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

Jump to bottom

improve performance of DepthwiseConv(NHWC) #31677

Merged
merged 11 commits into from
Apr 7, 2021
Merged

improve performance of DepthwiseConv(NHWC) #31677

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
2850f07
improve performance of DepthwiseConv(NWHC)
OuyangChao Mar 16, 2021
927487d
fix diff of DepthwiseConv(NHWC)
OuyangChao Mar 17, 2021
f15c262
optimize grids and blocks of DepthwiseConv(NHWC)
OuyangChao Mar 19, 2021
026c20e
improve backward performance of DepthwiseConv(NHWC)
OuyangChao Mar 20, 2021
57ef8a6
fix diff of DepthwiseConvTranspose(NHWC)
OuyangChao Mar 20, 2021
e7a1e05
improve performance of KernelDepthwiseConvFilterGradNHWC
OuyangChao Mar 21, 2021
7d3a4e8
improve backward performance of KernelDepthwiseConv(NHWC)
OuyangChao Mar 22, 2021
d1f877d
improve backward performance of KernelDepthwiseConvCFilterNHWC
OuyangChao Mar 24, 2021
81796de
improve performance with using data_layout as template param
OuyangChao Mar 25, 2021
06aadc3
improve performance of KernelDepthwiseConvFilterGrad
OuyangChao Mar 26, 2021
29bb5a9
fix conflict
OuyangChao Apr 1, 2021
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
104 changes: 29 additions & 75 deletions paddle/fluid/operators/conv_op.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -903,29 +903,19 @@ class DepthwiseConvKernel : public framework::OpKernel<T> {
"and input channel number is %d",
output->dims()[1], input->dims()[1]));
}
// transform tensor
Tensor transformed_input(input->type());
Tensor transformed_output(output->type());

if (channel_last) {
ResizeToChannelFirst<DeviceContext, T>(context, input,
&transformed_input);
TransToChannelFirst<DeviceContext, T>(context, input, &transformed_input);

ResizeToChannelFirst<DeviceContext, T>(context, output,
&transformed_output);

} else {
transformed_input = *input;
transformed_output = *output;
}

// update padding and dilation
auto in_dims = transformed_input.dims();
auto in_dims = input->dims();
auto filter_dims = filter.dims();

framework::DDim in_data_dims;
in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_format);
if (data_layout != framework::DataLayout::kNHWC) {
in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1);
}

framework::DDim filter_data_dims =
framework::slice_ddim(filter_dims, 2, filter_dims.size());
Expand All @@ -944,16 +934,12 @@ class DepthwiseConvKernel : public framework::OpKernel<T> {

if (fuse_relu) {
math::DepthwiseConvFunctor<DeviceContext, T, true> depthwiseConv;
depthwiseConv(dev_ctx, transformed_input, filter, strides, paddings,
dilations, &transformed_output);
depthwiseConv(dev_ctx, *input, filter, strides, paddings, dilations,
output, data_layout);
} else {
math::DepthwiseConvFunctor<DeviceContext, T, false> depthwiseConv;
depthwiseConv(dev_ctx, transformed_input, filter, strides, paddings,
dilations, &transformed_output);
}
if (channel_last) {
TransToChannelLast<DeviceContext, T>(context, &transformed_output,
output);
depthwiseConv(dev_ctx, *input, filter, strides, paddings, dilations,
output, data_layout);
}
}
};
Expand Down Expand Up @@ -981,33 +967,18 @@ class DepthwiseConvGradKernel : public framework::OpKernel<T> {
context.Attr<std::string>("padding_algorithm");
const std::string data_format = context.Attr<std::string>("data_format");

const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");

// transform Tensor
Tensor transformed_input(input->type());
Tensor transformed_output_grad(output_grad->type());

if (channel_last) {
ResizeToChannelFirst<DeviceContext, T>(context, input,
&transformed_input);
TransToChannelFirst<DeviceContext, T>(context, input, &transformed_input);

ResizeToChannelFirst<DeviceContext, T>(context, output_grad,
&transformed_output_grad);
TransToChannelFirst<DeviceContext, T>(context, output_grad,
&transformed_output_grad);

} else {
transformed_input = *input;
transformed_output_grad = *output_grad;
}

// update padding and dilation
auto in_dims = transformed_input.dims();
auto in_dims = input->dims();
auto filter_dims = filter.dims();

framework::DDim in_data_dims;
in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_format);
if (data_layout != framework::DataLayout::kNHWC) {
in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1);
}
framework::DDim filter_data_dims =
framework::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = framework::vectorize<int>(filter_data_dims);
Expand All @@ -1025,33 +996,18 @@ class DepthwiseConvGradKernel : public framework::OpKernel<T> {

if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
Tensor transformed_input_grad(input_grad->type());
if (channel_last) {
ResizeToChannelFirst<DeviceContext, T>(context, input_grad,
&transformed_input_grad);

} else {
transformed_input_grad = *input_grad;
}

set_zero(dev_ctx, &transformed_input_grad, static_cast<T>(0));
set_zero(dev_ctx, input_grad, static_cast<T>(0));

if (fuse_relu) {
math::DepthwiseConvInputGradFunctor<DeviceContext, T, true>
depthwiseConvInputGrad;
depthwiseConvInputGrad(dev_ctx, transformed_input, filter,
transformed_output_grad, strides, paddings,
dilations, &transformed_input_grad);
depthwiseConvInputGrad(dev_ctx, *input, filter, *output_grad, strides,
paddings, dilations, input_grad, data_layout);
} else {
math::DepthwiseConvInputGradFunctor<DeviceContext, T, false>
depthwiseConvInputGrad;
depthwiseConvInputGrad(dev_ctx, transformed_input, filter,
transformed_output_grad, strides, paddings,
dilations, &transformed_input_grad);
}
if (channel_last) {
TransToChannelLast<DeviceContext, T>(context, &transformed_input_grad,
input_grad);
depthwiseConvInputGrad(dev_ctx, *input, filter, *output_grad, strides,
paddings, dilations, input_grad, data_layout);
}
}

Expand All @@ -1061,15 +1017,13 @@ class DepthwiseConvGradKernel : public framework::OpKernel<T> {
if (fuse_relu) {
math::DepthwiseConvFilterGradFunctor<DeviceContext, T, true>
depthwiseConvFilterGrad;
depthwiseConvFilterGrad(dev_ctx, transformed_input,
transformed_output_grad, strides, paddings,
dilations, filter_grad);
depthwiseConvFilterGrad(dev_ctx, *input, *output_grad, strides,
paddings, dilations, filter_grad, data_layout);
} else {
math::DepthwiseConvFilterGradFunctor<DeviceContext, T, false>
depthwiseConvFilterGrad;
depthwiseConvFilterGrad(dev_ctx, transformed_input,
transformed_output_grad, strides, paddings,
dilations, filter_grad);
depthwiseConvFilterGrad(dev_ctx, *input, *output_grad, strides,
paddings, dilations, filter_grad, data_layout);
}
}
}
Expand Down
Loading