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conv1d_layer_wrapper.h
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conv1d_layer_wrapper.h
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/*
* Copyright 2021 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef LYRA_CODEC_CONV1D_LAYER_WRAPPER_H_
#define LYRA_CODEC_CONV1D_LAYER_WRAPPER_H_
#include <algorithm>
#include <memory>
#include <string>
#include <utility>
#include "absl/memory/memory.h"
#include "layer_wrapper.h"
#include "sparse_matmul/sparse_matmul.h"
namespace chromemedia {
namespace codec {
// Class that wraps the data and logic of conv1d layers.
template <typename WeightType, typename RhsType, typename OutputType,
typename DiskWeightType>
class Conv1DLayerWrapper
: public LayerWrapper<WeightType, RhsType, OutputType, DiskWeightType> {
public:
using Super = LayerWrapper<WeightType, RhsType, OutputType, DiskWeightType>;
static std::unique_ptr<
Conv1DLayerWrapper<WeightType, RhsType, OutputType, DiskWeightType>>
Create(const LayerParams& params) {
const std::string layer_prompt = "|" + params.prefix + "| layer: ";
// TODO(b/161015017): Support more general stride and kernel size
// combinations.
if (params.skip_connection) {
fprintf(stderr,
"%s Conv1D layer with skip connection is not supported.\n",
layer_prompt.c_str());
if (params.stride == 1) {
fprintf(stderr,
"%s Use DilatedConvolutionalLayerWrapper with |dilation| = 1 "
"and |stride| = 1 to allow skip connections.\n",
layer_prompt.c_str());
}
return nullptr;
}
if (params.dilation != 1) {
fprintf(stderr,
"%s Use DilatedConvolutionalLayerWrapper instead by setting "
"|params.type| to |kDilated|.\n",
layer_prompt.c_str());
return nullptr;
}
auto layer = Super::LoadAndCheckLayer(
params.from, params.prefix, layer_prompt, params.num_filters,
params.kernel_size * params.num_input_channels, params.num_threads);
if (layer == nullptr) {
return nullptr;
}
const int input_buffer_rows = layer->cols();
const int num_input_channels = input_buffer_rows / params.kernel_size;
const int output_rows = layer->rows();
return absl::WrapUnique(
new Conv1DLayerWrapper<WeightType, RhsType, OutputType, DiskWeightType>(
num_input_channels, output_rows, params.length, input_buffer_rows,
params.stride, params.relu, params.per_column_barrier,
std::move(layer)));
}
static std::unique_ptr<
Conv1DLayerWrapper<WeightType, RhsType, OutputType, DiskWeightType>>
Create(const LayerParams& params,
const WavegruBufferInterface& wavegru_buffer) {
const std::string layer_prompt = "|" + params.prefix + "| layer: ";
// TODO(b/161015017): Support more general stride and kernel size
// combinations.
if (params.skip_connection) {
fprintf(stderr,
"%s Conv1D layer with skip connection is not supported.\n",
layer_prompt.c_str());
if (params.stride == 1) {
fprintf(stderr,
"%s Use DilatedConvolutionalLayerWrapper with |dilation| = 1 "
"and |stride| = 1 to allow skip connections.\n",
layer_prompt.c_str());
}
return nullptr;
}
if (params.dilation != 1) {
fprintf(stderr,
"%s Use DilatedConvolutionalLayerWrapper instead by setting "
"|params.type| to |kDilated|.\n",
layer_prompt.c_str());
return nullptr;
}
auto layer = Super::LoadAndCheckLayer(
wavegru_buffer, params.prefix, layer_prompt, params.num_filters,
params.kernel_size * params.num_input_channels, params.num_threads);
if (layer == nullptr) {
return nullptr;
}
const int input_buffer_rows = layer->cols();
const int num_input_channels = input_buffer_rows / params.kernel_size;
const int output_rows = layer->rows();
return absl::WrapUnique(
new Conv1DLayerWrapper<WeightType, RhsType, OutputType, DiskWeightType>(
num_input_channels, output_rows, params.length, input_buffer_rows,
params.stride, params.relu, params.per_column_barrier,
std::move(layer)));
}
void Run(int tid, csrblocksparse::SpinBarrier* spin_barrier,
csrblocksparse::MutableVectorView<OutputType> output_view) override {
this->layer_->SpMM_bias(
csrblocksparse::VectorView<RhsType>(this->input_buffer_), &output_view,
this->relu_, tid, this->per_column_barrier_ ? spin_barrier : nullptr);
spin_barrier->barrier();
Reset(tid, spin_barrier);
}
// The part of |input_buffer_| updated by the previous layer corresponding to
// time = t (out of all past values). It is the bottom
// |num_inputs_to_update_| rows of the current column.
csrblocksparse::MutableVectorView<RhsType> InputViewToUpdate() override {
return csrblocksparse::MutableVectorView<RhsType>(
this->input_buffer_.data() + this->input_buffer_rows_ -
this->num_inputs_to_update_,
this->num_inputs_to_update_, this->length_, this->input_buffer_rows_);
}
private:
Conv1DLayerWrapper() = delete;
explicit Conv1DLayerWrapper(
int num_input_channels, int output_rows, int length,
int input_buffer_rows, int stride, bool relu, bool per_column_barrier,
std::unique_ptr<csrblocksparse::SparseLinearLayer<WeightType, RhsType>>
layer)
: Super(num_input_channels, output_rows, length, input_buffer_rows,
length, relu, per_column_barrier, std::move(layer)),
num_inputs_to_update_(
std::min(stride * num_input_channels, input_buffer_rows)) {}
// For Conv1D layers, |stride| > 1 is supported. Every time
// |stride| * |num_input_channels| elements are pushed in from the bottom.
//
// For example, for a layer of |kernel_size| = 3 and |stride| = 1, the memory
// layout of |input_buffer_| after loading v0 and calling Reset() should look
// like this (v0 is the input vector at t = 0, v1 is at t = 1, and so on):
//
// | | \ //
// |----| \ //
// | v0 | --> |kernel_size| stacks //
// |----| / //
// | | / //
//
// Leaving enough space to load v1. Then after loading v1, it should be:
//
// | | //
// |----| //
// | v0 | //
// |----| //
// | v1 | //
//
// On the other hand, If |stride| = 2, then after the first loading, which
// pushes in v0 and v1. Calling Reset() would move them two stacks up (and
// pushing v0 out), and the buffer looks like this:
//
// | v1 | //
// |----| //
// | | \ //
// |----| --> |stride| spaces //
// | | / //
//
// Leaving enough space to load the next 2 inputs. After the next loading,
// the buffer should look like this:
//
// | v1 | //
// |----| //
// | v2 | //
// |----| //
// | v3 | //
//
void Reset(int tid, csrblocksparse::SpinBarrier* spin_barrier) override {
// If |num_inputs_to_update_| == |input_buffer_rows_| it means that
// the whole buffer is overwritten evey time, so there is no need to move
// elements.
if (this->num_inputs_to_update_ < this->input_buffer_rows_) {
if (tid == 0) {
// Shift the current column up by |num_inputs_to_update_| elements.
std::move(this->input_buffer_.data() + this->num_inputs_to_update_,
this->input_buffer_.data() + this->input_buffer_rows_,
this->input_buffer_.data());
}
spin_barrier->barrier();
}
}
// Number of input elements to update after each matrix multiplication. Equal
// to the minimum between |input_buffer_rows_| and
// |num_input_channels_| * stride (not stored).
const int num_inputs_to_update_;
};
} // namespace codec
} // namespace chromemedia
#endif // LYRA_CODEC_CONV1D_LAYER_WRAPPER_H_