[triton/dnn/conv] merged optimizations branch

- Added forward/backward support for strided convolution
- Added support for bias
- Added support for reduction splitting

examples/python/pytorch/triton.py

@@ -2,7 +2,7 @@ import torch
 from torch.nn.modules.utils import _single, _pair, _triple
 import math
 
-torch.ops.load_library("/home/philippe/Development/triton/build/examples/python/pytorch/libtorch_triton.so")
+torch.ops.load_library("/home/philippe/development/triton/build/examples/python/pytorch/libtorch_triton.so")
 
 class ConvFunction(torch.autograd.Function):
 
@@ -37,7 +37,7 @@ class _ConvNd(torch.nn.Module):
                  padding, dilation, transposed, output_padding, groups, bias):
         super(_ConvNd, self).__init__()
         # not everything is supported by Triton
-        assert all(x==1 for x in stride)
+        assert all(x==1 or x==2 for x in stride)
         assert all(x==1 for x in dilation)
         assert transposed == False
         assert all(x==0 for x in output_padding)
@@ -46,6 +46,7 @@ class _ConvNd(torch.nn.Module):
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.kernel_size = kernel_size
+        self.stride = stride
         self.padding = padding
         self.weight = torch.nn.Parameter(torch.Tensor(
             in_channels, kernel_size[0], kernel_size[1], out_channels))
@@ -56,7 +57,7 @@ class _ConvNd(torch.nn.Module):
         self.reset_parameters()
 
     def forward(self, input):
-        return ConvFunction.apply(input, self.weight, self.bias, self.padding)
+        return ConvFunction.apply(input, self.weight, self.bias, self.stride, self.padding)
 
     def reset_parameters(self):
         n = self.in_channels