# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# We're not responsible for pytest decorators
# mypy: disallow_untyped_decorators = False

"""
Collection of some testing utilities for the Fairscale library. Please complement as
you see fit, but refrain from ad-hoc test utils within the different feature sets and
relative imports.
"""

import contextlib
import functools
import gc
import inspect
import logging
import multiprocessing
import os
import random
from statistics import mean
import subprocess
import sys
import tempfile
from typing import TYPE_CHECKING, Any, Callable, Dict, Generator, List, Optional, Tuple, Union

import numpy
import pytest
import torch
from torch import Tensor
import torch.distributed as dist
from torch.distributed import rpc
import torch.multiprocessing as mp
import torch.nn as nn

from fairscale.nn.model_parallel import destroy_model_parallel, initialize_model_parallel
from fairscale.nn.model_parallel.random import model_parallel_cuda_manual_seed

if TYPE_CHECKING:
    Base = nn.Module[Tensor]
else:
    Base = nn.Module

skip_if_cuda = pytest.mark.skipif(torch.cuda.is_available(), reason="Testing only on CPUs to save time")

skip_if_no_cuda = pytest.mark.skipif(
    not torch.cuda.is_available() or torch.cuda.device_count() < 1, reason="CUDA required"
)

skip_if_single_gpu = pytest.mark.skipif(
    not torch.cuda.is_available() or torch.cuda.device_count() < 2, reason="multiple GPUs required"
)

skip_if_less_than_four_gpu = pytest.mark.skipif(
    not torch.cuda.is_available() or torch.cuda.device_count() < 4, reason="4 GPUs or more required"
)

skip_if_py38 = pytest.mark.skipif(
    sys.version_info.major == 3 and sys.version_info.minor == 8, reason="Python3.8 is skipped"
)

skip_if_py39_no_cuda = pytest.mark.skipif(
    not torch.cuda.is_available() and sys.version_info.major == 3 and sys.version_info.minor == 9,
    reason="Python3.9 without CUDA is skipped",
)

available_devices = ["cpu"]
if torch.cuda.is_available():
    available_devices.append("cuda")


filename_mpi: Optional[str] = None


class IdentityLayer(Base):
    def __init__(self, size: int, scale: float = 1.0) -> None:
        super(IdentityLayer, self).__init__()
        self.weight = torch.nn.Parameter(scale * torch.randn(size))

    def forward(self, *_: Any, **__: Any) -> Tensor:
        return self.weight


def set_random_seed(seed: int) -> None:
    """Set random seed for reproducibility."""
    random.seed(seed)
    numpy.random.seed(seed)
    torch.manual_seed(seed)
    model_parallel_cuda_manual_seed(seed)


def torch_version() -> Tuple[int, ...]:
    numbering = torch.__version__.split("+")[0].split(".")[:3]

    # Catch torch version if run against internal pre-releases, like `1.8.0a0fb`,
    if not numbering[2].isnumeric():
        # Two options here:
        # - either skip this version (minor number check is not relevant)
        # - or check that our codebase is not broken by this ongoing development.

        # Assuming that we're interested in the second usecase more than the first,
        # return the pre-release or dev numbering
        logging.warning(f"Pytorch pre-release version {torch.__version__} - assuming intent to test it")
        numbering[2] = "0"

    return tuple(int(n) for n in numbering)


# Global variable to cache the results from the first nvidia-smi execution.
_smi_ver: Optional[str] = None


def torch_cuda_version(compiled: bool = False) -> Tuple[int, ...]:
    if compiled:
        numbering = torch.version.cuda.split(".")[:2]
    else:
        global _smi_ver
        if _smi_ver is None:

            def get_smi_ver() -> str:
                """Get CUDA version from nvidia-smi"""
                for line in subprocess.check_output("nvidia-smi".split()).decode("utf-8").split("\n"):
                    if "CUDA Version" in line:
                        res = line.split()[8]
                        assert res.startswith("10.") or res.startswith("11."), res
                        return res
                assert False

            _smi_ver = get_smi_ver()
        numbering = _smi_ver.split(".")[:2]
    return tuple(int(n) for n in numbering)


def dist_init(rank: int, world_size: int, filename: str, filename_rpc: str = "") -> bool:
    """
    Initialize torch distributed, based on a temporary file shared across ranks, which makes it possible for unrelated
    tests to be run concurrently.

    Return false if not enough GPUs present in the system.

    .. warning: This limits the usecase to all ranks being on the same node
    """

    try:
        torch.distributed.rpc.shutdown()
    except Exception:
        pass

    print(f"dist init r={rank}, world={world_size}")

    os.environ["WORLD_SIZE"] = str(world_size)
    os.environ["RANK"] = str(rank)
    url = "file://" + filename
    url_rpc = "file://" + filename_rpc

    if torch_version() >= (1, 6, 0):
        backend = "nccl" if torch.cuda.is_available() else "gloo"
        if backend == "nccl" and torch.cuda.device_count() < world_size:
            logging.warning("Requested world size cannot be reached on this machine, not enough GPUs")
            return False

        torch.distributed.init_process_group(backend=backend, rank=rank, world_size=world_size, init_method=url)

        tp_options = {"init_method": url_rpc}
        # Workaround for bug in torch v1.8.0. Should be fixed in v1.8.1
        if torch_version() == (1, 8, 0):
            if torch.cuda.is_available():
                # Workaround for https://github.com/pytorch/pytorch/issues/53844
                tp_options["_transports"] = ["ibv", "uv"]  # type: ignore
            else:
                # Workaround for https://github.com/pytorch/pytorch/issues/54266
                tp_options["_channels"] = ["mpt_uv", "basic", "cuda_ipc", "cuda_gdr", "cuda_xth", "cuda_basic"]  # type: ignore

        rpc.init_rpc(
            f"Test{rank}",
            rank=rank,
            world_size=world_size,
            backend=rpc.BackendType.TENSORPIPE,
            rpc_backend_options=rpc.TensorPipeRpcBackendOptions(**tp_options),
        )

    else:
        if world_size > 1:
            # TensorPipe is not available in Torch 1.5
            rpc.init_rpc(
                name=f"Test{rank}",
                rank=rank,
                world_size=world_size,
                rpc_backend_options=rpc.ProcessGroupRpcBackendOptions(init_method=url_rpc),
            )
        elif torch.cuda.is_available():
            torch.distributed.init_process_group(backend="nccl", rank=rank, world_size=world_size, init_method=url)
        else:
            return False

    if torch.cuda.is_available() and torch.cuda.device_count():
        torch.cuda.set_device(rank % torch.cuda.device_count())

    return True


def get_worker_map() -> Dict[Any, Any]:
    return {rank: f"Test{rank}" for rank in range(dist.get_world_size())}


def get_world_sizes() -> List[int]:
    limit = torch.cuda.device_count()
    return [x for x in [1, 2, 4, 8] if x <= limit]


def spawn_for_all_world_sizes(test_func: Callable, world_sizes: List[int] = get_world_sizes(), args: Any = []) -> None:

    for world_size in world_sizes:
        _, filename = tempfile.mkstemp()
        _, filename_rpc = tempfile.mkstemp()

        # (lefaudeux) Let mp handle the process joining, join=False and handling context has been unstable in the past
        mp.spawn(test_func, args=(world_size, filename, filename_rpc, *args), nprocs=world_size, join=True)


def worker_process(
    rank: int, world_size: int, filename: str, filename_rpc: str, func: Callable, args: Any, error_queue: Any
) -> None:
    """Main function for unit tests launced with torch_spawn"""

    if not dist_init(rank, world_size, filename, filename_rpc):
        logging.warning("failed initializing torch distributed")
        teardown()
        return

    kwargs = {}
    if "OMPI_COMM_WORLD_RANK" not in os.environ:
        kwargs["pipeline_backend"] = "gloo"

    initialize_model_parallel(1, world_size, **kwargs)

    try:
        func(*args)
        teardown()
    except BaseException as e:
        logging.warning(f" Rank {rank}: {e}")

        # Make sure that the group is properly destroyed, even for tests which check for exceptions being raised
        teardown()

        # If the function raises 'Skipped', this indicates pytest.skip(), so
        # forward it to parent so we can call pytest.skip() there
        if e.__class__.__name__ == "Skipped":
            error_queue.put(str(e))
            return

        raise e


def teardown() -> None:
    destroy_model_parallel()

    if torch.distributed.is_initialized():
        torch.distributed.destroy_process_group()
    try:
        # torch 1.5 hangs on shutdown if waiting for all processes
        torch.distributed.rpc.shutdown(graceful=False)
    except Exception:
        pass


def torch_spawn(world_sizes: Optional[List[int]] = None) -> Callable:
    if world_sizes is None:
        world_sizes = get_world_sizes()

    def prepare_test(func: Callable) -> Callable:
        """Function called with the test function as the argument. Generates a
        replacement which serves as the actual test function."""

        name = func.__name__
        parameters = inspect.signature(func).parameters

        if name.startswith("test"):
            raise ValueError(
                f"Tests marked with @torch_spawn (i.e. '{name}') should not have names beginning in 'test' as they will"
                " be picked up by pytest without running the spawn wrapper"
            )

        @functools.wraps(func)
        def replacement(*args: Any, **kwargs: Any) -> None:
            assert args == tuple()
            assert world_sizes is not None  # mypy crutch

            args = tuple(
                kwargs[p] for p in parameters if p != "rank"
            )  # converting named parameters to positional parameters to pass to `spawn`

            error_queue = multiprocessing.get_context("spawn").SimpleQueue()
            if "OMPI_COMM_WORLD_RANK" in os.environ:
                # TODO (Min): this global used to be assigned every time this file is imported.
                #     I changed it to be assigned on first use. Should be the same, but I am not
                #     sure this is used or is correct since different processes would have different
                #     file names to init_process_group below. By initing, here, we don't leave
                #     a temp file behind on importing time.
                global filename_mpi
                if filename_mpi is None:
                    filename_mpi = tempfile.mkstemp()[1]

                os.environ["RANK"] = os.environ["OMPI_COMM_WORLD_RANK"]
                os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"]
                torch.distributed.init_process_group("mpi", init_method=f"file://{filename_mpi}")

                world_size = torch.distributed.get_world_size()
                destroy_model_parallel()
                initialize_model_parallel(1, world_size)
                torch.cuda.set_device(torch.distributed.get_rank() % torch.cuda.device_count())
                if world_size in world_sizes:
                    try:
                        func(*args)
                        teardown()
                    except BaseException as e:
                        teardown()
                        import traceback

                        print(f"{traceback.format_exc()}")
                        raise e
                else:
                    pytest.skip("Requested world size doesn't match current world size")
            else:
                spawn_for_all_world_sizes(worker_process, world_sizes, (func, args, error_queue))

            if not error_queue.empty():
                msg = error_queue.get()
                pytest.skip(msg)

        # Register a function with the same name, prefixed with "test_" in the
        # calling module, so it will be picked up by pytest
        current_frame = inspect.currentframe()
        assert current_frame is not None
        caller_module = inspect.getmodule(current_frame.f_back)
        setattr(caller_module, f"test_{name}", replacement)

        return func

    return prepare_test


class _Block(Base):
    def __init__(self, embed_dim: int, num_heads: int) -> None:
        super().__init__()
        self.ln_1 = nn.LayerNorm(embed_dim)
        self.ln_2 = nn.LayerNorm(embed_dim)
        self.attn = nn.MultiheadAttention(embed_dim, num_heads)  # type: ignore
        self.mlp = nn.Sequential(nn.Linear(embed_dim, embed_dim * 4), nn.GELU(), nn.Linear(embed_dim * 4, embed_dim),)

    def forward(self, *inputs: Any, **kwargs: Any) -> Tensor:
        x = inputs[0]
        attn_mask = torch.full((len(x), len(x)), -float("Inf"), device=x.device, dtype=x.dtype)
        attn_mask = torch.triu(attn_mask, diagonal=1)

        x = self.ln_1(x)
        a, _ = self.attn(x, x, x, attn_mask=attn_mask, need_weights=False)
        x = x + a
        m = self.mlp(self.ln_2(x))
        x = x + m
        return x


class GPT2(Base):
    """
    GPT2 pytorch implementation, for testing purposes in the image-GPT context
    Credits: https://github.com/teddykoker/image-gpt"""

    def __init__(
        self, embed_dim: int, num_heads: int, num_layers: int, num_positions: int, num_vocab: int, num_classes: int
    ) -> None:
        super().__init__()

        self.embed_dim = embed_dim

        # start of sequence token
        self.sos = torch.nn.Parameter(torch.zeros(embed_dim))
        nn.init.normal_(self.sos)

        self.token_embeddings = nn.Embedding(num_vocab, embed_dim)
        self.position_embeddings = nn.Embedding(num_positions, embed_dim)

        self.layers = nn.ModuleList()
        for _ in range(num_layers):
            self.layers.append(_Block(embed_dim, num_heads))

        self.ln_f = nn.LayerNorm(embed_dim)
        self.head = nn.Linear(embed_dim, num_vocab, bias=False)
        self.clf_head = nn.Linear(embed_dim, num_classes)

    def forward(self, x: Tensor, classify: bool = False) -> Any:  # type: ignore
        """
        Expect input as shape [sequence len, batch]
        If classify, return classification logits
        """
        length, batch = x.shape

        h = self.token_embeddings(x)

        # prepend sos token
        sos = torch.ones(1, batch, self.embed_dim, device=x.device) * self.sos
        h = torch.cat([sos, h[:-1, :, :]], dim=0)

        # add positional embeddings
        positions = torch.arange(length, device=x.device).unsqueeze(-1)
        h = h + self.position_embeddings(positions).expand_as(h)

        # transformer
        for layer in self.layers:
            h = layer(h)

        h = self.ln_f(h)

        logits = self.head(h)

        if not classify:
            # return logits
            return logits

        h = torch.mean(h, dim=0)  # average pool over sequence
        # return classification logits and generative logits
        return self.clf_head(h), logits


def objects_are_equal(a: Any, b: Any, raise_exception: bool = False, dict_key: Optional[str] = None) -> bool:
    """
    Test that two objects are equal. Tensors are compared to ensure matching
    size, dtype, device and values.
    """
    if type(a) is not type(b):
        if raise_exception:
            raise ValueError(f"type mismatch {type(a)} vs. {type(b)}")
        return False
    if isinstance(a, dict):
        if set(a.keys()) != set(b.keys()):
            if raise_exception:
                raise ValueError(f"keys mismatch {a.keys()} vs. {b.keys()}")
            return False
        for k in a.keys():
            if not objects_are_equal(a[k], b[k], raise_exception, k):
                return False
        return True
    elif isinstance(a, (list, tuple, set)):
        if len(a) != len(b):
            if raise_exception:
                raise ValueError(f"length mismatch {len(a)} vs. {len(b)}")
            return False
        return all(objects_are_equal(x, y, raise_exception) for x, y in zip(a, b))
    elif torch.is_tensor(a):
        try:
            # assert_allclose doesn't strictly test shape, dtype and device
            shape_dtype_device_match = a.size() == b.size() and a.dtype == b.dtype and a.device == b.device
            if not shape_dtype_device_match:
                if raise_exception:
                    msg = f"sizes: {a.size()} vs. {b.size()}, "
                    msg += f"types: {a.dtype} vs. {b.dtype}, "
                    msg += f"device: {a.device} vs. {b.device}"
                    raise AssertionError(msg)
                else:
                    return False
            # assert_allclose.
            torch.testing.assert_allclose(a, b)
            return True
        except (AssertionError, RuntimeError) as e:
            if raise_exception:
                if dict_key and isinstance(e, AssertionError):
                    # Add dict key to the assertion error.
                    msg = e.args[0]
                    new_msg = f"For dict key '{dict_key}': {msg}"
                    raise AssertionError(new_msg) from None
                else:
                    raise e
            else:
                return False
    else:
        return a == b


def check_same_model_params(model_a: torch.nn.Module, model_b: torch.nn.Module, message: str = "") -> None:
    for p_a, p_b in zip(model_a.parameters(), model_b.parameters()):
        assert torch.allclose(p_a, p_b, atol=1e-3), f"Model parameters differ\n{p_a} {p_b}\n" + message

    for b_a, b_b in zip(model_a.buffers(), model_b.buffers()):
        assert torch.allclose(b_a, b_b), f"Model buffers differ {b_a} - {b_b}\n" + message


def check_same_models_across_ranks(
    model: torch.nn.Module, process_group: Any, params_should_be_equal: bool, check_broadcast_buffers: bool
) -> None:
    world_size = dist.get_world_size(process_group)
    rank = dist.get_rank(process_group)
    for param in model.parameters():
        # collect the params across the rank
        receptacle = [param.clone() for _ in range(world_size)]
        dist.all_gather(receptacle, param, group=process_group)

        if rank == 0:
            for sync_p in receptacle[1:]:
                assert not params_should_be_equal or torch.all(
                    torch.eq(receptacle[0], sync_p)
                ), f"Models differ in between ranks {receptacle[0]} - {sync_p}"

    # Check that all the buffers are in sync (authoritative rank is 0, its buffer is 0)
    if check_broadcast_buffers:
        for buffer in model.buffers():
            receptacle = [buffer.clone() for _ in range(world_size)]
            dist.all_gather(receptacle, buffer, group=process_group)
            if rank == 0:
                for sync_b in receptacle[1:]:
                    assert not params_should_be_equal or torch.all(
                        torch.eq(receptacle[0], sync_b)
                    ), f"Models differ in between ranks {receptacle[0]} - {sync_b}"


class DeviceAndTypeCheckModule(Base):
    """A simple module for checking Tensor devices and dtypes."""

    def __init__(
        self,
        expected_input_dtype: Optional[torch.dtype] = None,
        expected_input_device: Optional[torch.device] = None,
        expected_param_dtype: Optional[torch.dtype] = None,
        expected_param_device: Optional[torch.device] = None,
        expected_loss_dtype: Optional[torch.dtype] = None,
        expected_loss_device: Optional[torch.device] = None,
        expected_buffer_dtype: Optional[torch.device] = None,
    ):
        super().__init__()
        self.expected_input_dtype = expected_input_dtype
        self.expected_input_device = expected_input_device
        self.expected_param_dtype = expected_param_dtype
        self.expected_param_device = expected_param_device
        self.expected_loss_dtype = expected_loss_dtype
        self.expected_loss_device = expected_loss_device
        self.expected_buffer_dtype = expected_buffer_dtype

        self.linear = nn.Linear(5, 5)
        self.register_buffer("buffer", torch.rand((5,)))

    def _check(
        self,
        key: str,
        x: Union[torch.device, torch.dtype],
        expected: Union[Optional[torch.device], Optional[torch.dtype]],
    ) -> None:
        assert expected in {None, x}, f"{key} ({x}) != expected ({expected})"

    def forward(self, *input: Tensor, **kwargs: Any) -> Tensor:
        x = input[0]
        self._check("input.dtype", x.dtype, self.expected_input_dtype)
        self._check("input.device", x.device, self.expected_input_device)

        param = self.linear.weight
        self._check("param.dtype", param.dtype, self.expected_param_dtype)
        self._check("param.device", param.device, self.expected_param_device)
        self._check("buffer.dtype", self.buffer.dtype, self.expected_buffer_dtype)  # type: ignore
        x = x + self.buffer
        loss = (self.linear(x) + self.buffer).sum()
        self._check("loss.dtype", loss.dtype, self.expected_loss_dtype)
        self._check("loss.device", loss.device, self.expected_loss_device)

        return loss


@functools.lru_cache()
def get_cycles_per_ms() -> float:
    """Measure and return approximate number of cycles per millisecond for torch.cuda._sleep

    Copied from: github.com/pytorch/pytorch/blob/master/test/test_cuda.py
    """

    def measure() -> float:
        start = torch.cuda.Event(enable_timing=True)
        end = torch.cuda.Event(enable_timing=True)
        start.record()
        torch.cuda._sleep(1000000)
        end.record()
        end.synchronize()
        cycles_per_ms = 1000000 / start.elapsed_time(end)
        return cycles_per_ms

    # Get 10 values and remove the 2 max and 2 min and return the avg.
    # This is to avoid system disturbance that skew the results, e.g.
    # the very first cuda call likely does a bunch of init, which takes
    # much longer than subsequent calls.
    #
    # Tested on both Tesla V100, Quadro GP100, Titan RTX, RTX 3090 GPUs
    # and seems to return stable values. Therefore, we enable caching
    # using lru_cache decorator above.
    num = 10
    vals = []
    for _ in range(num):
        vals.append(measure())
    vals = sorted(vals)
    return mean(vals[2 : num - 2])


class DummyProcessGroup:
    def __init__(self, rank: int, size: int):
        self._rank = rank
        self._size = size

    def rank(self) -> int:
        return self._rank

    def size(self) -> int:
        return self._size


class SGDWithPausingCompute(torch.optim.SGD):
    def __init__(self, *args, **kwargs) -> None:  # type: ignore
        self.rank = kwargs["rank"]
        del kwargs["rank"]

        super().__init__(*args, **kwargs)

    def step(self, closure: Optional[Any] = None) -> Any:
        loss = super().step(closure=closure)

        # This is used to make sure that OSS and ShardedDDP enforce a proper stream synchronization
        # - Add a long cuda wait on a compute stream, non blocking from the CPU perspective
        with torch.cuda.stream(torch.cuda.Stream()):
            torch.cuda._sleep(100000000)

            # - optionally change the params on a per rank basis
            with torch.no_grad():
                for param_group in self.param_groups:
                    for param in param_group["params"]:
                        param *= 1.0 + self.rank / 10.0

        return loss


def state_dict_norm(state: Dict[str, torch.Tensor]) -> torch.Tensor:
    """Compute the norm from a state_dict for simple comparison."""
    norm = torch.zeros(1)
    for v in state.values():
        if not v.is_floating_point():
            v = v.float()
        norm += v.norm()
    return norm


def rmf(filename: str) -> None:
    """Remove a file like rm -f."""
    try:
        os.remove(filename)
    except FileNotFoundError:
        pass


@contextlib.contextmanager
def in_temporary_directory() -> Generator:
    """
    Context manager to create a temporary direction and remove
    it at the end of the context
    """
    old_cwd = os.getcwd()
    with tempfile.TemporaryDirectory() as temp_dir:
        os.chdir(temp_dir)
        yield temp_dir
        os.chdir(old_cwd)


@contextlib.contextmanager
def temp_files_ctx(num: int) -> Generator:
    """ A context to get tempfiles and ensure they are cleaned up. """
    files = [tempfile.mkstemp()[1] for _ in range(num)]

    yield tuple(files)

    # temp files could have been removed, so we use rmf.
    for name in files:
        rmf(name)


def dump_all_tensors(rank: int) -> None:
    """Useful tool for debugging memory issues from the python side."""
    if rank != 0:
        return
    for obj in gc.get_objects():
        try:
            ttype = str(type(obj))
            if torch.is_tensor(obj) or (hasattr(obj, "data") and torch.is_tensor(obj.data)):
                print(ttype, obj.shape, obj.dtype, obj.device, obj.storage().size())
        except Exception:
            pass
    print(torch.cuda.memory_summary())


def get_smi_memory() -> float:
    """Return process's GPU memory in MB."""
    pid = os.getpid()
    info_string = torch.cuda.list_gpu_processes()
    for line in info_string.splitlines():
        if str(pid) in line:
            toks = line.split()
            return float(toks[3])
    # If the process is not in the list, we are not using the GPU.
    return 0.0
