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提交 85db6f61 authored 作者: lamblin's avatar lamblin
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Merge pull request #1442 from nouiz/mixed2

[WIP] Use the new grad interface.
上级 51b39ada 3a1e910c
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doc/install_ubuntu.txt
浏览文件 @ 85db6f61
......@@ -124,6 +124,27 @@ Do like in the section "Updating Theano", but use
.. _install_ubuntu_gpu:
Manual Openblas instruction
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The openblas included in Ubuntu is limited to 2 threads. If you want
to use more cores at the same time, you will need to compile it
yourself. Here is some code that will help you.
.. code-block:: bash
# remove openblas if you installed it
sudo apt-get remove libopenblas-base
# Download the development version of OpenBLAS
git clone git://github.com/xianyi/OpenBLAS
cd OpenBLAS
make FC=gfortran
sudo make PREFIX=/usr/local/ install
cd /usr/local/lib
ln -s libopenblas.so /usr/lib/libblas.so
ln -s libopenblas.so.0 /usr/lib/libblas.so.3gf
Contributed GPU instruction
~~~~~~~~~~~~~~~~~~~~~~~~~~~
......
theano/gradient.py
浏览文件 @ 85db6f61
......@@ -8,6 +8,7 @@ __contact__ = "theano-dev <theano-dev@googlegroups.com>"
__docformat__ = "restructuredtext en"
import __builtin__
from itertools import izip
import logging
import warnings
_logger = logging.getLogger('theano.gradient')
......@@ -17,7 +18,6 @@ np = numpy
import theano
from itertools import izip
from theano import gof
from theano.gof import Variable
from theano.gof.python25 import OrderedDict
......@@ -78,10 +78,9 @@ def grad_not_implemented(op, x_pos, x, comment=""):
gradient is not implemented.
"""
return (NullType(
(
"This variable is Null because the grad method for "
"input %s (%s) of the %s op is not implemented. %s"
return (NullType((
"This variable is Null because the grad method for "
"input %s (%s) of the %s op is not implemented. %s"
) % (x_pos, x, op, comment)))()
......@@ -341,8 +340,8 @@ def Lop(f, wrt, eval_points, consider_constant=None,
known = dict(izip(f, grads))
ret = grad(cost=None, known_grads=known,
consider_constant=consider_constant, wrt=wrt,
disconnected_inputs=disconnected_inputs)
consider_constant=consider_constant, wrt=wrt,
disconnected_inputs=disconnected_inputs)
return format_as(using_list, using_tuple, ret)
......@@ -352,8 +351,8 @@ def Lop(f, wrt, eval_points, consider_constant=None,
#########################
def grad(cost, wrt, consider_constant=None,
disconnected_inputs='raise', add_names=True,
known_grads=None, return_disconnected='zero'):
disconnected_inputs='raise', add_names=True,
known_grads=None, return_disconnected='zero'):
"""
:type cost: Scalar (0-dimensional) Variable.
May optionally be None if known_grads is provided.
......@@ -406,17 +405,16 @@ def grad(cost, wrt, consider_constant=None,
if cost is not None and isinstance(cost.type, NullType):
raise ValueError("Can't differentiate a NaN cost."
"cost is NaN because " + \
cost.type.why_null)
"cost is NaN because " +
cost.type.why_null)
if cost is not None and cost.ndim != 0:
raise TypeError("cost must be a scalar.")
if isinstance(wrt, set):
raise TypeError("wrt must not be a set. sets have no defined "
"iteration order, so we can't return gradients in a matching"
" order.")
"iteration order, so we can't return gradients in a"
" matching order.")
using_list = isinstance(wrt, list)
using_tuple = isinstance(wrt, tuple)
......@@ -426,7 +424,7 @@ def grad(cost, wrt, consider_constant=None,
for elem in wrt:
if not isinstance(elem, Variable):
raise TypeError("Expected Variable, got " + str(elem) +
" of type "+str(type(elem)))
" of type " + str(type(elem)))
outputs = []
if cost is not None:
......@@ -435,7 +433,7 @@ def grad(cost, wrt, consider_constant=None,
outputs.extend(known_grads.keys())
var_to_app_to_idx = _populate_var_to_app_to_idx(
outputs, wrt, consider_constant)
outputs, wrt, consider_constant)
# build a dict mapping var to the gradient of cost with respect to var
grad_dict = OrderedDict()
......@@ -452,7 +450,8 @@ def grad(cost, wrt, consider_constant=None,
# g_cost may be Disconnected or NullType. A creative use of the function,
# sure, but nonetheless one we can and should support. So before we try
# to cast it make sure it even has a dtype
if hasattr(g_cost.type, 'dtype') and cost.type.dtype not in tensor.discrete_dtypes:
if (hasattr(g_cost.type, 'dtype') and
cost.type.dtype not in tensor.discrete_dtypes):
# Here we enforce the constraint that floating point variables have
# the same dtype as their gradient.
g_cost = g_cost.astype(cost.type.dtype)
......@@ -471,8 +470,8 @@ def grad(cost, wrt, consider_constant=None,
'Ambiguous whether %s should be made into tensor'
' or sparse theano variable' % str(type(g_var)))
if not isinstance(g_var.type, (NullType, DisconnectedType)) and 'float' \
not in str(g_var.type.dtype):
if (not isinstance(g_var.type, (NullType, DisconnectedType)) and
'float' not in str(g_var.type.dtype)):
raise TypeError("Gradients must always be NullType, "
"DisconnectedType, or continuous, but grad was "
"given a known_grad of type "+str(g_var.type))
......@@ -728,11 +727,13 @@ def _populate_var_to_app_to_idx(outputs, wrt, consider_constant):
return var_to_app_to_idx
class NullTypeGradError(TypeError):
"""
Raised when grad encounters a NullType.
"""
class DisconnectedInputError(ValueError):
"""
Raised when grad is asked to compute the gradient
......@@ -740,8 +741,9 @@ class DisconnectedInputError(ValueError):
disconnected_inputs='raise'.
"""
def _populate_grad_dict(var_to_app_to_idx,
grad_dict, wrt, cost_name=None):
grad_dict, wrt, cost_name=None):
"""
Helper function for grad function.
......@@ -783,7 +785,7 @@ def _populate_grad_dict(var_to_app_to_idx,
# list of bools indicating if each output is connected to the cost
outputs_connected = [not isinstance(g.type, DisconnectedType)
for g in output_grads]
for g in output_grads]
connection_pattern = _node_to_pattern(node)
......@@ -840,7 +842,7 @@ def _populate_grad_dict(var_to_app_to_idx,
# each destroyed input.
try:
dinputs = [node.inputs[x[0]] for x in
node.op.destroy_map.values()]
node.op.destroy_map.values()]
except AttributeError:
dinputs = []
......@@ -899,11 +901,11 @@ def _populate_grad_dict(var_to_app_to_idx,
if input_grads is None:
raise TypeError("%s.grad returned NoneType, "
"expected iterable." % str(node.op))
"expected iterable." % str(node.op))
if len(input_grads) != len(inputs):
raise ValueError(("%s returned the wrong number of" +\
" gradient terms.") % str(node.op))
raise ValueError(("%s returned the wrong number of" +
" gradient terms.") % str(node.op))
# must convert to list in case the op returns a tuple
# we won't be able to post-process out the Nones if it does that
......@@ -926,7 +928,7 @@ def _populate_grad_dict(var_to_app_to_idx,
# used to mean undefined, zero, or disconnected.
# We therefore don't allow it because its usage has become
# so muddied.
raise TypeError(('%s.grad returned None for' +\
raise TypeError(('%s.grad returned None for' +
' a gradient term, '
'this is prohibited. Instead of None,'
'return zeros_like(input), DisconnectedType()(),'
......@@ -964,7 +966,7 @@ def _populate_grad_dict(var_to_app_to_idx,
msg += "verifiably zeros."
msg = msg % (str(node.op), str(term),
str(type(term)), i)
str(type(term)), i)
if is_zero == 'no':
msg = "%s.grad returned %s of type %s for input"
......@@ -980,8 +982,8 @@ def _populate_grad_dict(var_to_app_to_idx,
#Check that op.connection_pattern matches the connectivity
#logic driving the op.grad method
for i, packed in \
enumerate(zip(inputs, input_grads, inputs_connected)):
for i, packed in enumerate(zip(inputs, input_grads,
inputs_connected)):
ipt, ig, connected = packed
actually_connected = \
not isinstance(ig.type, DisconnectedType)
......@@ -1027,11 +1029,11 @@ def _populate_grad_dict(var_to_app_to_idx,
if not isinstance(term, gof.Variable):
raise TypeError("%s.grad returned %s, expected"
" Variable instance." % (str(node.op),
type(term)))
type(term)))
if isinstance(term.type, NullType):
raise NullTypeGradError("tensor.grad "
"encountered a NaN. " +\
"encountered a NaN. " +
term.type.why_null)
#Don't try to sum up DisconnectedType placeholders
......@@ -1121,9 +1123,9 @@ class numeric_grad(object):
# For now, we use a heuristic that catches very bad gradients, but is not
# perfectly accurate.
type_eps = {'float64': 1e-7,
'float32': 3e-4,
numpy.dtype('float64'): 1e-7,
numpy.dtype('float32'): 3e-4}
'float32': 3e-4,
numpy.dtype('float64'): 1e-7,
numpy.dtype('float32'): 3e-4}
def __init__(self, f, pt, eps=None, out_type=None):
"""Return the gradient of f at pt.
......@@ -1243,15 +1245,13 @@ class numeric_grad(object):
"""
if len(g_pt) != len(self.gf):
raise ValueError(
'argument has wrong number of elements',
len(g_pt))
raise ValueError('argument has wrong number of elements',
len(g_pt))
errs = []
for i, (a, b) in enumerate(zip(g_pt, self.gf)):
if a.shape != b.shape:
raise ValueError(
'argument element %i has wrong shape %s' % (
i, str((a.shape, b.shape))))
raise ValueError('argument element %i has wrong shape %s' % (
i, str((a.shape, b.shape))))
errs.append(numeric_grad.abs_rel_err(a, b))
return errs
......@@ -1287,7 +1287,7 @@ class numeric_grad(object):
# max over the arrays in g_pt
max_arg = numpy.argmax(errs)
max_pos = pos[max_arg]
return (max_arg, pos[max_arg], abs_errs[max_arg], rel_errs[max_arg])
return (max_arg, max_pos, abs_errs[max_arg], rel_errs[max_arg])
def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
......@@ -1336,9 +1336,10 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
covers that case as well by using random projections.
"""
# The import is here to prevent circular import.
from theano import compile, shared
import theano.tensor
from theano.tensor import as_tensor_variable, cast, TensorType
from theano.tensor import as_tensor_variable, TensorType
assert isinstance(pt, (list, tuple))
pt = [numpy.array(p) for p in pt]
......@@ -1368,11 +1369,12 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
def function(inputs, output):
if mode is None:
f = compile.function(inputs, output, accept_inplace=True,
allow_input_downcast=True, on_unused_input='ignore')
allow_input_downcast=True,
on_unused_input='ignore')
else:
f = compile.function(inputs, output, accept_inplace=True,
allow_input_downcast=True, mode=mode,
on_unused_input='ignore')
allow_input_downcast=True, mode=mode,
on_unused_input='ignore')
return f
tensor_pt = [TensorType(
......@@ -1421,24 +1423,32 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
grad_fn = function(tensor_pt, symbolic_grad)
for test_num in xrange(n_tests):
num_grad = numeric_grad(cost_fn, [p.copy() for p in pt], eps, out_type)
try:
num_grad = numeric_grad(cost_fn, [p.copy() for p in pt],
eps, out_type)
analytic_grad = grad_fn(*[p.copy() for p in pt])
analytic_grad = grad_fn(*[p.copy() for p in pt])
# Since `tensor_pt` is a list, `analytic_grad` should be one too.
assert isinstance(analytic_grad, list)
# Since `tensor_pt` is a list, `analytic_grad` should be one too.
assert isinstance(analytic_grad, list)
max_arg, max_err_pos, max_abs_err, max_rel_err =\
num_grad.max_err(analytic_grad, abs_tol, rel_tol)
max_arg, max_err_pos, max_abs_err, max_rel_err = num_grad.max_err(
analytic_grad, abs_tol, rel_tol)
if max_abs_err > abs_tol and max_rel_err > rel_tol:
if max_abs_err > abs_tol and max_rel_err > rel_tol:
raise verify_grad.E_grad(max_arg, max_err_pos,
max_abs_err, max_rel_err, abs_tol, rel_tol)
raise verify_grad.E_grad(max_arg, max_err_pos,
max_abs_err, max_rel_err,
abs_tol, rel_tol)
# get new random projection for next test
if test_num < n_tests - 1:
t_r.set_value(random_projection(), borrow=True)
# get new random projection for next test
if test_num < n_tests - 1:
t_r.set_value(random_projection(), borrow=True)
except Exception, e:
e.args += ("\nThe error happened with the following inputs:", pt,
"\nThe value of eps is:", eps,
"\nThe out_type is:", out_type)
raise
class GradientError(Exception):
......@@ -1517,9 +1527,9 @@ def jacobian(expression, wrt, consider_constant=None,
rvals = []
for inp in args[2:]:
rval = grad(expr[idx],
inp,
consider_constant=consider_constant,
disconnected_inputs=disconnected_inputs)
inp,
consider_constant=consider_constant,
disconnected_inputs=disconnected_inputs)
rvals.append(rval)
return rvals
# Computing the gradients does not affect the random seeds on any random
......@@ -1527,8 +1537,8 @@ def jacobian(expression, wrt, consider_constant=None,
# just backtracking over old values. (rp Jan 2012 - if anyone has a
# counter example please show me)
jacobs, updates = theano.scan(inner_function,
sequences=arange(expression.shape[0]),
non_sequences=[expression] + wrt)
sequences=arange(expression.shape[0]),
non_sequences=[expression] + wrt)
assert not updates, \
("Scan has returned a list of updates. This should not "
"happen! Report this to theano-users (also include the "
......@@ -1537,7 +1547,7 @@ def jacobian(expression, wrt, consider_constant=None,
def hessian(cost, wrt, consider_constant=None,
disconnected_inputs='raise'):
disconnected_inputs='raise'):
"""
:type cost: Scalar (0-dimensional) Variable.
:type wrt: Vector (1-dimensional tensor) 'Variable' or list of
......
theano/sandbox/cuda/nnet.py
浏览文件 @ 85db6f61
......@@ -41,7 +41,7 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias (GpuOp):
float * sm_data, int sms0, int sms1,
float * am_data, int ams0)
{
const int row = blockIdx.x;
for (int row = blockIdx.x; row < M; row += gridDim.x){
const float * x = x_data + xs0 * row;
const int y_idx = (int)y_idx_data[row * y_idxs0];
......@@ -83,6 +83,7 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias (GpuOp):
+ log(sum);
}
am_data[row*ams0] = row_max_j;
}
}
"""
......@@ -168,7 +169,8 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias (GpuOp):
}
}
{
int n_blocks = CudaNdarray_HOST_DIMS(%(sm)s)[0];
int n_blocks = std::min(CudaNdarray_HOST_DIMS(%(x)s)[0],
NUM_VECTOR_OP_BLOCKS);
//TODO: launch more threads per row and do parallel sum and max reductions
int n_threads = 1;
int n_shared_bytes = 0; //n_threads * sizeof(float);
......@@ -195,8 +197,11 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias (GpuOp):
if (cudaSuccess != err)
{
PyErr_Format(PyExc_RuntimeError,
"Cuda error: %(classname)s %(nodename)s: %%s.\\n",
cudaGetErrorString(err));
"Cuda error: %(classname)s %(nodename)s: %%s.\\n"
"The kernel was launched with %%d threads,"
" %%d blocks and %%d shared memory\\n",
cudaGetErrorString(err),
n_threads, n_blocks, n_shared_bytes);
// no need to decref output vars the cleanup code will do it
%(fail)s;
}
......@@ -206,7 +211,7 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias (GpuOp):
def c_code_cache_version(self):
#return ()
return (3,)
return (4,)
gpu_crossentropy_softmax_argmax_1hot_with_bias = GpuCrossentropySoftmaxArgmax1HotWithBias()
......@@ -235,7 +240,7 @@ class GpuCrossentropySoftmax1HotWithBiasDx (GpuOp):
def c_code_cache_version(self):
#return ()
return (5,)
return (6,)
def c_code(self, node, nodename, inp, out, sub):
dnll, sm, y_idx = inp
......@@ -283,11 +288,12 @@ class GpuCrossentropySoftmax1HotWithBiasDx (GpuOp):
}
}
{
int n_blocks = std::min(CudaNdarray_HOST_DIMS(%(dx)s)[0],
NUM_VECTOR_OP_BLOCKS);
int n_threads = std::min(CudaNdarray_HOST_DIMS(%(dx)s)[1],256);
kCrossEntropySoftmax1HotWithBiasDx_%(nodename)s
<<<
CudaNdarray_HOST_DIMS(%(dx)s)[0],
std::min(CudaNdarray_HOST_DIMS(%(dx)s)[1],256)
>>>(
<<<n_blocks, n_threads>>>(
CudaNdarray_HOST_DIMS(%(dx)s)[0],
CudaNdarray_HOST_DIMS(%(dx)s)[1],
......@@ -310,9 +316,11 @@ class GpuCrossentropySoftmax1HotWithBiasDx (GpuOp):
if( cudaSuccess != err)
{
PyErr_Format(PyExc_RuntimeError,
"Cuda error: %%s: %%s.\\n",
"Cuda error: %%s: %%s.\\n"
"The kernel was launched with %%d threads and"
" %%d blocks\\n",
"kCrossEntropySoftmax1HotWithBiasDx_%(nodename)s",
cudaGetErrorString(err));
cudaGetErrorString(err), n_threads, n_blocks);
%(fail)s;
}
}
......
theano/sandbox/cuda/tests/test_nnet.py
浏览文件 @ 85db6f61
......@@ -25,7 +25,6 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
This is basic test for GpuCrossentropySoftmaxArgmax1HotWithBias
We check that we loop when their is too much threads
TODO: check that we loop when their is too much block(>32*1024)
"""
......@@ -100,13 +99,16 @@ def test_GpuCrossentropySoftmax1HotWithBiasDx():
This is basic test for GpuCrossentropySoftmax1HotWithBiasDx
We check that we loop when their is too much threads
TODO: check that we loop when their is too much block(>32*1024)
"""
n_in = 1000
batch_size = 4097
n_out = 1250
if not isinstance(mode_with_gpu, theano.compile.DebugMode):
n_in = 4098
n_out = 4099
# Seed numpy.random with config.unittests.rseed
utt.seed_rng()
......
theano/sandbox/linalg/ops.py
浏览文件 @ 85db6f61
......@@ -715,10 +715,9 @@ class ExtractDiag(Op):
implemented our own. """
x, = ins
z, = outs
# zero-dimensional matrices ...
if x.shape[0] == 0 or x.shape[1] == 0:
z[0] = numpy.zeros(0, dtype=x.dtype)
z[0] = node.outputs[0].type.value_zeros((0,))
return
if x.shape[0] < x.shape[1]:
......
theano/sandbox/linalg/tests/test_linalg.py
浏览文件 @ 85db6f61
......@@ -204,8 +204,8 @@ def test_rop_lop():
rop_f = function([mx, mv], yv)
sy, _ = theano.scan(lambda i, y, x, v: (tensor.grad(y[i], x) * v).sum(),
sequences=tensor.arange(y.shape[0]),
non_sequences=[y, mx, mv])
sequences=tensor.arange(y.shape[0]),
non_sequences=[y, mx, mv])
scan_f = function([mx, mv], sy)
rng = numpy.random.RandomState(utt.fetch_seed())
......@@ -561,6 +561,7 @@ class test_Eigh(test_Eig):
class test_Eigh_float32(test_Eigh):
dtype = 'float32'
def test_matrix_inverse_solve():
if not imported_scipy:
raise SkipTest("Scipy needed for the Solve op.")
......
theano/sparse/tests/test_basic.py
浏览文件 @ 85db6f61
......@@ -17,7 +17,7 @@ from theano.sparse import enable_sparse
from theano.gof.python25 import all, any, product
if enable_sparse == False:
if not enable_sparse:
raise SkipTest('Optional package sparse disabled')
from theano.sparse.basic import _is_dense, _is_sparse, _mtypes
......@@ -35,7 +35,7 @@ from theano.sparse import (
SparseFromDense,
Cast, cast, HStack, VStack, AddSSData, add_s_s_data,
structured_minimum, structured_maximum, structured_add,
mul_s_v, structured_add_s_v,
mul_s_v, structured_add_s_v,
SamplingDot, sampling_dot,
Diag, diag, SquareDiagonal, square_diagonal,
EnsureSortedIndices, ensure_sorted_indices, clean,
......@@ -372,17 +372,17 @@ class SparseInferShapeTester(utt.InferShapeTester):
[x, y],
[grads[0]],
[as_sparse_format(random_lil((4, 5),
config.floatX, 3), format),
config.floatX, 3), format),
as_sparse_format(random_lil((5, 3),
config.floatX, 3), format)],
config.floatX, 3), format)],
op)
self._compile_and_check(
[x, y],
[grads[1]],
[as_sparse_format(random_lil((4, 5),
config.floatX, 3), format),
config.floatX, 3), format),
as_sparse_format(random_lil((5, 3),
config.floatX, 3), format)],
config.floatX, 3), format)],
op)
def test_dense_from_sparse(self):
......@@ -398,8 +398,7 @@ class SparseInferShapeTester(utt.InferShapeTester):
self._compile_and_check([x],
[csc_from_dense(x)],
[numpy.random.randn(10, 40).astype(
config.floatX)],
config.floatX)],
csc_from_dense.__class__)
def test_sparse_from_list(self):
......@@ -674,27 +673,27 @@ class test_csm_properties(unittest.TestCase):
def test_csm_properties_grad(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
'csr': sp.csr_matrix}
for format in ['csc', 'csr']:
for dtype in ['float32', 'float64']:
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
verify_grad_sparse(lambda *x: CSMProperties()(*x)[0], [spmat],
structured=True)
structured=True)
verify_grad_sparse(lambda *x: CSMProperties()(*x)[1], [spmat],
structured=True)
structured=True)
verify_grad_sparse(lambda *x: CSMProperties()(*x)[2], [spmat],
structured=True)
structured=True)
verify_grad_sparse(lambda *x: CSMProperties()(*x)[2], [spmat],
structured=True)
structured=True)
def test_csm_properties(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
'csr': sp.csr_matrix}
for format in ['csc', 'csr']:
for dtype in ['float32', 'float64']:
......@@ -717,7 +716,7 @@ class test_csm(unittest.TestCase):
def test_csm_grad(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
'csr': sp.csr_matrix}
for format in ['csc', 'csr']:
for dtype in ['float32', 'float64']:
......@@ -732,7 +731,7 @@ class test_csm(unittest.TestCase):
Test support for gradients sparser than the input.
"""
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
'csr': sp.csr_matrix}
for format in ['csc', 'csr']:
for dtype in ['float32', 'float64']:
......@@ -742,7 +741,7 @@ class test_csm(unittest.TestCase):
s = tensor.ivector()
a = as_sparse_variable(sp_types[format](random_lil((4, 3),
dtype, 1)))
dtype, 1)))
f = theano.function([x, y, z, s],
tensor.grad(dense_from_sparse(
......@@ -751,7 +750,7 @@ class test_csm(unittest.TestCase):
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
res = f(spmat.data, spmat.indices, spmat.indptr,
numpy.asarray(spmat.shape, 'int32'))
numpy.asarray(spmat.shape, 'int32'))
assert len(spmat.data) == len(res)
......@@ -760,7 +759,7 @@ class test_csm(unittest.TestCase):
Test support for gradients of unsorted inputs.
"""
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
'csr': sp.csr_matrix}
for format in ['csr', 'csc', ]:
for dtype in ['float32', 'float64']:
......@@ -773,7 +772,7 @@ class test_csm(unittest.TestCase):
[1, 2, 1],
[1, 2, 1],
[1, 2, 1]],
dtype=dtype)[range(4)]
dtype=dtype)[range(4)]
# Make sure it's unsorted
assert not a.has_sorted_indices
a = as_sparse_variable(a)
......@@ -782,14 +781,15 @@ class test_csm(unittest.TestCase):
dense_from_sparse(a * CSM(format)(x, y, z, s))), x))
spmat = sp_types[format](random_lil((4, 3), dtype,
12))[range(4)]
12))[range(4)]
assert not spmat.has_sorted_indices
res = f(spmat.data, spmat.indices, spmat.indptr,
numpy.asarray(spmat.shape, 'int32'))
numpy.asarray(spmat.shape, 'int32'))
col1 = sp_types[format]((res, spmat.indices, spmat.indptr),
shape=numpy.asarray(spmat.shape, 'int32'))[:, 1].data
shape=numpy.asarray(spmat.shape,
'int32'))[:, 1].data
assert numpy.all(col1 == 2)
......@@ -808,7 +808,7 @@ class test_csm(unittest.TestCase):
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
res = f(spmat.data, spmat.indices, spmat.indptr,
numpy.asarray(spmat.shape, 'int32'))
numpy.asarray(spmat.shape, 'int32'))
assert numpy.all(res.data == spmat.data)
assert numpy.all(res.indices == spmat.indices)
......@@ -909,8 +909,7 @@ class test_structureddot(unittest.TestCase):
spmat = sp.csc_matrix(spmat)
images = tensor.Tensor(dtype='float32',
broadcastable=[False, False])(
'images')
broadcastable=[False, False])('images')
cscmat = CSC(kerns, spmat.indices[:spmat.size],
spmat.indptr, spmat.shape)
......@@ -931,7 +930,8 @@ class test_structureddot(unittest.TestCase):
#print 'type of kernvals = ', kernvals.dtype
bsize = 3
imvals = 1.0 * numpy.array(numpy.arange(bsize * spmat.shape[1]).\
reshape(bsize, spmat.shape[1]), dtype='float32')
reshape(bsize, spmat.shape[1]),
dtype='float32')
outvals = f(kernvals, imvals)
#print outvals
......@@ -949,10 +949,10 @@ class test_structureddot(unittest.TestCase):
f = theano.function([a, b], theano.Out(d, borrow=True))
topo = f.maker.fgraph.toposort()
for M, N, K, nnz in [(4, 3, 2, 3),
(40, 30, 20, 3),
(40, 30, 20, 30),
(400, 3000, 200, 6000),
]:
(40, 30, 20, 3),
(40, 30, 20, 30),
(400, 3000, 200, 6000),
]:
a_val = sp_mat[sparse_format_a](
random_lil((M, N), sparse_dtype, nnz))
b_val = sp_mat[sparse_format_b](
......@@ -969,10 +969,10 @@ class test_structureddot(unittest.TestCase):
f = theano.function([a, b], theano.Out(d, borrow=True))
for M, N, K, nnz in [(4, 3, 2, 3),
(40, 30, 20, 3),
(40, 30, 20, 30),
(400, 3000, 200, 6000),
]:
(40, 30, 20, 3),
(40, 30, 20, 30),
(400, 3000, 200, 6000),
]:
spmat = sp.csc_matrix(random_lil((M, N), sparse_dtype, nnz))
mat = numpy.asarray(numpy.random.randn(N, K), dense_dtype)
theano_times = []
......@@ -1017,10 +1017,10 @@ class test_structureddot(unittest.TestCase):
f = theano.function([a, b], d)
for M, N, K, nnz in [(4, 3, 2, 3),
(40, 30, 20, 3),
(40, 30, 20, 30),
(400, 3000, 200, 6000),
]:
(40, 30, 20, 3),
(40, 30, 20, 30),
(400, 3000, 200, 6000),
]:
spmat = sp.csr_matrix(random_lil((M, N), sparse_dtype, nnz))
mat = numpy.asarray(numpy.random.randn(N, K), dense_dtype)
t0 = time.time()
......@@ -1062,7 +1062,7 @@ class DotTests(utt.InferShapeTester):
self.v_10 = numpy.asarray(numpy.random.uniform(-1, 1, 10),
dtype=theano.config.floatX)
self.v_100 = numpy.asarray(numpy.random.uniform(-1, 1, 100),
dtype=theano.config.floatX)
dtype=theano.config.floatX)
def test_csr_dense(self):
x = theano.sparse.csr_matrix('x')
......@@ -1143,7 +1143,7 @@ class DotTests(utt.InferShapeTester):
a = sparse.csr_matrix('a', dtype='float32')
b = cuda.float32_shared_constructor(
numpy.random.rand(3, 4).astype('float32'))
numpy.random.rand(3, 4).astype('float32'))
d = sparse.dot(a, b)
f = theano.function([a], d)
......@@ -1281,7 +1281,7 @@ class UsmmTests(unittest.TestCase):
for node in topo]) == len(topo) - 5)
new_topo = []
for node in topo:
if not (isinstance(node.op, tensor.Elemwise) and \
if not (isinstance(node.op, tensor.Elemwise) and
isinstance(node.op.scalar_op,
theano.scalar.basic.Cast)):
new_topo.append(node)
......@@ -1360,8 +1360,8 @@ class test_zeros_like(unittest.TestCase):
x = theano.sparse.csr_matrix()
f = theano.function([x], theano.sparse.sp_zeros_like(x))
vx = scipy.sparse.csr_matrix(numpy.asarray(
numpy.random.binomial(1, 0.5, (100, 100)),
dtype=theano.config.floatX))
numpy.random.binomial(1, 0.5, (100, 100)),
dtype=theano.config.floatX))
fx = f(vx)
......@@ -1571,7 +1571,7 @@ class SpSumTester(utt.InferShapeTester):
shape=(10, 10))
z = theano.sparse.sp_sum(variable[0], axis=axis)
if axis == None:
if axis is None:
assert z.type.broadcastable == ()
else:
assert z.type.broadcastable == (False, )
......@@ -1951,24 +1951,26 @@ class Test_getitem(unittest.TestCase):
# the [] shortcut for getitem.
# x[a:b] is not accepted because we don't have sparse vectors
self.assertRaises(NotImplementedError,
x.__getitem__, (slice(a, b), c))
x.__getitem__, (slice(a, b), c))
# x[a:b:step, c:d] is not accepted because scipy silently drops
# the step (!)
self.assertRaises(ValueError,
x.__getitem__, (slice(a, b, -1), slice(c, d)))
x.__getitem__, (slice(a, b, -1), slice(c, d)))
self.assertRaises(ValueError,
x.__getitem__, (slice(a, b), slice(c, d, 2)))
x.__getitem__, (slice(a, b), slice(c, d, 2)))
# Advanced indexing is not supported
self.assertRaises(ValueError,
x.__getitem__, (tensor.ivector('l'), slice(a, b)))
x.__getitem__,
(tensor.ivector('l'), slice(a, b)))
# Indexing with random things is not supported either
self.assertRaises(ValueError,
x.__getitem__, slice(tensor.fscalar('f'), None))
x.__getitem__, slice(tensor.fscalar('f'), None))
self.assertRaises(ValueError,
x.__getitem__, (slice(None), slice([1, 3, 4], None)))
x.__getitem__,
(slice(None), slice([1, 3, 4], None)))
def test_GetItemScalar(self):
sparse_formats = ('csc', 'csr')
......@@ -1981,7 +1983,7 @@ class Test_getitem(unittest.TestCase):
n = 42
vx = as_sparse_format(self.rng.binomial(1, 0.5, (97, 100)),
format).astype(theano.config.floatX)
format).astype(theano.config.floatX)
f1 = theano.function([x, a, b], x[a, b])
r1 = f1(vx, 10, 10)
......@@ -2248,6 +2250,7 @@ def elemwise_checker(op, expected_f, gap=None, test_dtypes=None,
else:
self.gap_grad = gap
# Ensure the test's name is correct.
utt.seed_rng()
assert eval(self.__class__.__name__) is self.__class__
def test_op(self):
......@@ -2449,7 +2452,8 @@ TanTester = elemwise_checker(
ArcsinTester = elemwise_checker(
sparse.arcsin,
numpy.arcsin,
gap=(-1, 1))
gap=(-1, 1),
gap_grad=(-0.99, 0.99))
ArctanTester = elemwise_checker(
sparse.arctan,
......@@ -2501,7 +2505,7 @@ FloorTester = elemwise_checker(
numpy.floor,
grad_test=False,
test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes])
if not m in sparse.complex_dtypes])
Log1pTester = elemwise_checker(
sparse.log1p,
......@@ -2516,20 +2520,20 @@ Deg2radTester = elemwise_checker(
sparse.deg2rad,
numpy.deg2rad,
test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes])
if not m in sparse.complex_dtypes])
Rad2degTester = elemwise_checker(
sparse.rad2deg,
numpy.rad2deg,
test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes])
if not m in sparse.complex_dtypes])
TruncTester = elemwise_checker(
sparse.trunc,
numpy.trunc,
test_dtypes=[m for m in sparse.all_dtypes
if not m in sparse.complex_dtypes])
if not m in sparse.complex_dtypes])
SqrTester = elemwise_checker(
......@@ -2548,7 +2552,7 @@ class MulSVTester(unittest.TestCase):
def test_mul_s_v_grad(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
'csr': sp.csr_matrix}
for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']:
......@@ -2556,7 +2560,8 @@ class MulSVTester(unittest.TestCase):
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
theano.sparse.verify_grad_sparse(mul_s_v,
[spmat, mat], structured=True)
[spmat, mat],
structured=True)
def test_mul_s_v(self):
sp_types = {'csc': sp.csc_matrix,
......@@ -2590,7 +2595,8 @@ class StructuredAddSVTester(unittest.TestCase):
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
theano.sparse.verify_grad_sparse(structured_add_s_v,
[spmat, mat], structured=True)
[spmat, mat],
structured=True)
def test_structured_add_s_v(self):
sp_types = {'csc': sp.csc_matrix,
......@@ -2618,11 +2624,11 @@ class SamplingDotTester(utt.InferShapeTester):
x.append(sparse.csr_matrix())
#unsquare shape
a = [numpy.array(numpy.random.random_integers(5, size=(4, 3)) - 1,
dtype=theano.config.floatX),
dtype=theano.config.floatX),
numpy.array(numpy.random.random_integers(5, size=(5, 3)) - 1,
dtype=theano.config.floatX),
dtype=theano.config.floatX),
numpy.array(numpy.random.random_integers(2, size=(4, 5)) - 1,
dtype=theano.config.floatX)
dtype=theano.config.floatX)
]
a[2] = sp.csr_matrix(a[2])
......@@ -2672,7 +2678,7 @@ test_shared_options = theano.tensor.tests.test_sharedvar.makeSharedTester(
ref_fct_=lambda a: numpy.asarray((a * 2).todense()),
cast_value_=scipy.sparse.csr_matrix,
name='test_shared_options',
)
)
if __name__ == '__main__':
......
theano/tensor/sort.py
浏览文件 @ 85db6f61
......@@ -144,7 +144,17 @@ class ArgSortOp(theano.Op):
def grad(self, inputs, output_grads):
#No grad defined for intergers.
return [None, None]
inp, axis = inputs
inp_grad = theano.gradient.grad_not_implemented(
self, 0, axis,
"I'm not sure if argsort should have its gradient"
" implemented or is should be marked as undefined."
" So I mark it as not implemented for now.")
axis_grad = theano.gradient.grad_undefined(
self, 1, axis,
"argsort is not defined for non-integer axes so"
" argsort(x, axis+eps) is undefined")
return [inp_grad, axis_grad]
"""
def R_op(self, inputs, eval_points):
# R_op can receive None as eval_points.
......
theano/tests/run_tests_in_batch.py
浏览文件 @ 85db6f61
......@@ -185,7 +185,9 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
subprocess_extra_args.update(dict(
stdout=dummy_out.fileno(),
stderr=dummy_out.fileno()))
t0 = time.time()
subprocess.call(cmd, **subprocess_extra_args)
t1 = time.time()
# Recover failed test indices from the 'failed' field of the
# '.noseids' file. We need to do it after each batch because
# otherwise this field may get erased. We use a set because it
......@@ -193,8 +195,8 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
# to avoid duplicates.
failed = failed.union(cPickle.load(open(noseids_file, 'rb'))
['failed'])
print '%s%% done (failed: %s)' % ((test_range[-1] * 100) //
n_tests, len(failed))
print '%s%% done in %.3fs (failed: %s)' % (
(test_range[-1] * 100) // n_tests, t1 - t0, len(failed))
# Sort for cosmetic purpose only.
failed = sorted(failed)
if failed:
......
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