[Bug] LegalizeOps fails on valid onnx model: AssertionError: emit_te now only supports Tensor that has ShapeExpr shape #17964
Description
Expected behavior
TVM should compile the model correctly.
Actual behavior
When compiling the model with the default relax optimization pipeline, TVM crashes as follows:
Traceback (most recent call last):
File "/home/carla/Documents/test/test.py", line 51, in <module>
main()
File "/home/carla/Documents/test/test.py", line 45, in main
ex = relax.build(tvm_model, target="llvm", relax_pipeline=relax_pipeline)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/carla/Documents/tvm/python/tvm/relax/vm_build.py", line 253, in build
mod = relax_pipeline(mod)
^^^^^^^^^^^^^^^^^^^
File "/home/carla/Documents/tvm/python/tvm/ir/transform.py", line 238, in __call__
return _ffi_transform_api.RunPass(self, mod)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "tvm/ffi/cython/./function.pxi", line 212, in tvm.ffi.core.Function.__call__
File "tvm/ffi/cython/./function.pxi", line 265, in tvm.ffi.core.tvm_ffi_callback
File "/home/carla/Documents/tvm/python/tvm/relax/backend/cpu_generic/pipeline.py", line 73, in _pipeline
mod = seq(mod)
File "/home/carla/Documents/tvm/python/tvm/ir/transform.py", line 238, in __call__
return _ffi_transform_api.RunPass(self, mod)
File "tvm/ffi/cython/./function.pxi", line 212, in tvm.ffi.core.Function.__call__
File "tvm/ffi/cython/./function.pxi", line 265, in tvm.ffi.core.tvm_ffi_callback
File "/home/carla/Documents/tvm/python/tvm/relax/transform/legalize_ops/nn.py", line 509, in _nn_gelu
return bb.call_te(te_gelu, call.args[0], primfunc_name_hint="gelu")
File "/home/carla/Documents/tvm/python/tvm/relax/block_builder.py", line 356, in call_te
tir_func, call_args, output_sinfo, tir_vars = gen_call_tir_inputs(func, *args, **kwargs)
File "/home/carla/Documents/tvm/python/tvm/relax/utils.py", line 351, in gen_call_tir_inputs
te_args = _convert_te_arg(args)
File "/home/carla/Documents/tvm/python/tvm/relax/utils.py", line 289, in _convert_te_arg
new_arg = _convert_te_arg_helper(te_args)
File "/home/carla/Documents/tvm/python/tvm/relax/utils.py", line 273, in <genexpr>
return tuple(_convert_te_arg_helper(x) for x in arg)
File "/home/carla/Documents/tvm/python/tvm/relax/utils.py", line 273, in <genexpr>
return tuple(_convert_te_arg_helper(x) for x in arg)
File "/home/carla/Documents/tvm/python/tvm/relax/utils.py", line 224, in _convert_te_arg_helper
assert isinstance(
AssertionError: emit_te now only supports Tensor that has ShapeExpr shapeEnvironment
OS: Ubuntu 20.04
TVM: 0.21.dev0(bcb68b1)
CUDA: 11.8
Steps to reproduce
This bug can be reproduced by the following code with the model in the attachment. As shown in the code, the model can be executed by onnxruntime. However, tvm failed to compile this model with the default pipeline.
import sys
import numpy as np
import onnx
import onnxruntime
import tvm
from tvm import relax
from tvm.relax.frontend.onnx import from_onnx
import pickle
def main():
onnx_model = onnx.load("a674.onnx")
with open("inputs.pkl", "rb") as fp:
inputs = pickle.load(fp)
try:
ort_session = onnxruntime.InferenceSession(
onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]
)
ort_output = ort_session.run([], inputs)
except Exception as e:
print(e)
sys.exit(1)
print(ort_output)
# Convert the onnx model into relax through the onnx importer.
tvm_model = from_onnx(onnx_model, keep_params_in_input=True)
# Convert operators for inference mode.
tvm_model = relax.transform.DecomposeOpsForInference()(tvm_model)
# Legalize any relax ops into tensorir.
tvm_model = relax.transform.LegalizeOps()(tvm_model)
# Separate model from parameters.
tvm_model, params = relax.frontend.detach_params(tvm_model)
# Compile the relax graph into a VM then run.
#----------------------cpu-----------------------
with tvm.transform.PassContext(opt_level=0):
target = tvm.target.Target("llvm", host="llvm")
relax_pipeline = relax.pipeline.get_default_pipeline(target)
ex = relax.build(tvm_model, target="llvm", relax_pipeline=relax_pipeline)
if __name__ == "__main__":
main()
Triage
- needs-triage