2D Parallelism (Tensor Parallelism + FSDP)¶
2D Parallelism combines Tensor Parallelism (TP) and Fully Sharded Data Parallelism (FSDP) to leverage the memory efficiency of FSDP and the computational scalability of TP. This hybrid approach balances the trade-offs of each method, optimizing memory usage and minimizing communication overhead, enabling the training of extremely large models on large GPU clusters.
The Tensor Parallelism documentation and a general understanding of FSDP are a prerequisite for this tutorial.
Enable 2D parallelism¶
We will start off with the same feed forward example model as in the Tensor Parallelism tutorial.
import torch.nn as nn
import torch.nn.functional as F
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim):
super().__init__()
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
Next, we implement the LightningModule and override the configure_model() that applies the desired parallelism to our model.
import lightning as L
from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel
from torch.distributed.tensor.parallel import parallelize_module
from torch.distributed._composable.fsdp.fully_shard import fully_shard
class LitModel(L.LightningModule):
def __init__(self):
super().__init__()
self.model = FeedForward(8192, 8192)
def configure_model(self):
# Lightning will set up a `self.device_mesh` for you
# Here, it is 2-dimensional
tp_mesh = self.device_mesh["tensor_parallel"]
dp_mesh = self.device_mesh["data_parallel"]
if tp_mesh.size() > 1:
# Use PyTorch's distributed tensor APIs to parallelize the model
plan = {
"w1": ColwiseParallel(),
"w2": RowwiseParallel(),
"w3": ColwiseParallel(),
}
parallelize_module(self.model, tp_mesh, plan)
if dp_mesh.size() > 1:
# Use PyTorch's FSDP2 APIs to parallelize the model
fully_shard(self.model.w1, mesh=dp_mesh)
fully_shard(self.model.w2, mesh=dp_mesh)
fully_shard(self.model.w3, mesh=dp_mesh)
fully_shard(self.model, mesh=dp_mesh)
By writing the parallelization code in this special hook rather than hardcoding it into the model, we keep the original source code clean and maintainable. In addition to the tensor-parallel code from the Tensor Parallelism tutorial, this implementation now also shards the model’s parameters using FSDP along the data-parallel dimension.
Finally, configure the ModelParallelStrategy and configure the data-parallel and tensor-parallel sizes:
import lightning as L
from lightning.pytorch.strategies import ModelParallelStrategy
# 1. Create the strategy
strategy = ModelParallelStrategy(
# Define the size of the 2D parallelism
# Set these to "auto" (default) to apply TP intra-node and FSDP inter-node
data_parallel_size=2,
tensor_parallel_size=2,
)
# 2. Configure devices and set the strategy in Trainer
trainer = L.Trainer(accelerator="cuda", devices=4, strategy=strategy)
trainer.fit(...)
In this example with 4 GPUs, the Trainer will create a device mesh that groups GPU 0-1 and GPU 2-3 (2 groups because data_parallel_size=2, and 2 GPUs per group because tensor_parallel_size=2).
Later on when trainer.fit(model) is called, each layer wrapped with FSDP (fully_shard) will be split into two shards, one for the GPU 0-1 group, and one for the GPU 2-3 group.
Finally, the tensor parallelism will apply to each group, splitting the sharded tensor across the GPUs within each group.
Full training example (requires at least 4 GPUs).
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel
from torch.distributed.tensor.parallel import parallelize_module
from torch.distributed._composable.fsdp.fully_shard import fully_shard
import lightning as L
from lightning.pytorch.demos.boring_classes import RandomDataset
from lightning.pytorch.strategies import ModelParallelStrategy
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim):
super().__init__()
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class LitModel(L.LightningModule):
def __init__(self):
super().__init__()
self.model = FeedForward(8192, 8192)
def configure_model(self):
if self.device_mesh is None:
return
# Lightning will set up a `self.device_mesh` for you
# Here, it is 2-dimensional
tp_mesh = self.device_mesh["tensor_parallel"]
dp_mesh = self.device_mesh["data_parallel"]
if tp_mesh.size() > 1:
# Use PyTorch's distributed tensor APIs to parallelize the model
plan = {
"w1": ColwiseParallel(),
"w2": RowwiseParallel(),
"w3": ColwiseParallel(),
}
parallelize_module(self.model, tp_mesh, plan)
if dp_mesh.size() > 1:
# Use PyTorch's FSDP2 APIs to parallelize the model
fully_shard(self.model.w1, mesh=dp_mesh)
fully_shard(self.model.w2, mesh=dp_mesh)
fully_shard(self.model.w3, mesh=dp_mesh)
fully_shard(self.model, mesh=dp_mesh)
def training_step(self, batch):
output = self.model(batch)
loss = output.sum()
return loss
def configure_optimizers(self):
return torch.optim.AdamW(self.model.parameters(), lr=3e-3)
def train_dataloader(self):
# Trainer configures the sampler automatically for you such that
# all batches in a tensor-parallel group are identical
dataset = RandomDataset(8192, 64)
return torch.utils.data.DataLoader(dataset, batch_size=8, num_workers=2)
strategy = ModelParallelStrategy(
data_parallel_size=2,
tensor_parallel_size=2,
)
trainer = L.Trainer(
accelerator="cuda",
devices=4,
strategy=strategy,
max_epochs=1,
)
model = LitModel()
trainer.fit(model)
trainer.print(f"Peak memory usage: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB")
Note
2D Parallelism in PyTorch Lightning as well as PyTorch is experimental. The APIs may change in the future.
Beyond this toy example, we recommend you study our LLM 2D Parallel Example (Llama 3).
Effective use cases¶
In the toy example above, the parallelization is configured to work within a single machine across multiple GPUs. However, in practice the main use case for 2D parallelism is in multi-node training, where one can effectively combine both methods to maximize throughput and model scale. Since tensor-parallelism requires blocking collective calls, fast GPU data transfers are essential to keep throughput high and therefore TP is typically applied across GPUs within a machine. On the other hand, FSDP by design has the advantage that it can overlap GPU transfers with the computation (it can prefetch layers). Hence, combining FSDP for inter-node parallelism and TP for intra-node parallelism is generally a good strategy to minimize both the latency and network bandwidth usage, making it possible to scale to much larger models than is possible with FSDP alone.
from lightning.pytorch.strategies import ModelParallelStrategy
strategy = ModelParallelStrategy(
# Default is "auto"
# Applies TP intra-node and DP inter-node
data_parallel_size="auto",
tensor_parallel_size="auto",
)
Data-loading considerations¶
In a tensor-parallelized model, it is important that the model receives an identical input on each GPU that participates in the same tensor-parallel group. However, across the data-parallel dimension, the inputs should be different. In other words, if TP is applied within a node, and FSDP across nodes, each node must receive a different batch, but every GPU within the node gets the same batch of data.
If you use a PyTorch data loader, the Trainer will automatically handle this for you by configuring the distributed sampler. However, when you shuffle data in your dataset or data loader, or when applying randomized transformations/augmentations in your data, you must still ensure that the seed is set appropriately.
import lightning as L
trainer = L.Trainer(...)
# Define dataset/dataloader
# If there is randomness/augmentation in the dataset, fix the seed
dataset = MyDataset(seed=42)
dataloader = DataLoader(dataset, batch_size=8, shuffle=True)
# PyTorch Lightning configures the sampler automatically for you such that
# all batches in a tensor-parallel group are identical,
# while still sharding the dataset across the data-parallel group
trainer.fit(model, dataloader)
for i, batch in enumerate(dataloader):
...
Next steps¶
LLM 2D Parallel Example
Full example how to combine TP + FSDP in a large language model (Llama 3)
Pipeline Parallelism
Coming sooon
