# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. from copy import deepcopy from functools import partial from time import sleep from unittest import mock import pytest import torch from torch.optim import Adam from megatron.core import parallel_state from megatron.core.dist_checkpointing import ( ShardedTensor, load, load_plain_tensors, load_tensors_metadata, save, ) from megatron.core.dist_checkpointing.dict_utils import diff, nested_values from megatron.core.dist_checkpointing.optimizer import ( get_param_id_to_sharded_param_map, optim_state_to_sharding_state, ) from megatron.core.dist_checkpointing.serialization import get_default_save_sharded_strategy from megatron.core.dist_checkpointing.strategies.fully_parallel import ( FullyParallelSaveStrategyWrapper, ) from megatron.core.dist_checkpointing.utils import extract_sharded_tensors from megatron.core.tensor_parallel import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig from megatron.core.transformer.mlp import apply_swiglu_sharded_factory from megatron.training.arguments import parse_args from megatron.training.checkpointing import load_checkpoint, save_checkpoint from tests.unit_tests.dist_checkpointing import ( TempNamedDir, init_basic_mock_args, init_checkpointing_mock_args, initialize_gpt_model, setup_model_and_optimizer, setup_moe_model_and_optimizer, ) from tests.unit_tests.test_utilities import Utils class Model(torch.nn.Module): def __init__(self): super().__init__() self.conv = torch.nn.Conv1d(8, 16, 3) self.proj = torch.nn.Linear(8, 5) self.config = TransformerConfig(hidden_size=8, num_attention_heads=1, num_layers=1) def sharded_state_dict(self): sharded_state_dict = self.state_dict(keep_vars=True) # conv sharded_state_dict['conv.weight'] = ShardedTensor.from_rank_offsets( 'conv.weight', sharded_state_dict['conv.weight'], ( 1, parallel_state.get_tensor_model_parallel_rank(), parallel_state.get_tensor_model_parallel_world_size(), ), ) # bias is non-sharded sharded_state_dict['conv.bias'] = ShardedTensor.from_rank_offsets( 'conv.bias', sharded_state_dict['conv.bias'] ) # proj sharded_state_dict['proj.weight'] = ShardedTensor.from_rank_offsets( 'proj.weight', sharded_state_dict['proj.weight'], (0, Utils.rank, Utils.world_size) ) sharded_state_dict['proj.bias'] = ShardedTensor.from_rank_offsets( 'proj.bias', sharded_state_dict['proj.bias'], (0, Utils.rank, Utils.world_size) ) return sharded_state_dict class SwigluFactoryModel(torch.nn.Module): def __init__(self): super().__init__() self.linear = torch.nn.Linear( 5, 64 // parallel_state.get_tensor_model_parallel_world_size(), bias=False ) self.config = TransformerConfig(hidden_size=8, num_attention_heads=1, num_layers=1) def sharded_state_dict(self): sharded_state_dict = self.state_dict(keep_vars=True) sharded_state_dict['linear.weight'] = ShardedTensor.from_rank_offsets( 'linear.weight', sharded_state_dict['linear.weight'], ( ( 0, parallel_state.get_tensor_model_parallel_rank(), parallel_state.get_tensor_model_parallel_world_size(), ) ), replica_id=( ( parallel_state.get_pipeline_model_parallel_rank(), 0, parallel_state.get_data_parallel_rank(with_context_parallel=True), ) ), ) sharded_state_dict['linear.weight'] = apply_swiglu_sharded_factory( sharded_state_dict['linear.weight'], () ) return sharded_state_dict class Model1dFlattenTensor(torch.nn.Module): """This model is used to test whether a 1d flatten tensor can be correctly transformed into torch dist-ckpt form """ def __init__(self): super().__init__() self.config = TransformerConfig(hidden_size=128, num_attention_heads=1, num_layers=1) self.weight_1d = torch.nn.Parameter(torch.randn(self.config.hidden_size)) def sharded_state_dict(self): sharded_state_dict = self.state_dict(keep_vars=True) sharded_state_dict['weight_1d'] = ShardedTensor.from_rank_offsets( 'weight_1d', sharded_state_dict['weight_1d'], ( ( 0, parallel_state.get_tensor_model_parallel_rank(), parallel_state.get_tensor_model_parallel_world_size(), ) ), replica_id=( ( parallel_state.get_pipeline_model_parallel_rank(), 0, parallel_state.get_data_parallel_rank(with_context_parallel=True), ) ), ) return sharded_state_dict class TestOptimizer: def setup_method(self, method): pass def teardown_method(self, method): Utils.destroy_model_parallel() def test_optimizer_params(self, tmp_path_dist_ckpt): Utils.initialize_model_parallel(1, 1) model = Model() # Force optimizer state initialization for p in model.parameters(): p.grad = torch.ones_like(p.data) optim = Adam(model.parameters()) optim.step() model_state_dict = model.sharded_state_dict() param_map = get_param_id_to_sharded_param_map( model_state_dict, optim.param_groups[0]['params'] ) optim_state_dict = optim.state_dict() optim_state_to_sharding_state(optim_state_dict, param_map, exclude_keys=('step',)) optim_sharded_tensors = nested_values(extract_sharded_tensors(optim_state_dict)[0]) optim_sharded_keys = {sh_ten.key for sh_ten in optim_sharded_tensors} assert len(optim_sharded_keys) == 2 * len(model_state_dict) assert optim_sharded_keys == set( [ f'optimizer.state.{state_key}.{layer_name}' for state_key in ['exp_avg', 'exp_avg_sq'] for layer_name in model_state_dict ] ) def initialize_small_model(pre_process=True, post_process=True, seed=0, **config_kwargs): torch.manual_seed(seed) model_parallel_cuda_manual_seed(seed) return SwigluFactoryModel() def initialize_1d_flatten_tensor_model( pre_process=True, post_process=True, seed=0, **config_kwargs ): # This model is used to test whether a 1d flatten tensor can be correctly # transformed into torch dist-ckpt form torch.manual_seed(seed) model_parallel_cuda_manual_seed(seed) return Model1dFlattenTensor() def load_checkpoint_no_arg_checks(*args, **kwargs): with mock.patch('megatron.training.checkpointing.check_checkpoint_args'): with mock.patch('megatron.training.checkpointing.update_num_microbatches'): return load_checkpoint(*args, **kwargs) class TestDistributedOptimizer: def setup_method(self, method): pass def teardown_method(self, method): Utils.destroy_model_parallel() @pytest.mark.parametrize( "initialize_fn", [initialize_small_model, initialize_gpt_model, initialize_1d_flatten_tensor_model], ) @pytest.mark.parametrize("use_fpsl", [False, True]) # TODO: changing DP doesn't work in unit tests because of NCCL crashes @pytest.mark.parametrize( "tp_pp,src_dp,dest_dp", [ ((4, 1), 2, 2), # ((1, 1), 8, 1), # ((1, 1), 1, 8), # ((2, 1), 2, 1), # ((2, 1), 2, 2), ], ) @pytest.mark.flaky @pytest.mark.flaky_in_dev def test_dp_sharding(self, tmp_path_dist_ckpt, tp_pp, src_dp, dest_dp, use_fpsl, initialize_fn): src_world_size = tp_pp[0] * tp_pp[1] * src_dp dest_world_size = tp_pp[0] * tp_pp[1] * dest_dp assert src_world_size <= Utils.world_size, (tp_pp, src_dp) assert dest_world_size <= Utils.world_size, (tp_pp, dest_dp) sharding_type = 'fully_sharded_model_space' if use_fpsl else 'dp_zero_gather_scatter' Utils.initialize_model_parallel(*tp_pp) # sync=True to make sure other ranks wait for rank 0 to finish creating directory. with TempNamedDir(tmp_path_dist_ckpt / 'test_dp_sharding', sync=True) as ckpt_dir: try: Utils.set_world_size(src_world_size) if Utils.rank >= 0: # Save checkpoint A model, optimizer_A = setup_model_and_optimizer( seed=2, tp=tp_pp[0], pp=tp_pp[1], initialize_fn=initialize_fn ) save_strategy = get_default_save_sharded_strategy() if use_fpsl: save_strategy = FullyParallelSaveStrategyWrapper( save_strategy, parallel_state.get_data_parallel_group(with_context_parallel=True), True, ) save( optimizer_A.sharded_state_dict( model[0].sharded_state_dict(), sharding_type=sharding_type ), ckpt_dir, save_strategy, ) optim_param_state_A = optimizer_A.get_parameter_state_dp_zero() Utils.destroy_model_parallel() else: # this prevents NCCL errors when changing DP. TODO: fix it properly sleep(20) # Load checkpoint A with different TP/PP and save as checkpoint B Utils.set_world_size(dest_world_size) if Utils.rank == 0: print('_____________________') if Utils.rank >= 0: Utils.initialize_model_parallel(*tp_pp) model, optimizer_B = setup_model_and_optimizer( seed=3, tp=tp_pp[0], pp=tp_pp[1], initialize_fn=initialize_fn ) optim_param_state_B = optimizer_B.get_parameter_state_dp_zero() diffs = diff(optim_param_state_A, optim_param_state_B) # Expect a mismatch in values - diffs[2] nonempty if parallel_state.get_data_parallel_rank(with_context_parallel=True) == 0: assert not diffs[0] and not diffs[1] and diffs[2], diffs sharded_state_dict = optimizer_B.sharded_state_dict( model[0].sharded_state_dict(), is_loading=True, sharding_type=sharding_type ) optim_state_dict = load(sharded_state_dict, ckpt_dir) optimizer_B.load_state_dict(optim_state_dict) optim_param_state_B = optimizer_B.get_parameter_state_dp_zero() # Test both param state dicts are equal diffs = diff(optim_param_state_A, optim_param_state_B) assert not any(map(bool, diffs)), diffs else: # this prevents NCCL errors when changing DP. TODO: fix it properly sleep(20) finally: Utils.set_world_size() @pytest.mark.parametrize( ('src_tp_pp', 'dest_tp_pp', 'use_glu'), [((2, 2), (2, 4), False), ((1, 8), (4, 1), True), ((2, 4), (4, 2), False)], ) def test_finetune_doesnt_load_optimizer( self, tmp_path_dist_ckpt, src_tp_pp, dest_tp_pp, use_glu ): # sync=True to make sure other ranks wait for rank 0 to finish creating directory. Utils.initialize_model_parallel(*src_tp_pp) with TempNamedDir( tmp_path_dist_ckpt / 'test_finetune_doesnt_load_optimizer', sync=True ) as ckpt_dir: mock_args = parse_args(ignore_unknown_args=True) with mock.patch('megatron.training.checkpointing.get_args', new=lambda: mock_args): init_basic_mock_args(mock_args, tp=src_tp_pp[0], pp=src_tp_pp[1]) init_checkpointing_mock_args(mock_args, ckpt_dir, False) model, optimizer = setup_model_and_optimizer( seed=2, tp=src_tp_pp[0], pp=src_tp_pp[1], initialize_fn=partial(initialize_gpt_model, use_glu=use_glu), ) save_checkpoint(10, model, optimizer, None, 0) Utils.destroy_model_parallel() Utils.initialize_model_parallel(*dest_tp_pp) mock_args.tensor_model_parallel_size = dest_tp_pp[0] mock_args.pipeline_model_parallel_size = dest_tp_pp[1] model, optimizer = setup_model_and_optimizer( seed=3, tp=dest_tp_pp[0], pp=dest_tp_pp[1], initialize_fn=partial(initialize_gpt_model, use_glu=use_glu), ) model_unloaded_state_dict = deepcopy(model[0].state_dict()) optim_unloaded_state_dict = deepcopy(optimizer.state_dict()) # Load with different TPxPP should raise DistributeOptimizer error with pytest.raises(RuntimeError) as exc_info: load_checkpoint_no_arg_checks(model, optimizer, None) # "(TP, PP) mismatch" check is for backwards compatibility tests assert "(TP, PP) mismatch" in str( exc_info.value ) or "(TP, PP, encoder TP, encoder PP) mismatch" in str(exc_info.value) # Check that the state didn't change assert not any(diff(model[0].state_dict(), model_unloaded_state_dict)) assert not any(diff(optimizer.state_dict(), optim_unloaded_state_dict)) # Now test the same with a `finetune` flag mock_args.finetune = True load_checkpoint_no_arg_checks(model, optimizer, None) # Model weights should be different, but optimizer state is unchanged diffs = diff(model[0].state_dict(), model_unloaded_state_dict) # diffs[0] and diffs[1] is structural diff, diffs[2] is values diff - # we expect only values diff assert not diffs[0] and not diffs[1] and diffs[2] assert not any(diff(optimizer.state_dict(), optim_unloaded_state_dict)) # ... or `no_load_optim` flag model, optimizer = setup_model_and_optimizer( seed=3, tp=dest_tp_pp[0], pp=dest_tp_pp[1], initialize_fn=partial(initialize_gpt_model, use_glu=use_glu), ) mock_args.finetune = False mock_args.no_load_optim = True mock_args.no_load_rng = True load_checkpoint_no_arg_checks(model, optimizer, None) # Model weights should be different, but optimizer state is unchanged diffs = diff(model[0].state_dict(), model_unloaded_state_dict) # diffs[0] and diffs[1] is structural diff, diffs[2] is values diff - # we expect only values diff assert not diffs[0] and not diffs[1] and diffs[2] assert not any(diff(optimizer.state_dict(), optim_unloaded_state_dict)) class TestFP32Optimizer: def setup_method(self, method): pass def teardown_method(self, method): Utils.destroy_model_parallel() @pytest.mark.parametrize( ('src_tp_pp', 'dest_tp_pp'), [((2, 4), (2, 4)), ((2, 4), (4, 2)), ((8, 1), (1, 2))] ) def test_fp32_optimizer_resharding(self, tmp_path_dist_ckpt, src_tp_pp, dest_tp_pp): # sync=True to make sure other ranks wait for rank 0 to finish creating directory. def preprocess_fn(optim_common_dict): import copy preprocessed_optimzier_common_dict = copy.deepcopy(optim_common_dict) list = preprocessed_optimzier_common_dict['optimizer']['param_groups'] for dict_item in list: del dict_item['wd_mult'] return preprocessed_optimzier_common_dict Utils.initialize_model_parallel(*src_tp_pp) with TempNamedDir( tmp_path_dist_ckpt / 'test_fp32_optimizer_state_dict_A', sync=True ) as ckpt_dir_A: with TempNamedDir( tmp_path_dist_ckpt / 'test_fp32_optimizer_state_dict_B', sync=True ) as ckpt_dir_B: model_A, optimizer_A = setup_model_and_optimizer( seed=2, tp=src_tp_pp[0], pp=src_tp_pp[1], initialize_fn=initialize_small_model, bf16=False, ) save( optimizer_A.sharded_state_dict(model_A[0].sharded_state_dict()), ckpt_dir_A, preprocess_common_before_consistancy_check=preprocess_fn, ) Utils.destroy_model_parallel() # Load checkpoint A with different TP/PP and save as checkpoint B Utils.initialize_model_parallel(*dest_tp_pp) model_B, optimizer_B = setup_model_and_optimizer( seed=3, tp=dest_tp_pp[0], pp=dest_tp_pp[1], initialize_fn=initialize_small_model, bf16=False, ) load_sharded_state_dict = optimizer_B.sharded_state_dict( model_B[0].sharded_state_dict() ) state_dict = load(load_sharded_state_dict, ckpt_dir_A) optimizer_B.load_state_dict(state_dict) save(optimizer_B.sharded_state_dict(model_B[0].sharded_state_dict()), ckpt_dir_B) Utils.destroy_model_parallel() # Test both checkpoints are equal Utils.initialize_model_parallel(1, 1) plain_state_dict_A = load_plain_tensors(ckpt_dir_A) plain_state_dict_B = load_plain_tensors(ckpt_dir_B) diffs = diff(plain_state_dict_A, plain_state_dict_B) assert not any(map(bool, diffs)), diffs class TestOptimizerResharding: def setup_method(self, method): pass def teardown_method(self, method): Utils.destroy_model_parallel() @pytest.mark.parametrize( ('use_dist_opt', 'bf16', 'use_custom_fsdp'), ( (False, True, False), # regular BF16 (True, True, False), # DistOpt BF16 (True, True, True), # DistOpt + custom FSDP BF16 # (False, False), # FP32 ), ) @pytest.mark.parametrize( ('src_tp_pp', 'dest_tp_pp'), [((2, 4), (2, 4)), ((2, 4), (2, 2)), ((2, 4), (4, 2)), ((8, 1), (1, 2))], ) def test_optimizer_resharding( self, tmp_path_dist_ckpt, src_tp_pp, dest_tp_pp, use_dist_opt, bf16, use_custom_fsdp ): Utils.initialize_model_parallel(*src_tp_pp) with TempNamedDir( tmp_path_dist_ckpt / 'test_fp32_optimizer_state_dict_A', sync=False ) as ckpt_dir_A: with TempNamedDir( tmp_path_dist_ckpt / 'test_fp32_optimizer_state_dict_B', sync=False ) as ckpt_dir_B: extra_kwargs = {} if use_custom_fsdp: extra_kwargs['use_custom_fsdp'] = True model_A, optimizer_A = setup_model_and_optimizer( seed=2, tp=src_tp_pp[0], pp=src_tp_pp[1], bf16=bf16, dist_opt=use_dist_opt ) save(optimizer_A.sharded_state_dict(model_A[0].sharded_state_dict()), ckpt_dir_A) Utils.destroy_model_parallel() # Load checkpoint A with different TP/PP and save as checkpoint B Utils.initialize_model_parallel(*dest_tp_pp) model_B, optimizer_B = setup_model_and_optimizer( seed=3, tp=dest_tp_pp[0], pp=dest_tp_pp[1], bf16=bf16, dist_opt=use_dist_opt ) load_sharded_state_dict = optimizer_B.sharded_state_dict( model_B[0].sharded_state_dict() ) state_dict = load(load_sharded_state_dict, ckpt_dir_A) optimizer_B.load_state_dict(state_dict) save(optimizer_B.sharded_state_dict(model_B[0].sharded_state_dict()), ckpt_dir_B) Utils.destroy_model_parallel() # Test both checkpoints are equal Utils.initialize_model_parallel(1, 1) plain_state_dict_A = load_plain_tensors(ckpt_dir_A) plain_state_dict_B = load_plain_tensors(ckpt_dir_B) diffs = diff(plain_state_dict_A, plain_state_dict_B) assert not any(map(bool, diffs)), diffs if use_custom_fsdp and hasattr(torch.nn.parameter.Parameter, "main_grad"): # Custom fsdp adds the `main_grad` attribute function to the # torch Parameter, remove this attribute function so that # it doesn't conflict with the code in the non-custom fsdp # test branch. delattr(torch.nn.parameter.Parameter, "main_grad") @pytest.mark.parametrize(('use_dist_opt', 'bf16'), ((True, True),)) # DistOpt BF16 @pytest.mark.parametrize(('use_te', 'use_grouped_mlp'), ((False, False), (False, True))) @pytest.mark.parametrize('use_glu', [False, True]) @pytest.mark.parametrize( ('src_tp_pp_exp', 'dest_tp_pp_exp'), [ ((2, 2, 2), (2, 2, 2)), ((4, 1, 2), (1, 2, 2)), ((1, 1, 2), (1, 1, 4)), ((2, 1, 2), (1, 1, 8)), ], ) def test_chained_optimizer_resharding( self, tmp_path_dist_ckpt, src_tp_pp_exp, dest_tp_pp_exp, use_dist_opt, bf16, use_te, use_grouped_mlp, use_glu, ): src_tp, src_pp, src_exp = src_tp_pp_exp dest_tp, dest_pp, dest_exp = dest_tp_pp_exp with TempNamedDir( tmp_path_dist_ckpt / 'test_fp32_optimizer_state_dict_A', sync=False ) as ckpt_dir_A: with TempNamedDir( tmp_path_dist_ckpt / 'test_fp32_optimizer_state_dict_B', sync=False ) as ckpt_dir_B: Utils.initialize_model_parallel(src_tp, src_pp, expert_model_parallel_size=src_exp) model_A, optimizer_A = setup_moe_model_and_optimizer( seed=2, tp=src_tp, pp=src_pp, ep=src_exp, bf16=bf16, dist_opt=use_dist_opt, use_te=use_te, use_grouped_mlp=use_grouped_mlp, use_glu=use_glu, ) save(optimizer_A.sharded_state_dict(model_A[0].sharded_state_dict()), ckpt_dir_A) Utils.destroy_model_parallel() # Load checkpoint A with different TP/PP and save as checkpoint B Utils.initialize_model_parallel( dest_tp, dest_pp, expert_model_parallel_size=dest_exp ) model_B, optimizer_B = setup_moe_model_and_optimizer( seed=3, tp=dest_tp, pp=dest_pp, ep=dest_exp, bf16=bf16, dist_opt=use_dist_opt, use_te=use_te, use_grouped_mlp=use_grouped_mlp, use_glu=use_glu, ) load_sharded_state_dict = optimizer_B.sharded_state_dict( model_B[0].sharded_state_dict() ) state_dict = load(load_sharded_state_dict, ckpt_dir_A) optimizer_B.load_state_dict(state_dict) save(optimizer_B.sharded_state_dict(model_B[0].sharded_state_dict()), ckpt_dir_B) Utils.destroy_model_parallel() # Test both checkpoints are equal Utils.initialize_model_parallel(1, 1) plain_state_dict_A = load_plain_tensors(ckpt_dir_A) plain_state_dict_B = load_plain_tensors(ckpt_dir_B) diffs = diff(plain_state_dict_A, plain_state_dict_B) assert not any(map(bool, diffs)), diffs Utils.destroy_model_parallel()