[ROCM] Fixes compiling on ROCM 5 and accuracy on dense op

[masahi]

Fixes #13666

• Some bitcode files need to be updated
• There is a strange, device-dependent accuracy issue when using the default topi dense op schedule on AMDGPU (see the issue above). I confirmed that other schedule works fine.

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[mvermeulen]

I checked this with Radeon VII, gfx906 and also works on this platform.

[junrushao]

LGTM!

[wangzy0327]

Fixes #13666

• Some bitcode files need to be updated
• There is a strange, device-dependent accuracy issue when using the default topi dense op schedule on AMDGPU (see the issue above). I confirmed that other schedule works fine.

I checked this with Radeon VII, gfx906 and also works on this platform.

I test this with gfx908 but not works on the platform.

See details as #13666

@mvermeulen

[wangzy0327]

@ masahi @mvermeulen I use the commit masahi:rocm-t-fix to build and run code.But I get error as follow.

onnx_rocm.py

```

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

dtype="float32"
common_prefix_str = "onnx-model/vision/classification/"
tol_paras = [1e-7,1e-6,1e-5,1e-4,1e-3,1e-2]
input_name = "Input3"
input_size = (1,1,28,28)
output_size = (1,10)

import logging
logging.basicConfig(level=logging.ERROR)

import warnings
warnings.filterwarnings('ignore')

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")
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():
np.random.seed(0)
I_np = np.random.uniform(size = input_size).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_lib_output = build("rocm -libs=miopen",mod = mod,params = params,input_name = input_name,input_data = I_np, input = I_np.shape, output = output_size)
rocm_output = build("rocm",mod = mod,params = params,input_name = input_name,input_data = I_np, input = I_np.shape, output = output_size)
opencl_output = build("opencl",mod = mod,params = params,input_name = input_name,input_data = I_np, input = I_np.shape, output = output_size)
print(rocm_output[0][:10])
print(rocm_lib_output[0][:10])
print(opencl_output[0][:10])

main()

</details>

![image](https://user-images.githubusercontent.com/22990858/217220410-91c890d8-b74e-4189-98d6-d783a00fc4cd.png)