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Targeting the IPU from TensorFlow 2
Version: 2.2.0
  • 1. Introduction
    • 1.1. Document overview
  • 2. Targeting the Poplar XLA device
    • 2.1. Supported types
    • 2.2. Device selection
    • 2.3. Configuring system options
      • 2.3.1. TF_POPLAR_FLAGS environment variable
    • 2.4. Supported operations
    • 2.5. Unsupported operations
    • 2.6. Error Handling
      • 2.6.1. Construction and compilation errors
      • 2.6.2. Runtime errors
  • 3. Support for TensorFlow 2
    • 3.1. IPUStrategy
    • 3.2. Execution modes
      • 3.2.1. Graph mode with @tf.function
      • 3.2.2. Eager mode
    • 3.3. On-device loops
  • 4. Keras with IPUs
    • 4.1. Single IPU models
    • 4.2. Using steps_per_execution
    • 4.3. Gradient accumulation
    • 4.4. Model parallelism
      • 4.4.1. Sequential model
      • 4.4.2. Functional model
        • Pipelining a model you are writing yourself
        • Pipelining an existing functional model
    • 4.5. Automatic data parallelism
    • 4.6. Asynchronous callbacks
    • 4.7. Porting models from TensorFlow 2.1
      • 4.7.1. TF2.1
      • 4.7.2. TF2.4
    • 4.8. Implementation details
  • 5. Compiling and pre-compiling executables
    • 5.1. Caching of compiled executables
    • 5.2. Pre-compiling executables
      • 5.2.1. Unsupported Operations
  • 6. Training a model
    • 6.1. Training loops, data sets and feed queues
    • 6.2. Accessing outfeed queue results during execution
    • 6.3. Replicated graphs
      • 6.3.1. Selecting the number of replicas
      • 6.3.2. Performing parameter updates
    • 6.4. Pipelined training
      • 6.4.1. Sequential scheduling
      • 6.4.2. Interleaved scheduling
      • 6.4.3. Grouped scheduling
      • 6.4.4. Pipeline stage inputs and outputs
      • 6.4.5. Applying an optimiser to the graph
      • 6.4.6. Device mapping
      • 6.4.7. Concurrent pipeline stages
    • 6.5. Gradient accumulation
      • 6.5.1. Optimizers
      • 6.5.2. Pipelining
      • 6.5.3. Accumulation data type
    • 6.6. Optimizer state offloading
    • 6.7. Dataset benchmarking
      • 6.7.1. Accessing the JSON data
  • 7. Efficient IPU I/O
    • 7.1. Prefetch elements
    • 7.2. I/O Tiles
  • 8. Example using IPUEstimator
  • 9. Example using IPUPipelineEstimator
  • 10. Distributed training
    • 10.1. Example using IPUMultiWorkerStrategy
      • 10.1.1. The input function
      • 10.1.2. The model function
      • 10.1.3. Cluster definition
      • 10.1.4. Complete example
    • 10.2. Distributed training with Horovod
    • 10.3. Launching Horovod training
    • 10.4. Complete Horovod example
  • 11. Half-precision floating point and stochastic rounding
    • 11.1. Controlling the half-precision floating-point unit
    • 11.2. Resetting the global random number seed
    • 11.3. Debugging numerical issues
  • 12. IPU-optimised operations
    • 12.1. LSTM and GRU
    • 12.2. Dropout
    • 12.3. Embedding lookup
    • 12.4. Group normalisation
    • 12.5. Instance normalisation
    • 12.6. Layer normalisation
    • 12.7. GeLU activation
    • 12.8. Sequence slice
    • 12.9. Histogram
  • 13. IPU Outlined Functions
    • 13.1. Usage
    • 13.2. Examples
      • 13.2.1. Models with common structures
      • 13.2.2. Serializing large operations
  • 14. Writing custom operations
    • 14.1. Custom operation on the IPU
      • 14.1.1. Building the Poplar graph
      • 14.1.2. Gradient builders
      • 14.1.3. Metadata
      • 14.1.4. Compiling the IPU code
        • API level
        • PopLibs library code
        • Compiling the library file
      • 14.1.5. Using the custom op in TensorFlow
      • 14.1.6. Tensor allocation
      • 14.1.7. Examples
        • In-place operations
        • Operation attributes
        • Custom codelet
    • 14.2. Custom host CPU operations
      • 14.2.1. Gradient callback
  • 15. IPU host embeddings
    • 15.1. Usage
    • 15.2. Example
    • 15.3. Experimental functionality: IPU embeddings in remote buffers
      • 15.3.1. Partitioning strategies
        • Token strategy
        • Encoding strategy
        • Choosing a strategy for your application
  • 16. Retrieving information about compilation and execution
    • 16.1. TensorFlow options for reporting
    • 16.2. Dumping auxiliary Poplar information
      • 16.2.1. Poplar vertex graph
      • 16.2.2. Poplar interval report
    • 16.3. XLA graph file naming
  • 17. API changes
    • 17.1. Release 2.2
      • 17.1.1. Breaking changes
        • C++ Poplar TensorFlow libraries are private by default
        • Reports removed from ipu events
        • TensorFlow 2.1 to TensorFlow 2.4 Migration
      • 17.1.2. Non-breaking changes
        • IPULoggingTensorHook replication_factor deprecated
        • IPUInfeedQueue/IPUOutfeedQueue/IPULoggingTensorHook feed_name deprecated
        • Change of output location for profiling information
    • 17.2. Release 2.1
      • 17.2.1. Breaking changes
        • IPUPipelineEstimator change
        • Autosharding removed
        • IPU config change
        • IPU Keras changes [TensorFlow 2]
      • 17.2.2. Non-breaking changes
        • Recompute suggestions deprecated
        • IPUInfeedQueue/IPUOutfeedQueue replication_factor deprecated
        • IPUInfeedQueue data_to_prefetch deprecated
        • IPUOutfeedQueue data_to_prefetch deprecated
        • CTC loss ops deprecated
        • New configuration API
        • Support for grouped collectives
        • Environment variable changes
    • 17.3. Release 2.0
      • 17.3.1. Breaking changes
      • 17.3.2. Non-breaking changes
        • IPUPipelineEstimator change
        • Autosharding deprecated
        • IPU config change
        • IPU Keras changes [TensorFlow 2]
  • 18. Python API
    • 18.1. Operations and utilities related to the Graphcore IPU
    • 18.2. Distribution strategy for a single system
    • 18.3. Compiler interface
    • 18.4. Scoping contexts
    • 18.5. Infeed queue
    • 18.6. Outfeed queue
    • 18.7. General utilities
    • 18.8. Configuration utilities
    • 18.9. Looping utilities
    • 18.10. Distributed training
    • 18.11. Horovod
    • 18.12. Datasets
      • 18.12.1. Dataset benchmarking
      • 18.12.2. Dataset wrappers
    • 18.13. Estimators
      • 18.13.1. IPUEstimator
      • 18.13.2. IPUPipelineEstimator
      • 18.13.3. Run configs
      • 18.13.4. Session run hooks
    • 18.14. Keras
      • 18.14.1. IPU specific Keras extensions
    • 18.15. Keras layers
      • 18.15.1. Keras layer specializations for the Graphcore IPU
    • 18.16. Keras losses
      • 18.16.1. Keras loss functions for the Graphcore IPU
    • 18.17. Keras optimizers
      • 18.17.1. Keras Optimizer wrappers for the Graphcore IPU
    • 18.18. Operators
      • 18.18.1. Custom operations
      • 18.18.2. Functional operators
      • 18.18.3. Image operations
      • 18.18.4. Graphcore utility operations
      • 18.18.5. IPU specific maths operations
      • 18.18.6. Pipelining operators
      • 18.18.7. Popnn primitive neural network operators
      • 18.18.8. Popnn normalization operators
      • 18.18.9. Popnn recurrent neural network operators
      • 18.18.10. Popops all to all and all gather operators
      • 18.18.11. Popops cross replica operators
      • 18.18.12. Popops embedding operators
      • 18.18.13. Popops reduce scatter operator
      • 18.18.14. Poprand operators
      • 18.18.15. Utility operations to be used in replicated mode
      • 18.18.16. Slicing operators
      • 18.18.17. Statistics operators
      • 18.18.18. Summary operations for IPUs
    • 18.19. Optimisers
      • 18.19.1. Optimizer classes for the Graphcore IPU
    • 18.20. Sharding
      • 18.20.1. Utility functions for sharding graphs
  • 19. TensorFlow operators supported by the IPU
  • 20. Resources
    • 20.1. Graphcore
    • 20.2. TensorFlow
    • 20.3. Other
  • 21. Index
  • 22. Trademarks & copyright
Targeting the IPU from TensorFlow 2

Revision 4c70543b.