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Intelligence Processing Unit (IPU)

Lightning supports the Graphcore Intelligence Processing Unit (IPU), built for Artificial Intelligence and Machine Learning.

Note

IPU support is experimental and a work in progress (see Known limitations). If you run into any problems, please leave an issue.

IPU terminology

IPUs consist of many individual cores, called tiles, allowing highly parallel computation. Due to the high bandwidth between tiles, IPUs facilitate machine learning loads where parallelization is essential. Because computation is heavily parallelized, IPUs operate in a different way to conventional accelerators such as CPU/GPUs. IPUs do not require large batch sizes for maximum parallelization, can provide optimizations across the compiled graph and rely on model parallelism to fully utilize tiles for larger models.

IPUs are used to build IPU-PODs, rack-based systems of IPU-Machines for larger workloads. See the IPU Architecture for more information.

See the Graphcore Glossary for the definitions of other IPU-specific terminology.

How to access IPUs

To use IPUs you must have access to a system with IPU devices. To get access see getting started.

You must ensure that the IPU system has enabled the PopART and Poplar packages from the SDK. Instructions are in the Getting Started guide for your IPU system, on the Graphcore documents portal.

Training with IPUs

Specify the number of IPUs to train with. Note that when training with IPUs, you must select 1 or a power of 2 number of IPUs (i.e. 2/4/8..).

trainer = pl.Trainer(accelerator="ipu", devices=8)  # Train using data parallel on 8 IPUs

IPUs only support specifying a single number to allocate devices, which is handled via the underlying libraries.

Mixed precision & 16 bit precision

Lightning also supports training in mixed precision with IPUs. By default, IPU training will use 32-bit precision. To enable mixed precision, set the precision flag.

Note

Currently there is no dynamic scaling of the loss with mixed precision training.

import pytorch_lightning as pl

model = MyLightningModule()
trainer = pl.Trainer(accelerator="ipu", devices=8, precision=16)
trainer.fit(model)

You can also use pure 16-bit training, where the weights are also in 16-bit precision.

import pytorch_lightning as pl
from pytorch_lightning.strategies import IPUStrategy

model = MyLightningModule()
model = model.half()
trainer = pl.Trainer(accelerator="ipu", devices=8, precision=16)
trainer.fit(model)

Advanced IPU options

IPUs provide further optimizations to speed up training. By using the IPUStrategy we can set the device_iterations, which controls the number of iterations run directly on the IPU devices before returning to the host. Increasing the number of on-device iterations will improve throughput, as there is less device to host communication required.

Note

When using model parallelism, it is a hard requirement to increase the number of device iterations to ensure we fully saturate the devices via micro-batching. see Model parallelism for more information.

import pytorch_lightning as pl
from pytorch_lightning.strategies import IPUStrategy

model = MyLightningModule()
trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=32))
trainer.fit(model)

Note that by default we return the last device iteration loss. You can override this by passing in your own poptorch.Options and setting the AnchorMode as described in the PopTorch documentation.

import poptorch
import pytorch_lightning as pl
from pytorch_lightning.strategies import IPUStrategy

model = MyLightningModule()
inference_opts = poptorch.Options()
inference_opts.deviceIterations(32)

training_opts = poptorch.Options()
training_opts.anchorMode(poptorch.AnchorMode.All)
training_opts.deviceIterations(32)

trainer = Trainer(
    accelerator="ipu", devices=8, strategy=IPUStrategy(inference_opts=inference_opts, training_opts=training_opts)
)
trainer.fit(model)

You can also override all options by passing the poptorch.Options to the plugin. See PopTorch options documentation for more information.

PopVision Graph Analyser

PopVision Graph Analyser

Lightning supports integration with the PopVision Graph Analyser Tool. This helps to look at utilization of IPU devices and provides helpful metrics during the lifecycle of your trainer. Once you have gained access, The PopVision Graph Analyser Tool can be downloaded via the GraphCore download website.

Lightning supports dumping all reports to a directory to open using the tool.

import pytorch_lightning as pl
from pytorch_lightning.strategies import IPUStrategy

model = MyLightningModule()
trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(autoreport_dir="report_dir/"))
trainer.fit(model)

This will dump all reports to report_dir/ which can then be opened using the Graph Analyser Tool, see Opening Reports.

Model parallelism

Due to the IPU architecture, larger models should be parallelized across IPUs by design. Currently PopTorch provides the capabilities via annotations as described in parallel execution strategies.

Below is an example using the block annotation in a LightningModule.

Note

Currently, when using model parallelism we do not infer the number of IPUs required for you. This is done via the annotations themselves. If you specify 4 different IDs when defining Blocks, this means your model will be split onto 4 different IPUs.

This is also mutually exclusive with the Trainer flag. In other words, if your model is split onto 2 IPUs and you set Trainer(accelerator="ipu", devices=4) this will require 8 IPUs in total: data parallelism will be used to replicate the two-IPU model 4 times.

When pipelining the model you must also increase the device_iterations to ensure full data saturation of the devices data, i.e whilst one device in the model pipeline processes a batch of data, the other device can start on the next batch. For example if the model is split onto 4 IPUs, we require device_iterations to be at-least 4.

import pytorch_lightning as pl
import poptorch


class MyLightningModule(pl.LightningModule):
    def __init__(self):
        super().__init__()
        # This will place layer1, layer2+layer3, layer4, softmax on different IPUs at runtime.
        # BeginBlock will start a new id for all layers within this block
        self.layer1 = poptorch.BeginBlock(torch.nn.Linear(5, 10), ipu_id=0)

        # This layer starts a new block,
        # adding subsequent layers to this current block at runtime
        # till the next block has been declared
        self.layer2 = poptorch.BeginBlock(torch.nn.Linear(10, 5), ipu_id=1)
        self.layer3 = torch.nn.Linear(5, 5)

        # Create new blocks
        self.layer4 = poptorch.BeginBlock(torch.nn.Linear(5, 5), ipu_id=2)
        self.softmax = poptorch.BeginBlock(torch.nn.Softmax(dim=1), ipu_id=3)

    ...


model = MyLightningModule()
trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=20))
trainer.fit(model)

You can also use the block context manager within the forward function, or any of the step functions.

import pytorch_lightning as pl
import poptorch


class MyLightningModule(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.layer1 = torch.nn.Linear(5, 10)
        self.layer2 = torch.nn.Linear(10, 5)
        self.layer3 = torch.nn.Linear(5, 5)
        self.layer4 = torch.nn.Linear(5, 5)

        self.act = torch.nn.ReLU()
        self.softmax = torch.nn.Softmax(dim=1)

    def forward(self, x):

        with poptorch.Block(ipu_id=0):
            x = self.act(self.layer1(x))

        with poptorch.Block(ipu_id=1):
            x = self.act(self.layer2(x))

        with poptorch.Block(ipu_id=2):
            x = self.act(self.layer3(x))
            x = self.act(self.layer4(x))

        with poptorch.Block(ipu_id=3):
            x = self.softmax(x)
        return x

    ...


model = MyLightningModule()
trainer = pl.Trainer(accelerator="ipu", devices=8, strategy=IPUStrategy(device_iterations=20))
trainer.fit(model)

Known limitations

Currently there are some known limitations that are being addressed in the near future to make the experience seamless when moving from different devices.

Please see the MNIST example which displays most of the limitations and how to overcome them till they are resolved.

  • self.log is not supported in the training_step, validation_step, test_step or predict_step. This is due to the step function being traced and sent to the IPU devices. We’re actively working on fixing this

  • Multiple optimizers are not supported. training_step only supports returning one loss from the training_step function as a result

  • Since the step functions are traced, branching logic or any form of primitive values are traced into constants. Be mindful as this could lead to errors in your custom code

  • Clipping gradients is not supported

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