MegEngine/imperative/python/test/unit/utils/test_cgtools.py

# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 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
import megengine.utils.comp_graph_tools as cgtools
from megengine.core.ops.builtin import Elemwise
from megengine.core.tensor import megbrain_graph as mgb_graph
from megengine.core.tensor.megbrain_graph import apply_normal_varnode
from megengine.core.tensor.utils import astensor1d
from megengine.jit import trace
from megengine.utils.network import Network


def make_dev_tensor(value, dtype=None, device=None):
    return megengine.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, device=device)
    add_op = Elemwise(Elemwise.Mode.ADD)
    mul_op = Elemwise(Elemwise.Mode.MUL)
    a_plus_a = apply_normal_varnode(add_op, a.outputs[0], a.outputs[0])[0]
    a_plus_a_mul_const = apply_normal_varnode(mul_op, a_plus_a, const)[0]
    rst = apply_normal_varnode(add_op, a_plus_a_mul_const, a.outputs[0])[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, device=device)
    add_op = Elemwise(Elemwise.Mode.ADD)
    mul_op = Elemwise(Elemwise.Mode.MUL)
    a_plus_a = apply_normal_varnode(add_op, a.outputs[0], a.outputs[0])[0]
    old_opr = a_plus_a.op
    a_plus_a_mul_const = apply_normal_varnode(mul_op, a_plus_a, const)[0]
    a_mul_a = apply_normal_varnode(mul_op, a.outputs[0], a.outputs[0])[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)
    outputs = mgb_graph.load_graph(file).output_vars_list

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

    assert var_idx == 0


def test_load_refcnt():
    graph = mgb_graph.Graph()
    varnode = graph.make_const(0)
    buf, _ = mgb_graph.dump_graph([varnode])
    ret = mgb_graph.load_graph(io.BytesIO(buf))
    graph, (varnode,) = ret.graph, ret.output_vars_list
    del ret
    del graph
    varnode.owner


def test_get_opr_seq():
    class Net(M.Module):
        def __init__(self):
            super().__init__()
            self.data = megengine.tensor(
                np.random.random((1, 1, 4, 4)), dtype=np.float32
            )

        def forward(self, input):
            A = input.shape[0]
            shape = astensor1d((A, A), self.data, dtype="int32", device=input.device)
            x = F.reshape(self.data, shape)
            o = input + x
            return o

    net = Net()
    input = megengine.tensor(np.random.random((4, 4)), dtype=np.float32)

    @trace(symbolic=True, capture_as_const=True)
    def func(inp, *, net=None):
        return net(inp)

    func(input, net=net)
    file = io.BytesIO()
    func.dump(file, optimize_for_inference=False)
    file.seek(0)
    outputs = mgb_graph.load_graph(file).output_vars_list

    seq_1 = cgtools.get_oprs_seq(outputs, True)
    assert len(seq_1) == 5

    seq_2 = cgtools.get_oprs_seq(outputs, False)
    assert len(seq_2) == 6


def test_topological_sort():
    @trace(symbolic=True, capture_as_const=True)
    def func(x, y):
        a = x + y
        a1 = F.relu(a)
        a2 = F.abs(a)
        a3 = F.ceil(a) * 2
        a4 = F.floor(a)
        r = a1 - a2
        r1 = a3 / a4
        return r, r1

    file = io.BytesIO()
    func(megengine.tensor(1.0), megengine.tensor(2.0))
    func.dump(
        file, optimize_for_inference=False, keep_opr_name=True, keep_opr_priority=True
    )
    file.seek(0)
    g = Network.load(file)
    oprseq1 = g.all_oprs
    gt = [
        "Host2DeviceCopy",
        "Host2DeviceCopy",
        "ADD",
        "RELU",
        "ABS",
        "CEIL",
        "ImmutableTensor",
        "MUL",
        "FLOOR",
        "SUB",
        "TRUE_DIV",
    ]
    for op, mode in zip(oprseq1, gt):
        if op.type == "Elemwise":
            assert op.params["mode"] == mode
        else:
            assert op.type == mode


def test_graph_function():
    class Net(M.Module):
        def forward(self, a, b):
            return a - b, a * b

    net = Net()

    @trace(symbolic=True, capture_as_const=True)
    def function(a, b, *, net=None):
        return net(a, b)

    a = np.array([1, 2, 3])
    b = np.array([3])
    x, y = function(megengine.tensor(a), megengine.tensor(b), net=net)

    file = io.BytesIO()
    function.dump(
        file,
        arg_names=["a", "b"],
        output_names=["x", "y"],
        optimize_for_inference=False,
    )
    file.seek(0)

    graph = cgtools.GraphInference(file)
    results = graph.run(inp_dict={"a": a, "b": b})
    np.testing.assert_equal(x.numpy(), results["x"])
    np.testing.assert_equal(y.numpy(), results["y"])

    results = graph.run(a, inp_dict={"b": b})
    np.testing.assert_equal(x.numpy(), results["x"])
    np.testing.assert_equal(y.numpy(), results["y"])

    results = graph.run(a, b)
    np.testing.assert_equal(x.numpy(), results["x"])
    np.testing.assert_equal(y.numpy(), results["y"])

    file.seek(0)

    graph1 = cgtools.GraphInference(file, outputs=["x"])
    results = graph1.run(inp_dict={"a": a, "b": b})
    np.testing.assert_equal(x.numpy(), results["x"])
    assert "y" not in results