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Bbonev/disco refactor #28

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merged 9 commits into from
Dec 22, 2023
Merged

Bbonev/disco refactor #28

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53ccd15
Moved convolutions and exposed them directly
bonevbs Dec 17, 2023
4b623fb
Added transposition to the unit test
bonevbs Dec 17, 2023
dadad7f
Minor bugfix in CPU version of DISCO transpose code
bonevbs Dec 18, 2023
48e26b0
Adding convolution tests to CI
bonevbs Dec 18, 2023
fb75855
Added gradient check
bonevbs Dec 18, 2023
e364219
Checking the weight grad as well
bonevbs Dec 18, 2023
51451b0
Added test for anisotropic kernels
bonevbs Dec 19, 2023
92b6f38
Changed the code to only implicitly use sparse tensors in the modules…
bonevbs Dec 22, 2023
0a35814
Merge branch 'main' into bbonev/disco-refactor
bonevbs Dec 22, 2023
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4 changes: 3 additions & 1 deletion tests/test_convolution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -221,8 +221,10 @@ def test_disco_convolution(
in_shape, out_shape, kernel_shape, grid_in=grid_in, grid_out=grid_out, theta_cutoff=theta_cutoff
).to(self.device)

psi = torch.sparse_coo_tensor(conv.psi_idx, conv.psi_vals, size=(conv.kernel_size, conv.nlat_out, conv.nlat_in * conv.nlon_in)).to_dense()

self.assertTrue(
torch.allclose(conv.psi.to_dense(), psi_dense[:, :, 0].reshape(-1, nlat_out, nlat_in * nlon_in))
torch.allclose(psi, psi_dense[:, :, 0].reshape(-1, nlat_out, nlat_in * nlon_in))
)

# create a copy of the weight
Expand Down
32 changes: 20 additions & 12 deletions torch_harmonics/convolution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -222,10 +222,12 @@ def __init__(
idx, vals = _precompute_convolution_tensor(
in_shape, out_shape, kernel_shape, grid_in=grid_in, grid_out=grid_out, theta_cutoff=theta_cutoff
)
psi = torch.sparse_coo_tensor(
idx, vals, size=(self.kernel_size, self.nlat_out, self.nlat_in * self.nlon_in)
).coalesce()
self.register_buffer("psi", psi, persistent=False)
# psi = torch.sparse_coo_tensor(
# idx, vals, size=(self.kernel_size, self.nlat_out, self.nlat_in * self.nlon_in)
# ).coalesce()
self.register_buffer("psi_idx", idx, persistent=False)
self.register_buffer("psi_vals", vals, persistent=False)
# self.register_buffer("psi", psi, persistent=False)

# groups
self.groups = groups
Expand All @@ -248,10 +250,12 @@ def forward(self, x: torch.Tensor, use_triton_kernel: bool = True) -> torch.Tens
# pre-multiply x with the quadrature weights
x = self.quad_weights * x

psi = torch.sparse_coo_tensor(self.psi_idx, self.psi_vals, size=(self.kernel_size, self.nlat_out, self.nlat_in * self.nlon_in)).coalesce()

if x.is_cuda and use_triton_kernel:
x = _disco_s2_contraction_triton(x, self.psi, self.nlon_out)
x = _disco_s2_contraction_triton(x, psi, self.nlon_out)
else:
x = _disco_s2_contraction_torch(x, self.psi, self.nlon_out)
x = _disco_s2_contraction_torch(x, psi, self.nlon_out)

# extract shape
B, C, K, H, W = x.shape
Expand Down Expand Up @@ -317,10 +321,12 @@ def __init__(
idx, vals = _precompute_convolution_tensor(
out_shape, in_shape, kernel_shape, grid_in=grid_out, grid_out=grid_in, theta_cutoff=theta_cutoff
)
psi = torch.sparse_coo_tensor(
idx, vals, size=(self.kernel_size, self.nlat_in, self.nlat_out * self.nlon_out)
).coalesce()
self.register_buffer("psi", psi, persistent=False)
# psi = torch.sparse_coo_tensor(
# idx, vals, size=(self.kernel_size, self.nlat_in, self.nlat_out * self.nlon_out)
# ).coalesce()
self.register_buffer("psi_idx", idx, persistent=False)
self.register_buffer("psi_vals", vals, persistent=False)
# self.register_buffer("psi", psi, persistent=False)

# groups
self.groups = groups
Expand Down Expand Up @@ -351,10 +357,12 @@ def forward(self, x: torch.Tensor, use_triton_kernel: bool = True) -> torch.Tens
# pre-multiply x with the quadrature weights
x = self.quad_weights * x

psi = torch.sparse_coo_tensor(self.psi_idx, self.psi_vals, size=(self.kernel_size, self.nlat_in, self.nlat_out * self.nlon_out)).coalesce()

if x.is_cuda and use_triton_kernel:
out = _disco_s2_transpose_contraction_triton(x, self.psi, self.nlon_out)
out = _disco_s2_transpose_contraction_triton(x, psi, self.nlon_out)
else:
out = _disco_s2_transpose_contraction_torch(x, self.psi, self.nlon_out)
out = _disco_s2_transpose_contraction_torch(x, psi, self.nlon_out)

if self.bias is not None:
out = out + self.bias.reshape(1, -1, 1, 1)
Expand Down