import sys
import threading
import time
import unittest
from enum import Enum
import random
import torch
from datetime import timedelta
import torch.distributed as dist
import torch.distributed.autograd as dist_autograd
import torch.distributed.rpc as rpc
import torch.testing._internal.dist_utils
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.distributed.rpc import RRef
from torch.testing._internal.common_utils import IS_MACOS
from torch.testing._internal.dist_utils import (
    dist_init,
    initialize_pg,
    wait_until_node_failure,
    worker_name,
)
from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import (
    RpcAgentTestFixture,
)
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu


# Right now we test up to 3-layer nested rpc calls.
# rpc_done[1] and ctx_ids[1] represent rpc is done in prev rank, and context id
# sent from prev rank respectively.
# rpc_done[2] and ctx_ids[2] represents for prev of prev rank.
# rpc_done[3] and ctx_ids[3] represents for prev of prev of prev rank.
# rpc_done[0] and ctx_ids[0] represents for current rank, but mostly not used.
rpc_done = [False, False, False, False]
ctx_ids = [-1, -1, -1, -1]

known_context_ids = set()

requires_grad_tensor = torch.ones(3, 3, requires_grad=True)

# Send rpc done info and context_id to
# dst_rank = (self.rank + rank_distance) % self.world_size
# we don't need a lock here since the GIL is held while executing remote
# python UDFs, so access is serialized across several workers.
def _set_rpc_done(ctx_id, rank_distance):
    global rpc_done
    global ctx_ids
    global known_context_ids
    rpc_done[rank_distance] = True
    ctx_ids[rank_distance] = ctx_id
    known_context_ids.add(ctx_id)


def _check_rpc_done(rank_distance):
    while not rpc_done[rank_distance]:
        time.sleep(0.1)


def _torch_ones(sizes, requires_grad=False):
    return torch.ones(sizes, requires_grad=requires_grad)

# This method must be called on the rref owner, and verifies that the grad of
# rref tensor equals to the given grad.
def _compare_owner_value(context_id, rref, grad):
    grads = dist_autograd.get_gradients(context_id)
    return torch.equal(grads[rref.local_value()], grad)


def create_tensor():
    return torch.ones((3, 3), requires_grad=True)


@torch.jit.script
def create_torchscript_tensor() -> torch.Tensor:
    return torch.ones((3, 3)).requires_grad_()


def my_py_add(t1, t2):
    return torch.add(t1, t2)


def my_scalar_add(a, b):
    return a + b


def my_rref_add(rref_t1, t2):
    ret = torch.add(rref_t1.local_value(), t2)
    return ret


@torch.jit.script
def my_script_add(t1, t2):
    return torch.add(t1, t2)


@torch.jit.script
def my_script_ref_add(ref_t1: RRef[torch.Tensor], t2: torch.Tensor) -> torch.Tensor:
    t1 = ref_t1.to_here()
    return torch.add(t1, t2)


def my_nested_rref_add(dst, rref_t1, t2):
    return rpc.rpc_sync(dst, my_rref_add, args=(rref_t1, t2))


def ret_requires_grad():
    return requires_grad_tensor


def my_py_nested_call(t1, t2, dst, world_size, hops):
    next_dst = (dst + 1) % world_size
    if hops > 0:
        return rpc.rpc_sync(
            worker_name(next_dst),
            my_py_nested_call,
            args=(t1, t2, next_dst, world_size, hops - 1),
        )
    else:
        return rpc.rpc_sync(worker_name(next_dst), my_py_add, args=(t1, t2))


# after dist autograd context is cleaned up, it should be cleaned up on other
# nodes. This helper allows timeout_seconds for those RPCs to be completed, and
# ensures that all the contexts have been cleaned up in that timeframe.any
def _all_contexts_cleaned_up(timeout_seconds=10):
    global known_context_ids
    start = time.time()
    context_id_to_raised = set()
    while (
        time.time() - start < timeout_seconds
        and context_id_to_raised != known_context_ids
    ):
        for context_id in known_context_ids:
            try:
                dist_autograd._retrieve_context(context_id)
            except RuntimeError:
                context_id_to_raised.add(context_id)
    # all contexts have been cleaned up if trying to retrieve any context resulted in a RuntimeError.
    success = context_id_to_raised == known_context_ids
    return success


# This function creates a dis atugorad context, run rpc_sync on the given ps,
# and then blocks until the ps has verified the grads are correctly accumulated.
def _run_trainer(rref_t1, t2, ps, rank_diff):
    with dist_autograd.context() as context_id:
        ret = rpc.rpc_sync(ps, my_rref_add, args=(rref_t1, t2))
        dist_autograd.backward(context_id, [ret.sum()])
        # prevent deleting dist autograd context
        rpc.rpc_sync(ps, _set_rpc_done, args=(context_id, rank_diff))
        rpc.rpc_sync(ps, _check_rpc_done, args=(0,))

# This function is the same as _run_trainer, except rpc calls torchscript
# function "my_script_ref_add" instead of python funciton "my_rref_add"
def _run_trainer_torchscript(rref_t1, t2, ps, rank_diff):
    with dist_autograd.context() as context_id:
        ret = rpc.rpc_sync(ps, my_script_ref_add, args=(rref_t1, t2))
        dist_autograd.backward(context_id, [ret.sum()])
        # prevent deleting dist autograd context
        rpc.rpc_sync(ps, _set_rpc_done, args=(context_id, rank_diff))
        rpc.rpc_sync(ps, _check_rpc_done, args=(0,))


class SimulateBackwardError(Function):
    _simulate_error = True

    @staticmethod
    def forward(ctx, input):
        return input

    @staticmethod
    @once_differentiable
    def backward(ctx, input):
        if SimulateBackwardError._simulate_error:
            raise Exception("Simulate error on backward pass")
        else:
            return input


class ExecMode(Enum):
    LOCAL = 1  # Run the operation locally.
    RPC_SYNC = 2  # Run the operation using rpc_sync
    REMOTE = 3  # Run the operation using remote.
    RPC_ASYNC = 4  # Run the operation using rpc_async


class DistAutogradTest(RpcAgentTestFixture):
    def _exec_func_with_dst(self, dst, exec_mode, method, *args):
        if ExecMode.LOCAL == exec_mode:
            if len(args) == 1 and isinstance(args[0], list):
                return method(*args[0])
            return method(*args)
        elif ExecMode.RPC_SYNC == exec_mode:
            return rpc.rpc_sync(worker_name(dst), method, args=(args))
        elif ExecMode.REMOTE == exec_mode:
            return rpc.remote(worker_name(dst), method, args=(args)).to_here()
        elif ExecMode.RPC_ASYNC == exec_mode:
            fut = rpc.rpc_async(worker_name(dst), method, args=(args))
            return fut.wait()
        else:
            raise ValueError("Unrecognized ExecMode {}".format(exec_mode))

    def _exec_func(self, exec_mode, method, *args):
        return self._exec_func_with_dst(
            self._next_rank(), exec_mode, method, *args
        )

    def _next_rank(self):
        if hasattr(self, "dst_rank"):
            self.dst_rank = (self.dst_rank + 1) % self.world_size
            if self.dst_rank == self.rank:
                return self._next_rank()
        else:
            self.dst_rank = (self.rank + 1) % self.world_size
        return self.dst_rank

    def _check_rpc_done(self, rank_distance):
        _check_rpc_done(rank_distance)

    @dist_init
    def test_autograd_context(self):
        # Verify max possible id.
        max_auto_increment = 281474976710655
        self.assertEqual(
            max_auto_increment + (self.worker_id << 48), dist_autograd._get_max_id()
        )

        context_ids = []
        for i in range(200):
            with dist_autograd.context() as context_id:
                self.assertEqual(
                    context_id,
                    dist_autograd._retrieve_context(context_id)._context_id(),
                )
                # First 16 bits should be worker_id.
                self.assertEqual(self.worker_id, context_id >> 48)
                context_ids.append(context_id)

        for context_id in context_ids:
            with self.assertRaisesRegex(
                RuntimeError,
                "Could not find autograd context with id: {}".format(context_id),
            ):
                dist_autograd._retrieve_context(context_id)

    @dist_init
    def test_nested_context(self):
        with dist_autograd.context() as context_id:
            # Nested contexts not supported.
            with self.assertRaisesRegex(
                RuntimeError, "Already have an autograd context id for this thread"
            ):
                with dist_autograd.context() as context_id:
                    pass

    # For current context, this rank sends t1 and t2 tensors to dst_rank,
    # then get t3 = torch.add(t1, t2) result tensor.
    # For the current context in this rank, it expects graph like this:
    #  send function:
    #              rpcSendBackward
    #                  /          \
    #  t1.AccumulateGrad         t2.AccumulateGrad
    #
    #  recv function:
    #
    #            |
    #          t3.rpcRecvBackward
    #
    def _verify_graph_for_first_rpc_call(
        self, send_function, recv_function, t1, t2, ret
    ):
        # Retrieve the next functions in the graph.
        next_funcs = send_function.next_functions
        self.assertEqual(2, len(next_funcs))

        # We should now hit t1 and t2 in the autograd graph.
        self.assertEqual("torch::autograd::AccumulateGrad", next_funcs[0][0].name())
        self.assertEqual(t1, next_funcs[0][0].variable)
        self.assertEqual(0, next_funcs[0][1])
        self.assertEqual("torch::autograd::AccumulateGrad", next_funcs[1][0].name())
        self.assertEqual(t2, next_funcs[1][0].variable)
        self.assertEqual(0, next_funcs[1][1])

        # Test recv functions.
        self.assertEqual(ret.grad_fn, recv_function)

    # For a context passed from previous nested chain calls, this rank
    # receives two tensors t1 and t2, executes torch.add(t1, t2) and sends
    # result tensor t3 back.
    # For this context in this rank, it expects graph like this:
    #  send and recv functions:
    #       rpcSendBackward
    #           |
    #          t3.AddBackward0
    #          /             \
    # t1.recvRpcBackward    t2.recvRpcBackward
    def _verify_graph_for_rpc_call_exec(self, send_function):
        # Verify next function is AddBackward0
        next_funcs = send_function.next_functions
        self.assertEqual(1, len(next_funcs))
        add_backward_fn = next_funcs[0][0]
        self.assertEqual("AddBackward0", add_backward_fn.name())

        # Verify the next two functions are the same recv backward function.
        next_funcs = add_backward_fn.next_functions
        self.assertEqual(2, len(next_funcs))
        self.assertEqual(
            "torch::distributed::autograd::RecvRpcBackward", next_funcs[0][0].name()
        )
        self.assertEqual(
            "torch::distributed::autograd::RecvRpcBackward", next_funcs[1][0].name()
        )
        self.assertEqual(next_funcs[0][0], next_funcs[1][0])

    # For a context passed from previous nested chain calls, this rank
    # receives two tensors t1 and t2, forwards t1 and t2 tensors using
    # nested rpc call to next dst. In return route, receive result tensor t3
    # from next dst and forwarding t3 back to previous calls.
    # For this context in this rank, it expects graph like this:
    #  send and recv functions for receiving and forwarding t1 and t2:
    #       rpcSendBackward
    #          /          \
    # t1.recvRpcBackward    t2.recvRpcBackward
    #  send and recv functions for receiving and forwarding t3:
    #       rpcSendBackward
    #             |
    #           t3.recvRpcBackward
    def _verify_graph_for_nested_rpc_call(self, ctx):
        send_functions = ctx._send_functions()
        self.assertEqual(2, len(send_functions))

        # For send function when making nest rpc call,
        # next functions of the send function are two recv functions
        # for received two tensors from previous call
        next_funcs = list(send_functions.values())[0].next_functions
        self.assertEqual(2, len(next_funcs))
        self.assertEqual(
            "torch::distributed::autograd::RecvRpcBackward", next_funcs[0][0].name()
        )
        self.assertEqual(
            "torch::distributed::autograd::RecvRpcBackward", next_funcs[1][0].name()
        )
        self.assertEqual(next_funcs[0][0], next_funcs[1][0])

        # For send function when returning resonpose to previous call
        # next function of the send function is the recv function
        # for received tensor result returned from nested call
        next_funcs = list(send_functions.values())[1].next_functions
        self.assertEqual(1, len(next_funcs))
        self.assertEqual(
            "torch::distributed::autograd::RecvRpcBackward", next_funcs[0][0].name()
        )

    def _test_graph(self, fn, exec_mode):
        dst_rank = (self.rank + 1) % self.world_size

        initialize_pg(self.file_init_method, self.rank, self.world_size)

        with dist_autograd.context() as context_id:
            t1 = torch.ones(3, 3, requires_grad=True)
            t2 = torch.zeros(3, 3, requires_grad=True)
            if ExecMode.RPC_SYNC == exec_mode:
                ret = rpc.rpc_sync(worker_name(dst_rank), fn, args=(t1, t2))
            elif ExecMode.REMOTE == exec_mode:
                ret = rpc.remote(
                    worker_name(dst_rank), fn, args=(t1, t2)
                ).to_here()
            else:
                raise ValueError("Unrecognized ExecMode {}".format(exec_mode))

            rpc.rpc_sync(
                worker_name(dst_rank), _set_rpc_done, args=(context_id, 1)
            )

            # Verify graph for current context id.
            ctx = dist_autograd._current_context()
            self.assertEqual(context_id, ctx._context_id())
            send_functions = ctx._send_functions()
            self.assertEqual(1, len(send_functions))
            recv_functions = ctx._recv_functions()
            self.assertEqual(1, len(recv_functions))
            self._verify_graph_for_first_rpc_call(
                list(send_functions.values())[0],
                list(recv_functions.values())[0],
                t1,
                t2,
                ret,
            )

            # Wait for the prev rank to be done with rpc.
            self._check_rpc_done(1)
            # Verify graph for previous context id.
            ctx = dist_autograd._retrieve_context(ctx_ids[1])
            send_functions = ctx._send_functions()
            self.assertEqual(1, len(send_functions))
            self._verify_graph_for_rpc_call_exec(list(send_functions.values())[0])
            # this barrier is needed so one worker does not clean up their
            # autograd context before another worker tries to access it.
            dist.barrier()

        # autograd context should be cleaned up by now.
        with self.assertRaises(RuntimeError):
            ctx = dist_autograd._retrieve_context(context_id)

        # No autograd context available.
        with self.assertRaises(RuntimeError):
            ctx = dist_autograd._current_context()

    @dist_init
    def test_graph_for_builtin_call(self):
        self._test_graph(torch.add, ExecMode.RPC_SYNC)

    @dist_init
    def test_graph_for_python_call(self):
        self._test_graph(my_py_add, ExecMode.RPC_SYNC)

    @dist_init
    def test_graph_for_builtin_remote_call(self):
        self._test_graph(torch.add, ExecMode.REMOTE)

    @dist_init
    def test_graph_for_python_remote_call(self):
        self._test_graph(my_py_add, ExecMode.REMOTE)

    # 3-layer nested calls
    def _test_graph_for_py_nested_call(self, exec_mode):
        dst_rank = (self.rank + 1) % self.world_size

        initialize_pg(self.file_init_method, self.rank, self.world_size)

        with dist_autograd.context() as context_id:
            t1 = torch.ones(3, 3, requires_grad=True)
            t2 = torch.zeros(3, 3, requires_grad=True)
            nest_dst_rank = (dst_rank + 1) % self.world_size
            if ExecMode.RPC_SYNC == exec_mode:
                ret = rpc.rpc_sync(
                    worker_name(dst_rank),
                    my_py_nested_call,
                    args=(t1, t2, dst_rank, self.world_size, 1),
                )
            elif ExecMode.REMOTE == exec_mode:
                ret = rpc.remote(
                    worker_name(dst_rank),
                    my_py_nested_call,
                    args=(t1, t2, dst_rank, self.world_size, 1),
                ).to_here()
            else:
                raise ValueError("Unrecognized ExecMode {}".format(exec_mode))

            # Barrier to ensure all RPCs are done.
            dist.barrier()

            for rd in [1, 2, 3]:
                rpc.rpc_sync(
                    worker_name((self.rank + rd) % self.world_size),
                    _set_rpc_done,
                    args=(context_id, rd),
                )

            # Barrier to ensure all set_rpc_done have completed.
            dist.barrier()

            # For self.rank, it has 4 graphs to verify
            # One is for current context id when this rank send first rpc call.
            # Second one is for prev context id when this rank make 1st nested
            # call.
            # Third one is for prev prev context id when this rank make
            # 2nd nested call.
            # Last one is for prev prev prev context id when this rank
            # execute the torch.add() operator.

            # Verify first graph for current context id.
            ctx = dist_autograd._current_context()
            self.assertEqual(context_id, ctx._context_id())
            send_functions = ctx._send_functions()
            self.assertEqual(1, len(send_functions))
            recv_functions = ctx._recv_functions()
            self.assertEqual(1, len(recv_functions))
            self._verify_graph_for_first_rpc_call(
                list(send_functions.values())[0],
                list(recv_functions.values())[0],
                t1,
                t2,
                ret,
            )

            # Verify second graph for 1st nested call.
            ctx = dist_autograd._retrieve_context(ctx_ids[1])
            self._verify_graph_for_nested_rpc_call(ctx)

            # Verify third graph for 2nd nested call.
            ctx = dist_autograd._retrieve_context(ctx_ids[2])
            self._verify_graph_for_nested_rpc_call(ctx)

            # verify last graph for rpc call execution.
            ctx = dist_autograd._retrieve_context(ctx_ids[3])
            send_functions = ctx._send_functions()
            self.assertEqual(1, len(send_functions))
            self._verify_graph_for_rpc_call_exec(list(send_functions.values())[0])
            # this barrier is needed so one worker does not clean up their
            # autograd context before another worker tries to access it.
            dist.barrier()

    @dist_init
    def test_graph_for_py_nested_call(self):
        self._test_graph_for_py_nested_call(ExecMode.RPC_SYNC)

    @dist_init
    def test_graph_for_py_nested_remote_call(self):
        self._test_graph_for_py_nested_call(ExecMode.REMOTE)

    # Rank0->Rank1->Rank0
    def _test_graph_for_py_nested_call_itself(self, exec_mode):
        dst_rank = (self.rank + 1) % self.world_size

        initialize_pg(self.file_init_method, self.rank, self.world_size)

        with dist_autograd.context() as context_id:
            t1 = torch.ones(3, 3, requires_grad=True)
            t2 = torch.zeros(3, 3, requires_grad=True)
            if ExecMode.RPC_SYNC == exec_mode:
                ret = rpc.rpc_sync(
                    worker_name(dst_rank),
                    my_py_nested_call,
                    args=(
                        t1,
                        t2,
                        (self.rank - 1 + self.world_size) % self.world_size,
                        self.world_size,
                        0,
                    ),
                )
            elif ExecMode.REMOTE == exec_mode:
                ret = rpc.remote(
                    worker_name(dst_rank),
                    my_py_nested_call,
                    args=(
                        t1,
                        t2,
                        (self.rank - 1 + self.world_size) % self.world_size,
                        self.world_size,
                        0,
                    ),
                ).to_here()
            else:
                raise ValueError("Unrecognized ExecMode {}".format(exec_mode))

            rpc.rpc_sync(
                worker_name((self.rank + 1) % self.world_size),
                _set_rpc_done,
                args=(context_id, 1),
            )

            # For self.rank, it has 2 graphs to verify.
            # One is for current context id when this rank send first rpc
            # call and execute the torch.add() operator.
            # Another one is for prev context id when this rank make
            # nested call.
            ctx = dist_autograd._current_context()
            self.assertEqual(context_id, ctx._context_id())
            send_functions = ctx._send_functions()
            self.assertEqual(2, len(send_functions))
            recv_functions = ctx._recv_functions()
            self.assertEqual(2, len(recv_functions))
            self._verify_graph_for_first_rpc_call(
                list(send_functions.values())[0],
                list(recv_functions.values())[1],
                t1,
                t2,
                ret,
            )
            self._verify_graph_for_rpc_call_exec(list(send_functions.values())[1])

            # Verify two pairs of send and recv functions for nested
            # call
            self._check_rpc_done(1)
            ctx = dist_autograd._retrieve_context(ctx_ids[1])
            self._verify_graph_for_nested_rpc_call(ctx)
            # this barrier is needed so one worker does not clean up their
            # autograd context before another worker tries to access it.
            dist.barrier()

    @dist_init
    def test_graph_for_py_nested_call_itself(self):
        self._test_graph_for_py_nested_call_itself(ExecMode.RPC_SYNC)

    @dist_init
    def test_graph_for_py_nested_remote_call_itself(self):
        self._test_graph_for_py_nested_call_itself(ExecMode.REMOTE)

    def _test_no_graph_with_tensors_not_require_grad(self, exec_mode):
        initialize_pg(self.file_init_method, self.rank, self.world_size)
        dst_rank = (self.rank + 1) % self.world_size
        with dist_autograd.context() as context_id:
            t1 = torch.ones(3, 3, requires_grad=False)
            t2 = torch.zeros(3, 3, requires_grad=False)
            if ExecMode.RPC_SYNC == exec_mode:
                ret = rpc.rpc_sync(
                    worker_name(dst_rank), torch.add, args=(t1, t2)
                )
            elif ExecMode.REMOTE == exec_mode:
                ret = rpc.remote(
                    worker_name(dst_rank), torch.add, args=(t1, t2)
                ).to_here()
            else:
                raise ValueError("Unrecognized ExecMode {}".format(exec_mode))

            rpc.rpc_sync(
                worker_name(dst_rank), _set_rpc_done, args=(context_id, 1)
            )

            ctx = dist_autograd._current_context()
            send_functions = ctx._send_functions()
            self.assertEqual(len(send_functions), 0)
            recv_functions = ctx._recv_functions()
            self.assertEqual(len(recv_functions), 0)

            # Wait for the prev rank to be done with rpc.
            self._check_rpc_done(1)
            # NB: RRef.to_here() always passes the autograd context to the
            # the callee, as the caller does not know whether the return
            # value would contain a requires_grad tensor or not.
            #
            # rpc/remote with udf (_set_rpc_done here) also always passes the
            # autograd context to the callee due to the same reason.
            self.assertNotEqual(-1, dist_autograd._retrieve_context(ctx_ids[1]))
            dist.barrier()

    @dist_init
    def test_no_graph_with_tensors_not_require_grad(self):
        self._test_no_graph_with_tensors_not_require_grad(ExecMode.RPC_SYNC)

    @dist_init
    def test_no_graph_with_tensors_not_require_grad_remote(self):
        self._test_no_graph_with_tensors_not_require_grad(ExecMode.REMOTE)

    def _test_grad_only_on_return_value(self, exec_mode):
        initialize_pg(self.file_init_method, self.rank, self.world_size)
        dst_rank = (self.rank + 1) % self.world_size
        with dist_autograd.context() as context_id:
            if ExecMode.RPC_SYNC == exec_mode:
                ret = rpc.rpc_sync(worker_name(dst_rank), ret_requires_grad)
            elif ExecMode.REMOTE == exec_mode:
                ret = rpc.remote(
                    worker_name(dst_rank), ret_requires_grad
                ).to_here()
            else:
                raise ValueError("Unrecognized ExecMode {}".format(exec_mode))

            dist_autograd.backward(context_id, [ret.sum()])

            rpc.rpc_sync(
                worker_name(dst_rank), _set_rpc_done, args=(context_id, 1)
            )

            # Wait for the prev rank to be done with rpc.
            self._check_rpc_done(1)
            grads = dist_autograd.get_gradients(ctx_ids[1])
            self.assertEqual(1, len(grads))
            self.assertIn(requires_grad_tensor, grads)
            self.assertEqual(torch.ones_like(ret), grads[requires_grad_tensor])
            # due to the above get_gradients call, ensure that dist autograd
            # contexts aren't cleaned up until all workers exit context managers
            dist.barrier()

    @dist_init
    def test_grad_only_on_return_value(self):
        self._test_grad_only_on_return_value(ExecMode.RPC_SYNC)

    @dist_init
    def test_grad_only_on_return_value_remote(self):
        self._test_grad_only_on_return_value(ExecMode.REMOTE)

    def _test_rpc_complex_args(self, exec_mode):
        with dist_autograd.context() as context_id:
            num_tensors = 10
            tensors = []
            for i in range(num_tensors):
                tensors.append(torch.ones(3, 3, requires_grad=(i % 2 == 0)))

            dst_rank = self._next_rank()
            if ExecMode.RPC_SYNC == exec_mode:
                ret = rpc.rpc_sync(
                    worker_name(dst_rank), torch.stack, args=(tensors,)
                )
            elif ExecMode.REMOTE == exec_mode:
                ret = rpc.remote(
                    worker_name(dst_rank), torch.stack, args=(tensors,)
                ).to_here()
            else:
                raise ValueError("Unrecognized ExecMode {}".format(exec_mode))

            self.assertEqual(torch.stack(tensors), ret)

            # Verify appropriate tensors have been attached the autograd graph.
            next_funcs = list(
                dist_autograd._current_context()._send_functions().values()
            )[0].next_functions
            idx = 0
            for i in range(len(next_funcs)):
                self.assertEqual(
                    "torch::autograd::AccumulateGrad", next_funcs[i][0].name()
                )
                self.assertEqual(tensors[i], next_funcs[i][0].variable)

            # Verify that the worker id has been recorded in the context
            ctx = dist_autograd._current_context()
            worker_ids = ctx._known_worker_ids()
            self.assertEqual(len(worker_ids), 1)
            self.assertEqual(worker_ids, {dst_rank})

    @dist_init
    def test_rpc_complex_args(self):
        self._test_rpc_complex_args(ExecMode.RPC_SYNC)

    @dist_init
    def test_remote_complex_args(self):
        self._test_rpc_complex_args(ExecMode.REMOTE)

    def context_cleanup_test_helper(self, rpc_args, func, nested=False):
        initialize_pg(self.file_init_method, self.rank, self.world_size)

        # test that in dist autograd, in the case that tensors communicated over RPC do
        # NOT require grad, we still cleanup the dist autograd contexts created
        # on other nodes. This is because the autograd context is still
        # communicated over RPC even if tensor arguments do not require grad, as
        #  it is possible that the response could.
        if nested:
            dst_rank = (self.rank + 1) % self.world_size
            nested_dst_rank = (dst_rank + 1) % self.world_size
            dst_ranks = {dst_rank}
        else:
            dst_ranks = {rank for rank in range(self.world_size) if rank != self.rank}

        with dist_autograd.context() as context_id:
            for dst_rank in dst_ranks:
                rpc.rpc_sync(worker_name(dst_rank), func, args=rpc_args)
                rpc.rpc_sync(
                    worker_name(dst_rank), _set_rpc_done, args=(context_id, 1)
                )
                if nested:
                    rpc.rpc_sync(
                        worker_name(nested_dst_rank),
                        _set_rpc_done,
                        args=(context_id, 2),
                    )
        # the thread's context id should be cleaned up
        with self.assertRaises(RuntimeError):
            dist_autograd._retrieve_context(context_id)
        # Ensure all peers have finished mutating the
        # `known_context_ids` set.
        dist.barrier()
        # check that all contexts have been cleaned up.
        success = _all_contexts_cleaned_up()
        self.assertTrue(success)

    @dist_init
    def test_context_cleanup_tensor_with_grad(self):
        t1 = torch.ones(3, 3, requires_grad=True)
        t2 = torch.zeros(3, 3, requires_grad=True)
        self.context_cleanup_test_helper(rpc_args=(t1, t2), func=torch.add)

    @dist_init
    def test_context_cleanup_tensor_no_grad(self):
        t1 = torch.ones(3, 3, requires_grad=False)
        self.context_cleanup_test_helper(rpc_args=(t1, t1), func=torch.add)

    @dist_init
    def test_context_cleanup_no_tensors(self):
        self.context_cleanup_test_helper(rpc_args=(1, 1), func=my_scalar_add)

    @dist_init
    def test_context_cleanup_nested_rpc(self):
        t1 = torch.ones(3, 3, requires_grad=True)
        t2 = torch.zeros(3, 3, requires_grad=True)
        dst_rank = (self.rank + 1) % self.world_size
        args = (t1, t2, dst_rank, self.world_size, 0)
        self.context_cleanup_test_helper(
            rpc_args=args, func=my_py_nested_call, nested=True
        )

    @dist_init
    def test_worker_ids_recorded(self):
        dst_ranks = {rank for rank in range(self.world_size) if rank != self.rank}
        with dist_autograd.context() as context_id:
            # if no tensors require grad, we should still record worker_ids, as
            # the autograd context ID is still passed to other workers.
            t1 = torch.ones(3, 3, requires_grad=False)
            t2 = torch.zeros(3, 3, requires_grad=False)
            for dst_rank in dst_ranks:
                rpc.rpc_sync(worker_name(dst_rank), torch.add, args=(t1, t2))
                rpc.rpc_sync(
                    worker_name(dst_rank), _set_rpc_done, args=(context_id, 1)
                )
            # all worker_ids in dst_ranks should be recorded.
            ctx = dist_autograd._current_context()
            worker_ids = ctx._known_worker_ids()
            self.assertEqual(worker_ids, dst_ranks)

            # worker_ids should be recorded when tensors do require grad
            t1.requires_grad = True
            t2.requires_grad = True
            for dst_rank in dst_ranks:
                ret = rpc.rpc_sync(
                    worker_name(dst_rank), torch.add, args=(t1, t2)
                )
                rpc.rpc_sync(
                    worker_name(dst_rank), _set_rpc_done, args=(context_id, 1)
                )
            # all worker_ids in dst_ranks should be recorded.
            worker_ids = ctx._known_worker_ids()
            self.assertEqual(worker_ids, dst_ranks)

    @dist_init
    def test_dist_autograd_profiling(self):
        with dist_autograd.context() as context_id:
            t1 = torch.rand(3, 3, requires_grad=True)
            t2 = torch.rand(3, 3, requires_grad=True)
            loss = rpc.rpc_sync(worker_name(self._next_rank()), torch.add, args=(t1, t2)).sum()
            with torch.autograd.profiler.profile() as p:
                dist_autograd.backward(context_id, [loss])

        function_events = p.function_events

        def get_event(partial_key):
            return [event for event in function_events if partial_key in event.name][0]

        send_event = get_event("SendRpcBackward")
        recv_event = get_event("RecvRpcBackward")
        backward_event = get_event("torch::distributed::autograd::backward")
        # There should be at least 1 send and recv_events each, corresponding to send/recv functions executed.
        self.assertEqual(send_event.count, 1)
        self.assertEqual(recv_event.count, 1)
        # The CPU total for backward event should be great than send and recv, since
        # applying those functions in the backwards pass is a subset of the entire backward pass.
        self.assertGreater(backward_event.cpu_time_total, send_event.cpu_time_total)
        self.assertGreater(backward_event.cpu_time_total, recv_event.cpu_time_total)

    @dist_init
    def test_error_in_context(self):
        with dist_autograd.context() as context_id:
            t1 = torch.rand(3, 3, requires_grad=True)
            t2 = torch.rand(6, 6, requires_grad=True)

            with self.assertRaises(RuntimeError):
                # This should throw an error since matrix sizes don't match.
                rpc.rpc_sync(
                    worker_name(self._next_rank()), torch.matmul, args=(t1, t2)
                )

    def _verify_backwards(self, exec_mode, tensors, context_id, local_grads, *args):
        if exec_mode == ExecMode.LOCAL:
            torch.autograd.backward(tensors)
            return [arg.grad for arg in args]
        else:
            self._verify_backwards_remote(tensors, context_id, local_grads, *args)

    def _verify_backwards_remote(self, tensors, context_id, local_grads, *args):
        dist_autograd.backward(context_id, tensors)

        # Verify grads were accumulated appropriately.
        grads = dist_autograd.get_gradients(context_id)
        nargs = len(args)
        ngrads = 0
        for i in range(0, nargs):
            if local_grads[i] is not None:
                self.assertIn(args[i], grads)
                self.assertEqual(local_grads[i], grads[args[i]])
                ngrads += 1
            else:
                self.assertNotIn(args[i], grads)

        self.assertEqual(ngrads, len(grads))

    @dist_init
    def test_backward_no_grad_on_tensor(self):
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            loss = rpc.rpc_sync(
                worker_name(self._next_rank()),
                torch.add,
                args=(t1, t2)).sum()

            dist_autograd.backward(context_id, [loss], retain_graph=True)
            self.assertIsNone(t1.grad)
            self.assertIsNone(t2.grad)

            # Now populate .grad with local autograd engine and
            # verify dist autograd doesn't mess with it.
            loss_local = torch.add(t1, t2).sum()
            loss_local.backward()
            self.assertIsNotNone(t1.grad)
            self.assertIsNotNone(t2.grad)

            t1_grad_before = t1.grad
            t2_grad_before = t2.grad
            dist_autograd.backward(context_id, [loss])
            self.assertEqual(t1_grad_before, t1.grad)
            self.assertEqual(t2_grad_before, t2.grad)

    def _test_backward_simple(self, dst):
        # Run the same code locally and with dist autograd and verify gradients
        # are same.
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                ret = self._exec_func_with_dst(
                    dst, exec_mode, torch.add, t1, t2
                )
                loss = ret.sum()
                ret = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2
                )
                local_grads = ret if ret else local_grads

    @dist_init
    def test_backward_simple(self):
        self._test_backward_simple(self._next_rank())

    @dist_init
    def test_backward_simple_self(self):
        self._test_backward_simple(self.rank)

    # The current rank first creates a tensor on the rref_owner, and then passes
    # the rref with another tensor to the callee to run either my_rref_add or
    # my_nested_rref_add, depending on whether the callee is the rref owner.
    # The grad of tensor lives on the current rank, and the grad of the rref
    # tensor lives on the rref owner.
    def _test_backward_rref(self, callee, rref_owner):
        local_grads = None
        t1 = torch.ones((3, 3), requires_grad=True)
        t2 = torch.zeros((3, 3), requires_grad=True)

        local_ret = torch.add(t1, t2)
        local_ret.sum().backward()
        with dist_autograd.context() as context_id:
            rref_t1 = rpc.remote(
                rref_owner, _torch_ones, args=((3, 3),), kwargs={"requires_grad": True}
            )

            if callee == rref_owner:
                rref = rpc.remote(callee, my_rref_add, args=(rref_t1, t2))
            else:
                rref = rpc.remote(
                    callee, my_nested_rref_add, args=(rref_owner, rref_t1, t2)
                )
            ret = rref.to_here()
            dist_autograd.backward(context_id, [ret.sum()])

            # verify grads on caller
            grads = dist_autograd.get_gradients(context_id)
            self.assertIn(t2, grads)
            self.assertEqual(grads[t2], t2.grad)

            # verify grads on rref owner
            self.assertTrue(
                rpc.rpc_sync(
                    rref_owner,
                    _compare_owner_value,
                    args=(context_id, rref_t1, t1.grad),
                )
            )

    @dist_init
    def test_backward_rref(self):
        callee = worker_name(self._next_rank())
        rref_owner = callee
        self._test_backward_rref(callee, rref_owner)

    @dist_init
    def test_backward_rref_multi(self):
        if self.rank > 0:
            callee = "worker0"
            rref_owner = callee
            self._test_backward_rref(callee, rref_owner)

    @dist_init
    def test_backward_rref_nested(self):
        callee = worker_name((self.rank + 1) % self.world_size)
        rref_owner = worker_name((self.rank + 2) % self.world_size)
        self._test_backward_rref(callee, rref_owner)

    # In this test, every rank will serve as a parameter server (ps) and a
    # driver, and then kicks off trainers on the other three ranks. So, we have:
    # ps = rank0 with trainers = rank1/2/3
    # ps = rank2 with trainers = rank2/3/0
    # ps = rank3 with trainers = rank3/0/1
    # ps = rank4 with trainers = rank0/1/2
    #
    # These four test ps-trainer groups run on completely separate autograd
    # graphs, but they share the same set of underlying RpcAgents.
    def _test_trainer_ps(self, create_ref_fn, trainer_fn):
        local_grads = None
        t1 = torch.ones((3, 3), requires_grad=True)
        t2 = torch.zeros((3, 3), requires_grad=True)

        local_ret = torch.add(t1, t2)
        local_ret.sum().backward()

        # create rref on self
        rref_t1 = rpc.remote(
            worker_name(self.rank),
            create_ref_fn,
            args=())

        # kick off forward and backward pass on three other workers (trainers)
        rank_diffs = [1, 2, 3]
        futures = []
        for rank_diff in rank_diffs:
            futures.append(
                rpc.rpc_async(
                    worker_name((self.rank + rank_diff) % self.world_size),
                    trainer_fn,
                    args=(rref_t1, t2, worker_name(self.rank), rank_diff),
                )
            )

        # check if the trainers have done with their backward pass
        for rank_diff in rank_diffs:
            self._check_rpc_done(rank_diff)

        # trainers are done and holding the context for verification
        accumulate_grad_func = None
        for rank_diff in rank_diffs:
            # make sure grads are accumulated for the same tensors and values
            # are all correct
            ctx_id = ctx_ids[rank_diff]
            grads = dist_autograd.get_gradients(ctx_id)
            local_t1 = rref_t1.to_here()
            self.assertIn(local_t1, grads)
            self.assertEqual(grads[local_t1], t1.grad)

        # unblock trainers
        _set_rpc_done(None, 0)

        # wait until all trainers are done
        torch.futures.wait_all(futures)

    @dist_init
    def test_trainer_ps(self):
        self._test_trainer_ps(create_tensor, _run_trainer)

    @dist_init
    def test_trainer_ps_torchscript_functions(self):
        # TODO, need more investigation
        # there is rref leak when shutting down, suspect it is because
        # ref as arg is passed to pybind boundary, and the ref is not garbage
        # collected by python when calling shutdown()
        import torch.distributed.rpc.api as api
        api._ignore_rref_leak = True

        self._test_trainer_ps(create_torchscript_tensor, _run_trainer_torchscript)

    @dist_init
    def test_backward_multiple_round_trips(self):
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3))
        t3 = torch.rand((3, 3), requires_grad=True)
        t4 = torch.rand((3, 3))
        t5 = torch.rand((3, 3), requires_grad=True)

        for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                # Multiple RPCs between different nodes.
                val = self._exec_func(exec_mode, torch.add, t1, t2)
                val = self._exec_func(exec_mode, torch.mul, t3, val)
                s1 = self._exec_func(exec_mode, torch.stack, (t4, val))
                s2 = self._exec_func(exec_mode, torch.stack, (t5, val))
                val = self._exec_func(exec_mode, torch.bmm, s1, s2)
                val = self._exec_func(exec_mode, torch.matmul, val, val)
                loss = val.sum()

                ret = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2, t3, t4, t5
                )
                local_grads = ret if ret else local_grads

    @dist_init
    def test_backward_different_tensor_dims(self):
        local_grads = None
        t1 = torch.rand((4, 6), requires_grad=True)
        t2 = torch.rand((6, 5))
        t3 = torch.rand((5, 7), requires_grad=True)
        t4 = torch.rand((7, 9))

        for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                val = self._exec_func(exec_mode, torch.matmul, t1, t2)
                val = self._exec_func(exec_mode, torch.chain_matmul, [val, t3, t4])
                loss = val.sum()

                ret = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2, t2, t3, t4
                )
                local_grads = ret if ret else local_grads

    @dist_init
    def test_backward_unused_tensors(self):
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        t3 = torch.rand((3, 3), requires_grad=True)
        for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                s = self._exec_func(exec_mode, torch.stack, (t1, t2, t3))
                val = self._exec_func(
                    exec_mode,
                    torch.matmul,
                    torch.narrow(s, 0, 0, 1),
                    torch.narrow(s, 0, 2, 1),
                )

                loss = val.sum()
                ret = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2, t3
                )
                local_grads = ret if ret else local_grads

    @dist_init
    def test_backward_multiple_output_tensors(self):
        local_grads = None
        t = torch.rand((10, 2), requires_grad=True)
        for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                tensor_list = self._exec_func(exec_mode, torch.split, t, 2)
                t1 = tensor_list[0]
                t2 = tensor_list[2]
                t3 = tensor_list[4]

                val = self._exec_func(exec_mode, torch.chain_matmul, [t1, t2, t3])

                loss = val.sum()
                ret = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t
                )
                local_grads = ret if ret else local_grads

    def _run_test_backward_unused_send_function_in_thread(self):
        with dist_autograd.context() as context_id:
            t1 = torch.rand((3, 3), requires_grad=True)
            t2 = torch.rand((3, 3), requires_grad=True)

            # We don't use the result of an RPC function, as a result the
            # backward pass would hang in the "FAST" mode.
            res = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.add, args=(t1, t2)
            )

            val = torch.mul(t1, t2)

            # Run backward, this would hang forever.
            dist_autograd.backward(context_id, [val.sum()])

    @dist_init
    def test_backward_unused_send_function(self):
        # Run the test in a thread which would never finish.
        t = threading.Thread(
            target=self._run_test_backward_unused_send_function_in_thread
        )
        t.daemon = True
        t.start()
        t.join(10)  # Wait for 10s.

        # Verify thread is still alive (indicating backward hasn't completed yet).
        self.assertTrue(t.is_alive())

    @dist_init
    def test_backward_autograd_engine_error(self):
        with dist_autograd.context() as context_id:
            t1 = torch.rand((3, 3), requires_grad=True)
            t2 = torch.rand((3, 3), requires_grad=True)
            # Perform some ops before error simulation.
            tmp = (t1 + t2) * (t1 + t2)
            t3 = SimulateBackwardError.apply(tmp)

            # Run multiple round trips across different nodes and verify the
            # original node receives an error thrown on a node deep in the chain.
            val = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.add, args=(t2, t3)
            )
            val = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.mul, args=(val, t2)
            )
            val = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.matmul, args=(val, t2)
            )
            val = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.div, args=(val, t2)
            )

            with self.assertRaisesRegex(
                RuntimeError, "Error on Node [0-9]+: Simulate error on backward pass"
            ):
                # Run backwards, and validate we receive an error.
                dist_autograd.backward(context_id, [val.sum()])

    @dist_init(clean_shutdown=False)
    @unittest.skipIf(
        IS_MACOS,
        "Test is flaky on MacOS since libuv error handling is not as robust as TCP",
    )
    def test_backward_node_failure(self):
        rpc._set_rpc_timeout(5)  # 5 seconds
        initialize_pg(self.file_init_method, self.rank, self.world_size)

        with dist_autograd.context() as context_id:
            t1 = torch.rand((3, 3), requires_grad=True)
            t2 = torch.rand((3, 3), requires_grad=True)
            res = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.add, args=(t1, t2)
            )

            # Wait for all RPCs to be done.
            dist.barrier()

            # Kill all odd rank nodes.
            if self.rank % 2 == 0:
                shutdown_error_regex = self.get_shutdown_error_regex()
                # Wait for all other nodes to die.
                for rank in range(self.world_size):
                    if rank % 2 != 0:
                        wait_until_node_failure(rank, shutdown_error_regex)

                # Shutdown sequence is not very well defined and as a result
                # we might see any error given by get_shutdown_error_regex()
                with self.assertRaisesRegex(RuntimeError, shutdown_error_regex):
                    # Run backwards, and validate we receive an error since all
                    # other nodes are dead.
                    dist_autograd.backward(context_id, [res.sum()])
            else:
                # Exit all other nodes.
                pass

    @dist_init
    def test_backward_without_context(self):
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)

        context_id = 100  # dummy context_id
        with self.assertRaisesRegex(
            RuntimeError,
            "Could not find autograd context with id: {}".format(context_id),
        ):
            res = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.add, args=(t1, t2)
            )
            dist_autograd.backward(context_id, [res.sum()])

    @dist_init
    def test_backward_without_rpc(self):
        dst_rank = self.rank
        with dist_autograd.context() as context_id:
            t1 = torch.rand((3, 3), requires_grad=True)
            t2 = torch.rand((3, 3), requires_grad=True)
            t3 = torch.add(t1, t2)

            dist_autograd.backward(context_id, [t3.sum()])
            grads = dist_autograd.get_gradients(context_id)
            self.assertEqual(2, len(grads))
            self.assertIn(t1, grads)
            self.assertIn(t2, grads)
            self.assertEqual(torch.ones(3, 3), grads[t1])
            self.assertEqual(torch.ones(3, 3), grads[t2])

    @dist_init
    def test_backward_invalid_args(self):
        with dist_autograd.context() as context_id:

            with self.assertRaisesRegex(TypeError, "incompatible function arguments"):
                dist_autograd.backward(context_id, None)

            with self.assertRaisesRegex(TypeError, "incompatible function arguments"):
                dist_autograd.backward(None, None)

            with self.assertRaisesRegex(
                RuntimeError, "No tensors provided for gradient computation"
            ):
                dist_autograd.backward(context_id, [])

            with self.assertRaisesRegex(RuntimeError, "requires_grad not set on"):
                t = torch.rand(3, 3)
                dist_autograd.backward(context_id, [t])

            with self.assertRaisesRegex(
                RuntimeError, "is not a scalar, all roots need to be scalar"
            ):
                t = torch.rand(3, 3, requires_grad=True)
                dist_autograd.backward(context_id, [t])

            with self.assertRaisesRegex(
                RuntimeError, "does not have a valid gradient function"
            ):
                t = torch.rand(1, requires_grad=True)
                dist_autograd.backward(context_id, [t])

    @dist_init
    def test_backward_multiple_roots(self):
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC]:
            with dist_autograd.context() as context_id:
                r1 = self._exec_func(exec_mode, torch.add, t1, t2).sum()
                r2 = self._exec_func(exec_mode, torch.mul, t1, t2).sum()
                r3 = self._exec_func(exec_mode, torch.cos, t1).sum()
                r4 = self._exec_func(exec_mode, torch.div, t1, t2).sum()

                local_grads = self._verify_backwards(
                    exec_mode, [r1, r2, r3, r4], context_id, local_grads, t1, t2
                )

    @dist_init
    def test_backward_different_dtypes(self):
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True, dtype=torch.float32)
        t2 = torch.rand((3, 3), requires_grad=True, dtype=torch.float64)
        for exec_mode in [ExecMode.LOCAL, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                loss = self._exec_func(exec_mode, torch.add, t1, t2).sum()

                local_grads = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2
                )

    @dist_init
    def test_backward_simple_python_udf(self):
        # Run the same code locally and with dist autograd and verify gradients
        # are same.
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        for exec_mode in [ExecMode.LOCAL, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                ret = self._exec_func(exec_mode, my_py_add, t1, t2)
                loss = ret.sum()
                local_grads = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2
                )

    @dist_init
    def test_backward_simple_script_call(self):
        # Run the same code locally and with dist autograd and verify gradients
        # are same.
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        for exec_mode in [
            ExecMode.LOCAL,
            ExecMode.RPC_SYNC,
            ExecMode.RPC_ASYNC,
            ExecMode.REMOTE,
        ]:
            with dist_autograd.context() as context_id:
                forward_ret = self._exec_func(exec_mode, my_script_add, t1, t2)
                loss = forward_ret.sum()
                ret = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2
                )
                local_grads = ret if ret else local_grads

    @staticmethod
    def _complex_python_udf(t1, t2):
        t3 = torch.nn.functional.linear(t1, t2)
        t4 = torch.nn.functional.linear(t2, t3)
        t5 = torch.nn.functional.linear(t3, t4)
        return torch.chain_matmul(t1, t2, t3, t4, t5)

    @dist_init
    def test_backward_complex_python_udf(self):
        # Run the same code locally and with dist autograd and verify gradients
        # are same.
        local_grads = None
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        for exec_mode in [ExecMode.LOCAL, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                ret = self._exec_func(
                    exec_mode, DistAutogradTest._complex_python_udf, t1, t2
                )
                loss = ret.sum()
                local_grads = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2
                )

    @staticmethod
    def _python_udf_with_backward_error(t1, t2):
        t3 = t1 + t2
        t4 = SimulateBackwardError.apply(t3)
        return torch.chain_matmul(t1, t2, t3, t4)

    @staticmethod
    def _nested_rpc_call_backward_error(t1, t2, dst):
        t1 = t1 * t2
        t2 = t1 + t2
        res = rpc.rpc_sync(
            worker_name(dst),
            DistAutogradTest._python_udf_with_backward_error,
            args=(t1, t2),
        )
        return torch.chain_matmul(t1, t2, res)

    @dist_init
    def test_backward_python_udf_error(self):
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            loss = rpc.rpc_sync(
                worker_name(self._next_rank()),
                DistAutogradTest._nested_rpc_call_backward_error,
                args=(t1, t2, self._next_rank()),
            )
            with self.assertRaisesRegex(
                RuntimeError, "Simulate error on backward pass"
            ):
                dist_autograd.backward(context_id, [loss.sum()])

    _backward_done = False

    @dist_init(clean_shutdown=False)
    @unittest.skipIf(
        IS_MACOS,
        "Test is flaky on MacOS since libuv error handling is not as robust as TCP",
    )
    def test_backward_node_failure_python_udf(self):
        # Set a short timeout to quickly time out failed RPCs.
        rpc._set_rpc_timeout(5)  # 5 seconds
        initialize_pg(self.file_init_method, self.rank, self.world_size)

        with dist_autograd.context() as context_id:
            t1 = torch.rand((3, 3), requires_grad=True)
            t2 = torch.rand((3, 3), requires_grad=True)

            dst = self._next_rank()
            res = rpc.rpc_sync(
                worker_name(dst),
                my_py_nested_call,
                args=(t1, t2, dst, self.world_size, 1),
            )

            dist.barrier()

            # Kill rank 2 (last hop of nested rpc) and verify rank 0 receives an error.
            if self.rank == 2:
                return

            store = dist.distributed_c10d._get_default_store()
            if self.rank == 0:
                # Wait for rank 2 to die.
                shutdown_error_regex = self.get_shutdown_error_regex()
                wait_until_node_failure(2, shutdown_error_regex)
                # Shutdown sequence is not very well defined and as a result
                # we might see any error given by get_shutdown_error_regex().
                with self.assertRaisesRegex(RuntimeError, shutdown_error_regex):
                    # Run backwards, and validate we receive an error since rank 2 is dead.
                    dist_autograd.backward(context_id, [res.sum()])

                # Mark rank 0 is done in the store, since the RPC framework on
                # some nodes might be broken at this point (listenLoop() in
                # ProcessGroupAgent might've exited).
                store.set('test_backward_node_failure_python_udf_rank0_done', "True")
            else:
                # Wait for backward to finish on rank 0.
                store.wait(['test_backward_node_failure_python_udf_rank0_done'], timedelta(seconds=10))

    @staticmethod
    def _nested_python_udf(t1, t2, dst):
        t3 = t1 * t2
        t4 = t1 + t2
        res = rpc.rpc_sync(worker_name(dst), my_py_add, args=(t3, t4))
        return torch.chain_matmul(t1, t2, t3, t4, res)

    @dist_init
    def test_backwards_nested_python_udf(self):
        # Run equivalent of _nested_python_udf locally.
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        t3 = t1 * t2
        t4 = t1 + t2
        res = t3 + t4
        loss = torch.chain_matmul(t1, t2, t3, t4, res).sum()
        torch.autograd.backward([loss])

        # Now run distributed autograd.
        with dist_autograd.context() as context_id:
            loss = rpc.rpc_sync(
                worker_name(self._next_rank()),
                DistAutogradTest._nested_python_udf,
                args=(t1, t2, self._next_rank()),
            )
            dist_autograd.backward(context_id, [loss.sum()])

            grads = dist_autograd.get_gradients(context_id)
            self.assertEqual(t1.grad, grads[t1])
            self.assertEqual(t2.grad, grads[t2])

    _test_clean_context_backward_context_id = None

    class MyBackwardFunc(Function):
        @staticmethod
        def forward(ctx, input):
            return input

        @staticmethod
        @once_differentiable
        def backward(ctx, input):
            assert DistAutogradTest._test_clean_context_backward_context_id is not None

            # Release the context to simulate error (use barrier before releasing
            # context to ensure all nodes execute the backward function).
            dist.barrier()
            dist_autograd._release_context(
                DistAutogradTest._test_clean_context_backward_context_id
            )

            # Verify all contexts are cleaned up.
            assert _all_contexts_cleaned_up()

            return input

    @dist_init
    def test_clean_context_during_backward(self):
        """
        This test simulates the situation where the 'backward' call might throw
        an exception locally which would lead to the autograd context being
        cleaned up if we're using the context manager. As a result, the autograd
        context might be cleaned up while some threads are still using the
        autograd context.

        It is fine for the 'backward' call to throw an exception in this test,
        but the process should not crash.
        """
        initialize_pg(self.file_init_method, self.rank, self.world_size)

        context = dist_autograd._new_context()
        context_id = context._context_id()
        DistAutogradTest._test_clean_context_backward_context_id = context_id

        # Send the context id to all nodes.
        for i in range(0, self.world_size):
            if i != self.rank:
                rank_distance = (i - self.rank + self.world_size) % self.world_size
                rpc.rpc_sync(
                    worker_name(i),
                    _set_rpc_done,
                    args=(context_id, rank_distance),
                )

        dist.barrier()

        # Verify all context ids have been received.
        self.assertEqual(self.world_size - 1, len(known_context_ids))

        t1 = torch.rand((3, 3), requires_grad=True)
        for i in range(0, 100):
            dst = self._next_rank()
            t1 = rpc.rpc_sync(worker_name(dst), torch.add, args=(t1, t1))

        # Call MyBackwardFunc as the first op of the backward pass to
        # ensure we release the context early in the backward pass.
        t1 = DistAutogradTest.MyBackwardFunc.apply(t1)
        self.assertEqual(100, len(context._send_functions()))

        context_id = 100  # dummy context_id
        with self.assertRaisesRegex(
            RuntimeError,
            "Could not find autograd context with id: {}".format(context_id),
        ):
            dist_autograd.backward(context_id, [t1.sum()])

        # HACK: Killing workers since otherwise the autograd engine gets stuck on
        # other nodes. The proper fix would be addressing:
        # https://github.com/pytorch/pytorch/issues/27643, which would inform
        # other nodes about the failure.
        # The autograd engine gets stuck on other nodes since they're waiting to
        # receive gradients from the node that received an error (and as a
        # result it didn't execute the rest of the graph).
        dist.barrier()
        rpc.shutdown(graceful=False)
        sys.exit(0)

    @classmethod
    def _call_remote_embedding(cls, embedding_rref, input, offsets, per_sample_weights):
        embedding = embedding_rref.local_value()
        return embedding(input, offsets, per_sample_weights)

    @classmethod
    def _get_grad(cls, embedding_rref, context_id):
        embedding = embedding_rref.local_value()
        grad_map = dist_autograd.get_gradients(context_id)
        # Can't send sparse tensors over RPC: https://github.com/pytorch/pytorch/issues/30807
        return grad_map[embedding.weight].to_dense()

    @dist_init
    def test_embedding_bag_with_no_grad_tensors(self):
        dst = self._next_rank()
        remote_embedding = rpc.remote(
            worker_name(dst),
            torch.nn.EmbeddingBag,
            args=(16, 16),
            kwargs={"mode": "sum", "sparse": True},
        )
        local_embedding = torch.nn.EmbeddingBag(16, 16, mode="sum", sparse=True)

        input = torch.LongTensor([1, 2, 4, 5, 4, 3, 2, 9])
        # requires_grad = True to record send/recv functions
        per_sample_weights = torch.rand((8), requires_grad=True)
        offsets = torch.LongTensor([0, 4])

        local_res = local_embedding(input, offsets, per_sample_weights)

        # Run backward twice.
        torch.autograd.backward([local_res.sum()], retain_graph=True)
        torch.autograd.backward([local_res.sum()])
        local_grad = local_embedding.weight.grad

        with dist_autograd.context() as context_id:
            res = rpc.rpc_sync(
                worker_name(dst),
                DistAutogradTest._call_remote_embedding,
                args=(remote_embedding, input, offsets, per_sample_weights),
            )

            # Run backward twice to test accumulation of sparse gradients.
            dist_autograd.backward(context_id, [res.sum()], retain_graph=True)
            dist_autograd.backward(context_id, [res.sum()])

            remote_grad = rpc.rpc_sync(
                worker_name(dst),
                DistAutogradTest._get_grad,
                args=(remote_embedding, context_id),
            )

            self.assertEqual(local_grad.to_dense(), remote_grad)

    @classmethod
    def _mixed_requires_grad(cls, t1, t2):
        if t2.requires_grad:
            return t1 - t2
        else:
            return t1 * t2

    @dist_init
    def test_mixed_requires_grad(self):
        for exec_mode in [ExecMode.RPC_SYNC, ExecMode.REMOTE]:
            t1 = torch.rand((3, 3), requires_grad=True)
            t2 = torch.rand((3, 3), requires_grad=False)
            with dist_autograd.context() as context_id:
                ret = self._exec_func(
                    exec_mode, DistAutogradTest._mixed_requires_grad, t1, t2
                )
                self.assertEqual(t1 * t2, ret)
                dist_autograd.backward(context_id, [ret.sum()])
                self.assertTrue(t1.requires_grad)
                self.assertFalse(t2.requires_grad)
                grads = dist_autograd.get_gradients(context_id)
                self.assertIn(t1, grads)
                self.assertNotIn(t2, grads)
                self.assertEqual(t2, grads[t1])

    class TestDebugInfoFunc(Function):
        @staticmethod
        def forward(ctx, input):
            return input

        @staticmethod
        @once_differentiable
        def backward(ctx, input):
            debug_info = dist_autograd._get_debug_info()
            assert debug_info is not None
            backward_passes = int(debug_info["num_current_backward_passes"])

            # Hard to validate exact numbers because of the distributed nature.
            # We can't use a barrier() here since that would block the single
            # CPU thread available for autograd and can cause deadlocks.
            assert backward_passes >= 1 and backward_passes <= 4
            return input

    @dist_init
    def test_debug_info(self):
        initialize_pg(self.file_init_method, self.rank, self.world_size)

        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            i = 0
            res = {}
            res[i] = t1
            for rank in range(self.world_size):
                if rank != self.rank:
                    res[i + 1] = rpc.rpc_sync(
                        worker_name(rank), torch.add, args=(res[i], t2)
                    )
                    i += 1

            # Call custom function in middle of backward pass to ensure all
            # nodes are still waiting on a backward().
            res[i + 1] = DistAutogradTest.TestDebugInfoFunc.apply(res[i])
            i += 1

            for rank in range(self.world_size):
                if rank != self.rank:
                    res[i + 1] = rpc.rpc_sync(
                        worker_name(rank), torch.add, args=(res[i], t2)
                    )
                    i += 1

            dist_autograd.backward(context_id, [res[i].sum()])

            debug_info = dist_autograd._get_debug_info()
            num_autograd_context = int(debug_info["num_autograd_contexts"])
            # Need atleast one context and not more than 4.
            self.assertTrue(num_autograd_context >= 1 and num_autograd_context <= 4)

        for rd in range(self.world_size - 1):
            rpc.rpc_sync(
                worker_name((self.rank + rd + 1) % self.world_size),
                _set_rpc_done,
                args=(context_id, rd + 1),
            )

        dist.barrier()

        # Validate information
        debug_info = dist_autograd._get_debug_info()
        assert debug_info is not None
        self.assertEqual(0, int(debug_info["num_current_backward_passes"]))
        # only have `num_current_backward_passes` and `num_autograd contexts`
        self.assertTrue(len(debug_info) == 2)

        self.assertTrue(_all_contexts_cleaned_up())

        # All contexts should be cleaned up.
        debug_info = dist_autograd._get_debug_info()
        self.assertEqual(0, int(debug_info["num_autograd_contexts"]))

    @staticmethod
    def _workload_thread():
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            t3 = rpc.rpc_sync("worker0", torch.add, args=(t1, t2))
            t4 = rpc.rpc_sync("worker0", torch.mul, args=(t2, t3))
            t5 = rpc.rpc_sync("worker0", torch.matmul, args=(t3, t4))
            t6 = rpc.rpc_sync("worker0", torch.add, args=(t4, t5))

            dist_autograd.backward(context_id, [t6.sum()])

    @dist_init
    def test_async_dist_autograd(self):
        """
        This test ensures async processing for distributed autograd works
        appropriately. This is achieved by spawning multiple threads and
        hammering a single node with a lot of backward() calls.
        """

        initialize_pg(self.file_init_method, self.rank, self.world_size)
        if self.rank != 0:
            # All other ranks schedule work on rank 0.
            threads = []
            for i in range(20):
                t = threading.Thread(target=DistAutogradTest._workload_thread)
                t.start()
                threads.append(t)

            for thread in threads:
                thread.join()

        dist.barrier()

    @dist_init
    def test_backward_accumulate_grads(self):
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            t3 = torch.matmul(t1, t2)
            # Run backward twice.
            torch.autograd.backward([t3.sum()], retain_graph=True)
            torch.autograd.backward([t3.sum()])

            t3 = rpc.rpc_sync(
                worker_name(self._next_rank()), torch.matmul, args=(t1, t2)
            )
            # Run backward twice.
            dist_autograd.backward(context_id, [t3.sum()], retain_graph=True)
            dist_autograd.backward(context_id, [t3.sum()])

            # Verify the gradients are same for local and remote execution.
            grads = dist_autograd.get_gradients(context_id)
            self.assertEqual(2, len(grads))
            self.assertIn(t1, grads)
            self.assertIn(t2, grads)
            self.assertEqual(t1.grad, grads[t1])
            self.assertEqual(t2.grad, grads[t2])

    @staticmethod
    def _test_nested_backward_accumulate_grads(t1, t2, dst_rank):
        return rpc.rpc_sync(worker_name(dst_rank), torch.matmul, args=(t1, t2))

    @dist_init
    def test_nested_backward_accumulate_grads(self):
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            loss = rpc.rpc_sync(
                worker_name(self._next_rank()),
                DistAutogradTest._test_nested_backward_accumulate_grads,
                args=(t1, t2, self._next_rank()),
            ).sum()

            # Run backward twice.
            dist_autograd.backward(context_id, [loss], retain_graph=True)
            dist_autograd.backward(context_id, [loss])

    @dist_init
    def test_multiple_backward(self):
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            loss = rpc.rpc_sync(
                worker_name(self._next_rank()),
                torch.add,
                args=(t1, t2)).sum()

            # Run backward in a loop multiple times.
            for i in range(1000):
                dist_autograd.backward(context_id, [loss], retain_graph=True)

    @dist_init(clean_shutdown=False)
    def test_multiple_backward_with_errors(self):
        initialize_pg(self.file_init_method, self.rank, self.world_size)
        t1 = torch.rand((3, 3), requires_grad=True)
        t2 = torch.rand((3, 3), requires_grad=True)
        with dist_autograd.context() as context_id:
            loss = rpc.rpc_sync(
                'worker{}'.format(self._next_rank()),
                DistAutogradTest._python_udf_with_backward_error,
                args=(t1, t2)).sum()

            try:
                # Run backward in a loop multiple times.
                for i in range(100):
                    if i < 50:
                        with self.assertRaisesRegex(RuntimeError, "Simulate error on backward pass"):
                            dist_autograd.backward(context_id, [loss], retain_graph=True)
                    elif i > 50:
                        # Recovered from error.
                        dist_autograd.backward(context_id, [loss], retain_graph=True)
                    else:
                        dist.barrier()
                        SimulateBackwardError._simulate_error = False
                        dist.barrier()
            finally:
                # Sync before resetting flag.
                dist.barrier()

                # Reset the flag.
                SimulateBackwardError._simulate_error = True

    @dist_init
    def test_backward_verify_hooks(self):
        t1 = torch.ones((3, 3), requires_grad=True)
        # Double the gradient.
        t1.register_hook(lambda grad: grad * 2)
        t2 = torch.ones((3, 3), requires_grad=True)
        local_grads = None
        for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC, ExecMode.REMOTE]:
            with dist_autograd.context() as context_id:
                ret = self._exec_func(exec_mode, torch.matmul, t1, t2)
                loss = ret.sum()
                ret = self._verify_backwards(
                    exec_mode, [loss], context_id, local_grads, t1, t2
                )
                local_grads = ret if ret else local_grads

    @dist_init
    def test_no_grad_copy(self):
        '''
        Similar to test in test_autograd.py.
        '''
        # create autograd function that saves grad pointer as class static
        class MyFunc(Function):
            static_grad_ptr = None

            @staticmethod
            def forward(ctx, inp1, inp2):
                return inp1 + inp2

            @staticmethod
            def backward(ctx, grad):
                MyFunc.static_grad_ptr = grad.data_ptr()
                return grad, grad

        class MyFuncSingleGrad(Function):
            static_grad_ptr = None

            @staticmethod
            def forward(ctx, inp):
                return inp

            @staticmethod
            def backward(ctx, grad):
                MyFuncSingleGrad.static_grad_ptr = grad.data_ptr()
                return grad

        class NonContGradFunc(Function):
            @staticmethod
            def forward(ctx, inp1):
                ctx.size = inp1.size()
                return torch.tensor([1.])

            @staticmethod
            def backward(ctx, grad):
                return torch.ones(1).expand(ctx.size)

        a = torch.randn(5, 6, requires_grad=True)
        b = torch.randn(5, 6, requires_grad=True)
        # non-contiguous grad should be copied
        with dist_autograd.context() as context_id:
            dist_autograd.backward(context_id, [NonContGradFunc.apply(MyFunc.apply(a, b))])
            grads = dist_autograd.get_gradients(context_id)
            self.assertFalse(grads[a].data_ptr() == MyFunc.static_grad_ptr)
            self.assertFalse(grads[b].data_ptr() == MyFunc.static_grad_ptr)

        # test case that should trigger no copy for a
        with dist_autograd.context() as context_id:
            dist_autograd.backward(context_id, [MyFuncSingleGrad.apply(a)[1][0]])
            grads = dist_autograd.get_gradients(context_id)
            p_g = MyFuncSingleGrad.static_grad_ptr
            p_a = grads[a].data_ptr()
            # Verify there was no clone.
            self.assertTrue(p_a == p_g)

        # Test case that should trigger copy for both of a,b. This is
        # different in the distributed autograd case since we hold
        # a reference to all grads in a vector until all accumulation is done.
        with dist_autograd.context() as context_id:
            dist_autograd.backward(context_id, [MyFunc.apply(a, b)[1][0]])
            grads = dist_autograd.get_gradients(context_id)
            p_g = MyFunc.static_grad_ptr
            p_a = grads[a].data_ptr()
            p_b = grads[b].data_ptr()
            # check a,b uses different grad buffer
            self.assertFalse(p_a == p_b)
            # both should be copied.
            self.assertFalse(grads[a].data_ptr() == MyFunc.static_grad_ptr)
            self.assertFalse(grads[b].data_ptr() == MyFunc.static_grad_ptr)

    @dist_init
    def test_no_grad_copy_sparse(self):
        # create autograd function that saves grad pointer as class static
        class MyFunc(Function):
            static_grad_ptr = None

            @staticmethod
            def forward(ctx, inp):
                return inp

            @staticmethod
            def backward(ctx, grad):
                MyFunc.static_grad_ptr = grad._values().data_ptr()
                return grad

        class NonContGradFunc(Function):
            static_grad_ptr = None

            @staticmethod
            def forward(ctx, inp1, inp2):
                return inp1 + inp2

            @staticmethod
            def backward(ctx, grad):
                # Create a sparse tensor with non-contigous indices and values
                # and return as grad.
                v = torch.rand(1, 3)
                i = torch.ones(1, 1, dtype=torch.long)
                nv = v.expand(8, 3)
                ni = i.expand(1, 8)
                ngrad = torch.sparse.FloatTensor(ni, nv, torch.Size([10, 3]))
                NonContGradFunc.static_grad_ptr = ngrad._values().data_ptr()
                return ngrad, ngrad

        a = torch.randn(10, 3, requires_grad=True)
        b = torch.randn(10, 3, requires_grad=True)
        input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9])
        offsets = torch.tensor([0, 4])
        import torch.nn.functional as F

        # test case that should trigger no copy for a.
        with dist_autograd.context() as context_id:
            emb_matrix = MyFunc.apply(a)
            loss = F.embedding_bag(emb_matrix, input, offsets, sparse=True).sum()
            dist_autograd.backward(context_id, [loss], retain_graph=True)
            grads = dist_autograd.get_gradients(context_id)
            p_g = MyFunc.static_grad_ptr
            p_a = grads[a]._values().data_ptr()
            # check a uses the same buffer
            self.assertTrue(p_a == p_g)

            # Run backwards multiple times.
            for i in range(10):
                dist_autograd.backward(context_id, [loss], retain_graph=True)

        # non-contiguous indices and value, we should trigger a copy.
        with dist_autograd.context() as context_id:
            emb_matrix = NonContGradFunc.apply(a, b)
            loss = F.embedding_bag(emb_matrix, input, offsets, sparse=True).sum()
            dist_autograd.backward(context_id, [loss], retain_graph=True)
            grads = dist_autograd.get_gradients(context_id)
            p_g = NonContGradFunc.static_grad_ptr
            p_a = grads[a]._values().data_ptr()
            p_b = grads[b]._values().data_ptr()
            # check a,b uses different grad buffer
            self.assertFalse(p_a == p_b)
            # Verify we cloned both grads.
            self.assertFalse(p_a == p_g)
            self.assertFalse(p_b == p_g)

            # Run backwards multiple times to verify accumulation.
            for i in range(10):
                dist_autograd.backward(context_id, [loss], retain_graph=True)

    @dist_init
    def test_grad_copy_sparse_indices_extra_ref(self):
        # create autograd function that saves grad pointer as class static
        class MyFunc(Function):
            static_grad_ptr = None
            static_grad_indices_ref = None
            static_grad_values_ref = None

            @staticmethod
            def forward(ctx, inp):
                return inp

            @staticmethod
            def backward(ctx, grad):
                MyFunc.static_grad_ptr = grad._values().data_ptr()
                # indices() and values() return views, so holding onto
                # references of them would not increment refcount of indices
                # and values inside the sparse tensor.
                MyFunc.static_grad_indices_ref = grad._indices()
                MyFunc.static_grad_values_ref = grad._values()
                return grad

        a = torch.randn(10, 3, requires_grad=True)
        input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9])
        offsets = torch.tensor([0, 4])
        import torch.nn.functional as F

        with dist_autograd.context() as context_id:
            emb_matrix = MyFunc.apply(a)
            loss = F.embedding_bag(emb_matrix, input, offsets, sparse=True).sum()
            dist_autograd.backward(context_id, [loss], retain_graph=True)
            grads = dist_autograd.get_gradients(context_id)
            p_g = MyFunc.static_grad_ptr
            p_a = grads[a]._values().data_ptr()
            self.assertIsNotNone(MyFunc.static_grad_indices_ref)
            self.assertIsNotNone(MyFunc.static_grad_values_ref)
            # grad would be stolen, since static_grad_indices_ref and
            # static_grad_values_ref are holding onto views and don't bump the
            # refcount.
            self.assertTrue(p_g == p_a)

    @dist_init
    def test_post_hooks(self):
        self.hook_called_times = 0

        def post_hook_add_one(output_grads, input_grads):
            self.hook_called_times += 1
            return output_grads

        def post_hook_add_two(output_grads, input_grads):
            self.hook_called_times += 2
            return output_grads

        t = torch.rand(10, 10, requires_grad=True)
        a = t + t

        # Register post hooks
        accumulate_grad_0 = a.grad_fn.next_functions[0][0]
        accumulate_grad_0.register_hook(post_hook_add_one)
        accumulate_grad_0.register_hook(post_hook_add_two)

        accumulate_grad_1 = a.grad_fn.next_functions[1][0]
        accumulate_grad_1.register_hook(post_hook_add_two)

        with dist_autograd.context() as context_id:
            loss = a.sum()
            dist_autograd.backward(context_id, [loss])
            self.assertEqual(5, self.hook_called_times)
            grads = dist_autograd.get_gradients(context_id)
            self.assertEqual(1, len(grads))
            self.assertTrue(t in grads)

    @staticmethod
    def _slow_add(t1, t2):
        time.sleep(1)
        t3 = t1 + t2
        t3.requires_grad = True
        return t3

    @dist_init
    def test_thread_local_context_id(self):
        t1 = torch.rand((3, 3))
        t2 = torch.rand((3, 3))

        t3 = t1 + t2
        t3.requires_grad = True
        t3.sum().backward()

        dst = worker_name((self.rank + 1) % self.world_size)
        rref = rpc.remote(dst, DistAutogradTest._slow_add, args=(t1, t2))

        with dist_autograd.context() as context_id:
            loss = rref.to_here().sum()
            # due to slow add, the continuation of this backward pass will be
            # invoked by the previous rpc.remote thread which does not have a
            # valid context_id. So, this can test whether we propagate
            # thread_local states properly when jumping across threads on the
            # server side.
            dist_autograd.backward(context_id, [loss])
            self.assertTrue(
                rpc.rpc_sync(
                    dst,
                    _compare_owner_value,
                    args=(context_id, rref, t3.grad)
                )
            )

    @skip_if_lt_x_gpu(1)
    @dist_init
    def test_gpu_simple(self):
        t1 = torch.rand(3, 3, requires_grad=True, device="cuda:0")
        t2 = torch.rand(3, 3, requires_grad=True, device="cuda:0")
        (t1 + t2).sum().backward()
        with dist_autograd.context() as context_id:
            t3 = t1 + t2
            dist_autograd.backward(context_id, [t3.sum()])
            grads = dist_autograd.get_gradients(context_id)
            self.assertEqual(2, len(grads))
            self.assertEqual(t1.grad, grads[t1])
            self.assertEqual(t2.grad, grads[t2])

    @skip_if_lt_x_gpu(1)
    @dist_init
    def test_gpu_to_cpu_continuation(self):
        t1 = torch.rand(3, 3, requires_grad=True, device="cuda:0")
        t2 = torch.rand(3, 3, requires_grad=True)
        # Run a few iterations.
        for i in range(3):
            t1.grad = None
            t2.grad = None
            # Root is CPU
            local_grads = None
            for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC]:
                with dist_autograd.context() as context_id:
                    t3 = self._exec_func(exec_mode, torch.add, t2, t2)
                    t4 = t3.cuda(0) + t1
                    t5 = self._exec_func(exec_mode, torch.add, t4.cpu(), t2)
                    t6 = t5.cuda(0) + t4
                    t7 = self._exec_func(exec_mode, torch.add, t6.cpu(), t5)
                    # Autograd graph consists of CPU -> GPU -> CPU execution.
                    ret = self._verify_backwards(
                        exec_mode, [t7.sum()], context_id, local_grads, t1, t2
                    )
                    local_grads = ret if ret else local_grads

    @skip_if_lt_x_gpu(1)
    @dist_init
    def test_gpu_to_cpu_continuation_gpu_root(self):
        t1 = torch.rand(3, 3, requires_grad=True, device="cuda:0")
        t2 = torch.rand(3, 3, requires_grad=True)
        # Run a few iterations.
        for i in range(3):
            t1.grad = None
            t2.grad = None
            # Root is CPU
            local_grads = None
            for exec_mode in [ExecMode.LOCAL, ExecMode.RPC_SYNC]:
                with dist_autograd.context() as context_id:
                    t3 = self._exec_func(exec_mode, torch.add, t2, t2)
                    t4 = t3.cuda(0) + t1
                    t5 = self._exec_func(exec_mode, torch.add, t4.cpu(), t2)
                    t6 = t5.cuda(0) + t4
                    # Autograd graph consists of CPU -> GPU -> CPU execution.
                    ret = self._verify_backwards(
                        exec_mode, [t6.sum()], context_id, local_grads, t1, t2
                    )
                    local_grads = ret if ret else local_grads


class FaultyAgentDistAutogradTest(RpcAgentTestFixture):
    # Reusing a simplified helper function from DistAutogradTest to ensure
    # autograd context is successfully cleaned up even when RPCs are failing.
    def context_cleanup_test_helper(self, rpc_args, func):
        initialize_pg(self.file_init_method, self.rank, self.world_size)

        # test that in dist autograd, in the case that tensors communicated over RPC do
        # NOT require grad, we still cleanup the dist autograd contexts created
        # on other nodes. This is because the autograd context is still
        # communicated over RPC even if tensor arguments do not require grad, as
        # it is possible that the response could.
        dst_ranks = {rank for rank in range(self.world_size) if rank != self.rank}

        with dist_autograd.context() as context_id:
            for dst_rank in dst_ranks:
                rpc.rpc_sync(worker_name(dst_rank), func, args=rpc_args)
                rpc.rpc_sync(
                    worker_name(dst_rank), _set_rpc_done, args=(context_id, 1)
                )
        # the thread's context id should be cleaned up
        with self.assertRaises(RuntimeError):
            dist_autograd._retrieve_context(context_id)
        # Ensure all peers have finished mutating the
        # `known_context_ids` set.
        dist.barrier()
        # check that all contexts have been cleaned up.
        success = _all_contexts_cleaned_up()
        self.assertTrue(success)

    # no faulty_messages defined so this fails all retryable messages - see
    # faulty_rpc_agent_test_fixture.py for the list of retryable messages.
    @dist_init
    def test_context_cleanup_tensor_with_grad(self):
        t1 = torch.ones(3, 3, requires_grad=True)
        t2 = torch.zeros(3, 3, requires_grad=True)
        self.context_cleanup_test_helper(rpc_args=(t1, t2), func=torch.add)

    @dist_init
    def test_verify_backend_options(self):
        self.assertEqual(self.rpc_backend, rpc.backend_registry.BackendType.FAULTY_PROCESS_GROUP)
        self.assertEqual(self.rpc_backend_options.num_send_recv_threads, 8)
        self.assertEqual(self.rpc_backend_options.num_fail_sends, 3)
        self.assertEqual(len(self.rpc_backend_options.messages_to_fail), 4)

class TensorPipeDistAutogradTest(RpcAgentTestFixture):

    @skip_if_lt_x_gpu(4)
    def test_device_maps_backward_pass(self):
        options = self.rpc_backend_options
        dst = worker_name((self.rank + 1) % self.world_size)

        # The reverse of this device mapping should be used for the backward pass.
        options.set_device_map(dst, {self.rank: (self.rank + 1) % self.world_size})

        rpc.init_rpc(
            name=worker_name(self.rank),
            backend=self.rpc_backend,
            rank=self.rank,
            world_size=self.world_size,
            rpc_backend_options=options,
        )

        t1 = torch.rand(10, device=self.rank, requires_grad=True)
        t2 = torch.rand(10, device=self.rank, requires_grad=True)
        with dist_autograd.context() as context_id:
            res = rpc.rpc_sync(dst, torch.add, args=(t1, t2))
            dist_autograd.backward(context_id, [res.sum()])
            grads = dist_autograd.get_gradients(context_id)
            self.assertEqual(torch.ones(10), grads[t1])
            self.assertEqual(torch.ones(10), grads[t2])
            self.assertEqual(t1.device, grads[t1].device)
            self.assertEqual(t2.device, grads[t2].device)

        rpc.shutdown()

    class MyRemoteCompute(torch.nn.Module):
        def __init__(self):
            super().__init__()

        def forward(self, input):
            input = input * 2.0
            return input

    class MyLocalCompute(torch.nn.Module):
        def __init__(self, next_stage):
            super().__init__()
            self.next_stage = next_stage

        def forward(self, input):
            return self.next_stage.rpc_sync().forward(input)

    @skip_if_lt_x_gpu(4)
    def test_dist_autograd_sync_streams(self):

        options = self.rpc_backend_options
        dst = worker_name((self.rank + 1) % self.world_size)

        # The reverse of this device mapping should be used for the backward pass.
        options.set_device_map(dst, {self.rank: (self.rank + 1) % self.world_size})

        rpc.init_rpc(
            name=worker_name(self.rank),
            backend=self.rpc_backend,
            rank=self.rank,
            world_size=self.world_size,
            rpc_backend_options=options,
        )

        remote_compute = rpc.remote(dst, TensorPipeDistAutogradTest.MyRemoteCompute)
        local_compute = TensorPipeDistAutogradTest.MyLocalCompute(remote_compute)
        for _ in range(10):
            input = torch.rand([1000, 10000], device=self.rank, requires_grad=True)
            # Run local autograd
            result = input * 2.0
            r = random.random()
            loss = result.sum() * r
            loss.backward()

            # Run distributed autograd
            with dist_autograd.context() as context_id:
                result = local_compute(input)
                loss = result.sum() * r
                dist_autograd.backward(context_id, [loss])

                # Compare grads.
                grads = dist_autograd.get_gradients(context_id)
                self.assertEqual(input.grad, grads[input])

        rpc.shutdown()
