Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Remove deprecated torch.cuda.amp custom fwd and bwd #150

Merged
merged 2 commits into from
Sep 5, 2024
Merged

Remove deprecated torch.cuda.amp custom fwd and bwd #150

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
8119e43
change
snarayan21 Sep 5, 2024
7a8d2e8
change
snarayan21 Sep 5, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions megablocks/layers/glu.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ class MemoryOptimizedGroupedGLU(torch.autograd.Function):
"""GroupedMLP with manually scheduled memory reuse."""

@staticmethod
@torch.cuda.amp.custom_fwd
@torch.amp.autocast_mode.custom_fwd(device_type='cuda')
def forward(ctx, x, w1, v1, w2, batch_sizes, activation_fn):
# Cast inputs using ctx dtype from AMP
if ctx._fwd_used_autocast:
Expand Down Expand Up @@ -102,7 +102,7 @@ def forward(ctx, x, w1, v1, w2, batch_sizes, activation_fn):
return dsd_out

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.autocast_mode.custom_bwd(device_type='cuda')
def backward(ctx, ddsd_out):
if (not ctx.needs_input_grad[0] or not ctx.needs_input_grad[1] or not ctx.needs_input_grad[2]):
raise ValueError('Expected all MLP inputs to need grad.')
Expand Down
12 changes: 6 additions & 6 deletions megablocks/layers/mlp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,13 +18,13 @@
class ScaleGradient(torch.autograd.Function):

@staticmethod
@torch.cuda.amp.custom_fwd
@torch.amp.autocast_mode.custom_fwd(device_type='cuda')
def forward(ctx: Any, x: torch.Tensor, scale: float):
ctx.scale = scale
return x

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.autocast_mode.custom_bwd(device_type='cuda')
def backward(ctx: torch.Tensor, grad: torch.Tensor):
return grad * ctx.scale, None

Expand Down Expand Up @@ -188,7 +188,7 @@ class MemoryOptimizedMLP(torch.autograd.Function):
"""Sparse MLP with manually scheduled memory reuse."""

@staticmethod
@torch.cuda.amp.custom_fwd
@torch.amp.autocast_mode.custom_fwd(device_type='cuda')
def forward(ctx, x, w1, w2, topo, activation_fn):
# Cast inputs using ctx dtype from AMP
if ctx._fwd_used_autocast:
Expand Down Expand Up @@ -230,7 +230,7 @@ def forward(ctx, x, w1, w2, topo, activation_fn):
return dsd_out

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.autocast_mode.custom_bwd(device_type='cuda')
def backward(ctx, ddsd_out):
if (not ctx.needs_input_grad[0] or not ctx.needs_input_grad[1] or not ctx.needs_input_grad[2]):
raise ValueError('Expected all MLP inputs to need grad.')
Expand Down Expand Up @@ -398,7 +398,7 @@ class MemoryOptimizedGroupedMLP(torch.autograd.Function):
"""GroupedMLP with manually scheduled memory reuse."""

@staticmethod
@torch.cuda.amp.custom_fwd
@torch.amp.autocast_mode.custom_fwd(device_type='cuda')
def forward(ctx, x, w1, w2, batch_sizes, activation_fn):
# Cast inputs using ctx dtype from AMP
if ctx._fwd_used_autocast:
Expand Down Expand Up @@ -431,7 +431,7 @@ def forward(ctx, x, w1, w2, batch_sizes, activation_fn):
return dsd_out

@staticmethod
@torch.cuda.amp.custom_bwd
@torch.amp.autocast_mode.custom_bwd(device_type='cuda')
def backward(ctx: Any, ddsd_out: torch.Tensor):
if (not ctx.needs_input_grad[0] or not ctx.needs_input_grad[1] or not ctx.needs_input_grad[2]):
raise ValueError('Expected all MLP inputs to need grad.')
Expand Down
Loading