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Fix FutureWarning due to annotations #150

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Fix FutureWarning due to annotations #150

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16 changes: 8 additions & 8 deletions supar/structs/fn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -297,15 +297,15 @@ class Logsumexp(Function):
"""

@staticmethod
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float)
@torch.amp.custom_fwd(cast_inputs=torch.float, device_type='cuda')
def forward(ctx, x: torch.Tensor, dim: int = -1) -> torch.Tensor:
output = x.logsumexp(dim)
ctx.dim = dim
ctx.save_for_backward(x, output)
return output.clone()

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.custom_bwd(device_type='cuda')
def backward(ctx, g: torch.Tensor) -> Union[torch.Tensor, None]:
x, output, dim = *ctx.saved_tensors, ctx.dim
g, output = g.unsqueeze(dim), output.unsqueeze(dim)
Expand All @@ -317,14 +317,14 @@ def backward(ctx, g: torch.Tensor) -> Union[torch.Tensor, None]:
class Logaddexp(Function):

@staticmethod
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float)
@torch.amp.custom_fwd(cast_inputs=torch.float, device_type='cuda')
def forward(ctx, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
output = torch.logaddexp(x, y)
ctx.save_for_backward(x, y, output)
return output.clone()

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.custom_bwd(device_type='cuda')
def backward(ctx, g: torch.Tensor) -> Union[torch.Tensor, torch.Tensor]:
x, y, output = ctx.saved_tensors
mask = g.eq(0)
Expand All @@ -337,14 +337,14 @@ def backward(ctx, g: torch.Tensor) -> Union[torch.Tensor, torch.Tensor]:
class SampledLogsumexp(Function):

@staticmethod
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float)
@torch.amp.custom_fwd(cast_inputs=torch.float, device_type='cuda')
def forward(ctx, x: torch.Tensor, dim: int = -1) -> torch.Tensor:
ctx.dim = dim
ctx.save_for_backward(x)
return x.logsumexp(dim=dim)

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.custom_bwd(device_type='cuda')
def backward(ctx, g: torch.Tensor) -> Union[torch.Tensor, None]:
from torch.distributions import OneHotCategorical
(x, ), dim = ctx.saved_tensors, ctx.dim
Expand All @@ -354,7 +354,7 @@ def backward(ctx, g: torch.Tensor) -> Union[torch.Tensor, None]:
class Sparsemax(Function):

@staticmethod
@torch.cuda.amp.custom_fwd(cast_inputs=torch.float)
@torch.amp.custom_fwd(cast_inputs=torch.float, device_type='cuda')
def forward(ctx, x: torch.Tensor, dim: int = -1) -> torch.Tensor:
ctx.dim = dim
sorted_x, _ = x.sort(dim, True)
Expand All @@ -367,7 +367,7 @@ def forward(ctx, x: torch.Tensor, dim: int = -1) -> torch.Tensor:
return p

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.custom_bwd(device_type='cuda')
def backward(ctx, g: torch.Tensor) -> Tuple[torch.Tensor, None]:
k, p, dim = *ctx.saved_tensors, ctx.dim
grad = g.masked_fill(p.eq(0), 0)
Expand Down