TensorFlow - Marco Serafini · TensorFlow •Dataflow graph of operators, but not a DAG •Loops...
Transcript of TensorFlow - Marco Serafini · TensorFlow •Dataflow graph of operators, but not a DAG •Loops...
TensorFlow
Marco Serafini
COMPSCI 532Lecture 20
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Motivations• DistBelief: Previous iteration
• Parameter server• Limitations:
• Monolithic layers, difficult to define new ones• Difficult to offload computation with complex dependencies to parameter servers
• E.g. Apply updates based on gradients accumulated over multiple iterations• Fixed execution pattern
• Read data, compute loss function (forward pass), compute gradients for parameters (backward pass), write gradients to parameter server
• Not optimized for single workstations and GPUs
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TensorFlow• Dataflow graph of operators, but not a DAG
• Loops and conditionals• Deferred (lazy) execution
• Enables optimizations, e.g. pipelining with GPU kernels• Composable basic operators
• Matrix multiplication, convolution, ReLu• Concept of devices
• CPUs, GPUs, mobile devices• Different implementations of the operators
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Difference with Parameter Server• Parameter server
• Separate worker nodes and parameter nodes• Different interfaces
• TensorFlow: only tasks• Shared parameters (called operators): variables and queues• Tasks managing them are called PS tasks • PS task are regular tasks: they can run arbitrary operators• Uniform programming interface
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Example
b_1stateful operators
stateful operators
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Example• Data-parallel training looks like this
Stateful queues
Stateful variables
Concurrent steps for data parallelism
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Dataflow Graph• Vertex: unit of local computation
• Called operation in TensorFlow• Edges: inputs and outputs of computation
• Values along edges are called tensors
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Tensors• Edges in dataflow graph• Data flowing among operators• Format
• n-dimensional arrays• Elements have primitive types (including byte arrays)
• Tensors are dense• All elements are represented• User must find ways to encode sparse data efficiently
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Operations• Vertices in dataflow graph• State is encapsulated in operations
• Variables and queues• Access to state (and tensors)
• Variable op: Returns unique reference handle• Read op: Take reference handle, produce value of variable• Write ops: Take reference and value and update.
• Queues are also stateful operators• Get reference handle, modify through operations• Blocking semantics, backpressure, synchronization
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Execution Model• Step: client executes a subgraph by indicating:
• Edges to feed the subgraph with input tensors• Edges to fetch the output tensors• Runtime prunes the subgraph to remove unnecessary steps
• Subgraphs are run asynchronously by default• Can execute multiple partial, concurrent subgraphs
• Example: concurrent batches for data-parallel training
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Distributed Execution• Tasks: named processes that send messages
• PS tasks: store variables, but can also run computations• Worker tasks: the rest• Note: “informal” categories, not enforced by TensorFlow
• Devices: CPU, GPU, TPU, mobile, …• CPU is the host device• Device executes kernel for each operation assigned to it
• Same operation (e.g. matrix multiplication) has different kernels for different devices• Requirements for a device
• Must accept kernel for execution• Must allocate memory for inputs and outputs• Must transfer data to and from host memory
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Distributed Execution• Each operation
• Resides on a device• Corresponds to one or more kernel• More kernel can be specialized for different devices
• Operations are executed within a task
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Distributed Scheduling• TensorFlow runtime places operations on devices
• Implicit constraints: stateful operation on same device as state• Explicit constraints: dictated by the user• Optimal placement still open question
• Obtain per-device subgraphs• All operations assigned to device• Send and Receive operations to replace edges• Specialized per-device implementations
• CPU – GPU: CUDA memory copy• Across tasks: TCP or RDMA
• Placement preserved throughout session
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Dynamic Control Flow• How do enable dynamic control flow with static graph?• Example: recurrent neural network
• Train network for sequence of variable length without unrolling• Conditional: Switch and Merge
SwitchData input
Control input
op
op
op
op
Merge
input
dead
Output one non-dead
input
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Loops• Uses three additional operators
EnterData input op op Exit
NextIteration
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Scaling to Large Models• Model parallelism
• Avoids moving terabytes of parameters every time• Operations (typically implemented by library)
• Gather: reads tensor data from shard and computes• Part: Partitions the input across shards of parameters• Stitch: Aggregates all partitions
parameters
inputs
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Fault Tolerance• Long running tasks face failures and pre-emption
• Sometimes run at night on idle machines• Small operations, no need to tolerate individual failures
• Even RDDs are overkill• User use Save operation for checkpointing
• Each variable in a task connected to same save for batching• Asynchronous, not consistent
• Restore operation executed by clients at startup• Other use cases: transfer learning
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Coordination• TensorFlow is asynchronous by default
• Stochastic Gradient Descent tolerates asynchrony• Asynchrony increases throughput
• But synchrony has benefits• Using stale parameters slows down convergence
• System must support user-defined synchrony
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Synchronous Coordination• Use blocking queues for synchrony • Redundant tasks for stragglers
blocking queues on inputs and outputs
different colors = different versions
of parameter proactive (not reactive) backup
workers
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Implementation• Distributed master
• Obtain subgraphs for each participating device
• Dataflow executor• Handles requests from master • Schedules the execution of the kernels of local subgraph• Data transfer to device and over network
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Single-Machine Performance• Similar to COST analysis
• Comparison with single-server (not single-threaded) tools• Four convolutional models using one GPU
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Synchronous Microbenchmarks• Null training steps• Sparse performance is close to optimal (scalar)
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Scalability• Scalability bound by access to PS tasks (7 in the exp)• Synchronous coordination scales well• Backups are beneficial (but expensive way to do FT)