Class: TensorStream::Session

Inherits:
Object
  • Object
show all
Includes:
StringHelper
Defined in:
lib/tensor_stream/session.rb

Overview

TensorStream class that defines a session

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Methods included from StringHelper

#camelize, #constantize, #symbolize_keys, #underscore

Constructor Details

#initialize(evaluator = nil, thread_pool_class: Concurrent::ImmediateExecutor, log_device_placement: false, profile_enabled: false, evaluator_options: {}) ⇒ Session

Returns a new instance of Session.



9
10
11
12
13
14
15
16
17
18
 
# File 'lib/tensor_stream/session.rb', line 9

def initialize(evaluator = nil, thread_pool_class: Concurrent::ImmediateExecutor, log_device_placement: false, profile_enabled: false, evaluator_options: {})
  @thread_pool = thread_pool_class.new
  @closed = false
  @session_cache = {}
  @randomizer = {}
  @log_device_placement = log_device_placement
  @evaluator_options = evaluator_options.merge(profile_enabled: profile_enabled)
  get_evaluator_classes(evaluator)
  @evaluators = {}
end

Instance Attribute Details

#closed ⇒ Object (readonly)

Returns the value of attribute closed.



6
7
8
 
# File 'lib/tensor_stream/session.rb', line 6

def closed
  @closed
end

#last_session_context ⇒ Object (readonly)

Returns the value of attribute last_session_context.



6
7
8
 
# File 'lib/tensor_stream/session.rb', line 6

def last_session_context
  @last_session_context
end

#randomizer ⇒ Object

Returns the value of attribute randomizer.



7
8
9
 
# File 'lib/tensor_stream/session.rb', line 7

def randomizer
  @randomizer
end

#session_cache ⇒ Object (readonly)

Returns the value of attribute session_cache.



6
7
8
 
# File 'lib/tensor_stream/session.rb', line 6

def session_cache
  @session_cache
end

#target ⇒ Object (readonly)

Returns the value of attribute target.



6
7
8
 
# File 'lib/tensor_stream/session.rb', line 6

def target
  @target
end

Class Method Details

.default_session ⇒ Object 



38
39
40
 
# File 'lib/tensor_stream/session.rb', line 38

def self.default_session
  @session ||= Session.new
end

Instance Method Details

#assign_evaluator(tensor) ⇒ Object 



146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
 
# File 'lib/tensor_stream/session.rb', line 146

def assign_evaluator(tensor)
  device = @evaluator_classes.map { |klass|
    next nil if tensor.is_a?(Operation) && !klass.ops.include?(tensor.operation.to_sym)
    next klass.default_device if tensor.device.nil?

    klass.query_device(tensor.device)
  }.compact.first

  raise "no evaluator available to execute #{tensor.operation}" if device.nil?

  key = "#{device.evaluator}/#{device.name}"
  if @evaluators.key?(key)
    @evaluators[key]
  else
    @evaluators[key] = [device, device.evaluator.new(self, device)]
  end
end

#clear_session_cache ⇒ Object 



34
35
36
 
# File 'lib/tensor_stream/session.rb', line 34

def clear_session_cache
  @session_cache = {}
end

#close ⇒ Object 



100
101
102
103
104
105
 
# File 'lib/tensor_stream/session.rb', line 100

def close
  # unlink resources to save memory
  @last_session_context = nil
  @session_cache = {}
  @closed = true
end

#closed? ⇒ Boolean

Returns:



107
108
109
 
# File 'lib/tensor_stream/session.rb', line 107

def closed?
  @closed
end

#delegate_to_evaluator(tensor_arr, session_context, context) ⇒ Object 



132
133
134
135
136
137
138
139
140
141
142
143
144
 
# File 'lib/tensor_stream/session.rb', line 132

def delegate_to_evaluator(tensor_arr, session_context, context)
  if tensor_arr.is_a?(Array)
    tensor_arr.collect do |tensor|
      if tensor.is_a?(Array)
        delegate_to_evaluator(tensor, session_context, context)
      else
        run_with_session_context(tensor, session_context, context)
      end
    end
  else
    run_with_session_context(tensor_arr.op, session_context, context)
  end
end

#dump_internal_ops(tensor) ⇒ Object 



111
112
113
 
# File 'lib/tensor_stream/session.rb', line 111

def dump_internal_ops(tensor)
  dump_ops(tensor, ->(_k, n) { n.is_a?(Tensor) && n.internal? })
end

#dump_ops(tensor, selector) ⇒ Object 



119
120
121
122
123
124
125
126
 
# File 'lib/tensor_stream/session.rb', line 119

def dump_ops(tensor, selector)
  graph = tensor.graph
  graph.nodes.select { |k, v| selector.call(k, v) }.collect { |k, node|
    next unless @last_session_context[node.name]

    "#{k} #{node.to_math(true, 1)} = #{@last_session_context[node.name]}"
  }.compact
end

#dump_user_ops(tensor) ⇒ Object 



115
116
117
 
# File 'lib/tensor_stream/session.rb', line 115

def dump_user_ops(tensor)
  dump_ops(tensor, ->(_k, n) { n.is_a?(Tensor) && !n.internal? })
end

#get_evaluator_classes(evaluators) ⇒ Object 



20
21
22
23
24
25
26
27
28
29
30
31
32
 
# File 'lib/tensor_stream/session.rb', line 20

def get_evaluator_classes(evaluators)
  @evaluator_classes = if evaluators.is_a?(Array)
    if evaluators.empty?
      TensorStream::Evaluator.default_evaluators
    else
      evaluators.collect { |name| Object.const_get("TensorStream::Evaluator::#{camelize(name.to_s)}") }
    end
  elsif evaluators.nil?
    TensorStream::Evaluator.default_evaluators
  else
    [Object.const_get("TensorStream::Evaluator::#{camelize(evaluators.to_s)}")]
  end
end

#graph_ml(tensor, filename) ⇒ Object 



128
129
130
 
# File 'lib/tensor_stream/session.rb', line 128

def graph_ml(tensor, filename)
  TensorStream::Graphml.new(self).serialize(tensor, filename)
end

#list_devices ⇒ Object 



92
93
94
95
96
97
98
 
# File 'lib/tensor_stream/session.rb', line 92

def list_devices
  TensorStream::Evaluator.evaluators.collect { |_k, v|
    v[:class].query_supported_devices.collect do |device|
      device
    end
  }.flatten
end

#run(*args) ⇒ Object 



42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
 
# File 'lib/tensor_stream/session.rb', line 42

def run(*args)
  options = if args.last.is_a?(Hash)
    args.pop
  else
    {}
  end

  @evaluator_options[:thread_pool] = @thread_pool
  @evaluator_options[:log_intermediates] = options[:log_intermediates]

  context = {
    _cache: @session_cache,
    _options: options.merge(@evaluator_options),
    profile: {step: 0, operations: {}},
  }

  # scan for placeholders and assign value
  options[:feed_dict]&.each_key do |k|
    if k.is_a?(Placeholder)
      context[k.name.to_sym] = options[:feed_dict][k]
    elsif k.is_a?(String)
      target_graph = args[0].graph
      node = target_graph.get_node(k)
      raise "Cannot find placeholder with the name of #{k}" if node.operation != :placeholder

      context[k.to_sym] = options[:feed_dict][k]
    elsif k.is_a?(Operation) && k.operation == :placeholder
      context[k.name.to_sym] = options[:feed_dict][k]
    else
      raise "Invalid placeholder type passed key must be a string or a placeholder type"
    end
  end

  args.each { |t| prepare_evaluators(t, context) }
  @last_session_context = context

  if @log_device_placement
    context[:_cache][:placement].each do |k, v|
      puts "#{k} : #{v[0].name}"
    end
  end
  result = args.collect { |e|
    next e.value if e.is_a?(Tensor) && e.is_const && e.value

    value = delegate_to_evaluator(e, context, {})
    recursive_eval(value)
  }
  args.size == 1 ? result.first : result
end