VertexC opened a new issue #7476:
URL: https://github.com/apache/tvm/issues/7476
Hi,
I am trying to build onnx model with tvm but got following error
```bash
Check failed: oshape_sum == data_shape_sum (2048 vs. 131072) : Input tensor
shape and reshaped shape are not compatible
```
The value `131072` will change if I change the `batch_size` of following code
```python
import os
import time
import onnx
import numpy as np
import tvm
from tvm import te
import tvm.relay as relay
from tvm.contrib import graph_runtime
import util
def onnx2tvm_runner(model_name, batch_size=1, backend='cuda'):
model, shape = util.onnx_model(model_name)
data = np.random.rand(batch_size, *shape)
input_name = model.graph.input[0].name
shape_dict = {input_name: tuple([batch_size, *shape])}
mod, params = relay.frontend.from_onnx(model, shape_dict)
# TODO: how opt_level affects performance
opt_level = 3
if backend == 'llvm':
with tvm.transform.PassContext(opt_level=opt_level):
lib = relay.build(mod,
target='llvm',
target_host='llvm',
params=params)
ctx = tvm.cpu()
module = graph_runtime.GraphModule(lib["default"](ctx))
module.set_input(input_name, data)
else:
target = tvm.target.cuda()
with tvm.transform.PassContext(opt_level=opt_level):
lib = relay.build(mod, target, params=params)
ctx = tvm.gpu()
module = graph_runtime.GraphModule(lib["default"](ctx))
module.set_input(input_name, data)
dtype = "float32"
data = tvm.nd.array(data.astype(dtype))
def runner(data_size):
for _ in range(data_size // batch_size):
module.set_input(input_name, data)
module.run()
return runner
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="benchmark of onnx/tvm")
parser.add_argument("model", help="onnx model name")
parser.add_argument("--backend",
choices=['cuda', 'llvm'],
default='cuda',
help='backend target to run')
parser.add_argument("--batch", type=int, default=1, help='batch size')
parser.add_argument("--size", type=int, default=256, help='data size')
args = parser.parse_args()
os.environ['TVM_BACKTRACE'] = '1'
runner = onnx2tvm_runner(args.model,
batch_size=args.batch,
backend=args.backend)
duration = util.simple_bench(runner, args.size)
print(duration)
```
The models I used are
```bash
#!/bin/bash
SCRIPT=$(readlink -f "$0")
DIR=$(dirname "$SCRIPT")
echo $DIR
if [ ! -f ${DIR}/resnet50.onnx ]; then
wget -O ${DIR}/resnet50.onnx
https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-caffe2-v1-9.onnx
fi
if [ ! -f ${DIR}/mobilenet.onnx ]; then
wget -O ${DIR}/mobilenet.onnx
https://github.com/onnx/models/raw/master/vision/classification/mobilenet/model/mobilenetv2-7.onnx
fi
if [ ! -f ${DIR}/vgg16.onnx ]; then
wget -O ${DIR}/vgg16.onnx
https://github.com/onnx/models/raw/master/vision/classification/vgg/model/vgg16-7.onnx
fi
if [ ! -f ${DIR}/inception.onnx ]; then
wget -O ${DIR}/inception.onnx
https://github.com/onnx/models/raw/master/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx
fi
```
Meanwhile, when `batch_size=1`, all models built successfully. When
batch_size is not 1, only vgg16 pass the build but all others
(resnet50/mobilenet/inception) failed.
----------------------------------------------------------------
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]
