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README.md
⚡️ ShardFormer
📚 Table of Contents
🔗 Introduction
Shardformer is a module that automatically parallelizes the mainstream models in libraries such as HuggingFace and TIMM. This module aims to make parallelization hassle-free for users who are not from the system background.
🔨 Usage
Quick Start
The sample API usage is given below(If you enable the use of flash attention, please install flash_attn
. In addition, xformers's cutlass_op
provide a supplementary optimization):
from colossalai.shardformer import ShardConfig, ShardFormer
from transformers import BertForMaskedLM
import colossalai
# launch colossalai
colossalai.launch_from_torch(config={})
# create model
config = BertConfig.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config)
# create huggingface model as normal
shard_config = ShardConfig(tensor_parallel_process_group=tp_group,
pipeline_stage_manager=stage_manager,
enable_tensor_parallelism=True,
enable_fused_normalization=True,
enable_flash_attention=True,
enable_jit_fused=True,
enable_sequence_parallelism=True,
enable_sequence_overlap=True)
shard_former = ShardFormer(shard_config=shard_config)
sharded_model, shared_params = shard_former.optimize(model).to('cuda')
# do everything like normal
...
shardformer configuration
tensor_parallel_process_group
: the process group of tensor parallelism, it's necessary when using tensor parallel.
pipeline_stage_manager
: If using pipeline parallelism, it's necessary to specify a pipeline stage manager for inter-process communication in pipeline parallelism.
{{ autodoc:colossalai.pipeline.stage_manager.PipelineStageManager }}
enable_tensor_parallelism
: using tensor parallel, partition the model along the columns or along the rows
enable_fused_normalization
: using apex fused layernorm
enable_flash_attention
: using flash attention
enable_jit_fused
: using jit fused operators
enable_sequence_parallelism
: using sequence parallelism, partition these non-tensor parallel regions along the sequence dimension.
enable_sequence_overlap
: overlap the computation and communication in the sequence parallelism, it's used with enable_sequence_parallelism
.
Write your own policy
If you have a custom model, you can also use Shardformer to parallelize it by writing your own sharding policy. More information about the sharding policy can be found in API Design.
from colossalai.shardformer import Policy
class MyPolicy(Policy):
# implement your own policy
...
# init model and shard former
...
# use customized policy to shard model
my_policy = MyPolicy()
shard_former.optimize(model, my_policy)
🗺 Roadmap
We will follow this roadmap to develop Shardformer:
- API Design
- API Implementation
- Unit Testing
- Policy Implementation
model | tensor parallel | pipeline parallel | lazy initialization | xformer | flash attn2 | jit fused operator | fused layernorm | sequence parallel | overlap |
---|---|---|---|---|---|---|---|---|---|
bert | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] |
t5 | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [ ] | [ ] |
llama V1/V2 | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [ ] | [ ] |
gpt2 | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] |
opt | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [ ] | [ ] |
bloom | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] |
chatglm2 | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [x] |
vit | [x] | [x] | [ ] | [x] | [x] | [x] | [x] | [ ] | [ ] |
whisper | [x] | [x] | [x] | [x] | [x] | [x] | [x] | [ ] | [ ] |
sam | [x] | [ ] | [ ] | [x] | [x] | [x] | [x] | [ ] | [ ] |
blip2 | [x] | [ ] | [ ] | [x] | [x] | [x] | [x] | [ ] | [ ] |
roberta | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
albert | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
ernie | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
gpt-neo | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
gpt-j | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
beit | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
swin | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
swin V2 | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
qwen | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] | [ ] |
💡 API Design
We will discuss the major components of ShardFormer
below to help you better understand how things work.
This section serves as the design doc for Shardformer and the function signature might differ from the actual implementation.
Please refer to the code for more details.
Distributed Modules
ShardFormer
replaces the original PyTorch module with a distributed module.
The distributed module keeps the same attributes as the original module but replaces the original parameters with distributed parameters and defines a new forward
function to execute distributed computation.
Each distributed module implements its from_native_module
static method to convert the PyTorch module to its corresponding distributed module.
class ParallelModule(torch.nn.Module):
@abstractmethod
def from_native_module(module: torch.nn.Module, process_group: Union[ProcessGroup, Tuple[ProcessGroup]]) -> ParallelModule
"""
Convert a native module to a parallelized
Examples:
```python
# replace module
my_linear = Linear1D_Col.from_native_module(my_linear, process_group)
```
"""
Shard Config
ShardConfig
is a simple data class to tell ShardFormer
how sharding will be performed.
@dataclass
class ShardConfig:
tensor_parallel_process_group: ProcessGroup = None
enable_fused_normalization: bool = False
...
# Some possible future config fields
tensor_parallel_mode: Choice['1d', '2d', '2.5d', '3d'] # support different tensor parallel mode
inference_only: bool # only inject inference-suitable sharding policy
use_flash_attention: bool # whether to use flash attention to speed up attention
Policy
The Policy
class describes how to handle the model sharding.
It is merely a description, the actual sharding will be performed by ModelSharder
.
We abstract the policy into four stages:
- Preprocessing: call
Policy.preprocess
to do some prior work before sharding, for example, resizing the embedding - Providing
ModulePolicyDescription
: callPolicy.module_policy
to get a bunch ofModulePolicyDescription
to tellModelSharder
how the submodules's attributes, child parameters, and deeper submodules will be substituted. - Postprocessing: call
Policy.postprocess
to perform some postprocessing work, for example, binding the embedding and classifier head weights of the BERT model.
@dataclass
class ModulePolicyDescription:
r"""
Describe how the attributes and parameters will be transformed in a policy.
Args:
attribute_replacement (Dict[str, Any]): key is the attribute name, value is the attribute value after sharding
param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function must receive only one arguments: module.
sub_module_replacement (List[SubModuleReplacementDescription]): each element in the list is a ParamReplacementDescription
object which specifies the module to be replaced and the target module used to replacement.
method_replace (Dict[str, Callable]): key is the method name, value is the method for replacement
"""
attribute_replacement: Dict[str, Any] = None
param_replacement: List[Callable] = None
sub_module_replacement: List[SubModuleReplacementDescription] = None
method_replacement: Dict[str, Callable] = None
@dataclass
class SubModuleReplacementDescription:
r"""
Describe how a submodule will be replaced
Args:
suffix (str): used to get the submodule object
target_module (ParallelModule): specifies the module class used to replace to submodule
kwargs (Dict[str, Any]): the dictionary used to pass extra arguments to the `ParallelModule.from_native_module` method.
ignore_if_not_exist (bool): if the submodule does not exist, ignore it or raise an exception
"""
suffix: str
target_module: ParallelModule
kwargs: Dict[str, Any] = None
ignore_if_not_exist: bool = False
class Policy(ABC):
def __init__(self)
self.model = None
def set_model(self, model: nn.Module) -> None:
"""
Set model as an attribute of the Policy object so that we can access the model's attributes.
"""
self.model = model
@abstractmethod
def preprocess(self) -> nn.Module:
"""
Perform some preprocessing on the model, such as resizing the embedding size
"""
...
@abstractmethod
def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
"""
Return the dict for the modify policy, the key is the original layer class and the value is the
argument for the modify layer
"""
...
@abstractmethods
def postprocess(self) -> nn.Module:
"""
Perform some postprocessing on the model, such as binding the embedding with the weight of the classifier head
"""
...
Model Sharder
ModelSharder
is the class in charge of sharding the model based on the given policy.
class ModelSharder:
def __init__(self, model: torch.nn.Module, shard_config: ShardConfig, Policy: ShardPolicy = None):
#TODO: input is a cls or a obj
...
def shard(self) -> None:
"""
Shard model with parallelism with the help of pre-processing, replace_model_class, replace_module, and post-processing.
"""
...
def replace_module(self) -> None:
"""
Replace the layer according to the policy. Call Policy.module_policy() to get the module. Call _replace_module recursively.
"""
...
User-facing API
We only expose a limited number of APIs to the user to keep their user experience simple and clean.
class ShardFormer:
"""
Parallelize model based on the given config and policy
Example:
org_model = BertForMaskedLM.from_pretrained('bert-base-uncased')
shard_config = ShardConfig()
shard_former = ShardFormer(shard_config=shard_config)
model, shared_params = shard_former.optimize(org_model)
"""
def __init__(self, shard_config: ShardConfig):
"""
Do two things:
1. Create a distribute coordinator
2. serve as a store for shard config
"""
self.shard_config = shard_config
self.coordinator = DistCoordinator()
def optimize(self, model: nn.Module, policy: Policy = None) -> Tuple[nn.Module, List[Dict[int, Tensor]]]:
r"""
This method will optimize the model based on the given policy.
Args:
model (`torch.nn.Model`): the origin huggingface model
shard_config (`ShardConfig`): the config for distribute information
policy (`Policy`): the custom policy for sharding
Returns: the sharded model and the shared parameters
"""
sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy)
shared_params = sharder.shard()
return model, shared_params
⌨️ Development Notes
Add New Policy to Shardformer
This section serves as the guideline for writing new policies and register them into shardformer
.
- Step 1. Write your own model policy
You can create a new file in the colossalai/shardformer/policies
folder and name the file with the model name. You can implement your policy in this file. You should not import the any model zoo library at the header section of the file because we do not want to import the library when we do not use the policy. Libraries such as transformers
should be imported only in the function body when needed.
Please follow the following protocols when writing your policy:
-
You have to make a clear decision what you want to replace exactly in the original PyTorch module
- Use
ModulePolicyDescription.attribute_replacement
to replace the module attributes - Use
ModulePolicyDescription.param_replacement
to replace the module parameters - Use
ModulePolicyDescription.sub_module_replacement
to replace the submodules completely. The target module should implement thefrom_native_module
for the replacement. - Use
ModulePolicyDescription.method_replacement
to replace the module methods. These replacement methods should be put in theshardformer/modeling/<model-name>.py
.
- Use
-
You can implement the
ParallelModule
for primitive modules in theshardformer/layer/<model-name>.py
file. Primitive modules refer to modules which are not composed of other modules. For example, thetorch.nn.Linear
module is a primitive module while modules such asBertEncoder
module in thetransformers
library is a composite module. Primitive modules do not nested innernn.Module
members. For composite modules, you should consider usingModulePolicyDescription
to implement your replacement. -
ParallelModule
is meant to be used in two ways:ParallelModule.from_native_module
to convert native PyTorch module to theParallelModule
andParallelModule(...)
to instantiate the module directly just like a normal PyTorch module.ParallelModule
should be only implemented for modules whose weights are sharded. If you want to make your module compatible with theModulePolicyDescription.sub_module_replacement
and there is no weight sharding in your module, you can just implement thefrom_native_module
method without inheriting theParallelModule
likecolossalai/shardformer/layer/normalization.py
. -
Do not import any file in the
colossalai/shardformer/policies
andcolossalai/shardformer/modeling
to avoid unwanted import error. For example, a file in these folders accidentally importstransformers
library at the top of the file, then the user will have to installtransformers
library even if they do not use this file. Any file in themodeling
folder should be only imported by the policy file. A policy implementation should be only imported dynamically via the autopolicy or manually via theShardFormer
module. -
Try to keep your import statement on third-party libraries such as
transformers
within the function body instead of the header section of the file. This is because we do not want to import the library when we do not use the policy. -
Step 2. Register your policy to the autopolicy
Next, you need to register your policy in the colossalai/shardformer/policies/autopolicy.py
file.
For example, if we register the policy for the BERT model, we just add a key-value in the _POLICY_LIST
dictionary. The key if the qualname
of the model object (you can get it by model.__class__.__qualname__). The value is a PolicyLocation
object, which contains the file name and the class name of the policy. We do not import the policy directly because the policy file may contain libraries (such as transformers
) which we do not want to import when we do not use the policy.
_POLICY_LIST = {
# BERT
"transformers.models.bert.modeling_bert.BertModel":
PolicyLocation(file_name="bert", class_name="BertModelPolicy"),
}
Write Your Unit Testing
This section serves as the guideline for testing the shardformer
module.
- Step 1. Add your model to the model zoo in the test kits.
Add your model to the tests/kit/model_zoo
file. This allows you to define test-related components for this model. You can take tests/kit/model_zoo/transformers/llama.py
as an example for reference.
- Step 2. Write your unit testing for the model
Next, implement your unit test in the tests/test_shardformer
folder. Please refer to other similar tests for style consistency.
- Step 3. Execute your test
When you run tests locally, you should run tests for both your newly-added test file and the whole shardformer
module tests.
# test for your own test file
pytest tests/test_shardformer/test_model/<your-file>.py
# test for the whole shardformer module
pytest tests/test_shardformer
📊 Benchmarking
System Performance
We conducted benchmark tests to evaluate the performance improvement of Shardformer. We compared the training time between the original model and the shard model.
We set the batch size to 4, the number of attention heads to 8, and the head dimension to 64. 'N_CTX' refers to the sequence length.
In the case of using 2 GPUs, the training times are as follows.
N_CTX | org_model | shard_model |
---|---|---|
256 | 11.2ms | 17.2ms |
512 | 9.8ms | 19.5ms |
1024 | 19.6ms | 18.9ms |
2048 | 46.6ms | 30.8ms |
4096 | 160.5ms | 90.4ms |
In the case of using 4 GPUs, the training times are as follows.
N_CTX | org_model | shard_model |
---|---|---|
256 | 10.0ms | 21.1ms |
512 | 11.5ms | 20.2ms |
1024 | 22.1ms | 20.6ms |
2048 | 46.9ms | 24.8ms |
4096 | 160.4ms | 68.0ms |
As shown in the figures above, when the sequence length is around 1000 or greater, the parallel optimization of Shardformer for long sequences starts to become evident.
Convergence
To validate that training the model using shardformers does not impact its convergence. We fine-tuned the BERT model using both shardformer and non-shardformer approaches. The example that utilizes Shardformer simultaneously with Pipeline Parallelism and Data Parallelism (Zero1). We then compared the accuracy, loss, and F1 score of the training results.
the configurations are as follows:
batch_size = 2
epoch = 3
lr = 2.4e-5
accumulation_steps = 8
warmup_fraction = 0.03
accuracy | f1 | loss | GPU number | model sharded |
---|---|---|---|---|
0.82971 | 0.87713 | 0.23194 | 4 | True |
0.83797 | 0.88006 | 0.22683 | 2 | True |
0.84521 | 0.88700 | 0.21822 | 1 | False |
Overall, the results demonstrate that using shardformers during model training does not affect the convergence.