'''Copyright The Microsoft DeepSpeed Team'''

import deepspeed
from deepspeed.ops.op_builder import CPUAdamBuilder

from unit.common import DistributedTest, DistributedFixture
from unit.simple_model import *
from unit.util import required_minimum_torch_version

from unit.checkpoint.common import *

import pytest


class TestZeROCheckpoint(DistributedTest):
    world_size = 2

    @pytest.mark.parametrize('zero_stage, use_cpu_offload, adam_optimizer',
                             [(1,
                               False,
                               'Adam'),
                              (2,
                               False,
                               'Adam'),
                              (2,
                               True,
                               'deepspeed_adam'),
                              (3,
                               False,
                               'Adam'),
                              (3,
                               True,
                               'deepspeed_adam')])
    def test_load_optimizer_state(self,
                                  tmpdir,
                                  zero_stage,
                                  use_cpu_offload,
                                  adam_optimizer):
        if use_cpu_offload and not deepspeed.ops.__compatible_ops__[CPUAdamBuilder.NAME]:
            pytest.skip("cpu-adam is not compatible")

        config_dict = {
            "train_batch_size": 2,
            "steps_per_print": 1,
            "optimizer": {
                "type": 'Adam',
                "params": {
                    "lr": 0.00015,
                    "betas": [0.8,
                              0.999],
                    "eps": 1e-8,
                    "weight_decay": 3e-7
                }
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "wall_clock_breakdown": True,
            "zero_optimization": {
                "stage": zero_stage,
                "cpu_offload": use_cpu_offload
            }
        }
        hidden_dim = 10

        if zero_stage == 3:
            with deepspeed.zero.Init():
                models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]
        else:
            models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]

        checkpoint_correctness_verification(config_dict,
                                            models,
                                            hidden_dim,
                                            tmpdir,
                                            load_optimizer_states=True)

    @pytest.mark.parametrize('zero_stage, use_cpu_offload, adam_optimizer',
                             [(1,
                               False,
                               "Adam"),
                              (2,
                               False,
                               "Adam"),
                              (2,
                               True,
                               'deepspeed_adam'),
                              (3,
                               False,
                               'Adam'),
                              (3,
                               True,
                               'deepspeed_adam')])
    def test_not_load_optimizer_state(self,
                                      tmpdir,
                                      zero_stage,
                                      use_cpu_offload,
                                      adam_optimizer):
        if use_cpu_offload and not deepspeed.ops.__compatible_ops__[CPUAdamBuilder.NAME]:
            pytest.skip("cpu-adam is not compatible")

        config_dict = {
            "train_batch_size": 2,
            "steps_per_print": 1,
            "optimizer": {
                "type": 'Adam',
                "params": {
                    "lr": 0.00015,
                    "betas": [0.8,
                              0.999],
                    "eps": 1e-8,
                    "weight_decay": 3e-7
                }
            },
            "fp16": {
                "enabled": True
            },
            "zero_optimization": {
                "stage": zero_stage,
                "cpu_offload": use_cpu_offload
            }
        }
        hidden_dim = 10

        if zero_stage == 3:
            global DeepSpeedZeroOptimizer_Stage3
            from deepspeed.runtime.zero.stage3 import DeepSpeedZeroOptimizer_Stage3
            with deepspeed.zero.Init():
                models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]
        else:
            models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]

        checkpoint_correctness_verification(config_dict,
                                            models,
                                            hidden_dim,
                                            tmpdir,
                                            load_optimizer_states=False)

    @pytest.mark.parametrize('zero_stage', [1, 2])
    def test_hybrid_optimizer_state(self, tmpdir, zero_stage):
        config_dict = {
            "train_micro_batch_size_per_gpu": 2,
            "gradient_accumulation_steps": 2,
            "steps_per_print": 1,
            "zero_optimization": {
                "stage": zero_stage
            },
            "zero_allow_untested_optimizer": True,
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            }
        }
        hidden_dim = 10
        models = [SimpleModel(hidden_dim=hidden_dim) for _ in range(2)]
        optimizers = [HybridStateOptimizer(model.parameters()) for model in models]

        checkpoint_correctness_verification(config_dict,
                                            models=models,
                                            base_optimizers=optimizers,
                                            hidden_dim=hidden_dim,
                                            tmpdir=tmpdir,
                                            load_optimizer_states=True)

    @pytest.mark.parametrize('zero_stage', [0, 1, 2, 3])
    def test_load_module_only(self, tmpdir, zero_stage):
        config_dict = {
            "train_batch_size": 2,
            "optimizer": {
                "type": 'Adam'
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "zero_optimization": {
                "stage": zero_stage,
            }
        }
        hidden_dim = 10

        if zero_stage == 3:
            with deepspeed.zero.Init():
                models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]
        else:
            models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]

        checkpoint_correctness_verification(config_dict,
                                            models,
                                            hidden_dim,
                                            tmpdir,
                                            load_module_only=True)


class ws4_model_checkpoint(DistributedFixture):
    world_size = 4

    def run(self, class_tmpdir, elastic_save, load_optim):
        ds_config = {
            "train_batch_size": 4,
            "optimizer": {
                "type": 'Adam'
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "zero_optimization": {
                "stage": 2,
                "elastic_checkpoint": elastic_save
            }
        }
        hidden_dim = 10
        model = SimpleModel(hidden_dim)

        model, _, _, _ = deepspeed.initialize(config=ds_config,
                                            model=model,
                                            model_parameters=model.parameters())
        data_loader = random_dataloader(model=model,
                                        total_samples=8,
                                        hidden_dim=hidden_dim,
                                        device=model.device)
        for n, batch in enumerate(data_loader):
            loss = model(batch[0], batch[1])
            model.backward(loss)
            model.step()

        if load_optim:
            torch.save(model.optimizer.optimizer.state_dict(),
                       os.path.join(class_tmpdir,
                                    'opt-state-dict'))
        model.save_checkpoint(class_tmpdir)


@pytest.mark.parametrize("elastic_save", [True, False])
@pytest.mark.parametrize("elastic_load", [True, False])
@pytest.mark.parametrize("load_optim", [True, False])
class TestZeROElasticCheckpoint(DistributedTest):
    world_size = 2

    def test_elastic_checkpoint_fixed_dp(self,
                                         tmpdir,
                                         elastic_save,
                                         elastic_load,
                                         load_optim):
        ds_config = {
            "train_batch_size": 2,
            "optimizer": {
                "type": 'Adam'
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "zero_optimization": {
                "stage": 2,
                "elastic_checkpoint": elastic_save
            }
        }
        hidden_dim = 10

        # torch 1.2.* stores raw tensor id numbers in checkpoint state which leads to
        # false positive mismatches in checkpoint state comparisons.
        # Newer torch versions store tensor ids as 0, 1, 2, ...
        expected_mismatch_keys = [] if required_minimum_torch_version(1,
                                                                      4) else ['params']
        models = [SimpleModel(hidden_dim) for _ in range(2)]
        model, _, _, _ = deepspeed.initialize(config=ds_config,
                                            model=models[0],
                                            model_parameters=models[0].parameters())
        data_loader = random_dataloader(model=model,
                                        total_samples=8,
                                        hidden_dim=hidden_dim,
                                        device=model.device)
        for n, batch in enumerate(data_loader):
            loss = model(batch[0], batch[1])
            model.backward(loss)
            model.step()
        if load_optim:
            torch.save(model.optimizer.optimizer.state_dict(),
                       os.path.join(tmpdir,
                                    'opt-state-dict'))
        model.save_checkpoint(tmpdir)

        ds_config["zero_optimization"]["elastic_checkpoint"] = elastic_load
        model, _, _, _ = deepspeed.initialize(config=ds_config,
                                            model=models[1],
                                            model_parameters=models[1].parameters())
        model.load_checkpoint(tmpdir, load_optimizer_states=load_optim)

        if load_optim:
            saved_sd = torch.load(os.path.join(tmpdir, 'opt-state-dict'))
            curr_sd = model.optimizer.optimizer.state_dict()
            for curr_param_group, saved_param_group in zip(curr_sd['param_groups'], saved_sd['param_groups']):
                compare_state_dicts(curr_param_group,
                                    saved_param_group,
                                    expected_mismatch_keys)

        data_loader = random_dataloader(model=model,
                                        total_samples=8,
                                        hidden_dim=hidden_dim,
                                        device=model.device)
        for n, batch in enumerate(data_loader):
            loss = model(batch[0], batch[1])
            model.backward(loss)
            model.step()

    def test_elastic_checkpoint_change_dp(self,
                                          ws4_model_checkpoint,
                                          class_tmpdir,
                                          elastic_save,
                                          elastic_load,
                                          load_optim):
        ds_config = {
            "train_batch_size": 4,
            "optimizer": {
                "type": 'Adam'
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "zero_optimization": {
                "stage": 2,
                "elastic_checkpoint": elastic_load
            }
        }
        hidden_dim = 10
        model = SimpleModel(hidden_dim)

        # Load checkpoint with dp world size = 2
        model, _, _, _ = deepspeed.initialize(config=ds_config,
                                                model=model,
                                                model_parameters=model.parameters())
        if load_optim:
            with pytest.raises(deepspeed.runtime.zero.utils.ZeRORuntimeException):
                model.load_checkpoint(class_tmpdir, load_optimizer_states=load_optim)
        else:
            model.load_checkpoint(class_tmpdir, load_optimizer_states=load_optim)


class TestZeROSaveLoadEdgeCase(DistributedTest):
    world_size = 2

    @pytest.mark.parametrize('zero_stage', [0, 1, 2, 3])
    def test_immediate_save_load(self, tmpdir, zero_stage):
        config_dict = {
            "train_batch_size": 4,
            "optimizer": {
                "type": 'Adam'
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "zero_optimization": {
                "stage": zero_stage,
            }
        }
        hidden_dim = 10
        model = SimpleModel(hidden_dim)

        ds_model = create_deepspeed_model(config_dict=config_dict,
                                          model=model,
                                          base_optimizer=None)
        ds_model.save_checkpoint(tmpdir)
        ds_model.load_checkpoint(tmpdir,
                                 load_optimizer_states=False,
                                 load_lr_scheduler_states=False,
                                 load_module_only=False)

    @pytest.mark.parametrize('zero_stage', [0, 1, 2, 3])
    def test_load_immediate_save(self, tmpdir, zero_stage):
        config_dict = {
            "train_batch_size": 4,
            "optimizer": {
                "type": 'Adam'
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "zero_optimization": {
                "stage": zero_stage,
            }
        }
        hidden_dim = 10
        model = SimpleModel(hidden_dim)

        # 1. pretrain a model and save it
        dtype = torch.half
        ds_model = create_deepspeed_model(config_dict=config_dict,
                                          model=model,
                                          base_optimizer=None)
        data_loader = random_dataloader(model=ds_model,
                                        total_samples=1,
                                        hidden_dim=hidden_dim,
                                        device=ds_model.device,
                                        dtype=dtype)
        for _, batch in enumerate(data_loader):
            loss = ds_model(batch[0], batch[1])
            ds_model.backward(loss)
            ds_model.step()
        ds_model.save_checkpoint(tmpdir)

        # 2. load and immediately save a model with a fresh ds engine
        ds_model = create_deepspeed_model(config_dict=config_dict,
                                          model=model,
                                          base_optimizer=None)
        ds_model.load_checkpoint(tmpdir,
                                 load_optimizer_states=False,
                                 load_lr_scheduler_states=False,
                                 load_module_only=False)
        ds_model.save_checkpoint(tmpdir)

    @pytest.mark.parametrize('zero_stage', [0, 1, 2, 3])
    def test_save_before_accum_grad_is_done(self, tmpdir, zero_stage):
        config_dict = {
            "optimizer": {
                "type": 'Adam'
            },
            "fp16": {
                "enabled": True,
                "initial_scale_power": 8
            },
            "zero_optimization": {
                "stage": zero_stage,
                "stage3_gather_fp16_weights_on_model_save": True,
            },
            "gradient_accumulation_steps": 2,
            "train_micro_batch_size_per_gpu": 1,
            "train_batch_size": 4,
        }
        hidden_dim = 10
        model = SimpleModel(hidden_dim)

        # This test reproduces a bug where one tries to retrieve a 16bit model before grad_accum
        # cycle was completed.
        # So we config grad_accum=2 and step only once and save_16bit_model
        ds_model = create_deepspeed_model(config_dict=config_dict,
                                          model=model,
                                          base_optimizer=None)

        data_loader = random_dataloader(model=ds_model,
                                        total_samples=2,
                                        hidden_dim=hidden_dim,
                                        device=ds_model.device,
                                        dtype=torch.half)

        batch = next(iter(data_loader))
        loss = ds_model(batch[0], batch[1])
        ds_model.backward(loss)
        ds_model.step()

        # we stepped only once, and now save 16bit model before gradient_accumulation_steps=2 is complete
        ds_model.save_16bit_model(tmpdir, "model.pt")

        # let's test just as well that we can save the checkpoint too
        ds_model.save_checkpoint(tmpdir)
