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changed files for new graph format, adapted test.cu to not use pointers to Expr

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This commit is contained in:
Marcin Junczys-Dowmunt 2016-09-16 08:36:29 +02:00
parent 02629ca13b
commit 036284d39c
6 changed files with 59 additions and 63 deletions
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1
src/CMakeLists.txt
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@ -5,6 +5,7 @@ cuda_add_library(marian_lib
cnpy/cnpy.cpp
exception.cpp
expression_graph.cu
sgd.cu
tensor.cu
tensor_operators.cu
expression_operators.cu

20
src/sgd.cu
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@ -7,15 +7,11 @@
using namespace std;
namespace marian {
SGD::SGD(Expr& cost_func, Expr& inX, Expr& inY,
const std::vector<Expr*> params, float eta,
SGD::SGD(ExpressionGraph& g, float eta,
std::vector<float>& xData, size_t numFeatures,
std::vector<float>& yData, size_t numClasses,
size_t epochs, size_t batchSize)
: cost_function_(&cost_func),
inX_(&inX),
inY_(&inY),
params_(params),
: graph_(g),
eta_(eta),
xData_(xData),
numFeatures_(numFeatures),
@ -45,11 +41,11 @@ void SGD::Run()
size_t endId = startId + batchSize;
PrepareBatch(startId, endId, batchSize, shuffle, xt, yt);
*inX_ = xt;
*inY_ = yt;
graph_["x"] = xt;
graph_["y"] = yt;
cost_function_->forward(maxBatchSize_);
cost_function_->backward();
graph_.forward(maxBatchSize_);
graph_.backward();
UpdateModel();
@ -136,9 +132,9 @@ void SGD::PrepareBatch(
}
void SGD::UpdateModel() {
for (auto& param : params_) {
for (auto& param : graph_.params()) {
using namespace thrust::placeholders;
Element(_1 = _1 - eta_ * _2, param->val(), param->grad());
Element(_1 -= eta_ * _2, param.val(), param.grad());
}
}

10
src/sgd.h
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@ -3,7 +3,7 @@
#include <memory>
#include <iostream>
#include "expressions.h"
#include "expression_graph.h"
#include "thrust_functions.h"
#include "tensor_operators.h"
@ -11,8 +11,7 @@ namespace marian {
class SGD {
public:
SGD(Expr& cost_func, Expr& inX, Expr& inY,
const std::vector<Expr*> params, float eta,
SGD(ExpressionGraph& g, float eta,
std::vector<float>& xData, size_t numFeatures,
std::vector<float>& yData, size_t numClasses,
size_t epochs, size_t batchSize);
@ -20,10 +19,7 @@ class SGD {
void Run();
private:
Expr *cost_function_;
Expr *inX_;
Expr *inY_;
std::vector<Expr*> params_;
ExpressionGraph& graph_;
const float eta_;
std::vector<float>& xData_;
const size_t numFeatures_;

57
src/test.cu
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@ -14,42 +14,41 @@ int main(int argc, char** argv) {
int hidden_size = 5;
int num_inputs = 8;
std::vector<Expr*> X(num_inputs);
std::vector<Expr*> Y(num_inputs);
std::vector<Expr*> H(num_inputs);
std::vector<Expr> X;
std::vector<Expr> Y;
std::vector<Expr> H;
ExpressionGraph g;
for (int t = 0; t < num_inputs; ++t) {
X[t] = new Expr(input(shape={batch_size, input_size}));
Y[t] = new Expr(input(shape={batch_size, output_size}));
X.emplace_back(g.input(shape={batch_size, input_size}));
Y.emplace_back(g.input(shape={batch_size, output_size}));
}
Expr Wxh = param(shape={input_size, hidden_size}, init=uniform(), name="Wxh");
Expr Whh = param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh");
Expr bh = param(shape={1, hidden_size}, init=uniform(), name="bh");
Expr h0 = param(shape={1, hidden_size}, init=uniform(), name="h0");
Expr Wxh = g.param(shape={input_size, hidden_size}, init=uniform(), name="Wxh");
Expr Whh = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh");
Expr bh = g.param(shape={1, hidden_size}, init=uniform(), name="bh");
Expr h0 = g.param(shape={1, hidden_size}, init=uniform(), name="h0");
std::cerr << "Building RNN..." << std::endl;
H[0] = new Expr(tanh(dot(*X[0], Wxh) + dot(h0, Whh) + bh));
H.emplace_back(tanh(dot(X[0], Wxh) + dot(h0, Whh) + bh));
for (int t = 1; t < num_inputs; ++t) {
H[t] = new Expr(tanh(dot(*X[t], Wxh) + dot(*H[t-1], Whh) + bh));
H.emplace_back(tanh(dot(X[t], Wxh) + dot(H[t-1], Whh) + bh));
}
Expr Why = param(shape={hidden_size, output_size}, init=uniform(), name="Why");
Expr by = param(shape={1, output_size}, init=uniform(), name="by");
Expr Why = g.param(shape={hidden_size, output_size}, init=uniform(), name="Why");
Expr by = g.param(shape={1, output_size}, init=uniform(), name="by");
std::cerr << "Building output layer..." << std::endl;
std::vector<Expr*> Yp(num_inputs);
std::vector<Expr> Yp;
Expr* cross_entropy = NULL;
for (int t = 0; t < num_inputs; ++t) {
Yp[t] = new Expr(softmax_fast(dot(*H[t], Why) + by, name="pred"));
if (!cross_entropy) {
cross_entropy = new Expr(sum(*Y[t] * log(*Yp[t]), axis=1));
} else {
*cross_entropy = *cross_entropy + sum(*Y[t] * log(*Yp[t]), axis=1);
}
Yp.emplace_back(softmax_fast(dot(H[0], Why) + by));
Expr cross_entropy = sum(Y[0] * log(Yp[0]), axis=1);
for (int t = 1; t < num_inputs; ++t) {
Yp.emplace_back(softmax_fast(dot(H[t], Why) + by));
cross_entropy = cross_entropy + sum(Y[t] * log(Yp[t]), axis=1);
}
auto graph = -mean(*cross_entropy, axis=0, name="cost");
auto graph = -mean(cross_entropy, axis=0, name="cost");
for (int t = 0; t < num_inputs; ++t) {
Tensor Xt({batch_size, input_size});
@ -72,17 +71,17 @@ int main(int argc, char** argv) {
thrust::copy(values.begin(), values.end(), Xt.begin());
thrust::copy(classes.begin(), classes.end(), Yt.begin());
*X[t] = Xt;
*Y[t] = Yt;
X[t] = Xt;
Y[t] = Yt;
}
graph.forward(batch_size);
graph.backward();
g.forward(batch_size);
g.backward();
std::cerr << graph.val().Debug() << std::endl;
std::cerr << X[0]->val().Debug() << std::endl;
std::cerr << Y[0]->val().Debug() << std::endl;
std::cerr << X[0].val().Debug() << std::endl;
std::cerr << Y[0].val().Debug() << std::endl;
std::cerr << Whh.grad().Debug() << std::endl;
std::cerr << bh.grad().Debug() << std::endl;

16
src/train_mnist.cu
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@ -16,22 +16,24 @@ int main(int argc, char** argv) {
using namespace marian;
using namespace keywords;
Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
ExpressionGraph g;
Expr x = named(g.input(shape={whatevs, IMAGE_SIZE}), "x");
Expr y = named(g.input(shape={whatevs, LABEL_SIZE}), "y");
Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0");
Expr b = param(shape={1, LABEL_SIZE}, name="b0");
Expr w = named(g.param(shape={IMAGE_SIZE, LABEL_SIZE}), "w");
Expr b = named(g.param(shape={1, LABEL_SIZE}), "b");
std::vector<Expr*> params;
params.push_back(&w);
params.push_back(&b);
auto scores = dot(x, w) + b;
auto lr = softmax_fast(scores, axis=1, name="pred");
auto cost = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
auto lr = softmax_fast(scores);
auto cost = named(-mean(sum(y * log(lr), axis=1), axis=0), "cost");
cerr << "lr=" << lr.Debug() << endl;
SGD opt(cost, x, y, params, 0.9, trainImages, IMAGE_SIZE, trainLabels, LABEL_SIZE, 3, 24);
SGD opt(g, 0.9, trainImages, IMAGE_SIZE, trainLabels, LABEL_SIZE, 3, 24);
opt.Run();
return 0;
}

18
src/validate_mnist_batch.cu
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@ -59,13 +59,15 @@ int main(int argc, char** argv) {
std::cerr << "\tDone." << std::endl;
auto x = input(shape={whatevs, IMAGE_SIZE}, name="X");
auto y = input(shape={whatevs, LABEL_SIZE}, name="Y");
ExpressionGraph g;
auto w1 = param(shape={IMAGE_SIZE, 100}, name="W0", init=initW1);
auto b1 = param(shape={1, 100}, name="b0", init=initB1);
auto w2 = param(shape={100, LABEL_SIZE}, name="W1", init=initW2);
auto b2 = param(shape={1, LABEL_SIZE}, name="b1", init=initB2);
auto x = g.input(shape={whatevs, IMAGE_SIZE}, name="X");
auto y = g.input(shape={whatevs, LABEL_SIZE}, name="Y");
auto w1 = g.param(shape={IMAGE_SIZE, 100}, name="W0", init=initW1);
auto b1 = g.param(shape={1, 100}, name="b0", init=initB1);
auto w2 = g.param(shape={100, LABEL_SIZE}, name="W1", init=initW2);
auto b2 = g.param(shape={1, LABEL_SIZE}, name="b1", init=initB2);
std::cerr << "Building model...";
auto layer1 = tanh(dot(x, w1) + b1);
@ -86,7 +88,7 @@ int main(int argc, char** argv) {
xt << tmp;
x = xt;
predict.forward(BATCH_SIZE);
g.forward(BATCH_SIZE);
std::vector<float> results(LABEL_SIZE * BATCH_SIZE);
results << predict.val();
@ -113,7 +115,7 @@ int main(int argc, char** argv) {
xt << tmp;
x = xt;
predict.forward(endId - startId);
g.forward(endId - startId);
std::vector<float> results(LABEL_SIZE * BATCH_SIZE);
results << predict.val();