I am trying to import an onnx graph into tvm and I’m running into an issue with hitting a dimension matching assert:
onnx_model = onnx.load('xor.onnx')
xor_sym, xor_params = nnvm.frontend.from_onnx(onnx_model)
investigate xor_params:
import pprint
pprint.pprint(xor_params, indent=3)
OUT:
{ 'B': <tvm.NDArray shape=(2,), cpu(0)>
array([0., 0.], dtype=float32),
'B1': <tvm.NDArray shape=(2,), cpu(0)>
array([0., 0.], dtype=float32),
'W': <tvm.NDArray shape=(2, 2), cpu(0)>
array([[-0.5194443 , 0.92808163],
[ 0.68033445, -0.04199219]], dtype=float32),
'W1': <tvm.NDArray shape=(2, 2), cpu(0)>
array([[-0.9073467 , 0.44402432],
[ 0.77458537, 0.17634082]], dtype=float32)}
I then try to compile the graph:
import nnvm.compiler
target = 'llvm'
shape_dict = {'dense_1_input_0': (1,2)}
with nnvm.compiler.build_config(opt_level=0):
graph, lib, params = nnvm.compiler.build(xor_sym, target, shape_dict, params=xor_params)
OUT:
...
NNVMError: Error in operator elemwise_add0: [09:55:05] /home/agsim/repos/tvm/nnvm/src/top/nn/../elemwise_op_common.h:38: Check failed: assign(&dattr, (*vec)[i]) Incompatible attr in node elemwise_add0 at 1-th input: expected [1,2], got [2]
...
I think the cause is because in this network when we call nnvm.frontend.from_onnx(onnx_model) we get the following in the symbolic representation of the graph:
...
Op:matmul, Name=matmul0
Inputs:
arg[0]=dense_1_input_0(0) version=0
arg[1]=W(0) version=0
Variable:B
--------------------
Op:elemwise_add, Name=elemwise_add0
Inputs:
arg[0]=matmul0(0)
arg[1]=B(0) version=0
--------------------
...
compared to the super resolution which has conv followed by add…
...
--------------------
Op:conv2d, Name=conv2d0
Inputs:
arg[0]=1(0) version=0
arg[1]=2(0) version=0
Attrs:
channels=64
dilation=(1, 1)
groups=1
kernel_size=(5, 5)
padding=(2, 2)
strides=(1, 1)
use_bias=False
Variable:3
--------------------
Op:expand_dims, Name=expand_dims0
Inputs:
arg[0]=3(0) version=0
Attrs:
axis=1
num_newaxis=2
--------------------
Op:broadcast_add, Name=broadcast_add0
Inputs:
arg[0]=conv2d0(0)
arg[1]=expand_dims0(0)
--------------------
...
Is there any reason Conv -> Add in onnx is converted to BROADCAST_ADD(CONV_OUT, EXPAND_DIMS(BIAS)) and MatMul -> Add is converted to ELEMENTWISE_ADD(MATMUL_OUT, BIAS)?
Seems like i could fix this by hacking the xor_params Bias tensors to be shape 1,2 but that’s basically doing the expand_dims call myself.
Note: the xor.onnx was exported from pytorch and the Add does not have any attributes for the whereas super_resolution.onnx's Add has broadcast=1, axis=1. Was there something special done when exporting the onnx graph for super_resolution to generate these attributes?