mirror of https://github.com/hpcaitech/ColossalAI
[test] merge old components to test to model zoo (#4945)
* [test] add custom models in model zoo * [test] update legacy test * [test] update model zoo * [test] update gemini test * [test] remove components to testpull/4951/head
Hongxin Liu
GitHub
parent
3a41e8304e
commit
b8e770c832
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@ -9,6 +9,7 @@ from .comparison import (
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)
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from .pytest_wrapper import run_on_environment_flag
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from .utils import (
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DummyDataloader,
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clear_cache_before_run,
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free_port,
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parameterize,
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@ -34,4 +35,5 @@ __all__ = [
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"run_on_environment_flag",
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"check_state_dict_equal",
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"assert_hf_output_close",
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"DummyDataloader",
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]
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@ -273,3 +273,24 @@ def clear_cache_before_run():
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return _clear_cache
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return _wrap_func
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class DummyDataloader:
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def __init__(self, data_gen_fn: Callable, length: int = 10):
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self.data_gen_fn = data_gen_fn
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self.length = length
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self.step = 0
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def __iter__(self):
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self.step = 0
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return self
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def __next__(self):
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if self.step < self.length:
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self.step += 1
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return self.data_gen_fn()
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else:
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raise StopIteration
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def __len__(self):
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return self.length
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@ -1,29 +0,0 @@
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from . import (
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beit,
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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_layers,
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resnet,
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simple_net,
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)
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from .utils import run_fwd, run_fwd_bwd
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from . import albert # isort:skip
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__all__ = [
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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_layers",
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"resnet",
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"simple_net",
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"run_fwd_bwd",
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"albert",
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"beit",
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"run_fwd",
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]
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@ -1,62 +0,0 @@
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import torch
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from transformers import AlbertConfig, AlbertForSequenceClassification
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from .bert import get_bert_data_loader
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from .registry import non_distributed_component_funcs
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@non_distributed_component_funcs.register(name="albert")
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def get_training_components():
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hidden_dim = 8
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num_head = 4
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sequence_length = 12
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num_layer = 2
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vocab_size = 32
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def bert_model_builder(checkpoint: bool = False):
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config = AlbertConfig(
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vocab_size=vocab_size,
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gradient_checkpointing=checkpoint,
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hidden_size=hidden_dim,
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intermediate_size=hidden_dim * 4,
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num_attention_heads=num_head,
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max_position_embeddings=sequence_length,
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num_hidden_layers=num_layer,
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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)
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print("building AlbertForSequenceClassification model")
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# adapting huggingface BertForSequenceClassification for single unittest calling interface
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class ModelAdaptor(AlbertForSequenceClassification):
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def forward(self, input_ids, labels):
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"""
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inputs: data, label
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outputs: loss
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"""
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return super().forward(input_ids=input_ids, labels=labels)[0]
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model = ModelAdaptor(config)
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# if checkpoint and version.parse(transformers.__version__) >= version.parse("4.11.0"):
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# model.gradient_checkpointing_enable()
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return model
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is_distributed = torch.distributed.is_initialized()
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trainloader = get_bert_data_loader(
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n_class=vocab_size,
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batch_size=2,
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total_samples=10000,
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sequence_length=sequence_length,
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is_distributed=is_distributed,
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)
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testloader = get_bert_data_loader(
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n_class=vocab_size,
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batch_size=2,
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total_samples=10000,
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sequence_length=sequence_length,
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is_distributed=is_distributed,
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)
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criterion = None
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return bert_model_builder, trainloader, testloader, torch.optim.Adam, criterion
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@ -1,44 +0,0 @@
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import torch
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from timm.models.beit import Beit
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from colossalai.utils.cuda import get_current_device
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from .registry import non_distributed_component_funcs
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from .utils.dummy_data_generator import DummyDataGenerator
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class DummyDataLoader(DummyDataGenerator):
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img_size = 64
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num_channel = 3
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num_class = 10
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batch_size = 4
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def generate(self):
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data = torch.randn(
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(
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DummyDataLoader.batch_size,
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DummyDataLoader.num_channel,
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DummyDataLoader.img_size,
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DummyDataLoader.img_size,
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),
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device=get_current_device(),
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)
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label = torch.randint(
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low=0, high=DummyDataLoader.num_class, size=(DummyDataLoader.batch_size,), device=get_current_device()
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)
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return data, label
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@non_distributed_component_funcs.register(name="beit")
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def get_training_components():
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def model_builder(checkpoint=False):
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model = Beit(
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img_size=DummyDataLoader.img_size, num_classes=DummyDataLoader.num_class, embed_dim=32, depth=2, num_heads=4
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)
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return model
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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criterion = torch.nn.CrossEntropyLoss()
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return model_builder, trainloader, testloader, torch.optim.Adam, criterion
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@ -1,88 +0,0 @@
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import torch
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import transformers
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from packaging import version
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from torch.utils.data import SequentialSampler
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from transformers import BertConfig, BertForSequenceClassification
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from .registry import non_distributed_component_funcs
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def get_bert_data_loader(
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n_class,
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batch_size,
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total_samples,
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sequence_length,
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device=torch.device("cpu:0"),
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is_distributed=False,
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):
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train_data = torch.randint(
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low=0,
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high=n_class,
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size=(total_samples, sequence_length),
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device=device,
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dtype=torch.long,
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)
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train_label = torch.randint(low=0, high=2, size=(total_samples,), device=device, dtype=torch.long)
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train_dataset = torch.utils.data.TensorDataset(train_data, train_label)
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if is_distributed:
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sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
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else:
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sampler = SequentialSampler(train_dataset)
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train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=sampler)
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return train_loader
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@non_distributed_component_funcs.register(name="bert")
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def get_training_components():
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hidden_dim = 8
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num_head = 4
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sequence_length = 12
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num_layer = 2
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vocab_size = 32
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def bert_model_builder(checkpoint: bool = False):
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config = BertConfig(
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vocab_size=vocab_size,
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gradient_checkpointing=checkpoint,
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hidden_size=hidden_dim,
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intermediate_size=hidden_dim * 4,
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num_attention_heads=num_head,
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max_position_embeddings=sequence_length,
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num_hidden_layers=num_layer,
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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)
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# adapting huggingface BertForSequenceClassification for single unittest calling interface
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class ModelAdaptor(BertForSequenceClassification):
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def forward(self, input_ids, labels):
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"""
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inputs: data, label
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outputs: loss
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"""
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return super().forward(input_ids=input_ids, labels=labels)[0]
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model = ModelAdaptor(config)
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if checkpoint and version.parse(transformers.__version__) >= version.parse("4.11.0"):
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model.gradient_checkpointing_enable()
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return model
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is_distributed = torch.distributed.is_initialized()
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trainloader = get_bert_data_loader(
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n_class=vocab_size,
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batch_size=2,
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total_samples=10000,
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sequence_length=sequence_length,
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is_distributed=is_distributed,
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)
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testloader = get_bert_data_loader(
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n_class=vocab_size,
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batch_size=2,
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total_samples=10000,
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sequence_length=sequence_length,
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is_distributed=is_distributed,
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)
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criterion = None
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return bert_model_builder, trainloader, testloader, torch.optim.Adam, criterion
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@ -1,92 +0,0 @@
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import torch
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import torch.nn as nn
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from transformers import GPT2Config, GPT2LMHeadModel
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from colossalai.utils.cuda import get_current_device
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from .registry import non_distributed_component_funcs
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from .utils.dummy_data_generator import DummyDataGenerator
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class DummyDataLoader(DummyDataGenerator):
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vocab_size = 128
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batch_size = 4
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seq_len = 64
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def generate(self):
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input_ids = torch.randint(
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0,
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DummyDataLoader.vocab_size,
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(DummyDataLoader.batch_size, DummyDataLoader.seq_len),
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device=get_current_device(),
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)
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return input_ids, input_ids
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class GPTLMModel(nn.Module):
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def __init__(
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self,
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hidden_size=768,
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num_layers=12,
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num_attention_heads=12,
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max_seq_len=1024,
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vocab_size=50304,
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checkpoint=False,
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):
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super().__init__()
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self.checkpoint = checkpoint
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self.model = GPT2LMHeadModel(
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GPT2Config(
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n_embd=hidden_size,
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n_layer=num_layers,
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n_head=num_attention_heads,
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n_positions=max_seq_len,
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n_ctx=max_seq_len,
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vocab_size=vocab_size,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attn_pdrop=0.0,
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)
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)
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if checkpoint:
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self.model.gradient_checkpointing_enable()
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def forward(self, input_ids):
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# Only return lm_logits
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attention_mask = torch.ones_like(input_ids)
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return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=not self.checkpoint)[0]
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def gpt2_micro(checkpoint=True):
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return GPTLMModel(
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checkpoint=checkpoint, hidden_size=32, num_layers=2, num_attention_heads=4, max_seq_len=64, vocab_size=128
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)
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def gpt2_s(checkpoint=True):
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return GPTLMModel(checkpoint=checkpoint)
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def gpt2_m(checkpoint=True):
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return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16, checkpoint=checkpoint)
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class GPTLMLoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.loss_fn = nn.CrossEntropyLoss()
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def forward(self, logits, labels):
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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@non_distributed_component_funcs.register(name="gpt2")
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def get_training_components():
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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criterion = GPTLMLoss()
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return gpt2_micro, trainloader, testloader, torch.optim.Adam, criterion
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@ -1,48 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from colossalai.legacy.nn import CheckpointModule
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from .registry import non_distributed_component_funcs
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from .utils.dummy_data_generator import DummyDataGenerator
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class HangingParamModule(CheckpointModule):
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"""
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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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super().__init__(checkpoint=checkpoint)
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self.proj1 = nn.Linear(4, 8)
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self.weight = nn.Parameter(torch.randn(8, 8))
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self.proj2 = nn.Linear(8, 4)
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def forward(self, x):
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x = self.proj1(x)
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x = F.linear(x, self.weight)
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x = self.proj2(x)
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return x
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class DummyDataLoader(DummyDataGenerator):
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def generate(self):
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data = torch.rand(16, 4)
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label = torch.randint(low=0, high=2, size=(16,))
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return data, label
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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 HangingParamModule(checkpoint)
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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criterion = torch.nn.CrossEntropyLoss()
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from colossalai.nn.optimizer import HybridAdam
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return model_builder, trainloader, testloader, HybridAdam, criterion
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@ -1,49 +0,0 @@
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import torch
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import torch.nn as nn
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from colossalai.legacy.nn import CheckpointModule
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from .registry import non_distributed_component_funcs
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from .utils.dummy_data_generator import DummyDataGenerator
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class InlineOpModule(CheckpointModule):
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"""
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a module with inline Ops
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"""
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def __init__(self, checkpoint=False) -> None:
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super().__init__(checkpoint=checkpoint)
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self.proj1 = nn.Linear(4, 8)
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self.proj2 = nn.Linear(8, 8)
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def forward(self, x):
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x = self.proj1(x)
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# inline add_
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x.add_(10)
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x = self.proj2(x)
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# inline relu_
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x = torch.relu_(x)
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x = self.proj2(x)
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return x
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class DummyDataLoader(DummyDataGenerator):
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def generate(self):
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data = torch.rand(16, 4)
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label = torch.randint(low=0, high=2, size=(16,))
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return data, label
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@non_distributed_component_funcs.register(name="inline_op_model")
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def get_training_components():
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def model_builder(checkpoint=False):
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return InlineOpModule(checkpoint)
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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criterion = torch.nn.CrossEntropyLoss()
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from colossalai.nn.optimizer import HybridAdam
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return model_builder, trainloader, testloader, HybridAdam, criterion
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@ -1,38 +0,0 @@
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#!/usr/bin/env python
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class Registry:
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def __init__(self):
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self._registry = dict()
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def register(self, name):
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assert name not in self._registry
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def _register(callable_):
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self._registry[name] = callable_
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return _register
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def get_callable(self, name: str):
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return self._registry[name]
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def __iter__(self):
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self._idx = 0
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self._len = len(self._registry)
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self._names = list(self._registry.keys())
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return self
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def __next__(self):
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if self._idx < self._len:
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key = self._names[self._idx]
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callable_ = self._registry[key]
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self._idx += 1
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return callable_
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else:
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raise StopIteration
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non_distributed_component_funcs = Registry()
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model_parallel_component_funcs = Registry()
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__all__ = ["non_distributed_component_funcs", "model_parallel_component_funcs"]
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@ -1,47 +0,0 @@
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#!/usr/bin/env python
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import torch
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import torch.nn as nn
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from colossalai.legacy.nn import CheckpointModule
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from .registry import non_distributed_component_funcs
|
||||
from .utils.dummy_data_generator import DummyDataGenerator
|
||||
|
||||
|
||||
class NetWithRepeatedlyComputedLayers(CheckpointModule):
|
||||
"""
|
||||
This model is to test with layers which go through forward pass multiple times.
|
||||
In this model, the fc1 and fc2 call forward twice
|
||||
"""
|
||||
|
||||
def __init__(self, checkpoint=False) -> None:
|
||||
super().__init__(checkpoint=checkpoint)
|
||||
self.fc1 = nn.Linear(5, 5)
|
||||
self.fc2 = nn.Linear(5, 5)
|
||||
self.fc3 = nn.Linear(5, 2)
|
||||
self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
|
||||
|
||||
def forward(self, x):
|
||||
for layer in self.layers:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
|
||||
class DummyDataLoader(DummyDataGenerator):
|
||||
def generate(self):
|
||||
data = torch.rand(16, 5)
|
||||
label = torch.randint(low=0, high=2, size=(16,))
|
||||
return data, label
|
||||
|
||||
|
||||
@non_distributed_component_funcs.register(name="repeated_computed_layers")
|
||||
def get_training_components():
|
||||
def model_builder(checkpoint=False):
|
||||
return NetWithRepeatedlyComputedLayers(checkpoint)
|
||||
|
||||
trainloader = DummyDataLoader()
|
||||
testloader = DummyDataLoader()
|
||||
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
return model_builder, trainloader, testloader, torch.optim.Adam, criterion
|
||||
|
|
@ -1,37 +0,0 @@
|
|||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from torchvision.datasets import CIFAR10
|
||||
from torchvision.models import resnet18
|
||||
from torchvision.transforms import transforms
|
||||
|
||||
from colossalai.legacy.utils import get_dataloader
|
||||
|
||||
from .registry import non_distributed_component_funcs
|
||||
|
||||
|
||||
def get_cifar10_dataloader(train):
|
||||
# build dataloaders
|
||||
dataset = CIFAR10(
|
||||
root=Path(os.environ["DATA"]),
|
||||
download=True,
|
||||
train=train,
|
||||
transform=transforms.Compose(
|
||||
[transforms.ToTensor(), transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))]
|
||||
),
|
||||
)
|
||||
dataloader = get_dataloader(dataset=dataset, shuffle=True, batch_size=16, drop_last=True)
|
||||
return dataloader
|
||||
|
||||
|
||||
@non_distributed_component_funcs.register(name="resnet18")
|
||||
def get_resnet_training_components():
|
||||
def model_builder(checkpoint=False):
|
||||
return resnet18(num_classes=10)
|
||||
|
||||
trainloader = get_cifar10_dataloader(train=True)
|
||||
testloader = get_cifar10_dataloader(train=False)
|
||||
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
return model_builder, trainloader, testloader, torch.optim.Adam, criterion
|
||||
|
|
@ -1,53 +0,0 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from colossalai.legacy.nn import CheckpointModule
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
|
||||
from .registry import non_distributed_component_funcs
|
||||
from .utils.dummy_data_generator import DummyDataGenerator
|
||||
|
||||
|
||||
class SimpleNet(CheckpointModule):
|
||||
"""
|
||||
In this no-leaf module, it has subordinate nn.modules and a nn.Parameter.
|
||||
"""
|
||||
|
||||
def __init__(self, checkpoint=False) -> None:
|
||||
super().__init__(checkpoint=checkpoint)
|
||||
self.embed = nn.Embedding(20, 4)
|
||||
self.proj1 = nn.Linear(4, 8)
|
||||
self.ln1 = nn.LayerNorm(8)
|
||||
self.proj2 = nn.Linear(8, 4)
|
||||
self.ln2 = nn.LayerNorm(4)
|
||||
self.classifier = nn.Linear(4, 4)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.embed(x)
|
||||
x = self.proj1(x)
|
||||
x = self.ln1(x)
|
||||
x = self.proj2(x)
|
||||
x = self.ln2(x)
|
||||
x = self.classifier(x)
|
||||
return x
|
||||
|
||||
|
||||
class DummyDataLoader(DummyDataGenerator):
|
||||
def generate(self):
|
||||
data = torch.randint(low=0, high=20, size=(16,), device=get_current_device())
|
||||
label = torch.randint(low=0, high=2, size=(16,), device=get_current_device())
|
||||
return data, label
|
||||
|
||||
|
||||
@non_distributed_component_funcs.register(name="simple_net")
|
||||
def get_training_components():
|
||||
def model_builder(checkpoint=False):
|
||||
return SimpleNet(checkpoint)
|
||||
|
||||
trainloader = DummyDataLoader()
|
||||
testloader = DummyDataLoader()
|
||||
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
from colossalai.nn.optimizer import HybridAdam
|
||||
|
||||
return model_builder, trainloader, testloader, HybridAdam, criterion
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
from .dummy_data_generator import DummyDataGenerator
|
||||
from .executor import run_fwd, run_fwd_bwd
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
from abc import ABC, abstractmethod
|
||||
|
||||
|
||||
class DummyDataGenerator(ABC):
|
||||
def __init__(self, length=10):
|
||||
self.length = length
|
||||
|
||||
@abstractmethod
|
||||
def generate(self):
|
||||
pass
|
||||
|
||||
def __iter__(self):
|
||||
self.step = 0
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self.step < self.length:
|
||||
self.step += 1
|
||||
return self.generate()
|
||||
else:
|
||||
raise StopIteration
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
|
@ -1,4 +1,5 @@
|
|||
from . import diffusers, timm, torchaudio, torchrec, torchvision, transformers
|
||||
from . import custom, diffusers, timm, torchaudio, torchrec, torchvision, transformers
|
||||
from .executor import run_fwd, run_fwd_bwd
|
||||
from .registry import model_zoo
|
||||
|
||||
__all__ = ["model_zoo"]
|
||||
__all__ = ["model_zoo", "run_fwd", "run_fwd_bwd"]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,4 @@
|
|||
from .hanging_param_model import *
|
||||
from .nested_model import *
|
||||
from .repeated_computed_layers import *
|
||||
from .simple_net import *
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
import torch.nn as nn
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
|
||||
|
||||
class CheckpointModule(nn.Module):
|
||||
def __init__(self, checkpoint: bool = False):
|
||||
super().__init__()
|
||||
self.checkpoint = checkpoint
|
||||
self._use_checkpoint = checkpoint
|
||||
|
||||
def _forward(self, *args, **kwargs):
|
||||
raise NotImplementedError("CheckpointModule should implement _forward method instead of origin forward")
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
if self._use_checkpoint:
|
||||
return checkpoint(self._forward, *args, **kwargs)
|
||||
else:
|
||||
return self._forward(*args, **kwargs)
|
||||
|
||||
def train(self, mode: bool = True):
|
||||
self._use_checkpoint = self.checkpoint
|
||||
return super().train(mode=mode)
|
||||
|
||||
def eval(self):
|
||||
self._use_checkpoint = False
|
||||
return super().eval()
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from ..registry import model_zoo
|
||||
from .base import CheckpointModule
|
||||
|
||||
|
||||
class HangingParamModule(CheckpointModule):
|
||||
"""
|
||||
Hanging Parameter: a parameter dose not belong to a leaf Module.
|
||||
It has subordinate nn.modules and a nn.Parameter.
|
||||
"""
|
||||
|
||||
def __init__(self, checkpoint=False) -> None:
|
||||
super().__init__(checkpoint=checkpoint)
|
||||
self.proj1 = nn.Linear(4, 8)
|
||||
self.weight = nn.Parameter(torch.randn(8, 8))
|
||||
self.proj2 = nn.Linear(8, 4)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj1(x)
|
||||
x = F.linear(x, self.weight)
|
||||
x = self.proj2(x)
|
||||
return x
|
||||
|
||||
|
||||
def data_gen():
|
||||
return dict(x=torch.rand(16, 4))
|
||||
|
||||
|
||||
def loss_fn(x):
|
||||
outputs = x["x"]
|
||||
label = torch.randint(low=0, high=2, size=(16,), device=outputs.device)
|
||||
return F.cross_entropy(x["x"], label)
|
||||
|
||||
|
||||
def output_transform(x: torch.Tensor):
|
||||
return dict(x=x)
|
||||
|
||||
|
||||
model_zoo.register(
|
||||
name="custom_hanging_param_model",
|
||||
model_fn=HangingParamModule,
|
||||
data_gen_fn=data_gen,
|
||||
output_transform_fn=output_transform,
|
||||
loss_fn=loss_fn,
|
||||
)
|
||||
|
|
@ -2,10 +2,8 @@ import torch
|
|||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from colossalai.legacy.nn import CheckpointModule
|
||||
|
||||
from .registry import non_distributed_component_funcs
|
||||
from .utils import DummyDataGenerator
|
||||
from ..registry import model_zoo
|
||||
from .base import CheckpointModule
|
||||
|
||||
|
||||
class SubNet(nn.Module):
|
||||
|
|
@ -32,20 +30,24 @@ class NestedNet(CheckpointModule):
|
|||
return x
|
||||
|
||||
|
||||
class DummyDataLoader(DummyDataGenerator):
|
||||
def generate(self):
|
||||
data = torch.rand(16, 5)
|
||||
label = torch.randint(low=0, high=2, size=(16,))
|
||||
return data, label
|
||||
def data_gen():
|
||||
return dict(x=torch.rand(16, 5))
|
||||
|
||||
|
||||
@non_distributed_component_funcs.register(name="nested_model")
|
||||
def get_training_components():
|
||||
def model_builder(checkpoint=False):
|
||||
return NestedNet(checkpoint)
|
||||
def loss_fn(x):
|
||||
outputs = x["x"]
|
||||
label = torch.randint(low=0, high=2, size=(16,), device=outputs.device)
|
||||
return F.cross_entropy(x["x"], label)
|
||||
|
||||
trainloader = DummyDataLoader()
|
||||
testloader = DummyDataLoader()
|
||||
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
return model_builder, trainloader, testloader, torch.optim.Adam, criterion
|
||||
def output_transform(x: torch.Tensor):
|
||||
return dict(x=x)
|
||||
|
||||
|
||||
model_zoo.register(
|
||||
name="custom_nested_model",
|
||||
model_fn=NestedNet,
|
||||
data_gen_fn=data_gen,
|
||||
output_transform_fn=output_transform,
|
||||
loss_fn=loss_fn,
|
||||
)
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from ..registry import model_zoo
|
||||
from .base import CheckpointModule
|
||||
|
||||
|
||||
class NetWithRepeatedlyComputedLayers(CheckpointModule):
|
||||
"""
|
||||
This model is to test with layers which go through forward pass multiple times.
|
||||
In this model, the fc1 and fc2 call forward twice
|
||||
"""
|
||||
|
||||
def __init__(self, checkpoint=False) -> None:
|
||||
super().__init__(checkpoint=checkpoint)
|
||||
self.fc1 = nn.Linear(5, 5)
|
||||
self.fc2 = nn.Linear(5, 5)
|
||||
self.fc3 = nn.Linear(5, 2)
|
||||
self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
|
||||
|
||||
def forward(self, x):
|
||||
for layer in self.layers:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
|
||||
def data_gen():
|
||||
return dict(x=torch.rand(16, 5))
|
||||
|
||||
|
||||
def loss_fn(x):
|
||||
outputs = x["x"]
|
||||
label = torch.randint(low=0, high=2, size=(16,), device=outputs.device)
|
||||
return F.cross_entropy(x["x"], label)
|
||||
|
||||
|
||||
def output_transform(x: torch.Tensor):
|
||||
return dict(x=x)
|
||||
|
||||
|
||||
model_zoo.register(
|
||||
name="custom_repeated_computed_layers",
|
||||
model_fn=NetWithRepeatedlyComputedLayers,
|
||||
data_gen_fn=data_gen,
|
||||
output_transform_fn=output_transform,
|
||||
loss_fn=loss_fn,
|
||||
)
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from ..registry import model_zoo
|
||||
from .base import CheckpointModule
|
||||
|
||||
|
||||
class SimpleNet(CheckpointModule):
|
||||
"""
|
||||
In this no-leaf module, it has subordinate nn.modules and a nn.Parameter.
|
||||
"""
|
||||
|
||||
def __init__(self, checkpoint=False) -> None:
|
||||
super().__init__(checkpoint=checkpoint)
|
||||
self.embed = nn.Embedding(20, 4)
|
||||
self.proj1 = nn.Linear(4, 8)
|
||||
self.ln1 = nn.LayerNorm(8)
|
||||
self.proj2 = nn.Linear(8, 4)
|
||||
self.ln2 = nn.LayerNorm(4)
|
||||
self.classifier = nn.Linear(4, 4)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.embed(x)
|
||||
x = self.proj1(x)
|
||||
x = self.ln1(x)
|
||||
x = self.proj2(x)
|
||||
x = self.ln2(x)
|
||||
x = self.classifier(x)
|
||||
return x
|
||||
|
||||
|
||||
def data_gen():
|
||||
return dict(x=torch.randint(low=0, high=20, size=(16,)))
|
||||
|
||||
|
||||
def loss_fn(x):
|
||||
outputs = x["x"]
|
||||
label = torch.randint(low=0, high=2, size=(16,), device=outputs.device)
|
||||
return F.cross_entropy(x["x"], label)
|
||||
|
||||
|
||||
def output_transform(x: torch.Tensor):
|
||||
return dict(x=x)
|
||||
|
||||
|
||||
model_zoo.register(
|
||||
name="custom_simple_net",
|
||||
model_fn=SimpleNet,
|
||||
data_gen_fn=data_gen,
|
||||
output_transform_fn=output_transform,
|
||||
loss_fn=loss_fn,
|
||||
)
|
||||
|
|
@ -1,7 +1,15 @@
|
|||
from typing import Callable, Dict, Optional, Union
|
||||
|
||||
import torch
|
||||
from torch.nn import Module
|
||||
from torch.optim import Optimizer
|
||||
|
||||
from colossalai.interface import OptimizerWrapper
|
||||
|
||||
|
||||
def run_fwd(model, data, label, criterion) -> torch.Tensor:
|
||||
def run_fwd(
|
||||
model: Module, data: Dict, output_transform_fn: Callable, criterion: Optional[Callable] = None
|
||||
) -> torch.Tensor:
|
||||
"""run_fwd
|
||||
run fwd for the model
|
||||
|
||||
|
|
@ -14,18 +22,22 @@ def run_fwd(model, data, label, criterion) -> torch.Tensor:
|
|||
Returns:
|
||||
torch.Tensor: loss of fwd
|
||||
"""
|
||||
outputs = model(**data)
|
||||
outputs = output_transform_fn(outputs)
|
||||
if criterion:
|
||||
y = model(data)
|
||||
y = y.float()
|
||||
loss = criterion(y, label)
|
||||
loss = criterion(outputs)
|
||||
else:
|
||||
loss = model(data, label)
|
||||
|
||||
loss = loss.float()
|
||||
loss = next(iter(outputs.values())).sum()
|
||||
return loss
|
||||
|
||||
|
||||
def run_fwd_bwd(model, data, label, criterion, optimizer=None) -> torch.Tensor:
|
||||
def run_fwd_bwd(
|
||||
model: Module,
|
||||
data: Dict,
|
||||
output_transform_fn: Callable,
|
||||
criterion: Optional[Callable] = None,
|
||||
optimizer: Optional[Union[Optimizer, OptimizerWrapper]] = None,
|
||||
) -> torch.Tensor:
|
||||
"""run_fwd_bwd
|
||||
run fwd and bwd for the model
|
||||
|
||||
|
|
@ -38,7 +50,7 @@ def run_fwd_bwd(model, data, label, criterion, optimizer=None) -> torch.Tensor:
|
|||
Returns:
|
||||
torch.Tensor: loss of fwd
|
||||
"""
|
||||
loss = run_fwd(model, data, label, criterion)
|
||||
loss = run_fwd(model, data, output_transform_fn, criterion)
|
||||
if optimizer:
|
||||
optimizer.backward(loss)
|
||||
else:
|
||||
|
|
@ -359,9 +359,9 @@ output_transform_fn = lambda x: x
|
|||
# define loss funciton
|
||||
|
||||
loss_fn_for_bert_model = lambda x: torch.nn.functional.mse_loss(
|
||||
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
||||
x["last_hidden_state"], torch.ones_like(x["last_hidden_state"])
|
||||
)
|
||||
loss_fn = lambda x: x.loss
|
||||
loss_fn = lambda x: x["loss"]
|
||||
|
||||
config = transformers.BertConfig(
|
||||
hidden_size=128,
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ def data_gen():
|
|||
output_transform_fn = lambda x: x
|
||||
|
||||
# define loss funciton
|
||||
loss_fn_blip2_model = lambda x: x.loss
|
||||
loss_fn_blip2_model = lambda x: x["loss"]
|
||||
|
||||
config = transformers.Blip2Config()
|
||||
config.vision_config.patch_size = 14
|
||||
|
|
|
|||
|
|
@ -69,11 +69,11 @@ output_transform_fn = lambda x: x
|
|||
|
||||
# define loss function
|
||||
loss_fn_for_bloom_model = lambda x: torch.nn.functional.mse_loss(
|
||||
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
||||
x["last_hidden_state"], torch.ones_like(x["last_hidden_state"])
|
||||
)
|
||||
loss_fn_for_causal_lm = lambda x: x.loss
|
||||
loss_fn_for_classification = lambda x: x.loss
|
||||
loss_fn_for_question_answering = lambda x: x.loss
|
||||
loss_fn_for_causal_lm = lambda x: x["loss"]
|
||||
loss_fn_for_classification = lambda x: x["loss"]
|
||||
loss_fn_for_question_answering = lambda x: x["loss"]
|
||||
|
||||
config = transformers.BloomConfig(
|
||||
n_layer=2, n_head=4, vocab_size=250880, hidden_dropout=0, attention_dropout=0, hidden_size=64, pad_token_id=50256
|
||||
|
|
|
|||
|
|
@ -30,9 +30,9 @@ output_transform_fn = lambda x: x
|
|||
|
||||
# define loss function
|
||||
loss_fn_for_chatglm_model = lambda x: torch.nn.functional.mse_loss(
|
||||
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
||||
x["last_hidden_state"], torch.ones_like(x["last_hidden_state"])
|
||||
)
|
||||
loss_fn = lambda x: x.loss
|
||||
loss_fn = lambda x: x["loss"]
|
||||
|
||||
config = ChatGLMConfig(
|
||||
num_layers=2,
|
||||
|
|
|
|||
|
|
@ -87,13 +87,14 @@ output_transform_fn = lambda x: x
|
|||
|
||||
# define loss function
|
||||
loss_fn_for_gpt2_model = lambda x: torch.nn.functional.mse_loss(
|
||||
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
||||
x["last_hidden_state"], torch.ones_like(x["last_hidden_state"])
|
||||
)
|
||||
loss_fn = lambda x: x.loss
|
||||
loss_fn = lambda x: x["loss"]
|
||||
|
||||
config = transformers.GPT2Config(
|
||||
n_layer=2,
|
||||
n_head=4,
|
||||
n_embd=128,
|
||||
vocab_size=50258,
|
||||
attn_pdrop=0,
|
||||
embd_pdrop=0,
|
||||
|
|
|
|||
|
|
@ -42,9 +42,9 @@ if HAS_LLAMA:
|
|||
output_transform_fn = lambda x: x
|
||||
|
||||
# function to get the loss
|
||||
loss_fn = lambda output: output.last_hidden_state.mean()
|
||||
loss_fn_for_casual_lm = lambda output: output.loss
|
||||
loss_fn_for_seq_classification = lambda output: output.logits.mean()
|
||||
loss_fn = lambda output: output["last_hidden_state"].mean()
|
||||
loss_fn_for_casual_lm = lambda output: output["loss"]
|
||||
loss_fn_for_seq_classification = lambda output: output["logits"].mean()
|
||||
|
||||
config = LlamaConfig(
|
||||
num_hidden_layers=4,
|
||||
|
|
|
|||
|
|
@ -45,9 +45,9 @@ def data_gen_for_question_answering():
|
|||
|
||||
output_transform_fn = lambda x: x
|
||||
loss_fn_for_opt_model = lambda x: torch.nn.functional.mse_loss(
|
||||
x.last_hidden_state, torch.ones_like(x.last_hidden_state)
|
||||
x["last_hidden_state"], torch.ones_like(x["last_hidden_state"])
|
||||
)
|
||||
loss_fn_for_lm = lambda x: x.loss
|
||||
loss_fn_for_lm = lambda x: x["loss"]
|
||||
config = transformers.OPTConfig(
|
||||
hidden_size=128,
|
||||
num_hidden_layers=2,
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ def data_gen():
|
|||
output_transform_fn = lambda x: x
|
||||
|
||||
# define loss funciton
|
||||
loss_fn = lambda x: x.iou_scores.mean()
|
||||
loss_fn = lambda x: x["iou_scores"].mean()
|
||||
|
||||
config = transformers.SamConfig()
|
||||
config.vision_config.num_hidden_layers = 2
|
||||
|
|
|
|||
|
|
@ -44,9 +44,9 @@ def data_gen_for_t5_model():
|
|||
output_transform_fn = lambda x: x
|
||||
|
||||
# define loss function
|
||||
loss_fn_for_t5_model = lambda x: x.last_hidden_state.mean()
|
||||
loss_fn_for_encoder_only = lambda x: x.last_hidden_state.mean()
|
||||
loss_fn_for_conditional_generation = lambda x: x.loss
|
||||
loss_fn_for_t5_model = lambda x: x["last_hidden_state"].mean()
|
||||
loss_fn_for_encoder_only = lambda x: x["last_hidden_state"].mean()
|
||||
loss_fn_for_conditional_generation = lambda x: x["loss"]
|
||||
|
||||
# define model config
|
||||
config = transformers.T5Config(d_model=128, num_layers=2, dropout_rate=0, decoder_start_token_id=0)
|
||||
|
|
|
|||
|
|
@ -34,9 +34,9 @@ def data_gen_for_masked_image_modeling():
|
|||
output_transform_fn = lambda x: x
|
||||
|
||||
# function to get the loss
|
||||
loss_fn_for_vit_model = lambda x: x.pooler_output.mean()
|
||||
loss_fn_for_image_classification = lambda x: x.logits.mean()
|
||||
loss_fn_for_masked_image_modeling = lambda x: x.loss
|
||||
loss_fn_for_vit_model = lambda x: x["pooler_output"].mean()
|
||||
loss_fn_for_image_classification = lambda x: x["logits"].mean()
|
||||
loss_fn_for_masked_image_modeling = lambda x: x["loss"]
|
||||
|
||||
# register the following models
|
||||
# transformers.ViTModel,
|
||||
|
|
|
|||
|
|
@ -53,8 +53,8 @@ def data_gen_for_audio_classification():
|
|||
output_transform_fn = lambda x: x
|
||||
|
||||
# define loss funciton
|
||||
loss_fn = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state))
|
||||
loss_fn_attr = lambda x: x.loss
|
||||
loss_fn = lambda x: torch.nn.functional.mse_loss(x["last_hidden_state"], torch.ones_like(x["last_hidden_state"]))
|
||||
loss_fn_attr = lambda x: x["loss"]
|
||||
|
||||
config = transformers.WhisperConfig(
|
||||
classifier_proj_size=256,
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import torch
|
|||
import colossalai
|
||||
from colossalai.legacy.amp import convert_to_apex_amp, convert_to_naive_amp
|
||||
from colossalai.testing import assert_close_loose, clear_cache_before_run, rerun_if_address_is_in_use, spawn
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
|
||||
def check_equal(a, b):
|
||||
|
|
@ -25,13 +25,12 @@ def run_naive_amp():
|
|||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
# create layer
|
||||
test_models = ["repeated_computed_layers", "nested_model", "resnet18"]
|
||||
test_models = ["custom_repeated_computed_layers", "custom_nested_model", "torchvision_resnet18"]
|
||||
for test_name in test_models:
|
||||
get_component_func = non_distributed_component_funcs.get_callable(test_name)
|
||||
model_builder, train_dataloader, _, optim_class, _ = get_component_func()
|
||||
model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry(test_name).values()))
|
||||
|
||||
# create model
|
||||
naive_amp_model = model_builder(checkpoint=True).cuda()
|
||||
naive_amp_model = model_builder().cuda()
|
||||
apex_amp_model = copy.deepcopy(naive_amp_model)
|
||||
|
||||
# create optimizer
|
||||
|
|
@ -48,13 +47,12 @@ def run_naive_amp():
|
|||
apex_amp_model, apex_amp_optimizer = convert_to_apex_amp(apex_amp_model, apex_amp_optimizer, apex_amp_config)
|
||||
|
||||
# create data
|
||||
data_iter = iter(train_dataloader)
|
||||
data, label = next(data_iter)
|
||||
data = data.cuda()
|
||||
data = data_gen_fn()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
# forward pass
|
||||
naive_amp_output = naive_amp_model(data)
|
||||
apex_amp_output = apex_amp_model(data)
|
||||
naive_amp_output = naive_amp_model(**data)
|
||||
apex_amp_output = apex_amp_model(**data)
|
||||
assert_close_loose(naive_amp_output, apex_amp_output)
|
||||
|
||||
# backward
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import torch
|
|||
import colossalai
|
||||
from colossalai.legacy.amp import convert_to_apex_amp, convert_to_torch_amp
|
||||
from colossalai.testing import assert_close_loose, clear_cache_before_run, rerun_if_address_is_in_use, spawn
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
|
||||
def run_torch_amp():
|
||||
|
|
@ -18,13 +18,12 @@ def run_torch_amp():
|
|||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
# create layer
|
||||
test_models = ["resnet18", "simple_net"]
|
||||
test_models = ["torchvision_resnet18", "custom_simple_net"]
|
||||
for test_name in test_models:
|
||||
get_component_func = non_distributed_component_funcs.get_callable(test_name)
|
||||
model_builder, train_dataloader, _, optim_class, _ = get_component_func()
|
||||
model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry(test_name).values()))
|
||||
|
||||
# create model
|
||||
torch_amp_model = model_builder(checkpoint=True).cuda()
|
||||
torch_amp_model = model_builder().cuda()
|
||||
apex_amp_model = copy.deepcopy(torch_amp_model)
|
||||
|
||||
# create optimizer
|
||||
|
|
@ -41,13 +40,12 @@ def run_torch_amp():
|
|||
apex_amp_model, apex_amp_optimizer = convert_to_apex_amp(apex_amp_model, apex_amp_optimizer, apex_amp_config)
|
||||
|
||||
# create data
|
||||
data_iter = iter(train_dataloader)
|
||||
data, label = next(data_iter)
|
||||
data = data.cuda()
|
||||
data = data_gen_fn()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
# forward pass
|
||||
torch_amp_output = torch_amp_model(data)
|
||||
apex_amp_output = apex_amp_model(data)
|
||||
torch_amp_output = torch_amp_model(**data)
|
||||
apex_amp_output = apex_amp_model(**data)
|
||||
assert_close_loose(torch_amp_output, apex_amp_output)
|
||||
|
||||
for torch_amp_param, apex_amp_param in zip(torch_amp_model.parameters(), apex_amp_model.parameters()):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
import pytest
|
||||
import torch
|
||||
|
||||
import colossalai
|
||||
from colossalai.legacy.amp import AMP_TYPE
|
||||
from colossalai.legacy.core import global_context as gpc
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
CONFIG = dict(
|
||||
parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)), fp16=dict(mode=None), clip_grad_norm=1.0
|
||||
|
|
@ -15,29 +16,29 @@ CONFIG = dict(
|
|||
@parameterize("amp_mode", [AMP_TYPE.APEX, AMP_TYPE.TORCH, AMP_TYPE.NAIVE, None])
|
||||
def run_train(model_name, amp_mode):
|
||||
# FIXME: test bert
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
|
||||
train_dataloader = DummyDataloader(data_gen_fn)
|
||||
criterion = lambda x: x.sum()
|
||||
gpc.config.fp16["mode"] = amp_mode
|
||||
model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
|
||||
|
||||
model = model_builder(checkpoint=False)
|
||||
model = model_builder()
|
||||
engine, train_dataloader, *args = colossalai.legacy.initialize(
|
||||
model=model,
|
||||
optimizer=optimizer_class(model.parameters(), lr=1e-3),
|
||||
optimizer=torch.optim.Adam(model.parameters(), lr=1e-3),
|
||||
criterion=criterion,
|
||||
train_dataloader=train_dataloader,
|
||||
)
|
||||
|
||||
try:
|
||||
engine.train()
|
||||
for data, label in train_dataloader:
|
||||
for data in train_dataloader:
|
||||
engine.zero_grad()
|
||||
data = data.cuda()
|
||||
label = label.cuda()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
if criterion:
|
||||
output = engine(data)
|
||||
loss = engine.criterion(output, label)
|
||||
output = engine(**data)
|
||||
loss = engine.criterion(output)
|
||||
else:
|
||||
loss = engine(data, label)
|
||||
loss = engine(**data)
|
||||
engine.backward(loss)
|
||||
engine.step()
|
||||
break
|
||||
|
|
|
|||
|
|
@ -5,9 +5,9 @@ import colossalai
|
|||
from colossalai.legacy.amp.amp_type import AMP_TYPE
|
||||
from colossalai.legacy.trainer import Trainer
|
||||
from colossalai.logging import get_dist_logger
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils import MultiTimer
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
BATCH_SIZE = 4
|
||||
IMG_SIZE = 32
|
||||
|
|
@ -16,12 +16,14 @@ NUM_EPOCHS = 200
|
|||
CONFIG = dict(fp16=dict(mode=AMP_TYPE.TORCH))
|
||||
|
||||
|
||||
@parameterize("model_name", ["repeated_computed_layers", "resnet18", "nested_model"])
|
||||
@parameterize("model_name", ["custom_repeated_computed_layers", "torchvision_resnet18", "custom_nested_model"])
|
||||
def run_trainer(model_name):
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
|
||||
model = model_builder()
|
||||
optimizer = optimizer_class(model.parameters(), lr=1e-3)
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
|
||||
train_dataloader = DummyDataloader(data_gen_fn)
|
||||
test_dataloader = DummyDataloader(data_gen_fn)
|
||||
criterion = lambda x: x.sum()
|
||||
engine, train_dataloader, *_ = colossalai.legacy.initialize(
|
||||
model=model, optimizer=optimizer, criterion=criterion, train_dataloader=train_dataloader
|
||||
)
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import torch
|
|||
|
||||
from colossalai.nn.optimizer import CPUAdam, HybridAdam
|
||||
from colossalai.testing import clear_cache_before_run, parameterize
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
|
||||
def move_some_params_to_cuda(model, torch_model):
|
||||
|
|
@ -22,8 +22,7 @@ def check_params_equal(model, torch_model):
|
|||
@parameterize("nvme_offload_dir", ["./offload", None])
|
||||
@parameterize("adam_cls", [CPUAdam, HybridAdam])
|
||||
def test_nvme_adam(nvme_offload_fraction, nvme_offload_dir, adam_cls):
|
||||
get_components_func = non_distributed_component_funcs.get_callable("simple_net")
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry("custom_simple_net").values()))
|
||||
model = model_builder()
|
||||
torch_model = model_builder()
|
||||
move_some_params_to_cuda(model, torch_model)
|
||||
|
|
|
|||
|
|
@ -12,8 +12,7 @@ from colossalai.utils import set_seed
|
|||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from tests.components_to_test import run_fwd_bwd
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
|
||||
|
||||
PLACEMENT_CONFIGS = [
|
||||
{"placement_policy": "static", "shard_param_frac": 0.0}, # zero2
|
||||
|
|
@ -38,7 +37,7 @@ def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
|
|||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("keep_gather", [False, True])
|
||||
@parameterize("model_name", ["gpt2", "bert"])
|
||||
@parameterize("model_name", ["transformers_gpt_lm"])
|
||||
@parameterize("use_grad_checkpoint", [False, True])
|
||||
@parameterize("master_weights", [False, True])
|
||||
def exam_gpt_fwd_bwd(
|
||||
|
|
@ -49,17 +48,22 @@ def exam_gpt_fwd_bwd(
|
|||
master_weights: bool = True,
|
||||
):
|
||||
init_device = get_current_device()
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry(model_name).values())
|
||||
)
|
||||
|
||||
set_seed(42)
|
||||
model = model_builder(use_grad_checkpoint)
|
||||
model = model_builder()
|
||||
|
||||
set_seed(42)
|
||||
torch_model = model_builder(use_grad_checkpoint).cuda()
|
||||
torch_model = model_builder().cuda()
|
||||
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
||||
torch_p.data.copy_(p.data)
|
||||
|
||||
if use_grad_checkpoint:
|
||||
model.gradient_checkpointing_enable()
|
||||
torch_model.gradient_checkpointing_enable()
|
||||
|
||||
world_size = torch.distributed.get_world_size()
|
||||
config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
|
||||
config_dict[world_size]["chunk_size"] = 5000
|
||||
|
|
@ -77,25 +81,22 @@ def exam_gpt_fwd_bwd(
|
|||
torch_model = DDP(torch_model, device_ids=[rank])
|
||||
|
||||
set_seed(rank)
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
# you can only test a single fwd + bwd.
|
||||
# after bwd param is grad for Gemini, due to the chunk reuse optimization.
|
||||
if i > 0:
|
||||
break
|
||||
input_ids, label = input_ids.cuda(), label.cuda()
|
||||
|
||||
torch_optim.zero_grad()
|
||||
zero_optim.zero_grad()
|
||||
data = data_gen_fn()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
# set random seed is same as torch_model.eval()
|
||||
set_seed(42)
|
||||
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
|
||||
set_seed(42)
|
||||
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
|
||||
torch_optim.zero_grad()
|
||||
zero_optim.zero_grad()
|
||||
|
||||
assert torch.equal(torch_loss, loss)
|
||||
# set random seed is same as torch_model.eval()
|
||||
set_seed(42)
|
||||
torch_loss = run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
||||
set_seed(42)
|
||||
loss = run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
||||
|
||||
check_grad(model, torch_model)
|
||||
assert_close(torch_loss.float(), loss.float())
|
||||
|
||||
check_grad(model, torch_model)
|
||||
|
||||
|
||||
def run_dist(rank, world_size, port):
|
||||
|
|
|
|||
|
|
@ -3,38 +3,34 @@ import torch
|
|||
import torch.distributed as dist
|
||||
|
||||
import colossalai
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils import set_seed
|
||||
from colossalai.zero import GeminiDDP
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from colossalai.zero.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer
|
||||
from tests.components_to_test import run_fwd_bwd
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
|
||||
|
||||
# run gemini use the runtime memory tracer
|
||||
|
||||
|
||||
@parameterize("placement_policy", ["auto"])
|
||||
@parameterize("keep_gather", [False])
|
||||
@parameterize("model_name", ["repeated_computed_layers", "bert", "albert", "gpt2"])
|
||||
@parameterize("model_name", ["transformers_bert_for_sequence_classification"])
|
||||
@parameterize("use_grad_checkpoint", [False, True])
|
||||
def run_gemini_use_rmt(placement_policy, keep_gather, model_name: str, use_grad_checkpoint: bool = False):
|
||||
set_seed(42)
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
|
||||
|
||||
model = model_builder(use_grad_checkpoint).cuda()
|
||||
model = model_builder().cuda()
|
||||
if use_grad_checkpoint:
|
||||
model.gradient_checkpointing_enable()
|
||||
|
||||
print(f"model_name {model_name}")
|
||||
runtime_mem_tracer = RuntimeMemTracer(model)
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
if i > 0:
|
||||
break
|
||||
input_ids, label = input_ids.cuda(), label.cuda()
|
||||
|
||||
# mem tracing
|
||||
if i == 0:
|
||||
run_fwd_bwd(runtime_mem_tracer, input_ids, label, criterion, runtime_mem_tracer)
|
||||
runtime_mem_tracer = RuntimeMemTracer(model)
|
||||
data = data_gen_fn()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
run_fwd_bwd(runtime_mem_tracer, data, output_transform_fn, optimizer=runtime_mem_tracer)
|
||||
memstats = runtime_mem_tracer.memstats()
|
||||
runtime_tracer_non_model_data = runtime_mem_tracer._memstats._non_model_data_cuda_list
|
||||
print("runtime tracer non model data points: ", len(runtime_tracer_non_model_data))
|
||||
|
|
@ -62,16 +58,17 @@ def run_gemini_use_rmt(placement_policy, keep_gather, model_name: str, use_grad_
|
|||
)
|
||||
|
||||
set_seed(dist.get_rank())
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
train_dataloader = DummyDataloader(data_gen_fn)
|
||||
for i, data in enumerate(train_dataloader):
|
||||
# you can only test a single fwd + bwd.
|
||||
# after bwd param is grad for Gemini, due to the chunk reuse optimization.
|
||||
# print(f'iteration {i}')
|
||||
if i > 4:
|
||||
break
|
||||
input_ids, label = input_ids.cuda(), label.cuda()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
set_seed(42)
|
||||
run_fwd_bwd(model, input_ids, label, criterion, model)
|
||||
run_fwd_bwd(model, data, output_transform_fn, optimizer=model)
|
||||
|
||||
gemini_non_model_data = model.gemini_manager._mem_stats_collector._memstats.non_model_data_list("cuda")
|
||||
|
||||
|
|
|
|||
|
|
@ -7,13 +7,12 @@ from torch.testing import assert_close
|
|||
|
||||
import colossalai
|
||||
from colossalai.nn.optimizer import HybridAdam
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils import set_seed
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from tests.components_to_test import run_fwd
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo, run_fwd
|
||||
|
||||
PLACEMENT_CONFIGS = [
|
||||
{"placement_policy": "static", "shard_param_frac": 0.0}, # zero2
|
||||
|
|
@ -38,7 +37,7 @@ def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
|
|||
|
||||
# Compare gradients.
|
||||
for p0, p1 in zip(model.parameters(), torch_model.parameters()):
|
||||
assert_close(p0, p1.grad, rtol=1e-3, atol=5e-5)
|
||||
assert_close(p0, p1.grad, rtol=2e-3, atol=2e-2)
|
||||
|
||||
# Release gradient chunks and move them to gradient device.
|
||||
for grad_chunk, device in zip(grad_chunk_list, device_list):
|
||||
|
|
@ -48,21 +47,19 @@ def check_grad(model: GeminiDDP, torch_model: torch.nn.Module):
|
|||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("keep_gathered", [False, True])
|
||||
@parameterize("model_name", ["gpt2", "bert"])
|
||||
@parameterize("use_grad_checkpoint", [False, True])
|
||||
@parameterize("model_name", ["transformers_gpt_lm"])
|
||||
@parameterize("master_weights", [False, True])
|
||||
def exam_gemini_grad_acc(
|
||||
placement_config, keep_gathered: bool, model_name: str, use_grad_checkpoint: bool, master_weights: bool
|
||||
):
|
||||
def exam_gemini_grad_acc(placement_config, keep_gathered: bool, model_name: str, master_weights: bool):
|
||||
init_device = get_current_device()
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, _, _, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry(model_name).values())
|
||||
)
|
||||
|
||||
set_seed(42)
|
||||
gemini_model = model_builder(use_grad_checkpoint)
|
||||
gemini_model = model_builder()
|
||||
|
||||
set_seed(42)
|
||||
torch_model = model_builder(use_grad_checkpoint).cuda()
|
||||
torch_model = model_builder().cuda()
|
||||
for torch_p, p in zip(torch_model.parameters(), gemini_model.parameters()):
|
||||
torch_p.data.copy_(p.data)
|
||||
|
||||
|
|
@ -94,22 +91,23 @@ def exam_gemini_grad_acc(
|
|||
|
||||
set_seed(rank)
|
||||
accum_iter = 4
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
train_dataloader = DummyDataloader(data_gen_fn)
|
||||
for i, data in enumerate(train_dataloader):
|
||||
delay_unscale = False if (i + 1) % accum_iter == 0 else True
|
||||
input_ids, label = input_ids.cuda(), label.cuda()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
set_seed(42 + rank)
|
||||
torch_loss = run_fwd(torch_model, input_ids, label, criterion)
|
||||
torch_loss = run_fwd(torch_model, data, output_transform_fn, loss_fn)
|
||||
torch_loss = torch_loss / accum_iter
|
||||
with amp.scale_loss(torch_loss, torch_optim, delay_unscale=delay_unscale) as scaled_loss:
|
||||
scaled_loss.backward()
|
||||
|
||||
set_seed(42 + rank)
|
||||
gemini_loss = run_fwd(gemini_model, input_ids, label, criterion)
|
||||
gemini_loss = run_fwd(gemini_model, data, output_transform_fn, loss_fn)
|
||||
gemini_loss = gemini_loss / accum_iter
|
||||
gemini_optim.backward(gemini_loss)
|
||||
|
||||
assert torch.allclose(torch_loss, gemini_loss, rtol=1e-3, atol=1e-5)
|
||||
assert torch.allclose(torch_loss.float(), gemini_loss.float(), rtol=1e-3, atol=1e-5)
|
||||
|
||||
check_grad(gemini_model, torch_model)
|
||||
|
||||
|
|
|
|||
|
|
@ -7,12 +7,11 @@ from torch.testing import assert_close
|
|||
import colossalai
|
||||
from colossalai.legacy.amp import convert_to_apex_amp
|
||||
from colossalai.nn.optimizer import HybridAdam
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils import set_seed
|
||||
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from tests.components_to_test import run_fwd_bwd
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
|
||||
|
||||
PLACEMENT_CONFIGS = [
|
||||
{
|
||||
|
|
@ -51,12 +50,13 @@ def check_param(model: GeminiDDP, torch_model: torch.nn.Module):
|
|||
|
||||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("model_name", ["gpt2"])
|
||||
@parameterize("model_name", ["transformers_gpt_lm"])
|
||||
@parameterize("master_weights", [True, False])
|
||||
def exam_grad_clipping(placement_config, model_name: str, master_weights: bool):
|
||||
set_seed(1912)
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry(model_name).values())
|
||||
)
|
||||
|
||||
torch_model = model_builder().cuda()
|
||||
amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=32)
|
||||
|
|
@ -94,21 +94,17 @@ def exam_grad_clipping(placement_config, model_name: str, master_weights: bool):
|
|||
torch_model.train()
|
||||
|
||||
set_seed(dist.get_rank() * 3 + 128)
|
||||
for i, (data, label) in enumerate(train_dataloader):
|
||||
train_dataloader = DummyDataloader(data_gen_fn)
|
||||
for i, data in enumerate(train_dataloader):
|
||||
if i > 2:
|
||||
break
|
||||
data = data.cuda()
|
||||
label = label.cuda()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
zero_optim.zero_grad()
|
||||
torch_optim.zero_grad()
|
||||
|
||||
torch_loss = run_fwd_bwd(torch_model, data, label, criterion, torch_optim)
|
||||
loss = run_fwd_bwd(model, data, label, criterion, zero_optim)
|
||||
|
||||
# as no master weights leads to error accumulation, we don't check the loss
|
||||
if master_weights:
|
||||
assert_close(torch_loss, loss)
|
||||
run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
||||
run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
||||
|
||||
import apex.amp as apex_amp
|
||||
|
||||
|
|
|
|||
|
|
@ -9,13 +9,12 @@ from torch.testing import assert_close
|
|||
import colossalai
|
||||
from colossalai.legacy.amp import convert_to_apex_amp
|
||||
from colossalai.nn.optimizer import HybridAdam
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils import set_seed
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from tests.components_to_test import run_fwd_bwd
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo, run_fwd, run_fwd_bwd
|
||||
|
||||
PLACEMENT_CONFIGS = [
|
||||
{"placement_policy": "static", "shard_param_frac": 0.0}, # zero2
|
||||
|
|
@ -53,12 +52,11 @@ def single_chunk_init(model: torch.nn.Module, placement_config: dict):
|
|||
|
||||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("model_name", ["gpt2"])
|
||||
@parameterize("model_name", ["transformers_gpt_lm"])
|
||||
@parameterize("model_init_func", [single_chunk_init, multi_chunk_init])
|
||||
def exam_inference(placement_config: dict, model_name: str, model_init_func: Callable):
|
||||
set_seed(19360226)
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
|
||||
|
||||
torch_model = model_builder().cuda()
|
||||
amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=128)
|
||||
|
|
@ -79,29 +77,27 @@ def exam_inference(placement_config: dict, model_name: str, model_init_func: Cal
|
|||
torch_model.eval()
|
||||
|
||||
set_seed(dist.get_rank() * 3 + 128)
|
||||
train_dataloader = iter(train_dataloader)
|
||||
train_dataloader = iter(DummyDataloader(data_gen_fn))
|
||||
|
||||
def train_iter():
|
||||
input_ids, label = next(train_dataloader)
|
||||
input_ids, label = input_ids.cuda(), label.cuda()
|
||||
data = next(train_dataloader)
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
zero_optim.zero_grad()
|
||||
torch_optim.zero_grad()
|
||||
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
|
||||
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
|
||||
assert_close(torch_loss, loss, rtol=1e-5, atol=1e-5)
|
||||
torch_loss = run_fwd_bwd(torch_model, data, output_transform_fn, optimizer=torch_optim)
|
||||
loss = run_fwd_bwd(model, data, output_transform_fn, optimizer=zero_optim)
|
||||
assert_close(torch_loss.float(), loss.float(), rtol=1e-5, atol=1e-5)
|
||||
zero_optim.step()
|
||||
torch_optim.step()
|
||||
check_param(model, torch_model)
|
||||
|
||||
def inference_iter():
|
||||
input_ids, label = next(train_dataloader)
|
||||
input_ids, label = input_ids.cuda(), label.cuda()
|
||||
data = next(train_dataloader)
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
with torch.no_grad():
|
||||
torch_output = torch_model(input_ids)
|
||||
torch_loss = criterion(torch_output.float(), label)
|
||||
zero_output = model(input_ids)
|
||||
zero_loss = criterion(zero_output.float(), label)
|
||||
assert_close(torch_loss, zero_loss)
|
||||
torch_loss = run_fwd(torch_model, data, output_transform_fn)
|
||||
zero_loss = run_fwd(model, data, output_transform_fn)
|
||||
assert_close(torch_loss.float(), zero_loss.float(), rtol=1e-5, atol=1e-5)
|
||||
|
||||
train_iter()
|
||||
inference_iter()
|
||||
|
|
|
|||
|
|
@ -1,20 +1,18 @@
|
|||
import pytest
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from packaging.version import Version
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.testing import assert_close
|
||||
|
||||
import colossalai
|
||||
from colossalai.legacy.amp import convert_to_apex_amp
|
||||
from colossalai.nn.optimizer import HybridAdam
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils import set_seed
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from tests.components_to_test import run_fwd_bwd
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
|
||||
|
||||
PLACEMENT_CONFIGS = [
|
||||
{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 0.0}, # zero2
|
||||
|
|
@ -32,14 +30,17 @@ PLACEMENT_CONFIGS = [
|
|||
]
|
||||
|
||||
# this model is large enough to slice to chunks
|
||||
TEST_MODELS = ["gpt2"]
|
||||
TEST_MODELS = ["transformers_gpt_lm"]
|
||||
# these models are too small, all parameters in these models are compacted into one chunk
|
||||
EXAMPLE_MODELS = ["albert", "beit", "bert", "hanging_param_model", "nested_model", "repeated_computed_layers"]
|
||||
EXAMPLE_MODELS = [
|
||||
"transformers_bert_for_sequence_classification",
|
||||
"custom_hanging_param_model",
|
||||
"custom_nested_model",
|
||||
"custom_repeated_computed_layers",
|
||||
]
|
||||
|
||||
# bfloat16 cannot represent them exactly
|
||||
BF16_IGNORED_KEYS = [
|
||||
"albert.embeddings.word_embeddings.weight",
|
||||
"albert.embeddings.position_embeddings.weight",
|
||||
"masked_bias",
|
||||
]
|
||||
|
||||
|
|
@ -55,7 +56,7 @@ def check_param(model: GeminiDDP, torch_model: torch.nn.Module, dtype: torch.dty
|
|||
temp_zero_value = zero_dict[key].to(device=value.device)
|
||||
if dtype is torch.bfloat16 and any(k in key for k in BF16_IGNORED_KEYS):
|
||||
continue
|
||||
rtol, atol = 1e-3, 4e-3
|
||||
rtol, atol = 2e-3, 6e-3
|
||||
if dtype is torch.bfloat16:
|
||||
rtol, atol = 4e-3, 8e-3
|
||||
# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
|
||||
|
|
@ -74,8 +75,9 @@ def check_param(model: GeminiDDP, torch_model: torch.nn.Module, dtype: torch.dty
|
|||
@parameterize("master_weights", [True, False])
|
||||
def exam_model_step(placement_config, model_name: str, mixed_precision: torch.dtype, master_weights: bool):
|
||||
set_seed(42)
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry(model_name).values())
|
||||
)
|
||||
|
||||
torch_model = model_builder().cuda()
|
||||
# apex no master weights leads to nan, so we don't use it
|
||||
|
|
@ -104,19 +106,20 @@ def exam_model_step(placement_config, model_name: str, mixed_precision: torch.dt
|
|||
torch_model.eval()
|
||||
|
||||
set_seed(dist.get_rank() * 3 + 128)
|
||||
rtol, atol = 1e-4, 1e-5
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
rtol, atol = 4e-2, 4e-2
|
||||
train_dataloader = iter(DummyDataloader(data_gen_fn))
|
||||
for i, data in enumerate(train_dataloader):
|
||||
if i > 2:
|
||||
break
|
||||
input_ids, label = input_ids.cuda(), label.cuda()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
zero_optim.zero_grad()
|
||||
torch_optim.zero_grad()
|
||||
|
||||
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
|
||||
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
|
||||
torch_loss = run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
||||
loss = run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
||||
# as no master weights leads to error accumulation, we don't check the loss
|
||||
if master_weights:
|
||||
assert_close(torch_loss, loss, rtol=rtol, atol=atol)
|
||||
assert_close(torch_loss.float(), loss.float(), rtol=rtol, atol=atol)
|
||||
|
||||
zero_optim.step()
|
||||
torch_optim.step()
|
||||
|
|
@ -125,13 +128,14 @@ def exam_model_step(placement_config, model_name: str, mixed_precision: torch.dt
|
|||
check_param(model, torch_model, mixed_precision)
|
||||
|
||||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("placement_config", [PLACEMENT_CONFIGS[3]])
|
||||
@parameterize("model_name", EXAMPLE_MODELS)
|
||||
@parameterize("mixed_precision", [torch.half, torch.bfloat16])
|
||||
@parameterize("mixed_precision", [torch.half])
|
||||
def exam_tiny_example(placement_config, model_name: str, mixed_precision: torch.dtype):
|
||||
set_seed(2008)
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, loss_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry(model_name).values())
|
||||
)
|
||||
|
||||
torch_model = model_builder().cuda()
|
||||
amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=2)
|
||||
|
|
@ -159,26 +163,19 @@ def exam_tiny_example(placement_config, model_name: str, mixed_precision: torch.
|
|||
torch_model.eval()
|
||||
|
||||
set_seed(dist.get_rank() * 3 + 128)
|
||||
rtol, atol = 1.5e-6, 2e-5
|
||||
if mixed_precision is torch.bfloat16:
|
||||
rtol, atol = 2e-3, 2e-3
|
||||
elif Version(torch.__version__) >= Version("2.0.0"):
|
||||
rtol, atol = 4e-5, 3e-5
|
||||
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
train_dataloader = DummyDataloader(data_gen_fn)
|
||||
for i, data in enumerate(train_dataloader):
|
||||
if i > 2:
|
||||
break
|
||||
|
||||
input_ids = input_ids.cuda()
|
||||
label = label.cuda()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
zero_optim.zero_grad()
|
||||
torch_optim.zero_grad()
|
||||
|
||||
torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
|
||||
loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
|
||||
assert_close(torch_loss, loss, rtol=rtol, atol=atol) # atol should be 2e-5 for torch lower than 1.12
|
||||
|
||||
run_fwd_bwd(torch_model, data, output_transform_fn, loss_fn, optimizer=torch_optim)
|
||||
run_fwd_bwd(model, data, output_transform_fn, loss_fn, optimizer=zero_optim)
|
||||
zero_optim.step()
|
||||
torch_optim.step()
|
||||
|
||||
|
|
|
|||
|
|
@ -4,10 +4,9 @@ import numpy as np
|
|||
import pytest
|
||||
import torch
|
||||
|
||||
from colossalai.testing import clear_cache_before_run
|
||||
from colossalai.testing import DummyDataloader, clear_cache_before_run
|
||||
from colossalai.zero.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer
|
||||
from tests.components_to_test import run_fwd_bwd
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo, run_fwd_bwd
|
||||
|
||||
|
||||
@pytest.mark.skip("this is not used")
|
||||
|
|
@ -16,21 +15,22 @@ def test_runtime_mem_tracer():
|
|||
test_models = ["gpt2", "bert", "simple_net", "repeated_computed_layers", "nested_model", "albert"]
|
||||
|
||||
for model_name in test_models:
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, _, _, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry(model_name).values())
|
||||
)
|
||||
|
||||
model = model_builder(checkpoint=False).cuda()
|
||||
model = model_builder().cuda()
|
||||
|
||||
model_bk = deepcopy(model)
|
||||
runtime_mem_tracer = RuntimeMemTracer(model)
|
||||
|
||||
for i, (data, label) in enumerate(train_dataloader):
|
||||
train_dataloader = DummyDataloader(data_gen_fn)
|
||||
for i, data in enumerate(train_dataloader):
|
||||
if i > 1:
|
||||
break
|
||||
data = data.cuda()
|
||||
label = label.cuda()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
run_fwd_bwd(runtime_mem_tracer, data, label, criterion, optimizer=runtime_mem_tracer)
|
||||
run_fwd_bwd(runtime_mem_tracer, data, output_transform_fn, optimizer=runtime_mem_tracer)
|
||||
|
||||
for p1, p2 in zip(model_bk.parameters(), model.parameters()):
|
||||
torch.allclose(p1.to(torch.half), p2)
|
||||
|
|
|
|||
|
|
@ -5,40 +5,37 @@ import colossalai
|
|||
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils import get_current_device
|
||||
from colossalai.zero.gemini.chunk import init_chunk_manager, search_chunk_configuration
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
|
||||
def exam_search_chunk_size():
|
||||
world_size = torch.distributed.get_world_size()
|
||||
|
||||
get_components_func = non_distributed_component_funcs.get_callable("gpt2")
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry("transformers_gpt_lm").values())
|
||||
)
|
||||
|
||||
# make sure torch_model and model has the same parameter values
|
||||
model = model_builder()
|
||||
config_dict, *_ = search_chunk_configuration(
|
||||
model, search_range_m=1, search_interval=16, min_chunk_size_m=0, filter_exlarge_params=True
|
||||
model, search_range_m=1, search_interval=128, min_chunk_size_m=0, filter_exlarge_params=True
|
||||
)
|
||||
|
||||
for key in config_dict:
|
||||
chunk_size = config_dict[key]["chunk_size"]
|
||||
if world_size == 1 or True:
|
||||
assert chunk_size == 31616
|
||||
else:
|
||||
assert chunk_size == 1024
|
||||
assert chunk_size == 527872
|
||||
|
||||
|
||||
def exam_chunk_manager():
|
||||
world_size = torch.distributed.get_world_size()
|
||||
|
||||
get_components_func = non_distributed_component_funcs.get_callable("gpt2")
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry("transformers_gpt_lm").values())
|
||||
)
|
||||
|
||||
sharded_ddp_model = model_builder()
|
||||
chunk_manager = init_chunk_manager(
|
||||
sharded_ddp_model,
|
||||
get_current_device(),
|
||||
hidden_dim=16,
|
||||
hidden_dim=128,
|
||||
search_range_m=1,
|
||||
min_chunk_size_m=0,
|
||||
filter_exlarge_params=True,
|
||||
|
|
@ -46,7 +43,7 @@ def exam_chunk_manager():
|
|||
)
|
||||
config_dict = chunk_manager.dp_degree_chunk_size_dict
|
||||
assert len(config_dict) == 1
|
||||
assert config_dict[world_size] == 31616
|
||||
assert config_dict[world_size] == 527872
|
||||
|
||||
|
||||
def run_dist(rank, world_size, port):
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
|||
from colossalai.utils import set_seed
|
||||
from colossalai.zero import GeminiDDP
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
PLACEMENT_CONFIGS = [
|
||||
{"placement_policy": "static", "shard_param_frac": 0.0}, # zero2
|
||||
|
|
@ -26,15 +26,16 @@ def ignore_the_first_parameter(model: torch.nn.Module):
|
|||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("keep_gathered", [True, False])
|
||||
@parameterize("model_name", ["gpt2", "bert"])
|
||||
@parameterize("model_name", ["transformers_gpt_lm", "transformers_bert_for_sequence_classification"])
|
||||
@parameterize("master_weights", [False, True])
|
||||
def exam_state_dict(placement_config, keep_gathered, model_name: str, master_weights: bool):
|
||||
set_seed(431)
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
|
||||
|
||||
model = model_builder()
|
||||
|
||||
model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2
|
||||
|
||||
torch_model = model_builder()
|
||||
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
||||
torch_p.data.copy_(p.data)
|
||||
|
|
@ -54,29 +55,7 @@ def exam_state_dict(placement_config, keep_gathered, model_name: str, master_wei
|
|||
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
|
||||
assert_close(value, temp_zero_value, rtol=1e-3, atol=1e-5)
|
||||
|
||||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("keep_gathered", [True, False])
|
||||
@parameterize("model_name", ["gpt2", "bert"])
|
||||
@parameterize("master_weights", [False, True])
|
||||
def exam_load_state_dict(placement_config, keep_gathered, model_name: str, master_weights: bool):
|
||||
set_seed(431)
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
|
||||
model = model_builder()
|
||||
|
||||
set_seed(451)
|
||||
torch_model = model_builder() # get a different model
|
||||
|
||||
world_size = torch.distributed.get_world_size()
|
||||
config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
|
||||
config_dict[world_size]["chunk_size"] = 5000
|
||||
config_dict[world_size]["keep_gathered"] = keep_gathered
|
||||
|
||||
model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True, master_weights=master_weights)
|
||||
|
||||
torch_dict = torch_model.state_dict()
|
||||
# check load state dict
|
||||
model.load_state_dict(torch_dict, strict=False)
|
||||
zero_dict = model.state_dict(only_rank_0=False)
|
||||
|
||||
|
|
@ -85,23 +64,7 @@ def exam_load_state_dict(placement_config, keep_gathered, model_name: str, maste
|
|||
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
|
||||
assert_close(value, temp_zero_value, rtol=1e-3, atol=1e-5)
|
||||
|
||||
|
||||
@parameterize("placement_config", PLACEMENT_CONFIGS)
|
||||
@parameterize("model_name", ["gpt2", "bert"])
|
||||
@parameterize("master_weights", [False, True])
|
||||
def exam_state_dict_shard(placement_config, model_name: str, master_weights: bool):
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
|
||||
model = model_builder()
|
||||
|
||||
model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2
|
||||
|
||||
config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
|
||||
model = GeminiDDP(model, config_dict, **placement_config, master_weights=master_weights)
|
||||
model.train()
|
||||
|
||||
zero_dict = model.state_dict(only_rank_0=False)
|
||||
# check state dict shard
|
||||
accumulated_keys = set()
|
||||
# ensure number of shards > 1
|
||||
for shard, _ in model.state_dict_shard(max_shard_size=(model_size / 3), only_rank_0=False):
|
||||
|
|
@ -116,8 +79,6 @@ def run_dist(rank, world_size, port):
|
|||
config = {}
|
||||
colossalai.launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
||||
exam_state_dict()
|
||||
exam_load_state_dict()
|
||||
exam_state_dict_shard()
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
|||
from colossalai.utils import set_seed
|
||||
from colossalai.zero import GeminiDDP, GeminiOptimizer
|
||||
from colossalai.zero.gemini.chunk import search_chunk_configuration
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
|
||||
PLACEMENT_CONFIGS = [
|
||||
{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 0.0}, # zero2
|
||||
|
|
@ -22,8 +22,9 @@ PLACEMENT_CONFIGS = [
|
|||
@parameterize("keep_gathered", [True, False])
|
||||
def exam_zero_optim_state_dict(placement_config, keep_gathered):
|
||||
set_seed(431)
|
||||
get_components_func = non_distributed_component_funcs.get_callable("gpt2")
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
model_builder, data_gen_fn, output_transform_fn, *_ = next(
|
||||
iter(model_zoo.get_sub_registry("transformers_gpt_lm").values())
|
||||
)
|
||||
|
||||
model = model_builder()
|
||||
|
||||
|
|
@ -41,15 +42,15 @@ def exam_zero_optim_state_dict(placement_config, keep_gathered):
|
|||
|
||||
set_seed(dist.get_rank() * 3 + 128)
|
||||
model.train()
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
if i > 0:
|
||||
break
|
||||
optim.zero_grad()
|
||||
logits = model(input_ids)
|
||||
logits = logits.float()
|
||||
loss = criterion(logits, input_ids)
|
||||
optim.backward(loss)
|
||||
optim.step()
|
||||
data = data_gen_fn()
|
||||
data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
|
||||
|
||||
optim.zero_grad()
|
||||
outputs = model(**data)
|
||||
outputs = output_transform_fn(outputs)
|
||||
loss = next(iter(outputs.values())).sum()
|
||||
optim.backward(loss)
|
||||
optim.step()
|
||||
|
||||
optim_state_dict = optim.state_dict()
|
||||
optim.load_state_dict(optim_state_dict)
|
||||
|
|
|
|||
Loading…
Reference in New Issue