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提交 35e15192 authored 作者: abergeron's avatar abergeron
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Merge pull request #2171 from MarcCote/cumsum_3D

Support GpuCumsum on 3D array.
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theano/sandbox/cuda/extra_ops.py
浏览文件 @ 35e15192
import theano
import copy
from theano import Op, Apply
from theano import Op
from theano.gof import local_optimizer
from theano.sandbox.cuda import cuda_available, GpuOp
......@@ -14,8 +14,8 @@ if cuda_available:
class GpuCumsum(CumsumOp, GpuOp):
SUPPORTED_NDIMS = 2
__props__ = ('axis', 'max_threads_dim0', 'max_grid_size1')
SUPPORTED_NDIMS = 3
__props__ = ('axis', 'max_threads_dim0', 'max_grid_size1', 'max_grid_size2')
def __init__(self, axis):
"""
......@@ -24,6 +24,7 @@ class GpuCumsum(CumsumOp, GpuOp):
self.axis = axis
self.max_threads_dim0 = None
self.max_grid_size1 = None
self.max_grid_size2 = None
def perform(self, node, inp, out):
return Op.perform(self, node, inp, out)
......@@ -34,7 +35,7 @@ class GpuCumsum(CumsumOp, GpuOp):
raise TypeError('x must be a CudaNdarrayType', x)
if x.ndim > GpuCumsum.SUPPORTED_NDIMS:
raise NotImplementedError('Only cumsum on 1D and 2D array are supported right now!')
raise NotImplementedError('Only cumsum on 1D, 2D and 3D array are supported right now!')
if self.axis >= x.ndim:
raise ValueError('axis(={1}) out of bounds'.format(self.axis))
......@@ -44,7 +45,7 @@ class GpuCumsum(CumsumOp, GpuOp):
def make_thunk(self, node, storage_map, compute_map, no_recycling):
node_ = copy.copy(node)
assert node.op is node_.op
if node_.op.max_threads_dim0 is None or node_.op.max_grid_size1 is None:
if node_.op.max_threads_dim0 is None or node_.op.max_grid_size1 is None or node_.op.max_grid_size2 is None:
cuda = theano.sandbox.cuda
device_id = cuda.use.device_number
if device_id is None:
......@@ -59,6 +60,7 @@ class GpuCumsum(CumsumOp, GpuOp):
prop = cuda_ndarray.device_properties(device_id)
node_.op.max_threads_dim0 = prop['maxThreadsDim0']
node_.op.max_grid_size1 = prop['maxGridSize1']
node_.op.max_grid_size2 = prop['maxGridSize2']
return super(GpuCumsum, node_.op).make_thunk(node_, storage_map,
compute_map, no_recycling)
......@@ -67,7 +69,7 @@ class GpuCumsum(CumsumOp, GpuOp):
return "%s{%s}" % (self.__class__.__name__, self.axis)
def c_code_cache_version(self):
return (5,)
return (7,)
def c_support_code_apply(self, node, nodename):
return """
......@@ -96,28 +98,37 @@ class GpuCumsum(CumsumOp, GpuOp):
}
__device__
void k_fetchData_%(nodename)s(float* partialCumSum, float* input, int globalThreadID, dim3 dataStrides, int dataOffset) {
// blockIdx.y represents the # of the current independent cumsum
int idx_even = (globalThreadID*2 ) * dataStrides.x + (blockIdx.y + dataOffset) * dataStrides.y;
int idx_odd = (globalThreadID*2 + 1) * dataStrides.x + (blockIdx.y + dataOffset) * dataStrides.y;
void k_fetchData_%(nodename)s(float* partialCumSum, float* input, int globalThreadID, dim3 dataStrides, int offsetY, int offsetZ) {
// blockIdx.y and blockIdx.z represents the current independent cumsum
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];
}
__device__
void k_pushData_%(nodename)s(float* partialCumSum, float* output, int globalThreadID, dim3 dataStrides, int dataOffset) {
void k_pushData_%(nodename)s(float* partialCumSum, float* output, int globalThreadID, dim3 dataStrides, int offsetY, int offsetZ) {
__syncthreads();
// blockIdx.y represents the # of the current independent cumsum
int idx_even = (globalThreadID*2 ) * dataStrides.x + (blockIdx.y + dataOffset) * dataStrides.y;
int idx_odd = (globalThreadID*2 + 1) * dataStrides.x + (blockIdx.y + dataOffset) * dataStrides.y;
// blockIdx.y and blockIdx.z represents the current independent cumsum
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];
}
__global__
void k_cumadd_%(nodename)s(float* input, float* output, dim3 inputStrides, dim3 outputStrides, int dataOffset, int beforeLastElementIdx, int lastElementIdx) {
int dataOffsetY_input = (blockIdx.y + dataOffset) * inputStrides.y;
int dataOffsetY_output = (blockIdx.y + dataOffset) * outputStrides.y;
void k_cumadd_%(nodename)s(float* input, float* output, dim3 inputStrides, dim3 outputStrides, int offsetY, int offsetZ, int beforeLastElementIdx, int lastElementIdx) {
int idY = blockIdx.y + offsetY;
int idZ = blockIdx.z + offsetZ;
int dataOffsetY_input = idY * inputStrides.y + idZ * inputStrides.z;
int dataOffsetY_output = idY * outputStrides.y + idZ * outputStrides.z;
int idx_last_input = lastElementIdx*inputStrides.x + dataOffsetY_input;
int idx_last_output = lastElementIdx*outputStrides.x + dataOffsetY_output;
......@@ -127,39 +138,42 @@ class GpuCumsum(CumsumOp, GpuOp):
}
__global__
void k_finalCumSum_%(nodename)s(float* output, float* blockSum, int numElements, dim3 dataStrides, int dataOffset) {
void k_finalCumSum_%(nodename)s(float* output, float* blockSum, int nbElementsPerCumsum, dim3 dataStrides, int offsetY, int offsetZ) {
int globalThreadID = (blockIdx.x + 1) * blockDim.x + threadIdx.x;
// Check if current has data to process.
if (globalThreadID >= ceil(numElements/2.0)) {
if (globalThreadID >= ceil(nbElementsPerCumsum/2.0)) {
return;
}
const float currentBlockSum = blockSum[blockIdx.x*gridDim.y + blockIdx.y + dataOffset];
int idY = blockIdx.y + offsetY;
int idZ = blockIdx.z + offsetZ;
const float currentBlockSum = blockSum[blockIdx.x*(gridDim.y*gridDim.z) + idY*gridDim.z + idZ];
int dataOffsetY = (blockIdx.y + dataOffset) * (int)dataStrides.y;
int idx_even = (globalThreadID*2 ) * dataStrides.x + dataOffsetY;
int idx_odd = (globalThreadID*2 + 1) * dataStrides.x + dataOffsetY;
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;
}
__global__
void k_blockCumSum_%(nodename)s(float* input, float* output, int numElements, dim3 inputStrides, dim3 outputStrides, int dataOffset, float* blockSum) {
void k_blockCumSum_%(nodename)s(float* input, float* output, int nbElementsPerCumsum, dim3 inputStrides, dim3 outputStrides, int offsetY, int offsetZ, float* blockSum) {
// Regarding blockIdx and threadIdx, 'Cumsum' is always performed along the X axis.
// The Y axis will contain all the independent cumsums of the 2D case.
// The Y and Z axis of the grid will contain all independent cumsums of the 2D/3D case.
int globalThreadID = blockIdx.x * blockDim.x + threadIdx.x;
// Check if current thread has data to process.
if (globalThreadID >= ceil(numElements/2.0)) {
if (globalThreadID >= ceil(nbElementsPerCumsum/2.0)) {
return;
}
extern __shared__ float partialCumSum[];
// Load data in shared memory
k_fetchData_%(nodename)s(partialCumSum, input, globalThreadID, inputStrides, dataOffset);
k_fetchData_%(nodename)s(partialCumSum, input, globalThreadID, inputStrides, offsetY, offsetZ);
// Use a dichotomy approach to compute the cumsum (i.e. balanced binary tree).
// The tree is sweeped from the leaves to the root and from the root to the leaves.
......@@ -168,19 +182,19 @@ class GpuCumsum(CumsumOp, GpuOp):
k_reversePhase_%(nodename)s(partialCumSum);
// Write the final output to global memory
k_pushData_%(nodename)s(partialCumSum, output, globalThreadID, outputStrides, dataOffset);
k_pushData_%(nodename)s(partialCumSum, output, globalThreadID, outputStrides, offsetY, offsetZ);
if (blockSum != NULL){
if (threadIdx.x == blockDim.x - 1) {
blockSum[blockIdx.x*gridDim.y + blockIdx.y + dataOffset] = partialCumSum[threadIdx.x*2 + 1];
blockSum[blockIdx.x*(gridDim.y*gridDim.z) + (blockIdx.y + offsetY)*gridDim.z + blockIdx.z + offsetZ] = partialCumSum[threadIdx.x*2 + 1];
}
}
}
int cumSum_%(nodename)s(CudaNdarray* input, CudaNdarray* output, int maxThreads, int axis, int maxGridY) {
int shape[2] = { 1, 1 };
dim3 inputStrides(0,0,0);
dim3 outputStrides(0,0,0);
int cumSum_%(nodename)s(CudaNdarray* input, CudaNdarray* output, int axis, int maxThreads, int maxGridY, int maxGridZ) {
int shape[3] = { 1, 1, 1 };
dim3 inputStrides(0, 0, 0);
dim3 outputStrides(0, 0, 0);
switch (CudaNdarray_NDIM(input))
{
......@@ -197,8 +211,18 @@ class GpuCumsum(CumsumOp, GpuOp):
outputStrides.x = CudaNdarray_HOST_STRIDES(output)[0];
outputStrides.y = CudaNdarray_HOST_STRIDES(output)[1];
break;
case 3:
shape[0] = CudaNdarray_HOST_DIMS(input)[0];
shape[1] = CudaNdarray_HOST_DIMS(input)[1];
shape[2] = CudaNdarray_HOST_DIMS(input)[2];
inputStrides.x = CudaNdarray_HOST_STRIDES(input)[0];
inputStrides.y = CudaNdarray_HOST_STRIDES(input)[1];
inputStrides.z = CudaNdarray_HOST_STRIDES(input)[2];
outputStrides.x = CudaNdarray_HOST_STRIDES(output)[0];
outputStrides.y = CudaNdarray_HOST_STRIDES(output)[1];
outputStrides.z = CudaNdarray_HOST_STRIDES(output)[2];
break;
default:
printf("Only 1D and 2D cumsum is implemented yet.\\n");
return -1;
}
......@@ -207,75 +231,115 @@ class GpuCumsum(CumsumOp, GpuOp):
return 0;
}
if (axis == 1) {
int tmp = inputStrides.x;
// Perform cumsum on array of even size.
int nbElementsPerCumsum = shape[axis] - (shape[axis] %% 2);
// Determine how many elements can be processed in one block.
int dimBlockX = ceil( min(nbElementsPerCumsum, 2*maxThreads) / 2.0);
// Determine how many blocks are needed in total.
int dimGridX = ceil(nbElementsPerCumsum / (2.0*dimBlockX)); // Nb. of blocks needed per cumsum.
int dimGridY; // Nb. of independent cumsums (width).
int dimGridZ; // Nb. of independent cumsums (height).
int tmp;
switch (axis)
{
case 0:
dimGridY = shape[1];
dimGridZ = shape[2];
break;
case 1:
dimGridY = shape[0];
dimGridZ = shape[2];
tmp = inputStrides.x;
inputStrides.x = inputStrides.y;
inputStrides.y = tmp;
tmp = outputStrides.x;
outputStrides.x = outputStrides.y;
outputStrides.y = tmp;
}
break;
case 2:
dimGridY = shape[1];
dimGridZ = shape[0];
int numElements = shape[axis] - (shape[axis] %% 2);
int blockSize = ceil( min(numElements, 2*maxThreads) / 2.0);
int dimGridX = ceil(numElements / (2.0*blockSize)); // Nb. of elements to perform cumsum on.
int dimGridY = shape[1-axis]; // Nb. of independent cumsums.
const int shapeBlockSum[2] = { dimGridX, dimGridY };
tmp = inputStrides.x;
inputStrides.x = inputStrides.z;
inputStrides.z = tmp;
tmp = outputStrides.x;
outputStrides.x = outputStrides.z;
outputStrides.z = tmp;
break;
default:
return -1;
}
const int shapeBlockSum[2] = { dimGridX, dimGridY*dimGridZ };
CudaNdarray* deviceBlockSum = (CudaNdarray*) CudaNdarray_NewDims(2, shapeBlockSum);
for (int dataOffset = 0; dataOffset < dimGridY; dataOffset += maxGridY){
int localDimGridY = min(dimGridY - dataOffset, maxGridY);
dim3 dimBlock(blockSize, 1, 1);
dim3 dimGrid(dimGridX, localDimGridY, 1);
int sharedBytes = (2*blockSize) * sizeof(float);
k_blockCumSum_%(nodename)s<<<dimGrid, dimBlock, sharedBytes>>>
(
CudaNdarray_DEV_DATA(input),
CudaNdarray_DEV_DATA(output),
numElements,
inputStrides,
outputStrides,
dataOffset,
CudaNdarray_DEV_DATA(deviceBlockSum)
);
if (dimGridX > 1) {
// Do a cumsum over the blockSum (recursive).
if (cumSum_%(nodename)s(deviceBlockSum, deviceBlockSum, maxThreads, 0, maxGridY) == -1){
return -1;
}
// Perform `maxGridY`*`maxGridZ` cumsums in parallel.
for (int offsetY = 0; offsetY < dimGridY; offsetY += maxGridY){
int localDimGridY = min(dimGridY - offsetY, maxGridY);
// Since there are more than one block (i.e. `dimGridX > 1`)
// report partial cumsums of previous blocks to subsequents ones.
dim3 dimGrid(dimGridX, dimGridY, 1);
dim3 dimBlock(blockSize, 1, 1);
k_finalCumSum_%(nodename)s<<<dimGrid, dimBlock>>>
(
CudaNdarray_DEV_DATA(output),
CudaNdarray_DEV_DATA(deviceBlockSum),
numElements,
outputStrides,
dataOffset
);
}
for (int offsetZ = 0; offsetZ < dimGridZ; offsetZ += maxGridZ){
int localDimGridZ = min(dimGridZ - offsetZ, maxGridZ);
dim3 dimGrid(dimGridX, localDimGridY, localDimGridZ);
dim3 dimBlock(dimBlockX, 1, 1); // One cumsum per block.
int sharedBytes = (2*dimBlockX) * sizeof(float);
// If shape[axis] is odd, the last element is compute manually
if (shape[axis] != numElements){
dim3 dimGrid(1, localDimGridY, 1);
dim3 dimBlock(1, 1, 1);
k_cumadd_%(nodename)s<<<dimGrid, dimBlock>>>
k_blockCumSum_%(nodename)s<<<dimGrid, dimBlock, sharedBytes>>>
(
CudaNdarray_DEV_DATA(input),
CudaNdarray_DEV_DATA(output),
nbElementsPerCumsum,
inputStrides,
outputStrides,
dataOffset,
shape[axis]-2,
shape[axis]-1
offsetY,
offsetZ,
CudaNdarray_DEV_DATA(deviceBlockSum)
);
if (dimGridX > 1) {
// Do a cumsum over the blockSum (recursive).
if (cumSum_%(nodename)s(deviceBlockSum, deviceBlockSum, 0, maxThreads, maxGridY, maxGridZ) == -1){
return -1;
}
// Since there are more than one block (i.e. `dimGridX > 1`)
// report partial cumsums of previous blocks to subsequents ones.
dim3 dimGrid(dimGridX, localDimGridY, localDimGridZ);
dim3 dimBlock(dimBlockX, 1, 1);
k_finalCumSum_%(nodename)s<<<dimGrid, dimBlock>>>
(
CudaNdarray_DEV_DATA(output),
CudaNdarray_DEV_DATA(deviceBlockSum),
nbElementsPerCumsum,
outputStrides,
offsetY,
offsetZ
);
}
// If shape[axis] is odd, the last element is compute manually
if (shape[axis] != nbElementsPerCumsum){
dim3 dimGrid(1, localDimGridY, localDimGridZ);
dim3 dimBlock(1, 1, 1);
k_cumadd_%(nodename)s<<<dimGrid, dimBlock>>>
(
CudaNdarray_DEV_DATA(input),
CudaNdarray_DEV_DATA(output),
inputStrides,
outputStrides,
offsetY,
offsetZ,
shape[axis]-2,
shape[axis]-1
);
}
}
}
......@@ -293,7 +357,8 @@ class GpuCumsum(CumsumOp, GpuOp):
max_threads_dim0 = self.max_threads_dim0
max_grid_size1 = self.max_grid_size1
if max_threads_dim0 is None or max_grid_size1 is None:
max_grid_size2 = self.max_grid_size2
if max_threads_dim0 is None or max_grid_size1 is None or max_grid_size2 is None:
raise NotImplementedError("GpuCumsum.c_code should not be called "
"directly. It should be called by "
"make_thunk() that add some information "
......@@ -322,7 +387,7 @@ class GpuCumsum(CumsumOp, GpuOp):
}
{ // Namespace for kernel calls //
if (cumSum_%(nodename)s(%(x)s, %(z)s, %(max_threads_dim0)s, %(axis)s, %(max_grid_size1)s) == -1){
if (cumSum_%(nodename)s(%(x)s, %(z)s, %(axis)s, %(max_threads_dim0)s, %(max_grid_size1)s, %(max_grid_size2)s) == -1){
%(fail)s;
}
......
theano/sandbox/cuda/tests/test_extra_ops.py
浏览文件 @ 35e15192
......@@ -16,9 +16,8 @@ else:
from theano import tensor as T
import numpy as np
import theano
from theano import config
from theano.tensor.extra_ops import cumsum, CumsumOp
import itertools
class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
mode = mode_with_gpu
......@@ -45,68 +44,63 @@ class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
def test_Strides1D(self):
x = T.fvector('x')
# Stepped strides
f = theano.function([x], cumsum(x[::2]), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
a = np.random.randint(10, size=(42,)).astype("float32")
assert np.allclose(np.cumsum(a[::2]), f(a))
for axis in [0, None]:
a = np.random.random((42,)).astype("float32")
cumsum_function = theano.function([x], cumsum(x, axis=axis), mode=self.mode)
# Alternative stepped strides
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
a = np.random.randint(10, size=(42,)).astype("float32")
assert np.allclose(np.cumsum(a[::2]), f(a[::2]))
slicings = [slice(None, None, None), # Normal strides
slice(None, None, 2), # Stepped strides
slice(None, None, -1), # Negative strides
]
# Negative strides
f = theano.function([x], cumsum(x[::-1]), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
a = np.random.randint(10, size=(42,)).astype("float32")
assert np.allclose(np.cumsum(a[::-1]), f(a))
# Cartesian product of all slicings to test.
for slicing in itertools.product(slicings, repeat=x.ndim):
f = theano.function([x], cumsum(x[slicing], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[slicing], axis=axis), f(a))
assert np.allclose(np.cumsum(a[slicing], axis=axis), cumsum_function(a[slicing]))
def test_Strides2D(self):
x = T.fmatrix('x')
for shape_axis, axis in zip([0, 1, 0], [0, 1, None]):
for axis in [0, 1, None]:
a = np.random.random((42, 30)).astype("float32")
cumsum_function = theano.function([x], cumsum(x, axis=axis), mode=self.mode)
slicings = [slice(None, None, None), # Normal strides
slice(None, None, 2), # Stepped strides
slice(None, None, -1), # Negative strides
]
# Cartesian product of all slicings to test.
for slicing in itertools.product(slicings, repeat=x.ndim):
f = theano.function([x], cumsum(x[slicing], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[slicing], axis=axis), f(a))
assert np.allclose(np.cumsum(a[slicing], axis=axis), cumsum_function(a[slicing]))
def test_Strides3D(self):
x = T.ftensor3('x')
# Stepped strides along axis=0
f = theano.function([x], cumsum(x[::2], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[::2], axis=axis), f(a))
# Stepped strides along axis=1
f = theano.function([x], cumsum(x[:, ::2], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::2], axis=axis), f(a))
# Alternative stepped strides along axis=0
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[::2]), f(a[::2]))
for axis in [0, 1, 2, None]:
a = np.random.random((42, 30, 25)).astype("float32")
cumsum_function = theano.function([x], cumsum(x, axis=axis), mode=self.mode)
# Alternative stepped strides along axis=1
f = theano.function([x], cumsum(x), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::2]), f(a[:, ::2]))
slicings = [slice(None, None, None), # Normal strides
slice(None, None, 2), # Stepped strides
slice(None, None, -1), # Negative strides
]
# Negative strides along axis=0
f = theano.function([x], cumsum(x[::-1], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[::-1], axis=axis), f(a))
# Cartesian product of all slicings to test.
for slicing in itertools.product(slicings, repeat=x.ndim):
f = theano.function([x], cumsum(x[slicing], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[slicing], axis=axis), f(a))
assert np.allclose(np.cumsum(a[slicing], axis=axis), cumsum_function(a[slicing]))
# Negative strides along axis=1
f = theano.function([x], cumsum(x[:, ::-1], axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
assert np.allclose(np.cumsum(a[:, ::-1], axis=axis), f(a))
def test_GpuCumsum1D(self):
block_max_size = self.max_threads_dim0 * 2
......@@ -163,14 +157,63 @@ class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
assert np.allclose(np.cumsum(a, axis=axis), f(a))
# Use recursive cumsum
a_shape = [5, 3]
a_shape = [3, 3]
a_shape[shape_axis] = block_max_size*(block_max_size+1)+2
a = np.ones(a_shape, dtype="float32")
a = np.random.random(a_shape).astype("float32")
a = np.sign(a-0.5).astype("float32") # Avoid floating point error
assert np.allclose(np.cumsum(a, axis=axis), f(a))
def test_GpuCumsum3D(self):
# Should not use the GPU version.
block_max_size = self.max_threads_dim0 * 2
x = T.ftensor3('x')
for shape_axis, axis in zip([0, 1, 2, 0], [0, 1, 2, None]):
f = theano.function([x], cumsum(x, axis=axis), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, GpuCumsum)]
# Extensive testing for the first 1025 sizes
a_shape = [5, 5, 5]
a_shape[shape_axis] = 1025
a = np.random.rand(*a_shape).astype("float32")
slices = [slice(None), slice(None), slice(None)]
for i in xrange(a.shape[shape_axis]):
slices[shape_axis] = slice(i)
fa = f(a[slices])
npa = np.cumsum(a[slices], axis=axis)
assert np.allclose(npa, fa)
# Use multiple GPU threadblocks (along accumulation axis)
a_shape = [2, 2, 2]
a_shape[shape_axis] = block_max_size+2
a = np.random.random(a_shape).astype("float32")
assert np.allclose(np.cumsum(a, axis=axis), f(a))
# Use multiple GPU gridblocks (not along accumulation axis)
a_shape = [5, 5, 5]
a_shape[(shape_axis+1) % 3] = self.max_grid_size1+1
a = np.random.random(a_shape).astype("float32")
if axis is None:
a = np.sign(a-0.5).astype("float32") # Avoid floating point error
assert np.allclose(np.cumsum(a, axis=axis), f(a))
a_shape = [5, 5, 5]
a_shape[(shape_axis+2) % 3] = self.max_grid_size1+1
a = np.random.random(a_shape).astype("float32")
if axis is None:
a = np.sign(a-0.5).astype("float32") # Avoid floating point error
assert np.allclose(np.cumsum(a, axis=axis), f(a))
# Use recursive cumsum (along accumulation axis)
a_shape = [3, 3, 3]
a_shape[shape_axis] = block_max_size*(block_max_size+1)+2
a = np.random.random(a_shape).astype("float32")
a = np.sign(a-0.5).astype("float32") # Avoid floating point error
assert np.allclose(np.cumsum(a, axis=axis), f(a))
def test_GpuCumsum4D(self):
# Should not use the GPU version.
x = T.ftensor4('x')
f = theano.function([x], cumsum(x, axis=1), mode=self.mode)
assert [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, CumsumOp)]
theano/tensor/tests/test_extra_ops.py
浏览文件 @ 35e15192
......@@ -62,7 +62,7 @@ class TestCumsumOp(utt.InferShapeTester):
utt.verify_grad(self.op, [a]) # Test axis=None
for axis in range(len(a.shape)):
utt.verify_grad(self.op_class(axis=axis), [a])
utt.verify_grad(self.op_class(axis=axis), [a], eps=4e-4)
class TestCumprodOp(utt.InferShapeTester):
......@@ -493,10 +493,10 @@ class TestFillDiagonalOffset(utt.InferShapeTester):
# We can't use numpy.fill_diagonal as it is bugged.
assert numpy.allclose(numpy.diag(out, test_offset), val)
if test_offset >= 0:
assert (out == val).sum() == min( min(a.shape),
assert (out == val).sum() == min( min(a.shape),
a.shape[1]-test_offset )
else:
assert (out == val).sum() == min( min(a.shape),
assert (out == val).sum() == min( min(a.shape),
a.shape[0]+test_offset )
def test_gradient(self):
......@@ -505,13 +505,13 @@ class TestFillDiagonalOffset(utt.InferShapeTester):
def fill_diagonal_with_fix_offset( a, val):
return fill_diagonal_offset( a, val, test_offset)
utt.verify_grad(fill_diagonal_with_fix_offset,
utt.verify_grad(fill_diagonal_with_fix_offset,
[numpy.random.rand(5, 8), numpy.random.rand()],
n_tests=1, rng=TestFillDiagonalOffset.rng)
utt.verify_grad(fill_diagonal_with_fix_offset,
utt.verify_grad(fill_diagonal_with_fix_offset,
[numpy.random.rand(8, 5), numpy.random.rand()],
n_tests=1, rng=TestFillDiagonalOffset.rng)
utt.verify_grad(fill_diagonal_with_fix_offset,
utt.verify_grad(fill_diagonal_with_fix_offset,
[numpy.random.rand(5, 5), numpy.random.rand()],
n_tests=1, rng=TestFillDiagonalOffset.rng)
......
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