Module: TensorStream::OpenCLHelpers::RandomOps

Included in:
Evaluator::OpenclEvaluator
Defined in:
lib/tensor_stream/opencl/random_ops.rb

Overview

Collection of math functions for interfacing with OpenCL kernels

Constant Summary collapse

RAND_TABLE_SIZE = 
1024 * 1024

Class Method Summary collapse

Class Method Details

.included(klass) ⇒ Object 



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# File 'lib/tensor_stream/opencl/random_ops.rb', line 7

def RandomOps.included(klass)
  klass.class_eval do
    register_op :random_uniform do |context, tensor, inputs|
      maxval = tensor.options.fetch(:maxval, 1)
      minval = tensor.options.fetch(:minval, 0)
      seed = tensor.options[:seed]

      rand_buffer = @context[:_cache][:_cl_buffers]["_rand"] ||= begin
        @context[:_cache][:_cl_buffers]["_rand_seed_ptr"] = 0
        random = _get_randomizer(tensor, seed)
        rand_table = RAND_TABLE_SIZE.times.map { random.rand }
        convert_to_opencl(rand_table, [RAND_TABLE_SIZE], data_type: tensor.data_type, name: "rand_#{tensor.data_type}")
      end
      @context[:_cache][:_cl_buffers]["_rand_seed_ptr"] ||= 0

      seed_ptr = @context[:_cache][:_cl_buffers]["_rand_seed_ptr"]

      shape = read_final_result(complete_eval(inputs[0], context))
      shape = shape || tensor.shape.shape
      workgroup = [shape.reduce(:*) || 1 ] 
      cl_seed_ptr = OpenCL::Int1.new(seed_ptr)
      cl_min = OpenCL::Float1.new(minval)
      cl_max = OpenCL::Float1.new(maxval)

      @context[:_cache][:_cl_buffers]["_rand_seed_ptr"] = (seed_ptr + (shape.reduce(:*) || 0) ) % RAND_TABLE_SIZE
      buffer = _create_result_buffer(tensor.data_type, shape, tensor.name)
      buffer.op = _cl_program("random_uniform", dtype: tensor.data_type, tsize: RAND_TABLE_SIZE).send(:"random_uniform_#{tensor.data_type}", _opencl_queue, workgroup, cl_seed_ptr, cl_min, cl_max, rand_buffer.cl_buffer, buffer.cl_buffer)
      buffer
    end

    def _get_randomizer(tensor, seed)
      if tensor.graph.random_seed && seed
        Random.new(tensor.graph.random_seed ^ seed)
      elsif tensor.graph.random_seed
        @session.randomizer[tensor.graph.object_id] ||= Random.new(tensor.graph.random_seed)
        @session.randomizer[tensor.graph.object_id]
      elsif seed
        @session.randomizer[tensor.operation] ||= Random.new(seed)
        @session.randomizer[tensor.operation]
      else
        Random.new
      end
    end
  end
end