Class: TensorStream::Evaluator::BaseEvaluator

Inherits:
Object
  • Object
show all
Defined in:
lib/tensor_stream/evaluator/base_evaluator.rb

Overview

Evaluator base class

Direct Known Subclasses

OpenclEvaluator, RubyEvaluator

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(session, _device, thread_pool: nil, log_intermediates: false) ⇒ BaseEvaluator

Returns a new instance of BaseEvaluator.



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 23

def initialize(session, _device, thread_pool: nil, log_intermediates: false)
  @session = session
  @log_intermediates = log_intermediates
  @thread_pool = thread_pool || Concurrent::ImmediateExecutor.new
  @context[:compute_history] = [] if log_intermediates
end

Class Method Details

.default_device ⇒ Object

Select the best device available in the system for this evaluator



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 38

def self.default_device
  Device.new('cpu', :cpu, self)
end

.fetch_device(_query = []) ⇒ Object

Selects the best device with the specified query, query can be evaluator specific



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 45

def self.fetch_device(_query = [])
  Device.new('cpu', :cpu, self)
end

.ops ⇒ Object

gets all supported ops for this Evaluator class



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 101

def self.ops
  @ops ||= {}
end

.query_device(query) ⇒ Object

Select device using uri



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 51

def self.query_device(query)
  return default_device if query.nil? || query == :default

  all_devices = query_supported_devices
  substrs = query.split('/')
  substrs.each do |q|
    components = q.split(':')
    next if components.size.zero?
    if components[0] == 'device' # use tensorflow convention
      device_type = components[1]
      select_index = components[2].to_i

      devices = all_devices.select { |d| d.type == device_type.downcase.to_sym }
      return nil if devices.empty?

      select_index = [devices.size - 1, select_index].min
      return devices[select_index]
    elsif %w[cpu gpu].include?(components[0])
      device_type = components[0].to_sym
      select_index = components[1].to_i

      devices = all_devices.select { |d| d.type == device_type.downcase.to_sym }
      return nil if devices.empty?

      select_index = [devices.size - 1, select_index].min
      return devices[select_index]
    elsif components[0] == 'ts' # tensorstream specific
      evaluator_class = TensorStream::Evaluator.evaluators[components[1]][:class]
      return nil unless self == evaluator_class
      return evaluator_class.fetch_device(components[2..components.size]) if evaluator_class.respond_to?(:fetch_device)
      return nil
    end
  end
end

.query_supported_devices ⇒ Object

Query all supported devices



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 32

def self.query_supported_devices
  [Device.new('cpu', :cpu, self)]
end

.register_op(opcode, options = {}, &block) ⇒ Object

registers an op for the current evaluator class



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 88

def self.register_op(opcode, options = {}, &block)
  @ops ||= {}
  if opcode.is_a?(Array)
    opcode.each do |op|
      @ops[op.to_sym] = { options: options, block: block }
    end
  else
    @ops[opcode.to_sym] = { options: options, block: block }
  end
end

Instance Method Details

#invoke(tensor, execution_context) ⇒ Object

Raises:



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# File 'lib/tensor_stream/evaluator/base_evaluator.rb', line 105

def invoke(tensor, execution_context)
  return eval_tensor(tensor, execution_context) unless tensor.is_a?(Operation)
  raise UnsupportedOp.new(tensor), "op #{tensor.operation} is not yet supported" unless self.class.ops.key?(tensor.operation.to_sym)

  op = self.class.ops[tensor.operation.to_sym]
  op_options = op[:options]

  resolved_inputs = tensor.inputs.map do |i|
    next if i.nil?

    if i.is_a?(Array)
      next i.collect { |sub_item| sub_item.is_a?(Tensor) ? invoke(sub_item, execution_context) : sub_item }
    end

    if !op_options[:noop] && @context[:_cache][:placement][tensor.name] != @context[:_cache][:placement][i.name] # tensor is on another device or evaluator
      cache_key = "#{tensor.graph.object_id}_#{i.name}:#{object_id}"
      next @context[:_cache][cache_key] if @context[:_cache].key?(cache_key)

      result = @session.delegate_to_evaluator(i, @context, execution_context)
      convert_from_buffer(i, result).tap do |buffer|
        @context[:_cache][cache_key] = buffer if i.is_const
      end
    else
      prepare_input(i, execution_context, op_options)
    end
  end

  instance_exec(execution_context, tensor, resolved_inputs, &op[:block])
end