ALinrunrun opened a new issue, #19544:
URL: https://github.com/apache/tvm/issues/19544
### Expected behavior
TVM Relax should execute ONNX `NonMaxSuppression` consistently with ONNX
Runtime.
According to the ONNX `NonMaxSuppression` specification,
`max_output_boxes_per_class` defaults to `0`, which means no output. Therefore,
both of the following cases should return an empty tensor:
1. `max_output_boxes_per_class` is omitted.
2. `max_output_boxes_per_class` is explicitly provided as `0`.
### Actual behavior
TVM Relax returns selected indices even when `max_output_boxes_per_class` is
omitted or explicitly set to `0`:
```
no max input:
ORT: []
TVM: [[0, 0, 0], [0, 0, 2], [0, 0, 3]]
max=0:
ORT: []
TVM: [[0, 0, 0], [0, 0, 2], [0, 0, 3]]
```
The discrepancy appears when importing an ONNX NonMaxSuppression model
through the Relax ONNX frontend and compiling it for the llvm target.
### Environment
TVM: 0.14 environment / Relax ONNX frontend
ONNX Runtime: 1.23
Python: 3.11
Target: llvm
OS: Linux
### Steps to reproduce
```
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import onnx
import onnxruntime as ort
import tvm
from onnx import TensorProto, helper
from tvm import relax
from tvm.relax.frontend.onnx import from_onnx
def make_nms_model(with_max_input):
inputs = ["b", "s"] + (["m"] if with_max_input else [])
node = helper.make_node("NonMaxSuppression", inputs, ["y"])
model_inputs = [
helper.make_tensor_value_info("b", TensorProto.FLOAT, [1, 4, 4]),
helper.make_tensor_value_info("s", TensorProto.FLOAT, [1, 1, 4]),
]
if with_max_input:
model_inputs.append(
helper.make_tensor_value_info("m", TensorProto.INT64, [1])
)
graph = helper.make_graph(
[node],
"g",
model_inputs,
[helper.make_tensor_value_info("y", TensorProto.INT64, None)],
)
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("",
18)])
model.ir_version = 9
return model
def run_ort(model, feed):
sess = ort.InferenceSession(
model.SerializeToString(),
providers=["CPUExecutionProvider"],
)
return sess.run(None, feed)[0]
def run_tvm(model, feed):
mod = from_onnx(model)
with tvm.transform.PassContext(opt_level=3):
ex = tvm.compile(mod, target=tvm.target.Target("llvm"))
vm = relax.VirtualMachine(ex, tvm.cpu())
args = [tvm.runtime.tensor(v, tvm.cpu()) for v in feed.values()]
out = vm["main"](*args)
out = out[0] if isinstance(out, (list, tuple)) else out
return out.numpy()
base = {
"b": np.array(
[
[
[5.0, 5.0, 6.0, 6.0],
[5.05, 5.05, 6.05, 6.05],
[10.0, 10.0, 11.0, 11.0],
[20.0, 20.0, 21.0, 21.0],
]
],
dtype=np.float32,
),
"s": np.array([[[0.95, 0.85, 0.75, 0.65]]], dtype=np.float32),
}
model_no_max = make_nms_model(False)
ort_no_max = run_ort(model_no_max, base)
tvm_no_max = run_tvm(model_no_max, base)
print("no max input:")
print(" ORT:", ort_no_max.tolist())
print(" TVM:", tvm_no_max.tolist())
model_max_zero = make_nms_model(True)
feed_max_zero = {**base, "m": np.array([0], dtype=np.int64)}
ort_max_zero = run_ort(model_max_zero, feed_max_zero)
tvm_max_zero = run_tvm(model_max_zero, feed_max_zero)
print("max=0:")
print(" ORT:", ort_max_zero.tolist())
print(" TVM:", tvm_max_zero.tolist())
```
### Triage
* needs-triage
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