PyTorch for the IPU: User Guide
Version: 3.0.0
1. Introduction
1.1. Data batching
1.2. Parallel and Distributed execution
1.3. Constraints
1.4. Other resources
2. Installation
2.1. Version compatibility
2.2. Using a Python virtual environment
2.3. Setting the environment variables
2.4. Validating the setup
3. From PyTorch to PopTorch
3.1. Preparing your data
3.2. Creating your model
3.2.1. Training
3.2.2. Inference
3.3. The training loop
3.4. Multiple/custom losses
3.5. Optimizers
3.6. Going further
4. Features
4.1. Options
4.1.1. Setting options via config file
4.2. Model wrapping functions
4.2.1. poptorch.trainingModel
4.2.2. poptorch.inferenceModel
4.2.3. poptorch.PoplarExecutor
4.2.4. poptorch.isRunningOnIpu
4.3. Error handling
4.3.1. Recoverable runtime errors
4.3.2. Unrecoverable runtime errors
4.3.3. Application and other errors
4.4. Multi-IPU execution strategies
4.4.1. Annotations
Model partitioning using blocks
poptorch.Stage and poptorch.AutoStage
poptorch.Stage
poptorch.AutoStage
poptorch.Phase
Advanced annotation with strings
4.4.2. Available execution strategies
Pipelined execution
Sharded execution
Phased execution
Serial phased execution
Parallel phased execution
poptorch.Liveness
4.5. Optimizers
4.5.1. Loss scaling
4.5.2. Velocity scaling (SGD combined variant only)
4.5.3. Accumulation types
4.5.4. Constant attributes
4.5.5. Reading and writing optimizer state
4.6. PopTorch ops
4.6.1. poptorch.ctc_beam_search_decoder
4.6.2. poptorch.ipu_print_tensor
4.6.3. poptorch.identity_loss
4.6.4. poptorch.MultiConv
4.6.5. poptorch.nop
4.6.6. poptorch.dynamic_slice
4.6.7. poptorch.serializedMatMul
4.6.8. poptorch.set_available_memory
4.6.9. Miscellaneous functions
4.7. 16-bit float support
4.8. Automatic mixed-precision casting
4.9. PyTorch buffers
4.10. Creating custom ops
4.10.1. Implementing the custom op
4.10.2. Make the op available to PyTorch
4.10.3. Passing attributes to the custom op
4.11. Precompilation and caching
4.11.1. Caching
4.11.2. Precompilation
4.12. Environment variables
4.12.1. Logging level
4.12.2. Profiling
4.12.3. IPU Model
4.12.4. Wait for an IPU to become available
4.12.5. Enable executable caching
5. Efficient data batching
5.1. poptorch.DataLoader
5.2. poptorch.AsynchronousDataAccessor
5.2.1. Rebatching iterable datasets
5.3. poptorch.Options.deviceIterations
5.4. poptorch.Options.replicationFactor
5.5. poptorch.Options.Training.gradientAccumulation
5.6. poptorch.Options.outputMode
6. IPU supported operations
6.1. Torch operations
6.1.1. Tensor operations
Creation ops
Indexing, slicing, joining and mutating ops
Random samplers
6.1.2. Math operations
Pointwise ops
Reduction ops
Comparison ops
Other ops
BLAS and LAPACK Operations
6.2. Torch.nn operations
6.2.1. Containers
6.2.2. Convolution layers
6.2.3. Pooling layers
6.2.4. Padding layers
6.2.5. Activations
6.2.6. Normalization layers
6.2.7. Recurrent layers
6.2.8. Linear layers
6.2.9. Dropout
6.2.10. Sparse layers
6.2.11. Loss functions
6.2.12. Vision Layers
6.3. 16-bit float operations
6.4. 16-bit float migration
6.5. Gradient computation control
7. Debugging your model
7.1. Inspecting tensors
7.2. Anchoring tensors
7.3. Retrieving tensors
7.4. Inspecting optimiser state
8. Efficient IPU I/O
8.1. Prefetch and Multibuffering
8.2. Overlapping compute and I/O
9. Examples
9.1. MNIST example
10. Experimental features
10.1. Distributed execution without PopRun
10.2. torch.nn.CTCLoss
11. Legacy tracing frontend
11.1. Dispatcher support
11.2. Constraints when using tracing
11.3. 16-bit float operations when using tracing
11.3.1. Casting
11.3.2. Creation functions
11.3.3. Normalization
11.4. Automatic mixed-precision casting
11.4.1. Custom casting policies
12. API reference
12.1. Options
12.2. Helpers
12.3. PopTorch Ops
12.4. Model wrapping functions
12.5. Parallel execution
12.6. Optimizers
12.7. Data batching
12.8. Enumerations
12.9. Autocasting
13. Index
14. Legal notices
15. Changelog
15.1. v3.0 (Poplar SDK 3.0)
15.1.1. New features
15.1.2. API changes
15.1.3. Bug Fixes
15.2. v2.6 (Poplar SDK 2.6)
15.2.1. New features
15.2.2. API changes
15.2.3. Bug Fixes
15.3. v2.5 (Poplar SDK 2.5)
15.3.1. New features
15.3.2. API changes
15.3.3. Bug Fixes
15.4. v2.4 (Poplar SDK 2.4)
15.4.1. New features
15.4.2. API changes
15.4.3. Bug Fixes
15.5. v2.3 (Poplar SDK 2.3)
15.5.1. New features
15.5.2. Bug Fixes
15.5.3. API changes
15.6. v2.2 (Poplar SDK 2.2)
15.6.1. New features
15.6.2. API changes
15.7. v2.1 (Poplar SDK 2.1)
15.7.1. New features
15.7.2. API changes
15.7.3. Known issues
15.8. v2.0 (Poplar SDK 2.0)
15.8.1. New features
15.8.2. API changes
15.9. v1.0 (Poplar SDK 1.4)
15.9.1. New features
15.9.2. Known issues
15.10. v0.1 (Poplar SDK 1.3)
15.10.1. New features
PyTorch for the IPU: User Guide
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