#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.

import re
from collections import defaultdict
from enum import Enum

import gdb


class DiGraph(object):
    """
    Adapted from networkx: http://networkx.github.io/
    Represents a directed graph. Edges can store (key, value) attributes.
    """

    def __init__(self):
        # Map of node -> set of nodes
        self.adjacency_map = {}
        # Map of (node1, node2) -> map string -> arbitrary attribute
        # This will not be copied in subgraph()
        self.attributes_map = {}

    def neighbors(self, node):
        return self.adjacency_map.get(node, set())

    def edges(self):
        edges = []
        for node, neighbors in self.adjacency_map.items():
            for neighbor in neighbors:
                edges.append((node, neighbor))
        return edges

    def nodes(self):
        return self.adjacency_map.keys()

    def attributes(self, node1, node2):
        return self.attributes_map[(node1, node2)]

    def add_edge(self, node1, node2, **kwargs):
        if node1 not in self.adjacency_map:
            self.adjacency_map[node1] = set()
        if node2 not in self.adjacency_map:
            self.adjacency_map[node2] = set()
        self.adjacency_map[node1].add(node2)
        self.attributes_map[(node1, node2)] = kwargs

    def remove_node(self, node):
        self.adjacency_map.pop(node, None)
        for _, neighbors in self.adjacency_map.items():
            neighbors.discard(node)

    def subgraph(self, nodes):
        graph = DiGraph()
        for node in nodes:
            for neighbor in self.neighbors(node):
                if neighbor in nodes:
                    graph.add_edge(node, neighbor)
        return graph

    def node_link_data(self):
        """
        Returns the graph as a dictionary in a format that can be
        serialized.
        """
        data = {
            "directed": True,
            "multigraph": False,
            "graph": {},
            "links": [],
            "nodes": [],
        }

        # Do one pass to build a map of node -> position in nodes
        node_to_number = {}
        for node in self.adjacency_map.keys():
            node_to_number[node] = len(data["nodes"])
            data["nodes"].append({"id": node})

        # Do another pass to build the link information
        for node, neighbors in self.adjacency_map.items():
            for neighbor in neighbors:
                link = self.attributes_map[(node, neighbor)].copy()
                link["source"] = node_to_number[node]
                link["target"] = node_to_number[neighbor]
                data["links"].append(link)
        return data


def strongly_connected_components(G):  # noqa: C901
    """
    Adapted from networkx: http://networkx.github.io/
    Parameters
    ----------
    G : DiGraph
    Returns
    -------
    comp : generator of sets
        A generator of sets of nodes, one for each strongly connected
        component of G.
    """
    preorder = {}
    lowlink = {}
    scc_found = {}
    scc_queue = []
    i = 0  # Preorder counter
    for source in G.nodes():
        if source not in scc_found:
            queue = [source]
            while queue:
                v = queue[-1]
                if v not in preorder:
                    i = i + 1
                    preorder[v] = i
                done = 1
                v_nbrs = G.neighbors(v)
                for w in v_nbrs:
                    if w not in preorder:
                        queue.append(w)
                        done = 0
                        break
                if done == 1:
                    lowlink[v] = preorder[v]
                    for w in v_nbrs:
                        if w not in scc_found:
                            if preorder[w] > preorder[v]:
                                lowlink[v] = min([lowlink[v], lowlink[w]])
                            else:
                                lowlink[v] = min([lowlink[v], preorder[w]])
                    queue.pop()
                    if lowlink[v] == preorder[v]:
                        scc_found[v] = True
                        scc = {v}
                        while scc_queue and preorder[scc_queue[-1]] > preorder[v]:
                            k = scc_queue.pop()
                            scc_found[k] = True
                            scc.add(k)
                        yield scc
                    else:
                        scc_queue.append(v)


def simple_cycles(G):  # noqa: C901
    """
    Adapted from networkx: http://networkx.github.io/
    Parameters
    ----------
    G : DiGraph
    Returns
    -------
    cycle_generator: generator
       A generator that produces elementary cycles of the graph.
       Each cycle is represented by a list of nodes along the cycle.
    """

    def _unblock(thisnode, blocked, B):
        stack = set([thisnode])
        while stack:
            node = stack.pop()
            if node in blocked:
                blocked.remove(node)
                stack.update(B[node])
                B[node].clear()

    # Johnson's algorithm requires some ordering of the nodes.
    # We assign the arbitrary ordering given by the strongly connected comps
    # There is no need to track the ordering as each node removed as processed.
    # save the actual graph so we can mutate it here
    # We only take the edges because we do not want to
    # copy edge and node attributes here.
    subG = G.subgraph(G.nodes())
    sccs = list(strongly_connected_components(subG))
    while sccs:
        scc = sccs.pop()
        # order of scc determines ordering of nodes
        startnode = scc.pop()
        # Processing node runs 'circuit' routine from recursive version
        path = [startnode]
        blocked = set()  # vertex: blocked from search?
        closed = set()  # nodes involved in a cycle
        blocked.add(startnode)
        B = defaultdict(set)  # graph portions that yield no elementary circuit
        stack = [(startnode, list(subG.neighbors(startnode)))]
        while stack:
            thisnode, nbrs = stack[-1]
            if nbrs:
                nextnode = nbrs.pop()
                if nextnode == startnode:
                    yield path[:]
                    closed.update(path)
                elif nextnode not in blocked:
                    path.append(nextnode)
                    stack.append((nextnode, list(subG.neighbors(nextnode))))
                    closed.discard(nextnode)
                    blocked.add(nextnode)
                    continue
            # done with nextnode... look for more neighbors
            if not nbrs:  # no more nbrs
                if thisnode in closed:
                    _unblock(thisnode, blocked, B)
                else:
                    for nbr in subG.neighbors(thisnode):
                        if thisnode not in B[nbr]:
                            B[nbr].add(thisnode)
                stack.pop()
                path.pop()
        # done processing this node
        subG.remove_node(startnode)
        H = subG.subgraph(scc)  # make smaller to avoid work in SCC routine
        sccs.extend(list(strongly_connected_components(H)))


def find_cycle(graph):
    """
    Looks for a cycle in the graph. If found, returns the first cycle.
    If nodes a1, a2, ..., an are in a cycle, then this returns:
        [(a1,a2), (a2,a3), ... (an-1,an), (an, a1)]
    Otherwise returns an empty list.
    """
    cycles = list(simple_cycles(graph))
    if cycles:
        nodes = cycles[0]
        nodes.append(nodes[0])
        edges = []
        prev = nodes[0]
        for node in nodes[1:]:
            edges.append((prev, node))
            prev = node
        return edges
    else:
        return []


def get_stacktrace(thread_id):
    """
    Returns the stack trace for the thread id as a list of strings.
    """
    gdb.execute("thread %d" % thread_id, from_tty=False, to_string=True)
    output = gdb.execute("bt", from_tty=False, to_string=True)
    stacktrace_lines = output.strip().split("\n")
    return stacktrace_lines


def is_thread_blocked_with_frame(
    thread_id, top_line, expected_top_lines, expected_frame
):
    """
    Returns True if we found expected_top_line in top_line, and
    we found the expected_frame in the thread's stack trace.
    """
    if all(expected not in top_line for expected in expected_top_lines):
        return False
    stacktrace_lines = get_stacktrace(thread_id)
    return any(expected_frame in line for line in stacktrace_lines)


class MutexType(Enum):
    """Types of mutexes that we can detect deadlocks."""

    PTHREAD_MUTEX_T = "pthread_mutex_t"
    PTHREAD_RWLOCK_T = "pthread_rwlock_t"

    @staticmethod
    def get_mutex_type(thread_id, top_line):
        """
        Returns the probable mutex type, based on the first line
        of the thread's stack. Returns None if not found.
        """

        WAITLIST = [
            "__lll_lock_wait",
            "futex_abstimed_wait",
            "futex_abstimed_wait_cancelable",
            "futex_reltimed_wait",
            "futex_reltimed_wait_cancelable",
            "futex_wait",
            "futex_wait_cancelable",
        ]

        if is_thread_blocked_with_frame(thread_id, top_line, WAITLIST, "pthread_mutex"):
            return MutexType.PTHREAD_MUTEX_T
        if is_thread_blocked_with_frame(
            thread_id, top_line, WAITLIST, "pthread_rwlock"
        ):
            return MutexType.PTHREAD_RWLOCK_T
        return None

    @staticmethod
    def get_mutex_owner_and_address_func_for_type(mutex_type):
        """
        Returns a function to resolve the mutex owner and address for
        the given type. The returned function f has the following
        signature:

            f: args: (map of thread lwp -> thread id), blocked thread lwp
               returns: (lwp of thread owning mutex, mutex address)
                        or (None, None) if not found.

        Returns None if there is no function for this mutex_type.
        """
        if mutex_type == MutexType.PTHREAD_MUTEX_T:
            return get_pthread_mutex_t_owner_and_address
        if mutex_type == MutexType.PTHREAD_RWLOCK_T:
            return get_pthread_rwlock_t_owner_and_address
        return None


def print_cycle(graph, lwp_to_thread_id, cycle):
    """Prints the threads and mutexes involved in the deadlock."""
    for (m, n) in cycle:
        print(
            "Thread %d (LWP %d) is waiting on %s (0x%016x) held by "
            "Thread %d (LWP %d)"
            % (
                lwp_to_thread_id[m],
                m,
                graph.attributes(m, n)["mutex_type"].value,
                graph.attributes(m, n)["mutex"],
                lwp_to_thread_id[n],
                n,
            )
        )


def get_thread_info():
    """
    Returns a pair of:
    - map of LWP -> thread ID
    - map of blocked threads LWP -> potential mutex type
    """
    # LWP -> thread ID
    lwp_to_thread_id = {}

    # LWP -> potential mutex type it is blocked on
    blocked_threads = {}

    output = gdb.execute("info threads", from_tty=False, to_string=True)
    lines = output.strip().split("\n")[1:]
    regex = re.compile(r"[\s\*]*(\d+).*Thread.*\(LWP (\d+)\).*")
    for line in lines:
        try:
            thread_id = int(regex.match(line).group(1))
            thread_lwp = int(regex.match(line).group(2))
            lwp_to_thread_id[thread_lwp] = thread_id
            mutex_type = MutexType.get_mutex_type(thread_id, line)
            if mutex_type:
                blocked_threads[thread_lwp] = mutex_type
        except Exception:
            continue

    return (lwp_to_thread_id, blocked_threads)


def get_pthread_mutex_t_owner_and_address(lwp_to_thread_id, thread_lwp):
    """
    Finds the thread holding the mutex that this thread is blocked on.
    Returns a pair of (lwp of thread owning mutex, mutex address),
    or (None, None) if not found.
    """
    # Go up the stack to the pthread_mutex_lock frame
    gdb.execute(
        "thread %d" % lwp_to_thread_id[thread_lwp], from_tty=False, to_string=True
    )
    gdb.execute("frame 1", from_tty=False, to_string=True)

    # Get the owner of the mutex by inspecting the internal
    # fields of the mutex.
    try:
        mutex_info = gdb.parse_and_eval("mutex").dereference()
        mutex_owner_lwp = int(mutex_info["__data"]["__owner"])
        return (mutex_owner_lwp, int(mutex_info.address))
    except gdb.error:
        return (None, None)


def get_pthread_rwlock_t_owner_and_address(lwp_to_thread_id, thread_lwp):
    """
    If the thread is waiting on a write-locked pthread_rwlock_t, this will
    return the pair of:
        (lwp of thread that is write-owning the mutex, mutex address)
    or (None, None) if not found, or if the mutex is read-locked.
    """
    # Go up the stack to the pthread_rwlock_{rd|wr}lock frame
    gdb.execute(
        "thread %d" % lwp_to_thread_id[thread_lwp], from_tty=False, to_string=True
    )
    gdb.execute("frame 2", from_tty=False, to_string=True)

    # Get the owner of the mutex by inspecting the internal
    # fields of the mutex.
    try:
        rwlock_info = gdb.parse_and_eval("rwlock").dereference()
        rwlock_data = rwlock_info["__data"]
        field_names = ["__cur_writer", "__writer"]
        fields = rwlock_data.type.fields()
        field = [f for f in fields if f.name in field_names][0]
        rwlock_owner_lwp = int(rwlock_data[field])
        # We can only track the owner if it is currently write-locked.
        # If it is not write-locked or if it is currently read-locked,
        # possibly by multiple threads, we cannot find the owner.
        if rwlock_owner_lwp != 0:
            return (rwlock_owner_lwp, int(rwlock_info.address))
        else:
            return (None, None)
    except gdb.error:
        return (None, None)


class Deadlock(gdb.Command):
    """Detects deadlocks"""

    def __init__(self):
        super(Deadlock, self).__init__("deadlock", gdb.COMMAND_NONE)

    def invoke(self, arg, from_tty):
        """Prints the threads and mutexes in a deadlock, if it exists."""
        lwp_to_thread_id, blocked_threads = get_thread_info()

        # Nodes represent threads. Edge (A,B) exists if thread A
        # is waiting on a mutex held by thread B.
        graph = DiGraph()

        # Go through all the blocked threads and see which threads
        # they are blocked on, and build the thread wait graph.
        for thread_lwp, mutex_type in blocked_threads.items():
            get_owner_and_address_func = (
                MutexType.get_mutex_owner_and_address_func_for_type(mutex_type)
            )
            if not get_owner_and_address_func:
                continue
            mutex_owner_lwp, mutex_address = get_owner_and_address_func(
                lwp_to_thread_id, thread_lwp
            )
            if mutex_owner_lwp and mutex_address:
                graph.add_edge(
                    thread_lwp,
                    mutex_owner_lwp,
                    mutex=mutex_address,
                    mutex_type=mutex_type,
                )

        # A deadlock exists if there is a cycle in the graph.
        cycle = find_cycle(graph)
        if cycle:
            print("Found deadlock!")
            print_cycle(graph, lwp_to_thread_id, cycle)
        else:
            print("No deadlock detected. " "Do you have debug symbols installed?")


def load():
    # instantiate the Deadlock command
    Deadlock()
    print('Type "deadlock" to detect deadlocks.')


def info():
    return "Detect deadlocks"


if __name__ == "__main__":
    load()
