def testNegativeDim(self):
with self.session(use_gpu=True) as sess:
cdim = constant_op.constant(-2, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = self.evaluate(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
cdim = constant_op.constant(-3, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([1, 3, 5], dtypes.int32)
s2 = constant_op.constant([3, 3, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = self.evaluate(off)
self.assertAllEqual(ans, [[0, 0, 0], [2, 0, 0], [3, 0, 0]])
def testDimMismatch(self):
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5, 10], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
r"should contain 3 elem"):
self.evaluate(off)
def testNegativeDim(self):
with self.test_session(use_gpu=True) as sess:
cdim = constant_op.constant(-2, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = sess.run(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
cdim = constant_op.constant(-3, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([1, 3, 5], dtypes.int32)
s2 = constant_op.constant([3, 3, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = sess.run(off)
self.assertAllEqual(ans, [[0, 0, 0], [2, 0, 0], [3, 0, 0]])
def testNegativeDim(self):
with test_util.use_gpu():
cdim = constant_op.constant(-2, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = self.evaluate(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
cdim = constant_op.constant(-3, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([1, 3, 5], dtypes.int32)
s2 = constant_op.constant([3, 3, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = self.evaluate(off)
self.assertAllEqual(ans, [[0, 0, 0], [2, 0, 0], [3, 0, 0]])
def testNotVector(self):
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([[2, 3, 5]], dtypes.int32)
s1 = constant_op.constant([[2, 7, 5]], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
r"should be a vector"):
self.evaluate(off)
def testConcatDimOutOfRange(self):
cdim = constant_op.constant(4, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
r"Concat dim is out of range: 4 vs. 3"):
self.evaluate(off)
def testConcatDimOutOfRange(self):
cdim = constant_op.constant(4, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
r"Concat dim is out of range: 4 vs. 3"):
self.evaluate(off)
def testNotVector(self):
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([[2, 3, 5]], dtypes.int32)
s1 = constant_op.constant([[2, 7, 5]], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
r"should be a vector"):
self.evaluate(off)
def testDimMismatch(self):
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5, 10], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegex(errors_impl.InvalidArgumentError,
r"should contain 3 elem"):
self.evaluate(off)
def testBasic(self):
with self.test_session(use_gpu=True) as sess:
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = sess.run(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
def testBasic(self):
with self.session(use_gpu=True) as sess:
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = sess.run(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
def testBasic(self):
with test_util.use_gpu():
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = self.evaluate(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
def testSizeMismatch(self):
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 10], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegexp(
errors_impl.InvalidArgumentError,
r"All dimensions except 1 must match. Input 1 has shape \[2 7 10\] "
r"and doesn't match input 0 with shape \[2 3 5\]."):
self.evaluate(off)
def testBasic(self):
with self.session():
with self.test_scope():
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = self.evaluate(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
def testBasic(self):
with self.cached_session() as sess:
with self.test_scope():
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 5], dtypes.int32)
s2 = constant_op.constant([2, 20, 5], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
ans = self.evaluate(off)
self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
def testSizeMismatch(self):
cdim = constant_op.constant(1, dtypes.int32)
s0 = constant_op.constant([2, 3, 5], dtypes.int32)
s1 = constant_op.constant([2, 7, 10], dtypes.int32)
off = gen_array_ops.concat_offset(cdim, [s0, s1])
with self.assertRaisesRegexp(
errors_impl.InvalidArgumentError,
r"All dimensions except 1 must match. Input 1 has shape \[2 7 10\] "
r"and doesn't match input 0 with shape \[2 3 5\]."):
self.evaluate(off)
def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index):
"""Gradient for concat op.
Args:
op: An operation.
grad: `Tensor` or `IndexedSlices` representing the gradients with respect
to each output of the op.
start_value_index: An integer index of the first value in the op.inputs.
end_value_index: An integer index of the last value in the op.inputs.
dim_index: An interger index of concat_dim or axis parameter in op.inputs.
Returns:
Tensors representing the partial gradients with respect to each input
of the op.
Raises:
ValueError: if concat_dim/axis is not statically known.
"""
def _CreateDenseMaskAndBegin(sizes, concat_dim):
"""Create variables for iteratively slicing a dense gradients tensor."""
# Since shape is 1-D, shape_of_shape = [rank-of-inputs]
shape_of_shape = array_ops.shape(sizes[0])
# Make a vector of length equal to the input's dimensions,
# with 0's everywhere and 1 in the concat dim position.
# Note: Can't use sparse_to_dense since it isn't GPU-capable (for now)
mask = array_ops.concat([
array_ops.fill(array_ops.expand_dims(concat_dim, 0), 0), [1],
array_ops.fill(shape_of_shape - concat_dim - 1, 0)
], 0)
begin = array_ops.fill(shape_of_shape, 0)
return mask, begin
def _ExtractInputShapes(inputs):
"""Extract the shapes of a set of input tensors."""
if context.executing_eagerly():
return array_ops.shape_n(inputs)
sizes = []
fully_known = True
for x in inputs:
input_shape = array_ops.shape(x)
if not isinstance(input_shape,
ops.Tensor) or input_shape.op.type != "Const":
fully_known = False
break
sizes.append(input_shape)
if fully_known:
return sizes
else:
return array_ops.shape_n(inputs)
# Degenerate concatenation, just return grad.
if len(op.inputs) == 2:
return grad + [None] if end_value_index <= dim_index else [None] + grad
concat_dim = op.inputs[dim_index]
input_values = op.inputs[start_value_index:end_value_index]
out_grads = []
if isinstance(grad, ops.Tensor):
if context.executing_eagerly():
# Using mod here for convenience since concat_dim is already verified
# in concat implementation to be within the allowed [-rank, rank) range.
non_neg_concat_dim = (concat_dim._numpy().item(0) %
input_values[0]._rank()) # pylint: disable=protected-access
# All inputs are guaranteed to be EagerTensors in eager mode
sizes = pywrap_tensorflow.TFE_Py_TensorShapeSlice(
input_values, non_neg_concat_dim)
out_grads = array_ops.split(grad, sizes, non_neg_concat_dim)
else:
if constant_op.is_constant(concat_dim):
# If concat_dim is a constant defined in a different context,
# then we duplicate it in the current context to avoid passing it
# through an Enter node.
# This is a small optimization in general, but it is required when
# compiling with XLA, as XLA needs the concat input to be folded into a
# constant.
grad_context = control_flow_util.GetOutputContext(grad.op)
dim_context = control_flow_util.GetOutputContext(concat_dim.op)
if dim_context != grad_context:
value = tensor_util.constant_value(concat_dim)
concat_dim = constant_op.constant(value=value,
dtype=concat_dim.dtype)
# Using mod here for convenience since concat_dim is already verified
# in concat implementation to be within the allowed [-rank, rank) range.
non_neg_concat_dim = concat_dim % array_ops.rank(input_values[0])
# Get the inputs' tensor shapes
sizes = _ExtractInputShapes(input_values)
# The magic number of 16 was found through benchmarking a range of sizes
# on CPUs and a Maxwell TitanX. A speedup was seen in a large majority of
# cases when switching implementations at N=16, but it is possible that
# there will be a small number of performance regressions.
if len(sizes) > 16:
# extract the size of each input along the concat dimension
sizes = array_ops.squeeze(
array_ops.slice(array_ops.stack(sizes, axis=1),
[non_neg_concat_dim, 0], [1, -1]))
out_grads = array_ops.split(grad, sizes, non_neg_concat_dim)
else:
offset = gen_array_ops.concat_offset(non_neg_concat_dim, sizes)
for (begin, size) in zip(offset, sizes):
out_grads.append(array_ops.slice(grad, begin, size))
elif isinstance(grad, ops.IndexedSlices):
# Using mod here for convenience since concat_dim is already verified
# in concat implementation to be within the allowed [-rank, rank) range.
non_neg_concat_dim = concat_dim % array_ops.rank(input_values[0])
concat_dim_static = tensor_util.constant_value(concat_dim)
if concat_dim_static is None:
raise ValueError("Can only compute IndexedSlices gradient with "
"statically-known concat_dim")
if concat_dim_static < 0:
rank = tensor_util.constant_value(array_ops.rank(input_values[0]))
if rank is None:
raise ValueError(
"Can only compute IndexedSlices gradient with "
"negative concat_dim when first value rank is "
"statically-known.")
concat_dim_static %= rank
# Get the inputs' tensor shapes
sizes = [array_ops.shape(x) for x in input_values]
if concat_dim_static > 0:
# IndexedSlices, non_neg_concat_dim > 0. Each input gets IndexedSlices
# gradients with all the indices, but with grad.values sliced accordingly.
# This is like the Tensor case, except shape(grad.values)[0] is not equal
# to shape(sizes[i])[0], since only a subset of the dim-0 values are
# stored.
mask, begin = _CreateDenseMaskAndBegin(sizes, non_neg_concat_dim)
for size in sizes:
new_values = array_ops.slice(
grad.values, begin,
array_ops.concat(
[[-1], array_ops.slice(size, [1], [-1])], 0))
out_grads.append(
ops.IndexedSlices(new_values, grad.indices, size))
# Lint complains begin = begin + ...
begin = math_ops.add(begin, size * mask)
else:
# IndexedSlices, concat_dim == 0. Each input gets IndexedSlices gradients
# only for the relevant indices.
start = constant_op.constant(0, dtype=grad.indices.dtype)
for size in sizes:
size_concat_dim = array_ops.gather(size, non_neg_concat_dim)
if size_concat_dim.dtype != grad.indices.dtype:
size_concat_dim = math_ops.cast(size_concat_dim,
dtype=grad.indices.dtype)
end = start + size_concat_dim
# Compute the 1-D Tensor of indices relevant for this input.
indices_to_select = array_ops.squeeze(array_ops.where(
math_ops.logical_and(grad.indices >= start,
grad.indices < end)),
axis=[1])
new_indices = array_ops.gather(grad.indices,
indices_to_select) - start
new_values = array_ops.gather(grad.values, indices_to_select)
out_grads.append(
ops.IndexedSlices(new_values, new_indices, size))
start = end
else:
raise TypeError("Expected Tensor or IndexedSlices, got %s" %
type(grad))
return (out_grads + [None] if end_value_index <= dim_index else [None] +
out_grads)
def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index):
"""Gradient for concat op.
Args:
op: An operation.
grad: `Tensor` or `IndexedSlices` representing the gradients with respect
to each output of the op.
start_value_index: An integer index of the first value in the op.inputs.
end_value_index: An integer index of the last value in the op.inputs.
dim_index: An interger index of concat_dim or axis parameter in op.inputs.
Returns:
Tensors representing the partial gradients with respect to each input
of the op.
Raises:
ValueError: if concat_dim/axis is not statically known.
"""
def _CreateDenseMaskAndBegin(sizes, concat_dim):
"""Create variables for iteratively slicing a dense gradients tensor."""
# Since shape is 1-D, shape_of_shape = [rank-of-inputs]
shape_of_shape = array_ops.shape(sizes[0])
# Make a vector of length equal to the input's dimensions,
# with 0's everywhere and 1 in the concat dim position.
# Note: Can't use sparse_to_dense since it isn't GPU-capable (for now)
mask = array_ops.concat([
array_ops.fill(array_ops.expand_dims(concat_dim, 0), 0), [1],
array_ops.fill(shape_of_shape - concat_dim - 1, 0)
], 0)
begin = array_ops.fill(shape_of_shape, 0)
return mask, begin
def _ExtractInputShapes(inputs):
"""Extract the shapes of a set of input tensors."""
if context.executing_eagerly():
return array_ops.shape_n(inputs)
sizes = []
fully_known = True
for x in inputs:
input_shape = array_ops.shape(x)
if not isinstance(input_shape,
ops.Tensor) or input_shape.op.type != "Const":
fully_known = False
break
sizes.append(input_shape)
if fully_known:
return sizes
else:
return array_ops.shape_n(inputs)
# Degenerate concatenation, just return grad.
if len(op.inputs) == 2:
return grad + [None] if end_value_index <= dim_index else [None] + grad
concat_dim = op.inputs[dim_index]
input_values = op.inputs[start_value_index:end_value_index]
out_grads = []
if isinstance(grad, ops.Tensor):
if context.executing_eagerly():
# Using mod here for convenience since concat_dim is already verified
# in concat implementation to be within the allowed [-rank, rank) range.
non_neg_concat_dim = (
concat_dim._numpy().item(0) % input_values[0]._rank()) # pylint: disable=protected-access
# All inputs are guaranteed to be EagerTensors in eager mode
sizes = pywrap_tensorflow.TFE_Py_TensorShapeSlice(input_values,
non_neg_concat_dim)
out_grads = array_ops.split(grad, sizes, non_neg_concat_dim)
else:
if constant_op.is_constant(concat_dim):
# If concat_dim is a constant defined in a different context,
# then we duplicate it in the current context to avoid passing it
# through an Enter node.
# This is a small optimization in general, but it is required when
# compiling with XLA, as XLA needs the concat input to be folded into a
# constant.
grad_context = control_flow_util.GetOutputContext(grad.op)
dim_context = control_flow_util.GetOutputContext(concat_dim.op)
if dim_context != grad_context:
value = tensor_util.constant_value(concat_dim)
concat_dim = constant_op.constant(value=value, dtype=concat_dim.dtype)
# Using mod here for convenience since concat_dim is already verified
# in concat implementation to be within the allowed [-rank, rank) range.
non_neg_concat_dim = concat_dim % array_ops.rank(input_values[0])
# Get the inputs' tensor shapes
sizes = _ExtractInputShapes(input_values)
# The magic number of 16 was found through benchmarking a range of sizes
# on CPUs and a Maxwell TitanX. A speedup was seen in a large majority of
# cases when switching implementations at N=16, but it is possible that
# there will be a small number of performance regressions.
if len(sizes) > 16:
# extract the size of each input along the concat dimension
sizes = array_ops.squeeze(
array_ops.slice(
array_ops.stack(sizes, axis=1), [non_neg_concat_dim, 0],
[1, -1]))
out_grads = array_ops.split(grad, sizes, non_neg_concat_dim)
else:
offset = gen_array_ops.concat_offset(non_neg_concat_dim, sizes)
for (begin, size) in zip(offset, sizes):
out_grads.append(array_ops.slice(grad, begin, size))
elif isinstance(grad, ops.IndexedSlices):
# Using mod here for convenience since concat_dim is already verified
# in concat implementation to be within the allowed [-rank, rank) range.
non_neg_concat_dim = concat_dim % array_ops.rank(input_values[0])
concat_dim_static = tensor_util.constant_value(concat_dim)
if concat_dim_static is None:
raise ValueError("Can only compute IndexedSlices gradient with "
"statically-known concat_dim")
if concat_dim_static < 0:
rank = tensor_util.constant_value(array_ops.rank(input_values[0]))
if rank is None:
raise ValueError("Can only compute IndexedSlices gradient with "
"negative concat_dim when first value rank is "
"statically-known.")
concat_dim_static %= rank
# Get the inputs' tensor shapes
sizes = [array_ops.shape(x) for x in input_values]
if concat_dim_static > 0:
# IndexedSlices, non_neg_concat_dim > 0. Each input gets IndexedSlices
# gradients with all the indices, but with grad.values sliced accordingly.
# This is like the Tensor case, except shape(grad.values)[0] is not equal
# to shape(sizes[i])[0], since only a subset of the dim-0 values are
# stored.
mask, begin = _CreateDenseMaskAndBegin(sizes, non_neg_concat_dim)
for size in sizes:
new_values = array_ops.slice(
grad.values, begin,
array_ops.concat([[-1], array_ops.slice(size, [1], [-1])], 0))
out_grads.append(ops.IndexedSlices(new_values, grad.indices, size))
# Lint complains begin = begin + ...
begin = math_ops.add(begin, size * mask)
else:
# IndexedSlices, concat_dim == 0. Each input gets IndexedSlices gradients
# only for the relevant indices.
start = constant_op.constant(0, dtype=grad.indices.dtype)
for size in sizes:
size_concat_dim = array_ops.gather(size, non_neg_concat_dim)
if size_concat_dim.dtype != grad.indices.dtype:
size_concat_dim = math_ops.cast(
size_concat_dim, dtype=grad.indices.dtype)
end = start + size_concat_dim
# Compute the 1-D Tensor of indices relevant for this input.
indices_to_select = array_ops.squeeze(
array_ops.where(
math_ops.logical_and(grad.indices >= start,
grad.indices < end)),
axis=[1])
new_indices = array_ops.gather(grad.indices, indices_to_select) - start
new_values = array_ops.gather(grad.values, indices_to_select)
out_grads.append(ops.IndexedSlices(new_values, new_indices, size))
start = end
else:
raise TypeError("Expected Tensor or IndexedSlices, got %s" % type(grad))
return (out_grads + [None]
if end_value_index <= dim_index else [None] + out_grads)