| program(1.3) |
| [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] |
| { |
| func main<ios18>(tensor<fp16, [2, 1, 640]> c, tensor<fp16, [2, 1, 640]> h, tensor<int32, [1, 1]> token) { |
| int32 y_1_axis_0 = const()[name = string("y_1_axis_0"), val = int32(0)]; |
| int32 y_1_batch_dims_0 = const()[name = string("y_1_batch_dims_0"), val = int32(0)]; |
| bool y_1_validate_indices_0 = const()[name = string("y_1_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [13088, 640]> decoder_prediction_embed_weight_to_fp16 = const()[name = string("decoder_prediction_embed_weight_to_fp16"), val = tensor<fp16, [13088, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; |
| string token_to_int16_dtype_0 = const()[name = string("token_to_int16_dtype_0"), val = string("int16")]; |
| tensor<int16, [1, 1]> token_to_int16 = cast(dtype = token_to_int16_dtype_0, x = token)[name = string("cast_6")]; |
| tensor<fp16, [1, 1, 640]> y_1_cast_fp16_cast_uint16 = gather(axis = y_1_axis_0, batch_dims = y_1_batch_dims_0, indices = token_to_int16, validate_indices = y_1_validate_indices_0, x = decoder_prediction_embed_weight_to_fp16)[name = string("y_1_cast_fp16_cast_uint16")]; |
| tensor<int32, [3]> input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(2)]; |
| int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)]; |
| tensor<fp16, [1, 1, 640]> split_0_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h)[name = string("split_0_cast_fp16")]; |
| int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(2)]; |
| int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)]; |
| tensor<fp16, [1, 1, 640]> split_1_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c)[name = string("split_1_cast_fp16")]; |
| tensor<int32, [1]> input0_1_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("input0_1_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> input0_1_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input0_1_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("input0_1_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; |
| tensor<int32, [1]> input0_1_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("input0_1_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> input0_1_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input0_1_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("input0_1_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; |
| string input0_1_lstm_layer_0_direction_0 = const()[name = string("input0_1_lstm_layer_0_direction_0"), val = string("forward")]; |
| bool input0_1_lstm_layer_0_output_sequence_0 = const()[name = string("input0_1_lstm_layer_0_output_sequence_0"), val = bool(true)]; |
| string input0_1_lstm_layer_0_recurrent_activation_0 = const()[name = string("input0_1_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; |
| string input0_1_lstm_layer_0_cell_activation_0 = const()[name = string("input0_1_lstm_layer_0_cell_activation_0"), val = string("tanh")]; |
| string input0_1_lstm_layer_0_activation_0 = const()[name = string("input0_1_lstm_layer_0_activation_0"), val = string("tanh")]; |
| tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = string("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16752768)))]; |
| tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = string("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20029632)))]; |
| tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = string("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23306496)))]; |
| tensor<fp16, [1, 1, 640]> input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = y_1_cast_fp16_cast_uint16)[name = string("transpose_1")]; |
| tensor<fp16, [1, 1, 640]> input0_1_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> input0_1_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> input0_1_lstm_layer_0_cast_fp16_2 = lstm(activation = input0_1_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input0_1_lstm_layer_0_cell_activation_0, direction = input0_1_lstm_layer_0_direction_0, initial_c = input0_1_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input0_1_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input0_1_lstm_layer_0_output_sequence_0, recurrent_activation = input0_1_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_1_cast_fp16)[name = string("input0_1_lstm_layer_0_cast_fp16")]; |
| tensor<int32, [1]> input0_1_lstm_h0_squeeze_axes_0 = const()[name = string("input0_1_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> input0_1_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input0_1_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("input0_1_lstm_h0_squeeze_cast_fp16")]; |
| tensor<int32, [1]> input0_1_lstm_c0_squeeze_axes_0 = const()[name = string("input0_1_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> input0_1_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input0_1_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("input0_1_lstm_c0_squeeze_cast_fp16")]; |
| string input0_1_direction_0 = const()[name = string("input0_1_direction_0"), val = string("forward")]; |
| bool input0_1_output_sequence_0 = const()[name = string("input0_1_output_sequence_0"), val = bool(true)]; |
| string input0_1_recurrent_activation_0 = const()[name = string("input0_1_recurrent_activation_0"), val = string("sigmoid")]; |
| string input0_1_cell_activation_0 = const()[name = string("input0_1_cell_activation_0"), val = string("tanh")]; |
| string input0_1_activation_0 = const()[name = string("input0_1_activation_0"), val = string("tanh")]; |
| tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23311680)))]; |
| tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26588544)))]; |
| tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = string("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29865408)))]; |
| tensor<fp16, [1, 1, 640]> input0_1_cast_fp16_0, tensor<fp16, [1, 640]> input0_1_cast_fp16_1, tensor<fp16, [1, 640]> input0_1_cast_fp16_2 = lstm(activation = input0_1_activation_0, bias = concat_3_to_fp16, cell_activation = input0_1_cell_activation_0, direction = input0_1_direction_0, initial_c = input0_1_lstm_c0_squeeze_cast_fp16, initial_h = input0_1_lstm_h0_squeeze_cast_fp16, output_sequence = input0_1_output_sequence_0, recurrent_activation = input0_1_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input0_1_lstm_layer_0_cast_fp16_0)[name = string("input0_1_cast_fp16")]; |
| int32 var_33_axis_0 = const()[name = string("op_33_axis_0"), val = int32(0)]; |
| tensor<fp16, [2, 1, 640]> h_out = stack(axis = var_33_axis_0, values = (input0_1_lstm_layer_0_cast_fp16_1, input0_1_cast_fp16_1))[name = string("op_33_cast_fp16")]; |
| int32 var_34_axis_0 = const()[name = string("op_34_axis_0"), val = int32(0)]; |
| tensor<fp16, [2, 1, 640]> c_out = stack(axis = var_34_axis_0, values = (input0_1_lstm_layer_0_cast_fp16_2, input0_1_cast_fp16_2))[name = string("op_34_cast_fp16")]; |
| tensor<int32, [3]> output_1_perm_0 = const()[name = string("output_1_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp16, [1, 1, 640]> decoder_output = transpose(perm = output_1_perm_0, x = input0_1_cast_fp16_0)[name = string("transpose_0")]; |
| } -> (decoder_output, h_out, c_out); |
| } |
