I built from source code(v1.0.0), but Conv2 speed is slower than that installed with conda install command.
conv2d      cpu time - 709909.681us   cuda time - 709897.758us
conv2d      cpu time - 3431.992us       cuda time - 3430.720us
source code

class convT(nn.Module):
def __init__(self):
     super(convT, self).__init__()
     self.conv1=nn.Conv2d(1,1,5)

def forward(self, input):
     x = self.conv1(input)
     return x

if name == ‘main’:
x = torch.randn(1,1,200,200)
net = convT()
net.to(device)
x= x.cuda()

torch.cuda.synchronize()
with torch.autograd.profiler.profile(use_cuda=True) as prof:
    out = net(x)
torch.cuda.synchronize()

print(prof.key_averages().table(sort_by='cuda_time_total'))
print(out.size())


environment config
PyTorch version: 1.0.0a0+bb15580
Is debug build: No
CUDA used to build PyTorch: 9.0.176
OS: Ubuntu 16.04.6 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
CMake version: version 3.14.0
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 9.0.176
GPU models and configuration:
GPU 0: GeForce GTX TITAN X
GPU 1: GeForce GTX TITAN X
GPU 2: GeForce GTX TITAN X
GPU 3: GeForce GTX TITAN X
GPU 4: GeForce GTX TITAN X
GPU 5: GeForce GTX TITAN X
GPU 6: GeForce GTX TITAN X
GPU 7: GeForce GTX TITAN X
Nvidia driver version: 384.81
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so
/usr/lib/x86_64-linux-gnu/libcudnn.so.7
/usr/lib/x86_64-linux-gnu/libcudnn.so.7.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_static.a
/usr/lib/x86_64-linux-gnu/tmp/libcudnn.so.7.1.3
/usr/local/cuda-9.0/lib64/libcudnn.so
/usr/local/cuda-9.0/lib64/libcudnn.so.7
/usr/local/cuda-9.0/lib64/libcudnn.so.7.4.1
/usr/local/cuda-9.0/lib64/libcudnn_static.a
Versions of relevant libraries:
[pip] Could not collect
[conda] magma-cuda90              2.5.0                         1    pytorch
[conda] torch                     1.0.0a0+bb15580          pypi_0    pypi
[conda] torchfile                 0.1.0                    pypi_0    pypi