(8/n) Support 2D Parallelism - 2D Parallel Fabric Docs (#19887)
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@ -61,15 +61,15 @@ When loading a model from a checkpoint, for example when fine-tuning, set ``empt
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----
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********************************************
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Model-parallel training (FSDP and DeepSpeed)
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********************************************
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***************************************************
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Model-parallel training (FSDP, TP, DeepSpeed, etc.)
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***************************************************
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When training sharded models with :doc:`FSDP <model_parallel/fsdp>` or DeepSpeed, using :meth:`~lightning.fabric.fabric.Fabric.init_module` is necessary in most cases because otherwise model initialization gets very slow (minutes) or (and that's more likely) you run out of CPU memory due to the size of the model.
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When training distributed models with :doc:`FSDP/TP <model_parallel/index>` or DeepSpeed, using :meth:`~lightning.fabric.fabric.Fabric.init_module` is necessary in most cases because otherwise model initialization gets very slow (minutes) or (and that's more likely) you run out of CPU memory due to the size of the model.
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.. code-block:: python
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# Recommended for FSDP and DeepSpeed
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# Recommended for FSDP, TP and DeepSpeed
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with fabric.init_module(empty_init=True):
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model = GPT3() # parameters are placed on the meta-device
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@ -81,4 +81,4 @@ When training sharded models with :doc:`FSDP <model_parallel/fsdp>` or DeepSpeed
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.. note::
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Empty-init is experimental and the behavior may change in the future.
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For FSDP on PyTorch 2.1+, it is required that all user-defined modules that manage parameters implement a ``reset_parameters()`` method (all PyTorch built-in modules have this too).
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For distributed models on PyTorch 2.1+, it is required that all user-defined modules that manage parameters implement a ``reset_parameters()`` method (all PyTorch built-in modules have this too).
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@ -82,7 +82,6 @@ Get started
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.. displayitem::
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:header: Pipeline Parallelism
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:description: Coming soon
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:button_link:
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:col_css: col-md-4
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:height: 180
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:tag: advanced
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@ -110,6 +110,7 @@ In Fabric, define a function that applies the tensor parallelism to the model:
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parallelize_module(model, tp_mesh, plan)
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return model
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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.
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Next, configure the :class:`~lightning.fabric.strategies.model_parallel.ModelParallelStrategy` in Fabric:
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.. code-block:: python
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@ -222,6 +223,8 @@ Beyond this toy example, we recommend you study our `LLM Tensor Parallel Example
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----
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.. _tp-data-loading:
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***************************
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Data-loading considerations
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***************************
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@ -2,4 +2,286 @@
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2D Parallelism (Tensor Parallelism + FSDP)
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##########################################
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Content will be available soon.
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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.
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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.
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The :doc:`Tensor Parallelism documentation <tp>` and a general understanding of `FSDP <https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html>`_ are a prerequisite for this tutorial.
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.. note:: This is an experimental feature.
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----
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*********************
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Enable 2D parallelism
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*********************
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We will start off with the same feed forward example model as in the :doc:`Tensor Parallelism tutorial <tp>`.
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.. code-block:: python
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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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class FeedForward(nn.Module):
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def __init__(self, dim, hidden_dim):
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super().__init__()
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self.w1 = nn.Linear(dim, hidden_dim, bias=False)
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self.w2 = nn.Linear(hidden_dim, dim, bias=False)
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self.w3 = nn.Linear(dim, hidden_dim, bias=False)
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def forward(self, x):
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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Next, we define a function that applies the desired parallelism to our model.
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The function must take as first argument the model and as second argument the a :class:`~torch.distributed.device_mesh.DeviceMesh`.
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More on how the device mesh works later.
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.. code-block:: python
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from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel
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from torch.distributed.tensor.parallel import parallelize_module
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from torch.distributed._composable.fsdp.fully_shard import fully_shard
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def parallelize_feedforward(model, device_mesh):
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# Lightning will set up a device mesh for you
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# Here, it is 2-dimensional
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tp_mesh = device_mesh["tensor_parallel"]
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dp_mesh = device_mesh["data_parallel"]
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if tp_mesh.size() > 1:
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# Use PyTorch's distributed tensor APIs to parallelize the model
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plan = {
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"w1": ColwiseParallel(),
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"w2": RowwiseParallel(),
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"w3": ColwiseParallel(),
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}
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parallelize_module(model, tp_mesh, plan)
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if dp_mesh.size() > 1:
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# Use PyTorch's FSDP2 APIs to parallelize the model
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fully_shard(model.w1, mesh=dp_mesh)
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fully_shard(model.w2, mesh=dp_mesh)
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fully_shard(model.w3, mesh=dp_mesh)
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fully_shard(model, mesh=dp_mesh)
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return model
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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.
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In addition to the tensor-parallel code from the :doc:`Tensor Parallelism tutorial <tp>`, this function also shards the model's parameters using FSDP along the data-parallel dimension.
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Finally, pass the parallelization function to the :class:`~lightning.fabric.strategies.model_parallel.ModelParallelStrategy` and configure the data-parallel and tensor-parallel sizes:
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.. code-block:: python
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import lightning as L
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from lightning.fabric.strategies import ModelParallelStrategy
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strategy = ModelParallelStrategy(
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parallelize_fn=parallelize_feedforward,
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# Define the size of the 2D parallelism
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# Set these to "auto" (default) to apply TP intra-node and FSDP inter-node
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data_parallel_size=2,
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tensor_parallel_size=2,
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)
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fabric = L.Fabric(accelerator="cuda", devices=4, strategy=strategy)
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fabric.launch()
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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``).
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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.
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Finally, the tensor parallelism will apply to each group, splitting the sharded tensor across the GPUs within each group.
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.. collapse:: Full training example (requires at least 4 GPUs).
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.. code-block:: python
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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 torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel
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from torch.distributed.tensor.parallel import parallelize_module
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from torch.distributed._composable.fsdp.fully_shard import fully_shard
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import lightning as L
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from lightning.pytorch.demos.boring_classes import RandomDataset
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from lightning.fabric.strategies import ModelParallelStrategy
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class FeedForward(nn.Module):
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def __init__(self, dim, hidden_dim):
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super().__init__()
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self.w1 = nn.Linear(dim, hidden_dim, bias=False)
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self.w2 = nn.Linear(hidden_dim, dim, bias=False)
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self.w3 = nn.Linear(dim, hidden_dim, bias=False)
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def forward(self, x):
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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def parallelize_feedforward(model, device_mesh):
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# Lightning will set up a device mesh for you
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# Here, it is 2-dimensional
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tp_mesh = device_mesh["tensor_parallel"]
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dp_mesh = device_mesh["data_parallel"]
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if tp_mesh.size() > 1:
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# Use PyTorch's distributed tensor APIs to parallelize the model
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plan = {
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"w1": ColwiseParallel(),
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"w2": RowwiseParallel(),
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"w3": ColwiseParallel(),
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}
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parallelize_module(model, tp_mesh, plan)
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if dp_mesh.size() > 1:
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# Use PyTorch's FSDP2 APIs to parallelize the model
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fully_shard(model.w1, mesh=dp_mesh)
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fully_shard(model.w2, mesh=dp_mesh)
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fully_shard(model.w3, mesh=dp_mesh)
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fully_shard(model, mesh=dp_mesh)
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return model
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strategy = ModelParallelStrategy(
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parallelize_fn=parallelize_feedforward,
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data_parallel_size=2,
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tensor_parallel_size=2,
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)
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fabric = L.Fabric(accelerator="cuda", devices=4, strategy=strategy)
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fabric.launch()
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# Initialize the model
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model = FeedForward(8192, 8192)
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model = fabric.setup(model)
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# Define the optimizer
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optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3, foreach=True)
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optimizer = fabric.setup_optimizers(optimizer)
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# Define dataset/dataloader
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dataset = RandomDataset(8192, 128)
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dataloader = torch.utils.data.DataLoader(dataset, batch_size=8)
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dataloader = fabric.setup_dataloaders(dataloader)
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# Simplified training loop
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for i, batch in enumerate(dataloader):
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output = model(batch)
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loss = output.sum()
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fabric.backward(loss)
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optimizer.step()
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optimizer.zero_grad()
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fabric.print(f"Iteration {i} complete")
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fabric.print(f"Peak memory usage: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB")
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Beyond this toy example, we recommend you study our `LLM 2D Parallel Example (Llama 3) <https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples/fabric/tensor_parallel>`_.
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----
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*******************
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Effective use cases
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*******************
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In the toy example above, the parallelization is configured to work within a single machine across multiple GPUs.
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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.
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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.
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On the other hand, FSDP by design has the advantage that it can overlap GPU transfers with the computation (it can prefetch layers).
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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.
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.. code-block:: python
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from lightning.fabric.strategies import ModelParallelStrategy
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strategy = ModelParallelStrategy(
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# Default is "auto"
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# Applies TP intra-node and DP inter-node
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data_parallel_size="auto",
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tensor_parallel_size="auto",
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)
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----
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***************************
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Data-loading considerations
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***************************
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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.
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However, across the data-parallel dimension, the inputs should be different.
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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.
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If you use a PyTorch data loader and set it up using :meth:`~lightning.fabric.fabric.Fabric.setup_dataloaders`, Fabric will automatically handle this for you by configuring the distributed sampler.
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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.
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.. code-block:: python
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import lightning as L
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fabric = L.Fabric(...)
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# Define dataset/dataloader
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# If there is randomness/augmentation in the dataset, fix the seed
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dataset = MyDataset(seed=42)
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dataloader = DataLoader(dataset, batch_size=8, shuffle=True)
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# Fabric configures the sampler automatically for you such that
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# all batches in a tensor-parallel group are identical,
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# while still sharding the dataset across the data-parallel group
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dataloader = fabric.setup_dataloaders(dataloader)
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for i, batch in enumerate(dataloader):
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...
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----
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**********
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Next steps
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**********
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.. raw:: html
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<div class="display-card-container">
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<div class="row">
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.. displayitem::
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:header: LLM 2D Parallel Example
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:description: Full example how to combine TP + FSDP in a large language model (Llama 3)
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:col_css: col-md-4
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:button_link: https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples/fabric/tensor_parallel
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:height: 160
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:tag: advanced
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.. displayitem::
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:header: Pipeline Parallelism
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:description: Coming sooon
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:col_css: col-md-4
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:height: 160
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:tag: advanced
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.. raw:: html
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</div>
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</div>
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