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Implemented safe softmax (but doesn't solve the problem yet, we need log-softmax).
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@ -156,6 +156,7 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
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void forward() {
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// B = softmax(A).
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val_ = a_->val();
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SubtractMax(&val_); // Safe version of softmax.
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Softmax(&val_);
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}
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@ -95,4 +95,4 @@ class Adam {
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std::vector<Tensor> vt_;
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};
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}
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}
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@ -55,6 +55,56 @@ void SubtractMean(Tensor* Out, Tensor &Weights) {
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cudaStreamSynchronize(0);
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}
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__global__ void gSubtractMax(float* out, 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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if (j < rows) {
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extern __shared__ float _share[];
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float* _max = _share + blockDim.x;
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float* sp = out + j * cols;
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_max[threadIdx.x] = sp[threadIdx.x];
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for(int tid = 1; tid < cols; tid += blockDim.x) {
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int id = tid + threadIdx.x;
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if (id < cols) {
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if (sp[id] > _max[threadIdx.x]) _max[threadIdx.x] = sp[id];
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}
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}
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__syncthreads();
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int len = blockDim.x;
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while(len != 1) {
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__syncthreads();
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int skip = (len + 1) >> 1;
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if (threadIdx.x < (len >> 1)) {
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if (_max[threadIdx.x + skip] > _max[threadIdx.x]) {
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_max[threadIdx.x] = _max[threadIdx.x + skip];
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}
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}
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len = (len + 1) >> 1;
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}
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__syncthreads();
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for(int tid = 0; tid < cols; tid += blockDim.x){
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int id = tid + threadIdx.x;
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if(id < cols)
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sp[id] -= _max[0];
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}
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}
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}
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}
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void SubtractMax(Tensor* Out) {
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// Out is a m-by-k matrix, passed as input.
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// The max element of each row of Out is computed and subtracted from Out.
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// Out is both input and output.
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size_t m = Out->shape()[0];
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size_t k = Out->shape()[1];
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int blocks = std::min(MAX_BLOCKS, (int) m);
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int threads = std::min(MAX_THREADS, (int) k);
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int shared = sizeof(float) * threads * 2;
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gSubtractMax<<<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 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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@ -147,6 +147,10 @@ __global__ void gSubtractMean(float* out, float* weights,
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void SubtractMean(Tensor* Out, Tensor &Weights);
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__global__ void gSubtractMax(float* out, size_t rows, size_t cols);
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void SubtractMax(Tensor* Out);
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__global__ void gSoftMax(float* softMaxP, size_t rows, size_t cols);
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void Softmax(Tensor* Out);
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