wangzy0327 commented on issue #13666:
URL: https://github.com/apache/tvm/issues/13666#issuecomment-1370942424
@mashi
The minimal test case as follow.
<details>
<summary>onnx_rocm.py</summary>
```
from csv import writer
from pyexpat import model
import onnx
#from tvm.driver import tvmc
import numpy as np
import tvm
import tvm.relay as relay
from tvm.contrib import graph_executor
import tvm.testing
import numpy as np
import os
import logging
logging.basicConfig(level=logging.ERROR)
import warnings
warnings.filterwarnings('ignore')
//onnx-model mnist-7
def build(target:str,mod:tvm.IRModule, params:dict, input_name:str,
input_data:np.ndarray, input:tuple, output: tuple) -> np.ndarray:
tgt = tvm.target.Target(target=target, host="llvm")
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
dev = tvm.device(str(target), 0)
module = graph_executor.GraphModule(lib["default"](dev))
module.set_input(input_name, input_data)
module.run()
output_shape = output
tvm_output = module.get_output(0, tvm.nd.empty(output_shape)).numpy()
return tvm_output
def main(model_network : NetworkData):
# 设置随机种子
np.random.seed(0)
I_np = np.random.uniform(size = model_network.input).astype(dtype)
print(I_np[0][0][0][:10])
onnx_model =
onnx.load("onnx-model/vision/classification/mnist/model/mnist-7.onnx")
mod,params = relay.frontend.from_onnx(onnx_model,{"Input3":I_np.shape})
rocm_output = build("rocm",mod = mod,params = params,input_name =
model_network.input_name,input_data = I_np, input = I_np.shape, output =
model_network.output)
opencl_output = build("opencl",mod = mod,params = params,input_name =
model_network.input_name,input_data = I_np, input = I_np.shape, output =
model_network.output)
print(rocm_output[0][:10])
print(opencl_output[0][:10])
for name,each_network in MODEL_NAME.items():
print("-----------"+name+"----start----------------")
main(each_network)
```
</details>
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