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move gArgMax to tensor_operators. Write ArgMax() with tensors
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@ -1,5 +1,7 @@
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#include "tensor_operators.h"
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using namespace std;
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namespace marian {
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__global__ void gSubtractMean(float* out, float* weights,
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@ -53,6 +55,7 @@ void SubtractMean(Tensor* Out, Tensor &Weights) {
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cudaStreamSynchronize(0);
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}
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///////////////////////////////////////////////////////
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__global__ void gSoftMax(float* softMaxP, size_t rows, size_t cols) {
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for(int bid = 0; bid < rows; bid += gridDim.x) {
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int j = bid + blockIdx.x;
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@ -97,6 +100,35 @@ void Softmax(Tensor* Out) {
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gSoftMax<<<blocks, threads, shared>>>(Out->data(), m, k);
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cudaStreamSynchronize(0);
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}
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///////////////////////////////////////////////////////
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__global__ void gArgMax(float *out, const float *data, size_t rows, size_t cols) {
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size_t row = blockIdx.x;
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size_t startInd = row * cols;
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float maxScore = -99999;
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size_t maxInd;
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for (size_t col = 0; col < cols; ++col) {
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size_t ind = startInd + col;
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float score = data[ind];
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if (score > maxScore) {
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maxScore = score;
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maxInd = col;
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}
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}
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out[row] = maxInd;
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}
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void Argmax(Tensor* Out, const Tensor* In) {
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size_t m = In->shape()[0];
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size_t k = In->shape()[1];
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int blocks = m; //std::min(MAX_BLOCKS, (int) m);
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int threads = k; //std::min(MAX_THREADS, (int) k);
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//int shared = sizeof(float) * threads * 2;
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gArgMax<<<blocks, threads>>>(Out->data(), In->data(), m, k);
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cudaStreamSynchronize(0);
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}
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///////////////////////////////////////////////////////
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Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
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bool transA, bool transB, Float beta) {
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@ -137,4 +169,4 @@ Tensor Prod(Tensor C, const Tensor A, const Tensor B,
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return temp;
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}
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}
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}
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@ -151,6 +151,10 @@ __global__ void gSoftMax(float* softMaxP, size_t rows, size_t cols);
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void Softmax(Tensor* Out);
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__global__ void gArgMax(float *out, const float *data, size_t rows, size_t cols);
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void Argmax(Tensor* Out, const Tensor* In);
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Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
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bool transA, bool transB, Float beta);
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34
src/test.cu
34
src/test.cu
@ -8,22 +8,6 @@
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using namespace std;
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///////////////////////////////////////////////////////
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__global__ void gArgMax(float *out, const float *data, size_t rows, size_t cols) {
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size_t row = blockIdx.x;
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size_t startInd = row * cols;
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float maxScore = -99999;
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size_t maxInd = -1;
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for (size_t col = 0; col < cols; ++col) {
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size_t ind = startInd + col;
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float score = data[ind];
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if (score > maxScore) {
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maxScore = score;
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maxInd = col;
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}
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}
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out[row] = maxInd;
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}
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string output(const std::vector<float> &vec)
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{
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stringstream strm;
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@ -38,27 +22,27 @@ void temp()
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using namespace std;
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using namespace marian;
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std::vector<float> hVec({29,19, 49,39, 79,99, 79,39});
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std::vector<float> hVec({29,19, 49,39, 79,99, 79,39});
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cerr << "hVec =" << output(hVec) << endl;
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thrust::device_vector<float> dVec(8);
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thrust::copy(hVec.begin(), hVec.end(), dVec.begin());
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float *data = thrust::raw_pointer_cast(dVec.data());
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thrust::device_vector<float> dVec(8);
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thrust::copy(hVec.begin(), hVec.end(), dVec.begin());
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float *data = thrust::raw_pointer_cast(dVec.data());
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thrust::device_vector<float> dLabel(4);
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float *labelPtr = thrust::raw_pointer_cast(dLabel.data());
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gArgMax<<<4, 1, sizeof(float)>>>(labelPtr, data, 4, 2);
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gArgMax<<<4, 1, sizeof(float)>>>(labelPtr, data, 4, 2);
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std::vector<float> hVec2(8);
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thrust::copy(dVec.begin(), dVec.end(), hVec2.begin());
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cerr << "hVec2=" << output(hVec2) << endl;
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std::vector<float> hVec2(8);
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thrust::copy(dVec.begin(), dVec.end(), hVec2.begin());
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cerr << "hVec2=" << output(hVec2) << endl;
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std::vector<float> hLabel(4);
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thrust::copy(dLabel.begin(), dLabel.end(), hLabel.begin());
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cerr << "hLabel=" << output(hLabel) << endl;
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exit(0);
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exit(0);
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}
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///////////////////////////////////////////////////////
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