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Update scnn for newer pytorch versions #11

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Update scnn for newer pytorch versions #11

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d739531
update scnn.py
stephandooper Sep 8, 2023
8eafbd5
Update scnn.py
stephandooper Sep 8, 2023
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32 changes: 0 additions & 32 deletions conv2d_backward.cpp
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89 changes: 0 additions & 89 deletions cpp_functions.cpp
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25 changes: 9 additions & 16 deletions scnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@
from torch._six import container_abcs
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
from torch.utils.cpp_extension import load

from tqdm import tqdm

Expand All @@ -37,10 +36,6 @@ def forward_amp_decorator(func):
def backward_amp_decorator(func):
return func

# Load and compile cpp code to call cudnn conv2d backward function
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, "cpp_functions.cpp")
cpp_functions = load(name="cpp_functions", sources=[filename], verbose=False)

# inspired by torch/nn/modules/utils.py
def _ntuple(n):
Expand Down Expand Up @@ -125,9 +120,7 @@ def backward(ctx, grad_output):
# TODO: performance improvements possible by only backpropping valid input
# grad_input_padding = _grad_input_padding(grad_output, inpt.shape, stride, padding, (weight.shape[2], weight.shape[3]))
# TODO: use this!?
grad_in = cpp_functions.backward_input(inpt.shape, grad_output, weight.to(inpt.dtype), padding,
stride, dilation, groups,
torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic)
grad_in = torch.nn.grad.conv2d_input(inpt.shape, weight, grad_output, stride, padding, dilation, groups)
else:
grad_in = None

Expand Down Expand Up @@ -211,14 +204,14 @@ def backward(ctx, grad_output):

# Calculate the kernel gradients with the new unseen gradient values
relevant_grad = relevant_grad.contiguous()

grad_weight = cpp_functions.backward(weight.shape,
relevant_grad.to(weight.dtype),
relevant_input.to(weight.dtype),
(0, 0), # padding
stride[1:3], dilation, groups,
torch.backends.cudnn.benchmark, # benchmark
torch.backends.cudnn.deterministic) # deterministic
grad_weight = torch.nn.grad.conv2d_weight(relevant_input.to(weight.dtype),
weight.shape,
relevant_grad.to(weight.dtype),
stride[1:3],
(0, 0), # padding
dilation,
groups)

if bias is not None:
grad_bias = relevant_grad[0].sum((1, 2))
Expand Down