mirror of https://github.com/hpcaitech/ColossalAI
[test] align model name with the file name. (#2045)
Jiarui Fang
GitHub
parent
31c644027b
commit
1e885329f4
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@ -1,2 +1,11 @@
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from . import bert, gpt, inline_op_model, nested_model, no_leaf_module, repeated_computed_layer, resnet, simple_net
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from . import (
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bert,
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gpt2,
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hanging_param_model,
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inline_op_model,
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nested_model,
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repeated_computed_layer,
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resnet,
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simple_net,
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)
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from .utils import run_fwd_bwd
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@ -8,9 +8,10 @@ from .registry import non_distributed_component_funcs
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from .utils.dummy_data_generator import DummyDataGenerator
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class NoLeafModule(CheckpointModule):
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class HangingParamModule(CheckpointModule):
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"""
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In this no-leaf module, it has subordinate nn.modules and a nn.Parameter.
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Hanging Parameter: a parameter dose not belong to a leaf Module.
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It has subordinate nn.modules and a nn.Parameter.
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"""
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def __init__(self, checkpoint=False) -> None:
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@ -34,11 +35,11 @@ class DummyDataLoader(DummyDataGenerator):
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return data, label
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@non_distributed_component_funcs.register(name='no_leaf_module')
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@non_distributed_component_funcs.register(name='hanging_param_model')
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def get_training_components():
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def model_builder(checkpoint=False):
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return NoLeafModule(checkpoint)
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return HangingParamModule(checkpoint)
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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@ -14,7 +14,7 @@ from tests.components_to_test.registry import non_distributed_component_funcs
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def run_tracer(rank, world_size, port, use_grad_check=True):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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test_models = ['repeated_computed_layers', 'resnet18', 'no_leaf_module', 'bert']
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test_models = ['repeated_computed_layers', 'resnet18', 'hanging_param_model', 'bert']
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# test_models = ['bert']
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for model_name in test_models:
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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@ -50,7 +50,7 @@ def run_model(model, inputs, label, criterion, use_param_hook=False):
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def test_base_param_hook():
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test_models = ['repeated_computed_layers', 'resnet18', 'no_leaf_module', 'inline_op_model']
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test_models = ['repeated_computed_layers', 'resnet18', 'hanging_param_model', 'inline_op_model']
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# test_models = ['bert']
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for model_name in test_models:
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@ -41,7 +41,7 @@ def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
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# 'gpt2', 'bert',
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TEST_MODELS = ['no_leaf_module', 'gpt2', 'bert', 'simple_net', 'nested_model', 'repeated_computed_layers']
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TEST_MODELS = ['hanging_param_model', 'gpt2', 'bert', 'simple_net', 'nested_model', 'repeated_computed_layers']
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
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@ -1,77 +1,75 @@
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from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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from colossalai.nn import MoeLoss
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.zero.init_ctx import ZeroInitContext
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from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
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from colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16
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from colossalai.zero.sharded_model.utils import col_model_deepcopy
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.engine.gradient_handler import MoeGradientHandler
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from colossalai.context import MOE_CONTEXT
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from colossalai.testing import assert_equal_in_group
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from tests.test_zero.common import CONFIG, check_grads_padding, run_fwd_bwd
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from tests.test_moe.test_moe_zero_init import MoeModel
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@parameterize("enable_autocast", [False])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def run_model_test(enable_autocast, shard_strategy_class):
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shard_strategy = shard_strategy_class()
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get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
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_, train_dataloader, _, optimizer_class, _ = get_components_func()
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criterion = MoeLoss(aux_weight=0.01, loss_fn=torch.nn.CrossEntropyLoss)
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with ZeroInitContext(target_device=torch.device('cuda', torch.cuda.current_device()),
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shard_strategy=shard_strategy,
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shard_param=True):
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zero_model = MoeModel(checkpoint=True)
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zero_model = ShardedModelV2(zero_model, shard_strategy)
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# check whether parameters are identical in ddp
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for name, p in zero_model.named_parameters():
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if not p.colo_attr.param_is_sharded and p.colo_attr.is_replicated:
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assert_equal_in_group(p.colo_attr.data_payload)
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model = MoeModel(checkpoint=True).half()
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col_model_deepcopy(zero_model, model)
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model = model.cuda()
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grad_handler = MoeGradientHandler(model)
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for i, (data, label) in enumerate(train_dataloader):
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if i > 5:
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break
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data, label = cast_tensor_to_fp16(data).cuda(), label.cuda()
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run_fwd_bwd(model, data, label, criterion, enable_autocast)
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run_fwd_bwd(zero_model, data, label, criterion, enable_autocast)
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grad_handler.handle_gradient()
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check_grads_padding(model, zero_model, loose=True)
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def run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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MOE_CONTEXT.setup(seed=42)
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run_model_test()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2])
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@rerun_if_address_is_in_use()
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def test_moe_zero_model(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_moe_zero_model(world_size=2)
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from functools import partial
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import pytest
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import torch
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import torch.multiprocessing as mp
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import colossalai
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from colossalai.context import MOE_CONTEXT
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from colossalai.engine.gradient_handler import MoeGradientHandler
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from colossalai.nn import MoeLoss
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from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.zero.init_ctx import ZeroInitContext
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from colossalai.zero.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
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from colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16
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from colossalai.zero.sharded_model.utils import col_model_deepcopy
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_moe.test_moe_zero_init import MoeModel
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from tests.test_zero.common import CONFIG, check_grads_padding, run_fwd_bwd
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@parameterize("enable_autocast", [False])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def run_model_test(enable_autocast, shard_strategy_class):
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shard_strategy = shard_strategy_class()
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get_components_func = non_distributed_component_funcs.get_callable('hanging_param_model')
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_, train_dataloader, _, optimizer_class, _ = get_components_func()
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criterion = MoeLoss(aux_weight=0.01, loss_fn=torch.nn.CrossEntropyLoss)
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with ZeroInitContext(target_device=torch.device('cuda', torch.cuda.current_device()),
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shard_strategy=shard_strategy,
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shard_param=True):
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zero_model = MoeModel(checkpoint=True)
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zero_model = ShardedModelV2(zero_model, shard_strategy)
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# check whether parameters are identical in ddp
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for name, p in zero_model.named_parameters():
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if not p.colo_attr.param_is_sharded and p.colo_attr.is_replicated:
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assert_equal_in_group(p.colo_attr.data_payload)
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model = MoeModel(checkpoint=True).half()
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col_model_deepcopy(zero_model, model)
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model = model.cuda()
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grad_handler = MoeGradientHandler(model)
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for i, (data, label) in enumerate(train_dataloader):
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if i > 5:
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break
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data, label = cast_tensor_to_fp16(data).cuda(), label.cuda()
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run_fwd_bwd(model, data, label, criterion, enable_autocast)
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run_fwd_bwd(zero_model, data, label, criterion, enable_autocast)
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grad_handler.handle_gradient()
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check_grads_padding(model, zero_model, loose=True)
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def run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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MOE_CONTEXT.setup(seed=42)
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run_model_test()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2])
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@rerun_if_address_is_in_use()
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def test_moe_zero_model(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_moe_zero_model(world_size=2)
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@ -1,126 +1,124 @@
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from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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from colossalai.amp import convert_to_apex_amp
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from colossalai.nn import MoeLoss
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from colossalai.nn.optimizer import CPUAdam
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.zero.init_ctx import ZeroInitContext
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from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
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from colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_model.utils import col_model_deepcopy
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from colossalai.zero.sharded_optim import ShardedOptimizerV2
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from colossalai.zero.sharded_optim._utils import has_inf_or_nan
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from colossalai.utils import get_current_device
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.engine.gradient_handler import MoeGradientHandler
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from colossalai.context import MOE_CONTEXT
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from colossalai.testing import assert_equal_in_group
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from tests.test_zero.common import CONFIG, check_sharded_model_params
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from tests.test_moe.test_moe_zero_init import MoeModel
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def _run_step(model, optimizer, data, label, criterion, grad_handler):
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model.train()
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optimizer.zero_grad()
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if criterion:
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y = model(data)
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loss = criterion(y, label)
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else:
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loss = model(data, label)
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loss = loss.float()
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if isinstance(model, ShardedModelV2):
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optimizer.backward(loss)
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else:
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loss.backward()
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if grad_handler is not None:
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grad_handler.handle_gradient()
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optimizer.step()
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@parameterize("cpu_offload", [True])
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@parameterize("use_cpuadam", [True]) # We do not use Hybrid Adam right now, since it has a little bug
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@parameterize("reuse_fp16_shard", [True, False])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def _run_test_sharded_optim_v2(cpu_offload,
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shard_strategy_class,
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use_cpuadam,
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reuse_fp16_shard,
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gpu_margin_mem_ratio=0.0):
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shard_strategy = shard_strategy_class()
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if use_cpuadam and cpu_offload is False:
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return
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MOE_CONTEXT.reset_loss()
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get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
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_, train_dataloader, _, optimizer_class, _ = get_components_func()
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criterion = MoeLoss(aux_weight=0.01, loss_fn=torch.nn.CrossEntropyLoss)
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with ZeroInitContext(target_device=torch.device('cpu') if cpu_offload else get_current_device(),
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shard_strategy=shard_strategy,
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shard_param=True):
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zero_model = MoeModel(checkpoint=True)
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zero_model = ShardedModelV2(zero_model,
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shard_strategy,
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tensor_placement_policy='cpu' if cpu_offload else 'cuda',
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reuse_fp16_shard=reuse_fp16_shard)
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# check whether parameters are identical in ddp
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for name, p in zero_model.named_parameters():
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if not p.colo_attr.param_is_sharded and p.colo_attr.is_replicated:
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assert_equal_in_group(p.colo_attr.data_payload.to(get_current_device()))
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model = MoeModel(checkpoint=True).half()
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col_model_deepcopy(zero_model, model)
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model = model.cuda().float()
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if use_cpuadam:
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optimizer_class = CPUAdam
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optim = optimizer_class(model.parameters(), lr=1e-3)
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sharded_optim = optimizer_class(zero_model.parameters(), lr=1e-3)
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sharded_optim = ShardedOptimizerV2(zero_model,
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sharded_optim,
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initial_scale=2**5,
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gpu_margin_mem_ratio=gpu_margin_mem_ratio)
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amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False)
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apex_model, apex_optimizer = convert_to_apex_amp(model, optim, amp_config)
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apex_grad_handler = MoeGradientHandler(model)
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for i, (data, label) in enumerate(train_dataloader):
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if i > 5:
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break
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data, label = data.cuda(), label.cuda()
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_run_step(apex_model, apex_optimizer, data, label, criterion, apex_grad_handler)
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_run_step(zero_model, sharded_optim, data, label, criterion, None)
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check_sharded_model_params(model, zero_model, loose=True, reuse_fp16_shard=use_cpuadam)
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for param in model.parameters():
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assert not has_inf_or_nan(param)
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def _run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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MOE_CONTEXT.setup(seed=42)
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_run_test_sharded_optim_v2()
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# use_cpuadam = True can be used with cpu_offload = False
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2])
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@rerun_if_address_is_in_use()
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def test_moe_zero_optim(world_size):
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run_func = partial(_run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_moe_zero_optim(world_size=4)
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from functools import partial
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import pytest
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import torch
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import torch.multiprocessing as mp
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import colossalai
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from colossalai.amp import convert_to_apex_amp
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from colossalai.context import MOE_CONTEXT
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from colossalai.engine.gradient_handler import MoeGradientHandler
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from colossalai.nn import MoeLoss
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from colossalai.nn.optimizer import CPUAdam
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from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port, get_current_device
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from colossalai.zero.init_ctx import ZeroInitContext
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from colossalai.zero.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
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from colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_model.utils import col_model_deepcopy
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from colossalai.zero.sharded_optim import ShardedOptimizerV2
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from colossalai.zero.sharded_optim._utils import has_inf_or_nan
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_moe.test_moe_zero_init import MoeModel
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from tests.test_zero.common import CONFIG, check_sharded_model_params
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def _run_step(model, optimizer, data, label, criterion, grad_handler):
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model.train()
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optimizer.zero_grad()
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if criterion:
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y = model(data)
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loss = criterion(y, label)
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else:
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loss = model(data, label)
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loss = loss.float()
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if isinstance(model, ShardedModelV2):
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optimizer.backward(loss)
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else:
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loss.backward()
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if grad_handler is not None:
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grad_handler.handle_gradient()
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optimizer.step()
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@parameterize("cpu_offload", [True])
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@parameterize("use_cpuadam", [True]) # We do not use Hybrid Adam right now, since it has a little bug
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@parameterize("reuse_fp16_shard", [True, False])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def _run_test_sharded_optim_v2(cpu_offload,
|
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shard_strategy_class,
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use_cpuadam,
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reuse_fp16_shard,
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gpu_margin_mem_ratio=0.0):
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shard_strategy = shard_strategy_class()
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if use_cpuadam and cpu_offload is False:
|
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return
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MOE_CONTEXT.reset_loss()
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get_components_func = non_distributed_component_funcs.get_callable('hanging_param_model')
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_, train_dataloader, _, optimizer_class, _ = get_components_func()
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criterion = MoeLoss(aux_weight=0.01, loss_fn=torch.nn.CrossEntropyLoss)
|
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with ZeroInitContext(target_device=torch.device('cpu') if cpu_offload else get_current_device(),
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shard_strategy=shard_strategy,
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shard_param=True):
|
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zero_model = MoeModel(checkpoint=True)
|
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|
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zero_model = ShardedModelV2(zero_model,
|
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shard_strategy,
|
||||
tensor_placement_policy='cpu' if cpu_offload else 'cuda',
|
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reuse_fp16_shard=reuse_fp16_shard)
|
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|
||||
# check whether parameters are identical in ddp
|
||||
for name, p in zero_model.named_parameters():
|
||||
if not p.colo_attr.param_is_sharded and p.colo_attr.is_replicated:
|
||||
assert_equal_in_group(p.colo_attr.data_payload.to(get_current_device()))
|
||||
|
||||
model = MoeModel(checkpoint=True).half()
|
||||
col_model_deepcopy(zero_model, model)
|
||||
model = model.cuda().float()
|
||||
|
||||
if use_cpuadam:
|
||||
optimizer_class = CPUAdam
|
||||
optim = optimizer_class(model.parameters(), lr=1e-3)
|
||||
sharded_optim = optimizer_class(zero_model.parameters(), lr=1e-3)
|
||||
sharded_optim = ShardedOptimizerV2(zero_model,
|
||||
sharded_optim,
|
||||
initial_scale=2**5,
|
||||
gpu_margin_mem_ratio=gpu_margin_mem_ratio)
|
||||
|
||||
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False)
|
||||
apex_model, apex_optimizer = convert_to_apex_amp(model, optim, amp_config)
|
||||
apex_grad_handler = MoeGradientHandler(model)
|
||||
|
||||
for i, (data, label) in enumerate(train_dataloader):
|
||||
if i > 5:
|
||||
break
|
||||
data, label = data.cuda(), label.cuda()
|
||||
_run_step(apex_model, apex_optimizer, data, label, criterion, apex_grad_handler)
|
||||
_run_step(zero_model, sharded_optim, data, label, criterion, None)
|
||||
check_sharded_model_params(model, zero_model, loose=True, reuse_fp16_shard=use_cpuadam)
|
||||
for param in model.parameters():
|
||||
assert not has_inf_or_nan(param)
|
||||
|
||||
|
||||
def _run_dist(rank, world_size, port):
|
||||
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
MOE_CONTEXT.setup(seed=42)
|
||||
_run_test_sharded_optim_v2()
|
||||
|
||||
|
||||
# use_cpuadam = True can be used with cpu_offload = False
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize("world_size", [2])
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_moe_zero_optim(world_size):
|
||||
run_func = partial(_run_dist, world_size=world_size, port=free_port())
|
||||
mp.spawn(run_func, nprocs=world_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_moe_zero_optim(world_size=4)
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ from tests.components_to_test.registry import non_distributed_component_funcs
|
|||
@parameterize("enable_autocast", [True])
|
||||
@parameterize("shard_strategy_class", [BucketTensorShardStrategy])
|
||||
def run_model_test(enable_autocast, shard_strategy_class):
|
||||
test_models = ['repeated_computed_layers', 'resnet18', 'bert', 'no_leaf_module']
|
||||
test_models = ['repeated_computed_layers', 'resnet18', 'bert', 'hanging_param_model']
|
||||
shard_strategy = shard_strategy_class()
|
||||
for model_name in test_models:
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
|
|
|
|||
|
|
@ -1,25 +1,25 @@
|
|||
from functools import partial
|
||||
|
||||
import colossalai
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
import pytest
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.multiprocessing as mp
|
||||
from common import CONFIG, check_sharded_model_params
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
|
||||
import colossalai
|
||||
from colossalai.amp import convert_to_apex_amp
|
||||
from colossalai.nn.optimizer import CPUAdam
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use
|
||||
from colossalai.utils import free_port
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero.init_ctx import ZeroInitContext
|
||||
from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
|
||||
from colossalai.zero.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
|
||||
from colossalai.zero.sharded_model import ShardedModelV2
|
||||
from colossalai.zero.sharded_model.utils import col_model_deepcopy
|
||||
from colossalai.zero.sharded_optim import ShardedOptimizerV2
|
||||
from colossalai.zero.sharded_optim._utils import has_inf_or_nan
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
|
||||
from common import CONFIG, check_sharded_model_params
|
||||
|
||||
|
||||
def _run_step(model, optimizer, data, label, criterion, enable_autocast=False):
|
||||
|
|
@ -45,7 +45,7 @@ def _run_step(model, optimizer, data, label, criterion, enable_autocast=False):
|
|||
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
|
||||
@parameterize("gpu_margin_mem_ratio", [0.0, 0.7])
|
||||
def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam, gpu_margin_mem_ratio):
|
||||
test_models = ['repeated_computed_layers', 'resnet18', 'bert', 'no_leaf_module']
|
||||
test_models = ['repeated_computed_layers', 'resnet18', 'bert', 'hanging_param_model']
|
||||
shard_strategy = shard_strategy_class()
|
||||
|
||||
if use_cpuadam and cpu_offload is False:
|
||||
|
|
|
|||
Loading…
Reference in New Issue