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Implemented gradient of fast softmax.
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@ -119,9 +119,12 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
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}
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void backward() {
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// TODO
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Element(_1 += _2 * Exp(_3),
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a_->grad(), adj_, a_->val());
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// For each row, the Jacobian times vector is given by:
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// J * dy = p .* (dy - avg*1)
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// where avg = p'*dy and p is the softmax output (probabilities).
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Tensor result = adj_;
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SubtractMean(&result, val_);
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Prod(a_->grad(), adj_, result, false, false);
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}
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};
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@ -2,7 +2,57 @@
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namespace marian {
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// TODO: implement this.
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__global__ void gSubtractMean(float* out, float* weights,
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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* _sum = _share + blockDim.x;
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float* sp = out + j * cols;
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float* w = weights + j * cols;
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_sum[threadIdx.x] = 0.0;
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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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_sum[threadIdx.x] += w[id] * 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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_sum[threadIdx.x] += _sum[threadIdx.x + skip];
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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] -= _sum[0];
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}
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}
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}
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}
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void SubtractMean(Tensor* Out, Tensor &Weights) {
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// Out and Weights are both m-by-k matrices, passed as input.
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// A weighted average of each row of Out (according to the weights
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// specified in Weights) 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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gSubtractMean<<<blocks, threads, shared>>>(Out->data(), Weights.data(),
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m, k);
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cudaStreamSynchronize(0);
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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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@ -37,7 +87,6 @@ __global__ void gSoftMax(float* softMaxP, size_t rows, size_t cols) {
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}
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}
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// TODO: implement this.
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void Softmax(Tensor* Out) {
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size_t m = Out->shape()[0];
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size_t k = Out->shape()[1];
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@ -142,6 +142,11 @@ void Element(Functor functor,
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cudaStreamSynchronize(0);
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}
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__global__ void gSubtractMean(float* out, float* weights,
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size_t rows, size_t cols);
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void SubtractMean(Tensor* Out, Tensor &Weights);
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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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