m3at opened a new issue #6624: URL: https://github.com/apache/incubator-tvm/issues/6624
Following [discussion on the forum](https://discuss.tvm.apache.org/t/unable-to-build-relay-function-twice/7987) I'm opening this issue for what appear to be a bug when running `relay.build`, with potentially `nn.pad` being mutated in-place. In practice the issue can be side-stepped by making a `deepcopy` of the module before building. This issue is to find which pass is potentially mutating the module in-place. Steps to reproduce: ```sh # Obtain the model python3 -m pip install geffnet wget "https://github.com/rwightman/gen-efficientnet-pytorch/blob/master/onnx_export.py"; python3 onnx_export.py ./b4.onnx --model="tf_efficientnet_b4_ns" --img-size=380 ``` ```python import numpy as np import onnx import tvm from tvm import relay from tvm.contrib import graph_runtime # Prepare parameters input_shape = [1, 3, 380, 380] example_input = np.random.randn(*input_shape).astype(np.float32) target = "llvm -mcpu=core-avx2" ctx = tvm.cpu(0) # Get model from ONNX onnx_model = onnx.load("./b4.onnx") mod, params = relay.frontend.from_onnx( onnx_model, {"input0": input_shape}, ) # Build module with tvm.transform.PassContext(opt_level=3): graph_module = relay.build(mod, target=target, target_host=target, params=params) # Run, no issue runtime_module = graph_runtime.GraphModule(graph_module['default'](ctx)) runtime_module.set_input(key="input0", value=tvm.nd.array(example_input)) runtime_module.run() tvm_output = runtime_module.get_output(0).asnumpy() # Build again, or use autotvm.task.extract_from_program # Error (see below) with tvm.transform.PassContext(opt_level=3): graph_module = relay.build(mod, target=target, target_host=target, params=params ``` Relevant part of the error: ``` %45 = multiply(%43, %44); %46 = nn.pad(%45, pad_width=[[0, 0], [0, 0], [0, 1], [0, 1], [0, 0]]) an internal invariant was violated while typechecking your program [05:19:37] ../src/relay/op/nn/pad.cc:125: Check failed: data->shape.size() == param->pad_width.size(): There should be as many pad width pairs as shape dimensions but the shape has 4 dimensions and there are 5 pad width pairs. ; ; %47 = nn.conv2d(%46, meta[relay.Constant][38], strides=[2, 2], padding=[0, 0, 0, 0], groups=144, kernel_size=[3, 3]); ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
