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#!/usr/bin/python3
# Copyright 2019, The Android Open Source Project
#
# 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.
"""Spec Visualizer
Visualize python spec file for test generator.
Invoked by ml/nn/runtime/test/specs/visualize_spec.sh;
See that script for details on how this script is used.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import json
import os
import sys
from string import Template
# Stuff from test generator
import test_generator as tg
from test_generator import ActivationConverter
from test_generator import BoolScalar
from test_generator import Configuration
from test_generator import DataTypeConverter
from test_generator import DataLayoutConverter
from test_generator import Example
from test_generator import Float16Scalar
from test_generator import Float32Scalar
from test_generator import Float32Vector
from test_generator import GetJointStr
from test_generator import IgnoredOutput
from test_generator import Input
from test_generator import Int32Scalar
from test_generator import Int32Vector
from test_generator import Internal
from test_generator import Model
from test_generator import Operand
from test_generator import Output
from test_generator import Parameter
from test_generator import ParameterAsInputConverter
from test_generator import RelaxedModeConverter
from test_generator import SymmPerChannelQuantParams
TEMPLATE_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), "spec_viz_template.html")
global_graphs = dict()
def FormatArray(data, is_scalar=False):
if is_scalar:
assert len(data) == 1
return str(data[0])
else:
return "[%s]" % (", ".join(str(i) for i in data))
def FormatDict(data):
return "<br/>".join("<b>%s:</b> %s"%(k.capitalize(), v) for k, v in data.items())
def GetOperandInfo(op):
op_info = {"lifetime": op.lifetime, "type": op.type.type}
if not op.type.IsScalar():
op_info["dimensions"] = FormatArray(op.type.dimensions)
if op.type.scale != 0:
op_info["scale"] = op.type.scale
op_info["zero point"] = op.type.zeroPoint
if op.type.type == "TENSOR_QUANT8_SYMM_PER_CHANNEL":
op_info["scale"] = FormatArray(op.type.extraParams.scales)
op_info["channel dim"] = op.type.extraParams.channelDim
return op_info
def FormatOperand(op):
# All keys and values in op_info will appear in the tooltip. We only display the operand data
# if the length is less than 10. This should be convenient enough for most parameters.
op_info = GetOperandInfo(op)
if isinstance(op, Parameter) and len(op.value) <= 10:
op_info["data"] = FormatArray(op.value, op.type.IsScalar())
template = "<span class='tooltip'><span class='tooltipcontent'>{tooltip_content}</span><a href=\"{inpage_link}\">{op_name}</a></span>"
return template.format(
op_name=str(op),
tooltip_content=FormatDict(op_info),
inpage_link="#details-operands-%d" % (op.model_index),
)
def GetSubgraph(example):
"""Produces the nodes and edges information for d3 visualization."""
node_index_map = {}
topological_order = []
def AddToTopologicalOrder(op):
if op not in node_index_map:
node_index_map[op] = len(topological_order)
topological_order.append(op)
# Get the topological order, both operands and operations are treated the same.
# Given that the example.model.operations is already topologically sorted, here we simply
# iterate through and insert inputs and outputs.
for op in example.model.operations:
for i in op.ins:
AddToTopologicalOrder(i)
AddToTopologicalOrder(op)
for o in op.outs:
AddToTopologicalOrder(o)
# Assign layers to the nodes.
layers = {}
for node in topological_order:
layers[node] = max([layers[i] for i in node.ins], default=-1) + 1
for node in reversed(topological_order):
layers[node] = min([layers[o] for o in node.outs], default=layers[node]+1) - 1
num_layers = max(layers.values()) + 1
# Assign coordinates to the nodes. Nodes are equally spaced.
CoordX = lambda index: (index + 0.5) * 200 # 200px spacing horizontally
CoordY = lambda index: (index + 0.5) * 100 # 100px spacing vertically
coords = {}
layer_cnt = [0] * num_layers
for node in topological_order:
coords[node] = (CoordX(layer_cnt[layers[node]]), CoordY(layers[node]))
layer_cnt[layers[node]] += 1
# Create edges and nodes dictionaries for d3 visualization.
OpName = lambda idx: "operation%d" % idx
edges = []
nodes = []
for ind, op in enumerate(example.model.operations):
for tensor in op.ins:
edges.append({
"source": str(tensor),
"target": OpName(ind)
})
for tensor in op.outs:
edges.append({
"target": str(tensor),
"source": OpName(ind)
})
nodes.append({
"index": ind,
"id": OpName(ind),
"name": op.optype,
"group": 2,
"x": coords[op][0],
"y": coords[op][1],
})
for ind, op in enumerate(example.model.operands):
nodes.append({
"index": ind,
"id": str(op),
"name": str(op),
"group": 1,
"x": coords[op][0],
"y": coords[op][1],
})
return {"nodes": nodes, "edges": edges}
# The following Get**Info methods will each return a list of dictionaries,
# whose content will appear in the tables and sidebar views.
def GetConfigurationsInfo(example):
return [{
"relaxed": str(example.model.isRelaxed),
"use shared memory": str(tg.Configuration.useSHM()),
"expect failure": str(example.expectFailure),
}]
def GetOperandsInfo(example):
ret = []
for index, op in enumerate(example.model.operands):
ret.append({
"index": index,
"name": str(op),
"group": "operand"
})
ret[-1].update(GetOperandInfo(op))
if isinstance(op, Parameter):
ret[-1]["data"] = FormatArray(op.value, op.type.IsScalar())
elif isinstance(op, Input):
ret[-1]["data"] = FormatArray(example.feedDicts[0][0][op], op.type.IsScalar())
elif isinstance(op, Output) and not isinstance(op, IgnoredOutput):
ret[-1]["data"] = FormatArray(example.feedDicts[0][1][op], op.type.IsScalar())
return ret
def GetOperationsInfo(example):
return [{
"index": index,
"name": op.optype,
"group": "operation",
"opcode": op.optype,
"inputs": ", ".join(FormatOperand(i) for i in op.ins),
"outputs": ", ".join(FormatOperand(o) for o in op.outs),
} for index,op in enumerate(example.model.operations)]
# TODO: Remove the unused fd from the parameter.
def ProcessExample(example, fd):
"""Process an example and save the information into the global dictionary global_graphs."""
global global_graphs
print(" Processing variation %s" % example.testName)
global_graphs[str(example.testName)] = {
"subgraph": GetSubgraph(example),
"details": {
"configurations": GetConfigurationsInfo(example),
"operands": GetOperandsInfo(example),
"operations": GetOperationsInfo(example)
}
}
def DumpHtml(spec_file, out_file):
"""Dump the final HTML file by replacing entries from a template file."""
with open(TEMPLATE_FILE, "r") as template_fd:
html_template = template_fd.read()
with open(out_file, "w") as out_fd:
out_fd.write(Template(html_template).substitute(
spec_name=os.path.basename(spec_file),
graph_dump=json.dumps(global_graphs),
))
def ParseCmdLine():
parser = argparse.ArgumentParser()
parser.add_argument("spec", help="the spec file")
parser.add_argument("-o", "--out", help="the output html path", default="out.html")
args = parser.parse_args()
tg.FileNames.InitializeFileLists(args.spec, "-", "-", "-")
tg.FileNames.NextFile()
return os.path.abspath(args.spec), os.path.abspath(args.out)
if __name__ == '__main__':
spec_file, out_file = ParseCmdLine()
print("Visualizing from spec: %s" % spec_file)
exec(open(spec_file, "r").read())
Example.DumpAllExamples(DumpExample=ProcessExample, example_fd=0)
DumpHtml(spec_file, out_file)
print("Output HTML file: %s" % out_file)
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