test_cgtools.py

# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io

import numpy as np
import pytest

import megengine
import megengine.functional as F
import megengine.module as M
from megengine import cgtools
from megengine.core.tensor import megbrain_graph as mgb_graph
from megengine.core.tensor.raw_tensor import as_raw_tensor
from megengine.jit import trace


def make_dev_tensor(value, dtype=None, device=None):
    return as_raw_tensor(value, dtype=dtype, device=device)._dev_tensor()


def test_replace_vars():
    g = mgb_graph.Graph()
    g.options.async_exec_level = 0b100
    device = "xpux"
    dtype = np.float32
    a = mgb_graph.InputNode(device=device, dtype=dtype, graph=g)
    const = g.make_const(1.234)
    a_plus_a = F.add(a.outputs[0], a.outputs[0])
    a_plus_a_mul_const = F.mul(a_plus_a, const)
    rst = F.add(a_plus_a_mul_const, a.outputs[0])
    (new,) = cgtools.replace_vars([rst._node], {const._node: a_plus_a._node})
    out = mgb_graph.OutputNode(mgb_graph.VarNode(new))
    func = g.compile(out.outputs[0])
    func.execute()
    x = make_dev_tensor(5.0, device=device)
    a.set_value(x)
    res = out.get_value().numpy()
    np.testing.assert_equal(res, np.array([105.0]))


def test_replace_oprs():
    g = mgb_graph.Graph()
    g.options.async_exec_level = 0b100
    device = "xpux"
    dtype = np.float32
    a = mgb_graph.InputNode(device=device, dtype=dtype, graph=g)
    const = g.make_const(1.25)
    a_plus_a = F.add(a.outputs[0], a.outputs[0])
    old_opr = a_plus_a.op
    a_plus_a_mul_const = F.mul(a_plus_a, const)
    a_mul_a = F.mul(a.outputs[0], a.outputs[0])
    new_opr = a_mul_a.op
    (new,) = cgtools.replace_oprs(
        [a_plus_a_mul_const._node], {old_opr._node: new_opr._node}
    )
    out = mgb_graph.OutputNode(mgb_graph.VarNode(new))
    func = g.compile(out.outputs[0])
    func.execute()
    x = make_dev_tensor(5.0, device=device)
    a.set_value(x)
    res = out.get_value().numpy()
    np.testing.assert_equal(res, np.array([5.0 * 5.0 * 1.25]))


def test_graph_traversal():
    net = M.Conv2d(3, 32, 3)

    @trace(symbolic=True, capture_as_const=True)
    def fun(data):
        x = net(data)
        return x

    data = np.random.random([1, 3, 224, 224]).astype(np.float32)
    for _ in range(3):
        fun(megengine.tensor(data))

    file = io.BytesIO()
    fun.dump(file, optimize_for_inference=False)
    file.seek(0)
    cg, _, outputs = mgb_graph.load_graph(file)

    _, map_vars, var2oprs, *_ = cgtools.graph_traversal(outputs)
    input_var = map_vars[1]
    _, var_idx = var2oprs[input_var.id][0]

    assert var_idx == 0