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Zero-initialized L1, L2 gradient buffers to avoid garbage accumulation in gradients #138

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Merged
vbharadwaj-bk merged 1 commit into from
Jun 21, 2025
Merged

Zero-initialized L1, L2 gradient buffers to avoid garbage accumulation in gradients #138

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12 changes: 6 additions & 6 deletions openequivariance/extension/libtorch_tp_jit.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -167,8 +167,8 @@ tuple<torch::Tensor, torch::Tensor, torch::Tensor> jit_tp_backward(
const torch::Tensor &L3_grad) {

int64_t num_batch = L1_in.sizes()[0];
torch::Tensor L1_grad = torch::empty(L1_in.sizes(), L1_in.options());
torch::Tensor L2_grad = torch::empty(L2_in.sizes(), L2_in.options());
torch::Tensor L1_grad = torch::zeros(L1_in.sizes(), L1_in.options());
torch::Tensor L2_grad = torch::zeros(L2_in.sizes(), L2_in.options());
torch::Tensor W_grad = torch::empty(W.sizes(), W.options());

if(jit_instance->shared_weights == 1) {
Expand Down Expand Up @@ -207,8 +207,8 @@ tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor> jit_tp_double_
Stream stream = get_current_stream();

int64_t num_batch = L1_in.sizes()[0]; // Declaring outputs
torch::Tensor L1_grad = torch::empty(L1_in.sizes(), L1_in.options());
torch::Tensor L2_grad = torch::empty(L2_in.sizes(), L2_in.options());
torch::Tensor L1_grad = torch::zeros(L1_in.sizes(), L1_in.options());
torch::Tensor L2_grad = torch::zeros(L2_in.sizes(), L2_in.options());
torch::Tensor W_grad = torch::empty(W.sizes(), W.options());
torch::Tensor L3_dgrad = torch::empty(L3_grad.sizes(), L3_grad.options());

Expand Down Expand Up @@ -402,7 +402,7 @@ tuple<torch::Tensor, torch::Tensor, torch::Tensor> jit_conv_backward(
int64_t nnz = rows.sizes()[0];
int64_t node_count = L1_in.sizes()[0];
torch::Tensor L1_grad = torch::zeros(L1_in.sizes(), L1_in.options());
torch::Tensor L2_grad = torch::empty(L2_in.sizes(), L2_in.options());
torch::Tensor L2_grad = torch::zeros(L2_in.sizes(), L2_in.options());
torch::Tensor W_grad = torch::empty(W.sizes(), W.options());

torch::Tensor L1_in_contig = L1_in.contiguous();
Expand Down Expand Up @@ -452,7 +452,7 @@ tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor> jit_conv_doubl
int64_t nnz = rows.sizes()[0];
int64_t node_count = L1_in.sizes()[0];
torch::Tensor L1_grad = torch::zeros(L1_in.sizes(), L1_in.options());
torch::Tensor L2_grad = torch::empty(L2_in.sizes(), L2_in.options());
torch::Tensor L2_grad = torch::zeros(L2_in.sizes(), L2_in.options());
torch::Tensor W_grad = torch::empty(W.sizes(), W.options());
torch::Tensor L3_dgrad = torch::zeros(L3_grad.sizes(), L3_grad.options());

Expand Down
4 changes: 2 additions & 2 deletions openequivariance/implementations/TensorProduct.py
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Expand Up @@ -125,8 +125,8 @@ def backward_helper(
weights: torch.Tensor,
L3_grad: torch.Tensor,
) -> typing.List[torch.Tensor]:
L1_grad = torch.empty_like(L1_in)
L2_grad = torch.empty_like(L2_in)
L1_grad = torch.zeros_like(L1_in)
L2_grad = torch.zeros_like(L2_in)
weights_grad = torch.empty_like(weights)

if self.config.shared_weights:
Expand Down
4 changes: 2 additions & 2 deletions openequivariance/implementations/convolution/TensorProductConv.py
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Expand Up @@ -198,7 +198,7 @@ def backward_helper(
transpose_perm: Optional[torch.Tensor] = None,
) -> List[torch.Tensor]:
L1_grad = torch.zeros_like(L1_in)
L2_grad = torch.empty_like(L2_in)
L2_grad = torch.zeros_like(L2_in)
weights_grad = torch.empty_like(weights)

if self.config.shared_weights:
Expand Down Expand Up @@ -287,7 +287,7 @@ def double_backward_helper(
transpose_perm: Optional[torch.Tensor] = None,
) -> List[torch.Tensor]:
L1_grad = torch.zeros_like(L1_in)
L2_grad = torch.empty_like(L2_in)
L2_grad = torch.zeros_like(L2_in)
W_grad = torch.empty_like(W)
L3_dgrad = torch.zeros_like(L3_grad)

Expand Down