Module: TensorStream::OpenCLHelpers::NNOps
- Included in:
- Evaluator::OpenclEvaluator
- Defined in:
- lib/tensor_stream/opencl/nn_ops.rb
Overview
Collection of math functions for interfacing with OpenCL kernels
Class Method Summary collapse
Class Method Details
.included(klass) ⇒ Object
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# File 'lib/tensor_stream/opencl/nn_ops.rb', line 5 def NNOps.included(klass) klass.class_eval do # Fast in place multiply subtract assign register_op :apply_gradient_descent do |_context, tensor, inputs| _target_var, learning_rate, delta = inputs assign = tensor.inputs[0] || tensor assign.container_buffer.dirty = true # force buffer copy when variable is read externally output_buffer = assign.container_buffer work_group = [output_buffer.total_elements] event_wait_list = build_event_wait_list([assign.container_buffer, learning_rate, delta]) event = call_program("apply_gradient", output_buffer.data_type, work_group, delta.cl_buffer, learning_rate.cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event output_buffer end # updates for gradient descent with momentum register_op :apply_momentum do |_context, tensor, inputs| target_var, momentum_var, learning_rate, grad, momentum = inputs assign = tensor.inputs[0] || tensor assign_acc = tensor.inputs[1] assign.container_buffer.dirty = true # force buffer copy when variable is read externally assign_acc.container_buffer.dirty = true # force buffer copy when variable is read externally output_buffer = assign.container_buffer work_group = [output_buffer.total_elements] event_wait_list = build_event_wait_list([assign.container_buffer, assign_acc.container_buffer, learning_rate, grad, momentum]) method_call = :"apply_momentum_#{output_buffer.data_type}" event = _cl_program("apply_momentum", nesterov: tensor.[:use_nesterov], dtype: output_buffer.data_type). send(method_call, _opencl_queue, work_group, grad.cl_buffer, learning_rate.cl_buffer, momentum.cl_buffer, output_buffer.cl_buffer, assign_acc.container_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event assign_acc.container_buffer.op = event output_buffer end register_op :apply_adadelta do |context, tensor, inputs| _target_var, _accum, _accum_update, lr, rho, epsilon, grad = inputs assign = tensor.inputs[0] || tensor assign_acc = tensor.inputs[1] assign_acc_update = tensor.inputs[2] # mark variable buffers as dirty assign.container_buffer.dirty = true # force buffer copy when variable is read externally assign_acc.container_buffer.dirty = true # force buffer copy when variable is read externally assign_acc_update.container_buffer.dirty = true # force buffer copy when variable is read externally output_buffer = assign.container_buffer work_group = [output_buffer.total_elements] event_wait_list = build_event_wait_list(inputs) event = call_program('apply_adadelta', output_buffer.data_type, work_group, lr.cl_buffer, rho.cl_buffer, epsilon.cl_buffer, grad.cl_buffer, assign.container_buffer.cl_buffer, assign_acc.container_buffer.cl_buffer, assign_acc_update.container_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event assign_acc.container_buffer.op = event assign_acc_update.container_buffer.op = event output_buffer end # Adam optimization algorithm register_op :apply_adam do |_context, tensor, inputs| _target_var, _m, _v, beta1_power, beta2_power, lr_t, beta1_t, beta2_t, epsilon_t, grad = inputs assign = tensor.inputs[0] || tensor assign_m = tensor.inputs[1] assign_v = tensor.inputs[2] # mark variable buffers as dirty assign.container_buffer.dirty = true # force buffer copy when variable is read externally assign_m.container_buffer.dirty = true # force buffer copy when variable is read externally assign_v.container_buffer.dirty = true # force buffer copy when variable is read externally output_buffer = assign.container_buffer work_group = [output_buffer.total_elements] event_wait_list = build_event_wait_list(inputs) event = call_program("apply_adam", output_buffer.data_type, work_group, grad.cl_buffer, lr_t.cl_buffer, beta1_power.cl_buffer, beta2_power.cl_buffer, beta1_t.cl_buffer, beta2_t.cl_buffer, epsilon_t.cl_buffer, assign_m.container_buffer.cl_buffer, assign.container_buffer.cl_buffer, assign_v.container_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event assign_m.container_buffer.op = event assign_v.container_buffer.op = event output_buffer end register_op :apply_adagrad do |context, tensor, inputs| _target_var, _accum, lr, grad = inputs assign = tensor.inputs[0] || tensor assign_acc = tensor.inputs[1] assign.container_buffer.dirty = true assign_acc.container_buffer.dirty = true output_buffer = assign.container_buffer work_group = [output_buffer.total_elements] event_wait_list = build_event_wait_list(inputs) event = call_program('apply_adagrad', output_buffer.data_type, work_group, lr.cl_buffer, grad.cl_buffer, assign.container_buffer.cl_buffer, assign_acc.container_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event assign_acc.container_buffer.op = event output_buffer end register_op :apply_centered_rms_prop do |context, tensor, inputs| var, mg, ms, mom, lr, rho, momentum, epsilon, grad = inputs assign = tensor.inputs[0] assign_mg = tensor.inputs[1] assign_ms = tensor.inputs[2] assign_mom = tensor.inputs[3] assign.container_buffer.dirty = true assign_mg.container_buffer.dirty = true assign_ms.container_buffer.dirty = true assign_mom.container_buffer.dirty = true output_buffer = assign.container_buffer event_wait_list = build_event_wait_list(inputs) work_group = [output_buffer.total_elements] event = call_program('apply_centered_rms_prop', output_buffer.data_type, work_group, lr.cl_buffer, rho.cl_buffer, momentum.cl_buffer, epsilon.cl_buffer, grad.cl_buffer, assign.container_buffer.cl_buffer, assign_ms.container_buffer.cl_buffer, assign_mg.container_buffer.cl_buffer, assign_mom.container_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event assign_mg.container_buffer.op = event assign_ms.container_buffer.op = event assign_mom.container_buffer.op = event output_buffer end register_op :apply_rms_prop do |_context, tensor, inputs| var, ms, mom, lr, rho, momentum, epsilon, grad = inputs assign = tensor.inputs[0] assign_ms = tensor.inputs[1] assign_mom = tensor.inputs[2] assign.container_buffer.dirty = true assign_ms.container_buffer.dirty = true assign_mom.container_buffer.dirty = true output_buffer = assign.container_buffer event_wait_list = build_event_wait_list(inputs) work_group = [output_buffer.total_elements] event = call_program('apply_rms_prop', output_buffer.data_type, work_group, lr.cl_buffer, rho.cl_buffer, momentum.cl_buffer, epsilon.cl_buffer, grad.cl_buffer, assign.container_buffer.cl_buffer, assign_ms.container_buffer.cl_buffer, assign_mom.container_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event assign_ms.container_buffer.op = event assign_mom.container_buffer.op = event output_buffer end register_op :softmax do |_context, tensor, inputs| a = inputs[0] event_wait_list = build_event_wait_list(inputs) dtype = tensor.data_type output_buffer = _create_result_buffer(tensor.data_type, a.shape, tensor.name) m, n = a.shape raise "unsupported rank " if a.shape.size > 2 work_group = [m] n = m if n.nil? cl_n = OpenCL::Int1.new(n || 1) event = _cl_program("softmax", dtype: dtype).send(:"softmax_#{dtype}", _opencl_queue, work_group, cl_n, a.cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event output_buffer end register_op :log_softmax do |_context, tensor, inputs| a = inputs[0] # logits event_wait_list = build_event_wait_list(inputs) dtype = tensor.data_type output_buffer = _create_result_buffer(tensor.data_type, a.shape, tensor.name) m, n = a.shape raise "unsupported rank " if a.shape.size > 2 work_group = [m] n = m if n.nil? cl_n = OpenCL::Int1.new(n || 1) event = _cl_program("log_softmax", dtype: dtype).send(:"log_softmax_#{dtype}", _opencl_queue, work_group, cl_n, a.cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event output_buffer end register_op :softmax_cross_entropy_with_logits_v2 do |context, tensor, inputs| a = inputs[0] # logits b = inputs[1] # labels event_wait_list = build_event_wait_list(inputs) dtype = tensor.data_type output_buffer = _create_result_buffer(tensor.data_type, a.shape, tensor.name) output_buffer_backprop = _create_result_buffer(tensor.data_type, a.shape, "#{tensor.name}_2") rank = a.shape.size - 1 m, n = a.shape raise "unsupported rank " if a.shape.size > 2 work_group = [m] n = m if n.nil? cl_n = OpenCL::Int1.new(n || 1) event = _cl_program("softmax_cross", dtype: dtype).send(:"softmax_cross_#{dtype}", _opencl_queue, work_group, cl_n, a.cl_buffer, b.cl_buffer, output_buffer.cl_buffer, output_buffer_backprop.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event output_buffer_backprop.op = event loss = reduction(context, tensor, output_buffer, rank, :sum) TensorStream::Evaluator::OutputGroup.new([loss, output_buffer_backprop], [tensor.inputs[0].data_type, tensor.inputs[0].data_type]) end register_op :softmax_cross_entropy_with_logits_v2_grad do |_context, tensor, inputs| a = inputs[0] # logits b = inputs[1] # labels c = inputs[2] # grads event_wait_list = build_event_wait_list(inputs) dtype = tensor.data_type output_buffer = _create_result_buffer(tensor.data_type, a.shape, tensor.name) m, n = a.shape raise "unsupported rank " if a.shape.size > 2 work_group = [m] n = m if n.nil? cl_n = OpenCL::Int1.new(n || 1) event = _cl_program("softmax_cross_grad", dtype: dtype).send(:"softmax_cross_grad_#{dtype}", _opencl_queue, work_group, cl_n, a.cl_buffer, b.cl_buffer, c.cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event output_buffer end register_op :sparse_softmax_cross_entropy_with_logits do |context, tensor, inputs| a = inputs[0] # logits labels = read_final_result(complete_eval(inputs[1], context)) # labels labels = last_axis(labels) num_classes = a.shape.last labels = labels.map do |l| one_hot = Array.new(num_classes) { 0 } one_hot[l] = 1 one_hot end b = wrap_opencl(labels, data_type: inputs[0].data_type, name: "#{tensor.name}_label") event_wait_list = build_event_wait_list(inputs) dtype = tensor.data_type output_buffer = _create_result_buffer(tensor.data_type, a.shape, tensor.name) output_buffer_backprop = _create_result_buffer(tensor.data_type, a.shape, "#{tensor.name}_2") rank = a.shape.size - 1 m, n = a.shape raise "unsupported rank " if a.shape.size > 2 work_group = [m] n = m if n.nil? cl_n = OpenCL::Int1.new(n || 1) event = _cl_program("softmax_cross", dtype: dtype).send(:"softmax_cross_#{dtype}", _opencl_queue, work_group, cl_n, a.cl_buffer, b.cl_buffer, output_buffer.cl_buffer, output_buffer_backprop.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event output_buffer_backprop.op = event loss = reduction(context, tensor, output_buffer, rank, :sum) TensorStream::Evaluator::OutputGroup.new([loss, output_buffer_backprop], [tensor.inputs[0].data_type, tensor.inputs[0].data_type]) end register_op :softmax_grad do |_context, tensor, inputs| a, grad = inputs event_wait_list = build_event_wait_list(inputs) dtype = tensor.data_type output_buffer = _create_result_buffer(tensor.data_type, a.shape, tensor.name) m, n = a.shape raise "unsupported rank " if a.shape.size > 2 work_group = [m] n = m if n.nil? cl_n = OpenCL::Int1.new(n || 1) event = _cl_program('softmax_grad', dtype: dtype, size: n). send(:"softmax_grad_#{dtype}", _opencl_queue, work_group, cl_n, a.cl_buffer, grad.cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer.op = event output_buffer end %i[relu6].each do |op| register_op op, noop: true do |context, tensor, inputs| execute_func(op.to_s, tensor, inputs[0], context) end end # Fast per pixel parallel convolution operation register_op :conv2d do |_context, tensor, inputs| filter = inputs[1] batch, height, width, channel = inputs[0].shape filter_shape = filter.shape strides = tensor.[:strides] height_stride = strides[1] width_stride = strides[2] raise TensorStream::ValueError, " Current implementation does not yet support strides in the batch and depth dimensions." if strides[0] != 1 || strides[3] != 1 padding_option = tensor.[:padding] padding = (padding_option, filter_shape, height, width, height_stride, width_stride) event_wait_list = build_event_wait_list(inputs) f_height, f_width, _in_channels, out_channels = filter_shape out_h = (height - f_height + (padding[0] + padding[2])) / height_stride + 1 out_w = (width - f_width + (padding[1] + padding[3])) / width_stride + 1 out_shape = [batch, out_h, out_w, out_channels] output_buffer = _create_result_buffer(tensor.data_type, out_shape, tensor.name) cl_image_height = OpenCL::Int1.new(height) cl_image_width = OpenCL::Int1.new(width) cl_out_height = OpenCL::Int1.new(out_h) cl_out_width = OpenCL::Int1.new(out_w) work_dimen = [batch, out_h, out_w] output_buffer.op = _cl_program("conv2d", dtype: tensor.data_type, fh: f_height, fw: f_width, ch: channel, out_ch: out_channels, stride: [height_stride, width_stride], padding: padding). send(:conv2d, _opencl_queue, work_dimen, cl_image_height, cl_image_width, cl_out_height, cl_out_width, inputs[0].cl_buffer, inputs[1].cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer end register_op :conv2d_backprop_input do |context, tensor, inputs| image_shape, filter, grad = inputs filter_shape = filter.shape strides = tensor.[:strides] height_stride = strides[1] width_stride = strides[2] image_shape = read_final_result(complete_eval(image_shape, context)) event_wait_list = build_event_wait_list(inputs) output_buffer = _create_result_buffer(tensor.data_type, image_shape, tensor.name) batch, height, width, channels = image_shape f_height, f_width, in_channels, out_channels = filter_shape padding_option = tensor.[:padding] padding = (padding_option, filter_shape, height, width, height_stride, width_stride) work_dimen = [batch, height, width] out_h = (height - f_height + (padding[0] + padding[2])) / height_stride + 1 out_w = (width - f_width + (padding[1] + padding[3])) / width_stride + 1 cl_image_height = OpenCL::Int1.new(height) cl_image_width = OpenCL::Int1.new(width) cl_out_height = OpenCL::Int1.new(out_h) cl_out_width = OpenCL::Int1.new(out_w) output_buffer.op = _cl_program("conv2d_backprop_input", dtype: tensor.data_type, fh: f_height, fw: f_width, ch: channels, out_ch: out_channels, stride: [height_stride, width_stride], padding: padding). send(:conv2d_backprop_input, _opencl_queue, work_dimen, cl_image_height, cl_image_width, cl_out_height, cl_out_width, filter.cl_buffer, grad.cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer end register_op :conv2d_backprop_filter do |context, tensor, inputs| images, filter_shape, grad = inputs event_wait_list = build_event_wait_list(inputs) strides = tensor.[:strides] height_stride = strides[1] width_stride = strides[2] filter_shape = read_final_result(complete_eval(filter_shape, context)) output_buffer = _create_result_buffer(tensor.data_type, filter_shape, tensor.name) batch_size, height, width, channels = images.shape f_height, f_width, input_channels, output_channels = filter_shape work_dimen = [f_height, f_width, output_channels] padding_option = tensor.[:padding] padding = (padding_option, filter_shape, height, width, height_stride, width_stride) out_h = (height - f_height + (padding[0] + padding[2])) / height_stride + 1 out_w = (width - f_width + (padding[1] + padding[3])) / width_stride + 1 cl_batch_size = OpenCL::Int1.new(batch_size) cl_image_height = OpenCL::Int1.new(height) cl_image_width = OpenCL::Int1.new(width) cl_out_height = OpenCL::Int1.new(out_h) cl_out_width = OpenCL::Int1.new(out_w) output_buffer.op = _cl_program("conv2d_backprop_filter", dtype: tensor.data_type, fh: f_height, fw: f_width, ch: channels, out_ch: output_channels, stride: [height_stride, width_stride], padding: padding ). send(:conv2d_backprop_filter, _opencl_queue, work_dimen, cl_batch_size, cl_image_height, cl_image_width, cl_out_height, cl_out_width, images.cl_buffer, grad.cl_buffer, output_buffer.cl_buffer, event_wait_list: event_wait_list) output_buffer end def (padding_option, filter_shape, height, width, h_stride, w_stride) case padding_option when 'SAME' [ calc_pad(height, h_stride, filter_shape[0]), calc_pad(width, w_stride, filter_shape[1]), calc_pad(height, h_stride, filter_shape[0], true), calc_pad(width, w_stride, filter_shape[1], true) ] when 'VALID' [0, 0, 0, 0] else raise TensorStream::ValueError, "Unsupported padding value #{padding_option}, valid values 'SAME', 'VALID'" end end def calc_pad(w, stride, f_shape, ceil = false) r = ((w / stride - 1) * stride - w + f_shape) if ceil r.odd? ? r / 2 + 1 : r / 2 else r / 2 end end end end |