Module: Torch::Native::Generator

Defined in:
lib/torch/native/generator.rb

Class Method Summary collapse

Class Method Details

.generate_cpp_functions ⇒ Object 



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# File 'lib/torch/native/generator.rb', line 10

def generate_cpp_functions
  functions = grouped_functions
  generate_cpp_file("torch", :define_singleton_method, functions[:torch])
  generate_cpp_file("tensor", :define_method, functions[:tensor])
  generate_cpp_file("nn", :define_singleton_method, functions[:nn])
end

.grouped_functions ⇒ Object 



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# File 'lib/torch/native/generator.rb', line 17

def grouped_functions
  functions = functions()

  # remove functions
  skip_binding = ["unique_dim_consecutive", "einsum", "normal"]
  skip_args = ["bool[3]", "Dimname", "ScalarType", "MemoryFormat", "Storage", "ConstQuantizerPtr"]
  functions.reject! { |f| f.ruby_name.start_with?("_") || f.ruby_name.end_with?("_backward") || skip_binding.include?(f.ruby_name) }
  todo_functions, functions =
    functions.partition do |f|
      f.args.any? do |a|
        a[:type].include?("?") && !["Tensor?", "Generator?", "int?"].include?(a[:type]) ||
        skip_args.any? { |sa| a[:type].include?(sa) }
      end
    end

  # generate additional functions for optional arguments
  # there may be a better way to do this
  optional_functions, functions = functions.partition { |f| f.args.any? { |a| a[:type] == "int?" } }
  optional_functions.each do |f|
    next if f.ruby_name.start_with?("avg_pool") || f.ruby_name == "cross"
    opt_args = f.args.select { |a| a[:type] == "int?" }
    if opt_args.size == 1
      sep = f.name.include?(".") ? "_" : "."
      f1 = Function.new(f.function.merge("func" => f.func.sub("(", "#{sep}#{opt_args.first[:name]}(").gsub("int?", "int")))
      # TODO only remove some arguments
      f2 = Function.new(f.function.merge("func" => f.func.sub(/, int\?.+\) ->/, ") ->")))
      functions << f1
      functions << f2
    end
  end

  # todo_functions.each do |f|
  #   puts f.func
  #   puts
  # end

  nn_functions, other_functions = functions.partition { |f| f.python_module == "nn" }
  torch_functions = other_functions.select { |f| f.variants.include?("function") }
  tensor_functions = other_functions.select { |f| f.variants.include?("method") }

  {torch: torch_functions, tensor: tensor_functions, nn: nn_functions}
end