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Targeting the IPU from TensorFlow 2
Version: 2.5.1
  • 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
      • 4.4.3. Model subclass
        • Pipelining a model you are writing yourself
        • Pipelining an existing model
      • 4.4.4. Pipelining options
    • 4.5. Automatic data parallelism
    • 4.6. Asynchronous callbacks
    • 4.7. Configuring Infeeds and Outfeed
    • 4.8. Saving and loading Keras models
    • 4.9. 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. Optional simplification of infeeds and outfeeds
    • 6.3. Accessing outfeed queue results during execution
    • 6.4. Replicated graphs
      • 6.4.1. Selecting the number of replicas
      • 6.4.2. Performing parameter updates
    • 6.5. Pipelined training
      • 6.5.1. Grouped scheduling
      • 6.5.2. Interleaved scheduling
      • 6.5.3. Sequential scheduling
      • 6.5.4. Pipeline stage inputs and outputs
      • 6.5.5. Applying an optimiser to the graph
      • 6.5.6. Device mapping
      • 6.5.7. Concurrent pipeline stages
    • 6.6. Gradient accumulation
      • 6.6.1. Optimizers
      • 6.6.2. Pipelining
      • 6.6.3. Accumulation data type
    • 6.7. Optimizer state offloading
    • 6.8. Dataset benchmarking
      • 6.8.1. 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. Image operations
    • 12.2. Matmul serialisation
    • 12.3. LSTM and GRU
    • 12.4. Dropout
    • 12.5. Embedding lookup
    • 12.6. Group normalisation
    • 12.7. Instance normalisation
    • 12.8. Layer normalisation
    • 12.9. GeLU activation
    • 12.10. Sequence slice
    • 12.11. 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. IPU embedded application runtime
    • 16.1. Usage
    • 16.2. Pipelining and I/O tiles
      • 16.2.1. Parallel requests
      • 16.2.2. Timeout
      • 16.2.3. Engine restarts
    • 16.3. Example
    • 16.4. Error Handling
      • 16.4.1. Runtime errors
  • 17. Retrieving information about compilation and execution
    • 17.1. TensorFlow options for reporting
    • 17.2. XLA graph file naming
  • 18. IPU TensorFlow Addons
    • 18.1. Introduction
    • 18.2. Keras layers
      • 18.2.1. IPU implementations of standard Keras layers
      • 18.2.2. Layers without upstream equivalents
      • 18.2.3. Code example
    • 18.3. Optimizers
  • 19. TensorFlow API changes
    • 19.1. Release 2.5
      • 19.1.1. Breaking changes
        • IPU Keras changes
        • Removal of deprecated APIs
        • Other
      • 19.1.2. Non-breaking changes
        • Deprecated layers
        • Deprecated pipeline and gradient_accumulation options
        • RNN available_memory_proportion_fwd/available_memory_proportion_bwd deprecated
    • 19.2. Release 2.4
      • 19.2.1. Breaking changes
        • Summary ops
        • Removal of deprecated members
      • 19.2.2. Non-breaking changes
    • 19.3. Release 2.3
      • 19.3.1. Breaking changes
        • Custom user op metadata interface updates
        • The verified transfers feature has been removed
      • 19.3.2. Non-breaking changes
    • 19.4. Release 2.2
      • 19.4.1. Breaking changes
        • C++ Poplar TensorFlow libraries are private by default
        • Reports removed from ipu events
        • TensorFlow 2.1 to TensorFlow 2.4 Migration
      • 19.4.2. Non-breaking changes
        • IPULoggingTensorHook replication_factor deprecated
        • IPUInfeedQueue/IPUOutfeedQueue/IPULoggingTensorHook feed_name deprecated
        • Change of output location for profiling information
        • Warning when epsilon value is too low
    • 19.5. Release 2.1
      • 19.5.1. Breaking changes
        • IPUPipelineEstimator change
        • Autosharding removed
        • Old IPU option configuration API changes
        • IPU Keras changes [TensorFlow 2]
      • 19.5.2. 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
    • 19.6. Release 2.0
      • 19.6.1. Breaking changes
      • 19.6.2. Non-breaking changes
        • IPUPipelineEstimator change
        • Autosharding deprecated
        • IPU config change
        • IPU Keras changes [TensorFlow 2]
  • 20. TensorFlow Python API
    • 20.1. Operations and utilities related to the Graphcore IPU
    • 20.2. Distribution strategy for a single system
    • 20.3. Compiler interface
    • 20.4. Scoping contexts
    • 20.5. Infeed queue
    • 20.6. Outfeed queue
    • 20.7. General utilities
    • 20.8. Configuration utilities
    • 20.9. Looping utilities
    • 20.10. Distributed training
    • 20.11. Horovod
    • 20.12. Serving utilities
    • 20.13. Datasets
      • 20.13.1. Dataset benchmarking
      • 20.13.2. Dataset wrappers
    • 20.14. Estimators
      • 20.14.1. IPUEstimator
      • 20.14.2. IPUPipelineEstimator
      • 20.14.3. Run configs
      • 20.14.4. Session run hooks
    • 20.15. Keras
      • 20.15.1. IPU specific Keras extensions
    • 20.16. Keras layers
      • 20.16.1. Keras layer specializations for the Graphcore IPU
    • 20.17. Keras losses
      • 20.17.1. Keras loss functions for the Graphcore IPU
    • 20.18. Keras optimizers
      • 20.18.1. Keras Optimizer wrappers for the Graphcore IPU
    • 20.19. Operators
      • 20.19.1. Control flow operations.
      • 20.19.2. Custom operations
      • 20.19.3. Functional operators
      • 20.19.4. Image operations
      • 20.19.5. Graphcore utility operations
      • 20.19.6. IPU specific maths operations
      • 20.19.7. Pipelining operators
      • 20.19.8. Popnn primitive neural network operators
      • 20.19.9. Popnn normalization operators
      • 20.19.10. Popnn recurrent neural network operators
      • 20.19.11. Popops all to all and all gather operators
      • 20.19.12. Popops cross replica operators
      • 20.19.13. Popops embedding operators
      • 20.19.14. Popops reduce scatter operator
      • 20.19.15. Popops within replica operators
      • 20.19.16. Poprand operators
      • 20.19.17. Utility operations to be used in replicated mode
      • 20.19.18. Slicing operators
      • 20.19.19. Statistics operators
      • 20.19.20. Embedded application runtime
    • 20.20. Optimisers
      • 20.20.1. Optimizer classes for the Graphcore IPU
    • 20.21. Sharding
      • 20.21.1. Utility functions for sharding graphs
  • 21. TensorFlow operators supported by the IPU
  • 22. IPU TensorFlow Addons API changes
    • 22.1. Release 2.5
      • 22.1.1. Non-breaking changes
        • RNN available_memory_proportion_fwd/available_memory_proportion_bwd deprecated
    • 22.2. Release 2.4
  • 23. IPU TensorFlow Addons Python API
    • 23.1. Keras Layers
      • 23.1.1. Keras layers made for IPU TensorFlow
    • 23.2. Keras Optimizers
      • 23.2.1. Keras optimizers made for IPU TensorFlow
    • 23.3. Legacy TensorFlow Layers
      • 23.3.1. TensorFlow layers made for IPU TensorFlow
    • 23.4. Legacy TensorFlow Optimizers
      • 23.4.1. Optimizers made for IPU TensorFlow
  • 24. Resources
    • 24.1. Graphcore
    • 24.2. TensorFlow
    • 24.3. Other
  • 25. Trademarks & copyright
Targeting the IPU from TensorFlow 2

Revision d299c059.