import math

import pytest
import torch
import torch.distributed as dist
from torch.nn.utils.clip_grad import clip_grad_norm_

import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import (
    build_model_from_hybrid_plugin,
    check_loss,
    check_output_hidden_state,
    check_weight,
    run_forward_backward_with_hybrid_plugin,
    unwrap_model,
)


def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
        model_fn, loss_fn, test_config
    )

    org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
        org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
    )

    stage_manager = booster.plugin.stage_manager
    tp_group = booster.plugin.tp_group

    bert = unwrap_model(org_model, "BertModel", "bert")
    sharded_bert = unwrap_model(sharded_model, "BertModel", "bert")

    col_layer_for_check = ["encoder.layer[0].output.dense"]

    if test_config["precision"] == "fp32":
        atol, rtol = 1e-4, 1e-3
    elif test_config["precision"] == "fp16":
        atol, rtol = 5e-3, 5e-3
    else:
        atol, rtol = 2e-2, 2e-2

    dist.barrier()
    # Check gradient norm
    origin_norm = clip_grad_norm_(org_model.parameters(), test_config["max_norm"])

    # Calculate the gradient norm of the sharded optimizer
    device = origin_norm.device
    norm_groups = []
    for group_id in range(sharded_optimizer.num_param_groups):
        working_grads = sharded_optimizer._grad_store.get_working_grads_by_group_id(group_id)
        norm_group = sharded_optimizer._compute_grad_norm(gradients=working_grads)
        norm_groups.append(norm_group)
    total_norm = 0.0
    for norm in norm_groups:
        total_norm += norm**2.0
    hybrid_norm = torch.tensor(math.sqrt(total_norm)).to(device)

    # If using fp16 precision, divide by the initial scale
    if test_config["precision"] == "fp16":
        hybrid_norm /= test_config["initial_scale"]

    # Assert that the gradient norm of the original model is close to the gradient norm of the hybrid model
    assert torch.allclose(
        origin_norm, hybrid_norm, atol=atol, rtol=rtol
    ), f"Original model grad norm is not equal to sharded model grad norm\n{origin_norm}\n{hybrid_norm}"

    # optimizer executes step
    org_optimizer.step()
    sharded_optimizer.step()

    # check last hidden state & loss
    if stage_manager is None or stage_manager.is_last_stage():
        if test_config["precision"] == "fp32":
            atol, rtol = 1e-5, 1e-3
        elif test_config["precision"] == "fp16":
            atol, rtol = 5e-3, 5e-3
        else:
            atol, rtol = 2e-2, 2e-2
        if org_model.__class__.__name__ == "BertModel":
            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)

        check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)

    # check weights
    if test_config["precision"] == "fp32":
        atol, rtol = 5e-3, 1e-3
    else:
        atol, rtol = 5e-3, 5e-3
    if stage_manager is None or stage_manager.is_first_stage():
        check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)

    torch.cuda.empty_cache()


@parameterize(
    "test_config",
    [
        {
            "tp_size": 1,
            "pp_size": 2,
            "num_microbatches": 4,
            "zero_stage": 1,
            "enable_all_optimization": False,
            "use_lazy_init": False,
            "precision": "fp16",
            "max_norm": 5,
            "initial_scale": 1,
        },
        {
            "tp_size": 2,
            "pp_size": 1,
            "zero_stage": 2,
            "enable_all_optimization": False,
            "use_lazy_init": False,
            "precision": "fp16",
            "max_norm": 5,
            "initial_scale": 1,
        },
        {
            "tp_size": 2,
            "pp_size": 1,
            "zero_stage": 1,
            "enable_all_optimization": False,
            "use_lazy_init": False,
            "precision": "bf16",
            "max_norm": 5,
        },
    ],
)
def run_test(test_config):
    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")

    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)

    clear_layout_converter()
    Randomizer.reset_index()
    torch.cuda.empty_cache()


@parameterize(
    "test_config",
    [
        {
            "tp_size": 2,
            "pp_size": 2,
            "num_microbatches": 4,
            "zero_stage": 1,
            "enable_all_optimization": True,
            "use_lazy_init": False,
            "precision": "bf16",
            "max_norm": 5,
        },
        {
            "tp_size": 2,
            "pp_size": 2,
            "num_microbatches": 4,
            "zero_stage": 1,
            "enable_all_optimization": True,
            "use_lazy_init": False,
            "precision": "fp16",
            "max_norm": 5,
            "initial_scale": 1,
        },
    ],
)
def run_3d_test(test_config):
    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")

    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)

    clear_layout_converter()
    Randomizer.reset_index()
    torch.cuda.empty_cache()


def check_grad_clip_norm(rank, world_size, port):
    disable_existing_loggers()
    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
    run_test()


def check_grad_clip_norm_3d(rank, world_size, port):
    disable_existing_loggers()
    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
    run_3d_test()


@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_grad_clip_norm():
    spawn(check_grad_clip_norm, 4)


@pytest.mark.largedist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_grad_clip_norm_3d():
    spawn(check_grad_clip_norm_3d, 8)


if __name__ == "__main__":
    test_grad_clip_norm()
    test_grad_clip_norm_3d()
