(%x1: Tensor[(1, 224, 224, 3), int8], %x2: Tensor[(7, 7, 3, 64),
int8]) -> Tensor[(1, 224, 224, 64), int32] {
%0 = nn.pad(%x1, 0 /* ty=int32 */, pad_width=[[0, 0], [3, 3], [3, 3], [0,
0]]) /* ty=Tensor[(1, 230, 230, 3), int8] */;
nn.conv2d(%0, %x2, padding=[0, 0, 0, 0], data_layout="NHWC",
kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 224, 224, 64), int32]
*/
}
The Relay type checker is unable to show the following types match.
In particular dimension 1 conflicts: 218 does not match 224.dimension 2
conflicts: 218 does not match 224.
The Relay type checker is unable to show the following types match.
In particular `Tensor[(1, 224, 224, 64), int32]` does not match `Tensor[(1,
218, 218, 64), int32]`
note: run with `TVM_BACKTRACE=1` environment variable to display a backtrace.
```
## Reproduce Test Case
```Python
import tvm
from tvm import relay
class PadSimplifier(relay.ExprMutator):
def __init__(self):
super().__init__()
def visit_call(self, call):
call = super().visit_call(call)
if ((call.op != relay.op.get("nn.conv2d")) or
(not isinstance(call.args[0], relay.Call)) or
(call.args[0].op != relay.op.get("nn.pad"))):
return call
conv2d = call
pad, weight = conv2d.args
data, pad_value = pad.args
if ((pad.attrs.pad_mode != "constant") or
(not isinstance(pad_value, relay.Constant)) or
(pad_value.data.numpy() != 0)):
return conv2d
# For reproduce issue, so just return the conv2d operator.
return relay.Call(conv2d.op, [data, weight], conv2d.attrs,
conv2d.type_args, conv2d.span)
@relay.transform.function_pass(opt_level=0)
class SimplifyPad:
def transform_function(self, func, ir_mod, pass_ctx):
return PadSimplifier().visit(func)
dtype = "int8"
dshape = (1, 224, 224, 3)
kshape = (7, 7, 3, 64)
x1 = relay.var("x1", shape=dshape, dtype=dtype)
x2 = relay.var("x2", shape=kshape, dtype=dtype)
expr = relay.nn.pad(x1, [[0, 0], [3, 3], [3, 3], [0, 0]])
expr = relay.nn.conv2d(expr, x2, data_layout="NHWC", kernel_layout="HWIO",
out_dtype="int32")
nn_mod = tvm.IRModule.from_expr(expr)
passes = [
relay.transform.InferType(),
tvm.transform.PrintIR(),
SimplifyPad(),
]
with tvm.transform.PassContext(opt_level=3):
nn_mod = tvm.transform.Sequential(passes)(nn_mod)
print(nn_mod)
```
## Current Clue
We can see the first InferType pass works well, so before the pass
"SimplifyPad" the infered output shape of "nn.conv2d" is (1, 224, 224, 64),
after the pass "SimplifyPad" the operator "nn.pad" is removed, because
"SimplifyPad" is a function pass so the InferType pass will be executed
automatically, the error happened in this 2nd InferType pass.
https://github.com/apache/tvm/blob/683c5ebff2bc7f528458485d4c86f88f6c79394c/src/relay/ir/transform.cc#L157-L163
When the 2nd InferType call function Conv2DRel, the value of parameter
"types" is `"[TensorType([1, 224, 224, 3], int8), TensorType([7, 7, 3, 64],
int8), TensorType([1, 224, 224, 64], int32)]"`, the last item of parameter
"types" maybe wrong, because the value of this parameter during the 1st
InferType is `"[TensorType([1, 230, 230, 3], int8), TensorType([7, 7, 3, 64],
int8), IncompleteTypeNode(0, 0x5d6e270)]"`.
https://github.com/apache/tvm/blob/683c5ebff2bc7f528458485d4c86f88f6c79394c/src/relay/op/nn/convolution.h#L133-L136
The type solver relevant code is hard to understand, so I want to know is
this a bug? or the pass I write missing something?
Thanks a lot.
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