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@fiedorowicz1
fiedorowicz1 committed Jun 18, 2024
1 parent 46cf219 commit 8dc219b
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Showing 3 changed files with 41 additions and 31 deletions.
4 changes: 2 additions & 2 deletions include/lbann/utils/dnn_lib/cudnn/upsample.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -120,8 +120,8 @@ void upsample_nearest_backward(PoolingDescriptor const& poolingDesc,
TensorDescriptor const& dxDesc,
El::AbstractMatrix<TensorDataType>& dx)
{
auto multisync = El::MakeMultiSync(gpu::get_sync_info(dx),
gpu::get_sync_info(dy));
auto multisync =
El::MakeMultiSync(gpu::get_sync_info(dx), gpu::get_sync_info(dy));
auto handle_manager = internal::make_default_handle_manager(multisync);
upsample_nearest_backward(poolingDesc,
alpha_in,
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4 changes: 2 additions & 2 deletions include/lbann/utils/dnn_lib/miopen/upsample.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -96,8 +96,8 @@ void upsample_nearest_backward(PoolingDescriptor const& poolingDesc,
TensorDescriptor const& dxDesc,
El::AbstractMatrix<TensorDataType>& dx)
{
auto multisync = El::MakeMultiSync(gpu::get_sync_info(dx),
gpu::get_sync_info(dy));
auto multisync =
El::MakeMultiSync(gpu::get_sync_info(dx), gpu::get_sync_info(dy));
auto handle_manager = internal::make_default_handle_manager(multisync);
upsample_nearest_backward(poolingDesc,
alpha_in,
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64 changes: 37 additions & 27 deletions src/layers/transform/upsample.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -143,20 +143,22 @@ void upsample_layer<TensorDataType, Layout, Device>::bp_compute_dnn()
using ScalingType = dnn_lib::ScalingParamType<TensorDataType>;
const auto& local_gradient_wrt_output = this->get_local_prev_error_signals();
auto& local_gradient_wrt_input = this->get_local_error_signals();
if (local_gradient_wrt_output.Height() > 0 && local_gradient_wrt_output.Width() > 0) {
if (local_gradient_wrt_output.Height() > 0 &&
local_gradient_wrt_output.Width() > 0) {

// Useful constants
const auto alpha = El::To<ScalingType>(get_linear_size(m_scale_factors));
const auto zero = El::TypeTraits<ScalingType>::Zero();

// Perform backprop on GPU
dnn_lib::upsample_nearest_backward(m_pooling_dnn_desc,
alpha,
m_tensors_dnn_desc.get_prev_error_signals(),
local_gradient_wrt_output,
zero,
m_tensors_dnn_desc.get_error_signals(),
local_gradient_wrt_input);
dnn_lib::upsample_nearest_backward(
m_pooling_dnn_desc,
alpha,
m_tensors_dnn_desc.get_prev_error_signals(),
local_gradient_wrt_output,
zero,
m_tensors_dnn_desc.get_error_signals(),
local_gradient_wrt_input);
}
#endif // #ifndef LBANN_HAS_DNN_LIB
}
Expand Down Expand Up @@ -241,8 +243,9 @@ void upsample_layer<TensorDataType, Layout, Dev>::fp_compute_im2col()
// for (int j = 0; j < num_per_output_channel; ++j) {
// const TensorDataType* im2col_buffer =
// im2col_mat.LockedBuffer(channel * m_pool_size, j);
// TensorDataType output_entry = El::TypeTraits<TensorDataType>::Zero();
// for (int i = 0; i < m_pool_size; ++i) {
// TensorDataType output_entry =
// El::TypeTraits<TensorDataType>::Zero(); for (int i = 0; i <
// m_pool_size; ++i) {
// output_entry += im2col_buffer[i];
// }
// output_entry /= m_pool_size;
Expand All @@ -266,8 +269,9 @@ void upsample_layer<TensorDataType, Layout, Dev>::bp_compute_im2col()
// }

// // Local matrices
// const auto& local_gradient_wrt_output = this->get_local_prev_error_signals();
// auto& local_gradient_wrt_input = this->get_local_error_signals();
// const auto& local_gradient_wrt_output =
// this->get_local_prev_error_signals(); auto& local_gradient_wrt_input =
// this->get_local_error_signals();

// // Pool parameters
// const int local_width = local_gradient_wrt_output.Width();
Expand Down Expand Up @@ -381,7 +385,8 @@ upsample_layer<TensorDataType, T_layout, Dev>::get_distconv_adapter() const
}

template <typename TensorDataType, data_layout T_layout, El::Device Dev>
bool upsample_layer<TensorDataType, T_layout, Dev>::is_distconv_supported() const
bool upsample_layer<TensorDataType, T_layout, Dev>::is_distconv_supported()
const
{
return Dev == El::Device::GPU && T_layout == data_layout::DATA_PARALLEL;
}
Expand All @@ -396,7 +401,8 @@ dc::Shape upsample_distconv_adapter<TensorDataType, Layout, Device>::
this->layer());
auto scale_factors = layer.m_scale_factors;
std::reverse(std::begin(scale_factors), std::end(scale_factors));
auto output_spatial_local_shape = this->get_prev_activations(index).get_local_shape();
auto output_spatial_local_shape =
this->get_prev_activations(index).get_local_shape();
for (size_t i = 0; i < scale_factors.size(); i++) {
output_spatial_local_shape[i] *= scale_factors[i];
}
Expand All @@ -412,8 +418,8 @@ void upsample_distconv_adapter<TensorDataType, Layout, Device>::setup_layer(
m_dxdesc.create();
m_dydesc.create();

auto& l =
dynamic_cast<upsample_layer<TensorDataType, Layout, Device>&>(this->layer());
auto& l = dynamic_cast<upsample_layer<TensorDataType, Layout, Device>&>(
this->layer());

std::string mode;
switch (l.m_upsample_mode) {
Expand All @@ -429,23 +435,25 @@ template <typename TensorDataType, data_layout Layout, El::Device Device>
void upsample_distconv_adapter<TensorDataType, Layout, Device>::fp_compute(
bool const training)
{
auto& l =
dynamic_cast<upsample_layer<TensorDataType, Layout, Device>&>(this->layer());
auto& l = dynamic_cast<upsample_layer<TensorDataType, Layout, Device>&>(
this->layer());

auto& prev_activations = this->get_prev_activations();
auto& activations = this->get_activations();

auto xdesc = const_cast<dnn_lib::dnnTensorDescriptor_t>(m_xdesc.get());
auto ydesc = const_cast<dnn_lib::dnnTensorDescriptor_t>(m_ydesc.get());
dc_backend::setup_tensor_descriptor(xdesc, prev_activations,
dc_backend::setup_tensor_descriptor(xdesc,
prev_activations,
prev_activations.get_local_shape());
dc_backend::setup_tensor_descriptor(ydesc, activations,
dc_backend::setup_tensor_descriptor(ydesc,
activations,
activations.get_local_shape());

using ScalingType = dnn_lib::ScalingParamType<TensorDataType>;
const auto zero = El::TypeTraits<ScalingType>::Zero();
const auto alpha = El::To<ScalingType>(get_linear_size(l.m_scale_factors));

dnn_lib::upsample_nearest_forward(l.m_pooling_dnn_desc,
alpha,
m_xdesc,
Expand All @@ -459,17 +467,19 @@ void upsample_distconv_adapter<TensorDataType, Layout, Device>::fp_compute(
template <typename TensorDataType, data_layout Layout, El::Device Device>
void upsample_distconv_adapter<TensorDataType, Layout, Device>::bp_compute()
{
auto& l =
dynamic_cast<upsample_layer<TensorDataType, Layout, Device>&>(this->layer());
auto& l = dynamic_cast<upsample_layer<TensorDataType, Layout, Device>&>(
this->layer());

auto& prev_error_signals = this->get_prev_error_signals();
auto& error_signals = this->get_error_signals();

auto dxdesc = const_cast<dnn_lib::dnnTensorDescriptor_t>(m_dxdesc.get());
auto dydesc = const_cast<dnn_lib::dnnTensorDescriptor_t>(m_dydesc.get());
dc_backend::setup_tensor_descriptor(dxdesc, error_signals,
dc_backend::setup_tensor_descriptor(dxdesc,
error_signals,
error_signals.get_local_shape());
dc_backend::setup_tensor_descriptor(dydesc, prev_error_signals,
dc_backend::setup_tensor_descriptor(dydesc,
prev_error_signals,
prev_error_signals.get_local_shape());

using ScalingType = dnn_lib::ScalingParamType<TensorDataType>;
Expand All @@ -490,7 +500,7 @@ void upsample_distconv_adapter<TensorDataType, Layout, Device>::bp_compute()
template <typename TensorDataType, data_layout Layout, El::Device Device>
std::unique_ptr<Layer>
build_upsample_layer_from_pbuf(lbann_comm* comm,
lbann_data::Layer const& proto_layer)
lbann_data::Layer const& proto_layer)
{
LBANN_ASSERT_MSG_HAS_FIELD(proto_layer, upsample);

Expand All @@ -512,7 +522,7 @@ build_upsample_layer_from_pbuf(lbann_comm* comm,
}

#define PROTO_DEVICE(T, Device) \
template class upsample_layer<T, data_layout::DATA_PARALLEL, Device>; \
template class upsample_layer<T, data_layout::DATA_PARALLEL, Device>; \
LBANN_LAYER_BUILDER_ETI(upsample, T, Device)

#include "lbann/macros/instantiate_device.hpp"
Expand Down

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