SyncBN backend and double type

encoding/__init__.py

@@ -8,9 +8,12 @@
 ## LICENSE file in the root directory of this source tree 
 ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+import threading
 import torch
+import torch.cuda.nccl as nccl
 import torch.nn as nn
-from torch.autograd import Function
+from torch.autograd import Function, Variable
+from torch.nn.parameter import Parameter
 from ._ext import encoding_lib
 
 class aggregate(Function):
@@ -20,15 +23,26 @@ class aggregate(Function):
 		B, N, K, D = R.size()
 		E = A.new(B,K,D)
 		# TODO support cpu backend
+    if isinstance(A, torch.cuda.FloatTensor):
 		    encoding_lib.Encoding_Float_aggregate_forward(E, A, R)
+    elif isinstance(A, torch.cuda.DoubleTensor):
+		    encoding_lib.Encoding_Double_aggregate_forward(E, A, R)
+    else:
+        raise RuntimeError('unimplemented')
 		return E
 
 	def backward(self, gradE):
 		A, R = self.saved_tensors
 		gradA = A.new().resize_as_(A)
 		gradR = R.new().resize_as_(R)
+    if isinstance(A, torch.cuda.FloatTensor):
         encoding_lib.Encoding_Float_aggregate_backward(gradA, gradR, gradE, 
                 A, R)
+    elif isinstance(A, torch.cuda.DoubleTensor):
+        encoding_lib.Encoding_Double_aggregate_backward(gradA, gradR, gradE, 
+                A, R)
+    else:
+        raise RuntimeError('unimplemented')
 		return gradA, gradR
 
 
@@ -82,3 +96,131 @@ class Encoding(nn.Module):
 	def __repr__(self):
 		return self.__class__.__name__ + '(' \
 			+ 'N x ' + str(self.D) + '=>' + str(self.K) + 'x' + str(self.D) + ')'
+
+class sum_square(Function):
+    def forward(ctx, input):
+        ctx.save_for_backward(input)
+        B,C,H,W = input.size()
+        with torch.cuda.device_of(input):
+            xsum    = input.new().resize_(C).zero_()
+            xsquare = input.new().resize_(C).zero_()
+        if isinstance(input, torch.cuda.FloatTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Float_sum_square_Forward(
+                    input.view(B,C,-1), xsum, xsquare)
+        elif isinstance(input, torch.cuda.DoubleTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Double_sum_square_Forward( 
+                    input.view(B,C,-1), xsum, xsquare)
+        else:
+            raise RuntimeError('unimplemented') 
+        return xsum, xsquare
+
+    def backward(ctx, gradSum, gradSquare):
+        input, = ctx.saved_tensors
+        B,C,H,W = input.size()
+        with torch.cuda.device_of(input):
+            gradInput = input.new().resize_(B,C,H*W).zero_()
+        #    gradSum.view(1,C,1,1).expand_as(input) + \
+        #   2*gradSquare.view(1,C,1,1).expand_as(input)*input
+        if isinstance(input, torch.cuda.FloatTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Float_sum_square_Backward(
+                    gradInput, input.view(B,C,-1), gradSum, gradSquare)
+        elif isinstance(input, torch.cuda.DoubleTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Double_sum_square_Backward( 
+                    gradInput, input.view(B,C,-1), gradSum, gradSquare)
+        else:
+            raise RuntimeError('unimplemented') 
+        return gradInput.view(B,C,H,W)
+
+class batchnormtrain(Function):
+    def forward(ctx, input, gamma, beta, mean, std):
+        ctx.save_for_backward(input, gamma, beta, mean, std)
+        assert(input.dim()==3)
+        with torch.cuda.device_of(input):
+            invstd = 1.0 / std
+            output = input.new().resize_as_(input)
+        if isinstance(input, torch.cuda.FloatTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Float_batchnorm_Forward(output, 
+                    input, mean, invstd, gamma, beta)
+        elif isinstance(input, torch.cuda.DoubleTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Double_batchnorm_Forward(output, 
+                    input, mean, invstd, gamma, beta)
+        else:
+            raise RuntimeError('unimplemented')
+        return output 
+
+    def backward(ctx, gradOutput):
+        input, gamma, beta, mean, std = ctx.saved_tensors
+        invstd = 1.0 / std
+        with torch.cuda.device_of(input):
+            gradInput = gradOutput.new().resize_as_(input).zero_()
+            gradGamma = gradOutput.new().resize_as_(gamma).zero_()
+            gradBeta  = gradOutput.new().resize_as_(beta).zero_()
+            gradMean  = gradOutput.new().resize_as_(mean).zero_()
+            gradStd   = gradOutput.new().resize_as_(std).zero_()
+
+        if isinstance(input, torch.cuda.FloatTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Float_batchnorm_Backward(
+                    gradOutput, input, gradInput, gradGamma, gradBeta, 
+                    mean, invstd, gamma, beta, gradMean, gradStd,
+                    True) 
+        elif isinstance(input, torch.cuda.DoubleTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Double_batchnorm_Backward(
+                    gradOutput, input, gradInput, gradGamma, gradBeta, 
+                    mean, invstd, gamma, beta, gradMean, gradStd,
+                    True) 
+        else:
+            raise RuntimeError('unimplemented')
+        return gradInput, gradGamma, gradBeta, gradMean, gradStd
+
+class batchnormeval(Function):
+    def forward(ctx, input, gamma, beta, mean, std):
+        ctx.save_for_backward(input, gamma, beta, mean, std)
+        assert(input.dim()==3)
+        with torch.cuda.device_of(input):
+            invstd = 1.0 / std
+            output = input.new().resize_as_(input)
+        if isinstance(input, torch.cuda.FloatTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Float_batchnorm_Forward(output, 
+                    input, mean, invstd, gamma, beta)
+        elif isinstance(input, torch.cuda.DoubleTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Double_batchnorm_Forward(output, 
+                    input, mean, invstd, gamma, beta)
+        else:
+            raise RuntimeError('unimplemented')
+        return output 
+
+    def backward(ctx, gradOutput):
+        input, gamma, beta, mean, std = ctx.saved_tensors
+        invstd = 1.0 / std
+        with torch.cuda.device_of(input):
+            gradInput = gradOutput.new().resize_as_(input).zero_()
+            gradGamma = gradOutput.new().resize_as_(gamma).zero_()
+            gradBeta  = gradOutput.new().resize_as_(beta).zero_()
+            gradMean  = gradOutput.new().resize_as_(mean).zero_()
+            gradStd   = gradOutput.new().resize_as_(std).zero_()
+        if isinstance(input, torch.cuda.FloatTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Float_batchnorm_Backward(
+                    gradOutput, input, gradInput, gradGamma, gradBeta, 
+                    mean, invstd, gamma, beta, gradMean, gradStd,
+                    False) 
+        elif isinstance(input, torch.cuda.DoubleTensor):
+            with torch.cuda.device_of(input):
+                encoding_lib.Encoding_Double_batchnorm_Backward(
+                    gradOutput, input, gradInput, gradGamma, gradBeta, 
+                    mean, invstd, gamma, beta, gradMean, gradStd,
+                    False) 
+        else:
+            raise RuntimeError('unimplemented')
+        return gradInput, gradGamma, gradBeta, gradMean, gradStd
+

encoding/_ext/encoding_lib/__init__.py

+
+from torch.utils.ffi import _wrap_function
+from ._encoding_lib import lib as _lib, ffi as _ffi
+
+__all__ = []
+def _import_symbols(locals):
+    for symbol in dir(_lib):
+        fn = getattr(_lib, symbol)
+        locals[symbol] = _wrap_function(fn, _ffi)
+        __all__.append(symbol)
+
+_import_symbols(locals())

encoding/kernel/common.h

+// The maximum number of threads in a block
+const int WARP_SIZE = 32;
+const int MAX_BLOCK_SIZE = 512;
+
+// Number of threads in a block given an input size up to MAX_BLOCK_SIZE
+static int getNumThreads(int nElem) {
+  int threadSizes[5] = { 32, 64, 128, 256, MAX_BLOCK_SIZE };
+  for (int i = 0; i != 5; ++i) {
+    if (nElem <= threadSizes[i]) {
+      return threadSizes[i];
+    }
+  }
+  return MAX_BLOCK_SIZE;
+}
+
+__device__ __forceinline__ int getMSB(int val) {
+  return 31 - __clz(val);
+}

encoding/kernel/generic/device_tensor.h

@@ -12,13 +12,13 @@
 #define THC_GENERIC_FILE "generic/device_tensor.h"
 #else
 template <int Dim>
-THCDeviceTensor<float, Dim> devicetensor(THCState *state, THCTensor *t) {
+THCDeviceTensor<real, Dim> devicetensor(THCState *state, THCTensor *t) {
   if (!t) {
-    return THCDeviceTensor<float, Dim>();
+    return THCDeviceTensor<real, Dim>();
   }
   int inDim = THCTensor_(nDimension)(state, t);
   if (inDim == Dim) {
-    return toDeviceTensor<float, Dim>(state, t);
+    return toDeviceTensor<real, Dim>(state, t);
   }
   // View in which the last dimensions are collapsed or expanded as needed
   THAssert(THCTensor_(isContiguous)(state, t));
@@ -32,6 +32,6 @@ THCDeviceTensor<float, Dim> devicetensor(THCState *state, THCTensor *t) {
       size[Dim - 1] *= t->size[i];
     }
   }
-  return THCDeviceTensor<float, Dim>(THCTensor_(data)(state, t), size);
+  return THCDeviceTensor<real, Dim>(THCTensor_(data)(state, t), size);
 }
 #endif

encoding/kernel/generic/encoding_kernel.c

@@ -125,4 +125,399 @@ void Encoding_(Aggregate_Backward)(THCState *state, THCTensor *GA_,
 					GR, L, A, R);
 	THCudaCheck(cudaGetLastError());
 }
+
+
+/*+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
+// Returns the index of the most significant 1 bit in `val`.
+
+
+__global__ void Encoding_(BatchNorm_Forward_kernel) (
+    THCDeviceTensor<real, 3> output,
+    THCDeviceTensor<real, 3> input,
+    THCDeviceTensor<real, 1> mean,
+    THCDeviceTensor<real, 1> invstd,
+    THCDeviceTensor<real, 1> gamma,
+    THCDeviceTensor<real, 1> beta)
+{
+    int c = blockIdx.x;
+    //int N = input.getSize(0) * input.getSize(2);
+    /* main operation */ 
+    for (int b = 0; b < input.getSize(0); ++b) {
+        for (int x = threadIdx.x; x < input.getSize(2); x += blockDim.x) {
+            real inp = input[b][c][x].ldg();
+            output[b][c][x] = gamma[c].ldg() * (inp - mean[c].ldg()) * 
+                invstd[c].ldg() + beta[c].ldg();
+        }
+    }
+}
+
+void Encoding_(BatchNorm_Forward)(THCState *state, 
+        THCTensor *output_, THCTensor *input_, 
+        THCTensor *mean_, THCTensor *invstd_,
+        THCTensor *gamma_, THCTensor *beta_)
+/*
+ * batch norm forward function
+ * assuming the input is already flaghten
+ */
+{
+    /* Check the GPU index and tensor dims*/
+    THCTensor_(checkGPU)(state, 6, output_, input_, mean_, invstd_, 
+                         gamma_, beta_);
+    if (THCTensor_(nDimension)(state, output_) != 3 ||
+        THCTensor_(nDimension)(state, input_)  != 3 ||
+        THCTensor_(nDimension)(state, mean_)   != 1 ||
+        THCTensor_(nDimension)(state, invstd_) != 1 ||
+        THCTensor_(nDimension)(state, gamma_)  != 1 ||
+        THCTensor_(nDimension)(state, beta_)   != 1)
+        THError("BatchNorm2d forward: incorrect input dims. \n");
+    /* Device tensors */
+    THCDeviceTensor<real, 3> output = devicetensor<3>(state, output_);
+    THCDeviceTensor<real, 3> input  = devicetensor<3>(state, input_);
+    THCDeviceTensor<real, 1> mean   = devicetensor<1>(state, mean_);
+    THCDeviceTensor<real, 1> invstd    = devicetensor<1>(state, invstd_);
+    THCDeviceTensor<real, 1> gamma  = devicetensor<1>(state, gamma_);
+    THCDeviceTensor<real, 1> beta   = devicetensor<1>(state, beta_);
+    /* kernel function */
+    cudaStream_t stream = THCState_getCurrentStream(state);
+    dim3 blocks(input.getSize(1));
+    dim3 threads(getNumThreads(input.getSize(2)));
+    Encoding_(BatchNorm_Forward_kernel)<<<blocks, threads, 0, stream>>>(
+        output, input, mean, invstd, gamma, beta);
+    THCudaCheck(cudaGetLastError());
+}
+
+struct Encoding_(Float2){
+    real v1, v2;
+    __device__ Encoding_(Float2)() {}
+    __device__ Encoding_(Float2)(real x1, real x2) : v1(x1), v2(x2) {}
+    __device__ Encoding_(Float2)(real v) : v1(v), v2(v) {}
+    __device__ Encoding_(Float2)(int v) :  v1(v), v2(v) {}
+    __device__ Encoding_(Float2)& operator+=(const Encoding_(Float2)& a) {
+    v1 += a.v1;
+    v2 += a.v2;
+    return *this;
+  }
+};
+
+static __device__ __forceinline__ real Encoding_(rwarpSum)(real val) {
+#if __CUDA_ARCH__ >= 300
+  for (int i = 0; i < getMSB(WARP_SIZE); ++i) {
+    val += __shfl_xor(val, 1 << i, WARP_SIZE);
+  }
+#else
+  __shared__ real values[MAX_BLOCK_SIZE];
+  values[threadIdx.x] = val;
+  __threadfence_block();
+  const int base = (threadIdx.x / WARP_SIZE) * WARP_SIZE;
+  for (int i = 1; i < WARP_SIZE; i++) {
+    val += values[base + ((i + threadIdx.x) % WARP_SIZE)];
+  }
+#endif
+  return val;
+}
+
+static __device__ __forceinline__ Encoding_(Float2) Encoding_(warpSum)(Encoding_(Float2) value) {
+  value.v1 = Encoding_(rwarpSum)(value.v1);
+  value.v2 = Encoding_(rwarpSum)(value.v2);
+  return value;
+}
+
+struct Encoding_(GradOp) {
+    __device__ Encoding_(GradOp)(real m, THCDeviceTensor<real, 3> i, THCDeviceTensor<real, 3> g)
+        : mean(m), input(i), gradOutput(g) {}
+    __device__ __forceinline__ Encoding_(Float2) operator()(int batch, int plane, int n) {
+        real g = gradOutput[batch][plane][n].ldg();
+        real c = input[batch][plane][n].ldg() - mean;
+        return Encoding_(Float2)(g, g * c);
+    }
+    real mean;
+    THCDeviceTensor<real, 3> input;
+    THCDeviceTensor<real, 3> gradOutput;
+};
+
+// Sum across (batch, x/y/z) applying Op() pointwise
+__device__ Encoding_(Float2) Encoding_(reduce)(Encoding_(GradOp) op, THCDeviceTensor<real, 3> tensor, int plane) {
+  Encoding_(Float2) sum = (Encoding_(Float2))0;
+  for (int batch = 0; batch < tensor.getSize(0); ++batch) {
+    for (int x = threadIdx.x; x < tensor.getSize(2); x += blockDim.x) {
+      sum += op(batch, plane, x);
+    }
+  }
+
+  // sum over NumThreads within a warp
+  sum = Encoding_(warpSum)(sum);
+
+  // 'transpose', and reduce within warp again
+  __shared__ Encoding_(Float2) shared[32];
+
+  __syncthreads();
+  if (threadIdx.x % WARP_SIZE == 0) {
+    if (threadIdx.x / WARP_SIZE < 32) {
+        shared[threadIdx.x / WARP_SIZE] = sum;
+    }
+  }
+  if (threadIdx.x >= blockDim.x / WARP_SIZE && threadIdx.x < WARP_SIZE) {
+    // zero out the other entries in shared
+    shared[threadIdx.x] = (Encoding_(Float2))0;
+  }
+  __syncthreads();
+  if (threadIdx.x / WARP_SIZE == 0) {
+    sum = Encoding_(warpSum)(shared[threadIdx.x]);
+    if (threadIdx.x == 0) {
+      shared[0] = sum;
+    }
+  }
+  __syncthreads();
+
+  // Everyone picks it up, should be broadcast into the whole gradInput
+  return shared[0];
+}
+
+__global__ void Encoding_(BatchNorm_Backward_kernel) (
+    THCDeviceTensor<real, 3> gradoutput,
+    THCDeviceTensor<real, 3> input,
+    THCDeviceTensor<real, 3> gradinput,
+    THCDeviceTensor<real, 1> gradgamma,
+    THCDeviceTensor<real, 1> gradbeta,
+    THCDeviceTensor<real, 1> mean,
+    THCDeviceTensor<real, 1> invstd,
+    THCDeviceTensor<real, 1> gamma,
+    THCDeviceTensor<real, 1> beta,
+    THCDeviceTensor<real, 1> gradMean, 
+    THCDeviceTensor<real, 1> gradStd,
+    int train)
+{
+    /* declarations of the variables */
+    /* Get the index and channels */ 
+    int c = blockIdx.x; 
+    /* main operation */ 
+    //int N = input.getSize(0) * input.getSize(2);
+    //real norm;
+    //norm = 1.0 / N;
+
+    Encoding_(GradOp) g(mean[c], input, gradoutput);
+    Encoding_(Float2) res = Encoding_(reduce)(g, gradoutput, c);
+    real gradOutputSum = res.v1;
+    real dotP = res.v2;
+
+    //real projScale = dotP * norm * invstd[c].ldg() * invstd[c].ldg();
+    real gradScale = invstd[c].ldg() * gamma[c].ldg();
+    if (train && threadIdx.x == 0) {
+        gradMean[c] = - gradOutputSum * gamma[c].ldg() * invstd[c].ldg();
+        gradStd[c]  = - dotP * gamma[c].ldg() * invstd[c].ldg() * invstd[c].ldg();
+    }
+
+    if (gradinput.numElements() > 0) {
+        for (int batch = 0; batch < gradoutput.getSize(0); ++batch) {
+            for (int x = threadIdx.x; x < gradoutput.getSize(2); x += blockDim.x) {
+                gradinput[batch][c][x] = gradoutput[batch][c][x].ldg() * gradScale;
+            }
+        }
+    }
+
+    if (gradgamma.numElements() > 0) {
+        if (threadIdx.x == 0) {
+            gradgamma[c] += dotP * invstd[c].ldg();
+        }
+    }
+
+    if (gradbeta.numElements() > 0) {
+        if (threadIdx.x == 0) {
+            gradbeta[c] += gradOutputSum;
+        }
+    }
+}
+
+void Encoding_(BatchNorm_Backward)(THCState *state, 
+        THCTensor *gradoutput_, THCTensor *input_, THCTensor *gradinput_, 
+        THCTensor *gradgamma_, THCTensor *gradbeta_, THCTensor *mean_, 
+        THCTensor *invstd_, THCTensor *gamma_, THCTensor *beta_, 
+        THCTensor *gradMean_, THCTensor *gradStd_, int train)
+/*
+ * batch norm backward function
+ * assuming the input is already flaghten
+ */
+{
+    /* Check the GPU index and tensor dims*/
+    THCTensor_(checkGPU)(state, 6, gradoutput_, input_, gradinput_, 
+        gradgamma_, gradbeta_, mean_, invstd_, gamma_, beta_);
+    if (THCTensor_(nDimension)(state, gradoutput_) != 3 ||
+        THCTensor_(nDimension)(state, input_)      != 3 ||
+        THCTensor_(nDimension)(state, gradinput_)  != 3 ||
+        THCTensor_(nDimension)(state, gradgamma_)  != 1 ||
+        THCTensor_(nDimension)(state, gradbeta_)   != 1 ||
+        THCTensor_(nDimension)(state, mean_)   != 1 ||
+        THCTensor_(nDimension)(state, invstd_) != 1 ||
+        THCTensor_(nDimension)(state, gamma_)  != 1 ||
+        THCTensor_(nDimension)(state, beta_)   != 1 || 
+        THCTensor_(nDimension)(state, gradMean_) != 1 ||
+        THCTensor_(nDimension)(state, gradStd_)  != 1 )
+        THError("BatchNorm2d backward: incorrect input dims. \n");
+    /* Device tensors */
+    THCDeviceTensor<real, 3> gradoutput = 
+        devicetensor<3>(state, gradoutput_);
+    THCDeviceTensor<real, 3> input = 
+        devicetensor<3>(state, input_);
+    THCDeviceTensor<real, 3> gradinput = 
+        devicetensor<3>(state, gradinput_);
+    THCDeviceTensor<real, 1> gradgamma = 
+        devicetensor<1>(state, gradgamma_);
+    THCDeviceTensor<real, 1> gradbeta = devicetensor<1>(state, gradbeta_);
+    THCDeviceTensor<real, 1> mean     = devicetensor<1>(state, mean_);
+    THCDeviceTensor<real, 1> invstd   = devicetensor<1>(state, invstd_);
+    THCDeviceTensor<real, 1> gamma    = devicetensor<1>(state, gamma_);
+    THCDeviceTensor<real, 1> beta     = devicetensor<1>(state, beta_);
+    THCDeviceTensor<real, 1> gradMean = devicetensor<1>(state, gradMean_);
+    THCDeviceTensor<real, 1> gradStd  = devicetensor<1>(state, gradStd_);
+    /* kernel function */
+    cudaStream_t stream = THCState_getCurrentStream(state);
+    dim3 blocks(input.getSize(1));
+    dim3 threads(getNumThreads(input.getSize(2)));
+    Encoding_(BatchNorm_Backward_kernel)<<<blocks, threads, 0, stream>>>(
+        gradoutput, input, gradinput, gradgamma, gradbeta, mean, invstd, 
+        gamma, beta, gradMean, gradStd, train);
+    THCudaCheck(cudaGetLastError());
+}
+
+struct Encoding_(SumOp) {
+    __device__ Encoding_(SumOp)(THCDeviceTensor<real, 3> i)
+        : input(i){}
+    __device__ __forceinline__ Encoding_(Float2) operator()(int batch, int plane, int n) {
+        real g = input[batch][plane][n].ldg();
+        return Encoding_(Float2)(g, g * g);
+    }
+    real mean;
+    THCDeviceTensor<real, 3> input;
+};
+
+// Sum across (batch, x/y/z) applying Op() pointwise
+__device__ Encoding_(Float2) Encoding_(reduce_sum)(Encoding_(SumOp) op, THCDeviceTensor<real, 3> tensor, int plane) {
+  Encoding_(Float2) sum = (Encoding_(Float2))0;
+  for (int batch = 0; batch < tensor.getSize(0); ++batch) {
+    for (int x = threadIdx.x; x < tensor.getSize(2); x += blockDim.x) {
+      sum += op(batch, plane, x);
+    }
+  }
+
+  // sum over NumThreads within a warp
+  sum = Encoding_(warpSum)(sum);
+
+  // 'transpose', and reduce within warp again
+  __shared__ Encoding_(Float2) shared[32];
+
+  __syncthreads();
+  if (threadIdx.x % WARP_SIZE == 0) {
+    if (threadIdx.x / WARP_SIZE < 32) {
+        shared[threadIdx.x / WARP_SIZE] = sum;
+    }
+  }
+  if (threadIdx.x >= blockDim.x / WARP_SIZE && threadIdx.x < WARP_SIZE) {
+    // zero out the other entries in shared
+    shared[threadIdx.x] = (Encoding_(Float2))0;
+  }
+  __syncthreads();
+  if (threadIdx.x / WARP_SIZE == 0) {
+    sum = Encoding_(warpSum)(shared[threadIdx.x]);
+    if (threadIdx.x == 0) {
+      shared[0] = sum;
+    }
+  }
+  __syncthreads();
+
+  // Everyone picks it up, should be broadcast into the whole gradInput
+  return shared[0];
+}
+
+
+__global__ void Encoding_(Sum_Square_Forward_kernel) (
+    THCDeviceTensor<real, 3> input,
+    THCDeviceTensor<real, 1> sum,
+    THCDeviceTensor<real, 1> square)
+{
+    int c = blockIdx.x;
+    /* main operation */ 
+    Encoding_(SumOp) g(input);
+    Encoding_(Float2) res = Encoding_(reduce_sum)(g, input, c);
+    real xsum = res.v1;
+    real xsquare = res.v2;
+    
+    if (threadIdx.x == 0) {
+        sum[c]    = xsum;
+        square[c] = xsquare;
+    }
+}
+
+
+void Encoding_(Sum_Square_Forward)(THCState *state, 
+        THCTensor *input_, THCTensor *sum_, THCTensor *square_)
+/*
+ */
+{
+    /* Check the GPU index and tensor dims*/
+    THCTensor_(checkGPU)(state, 3, input_, sum_, square_);
+    if (THCTensor_(nDimension)(state, input_)   != 3 ||
+        THCTensor_(nDimension)(state, sum_)     != 1 ||
+        THCTensor_(nDimension)(state, square_)  != 1)
+        THError("Sum_Square forward: incorrect input dims. \n");
+    /* Device tensors */
+    THCDeviceTensor<real, 3> input  = devicetensor<3>(state, input_);
+    THCDeviceTensor<real, 1> sum    = devicetensor<1>(state, sum_);
+    THCDeviceTensor<real, 1> square = devicetensor<1>(state, square_);
+    /* kernel function */
+    cudaStream_t stream = THCState_getCurrentStream(state);
+    dim3 blocks(input.getSize(1));
+    dim3 threads(getNumThreads(input.getSize(2)));
+    Encoding_(Sum_Square_Forward_kernel)<<<blocks, threads, 0, stream>>>(
+        input, sum, square);
+    THCudaCheck(cudaGetLastError());
+}
+
+
+__global__ void Encoding_(Sum_Square_Backward_kernel) (
+    THCDeviceTensor<real, 3> gradInput,
+    THCDeviceTensor<real, 3> input,
+    THCDeviceTensor<real, 1> gradSum,
+    THCDeviceTensor<real, 1> gradSquare)
+{
+    int c = blockIdx.x;
+    /* main operation */ 
+    for (int batch = 0; batch < gradInput.getSize(0); ++batch) {
+        for (int x = threadIdx.x; x < gradInput.getSize(2); x += blockDim.x)
+        {
+            gradInput[batch][c][x] = gradSum[c] + 2 * gradSquare[c] *
+                input[batch][c][x];
+        }
+    }   
+}
+
+
+void Encoding_(Sum_Square_Backward)(THCState *state, 
+        THCTensor *gradInput_, THCTensor *input_, 
+        THCTensor *gradSum_, THCTensor *gradSquare_)
+/*
+ */
+{
+    /* Check the GPU index and tensor dims*/
+    THCTensor_(checkGPU)(state, 4, gradInput_, input_, gradSum_, 
+                         gradSquare_);
+    if (THCTensor_(nDimension)(state, gradInput_)  != 3 ||
+        THCTensor_(nDimension)(state, input_)      != 3 ||
+        THCTensor_(nDimension)(state, gradSum_)    != 1 ||
+        THCTensor_(nDimension)(state, gradSquare_) != 1)
+        THError("Sum_Square forward: incorrect input dims. \n");
+    /* Device tensors */
+    THCDeviceTensor<real, 3> gradInput  = devicetensor<3>(state, gradInput_);
+    THCDeviceTensor<real, 3> input      = devicetensor<3>(state, input_);
+    THCDeviceTensor<real, 1> gradSum    = devicetensor<1>(state, gradSum_);
+    THCDeviceTensor<real, 1> gradSquare =devicetensor<1>(state, gradSquare_);
+    /* kernel function */
+    cudaStream_t stream = THCState_getCurrentStream(state);
+    dim3 blocks(input.getSize(1));
+    dim3 threads(getNumThreads(input.getSize(2)));
+    Encoding_(Sum_Square_Backward_kernel)<<<blocks, threads, 0, stream>>>(
+        gradInput, input, gradSum, gradSquare);
+    THCudaCheck(cudaGetLastError());
+}
+
+
 #endif

encoding/kernel/generic/encoding_kernel.h

@@ -14,6 +14,26 @@
 
 void Encoding_(Aggregate_Forward)(THCState *state, THCTensor *E_, 
 							THCTensor *A_, THCTensor *R_);
+
 void Encoding_(Aggregate_Backward)(THCState *state, THCTensor *GA_, 
  	THCTensor *GR_, THCTensor *L_, THCTensor *A_, THCTensor *R_);
+
+void Encoding_(BatchNorm_Forward)(THCState *state, 
+        THCTensor *output_, THCTensor *input_, 
+        THCTensor *mean_, THCTensor *invstd_,
+        THCTensor *gamma_, THCTensor *beta_);
+
+void Encoding_(BatchNorm_Backward)(THCState *state, 
+        THCTensor *gradoutput_, THCTensor *input_, THCTensor *gradinput_, 
+        THCTensor *gradgamma_, THCTensor *gradbeta_, THCTensor *mean_, 
+        THCTensor *invstd_, THCTensor *gamma_, THCTensor *beta_, 
+        THCTensor *gradMean_, THCTensor *gradStd_, int train);
+
+void Encoding_(Sum_Square_Forward)(THCState *state, 
+        THCTensor *input_, THCTensor *sum_, THCTensor *square_);
+
+void Encoding_(Sum_Square_Backward)(THCState *state, 
+        THCTensor *gradInput, THCTensor *input_, 
+        THCTensor *gradSum_, THCTensor *gradSquare_);
+
 #endif

encoding/kernel/thc_encoding.cu

@@ -9,10 +9,14 @@
  *+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  */
 #include "thc_encoding.h"
+#include "common.h"
 
 #include "generic/device_tensor.h"
 #include "THC/THCGenerateFloatType.h"
 
+#include "generic/device_tensor.h"
+#include "THC/THCGenerateDoubleType.h"
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -20,6 +24,9 @@ extern "C" {
 #include "generic/encoding_kernel.c"
 #include "THC/THCGenerateFloatType.h"
 
+#include "generic/encoding_kernel.c"
+#include "THC/THCGenerateDoubleType.h"
+
 #ifdef __cplusplus
 }
 #endif

encoding/kernel/thc_encoding.h

@@ -26,6 +26,9 @@ extern "C" {
 #include "generic/encoding_kernel.h"
 #include "THC/THCGenerateFloatType.h"
 
+#include "generic/encoding_kernel.h"
+#include "THC/THCGenerateDoubleType.h"
+
 #ifdef __cplusplus
 }
 #endif

encoding/src/encoding_lib.cpp

@@ -20,6 +20,9 @@ extern "C" {
 #include "generic/encoding_generic.c"
 #include "THC/THCGenerateFloatType.h"
 
+#include "generic/encoding_generic.c"
+#include "THC/THCGenerateDoubleType.h"
+
 #ifdef __cplusplus
 }
 #endif

encoding/src/encoding_lib.h

@@ -24,3 +24,48 @@ int Encoding_Float_aggregate_forward(THCudaTensor *E, THCudaTensor *A,
 			THCudaTensor *R);
 int Encoding_Float_aggregate_backward(THCudaTensor *GA, THCudaTensor *GR, 
 		THCudaTensor *L, THCudaTensor *A, THCudaTensor *R);
+
+int Encoding_Float_batchnorm_Forward(THCudaTensor *output_, 
+        THCudaTensor *input_, THCudaTensor *mean_, 
+        THCudaTensor *invstd_, THCudaTensor *gamma_, THCudaTensor *beta_);
+
+int Encoding_Float_batchnorm_Backward(THCudaTensor *gradoutput_, 
+        THCudaTensor *input_, THCudaTensor *gradinput_, 
+        THCudaTensor *gradgamma_, THCudaTensor *gradbeta_, 
+        THCudaTensor *mean_, THCudaTensor *invstd_, 
+        THCudaTensor *gamma_,THCudaTensor *beta_, 
+        THCudaTensor *gradMean_, THCudaTensor *gradStd_, int train);
+
+int Encoding_Float_sum_square_Forward(THCudaTensor *input_, 
+        THCudaTensor *sum_, THCudaTensor *square_);
+
+void Encoding_Float_sum_square_Backward(
+        THCudaTensor *gradInput, THCudaTensor *input_, 
+        THCudaTensor *gradSum_, THCudaTensor *gradSquare_);
+
+/*++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
+
+int Encoding_Double_aggregate_forward(THCudaDoubleTensor *E, 
+        THCudaDoubleTensor *A, THCudaDoubleTensor *R);
+
+int Encoding_Double_aggregate_backward(THCudaDoubleTensor *GA, 
+        THCudaDoubleTensor *GR, THCudaDoubleTensor *L, 
+        THCudaDoubleTensor *A, THCudaDoubleTensor *R);
+
+int Encoding_Double_batchnorm_Forward(THCudaDoubleTensor *output_, 
+        THCudaDoubleTensor *input_, THCudaDoubleTensor *mean_, 
+        THCudaDoubleTensor *invstd_, THCudaDoubleTensor *gamma_, THCudaDoubleTensor *beta_);
+
+int Encoding_Double_batchnorm_Backward(THCudaDoubleTensor *gradoutput_, 
+        THCudaDoubleTensor *input_, THCudaDoubleTensor *gradinput_, 
+        THCudaDoubleTensor *gradgamma_, THCudaDoubleTensor *gradbeta_, 
+        THCudaDoubleTensor *mean_, THCudaDoubleTensor *invstd_, 
+        THCudaDoubleTensor *gamma_, THCudaDoubleTensor *beta_, 
+        THCudaDoubleTensor *gradMean_, THCudaDoubleTensor *gradStd_, int train);
+
+int Encoding_Double_sum_square_Forward(THCudaDoubleTensor *input_, 
+        THCudaDoubleTensor *sum_, THCudaDoubleTensor *square_);
+
+void Encoding_Double_sum_square_Backward(
+        THCudaDoubleTensor *gradInput, THCudaDoubleTensor *input_, 
+        THCudaDoubleTensor *gradSum_, THCudaDoubleTensor *gradSquare_);

encoding/src/generic/encoding_generic.c

@@ -34,4 +34,56 @@ int Encoding_(aggregate_backward)(THCTensor *GA, THCTensor *GR,
 	/* C function return number of the outputs */
 	return 0;
 }
+
+int Encoding_(batchnorm_Forward)(THCTensor *output_, THCTensor *input_, 
+        THCTensor *mean_, THCTensor *invstd_,
+        THCTensor *gamma_, THCTensor *beta_)
+/*
+ * 
+ */
+{
+    Encoding_(BatchNorm_Forward)(state, output_, input_, 
+        mean_, invstd_, gamma_, beta_);
+	/* C function return number of the outputs */
+	return 0;
+}
+
+int Encoding_(batchnorm_Backward)(THCTensor *gradoutput_, 
+        THCTensor *input_, THCTensor *gradinput_, 
+        THCTensor *gradgamma_, THCTensor *gradbeta_, THCTensor *mean_, 
+        THCTensor *invstd_, THCTensor *gamma_, THCTensor *beta_, 
+        THCTensor *gradMean_, THCTensor *gradStd_, int train)
+/*
+ */
+{
+    Encoding_(BatchNorm_Backward)(state, gradoutput_, input_, gradinput_, 
+        gradgamma_, gradbeta_, mean_, invstd_, gamma_, beta_, gradMean_, gradStd_,
+        train);
+	/* C function return number of the outputs */
+	return 0;
+}
+
+
+int Encoding_(sum_square_Forward)(THCTensor *input_, 
+        THCTensor *sum_, THCTensor *square_)
+/*
+ */
+{
+    Encoding_(Sum_Square_Forward)(state, input_, sum_, square_);
+	/* C function return number of the outputs */
+	return 0;
+}
+
+
+int Encoding_(sum_square_Backward)(
+        THCTensor *gradInput, THCTensor *input_, 
+        THCTensor *gradSum_, THCTensor *gradSquare_)
+/*
+ */
+{
+    Encoding_(Sum_Square_Backward)(state, gradInput, input_, gradSum_, 
+                                   gradSquare_);
+	/* C function return number of the outputs */
+	return 0;
+}
 #endif

encoding/src/generic/encoding_generic.h

-/*+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
- * Created by: Hang Zhang
- * ECE Department, Rutgers University
- * Email: zhang.hang@rutgers.edu
- * Copyright (c) 2017
- *
- * This source code is licensed under the MIT-style license found in the
- * LICENSE file in the root directory of this source tree 
- *+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
- */
-#ifndef THC_GENERIC_FILE
-#define THC_GENERIC_FILE "generic/encoding_generic.h"
-#else
-
-int Encoding_(aggregate_forward)(THCudaTensor *E, THCudaTensor *A,
-			THCudaTensor *R);
-#endif