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提交 008ab086 编写于 作者: T tangwei12
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Merge branch 'release/1.0.0' of github.com:PaddlePaddle/Paddle into release/1.0.0

上级 da4129f2 8d16de73
隐藏空白更改
内联 并排
Showing with 437 addition and 217 deletion
paddle/contrib/float16/float16_transpiler.py
浏览文件 @ 008ab086
......@@ -102,8 +102,8 @@ class Float16Transpiler:
continue
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
......@@ -187,7 +187,7 @@ class Float16Transpiler:
shape=var.shape,
persistable=var.persistable)
find_op(var)
var.op.rename_output(var_name, tmp_var_name)
var.op._rename_output(var_name, tmp_var_name)
self.block._insert_op(
i,
type="cast",
......
paddle/fluid/API.spec
浏览文件 @ 008ab086
......@@ -6,26 +6,9 @@ paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=
paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.Operator.__init__ ArgSpec(args=['self', 'block', 'desc', 'type', 'inputs', 'outputs', 'attrs'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.Operator.all_attrs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.attr_type ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.block_attr_id ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.blocks_attr_ids ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.has_kernel ArgSpec(args=['self', 'op_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.input ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.output ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.rename_input ArgSpec(args=['self', 'old_name', 'new_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.rename_output ArgSpec(args=['self', 'old_name', 'new_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.set_attr ArgSpec(args=['self', 'name', 'val'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Operator.to_string ArgSpec(args=['self', 'throw_on_error'], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.get_var ArgSpec(args=['name', 'program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.name_scope ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
......@@ -170,6 +153,13 @@ paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......@@ -241,13 +231,6 @@ paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwarg
paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sampling_id ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sum ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.slice ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.shape ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
......
paddle/fluid/framework/ir/graph_traits.cc
浏览文件 @ 008ab086
......@@ -14,6 +14,8 @@
#include "paddle/fluid/framework/ir/graph_traits.h"
#include <vector>
namespace paddle {
namespace framework {
namespace ir {
......
paddle/fluid/operators/sampling_id_op.cc
浏览文件 @ 008ab086
......@@ -53,15 +53,16 @@ class SamplingIdOpMaker : public framework::OpProtoAndCheckerMaker {
SamplingId Operator.
A layer for sampling id from multinomial distribution from the
input. Sampling one id for one sample.)DOC");
AddAttr<float>("min", "Minimum value of random. [default 0.0].")
AddAttr<float>("min", "Minimum value of random. (float, default 0.0).")
.SetDefault(0.0f);
AddAttr<float>("max", "Maximun value of random. [default 1.0].")
AddAttr<float>("max", "Maximun value of random. (float, default 1.0).")
.SetDefault(1.0f);
AddAttr<int>("seed",
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. [default 0].")
AddAttr<int>(
"seed",
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. (int, default 0).")
.SetDefault(0);
}
};
......
paddle/fluid/operators/sequence_slice_op.h
浏览文件 @ 008ab086
......@@ -75,11 +75,11 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
}
for (size_t i = 0; i < n; ++i) {
PADDLE_ENFORCE_LT(0, offset_data[i],
PADDLE_ENFORCE_LE(0, offset_data[i],
"The offset[%d] must greater than zero.", i);
PADDLE_ENFORCE_LT(0, length_data[i],
"The length[%d] must greater than zero.", i);
PADDLE_ENFORCE_LT(lod[0][i] + offset_data[i] + length_data[i],
PADDLE_ENFORCE_LE(lod[0][i] + offset_data[i] + length_data[i],
lod[0][i + 1], "The target tensor's length overflow.");
}
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 008ab086
......@@ -285,12 +285,12 @@ void BindOpDesc(pybind11::module *m) {
.def("set_output", &pd::OpDesc::SetOutput)
.def("input_arg_names", &pd::OpDesc::InputArgumentNames)
.def("output_arg_names", &pd::OpDesc::OutputArgumentNames)
.def("rename_input", &pd::OpDesc::RenameInput)
.def("rename_output", &pd::OpDesc::RenameOutput)
.def("_rename_input", &pd::OpDesc::RenameInput)
.def("_rename_output", &pd::OpDesc::RenameOutput)
.def("has_attr", &pd::OpDesc::HasAttr)
.def("attr_type", &pd::OpDesc::GetAttrType)
.def("attr_names", &pd::OpDesc::AttrNames)
.def("set_attr", &pd::OpDesc::SetAttr)
.def("_set_attr", &pd::OpDesc::SetAttr)
.def("attr", &pd::OpDesc::GetAttr)
.def("set_block_attr", &pd::OpDesc::SetBlockAttr)
.def("set_blocks_attr", &pd::OpDesc::SetBlocksAttr)
......@@ -300,8 +300,8 @@ void BindOpDesc(pybind11::module *m) {
std::string ser(seriralized);
self.SetAttr(name, ser);
})
.def("block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("_block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("_blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("check_attrs", &pd::OpDesc::CheckAttrs)
.def("infer_shape", &pd::OpDesc::InferShape)
.def("infer_var_type", &pd::OpDesc::InferVarType)
......
paddle/scripts/paddle_build.sh
浏览文件 @ 008ab086
......@@ -629,10 +629,10 @@ EOF
function gen_capi_package() {
if [[ ${WITH_C_API} == "ON" ]]; then
install_prefix="${PADDLE_ROOT}/build/capi_output"
rm -rf $install_prefix
make DESTDIR="$install_prefix" install
cd $install_prefix/usr/local
capi_install_prefix=${INSTALL_PREFIX:-/paddle/build}/capi_output
rm -rf $capi_install_prefix
make DESTDIR="$capi_install_prefix" install
cd $capi_install_prefix/
ls | egrep -v "^Found.*item$" | xargs tar -czf ${PADDLE_ROOT}/build/paddle.tgz
fi
}
......
python/paddle/fluid/backward.py
浏览文件 @ 008ab086
......@@ -38,8 +38,8 @@ def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None):
op_desc = op_descs[i]
if isinstance(op_desc, tuple):
op_desc = op_desc[0]
op_desc.rename_input(old_name, new_name)
op_desc.rename_output(old_name, new_name)
op_desc._rename_input(old_name, new_name)
op_desc._rename_output(old_name, new_name)
def _create_op_desc_(op_type, inputs, outputs, attrs):
......@@ -70,7 +70,7 @@ def _create_op_desc_(op_type, inputs, outputs, attrs):
if isinstance(val, framework.Block):
op_desc.set_block_attr(name, val.desc)
else:
op_desc.set_attr(name, val)
op_desc._set_attr(name, val)
return op_desc
......@@ -346,7 +346,7 @@ def _append_backward_ops_(block,
grad_sub_block_list = []
# If the op has its own sub-block, deal with the sub-block first
if op.has_attr("sub_block"):
sub_block = program.block(op.block_attr_id("sub_block"))
sub_block = program.block(op._block_attr_id("sub_block"))
grad_sub_block = program._create_block()
grad_sub_block._set_forward_block_idx(sub_block.idx)
cb = _callback_lookup_(op)
......@@ -382,7 +382,7 @@ def _append_backward_ops_(block,
for op_desc in grad_op_descs:
new_op_desc = target_block.desc.append_op()
new_op_desc.copy_from(op_desc)
new_op_desc.set_attr(op_role_attr_name, backward)
new_op_desc._set_attr(op_role_attr_name, backward)
grad_to_var["__current_op_desc__"] = new_op_desc
if callbacks is not None:
assert (isinstance(callbacks, list))
......@@ -408,7 +408,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
for op_idx in range(start_op_idx, block.desc.op_size()):
op_desc = block.desc.op(op_idx)
if op_desc.has_attr("sub_block"):
sub_block = block.program.block(op_desc.block_attr_id("sub_block"))
sub_block = block.program.block(op_desc._block_attr_id("sub_block"))
_append_backward_vars_(sub_block, 0, grad_to_var, grad_info_map)
new_vars = set()
# create new gradient variables
......@@ -438,12 +438,12 @@ def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map):
op_desc = block.desc.op(op_idx)
for name in op_desc.input_arg_names():
if name in var_map:
op_desc.rename_input(name, var_map[name])
op_desc._rename_input(name, var_map[name])
for name in op_desc.output_arg_names():
if block.desc.find_var(name.encode("ascii")):
new_name = unique_name.generate(name)
op_desc.rename_output(name, new_name)
op_desc._rename_output(name, new_name)
var_map[name] = new_name
for g, ng in six.iteritems(var_map):
......@@ -542,9 +542,9 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
if loss.op is None:
raise ValueError("loss.op is None. Should not happend")
loss.op.set_attr(core.op_proto_and_checker_maker.kOpRoleAttrName(),
int(core.op_proto_and_checker_maker.OpRole.Forward) |
int(core.op_proto_and_checker_maker.OpRole.Loss))
loss.op._set_attr(core.op_proto_and_checker_maker.kOpRoleAttrName(),
int(core.op_proto_and_checker_maker.OpRole.Forward) |
int(core.op_proto_and_checker_maker.OpRole.Loss))
if callbacks is not None:
isinstance(callbacks, list)
......@@ -631,7 +631,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
attr_val = [p.name, g.name]
if g.op.has_attr(op_role_var_attr_name):
attr_val.extend(g.op.attr(op_role_var_attr_name))
g.op.set_attr(op_role_var_attr_name, attr_val)
g.op._set_attr(op_role_var_attr_name, attr_val)
return params_and_grads
......
python/paddle/fluid/clip.py
浏览文件 @ 008ab086
......@@ -75,8 +75,8 @@ class ErrorClipByValue(BaseErrorClipAttr):
clip_op_desc.set_type("clip")
clip_op_desc.set_input("X", [grad_name])
clip_op_desc.set_output("Out", [grad_name])
clip_op_desc.set_attr("min", self.min)
clip_op_desc.set_attr("max", self.max)
clip_op_desc._set_attr("min", self.min)
clip_op_desc._set_attr("max", self.max)
def error_clip_callback(block, context):
......
python/paddle/fluid/framework.py
浏览文件 @ 008ab086
......@@ -37,11 +37,9 @@ from . import unique_name
__all__ = [
'Program',
'Operator',
'default_startup_program',
'default_main_program',
'program_guard',
'get_var',
'name_scope',
]
......@@ -654,11 +652,11 @@ class Operator(object):
self._update_desc_attr(attr_name, attr_val)
self.desc.check_attrs()
if self.has_kernel(type):
if self._has_kernel(type):
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)
def has_kernel(self, op_type):
def _has_kernel(self, op_type):
return op_type not in self.OP_WITHOUT_KERNEL_SET
def to_string(self, throw_on_error):
......@@ -699,7 +697,7 @@ class Operator(object):
"""
return self.desc.input(name)
def rename_input(self, old_name, new_name):
def _rename_input(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
......@@ -710,9 +708,9 @@ class Operator(object):
Returns:
None
"""
self.desc.rename_input(old_name, new_name)
self.desc._rename_input(old_name, new_name)
def rename_output(self, old_name, new_name):
def _rename_output(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
......@@ -723,7 +721,7 @@ class Operator(object):
Returns:
None
"""
self.desc.rename_output(old_name, new_name)
self.desc._rename_output(old_name, new_name)
@property
def input_names(self):
......@@ -787,7 +785,7 @@ class Operator(object):
"""
return self.desc.attr_type(name)
def set_attr(self, name, val):
def _set_attr(self, name, val):
"""
Set the value of attribute by attribute's name.
......@@ -820,7 +818,7 @@ class Operator(object):
isinstance(val, core.ProgramDesc):
self.desc.set_serialized_attr(name, val.serialize_to_string())
else:
self.desc.set_attr(name, val)
self.desc._set_attr(name, val)
@property
def attr_names(self):
......@@ -839,7 +837,7 @@ class Operator(object):
"""
return self.desc.attr(name)
def block_attr_id(self, name):
def _block_attr_id(self, name):
"""
Get the block attribute's id by name.
......@@ -849,9 +847,9 @@ class Operator(object):
Returns:
int: the block index.
"""
return self.desc.block_attr_id(name)
return self.desc._block_attr_id(name)
def block_attr(self, name):
def _block_attr(self, name):
"""
Get the block attribute by name.
......@@ -862,11 +860,11 @@ class Operator(object):
block: the block attribute.
"""
id = self.block_attr_id(name)
id = self._block_attr_id(name)
assert (id >= 0 and id < len(self.block.program.blocks))
return self.block.program.blocks[id]
def blocks_attr(self, name):
def _blocks_attr(self, name):
"""
Get the blocks attribute by name.
......@@ -877,13 +875,13 @@ class Operator(object):
list: list of the blocks attribute.
"""
attrs = []
for i in self.blocks_attr_ids(name):
for i in self._blocks_attr_ids(name):
assert (i >= 0 and i < len(self.block.program.blocks))
attrs.append(self.block.program.blocks[i])
return attrs
def blocks_attr_ids(self, name):
def _blocks_attr_ids(self, name):
"""
Get the blocks attribute's ids by name.
......@@ -894,7 +892,7 @@ class Operator(object):
list: list of the blocks ids.
"""
return self.desc.blocks_attr_ids(name)
return self.desc._blocks_attr_ids(name)
def all_attrs(self):
"""
......@@ -908,11 +906,11 @@ class Operator(object):
for n in attr_names:
attr_type = self.desc.attr_type(n)
if attr_type == core.AttrType.BLOCK:
attr_map[n] = self.block_attr(n)
attr_map[n] = self._block_attr(n)
continue
if attr_type == core.AttrType.BLOCKS:
attr_map[n] = self.blocks_attr(n)
attr_map[n] = self._blocks_attr(n)
continue
attr_map[n] = self.attr(n)
......@@ -1786,7 +1784,7 @@ class Program(object):
for j in six.moves.range(block.op_size()):
op = block.op(j)
if op.has_attr('is_test'):
op.set_attr('is_test', True)
op._set_attr('is_test', True)
res.blocks = [
Block(res, i) for i in six.moves.range(res.desc.num_blocks())
]
......@@ -2160,7 +2158,7 @@ def program_guard(main_program, startup_program=None):
switch_startup_program(startup_program)
def get_var(name, program=None):
def _get_var(name, program=None):
"""
Get a variable by name from the global block of a program.
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 008ab086
......@@ -284,7 +284,7 @@ def detection_output(loc,
target_box=loc,
code_type='decode_center_size')
compile_shape = scores.shape
run_shape = ops.shape(scores)
run_shape = nn.shape(scores)
scores = nn.flatten(x=scores, axis=2)
scores = nn.softmax(input=scores)
scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
......@@ -697,7 +697,7 @@ def ssd_loss(location,
raise ValueError("Only support mining_type == max_negative now.")
num, num_prior, num_class = confidence.shape
conf_shape = ops.shape(confidence)
conf_shape = nn.shape(confidence)
def __reshape_to_2d(var):
return nn.flatten(x=var, axis=2)
......@@ -724,7 +724,7 @@ def ssd_loss(location,
target_label.stop_gradient = True
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
actual_shape = ops.slice(conf_shape, axes=[0], starts=[0], ends=[2])
actual_shape = nn.slice(conf_shape, axes=[0], starts=[0], ends=[2])
actual_shape.stop_gradient = True
conf_loss = nn.reshape(
x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 008ab086
......@@ -29,110 +29,29 @@ from .. import unique_name
from functools import reduce
__all__ = [
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'chunk_eval',
'sequence_conv',
'conv2d',
'conv3d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'pool3d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'sequence_expand_as',
'sequence_pad',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'hsigmoid',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'squeeze',
'unsqueeze',
'lod_reset',
'lrn',
'pad',
'pad_constant_like',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'scatter',
'sequence_scatter',
'random_crop',
'mean_iou',
'relu',
'log',
'crop',
'rank_loss',
'elu',
'relu6',
'pow',
'stanh',
'hard_sigmoid',
'swish',
'prelu',
'brelu',
'leaky_relu',
'soft_relu',
'flatten',
'sequence_mask',
'stack',
'pad2d',
'unstack',
'sequence_enumerate',
'expand',
'sequence_concat',
'scale',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
'elementwise_mul',
'elementwise_max',
'elementwise_min',
'elementwise_pow',
'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d', 'conv3d',
'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'pool3d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'conv3d_transpose',
'sequence_expand', 'sequence_expand_as', 'sequence_pad', 'lstm_unit',
'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod',
'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'topk',
'warpctc', 'sequence_reshape', 'transpose', 'im2sequence', 'nce',
'hsigmoid', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'autoincreased_step_counter', 'reshape', 'squeeze', 'unsqueeze',
'lod_reset', 'lrn', 'pad', 'pad_constant_like', 'label_smooth', 'roi_pool',
'dice_loss', 'image_resize', 'image_resize_short', 'resize_bilinear',
'gather', 'scatter', 'sequence_scatter', 'random_crop', 'mean_iou', 'relu',
'log', 'crop', 'rank_loss', 'elu', 'relu6', 'pow', 'stanh', 'hard_sigmoid',
'swish', 'prelu', 'brelu', 'leaky_relu', 'soft_relu', 'flatten',
'sequence_mask', 'stack', 'pad2d', 'unstack', 'sequence_enumerate',
'expand', 'sequence_concat', 'scale', 'elementwise_add', 'elementwise_div',
'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
'elementwise_pow', 'uniform_random_batch_size_like', 'gaussian_random',
'sampling_id', 'gaussian_random_batch_size_like', 'sum', 'slice', 'shape'
]
......@@ -6463,6 +6382,246 @@ def expand(x, expand_times, name=None):
return out
from paddle.fluid.framework import convert_np_dtype_to_dtype_
@templatedoc()
def uniform_random_batch_size_like(input,
shape,
dtype='float32',
input_dim_idx=0,
output_dim_idx=0,
min=-1.0,
max=1.0,
seed=0):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('uniform_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='uniform_random_batch_size_like',
inputs={'Input': input},
outputs={'Out': out},
attrs={
'shape': shape,
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx,
'min': min,
'max': max,
'seed': seed,
'dtype': c_dtype
})
return out
@templatedoc()
def gaussian_random(shape,
mean=0.0,
std=1.0,
seed=0,
dtype='float32',
use_mkldnn=False):
"""
${comment}
Args:
shape (tuple|list): ${shape_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): Output data type.
use_mkldnn (Bool): Only used in mkldnn kernel.
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random',
outputs={'Out': out},
attrs={
'shape': shape,
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype,
'use_mkldnn': use_mkldnn
})
return out
@templatedoc()
def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
"""
${comment}
Args:
x (Variable): ${x_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Float): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('sampling_id', **locals())
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='sampling_id',
inputs={'X': x},
outputs={'Out': out},
attrs={'min': min,
'max': max,
'seed': seed})
return out
@templatedoc()
def gaussian_random_batch_size_like(input,
shape,
input_dim_idx=0,
output_dim_idx=0,
mean=0.0,
std=1.0,
seed=0,
dtype='float32'):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('gaussian_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random_batch_size_like',
inputs={'Input': input},
outputs={'Out': out},
attrs={
'shape': shape,
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx,
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype
})
return out
@templatedoc()
def sum(x, use_mkldnn=False):
"""
${comment}
Args:
x (Variable): ${x_comment}
use_mkldnn (Bool): ${use_mkldnn_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('x'))
helper.append_op(
type='sum',
inputs={'X': x},
outputs={'Out': out},
attrs={'use_mkldnn': use_mkldnn})
return out
@templatedoc()
def slice(input, axes, starts, ends):
"""
${comment}
Args:
input (Variable): ${input_comment}.
axes (List): ${axes_comment}
starts (List): ${starts_comment}
ends (List): ${ends_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('slice', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
helper.append_op(
type='slice',
inputs={'Input': input},
outputs={'Out': out},
attrs={'axes': axes,
'starts': starts,
'ends': ends})
return out
@templatedoc()
def shape(input):
"""
${comment}
Args:
input (Variable): ${input_comment}
Returns:
out (Variable): ${out_comment}
"""
helper = LayerHelper('shape', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
helper.append_op(
type='shape', inputs={'Input': input}, outputs={'Out': out})
return out
def _elementwise_op(helper):
op_type = helper.layer_type
x = helper.kwargs.get('x', None)
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 008ab086
......@@ -45,13 +45,6 @@ __all__ = [
'logical_or',
'logical_xor',
'logical_not',
'uniform_random_batch_size_like',
'gaussian_random',
'sampling_id',
'gaussian_random_batch_size_like',
'sum',
'slice',
'shape',
'maxout',
]
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ 008ab086
......@@ -1488,7 +1488,7 @@ def wrap_decoder(trg_vocab_size,
if weight_sharing:
predict = layers.matmul(
x=dec_output,
y=fluid.get_var(word_emb_param_names[0]),
y=fluid.framework._get_var(word_emb_param_names[0]),
transpose_y=True)
else:
predict = layers.fc(input=dec_output,
......
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 008ab086
......@@ -264,6 +264,25 @@ class TestLRDecay(TranspilerTest):
])
class TestDecayedAdagrad(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
opt = fluid.optimizer.DecayedAdagrad(learning_rate=0.1)
opt.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
class TestLRDecayConditional(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
......
python/paddle/fluid/tests/unittests/test_infer_shape.py
浏览文件 @ 008ab086
......@@ -76,8 +76,8 @@ class TestInferShape(unittest.TestCase):
mul_op_desc.set_input("X", ["x"])
mul_op_desc.set_input("Y", ["y"])
mul_op_desc.set_output("Out", ["out"])
mul_op_desc.set_attr("x_num_col_dims", 1)
mul_op_desc.set_attr("y_num_col_dims", 1)
mul_op_desc._set_attr("x_num_col_dims", 1)
mul_op_desc._set_attr("y_num_col_dims", 1)
mul_op_desc.check_attrs()
mul_op_desc.infer_shape(block)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 008ab086
......@@ -541,7 +541,7 @@ class TestBook(unittest.TestCase):
with program_guard(program):
input = layers.data(
name="input", shape=[3, 100, 100], dtype="float32")
out = layers.shape(input, name="shape")
out = layers.shape(input)
self.assertIsNotNone(out)
print(str(program))
......@@ -758,6 +758,65 @@ class TestBook(unittest.TestCase):
out = layers.expand(x, [1, 2])
print(str(program))
def test_uniform_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.uniform_random_batch_size_like(input, [-1, 11])
self.assertIsNotNone(out)
print(str(program))
def test_gaussian_random(self):
program = Program()
with program_guard(program):
out = layers.gaussian_random(shape=[20, 30])
self.assertIsNotNone(out)
print(str(program))
def test_sampling_id(self):
program = Program()
with program_guard(program):
x = layers.data(
name="X",
shape=[13, 11],
dtype='float32',
append_batch_size=False)
out = layers.sampling_id(x)
self.assertIsNotNone(out)
print(str(program))
def test_gaussian_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.gaussian_random_batch_size_like(
input, shape=[-1, 11], mean=1.0, std=2.0)
self.assertIsNotNone(out)
print(str(program))
def test_sum(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.sum(input)
self.assertIsNotNone(out)
print(str(program))
def test_slice(self):
starts = [1, 0, 2]
ends = [3, 3, 4]
axes = [0, 1, 2]
program = Program()
with program_guard(program):
input = layers.data(
name="input", shape=[3, 4, 5, 6], dtype='float32')
out = layers.slice(input, axes=axes, starts=starts, ends=ends)
def test_softshrink(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_protobuf_descs.py
浏览文件 @ 008ab086
......@@ -38,40 +38,40 @@ class TestOpDesc(unittest.TestCase):
self.assertEqual(['z'], op.output("Out"))
self.assertEqual(["Out"], op.output_names())
op.set_attr("int_attr", 1)
op._set_attr("int_attr", 1)
self.assertEqual(1, op.attr("int_attr"))
self.assertTrue(op.has_attr("int_attr"))
self.assertEqual(core.AttrType.INT, op.attr_type("int_attr"))
op.set_attr("float_attr", -1.32)
op._set_attr("float_attr", -1.32)
self.assertAlmostEqual(-1.32, op.attr("float_attr"), delta=1e-4)
self.assertTrue(op.has_attr("float_attr"))
op.set_attr("bool_attr", False)
op._set_attr("bool_attr", False)
self.assertFalse(op.attr("bool_attr"))
op.set_attr("string_attr", "abc")
op._set_attr("string_attr", "abc")
self.assertEqual("abc", op.attr("string_attr"))
self.assertTrue(op.has_attr("string_attr"))
op.set_attr("ints_attr", [1, 2, 3])
op._set_attr("ints_attr", [1, 2, 3])
self.assertEqual([1, 2, 3], op.attr("ints_attr"))
expected = [1.2, 2.3, 3.4]
op.set_attr("floats_attr", expected)
op._set_attr("floats_attr", expected)
for e, a in zip(expected, op.attr("floats_attr")):
self.assertAlmostEqual(e, a, delta=1e-4)
op.set_attr("strings_attr", ["a", "b", "c"])
op._set_attr("strings_attr", ["a", "b", "c"])
self.assertEqual(["a", "b", "c"], op.attr("strings_attr"))
op.set_attr("bools_attr", [True, False, True])
op._set_attr("bools_attr", [True, False, True])
self.assertEqual([True, False, True], op.attr("bools_attr"))
self.assertEqual(8, len(op.attr_names()))
op.set_block_attr("block_attr", program_desc.block(0))
self.assertEqual(0, op.block_attr_id("block_attr"))
op.set_block_attr("_block_attr", program_desc.block(0))
self.assertEqual(0, op._block_attr_id("_block_attr"))
mul_op = block.append_op()
mul_op.set_type("mul")
......
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ 008ab086
......@@ -128,7 +128,7 @@ def op_to_code(op):
attr_type = op.desc.attr_type(name)
if attr_type == core.AttrType.BLOCK:
a = "{name} = block[{value}]".format(
name=name, type=attr_type, value=op.block_attr_id(name))
name=name, type=attr_type, value=op._block_attr_id(name))
attrs_str += a
if i != len(attr_names) - 1:
attrs_str += ", "
......@@ -136,7 +136,7 @@ def op_to_code(op):
if attr_type == core.AttrType.BLOCKS:
a = "{name} = blocks{value}".format(
name=name, type=attr_type, value=op.blocks_attr_ids(name))
name=name, type=attr_type, value=op._blocks_attr_ids(name))
attrs_str += a
if i != len(attr_names) - 1:
attrs_str += ", "
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 008ab086
......@@ -470,7 +470,10 @@ class DistributeTranspiler(object):
"""
# remove optimize ops and add a send op to main_program
# FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay?
lr_ops = self._get_lr_ops()
delete_ops(self.origin_program.global_block(), self.optimize_ops)
delete_ops(self.origin_program.global_block(), lr_ops)
self.origin_program.__str__()
if wait_port:
......@@ -668,7 +671,7 @@ in a single call.")
__clone_lr_op_sub_block__(cloned_op, program, new_sub_block)
# reset the block of op
op.set_attr('sub_block', new_sub_block)
op._set_attr('sub_block', new_sub_block)
# append lr decay ops to the child block if exists
lr_ops = self._get_lr_ops()
......@@ -862,7 +865,7 @@ to transpile() call.")
if op.type in [
"gaussian_random", "fill_constant", "uniform_random"
]:
op.set_attr("shape", list(new_outputs["Out"].shape))
op._set_attr("shape", list(new_outputs["Out"].shape))
s_prog.global_block().append_op(
type=op.type,
inputs=new_inputs,
......@@ -1428,6 +1431,9 @@ to transpile() call.")
elif op_type == "rmsprop":
if varkey in ["Moment", "MeanSquare"]:
return param_shape
elif op_type == "decayed_adagrad":
if varkey == "Moment":
return param_shape
elif op_type == "sgd":
pass
return orig_shape
......
python/paddle/fluid/transpiler/inference_transpiler.py
浏览文件 @ 008ab086
......@@ -163,7 +163,7 @@ class InferenceTranspiler(object):
next_op = self.block.ops[i + 1]
if next_op.type == 'relu':
# modify bnorm OP to include relu
current_op.set_attr("fuse_with_relu", True)
current_op._set_attr("fuse_with_relu", True)
# remove relu OP
self.block._remove_op(i + 1)
i = i + 1
......@@ -377,7 +377,7 @@ class InferenceTranspiler(object):
type=old_var.type,
dtype=old_var.dtype,
shape=old_var.shape)
op.rename_input(old_param_name, new_param_name)
op._rename_input(old_param_name, new_param_name)
self.scope.var(new_param_name)
tensor = self.scope.find_var(new_param_name).get_tensor()
......@@ -463,8 +463,8 @@ class InferenceTranspiler(object):
current_op = self.block.ops[i]
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
......
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