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

Use two streams, one per FT slice. #126

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Use two streams, one per FT slice. #126

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
87 changes: 48 additions & 39 deletions feature_transformer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -367,6 +367,9 @@ def backward(ctx, grad_output):

return None, None, weight_grad, bias_grad

dft_stream_0 = cp.cuda.Stream()
dft_stream_1 = cp.cuda.Stream()

class DoubleFeatureTransformerSliceFunction(autograd.Function):

@staticmethod
Expand Down Expand Up @@ -418,31 +421,33 @@ def forward(ctx, feature_indices_0, feature_values_0, feature_indices_1, feature
max_active_features = feature_indices_0.shape[1]
output_size = weight.shape[1]

output0 = torch.empty(batch_size, output_size, dtype=torch.float32, device=device, requires_grad=True)
output1 = torch.empty(batch_size, output_size, dtype=torch.float32, device=device, requires_grad=True)

kernel = make_feature_transformer_slice_forward_kernel(max_active_features, output_size)
kernel(
grid=(batch_size,),
args=(
feature_indices_0.data_ptr(),
feature_values_0.data_ptr(),
weight.data_ptr(),
bias.data_ptr(),
output0.data_ptr()

with dft_stream_0:
output0 = torch.empty(batch_size, output_size, dtype=torch.float32, device=device, requires_grad=True)
kernel(
grid=(batch_size,),
args=(
feature_indices_0.data_ptr(),
feature_values_0.data_ptr(),
weight.data_ptr(),
bias.data_ptr(),
output0.data_ptr()
)
)
)

kernel(
grid=(batch_size,),
args=(
feature_indices_1.data_ptr(),
feature_values_1.data_ptr(),
weight.data_ptr(),
bias.data_ptr(),
output1.data_ptr()
with dft_stream_1:
output1 = torch.empty(batch_size, output_size, dtype=torch.float32, device=device, requires_grad=True)
kernel(
grid=(batch_size,),
args=(
feature_indices_1.data_ptr(),
feature_values_1.data_ptr(),
weight.data_ptr(),
bias.data_ptr(),
output1.data_ptr()
)
)
)

return output0, output1

Expand All @@ -465,27 +470,31 @@ def backward(ctx, grad_output_0, grad_output_1):
bias_grad = torch.zeros(output_size, dtype=torch.float32, device=device)

kernel = make_feature_transformer_slice_backward_kernel(max_active_features, output_size)
kernel(
grid=(batch_size,),
args=(
feature_indices_0.data_ptr(),
feature_values_0.data_ptr(),
weight_grad.data_ptr(),
bias_grad.data_ptr(),
grad_output_0.data_ptr()

# We can do it in two independent streams because all the writes in the kernel are atomic
with dft_stream_0:
kernel(
grid=(batch_size,),
args=(
feature_indices_0.data_ptr(),
feature_values_0.data_ptr(),
weight_grad.data_ptr(),
bias_grad.data_ptr(),
grad_output_0.data_ptr()
)
)
)

kernel(
grid=(batch_size,),
args=(
feature_indices_1.data_ptr(),
feature_values_1.data_ptr(),
weight_grad.data_ptr(),
bias_grad.data_ptr(),
grad_output_1.data_ptr()
with dft_stream_1:
kernel(
grid=(batch_size,),
args=(
feature_indices_1.data_ptr(),
feature_values_1.data_ptr(),
weight_grad.data_ptr(),
bias_grad.data_ptr(),
grad_output_1.data_ptr()
)
)
)

return None, None, None, None, weight_grad, bias_grad

Expand Down