Corrected bug in softmax; implemented cross-entropy node along with a few CUDA functions.

src/expression_operators.cu

@@ -125,4 +125,8 @@ Expr dot(Expr a, Expr b) {
   return Expr(a.graph(), new DotNodeOp(a, b));
 }
 
+Expr cross_entropy(Expr a, Expr b) {
+  return Expr(a.graph(), new CrossEntropyNodeOp(a, b));
+}
+
 }

src/expression_operators.h

@@ -112,4 +112,6 @@ inline Expr mean(Expr a, Args ...args) {
   }
 }
 
+ Expr cross_entropy(Expr a, Expr b);
+
 }

src/mnist_benchmark.cu

@@ -42,13 +42,12 @@ ExpressionGraph build_graph(const std::vector<int>& dims) {
             init=normal()));
 
   }
-  
+
-  auto probs = named(
+  auto scores = named(dot(layers.back(), weights.back()) + biases.back(),
-    softmax(dot(layers.back(), weights.back()) + biases.back()),
+                      "scores");
-    "probs"
+
-  );
+  auto cost = mean(cross_entropy(scores, y), axis=0);
-  
+  //auto cost = mean(-sum(y * log(softmax(scores)), axis=1), axis=0);
-  auto cost = -mean(sum(y * log(probs), axis=1), axis=0);
   auto costreg = named(
     cost, "cost"
   );
@@ -142,7 +141,7 @@ int main(int argc, char** argv) {
     g.forward(BATCH_SIZE);
     
     std::vector<float> bResults;
-    bResults << g["probs"].val();
+    bResults << g["scores"].val();
     results.insert(results.end(), bResults.begin(), bResults.end());
   }
   

src/node_operators.h

@@ -151,7 +151,7 @@ struct SoftmaxNodeOp : public UnaryNodeOp {
 
   void forward() {
     // B = softmax(A).
-    val_ = a_->val();
+    thrust::copy(a_->val().begin(), a_->val().end(), val_.begin());
     // Safe version of softmax.
     Softmax(&val_);
   }
@@ -441,4 +441,71 @@ struct DivNodeOp : public BinaryNodeOp {
 
 };
 
+// Cross-entropy node. It computes -b*log(softmax(a)), summing rowwise.
+struct CrossEntropyNodeOp : public BinaryNodeOp {
+  template <typename ...Args>
+    CrossEntropyNodeOp(ChainPtr a, ChainPtr b, Args ...args)
+    : BinaryNodeOp(a, b,
+                   keywords::shape=newShape(a, b),
+                   args...) { }
+
+  Shape newShape(ChainPtr a, ChainPtr b) {
+    Shape shape1 = a->shape();
+    Shape shape2 = b->shape();
+    UTIL_THROW_IF2(shape1[0] != shape2[0] || shape1[1] != shape2[1],
+                   "cross entropy requires dimensions to match");
+    shape1[1] = 1;
+    return shape1;
+  }
+
+  // We're caching the softmax probabilities here because we'll need them for
+  // the backward computation.
+  void forward() {
+    // C = -dot(B, log(softmax(A))).
+    if (probs_) {
+      probs_.set(0.0);
+    } else {
+      probs_.allocate(a_->val().shape(), 0.0);
+    }
+    thrust::copy(a_->val().begin(), a_->val().end(), probs_.begin());
+    Softmax(&probs_); // Safe version of softmax.
+    Tensor result(a_->val().shape());
+    Element(_1 = -_2 * Log(_3), result, b_->val(), probs_);
+    SumRowwise(result, val_);
+  }
+
+  // @TODO: In most cases it's wasteful to compute the derivative with respect
+  // to the second input which is typically an input node in the computation
+  // graph. In general the backward functions can skip the computation of
+  // gradients wrt input nodes.
+  void backward() {
+    // For each row, the first input derivative is given by adj * (p - y),
+    // where y is the gold label distribution (e.g. one hot vector) and
+    // p is the softmax output (probabilities).
+    // The second input derivative is -adj*log(p).
+    Tensor result(probs_.shape());
+
+    // Compute first input derivative.
+    Element(_1 = _2 -  _3, result, probs_, b_->val());
+    ScaleRowwise(result, adj_);
+    Element(_1 += _2, a_->grad(), result);
+
+    // Compute second input derivative.
+    Element(_1 = -Log(_2), result, probs_); // @TODO: use a cached log here.
+    ScaleRowwise(result, adj_);
+    Element(_1 += _2, b_->grad(), result);
+  }
+
+  virtual std::string graphviz() {
+    std::stringstream ss;
+    ss << "\"" << this << "\" [shape=\"box\", label=\"cross_entropy\", style=\"filled\", fillcolor=\"yellow\"]" << std::endl;
+    ss << "\"" << a_ << "\" -> \"" << this << "\"" << std::endl << std::endl;
+    return ss.str();
+  };
+
+ protected:
+  Tensor probs_;
+
+};
+
 }

src/tensor_operators.cu

@@ -227,4 +227,48 @@ Tensor Prod(Tensor C, const Tensor A, const Tensor B,
   return temp;
 }
 
+Tensor SumRowwise(cublasHandle_t handle, const Tensor A, Tensor result) {
+  size_t rows = A.shape()[0];
+  size_t cols = A.shape()[1];
+  thrust::device_vector<float> d_ones(cols, 1.f);
+  Float alpha = 1.f;
+  Float beta  = 0.f;
+  cublasSgemv(handle, CUBLAS_OP_T, cols, rows, &alpha,
+              A.data(), cols,
+              thrust::raw_pointer_cast(d_ones.data()), 1, &beta,
+              result.data(), 1);
+  return result;
 }
+
+Tensor SumRowwise(const Tensor A, Tensor result) {
+  Tensor temp = SumRowwise(cublasHandle, A, result);
+  return temp;
+}
+
+// @TODO: replace this by something else when broadcast elementwise operations
+// are ready.
+__global__ void gScaleRowwise(Float* out, const Float* scalingFactors,
+                              size_t rows, size_t cols) {
+  for(int bid = 0; bid < rows; bid += gridDim.x) {
+    int j = bid + blockIdx.x;
+    if(j < rows) {
+      Float* rowOut = out + j * cols;
+      for(int tid = 0; tid < cols; tid += blockDim.x) {
+        int i = tid + threadIdx.x;
+        if(i < cols) rowOut[i] *= scalingFactors[j];
+      }
+    }
+  }
+}
+
+void ScaleRowwise(Tensor Out, const Tensor ScalingFactors) {
+  Float* d_out = Out.data();
+  const Float* d_in = ScalingFactors.data();
+  int blocks  = std::min(MAX_BLOCKS, (int)Out.shape()[0]);
+  int threads = std::min(MAX_THREADS, (int)Out.shape()[1]);
+  gScaleRowwise<<<blocks, threads>>>(d_out, d_in,
+                                     Out.shape()[0], Out.shape()[1]);
+  cudaStreamSynchronize(0);
+}
+
+}

src/tensor_operators.h

@@ -165,4 +165,13 @@ Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
 Tensor Prod(Tensor C, const Tensor A, const Tensor B,
              bool transA, bool transB, Float beta = 0);
 
+Tensor SumRowwise(cublasHandle_t handle, const Tensor A, Tensor result);
+
+Tensor SumRowwise(const Tensor A, Tensor result);
+
+__global__ void gScaleRowwise(Float* out, const Float* scalingFactors,
+                              size_t rows, size_t cols);
+
+void ScaleRowwise(Tensor Out, const Tensor ScalingFactors);
+
 }