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提交 72c972df authored 作者: abergeron's avatar abergeron
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Merge pull request #4410 from kelvinxu/convert_gpucumsum

Convert gpucumsum
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theano/sandbox/gpuarray/__init__.py
浏览文件 @ 72c972df
......@@ -28,7 +28,7 @@ from .type import (GpuArrayType, GpuArrayVariable, GpuArrayConstant,
GpuArraySharedVariable, gpuarray_shared_constructor,
reg_context, get_context, ContextNotDefined)
from .basic_ops import as_gpuarray_variable
from . import dnn, opt, nerv
from . import dnn, opt, nerv, extra_ops
def transfer(x, target):
try:
......
theano/sandbox/gpuarray/extra_ops.py 0 → 100644
浏览文件 @ 72c972df
from __future__ import absolute_import, print_function, division
import os
from theano import Apply
from theano.tensor.extra_ops import CumsumOp
try:
from pygpu import gpuarray
except ImportError:
pass
from .basic_ops import (as_gpuarray_variable, GpuKernelBase, Kernel,
infer_context_name, GpuFromHost)
from .opt import register_opt as register_gpu_opt, op_lifter
class GpuCumsum(GpuKernelBase):
"""
Parameters
----------
axis
Can not be None. If you want the array flattened, do it before.
"""
SUPPORTED_NDIMS = 3
__props__ = ('axis',)
def __init__(self, axis):
self.axis = axis
def __str__(self):
return "%s{%s}" % (self.__class__.__name__, self.axis)
def c_code_cache_version_apply(self, node):
return (1,)
def c_headers(self):
return ['<numpy_compat.h>', '<gpuarray/types.h>', '<gpuarray_helper.h>']
def c_header_dirs(self):
return [os.path.dirname(__file__)]
def get_params(self, node):
return node.inputs[0].type.context
def make_node(self, x):
assert x.type.dtype == 'float32', "Only float32 supported for GpuCumSum"
x = as_gpuarray_variable(x, infer_context_name(x))
if x.ndim > GpuCumsum.SUPPORTED_NDIMS:
raise NotImplementedError('Only cumsum on 1D, 2D and\
3D arrays are supported right now!')
if self.axis >= x.ndim or self.axis < -x.ndim:
raise ValueError('axis(={0}) out of bounds'.format(self.axis))
return Apply(self, [x], [x.type()])
def gpu_kernels(self, node, nodename):
kernels = []
# cumadd
kname = "k_cumadd"
k_var = "k_cumadd_" + nodename
dtype_x = node.inputs[0].dtype
flags = Kernel.get_flags(dtype_x)
code = """
KERNEL void %(kname)s(float* input, float* output,
ga_ssize inputStrides_x,
ga_ssize inputStrides_y,
ga_ssize inputStrides_z,
ga_ssize outputStrides_x, ga_ssize outputStrides_y,
ga_ssize outputStrides_z, const int offsetY, const int offsetZ,
const int beforeLastElementIdx, const 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;
int idx_beforelast = beforeLastElementIdx*outputStrides_x + dataOffsetY_output;
output[idx_last_output] = input[idx_last_input] + output[idx_beforelast];
}
""" % locals()
params = [gpuarray.GpuArray, gpuarray.GpuArray, gpuarray.SSIZE,
gpuarray.SSIZE, gpuarray.SSIZE, gpuarray.SSIZE,
gpuarray.SSIZE, gpuarray.SSIZE,
'intc', 'intc',
'intc', 'intc',
]
kernels.append(Kernel(code=code, name=kname, params=params,
flags=flags, objvar=k_var))
# blockCumSum
kname = "k_blockCumSum"
k_var = "k_blockCumSum_" + nodename
params = [gpuarray.GpuArray, gpuarray.GpuArray, gpuarray.SIZE,
gpuarray.SSIZE, gpuarray.SSIZE, gpuarray.SSIZE,
gpuarray.SSIZE, gpuarray.SSIZE, gpuarray.SSIZE,
'int32', 'int32', gpuarray.GpuArray, ]
code = """
// helper functions
WITHIN_KERNEL
void k_reductionPhase(float* partialCumSum) {
// 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];
}
}
}
WITHIN_KERNEL
void k_fetchData(float* partialCumSum, 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
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];
}
WITHIN_KERNEL
void k_reversePhase(float* partialCumSum) {
// 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];
}
}
}
WITHIN_KERNEL
void k_pushData(float* partialCumSum, 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
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];
}
KERNEL void k_blockCumSum(float* input, float* output,
size_t nbElementsPerCumsum, 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.
int globalThreadID = blockIdx.x * blockDim.x + threadIdx.x;
// Check if current thread has data to process.
if (globalThreadID >= ceil(nbElementsPerCumsum/2.0)) {
return;
}
extern __shared__ float partialCumSum[];
// Load data in shared memory
k_fetchData(partialCumSum, input, globalThreadID, inputStrides_x, inputStrides_y, inputStrides_z, 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.
// Similar to http://www.umiacs.umd.edu/~ramani/cmsc828e_gpusci/ScanTalk.pdf
k_reductionPhase(partialCumSum);
k_reversePhase(partialCumSum);
// Write the final output to global memory
k_pushData(partialCumSum, 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];
}
}
}
"""
kernels.append(Kernel(code=code, name=kname, params=params,
flags=flags, objvar=k_var))
# k_finalCumSum
kname = "k_finalCumSum"
k_var = "k_finalCumSum_" + nodename
code = """
KERNEL void k_finalCumSum(float* output, float* blockSum, size_t nbElementsPerCumsum,
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)) {
return;
}
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 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;
}
"""
params = [gpuarray.GpuArray, gpuarray.GpuArray, gpuarray.SIZE,
gpuarray.SSIZE, gpuarray.SSIZE, gpuarray.SSIZE,
'int32', 'int32', ]
kernels.append(Kernel(code=code, name=kname, params=params,
flags=flags, objvar=k_var))
return kernels
def c_code(self, node, nodename, inp, out, sub):
if node.inputs[0].type.context.kind != 'cuda':
raise NotImplementedError("cuda only")
x, = inp
z, = out
axis = self.axis if self.axis is not None else 0
fail = sub['fail']
ctx = sub['params']
code = """
const size_t* shape = PyGpuArray_DIMS(%(x)s);
bool needAllocation = !%(z)s || PyGpuArray_NDIM(%(x)s) != PyGpuArray_NDIM(%(z)s);
int axis = %(axis)s;
if (axis < 0) {
// Convert negative axis to positive axis.
axis += PyGpuArray_NDIM(%(x)s);
}
if (theano_prep_output(&%(z)s, PyGpuArray_NDIM(%(x)s), PyGpuArray_DIMS(%(x)s), %(x)s->ga.typecode, GA_C_ORDER, %(ctx)s) != 0){
%(fail)s;
}
{ // Namespace for kernel calls //
size_t max_threads_dim0;
size_t max_grid_size1;
size_t max_grid_size2;
int err;
err = %(ctx)s->ops->property(%(ctx)s->ctx, NULL, NULL, GA_CTX_PROP_MAXLSIZE0, &max_threads_dim0);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "Could not fetch max_threads_dims0");
%(fail)s;
}
err = %(ctx)s->ops->property(%(ctx)s->ctx, NULL, NULL, GA_CTX_PROP_MAXGSIZE1, &max_grid_size1);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "Could not fetch max_grid_size1");
%(fail)s;
}
err = %(ctx)s->ops->property(%(ctx)s->ctx, NULL, NULL, GA_CTX_PROP_MAXGSIZE2, &max_grid_size2);
if (err != GA_NO_ERROR){
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){
%(fail)s;
}
}
""" % locals()
return code
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) {
size_t shape[3] = { 1, 1, 1 };
ssize_t inputStrides_x;
ssize_t inputStrides_y;
ssize_t inputStrides_z;
ssize_t outputStrides_x;
ssize_t outputStrides_y;
ssize_t outputStrides_z;
switch (PyGpuArray_NDIM(input))
{
case 1:
shape[0] = PyGpuArray_DIMS(input)[0];
inputStrides_x = PyGpuArray_STRIDES(input)[0] / sizeof(float);
outputStrides_x = PyGpuArray_STRIDES(output)[0] / sizeof(float);
break;
case 2:
shape[0] = PyGpuArray_DIMS(input)[0];
shape[1] = PyGpuArray_DIMS(input)[1];
inputStrides_x = PyGpuArray_STRIDES(input)[0] / sizeof(float);
inputStrides_y = PyGpuArray_STRIDES(input)[1] / sizeof(float);
outputStrides_x = PyGpuArray_STRIDES(output)[0] / sizeof(float);
outputStrides_y = PyGpuArray_STRIDES(output)[1] / sizeof(float);
break;
case 3:
shape[0] = PyGpuArray_DIMS(input)[0];
shape[1] = PyGpuArray_DIMS(input)[1];
shape[2] = PyGpuArray_DIMS(input)[2];
inputStrides_x = PyGpuArray_STRIDES(input)[0] / sizeof(float);
inputStrides_y = PyGpuArray_STRIDES(input)[1] / sizeof(float);
inputStrides_z = PyGpuArray_STRIDES(input)[2] / sizeof(float);
outputStrides_x = PyGpuArray_STRIDES(output)[0] / sizeof(float);
outputStrides_y = PyGpuArray_STRIDES(output)[1] / sizeof(float);
outputStrides_z = PyGpuArray_STRIDES(output)[2] / sizeof(float);
break;
default:
PyErr_SetString(PyExc_RuntimeError, "Unsupported Axis");
return -1;
}
if (shape[axis] <= 1) {
int err = pygpu_move(output, input);
return err;
}
// Perform cumsum on array of even size.
size_t nbElementsPerCumsum = 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);
// 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).
ssize_t 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];
tmp = inputStrides_x;
inputStrides_x = inputStrides_z;
inputStrides_z = tmp;
tmp = outputStrides_x;
outputStrides_x = outputStrides_z;
outputStrides_z = tmp;
break;
default:
PyErr_SetString(PyExc_RuntimeError, "Unsupported Axis");
return -1;
}
const size_t shapeBlockSum[2] = { dimGridX, dimGridY*dimGridZ };
PyGpuArrayObject* deviceBlockSum = pygpu_empty(2, shapeBlockSum, output->ga.typecode,
GA_C_ORDER, input->context, Py_None);
if (deviceBlockSum == NULL){
return -1;
}
// Perform `maxGridY`*`maxGridZ` cumsums 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 sharedBytes = (2*dimBlockX) * sizeof(float);
void* kernel_params[] = {(void*) input->ga.data,
(void*) output->ga.data,
(void*) &nbElementsPerCumsum,
(void*) &inputStrides_x,
(void*) &inputStrides_y,
(void*) &inputStrides_z,
(void*) &outputStrides_x,
(void*) &outputStrides_y,
(void*) &outputStrides_z,
(void*) &offsetY,
(void*) &offsetZ,
(void*) deviceBlockSum->ga.data
};
int err = GpuKernel_call(&k_blockCumSum_%(nodename)s, 3, dimBlock, dimGrid, sharedBytes, kernel_params);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "blockCumSum 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){
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.
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*) &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);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "finalCumSum call failed");
return -1;
}
}
// If shape[axis] is odd, the last element is compute manually
if (shape[axis] != nbElementsPerCumsum){
size_t dimGrid[3] = {1, localDimGridY, localDimGridZ};
size_t dimBlock[3] = {1, 1, 1};
size_t tmp0 = shape[axis]-2;
size_t tmp1 = shape[axis]-1;
void* kernel_params[] = {(void*) input->ga.data,
(void*) output->ga.data,
(void*) &inputStrides_x,
(void*) &inputStrides_y,
(void*) &inputStrides_z,
(void*) &outputStrides_x,
(void*) &outputStrides_y,
(void*) &outputStrides_z,
(void*) &offsetY,
(void*) &offsetZ,
(void*) &(tmp0),
(void*) &(tmp1)
};
int err = GpuKernel_call(&k_cumadd_%(nodename)s, 3, dimBlock, dimGrid, sharedBytes, kernel_params);
if (err != GA_NO_ERROR){
PyErr_SetString(PyExc_RuntimeError, "cumadd call failed");
return -1;
}
}
}
}
Py_XDECREF(deviceBlockSum);
return 0;
}
""" % locals()
return super(GpuCumsum, self).c_support_code_struct(node, nodename) + code
@op_lifter([CumsumOp])
def use_gpu_cumsumop(node, ctx_name):
if node.inputs[0].dtype == 'float32':
axis = node.op.axis
x = node.inputs[0]
if axis is not None and x.ndim > GpuCumsum.SUPPORTED_NDIMS:
return None
if axis is None and x.ndim > 1:
x = x.flatten()
x = GpuFromHost(ctx_name)(x)
# ``gpu_cumsum`` assume array has been flattened if needed.
if axis is None:
axis = 0
return GpuCumsum(axis)(x)
register_gpu_opt()(use_gpu_cumsumop)
theano/sandbox/gpuarray/tests/test_extra_ops.py 0 → 100644
浏览文件 @ 72c972df
# Skip test if cuda_ndarray is not available.
from __future__ import absolute_import, print_function, division
import itertools
import numpy as np
from six.moves import xrange
from theano import tensor as T
import theano
import theano.tensor.tests.test_extra_ops
from theano.tensor.extra_ops import cumsum, CumsumOp
from theano.tests.unittest_tools import SkipTest
from theano.tests import unittest_tools as utt
from .config import mode_with_gpu, test_ctx_name
from ..extra_ops import GpuCumsum
from ..type import get_context
class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
mode = mode_with_gpu
def setUp(self):
super(TestGpuCumsum, self).setUp()
test_ctx = get_context(test_ctx_name)
if test_ctx.kind != 'cuda':
raise SkipTest("Cuda specific tests")
self.max_threads_dim0 = test_ctx.maxlsize0
self.max_grid_size1 = test_ctx.maxgsize2
def test_Strides1D(self):
x = T.fvector('x')
for axis in [0, None, -1]:
a = np.random.random((42,)).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)]
utt.assert_allclose(np.cumsum(a[slicing], axis=axis), f(a))
utt.assert_allclose(np.cumsum(a[slicing], axis=axis),
cumsum_function(a[slicing]))
def test_Strides2D(self):
x = T.fmatrix('x')
for axis in [0, 1, None, -1, -2]:
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)]
utt.assert_allclose(np.cumsum(a[slicing], axis=axis), f(a))
utt.assert_allclose(np.cumsum(a[slicing], axis=axis),
cumsum_function(a[slicing]))
def test_Strides3D(self):
x = T.ftensor3('x')
for axis in [0, 1, 2, None, -1, -2, -3]:
a = np.random.random((42, 30, 25)).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)]
utt.assert_allclose(np.cumsum(a[slicing], axis=axis), f(a))
utt.assert_allclose(np.cumsum(a[slicing], axis=axis),
cumsum_function(a[slicing]))
def test_GpuCumsum1D(self):
block_max_size = self.max_threads_dim0 * 2
x = T.fvector('x')
f = theano.function([x], cumsum(x), 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 = np.random.random(1025).astype("float32")
for i in xrange(a.shape[0]):
utt.assert_allclose(np.cumsum(a[:i]), f(a[:i]))
# Use multiple GPU threadblocks
a = np.random.random((block_max_size + 2, )).astype("float32")
utt.assert_allclose(np.cumsum(a), f(a))
# Use recursive cumsum
a = np.ones((block_max_size * (block_max_size + 1) + 2,),
dtype="float32")
utt.assert_allclose(np.cumsum(a), f(a))
def test_GpuCumsum2D(self):
block_max_size = self.max_threads_dim0 * 2
x = T.fmatrix('x')
for shape_axis, axis in zip([0, 1, 0, 1, 0], [0, 1, None, -1, -2]):
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]
a_shape[shape_axis] = 1025
a = np.random.random(a_shape).astype("float32")
slices = [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)
utt.assert_allclose(npa, fa)
# Use multiple GPU threadblocks
a_shape = [5, 5]
a_shape[shape_axis] = block_max_size + 2
a = np.random.random(a_shape).astype("float32")
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
# Use multiple GPU gridblocks
a_shape = [4, 4]
a_shape[1 - shape_axis] = self.max_grid_size1 + 1
a = np.random.random(a_shape).astype("float32")
utt.assert_allclose(np.cumsum(a, axis=axis), f(a), rtol=5e-5)
# Use recursive cumsum
a_shape = [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
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
def test_GpuCumsum3D(self):
block_max_size = self.max_threads_dim0 * 2
x = T.ftensor3('x')
for shape_axis, axis in zip([0, 1, 2, 0, 2, 1, 0], [0, 1, 2, None, -1, -2, -3]):
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)
utt.assert_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")
utt.assert_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:
# Avoid floating point error
a = np.sign(a - 0.5).astype("float32")
utt.assert_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:
# Avoid floating point error
a = np.sign(a - 0.5).astype("float32")
utt.assert_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
utt.assert_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)]
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