simpler convdw
@@ -23,17 +23,17 @@ from tinygrad.llops.ops_cpu import unary_op, binary_op, reduce_op, movement_op
# ************* processing ops *************
-from tinygrad.helpers import get_conv_args
from tinygrad.ops import ProcessingOps
def convdw(x,grad_output,dw,C):
# NOTE: torch.nn.grad.conv2d_weight is wrong for groups in pytorch, wonder who it affects
# https://github.com/pytorch/pytorch/issues/51430
- grad_output = grad_output.reshape(C.bs, C.groups, C.rcout, C.oy, C.ox).repeat(1, 1, C.cin, 1, 1)
- grad_output = grad_output.reshape(C.bs * C.groups * C.rcout * C.cin, 1, C.oy, C.ox)
+ grad_output = grad_output.reshape(C.bs * C.groups, C.rcout, C.oy, C.ox).repeat(1, C.cin, 1, 1)
+ grad_output = grad_output.reshape(C.bs * C.groups * C.cin * C.rcout, 1, C.oy, C.ox)
x = x.reshape(1, C.bs * C.groups * C.cin, C.iy, C.ix)
+ # NOTE: this conv2d always has batch size 1.
grad_weight = torch.conv2d(x, grad_output, dilation=(C.ys, C.xs), groups=C.bs*C.groups*C.cin)
- grad_weight = grad_weight.reshape(C.bs, C.groups, C.cin, C.rcout, *grad_weight.shape[2:]).sum(dim=0).transpose(2, 1)
+ grad_weight = grad_weight.reshape(C.bs, C.groups, C.cin, C.rcout, *grad_weight.shape[2:]).transpose(3, 2).sum(dim=0)
dw[:] = grad_weight.reshape(C.groups*C.rcout, C.cin, *grad_weight.shape[3:])[:, :, :dw.shape[2], :dw.shape[3]]
def processing_op(op,x,w,ret,C):
@@ -42,7 +42,7 @@ def processing_op(op,x,w,ret,C):
ret[:] = torch.conv2d(x, w, stride=stride, groups=groups)
elif op == ProcessingOps.CONVT:
if stride == 1 or stride == (1,1):
- # strided needs weird padding: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
+ # strided needs weird "undilation": https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
w = w.reshape(C.groups, C.rcout, C.cin, C.H, C.W).flip(3, 4).transpose(2, 1).reshape(C.groups*C.cin, C.rcout, C.H, C.W)
ret[:] = torch.conv2d(x, w, padding=(C.H-1,C.W-1), groups=groups)
else:
