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Use two streams, one per FT slice. #126

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Use two streams, one per FT slice. #126

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87 changes: 48 additions & 39 deletions feature_transformer.py
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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