wangzy0327 commented on issue #13666:
URL: https://github.com/apache/tvm/issues/13666#issuecomment-1413100409
> Tried this with the following docker image I built from latest ROCm:
>
> ```
> docker pull mevermeulen/rocm-tvm:5.4.2
> ```
>
> I didn't have OpenCL built in that so I compared with CPU execution and I
don't see an issue:
>
> ```
> root@chilecito:/src/rocm-tvm/qa# python3 /home/mev/onnx_rocm.py
> [0.5488135 0.71518934 0.60276335 0.5448832 0.4236548 0.6458941
> 0.4375872 0.891773 0.96366274 0.3834415 ]
> [-0.22859086 -0.25806987 -0.43340546 0.4846983 -0.6018106 0.22698797
> 0.85465795 -0.9607101 0.5279621 -1.1830723 ]
> [-0.22859041 -0.25806972 -0.43340546 0.4846975 -0.6018108 0.2269876
> 0.8546581 -0.9607104 0.527962 -1.1830723 ]
> ```
>
> To compare against the CPU, I modified the last part of the program as
follows:
>
> ```
> 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("/home/mev/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 =
input_name,input_data = I_np, input = I_np.shape, output = output_size)
> cpu_output = build("llvm",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(cpu_output[0][:10])
> # print(opencl_output[0][:10])
> ```
>
> @wangzy0327 does my docker work for you? If so, a spot you can use for
comparison.
>
> Also can you cross check that your ROCm installation and driver is
properly installed. For example you can try:
>
> ```
> prompt% rocminfo
>
> prompt% cd /opt/rocm/share/hip/samples/0_Intro/square
> prompt% make
> prompt% cat square.out
> ```
@mvermeulen
I compare two platform `rocm` and `rocm -libs=miopen` on tvm v0.10.0 version
to run the code as follow.
<details>
<summary>onnx_rocm.py</summary>
```
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"](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():
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>
I get the error on AMD gfx908 device . The error is `ValueError:Cannot find
global function tvm.contrib.miopen.conv2d.setup` .
How to fix it ?
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