Module: TensorStream::ArrayOpsHelper

Included in:
Evaluator::OpenclEvaluator, Evaluator::RubyEvaluator, OpenCLBuffer
Defined in:
lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb

Overview

varoius utility functions for array processing

Instance Method Summary collapse

Instance Method Details

#broadcast(input_a, input_b) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 37

def broadcast(input_a, input_b)
  sa = shape_eval(input_a)
  sb = shape_eval(input_b)

  return [input_a, input_b] if sa == sb

  # descalar
  if sa.empty?
    input_a = [input_a]
    sa = [1]
  end

  if sb.empty?
    input_b = [input_b]
    sb = [1]
  end

  target_shape = shape_diff(sa, sb)

  if target_shape
    input_b = broadcast_dimensions(input_b, target_shape)
  else
    target_shape = shape_diff(sb, sa)
    raise "Incompatible shapes for op #{shape_eval(input_a)} vs #{shape_eval(input_b)}" if target_shape.nil?

    input_a = broadcast_dimensions(input_a, target_shape)
  end

  [input_a, input_b]
end

#broadcast_dimensions(input, dims = []) ⇒ Object

explicit broadcasting helper



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 69

def broadcast_dimensions(input, dims = [])
  return input if dims.empty?

  d = dims.shift

  if input.is_a?(Array) && (get_rank(input) - 1) == dims.size
    row_to_dup = input.collect do |input|
      broadcast_dimensions(input, dims.dup)
    end

    row_to_dup + Array.new(d) { row_to_dup }.flatten(1)
  elsif input.is_a?(Array)
    Array.new(d) { broadcast_dimensions(input, dims.dup) }
  else
    Array.new(d + 1) { input }
  end
end

#get_rank(value, rank = 0) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 162

def get_rank(value, rank = 0)
  return rank unless value.is_a?(Array)
  return rank + 1 if value.empty?

  get_rank(value[0], rank + 1)
end

#process_function_op(a, op) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 153

def process_function_op(a, op)
  # ruby scalar
  if (a.is_a?(Tensor) && a.shape.rank > 0) || a.is_a?(Array)
    vector_op(a, 0, op)
  else
    op.call(a, 0)
  end
end

#reduced_shape(input_shape, axes) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 26

def reduced_shape(input_shape, axes)
  return [] if axes.nil? # reduce to scalar
  axes = [ axes ] unless axes.is_a?(Array)
  return input_shape if axes.empty?

  axes.each do |dimen|
    input_shape[dimen] = 1
  end
  input_shape
end

#shape_diff(shape_a, shape_b) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 124

def shape_diff(shape_a, shape_b)
  return nil if shape_b.size > shape_a.size

  reversed_a = shape_a.reverse
  reversed_b = shape_b.reverse

  reversed_a.each_with_index.collect do |s, index|
    next s if index >= reversed_b.size
    return nil if reversed_b[index] > s
    s - reversed_b[index]
  end.reverse
end

#slice_tensor(input, start, size) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 4

def slice_tensor(input, start, size)
  return input if size.empty?
  start_index = start.shift
  dimen_size = start_index + size.shift

  input[start_index...dimen_size].collect do |input|
    if input.is_a?(Array)
      slice_tensor(input, start.dup, size.dup)
    else
      input
    end
  end
end

#softmax(arr) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 169

def softmax(arr)
  return arr if arr.empty?

  sum = if !arr[0].is_a?(Array)
    arr.map { |a| Math.exp(a - arr.max) }.reduce(:+)
  end

  arr.collect do |input|
    if input.is_a?(Array)
      softmax(input)
    else
      Math.exp(input - arr.max) / sum
    end
  end
end

#softmax_grad(arr) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 185

def softmax_grad(arr)
  return arr if arr.empty?

  arr.each_with_index.collect do |input, index|
    if input.is_a?(Array)
      softmax_grad(input)
    else
      arr.each_with_index.collect do |input2, index2|
        if index != index2
          -input * input2
        else
          input * (1.0 - input)
        end
      end
    end
  end
end

#tile_arr(input, dimen, multiples) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 137

def tile_arr(input, dimen, multiples)
  t = multiples[dimen]
  if dimen == multiples.size - 1
    return nil if t.zero?
    input * t # ruby array dup
  else
    new_arr = input.collect do |sub|
      tile_arr(sub, dimen + 1, multiples)
    end.compact

    return nil if new_arr.empty?

    new_arr * t
  end
end

#truncate(input, target_shape) ⇒ Object 



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# File 'lib/tensor_stream/evaluator/operation_helpers/array_ops_helper.rb', line 18

def truncate(input, target_shape)
  rank = get_rank(input)
  return input if rank.zero?

  start = Array.new(rank) { 0 }
  slice_tensor(input, start, target_shape)
end

#vector_op(vector, vector2, op = ->(a, b) { a + b }, switch = false, safe = true) ⇒ Object

handle 2 tensor math operations



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

def vector_op(vector, vector2, op = ->(a, b) { a + b }, switch = false, safe = true)
  if get_rank(vector) < get_rank(vector2) # upgrade rank of A
    duplicated = Array.new(vector2.size) do
      vector
    end
    return vector_op(duplicated, vector2, op, switch)
  end

  return op.call(vector, vector2) unless vector.is_a?(Array)

  vector.each_with_index.collect do |input, index|
    next vector_op(input, vector2, op, switch) if input.is_a?(Array) && get_rank(vector) > get_rank(vector2)

    if safe && vector2.is_a?(Array)
      next nil if vector2.size != 1 && index >= vector2.size
    end

    z = if vector2.is_a?(Array)
          if index < vector2.size
            vector2[index]
          else
            raise 'incompatible tensor shapes used during op' if vector2.size != 1
            vector2[0]
          end
        else
          vector2
        end

    if input.is_a?(Array)
      vector_op(input, z, op, switch)
    else
      switch ? op.call(z, input) : op.call(input, z)
    end
  end.compact
end