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debugger.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import sys
import six
import re
from .graphviz import GraphPreviewGenerator
from .proto import framework_pb2
from google.protobuf import text_format
_vartype2str_ = [
"UNK",
"LoDTensor",
"SelectedRows",
"FeedMinibatch",
"FetchList",
"StepScopes",
"LodRankTable",
"LoDTensorArray",
"PlaceList",
]
_dtype2str_ = [
"bool",
"int16",
"int32",
"int64",
"float16",
"float32",
"float64",
]
def repr_data_type(type):
return _dtype2str_[type]
def repr_tensor(proto):
return "tensor(type={}, shape={})".format(_dtype2str_[int(proto.data_type)],
str(proto.dims))
reprtpl = "{ttype} {name} ({reprs})"
def repr_lodtensor(proto):
if proto.type.type != framework_pb2.VarType.LOD_TENSOR:
return
level = proto.type.lod_tensor.lod_level
reprs = repr_tensor(proto.type.lod_tensor.tensor)
return reprtpl.format(
ttype="LoDTensor" if level > 0 else "Tensor",
name=proto.name,
reprs="level=%d, %s" % (level, reprs) if level > 0 else reprs)
def repr_selected_rows(proto):
if proto.type.type != framework_pb2.VarType.SELECTED_ROWS:
return
return reprtpl.format(
ttype="SelectedRows",
name=proto.name,
reprs=repr_tensor(proto.type.selected_rows))
def repr_tensor_array(proto):
if proto.type.type != framework_pb2.VarType.LOD_TENSOR_ARRAY:
return
return reprtpl.format(
ttype="TensorArray",
name=proto.name,
reprs="level=%d, %s" % (proto.type.tensor_array.lod_level,
repr_tensor(proto.type.lod_tensor.tensor)))
type_handlers = [
repr_lodtensor,
repr_selected_rows,
repr_tensor_array,
]
def repr_var(vardesc):
for handler in type_handlers:
res = handler(vardesc)
if res:
return res
def pprint_program_codes(program_desc):
reprs = []
for block_idx in range(program_desc.desc.num_blocks()):
block_desc = program_desc.block(block_idx)
block_repr = pprint_block_codes(block_desc)
reprs.append(block_repr)
return '\n'.join(reprs)
def pprint_block_codes(block_desc, show_backward=False):
def is_op_backward(op_desc):
if op_desc.type.endswith('_grad'): return True
def is_var_backward(var):
if "@GRAD" in var.parameter: return True
for arg in var.arguments:
if "@GRAD" in arg: return True
for var in op_desc.inputs:
if is_var_backward(var): return True
for var in op_desc.outputs:
if is_var_backward(var): return True
return False
def is_var_backward(var_desc):
return "@GRAD" in var_desc.name
if type(block_desc) is not framework_pb2.BlockDesc:
block_desc = framework_pb2.BlockDesc.FromString(
block_desc.desc.serialize_to_string())
var_reprs = []
op_reprs = []
for var in block_desc.vars:
if not show_backward and is_var_backward(var):
continue
var_reprs.append(repr_var(var))
for op in block_desc.ops:
if not show_backward and is_op_backward(op): continue
op_reprs.append(repr_op(op))
tpl = "// block-{idx} parent-{pidx}\n// variables\n{vars}\n\n// operators\n{ops}\n"
return tpl.format(
idx=block_desc.idx,
pidx=block_desc.parent_idx,
vars='\n'.join(var_reprs),
ops='\n'.join(op_reprs), )
def repr_attr(desc):
tpl = "{key}={value}"
valgetter = [
lambda attr: attr.i,
lambda attr: attr.f,
lambda attr: attr.s,
lambda attr: attr.ints,
lambda attr: attr.floats,
lambda attr: attr.strings,
lambda attr: attr.b,
lambda attr: attr.bools,
lambda attr: attr.block_idx,
lambda attr: attr.l,
]
key = desc.name
value = valgetter[desc.type](desc)
if key == "dtype":
value = repr_data_type(value)
return tpl.format(key=key, value=str(value)), (key, value)
def _repr_op_fill_constant(optype, inputs, outputs, attrs):
if optype == "fill_constant":
return "{output} = {data} [shape={shape}]".format(
output=','.join(outputs),
data=attrs['value'],
shape=str(attrs['shape']))
op_repr_handlers = [_repr_op_fill_constant, ]
def repr_op(opdesc):
optype = None
attrs = []
attr_dict = {}
is_target = None
inputs = []
outputs = []
tpl = "{outputs} = {optype}({inputs}{is_target}) [{attrs}]"
args2value = lambda args: args[0] if len(args) == 1 else str(list(args))
for var in opdesc.inputs:
key = var.parameter
value = args2value(var.arguments)
inputs.append("%s=%s" % (key, value))
for var in opdesc.outputs:
value = args2value(var.arguments)
outputs.append(value)
for attr in opdesc.attrs:
attr_repr, attr_pair = repr_attr(attr)
attrs.append(attr_repr)
attr_dict[attr_pair[0]] = attr_pair[1]
is_target = opdesc.is_target
for handler in op_repr_handlers:
res = handler(opdesc.type, inputs, outputs, attr_dict)
if res: return res
return tpl.format(
outputs=', '.join(outputs),
optype=opdesc.type,
inputs=', '.join(inputs),
attrs="{%s}" % ','.join(attrs),
is_target=", is_target" if is_target else "")
def draw_block_graphviz(block, highlights=None, path="./temp.dot"):
'''
Generate a debug graph for block.
Args:
block(Block): a block.
'''
graph = GraphPreviewGenerator("some graph")
# collect parameters and args
protostr = block.desc.serialize_to_string()
desc = framework_pb2.BlockDesc.FromString(six.binary_type(protostr))
def need_highlight(name):
if highlights is None: return False
for pattern in highlights:
assert type(pattern) is str
if re.match(pattern, name):
return True
return False
# draw parameters and args
vars = {}
for var in desc.vars:
# TODO(gongwb): format the var.type
# create var
if var.persistable:
varn = graph.add_param(
var.name,
str(var.type).replace("\n", "<br />", 1),
highlight=need_highlight(var.name))
else:
varn = graph.add_arg(var.name, highlight=need_highlight(var.name))
vars[var.name] = varn
def add_op_link_var(op, var, op2var=False):
for arg in var.arguments:
if arg not in vars:
# add missing variables as argument
vars[arg] = graph.add_arg(arg, highlight=need_highlight(arg))
varn = vars[arg]
highlight = need_highlight(op.description) or need_highlight(
varn.description)
if op2var:
graph.add_edge(op, varn, highlight=highlight)
else:
graph.add_edge(varn, op, highlight=highlight)
for op in desc.ops:
opn = graph.add_op(op.type, highlight=need_highlight(op.type))
for var in op.inputs:
add_op_link_var(opn, var, False)
for var in op.outputs:
add_op_link_var(opn, var, True)
graph(path, show=False)