testing a simple shiftnet

examples/python/pytorch/triton.py

@@ -1,6 +1,7 @@
 import torch
-from torch.nn.modules.utils import _single, _pair, _triple
 import math
+from torch.nn.modules.utils import _single, _pair, _triple
+from torch.distributions import categorical
 
 torch.ops.load_library("/home/philippe/development/triton/build/examples/python/pytorch/libtorch_triton.so")
 
@@ -93,13 +94,18 @@ class Conv2d(_ConvNd):
 class ShiftConvFunction(torch.autograd.Function):
 
     @staticmethod
-    def forward(ctx, input, weight, bias, stride, width):
+    def forward(ctx, input, weight, bias, stride, width, shift_h, shift_w):
         if bias is None:
           bias = torch.empty(0)
         ctx.save_for_backward(input, weight, bias)
         ctx.stride = stride
         ctx.width = width
-        output = torch.ops.triton.shift_conv_y(input, weight, bias, width[0], width[1], stride[0], stride[1])
+        ctx.shift_h = shift_h
+        ctx.shift_w = shift_w
+        output = torch.ops.triton.shift_conv_y(input, weight, bias,
+                                               width[0], width[1],
+                                               stride[0], stride[1],
+                                               shift_h, shift_w)
         return output
 
     @staticmethod
@@ -107,16 +113,70 @@ class ShiftConvFunction(torch.autograd.Function):
         input, weight, bias = ctx.saved_tensors
         stride = ctx.stride
         width = ctx.width
+        shift_h = ctx.shift_h
+        shift_w = ctx.shift_w
         dx = dw = dbias = None
         if ctx.needs_input_grad[0]:
-            dx = torch.ops.triton.shift_conv_dx(dy, weight, bias, width[0], width[1], stride[0], stride[1])
+            dx = torch.ops.triton.shift_conv_dx(dy, weight, bias, width[0], width[1], stride[0], stride[1], shift_h, shift_w)
         if ctx.needs_input_grad[1]:
-            dw = torch.ops.triton.shift_conv_dw(dy, input, bias, width[0], width[1], stride[0], stride[1])
+            dw = torch.ops.triton.shift_conv_dw(dy, input, bias, width[0], width[1], stride[0], stride[1], shift_h, shift_w)
         if ctx.needs_input_grad[2]:
             dbias = torch.sum(dy, (1, 2, 3))
-        return dx, dw, dbias, None, None
+        return dx, dw, dbias, None, None, None, None
 
 
+class _ShiftConvNd(torch.nn.Module):
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias):
+        super(_ShiftConvNd, self).__init__()
+        # initialize
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.weight = torch.nn.Parameter(torch.Tensor(in_channels, out_channels))
+        if bias:
+            self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.shift_h = self.make_shift(kernel_size[0])
+        self.shift_w = self.make_shift(kernel_size[1])
+        self.reset_parameters()
+
+    def forward(self, input):
+        return ShiftConvFunction.apply(input, self.weight, self.bias, self.stride,
+                                       self.kernel_size, self.shift_h, self.shift_w)
+
+    def make_shift(self, kernel_size):
+        if kernel_size == 3:
+            p = torch.Tensor([0.3, 0.4, 0.3])
+        elif kernel_size == 5:
+            p = torch.Tensor([0.1, 0.25, 0.3, 0.25, 0.1])
+        elif kernel_size == 7:
+            p = torch.Tensor([0.075, 0.1, 0.175, 0.3, 0.175, 0.1, 0.075])
+        elif kernel_size == 9:
+            p = torch.Tensor([0.05, 0.075, 0.1, 0.175, 0.2, 0.175, 0.1, 0.075, 0.05])
+        else:
+            raise RuntimeError('Unsupported kernel size')
+        return categorical.Categorical(p).sample((self.in_channels,)) - (kernel_size // 2)
+
+    def reset_parameters(self):
+        n = self.in_channels
+        for k in self.kernel_size:
+            n *= k
+        stdv = 1. / math.sqrt(n)
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.uniform_(-stdv, stdv)
+
+class ShiftConv2d(_ShiftConvNd):
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=False):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        super(ShiftConv2d, self).__init__(
+            in_channels, out_channels, kernel_size, stride, bias)
+
 #################################
 #########   BatchNorm ###########
 #################################
@@ -134,5 +194,27 @@ class BatchNormFunction(torch.autograd.Function):
     def backward(ctx, dy):
         eps = ctx.eps
         x, gamma, beta, mean, var  = ctx.saved_tensors
-        dx, dg, db = torch.ops.triton.batchnorm_dxdgdb(dy, x, gamma, mean, var, eps)
-        return dx, dg, db, None, None
+        dx, dg, db = torch.ops.triton.batchnorm_dxdgdb(dy.contiguous(), x, gamma, mean, var, eps)
+        return dx, dg, db, None
+
+
+class _BatchNorm(torch.nn.Module):
+
+    def __init__(self, num_features, eps=1e-5):
+        super(_BatchNorm, self).__init__()
+        self.num_features = num_features
+        self.eps = eps
+        self.weight = torch.nn.Parameter(torch.Tensor(num_features))
+        self.bias = torch.nn.Parameter(torch.Tensor(num_features))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        torch.nn.init.uniform_(self.weight)
+        torch.nn.init.zeros_(self.bias)
+
+    def forward(self, input):
+        return BatchNormFunction.apply(input, self.weight, self.bias, self.eps)
+
+class BatchNorm2d(_BatchNorm):
+
+    pass