# coding=utf-8

# 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.


# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch

import contextlib
from typing import Dict, Iterator, Set, Union

import torch
from torch.cuda import _lazy_call
from torch.utils.checkpoint import detach_variable

from .initialize import get_data_parallel_rank, get_model_parallel_rank

# Default name for the model parallel rng tracker.
_MODEL_PARALLEL_RNG_TRACKER_NAME = "model-parallel-rng"


def _set_cuda_rng_state(new_state: torch.ByteTensor, device: Union[int, str, torch.device] = -1) -> None:
    """Sets the random number generator state of the current GPU.

    Arguments:
        new_state (torch.ByteTensor): The desired state
    This function is adapted from PyTorch repo (torch.cuda.set_rng_state)
    with a single change: the input state is not cloned. Cloning caused
    major performance issues for +4 GPU cases.
    """
    if device == -1:
        device = torch.device("cuda")
    elif isinstance(device, str):
        device = torch.device(device)
    elif isinstance(device, int):
        device = torch.device("cuda", device)

    def cb() -> None:
        idx = device.index  # type: ignore
        if idx is None:
            idx = torch.cuda.current_device()
        default_generator = torch.cuda.default_generators[idx]  # type: ignore
        default_generator.set_state(new_state)

    _lazy_call(cb)


class CudaRNGStatesTracker:
    """Tracker for the cuda RNG states.

    Using the `add` method, a cuda rng state is initialized based on
    the input `seed` and is assigned to `name`. Later, by forking the
    rng state, we can perform operations and return to our starting
    cuda state.
    """

    def __init__(self) -> None:
        # Map from a string name to the cuda rng state.
        self.states_: Dict[str, torch.ByteTensor] = {}
        # Seeds are just for book keeping and ensure no seed is set twice.
        self.seeds_: Set[int] = set()

    def reset(self) -> None:
        """Set to the initial state (no tracker)."""
        self.states_ = {}
        self.seeds_ = set()

    def get_states(self) -> Dict[str, torch.ByteTensor]:
        """Get rng states. Copy the dictionary so we have direct
        pointers to the states, not just a pointer to the dictionary."""
        states = {}
        for name in self.states_:
            states[name] = self.states_[name]
        return states

    def set_states(self, states: Dict[str, torch.ByteTensor]) -> None:
        """Set the rng states. For efficiency purposes, we do not check
        the size of seed for compatibility."""
        self.states_ = states

    def add(self, name: str, seed: int) -> None:
        """Track the rng state.
        Arguments:
            name (str): The name of the seed
            seed (int): The seed value
        """
        # Check seed is not already used.
        if seed in self.seeds_:
            raise Exception("seed {} already exists".format(seed))
        self.seeds_.add(seed)
        # Check that state is not already defined.
        if name in self.states_:
            raise Exception("cuda rng state {} already exists".format(name))
        # Get the current rng state.
        orig_rng_state = torch.cuda.get_rng_state()
        # Set the new state and store it.
        torch.cuda.manual_seed(seed)
        self.states_[name] = torch.cuda.get_rng_state()
        # Reset rng state to what it was.
        _set_cuda_rng_state(orig_rng_state)

    @contextlib.contextmanager
    def fork(self, name: str = _MODEL_PARALLEL_RNG_TRACKER_NAME) -> Iterator[None]:
        """Fork the cuda rng state, perform operations, and exit with
        the original state."""
        # Check if we have added the state
        if name not in self.states_:
            raise Exception("cuda rng state {} is not added".format(name))
        # Store current rng state.
        orig_cuda_rng_state = torch.cuda.get_rng_state()
        # Set rng state to the desired one
        _set_cuda_rng_state(self.states_[name])
        # Do the stuff we wanted to do.
        try:
            yield
        finally:
            # Update the current rng state for later use.
            self.states_[name] = torch.cuda.get_rng_state()
            # And set the state to the original state we started with.
            _set_cuda_rng_state(orig_cuda_rng_state)


# RNG tracker object.
_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()


def get_cuda_rng_tracker() -> CudaRNGStatesTracker:
    """Get cuda rng tracker."""
    return _CUDA_RNG_STATE_TRACKER


def model_parallel_cuda_manual_seed(seed: int) -> None:
    """Initialize model parallel cuda seed.

    This function should be called after the model parallel is
    initialized. Also, no torch.cuda.manual_seed should be called
    after this function. Basically, this is replacement for that
    function.
    Two set of RNG states are tracked:
        default state: This is for data parallelism and is the same among a
                       set of model parallel GPUs but different across
                       different model paralle groups. This is used for
                       example for dropout in the non-model-parallel regions.
        model-parallel state: This state is different among a set of model
                              parallel GPUs, but the same across data parallel
                              groups. This is used for example for dropout in
                              model parallel regions.
    """
    # 2718 is just for fun and any POSITIVE value will work.
    offset = seed + 2718
    model_parallel_seed = offset + get_model_parallel_rank()
    # Data parallel gets the original sedd.
    data_parallel_seed = seed

    if torch.distributed.get_rank() == 0:
        print(
            "> initializing model parallel cuda seeds on global rank {}, "
            "model parallel rank {}, and data parallel rank {} with "
            "model parallel seed: {} and data parallel seed: {}".format(
                torch.distributed.get_rank(),
                get_model_parallel_rank(),
                get_data_parallel_rank(),
                model_parallel_seed,
                data_parallel_seed,
            ),
            flush=True,
        )
    if torch.cuda.is_available():
        _CUDA_RNG_STATE_TRACKER.reset()
        # Set the default state.
        torch.cuda.manual_seed(data_parallel_seed)
        # and model parallel state.
        _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, model_parallel_seed)


class CheckpointFunction(torch.autograd.Function):
    """This function is adapted from torch.utils.checkpoint with
    two main changes:
        1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
        2) the states in the model parallel tracker are also properly
           tracked/set/reset.
    """

    @staticmethod
    def forward(ctx, run_function, *args):  # type: ignore
        ctx.run_function = run_function

        # Copy the rng states.
        ctx.fwd_cpu_rng_state = torch.get_rng_state()
        ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state()
        ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()

        ctx.save_for_backward(*args)
        with torch.no_grad():
            outputs = run_function(*args)
        return outputs

    @staticmethod
    def backward(ctx, *args):  # type: ignore
        if not torch.autograd._is_checkpoint_valid():
            raise RuntimeError("Checkpointing is not compatible with .grad(), please use .backward() if possible")
        inputs = ctx.saved_tensors

        # Store the current states.
        bwd_cpu_rng_state = torch.get_rng_state()
        bwd_cuda_rng_state = torch.cuda.get_rng_state()
        bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()

        # Set the states to what it used to be before the forward pass.
        torch.set_rng_state(ctx.fwd_cpu_rng_state)
        _set_cuda_rng_state(ctx.fwd_cuda_rng_state)
        get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker)

        # Compute the forward pass.
        detached_inputs = detach_variable(inputs)
        with torch.enable_grad():
            outputs = ctx.run_function(*detached_inputs)

        # Set the states back to what it was at the start of this function.
        torch.set_rng_state(bwd_cpu_rng_state)
        _set_cuda_rng_state(bwd_cuda_rng_state)
        get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker)

        if isinstance(outputs, torch.Tensor):
            outputs = (outputs,)
        torch.autograd.backward(outputs, args)
        return (None,) + tuple(inp.grad for inp in detached_inputs)


def checkpoint(function, *args):  # type: ignore
    """Checkpoint a model or part of the model.
    This has been directly copied from torch.utils.checkpoint."""
    return CheckpointFunction.apply(function, *args)