cumsum -> cumop for both cumsum and cumprod

theano/gpuarray/extra_ops.py

 from __future__ import absolute_import, print_function, division
 import os
 from theano import Apply, Op
-from theano.tensor.extra_ops import CumsumOp
+from theano.tensor.extra_ops import CumsumOp, CumprodOp
 from .basic_ops import infer_context_name
 try:
     from pygpu import gpuarray
@@ -12,7 +12,7 @@ from .basic_ops import (as_gpuarray_variable, GpuKernelBase, Kernel, GpuReshape)
 from .opt import register_opt, op_lifter, register_opt2
 
 
-class GpuCumsum(GpuKernelBase, Op):
+class GpuCumOp(GpuKernelBase, Op):
     """
     Parameters
     ----------
@@ -20,10 +20,19 @@ class GpuCumsum(GpuKernelBase, Op):
         Can not be None. If you want the array flattened, do it before.
     """
     SUPPORTED_NDIMS = 3
-    __props__ = ('axis',)
+    __props__ = ('axis', 'mode')
 
-    def __init__(self, axis):
+    def __init__(self, axis, mode='add'):
         self.axis = axis
+        self.mode = mode
+
+    def __eq__(self, other):
+        if type(other) != type(self):
+            return False
+        return self.axis == other.axis and self.mode == other.mode
+
+    def __hash__(self):
+        return hash(self.axis) ^ hash(self.mode)
 
     def c_code_cache_version(self):
         return (3,)
@@ -38,14 +47,14 @@ class GpuCumsum(GpuKernelBase, Op):
         return node.inputs[0].type.context
 
     def make_node(self, x):
-        assert x.type.dtype == 'float32', "Only float32 supported for GpuCumSum"
+        assert x.type.dtype == 'float32', "Only float32 supported for GpuCumOp"
 
         context_name = infer_context_name(x)
 
         x = as_gpuarray_variable(x, context_name)
 
-        if x.ndim > GpuCumsum.SUPPORTED_NDIMS:
-            raise NotImplementedError('Only cumsum on 1D, 2D and\
+        if x.ndim > GpuCumOp.SUPPORTED_NDIMS:
+            raise NotImplementedError('Only cum op on 1D, 2D and\
                                        3D arrays are supported right now!')
 
         if self.axis >= x.ndim or self.axis < -x.ndim:
@@ -56,6 +65,7 @@ class GpuCumsum(GpuKernelBase, Op):
         kernels = []
         # cumadd
         kname = "k_cumadd"
+        op = {'mul': '*', 'add': '+'}[self.mode]
         k_var = "k_cumadd_" + nodename
         dtype_x = node.inputs[0].dtype
         flags = Kernel.get_flags(dtype_x)
@@ -75,7 +85,7 @@ class GpuCumsum(GpuKernelBase, Op):
             int idx_last_input = lastElementIdx*inputStrides_x + dataOffsetY_input;
             int idx_last_output = lastElementIdx*outputStrides_x + dataOffsetY_output;
             int idx_beforelast = beforeLastElementIdx*outputStrides_x + dataOffsetY_output;
-            output[idx_last_output] = input[idx_last_input] + output[idx_beforelast];
+            output[idx_last_output] = input[idx_last_input] %(op)s output[idx_beforelast];
             }
         """ % locals()
         params = [gpuarray.GpuArray, gpuarray.GpuArray, gpuarray.SSIZE,
@@ -86,9 +96,9 @@ class GpuCumsum(GpuKernelBase, Op):
                   ]
         kernels.append(Kernel(code=code, name=kname, params=params,
                               flags=flags, objvar=k_var))
-        # blockCumSum
-        kname = "k_blockCumSum"
-        k_var = "k_blockCumSum_" + nodename
+        # blockCumOp
+        kname = "k_blockCumOp"
+        k_var = "k_blockCumOp_" + nodename
         params = [gpuarray.GpuArray, gpuarray.GpuArray, gpuarray.SIZE,
                   gpuarray.SSIZE, gpuarray.SSIZE, gpuarray.SSIZE,
                   gpuarray.SSIZE, gpuarray.SSIZE, gpuarray.SSIZE,
@@ -96,109 +106,108 @@ class GpuCumsum(GpuKernelBase, Op):
         code = """
         // helper functions
         WITHIN_KERNEL
-        void k_reductionPhase(float* partialCumSum) {
+        void k_reductionPhase(float* partialCumOp) {
             // Traverse down from leaves to root building partial sums at internal nodes in the tree.
             for (unsigned int stride = 1; stride <= blockDim.x; stride *= 2) {
                 local_barrier();
                 unsigned int index = (threadIdx.x + 1) * (stride * 2) - 1;
                 if(index < blockDim.x*2) {
-                    partialCumSum[index] += partialCumSum[index - stride];
+                    partialCumOp[index] %(op)s= partialCumOp[index - stride];
                 }
             }
         }
 
         WITHIN_KERNEL
-        void k_fetchData(float* partialCumSum, float* input, int globalThreadID,
+        void k_fetchData(float* partialCumOp, float* input, int globalThreadID,
                          ga_ssize dataStrides_x, ga_ssize dataStrides_y, ga_ssize dataStrides_z,
                          int offsetY, int offsetZ) {
-            // blockIdx.y and blockIdx.z represents the current independent cumsum
+            // blockIdx.y and blockIdx.z represents the current independent cum op
             int idY = blockIdx.y + offsetY;
             int idZ = blockIdx.z + offsetZ; int offset = idY * dataStrides_y + idZ * dataStrides_z;
             int idx_even = (globalThreadID*2    ) * dataStrides_x + offset;
             int idx_odd  = (globalThreadID*2 + 1) * dataStrides_x + offset;
-            partialCumSum[threadIdx.x*2]     = input[idx_even];
-            partialCumSum[threadIdx.x*2 + 1] = input[idx_odd];
+            partialCumOp[threadIdx.x*2]     = input[idx_even];
+            partialCumOp[threadIdx.x*2 + 1] = input[idx_odd];
         }
 
         WITHIN_KERNEL
-        void k_reversePhase(float* partialCumSum) {
+        void k_reversePhase(float* partialCumOp) {
             // Traverse back up the tree building the scan from the partial sums
             for (unsigned int stride = exp2(ceil(log2((float)blockDim.x))); stride > 0; stride /= 2) {
                 local_barrier();
                 unsigned int index = (threadIdx.x + 1) * (stride * 2) - 1;
                 if(index + stride < blockDim.x*2) {
-                    partialCumSum[index + stride] += partialCumSum[index];
+                    partialCumOp[index + stride] %(op)s= partialCumOp[index];
                 }
             }
         }
 
         WITHIN_KERNEL
-        void k_pushData(float* partialCumSum, float* output, int globalThreadID,
+        void k_pushData(float* partialCumOp, float* output, int globalThreadID,
                         ga_ssize dataStrides_x, ga_ssize dataStrides_y, ga_ssize dataStrides_z,
                         int offsetY, int offsetZ) {
             local_barrier();
-            // blockIdx.y and blockIdx.z represents the current independent cumsum
+            // blockIdx.y and blockIdx.z represents the current independent cum op
             int idY = blockIdx.y + offsetY;
             int idZ = blockIdx.z + offsetZ;
             int offset = idY * dataStrides_y + idZ * dataStrides_z;
             int idx_even = (globalThreadID*2    ) * dataStrides_x + offset;
             int idx_odd  = (globalThreadID*2 + 1) * dataStrides_x + offset;
-            output[idx_even] = partialCumSum[threadIdx.x*2];
-            output[idx_odd]  = partialCumSum[threadIdx.x*2 + 1];
+            output[idx_even] = partialCumOp[threadIdx.x*2];
+            output[idx_odd]  = partialCumOp[threadIdx.x*2 + 1];
         }
 
-        KERNEL void k_blockCumSum(float* input, float* output,
-                                        size_t nbElementsPerCumsum, ga_ssize inputStrides_x,
+        KERNEL void k_blockCumOp(float* input, float* output,
+                                        size_t nbElementsPerCumOp, ga_ssize inputStrides_x,
                                         ga_ssize inputStrides_y,  ga_ssize inputStrides_z,
                                         ga_ssize outputStrides_x, ga_ssize outputStrides_y,
                                         ga_ssize outputStrides_z, int offsetY,
                                         int offsetZ, float* blockSum) {
-            // Regarding blockIdx and threadIdx, 'Cumsum' is always performed along the X axis.
-            // The Y and Z axis of the grid will contain all independent cumsums of the 2D/3D case.
+            // Regarding blockIdx and threadIdx, 'CumOp' is always performed along the X axis.
+            // The Y and Z axis of the grid will contain all independent cumops of the 2D/3D case.
 
             int globalThreadID = blockIdx.x * blockDim.x + threadIdx.x;
 
             // Check if current thread has data to process.
-            if (globalThreadID >= ceil(nbElementsPerCumsum/2.0)) {
+            if (globalThreadID >= (nbElementsPerCumOp+1)/2) {
                 return;
             }
 
-            extern __shared__ float partialCumSum[];
+            extern __shared__ float partialCumOp[];
 
             // Load data in shared memory
-            k_fetchData(partialCumSum, input, globalThreadID, inputStrides_x, inputStrides_y, inputStrides_z, offsetY, offsetZ);
+            k_fetchData(partialCumOp, input, globalThreadID, inputStrides_x, inputStrides_y, inputStrides_z, offsetY, offsetZ);
 
-            // Use a dichotomy approach to compute the cumsum (i.e. balanced binary tree).
+            // Use a dichotomy approach to compute the cum op (i.e. balanced binary tree).
             // The tree is sweeped from the leaves to the root and from the root to the leaves.
             // Similar to http://www.umiacs.umd.edu/~ramani/cmsc828e_gpusci/ScanTalk.pdf
-            k_reductionPhase(partialCumSum);
-            k_reversePhase(partialCumSum);
+            k_reductionPhase(partialCumOp);
+            k_reversePhase(partialCumOp);
 
             // Write the final output to global memory
-            k_pushData(partialCumSum, output, globalThreadID, outputStrides_x, outputStrides_y, outputStrides_z, offsetY, offsetZ);
+            k_pushData(partialCumOp, output, globalThreadID, outputStrides_x, outputStrides_y, outputStrides_z, offsetY, offsetZ);
 
             if (blockSum != NULL){
                 if (threadIdx.x == blockDim.x - 1) {
-                    blockSum[blockIdx.x*(gridDim.y*gridDim.z) + (blockIdx.y + offsetY)*gridDim.z + blockIdx.z + offsetZ] = partialCumSum[threadIdx.x*2 + 1];
+                    blockSum[blockIdx.x*(gridDim.y*gridDim.z) + (blockIdx.y + offsetY)*gridDim.z + blockIdx.z + offsetZ] = partialCumOp[threadIdx.x*2 + 1];
                 }
             }
         }
-        """
+        """ % locals()
         kernels.append(Kernel(code=code, name=kname, params=params,
                               flags=flags, objvar=k_var))
-        # k_finalCumSum
-        kname = "k_finalCumSum"
-        k_var = "k_finalCumSum_" + nodename
+        # k_finalCumOp
+        kname = "k_finalCumOp"
+        k_var = "k_finalCumOp_" + nodename
         code = """
-        KERNEL void k_finalCumSum(float* output, float* blockSum, size_t nbElementsPerCumsum,
+        KERNEL void k_finalCumOp(float* output, float* blockSum, size_t nbElementsPerCumOp,
                                                ga_ssize dataStrides_x,  ga_ssize dataStrides_y,  ga_ssize dataStrides_z,
                                                int offsetY, int offsetZ) {
             int globalThreadID = (blockIdx.x + 1) * blockDim.x + threadIdx.x;
 
             // Check if current has data to process.
-            if (globalThreadID >= ceil(nbElementsPerCumsum/2.0)) {
+            if (globalThreadID >= (nbElementsPerCumOp+1)/2)
                 return;
-            }
 
             int idY = blockIdx.y + offsetY;
             int idZ = blockIdx.z + offsetZ;
@@ -208,10 +217,10 @@ class GpuCumsum(GpuKernelBase, Op):
             int offset = idY * dataStrides_y + idZ * dataStrides_z;
             int idx_even = (globalThreadID*2    ) * dataStrides_x + offset;
             int idx_odd  = (globalThreadID*2 + 1) * dataStrides_x + offset;
-            output[idx_even] += currentBlockSum;
-            output[idx_odd] += currentBlockSum;
+            output[idx_even] %(op)s= currentBlockSum;
+            output[idx_odd] %(op)s= currentBlockSum;
         }
-        """
+        """ % locals()
         params = [gpuarray.GpuArray, gpuarray.GpuArray, gpuarray.SIZE,
                   gpuarray.SSIZE, gpuarray.SSIZE, gpuarray.SSIZE,
                   'int32', 'int32', ]
@@ -263,7 +272,7 @@ class GpuCumsum(GpuKernelBase, Op):
                     PyErr_SetString(PyExc_RuntimeError, "Could not fetch max_grid_size2");
                     %(fail)s;
                 }
-                if (cumSum_%(nodename)s(%(x)s, %(z)s, axis, max_threads_dim0, max_grid_size1, max_grid_size2) == -1){
+                if (cumOp_%(nodename)s(%(x)s, %(z)s, axis, max_threads_dim0, max_grid_size1, max_grid_size2) == -1){
                     %(fail)s;
                 }
             }
@@ -274,7 +283,7 @@ class GpuCumsum(GpuKernelBase, Op):
     def c_support_code_struct(self, node, nodename):
         code = """
 
-        int cumSum_%(nodename)s(PyGpuArrayObject* input, PyGpuArrayObject* output, int axis, size_t maxThreads, size_t maxGridY, size_t maxGridZ) {
+        int cumOp_%(nodename)s(PyGpuArrayObject* input, PyGpuArrayObject* output, int axis, size_t maxThreads, size_t maxGridY, size_t maxGridZ) {
             size_t shape[3] = { 1, 1, 1 };
             ssize_t inputStrides_x;
             ssize_t inputStrides_y;
@@ -316,14 +325,14 @@ class GpuCumsum(GpuKernelBase, Op):
                 int err = pygpu_move(output, input);
                 return err;
             }
-            // Perform cumsum on array of even size.
-            size_t nbElementsPerCumsum = shape[axis] - (shape[axis] %% 2);
+            // Perform cum op on array of even size.
+            size_t nbElementsPerCumOp = shape[axis] - (shape[axis] %% 2);
             // Determine how many elements can be processed in one block.
-            size_t dimBlockX = ceil((nbElementsPerCumsum > 2*maxThreads ? 2*maxThreads : nbElementsPerCumsum) / 2.0);
+            size_t dimBlockX = ((nbElementsPerCumOp > 2*maxThreads ? 2*maxThreads : nbElementsPerCumOp)+1)/2;
             // Determine how many blocks are needed in total.
-            size_t dimGridX = ceil(nbElementsPerCumsum / (2.0*dimBlockX));  // Nb. of blocks needed per cumsum.
-            size_t dimGridY;  // Nb. of independent cumsums (width).
-            size_t dimGridZ;  // Nb. of independent cumsums (height).
+            size_t dimGridX = (nbElementsPerCumOp+2*dimBlockX-1) / (2*dimBlockX);  // Nb. of blocks needed per cum op.
+            size_t dimGridY;  // Nb. of independent cum ops (width).
+            size_t dimGridZ;  // Nb. of independent cum ops (height).
             ssize_t tmp;
             switch (axis)
             {
@@ -365,18 +374,18 @@ class GpuCumsum(GpuKernelBase, Op):
             if (deviceBlockSum == NULL){
                 return -1;
             }
-            // Perform `maxGridY`*`maxGridZ` cumsums in parallel.
+            // Perform `maxGridY`*`maxGridZ` cum ops in parallel.
             for (size_t offsetY = 0; offsetY < dimGridY; offsetY += maxGridY){
                 size_t localDimGridY = (dimGridY - offsetY < maxGridY) ? (dimGridY - offsetY) : (maxGridY);
 
                 for (size_t offsetZ = 0; offsetZ < dimGridZ; offsetZ += maxGridZ){
                     size_t localDimGridZ = (dimGridZ - offsetZ < maxGridZ) ? (dimGridZ - offsetZ) : (maxGridZ);
                     size_t dimGrid[3] = {dimGridX, localDimGridY, localDimGridZ};
-                    size_t dimBlock[3] = {dimBlockX, 1, 1};  // One cumsum per block.
+                    size_t dimBlock[3] = {dimBlockX, 1, 1};  // One cum op per block.
                     size_t sharedBytes = (2*dimBlockX) * sizeof(float);
                     void* kernel_params[] = {(void*) input->ga.data,
                                              (void*) output->ga.data,
-                                             (void*) &nbElementsPerCumsum,
+                                             (void*) &nbElementsPerCumOp,
                                              (void*) &inputStrides_x,
                                              (void*) &inputStrides_y,
                                              (void*) &inputStrides_z,
@@ -387,39 +396,39 @@ class GpuCumsum(GpuKernelBase, Op):
                                              (void*) &offsetZ,
                                              (void*) deviceBlockSum->ga.data
                         };
-                    int err = GpuKernel_call(&k_blockCumSum_%(nodename)s, 3, dimBlock, dimGrid, sharedBytes, kernel_params);
+                    int err = GpuKernel_call(&k_blockCumOp_%(nodename)s, 3, dimBlock, dimGrid, sharedBytes, kernel_params);
                     if (err != GA_NO_ERROR){
-                        PyErr_SetString(PyExc_RuntimeError, "blockCumSum call failed");
+                        PyErr_SetString(PyExc_RuntimeError, "blockCumOp call failed");
                         return -1;
                     }
 
                     if (dimGridX > 1) {
-                        // Do a cumsum over the blockSum (recursive).
-                        if (cumSum_%(nodename)s(deviceBlockSum, deviceBlockSum, 0, maxThreads, maxGridY, maxGridZ) == -1){
+                        // Do a cum op over the blockSum (recursive).
+                        if (cumOp_%(nodename)s(deviceBlockSum, deviceBlockSum, 0, maxThreads, maxGridY, maxGridZ) == -1){
                             Py_DECREF(deviceBlockSum);
                             return -1;
                         }
                         // Since there are more than one block (i.e. `dimGridX > 1`)
-                        //  report partial cumsums of previous blocks to subsequents ones.
+                        //  report partial cum ops of previous blocks to subsequents ones.
                         size_t dimGrid[3] = {dimGridX, localDimGridY, localDimGridZ};
                         size_t dimBlock[3] = {dimBlockX, 1, 1};
                         void* kernel_params[] = {(void*) output->ga.data,
                                                  (void*) deviceBlockSum->ga.data,
-                                                 (void*) &nbElementsPerCumsum,
+                                                 (void*) &nbElementsPerCumOp,
                                                  (void*) &outputStrides_x,
                                                  (void*) &outputStrides_y,
                                                  (void*) &outputStrides_z,
                                                  (void*) &offsetY,
                                                  (void*) &offsetZ
                             };
-                        int err = GpuKernel_call(&k_finalCumSum_%(nodename)s, 3, dimBlock, dimGrid, sharedBytes, kernel_params);
+                        int err = GpuKernel_call(&k_finalCumOp_%(nodename)s, 3, dimBlock, dimGrid, sharedBytes, kernel_params);
                         if (err != GA_NO_ERROR){
-                            PyErr_SetString(PyExc_RuntimeError, "finalCumSum call failed");
+                            PyErr_SetString(PyExc_RuntimeError, "finalCumOp call failed");
                             return -1;
                         }
                     }
                     // If shape[axis] is odd, the last element is compute manually
-                    if (shape[axis] != nbElementsPerCumsum){
+                    if (shape[axis] != nbElementsPerCumOp){
                         size_t dimGrid[3] = {1, localDimGridY, localDimGridZ};
                         size_t dimBlock[3] = {1, 1, 1};
                         size_t tmp0 = shape[axis]-2;
@@ -450,26 +459,33 @@ class GpuCumsum(GpuKernelBase, Op):
             return 0;
         }
         """ % locals()
-        return super(GpuCumsum, self).c_support_code_struct(node, nodename) + code
+        return super(GpuCumOp, self).c_support_code_struct(node, nodename) + code
 
 
 @register_opt('fast_compile')
-@op_lifter([CumsumOp])
-@register_opt2([CumsumOp], 'fast_compile')
-def local_gpua_cumsumop(op, ctx_name, inputs, outputs):
-    if inputs[0].dtype == 'float32':
-        axis = op.axis
-        x = inputs[0]
-        if axis is not None and x.ndim > GpuCumsum.SUPPORTED_NDIMS:
-            return None
-
-        x = as_gpuarray_variable(x, ctx_name)
-
-        if axis is None and x.ndim > 1:
-            x = GpuReshape(1)(x, (-1,))
-
-        # ``gpu_cumsum`` assume array has been flattened if needed.
-        if axis is None:
-            axis = 0
-
-        return GpuCumsum(axis)(x)
+@op_lifter([CumsumOp, CumprodOp])
+@register_opt2([CumsumOp, CumprodOp], 'fast_compile')
+def local_gpua_cumop(op, ctx_name, inputs, outputs):
+    if inputs[0].dtype != 'float32':
+        return False
+    if isinstance(op, CumsumOp):
+        mode = 'add'
+    elif isinstance(op, CumprodOp):
+        mode = 'mul'
+    else:
+        return False
+    axis = op.axis
+    x = inputs[0]
+    if axis is not None and x.ndim > GpuCumOp.SUPPORTED_NDIMS:
+        return False
+
+    x = as_gpuarray_variable(x, ctx_name)
+
+    if axis is None and x.ndim > 1:
+        x = GpuReshape(1)(x, (-1,))
+
+    # ``gpu_cumop`` assume array has been flattened if needed.
+    if axis is None:
+        axis = 0
+
+    return GpuCumOp(axis, mode)(x)