| program(1.3) |
| [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.14.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios18>(tensor<fp32, [2, 1, 640]> c_in, tensor<fp32, [2, 1, 640]> h_in, tensor<int32, [1, 1]> targets) { |
| int32 input_batch_dims_0 = const()[name = string("input_batch_dims_0"), val = int32(0)]; |
| bool input_validate_indices_0 = const()[name = string("input_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [8193, 640]> d_embedding_weight_to_fp16 = const()[name = string("d_embedding_weight_to_fp16"), val = tensor<fp16, [8193, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; |
| string targets_to_int16_dtype_0 = const()[name = string("targets_to_int16_dtype_0"), val = string("int16")]; |
| string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("int32")]; |
| int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; |
| tensor<int16, [1, 1]> targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = string("cast_9")]; |
| tensor<int32, [1, 1]> cast_1 = cast(dtype = cast_1_dtype_0, x = targets_to_int16)[name = string("cast_8")]; |
| tensor<bool, [1, 1]> greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; |
| int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(8193)]; |
| tensor<int32, [1, 1]> add_2 = add(x = cast_1, y = slice_by_index_0)[name = string("add_2")]; |
| tensor<int32, [1, 1]> select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = string("select_0")]; |
| int32 input_cast_fp16_cast_uint16_axis_0 = const()[name = string("input_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; |
| string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; |
| tensor<int16, [1, 1]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; |
| tensor<fp16, [1, 1, 640]> input_cast_fp16_cast_uint16_cast_uint16 = gather(axis = input_cast_fp16_cast_uint16_axis_0, batch_dims = input_batch_dims_0, indices = select_0_to_int16, validate_indices = input_validate_indices_0, x = d_embedding_weight_to_fp16)[name = string("input_cast_fp16_cast_uint16_cast_uint16")]; |
| tensor<int32, [3]> input_batch_first_transpose_perm_0 = const()[name = string("input_batch_first_transpose_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)]; |
| string h_in_to_fp16_dtype_0 = const()[name = string("h_in_to_fp16_dtype_0"), val = string("fp16")]; |
| tensor<fp16, [2, 1, 640]> h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = string("cast_6")]; |
| 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_in_to_fp16)[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)]; |
| string c_in_to_fp16_dtype_0 = const()[name = string("c_in_to_fp16_dtype_0"), val = string("fp16")]; |
| tensor<fp16, [2, 1, 640]> c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = string("cast_5")]; |
| 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_in_to_fp16)[name = string("split_1_cast_fp16")]; |
| tensor<int32, [1]> o_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = string("o_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> o_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = o_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = string("o_lstm_layer_0_lstm_h0_squeeze_cast_fp16")]; |
| tensor<int32, [1]> o_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = string("o_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> o_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = o_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = string("o_lstm_layer_0_lstm_c0_squeeze_cast_fp16")]; |
| string o_lstm_layer_0_direction_0 = const()[name = string("o_lstm_layer_0_direction_0"), val = string("forward")]; |
| bool o_lstm_layer_0_output_sequence_0 = const()[name = string("o_lstm_layer_0_output_sequence_0"), val = bool(true)]; |
| string o_lstm_layer_0_recurrent_activation_0 = const()[name = string("o_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")]; |
| string o_lstm_layer_0_cell_activation_0 = const()[name = string("o_lstm_layer_0_cell_activation_0"), val = string("tanh")]; |
| string o_lstm_layer_0_activation_0 = const()[name = string("o_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(10487168)))]; |
| 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(13764032)))]; |
| 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(17040896)))]; |
| tensor<fp16, [1, 1, 640]> input_batch_first_transpose_cast_fp16 = transpose(perm = input_batch_first_transpose_perm_0, x = input_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_2")]; |
| tensor<fp16, [1, 1, 640]> o_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> o_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> o_lstm_layer_0_cast_fp16_2 = lstm(activation = o_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = o_lstm_layer_0_cell_activation_0, direction = o_lstm_layer_0_direction_0, initial_c = o_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = o_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = o_lstm_layer_0_output_sequence_0, recurrent_activation = o_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_batch_first_transpose_cast_fp16)[name = string("o_lstm_layer_0_cast_fp16")]; |
| tensor<int32, [1]> o_batch_first_lstm_h0_squeeze_axes_0 = const()[name = string("o_batch_first_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> o_batch_first_lstm_h0_squeeze_cast_fp16 = squeeze(axes = o_batch_first_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = string("o_batch_first_lstm_h0_squeeze_cast_fp16")]; |
| tensor<int32, [1]> o_batch_first_lstm_c0_squeeze_axes_0 = const()[name = string("o_batch_first_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [1, 640]> o_batch_first_lstm_c0_squeeze_cast_fp16 = squeeze(axes = o_batch_first_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = string("o_batch_first_lstm_c0_squeeze_cast_fp16")]; |
| string o_batch_first_direction_0 = const()[name = string("o_batch_first_direction_0"), val = string("forward")]; |
| bool o_batch_first_output_sequence_0 = const()[name = string("o_batch_first_output_sequence_0"), val = bool(true)]; |
| string o_batch_first_recurrent_activation_0 = const()[name = string("o_batch_first_recurrent_activation_0"), val = string("sigmoid")]; |
| string o_batch_first_cell_activation_0 = const()[name = string("o_batch_first_cell_activation_0"), val = string("tanh")]; |
| string o_batch_first_activation_0 = const()[name = string("o_batch_first_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(17046080)))]; |
| 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(20322944)))]; |
| 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(23599808)))]; |
| tensor<fp16, [1, 1, 640]> o_batch_first_cast_fp16_0, tensor<fp16, [1, 640]> o_batch_first_cast_fp16_1, tensor<fp16, [1, 640]> o_batch_first_cast_fp16_2 = lstm(activation = o_batch_first_activation_0, bias = concat_3_to_fp16, cell_activation = o_batch_first_cell_activation_0, direction = o_batch_first_direction_0, initial_c = o_batch_first_lstm_c0_squeeze_cast_fp16, initial_h = o_batch_first_lstm_h0_squeeze_cast_fp16, output_sequence = o_batch_first_output_sequence_0, recurrent_activation = o_batch_first_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = o_lstm_layer_0_cast_fp16_0)[name = string("o_batch_first_cast_fp16")]; |
| tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([1, 2, 0])]; |
| string transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("transpose_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; |
| int32 var_32_axis_0 = const()[name = string("op_32_axis_0"), val = int32(0)]; |
| tensor<fp16, [2, 1, 640]> var_32_cast_fp16 = stack(axis = var_32_axis_0, values = (o_lstm_layer_0_cast_fp16_1, o_batch_first_cast_fp16_1))[name = string("op_32_cast_fp16")]; |
| string var_32_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_32_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; |
| int32 var_33_axis_0 = const()[name = string("op_33_axis_0"), val = int32(0)]; |
| tensor<fp16, [2, 1, 640]> var_33_cast_fp16 = stack(axis = var_33_axis_0, values = (o_lstm_layer_0_cast_fp16_2, o_batch_first_cast_fp16_2))[name = string("op_33_cast_fp16")]; |
| string var_33_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_33_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; |
| tensor<fp32, [2, 1, 640]> c_out = cast(dtype = var_33_cast_fp16_to_fp32_dtype_0, x = var_33_cast_fp16)[name = string("cast_2")]; |
| tensor<fp32, [2, 1, 640]> h_out = cast(dtype = var_32_cast_fp16_to_fp32_dtype_0, x = var_32_cast_fp16)[name = string("cast_3")]; |
| tensor<fp16, [1, 640, 1]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = o_batch_first_cast_fp16_0)[name = string("transpose_1")]; |
| tensor<fp32, [1, 640, 1]> decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = string("cast_4")]; |
| } -> (decoder, h_out, c_out); |
| } |
