13. Changelog

13.1. v2.3 (Poplar SDK 2.3)

13.1.1. New features

• Support for torch.bitwise_and, torch.bitwise_or, torch.bitwise_xor

• Support for torch.logical_and, torch.logical_or,

• Support K-dimensional NLLLoss, K-dimensional CrossEntropyLoss

• Support for non-default affine parameter flags in normalisation ops

• Support for torch.Tensor.T

• Support for torch.bool in torch.zeros, torch.zeros_like, torch.ones, torch.ones_like

• Support for torch.scatter and its in-place variant

• Support for in-place modification to buffers on IPU

• Support for taking slices of scalars

• Support version of bilinear upsampling specifying intended output size instead of scale factors

• Add support for overlapping host IO on inputs via poptorch.set_overlap_for_input().

• Add option for setting number of IO tiles via numIOTiles in poptorch.Options (required for useIOTilesToLoad() and poptorch.set_overlap_for_input().)

• Improve PopTorch’s parity with PyTorch’s Softplus

• Improve implementation of torch.SiLU by using Poplar’s Swish operator

• Additional support for operation overloads

• Fix issue where PopTorch recalculated upsampling scales in fp16

• Fix issue where the last use of poptorch.set_available_memory would be pruned

• Add documentation on available memory proportion to incorporate embeddings and indexing operations

• Add documentation on how users can generate debug information

• Support replicated tensor sharding when running on multiple processes

• Allow selection for a non-constant x input.

• Support for enableConvDithering convolution option

13.1.2. API changes

• Default mean reduction strategies have changed from the deprecated PostAndLoss strategy to Post or Running based on optimiser accumulation type

• Mean reduction strategy can now be set via poptorch.Options.Training.setMeanAccumulationAndReplicationReductionStrategy.

• Add warning that IPU-specific optimiser states cannot be read from the host, when calling get_state() on poptorch.optim optimisers

13.2. v2.2 (Poplar SDK 2.2)

13.2.1. New features

• Migrated to PyTorch version 1.9.0

• Support for torch.roll

• Support for torch.clone

• Add modelName session option that can be passed to PopART

• Support List inputs to a model

• Tuples/Lists of constants can now be returned by a model

• Add enableProfiling convenience method in poptorch.Options to enable profile report generation

• Fix bug with torch.Tensor.repeat when applied to an input during training

• Fix bug with aten::to when applied to a constant used as an input to another node

• Improved error message when encountering untraceable types during compilation

• Support for torch.gather. Please note: this operator is known to cause long compilation times. Consider using a onehot-based solution instead or torch.index_select if appropriate.

• Using a convolution layer op with the value of padding greater than or equal to kernel_size` is now supported.

• Support for Poplar recoverable and unrecoverable errors.

• Support for torch.flip.

• Support for torch.Tensor.new_ones and torch.Tensor.new_zeros

13.2.2. API changes

• Removed accumulationReductionType which was deprecated in 2.1 in favour of accumulationAndReplicationReductionType in poptorch.Options.Training

• Removed runningVarianceAlwaysFloat which was deprecated in 2.1 and replaced by runningStatisticsAlwaysFloat in poptorch.Options.Precision,

13.3. v2.1 (Poplar SDK 2.1)

13.3.1. New features

• Support for torch.unbind

• Add option to set poptorch.Options using options specified in a config file.

• Add mode=poptorch.DataLoaderMode.AsyncRebatched

• Support for PopART name scopes via poptorch.NameScope

• Add mixed precision automatic casting

• Support for torch.cross

• Support for torch.functional.one_hot

• Support for torch.int8 data types

• Support for torch.median

• Support for torch.index_select

• Support for torch.scatter_add

• Add poptorch.Options.Precision.enableFloatingPointExceptions to control floating point exception behavior

• Support for inplace changes to inputs.

• Add option to log the number of IPU cycles used in executing the main graph

• Support for torch.nn.GRU

• Add automatic loss scaling option which can be enabled via poptorch.Options.Training.setAutomaticLossScaling.

• Add poptorch.BlockFunction decorating for assigning an existing function to a block.

• Add mechanism for inspecting arbitrary tensors

• Add custom operator for CTC beam search decoding: poptorch.ctc_beam_search_decoder

• Add a separate tensor variant (now default) to the SGD optimiser.

• Add a TensorFlow variant to the RMSProp optimiser.

13.3.2. API changes

• Removed Options.Popart which was deprecated in v2.0 and replaced with Options._Popart

• Removed MultiConvPartialsType which was deprecated in v2.0

• Deprecated poptorch.Options.Training.accumulationReductionType in favour of poptorch.Options.Training.accumulationAndReplicationReductionType

• Deprecated runningVarianceAlwaysFloat in favour of runningStatisticsAlwaysFloat in poptorch.Options.Precision, as this new option computes both the running mean and variance in FP32 when this option is set to True.

• Use of SGD via PyTorch’s or PopTorch’s API now results in use of the new separate tensor variant by default. To revert to the previous default variant, use poptorch.optim.SGD with use_combined_accum=True.

13.3.3. Known issues

• Using a convolution layer op with the value of padding greater than or equal to kernel_size` results in an error when training. Use a constant pad layer instead of the excess padding prior to the convolution.

13.4. v2.0 (Poplar SDK 2.0)

13.4.1. New features

• Support for the following activation functions:

  • torch.nn.acosh

  • torch.nn.asinh

  • torch.nn.atanh

  • torch.nn.Hardshrink

  • torch.nn.SiLU

  • torch.nn.Softplus

  • torch.nn.Softshrink

  • torch.nn.Threshold

• Support for the following random sampling operations:

  • torch.bernoulli

  • torch.distributions.Bernoulli

• Experimental support for torch.nn.CTCLoss

• Add Adam optimizer

• Support for torch.nn.AdaptiveAvgPool1d, torch.nn.AdaptiveAvgPool3d

• Migrated to PyTorch version 1.7.1

• Support for aten::index, aten::index_put_

• Support for torch.zeros_like, torch.ones_like

• Allow the user to specify which Optimizer attributes are constant or not.

• Allow the user to specify mode=poptorch.DataLoaderMode.Async in poptorch.DataLoader constructor instead of explicitly creating an AsynchronousDataAccessor

• Support for torch.nn.EmbeddingBag

• Support for torch.clamp_max and torch.clamp_min

• Support for torch.min(tensor, dim=.*, keepdim=.*) and torch.max(tensor, dim=.*, keepdim=.*) overloads.

• Support for poptorch.isRunningOnIpu. This function returns True when executing on IPU and False when executing the model outside IPU scope.

• Support for torch.amax and torch.amin

• Support for attributes in custom ops.

• Support for precompilation and reloading exported executables (poptorch.PoplarExecutor.compileAndExport and poptorch.load)

• Support for slices with variable start index (slice size must be constant).

• Add ipuHardwareVersion function to read the version of the IPU hardware present on the system.

• Changed default targetd Ipu version for the model and offline compilation to 2.

• Changed accumulationReductionType(reduction) option to now apply to replication reduction as well

• Add environment variable POPTORCH_CACHE_DIR

• Support for torch.fmod, and torch.remainder

• Support for torch.addcdiv

• Support for torch.bitwise_not

13.4.2. API changes

• Deprecated Options.Popart, Options._Popart may be used experimentally.

13.5. v1.0 (Poplar SDK 1.4)

13.5.1. New features

• Support for torch.nn.InstanceNorm1d, torch.nn.InstanceNorm2d and torch.nn.InstanceNorm3d

• Fixed issue with torch.nn.GroupNorm where only 4-dimensional inputs could be used

• Replaced Adam with AdamW optimizer.

• Support for the following loss functions:

  • torch.nn.KLDivLoss

  • torch.nn.PoissonNLLLoss

  • torch.nn.HingeEmbeddingLoss

  • torch.nn.BCEWithLogitsLoss

  • torch.nn.SmoothL1Loss

  • torch.nn.SoftMarginLoss

  • torch.nn.CosineEmbeddingLoss

  • torch.nn.MarginRankingLoss

  • torch.nn.TripletMarginLoss

  • torch.nn.NLLLoss for aten::nll_loss2d

• Support for torch.optim.RMSprop optimizer

• Support for bool inputs to models

• Improved support for half type models and inputs.

  • Using a mix of float 16 and float 32 inputs is now supported. Please see the documentation for cases in which a model might use different types compared to when run natively with PyTorch.

• Support for serialized matrix multiplications (poptorch.serializedMatMul)

• Support for POPTORCH_IPU_MODEL_VERSION environment variable.

• Support for torch.cumsum

• Support for pipelined / phased / sharded execution.

• Add PoplarExecutor.compile() to compile the model without executing it.

• Use sphinx-build to generate the documentation.

• Use Miniconda as build environment.

• Support for torch.meshgrid

• Support for torch.cartesian_prod

• Optimized torch.matmul implementation with limitations

  • Fused its input 0’s batch dimensions with the row dimension to avoid ReduceSum in its backward pass, for performance purpose

• Partial support for torch.einsum

  • Diagonals and ellipsis notation is unsupported

• Support for executable caching: poptorch.Options.enableExecutableCaching()

• Add optional title argument to poptorch.ipu_print_tensor

• Add len() method to poptorch.AsynchronousDataLoader

• Support for LAMB optimizer

• Support for recomputationCheckpoint()

• Support for torch.tensordot

• Support for rounding up the number of IPU used to allow models which specify of number of IPUs which is not a power of 2: poptorch.Options.autoRoundNumIPUs(True) NB, this will reserve but not use IPUs and so it is preferable to specify the model to use a number of IPUs which is a power of two

• Optimized torch.matmul implementation with limitations

  • Fused its input 0’s batch dimensions with the row dimension to avoid ReduceSum in its backward pass, for performance purpose

• Support for multi-convolutions with poptorch.MultiConv

• Support for PopART batch serialization settings

  • These can be set via poptorch.Options().Popart.set()

• Support for PopVision System Analyser added: tracing can be enabled by setting PVTI_OPTIONS='{"enable":"true"}'

13.5.2. Known issues

• Race condition in poptorch.DataLoader when using several workers resulting in the iteration sometimes finishing one element early.

  • Workaround: set num_workers to 0 or 1.

• poptorch.custom_op() doesn’t allow the user to set attributes.

  • Workaround: hardcode the attributes in the custom operation or pass them as regular inputs.

• Graphs containing block annotations (poptorch.Block or poptorch.BeginBlock) cannot be exported using torch.save()

  • Workaround: Make a soft copy of the model that doesn’t contain Blocks and use it to save /load the weights. (The weights should be shared between the two models).

• Lists of tensors are not supported as inputs.

  • Workaround: Use tuples instead.

    # Use a tuple
    assert inference_model((t1, t2)) # instead of [t1, t2]
    

13.6. v0.1 (Poplar SDK 1.3)

13.6.1. New features

• PopTorch now exposes PopART anchor options to choose how much data to return from a model. These are passed into the model wrapper via anchor_mode. options are Sum, All, Final and EveryN.

• Support for batched LSTM and batch first

• An Options object can now be passed to poptorch.trainingModel / poptorch.inferenceModel to configure the session and select IPUs

• The ‘profile’ option has been removed, instead profiling can be enabled by setting the environment variable POPLAR_ENGINE_OPTIONS='{autoReport.all:true, autoReport.directory:.}'

• Support for POPTORCH_IPU_MODEL and POPTORCH_WAIT_FOR_IPU environment variables.

• Support for the torch comparisons operations:

  • torch.eq

  • torch.ge

  • torch.gt

  • torch.le

  • torch.lt

  • torch.max

  • torch.min

  • torch.ne

  • torch.isnan

  • torch.topk

  • torch.min and torch.max only support (tensor, tensor) and (tensor) overloads. They do not support the (tensor, dim=, keepdim=) overload.

  • torch.topk only supports sorted=False and Largest=True

• Automatically synchronise the weights back to the Host after using the IPU for training. (i.e no need to explicitly call copyWeightsToHost() anymore)

• Support for non-linear activations torch.nn.PReLU and torch.nn.Hardtanh

• Support for Adam optimizer.

• Support for half type models and inputs.

  • Models that require operations on input tensors of mixed precision are not currently supported. For example:

    def forward(self, x, y):
      x # Half
      y # Float32
      return x + y # Not supported.
    

• Support for tensor.fill_, torch.full, torch.full_like

• Support for user provided custom operations. See PopART documentation for information on how to write them. They are exposed by poptorch.custom_op this takes in a list of input tensors, strings for the PopART op name and domain, the domain version, and a list of tensors the same shape and size as the expected output tensors. This is to ensure the pytorch trace remains valid as it traces on CPU so won’t actually execute the operation when building the graph.

• Support for torch.nn.Conv1D / torch.nn.Conv2D / torch.nn.Conv3D

• Support for torch.nn.Upsample (‘nearest’ mode only)

• Support for tensor.size

• Support for the following random sampling operations.

  • torch.rand

  • torch.uniform_

  • torch.distributions.Uniform

  • torch.randn

  • torch.normal

  • torch.normal_

  For repeatable random number generation use the randomSeed method of poptorch.Options

• Support for torch.clamp

• Adds poptorch.DataLoader

• Adds optimized poptorch.AsynchronousDataAccessor which allows for a dataloader to be offloaded to a background thread asynchronously.

• Support for torch.norm

• Upgraded from torch 1.5.0 to torch 1.6.0

• Experimental support for single host distributed execution

• Add torch.where and tensor.masked_fill