Class: TensorStream::Evaluator::BaseEvaluator

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

Overview

Evaluator base class

Base class to be used by all tensor_stream evaluators, provides support functions

Direct Known Subclasses

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 27

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 42

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 49

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 107

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 55

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 36

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 94

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 111

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 { |i|
    next if i.nil?
    next i if op_options[:noop]

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

    global_eval(tensor, i, execution_context, op_options)
  }

  start_time = if profile_enabled?
    time = Time.now
    time.to_i * (10**9) + time.nsec
  end

  instance_exec(execution_context, tensor, resolved_inputs, &op[:block]).tap do |result|
    if profile_enabled?
      time = Time.now
      end_time = time.to_i * (10**9) + time.nsec
      @context[:profile] ||= {step: 0, operations: {}}
      @context[:profile][:step] += 1
      @context[:profile][:operations][tensor.name] = {op: tensor.operation,
                                                       step: @context[:profile][:step],
                                                       eval_time: end_time - start_time,
                                                       shape: tensor.shape ? tensor.shape.shape : nil,
                                                       data_type: tensor.data_type,
                                                       tensor: tensor,}
    end
  end
end