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layers.hpp
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layers.hpp
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#ifndef LAYERS_HPP
#define LAYERS_HPP
#include "tensor.hpp"
#include "function.hpp"
struct ConvDesc {
miopenConvolutionDescriptor_t desc;
ConvDesc(int pad_h, int pad_w, int u, int v, int upscalex, int upscaley) {
CHECK_MIO(miopenCreateConvolutionDescriptor(&desc));
CHECK_MIO(miopenInitConvolutionDescriptor(desc, miopenConvolution, pad_h, pad_w, u, v, upscalex, upscaley));
}
// create with padding and stride, default upscale = 1
ConvDesc(int pad_h, int pad_w, int u, int v) : ConvDesc(pad_h, pad_w, u, v, 1, 1) {
}
// default stride = 1, upscale = 1
ConvDesc(int pad_h, int pad_w) : ConvDesc(pad_h, pad_w, 1, 1, 1, 1) {
}
// default pad = 0, stride = 1, upscale = 1
ConvDesc() : ConvDesc(0, 0, 1, 1, 1, 1) {
}
~ConvDesc() {
CHECK_MIO(miopenDestroyConvolutionDescriptor(desc));
}
};
// parameters for a 2D convolutional layer
struct ConvLayerDesc {
int batch_size;
int height;
int width;
int channels_in;
int channels_out;
int kernel_size;
int padding;
int stride;
};
static Dim getConvOutputDim(int padding, int stride, const TensorDesc& input, const TensorDesc& weights) {
int n, c, h, w;
ConvDesc d(padding, padding, stride, stride, 1, 1);
CHECK_MIO(miopenGetConvolutionForwardOutputDim(d.desc, input.desc, weights.desc, &n, &c, &h, &w));
return Dim(n, c, h, w);
}
struct ConvLayer : public ConvDesc, public ConvLayerDesc, public Layer {
Tensor weights;
Tensor dweights;
const Tensor* input_ref;
// algorithm selection:
miopenConvFwdAlgorithm_t fwd_algo;
miopenConvBwdWeightsAlgorithm_t bwd_weights_algo;
miopenConvBwdDataAlgorithm_t bwd_data_algo;
virtual std::ostream& write_name(std::ostream& os) const {
//return os << "Conv(" << kernel_size << "x" << kernel_size << ")";
return os << "Conv(" << kernel_size << "x" << kernel_size << ",pad=" << padding << ",s=" << stride << ")";
}
ConvLayer(const TensorDesc& input_dims, int channels_out, int kernel_size, int padding, int stride)
: ConvDesc(padding, padding, stride, stride, 1, 1),
ConvLayerDesc({input_dims.n, input_dims.h, input_dims.w, input_dims.c, channels_out, kernel_size, padding, stride}),
Layer((Dim&)input_dims, getConvOutputDim(padding, stride, input_dims, TensorDesc(channels_out, input_dims.c, kernel_size, kernel_size))),
weights(channels_out, input_dims.c, kernel_size, kernel_size),
dweights(channels_out, input_dims.c, kernel_size, kernel_size)
{
}
/* default stride = 1 */
ConvLayer(const TensorDesc& input_dims, int channels_out, int kernel_size, int padding)
: ConvLayer(input_dims, channels_out, kernel_size, padding, 1) {}
/* default padding = 0, stride = 1 */
ConvLayer(const TensorDesc& input_dims, int channels_out, int kernel_size)
: ConvLayer(input_dims, channels_out, kernel_size, 0, 1) {}
/* construct via conv parameters */
ConvLayer(const ConvLayerDesc& l)
: ConvLayer(TensorDesc(l.batch_size, l.channels_in, l.height, l.width), l.channels_out, l.kernel_size, l.padding, l.stride) {}
// estimate the number of muliplications for a direct implementation
double num_flops() {
return batch_size * 1.0 * height * width * channels_in * channels_out * kernel_size * kernel_size;
}
void init_forward(const Tensor& input, Tensor& output) override {
size_t fwd_workspace_size;
CHECK_MIO(miopenConvolutionForwardGetWorkSpaceSize(mio::handle(), weights.desc, input.desc, this->desc, output.desc, &fwd_workspace_size));
DEBUG("Init fwd " << *this << " req workspace: " << fwd_workspace_size);
DevBuffer& buffer = WorkSpace::get(fwd_workspace_size);
// find best algo, and benchmark!
miopenConvAlgoPerf_t perfs[4];
int returned_algos;
CHECK_MIO(miopenFindConvolutionForwardAlgorithm(mio::handle(), input.desc, input.data, weights.desc, weights.data, this->desc, output.desc, output.data, 4, &returned_algos, perfs, buffer.data, fwd_workspace_size, false));
INFO("\tMIOpen Found " << returned_algos << " fwd algorithms, choosing " << perfs[0].fwd_algo << ": ");
for (int i = 0; i < returned_algos; ++i) {
INFO("\t\t" << i << ") " << perfs[i].fwd_algo << " - time: " << perfs[i].time << ", Memory: " << perfs[i].memory);
}
fwd_algo = perfs[0].fwd_algo;
}
void find_bwd_data_algo(const Tensor& doutput, Tensor& dinput) {
size_t bwd_data_workspace_size;
CHECK_MIO(miopenConvolutionBackwardDataGetWorkSpaceSize(mio::handle(), doutput.desc, weights.desc, this->desc, dinput.desc, &bwd_data_workspace_size));
DEBUG("Init bwd_data " << *this << " req workspace: " << bwd_data_workspace_size);
DevBuffer& buffer = WorkSpace::get(bwd_data_workspace_size);
// find best algo, and benchmark!
miopenConvAlgoPerf_t perfs[5];
int returned_algos;
CHECK_MIO(miopenFindConvolutionBackwardDataAlgorithm(mio::handle(), doutput.desc, doutput.data, weights.desc, weights.data, this->desc, dinput.desc, dinput.data, 5, &returned_algos, perfs, buffer.data, bwd_data_workspace_size, false));
INFO("\tMIOpen Found " << returned_algos << " bwd_data algorithms, choosing " << perfs[0].fwd_algo << ": ");
for (int i = 0; i < returned_algos; ++i) {
INFO("\t\t" << i << ") " << perfs[i].fwd_algo << " - time: " << perfs[i].time << ", Memory: " << perfs[i].memory);
}
bwd_data_algo = perfs[0].bwd_data_algo;
}
void find_bwd_weights_algo(const Tensor& doutput, Tensor& input) {
size_t bwd_weights_workspace_size;
CHECK_MIO(miopenConvolutionBackwardWeightsGetWorkSpaceSize(mio::handle(), doutput.desc, input.desc, this->desc, weights.desc, &bwd_weights_workspace_size));
DEBUG("Init bwd_weights " << *this << " req workspace: " << bwd_weights_workspace_size);
DevBuffer& buffer = WorkSpace::get(bwd_weights_workspace_size);
// find best algo, and benchmark!
miopenConvAlgoPerf_t perfs[5];
int returned_algos;
CHECK_MIO(miopenFindConvolutionBackwardWeightsAlgorithm(mio::handle(), doutput.desc, doutput.data, input.desc, input.data, this->desc, dweights.desc, dweights.data, 5, &returned_algos, perfs, buffer.data, bwd_weights_workspace_size, false));
INFO("\tMIOpen Found " << returned_algos << " bwd_weights algorithms, choosing " << perfs[0].fwd_algo << ": ");
for (int i = 0; i < returned_algos; ++i) {
INFO("\t\t" << i << ") " << perfs[i].fwd_algo << " - time: " << perfs[i].time << ", Memory: " << perfs[i].memory);
}
bwd_weights_algo = perfs[0].bwd_weights_algo;
}
void init_backward(const Tensor& doutput, Tensor& dinput) override {
find_bwd_data_algo(doutput, dinput);
find_bwd_weights_algo(doutput, dinput);
}
void forward(const Tensor& input, Tensor& output) override {
float alpha = 1.f;
float beta = 0.f;
DevBuffer& buffer = WorkSpace::get();
CHECK_MIO(miopenConvolutionForward(mio::handle(), &alpha, input.desc, input.data, weights.desc, weights.data, this->desc, fwd_algo, &beta, output.desc, output.data, buffer.data, buffer.size));
// save for backward
input_ref = &input;
}
void backward(const Tensor& doutput, Tensor& dinput) override {
float alpha = 1.f;
float beta = 0.f;
DevBuffer& buffer = WorkSpace::get();
CHECK_MIO(miopenConvolutionBackwardData(mio::handle(), &alpha, doutput.desc, doutput.data, weights.desc, weights.data, this->desc, bwd_data_algo, &beta, dinput.desc, dinput.data, buffer.data, buffer.size));
CHECK_MIO(miopenConvolutionBackwardWeights(mio::handle(), &alpha, doutput.desc, doutput.data, input_ref->desc, input_ref->data, this->desc, bwd_weights_algo, &beta, dweights.desc, dweights.data, buffer.data, buffer.size));
}
};
struct PoolingLayer : public Layer {
miopenPoolingMode_t pool_mode;
miopenPoolingDescriptor_t desc;
// needed for backward: original input, original output, indeces (as workspace)
DevBuffer indeces_buf;
const Tensor* input;
const Tensor* output;
int kernel_size, padding, stride;
static Dim getOutputDim(const TensorDesc& input, int kernel_size, int padding, int stride, miopenPoolingMode_t pool_mode) {
int n, c, h, w;
miopenPoolingDescriptor_t pool_desc;
CHECK_MIO(miopenCreatePoolingDescriptor(&pool_desc));
CHECK_MIO(miopenSet2dPoolingDescriptor(pool_desc, pool_mode, kernel_size, kernel_size, padding, padding, stride, stride));
CHECK_MIO(miopenGetPoolingForwardOutputDim(pool_desc, input.desc, &n, &c, &h, &w));
CHECK_MIO(miopenDestroyPoolingDescriptor(pool_desc));
return Dim(n, c, h, w);
}
virtual std::ostream& write_name(std::ostream& os) const override {
if (pool_mode == miopenPoolingMax)
os << "MaxPool(";
else
os << "AvgPool(";
return os << kernel_size << "x" << kernel_size << ")";
}
PoolingLayer(const TensorDesc& input_dim, int kernel_size, int padding, int stride, miopenPoolingMode_t pool_mode)
: Layer((Dim&)input_dim, PoolingLayer::getOutputDim(input_dim, kernel_size, padding, stride, pool_mode)),
pool_mode(pool_mode),
kernel_size(kernel_size), padding(padding), stride(stride) {
CHECK_MIO(miopenCreatePoolingDescriptor(&desc));
CHECK_MIO(miopenSet2dPoolingDescriptor(desc, pool_mode, kernel_size, kernel_size, padding, padding, stride, stride));
}
~PoolingLayer() {
CHECK_MIO(miopenDestroyPoolingDescriptor(desc));
}
virtual void init_forward(const Tensor&, Tensor&) override {
size_t size;
CHECK_MIO(miopenPoolingGetWorkSpaceSize(output_desc.desc, &size));
indeces_buf = DevBuffer(size);
}
virtual void forward(const Tensor& input, Tensor& output) override {
float alpha = 1.f;
float beta = 0.f;
CHECK_MIO(miopenPoolingForward(mio::handle(), desc, &alpha, input.desc, input.data, &beta, output.desc, output.data, true, indeces_buf.data, indeces_buf.size));
// save for backward
this->input = &input;
this->output = &output;
}
virtual void backward(const Tensor& doutput, Tensor& dinput) override {
float alpha = 1.f;
float beta = 0.f;
CHECK_MIO(miopenPoolingBackward(mio::handle(), desc, &alpha, getOutputDesc().desc, output->data, doutput.desc, doutput.data, getInputDesc().desc, input->data, &beta, dinput.desc, dinput.data, indeces_buf.data));
}
};
struct MaxPool : public PoolingLayer {
MaxPool(const TensorDesc& input_dim, int kernel_size, int padding, int stride)
: PoolingLayer(input_dim, kernel_size, padding, stride, miopenPoolingMax) {}
};
struct AvgPool : public PoolingLayer {
AvgPool(const TensorDesc& input_dim, int kernel_size, int padding, int stride)
: PoolingLayer(input_dim, kernel_size, padding, stride, miopenPoolingAverage) {}
};
struct ReLU : public Layer {
miopenActivationDescriptor_t desc;
const Tensor* input_ref;
const Tensor* output_ref;
virtual std::ostream& write_name(std::ostream& os) const {
return os << "ReLU()";
}
ReLU(const TensorDesc& input_dim) : Layer(input_dim, input_dim) {
CHECK_MIO(miopenCreateActivationDescriptor(&desc));
CHECK_MIO(miopenSetActivationDescriptor(desc, miopenActivationRELU, 0.0, 0.0, 1.0));
}
~ReLU() {
CHECK_MIO(miopenDestroyActivationDescriptor(desc));
}
void forward(const Tensor& input, Tensor& output) {
float alpha = 1.f;
float beta = 0.f;
CHECK_MIO(miopenActivationForward(mio::handle(), desc, &alpha, input.desc, input.data, &beta, output.desc, output.data));
// save for backward
this->input_ref = &input;
this->output_ref = &output;
}
void backward(const Tensor& doutput, Tensor& dinput) {
float alpha = 1.f;
float beta = 0.f;
CHECK_MIO(miopenActivationBackward(mio::handle(), desc, &alpha, output_ref->desc, output_ref->data, doutput.desc, doutput.data, input_ref->desc, input_ref->data, &beta, dinput.desc, dinput.data));
}
};
void mm_blas(const Tensor& A, bool transA, const Tensor& B, bool transB, Tensor& C) {
assert(A.h == 1 && A.w == 1);
assert(B.h == 1 && B.w == 1);
assert(C.h == 1 && C.w == 1);
int M = transA ? A.c : A.n;
int K = transA ? A.n : A.c;
int N = transB ? B.n : B.c;
assert(transB ? K == B.c : K == B.n);
assert(C.n == M && C.c == N);
float alpha = 1.f;
float beta = 0.f;
int lda = A.c;
int ldb = B.c;
int ldc = C.c;
hipblasHandle_t blas_handle;
hipblasCreate(&blas_handle);
hipblasOperation_t opA = transA ? HIPBLAS_OP_T : HIPBLAS_OP_N;
hipblasOperation_t opB = transB ? HIPBLAS_OP_T : HIPBLAS_OP_N;
// call Sgemm with A<->B swapped (since we have rowmaj, but blas expects colmajor)
hipblasStatus_t err = hipblasSgemm(blas_handle, opB, opA, N, M, K, &alpha, (const float*)B.data, ldb, (const float*)A.data, lda, &beta, (float*)C.data, ldc);
assert(err == 0);
}
// (batch_size * size) -> (batch_size * size)
struct Linear : public Layer {
int batch_size;
int in_size;
int out_size;
Tensor weights; // dim (out_channels, in_channels, 1, 1)
Tensor dweights;
const Tensor* input_ref;
virtual std::ostream& write_name(std::ostream& os) const {
return os << "Linear(" << in_size << "," << out_size << ")";
}
Linear(const TensorDesc& input_dim, int out_size)
: Layer(input_dim, TensorDesc(input_dim.n, out_size, 1, 1)),
batch_size(input_dim.n),
in_size(input_dim.c * input_dim.h * input_dim.w),
out_size(out_size),
weights(out_size, in_size, 1, 1),
dweights(out_size, in_size, 1, 1)
{
}
void forward(const Tensor& input, Tensor& output) {
assert(batch_size == input.n);
assert(batch_size == output.n);
assert(out_size = output.c);
assert(in_size == input.c * input.h * input.w);
mm_blas(input, false, weights, true, output); // O <- I * W^T
input_ref = &input;
}
void backward(const Tensor& doutput, Tensor& dinput) {
// two MMs
mm_blas(doutput, true, *input_ref, false, dweights); // dW <- dO^T * I
mm_blas(doutput, false, weights, false, dinput); // dI <- dO * W
}
};
struct BatchNorm : public Layer {
// size of internal tensors (spatial: 1C11, per activation: 1CHW)
miopenBatchNormMode_t bn_mode;
TensorDesc bn_dim;
Tensor scale;
Tensor dscale;
Tensor bias;
Tensor dbias;
double exp;
Tensor running_mean;
Tensor running_var;
double epsilon;
Tensor saved_mean; // saved mean for backward
Tensor saved_ivar; // saved inverse variance for backward
const Tensor* input_ref; // save reference to input for backward pass
static TensorDesc get_bn_dim(const TensorDesc& input_dim, miopenBatchNormMode_t bn_mode) {
TensorDesc bn(0,0,0,0);
CHECK_MIO(miopenDeriveBNTensorDescriptor(bn.desc, input_dim.desc, bn_mode));
bn.update_get();
return bn;
}
BatchNorm(const TensorDesc& input_dim, miopenBatchNormMode_t bn_mode=miopenBNSpatial, double eps = 1e-05, double momentum = 0.1)
: Layer(input_dim, input_dim),
bn_mode(bn_mode),
bn_dim(get_bn_dim(input_dim, bn_mode)),
scale(bn_dim),
dscale(bn_dim),
bias(bn_dim),
dbias(bn_dim),
exp(momentum),
running_mean(bn_dim),
running_var(bn_dim),
epsilon(eps),
saved_mean(bn_dim),
saved_ivar(bn_dim)
{
}
virtual std::ostream& write_name(std::ostream& os) const {
return os << "BatchNorm()";
}
void forward(const Tensor& input, Tensor& output) {
float alpha = 1.f;
float beta = 0.f;
CHECK_MIO(miopenBatchNormalizationForwardTraining(mio::handle(),
bn_mode,
&alpha,
&beta,
input.desc,
input.data,
output.desc,
output.data,
bn_dim.desc,
scale.data,
bias.data,
exp,
running_mean.data,
running_var.data,
epsilon,
saved_mean.data,
saved_ivar.data));
input_ref = &input;
}
void backward(const Tensor& doutput, Tensor& dinput) {
float alpha = 1.f;
float beta = 0.f;
CHECK_MIO(miopenBatchNormalizationBackward(mio::handle(),
bn_mode,
&alpha,
&beta,
&alpha,
&beta,
input_ref->desc,
input_ref->data,
doutput.desc,
doutput.data,
dinput.desc,
dinput.data,
bn_dim.desc,
scale.data,
dscale.data,
dbias.data,
epsilon,
saved_mean.data,
saved_ivar.data));
}
};
struct Reshape : public Layer {
Reshape(const TensorDesc& input_dim, int n, int c, int h, int w)
: Layer(input_dim, TensorDesc(n, c, h, w)) {
assert(input_dim.n == n);
assert(input_dim.c * input_dim.h * input_dim.w == c*h*w);
}
void init_forward(const Tensor& input, Tensor& output) override {
output = input.viewAs(getOutputDesc());
}
void forward(const Tensor& input, Tensor& output) override {
output = input.viewAs(getOutputDesc());
}
void init_backward(const Tensor& doutput, Tensor& dinput) override {
dinput = doutput.viewAs(getInputDesc());
}
void backward(const Tensor& doutput, Tensor& dinput) override {
dinput = doutput.viewAs(getInputDesc());
}
};
#endif // LAYERS_HPP