# Owner(s): ["oncall: distributed"]

import copy
import sys
from unittest import mock

import torch
import torch.distributed as dist
import torch.nn as nn
from torch.distributed.fsdp.fully_sharded_data_parallel import (
    FullyShardedDataParallel as FSDP,
    ShardingStrategy,
)
from torch.distributed.fsdp.wrap import ModuleWrapPolicy
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
from torch.testing._internal.common_fsdp import FSDPTest
from torch.testing._internal.common_utils import run_tests, TEST_WITH_DEV_DBG_ASAN

if not dist.is_available():
    print("Distributed not available, skipping tests", file=sys.stderr)
    sys.exit(0)

if TEST_WITH_DEV_DBG_ASAN:
    print(
        "Skip dev-asan as torch + multiprocessing spawn have known issues",
        file=sys.stderr,
    )
    sys.exit(0)


class TestFSDPFineTune(FSDPTest):
    """Tests fine-tuning cases where some parameters are frozen."""

    NUM_LINEARS = 6

    @property
    def world_size(self) -> int:
        return 2

    def _init_seq_module(self) -> nn.Module:
        torch.manual_seed(42)
        modules = []
        for _ in range(self.NUM_LINEARS):
            modules += [nn.Linear(5, 5, device="cuda"), nn.ReLU()]
        seq = nn.Sequential(*modules)
        self._set_seq_module_requires_grad(seq, False)
        return seq

    def _set_seq_module_requires_grad(self, seq: nn.Module, requires_grad: bool):
        # Assume that the linears are leaf modules, meaning that we can pass
        # `recurse=True` to have this to work for both pre/post FSDP wrapping
        for i in range(self.NUM_LINEARS):
            # Only set for every other linear to test mixing frozen/non-frozen
            if i % 2 == 0:
                for param in seq[i * 2].parameters(recurse=True):
                    param.requires_grad = requires_grad

    @skip_if_lt_x_gpu(2)
    def test_backward_reshard_hooks(self):
        """
        Tests that the post-backward reshard happens even for flat parameters
        that do not require gradients.
        """
        self.run_subtests(
            {
                "sharding_strategy": [
                    ShardingStrategy.FULL_SHARD,
                    ShardingStrategy.SHARD_GRAD_OP,
                    ShardingStrategy.NO_SHARD,
                ],
                "use_orig_params": [False, True],
                "inp_requires_grad": [False, True],
                "unfreeze_params": [False, True],
            },
            self._test_backward_reshard_hooks,
        )

    def _test_backward_reshard_hooks(
        self,
        sharding_strategy: ShardingStrategy,
        use_orig_params: bool,
        inp_requires_grad: bool,
        unfreeze_params: bool,
    ):
        seq = self._init_seq_module()
        policy = ModuleWrapPolicy({nn.Linear})
        seq = FSDP(
            seq,
            auto_wrap_policy=policy,
            sharding_strategy=sharding_strategy,
            use_orig_params=use_orig_params,
        )
        orig_post_backward_reshard = (
            torch.distributed.fsdp._runtime_utils._post_backward_reshard
        )
        post_backward_reshard_count = 0

        def _post_backward_reshard_with_count(*args, **kwargs):
            nonlocal post_backward_reshard_count
            post_backward_reshard_count += 1
            return orig_post_backward_reshard(*args, **kwargs)

        with mock.patch(
            "torch.distributed.fsdp._runtime_utils._post_backward_reshard",
            _post_backward_reshard_with_count,
        ):
            num_steps = 2
            for step_idx in range(num_steps):
                if unfreeze_params and step_idx == num_steps - 1:
                    # Unfreeze the parameters on the last step to emulate some
                    # kinds of fine-tuning
                    self._set_seq_module_requires_grad(seq, True)

                inp = torch.randn(
                    (8, 5), device="cuda", requires_grad=inp_requires_grad
                )
                seq(inp).sum().backward()
                if step_idx < num_steps - 1 or not unfreeze_params:
                    # If the input does not require gradient, then the 0th
                    # frozen linear gets resharded in the catch-all reshard
                    # since we cannot register an autograd hook on it
                    expected_post_backward_reshard_count = (
                        self.NUM_LINEARS if inp_requires_grad else self.NUM_LINEARS - 1
                    )
                else:
                    # This follows the normal post-backward hook path
                    expected_post_backward_reshard_count = self.NUM_LINEARS
                self.assertEqual(
                    post_backward_reshard_count, expected_post_backward_reshard_count
                )
                post_backward_reshard_count = 0

    @skip_if_lt_x_gpu(2)
    def test_parity_with_ddp(self):
        """
        Tests parity with DDP when mixing flat parameters that require and do
        not require gradients.
        """
        self.run_subtests(
            {
                "sharding_strategy": [
                    ShardingStrategy.FULL_SHARD,
                    ShardingStrategy.SHARD_GRAD_OP,
                    ShardingStrategy.NO_SHARD,
                ],
                "use_orig_params": [False, True],
            },
            self._test_parity_with_ddp,
        )

    def _test_parity_with_ddp(
        self,
        sharding_strategy: ShardingStrategy,
        use_orig_params: bool,
    ):
        seq = self._init_seq_module()
        policy = ModuleWrapPolicy({nn.Linear})
        fsdp_seq = FSDP(
            copy.deepcopy(seq),
            auto_wrap_policy=policy,
            sharding_strategy=sharding_strategy,
            use_orig_params=use_orig_params,
        )
        ddp_seq = DDP(copy.deepcopy(seq), device_ids=[self.rank])
        fsdp_optim = torch.optim.Adam(fsdp_seq.parameters(), lr=1e-2)
        ddp_optim = torch.optim.Adam(ddp_seq.parameters(), lr=1e-2)
        torch.manual_seed(self.rank + 1)
        losses = []
        for _ in range(6):
            inp = torch.randn((8, 5), device="cuda")
            for seq, optim in ((fsdp_seq, fsdp_optim), (ddp_seq, ddp_optim)):
                loss = seq(inp).sum()
                losses.append(loss)
                loss.backward()
                optim.step()
                optim.zero_grad()
            torch.testing.assert_close(losses[0], losses[1])
            losses.clear()


if __name__ == "__main__":
    run_tests()
