提交 3099a8f3 编写于 作者: G guosheng

Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into add-reshape-reuse-input

test=develop
......@@ -515,20 +515,14 @@ void OpDesc::InferShape(const BlockDesc &block) const {
}
void OpDesc::InferVarType(BlockDesc *block) const {
// There are a few places that var type can be set.
// When VarDesc is created, default set to LOD_TENSOR.
// When output variable is created, default is defaut set to LOD_TENSOR.
// We limit here to be the only place that operator defines its customized
// var type inference. Hence, we don't do any "default" setting here.
auto &info = OpInfoMap::Instance().Get(this->Type());
if (info.infer_var_type_) {
info.infer_var_type_(*this, block);
} else {
// all output type is LoDTensor by default
VLOG(10) << this->Type()
<< " has not registered InferVarType. Set output variables to "
"LOD_TENSOR";
for (auto &out_pair : this->outputs_) {
for (auto &out_var_name : out_pair.second) {
block->FindRecursiveOrCreateVar(out_var_name)
.SetType(proto::VarType::LOD_TENSOR);
}
}
}
}
......
......@@ -324,10 +324,19 @@ class LayerHelper(object):
raise ValueError("no Parameter name %s found" % name)
return param
def create_tmp_variable(self, dtype, stop_gradient=False):
def create_variable_for_type_inference(self, dtype, stop_gradient=False):
"""Create a temporary variable that should be type inferred layer.
Note:
The default type will be set to LOD_TENSOR. However, when
the var is used as operator output, its type will be updated
based on operator's `VarTypeInference` implementation in
infer_var_type.
"""
return self.main_program.current_block().create_var(
name=unique_name.generate(".".join([self.name, 'tmp'])),
dtype=dtype,
type=core.VarDesc.VarType.LOD_TENSOR,
persistable=False,
stop_gradient=stop_gradient)
......@@ -388,7 +397,7 @@ class LayerHelper(object):
b = self.create_parameter(
attr=bias_attr, shape=size, dtype=input_var.dtype, is_bias=True)
tmp = self.create_tmp_variable(dtype=input_var.dtype)
tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
self.append_op(
type='elementwise_add',
inputs={'X': [input_var],
......@@ -414,7 +423,7 @@ class LayerHelper(object):
tmp = input_var
# NOTE(dzhwinter): some activation support inplace compution.
if not core.IsInplace(act_type):
tmp = self.create_tmp_variable(dtype=input_var.dtype)
tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
self.append_op(
type=act_type,
inputs={"X": [input_var]},
......
......@@ -80,8 +80,8 @@ def split_lod_tensor(input, mask, level=0):
"""
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_tmp_variable(dtype=input.dtype)
out_false = helper.create_tmp_variable(dtype=input.dtype)
out_true = helper.create_variable_for_type_inference(dtype=input.dtype)
out_false = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='split_lod_tensor',
inputs={
......@@ -131,7 +131,7 @@ def merge_lod_tensor(in_true, in_false, x, mask, level=0):
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
"""
helper = LayerHelper('merge_lod_tensor', **locals())
out = helper.create_tmp_variable(dtype=in_true.dtype)
out = helper.create_variable_for_type_inference(dtype=in_true.dtype)
helper.append_op(
type='merge_lod_tensor',
inputs={'X': x,
......@@ -524,7 +524,7 @@ class StaticRNN(object):
if not isinstance(o, Variable):
raise TypeError("step output takes a Variable")
tmp_o = self.helper.create_tmp_variable(dtype=o.dtype)
tmp_o = self.helper.create_variable_for_type_inference(dtype=o.dtype)
self.helper.append_op(
type='rnn_memory_helper',
inputs={'X': [o]},
......@@ -606,7 +606,8 @@ class StaticRNN(object):
pre_memories.append(mem.pre_mem.name)
mem_var = rnn_block.var(mem.mem.name)
assert isinstance(mem_var, Variable)
new_mem = self.helper.create_tmp_variable(dtype=mem_var.dtype)
new_mem = self.helper.create_variable_for_type_inference(
dtype=mem_var.dtype)
rnn_block.append_op(
type='rnn_memory_helper',
......@@ -813,7 +814,7 @@ def max_sequence_len(rank_table):
${out_comment}.
"""
helper = LayerHelper("max_seqence_len", **locals())
res = helper.create_tmp_variable(dtype="int64")
res = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="max_sequence_len",
inputs={"RankTable": rank_table},
......@@ -884,7 +885,7 @@ def array_to_lod_tensor(x, table):
lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
"""
helper = LayerHelper("array_to_lod_tensor", **locals())
tmp = helper.create_tmp_variable(dtype=x.dtype)
tmp = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="array_to_lod_tensor",
inputs={'X': x,
......@@ -915,7 +916,7 @@ def increment(x, value=1.0, in_place=True):
"""
helper = LayerHelper("increment", **locals())
if not in_place:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = x
helper.append_op(
......@@ -1012,7 +1013,7 @@ def less_than(x, y, force_cpu=None, cond=None, **ignored):
"""
helper = LayerHelper("less_than", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
......@@ -1051,7 +1052,7 @@ def equal(x, y, cond=None, **ignored):
"""
helper = LayerHelper("equal", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
helper.append_op(
......@@ -1098,7 +1099,7 @@ def array_read(array, i):
array,
Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
raise TypeError("array should be tensor array vairable")
out = helper.create_tmp_variable(dtype=array.dtype)
out = helper.create_variable_for_type_inference(dtype=array.dtype)
helper.append_op(
type='read_from_array',
inputs={'X': [array],
......@@ -1133,7 +1134,7 @@ def shrink_memory(x, i, table):
usage.
"""
helper = LayerHelper('shrink_memory', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='shrink_rnn_memory',
inputs={'X': [x],
......@@ -1170,7 +1171,7 @@ def array_length(array):
"""
helper = LayerHelper('array_length', **locals())
tmp = helper.create_tmp_variable(dtype='int64')
tmp = helper.create_variable_for_type_inference(dtype='int64')
tmp.stop_gradient = True
helper.append_op(
type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
......@@ -1590,7 +1591,7 @@ class DynamicRNN(object):
self.mem_dict = dict()
self.output_array = []
self.outputs = []
self.cond = self.helper.create_tmp_variable(dtype='bool')
self.cond = self.helper.create_variable_for_type_inference(dtype='bool')
self.cond.stop_gradient = False
self.while_op = While(self.cond)
self.input_array = []
......@@ -1924,7 +1925,7 @@ def reorder_lod_tensor_by_rank(x, rank_table):
helper.is_instance('x', Variable)
helper.is_instance('rank_table', Variable)
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reorder_lod_tensor_by_rank',
inputs={'X': [x],
......@@ -1958,7 +1959,7 @@ def is_empty(x, cond=None, **ignored):
"""
helper = LayerHelper("is_empty", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
elif not isinstance(cond, Variable):
raise TypeError("cond takes a variable")
......
......@@ -147,10 +147,11 @@ def rpn_target_assign(bbox_pred,
helper = LayerHelper('rpn_target_assign', **locals())
# Assign target label to anchors
loc_index = helper.create_tmp_variable(dtype='int32')
score_index = helper.create_tmp_variable(dtype='int32')
target_label = helper.create_tmp_variable(dtype='int32')
target_bbox = helper.create_tmp_variable(dtype=anchor_box.dtype)
loc_index = helper.create_variable_for_type_inference(dtype='int32')
score_index = helper.create_variable_for_type_inference(dtype='int32')
target_label = helper.create_variable_for_type_inference(dtype='int32')
target_bbox = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
helper.append_op(
type="rpn_target_assign",
inputs={
......@@ -282,7 +283,8 @@ def detection_output(loc,
scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
scores = nn.transpose(scores, perm=[0, 2, 1])
scores.stop_gradient = True
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
nmsed_outs = helper.create_variable_for_type_inference(
dtype=decoded_box.dtype)
helper.append_op(
type="multiclass_nms",
inputs={'Scores': scores,
......@@ -314,7 +316,7 @@ def iou_similarity(x, y, name=None):
"""
helper = LayerHelper("iou_similarity", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -351,7 +353,8 @@ def box_coder(prior_box,
helper = LayerHelper("box_coder", **locals())
if name is None:
output_box = helper.create_tmp_variable(dtype=prior_box.dtype)
output_box = helper.create_variable_for_type_inference(
dtype=prior_box.dtype)
else:
output_box = helper.create_variable(
name=name, dtype=prior_box.dtype, persistable=False)
......@@ -382,7 +385,7 @@ def polygon_box_transform(input, name=None):
"""
helper = LayerHelper("polygon_box_transform", **locals())
if name is None:
output = helper.create_tmp_variable(dtype=input.dtype)
output = helper.create_variable_for_type_inference(dtype=input.dtype)
else:
output = helper.create_variable(
name=name, dtype=prior_box.input, persistable=False)
......@@ -450,7 +453,7 @@ def detection_map(detect_res,
helper = LayerHelper("detection_map", **locals())
def __create_var(type):
return helper.create_tmp_variable(dtype=type)
return helper.create_variable_for_type_inference(dtype=type)
map_out = __create_var('float32')
accum_pos_count_out = out_states[0] if out_states else __create_var('int32')
......@@ -557,8 +560,9 @@ def bipartite_match(dist_matrix,
>>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
"""
helper = LayerHelper('bipartite_match', **locals())
match_indices = helper.create_tmp_variable(dtype='int32')
match_distance = helper.create_tmp_variable(dtype=dist_matrix.dtype)
match_indices = helper.create_variable_for_type_inference(dtype='int32')
match_distance = helper.create_variable_for_type_inference(
dtype=dist_matrix.dtype)
helper.append_op(
type='bipartite_match',
inputs={'DistMat': dist_matrix},
......@@ -644,8 +648,8 @@ def target_assign(input,
gt, matched_indices, mismatch_value=0)
"""
helper = LayerHelper('target_assign', **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
out_weight = helper.create_tmp_variable(dtype='float32')
out = helper.create_variable_for_type_inference(dtype=input.dtype)
out_weight = helper.create_variable_for_type_inference(dtype='float32')
helper.append_op(
type='target_assign',
inputs={
......@@ -816,9 +820,10 @@ def ssd_loss(location,
conf_loss = nn.reshape(
x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
conf_loss.stop_gradient = True
neg_indices = helper.create_tmp_variable(dtype='int32')
neg_indices = helper.create_variable_for_type_inference(dtype='int32')
dtype = matched_indices.dtype
updated_matched_indices = helper.create_tmp_variable(dtype=dtype)
updated_matched_indices = helper.create_variable_for_type_inference(
dtype=dtype)
helper.append_op(
type='mine_hard_examples',
inputs={
......@@ -998,8 +1003,8 @@ def prior_box(input,
max_sizes = [max_sizes]
attrs['max_sizes'] = max_sizes
box = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
box = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prior_box",
inputs={"Input": input,
......@@ -1337,8 +1342,8 @@ def anchor_generator(input,
'offset': offset
}
anchor = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
anchor = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="anchor_generator",
inputs={"Input": input},
......@@ -1384,7 +1389,7 @@ def roi_perspective_transform(input,
"""
helper = LayerHelper('roi_perspective_transform', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="roi_perspective_transform",
inputs={"X": input,
......@@ -1418,11 +1423,15 @@ def generate_proposal_labels(rpn_rois,
helper = LayerHelper('generate_proposal_labels', **locals())
rois = helper.create_tmp_variable(dtype=rpn_rois.dtype)
labels_int32 = helper.create_tmp_variable(dtype=gt_classes.dtype)
bbox_targets = helper.create_tmp_variable(dtype=rpn_rois.dtype)
bbox_inside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
bbox_outside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
labels_int32 = helper.create_variable_for_type_inference(
dtype=gt_classes.dtype)
bbox_targets = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
bbox_inside_weights = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
bbox_outside_weights = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
helper.append_op(
type="generate_proposal_labels",
......@@ -1504,8 +1513,10 @@ def generate_proposals(scores,
"""
helper = LayerHelper('generate_proposals', **locals())
rpn_rois = helper.create_tmp_variable(dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_tmp_variable(dtype=scores.dtype)
rpn_rois = helper.create_variable_for_type_inference(
dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_variable_for_type_inference(
dtype=scores.dtype)
helper.append_op(
type="generate_proposals",
inputs={
......
......@@ -954,7 +954,7 @@ def read_file(reader):
"""
helper = LayerHelper('read_file')
out = [
helper.create_tmp_variable(
helper.create_variable_for_type_inference(
stop_gradient=True, dtype='float32')
for _ in range(len(reader.desc.shapes()))
]
......
......@@ -202,10 +202,12 @@ def generate_layer_fn(op_type):
out_var = out[0] if (isinstance(out, list) or
isinstance(out, tuple)) else out
else:
out_var = helper.create_tmp_variable(dtype=dtype)
out_var = helper.create_variable_for_type_inference(dtype=dtype)
outputs[o_name] = [out_var]
for name in intermediate_output_names:
outputs[name] = [helper.create_tmp_variable(dtype=dtype)]
outputs[name] = [
helper.create_variable_for_type_inference(dtype=dtype)
]
helper.append_op(
type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs)
return helper.append_activation(out_var)
......@@ -229,7 +231,7 @@ def generate_layer_fn_noattr(op_type):
def func(x, name=None):
helper = LayerHelper(op_type, **locals())
output = helper.create_tmp_variable(dtype=x.dtype)
output = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": output})
return output
......
......@@ -58,11 +58,11 @@ def accuracy(input, label, k=1, correct=None, total=None):
"""
helper = LayerHelper("accuracy", **locals())
topk_out, topk_indices = nn.topk(input, k=k)
acc_out = helper.create_tmp_variable(dtype="float32")
acc_out = helper.create_variable_for_type_inference(dtype="float32")
if correct is None:
correct = helper.create_tmp_variable(dtype="int64")
correct = helper.create_variable_for_type_inference(dtype="int64")
if total is None:
total = helper.create_tmp_variable(dtype="int64")
total = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="accuracy",
inputs={
......@@ -124,8 +124,8 @@ def auc(input,
auc_out=fluid.layers.auc(input=prediction, label=label)
"""
helper = LayerHelper("auc", **locals())
auc_out = helper.create_tmp_variable(dtype="float64")
batch_auc_out = helper.create_tmp_variable(dtype="float64")
auc_out = helper.create_variable_for_type_inference(dtype="float64")
batch_auc_out = helper.create_variable_for_type_inference(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
# for batch auc
......
此差异已折叠。
......@@ -152,7 +152,7 @@ def cast(x, dtype):
result = fluid.layers.cast(x=data, dtype='float64')
"""
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='cast',
inputs={'X': [x]},
......@@ -184,7 +184,7 @@ def concat(input, axis=0, name=None):
out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
"""
helper = LayerHelper('concat', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='concat',
inputs={'X': input},
......@@ -221,7 +221,8 @@ def sums(input, out=None):
"""
helper = LayerHelper('sum', **locals())
if out is None:
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='sum',
inputs={'X': input},
......@@ -252,7 +253,7 @@ def assign(input, output=None):
"""
helper = LayerHelper('assign', **locals())
if output is None:
output = helper.create_tmp_variable(dtype=input.dtype)
output = helper.create_variable_for_type_inference(dtype=input.dtype)
if isinstance(input, Variable):
helper.append_op(
type='assign', inputs={'X': [input]}, outputs={'Out': [output]})
......@@ -311,7 +312,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
helper = LayerHelper("fill_constant", **locals())
if out is None:
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant',
inputs={},
......@@ -358,7 +359,7 @@ def fill_constant_batch_size_like(input,
${out_comment}.
"""
helper = LayerHelper("fill_constant_batch_size_like", **locals())
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant_batch_size_like',
inputs={'Input': input},
......@@ -396,7 +397,7 @@ def argmin(x, axis=0):
out = fluid.layers.argmin(x=in, axis=-1)
"""
helper = LayerHelper("arg_min", **locals())
out = helper.create_tmp_variable(VarDesc.VarType.INT64)
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_min',
inputs={'X': x},
......@@ -427,7 +428,7 @@ def argmax(x, axis=0):
out = fluid.layers.argmax(x=in, axis=-1)
"""
helper = LayerHelper("arg_max", **locals())
out = helper.create_tmp_variable(VarDesc.VarType.INT64)
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_max',
inputs={'X': x},
......@@ -477,8 +478,10 @@ def argsort(input, axis=-1, name=None):
out, indices = fluid.layers.argsort(input, axis=0)
"""
helper = LayerHelper("argsort", **locals())
out = helper.create_tmp_variable(dtype=input.dtype, stop_gradient=True)
ids = helper.create_tmp_variable(VarDesc.VarType.INT64, stop_gradient=True)
out = helper.create_variable_for_type_inference(
dtype=input.dtype, stop_gradient=True)
ids = helper.create_variable_for_type_inference(
VarDesc.VarType.INT64, stop_gradient=True)
helper.append_op(
type='argsort',
inputs={'X': input},
......@@ -562,7 +565,7 @@ def reverse(x, axis):
if isinstance(axis, int):
axis = [axis]
helper = LayerHelper("reverse", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reverse',
inputs={'Input': x},
......@@ -654,7 +657,7 @@ def has_inf(x):
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isinf", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isinf", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -670,7 +673,7 @@ def has_nan(x):
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isnan", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -687,6 +690,6 @@ def isfinite(x):
Variable: The tensor variable storing the output, contains a bool value.
"""
helper = LayerHelper("isfinite", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -151,7 +151,7 @@ class L2DecayRegularizer(WeightDecayRegularizer):
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
type=core.VarDesc.VarType.LOD_TENSOR)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
......@@ -228,7 +228,7 @@ class L1DecayRegularizer(WeightDecayRegularizer):
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
type=core.VarDesc.VarType.LOD_TENSOR)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
......
......@@ -30,7 +30,6 @@ class TestSliceVar(unittest.TestCase):
var = program.global_block().create_var(
name=str(random.randint(10000, 99999)),
persistable=True,
# dtype=core.VarDesc.VarType.LOD_TENSOR,
shape=shape)
var_list.append(var)
blocks = slice_variable(var_list, 10, min_size)
......
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