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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.

Open In Studio

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 define a function that applies the desired parallelism to our model. The function must take as first argument the model and as second argument the a DeviceMesh. More on how the device mesh works later.

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

def parallelize_feedforward(model, device_mesh):
    # Lightning will set up a device mesh for you
    # Here, it is 2-dimensional
    tp_mesh = device_mesh["tensor_parallel"]
    dp_mesh = 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(model, tp_mesh, plan)

    if dp_mesh.size() > 1:
        # Use PyTorch's FSDP2 APIs to parallelize the model
        fully_shard(model.w1, mesh=dp_mesh)
        fully_shard(model.w2, mesh=dp_mesh)
        fully_shard(model.w3, mesh=dp_mesh)
        fully_shard(model, mesh=dp_mesh)

    return model

By writing the parallelization code in a separate function 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 function also shards the model’s parameters using FSDP along the data-parallel dimension.

Finally, pass the parallelization function to the ModelParallelStrategy and configure the data-parallel and tensor-parallel sizes:

import lightning as L
from lightning.fabric.strategies import ModelParallelStrategy

strategy = ModelParallelStrategy(
    parallelize_fn=parallelize_feedforward,
    # 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,
)

fabric = L.Fabric(accelerator="cuda", devices=4, strategy=strategy)
fabric.launch()

In this example with 4 GPUs, Fabric 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 fabric.setup(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.fabric.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))


def parallelize_feedforward(model, device_mesh):
    # Lightning will set up a device mesh for you
    # Here, it is 2-dimensional
    tp_mesh = device_mesh["tensor_parallel"]
    dp_mesh = 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(model, tp_mesh, plan)

    if dp_mesh.size() > 1:
        # Use PyTorch's FSDP2 APIs to parallelize the model
        fully_shard(model.w1, mesh=dp_mesh)
        fully_shard(model.w2, mesh=dp_mesh)
        fully_shard(model.w3, mesh=dp_mesh)
        fully_shard(model, mesh=dp_mesh)

    return model


strategy = ModelParallelStrategy(
    parallelize_fn=parallelize_feedforward,
    data_parallel_size=2,
    tensor_parallel_size=2,
)

fabric = L.Fabric(accelerator="cuda", devices=4, strategy=strategy)
fabric.launch()

# Initialize the model
model = FeedForward(8192, 8192)
model = fabric.setup(model)

# Define the optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3)
optimizer = fabric.setup_optimizers(optimizer)

# Define dataset/dataloader
dataset = RandomDataset(8192, 128)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
dataloader = fabric.setup_dataloaders(dataloader)

# Simplified training loop
for i, batch in enumerate(dataloader):
    output = model(batch)
    loss = output.sum()
    fabric.backward(loss)
    optimizer.step()
    optimizer.zero_grad()
    fabric.print(f"Iteration {i} complete")

fabric.print(f"Peak memory usage: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB")

Note

2D Parallelism in Lightning Fabric 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.fabric.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 and set it up using setup_dataloaders(), Fabric 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

fabric = L.Fabric(...)

# 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)

# Fabric 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
dataloader = fabric.setup_dataloaders(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)
advanced

Pipeline Parallelism

Coming sooon

advanced


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